c ifi REGIONAL WORK FORCE CHARACTERISTICS AND MIGRATION DATA: A Handbook on the Social Security Continuous Work History Sample and Its Application U.S. DEPARTMENT OF COMMERCE Bureau of Economic Analysis Vso^* REGIONAL WORK FORCE CHARACTERISTICS AND MIGRATION DATA: A Handbook on the Social Security Continuous Work History Sample and Its Application U. S. DEPARTMENT OF COMMERCE Elliot L Richardson, Secretary Edward 0. Vetter, Under Secretary BUREAU OF ECONOMIC ANALYSIS George Jaszi, Director Daniel Garnick, Associate Director for Regional Economics *K or J°\ December 1976 o XI O 9 3 This handbook was prepared pursuant to Interagency Agreement Number H-67-75, funded by the Department of Housing and Urban Development, Office of Policy Develop- ment and Research. For sale by the Superintendent of Documents, U.S. Government Printing Office Washington, DC. 20402 - Price $5.10 Stock No 003-010-00055-4 FOREWORD As part of its program of maintaining and extending statistical measures and techniques for regional economic analysis and projection, the Bureau of Economic Analysis (BEA) about one decade ago began to develop a system for summarizing and making available through analytical tables the work force structure and migration data included in the 1-percent Con- tinuous Work History Sample (CWHS). With the cooperation of the Social Security Ad- ministration (SSA), the BEA established a CWHS data system service that provides tabula- tions for a broad and expanding group of users. These two agencies have a joint ongoing program for strengthening and improving the usefulness of the CWHS data for regional economic and demographic analysis. The BEA-SSA cooperative program recently received interagency sponsorship for its development of a 10-percent CWHS data file for 1971 and 1973. The Department of Hous- ing and Urban Development had long been interested in the applicability of these files for State and substate planning, and it took the leadership role in the interagency funding arrangement for the 10-percent sample. Its sponsorship of this handbook is a logical exten- sion of its efforts in behalf of the CWHS data program. Stevenson Weitz, of the Office of Policy Development and Research, Department of Housing and Urban Development, provided guidance in the organization of material for the handbook and in the selection of an advisory committee which reviewed the initial plans and commented on early drafts of the chapters. The preparation of this handbook has involved the efforts of many persons in the BEA and the SSA. The Regional Economic Analysis Division in the BEA led the effort, under the project direction of David Cartwright, who made contributions throughout. Special recognition is noted for the work of Vernon Renshaw, who drafted large portions of the study, including the chapters on applications and comparability, and of Bruce Levine, who drafted the chapters documenting the CWHS files and their limitations. Howard Friedenberg and James Younger edited and coordinated the production of the entire report. Kathryn Nelson originally suggested the idea of the handbook and served as consultant to the BEA on this project, extending for this study her previous research on census and CWHS migration rates. Large contributions to the chapters documenting the CWHS files and their limitations were made by Warren Buckler, Vincent Libertore, Robert Finch, and John Kulick, under the direction of Henry Patt (Chief of the Division of Statistics) and Thomas Jabine (Chief Mathematical Statistician), of the Office of Research and Statistics, SSA. Additional acknowledgement is given for the efforts in the BEA of Kenneth Johnson, Evelyn Richardson, Shirley Bell, Kenneth Horowitz, Jamila Bomani, and Marie Watson. Renae Pitt, Mary Moul, and Bettie Mills typed the handbook. Billie Jo Hurley provided the graphic services. v --Uric < ( H < ■"' W™ <( l( Bureau of Economic Analysis m ADVISORY COMMITTEE Mr. Ralph Allyn U.S. Department of Labor Ms. Margaret Barnes U.S. Department of Health, Education, and Welfare Mr. Stuart Bendelow Metropolitan Washington Council of Governments Mr. Arthur Benjamin State of Maryland Mr. David Birch Joint Center for Urban Studies, M.I.T. Mr. Martin Costello U.S. Department of Transportation Mr. G. E. Alan Dever State of Georgia Mr. Richard Dowdall City of Louisville, Kentucky Mr. Thomas Dundas State of Montana Mr. Gerald Duskin U.S. Department of Commerce Mr. Sho Maruyama Delaware Valley Regional Planning Commission Mr. Richard Metcalf U.S. Department of Housing and Urban Development Mr. Peter Morrison The Rand Corporation Mr. Jerome Picard Appalachian Regional Commission Mr. Nels Rasmussen State of California Mr. James Rose Baltimore Regional Planning Commission Mr. Alvin Sanders State of Massachusetts Mr. John Smith State of New York Mr. George Stolnitz The University of Indiana Mr. David Word U.S. Department of Commerce IV CONTENTS Chapter Page Foreword iii List of Tables viii List of Figures and Map xii Glossary xiii I. Introduction and Summary 1 The Continuous Work History Sample and Its Use 1 Organization and Content of This Handbook 2 Use of CWHS Work Force and Migration Data: A Review of Past Studies (Chapter II) 3 Use of CWHS Work Force and Migration Data: An Example for New York State (Chapter III) 3 Description of CWHS Data (Chapter IV) 4 Availability of CWHS Data (Chapter V) 4 Limitations of CWHS Data (Chapter VI) 5 A Comparison of the CWHS with Other Data Sets (Chapter VII) 5 II. Use of CWHS Work Force and Migration Data: A Review of Past Studies 7 Introduction 7 Analysis of Work Force Structure 8 Existing Studies 8 Potential Applications 9 Analysis of Work Force Change 10 Introduction 10 Geographic Mobility 10 Migration over time 10 Migration streams 1 1 Multiple migration 12 Returns to migration 12 Industrial Mobility 13 Extent and characteristics 13 Origin-destination matrix 13 Role of industry structure 14 Returns to labor mobility 14 Predicting Migration 14 Assessing Policy Impacts '. 15 III. Use of CWHS Work Force and Migration Data: An Example for New York State 17 The New York State Migration Project 17 The New York Tabulations 17 Work Force Structure 18 Migration Structure 19 Migration Flows 19 CWHS and Census Data 20 Employment Data 20 Migration Data 21 Conclusion 22 CONTENTS— Continued Chapter Page IV. Description of CWHS Data 37 Files Available from the Social Security Administration 37 1-Percent Annual Employee-Employer File 37 1-Percent Annual Self-Employed File 37 1-Percent Longitudinal Employee-Employer Data (LEED) File 38 1-Percent 1937-to-Date Continuous Work History Sample File 38 0.1-Percent 1937-to-Date Continuous Work History Sample File 38 First Quarter Files 38 General Characteristics 38 Sources 38 Processing Procedures 39 Establishment Reporting Plan (ERP) 39 Maintenance of the employer file 39 Estimated annual wages 39 Processing cutoff 40 Unique case numbers 40 Sampling Procedures 40 Coverage 41 Geographic and Industrial Coding 41 New Developments 43 10- Percent Sample 43 Residence Coding 43 Extensions of Coverage 43 Effects of Annual Reporting on the CWHS 45 Conversion from Quarterly to Annual Reporting 45 Implications for the CWHS 45 Availability and timing of file 45 Coverage 45 Content 46 Establishment Reporting Plan (ERP) 46 Effect on migration analysis 46 V. Availability of CWHS Data 47 Files Maintained in the BEA System 47 First Quarter and Annual Files 47 Longitudinal First Quarter File 48 10-Percent File 48 New Developments 49 Standard Tabulations Available from the BEA 49 Migration Summary Tabulation 49 Structure of Migrants, Nonmigrants, Entrants, and Exits 50 Work Force Structure 51 Migration Matrix 51 Longitudinal Analysis 51 Commuting Tabulations 51 Selecting the Appropriate File for Tabulation 51 Procedures for Acquiring Special Tabulations 52 VI. Limitations of CWHS Data 71 Coverage 71 Sampling Variability 72 Reporting Errors 75 Other Limitations 76 Unclassified Workers 76 vi CONTENTS— Continued Chapter Page Unclassified by State, county, and industry 76 Unclassified by county 77 Unclassified by industry 77 Unclassified by sex, race, or age 77 Lack of Timeliness 77 Lack of Geographic Data for Military Personnel 77 Lack of Migration Data on Entrants and Exits 78 VII. A Comparison of the CWHS with Other Data Sets 91 Work Force Coverage 91 By Industry and Area 91 CBP employment 91 CBP wages 91 Job count employment 92 Counts of workers 92 Geographic variations 93 Census employment 93 By Demographic Characteristics 95 BLS and CPS employment 95 Decennial census employment 95 Migration 97 Previous Comparisons of CWHS with Census Data 97 Using CWHS annual net migration rates to estimate population 97 Comparing census and CWHS rates of gross inmigration 98 The Usefulness of Adjustment Factors 99 Estimating State population rates of inmigration and outmigration 99 Differences by sex, race, and age in CWHS and census measures of work force migration 99 Regional biases in social security measures of inmigration, outmigration, and net migration 100 Further Comparisons of Net Migration 100 Conclusion 101 Bibliography 119 Appendix A. Forms A- 1-1 Appendix B. Geographic Codes B- 1-1 Appendix C. Flow Charts and Formats C- 1-1 Appendix D. Employment Tables D-l-1 Vll LIST OF TABLES Page III- 1 . Work Force Structure Based on Social Security Continuous Work History Sample (1%), First Quarters of 1967, 1970, and 1973, United States 23 III-2. Work Force Structure Based on Social Security Continuous Work History Sample (1%), First Quarters of 1967, 1970, and 1973, New York State 24 III-3. Migrant Structure Based on Social Security Continuous Work History Sample (1%), First Quarters of 1967 and 1970, New York State Outmigrants and Inmigrants.... 25 III-4. Migrant Structure Based on Social Security Continuous Work History Sample (1%), First Quarters of 1967 and 1970, New York State Nonmigrants 26 III-5. Migrant Structure Based on Social Security Continuous Work History Sample (1%), First Quarters of 1967 and 1970, New York State Exits and Entrants 27 III-6. Migrant Structure Based on Social Security Continuous Work History Sample (1%), First Quarters of 1970 and 1973, New York State Outmigrants and Inmigrants.... 28 III-7. Migrant Structure Based on Social Security Continuous Work History Sample (1%), First Quarters of 1970 and 1973, New York State Nonmigrants 29 III-8. Migrant Structure Based on Social Security Continuous Work History Sample (1%), First Quarters of 1970 and 1973, New York State Exits and Entrants 30 III-9. Migration Summary Based on Social Security Continuous Work History Sample (1%), First Quarters of 1967-70-73, New York, All Workers, All Ages 31 I II- 10. Migration of New York State Workers, by Selected Characteristics, Based on 1-Percent CWHS Tabulations, for the First Quarters of 1967-70-73 32 1 1 1 - 1 1. Migration Matrix, Employment and Mean Wages, 1967-70, All Workers, New York State 33 111-12. Migration Matrix, Employment and Mean Wages, 1970-73, All Workers, New York State 34 III- 13. Census and CWHS Employment, New York State and Census Regions, by Age Group and Race, 1969 35 IV-l(A). Estimated Annual Wages of Nonfarm Workers, Using Method "B" 40 IV-l(B). Estimated Annual Wages of Farm Workers, Using Method "B" 40 IV-2. Differences between Standard Industrial Classification and SSA Equivalents Used on CWHS Files 42 IV-3. Distribution of Government Workers, by Industry, 1-Percent CWHS Major Job Summary, 1970 44 viii LIST OF TABLES— Continued Page V-l. CWHS Control Counts, 1960-72 48 V-2. Average Percent Difference in Number of Workers between Edited Results and Unedited Sample Value from Migrant Structure Summary Files 50 V-3. ' Migration Summary Based on Social Security Continuous Work History Sample (1%), First Quarter of 1967-70-73, New York, All Workers, All Ages 53 V-4. Migration Summary Based on Social Security Continuous Work History Sample (1%), First Quarter of 1967-70-73, New York, White Males, 40-44 Years of Age 54 V-5. Migration Summary Based on Social Security Continuous Work History Sample (1%), First Quarter 1967-70-73, New York, White Males, Manufacturing 55 V-6. Migrant Structure Based on Social Security Continuous Work History Sample (10%), First Quarters of 1971 and 1973, New York Outmigrants and Inmigrants 56 V-7. Nonmigrant Structure Based on Social Security Continuous Work History Sample (10%), First Quarters of 1971 and 1973, New York 57 V-8. Structure of Exits and Entrants Based on Social Security Continuous Work History Sample (10%), First Quarters of 1971 and 1973, New York 58 V-9. Work Force Structure Based on Social Security Continuous Work History Sample (1%), First Quarters of 1963, 1964, and 1965, New York 59 V-10. Work Force Structure Based on Social Security Continuous Work History Sample (10%), First Quarters of 1971 and 1973, New York 60 V-l 1 . Migration Matrix, Employment and Mean Wages, 1967-70, White Males, Durable Goods, New York 61 V-12. Longitudinal Analysis of 1960 Workers Based on Social Security Continuous Work History Samples (1%), First Quarters of 1960-68, New York 62 V-l 3. Longitudinal Analysis of 1968 Workers Based on Social Security Continuous Work History Samples (1%), First Quarters of 1968-60, New York 63 V-14(A). Intercounty Commuting Patterns Based on Social Security Continuous Work History Sample (1%), First Quarter of 1972, Total Commuting Patterns for Manhattan 64 V-14(B). Intercounty Commuting Patterns Based on Social Security Continuous Work History Sample (1%), First Quarter of 1972, White Commuting Patterns for Manhattan 65 V-14(C). Intercounty Commuting Patterns Based on Social Security Continuous Work History Sample (1%), First Quarter of 1972, Black Commuting Patterns for Manhattan 66 V-15. Comparison of Work Force Distributions, by State, from Two 1972 Major Job Summary Files 67 V-l 6. Comparison of 1972 Work Force Distributions from Final First Quarter and Annual Major Job Summaries, by Selected Characteristics 68 ix LIST OF TABLES— Continued Page V- 1 7. Comparison of Interstate Migration Rates, from the CWHS Final First Quarter and Annual Major Job Summary Files, 1970-72 69 VI- 1. Social Security Coverage of State and Local Government Employment, March 1969.... 72 VI-2. Approximate Standard Errors of Estimated Percentages of Persons (1 -Percent CWHS) 73 VI-3. Approximate Standard Errors of Estimated Percentages of Persons (10- Percent CWHS) 74 VI-4. Approximate Standard Errors for Estimated Number of Persons with Specific Characteristics, Total Covered Work Force of 100 Million 74 VI-5. Relative Standard Errors for Estimated Average Total Earnings 75 VI-6. Distribution of Cells — County x Origin or Destination x Sex x Race x Age Group 78 VI-7. Distribution of Cells — County x Sex x Race x Age 79 VI-8. Comparison of 100-Percent and 1 -Percent Data on All Workers, Taxable Earnings and Mean Taxable Earnings, by Age, 1972 80 VI-9. Comparison of 100-Percent and 1-Percent Data on All Workers, Taxable Earnings and Mean Taxable Earnings, by Earnings Interval, 1972 81 VI-10. Effect of SSA Corrections on Interstate Migration Flows, 1970-71 82 VI-1 1 . Effect of SSA Corrections on Interstate Migration Flows, 1971-72 84 VI-12. Potential Migrant Errors, 1965-70 86 VI- 13. Comparison of the Structural Characteristics of Workers Unclassified in the Preliminary First Quarter File and of All Workers in the Final First Quarter File, 1972 87 VI- 14. Migration Rates of Workers Who Were Unclassified in the Preliminary 1972 First Quarter File, 1970-72 (number of 1-percent sample cases) 88 VI-1 5. State Migration Flows to and from the Military and Reserves, 1971-72 (number of 1 -percent sample cases) 89 VII-1. Employment, by Major Industry Group, 1971 and 1973, United States 102 VII-2. 1-Percent CWHS and Census Employment, by Major Industry Group, 1960 and 1970, United States 103 VII-3. CWHS and Census Employment, by Sex and Age Group, 1960 and 1970, United States 104 VII-4. CWHS Employment and Census Population, by Sex, Race, and Age Group, 1960and 1970, United States 105 VII-5. Standard Error Factors of Relationships Between Interstate Migration Rates Derived From the 1970 Census of Population (M c ) and the Social Security 1-Percent CWHS (M s ), by Sex and Race, 1965-70 106 LIST OF TABLES— Continued Page VII-6. Adjustment Factors (Ratio of Average Census Migration Rate to Average Social Security Migration Rate), by Sex, Race, and Age, for 1965-70, 1970-71, and 1970-73 107 VII-7. Standard Error Factors of Linear Relationships M c = aM s between Census and Social Security Rates of Inmigration in 28 Metropolitan Areas, by Sex, Race, and Age, 1965-70 108 VII-8. Net Migration Rates, by State, 1971-73 109 D-l. Employment by Major Industry Group, by State, 1971 and 1973 D-l-1 D-2. Employment by Major Industry Group for Census Divisions, 1960 and 1970 D-2-1 D-3. 1-Percent CWHS and Census Employment, by Major Industry Group and BEA Economic Area, 1960and 1970 D-3-1 D-4. CWHS and Census Employment, by State, Sex, and Age Group, 1960 and 1970 D-4-1 D-5. CWHS Employment and Census Population, by Census Region and State and • Sex, Race, and Age Group, 1960 and 1970 D-5-1 xi LIST OF FIGURES AND MAP Page VII- 1. Census and Social Security Rates of White Male Migration Into Each State, 1965-70.... 110 VII-2. Census and Social Security Rates of White Male Migration Out of Each State, 1965-70 Ill VII-3. Census and Social Security Rates of White Female Migration Into Each State, 1965-70 112 VII-4. Census and Social Security Rates of White Female Migration Out of Each State, 1965-70 113 VII-5. Census and Social Security Rates of Black Male Migration Into Each State, 1965-70 .... 1 14 VII-6. Census and Social Security Rates of Black Male Migration Out of Each State, 1965-70 115 VII-7. Census and Social Security Rates of Black Female Migration Into Each State, 1965-70 116 VII-8. Census and Social Security Rates of Black Female Migration Out of Each State, 1965-70 117 VII-9. Percentage Point Deviation From Average Adjustment Factor (Ratio of Census/Social Security Average Migration Rates) of Regional Inmigration and Outmigration Rates, by Race 118 Map BEA Economic Areas Preceding D-3-1 XII GLOSSARY BEA — Bureau of Economic Analysis, U.S. Department of Commerce (formerly the Office of Business Economics). BLS — Bureau of Labor Statistics, U.S. Department of Labor. CBP — County Business Patterns, an annual publication of the Bureau of the Census, U.S. Department of Commerce. CPS — Current Population Survey, an annual survey of the Bureau of the Census, U.S. Department of Commerce. CWHS — Continuous Work History Sample of the Social Security Administration. ERP — Establishment Reporting Plan (see Chapter IV). File — Used in this handbook to refer to a physical assemblage of data, such as a magnetic tape. For example, the CWHS, for each time period, is referred to as a separate file. HEW — U.S. Department of Health, Education, and Welfare. HUD — U.S. Department of Housing and Urban Development. LEED — Longitudinal employee-employer file (see Chapter IV). Local area — Used in this handbook to refer to counties and multi-county regions, such as SMSA's or State planning districts. OASDHI — Old-Age, Survivors 1 , Disability, and Health Insurance. OBERS — A set of decennial projections to the year 2020 of income and employment, by industry, for BEA economic areas, States, SMSA's and water resource planning areas. Produced for the U.S. Water Resources Council by the Bureau of Economic Analysis (formerly Office of Business Economics), U.S. Department of Commerce, and the Economic Research Service, U.S. Department of Agriculture. ORS SSA UI Office of Research and Statistics, Social Security Administration. Social Security Administration, U.S. Department of Health, Education, and Welfare. Unemployment Insurance. Used in this handbook to refer to data collected by the States, as part of their Unemployment Insurance programs, and published by the U.S. Department of Labor. xin CHAPTER I INTRODUCTION AND SUMMARY The Continuous Work History Sample and Its Use The Social Security Administration's (SSA) Con- tinuous Work History Sample (CWHS) is a uniquely detailed source of information on work force characteristics and the components of work force change for States and substate areas for intercensal years. These data can help planners monitor and evaluate the effects on area workers of economic events and policies. The CWHS is a sample of workers' earnings records from employers' quarterly reports to the SSA. 1 The sample is based on specific digits in workers' social security numbers. Because the same social security numbers are included in the sample for each period, work histories for workers in the sample can be as- sembled by linking the data files for successive periods. Work histories include data on sex, race, year of birth, and, for each time period, the State, county, and industry of employment, as well as an es- timate of wages earned from each social-security- covered job. 2 The area work force and migration data available from these administrative records can be assembled less expensively and updated more frequently than equivalent data from special surveys. Moreover, with year-by-year data for the same workers, the CWHS has unique capabilities for tracing the processes by which work force changes occur. Although CWHS data cover fewer persons (the working population with social-security-covered jobs rather than total population) and fewer demographic characteristics than census data, they are free of the memory biases often found in survey responses. 'Beginning in 1978 employers will report on an annual basis only (see "Effects of Annual Reporting on the CWHS" in Chapter IV). : Social security numbers are scrambled to conceal the identities of workers in the sample. Most data files used in economic and demographic analysis are static, macro-data sets. They are static in that they contain observations about the economic or demographic characteristics of a given area at a given point in time. They are macro in that the data cells generally contain aggregations rather than informa- tion pertaining to individual workers or firms. Inferences are customarily made from the observed changes in the macro data about the processes by which the changes took place. The CWHS is, however, a micro-data file in that it contains informa- tion for individual workers. Inferences can be made from this file about the processes through which economic and demographic changes take place in areas. The primary application of CWHS data described in this handbook is in making and evaluating estimates of migration and work force change. Additional uses, such as monitoring the results of manpower training or economic development programs, are also suggested. Other program areas where the CWHS data may be useful include: 1 . Housing. Those who plan for housing may be especially interested in the earnings characteristics of migrants and in shifts in the earnings distributions of the resident work force. Indicators of past mobility may also help in better predicting the relative demand for rented rather than owned un- its. 2. Government services and finance. The ser- vice needs of the population may be better anticipated with data on sex, race, age, and earnings. Projected tax revenues are closely tied to both employment changes and in- come of residents. With the possible development of intercensal commuting data (see Chapter IV), local governments will be able to monitor the movement to the suburbs of those who retain central-city jobs and of those who shift job location as well. Manpower development. CWHS data may be used to measure the wage changes as- sociated with manpower movements and to examine the distribution of wages among different groups of workers. The patterns and results of manpower development may be studied in detail. The joint use of data on mobility, wage changes, and industry permits analysis of the impact on local areas of such phenomena as growth or decline in selected industries, changes in the demographic composition of the work force, and changes in the distribution of wages. Transportation. If developed on an ongoing basis, intercensal commuting data would be extremely valuable to metropolitan tran- sportation planners, especially in combina- tion with data on commuter employment and earnings characteristics also included in the files. 5. Health. Health on population planners seek data size and composition when planning for hospital and health care needs. Annual data on age, race, sex, in- dustry, and earnings help planners identify those segments of the population most in need of particular health services. 6. Unemployment insurance. At least one State agency has already shown interest in the use of CWHS data on worker earnings dis- tributions to forecast the size of the State unemployment insurance fund. Despite its advantages, use of the CWHS by State and substate administrators and planners has been hampered by: 1. the inadequacy of the 1 -percent sample for the analysis of small area work forces, 2. the difficulty and expense of processing the massive CWHS files available from the SSA, and 3. a lack of information about the nature of the CWHS files and the availability of data tabulations from them. The limitations of the 1 -percent sample for the analysis of small areas have been substantially reduced by the development of a 10-percent sample. With interagency sponsorship from the Department of Housing and Urban Development and others, the BEA and the SSA have developed data on migration and work force characteristics from 10-percent CWHS files for the first quarters of 1971 and 1973; and work is underway to establish a 10-percent sam- ple for the first quarter of 1975. Tabulations as well as summary tapes for States, BEA economic areas, SMSA's, and counties are available from the BEA at marginal cost. The difficulty and expense of processing the massive CWHS files available from the SSA (approximately 1 .5 million records per year for the 1-percent sample) ordinarily would permit only a few large research in- stitutions and Federal agencies to regularly acquire and process these files. Because of the usefulness of the CWHS for regional analysis, the BEA has developed a system which makes the data available to a broad group of users and provides a data tabulation service based on the summary files that it regularly establishes. To satisfy the need for a systematic compendium of information about the CWHS data and their use is the principal purpose of this handbook. In seeking both to describe the data and their potential to persons unfamiliar with the CWHS and to com- prehensively document the technical information re- quired for the intelligent application of the data, this handbook includes diverse materials. Those un- familiar with the data may wish to defer considera- tion of the technical details, whereas experienced users will be interested primarily in the technical documentation. The remainder of this chapter ex- plains the organization of this handbook and provides a summary of each chapter. Organization and Content of this Handbook The handbook is divided into two sections: (1) ap- plications of the data (Chapters II and III) and (2) documentation of the nature, availability, limita- tions, and comparability of the data (Chapters IV - VII). An annotated bibliography describes works dealing with the CWHS; when cited in the text, these works are denoted by parenthetical numbers cor- responding to bibliographic entry numbers. In addi- tion, Appendices A through D provide information on (1) administrative forms from which the SSA derives its sample data, (2) the numbers used by the SSA to code geographic areas of the U.S., (3) the data items contained on the CWHS files and the BEA summary files, together with flow charts of BEA processing procedures, and (4) comparisons of CWHS data with related data from other sources for States, Census Bureau divisions, and BEA economic areas. Chapters II and III address the issues most likely to be raised by those who are unfamiliar with the CWHS— its use and its -applicability. Chapter II provides an overview of past and potential uses of the data. It is a representative rather than comprehensive discussion of such uses without reference to the limitations of the data or the technical difficulties in- volved. Chapter III presents an example of the analysis of CWHS data for the State of New York. After the reader determines whether the data are rele- vant to his work, he can proceed to the more detailed data discussions contained in Chapters IV through VII. Chapter IV documents the sources, procedures, and concepts used by the SSA in establishing the CWHS files. It discusses new developments un- derway at both the SSA and the BEA to improve and extend the data. Chapter V describes the data files and presents examples of the variety of tabulations available from the BEA. It also discusses the advan- tages and disadvantages of using first quarter rather than annual files. Chapter VI contains much-needed documentation of the limitations of CWHS data. An understanding of limitations is vital to the effective use of the data. Chapter VII documents the reliability of the data at the State and regional level through comparisons with related data series. Brief summaries of the key points in each chapter follow. Use of CWHS Work Force and Migration Data: A Review of Past Studies (Chapter II) The CWHS indicates the age, sex, race, industry, and earnings characteristics of a local-area (a county or group of counties) work force, annually from 1957 forward. The SSA publications Earnings Distribu- tions in the United States show these data (excluding industry) for States (including metropolitan- nonmetropolitan breakdowns) and large metropolitan areas, for 1967-69, with updates plan- ned at 5-year intervals. The Bureau of Labor Statistics series Annual Earnings and Employment Patterns of Private N onagri cultural Employees show these data for broad geographic regions, for 1965-67 and 1970-71, with annual updates planned. Data on work force characteristics permit comparison of the earnings patterns of blacks and whites. If combined with data on residence of the workers, they can provide commuting data which may be useful in meeting Federal requirements for housing assistance or transportation plans. The CWHS can be used to trace the work experience of individuals over time. It can help planners analyze the short-run effects of policy decisions on the work force via data on the number and characteristics of workers who move into and out of an area during a year. Planners interested in the effects of national economic developments on regional migration pat- terns can evaluate data on the geographic origins and destinations of movers. Planners concerned with the types of housing and public services required in local areas can use data on workers who move frequently. Decisions among alternative industrial development plans can profit from local-area data on workers who increased their earnings by changing their industry of employment. Planners concerned with the condition of low-income workers can study these workers' geographic and industrial mobility and their propen- sity either to gain stable employment, to be tem- porarily absent from the work force, or to drop out of the work force. Regional economic forecasters can interpret trends via annual data on work force change and its sources. Finally, those engaged in evaluating manpower development programs can have regularly updated information with which to monitor the behavior of an area's work force. Use of CWHS Work Force and Migration Data: An Example for New York State (Chapter III) CWHS data for New York State illustrate applica- tions of the data for monitoring migration and work force change. New York was chosen because the State Economic Development Board has an ongoing "Migration Project" which utilizes CWHS data and because recent changes in the State's economy provide the opportunity for illustrating a broad range of CWHS capabilities. Among the important migration developments in New York revealed by the CWHS were: 1. a major increase in the overall rate of worker net outmigration between the late 1960's and the early 1970's, led by in- creased net migration to the South, 2. a reversal of black worker migration from net inmigration in the late 1960's to net outmigration in the early 1970's, and 3. the dominance of the New York City area in the overall migration reversals for the State. The CWHS data also revealed significant structural shifts in the New York work force between the late 1960's and early 1970's. The share of total employ- ment accounted for by government increased much faster in the State than in the Nation. Wage levels, which were already above national levels, increased at above-average rates in the State. In spite of a rever- sal of the net inmigration pattern for blacks, the share of black employment in New York increased relative to the national average. A comparison of CWHS and census work force data, moreover, suggested that the 1970 census undercount for blacks in New York may have been particularly severe. marization of these files will soon be merged with the 10-percent and 1 -percent CWHS files. Availability of CWHS Data (Chapter V) Description of CWHS Data (Chapter IV) The Social Security Administration (SSA) prepares sample data from its administrative records (mainly employers' wage reports) for use by its own and other researchers. These data show the following characteristics for the covered work force: age, sex, race, wages, and industry and location of employ- ment. Among several sample data files, two are of primary importance — a 1-percent annual (1957 forward) employee-employer file and a 1 -percent an- nual self-employed file. They provide the basis for the analysis of work force characteristics and worker migration for local areas. These annual files are available approximately 2-1/2 years after the end of a subje - t year. The BEA, together with the Census Bu: jau, also obtains preliminary employee-employer tiles based entirely on first quarter employer reports. These files are available approximately 15 months after the end of a reference quarter (or 2 years before the annual file). Persons covered by the CWHS include workers whose participation in the social security program is mandatory — employees in nonfarm industries, cer- tain farm employees, most domestic employees who work on a regular basis, and Federal employees not covered by the Federal Retirement System — and those whose coverage is elective — ministers, self- employed persons, and State and local government workers. Nonfarm employers report quarterly, while farm employers and self-employed persons report an- nually. Wages are reported only up to the taxable limit for the year, and total yearly wages are es- timated by an SSA-developed procedure. Confidentiality is assured by a system of scrambling individual identification numbers; although scrambled, these numbers permit the examination of data for the same individuals from year to year. The BEA, jointly with the SSA, has developed 10- percent files (containing 8 million records) for the first quarters of 1971 and 1973, and has under development a 10-percent file for the first quarter of 1975. They permit the analysis of labor force mobility patterns at a finer level of demographic detail and for smaller geographic areas than do the 1-percent sam- ples. In addition, the BEA has acquired a 10-percent sample of the personnel records of Civil Service workers and a 10.4-percent sample of workers covered by the Railroad Retirement Act; a sum- The BEA can assemble CWHS data, on a quick- response basis, into five types of analytical tables for any county or group of counties. 1 . A migration summary tabulation (see Tables V-3 through V-5) displays work force changes for a specified area, timespan, and segment of the work force. The following components of change are measured: in- migrants, outmigrants, entrants, exits, and net movements to and from the military and unclassified areas. (Entrants are workers who were not in social-security- covered employment at the beginning of the time period under study, and exits are workers who were not in covered employ- ment at the end of that period.) This type of tabulation also shows the geographic origins of inmigrants and the destinations of outmigrants. 2. Structure tabulations (see Tables V-6 through V-8), available from the 10- percent CWHS, describe migrants, non- migrants, entrants, and exits for a specified area for 1971-73, in terms of demographic and economic characteristics. Numbers of workers and mean wages are shown, ac- cording to sex, race, age group, industry of employment, and wage class. The planner is thus able to observe the differential characteristics of those workers entering and leaving an area of study and to assess the impact which migration has on the total work force structure. These tabulations are also available from the 1 -percent sample for a wider choice of timespans. Other structure tabulations (see Tables V-9 and V-10) describe the structure for the en- tire work force. Year-to-year changes in work force structure can be observed in terms of sex, race, age, industry, and wage class. 3. A migration matrix presents employment and mean wages, for a specified timespan and segment of the work force, in a con- venient 9x9 matrix, where rows and columns represent either areas or industries of origin and destination. The example shown in Table V-l 1 pertains to New York State planning regions. It shows that 106,700 of the white male workers in the durable goods manufacturing industry employed in the Western Region (Region 1) in 1967 were still employed there in 1970, but that 1,100 of the workers employed in the Western Region in 1967 had migrated to the Finger Lakes Region (Region 2) by 1970, and so on. 4. Longitudinal tables measure the work ex- perience of a particular group of workers, for the years prior to or following a given year. The group of workers can be defined in terms of State or county of employment and according to sex, race, age, industry, and wage class. Table V-12 shows workers whose major job in 1960 was in New York State and the migration status of these workers for the years 1963 to 1968. In 1966, for example, 2,408,500 workers had been working in New York in the same industry since 1960. An additional 968,400 of the 1966 workers had also been working in New York since 1960, but in different in- dustries. The table shows 1960 workers who were employed in another State in at least one of the years between 1960 and 1966 — 52,900 in the same industry and 73,600 in a different industry. It also shows persons whose major job was in either the armed forces or military reserves in one or more of the years between 1960 and 1966, persons who worked in 1960 and 1966 but who did not work in at least one of the in- tervening years, migrants, persons working in one of the two military categories in 1966, persons who had not worked in covered employment since 1960, and persons who worked in 1960 and in at least one of the years between 1960 and 1966 but who were not working in 1966. In obtaining tabulations from the BEA, the planner has the option of requesting either first quarter or an- nual data. For migration analysis, the first quarter data are preferred: they are timelier; wages before migration can be separated more precisely from wages after migration; and comparisons between CWHS employment estimates and other data series can more easily be made. The planner will find that the annual data have their advantages, too: the number of records for the workers whose geographic location is unknown is less than that in the first quarter file; certain industries with strong seasonal patterns of employment are not likely to be understated or overstated; data on farm workers are available; and information on the self- employed can be incorporated. Limitations of CWHS Data (Chapter VI) A number of limitations in the CWHS will confront the planner. Most can be overcome, or at least con- trolled, through careful study design and the astute interpretation of results. Among major limitations is sampling variability, which indicates how much a sample estimate can be expected to deviate from the "true" (total population) result. The variation as- sociated with CWHS estimates depends principally on the size of the sample — the larger the sample, the smaller the variation. The variation associated with estimates of numbers of persons, for example, tends to be only about one-third as large in the 10-percent sample as in the 1-percent sample. Another major limitation is the incomplete coverage of workers. Almost 10 percent of the workers in paid employment are excluded from the social security system, including most Federal civil servants, some State and local government employees, certain agricultural and domestic workers, and certain employees of nonprofit organizations. Among other restrictions is the existence of reporting errors, which, if undetected, permit erroneous conclusions to be drawn. For example, faulty reporting by multi- establishment firms of the locations of individual es- tablishments can result in "spurious" migration flows. When workers are unclassified by State, county, and/or industry, local area estimates of total work force and gross migration can be understated. Other limitations include the lack of State and county information on the employment of military and military reserve personnel and the lack of migration data on entrants and exits. Gross migration flows are underestimated by the extent of the migration of those entering and leaving the work force. Efforts are being made to increase sample size, to ex- tend coverage through the use of Civil Service and Railroad Retirement Board files, and to identify and correct reporting errors. A Comparison of the CWHS With Other Data Sets (Chapter VII) Depending on the planner's needs, various data sources may be alternatives to the CWHS: other Social Security Administration records (especially from County Business Patterns — CBP), State un- employment insurance (UI) records (notably those of the Bureau of Labor Statistics — BLS — -and the BEA employment series), and Census Bureau household surveys and censuses (particularly the Current Population Survey — CPS — and decennial population censuses). Alternative sources for employment by industry are the CBP. UI, and decennial census; for employment by demographic characteristics, the BLS, CPS, and census; and for migration, the census and CPS. From a conceptual standpoint, CWHS employment estimates may be lower or higher than similar CBP and UI estimates. For example, UI and CBP data count a worker two or more times if he holds two or more jobs during a reference period, while the CWHS counts a worker only once, no matter how many jobs he holds during the period. At the same time, however, the CWHS counts workers who are employed at any time during a quarter or a year, un- like the UI and CBP, which count only workers employed during a specific pay period. In a comparison between decennial census and CPS household-survey employment data and CWHS employment data, several differences become ap- parent: (1) the former measure employment by place of residence, while the CWHS data measure it by place of work; (2) the census and CPS provide point estimates for the previous week, and the CWHS for a minimum of a quarter; (3) the census and CPS in- clude data not only for industries not completely covered by social security legislation but also for unpaid — and hence noncovered — workers in family farms and businesses; and (4) the census and CPS cover all workers (including the self-employed), while quarterly CWHS data include only wage and salary workers. The discrepancies between CWHS and Census Bureau data are not uniform among different sex, race, and age groups, nor have they remained stable over time. For planners, perhaps the most significant differences between CWHS and Census data are in migration es- timates. While the CWHS measures work force migration, the decennial census and the CPS measure population migration. The CWHS can be used to measure migration by place of work and for variable time intervals while the census and CPS measure migration by place of residence between two fixed points in time. The CWHS has certain disadvantages when employed to measure migration flows. Unlike Census Bureau data, it does not record the migration of (1) nonworkers, (2) persons before they enter or after they exit the work force, (3) persons who are un- employed during either a beginning or ending reference period, and (4) persons who move between noncovered jobs or between covered and noncovered jobs. But CWHS data also have advantages relative to census and CPS gross migration data. They permit study of the frequency and timing of migration in these ways: (1) they provide data for various time periods rather than for only one time period preceding each decennial census; (2) they can provide information on multiple moves occurring during a period under study and can record as migrants persons who move and then return to places of origin before the end of the period; and (3) they can link in- dividuals over time to provide profiles of individual movements over several time periods. Although CWHS work force gross migration rates for all demographic groups tend to be significantly higher than census work force and population migra- tion rates, simple proportional adjustments to the CWHS rates are shown to result in close cor- respondences between CWHS and census rates. Because CWHS-census discrepancies differ among demographic groups, the separate adjustment of each sex-race-age group is recommended. CHAPTER II USE OF CWHS WORK FORCE AND MIGRATION DATA: A REVIEW OF PAST STUDIES Introduction The CWHS data files that have the greatest relevance for State and local-area planners are the annual 1- percent employee-employer and self-employed files and the first quarter 1-percent and 10-percent employee-employer files (see Chapter IV). These files contain information on the sex, age, and race of workers and the industry and county of employment and earnings associated with each social-security- covered job. Through tabulations from the individual CWHS records, data on the demographic, industrial, and earnings characteristics of the work force of a .particular area can be developed (see Chapter V). And by linking files for two or more periods, the characteristics of workers who enter and leave the work force, change county of work (migrate), and/or change industry of employment can be specified. State and local-area planners will find the CWHS of interest because it is the only reasonably comprehen- sive source of intercensal data on the demographic characteristics of the work force for relatively small geographic areas. In addition, perhaps the most valuable potential uses of the CWHS relate to the regular monitoring of migration and other aspects of work force change. The CWHS is unique among large-scale data sources in being able to trace individual worker movements and earnings changes over time. Although the decen- nial census collects migration information from in- dividuals (such as place of residence 5 years ago), such data cover limited timespans and depend heavily on the reliability of census respondents' memories. The CWHS, in contrast, monitors changes in earn- ings, industry, and location of work on a quarterly or annual basis (as long as an individual in the sample is working in covered employment) and relies on regularly maintained administrative records rather than workers' memories. This chapter will review past and potential applica- tions of CWHS data, with particular emphasis on studies which apply to State and local-area research and planning concerns. The State of New York has recently begun acquiring CWHS data on a regular basis for monitoring migration and work force change. Since the New York case represents one of the few efforts to develop an ongoing State and sub- state analysis program using CWHS data, it will be discussed in a separate chapter (see Chapter III). 1 One of the earliest area studies was conducted at the BEA by Hirschberg for the Appalachia Region, 1957- 63 (19). The Appalachia study also marked the begin- ning of the BEA tabulation system, which has provided special CWHS tabulations for studies con- ducted at various research and planning institutions (see Chapter V). One of the most detailed studies of a particular area using CWHS data was a recently com- pleted analysis by Stolnitz for the State of Indiana, 1960-68 (46). This study, which utilized tabulation services available at the University of Indiana (see Chapter V), was unique among CWHS studies, in part because of its heavy emphasis on interindustrial mobility. Since most CWHS studies have been designed to answer relatively specific research or policy ques- tions, the discussion of applications in this chapter is organized mainly by the kinds of issues that have been addressed with the data. The first part of the chapter reviews applications of CWHS data to the delineation of work force structure. Programs for the development of work force structure data are the only ones which have led to the regular publication of CWHS data. These publications offer the analyst a convenient way of becoming familiar with the data and some of their potential; the publications are, 'Lack of information about CWHS data has hampered their use at the State and local-area level. Moreover, for small areas, large CWHS files are costly to process, and a 1-percent sample is fre- quently too limited for detailed analyses. To some extent these limitations are being overcome by the development of a new 10- percent sample (see Chapter IV) and the development of special tabulation systems which reduce the costs of obtaining data (see Chapter V). therefore, emphasized in the review of existing studies of work force structure. The second and more extensive part of this chapter reviews applications of CWHS data to the analysis of work force change. This discussion is divided ac- cording to various aspects of geographic mobility, industrial mobility, and other policy concerns. Generally, the reviews are intended to suggest poten- tial applications of CWHS data at the State and local-area level. 2 Complete references for and brief descriptions of the studies reviewed in this chapter, as well as other CWHS studies, can be found in the an- notated bibliography. Analysis of Work Force Structure Like the decennial census and periodic household surveys, such as the Current Population Survey (CPS), the CWHS provides data on work force characteristics. 3 The CWHS, however, is available for intercensal years and for areas too small to be covered adequately by national household surveys. Moreover, it is the only administrative-record source of data which provides employment and earnings classified by the sex, age, and race of the workers. Existing Studies One of the most ambitious applications of CWHS data to the description of work force structure is the publication series Earnings Distributions in the United States (56), prepared by the Office of Research and Statistics, Social Security Administration (SSA). This series includes separate publications covering 1967, 1968, and 1969, with updates planned at 5-year inter- vals beginning with 1974 data. The published data are derived from the annual 1-percent employee- employer and self-employment files of the CWHS and are presented for the Nation, States (including metropolitan-nonmetropolitan breakdowns), and large metropolitan areas. The data include employ- ment by sex, race (total, white, Negro), age (under 25, 25-44, 45-64, 65 and over), and earnings (six categories). Similar data are also published for the subset of all employees who worked in each of the four quarters of the year. No data, however, are in- cluded on the industrial distribution of employment or earnings. The data which have been published thus far can be used at the State and metropolitan-area levels to as- : Sometimes sample size or other dala limitations make it inap- propriate to adapt studies conducted at the national or broad regional level to small areas. Chapter VI contains a detailed discus- sion of various CWHS limitations. For a discussion of differences in coverage among these series, see Chapter VII. sess the trends in the demographic and earnings characteristics of the work force in the late 1960's. The 1969 data, moreover, can be compared with data on 1969 employment taken from the 1970 census in order to assess the strengths and weaknesses of these alternative data sources on worker characteristics. The 1974 data will provide a mid-decade reading on work force characteristics which has not previously been available for States and metropolitan areas. A general assessment of the comparability of CWHS with census data is presented in Chapter VII. In addi- tion, a comparison of 1969 employment data from the 1970 census with data from Earnings Distributions in the United States, for New York State and the four broad census regions, is presented in Chapter III (see Table 111-13). Although incomplete worker coverage and the lack of place-of-residence data in the CWHS often make CWHS-census comparisons difficult to interpret, such comparisons (see Chapter III) provide some indication of the extent of the apparently sub- stantial census undercount among selected demographic groups (particularly blacks). The census undercount of workers is probably concentrated among irregular workers; the Earnings Distributions data permit assessment of the relative importance of irregular workers versus stable (four-quarter) workers in various demographic and earnings categories. The most extensive source of data on worker characteristics by industry which has been developed from the CWHS is Annual Earnings and Employment Patterns of Private Nonagricultural Employees, published in the Bureau of Labor Statistics (BLS) Bulletin Series (60). Detailed reports cover the years 1965-67 and 1970; a summary report is available for 1971; and annual reports are planned for subsequent years. The BLS data, which are available for broad geographic regions, were developed from the annual CWHS 1-percent employee-employer file, sup- plemented with data on workers covered by the Railroad Retirement Act. The BLS data are the first to have merged the railroad data with the CWHS. In most of the BLS industry tabulations, a worker is assigned to the industry in which he receives the largest share of his total estimated earnings for the year. In some of the tabulations, however, distinc- tions are drawn between the earnings a worker receives in his major industry and the earnings he receives in other industries. Distinctions are also made between all workers and four-quarter workers. The BLS does not publish CWHS industry data for States or local areas, but, within the limits of sample size, similar data could be developed for such areas. Presently, the census is the only alternative source of State-level employment based on records for in- dividuals. The census, however, inquires only about current or most recent industry of employment as of the census date, while census earnings data refer to the preceding year. Unlike the CWHS, therefore, the census cannot unambiguously identify an individual's earnings classified on the basis of the industry in which they were earned, nor does the census identify the number of jobs and industries in which an in- dividual had earnings, as does the CWHS. Thus, the CWHS offers' the State and local-area planner a uni- que source of information for examining questions relating to the industrial sources of earnings for such groups as low-wage or irregular workers. The BLS data suggest that, during any one year, about 25 percent of private nonagricultural workers in the U.S. are employed in more than one industry. In general, seasonal industries, such as construction, and low-wage industries, such as certain types of trade and services, tend to have the greatest propor- tions of both part-year workers and workers with earnings from more than one industry. These in- dustries also tend to have the highest ratios of total workers to workers receiving the major shares of their earnings from the industry. Industries with the lowest ratios of total workers to major earners (that is, industries with the strongest attachment of workers to the industry) tend to be in the mining, manufacturing, and transportation groups. In addition to providing CWHS data in considerable industrial detail (generally three-digit SIC at the national level), the BLS bulletins provide data by sex, age, and race as well as data for broad geographic regions. An example of a study using CWHS work force structure data prepared at the BLS is a work by Stras- ser which compared the earnings patterns of blacks and whites for 1966 (61). Blacks generally had lower earnings than whites. Part of the difference was due to a greater degree of intermittent unemployment among black males, but black-white differentials characterized four-quarter workers as well. For all black males the median level of earnings in 1966 was $2,990, compared with $5,578 for white males. Among four-quarter workers the medians were $4,316 and $6,802 for black and white males, respectively. Despite the substantial differentials, Strasser found that, for four-quarter male workers, the overlap of the black and white distributions amounted to 64 per- cent (that is, when the two distribution curves plot- ting percent of workers against income were com- pared, the area in common to both curves was 64 per- cent of the total area of each curve). The extent of overlap and the wage differentials, however, varied among industries and regions. Black-white differen- tials, for example, were larger in construction than in transportation and larger in the South than in other regions. The SSA and BLS reports which profile the work force are the only publications providing extensive CWHS data on a "regular" basis. The BEA, however, has also developed standard work force structure tabulations which can be assembled for any county or group of counties and for any time period for which the basic files are available (see Chapter V). Potential Applications The applications of the CWHS to the analysis of work force structure can be extended by combining existing files with other data sources. Possible joint uses of the CWHS include (1) supplementing the current information on CWHS files with informa- tion, such as location of residence, from other micro- data files, and (2) developing, via the CWHS, an age and sex distribution for the BEA annual employment series for States. Although these examples (discussed below) treat States and local areas within a national perspective, the statistical methods and data series developed should be useful to planners interested in particular geographic areas. The SSA has already made test efforts to obtain place-of-residence information for workers in the CWHS files, and more extensive efforts are planned for the future (see Chapter IV). The most obvious use of the new place-of-residence information would be in combination with the place-of-work data already in the CWHS file to develop data on worker com- muting patterns. Such intercensal commuting data can benefit numerous programs: 1. The Housing and Community Develop- ment Act of 1974 encourages municipalities to consider the number of low-income commuters in planning for low-income housing assistance in their jurisdictions. 2. The Comprehensive Employment and Training Act of 1973 requires labor force data by place of residence. Currently, these data must be developed from work force data which must be converted to a place of residence basis. 3. The BEA's local-area personal income es- timates require that wage and salary data by place of work be "residence adjusted." Recently, the CWHS data, after adjustment for com- muting, multiple-job holders, and age-distribution differences with related census data, were used to derive an age-sex structure for the BEA State employ- ment series for the period 1958-72. This series, in turn, will be used to develop employment and pop- ulation projections, by age and sex, as a part of the BEA area economic projection program. The BEA and CWHS employment series are not strictly com- parable, because BEA employment is a count of jobs while CWHS employment is usually tabulated as a count o( workers. The age distributions, however, should be similar, and CWHS data can be tabulated on a job-count basis. CWHS data, moreover, are more appropriate than census data for indicating the age-sex distribution of BEA employment, because census data are residence rather than establishment data and cannot be used to develop a job-count measure of employment. Analysis of Work Force Change Introduction The CWHS work force structure data discussed above can be used to monitor aspects of work force change for both long periods (census decades) and short periods (years or quarters). The CWHS, however, can best be used for analyzing change through its ability to follow an individual's work ex- perience over time. This permits the analysis of gross movements of workers from job to job and place to place, as well as into and out of the covered work force. When the work experience cannot be traced on an individual basis, only net changes in the composi- tion of the work force can be monitored. As the studies reviewed in this section show, net changes often conceal a great deal of change associated with gross interregional migration, interindustry mobility, and intraindustry mobility. The CWHS is the only large scale data source with this "longitudinal" feature of tracing an individual's work experience over time. The decennial census and CPS ask questions about work force status or ac- tivities in preceding periods, but the answers provide only limited information about mobility (for exam- ple, State of birth and residence 5 years ago) and may suffer from imperfect recall. Although most CWHS studies of work force change cover a year or more, each annual file contains much data for monitoring short-run change. In fact, prior to the development of longitudinal files which con- tain individual records for a series of years (see Chapter V), mobility studies restricted to a single year were common. The classic single-year studies are by Bogue (4), covering Ohio and Michigan for 1947, and Bunting (7), covering Georgia and the Carolinas for 1953. The simplest concept of mobility used in these studies was based on multiple job holding dur- ing the year (working in more than one county, in more than one industry, or for more than one employer). Unfortunately, this concept included as "mobile" those workers who held two or more jobs simultaneously. An alternative approach to the measurement of mobility was to trace changes in the major job (the one with the greatest earnings) from quarter to quarter. Bogue noted that this approach resulted in less geographic mobility than did a defini- tion based on the employment of an individual in more than one county during the year. Although much can be learned about worker mobility from CWHS data for a single year, more can be learned by utilizing data for more than 1 year. Among the pioneering CWHS studies which trace in- dividual worker records for more than 1 year are the Gallaway studies of interindustry (58) and geographic (57) mobility in the United States during the 1957-60 period. These studies were based on tabulations prepared at the Office of Research and Statistics of the SSA. The focus was on major in- dustrial groups and multistate geographic regions, in considerable demographic detail. Like the Gallaway studies, subsequent CWHS mobility studies have often treated geographic mobility and industrial mobility separately. This distinction is maintained in the following discussion. Geographic Mobility Migration over time In contrast to the decennial census, which typically provides place-of-residence data for two points in time (based on a question about the respondent's residence 5 years earlier), the CWHS can provide quarterly or annual time-series migration data. Census migration rates based only on individuals' locations at the beginning and end of a 5-year period may substantially understate total movement, because CWHS data suggest that many workers change county of employment frequently. In analyz- ing 1-year CWHS migration rates for large metropolitan areas, for example, Morrison noted that "the working-age population of the modern American metropolis is far more fluid than would otherwise be supposed, and conventional measures (for example, 1-year net or 5-year gross migration) severely understate its actual migratory comings and goings." (28) Using CWHS time-series data, Morrison also found considerable variations in gross migration rates among metropolitan areas, large gross flows relative to net flows for nearly all areas, and persistent in- terarea differences in gross migration rates over time. 4 Gross migration rates varied from a low of 4 J ln an analysis of gross migration rates for nine metropolitan areas. Nelson attributed the substantial interarea differences partly to differences in industrial structure and differential geographic mobility rates by industry but added that "they seem more closely related to the rate of employment growth and new job oppor- tunities in each area." (34) 10 percent per year to a high of 20 percent, and for most areas net migration rates tended to be considerably smaller than either the gross inmigration or out- migration rates. Hence, most areas experienced sub- stantial migration but little net redistribution of the work force. Analysis by Birch and others, moreover, suggested that an area's inmigrants and outmigrants frequently have very similar characteristics (3). The time-series aspect of the CWHS migration data can be particularly valuable to local-area planners, because it can measure the effects of short-run forces that cannot be ascertained from cross-sectional data. An analysis by Renshaw, which used CWHS time- series and cross-sectional data, for example, suggested that short-run increases in employment op- portunities in metropolitan areas generally resulted in reduced outmigration and increased inmigration, even though the long-run effect of higher rates of employment growth was likely to be an increasingly mobile work force and rising outmigration (39). Conversely, the short-run effect of reduced employ- ment opportunities was likely to be both increased outmigration and reduced inmigration. Thus, economic policies can be used to influence both out- migration and inmigration, but with short-run results different from long-run impacts on population struc- ture and mobility. 5 toward the suburbs and increased office employment in the city. In addition to bringing in highly paid ex- ecutives, increased office employment was associated with relative increases in comparatively low wage female and minority workers (presumably engaged in clerical and service support activities). More recently, Smith and Batutis (43) used CWHS data to study changing migration patterns for New York State in the early 1970's (see Chapter III). One of the more comprehensive uses of CWHS data for a particular area was Nelson's study of Atlanta (66). The study covered the 1962-67 period and examined many features of work force change in Atlanta, in- cluding the origins of inmigrants and the destinations of outmigrants. As might be expected, the most pop- ular destinations for outmigrants also tended to be the greatest suppliers of inmigrants. The interchanges with distant regions tended to be balanced, and dominated by intermetropolitan moves. Interchanges with areas in the South tended to involve net migra- tion to Atlanta from nonmetropolitan areas. Although many other migrant characteristics were noted in the study, far more might have been done (for example, cross-classification of particular streams by the sex, age, and race of the migrants) had it not been for the limitations of a 1 -percent sample size. . Migration streams In addition to providing time-series and cross- sectional data on gross migration of workers into and out of geographic areas, the CWHS can provide data on place-to-place flows (based on changes in county of employment). These data are also valuable to local-area planners concerned with the likely effect of both local and national developments on migration patterns. Unfortunately, past use of both the time- series and place-to-place features of the CWHS data is limited, but several revealing studies are available. In a study of the New York metropolitan area Johnson documented the 1962-66 movement of par- ticular kinds of jobs and workers from the city to sub- urban counties, as well as changes in the overall com- position of employment (22). As might be expected, the study showed faster job growth in the suburbs and net migration of workers from the city to the sub- urbs. The city gained high income jobs relative to the suburbs and lost low income and middle income jobs. These changes are consistent with the movement of manufacturing and certain trade and service jobs 'Using cross-sectional census data, Lowry failed to find a consis- tent relationship between outmigration and economic factors (25). Both inmigration and outmigration rates were faster in fast growth areas than in slow growth areas. Lowry, however, failed to stress the fact that growth forces tend to attract highly mobile people to an area and to bring rapid change which is conducive to high rates of worker turnover. In the past, Smith and Chenareddy (65) and Hathaway and Perkins (18) used CWHS migration data to analyze rural-urban migration flows. These studies found substantial movement both into and out of the farming sector. It was concluded that bet- ter training for off-farm movers and increased non- farm job opportunities would be needed to smooth adjustments associated with a declining farm sector. Recent data, however, suggest a slowing of agricultural decline and a resurgence of growth in many nonmetropolitan areas. The BEA is currently using CWHS data to analyze recent reversals in the pattern of metropolitan/nonmetropolitan migration. The advent of the 10-percent sample should greatly increase the scope for using CWHS data for local- area analysis. Although the 10-percent sample is new, the BEA has already developed a standard format permitting special tabulations of migration streams for local areas. The origins and destinations specified include States, same or different BEA economic areas, and same or different SMSA's (see Chapter V). The standard tabulations can be cross-classified by sex, race, and age; sex, race, and industry; or sex, race, and wage class. Mean wages are included for nonmigrants, entrants, and exits as well as for migrants. The 10-percent samples for 1971 and 1973 have been used to a limited extent. Tabulations for New York State (not cited in the following chapter), for exam- 11 pie. reveal a trend of substantial net outmigration which characterized the large industrial States in the early 1970's. The estimates suggest net outmigration from New York for essentially all demographic groups, but groups vary in the rate of net outmigra- tion and. especially, in the rates of net interchange with other regions. Black males, for example, actually had a higher rate of net outmigration than white males over the period (although black males also had higher entrant rates). The greatest net loss of New York migrants was to the South Atlantic Census division. Multiple migration Unlike census data, CWHS data are able to measure repeat migration by the same individuals year after year. In a study using CWHS data, Morrison noted a strong tendency toward "chronic" movement by some individuals, while others remained immobile (29). The study analyzed individual worker tenden- cies to move or not over the 1957-66 period. Among those working in each of the 10 years, a maximum of nine possible intercounty moves was identified (one between each pair of years). Only 4 percent of those males who had not moved at all in the earlier periods moved between 1965 and 1966, while 70 percent of those who had moved in each of the earlier periods also moved in the last period. Those who had moved once prior to the last period were more than three times as likely to move in the last period than were those who had not moved in the first 9 years, and the proportion of movers in the last period increased steadily with the amount of previous moving activity. A few studies have examined the impact of multiple migration on particular areas. Fisher and Purnell, for example, studied return migration to the South and multiple migration associated with movement from the rural South between 1957 and 1966 (10). In addi- tion, Kiker and Traynham studied the relative impor- tance of return migration for the economy of the Southeast (23). In three out of four periods during the 1960's for which return migrants were identified, they constituted more than 30 percent of inmigrants to the Southeast. Moreover, 30 percent or more of those leaving the Southeast during a given 2-year period in the 1960's had returned by the end of the subsequent 2-year period. The wage levels of return migrants were generally lower than for nonreturning migrants, but in most years they compared favorably with the wage levels of nonmovers. In general, the study of multiple migration has been limited by the large amount of data processing re- quired. In the future, however, such studies will be greatly facilitated by processing routines developed at the BEA which can trace the mobility status of all those working in a particular geographic area in a given "base" year for six subsequent (or preceding) periods (see Chapter V). The data can identify those who have migrated and returned (as of the third or subsequent period) in terms of their demographic characteristics. Additional study, via CWHS data, of the origins and destinations, as well as industry, earnings, and demographic characteristics of multiple movers, might provide important insights into the dynamics of work force trends in particular areas. Morrison and Relies, for example, have shown that rapid growth and high inmigration rates for an area lead to rapid worker turnover because of the high incidence of multiple migration (30). Declining areas, in con- trast, might be expected to have much lower tur- nover. High levels of multiple migration can have important implications for local planners. In the area of hous- ing, for example, frequent movers may prefer rental to owner-occupied housing. Also, the kind of housing demanded is likely to depend on the income and demographic characteristics of the migrants. These characteristics, moreover, are likely to affect the de- mand for a variety of public services. Returns to migration CWHS migration data are superior to census data for analyzing the monetary returns to migration, because CWHS earnings are available for both before and after the move, while census earnings are available for only 1 year. With CWHS data, therefore, the earnings level of migrants can be compared with that of nonmigrants, and the changes in earnings for both groups can be compared, in order to evaluate the economic success or failure of migration. CWHS data suggest that migrants generally have lower initial earnings, on the average, than non- migrants, but increase their earnings at a faster rate than nonmigrants. Although part of this difference is related to their younger average age, migrants appear to increase their earnings faster than nonmigrants even when age is accounted for. As might be ex- pected, the migrants showing the largest earnings in- creases are those moving from low income (usually nonmetropolitan ) areas to high income (metropolitan) areas. Nelson found that even those migrants from large, high wage metropolitan areas to nonmetropolitan areas increased their wages more rapidly than workers who stayed in the metropolitan areas (34). She also found, however, that metropolitan- nonmetropolitan movement did not effect financial gains for all metropolitan outmigrants, and that areas where inmigrants had the largest earnings gains did not tend to experience high rates of inmigration. At the State level, Stolnitz has documented the earnings 12 gains associated with migration into, out of, and within Indiana (46). Using CWHS data to analyze the income characteristics of interregional migrants for the period 1960-65, Trott, Matson, and Smith also found that migrants generally increased their earnings more than nonmigrants, and that movers to high income regions had the largest absolute income gains (50). However, migrants to low income regions were more likely to improve their relative earnings position than were movers to high income regions. This was a pos- sible cause of the net inmigration of whites to the Southeast during the early 1960's. Blacks as well as whites generally improved their earnings by migrating, but migration was not so consistently profitable for blacks as for whites. CWHS earnings and migration data permit the development of a wide variety of analytical models for examining the economic causes and consequences of migration. Trott, for example, found that out- migration from BEA economic areas tended to be negatively related to the areas' relative earnings, ad- justed for industrial structure (49). Although economically rational, this finding contrasts with many results based on other data sources. In a study of human capital associated with migration into and out of the Southeast during the 1960-66 period, Laber found that, although the South had a net outflow of workers, it experienced a slight increase in the stock of human capital, because the inmigrants tended to have higher incomes than the outmigrants (24). The new 10-percent sample will make similar studies possible for small areas and increase the detail of data which can be analyzed for large areas. Industrial Mobility Extent and characteristics As in the case of migration analysis, the CWHS data files offer wide-ranging possibilities for studying in- dustrial mobility. There have, however, been even fewer studies of industrial mobility, using CWHS data, than of geographic mobility. The most exten- sive industrial data generated from the CWHS have been developed in connection with the BLS publica- tion series Annual Earnings and Employment Patterns of Private Nonagrieultural Employees (60). These data are for 1 year at a time and are not directly focused on questions of interindustry mobility, but they do provide some information on workers holding jobs in more than one industry during a given year. These data reveal a substantial amount of interindustry movement and multiple job holding. They do not, however, utilize the unique capability of the CWHS for tracing individual workers over several periods of time. The pioneering Gallaway study noted earlier is one of the most extensive industry studies (58). Stolnitz did detailed industry studies at the national level (67) and for Indiana (46). The Indiana study is probably the most comprehensive analysis of industrial mobility carried out at the State level. The Stolnitz national study revealed that over two- fifths of U.S. workers in the CWHS file for both 1960 and 1968 had a different major industry of employ- ment in the 2 years. Generally, blacks tend to change industries more often than whites, and interindustry mobility (which is often associated with geographic mobility) tends to decline with age. Interindustry movers tend to have low incomes, but they tend also to achieve significant income increases through movement. In comparison with geographic mobility, interindustry mobility appears to be more con- centrated among low income groups. Industrial mobility tends to vary considerably by in- dustry, and among geographic areas for the same in- dustry. For example, Nelson found turnover in low- wage retail trade and service industries to be much more frequent than in higher-wage industries (66). The low-wage industries generally hired large numbers of new entrants who subsequently moved to jobs with higher pay. Within single industries, the author also found substantially higher turnover in fast-growth areas than in slow-growth areas. As in the case of geographic mobility, the analysis of industry mobility for particular geographic areas will be greatly facilitated by the new 10-percent sample. The standard tabulation formats thus far developed do not yet provide industry-to-industry flows. They do, however, classify migrants, nonmigrants, entrants, and exits from the covered work force, by industry and wage characteristics. The basic files will permit extensive analysis of in-and-out flows for par- ticular industries as well as industry-to-industry flows. Origin-destination ma trix An origin-destination matrix, showing worker move- ments among industries, as well as nonmovers, entrants, and exits, is one of the best ways to study variations in mobility from industry to industry. Stolnitz constructed such a matrix for the U.S. for the period 1960-68 (67). Among 12 industry groups, he found that between 25 percent and 50 percent of the 1968 work force were not in the 1960 work force and that generally between 25 percent and 50 percent of the workers in each industry in 1968 were in the same industry in 1960. The industries with the greatest proportions of entrants tended to be low-wage and/or rapidly growing (for example, trade and professional services), while those with the greatest proportion of stayers tended to be high-wage and/or 13 relatively declining (for example, durable manufac- turing and farming). The trade industry generally tended to be the largest supplier (after entrants) of workers for other industries. The proportion of interindustry movers and entrants revealed by an origin-destination matrix will vary, depending on the length of the time period covered and the level of industrial detail examined. Dif- ferences in patterns of mobility by industry, however, are persistent, and CWHS data permit a more thorough analysis of these differences than is possible from other data sources. Role of industry structure The factors associated with industry mobility were revealed in a study by Alexander (1). Using CWHS data on the interindustry movements of white male workers between 1965 and 1966, Alexander classified four-digit SIC industries into three broad classifications — "manorial, " "guild," and "unstruc- tured." Manorial industries were characterized by generally low mobility (fewer than 10 percent of the workers changed employers), guild industries by high interfirm movement relative to interindustry move- ment (firm mobility minus interindustry mobility ex- ceeded 10 percent), and unstructured industries by high mobility (over 20 percent of the workers changed firms) which did not follow the guild pattern of movement. Generally, industries listed in the manorial group in- cluded those (for instance, in durable manufacturing) where unions are strong and seniority systems com- mon. Guild industries tended to involve heavy use of craft union workers (as in the building industries, where there is often a strong attachment to the in- dustry and the union and a prevalence of short-term job opportunities). The unstructured group consists of a variety of industries, including many where un- ionization is weak and/or wage rates low. Alexander further tested a number of hypotheses concerning the relationship between industry struc- ture and such factors as work experience, earnings, and age of workers. He found, for example, that length of employment in a particular firm tended to raise earnings for workers in the lower and middle income classes, but not those at the higher income levels. Although the study did not include geographic analysis, the issues of industrial structure, mobility, and earnings have important implications at the local level, because of sizable geographic variations in these work force characteristics. Analysis of these characteristics constitutes an important area for future applications of CWHS data. In particular, local planners might use CWHS data in efforts to predict the probable impact of alternative industrial development plans on the demographic characteristics of the work force and on factors such as worker turnover and earnings. Returns to labor mobility Perhaps the most consistent characteristic of in- dustrial mobility as reflected in CWHS data is the low average income of workers changing industry combined with a high average rate of increase in earnings associated with the move. Undoubtedly, a major factor in this pattern is the strong tendency for workers initially to enter low paying industries and subsequently to move to higher paying industries. Once in higher paying industries, however, workers would appear to shift both industry and employer much less frequently. Firm seniority (as shown by Alexander) tends to be an important factor influencing the earnings of low and middle income classes. High income movers are more likely to move geographically or change employers in the same industry, while low income workers are more likely to change industry of employment without moving geographically. Interindustry movement by low income workers often results from employer layoffs and, consequent- ly, may not result in significant income gains. The CWHS data cannot directly distinguish voluntary from involuntary movements. They can, however, in- dicate the number of employers and earnings in each job on a quarterly basis. CWHS files provide an ex- cellent basis for tracing the work experience of those with low earnings and irregular attachment to the work force. Not only can sex, race, and age characteristics be identified, but the progress of these workers can be traced to indicate geographic move- ment, the industries in which they work, the periods during which they are absent from the work force, and the propensity for workers to eventually gain stable employment and increased earnings or to drop completely out of the work force. Predicting Migration Migration is the most difficult component of popula- tion change to predict at State and local levels. Typically, information on trends must be based on census data, which offer very few points of observa- tion. The CWHS data, however, can provide annual data on gross migration and employment changes, classified by demographic characteristics. This time- series feature of the CWHS is important to the local planner, who must analyze and interpret employment and migration trends when making short- and long- term forecasts. At least two major efforts are currently underway to utilize CWHS time-series data in predicting migra- tion and growth for local areas. At the Rand Cor- 14 poration, Morrison and Relies devised a model for projecting gross inmigration and outmigration for in- dividual metropolitan areas from projections of employment growth and data on past migration trends (30). The CWHS data used in the study in- cluded migration for seven time periods (1959-65) and 85 metropolitan areas. Because the time series was short, estimated relationships between annual migration and both employment change and past migration were developed, using cross-sectional regressions. Employment growth was found to be significantly related to inmigration but not to out- migration. Outmigration, however, revealed a strong relationship to previous outmigration and inmigra- tion levels (a complex result of repeated migration). When the inmigration model was tested, by making projections for individual areas, the results, although not decisive, appeared to improve on standard trend- extrapolation techniques. The BEA is also utilizing time-series CWHS data in developing its next set of State and local-area projec- tions. The principal applications involve the in- troduction of demographic detail into the employ- ment and population projections generated by the model. An initial step in the process involves develop- ing a time series of State employment estimates, by age and sex, for the period 1958-72, using CWHS .data in conjunction with the standard BEA employ- ment series. These data are then to be used with an- nual population data, by age and sex, to compute employment participation rates, which can be pro- jected as a part of the model planned for producing population projections by age and sex. Population projections are to be derived from the employment projections. Net population migration estimates can be derived by comparing these projections with those derived by the Census Bureau under the assumption of no net migration among States. The BEA is also working to develop a "gross flows" model, using CWHS data in order to permit direct projections of gross migration. While time-series data are useful in comprehensive projection models covering many regional areas, they are even more important for planners concerned with only a few regions. The use of cross-sectional models is not feasible in such cases unless efforts are made to acquire and analyze numerous data for areas not directly under consideration. Both time-series and cross-sectional analyses yield important insights into the processes of migration and growth. Time-series data, however, will be particularly important in short-term and single-area forecasts. The CWHS is unique in providing regional migration data on both a time-series and cross-sectional basis. It can be an in- valuable resource in forecasting population and employment trends. Assessing Policy Impacts Beyond its usefulness as a source of aggregate statistics for particular areas, the CWHS has the uni- que advantage of displaying the experiences of a very large sample of individuals over time, thereby providing insights into the impacts of policy decisions affecting members of the work force. The National Academy of Sciences, in 1974, recommended greater use of the CWHS in the evaluation of manpower programs (31). The chief advantages cited were the low cost, the absence of recall or interviewer bias in determining the earnings pattern of former program participants, and the elimination of the nonresponse problem of sample survey techniques. The use of CWHS data in manpower program evaluation involves the matching of CWHS files with Manpower Automated Reporting System files. In combination, these files contain a wealth of informa- tion on the characteristics of workers, which would aid in studying the effects of manpower programs on individual workers. For example, using CWHS data, Jacobson studied the effects of manpower training on earnings and found that earlier studies had un- derestimated the effects of such programs on earnings (21). The regular updating of the CWHS, moreover, provides an excellent basis for the continuous monitoring of work force behavior (for example, entry, exit, migration, and interfirm and interindustry mobility) and of personal earnings. The CWHS can also be used to assess the impact of major policy decisions which tend to alter the pattern of growth and development of particular industries and regions. Jacobson used CWHS data to study the earnings patterns of workers who left the steel in- dustry under varying economic circumstances (20). The purpose of the study was to predict the probable losses that would be incurred by displaced steel workers if import restrictions on steel were eliminated. The conclusion was that the total losses to displaced workers probably would be small. Since social security data are collected on an es- tablishment basis, the possibility exists of tracing not only region-wide or industry-wide impacts of general policies, but also the behavior of workers directly af- fected by specific events, such as major plant loca- tions or closings. Although sample size and issues related to confidentiality often constrain the analysis of specific events, Pursell and others were able to use social security records to analyze the effects of one plant closing by obtaining special clearances from the SSA and the permission of the workers involved (38). The Pursell study was designed to analyze the effect on displaced workers of benefits awarded under 15 provisions of the Trade Expansion Act. The analysis involved the use of survey data and social security records. The work force involved consisted mainly of females and blacks. The analysis revealed high rates of industrial mobility and low rates of geographic mobility following the plant closing. An examination of earnings records revealed that the females and blacks appeared generally to adjust less well (had earnings declines or lower gains) than white males. There was little evidence that special training benefits provided by the Act contributed significantly to im- proving worker earnings. In fact, those accepting training appeared primarily to treat it as income maintenance and generally ended up with earnings lower than those of other workers. The range of potential policy-oriented analyses that could utilize CWHS data is, of course, very wide. The effects of the decline of particular industries on different kinds of workers or different areas, for ex- ample, can be traced with these data. Also work force impacts of such large-scale events as the Vietnam war or the energy crisis might be examined. For ex- ample, the CWHS can show what kinds of jobs (and where) were taken by young persons exiting the military during and after the Vietnam war. Or changes in the earnings and mobility of low income workers could be analyzed, following such events as changes in the minimum wage. Lastly, the work force impacts of numerous area-specific events might also be examined. 16 CHAPTER USE OF CWHS WORK FORCE AND MIGRATION DATA: AN EXAMPLE FOR NEW YORK STATE This chapter illustrates several ways to use the CWHS for the intercensal monitoring of migration and work force change through references to a New York State example. The New York discussion is organized to show how a planner might analyze and evaluate a large mass of CWHS work force and migration data for a State or local area. New York State was chosen to illustrate applications of the CWHS data, because it has a substantial program (the New York State Migration Project) for using the CWHS in its economic and demographic planning, unlike most other States. Recent changes in New York migration patterns, moreover, illustrate the importance of a system for monitoring change. The next section summarizes the major findings of the New York State Migration Project. Succeeding sections supplement these findings with a general dis- cussion of the work force and migration tabulations for New York State. The New York State Migration Project The New York CWHS migration research project has been described in a series of papers by John Smith and Michael Batutis of the New York State Economic Development Board (43). The stated pur- poses of the project were: 1. to extend the general knowledge of post- 1970-census migration patterns in terms of migrant characteristics, 2. to ascertain the economic development policy implications of recent migration patterns, and 3. to improve the methodology and techni- ques underlying the State's demographic projection series. Based on the analysis of post-1970 New York migra- tion patterns completed thus far by the New York project, the most significant findings can be sum- marized, as follows: 1. New York experienced a major increase in its overall rate of worker net outmigration from the late 1960's to the early 1970's. 2. The greatest increase in net outmigration involved migration to the South. 3. The migration of blacks reversed from net migration into New York in the late 1960's to net migration out of the State in the ear- ly 1970's. 4. Net migration for the key age group, 25-29, shifted from inmigration during the late 1960's to outmigration in the early 1970's. 5. The New York City area generally dominated the overall migration reversals for the State. The New York Tabulations The analysis of these migration patterns was based on standard tabulations from the 1-percent CWHS first quarter files. (A detailed description of the basic CWHS files is contained in Chapter IV, and the BEA system from which the tabulations were developed is discussed in Chapter V.) The tabulations included: 1. the BEA work force structure tabulations for the years 1965, 1967, 1970, 1972, and 1973 for the State, 2. the BEA migrant, nonmigrant, and entrant and exit structures for the 1965-70, 1970- 72, 1967-70, and 1970-73 periods for the State, 17 3. the BEA migration summary tabulations for the same periods as the migration struc- ture tabulations; migration summaries were prepared for New York City and the New York City metropolitan area as well as the State (see Tables III-9 and 111-10, below), and 4. two BEA origin-destination matrices for New York (see Tables III-ll and III- 12, below): a. the first matrix traced flows among seven substate regions, the New Jersey and Connecticut part of the Tri- State Region, and the remainder of the Na- tion, and b. the second matrix traced flows between two New York areas (the Tri-State Region and the remainder of the State), on the one hand, and seven regions of the remainder of the Nation, on the other hand. Work Force Structure Tables III-l and 1 1 1-2 are work force structure tabulations for the United States and New York, for 1967. 1970, and 1973. The 1967 and 1970 data are based on the 1 -percent final first quarter CWHS file, and the 1973 data are based on the preliminary 1- percent first quarter file. The data suggest that the median age of the social- security-covered work force in New York was about 3 years higher than the median age nationally, that females and blacks made up somewhat larger propor- tions of the covered work force in New York than in the U.S., and that the median wage of covered workers in New York was about 20 percent above the national median in 1973. Between 1967 and 1973 the median age of the New York work force dropped by more than 2 years, the proportions of blacks and females in the work force increased somewhat, and median wages increased by 46 percent. Generally, the New York changes in work force structure paralleled the corresponding national changes over this period, but the rates of change in New York deviated somewhat from the national rates. The median age of the work force declined slightly less in New York than nationally; females increased their share of the work force less rapidly than they did nationally, and blacks increased their share more rapidly. Median wages in New York increased faster than nationally. The work force structure tables reveal that average wage levels in New York increased more rapidly from 1967 to 1973 than did national wage rates, even though wages were already substantially above the national levels in 1967. Wages in government and ser- vices increased at faster than average rates, both in New York and nationally. In both industries wage levels increased faster in New York than nationally, as did the share of total employment. In government (excluding Federal and other noncovered workers), mean wages increased 78.4 percent in New York and only 61.4 percent nationally, and the government share of New York employment increased by 3.1 percentage points (from 4.9 percent) compared to a national increase of only .3 percentage points (from 4.5 percent). In services, mean New York wages in- creased 50 percent compared to a 48.9-percent in- crease nationally, and the share of New York employment in services increased by 1.8 percentage points compared to a 1.4-percentage-point increase nationally. The largest category of services employ- ment in New York, as measured by the CWHS (educational services), is made up primarily of public employees, and, thus, the rapid rises of government and services wages in the State are somewhat related. 1 Increases in private-sector wages also contributed to the relatively high growth rate of wages in New York over the 1967-73 period. In the above-average-wage finance, insurance, and real estate industry, for exam- ple, the wage level in New York increased faster than nationally (52.6 percent v. 41.8 percent), and the share of New York employment in this industry also increased faster than nationally. New York experienced its largest relative employ- ment decline between 1967 and 1973 in trade. Declin- ing employment in the low-wage trade sector may have contributed significantly to the growth in mean wages for the State. In fact, the 1967 and 1973 struc- ture data are generally consistent with the earlier Johnson (22) findings for the New York City area (see Chapter II), in which it was suggested that employment trends in New York City were 'The particularly sharp increase in New York's government employment from 1970 to 1973, in Table 1 1 1-2, may have resulted partly from an apparent reclassification of some public service workers into the government category. Moreover, the increasing share of government workers nationally, shown in Table III-l, may have been understated somewhat because of a large increase in the number of workers unclassified by industry between 1970 and 1973. (In at least one State (Pennsylvania) a large number of government workers appears to have been placed in the unclas- sified category in 1973.) Even if allowance is made for possible classification changes and problems, however, the share of public sector employment increased substantially faster in New York than nationally between 1967 and 1973. 18 characterized by heavy losses in the number of medium- and low-wage jobs in manufacturing and certain trade industries and by relative gains in the number of high-wage professional and managerial jobs and of low-wage clerical and service jobs. The structure tables examined here suggest that dur- ing the 1967-73 period, at least, the impact of relative gains in high-wage office employment more than off- set increases in low-wage service jobs in terms of in- fluencing overall wage trends in New York relative to the Nation as a whole. Migration Structure As would be expected, new entrants tend to have sub- stantially lower wages, on the average, than non- migrants, and they tend to be disproportionately con- centrated in such low-wage industries as trade and services; work force exits exhibit similar patterns, although to a lesser degree. Migrants tend to have higher wages than exits or entrants, but from 1970 to 1973 they had lower average wages than non- migrants in New York (in part, because they tend to be younger than nonmigrants). Also from 1970 to 1973 New York outmigrants tended to have relatively lower wages (before migrating) than inmigrants (after migrating).- This apparent selection process helps ex- plain the increase in New York wage levels relative to national levels over the 1970-73 period. The CWHS also permits a direct examination of the characteristics of migrants and other workers in- volved in work force change. Tables III-3 through III-8 show the age, race, sex, industry, and earnings characteristics of inmigrants, outmigrants, non- migrants, entrants, and exits for the 1967-70 and 1970-73 periods for New York State. Even though the net changes in work force size were not large between 1967 and 1973, Tables III-3, III-5, III-6, and III-8 reveal a considerable volume of work force turnover. The characteristics of workers engag- ing in the various forms of turnover, moreover, differ considerably from those of nonmigrants (which as defined here exclude entrants, exits, and interstate migrants, but not intrastate migrants or those chang- ing jobs locally). As would be expected, entrants to the work force tend to be considerably younger, on the average, than nonmigrants. Migrants tend to be older than entrants, but still considerably younger than nonmigrants. Surprisingly, the median age of work force exits is also somewhat lower than that of nonmigrants (although substantially higher than for entrants and migrants). This seeming paradox is the result of relatively high rates of exit and entrance among the youngest age groups. Females constituted only about 40 percent of New York employment in the late 1960's and early 1970's, but they accounted for about half of the work force exits and entrants. On the other hand, females ac- counted for a less than proportional share of inter- state work force migration (perhaps because a relatively high propensity to enter and leave the work orce increases the likelihood that female movers will not be recorded as migrants in the CWHS). Blacks, as a group, also tend to be disproportionately represented among entrants and exits and somewhat underrepresented among interstate migrants. Migration Flows The conclusions reached by Smith and Batutis (43), concerning the changing pattern of migration for New York since 1970, were based largely on standard BEA migration summary tabulations and in- terregional migration tables prepared for New York. These tables contain numerous details, by migrant sex, age, race, industry, wage class, origin, and destination. Table III-9 shows that between 1967-70 and 1970-73 net outmigration from New York State more than doubled, increasing from 49,900 to 137,100. This change was associated with a decline of 70,300 in gross inmigration and an increase of 16,900 in gross outmigration. A relatively large number of workers in the 1973 CWHS file were unclassified by State and county, and the gross migration estimates for 1970-73 may be understated somewhat, relative to 1967-70. But even allowing for such a bias, the data suggest that the changing net migration for New York resulted more from declining inmigration than in- creasing outmigration. Table III-9 reveals that the sharpest migration changes for New York occurred in interchanges with southern States. In many cases the State-to-State flows may be distorted by sampling variability, or by "spurious" migration (for example, errors due to misreporting by multi-establishment firms — see Chapter VI). Overall, however, there is a definite pat- tern of declining migration from, and increasing : During the 1967-70 period New York inmigrants had lower wages relative to both outmigrants and nonmigrants than during the 1970-73 period. The contrast between the two periods can be explained partly by a reduced inmigration of low-income blacks in the post-1970 period. 19 migration to. the South. (Interchanges with the State of Florida alone may have accounted for nearly a quarter of the increased net outmigration from New York between the 1967-70 and 1970-73 periods.) Table 1 1 1- 10 shows migration patterns for selected demographic groups. It shows the shifts in particular groups which explain the overall increase in net out- migration for New York. Although blacks comprised only about 12 percent of the total New York work force, for example, they accounted for nearly a quarter of the increase in the State's net outmigration between 1967-70 and 1970-73. The estimated net migration for all black workers shifted from a net in- migration of 5,700 to a net outmigration of 15,600. For black males the shift Was from a net inmigration of 8,300 to a net outmigration of 7,400. These es- timates suggest that most of the change in net migra- tion for black males resulted from reduced gross in- migration (especially from the South) rather than in- creased gross outmigration. The 25-29 age group was i aother important demographic group which contributed dispropor- tionately to the net migration reversal for New York. This was particularly true of the white males in that age group. Table 111-10 shows that estimated net migration for these white males changed from a net inmigration of 5,600 during 1967-70 to a net out- migration of 18,400 during 1970-73. This shift alone accounted for over a quarter of the total estimated in- crease in net outmigration for the State. In contrast to the total shift and the shift for black males, however, the shift in net migration for white males, 25-29, was accounted for by approximately equal shifts in gross inmigration and outmigration. Tables III-ll and 111-12 show estimated migration data for seven regions in New York State for 1967-70 and 1970-73. These tables reveal the dominant impact of the New York City area on overall State trends in net migration. In fact, these estimates suggest that the increase in net outmigration from the Tri-State Region (which contains New York City) actually ex- ceeded the increase for the State as a whole. The remaining six New York regions as a group ex- perienced a small reduction in net outmigration. While the balance of net migration with out-of-State areas "deteriorated" for all New York regions between 1967-70 and 1970-73, the upstate New York regions generally "improved" their overall net migra- tion balances, because a part of the greatly increased net outmigration from the New York City area found its way into upstate areas. Caution should be exer- cised in interpreting these data, however, because sampling variability, or other problems, may be in- fluencing the flows (see Chapter VI). In particular, the heavy inmigration to the Upper Hudson Region may be, in part, a spurious consequence of the New York State government's policy of reporting all State employees out of the capital in Albany. Also, the 1967-70 flow of migrants from the Southern Tier Region to the Tri-State Region is abnormally large and may be the result of spurious migration. In spite of such problems, however, the CWHS data reveal a remarkably consistent overall pattern of changing migration trends for New York State and its regions. CWHS and Census Data The objectives of the New York State Migration Pro- ject include the use of the CWHS to monitor and pro- ject population change as well as work force change. From this perspective, the relationship between CWHS work force data and decennial census work force and population data is crucial. This relationship, for New York State, is discussed below. (Chapter VII presents a more extensive comparison of CWHS data with census and other data.) Employment Data Table 111-13 shows 1969 census and CWHS employ- ment for New York and the four broad Census regions with selected demographic detail. The census data are based on information provided in the 1970 census, which identifies anyone who worked for any length of time (at any location) in 1969 and who lived in the designated area at the time the census was taken. The CWHS data are taken from the Social Security Administration (SSA) publication Earnings Distributions in the United States 1969 (56) and in- clude all workers with social-security-covered earn- ings in 1969 on the basis of the location of their major jobs in 1969. For most areas, the census typically shows greater employment than the CWHS. This is because some workers (for example, many career Federal Civil Ser- vice employees) had no social-security-covered job during 1969, and others (particularly those in the military) showed no geographic location in SSA records (see Chapter VI). For New York, despite these gaps in the CWHS data, the CWHS shows greater employment than the census. A major reason is the fact that large numbers 20 of persons (especially commuters) worked primarily in New York in 1969 but lived outside of New York in April 1970, when the census was taken. Another reason for the high CWHS-census ratios for New York is the tendency for multi-establishment employers to erroneously report some employees as being located at corporate headquarters rather than at their actual places of work (see Chapter VI). Limitations in census data, particularly under- counting, also contribute substantially to the high CWHS-census ratios for New York. The census un- dercount is known to be high among young workers and blacks, and the CWHS-census ratios for New York in Table III- 13 tend to be highest for these groups. Among blacks, for example, the ratio is 1.23, compared with an overall ratio of 1.09 and a ratio of 1.05 for nonblacks. Since the New York ratio for biacks is unlikely to be biased upward relative to whites by factors such as commuting, its magnitude would seem to indicate a particularly serious census undercount for blacks. Migration Data Both CWHS and census migration data are useful for interpreting and projecting work force and popula- tion changes. Meaningful comparisons between these two kinds of data, however, are often difficult to make. 3 CWHS migration data are restricted to the work force while census migration data cover the en- tire population. CWHS data, moreover, refer to changes in place of work while census data refer to changes in place of residence. For New York, as for other large industrial States in the Northeast in the late 1960's and early 1970's, the net outmigration of the population appears to be un- derstated by the CWHS. 4 This apparent bias suggests that nonworkers and others whose migration is not likely to be covered in the CWHS may be leaving large industrial States on net at faster rates than are the migrants recorded in the CWHS. As noted above, the CWHS data show Florida to be a major destination of New York outmigrants. 'Chapter VII presents detailed comparisons between CWHS and census migration data. It also provides the State and local-area planner with guidelines on how to adjust CWHS migration rates so that they will more closely reflect census work force and popula- tion migration rates. ""Chapter VII suggests that the bias holds for several time periods and in comparisons of the CWHS with several sources of regional estimates of population net migration rates (including the Current Population Survey and post- 1970 Census Bureau State population estimates, as well as 1970 census data). Because many movers from New York to Florida are retirees, however, the CWHS may not record them as migrants. (They would not be recorded as migrants unless they worked in a covered job both before and after the move.) Therefore, CWHS net migration flows from States such as New York to Florida and other retirement areas are likely to understate pop- ulation migration. One reason New York appears to be experiencing net outmigration of retirees is its relatively high cost of living. This factor may also tend to discourage migra- tion to New York by large families with low labor force participation rates. 5 Unfortunately, the data are inadequate for determining the extent of bias in CWHS net migration rates resulting from nonworker migration. Although nonworkers are not recorded in the CWHS, irregular workers will be recorded during periods in which they have at least one covered job. The longer the time period over which CWHS work force status is checked to determine migration status, the more precise will be the measure of the potential number of irregular workers who might be identified as migrants. For example, if the period over which work force status is checked is shifted from a first quarter to an annual basis (that is, if a worker is defined as one who worked at any time during a year rather than only during a first quarter), then an area's inmigration and outmigration rates will increase because the annual data will include more irregular workers than the first quarter data (see Chapter V — Table V-17). In the case of New York, the shift from a first quarter to an annual basis increases the out- migration rate more than the inmigration rate and thereby results in an increased estimate of net out- migration. This result is consistent with the general proposition that irregular workers and nonworkers are leaving New York on net at faster rates than persons with strong work force attachments. In addition to problems associated with retirees, non- workers, and irregular workers, migration on the part of new entrants to the work force causes discrepan- cies between CWHS and census migration. CWHS net migration rates, however, can be adjusted to ac- count for new entrants. 6 An adjustment, which was High welfare payments, however, may tend to offset the effects of living costs for some groups. "A related adjustment process is described in Chapter VII. It is based on a CWHS-census comparison for 1965-70, which found that the migration rates of new entrants to the work force tend to follow the migration rates of work force participants with similar demographic characteristics. 21 performed for two periods (1967-70 and 1970-73), suggests that the increased rate of net outmigration for New York since 1970 may have been even greater than implied by the data shown in this chapter. Conclusion The data and analysis presented in this chapter are in- tended to illustrate the usefulness of CWHS data for the monitoring of migration and work force change by State and local-area planners. Of course, the kinds of data tabulations and analytical techniques used in a particular study will depend on the characteristics and policy concerns of the area under consideration. For example, in the case of small areas for which a 1- percent sample is less adequate than for New York, a planner might wish to concentrate his analysis on tabulations from the new 10-percent CWHS files — even though the 10-percent data provide less scope for historical analysis than the 1 -percent data (see Chapter IV). The remainder of this handbook provides technical information to guide researchers and planners in their use of CWHS data. Chapter IV presents detailed descriptions of the basic CWHS files. Chapter V discusses data availability, with special emphasis on the tabulation services provided by the BE A. Before attempting to acquire specific files or tabulations, however, a planner should understand the limitations of CWHS data and how they might af- fect the area under study. Chapter VI discusses these limitations. Finally, a planner or researcher will probably want to use CWHS data in conjunction with other data and information relating to his study area(s). To facilitate the interpretation of CWHS data in the context of broader data analysis systems, Chapter VII compares CWHS data with related data series. 22 < UJ UJ C7 s: < >- r- or c O -< M^ O uj ct r~- ct c o- 3 3-1 I— UU1 • 3 3 1^ iroo .— OZh or -- or o ct < S 3 3 1- es ^00 (TO m a* o O •© C*l CO •-« r- •— o^ — ' in i/) O CL <\J O f\J O CM Z LjJ • • • • • < *<: IM in r~ o^ m in or 00 4- m ^ m 3 o r^ o h- o* (» O 3 X m in oo in r- 1- li- r^ -4- cm -o < ^ t/> or D O O 3 o < r- M? 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O -a CD S- cu > o CO i- CD o CD ON oo ON ON CD CX> CD ON CNJ 1 CNJ 1 CD ro CNJ 1 CD ro CNJ 1 ro E CD CNJ ro E CD OJ 1 LO LO E LO E LO M- LO <4- LO CVJ J*! CNJ CD csj ^z CNJ CD CNJ ->^ CNJ CD u CD ■P CD o CD +J CD O CD cn ro Ol • ( — en ro CD •r"* CD ro CD e£ ■ — < JC < ■ — < JC < i — < CO 0Q 32 TABLE 1 1 1 — 11 NEW YORK STATE MIGRATION MATRIX EMPLOYMENT AND MEAN WAGES. 1967 - 1970 ALL WORKERS ALL WORKERS WORKERS FROM! TABULATION 1 COVERED _ .- .,-„ , — ., MILITARY LEFT WORK REGION REGION REGION REGION REGION REGION REGION REGION REGION AND NON- LABOR FORCE 123456789 OTHER MIGRANTS FORCE 1967 WORKERS (000) 321.0 4.4 1.5 .4 3.0 4.0 7.1 2.0 19.1 12.6 MEAN WAGFS 1967 6,276 5,940 6,107 3,460 3,935 5,559 7,392 7,697 6.254 5,555 MEAN nAGES 1970 7,688 7,436 11,310 3,168 6,157 6,068 8,402 10,185 7,994 6,921 % CHANGE 22.5 25.2 85.2 -8.4 56.5 9.2 13.7 32.3 27.8 24.6 REGION 2 - FINGER LAKES WORKFRS (0001 4.5 256.1 1.9 1.2 1.6 1.7 5.2 1.7 16.1 9.0 MEAN WAGES 1967 5,647 7,476 5.420 5,100 2,614 5,584 6,160 9,081 6,367 4,099 MEAN WAGES 1970 7,518 9,146 7,757 5,393 4,504 6,938 8,050 11,404 7,413 5,023 * CHANGE 33.1 22.3 43.1 5.7 72.3 24.2 30.7 25.6 16.4 22.5 REGION 3 - CENTRAL NEW YORK WORKFRS (000) 2.1 1.5 157.5 3.0 4.2 2.6 5.3 .9 14.5 10.1 MEAN WAGFS 1967 4,233 6,765 5,905 4,405 4,984 4,196 6,459 7,340 6.572 4,724 MEAN WAGES 1970 7,059 8,656 7,158 5,873 7,703 5,980 7,617 9,437 8,527 5,249 % CHANGE 66.8 28.0 21.2 33.3 54.6 42,5 17.9 28.6 29.7 11.1 REGION 4 - UPPER MOHAWK, BLACK RIVER, ST. LAWRENCE, AND LAKE GEORGE-LAKE CHAMPLAIN WORKFRS (000) .9 1.3 5.1 141.0 4.9 1.1 3.0 1.5 9.9 7.3 MEAN WAGES 1967 5,528 3,013 4,572 5,830 3,641 3,652 3,394 3,809 4,975 2,528 MEAN WAGES 1970 6,644 5,345 5,291 6,972 5,670 5,417 5,651 5,247 6,150 4,225 % CHANGE 20.2 77.4 15.7 19.6 55.7 48.3 66,5 37.8 23.6 67.1 REGION 5 - UPPER HUDSON WORKFRS (000) 2.1 1.5 1.6 3.6 297.7 1.6 11.5 3.0 17.3 7.9 MEAN WAGES 1967 3.787 5,022 4,276 4,528 6,499 2,461 4,975 5,890 6,115 3,668 MEAN WAGES 1970 6,478 8,157 6,016 5,515 7,908 5,081 6,785 7,889 7,641 4,397 % CHANGE 71.1 62.4 40.7 21.8 21.7 106,5 36.4 33.9 25.0 19.9 REGION 6- SOUTHERN TIER EAST, SOUTHERN TIER CENTRAL, AND SOUTHERN TIER WEST WORKFRS 1000) 2.5 2.7 3.8 .7 3.3 187.8 14.7 .9' 23.6 12.8 MEAN WAGES 1967 4,972 4,954 4,421 5.096 4,966 5,918 7,789 5,682 6,108 5,658 MEAN WAGFS 1970 6,304 7,193 5,172 4,890 7,282 7,170 11,137 7,474 7,877 7,343 * CHANGE 26.8 45.2 17.0 -4.0 46.6 21.2 43.0 31.5 29.0 29.8 REGION 7 - TRI-STATE (NEW YORK PART): NEW YORK CITY. NASSAU-SUFFOLK. AND MID-HUpSON WORKFRS (000) 4.4 3.9 4.4 4.4 4.5 3093.8 89.5 322.5 191.4 MEAN WAGES 1967 5,138 8,352 5,537 6,130 5,232 5,455 6,719 7,021 7,110 6,218 MEAN WAGFS 1970 6,881 10,676 8,024 7,638 7,134 6,101 8,634 9,299 8,674 8,658 % CHANGE 33.9 27.8 44.9 24.6 36.4 11.8 28.5 32.4 22.0 39.2 REGION 8- TRI-STATE (CONNECTICUT AND NEW JERSEY PARTS)* WORKERS (000) .7 1.3 .9 .9 3.1 1.6 83.4 1.7 MEAN WAGES 1967 3,104 6,173 7,856 6,240 5,849 8,663 6,484 4,601 MEAN WAGES 1970 5,754 9,445 10,146 8,470 7,125 9,707 8,682 6,646 % CHANGE 85.4 53.0 29.1 35.7 21.8 12.1 33.9 44.4 REGION 9 - REST OF UNITED STATES WORKERS (000) 16.2 15.3 10.5 9.2 17.5 21.7 290.5 11.7 MEAN WAGES 1967 6,129 4,879 5.972 6,048 6,527 5,743 6,191 8,099 MEAN WAGES 1970 8,777 7,765 8,623 7,525 9,506 7,862 8,471 11,325 96 CHANGE 43.2 59.2 44.4 24.4 45.6 36.9 36,8 39.8 MILITARY 6 OTHER WORKFRS (000) 15.5 11.0 6,4 5.9 12.6 12.7 174.1 2.2 16.4 66.4 MEAN WAGES 1967 3,280 2,541 3,189 2,540 2,922 3,301 4,184 6,641 7,846 4,390 MEAN WAGES 1970 6,927 6,864 6,503 5,672 6,651 6,083 7,566 10,240 10,286 5,823 % CHANGE 111.2 170.1 103.9 123.3 127.6 84.3 80.3 54.2 31.1 32.6 ENTERED LABOR FORCE WORKERS (000) 109.3 85.7 57.3 56.7 101.7 66.0 1319.5 45.9 MEAN WAGES 1967 MEAN WAGFS 1970 3,334 3,881 3,287 3,325 3,967 3,141 4,092 3,195 * CHANGE COVERED WORK FORCE 1970 WORKERS (000) 479.2 384,7 250.9 227.0 470.9 305,3 5008.1 101.7 439,4 377.0 MEAN WAGFS 1970 6,678 7.817 6,298 6,010 6,990 6,257 7,391 9,256 8.513 7,111 80.6 4,251 62.6 4,252 47.4 3,717 41.6 3,417 73.8 4,625 58.2 3,947 937,4 4,347 50.2 3,039 455.9 5,892 361,6 6,701 249.1 5,436 217.6 5,064 421.6 5,997 311.0 5,595 4677.5 6,246 93.6 6,447 392.6 6,175 373.4 4,041 07/08/76 * FAIRFIELD AND NEW HAVEN COUNTIES IN CONNECTICUT AND BERGEN, PASSAIC, ESSEX, HUDSON, UNION MORRIS SOMERSET MIDDLESEX, AND MONMOUTH COUNTIES IN NEW JERSEY. ... iiui, iiukko, iuntKiti , 33 TABLE 1 1 1-12 MIGRATION MATRIX EMPLOYMENT AND MEAN WAGES. 1970 NEW YORK STATE ALL WORKERS ALL WORKERS WORKERS FKC'' = TABULATION I COVERED MILITARY LEFT WORK REGION REGION REGION REGION REGION REGION REGION REGION REGION AND NON- LABOR FORCE 123*56789 OTHER MIGRANTS FORCE 1970 REGION 1- WESTERN WORKERS 1000) MEAN ..AGES 1970 MEAN ,AGES 1973 * CHANGE REGIUN 2- FINGER LAKES WORKERS (000) MEAN WAGES 1970 MEAN *AGE5 1973 % CHANGE 312.5 6.0 1.3 1,1 3.1 7,283 5.61* 8,81* 5,096 5.17* 9,512 7,917 11,608 5,913 7,7*6 30.6 36.2 31.7 16.0 *9.7 3.5 260.3 3.0 3,516 b.7U5 5,075 7,122 11,262 8,983 29.1 29.* 77.0 1.3 1.6 .108 10.1*6 ■363 1*,282 »*.l '.i.1.8 2.9 5,5^3 7,6*0 38.3 2.1 .756 .112 7.5 5.6 5, 761 7,71* 33.9 8.*62 11 ,*57 35.* .9 5.91 7 5,583 -5.6 .8 .516 ,670 2.0 23.5 6,716 8,697 29,5 17,* 6.66* 8,889 33.* 20,2 = ,998 9.098 30,0 12.* 7,083 8,560 20,9 102.1 *,930 78.* 5.500 *79.2 6.678 38*. 7 7,817 REGION 3- CENTRAL NEW YORK WORKERS (000) MEAN WAGES 1970 MtAN WAGES 1973 % CHANGE 2,0 2.2 152.* 6,*63 7,885 7,052 8,810 11,723 9,156 36.3 *e.7 29,8 *.0 2.5 5,797 5,052 7,*87 7,50* 29.2 *fl.5 REUIO^I *" UPPER MOHAWK, BLACK RIVER, ST. LAWRENCE, AND LAKE GEORGE-LAKE CHAMPLAIN WORKERS (000) .5 1.1 3.7 138.9 *.5 MEAN WAGES 1970 9,6*2 5.*60 6,193 6,563 5.80* MEAN WAGES 1973 10,190 9,770 8,127 8,2*6 8,599 % CHANGE 5.7 78.9 31.2 25.6 *8.2 REGION 5 " UPPER HUDSON WORKERS (000) MEAN WAGES 1970 MEAN WAGES 1973 « CHANGE 2.2 1.6 5,2*0 3,**7 7,850 7,C*3 *9.8 10*. 3 2.7 3,3 315,5 b»l*2 5,795 7,603 8,853 8,193 10.073 **.l *1.* 32.5 2.2 *,367 7,022 60.8 .8 *,396 5,951 35.* 2.4 *,790 8,508 77.6 3.3 6,2 ro 8,5 73 36.7 *.l 7,*79 8,2*2 10.2 9.8 6,009 9,782 62.8 8,589 13,158 53,2 1.0 6.*18 7,995 2*. 6 1.5 7,201 10,067 39.8 1*,9 5.620 7,552 3*,* 10,6 5.*63 7,092 29.8 20.5 8.068 9.626 19.3 13.5 6,3*3 9,**7 10.* *,297 6,880 60,1 11.* 6,085 8,253 35.6 52.9 *,370 51.* *.855 100,0 5.2*3 250.9 6,298 227.0 6,010 *70,9 6,990 RE^Iun 6- SOUTHERN TIER EAST, SOUTHERN TIER CENTRAL, AND SOUTHERN TIER WEST WORKERS (000) 2.0 2.7 3.0 ,9 3.6 191.5 MEAN WAGES 1970 6,66* 6,285 5,517 3.92* 5.*1* 6,929 MEAN WAGES 1973 9,890 8,528 7,389 6.*96 7.126 8,707 * CHANGE *8.* 35.7 33.9 65.5 31.6 25.7 REGION 7- TRI-STATE (NEW YORK PART): NEW YORK CITY, NASSAU-SUFFOLK, AND MID-HUDSON WORKERS (000) *.9 3.9 6.2 3.0 22.3 *.2 MEAN WAGES 1970 5,806 6,172 6,830 7,991 5,607 5,912 MEAN WAGES 1973 8,510 8,016 8,778 9,013 8.083 6,281 % CHANGE *6.6 29.9 28.5 12.8 **,2 6.2 REGION 8 - TRI-STATE (CONNECTICUT AND NEW JERSEY PARTS)* WORKERS (000) 1.1 1.8 1.0 MEAN WAGES 1970 *»*35 8,50* 12,010 MEAN WAGES 1973 *,866 11,1*9 17.89* % CHANGE 9.7 31.1 *9.0 ,9 3,110 7.961 30.3 2.6 6.776 10.168 50.1 REUIU'i 9- REST OF UNITED STATES WORKERS (000) 1*.2 12.* 11.6 MEAN WAGES 1970 6.3*5 5.813 6,597 MEAN WAGES 1973 8,7*5 9,023 9,151 5S CHANGE 37,8 55.2 38.7 8.3 6.985 8.28* 18.6 22.2 6.683 9.035 35.2 1.* 6,*1* 7,39* 15.3 18.9 5,885 8,305 *1.1 3.* 5,881 7,067 20.2 3002.9 8,129 10,552 29,8 69.3 7,2*6 9, 762 3*. 7 2**. 7 7,351 10,081 37.1 2.0 8,151 9,6*7 18,* 83,* 8,528 10,512 23.3 2 3.7 6.593 8,070 22. ESS "' hudson - UNI0N ' M0RRIS ' S0HERSET ' 34 Table 1 1 i-i 3 Census and CWHS Employment, New York State and Census Regions, by Age Group and Race, 1969 (employment in thousands) Males Females State and • Percent Percent of total CWHS/ of total CWHS/ region Census census CWHS census Census census CWHS census New York: Total 4,978.3 100.0 5,400.6 1.085 3,388.0 100.0 3,712.0 1 .096 <25 946.7 19.0 1,101.0 1.163 871.4 25.7 1,031.7 1 .184 25-44 2,010.1 40.4 2,181.9 1.085 1,169.7 34.5 1,268.2 1 .084 45-64 1,726.5 34.7 1,804.9 1.045 1,176.7 34.8 1,242.0 1 .055 65+ 295.0 5.9 312.8 1.060 170.2 5.0 170.1 .999 Black, all ages 470.0 9.4 575.3 1.224 422.0 12.5 518.5 1 .229 Northeast: Total 13,658.7 100.0 13,531.2 .991 9,207.6 100.0 9,175.2 .996 <25 2,795.7 20.4 2,943.0 1.053 2,472.2 26.9 2,616.7 I .058 25-44 5,364.2 39.3 5,308.5 .990 3,121.9 33.9 3,091.1 .990 45-64 4,737.8 34.7 4,554.5 .961 3,168.1 34.4 3,073.5 .970 65+ 761.0 5.6 725.2 .953 445.4 4.3 393.9 .884 Black, all ages 960.0 7.0 1,125.3 1.172 851.1 9.2 976.2 1 .147 North Centra 1: Total 15,944.4 100.0 14,995.1 .940 10,303.0 100.0 9,373.1 .910 <25 3,689.2 23.1 3,507.8 .951 3,033.2 29.4 2,927.1 .965 25-44 6,216.4 39.0 5,937.8 .955 3,596.3 34.9 3,258.6 .906 45-64 5,128.8 32.2 4,810.1 .938 3,168.8 30.8 2,839.1 .896 65+ 910.0 5.7 739.4 .813 504.7 4.9 348.3 .690 Black, all ages 1,025.8 6.4 1 -,098.7 1.071 839.2 8.1 836.6 .997 South: Total 17,012.4 100.0 15,0-, 1 .5 .882 11,116.5 100.0 9,884.8 .889 <25 4,063.1 23.9 3,638.1 .895 2,909.2 26.2 2,797.5 .962 25-44 6,872.8 40.4 6,035.7 .878 4,383.2 39.4 3,863.5 .881 45-64 5,213.2 30.6 4,660.1 .894 3,345.9 30.1 2,899.0 .866 65+ 863.3 5.1 677.6 .785 478.2 4.3 324.8 .679 Black, all ages 2,574.0 63.4 2,526.3 .981 2,171.1 19.5 1,872.9 .863 West: Total 10,073.2 100.0 8,605.0 .854 6,409.9 100.0 5,541.1 .864 <25 2,468.6 24.5 2,062.0 .835 1,816.3 28.3 1,693.5 .932 25-44 4,108.9 40.8 3,597.9 .876 2,456.8 38.3 2,117.7 .862 45-64 3,067.5 30.5 2,620.8 .854 1,898.3 29.6 1,567.5 .826 65+ 428.2 4.3 324.3 .757 238.0 3.7 162.4 .682 Black, all ages 411.9 4.1 370.2 .899 311.0 4.9 268.3 .863 35 CHAPTER IV DESCRIPTION OF CWHS DATA The CWHS is a system of multipurpose research files assembled and maintained by the Office of Research and Statistics of the Social Security Administration (SSA); it is used by the SSA for the evaluation of ex- isting and proposed social insurance programs. Each file in the system consists of a sample of earn- ings records for individual workers, based on employers' reports to the SSA. The sample is selected on the basis of specified digits in the workers 1 social security numbers so that the same persons are in- cluded in the sample each year. Thus, records for in- dividuals for different time periods can be grouped to form work histories for those individuals. The infor- mation contained in the files includes the workers' sex, race, year of birth, and wages earned, as well as the State, county, and industry of employment. This chapter describes the CWHS files available from the SSA. It details the sources, concepts, and process- ing and sampling procedures used in creating the files. (The work of the BEA in assembling and tabulating those data that are especially valuable for State and local-area economic and demographic analysis is discussed in Chapter V.) The last section of this chapter describes changes in the social security program expected to go into effect around 1978; these changes will have important implications for the CWHS. Files Available from the Social Security Administration The initial CWHS files were designed in 1940, originally for in-house use only. The potential usefulness of these files for socio-economic analyses was soon recognized, and they were made available to other organizations. As the use of the CWHS in- creased, new files were added to the system, and to- day five separate data files are produced on a regular basis. These files can be obtained by writing to the Division of Statistics, Office of Research and Statistics, Room 2E, Meadows East Building, 6401 Security Boulevard, Baltimore, Maryland 21235. A brief description of each file follows. However, since only the employee-employer and self-employed files contain information on the State, county, and in- dustry of employment (vital for local-area work force and migration analysis), most of this chapter pertains specifically to these two files. 1-Percent Annual Employee-Employer File This is the primary file used for regional work force analysis. It is available for the years 1957-72. A new file is available approximately 2-1/2 years after the end of each year, and it contains one record for each job held for each individual during that year. The 1972 file contains approximately 1.4 million records for approximately 900,000 workers. Basic data elements include: 1. personal characteristics — sex, race, and year of birth, 2. wage information — quarterly taxable and a total estimate, and 3. employer information — State and county and industry; coverage group indicator (farm, household, State and local govern- ment, other). 1-Percent Annual Self-Employed File This file contains records of self-employed in- dividuals who filed Schedule SE of Internal Revenue Service (IRS) Form 1040. Basic data elements include: 1 . personal characteristics — year of birth, sex, and race, and 2. self-employment data — taxable income, net earnings, taxable earnings (including 37 wages, if any), farm or nonfarm indicator. State, county, and industry. The 1 -percent annual self-employed file, which for 1972 contains approximately 61.000 records, is usual- 1\ available concurrently with the annual employee- employer file. 1-Percent Longitudinal Employee-Employer Data (LEED) File This file (for 1957 forward) is assembled from data derived from the 1-percent sample annual employee- employer file. The original records are skeletonized, resequenced, and merged, so that all records as- sociated with an employee over the file's timespan ap- pear together. Although its large size and variable length records make it difficult to work with, the file is very useful for following individual workers over several years. Major data elements are identical to those in the 1- percent sample annual file. 1-Percent 1937-to-Date Continuous Work History Sample File This is the "grandfather" of the CWHS file. Initially designed to provide information for the administra- tion of the social security system, it contains more in- formation for all workers and for a longer period of time than does the annual employee-employer file. The major limitation is that the file does not contain information on the State, county, or industry of employment and, therefore, can be used only for national work force analyses. It is the largest file in the CWHS system and consists of 2.2 million records for 1972. 0.1 -Percent 1937-to-Date Continuous Work History Sample File This file contains only one-tenth the social security numbers of the 1-percent file and, being smaller, is more adaptable for many national work force analyses. It also contains more detailed earnings in- formation. First Quarter Files In addition to the previously mentioned annual files, two others were created at the request of the Bureau of the Census and the BEA — the 1-percent and 10- percent first quarter files. (They are not, however, part of the regular CWHS system.) Their major ad- vantage is their availability nearly 1-1/2 years before the annual file. Disadvantages, however, include the absence of farm workers (who are included in the an- nual file) and of late reports (whose data are incor- porated into the annual file) and a high incidence (6 percent of the 1973 file) of workers unclassified by State and county (most of whom are classified in the annual file). Appendix C includes tape formats, technical specifications, and costs associated with all files. The user should remember that reformatting the files to his own specifications and processing these files for tabulation purposes will lead to substantial costs. Unless the user has an ongoing project and an ef- ficient computer system, it will be quicker and less ex- pensive to request summarized data from one of the tabulation systems described in Chapter V. General Characteristics Sources Data on earnings and employment are derived from reporting forms submitted to the SSA by employers and self-employed persons. The taxable wages of employees are reported quarterly on Form 942 by household employers, on Form OAR-S3 by State and local government employers, and on Form 941 by most remaining employers. Farm employers report annually on Form 943, and self-employed persons annually on Schedule SE of Form 1040. Examples of these forms are included in Appendix A. Employers list employees by social security number and name and report the amount of taxable wages paid to each worker during the reporting period. Data on the age, sex, and race of employees are ob- tained from applications for social security numbers (Form SS-5). These data constitute the "personal characteristics file." No guidelines are provided on the form for choosing among the race categories ("white," "Negro," and "other"), and problems sometimes arise due to personal interpretations. Variations frequently occur among Spanish- Americans, some of whom place themselves under the "other" category, while others place themselves under the "white" category. Geographic and industrial data for employers are ob- tained from applications for identification numbers (Form SS-4) and from other forms used periodically to update these applications (Forms OAA-100, OAA-103, and SSA-5019), as well as from periodic updates with data from the most recent economic census. 1 The information derived from these forms is referred to as the "employer identification file." 'It is hoped that in the future the employee-employer file wil contain place-of-residence as well as place-of-work information. 38 Employers are assigned classifications based on the location and nature of their businesses. Processing Procedures Earnings data from the various quarterly and annual reporting forms are punched, scanned, or reported directly on magnetic tape. The machine readable in- formation includes social security number, employer identification number, reporting unit number, and taxable wages paid during the reporting period. Earn- ings records for individuals whose social security numbers fall in one of the CWHS samples are ac- cumulated separately to form the sample files. The file is considered complete 9 months after the end of the reference period when it is merged with the files of previous quarters to form an annual record. The file is then sequenced by employer number and matched with an employer identification file to ob- tain geographic and industrial data. (Earnings records which have no match in the employer file are carried as unclassified by State, county, or industry.) The file is then resequenced by social security number and matched with the personal characteristics file to obtain data on sex, race, and age. Establishment Reporting Plan (ERP) Special procedures are necessary when an employer owns more than one establishment and has only one employer number. To permit classification by es- tablishment, the SSA requests that all multi- establishment employers having at least 50 employees (with at least 6 in a separate location) use the Establishment Reporting Plan, whereby workers are grouped by establishment. Multi-establishment firms are identified either through the application for an identification number or through a submitted wage report. A multi-unit employer is asked to complete Form SSA-5019 (see Appendix A), showing establishment location, industrial activity, and approximate number of employees, and to provide a unique four- digit number to identify each of his establishments. 2 Upon return of a Form SSA-5019 the SSA assigns geographic and industrial codes to each establish- ment. The multi-unit employer identification file is then augmented with a record for each establishment, containing employer number, establishment number, geographic code, and industrial code. When filling out subsequent wage reports, a multi- establishment employer is requested to group Employers are allowed to group establishments which have the same industrial activity and are in the same county. employees by establishment, to identify each group with an establishment number, and to attach Form SSA- 1 94 1 . This is a recapitulation of the wage report, which assists in checking and balancing the report by identifying groups of employees by the establishment numbers provided previously on Form SSA-5019. Maintenance of the employer file Geographic and industrial information is vital to many uses of the CWHS, and it is imperative to keep the employer identification file as current as possible. Updated information is obtained in several ways: 1. the employer informs the SSA, 2. an analysis of a submitted wage report shows a discrepancy with information con- tained in the current employer identifica- tion file, 3. County Business Patterns mail surveys or Census Bureau employer record listings in- dicate new or corrected information con- cerning establishments, 4. the employer file is updated (this is per- formed semi-annually) with additions (new businesses) and corrections, and 5. the employer file is matched (this is per- formed periodically) with a file consisting of information from the latest Census com- pany organization file, the latest first quarter County Business Patterns data, and employer information from the most recent economic census. When a correction is indicated and the employer has only one establishment, the SSA updates the file directly. If the employer has two or more establish- ments, he is requested to remit a Form SSA-5019, so that current information can be obtained on all his establishments. Estimated annual wages Employers report wages only up to the taxable limit for the year — on a quarterly basis for nonfarm workers and on an annual basis for farm workers. Total yearly nonfarm wages are estimated by an SSA- developed procedure, which is based on the quarterly pattern of taxable wages. Similarly, total yearly farm wages are estimated by a procedure based on a pro- jected distribution of wages above the maximum tax- able wage. Two methods are used to estimate total wages. The first (A) is applied to nonfarm workers who reach the 39 taxable limit during the second quarter or subsequent quarters. The quarter in which the taxable limit is reached is first determined (the limit quarter). Then the wages in the quarter prior to the limit quarter are substituted for those in the limit quarter and subse- quent quarters if they are larger than the limit quarter wages. If no other quarter has wages larger than those earned in the limit quarter, the limit quarter wages are substituted in subsequent quarters. The resulting sum of these quarterly wages is the estimate of annual wages for nonfarm employees. The second method (B) is used for those nonfarm workers who meet the taxable limit during the first quarter and for all farm workers, who report annual- ly. This method assigns a fixed amount of wages that is determined by an estimation process based on a Pareto curve fitted to taxable wages. Table IV- 1 -A shows estimated annual wages for nonfarm employees who met the taxable limit during the first quarter, as determined by Method B. Table IV-l-A — Estimated Annual Wages of Nonfarm Workers, Using Method " B" Estimated Annual Calendar Taxable Wages Year(s) Limit Males Females 1957-58 $4,200 $27,000 $24,000 1959-65 4,800 32,000 25,000 1966-67 6,600 42,000 33,000 1968-71 7,800 51,000 45,000 1972 9,000 56,100 50,300 1973 10,800 73,500 60,900 Thus, if a male nonfarm worker reached the taxable limit of $10,800 in the first quarter of 1973, his annual wage was estimated at $73,500. For females who reached the taxable limit in the same quarter, the an- nual wage was estimated at $60,900. Table IV-l-B shows estimated annual wages for farm employees, again as determined by Method B of the SSA procedure. Table IV-l-B — Estimated Annual Wages of Earm Workers, Using Method " B" Calendar Year(s) Estimated Annual Taxable Wages Limit Males Females 1957-58 $4,200 $7,400 $7,000 1959-65 4,800 8,000 7,300 1966-67 6,600 10,700 l >,hOU 1968-71 7,800 12,600 9,900 1972 9,000 14,300 11,100 1973 10,800 15,800 15,800 For those farm workers, both male and female, whose wages reached the $10,800 taxable limit at any time during 1973, annual wages were estimated at $15,800. Processing cutoff The processing cutoff date for each of the five CWHS data files is 9 months after the end of the reference period. The employee-employer files are not revised after the cutoff date for each year's file. Data on all workers and their reported earnings generally comprise about 97 percent to 98 percent of actual covered employ- ment and earnings during the year. In the 1937-to-date files, data for a given year in- crease with each year's additional processing, since the CWHS is updated to reflect reported information for any year for persons included in the sample. Representation of covered earnings in the sample ap- proaches 100 percent after 3 years of continued ac- cumulation of reports. Information relating to the cumulative number of workers with earnings at any time since the beginning of the program is also representative for more than 99-1/2 percent of the total employed through the last year of a given 1937- to-date CWHS. Unique case numbers Longitudinal analysis requires that a unique code be assigned to each employee and employer so that data for a current year can be linked to existing data for that record. In order to eliminate all possibility of identifying in- dividuals in the files, the SSA removes both the social security numbers and the employer identification numbers and replaces them with unique case numbers before the files are released by the SSA. Sampling Procedures The population, or sampling frame, from which the CWHS is selected consists of the one billion possible nine-digit social security numbers. These numbers have the following digital arrangement: Area in which number assigned (three digits) XXX Group number (two digits) XX Serial number (four digits) xxxx In the issuance of social security numbers, each State is assigned one or more area numbers.' Each area 'An exception to the use of area numbers as a State code was made for persons covered (prior to August 1963) under the Railroad Retirement Act, for whom a special block of social security numbers was assigned. Since July 1963, however, railroad workers have been assigned numbers in the regular series. 40 number, in combination with a given group number, defines a stratum. The population assigned social security numbers is thus stratified geographically (by place of application for social security number) and chronologically (by the process of assigning these numbers). Each number is an element of a given stratum, and the population represented by the possi- ble one billion elements constitutes the sampling frame. The CWHS is a longitudinal sample of persons with covered employment. The sample consists of all persons who have social security numbers with specified digits in certain of the serial-number posi- tions and who have covered employment during any defined reference period. The digital selection pattern remains constant. The employment and earnings histories for persons in the sample are available from 1957 forward, with limited additional earnings data going back to 1937. The 1 -percent CWHS may be described as a stratified cluster probability sample of all possible social security numbers. A stratum consists of all social security numbers with the same area-group number. In a stratum for which all numbers have been issued, the 1 -percent sample consists of 100 of the 9,999 social security numbers issued. (Numbers ending in 0000 are not assigned.) The clustering within a stratum arises from the par- ticular digital selection procedure used, in combina- tion with past methods of assigning social security numbers. Because of the clustering, sampling errors of estimates from the 1-percent CWHS are slightly larger than those that would result from a stratified random sample of the same size. The present design of the 1 -percent sample evolved from earlier sample designs — an initial 20-percent sample and a later 4-percent sample. All past designs have used the same stratification modes as are used in the present design. The 10-percent CWHS is equivalent to a stratified random sample. The strata are the same as those used for the 1 -percent sample, and the digital selection procedure within strata is such that there is no clustering effect. Therefore, sampling errors of es- timates from the 10-percent CWHS are about the same as or slightly smaller than those that would result from a simple random sample of the same size. Further information on sampling variability, in- cluding tables of sampling errors for estimates of counts of persons, percents, and mean earnings ap- pear in Chapter VI. Coverage 4 The data in the CWHS system is limited in scope and content to that covered by the social security program, and no major revisions in the relevant coverage provisions of this program have occurred since 1957. Optional coverage, however, is a factor affecting CWHS industrial and geographic data and is tantamount to a partial coverage exclusion. Between 1957 and 1975 the portion of the labor force covered by the CWHS increased from about 85 per- cent to about 90 percent. In 1975 wage-and-salary workers covered by the CWHS accounted for about 82 percent of the labor force; and self-employed persons, for about 7 percent. The largest group ex- cluded from coverage was State and local govern- ment workers in units which have opted against coverage; Federal Civil Service (career and career conditional) workers and noncovered self-employed persons also represented significant exclusions from CWHS coverage. Geographic and Industrial Coding The geographic codes used on the CWHS files for 1963 and before consisted of a two-digit State code and a two-digit county code (except in States with more than 100 counties, where the code referred to groups of counties). Beginning in 1964, a third digit was added to the county code so that county group- ings were no longer necessary and each county had a unique identifier. The State and county codes, as presently used in the CWHS, are included in Appen- dix B. The assignment of industrial codes to establishments of multi-unit employers is based on information sup- plied on Form 5019. For single-unit employers it is based on information appearing on applications for employer identification numbers. These codes are modified from the standard industrial classification (SIC). The slight differences between the 1967 SIC and SSA equivalents are shown in Table IV-2. The CWHS files for 1974 and later (with the exception of the 1975 10-percent first quarter file) will contain 1972 SIC codes with similar modifications. An important difference between the industrial clas- sifications used for the CWHS and those for other similar work force data arises from the treatment of government workers. The SSA classifies policymak- ing government activities in industry 901 1 . Operating government activities, in contrast, are given, wherever possible, appropriate four-digit industry codes. For instance, a State board of education is J For a more complete discussion of the coverage limitations of the CWHS, see Chapter VI. 41 Table IV-2 Differences between Standard Industrial Classification and SSA Equivalents Used on CWHS Files SSA SIC Industry title equivalent 011-019 Agricultural production 0100 0712 Agricultural services 0711 0719 Crew leaders 0718 0722-0729 Animal husbandry services 0721 0811-0861 Forestry 0891 0912-0914 Finfish, shellfish, and whale products 0915 0989 Fish hatcheries, farms, and preserves 0911 4212, 4213 Trucking, local and long distance 4211 4452-4459 Local water transportation 4451 4511, 4521 Air transportation 4511 4582, 4583 Fixed facilities and services related to air transportation 4581 4612, 4613 Crude and refined petroleum pipelines 4611 4742, 4743 Rental of railroad cars 4741 4738-4789 Miscellaneous transportation services, excluding inspection and weighing 4781 4832, 4833 Radio and television broadcasting 4831 4922-4925 Gas companies and systems 4921 4952-4959 Sanitary services 4951 5462, 5463 Retail bakeries 5461 5812, 5813 Eating and drinking places 5811 5932, 5933 Antique and secondhand stores 5931 5962, 5969 Farm and garden supply stores 5961 6022-6028 Commercial and stock savings banks 6021 6032-6034 Mutual savings banks 6031 6042, 6044 Nondeposit trust companies 6041 6052-6059 Banking and related functions 6051 6122-6125 Savings and loan associations 6121 6142-6149 Personal credit institutions 6141 6152-6159 Business credit institutions 6151 6312-6319 Life insurance carriers 6311 6332-6339 Fire, marine, and casualty insurance 6331 6512-6519 Real estate operators (except develooers) 6511 6722-6725 Investment companies 6721 6792, 6793 Oil royalty and commodity trading companies 6791 (8099) Clinics and dispensaries not operated by hospitals or groups of physicians, group health associations providing medical and health services (not including visiting nurses' associations), and psychiatric clinics 8098 8221, 8222 Colleges, universities, professional schools, junior colleges, and normal schools 8221 91XX-93XX Federal, State, and local government 9011 94XX International government 99 Nonclassifiable establishments 9911 42 classified under industry 9011, whereas a State university is classified under industry 8221. Unemployment insurance data, however, classify all government workers under SIC 91, 92, or 93 (Federal, State, or local government), and County Business Patterns excludes government workers en- tirely. Table IV-3 shows the distribution of govern- ment workers from the 1970 1 -percent first quarter major job summary file in order to indicate the extent of industrial coding for government workers available from the CWHS. New Developments Developmental efforts have been underway at the SSA and the BEA to improve the usefulness of CWHS data for local-area planning. 10-Percent Sample The Department of Housing and Urban Develop- ment (HUD), together with the Economic Develop- ment Administration, the Bureau of the Census, the Department of Health, Education, and Welfare (HEW), and other Federal agencies, have sponsored a joint project by the BEA and the SSA to construct a 10-percent CWHS file for the first quarters of 1971 and 1973. This file will provide estimates with much greater accuracy for States and local areas than data from the 1 -percent sample. The 10-percent file was first developed by the SSA, utilizing the procedures established for the com- parable 1 -percent sample employee-employer file. The completed file, with scrambled social security and employer identification numbers, was then forwarded to the BEA for further processing. 5 A comparable 10-percent sample for the first quarter of 1975 is currently being processed. If funding is available, it will be matched to the 1971 file and sum- marized to produce migration data for the 1971-75 period. Residence Coding The SSA has tested procedures for incorporating place-of-residence codes for workers into the CWHS files. Thus far, the use of the residence coding has 5 The BEA has established procedures which permit the tabula- tion of a maximum amount of releasable information for work force and migration analysis, at minimum costs to the State and local-area user. Carefully designed summary files have been created so that the entire file of approximately 10 million records would not have to be processed anew with each request for data. The BEA also maintains (with appropriate security precautions) files of individual work records from which alternative tabulations may be created on request. Descriptions of summary files and of standard tabulations available from the BEA can be found in Chapter V. been focused on comparisons of residence and work locations to determine their consistency and thereby isolate possible errors in geographic coding. (See Chapter VI for a discussion of reporting errors in place-of-work coding.) If incorporated as a regular part of the CWHS program, however, residence coding would have two major implications beyond improvements in geographic coding: 1. it would facilitate the tabulation of migra- tion by both place of residence and place of work, thus eliminating one of the major limitations of CWHS data for population estimation and projection purposes, and 2. it would facilitate the tabulation (within the limits of sampling variability and reporting errors) of commuting flows by county, since workers' county of residence and county of work would appear on the CWHS file; commuters could be described in terms of sex, race, age, industry of employment, and wages. The potential usefulness of this new development is great. Planners have long sought a method to es- timate the rapidly changing commuting patterns of the Nation during intercensal years. This may prove to be the most promising and least expensive way of developing such estimates on a regular basis. Test commuting data have been developed using the 1972 1 -percent CWHS file (see Chapter V). The levels of sampling variability associated with the 1-percent files are, however, prohibitively high for the detailed information necessary for commuting pattern analysis, except in the case of densely populated regions. Plans for developing commuting data from the CWHS, therefore, are currently being focused on the new 10-percent samples. Extensions of Coverage As noted above, about 90 percent of workers in paid employment are covered by the social security program. To extend the coverage of its CWHS files for work force structure and migration analysis, the BEA has acquired a 10-percent sample of the person- nel records of Civil Service workers and a 10.4- percent sample of workers covered by the Railroad Retirement Act. These files are in the process of being summarized in order to merge them with the 10- percent and 1 -percent CWHS files. The social security numbers in these samples are consistent with those of the CWHS samples. They were scrambled by the SSA prior to their release to the BEA to prevent the identification of individual workers and to permit a match with the "scrambled" CWHS files. 43 Table IV-3 Distribution of Government Workers, by Industry, 1 -Percent CWHS Major Job Summary, 1970 Number of sample cases State and 1< Deal Federal 2 32 1 658 2 10 12 25 2 SIC Industry 01 Agricultural production 08 Forestry 15 General building contractors 16 Heavy construction contractors 20 Food and kindred products 21 Tobacco manufacturers 35 Machinery, except electrical 36 Electrical equipment and supplies 37 Transportation equipment 41 Local and suburban transportation 44 Water transportation 45 Air transportation 47 Transportation services 48 Communications 49 Electric, gas, and sanitary services 50 Wholesale trade 53 Retail general merchandise 56 Apparel and accessory stores 58 Eating and drinking places 59 Miscellaneous retail 60 Banking 61 Credit agencies other than banks 63 Insurance carriers 65 Real estate 70 Hotels and other lodging places 73 Miscellaneous business services 75 Auto repair services and garages 78 Motion pictures 79 Amusement and recreation services 80 Medical and other health services 81 Legal services 82 Educational services 84 Museums, etc. 86 Nonprofit membership organizations 89 Miscellaneous services 90 Government 00 Unclassified Total 47,618 1,746 713 139 5 1 24 1 381 1 2 115 1 129 107 1 3 1 24 1 21 6 336 3 2 5 5 9 1,659 2 1 21,901 3 3 349 9 1 3 19,992 1,390 1,266 44 The Civil Service file contains nearly 5 million records covering the period 1962-74. A procedure is under development that will summarize personnel ac- tions and produce quarterly Civil Service employ- ment and earnings information. A 1-percent sample of this file will be used to test the adequacy of the summarization procedure and the procedures for matching this file with the CWHS. The major variables of the Civil Service file are sex, age, occupation, grade level, part- or full-time employment status, approximate earnings, and State, county, and city of employment. This information, however, may be considerably less accurate than that provided by the CWHS, because it is derived from personnel rather than payroll records. The personnel records include only the rates of pay and not actual amounts earned. Thus, "there is no way to account for the hours worked by part-time, intermittent, and hourly workers, nor for the overtime paid to salaried workers. This file, hopefully, will provide accurate work force and earnings estimates for Civil Service workers. At the least, it will provide the State and county of work and the approximate earnings levels of workers who move between social-security- covered jobs and Civil Service jobs. As to the sample file of Railroad Retirement Act workers, its major use will be to identify those workers moving between social-security-covered employment and railroad employment. The major limitation of this file is that it does not show State and county of employment. Effects of Annual Reporting on the CWHS 6 Conversion From Quarterly to Annual Reporting Public Law 94-202, passed by the 94th Congress in 1975, includes amendments to the Social Security Act and the Internal Revenue Code. These amendments allow the Departments of Treasury and HEW to ex- change information as needed in order to permit the conversion of social security tax reporting by employers from a quarterly to an annual basis. Under "annual reporting," as the new system is called, the employer who now files five reports per year for each employee (four quarterly reports on Form 941-A and one annual report on Form W-2) will be able to file a single consolidated annual wage report for each employee. The single report will show total annual earnings and any additional information needed to determine quarters of coverage under social security. ""Section prepared by Thomas Jabine, Chief Mathematical Statistician, Office of Research and Statistics, SSA. The annual reporting procedure will apply to earn- ings received in 1978 and thereafter. The W-2 forms will be the vehicle for the consolidated annual report, and those submitted by employers will be processed initially by the SSA. A processing operation is designed to provide both IRS and SSA with the infor- mation necessary to carry out their respective responsibilities in an efficient manner. The new legislation, however, does not provide for what is sometimes called "true annual reporting." Rather, it requires that insured status for social security purposes be determined on the basis of quarters of coverage. Thus, quarterly information will be required on the Form W-2, and it will most likely take the form of a check-box indicator for each quarter in which the employee had covered earnings of $50 or more. Implications for the CWHS The implications of annual reporting for the CWHS cannot be fully determined at this time. They will de- pend on a number of factors as yet unknown: 1. the precise content of the consolidated an- nual report (Form W-2), 2. the instructions to be given to employers for completing the W-2 form, and 3. the nature of the information to be ex- tracted from the W-2 form for data entry into SSA operating record systems. The SSA is working to insure that maximum use of the data for research and statistical purposes will be consistent with the basic program requirements. The content of the W-2 form, and therefore the input to the CWHS, will be limited to those data which are es- sential for program purposes. The SSA anticipates the following changes in the out- puts of the CWHS system. Availability and timing of file A first quarter file for the years after 1977 will no longer be available. All other files will continue to be available, however. The SSA will speed up the availability of the annual employee-employer file. Coverage Coverage will be substantially improved, inasmuch as the SSA processing system will include W-2 forms for noncovered earnings. Thus, nearly all earnings in noncovered employment will be included, as will data for persons whose only earnings are from these 45 sources. The SSA is developing the necessary arrangements with the IRS in order to include this in- formation in the CWHS svstem. Content The new system will provide actual information on both total and covered earnings for the full year. Currently, total earnings are only estimated for each person reaching the maximum covered earnings dur- ing the year. The SSA will not be able to provide a bridge between the two procedures, however, and es- timates of changes in total earnings between 1977 and 1978 will be adversely affected. The information presently used to estimate total earnings will no longer be available under the annual reporting system. For persons with covered earnings, the SSA will have data on the quarters of coverage obtained during the year and will be able to identify the specific quarters in which each person had $50 or more of covered earnings with each employer. The SSA expects to have information on both place of work and place of residence for all persons with covered employment and/or noncovered employ- ment. Establishment Reporting Plan (ERP) The present ERP permits assignment of industrial (SIC) and geographic codes on an establishment, or reporting-unit, basis for employers with more than one establishment. Arrangements are made with these employers to report information on the covered earnings of their employees separately for each reporting unit. These arrangements will continue under annual reporting. A control form (W-3), will be used to forward the W-2 forms, and multi-unit employers will be requested to batch and control these forms by reporting unit. Effect on migration analysis First quarter employee-employer files have been regularly used for most CWHS basic migration studies. Migration rates based on changes in prin- cipal place of employment between the first quarters of different years are easier to interpret than those based on differences in principal place of employ- ment during each year under study. On the other hand, first quarter files have the disadvantage of ex- cluding persons whose earnings are reported on an annual basis (such as farm workers and the self- employed). Users will now have to develop methods of migration analysis based on the annual files. For most workers, it will still be possible to determine the employer worked for (and hence, the place of work) in each quarter. However, for those persons with more than one employer in a single quarter, a basis other than amount of earnings will probably have to be used in order to specify a "principal employer." Once the precise nature of the information available for inclusion in the CWHS system is known, the SSA will consult with users to determine how this infor- mation can best be structured to meet user require- ments. 46 CHAPTER V AVAILABILITY OF CWHS DATA The CWHS files consist of approximately 1 .4 million records per year for the annual 1 -percent employee- employer file, and 8 million records for the 10- percent first quarter files. They are difficult and cost- ly to process, particularly for State and local agencies interested in local-area data. In order to overcome these limitations and to make the data available to a large number of users, the BEA has systematically produced, from the employee-employer files for individual years, 1957 forward, a series of summary files from which tabula- tions of work force characteristics and migration can easily be provided. This chapter describes the data files and standard tabulations available from the BEA. Other organizations, including Indiana University, the Rand Corporation, the Tennessee Valley Authority, and the Oak Ridge National Laboratory, have systematically acquired CWHS data and developed tabulations. Some of these organizations also provide tabulations on request. Indiana University has developed, under the direc- tion of Professor George J. Stolnitz, a longitudinal major-job summary file for the years 1960, 1963, and 1966-68. Unlike the BEA first quarter file, the In- diana file is composed of annual summaries (workers with covered wages at any time during the year, clas- sified by major employer for that year). The Indiana file is unique, in that it includes data for self- employed workers. The University provides a data service for local-area researchers. For more informa- tion, contact Professor George J. Stolnitz, Depart- ment of Economics, Indiana University, Bloomington, Indiana 47401. Files Maintained in the BEA System The BEA has constructed from the CWHS a unique set of files (available on magnetic tape) for migration analysis purposes. Together with an access system for producing analytical tables for specified regions (also available on magnetic tape), these files are referred to as the BEA Migration Analysis Data System. This system is available to public and private organiza- tions. The BEA has acquired the 1-percent annual employee-employer files for the years 1957 to 1972, the 1 -percent annual self-employed files for 1960 to 1971, the 1 -percent first quarter files for 1970 to 1975, and the 10-percent first quarter files for 1971 and 1973. The CWHS contains one record for each job held by each individual in the sample. In the BEA process, the file is sequence-checked to insure that all records for an individual are together. Then the records are summarized for each employee by major job; that is, each worker is assigned the State, county, and in- dustry codes of the employer paying the largest proportion of his total wages during the reference period, and the wages from each job are aggregated to a total for that period. During the major job sum- mary process workers coded under "other race" or "unknown race" are recoded under "white," and workers coded under "unknown sex" are recoded un- der "male" — the "white" and "male" categories be- ing the largest by far and, therefore, the least suscep- tible to distortion by the addition of the relatively few ambiguously coded items. Only pertinent economic and demographic information is extracted from the initial CWHS records. Table V-l shows the number of employee-employer combinations from the annual CWHS files and the number of workers tabulated by the BEA.' First Quarter and Annual Files The first quarter file contains records for individuals who worked in the first quarter. First quarter wages are expressed at annual rates. The BEA routinely ac- quires first quarter CWHS files approximately one year after the end of a subject quarter. 'The data items contained on the CWHS files and the BEA ma- jor job summary files, together with flow charts of BEA processing procedures, are shown in Appendix C. 47 Table V-l CWHS Control Counts, 1960-72 Year Number of employee- employer combinations Number of workers Averac jobs e number of per worker Number of first quarter workers 1960 990,877 660,184 1.5 540,359 1961 983,172 664,342 1.5 538,531 1962 1,020,040 680,617 1.5 554,845 1963 1,042,024 695,033 1.5 565,963 1964 1,083,418 714,753 1.5 581 ,391 1965 1,153,522 745,484 1.5 600,817 1966 1,270,193 784,992 1.6 630,788 1967 1,265,750 805,577 1.6 659,461 1968 1,320,975 833,399 1.6 677,826 1969 1,378,324 863,126 1.6 704,051 1970 1,333,408 870,503 1.5 718,779 1971 1,297,842 871,330 1.5 719,247 1972 1,368,066 897,821 1.5 723,766 The data derived from the first quarter file are referred to as "preliminary first quarter," inasmuch as this file excludes certain workers who are reported on an annua! basis only (see Chapter IV) and late reports, which are included in the annual file. Another exclusion is the large number of workers un- classified by State, county, or industry. These preliminary data are replaced by the first quarter in- formation from the annual file received 1-1/2 years later. : The annual file contains records for all individuals in the sample who worked in covered employment at any time during the year, classified by the major employer during that year. The annual CWHS file is available approximately 2-1/2 years after the end of a reference year. Both first quarter and annual files can be used to analyze migration and work force structure. Longitudinal First Quarter File The first quarter major job summary files have been linked to produce a longitudinal file of records for each individual in the sample containing work history information for 1960 and the most recent 11 years (currently 1963-73). The file is used as input to most of the standard tabulations. The longitudinal file is updated whenever a new first quarter file is available. (Major job summary files for the earlier years are retained for reincorporation into the file.) With each updating of the longitudinal file.a new interstate migration matrix is generated. Show- ing moves between the year being added to the file and the previous year, these matrices are used as edits to detect unusual movements indicating classification errors. The matrices and first quarter files are forwarded regularly to the Census Bureau for its migration analysis programs. 10-Percent File The 10-percent migration analysis file, recently con- structed by the SSA and the BEA under the spon- sorship of several Federal departments,- 1 permits the analysis of labor force mobility patterns at a finer level of demographic detail and for smaller geographic areas than did the 1-percent samples. The BEA treats the 10-percent file of individuals, with its social security numbers and employer iden- tification numbers scrambled to avoid identification of individuals or firms, as confidential information. Data are held under lock and key, and they may not be released without the written approval of the SSA. A number of public use summary files, however, are available from the BEA. differences in the State migration rates derived from the preliminary and final first quarter files are discussed in Chapter VI. 'See "New Developments, 10-Percent Sample," in Chapter IV. 48 The summary files that have been created so far in- clude three basic types: 4 1. Files of nonmigrants and work force entrants or exits (those who worked in only one of the two time periods) are classified by: a. county, sex, race, and age; and b. county, sex, race, and industry. A summary by wage class is planned. 2. Files of inmigrants and outmigrants are classified by: a. county and origin or destination (same or different BEA economic area, SMSA, and non-SMSA, and State); b. sex, race, and age; and c. sex, race, and industry. 3. Files of inmigrants, outmigrants, non- migrants, entrants, and exits are classified by: a. county; b. sex, race, age, and in- dustry; and c. wage class characteristics. These summaries were created as inputs to the stan- dard tabulating procedures. Other summary files can be created on request from the individual records maintained at the BEA. In order to protect the confidentiality of data cells containing only one or two observations, the sum- mary files are edited in such a way that the cells are counted as either zero or three. Cells with one obser- vation are counted as three in one instance out of three and as zero in the other two. Cells with two observations are counted as three in two instances out of three and as zero in the remaining one. This procedure, unfortunately, introduces distortions for small counties and particular flows; for larger areas, however, distortions average out. Table V-2 shows the average 1971 differences in the number of workers between the edited results for States and the unedited sample values obtained from the structure summary files. The largest distortions among in- migrants are in the categories "other females," the lowest age group, and the two highest age groups; and among outmigrants, the same categories plus "other males." To prevent the release of county industrial data that might reveal the identity of a particular establish- ment, only that industry detail appearing in the Census Bureau's County Business Patterns is made available. These publications have been edited to pre- vent disclosure of data relating to individual es- tablishments, and any deleted industry is recoded un- der the "unclassified industry" category. 4 For a description of these summary files see Appendix C. New Developments The BEA is actively engaged in developing new systems to improve its data base and provide new analytical capabilities. Among the projects planned or underway are: 1 . development of a 10-percent CWHS file for the first quarter of 1975, 2. development of commuting tabulations from the 1 -percent and 10-percent files, 3. development of a longitudinal file from the annual major job summaries, 4. incorporation of data from the self- employed, Civil Service, and Railroad Retirement Board samples into the longitudinal files, 5. incorporation of a death indicator into the longitudinal files, 6. development of tabulations that measure repeat and return migration phenomena, for use with the longitudinal file, and 7. intensified efforts to correct classification and reporting errors. Standard Tabulations Available from the BEA The examination, for any two time periods, of all possible cross-classifications in the sample, by migra- tion status and demographic characteristics, would result in the creation of many millions of items of data. The cross-classification of intercounty migra- tion, for example, yields a potential 10,240,000 records (3,200 counties x 3,200 counties). The further cross-classification of the data by sex (two classes), race (two classes), and industry division (nine classes) yields a potential 368,640,000 records (36 classes x 10,240,000 records). The BEA, in response to the massive nature of these files, has prepared few magnetic tapes and printed tables containing tabulated information. Rather, it has developed special computer techniques and data retrieval routines designed to permit the ready tabulation of specific types of information. Migration Summary Tabulation This tabulation displays the components of work force change for a specified region. It also shows the origins of inmigrants and the destinations of out- migrants. Mean wages are shown for each group of 49 Table V-2 Average Percent Difference in Number of Workers between Edited Results and Unedited Sample Value From M grant Struct ure Suranary Files 1/ Inmiqrants Outmiqrants Total migrants .05 .03 White males .08 .09 Black males 1.55 1.54 Other males 3.51 5.79 White females .26 .31 Black females 3.06 1.86 Other females 6.13 6.65 Migrants by age (1973) Less than 19 6.63 7.17 19 - 21 1.39 1.22 22 - 24 .76 .57 25 - 29 .37 .45 30 - 34 .(,? .53 35 - 39 .72 1.06 40 - 44 1.61 1.37 45 - 49 1.16 1.21 50 - 54 1.89 2.00 55 - 59 2.71 1.88 60 - 64 4.07 3.41 65 - 69 7.33 6.85 70 and over 7.82 6.15 Unclassified age .00 2.08 Migrants by wage class Under $2,000 .49 .31 $2,000 - $2,999 1.36 1.10 3,000 - 3,999 1.52 .89 4,000 - 4,999 1.23 1.21 5,000 - 5,999 1.01 .92 6,000 - 6,999 .94 1.60 7,000 - 7,999 1.51 1.47 8,000 - 8,999 1.02 1.63 9,000 - 9,999 1.65 2.27 10,000 - 14,999 .70 .82 15,000 - 24,999 1.78 2.10 25,000 and over 3.05 4.61 1/ Averages are computed for those States for which the sample contained 25 or more workers in a given category. workers, at both the beginning and end of the time period, enabling the analyst to observe the relative wage gains or losses attributable to migration. The entire table can be cross-classified by economic and demographic characteristics. Table V-3 is a typical migration summary tabulation from the 1 -percent sample, with two time periods shown on the same page. Table V-4 shows a cross- classification by sex, race, and age; and Table V-5, a cross-classification by sex, race, and industry. The first line of each table shows the initial covered work force. If one adds to this figure inmigrants (line 2), net inmigration from the military (line 6), and entrants (line 7), and if one subtracts outmigrants (line 3) and exits (line 8), the result is the final covered force (line 9). Inmigrants are defined as individuals whose major jobs were in a known location outside a study area at the beginning of a time period and whose major jobs were in the study area at the end of the same time period. Outmigrants are workers whose major jobs were in a known outside location at the end of the time period. The "net military and other" line is the net flow of workers between a study area and unknown locations (both military and nonmilitary). Many of these workers are probably nonmigrants in the area of study, but since their proportion cannot be deter- mined, the entire group is tabulated separately from the known migrants and nonmigrants. Entrants are workers who were not in covered employment at the beginning of the time period un- der study, while exits are workers who were not in covered employment at the end of that period. The origin and destination classifications used in Tables V-3 through V-5 are flexible in terms of coun- ties and combinations of counties. Classifications may be specified to meet users' needs. Structure of Migrants, Nonmigrants, Entrants, and Exits Tables V-6 through V-8 are structure tables available from the 10-percent CWHS. Tabulations are by one- digit SIC code industry. These tables describe total inmigrants and outmigrants of an area in terms of their demographic and economic characteristics, in contrast to the migration summary tabulations, which show migration by origin and destination clas- sifications. The analyst is able to observe the differen- tial characteristics of those workers entering and leaving an area of study and to assess the impact which migration has on the total work force struc- ture. These differentials are sometimes substantial, even for areas with near-zero net migration. Migrant structures, including those showing relative gains in mean wages, can be compared with struc- tures of nonmigrants and work force entrants or exits of the same area. Age, unless otherwise specified, is computed as of the end of the reference time period. Industry and wage classes for outmigrants and work force exits are those existing at the beginning of the period; for inmigrants and work force entrants, they are those existing at the end of the period; and for nonmigrants, those existing at both beginning and end. As discussed above, the 10-percent data have been subjected to a random rounding procedure, for cells containing less than three workers (at the county level), and an industrial disclosure edit. An asterisk following industry data indicates that the data for one or more component counties have been suppres- sed (recoded to "unclassified"). Suppressions corres- pond with those shown in County Business Patterns. Tables V-6 through V-8 are also available from the 1- percent sample. 50 Work Force Structure Tables V-9 and V-10 are similar to the migrant struc- ture tabulation (Table V-6). Unlike that tabulation, however, they include an area's entire covered work force. Changes in work force structure can be observed in terms of sex, race, age, industry, and wage class. The age, industry, and wage classifica- tions are flexible and can be specified to meet various requirements. These tables are useful as an editing device and sup- plement to the migration tables. They facilitate the identification of reporting, coding, or processing errors which appear as large changes in an area's work force structure. They also permit the identifica- tion oi' coverage, classification, or sampling variability problems through comparison with other employment series. Migration Matrix This tabulation presents migration data in a con- venient 9x9 matrix form where rows represent areas (or industries) of origin and columns, areas (or in- dustries) of destination. Table V- 1 1 is such a matrix of New York State plan- ning regions. The cells of the matrix represent migrants while the margins account for new entrants to the covered work force, exits from the covered work force, movements to and from the "military and other" classification, and total covered work force at the beginning and end of the time period. This table can also be prepared for specific sex, race, industry, or wage classes and adapted to a variety of uses, among them industry-to-industry, city-size-to- city-size, or wage-class-to-wage-class movements. Longitudinal Analysis The tabulations previously described measure the How of workers to and from a given area or industry of interest. The longitudinal tables, in contrast, measure the work experience of a particular group of workers for the years prior to or following a given reference point. A work force can be defined in terms of State or county of employment in addition to sex, race, age, industry, and wage class. Migration status categories and years are flexible and can be altered to meet the needs of each study. Table V-12 shows workers whose major job in 1960 was in New York and the migration status of these workers for the years 1963-68. "Nonmigrants" are 1960 workers who had not left the State. In 1966, for example, 2,408,500 workers had been working in New York in the same industry since 1960. Another 968,400 workers, although in New York throughout the 1960-66 period, changed industries one or more times. "Return migrants" are 1960 workers who were employed in another State in at least one of the years between 1960 and 1966 — 52,900 in the same industry and 73,600 in a different industry. "Return from military" refers to persons whose major job was in either the armed forces or military reserves in one or more of the years between 1960 and 1966 (29,400 workers). "Re-entered WF" refers to persons who worked in 1960 and 1966 but who did not work in at least one of the intervening years (410,300 workers — 161,300 who re-entered the same industry and 249,000 who re-entered a different industry). "Migrants" are those working in another State in 1966 (473,000 workers). "Military and reserves" refers to those working in one of these two categories in 1966 (25,500). "Continuously left labor force" are those who had not worked in covered employment since 1960 (836,600 workers). "Other left labor force" are persons who worked in 1960 and in at least one of the years between 1960 and 1966 but who were not working in 1966 (888,000 workers). Table V-13 is a tabulation of workers whose major job in 1968 was in New York. The table displays their migration status for the years prior to 1968. Commuting Tabulations Table V-14 is a sample of commuting tabulations developed from the 1972 CWHS file. The residence coding of the file was not complete and there was a problem in coordinating the timing of the place-of- residence and place-of-work information, but the tables do illustrate some of the kinds of commuting data which could potentially be developed on an ongoing basis. The problems of incomplete and im- properly timed coding which affected the 1972 tests have been largely resolved, so subsequent work, par- ticularly with a 10-percent sample, could be expected to produce much improved commuting data. Selecting the Appropriate File for Tabulation 5 The analyst must choose between the two files main- tained by the BEA: the first quarter and annual files. For migration analysis the first quarter file is preferred to the annual file for several reasons: 1. The first quarter file is more timely — the BEA receives a preliminary first quarter file 1-1/2 years before an annual file is available. s See "Effects of Annual Reporting on the CWHS," Chapter IV. 51 2. Wages before migration can be separated more precisely from wages after migration by using the first quarter file. The use of an- nual data has the effect of biasing downward an estimated "return to migra- tion." In contrast, the use of the first quarter file will produce a bias only when migration occurs during the first quarter — a small bias, since, historically, most migra- tion occurs during second and third quarters. 3. Comparisons between CWHS employment estimates and other data series (such as County Business Patterns and decennial censuses) can more easily be made by using first quarter data. It is important to bear in mind that CWHS first quarter data measure the number of people who worked in covered employment during the entire first quarter, whereas other series measure the number of persons working at a given point in time during the first quarter. The annual file has advantages t >o: 1. It does not, in contrast to the preliminary first quarter file, contain records for a large number of workers whose geographic loca- tions are unknown. 2. Use of the annual file can partially offset the bias found in the quarterly file where certain industries with a strong seasonal pattern of employment are likely to be un- derstated or overstated. 3. The annual file, unlike the first quarter file, includes information on the migration of individuals who do not work in the first quarters of either the first or last years of the period under study. 4. The annual file contains information on farm workers, unlike the first quarter file. 5. The BEA receives from the SSA, on an an- nual basis, a 1-percent sample of self- employed persons. The sample is drawn on the same basis as the CWHS, and it is therefore possible to incorporate informa- tion on the self-employed into the annual file. The distributions of covered employment by State, as shown in the two files are, as one would expect, very similar. However, the annual summary includes paid farm workers, while the first quarter file excludes most of them. This tends to increase the relative weight of agricultural States and to slightly reduce that of industrial States. This effect, however, is bare- ly discernible in Table V-15. A striking difference appearing in Table V-15 is the consistently lower mean wages recorded by the an- nual summary, reflecting the larger number of part- time and short-term workers included in this file. The same difference is again illustrated in Table V-16, which compares work force distributions by selected demographic and economic characteristics. The an- nual file records, as expected, more workers in all categories and lower mean wages in almost all categories. It also includes many more paid farm workers (who are reported to the SSA on an annual basis only) and relatively more female, black, young (under 25), and part-time or short-term workers (those earning under $2,000 per year). A comparison of 1970-72 interstate migration rates derived from the final first quarter and annual major job summary files is shown in Table V-17. The rates show migrants as a percent of the total matched work force for each State (matched work force is defined as inmigrants plus nonmigrants). The migration rates from the annual file range from 4 percent to 50 per- cent higher than those in the final first quarter file. Thus the short-term workers recorded in the annual file but not in the first quarter file have much higher rates of migration than workers in the first quarter file. Procedures for Acquiring Special Tabulations The standard BEA tabulations outlined previously were designed to be flexible enough to meet most tabulation requirements. The BEA will, time and resources permitting, prepare special tabulations on request. Costs vary widely, depending on the com- plexity of the task. Inquiries should be sent to: David Cartwright, Data and Systems Branch, Regional Economic Analysis Division, Bureau of Economic Analysis, U.S. Department of Commerce, Washington, D.C. 20230. Table V-15 illustrates the differences in work force coverage obtained from the 1972 final first quarter and annual major job summary files.'' There were 24 percent more persons who worked in social-security- covered jobs during the calendar year 1972 than dur- ing the first quarter of 1972. 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Ol ^oo^oo^o^c^ct^c* c* o 0* > UJ O 13 < ■t o ^ o ^t (T 13 • rgro^tingj^oo CX 4- -t a «r >- < rsl m m ^» ^f in in o >- U.1 UJ o — o ♦ z >- M ^ pg O 3 >n UJ CD I z: o rr u cr \J •-• h- CD z cr -J :-U V ►- 1 e ! 1 B ) 1 o <; UL ) 3 UJ z rr 1 1 1 1 t 1 1 1 ■ 1 < z m LU < 1— tJ UJ z. •— 1LI t- h- »— » _1 » i/) aJ UJ UJ < > -IS. ~> « lj in j^ o in o in o in < □ u m _i u u • < UJ UJ I UCiOOOOOOO o o o o >-■ < > 4 1UI 1 a: to rg m m -j- - 3 ZJ JJ u_ •.»»»•■»• • £ _J u rr i? z z < o z cr > rg ro >j- xv «o r^ oo o o in in it r\j »— 1C r 3 ■x. 59 -ZL UJ <: u uj < ST 3 O < t t— St o in in >T O O* CO O^ O O a a: -^ in ci --< c o m in or in p- rsj c co r- 3 O r- >r ^ ^ - < OI-3 X -- 3 3 I- u m uj or in _ X) x or uj < z (*>«*■ m r- r- ■* m o g* -^ in co ^- p- o in r- r- o> o> -4- o> •— * r- p— m m m o in rsj 43 c p- — » (T l oo , ■-'*^^-^-J'ln<^J^oc^•4■ OHNOinOOSI<}4)(DOa)0D ^■ocof*>fMcorMOP^(Mmoo>fn -^mmcooo--*'- o— 1 .-1 — «in<\j-4-mao>o»j-ccP^— « m mMO(^OO l O ( 00'CO'0(MH 0> orsjm>rjmo>oa)(*icoH « m in -i -j ^ ^ o im«i ■4"m^-»fio '0 ^O^^^-^^iO'fNJ'OP^ in^HOif # r- oaooco injcDp^om— • a a o m^r-occinp-cNj * * * * ^•nOP-— • — "OOP*- -CO* -•O 1 am ■an %op-p-o • ••••••••• (^JO>J■(^J^^o^lnCi^ •© t\i — i-Hinp^p-*o-4'>3'f<">m ^co— 4'^^-i-*-m (nmooo*tirMno , >0'0't(M «•••••• ••« • • o mmmop**p*r- *o in ^ nJ- O r\j rg -j- co O O — • --« m -0 00000" 0*- co co 00 P^ in ao ^co^-i^Hr-min^mmr-inr-^^in ^r-moo K ,, ooo s P--4-'v r\i 00 m -4- o> ^(*imrain h — •-OMOOMCHf.H^ ■-•'l-0-iff>t(MVJOCCi in o c> co p^ -o r- p-co-o fNirgo-^^-rsiOP-o-J- m -o p- r-t 00 Oininp~-pj-* in tn j ao in L/1 00 CO — « O CL f*- m 04 -* •o oo rg 2 aJ < * <\J P- P^ tsj.ro in or -xi — 1 t\j o» •J" 30 -C 3 D* >o < m m s • • * hff , Gia3mo>0(\i»j -4- in uo -o in m>ro v P^'0-op-p--NOinm^ r\! co in -J- in ac en O s o s in o^'*ir-0'£im-tfi-r4 o --»- or ^ in in in a: ^ in in uj lu lu o u. 1 iu y j j j 3. _i 3 3 < < < O ^ £ 5_ UJ UJ LU UJ u_ u_ u_ c lu ^ or u.' k u uj 'U if cr >m00'-'< u_ I I I I 1 I I I I I i 3 -- '~ <£ m o> ~> a < 7* hi cr O* 0^ CT a CT 4 C.T' o< O 0" or t Or 1— -a or 0> a O" o> cr o> cr O o* UJ :> uj m ^9 or ~> -; r«j m t in c P- cr> 0> -I ** c? in V tfl tfi fA \P. w> (A rsj < > UJ n Ml s: ?■ UJ > rvj tf= & 3 oj or V-*> rr 2 1— »-• aj z: 3 3 C ■ aJ 2 U- 1 1 1 1 r ■ 1 t 1 l <.'Z h- O 3 I/) UJ cr UJ 3 U- 1 a - y 7- u <\j tn j- IT •O 1^ X O O .n in ^ <& O <7 or x LU z V M KH tn w ** i#l w (H AGES 1970 3,925 a, 856 a, ooo % CHANGE -9.1 18.3 56. 7 6,8 73 7,822 13.8 5,400 8,600 59.3 .2 .3 9i>510 4,053 12,135 6,3*0 23,7 56.4 8,460 1U.8 73 11,493 14,692 27.8 5,853 8,253 41.0 2,750 7,505 172.9 11,233 13,430 19.6 ,1 6,240 6,310 1.1 8 ,498 10,4/1 2.6 10,02 7 10,781 a ,004 11,045 .1 '.590 12,350 62.7 REGION 4 - UPPER MOHAWK, BLACK RIVER, ST. LAWRENCE, AND LAKE GEORGE-LAKE CHAMPLAIN WORKERS (000) .2 .7 29.9 .1 MEAN AAGES 1967 2,385 5,061 7,489 1,140 MEAN WAGES 1970 8.2B5 6,001 8,420 7,710 % CHANGE 247.4 18.6 12.4 576.3 520 5,230 905.8 1.0 5,883 7.2 75 32.0 7,313 REG 10 v 5 - UPPER HUDSON WORKERS (000) MEAN /.AGES 1967 MEAN WAGES 1970 * CHANGE .2 2,870 6,415 123.5 .1 .2 7,500 2,680 15,000 8,075 100.0 201.3 26.5 7,633 3.344 9.3 .5 3,384 8,952 164.5 ),99U *,3 72 34.1 i. 2 • ooo ,902 31.2 r .42 i REGION 6- SOUTHERN TIER EAST, SOUTHERN TIER CENTRAL, AND SOUTHERN TIER WEST WORKERS (000) .3 .3 .4 ,1 52.7 9.9 .1 4.9 .1 MEAN WAGES 1967 3,740 6,093 8,173 7,100 7 , 7 32 9,299 6,690 7.736 5,180 MEAN WAGES 1970 9,400 10,lr3 8,455 5,430 9,027 13,354 6.8UU 1 HUDSON, UNION MORRIS SOMERSET MIDDLESEX, AND MONMOUTH COUNTIES IN NEW JERSEY. 61 tni/i i— «r-r-*>c^mm4r*>g>-4'Oor'- ■ouj I'Mrsi^oooD^r-.oooom IM^ li>IMD««^^ i n« •-• Nmrsi-j 4r -* gr hhq: t ^h _ ^ ^ 4) | o itrcDH^^^iNj^mommmrt o* o i- o i •••♦•••••••••• in • ioo ir^f«i-4 - 4"O v o^)cO'4'ir*c\jo4rpg ac z i • ••••••••••••• aJoofn>*Or-tf\i<\i,of*- * /» I 4> ^ 4" O "KMOOflO'inOBN a: 3 I 4) o ^^^rsj o* 43 in O O I • • •■ 5 X I (M -< 4f (- I — I Lii |0 1 ^3D-i^>f»OHf\jrj * I 43 ^h 4> O r\|(J*4>4r4)— 44)inm tricro — t .*-« m —i m r^rg^ 4J" tO l4--4-c>r-m r- Oin ir-momrgaooocgo^^r-m mcoor44j)C7 k s lm4r«omm^mcg4>4fwrr»rn4/ o ia>^r\ja>m'0'Nimr-mr-omr- 43 tr-r-comcoo-^4'r*i4roo4/ , f>J < UJ o < a */> ODIt _l ^ o o < z z o — a « OI-UJ Q(?ZI- d -«oa »- u < -• U_ 3 e? 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Table VI-13 Comparison of the Structural Characteristics of Workers Unclassified in the Preliminary First Quarter File and of All Workers in the Final First Quarter File, 1972 Unclassified workers preliminary first quarter All workers final first quarter Thousands of workers Percent of total Mean Wages Thousands of workers Percent of total Mean Wages Total covered work force 2,279.0 100.0 $7,517 72,427.2 100.0 $7,070 Male 1,388.0 60.9 9,465 44,550.3 61.5 8,683 Fema 1 e 891.0 39.1 4,484 27,876.9 38.5 4,492 White and other 2,049.4 89.9 7,768 64,848.7 89.5 7,293 Black 229.6 10.1 5,280 7,578.5 10.5 5,161 Work force by age Less than 25 490.9 21.5 3,556 16,424.7 22.7 3,595 25 - 29 305.8 13.4 7,027 9,753.7 13.5 6,820 30 - 34 239.7 10.5 8,735 7,594.7 10.5 8,182 35 - 39 218.4 9.6 9,211 6,674.4 9.2 8,687 40 - 44 218.9 9.6 9,575 6,806.3 9.4 8,868 45 - 49 230.2 10.1 9,717 6,974.4 9.6 8,931 50 - 54 212.5 9.3 9,211 6,498.6 9.0 8,704 55 - 59 177.1 7.8 8,468 5,376.6 7.4 8,257 60 and over 185.5 8.1 7,238 6,323.8 8.7 6,764 Work force by industry Unclassified 2,251.8 98.8 7,455 886.0 1.2 6,257 Agriculture .0 .0 330.3 .5 5,150 Mining .0 .0 632.9 .9 9,623 Contract construction .3 .0 4,889 3,808.0 5.3 8,464 Manufacturing 12.3 .5 13,963 19,829.7 27.4 8,261 Transportation, communi- cation and public util i ties .1 .0 7,916 4,189.3 5.8 9,427 Wholesale and retail trade 11.8 .5 12,462 16,558.2 22.9 5,667 Finance, insurance and real estate .5 .0 8,404 4,064.6 5.6 7,759 Services .2 .0 6,549 15,809.3 21 .8 6,022 Government 2.0 .1 9,694 3,680.5 5.1 7,277 Military .0 .0 2,562.7 3 5 6,017 Reserves .0 .0 95.7 .'1 4,130 Work force by wage class Under $2,000 400.4 17.6 825 12,586.3 17.4 924 $2,000 - 2,999 134.6 5.9 2,493 5,013.9 6.9 2,493 3,000 - 3,999 155.2 6.8 3,520 5,939.9 8.2 3,518 4,000 - 4,999 193.1 8.5 4,521 6,952.8 9.6 4,479 5,000 - 5,999 193.9 8.5 5,484 6,079.6 8.4 5,479 6,000 - 6,999 174.8 7.7 6,487 5,627.3 7.8 6,476 7,000 - 7,999 160.0 7.0 7,486 5,105.8 7.0 7,483 8,000 - 8,999 137.1 6.0 8,485 4,415.7 6.1 8,479 9,000 - 9,999 128.8 5.7 9,478 3,853.8 5.3 9,475 10,000 - 14,999 381.4 16.7 12,057 11,157.3 15.4 12,017 15,000 - 24,999 174.6 7.7 18,401 4,566.2 6.3 18,205 25,000 and over 45.1 2.0 32,302 1,128.6 1.6 32,577 87 Table VI-14 Migration Rates of Workers Who Were Unclassified in the Preliminary 1972 First Quarter File, 1970-72 (number of 1 -percent sample cases) State Formerly Out- unclassified Non- workers Total work force Out- Non- migrants migrants Rate migrants migrants Rate Alaska 3 8 27.3 235 493 32.3 Maine 15 48 23.8 175 2,048 7.9 New Hampshire 16 74 17.8 245 1,825 11.8 Vermont 6 20 23.1 132 976 11.9 Massachusetts 84 321 20.7 1,336 13,960 8.7 Rhode Island 13 52 20.0 264 2,493 9.6 Connecticut 69 183 27.4 908 8,177 10.0 New York 279 1,360 17.0 4,917 52,460 8.6 New Jersey 104 332 23.9 2,306 18,237 11.2 Pennsylvania 131 613 17.6 2,518 32,019 7.3 Hawaii 9 17 34.6 166 1,815 8.4 Ohio 142 547 20.6 2,254 25,530 8.1 Indiana 97 155 38.5 1,628 13,458 10.8 Illinois 188 864 17.9 2,852 27,976 9.3 Michigan 75 694 9.8 1 ,420 23,556 5.7 Wisconsin 64 161 28.4 793 11,049 6.7 Minnesota 31 260 10.7 810 9,089 8.2 Iowa 54 153 26.1 720 6,159 10.5 Missouri 69 254 21.4 1,351 11,326 10.7 North Dakota 2 19 9.5 124 1,002 11.0 South Dakota 5 20 20.0 112 1,066 9.5 Nebraska 13 63 17.1 365 3,330 9.9 Kansas 17 62 21.5 727 4,448 14.0 Florida 123 321 27.7 1,832 14,502 11.2 Delaware 16 18 47.1 212 1,407 13.1 Maryland 192 303 38.8 1,259 8,468 12.9 Virginia 103 198 34.2 1,206 10,040 10.7 West Virginia 28 50 35.9 319 3,610 8.1 North Carolina 81 621 11.5 1,142 13,728 7.7 South Carolina 36 137 20.8 595 6,257 8.7 Georgia 115 283 28.9 1,848 11,246 14.1 Kentucky 40 107 27.2 650 6,092 9.6 Tennessee 49 228 17.7 1,013 9,814 9.4 Alabama 6 3 184 25.5 843 7,422 10.2 Mississippi 48 86 35.8 474 4,099 10.4 Arkansas 40 86 31.7 420 3,630 10.4 Louisiana 82 171 32.4 798 6,451 11.0 Oklahoma 33 91 26.6 622 5,127 10.8 Texas 146 844 14.7 2,534 24,724 9.3 Montana 6 19 24.0 123 1,220 9.2 Idaho 6 12 33.3 181 1,285 12.3 Wyoming 6 26 18.8 111 660 14.4 Colorado 41 64 39.0 671 4,179 13.8 New Mexico 9 28 24.3 254 1,675 13.2 Arizona 31 132 19.0 732 3,580 17.0 Utah 11 30 26.8 218 2,241 8.9 Nevada 7 84 7.7 228 1,045 17.9 Washington, D.C. 29 60 32.6 802 2,159 27.1 Washington 49 157 23.8 886 7,363 10.7 Oregon 26 98 21.0 480 4,792 9.1 California 244 1,713 12.5 3,919 44,490 8.1 United States 3,146 12,431 20.2 50,730 483,798 9.5 88 Table VI-15 State Migration Flows to and from the Military and Reserves, 1971-72 (number of 1 -percent sample cases) State Maine New Hampshire Vermont Massachusetts Rhode Island Connecticut New York New Jersey Pennsylvania Delaware Maryland Washington, D.C. Michigan Ohio Indiana Illinois Wisconsin Minnesota Iowa Missouri North Dakota South Dakota Nebraska Kansas Virginia West Virginia Kentucky Tennessee North Carolina South Carolina Georgia Florida Alabama Mississippi Louisiana Arkansas Oklahoma Texas New Mexico Arizona Montana Idaho Wyoming Colorado Utah Washington Oregon Nevada California Alaska Hawa i i United States Migrants Migrants Net from the to the movement mil itary military from the and reserves and reserves military and reserves 21 13 8 9 14 -5 13 6 7 119 60 59 25 12 13 71 33 38 307 184 123 114 68 46 246 143 103 16 12 4 105 65 40 28 24 4 235 103 132 278 181 97 160 99 61 293 148 145 134 69 65 91 62 29 77 41 36 137 69 68 17 4 13 16 10 6 51 37 14 68 38 30 122 62 60 55 22 33 84 45 39 133 51 82 166 75 91 79 38 41 155 59 96 214 114 100 84 49 35 40 25 15 84 40 44 38 22 16 86 35 51 320 211 109 31 17 14 64 35 29 14 10 4 17 13 4 7 9 -2 93 43 50 31 14 17 82 54 28 54 34 20 19 10 9 454 247 207 20 6 14 36 15 21 5,213 2,850 2,363 89 CHAPTER VII A COMPARISON OF THE CWHS WITH OTHER DATA SETS This chapter compares CWHS work force and migra- tion estimates with employment and population es- timates from other data series. The emphasis is on the conceptual differences among the series and their im- plications. Much of the comparison of work force es- timates is summarized from a study conducted by the BEA, under the sponsorship of the Department of Labor (52). The comparisons are based on data for the Nation as a whole; similar data for States, BEA economic areas, and Census divisions are included in Appendix D. In addition, this chapter summarizes research con- ducted at the Oak Ridge National Laboratory, under the sponsorship of the Department of Housing and Urban Development, on the relationship between census and CWHS migration estimates. Also in- cluded are some important findings from an exten- sion of that research performed especially for this handbook. Work Force Coverage This section compares CWHS data on work force characteristics with (1) data derived from social security records, particularly County Business Pat- terns (CBP) data; (2) data derived principally from State unemployment insurance (UI) records, especial- ly Bureau of Labor Statistics (BLS) and BEA employment series; and (3) data compiled from household surveys and censuses conducted by the Census Bureau, particularly the Current Population Survey (CPS) and the decennial population census. In some cases the effects of particular conceptual dif- ferences among the series are readily discernible, but often it is difficult to assess the relative importance of the factors responsible for the differences. By Industry and Area CBP employment CWHS and CBP employment data ought to corres- pond closely, because both are derived from ad- ministrative records of the Social Security Ad- ministration (SSA). The two series, however, differ in several ways. 1. CWHS employment is aggregated from a sample of employer reports on individual workers, while CBP data are based on sum- mary reports of employment for individual business establishments. 2. CWHS employment, as usually tabulated, is a count of workers employed at some time during a quarter (or year), classified by the job which paid the most wages, while CBP employment is a count of jobs held during a single (mid-March) pay period, resulting in a double counting of dual job holders. Although job count data may be obtained from the CWHS, employ- ment which conforms precisely to CBP concepts cannot be derived, because in- dividual pay periods are not identified. 3. CWHS employment differs from CBP data because of sampling variability in the CWHS and because of adjustments made in the CBP data to reflect establishment reporting problems encountered in the social security files (see Chapter VI). CBP wages In contrast to employment, conceptual consistency between CWHS and CBP wages can be achieved, ex- cept for sampling variability and geographic and in- dustrial adjustments made in the CBP data to reflect the results of special establishment surveys conducted by the Census Bureau.' A comparison of CWHS with CBP wage estimates, by State and 2-digit SIC detail, revealed some of the effects on CWHS wages of the 'Adjustments are for (1) assignment of out-of-State employment and wage data to States in which corporate headquarters are located and (2) inclusion of administrative and auxiliary units in the 2-digit SIC categories assigned in social security records. 91 inability to incorporate the CBP adjustments (52). Generally, the CWHS data showed a slightly exces- sive concentration of wages in key industrial States, such as New York, Pennsylvania, Michigan, Illinois, Ohio, California, and Texas. The machinery and fabricated metals industries were important sources of the geographically biased wage distribution. Job count employment Job count employment estimates, by State and in- dustry, derived from the first quarter CWHS file were compared with CBP estimates and with BLS es- timates derived from reports of firms covered by UI records (52). : Although the CWHS job counts covered the entire first quarter while the CBP and UI job counts covered a single mid-March pay period, the employment comparisons, nevertheless, revealed geographic and industrial biases similar to those in the wage comparisons. If anything, the biases toward CWHS concentration in key industrial States ap- peared stronger in the employment than in the wage data. Generally, the correspondence between CBP and UI employment was closer than the cor- respondence of either to CWHS data (even allowing for sampling variability). Counts of workers Table VII- 1 compares U.S. industrial employment es- timates based on the first quarter CWHS 1 -percent and 10-percent files (with each worker assigned to the industry that paid the bulk of his wages) with es- timates from CBP and UI sources. Such a com- parison is appropriate, because the "major job" con- cept is the most commonly used basis for tabulating CWHS employment. (Similar comparisons for States are presented in Appendix Table D-l.) Conceptually, CWHS employment estimates may be lower or higher than similar CBP and UI estimates. CWHS estimates may be lower, because the CWHS counts a worker only once, while the UI and CBP data count him more than once, if he held more than one job in the reference period. On the other hand, the CWHS counts workers who were employed at any time during the first quarter rather than just those employed during the mid-March pay period. CWHS industry estimates for 1973 are typically lower than the corresponding CBP and UI estimates, however, because of a major problem associated with workers who could not be allocated by State or in- dustry (see Chapter VI). An industry-by-industry comparison of the three data sets follows. Agriculture. CWHS agriculture employment (mainly agricultural service workers) tends to be higher than CBP or UI estimates, because some farm workers who normally appear only in the annual file are in- cluded in the first quarter file. CBP and UI estimates exclude farm workers entirely. Services. CWHS services employment tends to be higher because of the inclusion of many public service workers (for example, in educational institutions or hospitals) who are either classified as government workers in the CBP and UI data or are excluded. - The CWHS, moreover, includes private household workers, who are not included in the other data, and covers nonprofit service organizations differently. Construction. The CWHS estimate is higher relative to the CBP and UI estimates in construction than in any other comprehensively covered industry because of the presence of some government construction workers in the CWHS and because of the intermittent nature of construction employment during the winter months. Some construction employees who work a part of the first quarter, but miss some pay periods because of bad weather or other factors, would appear in the CWHS but not in the CBP or UI, because the latter refer to a single pay period. (States where CWHS estimates exceed CBP and UI estimates are in the North, where employment is sometimes intermittent due to adverse weather con- ditions.) On the other hand, the CWHS may also classify some workers who were employed in con- struction in the mid-March pay period into other in- dustries, if they worked and earned more in the other industries during the first quarter. Individual CWHS records suggest that construction workers are more likely to have additional earnings from other in- dustries than, for example, manufacturing or tran- sportation workers. Trade. Secondary jobs tend to be in low-wage in- dustries, such as trade. Thus, CWHS employment for trade is more likely to be low, when compared with CBP and UI data, than would be CWHS employ- ment for construction. The ratios of CWHS employ- ment in trade to CBP and UI trade employment are significantly lower than the comparable ratios for construction. However, these ratios are not uniform- ly lower than those for other industries, such as tran- sportation and utilities, which employ relatively few secondary workers. The tendency, therefore, for secondary workers to reduce CWHS estimates of trade employment relative to CBP and UI appears largely offset by the number of intermittent workers who, although classified in the trade industry, do not appear in the mid-March pay period. : A job count comparison of employment is the most valid, con- ceptually. 'The CWHS also classifies other public workers in nongovern- ment categories, but the effect on national estimates is minimal. No CBP estimates of government employment are currently available for comparison. 92 Finance, insurance, and real estate. In finance, in- surance, and real estate the CWHS-UI employment ratio tends to exceed the CWHS-CBP ratio, even more so than in construction and trade. The CWHS- CBP comparison suggests that secondary workers in the finance, insurance, and real estate industry are relatively more important than intermittent workers in the same industry, even though the CWHS-UI comparison suggests the opposite conclusion. The CWHS-CBP comparison (based on social security records) is probably more accurate, because in the UI data the definition of what constitutes an employee is not uniform from State to State, and it tends fre- quently to be less inclusive than that used in the social security system. Unallocated. Generally, rhe industry-to-industry variations in the CWHS-CBP employment ratios at the national level are small and in directions which might be anticipated from the conceptual differences between the two measures. The 1973 ratios, however, are low overall because of the exclusion of approx- imately 6 percent of the workers in the CWHS — those who are not classified by State and industry in the 10-percent file. 4 For most industries an allocation of these workers would raise the CWHS-CBP ratios closer to one. Geographic variations CWHS-CBP and CWHS-UI industry employment comparisons often tend to be more difficult to in- terpret at the State level than at the national level (see Appendix Table D- 1 ). In some States and in some in- dustries sampling variability in the CWHS data (par- ticularly in the 1 -percent file) is a significant factor in- fluencing the comparisons. In addition, however, dif- ferences in geographic coding and data editing and processing procedures influence the comparisons. The Census program for adjusting incomplete or erroneous geographic and industry reporting by multi-establishment firms to the SSA is an important factor affecting CWHS-CBP comparisons. These ad- justments are sometimes substantial and readily ap- parent. 5 Among those industries in which CWHS, CBP, and UI coverage are essentially comparable, the closest State-by-State correspondences are generally in trade and finance, probably because of the relatively The proportion of unallocated workers is somewhat less in the 1-percent than in the 10-percent CWHS. This discrepancy is greater lor 1971 than for 1973 (see Table VII- 1) because both the 1971 and 1973 10-percent files are preliminary, while the 1971 1- percent file is "finar and the 1973 1-percent file is preliminary. The industrial composition of unallocated workers is not known, and it is not possible to determine the extent to which their omis- sion distorts the CWHS industry composition and, therefore, the industry ratios in Table V1I-1. 5 For a discussion of reporting inaccuracies, see Chapter VI. greater importance of small intrastate firms in these industries. The most serious geographic allocation problems in the CWHS generally appear to be in manufacturing, as well as in transportation, com- munication, and public utilities. The CWHS-CBP and CWHS-UI employment ratios for construction vary more from State to State than those for trade or finance, but somewhat less than those for manufac- turing. Census employment CWHS data on the number of workers are not strict- ly comparable to decennial census or CPS household- survey employment data. 1. The census and CPS measure employment by place of residence, whereas CWHS data measure employment by place of work. 2. The census and CPS essentially provide point estimates of employment for the previous week, while the CWHS covers a minimum of a quarter. 3. The census and CPS include data for in- dustries that are not completely covered by social security programs as well as data for unpaid (and hence uncovered) workers in family farms and businesses. 4. The census and CPS cover all workers (including the self-employed), while quarterly CWHS data include wage and salary workers only. When national nonagricultural employment es- timates based on first quarter CWHS data were com- pared with data based on the March CPS, the CPS revealed larger numbers of workers because of its more inclusive coverage (52). CWHS and CPS es- timates became closer over time, however, due to the declining relative importance of groups not included in the CWHS. When unpaid family workers, the self- employed, and government workers were excluded from the comparison, the CWHS yielded larger employment estimates than the CPS (as would be ex- pected on the basis of the longer time period involved in the CWHS). No reconciliation for the longer time period involved in the CWHS estimates was possible. In order to provide a geographic and industrial com- parison between CWHS and household data, first quarter CWHS employment estimates by industry for 1960 and 1970 are compared with census employment estimates for BEA economic areas (see Table VII-2 and Appendix Table D-3). The use of BEA areas minimizes differences between the two series due to discrepancies between place of work and place of residence. Even without the distortion due to com- 93 muting, however, other factors make meaningful comparisons difficult. 1. The 1-percent sample of the CWHS results in substantial sampling .variability for many industries in small areas. 2. In principle, census industrial classification systems should be more nearly comparable with CWHS than should CBP or UI, because both the Census Bureau and the SSA try to classify government workers not in public administration into an ap- propriate nongovernmental SIC category (for example, classifying public school workers into the educational services in- dustry). This classification comparability, however, may do little to improve overall comparability because of incomplete CWHS coverage of government workers. 3. CWHS industrial classifications are based on information supplied by employers (for example. State and local government reporting units), while census classifica- tions are based on information supplied by employees. In many cases government units reporting to the SSA are responsible for a variety of activities for which public administration is not separated from other governmental employment. Hence, the drivers for a local public bus system might be less likely to be counted by the SSA in the transportation industry than they would by Census. The CWHS-census employment ratios for BEA areas generally reveal more workers in the census than in the CWHS. The greater census counts result mainly from incomplete social security coverage (which is concentrated in a few industries) and the absence of self-employed workers from the first quarter CWHS files used in the comparisons. Partially offsetting the more inclusive coverage of the census is the restric- tion of census employment to those working in the week preceding the census. Agriculture. The very small CWHS-census ratios in agriculture are due to the exclusion of self-employed farmers and most paid farm workers in the first quarter CWHS file. Government. Next to agriculture, government generally has the lowest CWHS-census employment ratios. This is due mainly to the absence of most Federal workers and the elective coverage of State and local government workers under social security. In some BEA areas, however, the CWHS suggests substantially more workers than does the census. In most cases this reflects a tendency for State govern- ments to report all State employees in one or several reporting units based at the State capital, even though much of the actual employment may be located in BEA areas in which the capital is not located. In some cases a high CWHS-census ratio for government may also reflect a tendency for the CWHS government data to include employment which would be reclassified into a nongovernment in- dustry if the social security reporting unit breakdowns were sufficiently fine. Transportation, communication, and public utilities. Transportation, communication, and public utilities have low CWHS-census employment ratios because of the exclusion of most railroad employment from social security coverage. In addition, other segments of the transportation industry (particularly street railways and bus lines) and the utilities industry (par- ticularly water supply and sanitary services) contain significant numbers of government-owned opera- tions. The government operations would be included in a census, but might not be covered or might not be separated from general government in social security reports. Services. Government service facilities (for example, in health and education) may also contribute to low CWHS-census employment ratios for the services in- dustry. A key factor is the relatively large number of self-employed workers (excluded from the first quarter CWHS) in many service industries. In addi- tion, incomplete social security coverage of non- profit service organizations and private household workers contributes to the low ratios. Construction. In the construction industry both the self-employed and government employment con- tribute to low CWHS-census employment ratios. After government, agriculture, and services, the census shows the construction industry to have the smallest proportion of private wage and salary workers (about 75 percent in 1970) among all major industry groups. Other industries. In mining, manufacturing, trade, and finance, the average CWHS-census employment ratios are generally above .9, although the ratios sometimes vary substantially from area to area because of sampling variability (particularly in mining) and geographic and industrial classification problems. In mining and manufacturing census and 94 CWHS correspond closely, because these industries have few self-employed or other noncovered workers. In trade and finance, the comparisons are influenced by a greater incidence of self-employment than in manufacturing and mining (although not as great as in services or construction). The exclusion of the self- employed in trade and finance, however, tends in many instances to be offset by the longer time period covered by the CWHS, which results in the inclusion of intermittent workers who may not have been working in the census reference week. By Demographic Characteristics The CWHS is the only major administrative-record source of work force data by demographic characteristics. In 1972 the BEA made national-level comparisons between annual CWHS data and annual BLS work experience data (collected in the March CPS for the previous year), and between first quarter CWHS data and March employment data from the CPS (52). In addition, first quarter CWHS data for selected metropolitan areas were compared with data from the 1970 census. A comparison of the coverage, concepts, data collection techniques, and processing procedures of the various files sheds light on the strengths and weaknesses of CWHS demographic data. BLS and CPS employment CWHS work force coverage is incomplete, and BLS work experience data yield larger estimates of employment than does the CWHS. These discrepan- cies, however, are not uniform for different sex, age, and race groups, nor have they remained stable over time. Compared with BLS data, the CWHS generally reveals greater coverage for males than females, for whites than blacks, and for prime-age workers than older workers. For young workers, the CWHS-BLS comparison has been mixed. Young workers often are concentrated in categories poorly covered by the CWHS (or not covered at all), such as unpaid family workers and low-wage farm and service workers. BLS data, on the other hand, count all workers above a specified age. In the early 1960's, when the cutoff age was 14 years, the CWHS-BLS comparison showed its greatest dis- crepancies for the age group of workers under 20 years. In the late 1960's, however, when the age cutoff had been raised to 16 years, the CWHS estimates of workers under 20 years actually exceeded the BLS es- timates. For other age groups, too, the gap between CWHS and BLS estimates has tended to decrease over time, as employment in categories covered by the CWHS rose faster than those not covered. In an effort to obtain a closer comparison between CWHS and BLS employment by sex, the 1972 BEA study excluded the agriculture, railroad, and govern- ment sectors and self-employed and unpaid family workers from the employment estimates, thus eliminating most of the difference in total employ- ment between the two series. Differences by sex, however, were not reduced to the same extent. In the adjusted data, the BLS had a higher estimate for females and the CWHS a higher estimate for males. A number of imperfections in both data series could account for the discrepancies by sex. In the CWHS female understatement, a predominant factor is the partial coverage of the private household sector and certain nonprofit service industries in which female employment is disproportionately large. In the CWHS male overstatement, the discrepancy is pos- sibly due to men who hold primary jobs in industries excluded from coverage (for example, postal workers) and secondary jobs in covered work. Such workers were deleted from the BLS (major job) data but would appear in the CWHS data because of their secondary jobs. Comparisons by race further underline the in- complete social security coverage of private household and other low-wage service jobs in which females and blacks are disproportionately represented. For example, the gap between CWHS and BLS estimates of 1967 black employment is proportionately nearly twice as great as the cor- responding gap between the two estimates of white employment. For females the black-white dis- crepancy was slightly greater than for males, due probably to the concentration of black female employment in the private household sector. The comparison of first-quarter CWHS data with March CPS data on nonagricultural wage and salary employment, by demographic group, generally in- dicated coverage differences similar to those found by CWHS and BLS comparisons. The CPS, however, did not reveal as large an employment excess over the CWHS as had the BLS data. While the CWHS covered the entire first quarter, the CPS referred to a single week only. This difference in time references made the CWHS-CPS comparisons difficult to in- terpret. This was also true of the CWHS-census com- parisons for metropolitan areas. For some of these areas there was an additional distortion, due to the presentation of CWHS data by place of work and of census data by place of residence. Decennial census employment Differences between CWHS and census data, and the approximate magnitude of these differences, are im- portant considerations when using the CWHS work force data. To illustrate some of these differences, a comparison of U.S. first quarter CWHS and census 95 work force data, by sex and 5-year age groups, for 1960 and 1970. is presented in fable VII-3. (Similar data for States are presented in Appendix Table D-4.) A number of factors contributing to discrepancies are apparent. Census work force estimates tend to exceed the CWHS estimates, because the former includes self-employed and agricultural workers (excluded in the first quarter CWHS), unpaid family workers, and workers in industries not covered by social security. However, the CWHS exclusions are partially offset b\ the greater time period covered by the CWHS. The first quarter CWHS, compared to the census, reveals a relatively greater CWHS understatement among males than among females, due primarily to the exclusion of the self-employed and agricultural workers (who are predominately male) from the CWHS. An additional factor is the preponderance of females in short-term jobs; this factor increases the likelihood of females working during a first quarter (and. thus, appearing in the first quarter CWHS) but not being employed in a census reference week. For all but the youngest age group (15 to 19) in 1970, these factors seem to offset the tendency for females to be disproportionately represented in uncovered or poor- ly covered occupations, such as babysitting and other private household services. For both males and females the highest CWHS- census work force ratios are for workers in the 20-to- 29 age groups. For the age group 20-to-24, in 1970, the CWHS estimates actually exceed the census es- timates. A major factor contributing to such high CWHS-census ratios for this age group is the large number of persons with limited work force attach- ment (such as students) who accumulate some social- security-covered earnings during the quarter, but do not work during the census reference week. Moreover, the census work force estimates for persons in the 20-to-29 age groups (especially black males) are likely to be somewhat low, because the census undercount tends to be disproportionately concentrated in those age groups. The factors con- tributing to the large census undercounts among persons in their twenties, compared to persons aged 15 through 19, may also help to explain the low CWHS-census ratios for the 15-to-19 age group com- pared to the 20-to-24 age group. A greater tendency for persons in the younger age group to work in un- covered jobs, however, may also contribute to the contrast in CWHS-census ratios. From 1960 to 1970, CWHS-census work force ratios increased in all sex-age categories at the national level. In general, the ratios for males increased somewhat more than those for females, due probably to the decrease during this period in the number of agricultural workers and self-employed. The number of uncovered jobs in industries containing concentra- tions of females (for example, the private household sector) also declined, although not to the same extent as in male-dominated industries (such as farming). At the same time, the ratios for the youngest and oldest workers increased somewhat more than those for workers in the prime working ages. The relatively large increases in CWHS-census ratios for young and old workers partially reflect their tendency to enter and leave the work force more frequently than prime- age workers. These increases also reflect the decline in the proportion of short-term jobs which are not reflected in the first quarter CWHS (for example, agricultural and private household jobs). This decline may also be a factor in the relative declines in labor force participation rates of the two age groups. State-by-State comparisons of CWHS-census work force ratios reveal considerable regional variations in coverage. Overall comparisons and comparisons by sex, however, do tend to show a consistent regional pattern. Historically, the highest CWHS-census ratios have been concentrated in the industrial States ol' the Northeast and North Central regions, with the lowest ratios in the agricultural States of the Plains, the West, and the South. From 1960 to 1970, however, the agricultural States, especially those of the South, tended to increase their CWHS-census ratios more than the industrial States because of declines in agricultural employment. State-to-State variations in the ratios tend to be less pronounced for female workers than for male workers. The greater variance for males is not surprising, since the regional patterns appear to be closely related to the geography of the male-dominated agricultural sector. Agriculture is not the only factor influencing State- to-State variations in CWHS-census work force ratios. Many of the ratios are influenced by the dis- tinction between place-of-work data (CWHS) and place-of-residence data (census). This effect is par- ticularly strong in the District of Columbia where the CWHS-census ratio is greater than one, even though the largest employer, the Federal government, is largely uncovered by social security. Other problems are associated with the erroneous geographic coding of place of work in the CWHS. In the State-to-State variation of CWHS-census ratios, it is unfortunately difficult to separate data problems from other fac- tors. (See Chapter VI for a discussion of reporting problems.) The census data discussed above refer to employment in the week preceding the decennial census date. The Census Bureau also collects data on employment in the year preceding the decennial census (classified by- place of residence at the time of the census). These annual census data can be compared with annual CWHS data, which, unlike first quarter CWHS data, include the self-employed and agricultural workers. 6 'Chapter ill. Table 111-13, shows an annual comparison for 1969 for New York State and the four broad Census regions. 96 The use of the same timespan in the comparison (1 year) eliminates the upward bias in CWHS data that occurs when quarterly CWHS data are compared with census data based on the week preceding the census. In the comparisons based on annual data, as in those based on first quarter data, census employment generally tends to exceed CWHS employment because of incomplete social security coverage of workers and because of the absence of geographic data on military workers in the CWHS. Regional dif- ferences reflect differences in the importance of in- dustrial employment groups not covered in the sub- national CWHS data. The concentration of military bases in the South and West, for example, contributes to relatively low CWHS-census ratios for young males in these areas. Differences among demographic groups reflect, in part, census undercounts (see Chapter III). In particular, census undercounts might be an important factor in relatively high CWHS- census ratios for blacks. The factors underlying the difference between CWHS work force coverage and that reported in the census generally cause similar variations in the CWHS coverage of population numbers. This coverage will not be uniform because of variations among demographic groups and regions in labor force par- ticipation rates and unemployment rates. National comparisons between CWHS work force and total census population, by age group, sex, and race, are presented in Table VI 1-4. (State comparisons are presented in Appendix Table D-5.) Migration This section compares CWHS migration data with migration data from the Census of Population and the CPS. The CWHS measures work force migration, by place of work, for variable time intervals. The decen- nial census and the CPS, in contrast, measure popula- tion migration, by place of residence, generally for two fixed points in time. Each of these data sources has unique strengths and weaknesses for measuring migration. Because the CWHS only records worker moves between jobs covered by social security, it necessarily omits: 1. moves of nonworkers, 2. moves occurring before entry into or after exit from the covered work force, 3. moves of those unemployed for either a beginning or ending reference period, and 4. moves between noncovered jobs or between covered and noncovered jobs. With continuing efforts to obtain place-of-residence information for CWHS workers (see Chapter IV), some of these coverage limitations may be reduced, but CWHS coverage will still fall short of the census' (in principle) complete population coverage. Because of the incomplete coverage of migration, CWHS migration rates should be computed on a base of only those workers working in both time periods. Census gross migration data also have limitations, particularly for studying the frequency and timing of migration. 1. The census provides migration data only for one time period preceding each decen- nial census (for example, 1965-70). 2. The census does not provide information on multiple moves occurring during the period under study, nor does it record as a migrant a person who moved but returned to his place of origin before the end of the period. 3. Successive censuses cannot link individuals over time to provide a profile of individual movements over several periods. CWHS data, in contrast to census data, provide great flexibility in the choice of time periods for analysis and also permit analysis of repeat and return migra- tion (see Chapter II). The direct linkage of CWHS files also permits the tracing of changes in individual worker characteristics (for example, earnings) over several periods. (Although it cannot trace the characteristics of individual migrants over time, the census collects information on certain characteristics — for example, education — that are not available in the CWHS files.) Census and CPS are the most common and familiar sources of migration data. The remainder of this sec- tion describes their comparability with CWHS migra- tion data. Previous Comparisons of CWHS with Census Data Using CWHS annual net migration rates to estimate population Because of the availability of intercensal migration data from the CWHS, the Census Bureau has tested the potential of the CWHS for population estimation (see especially Zitter and Nagy (53) and Zitter and Word (68)). Annual CWHS State net migration rates were used, in a standard population estimation for- 97 mat, to predict 1970 State population by race from a 1960 population base. Although the overall test results were satisfactory, they were not superior to results obtained from more standard estimation procedures used by the Bureau. The tests revealed particular weaknesses in using CWHS migration rates to estimate population for areas where com- muting is important and for the black population. 7 Comparing census and CWHS rates of gross inmigration The Census Bureau's comparison may have been af- fected by such factors as commuting, labor force par- ticipation rates, sample size, and differences in in- dustrial coverage. An evaluation by Nelson (33) tried to control for the effects of these factors. Using the 1- percent Public Use Sample from the 1970 census and the CWHS, the study compared measures of 1965-70 migration by the covered work force into 28 metropolitan areas/ Although this controlled study found systematic differences in the measurement of work force migration between the CWHS and the census, by far the most important of these differences was the consistent tendency of the CWHS to record higher migration rates, especially for blacks and older workers. Simple adjustments to take account of these systematic differences appear to improve the use of the CWHS as a proxy for census work force and population migration rates. The main results of the study were: 1. Rates of 1965-to-1970 inmigration for the social-security-matched covered work force were consistently higher than those recorded in the 1970 census for the total covered work force. Since controls focused the study on the covered work force in specific areas where workers can be as- sumed to both live and work, the higher CWHS estimates were not due to com- muting patterns nor to differences in in- dustrial structure or labor force participa- tion rates. Rather, the CWHS may have recorded the migration of persons who did not report their 1965 residence in the 1970 census. 2. The inability of the CWHS to record new entrant migration did not reduce its utility The CWHS may have underestimated black population because of the small sample size and a tendency to understate South-North black migration. It is hypothesized that such migration often oc- curs prior to work force entry or involves a move from a non- covered job. 'The data format of the Public Use Sample, 1970 Census limited the study to inmigration. for estimating the range of migration rates into metropolitan areas. Indeed, the migra- tion of young adults entering the covered work force was shown to be related to the inmigration patterns of those in the matched work force of the same sex, race, and age. Furthermore, inmigration rates for the total work force (including new entrants and returnees from military ser- vice) were reliabily predicted. 3. In previous models social security and census rates were implicitly assumed to be equivalent. A model that explicitly corrects for systematically higher social security rates, however, substantially increases the precision with which census migration rates can be proxied by CWHS data. Incremen- tal improvements for different SMSA's resulted from adjusting for differences in the proportion of workers who were new entrants. The greatest improvement, however, came from simple adjustments (by sex, race, and age) which compensated for consistent differences between CWHS and census mean levels of migration. These adjustments roughly halved the unex- plained variance in census rates for impor- tant demographic groups. The improve- ments in black male migration rates were particularly striking. 4. The adjustments in migration rates are equivalent to the ratio between average census and average social security migra- tion rates in any group which is demographically or geographically defined. Thus, these adjustment factors should be useful when estimating migration in areas other than those studied. 5. Extension of the results for work force migration showed that rates of migration for the total resident adult population (those moving in and those moving out of each metropolitan area) could be proxied by CWHS rates of work force migration to and from noncontiguous States. The CWHS-census relationship persisted even when controls for commuting and labor force participation differences were relax- ed. Apparently, the difference in the levels of reported migration is a greater source of discrepancy between the census and the CWHS than either commuting or labor force participation. Once again, adjust- ments for mean differences were particular- ly helpful in explaining discrepancies in black migration. 98 In short, the study's major finding was that an adjust- ment factor derived from the ratio of average rates of census and social security migration could be used to estimate the range of census rates of inmigration from that of social security rates. Census rates of work force inmigration could be proxied almost as reliably with these simple adjustments as with fitted regressions. Table VII-5 summarizes the results for four sex/race groups. The use of the adjustment fac- tors resulted in reductions in variance that were equivalent to those found through fitted regressions for all sex/race groups, except for black females. In addition to providing reliable estimates of in- migration rates, the CWHS data recorded a geographic distribution of inmigrants that was similar to that in the census. In addition, CWHS and census data showed the same direction of white migration (lows for the four national Census regions. Indications, however, that adjustment factors might vary by region and direction of migration raised questions about a possible differential bias in the CWHS measures of net migration for particular regions. force participation.) The results of this examination (see Table VII-5) indicated that adjustment factors were quite useful for predicting both outmigration and inmigration and that they were better at es- timating population migration of the 50 States than work force migration into the 28 study areas (as shown by the reduced standard error factors). Moreover, the adjustment factors for the total pop- ulation migration in the United States were remarkably similar to those used for the migration of the work force into the 28 metropolitan areas. Figures VII-1 through VII-8 plot the social security and census rates of inmigration and outmigration for the sex/race groups in order to identify the outliers. From these plots, it appears that the outliers from the general pattern can be characterized as States af- fected by commuting, military migration, and spurious migration. The plots also reveal that less regional variation exists in the measurement of out- migration than in the measurement of inmigration. Blacks appear to be misrepresented in States with low sample sizes (such as Colorado, Washington, and Minnesota). The Usefulness of Adjustment Factors Since the above-noted study compared only inmigra- tion rates, 1965-70, it was unclear whether the adjust- ment factors that were derived would also be useful for proxying outmigration rates and for relating CWHS migration estimates to the CPS measures available in intercensal years. In this handbook, therefore, the applicability of adjustment factors for relating CWHS and census or CPS migration rates is evaluated in greater detail. Equivalent national and regional adjustment factors are derived in order to relate CWHS work force migration to the migration of the population and of employed workers over three time periods, as shown by the 1970 census and the 1971 and 1973 CPS. (For easy use, these adjust- ment factors should be stable over time, and they should be similar across different regions of the country.) As shown below, the differential pattern of adjustment by sex, race, and age found earlier ap- pears to hold over time, when national data are studied, and the suggested simple adjustment factors help "proxy" census State population migration rates (both in and out) from CWHS data. Regional biases, however, are evident in the social security measures of migration. Differences by sex, race, and age in CWHS and census measures of work force migration In the study of 28 metropolitan areas, CWHS- matched work force migration rates were systematically higher than equivalent census migra- tion rates, and the disparity varied by sex, race, and age. The disparity was least for young white males and females, larger for older whites, and largest for blacks. Average census migration rates were found to be roughly 70 percent of equivalent social security rates for white males and females and for black females. For black males, however, the disparity was greater — census rates were half those of the CWHS. For all four sex/race groups, the least adjustment was necessary for persons aged 25 years to 34 years, and the disparities increased with age. An explanation of these disparities is not obvious, but possibly they result from census undercounts of migration, social security overcounts of migration, and other reporting inaccuracies. Social security data may, nevertheless, be used as intercensal proxies if the two measures of migration can be consistently mapped into each other and adjustment factors are stable over time. Estimating State population rates of inmigration and outmigration The utility of adjustment factors in estimating the range of population migration rates, both in and out of all 50 States, was examined for this handbook. (Data were not controlled for commuting and labor Table VI 1-6 shows the adjustment factors by sex, race, and age group for both the metropolitan area and State migration studies. A similar pattern of age and race disparity in both studies suggests that these adjustment factors are stable over time. With such adjustment factors, migration by age and by change in earnings could be predicted from social security 99 data almost as reliably as migration for total sex/race groups. The standard error factors for different sex/ race age groups are shown in Table VII-7, which in- dicates the trade-offs required in terms of loss of precision to gain added detail on the demographic characteristics of migrants. The inability of the social security data to record new entrant migration, however, indicates that these data present a biased picture of the age composition of migrants, even though the rates of migration of new entrants into the work force parallel those of people (of the same sex, race, and age in the work force) for whom social security can measure migration. Because entrants are concentrated in the younger age groups, social security data underrepresent their migration, and thus do not adequately measure the number of young migrants to or from an area. In the case of in- migration, the number of young inmigrants can be approximated from social security data by applying known inmigration rates to the number of new entrants in the work force. For young outmigrants, however, there is no comparable means of adjust- ment. Adjustments for variations in the proportion of new entrants in the work force result in only minor changes in predicting migration. Nevertheless, users should be aware of this problem if they are concerned with the age composition of migrants to and from their area of study. Regional biases in social security measures of inmigration, outmigration, and net migration Although the study of 28 metropolitan areas dealt only with inmigration, examination of the migration flows between the four Census regions of the Nation suggested that the adjustment factors might vary by region. If so, the use of these national adjustment fac- tors might result in biased measures of net migration for particular areas. The 1 965-to- 1970 migration rates suggest that there was more variation by region for inmigration than for outmigration. Figure VII-9 shows the difficulty of generalizing about the interregional flows and regional rates of in- migration and outmigration of these four regions over three time periods. For whites, it appears that social security data consistently overmeasure net migration for the Northeast region (tend to show more inmigration and less outmigration than does the census): the pattern holds for all time periods. At the same time, however, social security data appear to undermeasure the net migration for the West region (show less inmigration and more outmigration than does the census). For the North Central and South regions, regional biases in measurement are small. For the older North Central region, social security data tend to show more net migration into the region (or less net migration out of the region) than do the census data, while for the South region, social security data tend to underestimate net in- migration. For blacks, regional disparities are more extensive, and the patterns less consistent, thus making characterization of tendencies more difficult. As for whites, social security data consistently tend to un- derestimate the extent of net inmigration to the West region. Furthermore, these data tend to overestimate the net inmigration of blacks to the South region, although this finding is reversed for the most recent (1970-73) CPS comparisons. For the North Central region, social security data generally undermeasure net migration, although the level of bias varies greatly between 1971 and 1973. The results are most confus- ing for the Northeast region: the CWHS appears to undermeasure the net migration relative to the 1970 census, and to overmeasure the net migration for the years after 1970. A simple evaluation of these results is difficult to per- form. For whites, it is safe to say that CWHS data measure migration to and from the North Central and South regions in a fashion similar to that of the census and the CPS. CWHS data, however, tend to overestimate net migration for the older Northeast region and to underestimate it for the growing West region, possibly because of the spurious migration of employees to the Northeast region headquarters of national companies. For blacks, it is difficult to make generalizations. However, part of the disparity between census and CWHS data in the measurement of black migration rates seems to reflect better recording by the CWHS of the migration of those young black males who were severely undercounted by the 1970 census. (Furthermore, the sample size of blacks for the CPS is admittedly weak.) Thus, more credence should perhaps be given to the results shown by CWHS black migration data. Further Comparisons of Net Migration Athough national adjustment factors help resolve disparities in gross inmigration and outmigration rates between census and CWHS data, they do not appear to be useful proxies for net migration because of regional biases. The following comparison of State net migration rates, 1971-73, based on the CWHS 10- percent files and Census Bureau State population es- timates, further illustrates some of the conceptual dif- ferences between work force net migration and pop- ulation net migration data. In Table VII-8 each of the States is ranked on the basis o\~ the 1971-73 net migration rates computed from CWHS data and from Census population es- timates. The net population migration rates refer to the civilian population under age 65. The Census Bureau derives its rates by first subtracting estimated 100 natural increase in the population (births minus deaths), net movement of persons from military to civilian status, and an estimate of net migration for the population 65 and over from the Census Bureau's "best" estimate of the change in civilian population from 1971 to 1973. The population migration rates were then computed by dividing the average migra- tion estimate for the civilian under-65 population by the 1971 average population estimate for this group. The CWHS migration rates were computed by dividing net migration (inmigrants less outmigrants) by the sum of matched nonmigrants and outmigrants for the States. Movers to and from the military and other unclassified geographic areas, as well as entrants and exits, were excluded in the computation of the CWHS migration rates. Census net migration estimates, 1971-73, generally tend to be somewhat higher than the corresponding CWHS rates partly because the Census Bureau rates include net immigrants from abroad (who would show up initially as entrants, if they appear in the CWHS at all). Allowing for this difference in average levels of net migration, the range of the CWHS and Census Bureau estimates is generally similar, although the rankings of the States based on the two series differ. In 1 7 States and the District of Columbia, CWHS net migration rates, 1971-73, exceed Census Bureau rates. In some of these cases the CWHS data are dis- torted by "spurious" inmigration . Generally, the large industrial States (Massachusetts, New York, New Jersey, Pennsylvania, Ohio, Illinois, and California), which experienced significant net out- migration or declining net inmigration after 1970, show higher CWHS rates (more net inmigration or less net outmigration). The CWHS does not record the migration of nonworking dependents, and the CPS suggests that proportionately more of the migrants from metropolitan to nonmetropolitan areas have dependents than is the case for the reverse flow. Another factor contributing to the higher CWHS rates is the business cycle. The recession of early 1971 had a differentially large impact on the in- dustrial States, and many of the unemployed may have migrated to areas with better job prospects by 1973. If they did not work at all in the first quarter of 1971, they would appear as entrants in the CWHS file, thus biasing downward the outmigration rates for high-unemployment States and the inmigration rates for other States. High Census Bureau rates relative to CWHS rates oc- c ir in such agricultural States as Iowa, North Dakota, and Nebraska. Since the first quarter CWHS does not include farmers and farm workers, it may not accurately reflect population migration for these States. In the past it might have been expected that CWHS rates would understate net outmigration for such States, because the past heavy outmigration from farming (often to more industrial States) would not be recorded in the CWHS. Data for the post-1970 period, however, suggest that the movement out of farming has slowed dramatically and even may have reversed in some areas. Increasingly, moreover, many farmers and farm laborers also work at least part time in nonfarm jobs which are covered in the CWHS. Therefore, it cannot be assumed that the CWHS necessarily understates net outmigration for farm areas. Conclusion As this chapter suggests, many factors influence the comparability of CWHS data with other related data. Even so relatively straight-forward a concept as employment can be defined and measured in various ways. For example, employment measures for a par- ticular area depend on whether jobs or workers are being counted, whether workers are counted by place of work or place of residence, and on the time period being considered (for example, a week or a quarter). Unlike most other data sources, the CWHS can provide either job counts or worker counts, but it cannot measure employment for a particular week or pay period. Thus, precise reconciliation of the CWHS with other employment series is generally not possi- ble. And in the case of CWHS migration data, the problems of reconciliation can be even more difficult. Despite the problems involved, comparisons of the CWHS with other series can be very useful at the State and regional levels. The attempt to reconcile data differences requires familiarity with the many definitional and statistical details involved in data construction. These details are crucial for being able to draw accurate inferences from the data. This is particularly true at the State and regional levels, because many of the factors affecting CWHS data comparability and interpretation vary considerably among geographic areas. Moreover, State and regional planners will need to understand the relationship between CWHS data and other informa- tion they may possess in order to decide which CWHS data to acquire. Comparisons of alternative data sources can also be used to isolate particular data problems which might otherwise mislead a regional analyst. An analyst's specialized knowledge of regional economic condi- tions is especially useful in isolating such problems. 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LT (M u> p-oocT>aoaocijaojocr-oo .n<\JOf*- -o xcr-ao.n.J-cisO-l-rNjrom r\i -J- LTilNIXlrOOOCOr-OO'-t >f HI O 1 ^t -O O - lOOrOO'-i N-l'NM'M^lCirvitM'- 1 >-l o o o o o o o o -a cr o in o c o o o o o o o o o o — • in o o m w p- o CM (M\0-j-inoo»ot^-rTicccr — i — «4-c\jr\)r\jr\jrMCMr\j— « .0 p- JO in o -o p- o o CO r- >- o CO _-J CO z LU <_> Q < CO 1 CI 1 X Id 1 _s UJ 1 C-V h- < cT, '" JJ >- 2 -i CL J 1 O uJ 3 >- a Jj >— 13 l/J <* (t^i CC f^ CP T f 1 - N 1i f\; O ^ 0(3>vfOf---&vJ-(NiX)OvO -i iO cm — • m —> j x\ -r u in y- cr cp j:i — p- >♦- o 4- oo o 3-' p- r» .a cr o a - ' cm -j r- -j- p- Om «t si r- -_ — « m o ^-l T- n — « o^ *n x? r^i crj a> 7- O r\j o in r-i -f C -.0 — i •- -T r-l ^l-jnxijrij-j-rntMrsi oooooooo o a o ooooooo ooco in ro C 03 vO -o r- C i-t ^r -o ao O O a^ •*■ o a m x -r o m <»- CT -T C7^ f lU o i i i i i i i i i i nomomoriOJ^O r-tr\if\jr ac in r- cr- -t r- r>- in -43''\i'\ijrvin —• o o o o ooooooo o ooooooooooo o ■n— • m og MniiOM^c-tmm ^ra»mo*-oaounmo>o m a r- o — i o x m oo ~o r ■,•- ^ sT o j- ?■ > -f jj — i(M r \jr f irn^-^j-xiin-0> I I till moinomomomo I— o p- o o CM P~ < 104 STATE - UNITED STATES TABLE VII-4 CWHS EMPLOYMENT AND CENSUS POPULATION, BY SEX, RACE, AND AGE GROUP, 1960 AND 1970 AGE GROUP CWHS WORK FORCE I960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCE 1970 CENSUS POPULATION :whs/cem WHITE MALES UNDER 25 4,186.900 34,985,316 .120 25 - 29 3,414,400 4,721,074 .723 30 - 34 3,800,600 5,217,653 .728 35 - 39 3,849,800 5,446,399 .707 40 - 44 3,535,400 5,116,691 .691 45 - 49 3,274,200 4,827,973 .678 50 - 54 2,776,400 4,285,945 .648 55 - 59 2,187,500 3,728,586 .587 60 & OVER 2,933,800 10,029,656 .293 TOTAL 29,959,000 78,359,293 .382 6,968,100 40,273,241 .173 4,717,100 5,849,792 .806 3,988,500 4,925,069 .810 3,570,100 4,784,375 .746 3,769,000 5,194,497 .726 3,774,300 5,257,619 .718 3,342,500 4,832,555 .692 2,886,600 4,310,921 .670 3,575,800 11,292,918 .317 6,592,000 86.720,987 .422 BLACK MALES UNDER 25 474,600 5,272,942 .090 25 - 29 391,800 611,122 .641 30 - 34 398,400 627,911 .634 35 - 39 397,100 632,590 .628 40 - 44 360,500 558,768 .645 45 - 49 333,300 529,718 .629 50 - 54 273,900 448,788 .610 55 - 59 193,800 398,645 .486 60 (, OVER 220,400 882,736 .250 TOTAL 3,043.800 9,963,220 .306 881,100 6 ,782,607 .130 539,700 771,775 .699 460,100 670,721 .686 421,000 628,048 .670 406,300 624,316 .651 390,300 593,715 .657 329,500 515,361 .639 285,000 454,900 .627 331,600 1 ,149,762 .288 4,044,600 12 ,191,205 .332 WHITE FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - ^4 55 - 59 60 & 3VER TOTAL 3,185,400 1,484,800 1,525,200 1,848,100 1,935,200 1,880,000 1,673,400 1 ,240,900 1,434,600 16,207,600 34,171,535 4,833,455 5,370,325 5,693,711 5,305,808 4,956,872 4,407,465 3,897,593 11,824,852 80,461,616 .093 .307 ,284 .325 ,365 .379 ,380 .318 .121 .201 5,830,200 39,380,629 .148 2,544,800 5,962,122 .427 1,896,400 5,042,368 .376 1 ,982,300 4,936,494 .402 2.333,700 5,412,335 .431 2,549,900 5,587,023 .456 2,308,900 5,169,302 .447 1,935,000 4,695,581 .412 2,252,400 14,842,134 .152 23,633,600 91,027,988 .260 BLACK FEMALES UNDER 25 246,200 5 ,368,291 .046 25 - 29 210,900 702,156 .300 30 - 34 229,800 732,244 .314 35 - 39 248,600 707,550 .351 40 - 44 227,400 618,359 .368 45 - 49 205,900 564,564 .365 50 - 54 177,400 463,619 .383 55 - 59 113,400 405,008 .280 60 6 OVER 117,600 964,735 .122 TOTAL 1 ,777,200 10 ,526,526 .169 677,800 6 ,904,944 .098 405,000 893,304 .453 356,600 792,278 .450 320,200 757,934 .422 330,400 749,806 .441 313,600 677,582 .463 261,700 586,800 .446 201,800 511,626 .394 230,500 1 ,397,472 .165 3,097,600 13 ,271,746 .233 GRAND TOTAL 50,987,600 179,310,655 .284 67,367,800 203,211,926 .332 105 Table VII-5 Standard Error Factors of Relationships Between Interstate Migration Rates Derived From the 1970 Census of Population (M ) and the Social Security 1- Percent CWHS (M ), by S^x and Race, 1965-70 Regression: M . Proportional Relationship aM„ M = aM s c s Rates of inmigration from noncontiguous States, by the total covered work force into 28 metropolitan areas ]_/ white males black males white females black females , Standard error „. factor — M & Standard error „. factor— Adjustment 10.5 .84 factor (a) 3/ 1.20 1.20 .70 1.45 7.0 .65 1.45 .49 1.24 8.8 .86 1.24 .71 1.54 8.0 .46 1.67 .67 Rates of inmigration from different States, by the Census population into 50 States 4/ white males. . . black males. . . white females, black females. 1.24 12.0 .72 1.20 .68 1.29 7.0 .78 1.29 .49 1.22 10.6 .79 1.22 .83 1.30 5.8 .79 1.23 .59 Rates of outmigration to different States, by the Census population from 50 States 4/ white males. . . black males. . . white females, black females. 1.18 12.2 .70 1.12 .68 1.23 7.2 .69 1.23 .49 1.22 10.9 .62 1.22 .83 1.30 5.8 .53 1.25 .59 1/ The "total covered work force" comprises all workers, including new entrants, in industries covered by the Social Security program. Census migration rates were calculated from the 1970 1 -percent county group Public Use Sample . 2/ The standard error factor (f) is the antilog of the standard error of the estimate lny/lnx" In terms of the original variables, M it may be considered as de- lineating the standard deviation around the geometric mean, and the following con- fidence interval should hold: Probability^ < M < R c c f} ^ .67 3/ The adjustment factor used in the proportional relations between Census and Social Security migration rates is the ratio of the average Census migration rate for the group being studied to the average Social Security migration rate: number of Census migrants in group i base Census population in group i number of Social Security migrants in group i base Social Security work force in group i 4/ For black males and females, the sample is comprised of only those 29 States for which the base of the CWHS migration rate was at least 50. 106 03 O O r— CD r— > r~- i — (O C71 n TO 0) S- (J EU a: =c ml 3, \, "3-1 +J C CD E >■ o O p^ 1 o. in E T> 01 CJ1 CO 13 i/) c o> CJ I— «* ~l t/1 CD EC U c/1 s- £ o CO 1- co ^ CM '^ o 3 o r^ "D i CD LTl ^- UD CU O > O U *d- «=*■ *3- "=d- ^f «3- cxi co (percent) (percent) Maine 1.3 16 .1 24 6.1 38 New Hampshire 3.0 5 2.1 9 12.3 10 Vermont 1.0 21 -3.2 49 5.0 44 Massachusetts -.3 37 .4 21 5.1 42 Rhode Island .6 24 .2 23 7.7 31 Connecticut -.8 45 -1.3 42 3.0 4'i New York -2.0 50 -1.4 46 .3 51 New Jersey -.2 36 -.0 25 5.3 40 Pennsylvania -1.1 47 -.7 35 1.6 ',0 Delaware .5 28 4.2 4 11.4 13 Maryland .5 27 -1.3 45 5.2 41 District of Columbia -3.3 51 -.4 30 3.7 4,-, Michigan -.6 41 -.6 31 6.8 34 Ohio -1.4 48 -.7 34 5.1 43 Indiana -.7 43 -1.7 47 7.9 27 11 linois -1.5 49 -1.2 41 4.5 47 Wisconsin .4 30 -.7 33 7.8 29 Minnesota -.6 42 -1.2 40 7.8 30 Iowa -.5 40 -2.2 48 6.6 36 Missouri -.0 33 -.3 29 5.9 39 North Dakota -.7 44 -3.7 51 6.2 3/ South Dakota -.1 34 -.6 32 11.5 12 Nebraska .6 25 -1.1 39 7.5 3:' Kansas -.4 39 -1.0 38 9.9 19 Virginia 1.7 12 1.0 14 11.1 16 West Virginia -.2 35 -.7 36 4.6 46 Kentucky .1 31 2.8 7 12.7 9 Tennessee 1.0 19 -.0 26 9.4 20 North Carolina 1.0 20 .5 19 11.1 15 South Carolina 1.1 IS -.9 37 9.1 23 Georgia 1.2 17 -1.3 44 7.9 28 Florida 8.4 1 6.4 1 19.0 1 Alabama .0 32 -1.3 43 8.0 26 Mississippi .5 26 .8 16 11.2 14 Louisiana -.3 38 -.0 27 6.7 35 Arkansas 2.1 9 1.0 15 11.6 11 Oklahoma 1.0 22 2.1 10 9.3 21 Texas 1.5 14 .2 22 9.3 22 New Mexico 1.4 15 1.4 12 13.2 7 Arizona 8.2 2 3.9 5 16.7 3 Montana 1.0 23 1.2 13 9.0 24 Idaho 2.8 6 1.5 11 10.8 17 Wyoming 1.5 13 .7 18 13.0 8 Colorado 6.9 3 6.2 2 18.6 2 Utah 1.7 11 2.4 8 15.8 4 Washington -1.0 46 -.2 28 7.5 33 Oregon 2.8 7 3.9 6 14.2 5 Nevada 5.5 4 4.9 3 13.9 6 California .4 29 .7 17 8.3 25 Alaska 2.2 8 -3.5 50 4.9 45 Hawaii 1.9 10 .5 20 10.1 IK 109 z. 3 » 0) c o 1 i 1 1 a o +3 CD k. 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O (0 +-» o CO :3 CM LO LO CN O CM LO LO uoi^egndod ajeiuaj >|oe;g snsusQ &m j.o uoiiej6sujino |o saiey CO 'IAI 17 4) 0) CO im 4) > < o 3 O o ■ *■ o o w 3 s O *+- o o 0) o CO K > CO ec E o 2 CD cz c/5 LU ft E cc 09 1b. 3 stment Fact igration and s IS Li- Average Adju Regional Inm X H D O - ..'.-■- SOUTH i 1 . i 1 ._ _ i E o (A o oc 1.1 *l is o w c o u fa (Jl c P; > *■ o o J= O Cb i: .2 S to > I§ i! D 18 BIBLIOGRAPHY This bibliography lists the books, articles, papers, reports, and unpublished material referred to in the text plus other works dealing with the Continuous Work History Sample. It is not intended, however, to be a com- prehensive bibliography on the CWHS. Several of the annotations were taken from: U.S. Department of Health, Education, and Welfare. Public Health Service. National Institutes of Health. Rural — Urban Migration Research in the United States, by Daniel O. Price and Melanie M. Sikes. Publication No. (NIH) 75-565. Washington, D.C.: Government Printing Office, 1975. 1. Alexander, Arthur J. "Income, Experience, and the Structure of Internal Labor Markets." Quarterly Jour- nal of Economics, LXXXVIII (February 1974), 63-85. The 1957-66 longitudinal file is used to examine the effect of industry and firm experience on the 1965 earn- ings of adult white males in industries with manorial, guild, and unstructured patterns of labor organiza- tion. This article concludes that variations in mobility patterns are linked to different internal labor market structures, and that the relative importance for earnings of firm-specific experience and general experience varies across income tlass but not across the structural types of industries. 2. Alexander, Arthur J. Structure, Income, and Race: A Study in Internal Labor Markets. Report No. R-577- OEO. Santa Monica, Calif.: Rand Corporation, 1970. The 1957-66 longitudinal file is used to examine the effect of industry and firm experience on the 1965 earn- ings of black males in manorial industries. The data suggest that there are separate promotion ladders for whites and nonwhites, and that nonwhites are relegated to dead-end jobs in southern manorial industries. 3. Birch, David L.; Allaman, Peter M.; and Martin, Elizabeth A. Level and Composition of Migration Streams Into and Out of Metropolitan and Rural Areas, Working Paper No. 3. Cambridge, Mass.: M.I.T. — Harvard Joint Center for Urban Studies, 1975. Data on the level and demographic composition of gross migration flows for 315 subdivisions of the United States are analyzed for annual intervals 1959-65 and 1967-68. Prominent features include large gross migration flows relative to net migration (which is true of total flows and flows for separate demographic groups) for most areas and substantial variation from area to area in rates of migration. 4. Bogue, Donald J. A Methodological Study of Migration and Labor Mobility. in Michigan and Ohio in 1947, Studies in Population Distribution, No. 4. New York: Scripps Foundation, 1952. Social security records for 1947 are used to study the mobility patterns of workers in Ohio and Michigan. Mobility is defined as a change in county of employment, industry of employment, or employer during the year. The study concludes that about one-third of all Michigan and Ohio workers changed county, in- dustry, and/or employer during the study year. Almost all moves (98 percent) involved a change of employer; about 85 percent involved an industry change; and about 50 percent, a change of county. 5. Brecher, Charles. Upgrading Blue Collar and Service Workers. Baltimore: Johns Hopkins University Press, 1972. This is a study of workers who stayed in any of five specific industries in New York City for the years 1962- 66. The occupational structure of these five industries was the major determinant of these industries' up- grading possibilities. For instance, apparel manufacturing and retail eating and drinking places had small proportions of higher-paying jobs, while construction had many higher-level job opportunities. Upgrading was the dominant method of reaching the higher levels within an industry. Blacks and females held significantly smaller portions of high-level jobs, probably due to discrimination. 119 6. Bunting. Robert L. "Labor Mobility and Wage Improvement."' Paper presented at a conference of the Human Resources Subcommittee of the Committee on Urban Economics, Resources for the Future, Washington. D.C.. November 16-17. 1962. Data for workers in North Carolina, South Carolina, and Georgia during 1953 are extracted from the an- nual CWHS file. These data are used to compare the wages of mobile workers before and after job changes relative to the wages of workers who do not change jobs. Workers who voluntarily changed jobs are shown to have experienced wage increases several percentage points greater than those who did not change jobs. 7. Bunting, Robert L. "Labor Mobility: Sex, Race, and Age." Review of Economics and Statistics, XLII (May I960). 229-31. Social security data for 1953 are used to study mobility patterns for workers in North Carolina, South Carolina, and Georgia. Mobility is defined as a change of employer in 1953. The age, sex, and race characteristics of workers are cross-classified by number of employers during the year. Males tended to be more mobile than females; young workers, more mobile than older workers; and black males, more mobile than white males. 8. Bunting, Robert L.; Ashby, Lowell D.; Prosper, Peter A., Jr. "Labor Mobility in Three Southern States." Industrial and Labor Relations Review, XIV (April 1961), 432-45. An employee-employer file for 1953 was used to study workers in North Carolina, South Carolina, and Georgia, cross-classified by sex, race, and age. Mobility varied with sex, race, and age. Young workers were 45 percent more mobile than older workers, black males more mobile than white males, and white females slightly more mobile than black females. Within age, sex, and race categories, intercounty moves were more frequent in nonmetropolitan areas than in metropolitan areas. Net outmigration was greater among males and blacks than among females and whites. The proportion of young males and blacks increased as dis- tance of move increased. The article concludes that, despite limitations, social security data provide reliable information on labor mobility. 9. Fisher. I. N. and Purnell, S. W. The North-South Migration Process: Some Observations on Urban-Rural Migration. Santa Monica, Calif.: Rand Corporation, 1973. The 1957-66 LEED file was used to identify those persons working one or more years in nine large northern metropolitan areas, and to study the characteristics, migration patterns, and destinations of those moving to rural areas of the South. The variety of groups moving from northern cities to the South was found to be quite diverse — including both intermetropolitan migrants and return migrants. The economic returns to movement to the rural South were found to be very low, especially among blacks. 10. Fisher, I. N. and Purnell, S. W. The Rural-Urban Migration Process: A Longitudinal Analysis of Rural Out- migration. Santa Monica, Calif.: Rand Corporation, 1972. The 1957-66 LEED file was used to identify persons working one or more years in the rural South and to trace the geographic and urban-rural pattern of their moves. Over half the work force in the rural southern areas moved elsewhere during the study period. Although most still lived in the South, a substantial number moved to the North. A significant proportion migrated in a series of moves, including stopovers in intermediate-sized cities and regional centers. Outmigration was highest in the younger, more highly skilled component of the work force. 11. Gallaway, Lowell. "Age and Labor Mobility Patterns." Southern Economic Journal, XXXVI (October 1969). 171-80. CWHS data for 1957 and 1960 are used to consider patterns of geographic and interindustry mobility of males, by age. About 25 percent of the sample was found to have changed major industry and 7 percent to have changed Census region. Age inhibits worker mobility. In the case of geographic mobility, an annual earnings differential of $85 was sufficient to compensate a worker for the incremental costs of movement associated with an additional year of age. In the case of interindustry movement, these costs were somewhat higher. 1 2. Gallaway, Lowell E. "The Effect of Geographic Labor Mobility on Income: A Brief Comment." Journal of Human Resources, IV (Winter 1969), 103-9. This article uses 1957-60 data, cross-classified by region and industry of employer and by sex, race, age, and earnings to comment on Lansing and Morgan, "The Effect of Geographic Mobility on Income," Journal of 120 Human Resources, II (Fall 1967), 449-60. Gallaway agrees with Lansing and Morgan that income levels of geographically mobile workers are less than those of nonmobile workers, although this relationship tends to disappear for workers of similar characteristics. Gallaway adds confirming evidence, using additional data provided by the CWHS. 13. Gallaway, Lowell E. "Geographic Flows of Hired Agricultural Labor: 1957-60." American Journal of Agricultural Economics, L (May 1968), 199-212. Gallaway, uses 1957 and 1960 files for the demographic analysis of farm workers, cross-classified by area, age, and earnings, to conclude that the movement of labor is related to earnings differentials between regions, distance, age-group earnings, and, in some cases, demand for agricultural labor — all of which is consistent with formal labor market theory. A number of noneconomic barriers to interregional mobility (for instance inadequate nonfarm job opportunity information) limit the possibility of bettering the relative economic position of hired agricultural labor. 14. Gallaway, Lowell E. "Mobility of Hired Agricultural Labor, 1957-60.'" Journal of Farm Economics, XLIX (February 1967), 32-53. Using the 1957-60 longitudinal data, Gallaway establishes a substantial outflow of workers from the agricultural sector and shows that agricultural workers are responsive to differential economic advantages when moving into the nonagricultural sector. Although the outflow occurs primarily among the young, there is a sizeable "reverse" flow of older workers, comprised of many involuntarily mobile workers who are able to find job openings in agriculture at relatively low earnings levels. No significant increase of relative earnings of agricultural workers occurred between 1957 and 1960. The economic costs of move- ment and the artificial barriers to mobility between agricultural and nonagricultural sectors have a much greater effect on older agricultural workers than on older workers in general. 15. Gallaway, Lowell E. "The Negro and Poverty." Journal of Business of the University of Chicago, XL (January 1967), 27-35. Using the 0.1-percent CWHS for 1955-58 by sex, race, and age, Gallaway finds that educational differences and race influence income transition rates. The incidence of poverty is greater among those workers with a prior history of poverty-level incomes, expecially Negroes. Noneconomic forces affecting poverty include discrimination and the social attitudes of Negroes. 16. Goldsmith, John R. and Hirschberg, David A. "Mortality and Industrial Employment." Journal of Oc- cupational Medicine, X (March 1976), 161-64. This is a preliminary report comparing mortality rates based on social security data and expected mortality rates for a few specific industries. The authors use longitudinal data for workers in the 1965 labor force, and follow employment patterns through 1972. The aim is to identify industries where preventive programs may prove useful. 17. Hathaway, Dale E. and Perkins, Brian B. "Farm Labor Mobility, Migration, and Income Distribution." American Journal of Agricultural Economics, L (May 1968), 342-56. CWHS data (1955-63) show that although a large proportion of farm workers experienced losses on mov- ing into nonfarm occupations, the rate of mobility out of agriculture remained high. The rate of "back movement" into agriculture offset about 90 percent o( the mobility out of the industry. These "back movers," were likely to be blacks and individuals from low-income areas who failed to improve incomes in nonfarm occupations. 18. Hathaway, Dale E. and Perkins, Brian B. "Occupational Mobility and Migration From Agriculture," in Rural Poverty in the United States. A report by the President's National Advisory Commission on Rural Poverty, May 1968. Washington, D.C.: Government Printing Office, 1968. CWHS data for 1957-63 show that off-farm movers from low-income rural areas had lower earnings gains and were more likely to return to agriculture than off-farm movers from high income areas. Significant earnings gains from off-farm movement were associated with long-distance moves, moves to large cities, and stable nonfarm employment. Off-farm movers taking jobs in government or trade received lower earn- ings than movers to other industries. 19. Hirschberg, David A. "The Impact of Geographic Mobility on the Appalachian Region, 1957-1963." Un- published master's thesis. New York University, 1968. 121 Hirschberg uses CWHS data to examine the demographic and earnings characteristics of Appalachian migrants, nonmigrants, and work force entrants and exits. Employment growth in Appalachia lagged behind the national rate; earnings levels were below average and grew at below average rates. The CWHS data did not indicate substantial net outmigration flows probably because many young people left the region prior to taking their first covered jobs. 20. Jacobson, Louis. Earnings Losses of Workers Displaced from the Steel Industry by Imports of Steel. Report No. PRI 197-75. Arlington, Virginia: Public Research Institute, Center for Naval Analyses, 1975. The CWHS LEED file is used to follow the earnings patterns of steel workers to determine if displaced workers had difficulty finding suitable alternative employment. Under "realistic" assumptions about future economic conditions, losses to workers displaced by the removal of restrictions on the importation of steel would be relatively small, affecting only about 2,500 workers of a total of 600,000. 21. Jacobson, Louis. The Use of Longitudinal Data to Assess the Impact of Manpower Training on Earnings. Report No. PRI 73-2. Arlington, Virginia: Public Research Institute, Center for Naval Analyses, 1973. CWHS data are used in a regression analysis to test a model of income determination designed to study the effects of manpower training on earnings. Earlier studies understated the impact of training on earnings because they failed adequately to consider that trainees had been selected because of difficulties in finding and holding employment. 22. Johnson, William A. Changing Patterns of Employment in the New York Metropolitan Area. Report No. R- 571 -NYC. Santa Monica, Calif.: Rand Corporation, 1971. Johnson uses CWHS data to analyze the changing demographic and industrial composition of employment in New York City and in the surrounding metropolitan area, during 1962-66. New York City lost low- and middle-income jobs to the suburbs, and the city's employment composition shifted increasingly to office and service activities. 23. Kiker, B. F. and Traynham, Earle C, Jr. Return and Nonreturn Migration for the Southeast: 1960-1970. Es- says in Economics, No. 30. Columbia: University of South Carolina, 1974. CWHS data are used to identify workers who left the Southeast during selected intervals of the 1960's and to determine the proportion of workers who had returned by 1970. Over a third of the workers who left the Southeast between 1960 and 1968 returned by 1970. 24. Laber, Gene. "Human Capital in Southern Migration." Burlington, Vt., no date. (Mimeographed.) Laber uses CWHS data to examine the earnings patterns of migrants into and out of the Southeast during 1960-66. Although the South experienced a net outmigration of workers, inmigrants had higher (southern) earnings than outmigrants, and the South probably experienced a net increase in human capital as a result of migration. 25. Lowry, Ira S. Migration and Metropolitan Growth: Two Analytical Models. San Francisco: Chandler, 1966. Rates of inmigration to geographic areas are generally sensitive to economic opportunities, while rates of outmigration are not. 26. McCall, John J. Earnings Mobility and Economic Growth. Report No. R-576-OEO. Santa Monica, Calif.: Rand Corporation, 1970. The earnings mobility of 25-55 year old males and females are investigated longitudinally, 1957-66, and the movements across three earnings levels ($1,500, $3,000, and $4,500) are measured. Given the low earnings levels of 1957, the probability of remaining in a low-earnings category in all 10 years was significantly greater than zero and was higher for nonwhites and females than for white males. The probability of mov- ing out of the low-earnings category was positively related to percentage change in GNP, with the probability higher for nonwhites. Most, but not all, low-earnings workers benefited from growth. 27. McCall, John J. Income, Mobility, Racial Discrimination and Economic Growth. Lexington, Mass.: D. C. Heath, 1973. Longitudinal data (1957-66), by race, sex, age, and earnings, show that black males were more likely to stay at low-earnings levels than were white males, and that in periods of economic growth, nonwhite males ad- 122 vanced more rapidly than white males. Females benefited from growth, but not as much as males. Growth led to higher female labor force participation rates. Although racial discrimination was evident, during periods of growth employers were more likely to hire blacks. 28. Morrison, Peter A. "An Analysis of Annual Migration Flows Into and Out of Metropolitan Areas." Paper presented at the annual meeting of the Population Association of America, Seattle, Washington, April 17, 1975. Morrison 'analyzes annual CWHS gross inmigration and outmigration rates for 98 SMSA's, 1959-65, and runs cross-sectional regressions on the annual data, relating inmigration and outmigration to such variables as employment change and past migration rates. Annual migration rates were substantially higher than might have been expected from 5-year census rates. Unlike outmigration, inmigration was related systematically to employment change. 29. Morrison, Peter A. "Chronic Movers and the Future Redistribution of Population: A Longitudinal Analysis." Demography, VIII (May 1971), 171-84. Individual CWHS records for 1957-66 are examined to determine the extent of repeat migration among workers. The individual propensity to move in the later years of the study was higher for those who had moved recently, and the probability of movement increased substantially with the number of previous moves. 30. Morrison, Peter A. and Relies, Daniel A. Recent Research Insights into Local Migration Flows. Working Paper P-5379. Santa Monica, Calif.: Rand Corporation, 1975. Annual CWHS migration data for metropolitan areas, 1959-65, were used to develop and test a model for projecting migration flows from employment projections. Inmigration was systematically related to employment growth, but employment growth was not useful for projecting outmigration. Outmigration could be predicted well, as a function of lagged outmigration. 3 1 . National Academy of Sciences. Panel on Manpower Training Evaluation. The Use of Social Security Earn- ings Data for Assessing the Impact of Manpower Training Programs. Washington, D.C.: National Academy of Sciences, 1974. Information from SSA earnings records is matched with data from the Manpower Automated Reporting System in order to evaluate the effect of participation in manpower training programs. The panel recom- mends that social security data (including CWHS) be more widely used in the evaluation of manpower training programs. The accuracy of social security earnings data is considerably higher than comparable data from other retrospective sample surveys. The data can complement the results of field evaluation studies at low cost. SSA data are cheaper than data from sample surveys, and they provide a more reliable means for comparing earnings performance of trainees and nontrainees than do sample surveys. 32. Nelson, Kathryn P. Evaluating Social Security Measures of Migration: Basic Considerations. Report ORNL-UR-119. Oak Ridge, Tenn.: Oak Ridge National Laboratory, 1975. This report describes the coverage and quality of the CWHS as a source of data on work force migration. The undercoverage of the migration of young adults may be a major, but correctable, source of bias in CWHS estimates of migration. This hypothesis can be tested through detailed comparison with the 1970 Census of Population. 33. Nelson, Kathryn P. Evaluating Social Security Measures of Migration: Results for 28 Metropolitan Areas by Sex, Race, Age, and Earnings, 1965-1970. Report ORNL-UR-130. Oak Ridge, Tenn.: Oak Ridge National Laboratory, forthcoming. Rates of 1965-70 CWHS work force migration into 28 large metropolitan areas are compared with census migration rates for both the covered work force and the population, by sex, race, age, and earnings. Based on census data, migration rates of entrants into the 1970 work force parallel those of workers in covered industries in both 1965 and 1970, who were of the same sex, race and age. After controlling for sources of error such as commuting and differences in labor force participation and industrial coverage, and for migration of entrants and exits, Nelson finds that CWHS migration rates are systematically higher than equivalent census migration rates. Adjustments for this difference in average rates of migration make CWHS migration rates serve as a reliable proxy for census migration rates, by sex, race, age, and earnings. The differences in level of migration and the corresponding improvements from use of adjustment factors are greatest for black males and older workers. 123 34. Nelson. Kathryn P. "'The Migration of Workers: Assessing Its Metropolitan Impacts." Paper presented at the NICHD Conference on Population Distribution, Belmont, Md., January 27, 1975. The 1962-67 migration rates and streams of workers, by sex, race, age, and earnings, in nine metropolitan areas are examined to demonstrate ways in which the CWHS can be used for assessing and projecting the local impacts of migration. Information from the CWHS can be used to evaluate intercensal population es- timates and to monitor the ways in which local migration patterns diverge from the migration trends as- sumed in population projections. Differences in outmigration rates by age and earnings, in the geographic directions of migration streams, and in the economic returns to migration are cited as examples. 35. Perkins. Brian B. "Labor Mobility Between the Farm and Nonfarm Sector." Unpublished Ph.D. disserta- tion. Michigan State University, 1964. Based on 1955-59 CWHS data, rates of off-farm and in-farm mobility were high. High in-farm rates are at- tributed to the failure of many off-farm movers to find off-farm jobs. Policies to maintain full employment and to retrain off-farm movers are needed. 36. Perkins. Brian B. and Hathaway, Dale E. The Movement of Labor Between Farm and Nonfarm Jobs. Agriculture Experimental Station Research Bulletin 13. East Lansing, Michigan: Michigan State Univer- sity, 1966. Data for 1955-59 show that a large "back movement" of workers returning to farm employment from non- farm employment resulted in a net outflow of only 3.5 percent, despite an average outmigration rate of 14 percent. Mobility rates are higher for youths, for workers with previous nonfarm experience, and for those with multiple jobs. Workers who made income gains remained in an off-farm category, while those who ex- perienced losses returned to farm employment. The rate of "back movement" increased noticeably during the recession of 1957-58. 37. Pursell, Donald E. "Determinants of Male Labor Mobility." Demography, IX (May 1972), 257-61. CWHS data are used in a cross-sectional study of male worker migration from 1960 to 1965 for 84 metropolitan areas. Both inmigration and outmigration of male workers are significantly related to employment change. The relationship is somewhat stronger for inmigration than outmigration. 38. Pursell, Donald E.; Schriver, William R.; and Bowlby, Roger. Trade Adjustment Assistance: An Analysis of Impacted Worker Benefits of Displaced Workers in the Electronics Industry. Center for Manpower Studies. Memphis, Tenn.: Memphis State University, 1975. The effects of training benefits under the Trade Expansion Act were studied, using the social security earn- ings records of cooperating workers who were affected by the closing of an electronics plant in Memphis in 1970. There was no evidence that training benefits improved the earnings records of displaced workers. This may have been due to a large number of females who did not agressively seek alternative employment after the plant closed. 39. Renshaw, Vernon. "The Relationship of Gross Migration to Net Migration: A Short Run, Long Run Distinction." Regional Science Perspectives. V (February 1975), 109-24. Annual CWHS migration rates for 224 SMSA's were examined for 1960-65. Simple cross-sectional regres- sions between rates of migration and employment change show that inmigration was systematically related to employment change, while outmigration was not. When year-to-year deviations from average migration rates were examined.both inmigration and outmigration were systematically related to employment change. 40. Renshaw, Vernon. "The Role of Migration in Labor Market Adjustment." Unpublished Ph.D. disserta- tion, Massachusetts Institute of Technology, 1970. Renshaw analyzes a combined annual time series and cross-section of CWHS gross migration data for 224 metropolitan areas for the years 1960-65. Simple cross-sectional correlations show a significant relationship between employment growth and inmigration, but not outmigration. Examination of changing patterns of migration over time show both outmigration and inmigration to be significantly related to employment growth. 41. Renshaw, Vernon. "Using Gross Migration Data Compiled from the Social Security File." Demography, XI (February 1974), 143-48. 124 CWHS work force data illustrate the interrelationships among the components of work force change for metropolitan areas, 1960-65. Cross-sectional correlations reveal positive interrelationships among the com- ponents of employment growth — inmigrants, outmigrants, entrants, and exits. This reflects mobility dif- ferences among the work forces in different areas and does not indicate a "perverse' 1 response of out- migrants and exits to employment opportunities. 42. Schiller, Bradley R. "Equality, Opportunity, and the 'Good JobV , Public Interest (Spring 1976), 111-20. Of a sample of LEED file workers earning at least $1,000 in 1957, and still working in 1971, 71 percent moved at least 5 percentile points, up or down the earnings distribution. The average change of relative earnings was 21 percentiles. Blacks were less "mobile" even though they started at relatively lower positions in the 1957 earnings distribution. Blacks who were near the top of the distribution in 1957 were more likely to fall than their white counterparts. Blacks, on the whole, failed to move up in the earnings distribution. 43. Smith, John E. and Batutis, Michael J. "Changing Growth Patterns: The Case of New York State." Post Industrial America: Metropolitan Decline and Interregional Job Shifts. Edited by George Sternlieb and James W. Hughes. New Brunswick, New Jersey: Rutgers University, 1975. CWHS data are used to contrast post-1970 with late 1960's migration patterns for New York State. Sub- stantial shifts in net migration patterns occurred after 1970, including a reversal of black migration from net inmigration to net outmigration and the emergence of a strong pattern of net outmigration to the South. 44. Steinberg, Edward. "Upward Mobility in the Internal Labor Market." Industrial Relations, XIV (May 1975), 259-65. National work force data, 1965-70, are used to examine worker attachment to firm and industry and the in- cidence of "advancement" (earnings increases of more than $2,000), by sex, race, age, and 1965 earnings. Half the workers remained with the same employer, and another 17 percent remained in the same 2-digit in- dustry. Firm and industry attachments were strongest among middle-income workers, females, and older workers. Advancement rates were highest for middle-income workers, males, and whites. 45. Steinberg, Edward. "Upward Mobility in Low-Income Workers: A Study of Internal Labor Markets in New York City." Journal of Behavioral Economics, IV (Summer 1975), 1-16. Steinberg analyzes the upward mobility and interfirm and interindustry mobility of New York City workers who earned $3,000-$5,000 in 1965 and were still employed in New York in 1970. Low-income women were more likely to remain with the same firm or industry. Men were more likely to increase earn- ings. Age was negatively associated with mobility. Among "firm stayers," blacks and whites were equally likely to increase their earnings. In New York State advancement was most common in banking, which had a large proportion of "firm stayers," as did apparel, despite low incomes. 46. Stolnitz, George J. Mobility and Earnings of the Labor Force in Indiana, 1960-1968. Final Report, Project No. Ind. PD-1, prepared under contract for the U.S. Department of Housing and Urban Development. Bloomington: Indiana University, 1975. A CWHS tabulation system developed at Indiana University is used to analyze geographic and industrial mobility of the Indiana work force, 1960-68. 47. Strasser, Arnold. "Annual Earnings in Construction." Construction Review, XVI (March 1970), 4-10. A BLS tabulation of the CWHS compares earnings in construction with earnings in other industries, 1964- 67. Although construction workers had high hourly wages, the median annual earnings of those whose ma- jor job was in construction were relatively low because of seasonality. For four-quarter workers, earnings in construction were higher than in other industries. 48. Trott, Charles E. "An Analysis of Outmigration," Proceedings of the American Statistical Association, Business and Economic Statistics Section, 1971. Using a model of outmigration based on CWHS data for BEA economic areas, 1960-63 and 1963-66, Trott finds that areas experiencing rapid employment growth in the 1950's were the most likely to experience rapid outmigration after 1960. 125 49. Trott, Charles E. "Differential Responses in the Decision to Migrate." Paper presented at the meeting of the Regional Science Association, Ann Arbor, Michigan, November 12, 1971. CWHS data for selected demographic groups for 56 BEA economic areas, 1960-63 and 1963-66, reveal that although black and white males had different patterns of interregional net migration, they had similar out- migration rates and moved for similar reasons. 50. Trott. Charles E.; Matson, Roger A.; and Smith, Wesley G. "Relative Income Characteristics of Interregional Migrants." Regional Science Perspectives, IV (March 1974), 126-47. CW'HS data on the demographic and earnings characteristics of migrants among the eight BEA regions, 1960-65, show that migrants to low-income regions such as the Southeast had smaller income increases than other migrants, but greater income improvement relative to other workers in the same region. 51. U.S. Department of Commerce. Bureau of Economic Analysis. "Earnings Increases, 1969-73," by Edward Steinberg. Survey of Current Business, LVI (April 1976), 19-21. Longitudinal CWHS data show that blacks earning less than $3,000 a year were less likely to advance than whites; at higher income levels they were more likely to advance. For all sex-race groups the likelihood of advancement was positively related to earnings level and negatively related to age. 52. U.S. Department of Commerce. Bureau of Economic Analysis. "An Evaluation of the Usefulness of the Social Security Administration's Continuous Work History Samples". Unpublished report prepared for the Department of Labor, Manpower Administration. Washington, D.C., 1972. CWHS data are compared with data from other sources to evaluate the potential usefulness of the CWHS. The CWHS offers unique possibilities for work force analysis, particularly because of its ability to trace in- dividuals and groups of workers over time. Inadequate sample size for many local-area applications is con- sidered the major shortcoming of the data. Problems of incomplete coverage and reporting errors are also noted. 53. U.S. Department of Commerce. Bureau of the Census. "Use of Social Security's Continuous Work History Sample for Population Estimation," by Meyer Zitter and Elizabeth S. Nagy. Current Population Report, Series P-23, No. 31. Washington, D.C.: Government Printing Office, 1970. The CWHS can be used to develop post-censal migration estimates at a level of geographic and demographic detail for which alternative migration data are not available. However, work" force migration may not necessarily parallel resident population migration; the CWHS sample is inadequate for the reliable estimation of net migration for small areas; and the timeliness of the CWHS data is not adequate for use in a regular program of current intercensal population estimates. 54. U.S. Department of Health, Education, and Welfare. Social Security Administration. "Measures of Labor Mobility and OASDHI Data," by Sebastia Svolos. Social Security Bulletin. XXIX (April 1966), 38-44. This is an illustrative article which shows how the OASDHI sample can be used to study industrial mobility. 55. U.S. Department of Health, Education, and Welfare. Social Security Administration. "Social Security Statistical Data, Social Science Research, and Confidentiality," by J. Steinberg and H. C. Cooper. Social Security Bulletin, XXX (October 1967). The potential use of Social Security Administration records, containing information generally not available from other sources, is wide. In the use of these records individual rights to privacy must be safeguarded. 56. U.S. Department of Health, Education, and Welfare. Social Security Administration. Office of Research and Statistics. Earnings Distributions in the United States, 1969. Washington, D.C.: Government Printing Office, 1975. This data publication presents total annual and four-quarter social-security-covered employment for the United States, census regions, States, metropolitan and nonmetropolitan portions of States and regions, and large metropolitan areas. The employment data are cross-classified by sex, race (two categories), age (four categories), and earnings levels (six categories). 126 57. U.S. Department of Health, Education, and Welfare. Social Security Administration. Office of Research and Statistics. Geographic Labor Mobility in the United States, 1957 to 1960, by Lowell E. Gallaway. Research Report No. 28. Washington, DC: Government Printing Office, 1969. CWHS data, by sex and race, showed that for whites, but not for blacks, the proportion of nonmovers in any given region was positively related to the mean annual earnings in the same region. For all males and white females interregional gross flow differentials were positively related to both earnings and distance. Interregional earnings variations explained many of the interregional differences in net flows. 58. U.S. Department of Health, Education, and Welfare. Social Security Administration. Office of Research and Statistics. Interindustry Labor Mobility in the United States, 1957 to 1960, by Lowell E. Gallaway. Research Report No. 18. Washington, D.C.: Government Printing Office, 1967. CWHS data on U.S. interindustry mobility by sex, age, race, and earnings show that interindustry move- ment and entry into and exit from the work force were rapid. Movers were responsive to economic incen- tives, but high mobility rates implied high levels of frictional unemployment. 59. U.S. Department of Labor. Bureau of Labor Statistics. Annual Earnings and Employment Patterns of Labor, by Arnold Strasser. Bulletin No. 1675. Washington, D.C.: Government Printing Office, 1970. Quarterly CWHS data by race and industry for the United States for 1965 show that the greatest concentra- tion of employees with low earnings was in retail trade and services. The earnings of whites were slightly higher than those of blacks, partially because fewer blacks than whites worked all four quarters. A greater proportion of blacks was in the low-earnings industries, and even in the same industry, blacks did not earn as much as whites. 60. U.S. Department of Labor. Bureau of Labor Statistics. Annual Earnings and Employment Patterns of Private Nonagricultural Employees, 1970. Washington, D.C.: Government Printing Office, 1975. This report is one of the most recently published of a series, which also covers the years 1965-67. The 1970 report presents CWHS data on national employment and earnings, by 3-digit industry, for selected demographic groups, four-quarter workers, and all workers. The CWHS data are supplemented with in- formation on workers covered under the Railroad Retirement Act. 61 . U.S. Department of Labor. Bureau of Labor Statistics. "Differentials and Overlaps in Earnings of Blacks and Whites," by Arnold Strasser. Monthly Labor Review, XCIV (December 1971), 16-26. CWHS earnings for blacks and whites, males and females, for 1966 show that black earnings were substan- tially lower than white earnings, but that differentials were less for four-quarter workers than all workers. Black-white differentials were greater in the South than in other regions. 62. U.S. Department of Labor. Bureau of Labor Statistics. "Worker Mobility in a Labor Surplus Area," by Vincent F. Gegan and Samuel H. Thompson. Monthly Labor Review (December 1957), 1,451-56. This study of migration in Harrison County, West Virginia, shows that young workers migrated more than older workers in 1953-55, males migrated more than females, outmigrants had lower 1953 incomes than nonmigrants, and nonmigrants had higher 1955 incomes than outmigrants. 63. U.S. Tennessee Valley Authority. National Fertilizer Development Center. Metropolitan Labor Force Migration in the Southeast, by W. G. Smith, R. A. Matson, and C. W. Mann. Bulletin Y-36. Muscle Shoals, Alabama: National Fertilizer Center, 1971. CWHS migration matrices for the 33 largest SMSA's in the Southeast, for each pair of successive years between 1960 and 1965, show that intermetropolitan migration only moderately affected labor force growth. Migration from nonmetropolitan to metropolitan areas in the Southeast was roughly twice as large as intermetropolitan migration. Migration to and from areas outside the Southeast was about as significant as intermetropolitan migration. Most of the labor force growth resulted from the entry of new workers without job experience. 64. U.S. Tennessee Valley Authority. National Fertilizer Development Center. Mobility of the Tennessee Valley Labor Force, 1957-63, by Wesley G. Smith and Roger A. Matson. Bulletin Y-23. Muscle Shoals, Alabama: National Fertilizer Center, 1971. .27 CWHS data on change in the labor force by sex and race and by origin and destination of migrants, 1957- 63. show that the Tennessee Valley increased its income-generating capacity through the inmigration of skilled workers from the North and the outmigration of low income workers. 65. U.S. Tennessee Valley Authority. National Fertilizer Development Center. Movement of Labor Between Farm and Nonfarm Sectors and Multiple Job-Holding by Farm Operators in the Tennessee Valley, by Wesley G. Smith and Venkareddy Chennareddy. Report T67-4AE. Muscle Shoals, Alabama: National Fertilizer Center. 1967. CWHS data on agricultural workers in a 125-county Tennessee Valley region, 1955-59, show high rates of movement in both directions between the farm and nonfarm sectors. Most off-farm movers did not im- mediately improve their earnings. Those farmers who held multiple jobs substantially increased their earn- ings. 66. U.S. Tennessee Valley Authority. National Fertilizer Development Center. "Urban Growth Patterns: The Atlanta Experience," by Kathryn P. Nelson. The Labor Force, Migration, Earnings, and Growth. Proceedings of a conference held June 22 and 23, 1972 and sponsored by the Social Security Administration and the Tennessee Valley Authority. Bulletin Y-63. Muscle Shoals, Alabama: National Fertilizer Center, 1973. CWHS work force and migration data, 1962 to 1967, show the rapid growth of a highly mobile work force for the Atlanta area. Inmigration from the rest of the South was a major factor in this growth. In several in- dustries, especially manufacturing and trade, employment growth in the central county of the SMSA lagged behind growth in suburban counties. 67. U.S. Tennessee Valley Authority. National Fertilizer Development Center. "U.S. Interindustry Mobility — 1960-1968," by George J. Stolnitz. The Labor Force.Migration, Earnings, and Growth. Proceedings of a con- ference held June 22 and 23, 1972 and sponsored by the Social Security Administration and the Tennessee Valley Authority. Bulletin Y-63. Muscle Shoals, Alabama: National Fertilizer Center, 1973. A CWHS data matrix of U.S. interindustry movers, 1960-68, shows that interindustry movers had lower average earnings than did nonmovers. Movers' earnings grew faster than those of nonmovers. The trade sector was a major source of jobs for work force entrants and a major supplier of workers to other in- dustries. Blacks changed industries more than whites, and blacks with intermittent employment changed in- dustries more frequently than did other blacks. 68. Zitter, M. and Word, D. "Use of Administrative Records for Small-Area Population Estimates." Paper presented at the Population Association of America Conference, New Orleans, La., April 1973. CWHS data are used to estimate the working age population of the 50 States, by race. CWHS net migration data, 1965-70, are compared with corresponding data from the 1970 census. The CWHS does not appear to be a good resource for directly measuring net residential migration and population change. 128 APPENDIX A A-l. Form 941, Employer's Quarterly Federal Tax Return A-2. Form 942, Employer's Quarterly Tax Return for Household Employees A-3. Form 943, Employer's Annual Tax Return for Agricultural Employees A-4. Form 0AR-S3, State's Quarterly Report of Wages Paid A-5. Form SS-5, Application for a Social Security Number A-6. Form SS-4, Application for Employer Identification Number A-7. Form OAA-100, Employer Information Schedule A-8. Form OAA-103, Employer Industrial Activity Schedule A-9. Form SSA-5019, Establishment Reporting Plan A-10. Form SSA-1941 , Recapitulation for Establishment Reporting A-l 1 . IRS Form 1040, Schedule SE, Computation of Social Security Self-Employment Tax Form 941 (Rev. Oct. 1973) Department of tits Trenurv Appendix A-l Employer's Quarterly Federal Tax Return Form 941 only) 2. oiel peg neludmg nt pages OtV I't '141 ► a lots of er (eicepi V loyed in the rch 12th t> iployees List (or each nonagricultural employee the WAGES taxable under the FICA which were paid during the quarter. If you pay an employee more than $10,800 In a calendar year report only the first $10,800 of such wages. In the case of "Dp Income" see instructions on page 4. •nployee's name and number exactly as shown on his Social Security card. 6. TAXABLE FICA WAGES Piid to Employee In Quarter (Befor* deduction*) '. TAXABLE TIPS REPORTED (See page 4) □ j need more space tor lilting < Totals for this page Ttployees, use Schedule —Wage total in o continuation sheet). Form 341a. imn 6 and tip total in colun 8. TOTAL WAGES TAXABLE UNDER FICA PAID DURING QUARTER. (Total of column 6 on thii page end continuation ;heets.) Enter here and In Item 14 below . $. 9. TOTAL TAXABLE TIPS REPORTED UNDER FICA DURING QUARTER. (If no tips reported, write "None.") (Total of column 7 on thii pagt and continuation sheets.) Enter here and in Item 15 below Name (is distinguished f. Employer's Trade name, if any name, r address, employer I dentitl cation number, and calendar quarter. (If not correct, P lflflM Addrau and ZIP code change) lame) Date quarter ended — i Employer Identification No. Addrau and ZIP cod* „„£ntriM muit be mad* both above ind below thii Una; if aadroii different from previous return chici hire q Nim* (•■ dlitlngulihtd from trad* mm*} t Trade njme. If in) i L Dat* quartar ended Employer Identification No. 10. TOTAL WAGES AND TIPS SUBJECT TO WlTKHOLOl N3 PLUS OTHER COMPENSATION 11. AMOUNT OF INCOME TAX WITHHELD FROM WAGES, TIPS, ANNUITIES, etc. (See Instruction] 12. ADJUSTMENT FOR PRECEDING QUARTERS OF CALENDAR TEAR 13. ADJUSTED TOTAL OF INCOME TAX WITHHELD 14. TAXABLE FICA WAGES PAID litem 8) . . 15. TAXABLE TIPS REPORTED (Item 9) . . . 16. TOTAL FICA TAXES (Item 14 plus Item IS) 17. adjustment (See Instructions) . . . 18. ADJUSTED TOTAL OF FICA TAXES . . . * multiplied by 11.7%— TAX $ multiplied by 5. 85%— TAX 19. TOTAL TAXES (Item 13 plus Item 18) 20. TOTAL DEPOSITS FOR QUARTER (INCLUDING FINAL DEPOSIT MADE FOR QUARTER) AND OVERPAYMENT FROM PREVIOUS QUARTER LIST IN SCHEDULE B (See Instructions on page 4) Note: If undeposlted taxes at the end of the quarter are $200 or more, the full amount must be deposited with an authorized commercial bank or a Federal Reserve bank. This deposit must be entered In Schedule B and included in item 20. 21. UNDEPOSITED TAXES DUE (ITEM 19 LESS ITEM 20— THIS SHOULD BE LESS THAN $200). PAY TO INTERNAL REVENUE SERVICE AND ENTER HERE > 22. IF ITEM 20 IS MORE THAN ITEM 19, ENTER EXCESS HERE » J AND CHECK IF YOU WANT IT fj APPLIED TO NEXT RETURN. OR rj REFUNDED. 23. If nol I lor r i succeeding c "FINAL" lete of fin '•• ying schedules end statements, and to the best ol my knowledge end belie - Title (Owner, etc ) A-l-1 Form 941 (Rev. Oct. 1973) General Instructions ■ 3Re 2 Circular E. Employer's Tax Guide, explains the rules for withholding, paying, depositing, and reporting Federal income tax. social se- curity (FICA) taxes, and Federal unemployment tax. Special instructions for agricultural and household employers are given below. Circular F_ as well as Circular A, Agricul- tural Employer's Tax Guide, may be obtained tree from any Internal Revenue office. Purpose of Form 94-1. — This form combines the reporting of income tax withheld from wages, tips, annuities, supplemental unemploy- ment compensation benefits, and taxes under the Federal Insurance Contributions Act (FICA). If you have only one of these taxes to report, fill in only the applicable portions. NOTE. — State and local government em- ployers should use Form 941E to report in- come tax withheld but should send social se- curity payments and reports to appropriate State officials. Form 941E is also prescribed for tax-exempt organizations, certain payers of annuities, and employers who pay supplemental unemploy- ment compensation benefits that do not re- port social security taxes to Internal Revenue. Who must file. — If you have one or more employees, you must file a return for the first quarter in which you are required to with- hold income tax, or in which you pay wages subject to social security tax, and for each quarter thereafter. If you temporarily discontinue paying wages (for example, seasonal activities), you must still file returns. If you no longer expect to pay wages subject to any of the taxes report- able on this form, you must file a "Final Return." Once you have filed a return, a preaddressed Form 941 will be sent to you every three months. If the form fails to reach you, request one in time to file. Sale or transfer of business. — If a business is sold or transferred by one employer to an- other, each must file a separate return. But neither should report wages paid by the other. Such a transfer occurs, for example. If a sole proprietor forms a partnership or a corporation. If there has been a change of ownership or other transfer of the business during the quar- ter, attach a statement showing the name of the present owner; whether the present owner is an individual, a partnership, or a corpora- tion; the nature of the change or transfer; and its date. When a statutory merger or consolidation occurs, the obligation of the continuing cor- poration to file a Form 941 and report wages is the same as if the continuing and dissolved corporations constituted one person. When to file. — A return must be filed for each quarter of the calendar year, as follows: if the return s Quarter covered Jnnuorv, February, March April. May, June July, August, September October. November, December Quarter Dueo March 31 April 30 June 30 July 31 September 30 October 31 December 31 January 31 ie'y deposits (Form 501) in full payment of the taxes due for the entire quarter, the return may be filed on or before the tenth day of the second month following the quarter. Where to file. I pla< New Jersey. New York Cily and counties of Nassau, Rockland, SuHolk. and We I. scste Internal Revenue Service 1040 Waverly Avenue Hollsvilk. NY. 11799 New York (all other counlies), Connecticut, Maine. Massachusetts, New Hampshire, Rhode Island. Vermont Internal Revenue Service Center 310 Lowell Street Andover, Mass. 01812 District ol Columbia, Delaware, Maryland, Pennsylvania Internal Revenue Service Center 11&01 Roosevelt Boulevard Philadelphia, Pa. 19155 Alabama, Florida. Cco[.:i;i, Miss.ssippi, South Carolina Internal Revenue Service Center 4800 Butord Highway Chamblee. Georgia 30006 Michigan, Ohio Internal Revenue Service Center Cincinnati, Ohio 45398 Arkansas. Kansas, Louisiana, New Mexico, Oklahoma, Texas Internal Revenue Sorvico Center 3651 S Interregional Hwy. Auslin, Te*as 78740 Alaska. Arizona. Colorado. Idaho, Minnesota, Montaru Nebraska, Nevada. North Dakota. Oregon. South Dakota, Utah, Washington, Wyoming Missouri, Wisconsin Internal Revenue Service Center 2306 E. Bannister Road Kansas City. Mo. 64170 California., Hawaii Internal Revenue Service Center 5045 East Butler Avenue Fresno. California 93888 North Carolina, Tenness Virginia, West Virginia Iniernal Revenue Service a Center 3131 Democrat Road Memphis, Tenn. 38110 If you have no legal residence or principal place of business in any Internal Revenue dis- trict, file with the Internal Revenue Service Center, 11601 Roosevelt Boulevard, Philadel- phia. Pa. 19155. Employer identification number, name, and address. — Use the preaddressed Form 941 mailed to you. If you lose it, request another. If you must use a nonpreaddressed form, enter your employer identification number and name exactly as shown on your previous re- turns and the last date of the quarter for which the return is filed. Do not use the identification number assigned to a prior owner. If you do not have an employer identification number, apply for one on Form SS-4, avail- able from any Internal Revenue or Social Se- curity Administration district office. Penalties and Interest.— The law provides penalties for filing a return late, paying taxes late, or for making deposits late, unless reason- able cause is shown for the delay. If you are late in doing any of these, attach an explana- tion to your return. There are also penalties for filing false or fraudulent returns, submitting bad checks, and willfully failing to collect and pay tax, furnish statements to employees, keep records and file returns. FORMS W-2, W-2P, 1099R, AND W-3 On or before February 28. or when filing a final return on Form 941 if you make final payments before the end of the year, send Copies A of all Forms W-2, W-2P. and 1099R. issued for the year, and Form W-3P (if appli- cable) with a Form W-3 to the Internal Rev- enue Service Center where you file Forms 941. Form W-3P is to be used only by governmental agencies or retirement systems, and insurance companies. You may furnish magnetic tape re- ports of Forms W-2, W-2P. and 1099R. See the applicable Revenue Procedures available from any Internal Revenue Service Center. Form W-3 will be mailed to you for filing with your return for the fourth quarter. If you file a final return before the end of the year, request Form W-3 from your District Director. Instructions for filing are printed on the back of that form. Statements must be furnished to employees by January 31, if at the close of the year the employee is in your employ. If his employment is terminated before December 31, the state- ments should be furnished within 30 days after the last payment of wages. AGRICULTURAL AND HOUSEHOLD EMPLOYERS Income tax withholding. — If your employee requests and you agree that Federal income tax be withheld from his remuneration, you must withhold the proper amount from each payment based on the information he furnishes on Form W-4. Otherwise, income tax does not have to be withheld. Cash paid to such em- ployees may be taxable for FICA purposes. Agricultural employers. — Do not report agri- cultural wages on Form 941. Obtain a copy of Circular A from Internal Revenue and ask to have your name placed on the mailing list to receive Form 943, "Employer's Annual Tax Return for Agricultural Employees." Household employees in a private home on a farm oper- ated for profit are agricultural employees. Household employers. — FICA taxes apply in the case of each employee to whom you pay cash wages of $50 or more in a quarter for domestic service in your private (nonfarm) home. If you file Form 941 for business em- ployees, you may include household employees on this form. Otherwise, report them on Form 942. If you report both business and household employees on Form 941, identify the house- hold employees as such in Schedule A by grouping them under a heading "Household," or by writing the letter "H" at the right-hand side of column 7, opposite the name of each. age ■ Schedule B must be used by employers required to make than $2,000 for any month, list the amount of your liability for deposits of taxes reportable in this return. List deposits in that month on the "total" line for that month. Schedule B and show the total in item 20 on page 1. if you made more than one deposit for a period, attach a In Column A, list your tax liability for each quarter-monthly statement showing the amount and date of each deposit, period in which a payday occurs. If your total taxes are less SCHEDULE B— RECORD OF FEDERAL TAX DEPOSITS Deposit period ending: Overpayment from previous quarter . 1st through 7th day . . 8th through 15th day . . 16th through 22d day . . 23d through last day . . First month quarter 1 First month total 1st through 7th day . 8th through 15th day . 16th through 22d day . 23d through last day . Second month quarter 2 Second month total . . . , 1st through 7th day , 8th through 15th day , 16th through 22d day . 23d through last day quarter LI LI B :■: 3 Third month total 4 Total for quarter (total of items 1, 2, and 3) . 5 Final deposit made for quarter. (Enter zero if the final deposit made for the quarter is included in item 4.) 6 Total deposits for quarter (total of items 4 and 5) — enter here and in item 20, page 1 A- 1-2 . Oct. 1973) Specific Instructions . 4 Note. — Stale and local government em- ployers should deposit income tax withheld with Form 501 and report the tax on Form 941E but send social security payments and earnings reports to appropriate State officials. Item 1. (First quarter only.) Number of employees. — Exclude household employees, persons receiving no compensation during the pay period, pensioners, and members of the Armed Foices. If you have only household employees in the pay period, enter zero (0). The number you enter will not necessarily be the same as the total number of employees listed in Schedule A. Item 4. Employee's social security num- ber. — Enter the social security number as- signed to each employee as shown on his social security card. If a new employee does not have a social security card, have him apply for one at any social security office. Item 5. Name of employee. — Type or print the name of each employee exactly as it ap- pears on his social security card. You may use initials instead of given names. If a new employee has a social security card but it shows a different name than the one you will use for your records, have the employee obtain a corrected card from any social security office. Until the employee shows you a cor- rected card, report his wages under the name shown on his present card. Item 6. Taxable F1CA wages. — Enter the total wages (betore deductions and excluding tips) taxable under the PICA that you paid to each employee during the quarter. After you re- port $10,800 for an employee in a calendar year, excluding tips, do not report any amount you later paid him in the same year. (You should continue to withhold income tax on tips reported to you, even though the wages and tips have reached $10,800.) Do not use tips in computing maximum wages subject to your share of social security tax. Item 7. Taxable tips. — Cash tips that total $20 or more in a month must be reported to you by the employee by the 10th day of the next month. Enter the total amount of tip income the employee reported during the quar- ter on the written reports or Forms 4070, regardless of whether the employee tax (5.85 percent of total) has been withheld. When the combined total of tips and wages reported for FICA purposes reaches $10,300, no additional tips should be reported for FICA purposes. Other use of Column 7. — If you do not use column 7 for tip income, you may use it for any payroll or State unemployment infor- mation that will facilitate your recordkeeping. If so. enter a check-mark in the block in column 7. Magnetic Tape Reporting. — You may use magnetic tape to furnish information required by Forms W-2, W-2P, 1099R, Schedule A Ite vithheld on (Form 941), and Form 941. To do so: (a) For Forms W-2, W-2P, and 1099R only, see Rev. Proc. 73-13, available from any Internal Revenue Service Center. (b) For Forms W-2, W-2P and Schedule A, see Rev. Proc. 71-18, available from any Internal Revenue Service Center, or the Social Security Administration, Baltimore, Maryland 21235; (c) For Schedule A only, see Technical In- structions Bulletin £3 available from the Social Security Administration; and (d) For Form 941 and for filing composite returns, see Rev. Proc. 72-37, available from any Internal Revenue Service Center. Item 10. — Enter the combined amounts of total wages paid, tips reported, and other com- pensation paid to your employees, whether or not subject to income tax withholding or FICA tax. Exclude annuities or supplemental unem- ployment compensation benefits whether or not you withheld income tax on them. -Enter the amount of income tax es, including tips reported, .Lipplementa! unemployment compensation benefits. Item 12. — Adjustment of income tax with- held. — Use item 12 to correct errors made in withholding income tax from wages paid in the preceding quarters of the same calen- dar year. (Consult the District Director before correcting a prior-year undercollection. If the tax was overcollected in a prior year, do not make an adjustment.) Explain any amount in item 12 in an attached statement. This statement must set forth: (a) An explanation of the error the entry is intended to correct; (b) The return period or periods to which the error relates; (c) The amount chargeable to each period; (d) The tax-return period in which the error was determined; and (e) How you and the employees have set- tled any overcollection or undercollec- tion of income tax withheld. Item 17. — Adjustment of taxes under FICA.— Use item 17 to correct amount of FICA tax as reported on a prior return, or credits for overpayments of penalty or interest paid with respect to tax for prior periods. If you report both an underpayment and an overpayment, enter only the difference. Except as provided below with respect to fractions of cents, ex- plain any amount in Item 17 in an attached statement or on Form 941c (Rev, July 1971 or later). This statement must set forth: (a) An explanation of the error the entry is intended to correct; (b) The return period or periods to which the error relates; (c) The amount chargeable to each period; (d) The tax-return period in which the er- ror was determined; (e) That you repaid FICA tax overcollected; if the entry corrects an oveicoilection Of I and (0 II the entry corrects FICA tax over- collected in a prior year, that you have obtained from the employee a written statement that he has not claimed and will not claim a refund or credit of the amount overcollected. If wages or tips were mistakenly reported or omitted on prior returns, submit on Form 941c or include in the statement: (a) The name and social security number of each employee whose wages or tips were mistakenly reported or omitted; (b) The amount of wages or tips mistakenly reported for each quarter for each em- ployee (if none, so state); and (c) The amount of wages or tips which should have been reported for each quarter for each employee (if none, so state). Use a separate Form 941c for tips modify- ing the headings in columns 4 and 5, if neces- sary. Obtain Forms 941c from your local In- ternal Revenue office. Adjustments of FICA tax on tips. — Include in Item 17 the total uncollected employee FICA tax included on Item 15 and the total adjustments where employee FICA tax is not applicable to amounts included in Item 14. Attach a statement explaining each adjustment. For details see Circular E. Fractions of cents. — If there is a difference between the total employee tax included in Item 16 and the total deducted from the re- muneration of employees, due to fractions of cents added or dropped in collecting employee tax, report this difference in Item 17 as a deduction or an addition, as appropriate. If such a difference is the only entry made, write "Fractions only" in the margin of the form. Item 21. Undeposited taxes due. — If you followed the deposit requirements, any balance on this line will be less than $200. The balance may either be paid with the return or de- posited. If deposited, be sure to enter the amount of the deposit in Schedule B. Item 22. Overpayment. — If you deposited more than the correct amount for a quarter, you may elect to have the overpayment re- funded or applied to your next return. Any amount appt.ed should be entered in Schedule G ■; iyoui *t retu Deposit Requirements Generally, you must deposit the income tax withheld and both the employer and employee social security taxes with an authorized com- mercial bank or a federal Reserve bank. A Federal Tax Deposit Form 501 must accompany each deposit. The amount of taxes determines the frequency of the deposits. The following rules show how often you must make deposits. (1) If at the end of a quarter the total amount of undeposited taxes is less than $.200, you are not required to make a deposit. You may either pay the taxes directly to Internal Revenue along with your quarterly Form 94 1 or make a deposit. (2) If at the end of a quarter the total amount of undeposited taxes is $200 or more, you must deposit the entire amount on or before the last day of the first month after the end of the quarter. If $2,000 or more, see rule 4 below. (3) If at the end of any month (except the last month of a quar- ter) the cumulative amount of undeposited taxes for the quarter is $200 or more and less than $2,000, you must deposit the taxes within 15 days after the end of the month. (This does not apply if you made a deposit for a quarter-monthly period that occurred during the month under the $2,000 rule in 4 below.) (4) If at the end of any quarter-monthly period the cumulative amount of undeposited taxes for the quarter is $2,000 or more, you must deposit the taxes within three banking days after the end of the quarter-monthly period. (A quarter-monthly period ends on the 7th, 15th, 22d, and last day of the month.) In determining banking days exclude local banking holidays observed by authorized commercial banks, as v/ell as Saturdays, Sundays, and legal holidays. The deposit requirements are considered met if: (a) you deposit at least 90 per- cent of the actual tax liability for the deposit period, and (b) if the quarter-monthly period occurs in a month other than the third month ot a quarter, you deposit any underpayment with your first deposit that is required to be made after the 15th day of the following month. Any underpayment that is $200 or more for a quarter-monthly period that occurs during the third month of the quarter must be deposited on or before the last day of the nevt month. SCHEDULE B— RECORD OF FEDERAL TAX DEPOSITS Deposit Overpayr period ending: A. Tax liability B. Amount deposited C. Date of deposit 1st through 7th day 8th through 15th day 16th through 22d day month of 1 First month total | 1 1st through 7th day of 16th through 22d day quarter 2 Secon 1st through 7th day 8th through 15th day 16th through 22d day 23d through last day of quarter 3 Third r nonth total [* 5 Final deposit made for quarter. (Enter zero if the final deposi is included in item 4.) : made for the quarter here and in item 20, 6 Total page deposits for quarter (total of items 4 and 5) — enter 1 A- 1-3 942 (Rev. October 1974) Department o* the Treasury Internal Revenue Service Appendix A-2. Form 942 Employer's Quarterly Tax Return for Household Employees (For Social Security (FICA) and Withheld Income Tax) For FICA purposes, list each household employee to whom you paid cash wages of $50 or more in the calendar quar- ter covered by this return. If you pay an employee more than $13,200 in a calendar year, report only the first $13,200 of the wages for FICA. For income tax withholding, see instructions on pages 2 and 4. Employee's Social Security No. 000 00 0000 Employee's Name (PLEASE TYPE OR PRINT IN INK AS SHOWN ON SOCIAL SECURITY CARD) Cash Wages Paid in Quarter (BEFORE TAX DEDUCTION) Total cash wages m City, State, and ZIP code Name Your name, address, strce , address employer ► identification number, and calendar quarter Name of return. (" no ' Street address correct, K please Date quarter ended Employer identification no. Make entries both above and below this line Date quarter ended Employer identification no change.) City, State, and ZIP code Taxes 1 FICA tax: Total cash wages as shown above $ x 11.7% 2 Federal income tax withheld, if requested by your employee | If you will NOT need to file Form 942 in the future, check here fj. If no tax is due, write "None" above J^ Important: Form W-2, Wage and Tax Statement, should be furnished to employees and filed with IRS — see instructions on page 4 and on back of Copy D, Form W-2, Under the penalties of perjury, I declare that I have exan Signature ^ ined this return, and to the best ot my knowledge and belief it is true, correct, and complete. A-2-1 General Information Purpose. — Use this form to report and pay employer and employee FICA taxes and in- come tax (if any) withheld at the employee's request. The Taxes. FICA. — FICA taxes are imposed on both the employer and the employee on cash wages of household employees and other workers who perform services of a household nature in or about a private home of the em- ployer (other than on a farm operated for profit). In general, these services include those performed by cooks, waiters, butlers, house- keepers, governesses, maids, cleaning women, valets, babysitters, janitors, laundresses, care- takers, handymen, gardeners, and chauffeurs of automobiles for family use. The combined rate of employer and employee FICA tax is 11.7% and applies ONLY to the first $13,200 of cash wages for 1974. How to Determine if FICA Taxes are Due. The $50-a-quarter Test. — FICA taxes are due if you pay an employee cash wages of $50 or more in a calendar quarter for household services. The taxes apply to all cash wages paid in the quarter regardless of when earned. The $50-a-quarter test applies separately to each household employee. No FICA taxes are due on amounts paid to workers who are not your employees such as carpenters, painters, plumbers or repairmen working for you as in- dependent contractors. If you are not sure whether the taxes apply to a worker, request advice from the Interna! Revenue Service. Em- ployers with workers on a farm operated for profit may refer to Circular A for additional in- formation; other business employers may refer to Circular E. Both circulars are available free from any Internal Revenue Service office. What are Taxable FICA Wages. — FICA taxes apply only to cash wages paid to household employees who meet the $50-a-quarter test. Checks, money orders, etc., are the same as cash, but the value of food, lodging, clothing, car tokens, and other noncash items furnished to household employees are not subject to FICA taxes. But regard cash given instead of these items as wages. It does not matter whether payments are based on the hour, day, week, month, or year, or on piecework. Social security taxes do not apply to cash wages for domestic service in your home if performed by your spouse, or by your son or daughter under the age of 21. Nor do these taxes apply to cash wages for domestic service performed by your mother or father unless: (a) you have in your home a son or daughter who is under age 18 or has a physical or mental condition that requires the personal care of an adult for at least four continuous weeks in the quarter, and (b) you are a widow or widower, or are divorced, or you have a spouse in your home who. because of a physical or mental condition, is incapable of caring for your son or daughter for at least four continuous weeks in the quarter. In reporting cash wages on your quarterly return, show the full amount before tax was deducted. Deducting Employee FICA Tax. — Deduct the employee FICA tax of 5.85% from each pay- ment of cash wages if you expect the em- ployee to meet the $50 a-quarter test. Al- though you may not be sure the $50-a-quarter test will be met when you pay the wages, you may still deduct the employee FICA tax. If you do not deduct employee FICA tax, or if you deduct less than the correct amount, adjust the eiror by deducting the tax from a later payment to the same employee. If you deduct employee FICA tax when no tax is due, or if you deduct more than the correct amount, you should repay your employee. If you prefer to pay the employee FICA tax without deducting it from your employee's wages, you may do so. If you do not deduct employee FICA tax from taxable wages, you are nevertheless required to pay the tax. Any employee FICA tax you pay for an em- ployee is additional income to him and you should include it in block 2, "Wages, tips, and other compensation," on his Form W-2 (see "Form W-2, Wage and Tax Statement," on page 4). However, do not count such amounts as cash wages for FICA purposes. Income Tax. — If an employee wants to have Federal income tax withheld from his wages, he must give you a completed Form W-4, Employee's Withholding Allowance Certificate. If an employee requests income tax with- holding and you agree, you must withhold an amount from each payment based on the in- formation shown on the Form W-4 he gives you. Show the total of any income tax with- held on line 2. Any income tax withholding you pay for an employee without deducting it from the em- ployee's wages is additional income to him, and you should include it on his Form W-2. For Federal income tax withholding tables and other withholding information, see Circular E, Employer's Tax Guide, available free from any IRS office. What are Wages Subject to Income Tax Withholding. — If your employee requests in- come tax withholding, wages subject to income tax withholding consist of all remuneration, whether in cash or other forms, paid to your employee for services performed. The word "wages" covers all kinds of employee remu- neration, including salaries, vacation allow- ances, bonuses, meals (unless furnished for your convenience and on your premises), lodging (unless furnished on your premises, for yojr convenience, and as a condition of employment), clothing, car tokens, and other noncash items. Measure wages you pay in any form other than money by the value of the goods, lodging, meals, or other consideration you give in payment for services. Employee's Social Security Number. — When you employ a household worker, record his name and social security number exactly as they appear on his social security card. Reporting the employee's name and number accurately will insure proper credit to his social security earnings record. If your employee does not have a social se- curity number, he should apply for one on Form SS-5, available at the nearest Social Security office, post office, or Internal Revenue office. Employer Identification Number. — Your Form 942 should show the employer identifi- cation number that was assigned to you as an employer of household employees. If you do not have an employer identification number, do not apply for one. Instead, write "NONE" in the space provided for the number. The In- ternal Revenue Service will then assign you a number and send you a Form 942 each quar- ter. It is important that you keep a record of your employer identification number. When to File. — Beginning with the first cal- endar quarter in which you pay taxable wages to one or more household employees, you must file quarterly returns leporting taxable wages paid and the total of the employer's and employee's FICA taxes and any income tax withheld in the quarter. The calendar quarters of the year and the last day for filing a return for each quarter are as follows: Quarters Quarter Ending Return Due Jan. -Feb. Mar. . . . Mar. 31 . . Apr. 30 April May-June . June 30 . . July 31 July-Aug -Sept. . . . Sept. 30 . . Oct. 31 Oct -Nov.-Dec. . . . Dec. 31 . . Jan. 31 If you need more space, attach a statement showing your employees' social security num- bers, names, and wages in the same arrange- ment as on Form 942. Also be sure to write at the top of the statement your own name, address, employer identification number, and the calendar quarter covered, so that it can be identified if it becomes separated from your return. Include any wages reported on the statement in the "Total cash wages" on your return. If you receive Form 942 for a quarter when you did not pay any taxable wages, indicate this on the form and return it to the Internal Revenue Service. Final Return. — If you do not expect to pay taxable wages in the future, check the box on the return. If you later resume paying taxable wages, please notify the Internal Revenue Service. Fayment of Taxes. — You may pay FICA and withheld income taxes either by mail or in person. Make checks or money orders payable to Internal Revenue Service. To avoid loss. do not mail cash. Postage stamps are not acceptable for paying taxes. Correcting Mistakes. — If, after filing a re- turn on Form 942, you find that you paid more than the correct amount of FICA tax, you may subtract the difference on your next quarterly return. If you paid less than the correct amount of FICA tax, and an additional payment has not been requested, add the difference to your next quarterly return. In either event, attach an ex- planation to the return on which you make the correction. If you find a mistake in the name, social se- curity number, or wages of any employee, inform the Internal Revenue Service what the mistake was and the quarterly return on which it was made, and give the correct information. Where to File: If you are located in File with New Jersey, New York City Internal Revenue Service and counties o( Nassau, Center Rockland, Suffolk, and 1040 Waverly Avenue Westchester Haltsville, New York 11799 (Continued on page 4) Page A-2-2 New York (all other counties). Connecticut, Mame, Massachusetts, New Hampshire, Rhode Island, Vermont Internal Revenue S Center 310 Lowell Street Andovcr, Mass. 01812 ce District of Columbia, Delaware. Maryland, Pennsylvania Internal Revenue Service Center 11601 Roosevelt Boulevard Philadelphia. Pa. 19155 Alabama, Florida, Georgia, Mississippi, South Carolina Internal Revenue Service Center 4800 Buford Highway Chamblee, Georgia 30006 Internal Revenue Service Center Cincinnati. Ohio 45298 Arkansas, Kansas, Louisiana, New Mexi Oklahoma. Texas Internal Revenue Service Center 3651 S. Interregional Hwy. Austin. Texas 78740 Alaska. Arizona, Colorado, Idaho, Minnesota, Montana, Nebraska, Nevada, North Dakota, Oregon, South Dakota, Utah. Washington, Wyoming Internal Revenue Service Center 1160 West 1200 South St. Ogden. Utah 84201 Illinois, Wiscons Iowa, Missouri, n Internal Revenue Service Center 2306 E. Bannister Road Kansas City, Mo. 64170 Californ a. Hawaii Internal Revenue Service Center 5045 East Butler Avenue Fresno. California 93888 Indiana Carolina Virginia Kentucky, North , Tennessee, West Virginia Internal Revenue Service Center 3131 Democrat Road Memphis, Tenn. 38110 Keeping Records. — Keep your copies of Forms 942 and W-2. Also keep details such as your employee's social security number and name, dates and amounts of cash wage pay- ments, and employee FICA and income tax. if any, deducted. You may keep the records in any manner you wish. Form W-2, Wage and Tax Statement. — With your Form 942 for the quarter ending December 31 (or with your final return for an earlier quarter), you should file Copy A of the enclosed Form W-2. Wage and Tax Statement, for each employee. (See the instructions on the back of Copy D.) Space is provided on Form W-2 for both income tax information and social security information. Complete all applicable entries on Form W-2 and leave blank those blocks that do not apply. In most instances, only boxes 1, 2, 3, and 4 will apply. Fill in your identification number, name, and address; your employee's social se- curity number, name, and address; and the following information, if applicable: Box 1. — Enter total Federal income tax with- held (if any). Sox 2. — Enter total wages paid whether or not income tax was withheld. (See "What are Wages Subject to Income Tax Withholding" on page 2.) Box 3. — Enter total amount of FICA em- ployee tax (not the employer tax) deducted and withheld or paid by you for the employee. (See "Deducting Employee FICA Tax" on page 2.) Sox 4. — Enter total wages paid subject to FICA. (See "What are Taxable FICA Wages" on page 2.) (The first two boxes under "Other Infomia tion" on Form W-2 were added because of pending pension reform legislation. It was later learned that this legislation will not affect the 1974 Form W-2.) You should give the appropriate copies of Form W-2 for the calendar year to each em- ployee on or before January 31 of the next year. If a worker leaves your employ before the end of the calendar year, you should give him the appropriate copies of Form W-2 within 30 days after you pay his last wages. Penalties. — Avoid penalties and interest by filing timely returns and paying tax when due. The law provides a penalty for late filing of a return or payment of the tax unless you show reasonable cause for the delay. If you are unavoidably late in filing a return or paying the tax, attach an explanation to your return. Optional Use of Whole Dollar Amounts for FICA Taxes. — You may round off the amount of cash wages paid to the nearest whole dollar in determining whether the $50-a-quarter test is met, in figuring employee tax deductions, and in reporting wages on your return. For ex- ample, if you paid an amount between $104.50 and $105.49, inclusive, you may re- port $105 as the taxable wage. If you use this method in a quarter, you must use it for all wage payments to house- hold employees in that quarter. A 5.85 percent employee FICA tax deduction table based on whole dollars is shown below for 1974 and 1975. 1974 and 1975 Employee FICA (5.85%) Tax Deduction Table. (For income tax withholding tables, see Circular E.) Note: You may use this table to figure the amount of employee FICA tax to deduct from each wage payment. You may also use this table to figure the total FICA tax to report on Form 942 by doubling the tax amounts. For example, if you pay total wages of $114 during the quarter, the employee tax is $6.67 ($5.85 tax for $100, plus $0.82 for $14 wages). The tax you report on Form 942 would be double the amount of em- ployee tax, or $13.34 ($6.67 employee tax, plus $6.67 employer tax). The em- The em- The em- The em- The em- If wage ployee tax If wage ployee tax If wage ployee tax If wage ployee tax If wage ployee tax payment to be payment to be payment to be payment to be payment to be IS — deducted is — is — deducted is — deducted IS — IS deducted IS — deducted is — $i $0.06 $21 $1.23 $41 $2.40 $61 $5.57 $81 $4.74 2 .12 22 1.29 42 2.46 62 3.63 82 4.80 3 .18 23 1.35 43 2.52 63 3.69 83 4.86 4 .23 24 1.40 44 2.57 64 3.74 84 4.91 5 .29 25 1.46 45 2.63 65 3.80 85 4.97 6 .35 26 1.52 46 2.69 66 3.86 86 5.03 7 .41 27 1.58 47 2.75 67 3.92 87 5.09 8 .47 28 1.64 48 2.81 68 3.98 88 5.15 9 .53 29 1.70 49 2.87 69 4.04 89 5.21 10 .59 30 1.76 50 2.93 70 4.10 90 5.27 11 .64 31 1.81 51 2.98 71 4.15 91 5.32 12 .70 32 1.87 52 3.04 72 4.21 92 5.38 13 .76 33 1.93 53 3.10 73 4.27 93 5.44 14 .82 34 1.99 54 3.16 74 4.33 94 5.50 15 .88 35 2.05 55 3.22 75 4.39 95 5.56 16 .94 36 2.11 56 3.28 76 4.45 96 5.62 17 .99 37 2.16 57 3.33 77 4.50 97 5.67 18 1.05 38 2.22 58 3.39 78 4.56 98 5.73 19 1.11 39 2.28 59 3.45 79 4.62 99 5.79 20 1.17 40 2.34 60 3.51 80 4.68 100 5.85 Page 4 A-2-3 943 Appendix A-3. Form 943 EMPLOYER'S ANNUAL TAX RETURN FOR AGRICULTURAL EMPLOYEES (For Social Security (FICA) and Withheld Income Tax) )75 SCHEDULE A. — Annual Report of Taxable Cash Wages Paid for Agricultural Labor Total pages of this return including W this page and any pages of Form 941a employees listed List each employee (a) to whom you paid $150 or more cash wages in the year tor agricultural labor or (b) who performed agricultural labor tor you on 20 or more days during such year for any amount ot cash wages computed on a time basis Do not list any employee who does not meet either of these tests. If you paid an employee more than $14,100 in this year, report only the first $14,100 paid to that employee. If you paid wages for any services other than agricultural labor, do not report such wages on this form. See instructions. Save time and money — If your report shows each employee's name and number exactly as they appear on the employee's social security card, it will not be necessary to write back to you to ask for the correct information. 1. EMPLOYEE'S SOCIAL SECURITY NUMBER (II number is unknown, im Instructions) 003 00 0000 2. NAME OF EMPLOYEE (Please type or print) 3. TAXABLE FICA WAGES Paid to employe* in year (before deductions) Dollars Cants If you need more space (ot listing employees, us Total wages reported in column 3 on t e Schedule A continuation sheets, Form 941a. $ 4. Total wa Form 94 ges taxable under FICA paid during year (Total wages shown on this page and on any continuation sheets, $ I Name (as distinguished from trade name) Employer's name - Trade name. If any address, ^ employer identification Addr «« and zip code number, and calendar year. (If not correct, please ► T,adfl nam8 ' " ■"" change) Address and ZIP coda Calendar yeai 1975 Employer Identification No. Entries must be made both above and below this line. It address is different from previous return, check here Q Name (as distinguished from trade name) Calendar year 1975 Employer Identification No. 5. Total taxable cash wages paid in year (from line 4) 6. FICA taxes, 11.7% of wages on line 5 (5.85% employer tax and 5.85% employee tax) 7. Adjustments (attach statement — see instructions) 8. FICA taxes as adjusted 9. Amount of income tax withheld (see instructions) 10. Total taxes (line 8 plus line 9) 11. Total deposits for year (including final deposit made for year) and overpayment from previous year listed in Schedule B (see instructions on page 4) Note: If undeposited taxes at the end of the year are $200 or more, you must deposit the lull amount with an author- ized commercial bank or a Federal Reserve bank. Enter this deposit in Schedule B and include it on line 11. 12. Undeposited taxes due (line 10 less line 11 — This should be less than $200). Pay to Internal Revenue Service . . 13. If line 11 is mo'e th^n line 10. enter '■■<:• ss here ^ $ and check if to be fj applied to next return, or □ refunded. 14. If you do not expect to be liable for retur ns in the future, w rite 'Final Return" here l>> Under penalties ot perjury, I declare that 1 have eiamir saw and complete. Date Signature Tltla (Owner, etc.) See "Where to File" on page 2. Please use the preaddressed envelope provided. Form 943 1975 A-3-1 furnished you with a crew of farm workers and paid their wages. List your own employees on Schedule A c nly and not in this space. Name of crew leader Hone address Employer identification number Nine out of 10 working people in the United States are now building protec- tion for themselves and their families under the social security program. The three kinds of monthly benefits under social security are: 1. Retirement — at age 65. (Reduced benefits are payable as early as 62.) 2. Disability — when a worker under 65 becomes unable to work because of a disability. 3. Survivors — when a worker dies. In addition to cash benefits, health insurance benefits are available for peo- ple 65 or over (whether or not the worker is retired). Instructions The following instructions are for the pre- paring and filing of Form 943. Additional in- structions are in Circular A and Circular E, copies of which are available from any Internal Rev- enue Service office. If you need more space for listing employees, request continuation sheets Form 941a, from your local Internal Revenue Service office. Who Must File Form 943. — Every em- ployer who paid cash wages to one or more employees who met either one of the tests described in column 3 on this page, or who voluntarily agreed to withhold income tax from remuneration paid for agricultural labor, must file a Form 943 for each calendar year, beginning with the first year in which the employer paid such wages or withheld income tax. After you have once filed a return, the Internal Revenue Service will mail you the forms needed for future use. If you are required to file a return but do not receive the necessary form, request one from the Internal Revenue Service so you can file on time. If you receive a form for a year in which none of your employees met either one of the tests described in column 3 and you have no liability for income tax withheld, write "NONE" on line 10 and send the form back to the Internal Revenue Service. If you do not expect to be liable for the taxes reportable on this form in the future, write "FINAL RETURN" on line 14. If you later become liable for any of the taxes, notify the Internal Revenue Service. When to file.— Form 943 for 1975 must be filed on or before January 31, 1976. However, if you made timely deposits in full payment of the taxes due for the year, you may file the return on or before Febru- ary 10, 1976. If the due date for filing a return or mak- ing a tax deposit falls on Saturday, Sunday, or a legal holiday, you may file the return or make the deposit on the first succeeding day that is not a Saturday, Sunday, or legal holiday. Where to File If your principal pla of business, office, agency is located In Connecticut, ass achu setts, Nc* .'. Rhode Island, 310 Lowell Street Andover, Masiachu; 01812 Vermont : ■'. ■ ■ « ■ ■:■ 1. , Delaware, Maryland, Pennsylvania t IMl Hoor.evell (Joulcvani Philadelphia, Pennsylvania 19155 Rial ama, Flonda, Geoi| la Mississippi. South Carolm 4800 Bulord Highway Chamblee. Georgia 10006 Michigan, Ohio Cincinnati. Ohio 45298 Arkansas, Kansas. Louisia New Mexico. Oklahoma, Texas a. 3651 S. Intertegionai Hwy. Austin. Teias 78740 Ala; . C:-l0 Idaho, Minnesota, Montan Nebraska, Nevada, North Oakota, Oregon, South Dakota, Utah, Washington, Wyoming Illinois Iowa. Missouri, 2306 E Bannister Road Kansas City. Missour, 64170 Cah lom a, Hawaii S045 East Butler Avenue Fresno, Calif. 93888 1 no. ana Carolina Kentucky, North , Tennessee, West Virginia 3131 Democrat Road Memphis. Tennessee 38110 If you have no legal residence or princi- pal place of business in any Internal Rev- enue Service district, file the return with the Internal Revenue Service Center, 11601 Roosevelt Boulevard, Philadelphia, Penn- sylvania 19155. The Tests — $150 a year or 20 days a year. — The employer tax and employee tax under the Federal Insurance Contributions Act (F1CA) apply to each employee (a) to whom you pay $150 or more cash wages in a calendar year for agricultural labor or (b) who performs agricultural labor for you on 20 or more days during a calen- dar year for any amount of cash wages computed on a time basis. Withholding of Income Tax. — If an em- ployee requests income tax withholding and you ^gree, you must withhold the proper amount of tax from all remunera- tion paid to the employee, cash as well as noncash. Show the total of any income tax withheld on line 9, page 1. If an employee wants to have Federal income tax withheld from wages, the em- ployee must give you a completed Form W-4. Any withholding tax you pay for an em- ployee is additional income to the employee and must be included on the employee's Form W-2. For Federal income tax withholding tables and other withholding information, see Circular E, Employer's Tax Guide, avail- able from any Internal Revenue Service office. Forms W-2 and W-3 You must prepare a Form W-2 for every employee to whom you paid cash wages subject to the employee FtCA tax or from whose wages, in voluntary agreement with the employee, you withheld Federal income tax. Send Copy A of Forms W-2 to your In- ternal Revenue Service Center with a com- pleted Form W-3, Transmittal of Income and Tax Statements, no later than March 1, 1976. You may furnish magnetic tape reports instead of Forms W-2. See Rev. Proc. 75-20, available from any Internal Revenue Service Center or District office. For further instructions, see Circular A and Circular E. Schedule A Instructions. — Fill in Sched- ule A before you make any other entries on the form. List each employee (a) to whom you paid $150 or more cash wages in the calendar year for agricultural labor or (b) who performed agricultural labor for you on 20 or more days during the year for any amount of cash wages computed on a time basis. Cash wages include checks, money orders, etc. If an employee does (Instructions continued on page 4) Schedule B must be used by employers required to make deposits of taxes reportable on this return. List deposits in Schedule 6 and show the total on line 11, page 1. In column A, list your tax liability for each month. If your total taxes are $2,000 or more for any month, attach a state- ment showing the tax liability, amount of deposit, and date of deposit for each quarter-monthly period during that month. SCHEDULE B. — Record of Federal Tax Deposits Deposit period ending: Overpayment from previous year 1 January 31 2 February 28 3 March 31 4 April 30 5 May 31 6 June 30 7 July 31 8 August 31 9 September 30 10 October 31 11 November 30 12 December 31 13 Total for year 14 Final deposit made for year. (Enter zero if you include the final deposit made for the year on line 13) 15 Total deposits for year (total of lines 13 and 14)- page 1 enter here and on line 11. Page 2 A-3-2 Instructions (Continued from page 2) Name of crew leader Homo address Employer identification number not meet at least one of these tests in the year, do not include the employee in Sched- ule A. Column 1. Employee's social security number. — Enter each employee's social se- curity number as shown on the employee's social security card If a new employee does not have a social security card, have the employee apply for one at any Social Se- curity Administration office and do not make any entry in this space. The Social Security Administration will request the em ployee's number by contacting you at a later date. Column 2. Name of employee. — Type or print your employee's full name exactly as it appears on the employee's social se- curity card. Column 3. Taxable FICA wages paid to employee during year. — Enter the total tax- able cash wages you paid each employee in the calendar year for agricultural labor. Do NOT include (a) the value of noncash items such as food and lodging, or (b) re- muneration for services other than agricul- tural labor. Report the full amount of cash wages before the tax was deducted. If you paid an employee more than $14,100, re- port only $14,100. If you paid taxable wages to an employee for services other than agricultural labor, do not report such wages on Form 943. Instead, use Form 941 (or Form 942 if you paid wages for domestic service in your private, nonfarm home). The taxes apply only to the first $14,100 of taxable wages you paid to the employee, regardless of the kinds of service performed. The Internal Revenue Service will furnish you the necessary forms and instructions, upon request. Line 4. Total taxable wages.— Enter on this line the total of the amounts shown in column 3. If you use continuation sheets (Form 941a) include all wages reported on those sheets. Magnetic Tape Reporting. — You may use magnetic tape to furnish information re- quired by Form W-2 and Schedule A, Form 943. For Form W-2, see Rev. Proc. 75-20, available from any Internal Revenue Serv- ice Center or District office. For Schedule A, see Technical Information Bulletin #3 (TIB-3) available from any Internal Rev- enue Service Center and from the Social Security Administration, Bureau of Data Processing, P.O. Box 2317, Baltimore, Md. 21203. Line 7. Adjustments of Taxes under FICA. — If you deduct employee tax when no tax is due, or if you deduct more than the correct amount, you should repay the employee, if possible. If you are unable for any reason to repay the employee before you file your return, you must include the amount with your tax deposit or pay the amount with your re- turn. Enter the amount of the overcollec- tion on line 7. Attach to the return a state- ment on a separate sheet of paper explain- ing the overcollection, and showing the em- ployee's social security number (if known), name, and the amount you overcollected and did not repay the employee. Other Errors. — Use line 7 to correct underpayments or overpayments of FICA tax as reported on a prior return, or credits for overpayments of penalty or interest paid with respect to such tax for prior periods. If both an underpayment and an overpay- ment are reported, enter the difference be- tween the two on line 7. Except as provided below for fractions of cents, any entry on line 7 must be explained by a statement attached to the return or on Form 941c (Revised July 1971 or later). This state- ment must set forth: (a) An explanation of the error the entry is intended to correct; (b) The year or years to which the er- ror relates; (c) The amount of error chargeable to each year; (d) The year in which the error was found; and (e) The fact that you repaid FICA tax overcollected from an employee, if the entry corrects an overcollection of tax, and that you have obtained from the employee a written statement that the employee has not claimed and will not claim refund or credit of the amount of the overcollection. Fractions of Cents.— If adding and drop ping fractions of cents in collecting em- ployee tax has caused a difference be- tween the amount included on line 6 and the amount actually deducted from the employees, report this difference on line 7. If this is the only entry on line 7, write "Fractions only" in the margin of the form. If, on a prior Form 943, you did not re- port or incorrectly reported an employee's wages, include in the statement or on a Form 941c: (a) The name and social security num- ber of each employee whose wages were erroneously reported or omitted; (b) The amount of wages, if any, errone- ously reported for each year for each em- ployee (if none, so state); and (c) The amount of wages, if any, which should have been reported for each year for each employee (if none, so state). You may obtain Forms 941c from your local Internal Revenue Service office. Line 11. Note: At the time we printed this form, changes in the rules for depositing with- held income and social security taxes were under consideration. If the rules are changed, the internal Revenue Service will send employers a notice explaining the new rules. Deposit Requirements. — Generally, you must deposit the income tax withheld and both the employer and employee social se- curity taxes with an authorized commercial bank or a Federal Reserve bank. A Federal Tax Deposit Form 511 must accompany each deposit. The amount of taxes determines the frequency of the deposits. The following rules show how often you must make deposits: (1) If at the end of the year the total amount of undeposited taxes is less than $200, you are not required to make a deposit. You may either pay the taxes directly to Internal Revenue along with your Form 943 or make a deposit. (2) If at the end of the year the total amount of undeposited taxes is $200 or more, you must deposit the entire amount on or before January 31. If $2,000 or more, see rule 4 below. (3) If at the end of any month (except December) the cumulative amount of un- deposited taxes for the year is $200 or more but less than $2,000, you must de- posit the taxes within 15 days after the end of the month. (This does not apply if you made a deposit for a quarter-monthly period that occurred during the month un- der the $2,000 rule in 4 below.) (4) If at the end of any quarter-monthly period the cumulative amount of unde- posited taxes for the year is $2,000 or more, you must deposit the taxes within three banking days after the end of the quarter-monthly period. (A quarter-month- ly period ends on the 7th, 15th, 22nd, and last day of the month.) In determining banking days, exclude any local banking holidays observed by authorized commer- cial banks, as well as Saturdays, Sundays, and legal holidays. The deposit require- ments are considered met if: (a) you de- posit at least 90 percent of the actual tax liability for the deposit period, and (b) if the quarter-monthly period occurs in a month other than December, you deposit any underpayment with the first deposit you are required to make after the 15th day of the following month. Any under- payment that is $200 or more for a quarter- monthly period that occurs during Decem- ber must be deposited on or before January 31. Line 12. Undeposited taxes due. — If you followed the deposit requirements, any bal- ance on this line will be less than $200. The balance may either be paid with the re- turn or deposited. If deposited, be sure to enter the amount of the deposit in Sched- ule B. SCHEDULE B. — Record of Federal Tax Deposits Deposit period ending: Overpayment from previous year 1 January 31 2 February 28 3 March 31 4 April 30 5 May 31 6 June 30 7 July 31 8 August 31 9 September 30 10 October 31 11 November 30 12 December 31 13 Total for year 14 Final deposit made for year. (Enter zero if you include the final deposit made for the year on line 13) 15 Total deposits for year (total of lines 13 and 14) — enter here and on line 11, page 1 B. Amount deposited Pago 4 A-3-3 Form OAR-S3 Department of Health, Education, and Welfare Appendix A-4. Form 0AR-S3 STATE'S QUARTERLY REPORT OF WAGES PAID (BY REPORTING ENTITY) See instructions on back of this page. Do not send this form to Internal Revenue Service. Form approved OMB No. 72-R0438 1. Reporting entity's identifying number, name, and address 2. Date quarter ended .Total pages of this report, including this page and any con- tinuation pages. . Total number of em- ployees listed. . Number of persons employed during pay period containing March 12th. (See in- structions.) Do not report wages in excess of S1 2,600 paid in any calendar year after 1973. S10.800 for 1973; $9,000 tor 1972. S7.800 for years 1969 thru 1971 For prior years, consult your State agency IF YOU HAVE NO EMPLOYEES TO REPORT, ENTER "NO COVERED WAGES PAID" BELOW (6) EMPLOYEE'S SOCIAL SECURITY NUMBER OOO 00 0000 (7) NAME OF EMPLOYEE (Please type or print exactly as shown on the employee's Social Security Card) (8) COVERED WAGES Paid to Employee During Quarter (Before deductions) ^ Dollars and Cents (8A| If more space is needed for listing employees, use forms 0AR-S3a (continuation sheets). Total wages reported in column 8 for this page TOTALS 9. Total covered wages paid during quarter — , 10. Contributions— Multiply the total wages entered in item 9 by 11.7% for wages paid in 1973 or 1974; by 10.4% for wages paid in 1971 or 1972; by 9.6% for wages paid in 1969 or 1970. For wages paid prior to 1969, consult your State agency USE ONLY AS INSTRUCTED BY STATE AGENT 11. Adjustment for overpayment or underpayment of contributions. 12. Contributions as adjusted by item 11_ DO NOT USE FORMS OF DIFFERENT DESIGN UNLESS APPROVED BY THE SOCIAL SECURITY ADMINISTRATION A-4-1 GENERAL INSTRUCTIONS A form OAR-S3 must be completed for each reporting entity whether or not any wages were paid in the calendar quarter. If no wages were paid, enter "No Covered Wages Paid" in the space provided for listing employee wage data. After this form has been completed, the original must be sent to the State agency; the duplicate copy must be retained by the reporting entity or State agency; and the triplicate copy, if pre- pared, shall be used as directed by the State agency. The quarterly wage reports should be filed on or before the date required by your State agency. SPECIFIC INSTRUCTIONS Item 5 — January-March quarter only. Enter in this item a count of all covered employees who earned wages during the pay period con- taining the 12th day of the 3rd month, whether or not they are listed in this report. If there were no covered employees in this pay period, enter zero (0). Item 6 — Enter the social security number assigned to each employee. If the employee does not have a social security number but has a receipt showing that he applied for one, enter the information from his receipt on the wage report preceded by the words "Receipt Issued." If the employee has neither a card nor a receipt, request him to complete a Form SS-5, Applica- tion for Social Security Number. Attach a copy of this form to the wage report. Retain a copy for your records and send the original to the nearest social security office. Form SS-5 may be obtained from any social security office or post office. Item 7 — Enter the name of each employee who received covered wages during the quarter exactly as it appears on the employee's social security card. If the given name, initial or sur- name the employee uses in employment is dif- ferent than the one on the employee's card (for example, because of marriage), report the em- ployee's name exactly as shown on the card. Then instruct the employee to obtain a social security card bearing the name which he uses in employment. Forms OAAN-7003, Request For Change In Social Security Records, are available from any social security office for recording changes in name information as well as other corrections of identifying information. Item 8 — Enter the total covered wages paid to each employee during the quarter. If wages paid in a previous quarter were errone- ously reported or omitted from the report for that quarter, do not include such corrections on this report. Instead, report these corrections on Form 0AR-S4, State's Report of Adjustments, available from your State agency. Item 9 — Enter the total of the wage amounts listed in column 8 for all pages of this report. Where a political subdivision or coverage group is reporting each payroll record unit separately, enter only the total wage amount for that unit. HOW TO REPORT AGRICULTURAL EMPLOYEES Report agricultural employees in the regular manner when the agricultural exclusion has not been taken. Otherwise, group such employees together on the wage report under the heading "AGRICULTURAL EMPLOYEES." TIPS If any of your employees receive tips as com- pensation for services, contact your State agency for reporting instructions. A-4-2 Appendix A-5. Form SS-5 APPLICATION FOR SOCIAL SECURITY NUMBER (Or Replacement of Lost Card) Information Furnished On This Form Is CONFIDENTIAL See Instructions on Back. Print in Black or Dark Blue Ink or Use Typewriter. DO NOT WRITE IN THE ABOVE SPACE a CO Print FULL NAME YOU WILL USE IN WORK OR BUSINESS {First Name) {Middle Nome or Initial — If none, draw tine — ) (Last Name] Print FULL NAME GIVEN YOU AT BIRTH PLACE OF BIRTH (Cry) (Counfy tf known) {State) MOTHER'S FULL NAME AT HER BIRTH (Her maiden name) FATHER'S FULL NAME (Regardless of whether living or dead) YOUR (Month) (Day) (Year) DATE OF BIRTH YOUR PRESENT AGE (Age on fosf birthday) YOUR SEX a a YOUR COLOR OR RACE WHITE NEGRO OTHER nan HAVE YOU EVER BEFORE APPLIED I] FOR OR HAD A SOCIAL SECURITY, * RAILROAD, OR TAX ACCOUNT NUMBER? NO KNOW YES-rf 1 nnff (If Yes - ' Print STATE in which you applied and DATE you applied and SOCIAL SE CURITY NUMBER if known) YOUR MAILING ADDRESS (Number and street) [City) {State) {ZIP Code) TODAY'S DATE € Sign YOUR NAME HERE (Do Not Print) TREASURY DEPARTMENT Internal Revenue Service Form SS-5 (12-64) Return completed application to nearest SOCIAL SECURITY ADMINISTRATION DISTRICT OFFICE HAVE YOU COMPLETED ALL 13 ITEMS? INSTRUCTIONS One Account Number Is All You Ever Need For Social Security And Tan Purposes Special Attention Should Be Given To Items Listed Below Fill, in this form completely and correctly. If any information is not known and is unavailable, write "unknown." Use typewriter or print legibly in dark ink. a Your account number card will be typed with the name you show in item 1 How- ever, if you want to use the name shown in item 2, attach a signed request to this form. Kj If not born in the USA, enter the name of the country in which you were born. Glf a stepfather, adopting father, or foster father is shown, include the relationship after name; for example, "John H. Jones, stepfather." If you have ever before filled out an application like this for a social security, rail- road, or tax number, check "yes" even if you never received your card. If you check "yes," give the name of the State and the approximate date on which you applied. Also enter your social security number if you did receive the card and remember the number. You may find your number on an old tax return, payroll slip, or wage statement. If you get your mail in the country, without a street address, show your R.F.D. Route, and Box number,- if at the post office, show your P.O. Box No.; if there is no such way of showing your mail address, show the town or post office name. If mail under your name is not normally received at the address which you show, use an "in care of" address. Sign your name as usually written. Do not print unless this is your usual signa- ture. (If unable to write, make a mark witnessed by two persons who can write. The witnesses preferably should be persons who work with the applicant and both must sign this application. A parent, guardian, or custodian who completes this form on behalf of another person should sign his own name followed by his title or relationship to the applicant; for example, "John Smith, father") a ai $ 1966 OF — 240-456 FOR DISTRICT OFFICE USE FOR BUREAU OF DATA PROCESSING AND ACCOUNTS A-5-1 Appendix A-6 . Form SS-h- APPLICATION FOR EMPLOYER IDENTIFICATION NUMBER ISee Insltutllons on Reverse) PLEASE LEAVE BLANK 1. NAME (TRUE name as distinguished from TRADE name.) 2. trade; name, if any (Enter name under which business is operated, if different from item 1.) 3. ADDRESS OF PRINCIPAL PLACE OF BUSINESS (No. and Street, City, Zone, State) 4. County 5. check (X) TYPE OF ORGANIZATION (If "other" specify, such as "Estate," etc.) 1 1 Indi- | 1 Corpo- 1 1 Partner- | 1 Other 1 1 vidual | | ration | | ship 1 I (Specify) 6. If individual, enter your social security account number 7. REASON FOR APPLYING (If "other" specify such as "Corporate structure Started Purchased change," "Acquired by gift or trust," etc.) 1 1 business I I business I I 8. Date you acquired or started business (Mo., day, year) 9. First date you paid or will pay wages 10. NATURE OF BUSINESS (See Instructions) 11. NUMBER OF -*• EMPLOYEES Agricultural Non-agricultural 12. II nature of business is MANUFACTURING, list in order of their importance the principal products manufactured and the estimated 1 % PLEASE LEAVE BLANK percentage of the total value of all products which each represents. CODES FR 3 % .: % Reg 13. Do you operate more than one place of business lyes 1 No DO a. Name and address. b. Nature of business. c. Number of employees. A 14. To whom do you sell most of your products or services? CC I - IBusiness [ 1 Uenercl 1 1 Other 1 (establishments I I public | | (Specify) SC _ PLEASE | G eo 1 Ind. 1 Cla LEAVE BLANK -*■ Size 1 Seas, for Appl. Bus. Bir Date DO NOT DETACH PLEASE LEAVE BLANK 1. NAME (TRUE name as distinguished from TRADE name.) 2. TRADE NAME. IF ANY (Enter name under which business is operated, if different from item X.) I 4. County 3. ADDRESS OF PRINCIPAL PLACE OF BUSINESS Wo. and Street, City, Zone, State) 5. CHECK (X) TYPE OF ORGANIZATION < If "other" specify, such as "Estate," etc.) □ CorDO- I 1 Partner- i 1 Other ration |_J ship |_| (Specify) |— 1 Indi- I I vidual 6. If individual, enter your social security account number 7. REASON FOR APPLYING (If "other" specify such as "Corporate structure Started Purchased chan ge," "Acquired by gift or trust," etc.) □ new I I going I I other business I 1 business ' > 8. Date you acquired or started business (Mo., day, year) 9. First date you . paid or will pay wages lO- NATURE OF BUSINESS (Sec Instructions! NUMBER OF ->- EMPLOYEES Agricultural Non-agricultural 12. Have you ever applied tor an identilication number for this or any other business? □ ves Ql // "Yes," enter previous number, if known, or the approximate DATE and STATE where you first applied ■■--— > SIGNATURE A-6-1 FORM SS-- PART 3 US TREASURY DEPARTMENT— INTERNAL REVENUE SERVICE APPLICATION FOR EMPLOYER IDENTIFICATION NUMBER (See Instructions on Reverse) 1. NAME (TRUE name as distinguished from TRADE name.) 2. TRADE NAME. IF ANY (Enter name under which business is operated, if different from item U 3. ADDRESS OF PRINCIPAL PLACE OP BUSINESS (No. and Street, City, Zone, State) 4. County 5. CHECK ( x ) TYPE OF ORGANIZATION (If "other" specify, such as "Estate," etc.) □ Indi- I 1 Corpo- ] 1 Part vidua! j | ration I I ship Indi- | 1 Corpo- | 1 Partner- I 1 Other | I (Specify) 6. If individual, enter your social : account number 7. REASON for APPLYING (If "other" specify such as "Corporate structure Started Purchased change" "Acquired by gift or trust," etc.) □ i □ a 8. Date you acquired or started business (Mo., day, year) 9. First date you paid or will pay wages lO. NATURE OF BUSINESS (See Instructions) Agricultural Non-agricultural NUMBER OF -** EMPLOYEES 12. Ii nature ol business is MANUFACTURING, list in order of their importance the principal products manufactured and the estimated percentage of the total value of all products which each represents. 13. Do you operate more than one place of business j K // "Yes," attach a list showing for each separate establishment: ' — ' □> a. Name and address. b. Nature of business. c. Number of employees. 14. To whom do you sell most of your products or services? □ Business l 1 Gen establishments | [ pub | General ublic □ Other (Specify) PLEASE LEAVE BLANK Reg I Reas. for Appl. Bus. Bii. Date FORM SS-4 PART A NOTICE OF EMPLOYER IDENTIFICATION NUMBER Please make a separate record of this number lor use in case this notice -^ should be lost or destroyed, The Identification Number shown above will be used by the Internal Revenue Service to identify your Federal business tax returns, i.e., 1120. 940, 941, etc., and your payments of the taxes reported on such returns. Your Identification Number should be shown on such returns and on any related forms of correspondence. If you change your address, please report the new address to the District Director for the Internal Revenue District in which the new address is located. You should continue to use the same Identification Number even though you change the address of your principal place of business. District Director of Internal Revenue A-6-2 INSTRUCTIONS WHO .MUST FILE THIS APPLICATION? Ever} person who has not previously secured an identification number and who (a) pays wages to one or more employtes, or (b) is required to have an identification number lor inclusion in any return, statement or ctner document. Only one application lor an identification number should be fiied, regardless of the number of establishments operated. This is true even thougu :ne business is conducted under one or more business or trade names. Each corporation of an affiliated group must be treated separately, and each must file a separate application. If a business is sold or transferred and. the new owner does not have nn identification number, he should not use the identification number assigned to the previous owner, but must ti'.c an application on Form SS-1 for a new identification number. WHERE MUST THIS APPLICATION BE FILED? With the U.S. District Director of Internal Revenue with whom the Federal tax returns are filed. WHEN MUST THIS APPLICATION BE FILED? (a) By those who pay wages, on or before the seventh day after the date on which business begins, (b) By others in sufficient time for the identification number to be included in return, statement, or other document. BOW THIS APPLICATION SHOULD BE FILLED IN. All answers shouid be typewritten or printed plainly with ballpoint pen in black or dark blue ink. Items 1 and 2. Enter in Item 1 the true name of the applicant and enter in Item 2 the trade name, if any, adopted for business purposes. For example, if John W. Jones, an individual owner, operates a restaurant under the trade name of "Busy Eee Restaurant," "John W. Jones" should be entered in Item 1 and "Busy Bee Restaurant" in Item 2. NOTE: — If created by statute, court order or decree, charter, oral or written agreement, will, declaration of trust, or other legal instrument, enter in Item I the full name recognized thereunder. If a corporation! enter in Item 1 the corporate name as set forth in its charter or other legal document issued by the Government creating It. In the case of a trust, the name of the trust estate should be entered in Item 1, and the name of the trustee in Item 2. In the case of an estate of a decedent, insolvent, etc., the name cf the estate should be entered in Item i and the name of the administrator or other fiduciary in Item 2, If the true name is unusually long, it should be shown in a statement attached to this form. In such case, a ^hort version of the name should be adopted for purposes of this form and entered in Item 1, SO NOT DETACH Item 10. Describe the kind of business carried on by applicant In Item 1, The following examples illustrate the type of information needed. (a) MINING AND QUARRYING: State the process and the principal product; i.e., mining bituminous coal, mining bauxite, contract drilling for oil, quarrying dimension stone, etc, (b) CONTRACT CONSTRUCTION: State whether general contractor or special trade contractor and show type of work nor- mally performed; i.e., general contractor for residential buildings, general contractor on streets and highways, electrical subcontractor, plumbing subcontractor, etc. (c) TRADE: State the type of sale and the principal line of goods sold; i.e., wholesale dairy products, manufacturer's repre- sentative for mining machinery, wholesale petroleum-bulk station, retail hardware, retail men's clothing, etc. (d) MANUFACTURING: State type of establishment operated; i.e., sawmill, vegetable cannery, by-product coke oven, steel cold-rolling mill, etc. In Item 12, Part 1, list the principal products manufactured. (e) NONPROFIT: State whether organized for religious, charitable, scientific, literary, educational, or humane purposes and state the principal activity; i.e., religious organization — hospital; charitable organization — home for the aged, etc. (f) OTHER ACTIVITIES: State exact type of business operated, i.e., advertising agency, dry cleaning plant, farm, labor union, motion picture theater, real estate agent, steam laundry, rental of coin-operated vending machines, etc. A-6-3 form oAA-ioo (4-711 Appendix A-7. Form OAA-100 EMPLOYER INFORMATIONAL SCHEDULE DEPARTMENT OF HEALTH, EDUCATION, AND WELFARE Social Security Administration P.O. BOX 2115. BALTIMORE. MARYLAND 21203 r i Form Approved OMB No. 72-R0238 DO NOT FILL IN THESE ITEMS CODE GEOGRAPHIC INDUSTRY L J In order to classify your business accurately, we need a detailed description of your principal activity. This information permits the Ad- ministration to prepare business statistics for itself, for other government agencies and for the general public as an economical by-product of the employers federal tax returns. To avoid correspondence fill in the answers to questions on this form even though you may have no employees at this time. PLEASE RETURN THE COMPLETED FORM IN THE ENCLOSED ENVELOPE WITHIN 15 DAYS OF RECEIPT. COMPLETE ITEMS A THRU G FOR YOUR PLACE OF BUSINESS SHOWN IN THE ABOVE ADDRESS LABEL. IF YOU OPERATE MORE THAN ONE PLACE OF BUSINESS, REPORT ADDITIONAL PLACE(S) OF BUSINESS IN ITEM H A. PHYSICAL LOCATION OF THIS ESTABLISHMENT B. NUMBER OF EMPLOYEES DURING PEAK OPERATION NON-AGRICULTURAL AGRICULTURAL C. DESCRIBE THE KIND OF BUSINESS OR ACTIVITY OF THIS ESTABLISHMENT. D. MARK (X) THE BOX OR BOXES FOR THE ACTIVITIES IN WHICH YOUR ESTABLISHMENT IS ENGAGED IF MORE THAN ONE BOX IS MARKED. ENCIRCLE THE BOX REPRESENTING THE MAJOR ACTIVITY. I Sec definitions on reverse side.) ^2 AGRICULTURAL PRODUCTION ~J MINING (include juels) OR QUARRYING 3] MANUFACTURING Qj CONSTRUCTION OR BUILDING TRADE CONTRACTOF fj WHOLESALE TRADE ~^\ RETAIL TRADE ^) REPAIR SERVICES (Specify) | | SOCIAL AND REHABILITATION SERVICES (Specify) | 1 OTHER (Specify) LIST BELOW THE PRINCIPAL PRODUCTS SOLD OR SERVICES PERFORMED BY THIS ESTABLISHMENT AND INDICATE APPROX- IMATE PERCENTAGE EACH WAS OF YOUR TOTAL RECEIPTS DURING THE PAST 12 MONTHS F. IF YOU MANUFACTURE, WHAT ARE THE PRINCIPAL RAW MATERIALS USED? G. TO WHOM DO YOU SELL MOST OF YOUR PRODUCTS OR SERVICE? _j BUSINESS ESTABLISHMENTS \^\ CONSTRUCTION CONTRACTORS ^] puBlIc* 1 " ,, PLEASE LIST BELOW ANY ADDITIONAL ESTABLISHMENTS REPORTED UNDER THE EMPLOYER IDENTIFICATION NUMBER FRINTED ON THIS FORM. (Attach additional sheets if necessary.) TRADE NAME AND ADDRESS Or EACH PLACE Of EMPLOYMENT DESCRIBE THE PRINCIPAL BUSINESS ACTIVITY AND PRODUCTS OR SERVICES SOLD OR PRODUCTS MANUFACTURED NO OF EMPLOYEES 1. 2. SIGNATURE TITLE TELE. NO. INCLUDE ■* R E A CODE DATE A-7-1 DEFINITIONS OF MAJOR BUSINESS ACTIVITIES (Item D on reverse side) Agricultural production — Production of field crops (cotton, grains, tobacco, etc.). fruits, tree nuts, vegetables, livestock (cattle, poultry, hogs, etc.), norticultural specialties (flowers, plants, shrubs, etc.), and animal specialties (honey, furs, etc.). Mining (including fuels) and quarrying — Ex- traction or quarrying of minerals' (such as metallic and non-metallic ores, coal, petroleum, stone, sand, etc.), minerals preparation and services, and minerals ex- ploration and development. Manufacturing — Fabrication, assembly or processing materials into new products. Ap- parel jobbing; logging; publishing, printing; ready-mixed concrete production; machine shop repair; etc. are included in this activity. Construction or building trade contractors — Construction by general contractors of buildings, roads, etc., and piumbing, carpen- try, electrical work, concreie work, etc. by special trade contractors. Wholesale trade — Selling merchandise to business firms; professional, institutional users; and to government. The principal types include merchant wholesalers, merchandise agents or brokers, manufacturers' sales branches. Retail trade — Selling merchandise to the general public. Also included are eating and drinking places and gasoline service stations. (If selling primarily door to door, through vending machines or through the mails, so indicate on the reverse side, item C). Repair services — Repairing merchandise such as apparel, shoes, automobiles, household appliances, radio, television, etc. This activity does not include repairs to fixed structures. Social and rehabilitation services — Individual and family services; jobtraining and vocational rehabilitation services; child day care services, etc. Other — Agricultural services; fisheries; transportation, communication; finance, in- surance, and real estate; lodging places (hotels, etc); personal, business, recreational services; medical, legal, educational, accoun- ting, architectural services; etc. (If your business activity is dry cleaning and the work is done in your own plant, please indicate on the reverse side, item C). A-7-2 Appendix A-8. Form OAA-103 FORM OAA-103 (5-74) EMPLOYER INDUSTRIAL ACTIVITY SCHEDULE DEPARTMENT OF HEALTH, EDUCATION, AND WELFARE SOCIAL SECURITY ADMINISTRATION P.O. BOX 2115, BALTIMORE, MARYLAND 21203 r ~i Form Approved 0MB No. 72-R0592 Contract Construction and Real Estate Industries DO NOT FILL IN THESE ITEMS CODE GEOGRAPHIC INDUSTRY L J In order to classify your business accurately, we need a detailed description of your principal activity. This information perm'ts the Administra- tion to prepare business statistics for itself, for other government agencies and for the general public as an economical by-product of the Employ- er's Federal tax returns. To avoid correspondence fill in the answers to questions on this form even though you may have no employees at this time. P L EASE RETURN THE COMPLETED FORM IN THE ENCLO SED ENVEL OPE WITHIN 15 DAYS OF RECEIPT. COMPLETE ITEMS A THRU C FOR YOUR PLACE OF BUSINESS OR PRIMARY PLACE OF BUSINESS, IF YOU OPERATE MORE THAN ONE (See Item D continuation tor reporting additional place(s) ot business A PHYSICAL LOCATION OF YOUR PRIMARY PLACE OF BUSINES 1 B NUMBER OF EMPLOYEES DURING PEAK OPERATION NON- AGRICULTURAL AGRICULTURAL C Please circle the number of the description below that best descri activity, circle the numbers of those that apply and show the appr preceeding 12 months. If none of the descriptions in items 1 throu s the nature of your business. If you engage in more than o imate percentage of each based upon the gross receipts for 26 apply, describe the nature of your business in item 27. CONSTRUCTION OF BUILDING % SPECIAL TRADE CONTRACTOR (Continued) % 1 1511 CONSTRUCTION OF BUILDINGS FOR OTHERS ON A CON- STRUCTION CONTRACT BASIS (GENERAL CONTRACTOR) 14 1794 EARTH MOVING CONNECTED WITH BUILDING CONSTRUCTION 15 1731 ELECTRICAL WORK (WIRING BUILDINGS) 2 1531 CONSTRUCTION OF BUILDINGS ON OWN ACCOUNT FOR SALE TO OTHERS (OPERATIVE BUILDER) 16 1752 FLOOR LAYING AND OTHER FLOOR WORK 3 6511 CONSTRUCTION OF BUILDINGS ON OWN ACCOUNT FOR RENT TO OTHERS 17 1741 MASONRY, STONE SETTING AND OTHER STONEWORK 18 1721 PAINTING AND PAPER HANGING 4 9911 CONSTRUCTION OF A HOME FOR MY PERSONAL USE 19 1742 PLASTERING AND LATHING 5 CONSTRUCTION OF A BUILDING FOR MY BUSINESS WHICH IS: (PLEASE EXPLAIN) 20 1711 PLUMBING, HEATING (EXCEPT ELECTRIC) AND AIR CONDITIONING 21 1761 ROOFING AND SHEET METAL WORK 6 CONSTRUCTION OF BUILDINGS OTHER THAN THOSE LISTED IN ITEMS 1 THROUGH 5 ABOVE: (PLEASE EXPLAIN) 22 SPECIAL TRADE CONTRACTORS OTHER THAN THOSE LISTED IN ITEMS 10 THROUGH 21 (PLEASE EXPLAIN) CONSTRUCTION OTHER THAN BUILDINGS REAL ESTATE 7 161 1 CONSTRUCTION OF STREETS AND HIGHWAYS (GENERAL CONTRACTOR) 3 6531 AGENT. BROKER OR MANAGER OF REAL ESTATE FOR OTHERS 24 6552 DEVELOPER AND SUBDIVIDER (EXCEPT CEMETERIES) 8 1621 ELECTRIC POWER LINE CONSTRUCTION (GENERAL CONTRACTOR) 25 651 1 OWNERS, OPERATORS AND LESSORS OF REAL PROPERTY 26 REAL ESTATE OPERATORS OTHER THAN THOSE LISTED IN ITEMS 23 THROUGH 25 (PLEASE EXPLAIN) 9 1621 HEAVY CONSTRUCTION (DAMS, WATER MAINS, BRIDGES, EARTH MOVING PROJECTS, ETC.): GENERAL CONTRACTOR (PLEASE EXPLAIN) MISCELLANEOUS SPECIAL TRADE CONTRACTOR 27 IF YOU ENGAGE IN ANY ADDITIONAL OR OTHER ACTIVITY NOT LISTED IN ITEMS 1 THROUGH 26 PLEASE DESCRIBE EACH SUCH ACTIVITY 10 1752 ASPHALT TILE AND LINEOLEUM INSTALLATION 11 1751 CARPENTRY 12 1743 CERAMIC TILE, TERRAZZO, MARBLE AND MOSAIC WORK 1771 CONCRETE WORK TOTAL OF ITEMS 1 THROUGH 27 100% D Attach a list including all establishments reported each establishment, please give the following: (a) under the employer identification number printed on this form. For the name and cddress, (b) nature of business, (c) number of employees. SIGNATURE TELE. NO. INCLUDE AREA CODE DATE A-8-1 Appendix A-9 . Form SSA-5019 z z = < O _l z *£ 1- LU Oi or O or a- o S|2 UJ X 1/1 CO _l < m 1- < t- 1.1 LL UJ O O ^ u Ld : a >- H UJ o til m _j 2 Q. (J u. 3 5 < 6 u. 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X 3 G G U E OJ CX c CU O 3 CU E '3 u 11 C/3 CU c 'J o 4-1 O o X 3 3 3 4-J u cX 3 CO G G 3 w "3 X: -3 CU _E J^ CO y, c "3 X O Ul u E ui C X X u E X CJ a, 03 Cfl C3 CU ^ CU X G TJ O Ul 11 '5 -3 cTL G CU > Cfl G E 0) CU ui Ul a. 03 ex CU -o O *-i Cu 03 E E D a, o3 60 2 C c E 3 O U n c X o *■" co 3 (jj Cfl "° J»d CU -3 _2 cu Cu cu E x CU UJ c E o U G 3 11 X UJ £ X CO G 3 X CU 3 ■ -1 Uj O X G 3 Ukj t-l TJ CO 1) X "3 G £ G U X X 3 Cfl C Uj c UJ G C 3 X £ Cfl Uj 3 G 60 3 G Ui 3 ' G G ■ l i X E G u X UJ Cfl G I) 3 u- X 3 CO X 3 CO X u W CO UJ Cfl '5b 3 CO TJ UJ "d CO 3 Uj 3 o G X u E 60 uH « G X Ul £ 3 G CO X rt 3 03 X UJ 3 o G cfl G CO uj CO G G Uj O -a CO x UJ Uj 3 E « c G G 's >■ C 3 A-9-2 Appendix A-10. Form SSA-1941 FORM SSA 1941 18-72) Deportment of health, education and weifare RECAPITULATION FOR ESTABLISHMENT REPORTING Form approved OMB No. 72-RO 706 al S Enp'oyer Identification Number Date Quartef Ended Employer Name and Address INSTRUCTIONS for the preparation of this recapitulation sheet are on the back of this form. Your cooperation in following the in- structions will be greatly appreciated. ESTABLISHMENT OR REPORTING UNIT NUMBER BEGINNING PAGE NUMBER NUMBER OF EMPLOYEES LISTED PAY PERIOD EMPLOYMENT I TAXABLE F. I. C. A. WAGES TAXABLE TIPS REPORTED 3 A-10-1 INSTRUCTIONS Employers reporting under the Establishment Re- porting Plan should prepare a recapitulation for their Form 941, Employer's Quarterly Federal Tax Return, and Forms 941 A, continuation sheet. The recapitulation is used only by the Social Security Administration. It should be prepared on Forms SSA-1941 and securely attached to Form 941. A supply of Forms SSA-1941 will be mailed to you once each year. If additional forms are needed before the next yearly distribution, please tele- phone or write any social security office, or write to the Social Security Administration, P.O. Box 2115, Baltimore, Maryland 21203. The recapitulation should be typed to show a one-line summary for each establishment or report- ing unit. The information must be listed in the same page number sequence as the report. The exhibit shown below contains examples of the information to be entered in each field on the front of the form. 1 ESTABLISHMENT BEGINNING NUMBER OF PAY PERIOD TAXABIE TAXABLE TIPS -J OR REPORTING PAGE NUMBER EMPLOYEES EMPLOYMENT F. 1. C A. WAGES REPORTED 3 UNIT NUMBER LISTED 1673 1 10 10 25,015.00 0002 2 2250 1925 6.725,104.30 0001 243 150 125 450.008.59 2,476.00 1000 250 42 32 85,426.37 875.00 2 3 1 BEGINNING PAGE NUMBER: The first page number of the establishment or re- porting unit, as shown on the Form 941 A. 2 PAY PERIOD EMPLOYMENT: Number of persons employed during the pay period which includes March 12. This information is required only on the first quarterly report each year. 3 TAXABLE TIPS REPORTED: The total taxable tips per establishment or report- ing unit as reported on the Forms 941 A for the quarter. If none of your units report taxable tips, you may enter in this column payroll data, State unemployment information, plant identification or any other information you will need for your rec- ordkeeping. REPORTING NEW ESTABLISHMENTS OR REVISED NUMBERING SYSTEMS: If you report new estab- lishments or reassign establishment numbers to identify other locations or activities, please pre- pare new Forms SSA-5019, list of establishments or reporting units. Keep the yellow copy and mail the other copies to the Social Security Administra- tion at the above address. For blank Forms SSA- 5019 follow the instructions for obtaining Forms SSA-1941 given above. A-10-2 Appendix A- 11 . IRS Form 1040, Schedule SE SCHEDULE SE (Form 1040) Department of the Treasur Internal Revenue Service Computation of Social Security Self-tmployment Tax £». Each self-employed person must file a Schedule SE. ^ Attach to Form 1040. ^ See Earned Income Credit Instructions on page 8 and Instructions for Schedule SE (Form 1040). 75 • If you had wages, including tips, of $14,100 or more that were subject to social security or railroad retirement taxes, do not fill in this schedule unless you are eligible for the Earned Income Credit. See Instructions. • If you had more than one business, combine profits and losses from all your businesses and farms on this Schedule SE. Important. — The self-employment income reported below will be credited to your social security record and used in figuring social security benefits. NAME OF SELF-EMPLOYED PERSON (AS SHOWN ON SOCIAL SECURITY CARD) Social security number of self-employed person ► Business activities subject to self-employment tax (grocery store, restaurant, farm, etc.) ► • If you have only farm income complete Parts I and III. • If you have only nonfarm income complete Parts II and III. • If you have both farm and nonfarm income complete Parts I, II, and III. ISjUM Computation of Net Earnings from FARM Self-Employment You may elect to compute your net farm earnings using the OPTIONAL METHOD, line 3, instead of using the Regular Method, line 2, if your gross profits are: (1) $2,400 or less, or (2) more than $2,400 and net profits are less than $1,600. However, lines 1 and 2 must be completed even if you elect to use the FARM OPTIONAL METHOD. REGULAR METHOD I (a) Schedule F, line 54 (cash method), or line 74 (accrual method) 1 Net profit or (loss) from: | (b) Farm partnerships 2 Net earnings from farm self-employment (add lines 1(a) and (b)) FARM OPTIONAL METHOD 3 If gross profits from farming ' are: (a) Not more than $2,400, enter two-thirds of the gross profits . (b) More than $2,400 and the net farm profit is less than $1,600, enter $1,600 1 Gross profits from farming are the total gross profits from Schedule F, tine 28 (cash method), or line 72 (accrual method), plus the distributive share of gross profits from farm partnerships (Schedule K-l (Form 1065), line 14) as explained in instructions tor Schedule SE. 4 Enter here and on line 12(a), the amount on line 2, or line 3 if you elect the farm optional method . Computation of Net Earnings from NONFARM Self-Employment REGULAR METHOD 5 Net profit or (loss) from: (a) Schedule C, line 21. (Enter combined amount if more than one business.) . (b) Partnerships, joint ventures, etc. (other than farming) (c) Service as a minister, member of a religious order, or a Christian Science prac- titioner. (Include rental value of parsonage or rental allowance furnished.) If you filed Form 4361, check here ► rj and enter zero on this line (d) Service with a foreign government or international organization ....... , . „ , < SsB F °™ 104u in- „ ., . (e) Other stations tor line 35.) Specify ► 6 Total (add lines 5(a) through (e)) 7 Enter adjustments if any (attach statement) 8 Adjusted net earnings or (loss) from nonfarm self-employment (line 6, as adjusted by line 7) . . . . If line 3 is $1,600 or more OR if you do not elect to use the Nonfarm Optional Method, omit lines 9 through 11 and enter amount from line8on line 12(b), Part III. Note: You may use trie nonfarm optional method (line 9 through line 11) only it line 8 is less than $1,600 and less than two-thirds of your gross nonfarm profits,' and you had actual net earnings from self-employment of $400 or more for at least 2 of the 3 following years: 1972, 1973, and 1974. The nonfarm optional method can only be used for 5 taxable years. NONFARM OPTIONAL METHOD 9 (a) Maximum amount reportable, under both optional methods combined (farm and nonfarm) .... (b) Enter amount from line 3. (If you did not elect to use the farm optional method, enter zero.) . (c) Balance (subtract line 9(b) from line 9(a)) 10 Enter two-thirds of gross nonfarm profits ! or $1,600, whichever is smaller 11 Enter here and on line 12(b), the amount on line 9(c) or line 10, whichever is smaller 2 Gross profits from nonfarm business are the total of the gross profits from Schedule C, line 3, plus the distributive share of gross profits from nonfarm partnerships (Schedule K-l (Form 1065), line 14) as explained in instructions for Sch edule SE. Also, include gross profits from services reported on lines 5(c), (d). and (e). as adjusted by line 7. i 00 ill ■ i^Tilll^ Computation of Social Security Self-Employment Tax 12 Net earnings or (loss): (a) From farming (from line 4) (b) From nonfarm (from line 8, or line 11 if you elect to use the Nonfarm Optional Method) . . . . 13 Total net earnings or (loss) from self-employment reported on line 12. (If Line 13 is less than $400, you are not subject to self-employment tax. Do not fill in rest of schedule.) . 14 The largest amount of combined wages and self-employment earnings subject to social security or railroad retirement taxes for 1975 is . . |14,100 00 15 (a) Total "FICA" wages and "RRTA" compensation " ' WB. (b) Unreported tips subject to FICA tax from Form 4137, line 9 or to RRTA . h (c) Total of lines 15(a) and (b) 16 Balance (subtract line 15(c) from line 14) . . 17 Self-employment income — line 13 or 16, whichever is smaller 18 Self-employment tax. (If line 17 is $14,100.00, enter $1,113.90; if less, multiply the amount on line 17 by .079.) Enter here and on Form 1040, line 59 ■ft U.S. GOVERNMENT PRINTING OFFICE - 1975— 0-575-368 E.l. ll-Zb$7299 A-ll-1 APPENDIX B GEOGRAPHIC CODES FOR RECORDS OF EMPLOYERS, WORKERS, AND SELF-EMPLOYED PERSONS SOCIAL SECURITY ADMINISTRATION Office of Research and Statistics 1974 NUMERICAL LISTING OF STATES BY STATE CODE Code State Code State *02 Alaska 51 Delaware 11 Ma i ne 52 Maryland 12 New Hampshire 53 Virginia 13 Vermont 55 West Virginia 14 Massachusetts 56 North Carolina 15 Rhode Island 57 South Carolina 16 Connecticut 58 Georgia 21 New York 61 Kentucky 22 New Jersey 63 Tennessee 23 Pennsylvania 64 Alabama 24 American Samoa 65 Mississippi *25 Alaska 71 Arkansas 26 Hawaii 72 Louisiana 27 Puerto Rico 73 Oklahoma 28 Virgin Islands 74 Texas 29 Guam 81 Montana 31 Ohio 82 Idaho 32 Indiana 83 Wyomi ng 33 Illinois 84 Colorado 35 Michigan 85 New Mexico 36 Wisconsin 86 Arizona 41 Minnesota 87 Utah 42 Iowa 88 Nevada 43 Missouri 90 District of Columbia 45 North Dakota 91 Washington 46 South Dakota 92 Oregon 47 Nebraska 93 Cal ifornia 48 Kansas 98 International Operations 50 Florida 99 Ships at sea *Alaska: Through 1968 - 25 After 1968 - 02 B-2 ALPHABETIC LISTING OF STATE-COUNTY CODES ' ALABAMA - 64 Code County Code County Code County 64000 Autauga 64230 Dallas 64460 Marion 64010 Baldwin 64240 De Kalb 64470 Marshall 64020 Barbour 64250 Elmore 64480 Mobile 64030 Bibb 64260 Escambia 64490 Monroe 64040 Blount 64270 Etowah 64500 Montgomery 64050 Bullock 64280 Fayette 64510 Morgoan 64060 Butler 64290 Franklin 64520 Perry 64070 Calhoun 64300 Geneva 64530 Pickens 64080 Chambers 64310 Greene 64540 Pike 64090 Cherokee 64320 Hale 64550 Randolph 64100 Chilton 64330 Henry 64560 Russell 64110 Choctaw 64340 Houston 64570 Saint Clair 64120 Clarke 64350 Jackson 64580 Shelby 64130 Clay 64360 Jefferson 64590 Sumter 64140 Cleburne 64370 Lamar 64600 Talladega 64150 Coffee 64380 Lauderdale 64610 Tallapoosa 64160 Colbert 64390 Lawrence 64620 Tuscaloosa 64170 Conecuh 64400 Lee 64630 Wal ker 64180 Coosa 64410 Limestone 64640 Washington 64190 Covington 64420 Lowndes 64650 Wilcox 64200 Crenshaw 64430 Macon 64660 Winston 64210 Cullman 64440 Madison 64990 Statewide 64220 Dale 64450 Marengo B-3 Code 25 (through 1968) ALASKA - 02 (after 1968) County Equivalent ■■ Judicial Division (through 1968) Code Judicial Division 25010 First 25020 Second Code Judicial Division 25030 Third 25040 Fourth Judicial Division 25990 Statewide County Equivalent - Borough (1969-1970) Code Borough Code Borough 02009 Bristol Bay 02015 Gateway 02017 Greater Anchorage 02019 Greater Juneau 02021 Greater Sitka 02023 Kenai Peninsula (NW) 02025 Kenai Peninsula (SE) 02029 Kodiak Island 02035 Matanuska Susitna 02039 North Star 02098 Area outside boroughs 02990 Statewide County Equivalent - Census Division (1971--) Code Division Code Division 02010 Aleutian Islands 02110 Juneau 02020 Anchorage 02120 Kenai -Cook Inlet 02030 Angoon 02130 Ketchikan 02040 Barrow-North Slope 02140 Kobuk 02050 Bethel 02150 Kodiak 02060 Bristol Bay Borough 02160 Kuskokwim 02070 Bristol Bay 02170 Matanuska-Susitna 02080 Cordova-McCarthy 02180 Nome 02090 Fairbanks 02190 Outer Ketchikan 02100 Haines 02200 Prince of Wales B-4 ALASKA - 25, 02 (cont.) Code 02210 02220 02230 02240 02250 Division Seward Sitka Skagway-Yakutat Southeast Fairbanks Upper Yukon Code 02260 02270 02280 02290 02990 Division Valdez-Chitina-Whittier Wade Hampton Wrangel 1 -Petersburg Yukon-Koyukuk Statewide AMERICAN SAMOA - 24 Code County Code County Code County 24000 Faleasao 24060 Ofu 24110 Ta'u 24010 Fitiuta 24070 Olosega 24120 Tualauta 24020 Ituau 24080 Saole 24130 Taulatai 24030 Lealataua 24090 Sau 24140 Vaifanua 24040 Leasina 24100 Swains 24990 Territorywide 24050 Mauputasi Island ARIZONA - 86 Code County Code County Code County 86000 Apache 86050 Greenlee 86100 Pinal 86010 Cochise 86060 Maricopa 86110 Santa Cruz 86020 Coconino 86070 Mohave 86120 Yavapai 86030 Gila 86080 Navajo 86130 Yuma 86040 Graham 86090 Pima 86990 Statewide ARKANSAS - 71 Code County Code County Code County 71000 Arkansas 71050 Bradley 71100 Clay 71010 Ashley 71060 Calhoun 71110 Cleburne 71020 Baxter 71070 Carroll 71120 Cleveland 71030 Benton 71080 Chicot 71130 Columbia 71040 Boone 71090 Clark 71140 Conway B-5 ARKANSAS ■ - 71 (cont.) Code County Code County Code County 71150 Craighead 71360 Lafayette 71560 Polk 71160 Crawford 71370 Lawrence 71570 Pope 71170 Crittenden 71380 Lee 71580 Prairie 71180 Cross 71390 Lincoln 71590 Pulaski 71190 Dallas 71400 Little River 71600 Randolph 71200 Desha 71410 Logan 71610 St. Francis 71210 Drew 71420 Lonoke 71620 Saline 71220 Faul kner 71430 Madison 71630 Scott 71230 Franklin 71440 Marion 71640 Searcy 71240 Fulton 71450 Miller 71650 Sebastian 71250 Garland 71460 Mississippi 71660 Sevier 71260 Grant 71470 Monroe 71670 Sharp 71270 Greene 71480 Montgomery 71680 Stone 71280 Hempstead 71490 Nevada 71690 Union 71290 Hot Spring 71500 Newton 71700 Van Buren 71300 Howard 71510 Ouachita 71710 Washington 71310 Independence 71520 Perry 71720 White 71320 Izard 71530 Phillips 71730 Woodruff 71330 Jackson 71540 Pike 71740 Yell 71340 Jefferson 71550 Poinsett 71990 Statewide 71350 Johnson CALIFORNIA - 93 Code County Code County Code County 93000 Alameda 93100 Glenn 93310 Marin 93010 Alpine 93110 Humboldt 93320 Mariposa 93020 Amador 93120 Imperial 93330 Mendocino 93030 Butte 93130 Inyo 93340 Merced 93040 Calaveras 93140 Kern 93350 Modoc 93050 Colusa 93150 Kings 93360 Mono 93060 Contra Costa 93160 Lake 93370 Monterey 93070 Del Morte 93170 Lassen 93380 Napa 93080 Eldorado 93200 Los Angeles 93390 Nevada 93090 Fresno 93300 Madera 93400 Orange B-6 CALIFORNIA - 93 (cont.) Code County Code County Code County 93410 Placer 93510 San Mateo 93610 Sutter 93420 Plumas 93520 Santa Barbara 93620 Tehama 93430 Riverside 93530 Santa Clara 93630 Trinity 93440' Sacramento 93540 Santa Cruz 93640 Tulare 93450 San Benito 93550 Shasta 93650 Tuolumne 93460 San Bernardino 93560 Sierra 93660 Ventura 93470 San Diego 93570 Siskiyou 93670 Yolo 93480 San Francisco 93580 Solano 93680 Yuba 93490 San Joaquin 93590 Sonoma 93990 Statewide 93500 San Luis Obispo 93600 Stanislaus COLORADO - 84 Code County Code County Code County 84000 Adams 84220 Garfield 84430 Morgan 84010 Alamosa 84230 Gilpin 84440 Otero 84020 Arapahoe 84240 Grand 84450 Ouray 84030 Archuleta 84250 Gunnison 84460 Park 84040 Baca 84260 Hinsdale 84470 Phillips 84050 Bent 84270 Huerfano 84480 Pitkin 84060 Boulder 84280 Jackson 84490 Prowers 84060 Chaffee 84290 Jefferson 84500 Pueblo 84080 Cheyenne 84300 Ki owa 84510 Rio Blanco 84090 Clear Creek 84310 Kit Carson 84520 Rio Grande 84100 Conejos 84320 Lake 84530 Routt 84110 Costilla 84330 La Plata 84540 Saguache 84120 Crowley 84340 Larimer 84550 San Juan 84130 Custer 84350 Las Animas 84560 San Miguel 84140 Delta 84360 Lincoln 84570 Sedgwick 84150 Denver 84370 Logan 84580 Summit 84160 Dolores 84380 Mesa 84590 Tel ler 84170 Douglas 84390 Mineral 84600 Washington 84180 Eagle 84400 Moffatt 84610 Weld 84190 Elbert 84410 Montezuma 84620 Yuma 84200 El Paso 84420 Montrose 84990 Statewide 84210 Fremont B-' CONNECTICUT - 16 Code County Code County Code County 16000 Fairfield 16030 Middlesex 16060 Tolland 16010 Hartford 16040 New Haven 16070 Windham 16020 Litchfield 16050 New London 16990 Statewide DELAWARE - 51 Code County Code County 51000 Kent 51020 Sussex 51010 New Castle 51990 Statewide DISTRICT OF COLUMBIA - 90 Code City 90000 Wash ington FLORIDA - 50 Code County Code County Code County 50000 Alachua 50150 Duval 50300 Indian River 50010 Baker 50160 Escambia 50310 Jackson 50020 Bay 50170 Flagler 50320 Jefferson 50030 Bradford 50180 Franklin 50330 Layfayette 50040 Brevard 50190 Gadsden 50340 Lake 50050 Broward 50200 Gilchrist 50350 Lee 50060 Calhoun 50210 Glades 50360 Leon 50070 Charlotte 50220 Gulf 50370 Levy 50080 Citrus 50230 Hamilton 50380 Liberty 50090 Clay 50240 Hardee 50390 Madison 50100 Collier 50250 Hendry 50400 Manatee 50110 Columbia 50260 Hernando 50410 Marion 50120 Dade 50270 Highlands 50420 Martin 50130 De Soto 50280 Hills- borough 50430 Monroe 50140 Dixie 50290 Holmes 50440 Nassau B-£ FLORIDA - 50 (cont.) Code County Code County Code County 50450 Okaloosa 50530 Putnam 50610 Taylor 50460 Okeechobee 50540 St. Johns 50620 Union 50470 Orange 50550 St, Lucie 50630 Volusia 50430 Osceola 50560 Santa Rosa 50640 Wakulla 50490 Palm Beach 50560 Sarasota 50650 Walton 50500 Pasco 50580 Seminole 50660 Washington 50510 Pinellas 50590 Sumter 50990 Statewide 50520 Polk 50600 Suwannee GEORGIA - 58 Code County Code County Code County 58001 Appling 58181 Candler 58202 Dade 58011 Atkinson 58190 Carroll 58351 Dawson 58002 Bacon 58201 Catoosa 58360 Decatur 58021 Baker 58210 Charlton 58370 De Kalb 58031 Baldwin 58220 Chatham 58380 Dodge 58040 Banks 58230 Chattahoo- chee 58342 Dooly 58050 Barrow 58240 Chattooga 58390 Dougherty 58060 Bartow 58250 Cherokee 58400 Douglas 58071 Ben Hill 58260 Clarke 58411 Early 58081 Berrien 58271 Clay 58421 Echols 58090 Bibb 58281 Clayton 58132 Effingham 58101 Bleckley 58012 Clinch 58431 Elbert 58110 Brantley 58290 Cobb 58440 Emanuel 58120 Brooks 58003 Coffee 58182 Evans 58131 Bryan 58300 Colquitt 58451 Fannin 58140 Bulloch 58310 Columbia 58282 Fayette 58150 Burke 58082 Cook 58460 Floyd 58161 Butts 58320 Coweta 58352 Forsyth 58022 Calhoun 58330 Crawford 58432 Frank! in 58170 Camden 58341 Crisp 58470 Fulton B-9 GEORGIA - 58 (cont.) Code County Code County Code County 58452 Gilmer 58683 Mcintosh 58733 Stewart 58481 Glascock 58710 Macon 58870 Sumter 58490 Glynn 58720 Madison 58881 Talbot 58500 Gordon 58731 Marion 58692 Tal iaferro 58510 Grady 58740 Meriwether 58183 Tattnall 59521 Greene 58412 Miller 58882 Taylor 58530 Gwinnett 58750 Mitchell 58772 Telfair 59541 Habersham 58760 Monroe 58672 Terrell 58550 Hall 58771 Montgomery 58890 Thomas 58561 Hancock 58522 Morgan 58900 Tift 58570 Haralson 58453 Murray 58184 Toombs 58580 Harris 58780 Muscogee 58544 Towns 58433 Hart 58791 Newton 58642 Treutlen 58590 Heard 58801 Oconee 58910 Troup 58283 Henry 58802 Oglethorpe 58073 Turner 58601 Houston 58810 Paulding 58603 Twiggs 58072 Irwin 58602 Peach 53454 Union 58610 Jackson 58354 Pickens 58920 Upson 58562 Jasper 58820 Pierce 58203 Walker 58004 Jeff Davis 58163 Pike 58930 Walton 58620 Jefferson 58830 Polk 58940 Ware 58630 Jenkins 58102 Pulaski 58483 Warren 58641 Johnson 58563 Putnam 58950 Washington 58650 Jones 58272 Quitman 58960 Wayne 58162 Lamar 58542 Rabun 58734 Webster 58422 Lanier 58273 Randolph 58773 Wheeler 58660 Laurens 58840 Richmond 59545 White 58671 Lee 58792 Rockdale 58970 Whitfield 58681 Liberty 58732 Schley 58074 Wilcox 58691 Lincoln 58850 Screven 58693 Wilkes 58682 Long 58413 Seminole 58032 Wilkinson 58700 Lowndes 58860 Spalding 58980 Worth 58353 Lumpkin 58543 Stephens 58990 Statewide 58482 McDuffie B-10 GUAM - 29 Alphabetic Municipality L i sting Code Muncipality Code Muncipality Code Muncipality 29000 Agana 29070 Inarajan 29150 Sinajana 29010 Agana Heights 29080 Maina 29160 Talofofo 29020 Agat 29090 Mangilao 29170 Tamuning 29000 Anigua (West Agana) 29100 Men* zo 29180 Toto 29130 Aprah Heights 29110 Mongmong 29190 Umatac 29030 Asan 29120 Ordot 29200 Yigo 29040 Barrigada 29130 Pi ti 29210 Yona 29050 Chalan Pago 29140 Santa Rita 29990 Territorywide 29060 Dededo HAWAII - 26 Code county Code County Code County 26010 Hawaii 26040 Kauai 26990 Statewide 26020 Honolulu 26050 Maui (in- cludes Kalawao) IDAHO - 82 Code County Code County Code County 82000 Ada 82160 Clark 82310 Lincoln 82010 Adams 82170 Clearwater 82320 Madison 82020 Bannock 82180 Custer 82330 Minidoka 82030 Bear Lake 82190 Elmore 82340 Nez Perce 82040 Benewah 82200 Franklin 82350 Oneida 82050 Bingham 82210 Fremont 82360 Owyhee 82060 Blaine 82220 Gem 82370 Payette 82070 Boise 82230 Gooding 82380 Power 82080 Bonner 82240 Idaho 82390 Shoshone 82090 Bonneville 82250 Jefferson 82400 Teton B-ll IDAHO - 82 (cont.) Code County Code County Code County 82100 Boundary 82260 Jerome 82410 Twin Falls 82110 Butte 82270 Kootenai 82420 Valley 82120 Camas 82280 Latah 82430 Washington 82130 Canyon 82290 Lemhi 82990 Statewide 82140 Caribou 82300 Lewis 82150 Cassia ILLINOIS - 33 Code County Code County Code County 33000 Adams 33440 Henderson 33780 Moultrie 33010 Alexander 33450 Henry 33790 Ogle 33020 Bond 33460 Iroquois 33800 Peoria 33030 Boone 33470 Jackson 33810 Perry 33041 Brown 33480 Jasper 33820 Piatt 33050 Bureau 33490 Jefferson 33830 Pike 33060 Calhoun 33500 Jersey 33522 Pope 33070 Carroll 33510 Jo Daviess 33850 Pulaski 33080 Cass 33521 Johnson 33860 Putnam 33090 Champaign 33530 Kane 33870 Randolph 33100 Christian 33540 Kankakee 33880 Richland 33110 Clark 33550 Kendall 33890 Rock Island 33120 Clay 33560 Knox 33900 St. Clair 33130 CI inton 33570 Lake 33910 Sal ine 33140 Coles 33580 La Salle 33920 Sangamon 33200 Cook 33590 Lawrence 33042 Schuyler 33150 Crawford 33600 Lee 33940 Scott 33160 Cumberland 33610 Livingston 33950 Shelby 33170 De Kalb 33620 Logan 33960 Stark 33180 De Witt 33630 McDonough 33970 Stephenson 33190 Douglas 33640 McHenry 33980 Tazewell 33250 Du Page 33650 McLean 33210 Union 33310 Edgar 33660 Macon 33220 Vermil ion 33320 Edwards 33670 Macoupin 33230 Wabash 33330 Effingham 33680 Madison 33240 Warren 33340 Fayette 33690 Marion 33260 Washington 33350 Ford 33700 Marshall 33270 Wayne 33360 Frankl in 33710 Mason 33280 White 33370 Fulton 33720 Massac 33290 Whiteside 33381 Gallatin 33730 Menard 33300 Will B-12 ILLINOIS - 33 (cont.) Code County Code County Code County 33390 Greene 33740 Mercer 33430 Williamson 33400 Grundy 33750 Monroe 33840 Winnebago 33410 Hamilton 33760 Montgomery 33930 Woodford 33420 Hancock 33770 Morgan 33990 Statewide 33382 Hardin INDIAN/5 - 32 Code County Code County Code County 32000 Adams 32310 Hendricks 32620 Pike 32010 Allen 32320 Henry 32630 Porter 32020 Bartholomew 32330 Howard 32640 Posey 32030 Benton 32340 Huntington 32650 Pulaski 32040 Blackford 32350 Jackson 32660 Putnam 32050 Boone 32360 Jasper 32670 Randolph 32060 Brown 32370 Jay 32680 Ripley 32070 Carroll 32380 Jefferson 32690 Rush " 32080 Cass 32390 Jennings 32700 St. Joseph 32090 Clark 32400 Johnson 32710 Scott 32100 Clay 32410 Knox 32720 Shelby 32110 Clinton 32420 Kosciusko 32730 Spencer 32120 Crawford 32430 Lagrange 32740 Starke 32130 Daviess 32440 Lake 32750 Steuben 32140 Dearborn 32450 La Porte 32760 Sull ivan 32150 Decatur 32460 Lawrence 32770 Switzerland 32160 De Kalb 32470 Madison 32780 Tippecanoe 32170 Delaware 32480 Marion 32790 Tipton 32180 Dubois 32490 Marshall 32800 Union 32190 Elkhart 32500 Martin 32810 Vanderburg 32200 Fayette 32510 Miami 32820 Vermillion 32210 Floyd 32520 Monroe 32830 Vigo 32220 Fountain 32530 Montgomery 32840 Wabash 32230 Franklin 32540 Morgan 32850 Warren 32240 Fulton 32550 Newton 32860 Warrick 32250 Gibson 32560 Noble 32870 Washington 32260 Grant 32570 Ohio 32880 Wayne 32270 Greene 32580 Orange 32890 Wells 32280 Hamilton 32590 Owen 32900 White 32290 Hancock 32600 Parke 32910 Whitley 32300 Harrison 32610 Perry 32990 Statewide B-13 IOWA - 42 Code County Code County Code County 42000 Adair Adams 42340 Frankl in 42670 Monroe 42010 42350 Fremont 42680 Montgomery 42020 Allamakee 42360 Greene 42690 Muscatine 42030 Appanoose 42370 Grundy 42700 O'Brien 42040 Audubon 42380 Guthrie 42710 Osceola 42050 Benton 42390 Hamilton 42720 Page 42060 Black Hawk 42400 Hancock 42730 Palo Alto 42070 Boone 42410 Hardin 42740 Plymouth 42080 Bremer 42420 Harrison 42750 Pocahontas 42090 Buchanan 42430 Henry 42760 Polk 42100 Buena Vista 42440 Howard 42770 Pottawattamie 42110 Butler 42450 Humboldt 42780 Poweshiek 42120 Calhoun 42460 Ida 42790 Ringgold 42130 Carroll 42470 Iowa 42800 Sac 42140 Cass 42480 Jackson 42810 Scott 42150 Cedar 42490 Jasper 42820 Shelby 42160 Cerro Gordo 42500 Jefferson 42830 Sioux 42170 Cherokee 42510 Johnson 42840 Story 42180 Chickasaw 42520 Jones 42850 Tama 42190 Clarke 42530 Keokuk 42860 Taylor 42200 Clay 42540 Kossuth 42870 Union 42210 Clayton 42550 Lee 42880 Van Buren 42220 Clinton 42560 Linn 42890 Wapello 42230 Crawford 42570 Louisa 42900 Warren 42240 Dallas 42580 Lucas 42910 Washington 42250 Davis 42590 Lyon 42920 Wayne 42260 Decatur 42600 Madison 42930 Webster 42270 Delaware 42610 Mahaska 42940 Winnebago 42280 Des Moines 42620 Marion 42950 Winneshiek 42290 Dickinson 42630 Marshall 42960 Woodbury 42300 Dubuque 42640 Mills 42970 Worth 42310 Emmet 42650 Mitchell 42980 Wright 42320 Fayette 42660 Monona 42990 Statewide 42330 Floyd B-14 KANSAS - 48 Code County Code County Code County 48000 Allen 48360 Greenwood 48710 Ottawa 48010 Anderson 48371 Hamilton 48720 Pawnee 4802Q Atchison 48380 Harper 48730 Phillips 48030 Barber 48390 Harvey 48740 Pottawatomie 48040 Barton 48332 Haskell 48750 Pratt 48050 Bourbon 48410 Hodgeman 48760 Rawlins 48060 Brown 48420 Jackson 48770 Reno 48070 Butler 48430 Jefferson 48780 Republic 48080 Chase 48440 Jewell 48790 Rice 48090 Chautauqua 48450 Johnson 48800 Riley 48100 Cherokee 48372 Kearny 48810 Rooks 48110 Cheyenne 48470 Kingman 48820 Rush 48120 Clark 48480 Kiowa 48830 Russell 48130 Clay 48490 Labette 48840 Sal ine 48140 Cloud 48501 Lane 48502 Scott 48150 Coffey 48510 Leavenworth 48860 Sedgwick 48160 Comanche 48520 Lincoln 48870 Seward 48170 Cowley 48530 Linn 48880 Shawnee 48180 Crawford 48541 Logan 48890 Sheridan 48190 Decatur 48550 Lyon 48900 Sherman 48200 Dickinson 48560 McPherson 48910 Smith 48210 Doniphan 48570 Marion 48920 Stafford 48220 Douglas 48580 Marshall 48642 Stanton 48230 Edwards 48590 Meade 48940 Stevens 48240 Elk 48600 Miami 48950 Sumner 48250 Ellis 48610 Mitchell 48960 Thomas 48260 Ellsworth 46820 Montgomery 48970 Trego 48270 Finney 48630 Morris 48980 Wabaunsee 48280 Ford 48641 Morton 48542 Wallace 48290 Franklin 48650 Nemaha 48400 Washington 48300 Geary 48660 Neosho 48352 Wichita 48310 Gove 48670 Ness 48460 Wilson 48320 Graham 48680 Norton 48850 Woodson 48331 Grant 48690 Osage 48930 Wyandotte 48340 Gray 48700 Osborne 48990 Statewide 48351 Greeley B-15 KENTUCKY - 61 Code County Code County Code County 61001 Adair 61360 Frankl in 61710 Lyon 61010 Allen 61192 Fulton 61720 McCracken 61021 Anderson 61073 Gallatin 61730 McCreary 61030 Ballard 61390 Garrard 61740 McLean 61040 Barren 61401 Grant 61750 Madison 61051 Bath 61410 Graves 61760 Magoffin 61060 Bell 61420 Grayson 61770 Marion 61071 Boone 61002 Green 61780 Marshall 61080 Bourbon 61440 Greenup 61790 Martin 61090 Boyd 61450 Hancock 61800 Mason 61100 Boyle 61461 Hardin 61462 Meade 61110 Bracken 61470 Harlan 61052 Menifee 61120 Breathitt 61480 Harrison 61830 Mercer 61130 Breckinridge 61490 Hart 61003 Metcalfe 61141 Bullitt 61500 Henderson 61850 Monroe 61151 Butler 61510 Henry 61860 Montgomery 61160 Caldwell 61193 Hickman 61312 Morgan 61170 Calloway 61530 Hopkins 61880 Muhlenberg 61180 Campbell 61540 Jackson 61890 Nelson 61191 Carlisle 61550 Jefferson 61900 Nicholas 61072 Carroll 61560 Jessamine 61910 Ohio 61210 Carter 61570 Johnson 61920 Oldham 61220 Casey 61580 Kenton 61930 Owen 61230 Christian 61590 Knott 61642 Owsley 61240 Clark 61600 Knox 61402 Pendleton 61250 Clay 61610 Larue 61960 Perry 61261 Clinton 61620 Laurel 61970 Pike 61270 Crittenden 61630 Lawrence 61322 Powel 1 61262 Cumberland 61641 Lee 61200 Pulaski 61290 Daviess 61650 Leslie 61342 Robertson 61152 Edmonson 61660 Letcher 61280 Rockcastle 61311 Elliott 61670 Lewi s 61313 Rowan 61321 Estill 61680 Lincoln 61263 Russell 61330 Fayette 61690 Livingston 61300 Scott 61341 Fleming 61701 Logan 61370 Shelby 61350 Floyd B-16 KENTUCKY - 61 (cont.) Code County Code County Code County 61702 Simpson 61810 Trimble 61940 Webster 61142 Spencer 61820 Union 61950 Whitley 61380 Taylor 61840 Warren 61643 Wolfe 61430 , Todd 61022 Washington 61980 Woodford 61520 Trigg 61870 Wayne 61990 Statewide LOUISIANA - 72 Code Parish Code Parish Code Parish 72000 Acadia 72150 De Soto 72300 Lincoln 72010 Allen 72160 East Baton Rouge 72310 Livingston 72020 Ascension 72170 East Carroll 72320 Madison 72030 Assumption 72180 East Feli- ciana 72330 Morehouse 72040 Avoyelles 72190 Evangeline 72340 Natchitoches 72050 Beauregard 72200 Franklin 72350 Orleans 72060 Bienville 72210 Grant 72360 Ouachita 72070 Bossier 72220 Iberia 72370 Plaquemines 72080 Caddo 72230 Iberville 72380 Pointe Coupee 72090 Calcasieu 72240 Jackson 72390 Rapides 72100 Caldwell 72250 Jefferson 72400 Red River 72110 Cameron 72260 Jefferson Davis 72410 Richland 72120 Catahoula 72270 Lafayette 72420 Sabine 72130 Claiborne 72280 Lafourche 72430 St. Bernard 72140 Concordia 72290 La Salle 72440 St. Charles 72450 St. Helena 72520 Tangipahoa 72590 Webster 72460 St. James 72530 Tensas 72600 West Baton Rouge 72470 St. John The Baptist 72540 Terrebonne 72610 West Carroll 72480 St. Landry 72550 Union 72620 West Feliciana 72490 St. Martin 72560 Vermil ion 72630 Winn 72500 St. Mary 72570 Vernon 72990 Statewide 72510 St. Tammany 72580 Washington B-17 MAINE -11 Code County Code County Code County 11000 Androscoggin 11060 Knox 11120 Somerset 11010 Aroostook 11070 Lincoln 11130 Waldo 11020 Cumberland 11080 Oxford 11140 Washington 11030 Franklin 11090 Penobscot 11150 York 11040 Hancock 11100 Piscataquis 11990 Statewide 11050 Kennebec 11110 Sagadahoc MARYLAND - 52 Code County Code County Code County 52000 Allegeny 52090 Dorchester 52170 Queen Annes 52010 Anne Arundel 52100 Frederick 52180 St. Marys 52020 Baltimore 52110 Garrett 52190 Somerset 52040 Calvert 52120 Harford 52200 Talbot 52050 Caroline 52130 Howard 52210 Washington 52060 Carroll 52140 Kent 52220 Wicomico 52070 Cecil 52150 Montgomery 52230 Worcester 52080 Charles 52160 Prince Georges 52990 Statewide Independent City 52030 Baltimore Ci ty MASSACHUSETTS - 14 Code County Code County Code County 14000 Barnstable 14060 Franklin 14130 Norfolk 14010 Berkshire 14070 Hampden 14150 Plymouth 14020 Bristol 14080 Hampshire 14160 Suffolk 14030 Dukes 14090 Middlesex 14170 Worcester 14040 Essex 14120 Nantucket 14990 Statewide B-18 MICHIGAN - 35 Code County Code County Code County 35000 Alcona 35250 Gladwin 35500 Manistee 35010 Alger 35260 Gogebic 35510 Marquette 35020 Allegan 35270 Grand Traverse 35520 Mason 35030 Alpena 35280 Gratiot 35530 Mecosta 35040 Antrim 35290 Hillsdale 35540 Menominee 35050 Arenac 35300 Houghton 35550 Midland 35060 Baraga 35310 Huron 35560 Missaukee 35070 Barry 35320 Ingham 35570 Monroe 35080 Bay " 35330 Ionia 35580 Montcalm 35090 Benzie 35340 Iosco 35590 Montmorency 35100 Berrien 35350 Iron 35600 Muskegon 35110 Branch 35360 Isabella 35610 Newaygo 35120 Calhoun 35370 Jackson 35620 Oakland 35130 Cass 35380 Kalamazoo 35630 Oceana 35140 Charlevoix 35390 Kalkaska 35640 Ogemaw 35150 Cheboygan 35400 Kent 35650 Ontonagon 35160 Chippewa 35410 Keweenaw 35660 Osceola 35170 Clare 35420 Lake 35670 Oscoda 35180 Clinton 35430 Lapeer 35680 Otsego 35190 Crawford 35440 Leelanau 35690 Ottawa 35200 Delta 35450 Lenawee 35700 Presque Isle 35210 Dickinson 35460 Livingston 35710 Roscommon 35220 Eaton 35470 Luce 35720 Saginaw 35230 Emmet 35480 Mackinac 35730 Saint Clair 35240 Genesee 35490 Macomb 35740 Saint Joseph 35750 Sanilac 35780 Tuscola 35810 Wayne 35760 Schoolcraft 35790 Van Buren 35830 Wexford 35770 Shiawassee 35800 Washtenaw 35990 Statewide MINNESOTA - 41 Code County Code County Code County 41000 Aitkin 41300 Itasca 41590 Polk 41010 Anoka 41310 Jackson 41600 Pope 41020 Becker 41320 Kanabec 41610 Ramsey 41030 Beltrami 41330 Kandiyohi 41620 Red Lake 41040 Benton 41340 Kittson 41630 Redwood B-19 MTNNFSOTA - ■ 61 (cont.) Code County Code County Code County 41050 Big Stone 41350 Koochiching 41640 Renville 41060 Blue Earth 41360 Lac qui Parle 41650 Rice 41070 Brown 41370 Lake 41660 Rock 41080 Carlton 41380 Lake of the Woods 41670 Roseau 41090 Carver 41680 St. Louis 41100 Cass 41390 Le Sueur 41690 Scott 41110 Chippewa 41400 Lincoln 41700 Sherburne 41120 Chisago 41410 Lyon 41710 Sibley 41130 Clay ' 41420 McLeod 41720 Stearns 41140 Clearwater 41430 Mahnomen 41730 Steele 41150 Cook 41440 Marshall 41740 Stevens 41160 Cottonwood 41450 Martin 41750 Swift 41170 Crow Wing 41460 Meeker 41760 Todd 41180 Dakota 41470 Mil 1 e Lacs 41770 Traverse 41190 Dodge 41480 Morrison 41780 Wabasha 41200 Douglas 41490 Mower 41790 Wadena 41210 Faribault 41500 Murray 41800 Waseca 41220 Fillmore 41510 Nicollet 41810 Washington 41230 Freeborn 41520 Nobles 41820 Watonwan 41240 Goodhue 41530 Norman 41830 Wilkin 41250 Grant 41540 Olmsted 41840 Winona 41260 Hennepin 41550 Otter Tail 41850 Wright 41270 Houston 41560 Pennington 41860 Yellow Medicine 41280 Hubbard 41570 Pine 41990 Statewide 41290 Isanti 41580 Pipestone MISSISSIPPI - 65 Code County Code County Code County 65000 Adams 65280 Itawamba 65560 Pike 65010 Alcorn 65290 Jackson 65570 Pontoboc 65020 Ami te 65300 Jasper 65580 Prentiss 65030 Attala 65310 Jefferson 65590 Quitman 65040 Benton 65320 Jefferson Davis 65600 Rankin B-20 MISSISSIPPI - 65 (cont.) Code County Code County Code County 65050 Bolivar 65330 Jones 65610 Scott 65060 Calhoun 65340 Kemper 65620 Sharkey 65070 Carroll 65350 Lafayette 65630 Simpson 65080 , Chickasaw 65360 Lamar 65640 Smith 65090 Choctaw 65370 Lauderdale 65650 Stone 65100 Claiborne 65380 Lawrence 65660 Sunflower 65110 Clarke 65390 Leake 65670 Tallahatchie 65120 Clay 65400 Lee 65680 Tate 65130 Coahoma 65410 Leflore 65690 Tippah 65140 Copiah 65420 Lincoln 65700 Tishomingo 65150 Covington 65430 Lowndes 65710 Tunica 65160 De Soto 65440 Madison 65720 Union 65170 Forrest 65450 Marion 65730 Walthall 65180 Franklin 65460 Marshall 65740 Warren 65190 George 65470 Monroe 65750 Washington 65200 Greene 65480 Montgomery 65760 Wayne 65210 Grenada 65490 Neshoba 65770 Webster 65220 Hancock 65500 Newton 65780 Wil kinson 65230 Harrison 65510 Noxubee 65790 Winston 65240 Hinds 65520 Oktibbeha 65800 Yalobusha 65250 Holmes 65530 Panola 65810 Yazoo 65260 Humphreys 65540 Pearl River 65990 Statewide 65270 Issaquena 65550 MISSOURI Perry - 43 Code County Code County Code County 43000 Adair 43350 Franklin 43700 Morgan 43010 Andrew 43360 Gasconade 43710 New Madrid 43021 Atchison 43371 Gentry 43720 Newton 43030 Audrain 43380 Greene 43730 Nodaway 43040 Barry 43390 Grundy 43741 Oregon B-21 MISSOURI - 43 (cont.) Code County Code County Code County 43050 Barton 43401 Harrison 43750 Osage 43060 Bates 43410 Henry 43332 Ozark 43070 Benton 43142 Hickory 43770 Pemiscot 43080 Bollinger 43022 Holt 43780 Perry 43090 Boone 43440 Howard 43790 Pettis 43100 Buchanan 43450 Howell 43800 Phelps 43110 Butler 43461 Iron 43810 Pike 43120 Caldwell 43470 Jackson 43820 Platte 43130 Callaway 43480 Jasper 43282 Polk 43141 Camden 43490 Jefferson 43840 Pulaski 43150 Cape Girardeau 43500 Johnson 43850 Putnam 43160 Carroll 43511 Knox 43860 Ralls 43171 Carter 43520 Laclede 43870 Randolph 43180 Cass 43530 Lafayette 43880 Ray 43191 Cedar 43540 Lawrence 43172 Reynolds 43200 Chariton 43222 Lewis 43900 Ripley 43211 Christian 43560 Lincoln 43910 St. Charles 43221 Clark 43570 Linn 43192 St. Clair 43230 Clay 43580 Livingston 43930 St. Francois 43240 Clinton 43590 McDonald 43940 St. Louis County 43250 Cole 43600 Macon 43960 Ste. Genevieve 43260 Cooper 43462 Madison 43970 Saline 43270 Crawford 43620 Maries 43980 Schuyler 43281 Dade 43630 Marion 43512 Scotland 43290 Dallas 43402 Mercer 43420 Scott 43300 Daviess 43650 Miller 43742 Shannon 43310 De Kalb 43660 Mississippi 43430 Shelby 43320 Dent 43670 Moniteau 43550 Stoddard 43331 Douglas 43680 Monroe 43212 Stone 43340 Dunklin 43690 Montgomery 43610 Sullivan 43213 Taney 43890 Washington 43372 Worth 43641 Texas 43173 Wayne 43642 Wright 43760 Vernon 43920 Webster 43990 Statewide 43830 Warren Indepe ndent City 43950 St. Louis City B-22 MONTANA - 81 Code County Code County Code County 81000 Beaverhead 81100 Dawson 81200 Hill 81010 Big Horn 81110 Deer Lodge 81210 Jefferson 81020 Blaine 81120 Fallon 81220 Judith Basin 81030 Broadwater 81130 Fergus 81230 Lake 81040 Carbon 81140 Flathead 81240 Lewis and Clark 81050 Carter 81150 Gallatin 81250 Liberty 81060 Cascade 81160 Garfield 81260 Lincoln 81070 Chouteau 81170 Glacier 81270 McCone 81080 Custer 81180 Golden Valley 81280 Madison 81090 Daniels 81190 Granite 81290 Meagher 81300 Mineral 81400 Ravalli 81500 Toole 81310 Missoula 81410 Richland 81510 Treasure 81320 Musselshell 81420 Roosevelt 81520 Valley 81330 Park 81430 Rosebud 81530 Wheatland 81340 Petroleum 81440 Sanders 81540 Wibaux 81350 Phillips 81450 Sheridan 81550 Yellowstone 81360 Pondera 81460 Silver Bow 81560 Yellowstone 81370 Powder River 81470 Stillwater National Park 81380 Powell 81480 Sweet Grass 81990 Statewide 81390 Prairie 81490 Teton NEBRASKA - 47 Code County Code County Code County 47000 Adams 47200 Custer 47400 Hamilton 47010 Antelope 47210 Dakota 47410 Harlan 47020 Arthur 47220 Dawes 47420 Hayes 47030 Banner 47230 Dawson 47430 Hitchcock 47040 Blaine 47240 Deuel 47440 Holt 47050 Boone 47250 Dixon 47450 Hooker 47060 Box Butte 47260 Dodge 47460 Howard 47070 Boyd 47270 Douglas 47470 Jefferson 47080 Brown 47280 Dundy 47480 Johnson 47090 Buffalo 47290 Fillmore 47490 Kearney 47100 Burt 47300 Frankl in 47500 Keith 47110 Butler 47310 Frontier 47510 Keya Paha 47120 Cass 47320 Furnas 47520 Kimball 47130 Cedar 47330 Gage 47530 Knox 47140 Chase 47340 Garden 47540 Lancaster B-23 NEBRASKA - 47 (cont.) Code County Code County Code County 47150 Cherry 47350 Garfield 47550 Lincoln 47160 Cheyenne 47360 Gosper 47560 Logan 47170 Clay 47370 Grant 47570 Loup 47180 Colfax 47380 Greeley 47580 McPherson 47190 Cuming 47390 Hall 47590 Madison 47600 Merrick 47720 Red Willow 47830 Stanton 47610 Morrill 47730 Richardson 47840 Thayer 47620 Nance 47740 Rock 47850 Thomas 47630 Nemaha 47750 Sal ine 47860 Thurston 47640 Nuckolls 47760 Sarpy 47870 Valley 47650 Otoe 47770 Saunders 47880 Washington 47660 Pawnee 47780 Scotts Bluff 47890 Wayne 47670 Perkins 47790 Seward 47900 Webster 47680 Phelps 47800 Sheridan 47910 Wheeler 47690 Pierce 47810 Sherman 47920 York 47700 Platte 47820 Sioux 47990 Statewide 47710 Polk NEVADA -88 Code County Code County Code County 88000 Churchill 88050 Eureka 88110 Nye 88010 Clark 88060 Humboldt 88130 Pershing 88020 Douglas 88070 Lander 88140 Storey 88030 Elko 88080 Lincoln 88150 Washoe 88040 Esmeralda 88090 Lyon 88160 White Pine 88100 Mineral 88990 Statewide Indeper dent City 88120 Carson City B-24 NEW HAMPSHIRE - 12 Code County Code County Code County 12000 Belknap 12040 Grafton 12080 Strafford 12010 Carroll 12050 Hillsboro 12090 Sullivan 12020 Cheshire 12060 Merrimack 12990 Statewide 12030 Coos 12070 Rockingham NEW JERSEY - 22 Code County Code County Code County 22000 Atlantic 22230 Hudson 22320 Passaic 22100 Bergen 22250 Hunterdon 22340 Salem 22150 Burlington 22260 Mercer 22350 Somerset 22160 Camden 22270 Middlesex 22360 Sussex 22180 Cape May 22290 Monmouth 22370 Union 22190 Cumberland 22300 Morris 22390 Warren 22200 Essex 22310 Ocean 22990 Statewide 22220 Gloucester NEW MEXICO - 85 Code County Code County Code County 85000 Bernalillo 85110 Hidalgo 85210 Sandoval 85010 Catron 85120 Lea 85220 San Juan 85020 Chaves 85130 Lincoln 85230 San Miguel 85030 Colfax 85310 Los Alamos 85240 Sante Fe 85040 Curry 85140 Luna 85250 Sierra 85050 De Baca 85150 Mc Kin ley 85260 Socorro 85060 Dona Ana 85160 Mora 85270 Taos 85070 Eddy 85160 Otero 85280 Torrance 85080 Grant 85180 Quay 85290 Union 85090 Guadalupe 85190 Rio Arriba 85300 Valencia 85100 Harding 85200 Roosevelt 85990 Statewide B-25 NEW YORK - 21 Code County Code County Code County 21000 Albany 21320 Herkimer 21123 Richmond 21010 Allegany 21330 Jefferson 21620 Rockland 21122 Bronx 21124 Kings 21630 St. Lawrence 21030 Broome 21340 Lewis 21640 Saratoga 21040 Cattaraugus 21350 Livingston 21650 Schnectady 21050 Cayuga 21360 Madison 21660 Schoharie 21060 Chautauqua 21370 Monroe 21670 Schuyler 21070 Chemung 21380 Montgomery 21680 Seneca 21080 Chenango 21400 Nassau 21690 Steuben 21090 Clinton 21120 New York (City-wide) 21700 Suffolk 21200 Columbia 21710 Sullivan 21210 Cortland 21121 New York 21720 Tioga 21220 Delaware (Manhattan) 21730 Tompkins 21230 Dutchess 21500 Niagara 21740 Ulster 21240 Erie 21510 Oneida 21750 Warren 21260 Essex 21520 Onondaga 21760 Washington 21270 Franklin 21530 Ontario 21770 Wayne 21280 Fulton 21540 Orange 21800 Westchester 21290 Genesee 21550 Orleans 21900 Wyoming 21300 Greene 21560 Oswego 21910 Yates 21310 Hamilton 21570 Otsego 21990 Statewide 21580 Putnam 21125 Queens 21600 Rensselaer NORTH CAROLINA - 56 Code County Code County Code County 56000 Alamance 56050 Avery 56100 Buncombe 56010 Alexander 56060 Beaufort 56110 Burke 56020 Alleghany 56070 Bertie 56120 Cabarrus 56030 Anson 56080 Bladen 56130 Caldwell 56040 Ashe 56090 Brunswick 56141 Camden 56150 Carteret 56440 Henderson 56730 Pitt 56160 Caswell 56450 Hertford 56740 Polk 56170 Catawba 56460 Hoke 56750 Randolph 56180 Chatham 56470 Hyde 56760 Richmond 56190 Cherokee 56480 Iredell 56770 Robeson B-26 NORTH CAROLINA - 56 (cont.) Code County Code County Code County 56200 Chowan 56490 Jackson 56780 Rockingham 56210 Clay 56500 Johnston 56790 Rowan 56220 Cleveland 56510 Jones 56800 Rutherford 56230 Columbus 56520 Lee 56810 Sampson 56240 Craven 56530 Lenoir 56820 Scotland 56250 Cumberland 56540 Lincoln 56830 Stanly 56142 Currituck 56550 McDowell 56840 Stokes 56270 Dare 56560 Macon 56850 Surry 56280 Davidson 56570 Madison 56860 Swain 56290 Davie 56580 Martin 56870 Transylvania 56300 Duplin 56590 Mecklenburg 56880 Tyrrel 1 56310 Durham 56600 Mitchell 56890 Union 56320 Edgecombe 56610 Montgomery 56900 Vance 56330 Forsyth 56620 Moore 56910 Wake 56340 Franklin 56630 Nash 56920 Warren 56350 Gaston 56640 New Hanover 56930 Washington 56360 Gates 56650 Northampton 56940 Watauga 56370 Graham 56660 Onslow 56950 Wayne 56380 Granville 56670 Orange 56960 Wilkes 56390 Greene 56680 Pamlico 56970 Wi 1 son 56400 Guilford 56690 Pasquotank 56980 Yadkin 56410 Halifax 56700 Pender 56260 Yancey 56420 Harnett 56710 Perquimans 56990 Statewide 56430 Haywood 56720 Person NORTH DAKOTA - 45 Code County Code County Code County 45000 Adams 45180 Grant 45360 Ransom 45010 Barnes 45190 Griggs 45370 Renville 45020 Benson 45200 Hettinger 45380 Richland 45030 Billings 45210 Kidder 45390 Rolette 45040 Bottineau 45220 La Moure 45400 Sargent B-27 NORTH DAKOTA - 45 (cont.) Code County Code County Code County 45050 Bowman 45230 Logan 45410 Sheridan 45060 Burke 45240 McHenry 45420 Sioux 45070 Burleigh 45250 Mcintosh 45430 Slope 45080 Cass 45260 McKenzie 45440 Stark 45090 Cavalier 45270 McLean 45450 Steele 45100 Dickey 45280 Mercer 45460 Stutsman 45110 Divide 45290 Morton 45470 Towner 45120 Dunn 45300 Mountrail 45480 Traill 45130 Eddy 45310 Nelson 45490 Walsh 45140 Emmons 45320 01 iver 45500 Ward 45150 Foster 45330 Pembina 45510 Wells 45160 Golden Valley 45340 Pierce 45520 Williams 45170 Grand Fords 45350 Ramsey 45990 Statewide OHIO - 31 Code County Code County Code County 31000 Adams 31100 Champaign 31210 Delaware 31010 Allen 31110 Clark 31220 Erie 31020 Ashland 31120 Clermont 31230 Fairfield 31030 Ashtabula 31130 Clinton 31240 Fayette 31040 Athens 31140 Columbiana 31250 Franklin 31050 Auglaize 31150 Coshocton 31260 Fulton 31060 Belmont 31160 Crawford 31270 Gallia 31070 Brown 31170 Cuyahoga 31280 Geauga 31080 Butler 31190 Darke 31290 Greene 31090 Carroll 31200 Defiance 31300 Guernsey 31310 Hamilton 31520 Marion 31720 Ross 31330 Hancock 31530 Medina 31730 Sandusky 31340 Harkin 31540 Meigs 31740 Scioto 31350 Harrison 31550 Mercer 31750 Seneca 31360 Henry 31560 Miami 31760 Shelby B-28 OHIO - 31 (cont.) Code County Code County Code County 31370 Highland 31570 Monroe 31770 Stark 31380 Hocking 31580 Mongtomery 31780 Summit 31390 Holmes 31590 Morgan 31790 Trumbull 31400 Huron 31600 Morrow 31800 Tuscarawas 31410 ' Jackson 31610 Muskingum 31810 Union 31420 Jefferson 31620 Noble 31820 Van Wert 31430 Knox 31630 Ottawa 31830 Vinton 31440 Lake 31640 Paulding 31840 Warren 31450 Lawrence 31650 Perry 31850 Washington 31460 Licking 31660 Pickaway 31860 Wayne 31470 Logan 31670 Pike 31870 Williams 31480 Lorain 31680 Portage 31880 Wood 31490 Lucas 31690 Preble 31890 Wyandot 31500 Madison 31700 Putnam 31990 Statewide 31510 Mahoning 31710 OKLAHOMA Richland - 73 Code County Code County Code County 73000 Adair 73100 Cherokee 73200 Delaware 73010 Alfalfa 73110 Choctaw 73210 Dewey 73020 Atoka 73120 Cimarron 73220 Ellis 73030 Beaver 73130 Cleveland 73230 Garfield 73040 Beckham 73140 Coal 73240 Garvin 73050 Blaine 73150 Comanche 73250 Grady 73060 Bryan 73160 Cotton 73260 Grant 73070 Caddo 73170 Craig 73270 Greer 73080 Canadian 73180 Creek 73280 Harmon 73090 Carter 73190 Custer 73290 Harper 73300 Haskell 73460 Major 73620 Pottawatomie 73310 Hughes 73470 Marshall 73630 Pushmataha 73320 Jackson 73480 Mayes 73640 Roger Mills 73330 Jefferson 73490 Murray 73650 Rogers 73340 Johnston 73500 Muskogee 73660 Seminole B-29 OKLAHOMA - 73 (cont.) Code County Code County Code County 73350 Kay 73510 Noble 73670 Sequoyah 73360 Kingfisher 73520 Nowata 73680 Stephens 73370 Ki owa 73530 Okfuskee 73690 Texas 73380 Latimer 73540 Oklahoma 73700 Tillman 73390 Le Flore 73550 Okmulgee 73710 Tulsa 73400 Lincoln 73560 Osage 73720 Wagoner 73410 Logan 73570 Ottawa 73730 Washington 73420 Love 73580 Pawnee 73740 Washita 73430 McClain 73590 Payne 73750 Woods 73440 McCurtain 73600 Pittsburg 73760 Woodward 73450 Mcintosh 73610 Pontotoc 73990 Statewide OREGON - 92 Code 92000 92010 92020 92030 92040 92050 92060 92070 92080 92090 92100 92110 92120 County Baker Benton Clackamas Clatsop Columbia Coos Crook Curry Deschutes Douglas Gilliam Grant Harney Code 92130 92140 92150 92160 92170 92180 92190 92200 92210 92220 92230 92240 92250 County Hood River Jackson Jefferson Josephine Klamath Lake Lane Lincoln Linn Malheur Marion Morrow Multnomah Code 92260 92270 92280 92290 92300 92310 92320 92330 92340 92350 92990 County Polk Sherman Tillamook Umatilla Union Wallowa Wasco Washington Wheeler Yamhill Statewide B-30 PENNSYLVANIA - 23 Code County Code County Code County 23000 Adams 23310 Elk 23580 Montour 23010 Allegheny 23320 Erie 23590 Northampton 23070 Armstrong 23330 Fayette 23600 Northumberland 23080 Beaver 23340 Forest 23610 Perry 23100 ' Bedford 23350 Franklin 23620 Philadelphia 23110 Berks 23360 Fulton 23630 Pike 23120 Blair 23370 Greene 23640 Potter 23130 Bradford 23380 Huntingdon 23650 Schuylkill 23140 Bucks 23390 Indiana 23670 Snyder 23150 Butler 23400 Jefferson 23680 Somerset 23160 Cambria 23410 Juniata 23690 Sullivan 23180 Cameron 23420 Lackawanna 23700 Susquehanna 23190 Carbon 23440 Lancaster 23710 Tioga 23200 Centre 23450 Lawrence 23720 Union 23210 Chester 23460 Lebanon 23730 Venango 23220 Clarion 23470 Lehigh 23470 Warren 23230 Clearfield 23480 Luzerne 23750 Washington 23240 Clinton 23510 Lycoming 23760 Wayne 23250 Columbia 23520 McKean 23770 Westmoreland 23260 Crawford 23530 Mercer 23790 Wyoming 23270 Cumberland 23540 Mifflin 23800 York 23280 Dauphin 23550 Monroe 23990 Statewide 23290 Delaware 23560 Montgomery PUERTO RICO - 27 Alphabetic Municipality Listing Code Municipality Code Municipality Code Municipality 27440 Adjuntas 27230 Anasco 27010 Bayamon 27210 Aguada 27120 Arecibo 27340 Caba Rojo 27220 Aguadilla 27540 Arroyo 27560 Caguas 27520 Aguas Buenas 27131 Barceloneta 27240 Camuy 27530 Aibonito 27550 Barranquitas 27712 27020 Canovanas Carolina B-31 PUERTO RICO - 27 Alphabetic Municipality Listing (cont. ) Code Code Municipality Code Municipal ity Municipality 27030 Catano 27460 Jayuya 27310 Quebradillas 27570 Gayey 27470 Juana Diaz 27320 Rincon 27640 Ceiba 27690 Juncos 27740 Rio Grande 27140 Ciales 27380 Lajas 27080 Rio Piedras 27580 Cidra 27270 Lares 27410 Sebana Grande 27450 Coamo 27280 Las Marias 27630 Salinas 27590 Comerio 27700 Las Piedras 27420 San German 27040 Corozal 27711 Loiza 27080 San Juan 27650 Culebra 27720 Luquillo 27750 San Lorenzo 27150 Dorado 27160 Manati 27330 San Sebastian 27660 Fajardo 27290 Maricao 27500 Santa Isabel 27132 Florida 27610 Maunabo 27090 Toa Alta 27350 Guanica 27390 Mayaguez 27100 Toa Baja 27600 Guayama 27300 Moca 27110 Trujillo Alto 27360 Guayanilla 27170 Morovis 27180 Utuado 27050 Guaynabo 27730 Naguabo 27190 Vega Alta 27670 Gurabo 27060 Naranjito 27200 Vega Baja 27250 Hatillo 27480 Orocovis 27760 Vieques 27370 Hormigueros 27620 Patillas 27510 Vi 11 alba 27680 Humacao 27400 Peneulas 27700 Yabucoa 27260 Isabela 27490 RHODE Ponce ISLAND - 15 27430 27990 Yauco Puerto Rico, NFD Code County Code County Code County 15000 Bristol 15020 Newport 15050 Washington 15010 Kent 15030 SOUTH Providence CAROLINA - 57 15990 Statewide Code County Code County Code County 57000 Abbeville 57050 Barnwell 57100 Cherokee 57010 Ai ken 57060 Beaufort 57110 Chester 57020 Allendale 57070 Berkeley 57120 Chesterfield 57020 Anderson 57080 Calhoun 57130 Clarendon 57040 Bamberg 57090 Charleston 57140 Colleton B-32 SOUTH CAROLINA - 57 (Cont.) Code County Code County Code County 57150 Darlington 57260 Jasper 57370 Orangeburg 57160 Dillon 57270 Kershaw 57380 Pickens 57170 Dorchester 57280 Lancaster 57390 Richland 57180 Edgefield 57290 Laurens 57400 Saluda 57190 Fairfield 57300 Lee 57410 Spartanburg 57200 Florence 57310 Lexington 57420 Sumter 57210 Georgetown 57320 McCormick 57430 Union 57220 Greenville 57330 Marion 57440 Williamsburg 57230 Greenwood 57340 Marl boro 57450 York 57240 Hampton 57350 Newberry 57990 Statewide 57250 Horry 57360 Oconee SOUTH DAKOTA - 46 Code County Code County Code County 46010 Aurora 46210 Douglas 46410 Lincoln 46020 Beadle 46220 Edmunds 46420 Lyman 46030 Bennett 46230 Fall River 46430 McCook 46040 Bon Homme 46240 Faulk 46440 McPherson 46050 Brookings 46250 Grant 46450 Marshall 46060 Brown 46260 Gregory 46460 Meade 46070 Brule 46270 Haakon 46470 Mellette 46080 Buffalo 46280 Hamlin 46480 Miner 46090 Butte 46290 Hand 46490 Minnehaha 46100 Campbell 46300 Hanson 46500 Moody 46110 Charles Mix 46310 Harding 46510 Pennington 46120 Clark 46320 Hughes 46520 Perkins 46130 Clay 46330 Hutchinson 46530 Potter 46140 Codington 46340 Hyde 46540 Roberts 46150 Corson 46350 Jackson 46550 Sanborn 46160 Custer 46360 Jerauld 46560 Shannon 46170 Davison 46370 Jones 46570 Spink 46180 Day 46380 Kingsburg 46580 Stanley 46190 Deuel 46390 Lake 46590 Sully 46200 Dewey 46400 Lawrence 46600 Todd 46610 Tripp 46640 Walworth 46680 Ziebach 46620 Turner 46650 Washabaugh 46990 Statewide 46630 Union 46670 Yankton B-33 TENNESSEE - 63 Code County Code County Code County 63000 Anderson 63250 Franklin 63500 Lewis 63010 Bedford 63260 Gibson 63510 Lincoln 63020 Benton 63270 Gi 1 es 63520 Loudon 63030 Bledsoe 63280 Grainger 63530 McMinn 63040 Blount 63290 Greene 63540 McNai ry 63050 Bradley 63300 Grundy 63550 Macon 63060 Campbell 63310 Hamblen 63560 Madison 63070 Cannon 63320 Hamilton 63570 Marion 63080 Carroll 63330 Hancock 63580 Marshall 63090 Carter 63340 Hardeman 63590 Maury 63100 Cheatham 63350 Hardin 63600 Meigs 63110 Chester 63360 Hawkins 63610 Monroe 63120 Claiborne 63370 Haywood 63620 Montgomery 63130 Clay 63380 Henderson 63630 Moore 63140 Cocke 63390 Henry 63640 Morgan 63150 Coffee 63400 Hickman 63650 Obion 63160 Crockett 63410 Houston 63660 Overton 63170 Cumberland 63420 Humphreys 63670 Perry 63180 Davidson 63430 Jackson 63680 Pickett 63190 Decatur 63440 Jefferson 63690 Polk 63200 De Kalb 63450 Johnson 63700 Putnam 63210 Dickson 63460 Knox 63710 Rhea 63220 Dyer 63470 Lake 63720 Roane 63230 Fayette 63480 Lauderdale 63730 Robertson 63240 Fentress 63490 Lawrence 63740 Rutherford 63750 Scott 63820 Sumner 63890 Washington 63760 Sequatchie 63830 Tipton 63900 Wayne 63770 Sevier 63840 Trousdale 63910 Weakley 63780 Shelby 63850 Unicoi 63920 White 63790 Smith 63860 Union 63930 Williamson 63800 Stewart 63870 Van Buren 63940 Wilson 63810 Sullivan 63880 Warren TEXAS - 74 63990 Statewide Code County Code County Code County 74001 Anderson 74192 Burleson 74351 Cottle 74011 Andrews 74142 Burnet 74361 Crane 74020 Angelina 74102 Caldwell 74202 Crockett 74031 Aransas 74033 Calhoun 74352 Crosby 74041 Archer 74231 Callahan 74371 Culberson B-34 Code County TEXAS - 74 (cont.) Code County Code County 74051 Armstrong 74240 Cameron 74381 Dallam 74061 Atascosa 74251 Camp 74390 Dallas 74071 Austin 74053 Carson 74012 Dawson 74081 Bail ey 74260 Cass 74272 Deaf Smith 74091 Bandera 74271 Castro 74401 Delta 74101 Bastrop 74280 Chambers 74410 Denton 74111 Baylor 74002 Cherokee 74421 De Witt 74032 Bee 74291 Childress 74353 Dickens 74121 Bell 74042 Clay 74431 Dimmit 74130 Bexar 74082 Cockran 74054 Donley 74141 Blanco 74301 Coke 74441 Duval 74151 Borden 74222 Col eman 74451 Eastland 74161 Bosque 74311 Coll in 74362 Ector 74170 Bowie 74292 Coll ingsworth 74461 Edwards 74180 Brazoria 74072 Colorado 74470 Ellis 74191 Brazos 74321 Comal 74480 El Paso 74201 Brewster 74223 Comanche 74491 Erath 74052 Briscoe 74331 Concho 74501 Falls 74211 Brooks 74341 Cooke 74510 Fannin 74221 Brown 74122 Coryel 1 74193 Fayette 74521 Fisher 74492 Hood 74582 Liberty 74354 Floyd 74402 Hopkins 74502 Limestone 74112 Foard 74661 Houston 74386 Lipscomb 74530 Fort Bend 74152 Howard 74761 Live Oak 74252 Franklin 74372 Hudspeth 74145 Llano 74541 Freestone 74670 Hunt 74363 Loving 74062 Frio 74385 Hutchinson 74770 Lubbock 74013 Gaines 74302 Irion 74085 Lynn 74550 Galveston 74681 Jack 74332 McCulloch 74335 Garza 74035 Jackson 74780 McLennan 74143 Gillespie 74691 Jasper 74762 McMullen 74561 Glasscock 74203 Jeff Davis 74662 Madison 74034 Goliad 74700 Jefferson 74622 Marion 74422 Gonzales 74711 Jim Hogg 74014 Martin 74055 Gray 74442 Jim Wells 74146 Mason 74342 Grayson 74721 Johnson 74790 Matagorda 74571 Gregg 74232 Jones 74433 Maverick 74581 Grimes 74423 Karnes 74063 Medina 74322 Guadalupe 74730 Kaufman 74742 Menard 74356 Hale 74092 Kendall 74562 Midland B-35 TEXAS - 74 (cont.) Code County Code County Code County 74056 Hall 74212 Kenedy 74195 Mi 1 am 74590 Hamilton 74632 Kent 74147 Mills 74382 Hansford 74093 Kerr 74153 Mitchell 74113 Hardeman 74741 Kimble 74800 Montague 74600 Hardin 74357 King 74583 Montgomery 74610 Harris 74462 Kinney 74387 Moore 74621 Harrison 74443 Kleberg 74253 Morris 74383 Hartley 74114 Knox 74358 Motley 74631 Haskell 74750 Lamar 74811 Nacogdoches 74103 Hays 74084 Lamb 74820 Navarro 74384 Hemphil 1 74144 Lampasas 74692 Newton 74641 Henderson 74432 La Salle 74522 Nolan 74650 Hidalgo 74424 Lavaca 74830 Nueces 74162 Hill 74194 Lee 74388 Ochiltree 74083 Hockley 74542 Leon 74273 Oldham 74840 Orange 74148 San Saba 74208 Upton 74682 Palo Pinto 74743 Schleicher 74464 Uvalde 74623 Panola 74523 Scurry 74465 Val Verde 74722 Parker 74233 Shackelford 74642 Van Zandt 74274 Parmer 74814 Shelby 74037 Victoria 74204 Pecos 74380 Sherman— 74663 Walker 74851 Polk 74890 Srni th 74073 Waller 74861 Potter 74493 Somervell 74365 Ward 74205 Presidio 74900 Starr 74197 Washington 74871 Rains 74452 Stephens 74950 Webb 74862 Randal 1 74303 Sterling 74074 Wharton 74206 Reagan 74633 Stonewall 74293 Wheeler 74463 Real 74744 Sutton 74960 Wichita 74254 Red River 74275 Swisher 74115 Wilbarger 74364 Reeves 74910 Tarrant 74213 Wi 1 1 acy 74036 Refugio 74234 Taylor 74970 Williamson 74389 Roberts 74207 Terrel 1 74323 Wilson 74196 Robertson 74086 Terry 74366 Winkler 74312 Rockwall 74921 Throckmorton 74723 Wise 74333 Runnel s 74255 Titus 74872 Wood 74572 Rusk 74930 Tom Green 74980 Yoakum 74812 Sabine 74940 Travis 74922 Young 74813 San Augustine 74853 Trinity 74712 Zapata 74852 San Jacinto 74854 Tyler 74434 Zavala 74880 San Patricio 74573 Upshur 74990 Statewide 1/ The tenth county in a group of counties formerly assigned code 7438, B-36 UTAH - 87 Code County Code County Code County 87000 Beaver 87100 Iron 87200 Sevier 87010 Box Elder 87110 Juab 87210 Summit 87020 Cache 87120 Kane 87220 Tooele 87030 . Carbon 87130 Millard 87230 Uintah 87040 Daggett 87140 Morgan 87240 Utah 87050 David 87150 Piute 87250 Wasatch 87060 Duchesne 87160 Rich 87260 Washington 87070 Emery 87170 Salt Lake 87270 Wayne 87080 Garfield 87180 San Juan 87280 Weber 87090 Grand 87190 Sanpete VERMONT - 13 87990 Statewide Code County Code County Code County 13000 Addison 13050 Franklin 13100 Rutland 13010 Bennington 13060 Grand Isle 13110 Washington 13020 Caledonia 13070 Lamoille 13120 Windham 13030 Chittenden 13080 Orange 13130 Windsor 13040 Essex 13090 Orleans VIRGINIA - 53 13990 Statewide Code County Code County Code County 53000 Accomack 53240 Cumberland 53500 King Will iam 53010 Albemarle 53250 Dickenson 53510 Lancaster 53020 Alleghany 53260 Dinwiddie 53520 Lee 53030 Amel ia 53280 Essex 53530 Loudoun 53040 Amherst 53290 Fairfax 53540 Louisa 53050 Appomattox 53300 Fauquier 53550 Lunenburg 53060 Arlington 53310 Floyd 53560 Madison 53070 Augusta 53320 Fluvanna 53570 Mathews 53080 Bath 53330 Franklin 53580 Mecklenburg 53090 Bedford 53340 Frederick 53590 Middlesex 53100 Bland 53350 Giles 53600 Montgomery 53110 Botetourt 53360 Gloucester 53620 Nelson 53120 Brunswick 53370 Goochland 53182 New Kent 53130 Buchanan 53380 Grayson 53650 Northampton 53140 Buckingham 53390 Greene B-37 VIRGINIA - 53 (cont.) Code County Code County Code County 53150 Campbell 53400 Greensville 53660 Northumberland 53160 Caroline 53410 Halifax 53670 Nottoway 53170 Carrol 1 53420 Hanover 53680 Orange 53181 Charles City 53430 Henrico 53690 Page (Co.) 53440 Henry 53700 Patrick 53190 Charlotte 53450 Highland 53710 Pittsylvania 53200 Chesterfield 53460 Isle of Wight 53720 Powhatan 53210 Clarke 53470 James City (Co.) 53730 Prince Edward 53220 Craig 53480 King and Queen 53740 Prince George 53230 Culpeper 53490 King George 53760 Prince Will iam 53770 Pulaski 53850 Shenandoah 53930 Warren 53780 Rappahannock 53860 Smyth 53950 Washington 53790 Richmond 53870 Southampton 53960 Westmoreland 53800 Roanoke 53880 Spotsylvania 53970 Wise 53810 Rockbridge 53890 Stafford 53980 Wythe 53820 Rockingham 53900 Surry 53630 York 53830 Russell 53910 Sussex 53990 Statewide 53840 Scott 53920 Tazewell Alphabetic Independent Ci ty Listing 53061 Alexandria 53291 Falls Church 53261 Petersburg 53091 Bedford 53871 Frankl in 53642 Portsmouth 53951 Bristol 53881 Fredericksburg 53601 Radford 53811 Buena Vista 53171 Galax 53431 Richmond 53011 Charlottesville 53270 Hampton 53801 Roanoke 53640 Chesapeake City 53821 Harrisonburg 53802 Salem 53022 Clifton Forge 53741 Hopewel 1 53411 South Boston 53202 Colonial Heights 53812 Lexington 53071 Staunton 53151 Lynchburg 53611 Suffol k 53021 Covington 53441 Martinsville 53750 Virginia Beach 53711 Danville 53610 Nansemond 53072 Waynesboro 53401 Emporia 53940 Newport News 53631 Will iamsburg 53292 Fairfax 53641 Norfolk 53341 VIRGIN Winchester ISLANDS - 28 53971 Norton Code Island Code Island Code Island 28010 Saint Croix 28030 Saint Thomas 28990 Virgin Islands, 28020 Saint John NFD B-3X WASHINGTON - 91 Code County Code County Code County 91000 91010 91020 91030 91040 Adams Asotin Benton Chelan Clallam 91140 91150 91160 91170 91180 Island Jefferson King Kitsap Kittitas 91270 91280 91290 91300 91310 San Juan Skagit Skamania Snohomish Spokane 91050 91060 91070 9108Q 91090 Clark Columbia Cowl i tz Douglas Ferry 91190 91200 91210 91220 91230 Kl ickitat Lewis Lincoln Mason Okanogan 91320 91330 91340 91350 91360 Stevens Thurston Wahkiakum Walla Walla Whatcom 91100 91110 91120 91130 Franklin Garfield Grant Grays Harbor 91240 91250 91260 Pacific Pend Oreille Pierce 91370 91380 91990 Whitman Yakima Statewide WEST VIRGINIA - 55 Code County Code County Code County 55000 55010 55020 55030 55040 Barbour Berkeley Boone Braxton Brooke 55190 55200 55210 55220 55230 Kanawha Lewi s Lincoln Logan McDowel 1 55370 55380 55390 55400 55410 Pocahontas Preston Putnam Raleigh Randol ph 55050 55060 55070 55080 55090 Cabell Calhoun Clay Doddridge Fayette 55240 55250 55260 55270 55280 Marion Marshal 1 Mason Mercer Mineral 55420 55430 55440 55450 55460 Ritchie Roane Summers Taylor Tucker 55100 55110 55120 55130 55140 Gilmer Grant Greenbrier Hampshire Hancock 55290 55300 55310 55320 55330 Mingo Monongalia Monroe Morgan Nicholas 55470 55480 55490 55500 55510 Tyler Upshur Wayne Webster Wetzel 55150 55160 55170 55180 Hardy Harrison Jackson Jefferson 55340 55350 55360 Ohio Pendleton Pleasants 55520 55530 55540 55990 Wirt Wood Wyoming Statewide B-39 WISCONSIN - 36 Code County Code County Code County 36000 Adams 36050 Buffalo 36100 Columbia 36010 Ashland 36060 Burnett 36110 Crawford 36020 Barron 36070 Cal umet 36120 Dane 36030 Bayfield 36080 Chippewa 36130 Dodge 36040 Brown 36090 Clark 36140 Door 36150 Douglas 36350 Manitowoc 36530 Rusk 36160 Dunn 36360 Marathon 36540 St. Croix 36170 Eau Claire 36370 Marinette 36550 Sauk 36180 Florence 36380 Marquette 36560 Sawyer 36190 Fond du Lac 36710 Menominee 36570 Shawano 36200 Forest 36390 Milwaukee 36580 Sheboygan 36210 Grant 36400 Monroe 36590 Taylor 36220 Green 36410 Oconto 36600 Trempealeau 36230 Green Lake 35420 Oneida 36610 Vernon 36240 Iowa 36430 Outagamie 36620 Vilas 36250 Iron 36440 Ozaukee 36630 Walworth 36260 Jackson 36450 Pepin 36640 Washburn 36270 Jefferson 36460 Pierce 36650 Washington 36280 Juneau 36470 Polk 36660 Waukesha 36290 Kenosha 36480 Portage 36670 Waupaca 36300 Kewaunee 36490 Price 36680 Waushara 36310 La Crosse 36500 Racine 36690 Winnebago 36320 Lafayette 36510 Richland 36700 Wood 36330 Langlade 36520 Rock 36990 Statewide 36340 Lincoln WYOMING - 83 Code County Code County Code County 83000 Al bany 83090 Johnson 83180 Sweetwater 83010 Big Horn 83100 Laramie 83190 Teton 83020 Campbell 83110 Lincoln 83200 Uinta 83030 Carbon 83120 Natrona 83210 Washakie 83040 Converse 83130 Niobrara 83220 Weston 83050 Crook 83140 Park 83060 Fremont 83150 Platte 83070 Goshen 83160 Sheridan 83080 Hot Springs 83170 Sublette 83990 Statewide B-40 APPENDIX C C-l. Social Security Administration Processing of the Continuous Work History Samples (flow chart) C-2. SSA Format: Annual Employee-Employer File C-3. SSA Format: First Quarter Employee-Employer File C-4. SSA Format: Self -Employed File, Record Format C-5. SSA Tape Characteristics C-6. BEA Processing Procedures (1%) (flow chart) C-7. BEA Format: 1 -Percent CWHS First Quarter Major Job Summary File C-8. BEA Format: 1 -Percent CWHS First Quarter Longitudinal File C-9. BEA Format: 1-Percent CWHS Annual Major Job Summary File C-l 0. BEA Format: 10-Percent CWHS Nonmigrant Summary File, by Sex, Race, and Age C-ll. BEA Format: 10-Percent CWHS Migrant Summary Files, by Sex, Race, and Age C-l 2. BEA Format: 10-Percent CWHS Entrant and Exit Summary File, by Sex, Race, and Age C-l 3. BEA Format: 10-Percent CWHS County Summary Files Appendix C-1 SSA Processing of the CWHS Employer Earnings Reports (Form 941, etc.) Employer Application for Identification (SS-4) Extract a Sample of Wage Items According to CWHS Criteria Request Compliance With Establishment Reporting Plan Sequence Sample Wage Records by Employer identification Number Combine Employer Identification With Earnings Information Sequence Records by Social Security Number Code Each Unit by Industry and County Individual's Application for a Social Security Number (SS-5) Combine Employer Information With Personal Characteristics C-l-1 Appendix C-2 SSA Format: Annual Employee-Employer File (The 1972 final coded employee-employer record is 108 positions in length and is blocked in groups of 18.) No. of positions 9 9 4 1 Description Account number Employer number Establishment number Type of schedule Coding source Annual wages First quarter wages Second quarter wages Third quarter wages Fourth quarter wages Wage items Coding B, 1 Insured status 1/1/73 1 2 3 4 5 6 1 1 Benefit status (future yr.) 1/1/73 Benefit status (current yr.) 1/1/72 7 AJ B,2 C,3 - D,4 - 941 Civilian report 943 Farm labor report 941 Military report 942 Household report c Fully and currently Currently only Fully but not currently Uninsured Permanently and currently Permanently but not currently Deceased Nonentitled living Living entitled Deceased entitled previously living entitled Deceased entitled (survivors)- worker fully insured and not previously Entitled (survivors)-- worker was currently but not fully insured C-2-l No. of Positions (1) Appendix C-2-Continued Description Coding Benefit status (current yr.) E,5 - Entitled (survivors)--worker 1/1/72 (Continued] NOTE: Alpha code indicates veteran credits have been used. Current year Estimated wages deemed fully insured, death prior 9/1/50, 6 or more QC F,6 - Living worker entitled to disability insurance 7 - Nonentitled; claim disallowed 8 - Nonentitled; proof of death 9 - Lump-sum 195 Act 72 Taxable wages if less than the maxi- mum. If taxable wages are greater than the maximum: Nonfarm : consider as the "limit quarter" the one in which the taxable limit is reached. Determine for the next prior quarter the wages that are equal or greater than wages in the "limit quarter" and substitute those wages for the "limit quarter" and all subsequent quarters in the year. If no prior quarter has wages greater than the "limit quarter," the "limit quarter" wages are used in subsequent quarters. An exception to this is when the tax- able limit is reached in the first quarter; for nonfarm--$56,100 for males and $50,300 for females; for farm--$14,300 for males and $11,100 for females. C-2-2 Appendix C-2-Continued No. of Positions Description Coding 1 Sex 1 - Male 2 - Female 3 - Unknown ' 1 Race 1 - White 2 - Negro 3 - 0/T 1 or 2 2 Month of birth 01 - 12; 00 = Unknown 3 Year of birth 774 - = Unknown 1 Dual coverage b = None 2 = Military 3 = Tip 4 = Regular 1 Collector code b = None 2 = Household = Federal civilian 3 = Nonprofit 4 = Federal 5 = Reg. 1 Indication of 999 quarter 0- None; 2-4th qtr.; 9-3rd qtr. ; 6-2nd qtr. ; 3-1 st qtr. 1 Empl oye r/i denti fi cati on S - single; M - multi 1 Establishment indication s - single; M - multi 1 Referendum code Ir ERP IRD 69 only; all other IRD's blank; b, 1 , 2, 3, 9, o, A, B, 5 2 Size of the employer 00- 56, bo; if signed, 2 or more records combined 1 Class N - nonprofit; H-household; F- Federal ; blank or - other coverage; B, C, D-IRS 69; G- reserves 5 State and county See Appendix B Industry Coverage Year of entitlement or death Blank Standard UNIVAC CFH format 1 - Farm labor a/ 2-State and local a/ 5-Nonprofit a/ 6-Federal civilian 7-Self-reported tips a/ 8-Household b/ M-Military b/ R-Reserves T-Reported tips 0-0ther 00, 39 - Current year a/ Plus zone indicates tips. b/ Numeric equivalent indicates tips. C-2-3 Appendix C-3 SSA Format: First Quarter Employee-Employer File Positions Description 1-9 Social Security number 10-18 Employer identification 19-22 Establishment number 23 etc. Type of schedule Coding 24 25-31 32-38 39-42 43 44 45 number Match code Filler 1st quarter wages No. of wage items Dual coverage Employer coverage Source of size A = farm (943) T = IRD 79-self reported tips (941) 8 = household (942) = 941 report B = match on EIN to ERP file C = match on EIN to NERP file Blank = unmatched Blanks Dollars only 2 = military or reserve 3 = TIP 4 = regular 2 = household 3 = nonprofit 4 = Federal 5 = regular A = correction B = business birth C = 03/65/OCS D = AGR-12/65 E = SS4 original F = 1963 census G = 03/64 CBP H = 1970 census C 1 = size not corrected by census record 2 = size corrected by matching census record (code 1 of FN 5) 3 = size from 1969 coordination 4 = size not corrected in 1969 coordination C-3-1 Appendix C-3--Continued Positions Description Coding 46 9999 establishment = no 9999 establ i 3 = last reported i shment n first quarter 6 = last reported in second quarter 9 = last reported in third quarter 2 = last reported in fourth quarter G = reported on 943 (AG) 47 Single/multiple S = reported by one ER employer indication M = reported by more than one employer 48 Single/multiple S = reported in only one establishment establishment indication by this employer M = reported in more than one estab- lishment by this employer 49 Combined establishment Blank = not combined indication 1 = combined 50 Referendum code (IRD 69 only - otherwise blank) 1,2,3,9,A,B,C,S 51-52 Size code number Blank = unknown g-49 = 0-49 50 = 50-99 51 = 100-249 52 = 250-499 53 = 500-999 54 = 1000-2499 55 = 2500-4999 56 = 5000 & over 53 Auxiliary and admin. Blank = not present indication 1 = present 54 Class code number B,C,D = State & local 2,3,4, = IRD 69 H = household N = nonprofit F = Federal G = Federal Reserve Blank = al 1 others 55-56 State code 57-59 County code C-3-2 Appendix C-3--Continued Positions Descriptions 60-61 Major industry code 62-63 Minor industry code 64 Coverage code Coding 1 = farm 2 = State & local B = State & local tips 5 = nonprofit 6 = Federal civilian E = nonprofit tips F = Federal civilian tips 7 = self -reported tips 8 = household H = household tips M = military 4 = military tips R = reserve 9 = reserve tips T = regular tips = all others 65 66-67 Space Birth month 68-70 71 Birth year Sex code 1 = male 2 = female 3 = unknown 72 Race code 1 = white 2 = negro 3 = other 4 = unknown C-3-3 Appendix C-4 SSA Format: Self -Employed File, Record Format File identification: Record format : Trailer Selection DM 5153 80 characters blocked in 5's. Beginning in 1968, 84 characters blocked in 20 ' s . Density : 800 1600 beginning in 1968 Track : 7-Track 9-Track beginning in 1968 Recording mode : EBDIC Header : 1960-65 - 79 positions wi position. Beginning in 1966, 360 0S standard labels are used. Beginning in 1968, no labels. None for 1960-65. Beginning in 1966, 360 0S standard trailers are used. Beginning in 1968, no trailers. All 1% sample workers having any self -employment income in current SE year. NOTE: The current SE year is the prior CW year. 1% sample consists of accounts with digits 2 or 7 in the sixth digit of the account number and 05, 20, 45, 70, or 95 in the eighth and ninth digits. Positions 1-9 10 11 Description Coding Account number Sex Race 12-13 2/ Blank 14-18 3/ Current SE year - SE income Actual 1 = Male V 2 = Female 3 = Unknown 1 = White 1/ 2 = Negro 3 = Other 4 = Unknown Actual 00000 = Unknown C-4-1 DM 5153 Positions Appendix C-4-Continued Description Coding 19-24 3/ Current SE year - SE net 25-28 29-30 31-33 34-35 36 37 38-42 43 44 45 46-47 48-49 50 Current SE year - industry Current SE year - State Current SE year - county code 4/ Current SE year - occupation code Current SE year - farm option Current SE year - SE coverage Current SE year - taxable earnings Minister waiver Current SE year - insured status Current SE year - benefit status First year employed First year with SE Current SE year - type of SE Actual 000000 = Unknown Actual See standard industrial classification index Unknown Codes: 0000 or OObb = Nature of business Not shown 9900 or 99bb = Not coded by IRS 9999 = IRS code not valid See Appendix B See Appendix B Actual bb = Unknown 5/ = Option not used 1 = Option used 2 = SE only 6/ 3 = SE and wage Actual = No 7/ 1 = Yes Actual 01 = Unknown Actual 1 = Nonfarm SE only 8/ 2 = Farm SE only 3 = Both farm and nonfarm SE C-4-2 DM 5153 Positions 51 52 Appendix C-4-Continued Description Current SE year - type of wages Minister exemption Coding 1 = Nonfc arm W/S on y 87 2 = Farm W/S on i.y 3 = Both farm and non- farm W/S = No wages 1 = Yes 9/ = No 53-54 Blanks 55-57 2/ Year of birth 58-73 Blanks 74-76 77 Random group number Blank 78-79 SE year 80-84 12/ Blank Actual 744 = Unknown 10/ Actual 3-digit code Y\J Actual SE year ]_/ Coding prior to the 1968 file was: Sex: 1 = 0/T female 2 = female Race: 1 = white, 0/T Negro or unknown 2 = Negro 3 = Unknown 2/ Format prior to 1968 file was: Year of birth: positions 12-14 SE income: positions 15-18 (four positions) Beginning with 1968 file, SE income was made five positons and the year of birth was moved from positions 12-14 to 55-57. Positions 55-57 are blank prior to 1968. 3/ SE net could be six positions prior to 1968 . After 1968, the high-order position is zero-filled. 4-/ Can be right-justified with blanks. C-4-3 DM 5153 Appendix C-4-Continued 5/ Discontinued beginning in 1965. 6/ 1960-62 code for SE only is 1. Beginning in 1963, code for SE only is 2. 7/ Beginning in 1963, minister waiver is shown here. Beginning in 1968, check for minister exemption code also. 8/ For 1962, codes 1 and 2 are reversed. (1 = farm SE (W/S) only; 2 = nonfarm SE (W/S) only). 9/ Minister exemption beginning in 1968. 10/ Additional data was included for 1966 SE year only: 65 - 69 1967 taxable earnings 70 - 73 1967 coverage indication 74 - 75 1966 SE net coded 76 - 77 Age in 1966 coded 11/ Random group number included for years 1967-70. 12 / For years 1960-67, record is 80 positions length with record mark in position 80. C-4-4 < to mmco Klr-Ol r-OU'DUITJ OO O "O =C Ll_ V C O C CO i— aj r^ u i— I ,LO a> aj C0W*r-^OOlT3UITj OO onuinj30 -o =1 u_ s- c o r^ in to tc to o>on3 lo CO LO CO O *ct o -o "O lo o ■=j- c\j lo oj o -a i— i a) s- ojo C\J LO . — LO OD-i ro >,!« w, -_Q .- O O TCI I s- "=i- ooaitioi'O »io S- id ro CO LOO TJ LO O > O A3 >>LO , •- i— i -o -feo- :>, o > i ^ S- CM c\l| ro o OJ r— co o cr> "a o •— o -o i— i LO O O *o co o s- re OJ OJ O (13 ,lo a> •r- - >,0 E csj o ■t^-l — r~~ lOcoor^unLOcocnoocOr^ » . — lONCONCMCOrNOli — COLO I CTt LO LO P-- LO C\J i — LO LO C\J LO CO s-|o r-wn^-Loiorvcnoio^- rrj LO IDlDkDlOOkDlDlDlDrNN OJ CTi CT>CTiCTtCTiCT»CTiCTiCT>CTt n| . cm ro o LO «^j" CO CM • cm en lo r*-. c\j o «=d* i lo t— r-- o~> on <3* CO I #x * « „ *s <* n . co O lo o co ro r^ lo r-» kD cm r^ ro oi i — cm cm ro ro ro cm 1. ID O N CO CTi O i- +-> to ex to OJ LO aj LO OJ O s_ u i- a 3 s- ^^ s- . 4-> O OJ O T- u U CL.C 4-> • 3 CD A3 -M <_> o. S- S- +-> CT> A3 • 4-J c 4- _0 LO 4- 4- OJ O O r— CTI OJ C >, "O s- s- -o •i- +J >> O OJ OJ s_ -^ •(— _\i +-> O HJ3 O CJ LO u EEu O C ro S- +-> 3 3 J) •— OJ i_ A3 -r- zza CQ O 1— a. CO oi as. oj c o or ec 4- ior^i^t-^cvjo-^-00 HOIOONN'*W(- INr-KCOr-OO^f *tOlOOfOO(MOO LOOr^CT1COCNJ"3-CO 'CTCTtCTiCTiCTiOOO i- N CO Ol O r- CVJCO=3- A3LOLOLOLOLOLOLOLO OJ CTi CTi CTt CTi CTt CTi CTi CTt >t m iDrvco A3^| CM| C-5-1 Appendix C-6 BEA Processing Procedures (1 Percent CWHS) Additional Summarization, by County Work-force Structure Tabulations E.I. Edits Sent to SSA for Investigation Summarize, by Major Job Concept Update BEA Longitudinal File Extract the 2 Most Recent Years of Data Matrix File Sent to Census C-6-1 Appendix C-7 BEA Format: 1 -Percent CWHS First Quarter Major Job Summary File Positions Description 1-9 Social security number 10-12 Year of birth 13 Sex/race 14-18 Place of work (State and county) 19-22 Industry (4-digit) 23-27 Wages 28-32 Place of residence (State and county) 33-41 Employer ID number 42-45 Multi-establishment number 46 Number of jobs held during quarter 47-48 Year of reference (e.g., 72) C-7-1 Appendix C-8 BEA Format: 1 -Percent CWHS First Quarter Longitudinal File Position 1-9 10-12 13 14-15 16-18 19-20 21-24 25-35 36-46 47-57 58-68 69-79 80-90 91-101 102-112 113-123 124-134 135-145 Logical record length: 145 Physical record length: 2,030 Sort: 1-9 Social security number (scrambled) Year of birth Sex/race code 1960 SSA State ( :ode 1960 SSA county code 1960 2-di "git SIC indus - try code 1960 wages (in ■ tens of do' liars) Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9 Year 10 Year 11 Year 12 Note: Years 2-12 are the most recent eleven years available. Each 11 digits are subdivided as in the 1960 data (positions 14-24) C-8-1 Appendix C-9 BEA Format: 1 -Percent CWHS Annual Major Job Summary File Positions Description 1-9 Social security number 10-12 Year of birth " 13 Sex/race 14-18 Place of work (State and county) 19-22 Industry (4-digit) 23-27 Estimated annual wages 28-32 Place of residence (State and county) 33-36 Quarterly work history 37-41 First quarter place of work (State and county) 42-43 First quarter industry (2-digit) 44-48 First quarter wages 49 Number of jobs held during year C-9-1 Appendix C-10 BEA Format: 10-Percent CWHS Nonmi grant Summary File, by Sex, Race, and Age Position 1-2 SSA State code 3-5 SSA county Code 6-8 BEA economic area code 9-12 FIPS SMSA code 13 Sex/race code 1/ 14-15 Age group code 2/ 16-20 Number of nonmigrants 21-30 Aggregate wages, 1971 31-40 Aggregate wages, 1973 Logical record length: 40 Physical record length: 2,000 Sort: 1-5, 13-15 1/ Sex/Race codes: 1 White males 2 Black males 3 Other males 4 White females 5 Black females 6 Other females Age group codes: 01 Under 19 08 45-49 02 19-21 09 50-54 03 22-24 10 55-59 04 25-29 11 60-64 05 30-34 12 65-69 06 35-59 13 70 and over 07 40-44 14 Of unclassified age C-10-1 Appendix C-11 BEA Format: 10-Percent CWHS Migrant Summary Files, by Sex, Race, and Age Positions 1-2 SSA State code 3-5 SSA county code 6-8 BEA economic area code 9-12 FIPS SMSA code 13-17 Origin or destination code 1/ 18 Sex/race code 2/ 19-20 Age group code 2/ 21-25 Number of outmigrants or inmigrants 26-35 Aggregate 1971 wages 36-45 Aggregate 1973 wages Logical record length: 45 Physical record length: 2,025 Sort: 1-5, 13-20 1/ Position 13 - Same BEA economic area 1 - Other BEA economic area 14 - Same SMSA 1 - Other SMSA 2 - NonSMSA 15 - Same State 1 - Other State 16-17 State of origin or destination 2/ See documentation of nonmigrant sex-race-arid-age summary C-ll-l Appendix C-12 BEA Format: 10-Percent CWHS Entrant and Exit Summary File, by Sex, Race, and Age Position 1-2 SSA State code 3-5 SSA county code 6-8 BEA economic area code 9-12 FIPS SMSA code 13 Sex/race code — 1/ 14-15 Age group code — 16-20 Number of workers 21-30 Aggregate wages 31 1 = Entrant (wages for 1973) 2 = Exit (wages for 1971) Logical record length: 31 Physical record length: 2,015 Sort: 1-5, 13-15 1/ See documentation of nonmigrant sex-race-and-age summary. C-12-1 Appendix C-13 BEA Format: 10-Percent CWHS County Summary Files Position' 1-2 SSA State code 3-5 SSA county code 6-8 Item code 1/ 9-14 Number of workers 15-24 Aggretate 1971 wages 2/ 25-34 Aggregate 1973 wages 3/ 35-37 BEA economic area code 38-41 FIPS SMSA code Logical record length: 41 Physical record length: 2,009 Sort: 1- ■8 1/ Item Codes: Item Codes: 000 Total 302 Mining 101 White males 303 Contract construction 102 Black males 304 Manufacturing 103 Other males 305 Transportation, communication 104 White females and public utilities 105 Black females 306 Wholesale and retail trade 106 Other females 307 Finance, insurance, and real 201 Less than 19 years of age estate 202 19-21 years of age 308 Services 203 22-24 " ii 309 Government 204 25-29 " n 310 Unclassified 205 30-34 " ii 401 Under $2,000 206 35-39 " 1! 402 $ 2,000-$ 2,999 207 40-44 " II 403 $ 3,000-$ 3,999 208 45-49 " " 404 $ 4,000-$ 4,999 209 50-54 " " 405 $ 5,000-$ 5,999 210 55-59 " II 406 $ 6,000-$ 6,999 211 60-64 " II 407 $ 7,000-$ 7,999 212 65-69 " II 408 $ 8,000-$ 8,999 213 70 years of age and older 409 $ 9,000-$ 9,999 214 Of unclassified age 410 $10,000-$14,999 301 Agriculture 411 $15,000-$24,999 501-599 2-digit SIC code 412 $25,000 and over industries 01 to 99 2/ Zeros on entrant tape 3/ Zeros on exit tape C-13-1 APPENDIX D D-l. Employment by Major Industry Group, by State, 1971 and 1973 D-2. Employment by Major Industry Group for Census Divisions, 1960 and 1970 D-3. One-Percent CWHS and Census Employment, by Major Industry Group and BEA Economic Area, 1960 and 1970 D-4. CWHS and Census Employment, by State, Sex, and Age Group, 1960 and 1970 D-5. CWHS Employment and Census Population, by Census Region and State and Sex, Race, and Age Group, 1960 and 1970 ALABAMA INDUSTRY APPENDIX TABLE D-l EMPLOYMENT BY MAJOR INDUSTRY GROUP. BY STATE. 1971 AND 1973 CWH5 \0%/ CWHS IB UI ICBP AG. FORESTRY £, F I SH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM £, P UTILS TRADE FIN, INS. f, SERVICES GOVERNMENT REAL FST '..iso 6,630 S3, 820 294,400 47,670 206,080 48,130 221,150 70,530 952,560 5,400 7,500 58,500 324,600 56,600 225,600 45,900 223,500 69,900 2,038 7,795 45,000 314,827 45,365 174,324 36,122 73,516 58,430 757.417 3.820 8,209 54,541 308,755 48,842 196,611 42,396 116,527 ,769 2.036 1 .086 .884 .851 .808 .920 1.196 .987 .907 .935 .954 .842 1.051 .976 .913 1.182 1.048 1.049 1.332 1.135 .989 3.0O8 1 .898 1.009 1.207 19 7 3 — AG. FORESTRY (, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM f, P UTIL TRADE FIN, INS. 6 SERVICES GOVERNMENT TOTAL REAL EST 4,880 7,570 58,210 313,450 50,860 215,460 46,160 227,270 80,920 9,500 7,500 62,600 314,300 57,300 225,400 48,200 225,100 78,300 1,028,200 4,740 8,216 58,295 339,093 50,641 215,124 44,244 126,781 85,104 932,238 4,986 7,674 65,238 332,286 51,759 221,588 47,490 133,650 .514 1.030 .979 1.009 .921 .986 .930 .999 .892 .997 .924 .943 .888 1.004 .983 .956 1.002 .972 .958 1.043 .972 1.010 1.793 1.700 1.033 .951 .977 ALASKA A5. FORESTRY (, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM 6 P UTILS TRADE FIN. INS. £, REAL EST SERVICES GOVERNMENT TOTAL 19 7 1 290 1.900 5,200 17,830 9,390 24,470 8,640 17,240 13,400 98,360 100 1,400 5,600 18,000 11,000 25.900 11.300 22.400 15.000 110,700 130 2,362 4.456 5.358 8.914 15.035 3.080 10.372 17,326 67.033 472 1.832 3.329 6.298 8,213 14,828 3,296 10,565 1.357 .804 1 .037 ,929 1.167 1.562 .991 3.328 2.831 .854 1.053 1.143 .945 1.628 1.650 .765 2.805 2.621 .770 1.662 1.632 .893 .773 19 7 3 AG. FORESTRY & FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM 6 P UTILS TRADE FIN, INS, £, REAL EST SERVICES GOVERNMENT TOTAL 570 1,130 6,950 14,400 8,000 21,420 9,510 20,700 15,630 98,310 1,700 600 7,100 12.800 10.100 22,300 10,400 21,500 14.500 101.000 512 1.807 5.184 7.599 9.016 16.975 4.078 13.495 21.096 79,762 710 1,437 4,606 6,856 8,635 17,321 3,903 13,074 .335 1.113 .803 1.883 .625 .786 .979 1.341 1.509 1.125 1,895 2.100 .792 .887 .926 .961 1.262 1 .237 .914 2.332 2.437 .963 1.534 1 .583 1.078 .741 D-l-1 ARIZONA INDUSTRY AS. FORESTRY f. FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM f, P UTILS TRADE FIN, INS, J, REAL EST SERVICES GOVERNMENT APPENDIX TABLE D-l EMPLOYMENT BY MAJOR INDUSTRY GROUP. BY JTATE, 1971 AND 1973 CiJHS 10* TOTAL 4,330 6,970 46,930 70,140 27,470 133,390 33,090 155,300 39,080 516,700 19 7 1 3.800 20,600 49.300 78.500 28.300 147.400 35,600 154,600 37.200 555,300 1.580 22.871 39.405 85.020 35.200 127,013 27,884 65,986 29,894 434,853 2,810 19,602 41 ,474 85,889 25,064 133,571 33,872 99,588 CWHS 10«/ CWHS 1% UI 1.139 2.741 1.541 .338 .305 .356 .952 1.191 1.132 .894 .825 .817 .971 .780 1.096 .905 1.050 .999 .929 1.187 .977 1.005 2.354 1.559 1.051 1.307 19 7 3 AG, FORESTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COM'! 6 P UTILS TRADE FIN. INS. 6 REAL EST SERVICES GOVERNMENT TOTAL 5.010 13,090 57,770 65.820 30,710 157,110 40,390 175,280 43,130 588,310 7,600 19,300 60,300 65,100 32,300 158.700 38.000 170,600 43.700 595,600 2,775 25,344 59,578 105,339 32,4*0 160,782 39.319 115.402 55.627 596.626 3,820 22,419 57,408 106,502 29,574 167,782 43,720 118,667 .659 .678 .516 .584 .958 .970 1.006 1.011 .625 .618 .951 .946 1.038 .990 .977 .936 1.063 1.027 .924 1,027 1.519 1 .477 .987 .775 .988 ARKANSAS AG, FORESTRY & FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM £, P UTILS TRADE FIN, INS, 6 REAL EST SERVICES GOVERNMENT TOTAL 19 7 1 4,660 2,730 24,680 162,800 26,060 110,410 21,610 125,840 24,770 503,560 5,500 4,600 23.900 167,300 27,200 1 10,900 22,500 131.900 25.700 519.500 4,356 4.351 24.372 166.417 25.261 105.520 20.054 45.259 18.198 413.788 3.267 3.725 24,153 162,860 24,686 107,016 21,472 65,978 .847 1.O70 1.426 .593 .627 .733 1.033 1.013 1.022 .973 .978 1.000 .958 1.032 1.056 .996 1.046 1.032 .960 1.078 1.006 .954 2.780 1.907 .964 1.361 AG, FORESTRY 6 FISH MIMING CON. CONSTRUCTION MANUFACTURING TRANS, COMM (, P UTILS TRADE FIN, INS, 6 REAL EST SERVICES GOVERNMENT TOTAL 5,130 2,270 26,990 175,000 28,270 120,250 24,110 132,860 25,400 540,280 7,300 3,500 26,300 175,800 25,500 121,100 24,400 143,200 25,200 552,300 4,668 4,097 30,572 195,398 28,701 123.228 24,108 69.227 30.618 510,617 3,431 3,703 30,385 189,058 27,415 125,066 25,758 75.777 .703 .649 .554 .613 1.026 .883 .888 .995 .896 .926 1.109 .985 1.031 .993 .976 .961 .988 1.000 .936 .928 1.919 1.753 1.008 .830 .978 D-l -2 APPENDIX TABLE D-l EMPLOYMENT RY MAJOR INDUSTRY GROUP. BY STATE. 1971 AND 1973 CALIFORNIA INDUSTRY CWHS 10*/ CWHS IS UI A3. FORESTRY (, FISH MIMING CON. CONSTRUCTION MANUFACTURING TRANS. COMM f, P UTILS TRADE FIN, INS. £, REAL EST SERVICES GOVERNMENT TOTAL 38,180 23,670 303,530 1, 467, 350 427,540 1,547,270 389,680 1 ,346,260 300,820 5,844,300 40,200 30,400 320,300 1 ,528,700 442.900 1 .617.500 411,900 1,414,000 314,600 6,120,500 30.542 29.523 277.414 1 ,441 ,998 404,479 1 ,510,397 381,530 967,696 331.129 5,374,708 26,633 31 ,224 288,905 1 .450,329 410,414 1 ,550,831 391,024 1,161,375 950 1.250 1.434 779 .802 .758 948 1.094 1.051 960 1.018 1.012 965 1.057 1.042 957 1.024 .998 Q4fc 1.021 .997 952 1 .391 1.159 956 .908 19 7 3 AG. FORESTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM £, P UTILS TRADE FIN, INS, 6 REAL EST SERVICES GOVERNMENT TOTAL 44,310 23,250 313,850 1,518,480 388,390 1,570,310 401,410 1 ,448,270 320,010 6,028,280 78,300 25.500 311.300 1.492.800 390,700 1,579,000 409,100 1,480,500 330,600 6,097,800 41,498 29,545 301,952 1,592,529 417,127 1.671,580 430,250 1 ,294,997 714,042 6,493,520 32,309 29,630 308.493 1,601,480 425,928 1,678,025 441,275 1 ,304,823 5,821,963 .566 1 .068 1.371 .912 .787 .785- 1.008 1.039 1.017 1.017 .954 .948 .994 .931 .912 .994 .939 .936 .981 .933 .910 .978 1.118 1.110 .968 .448 COLORADO AG. FORESTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM !, P UTILS TRADE FIN, INS, 6 REAL EST SERVICES GOVERNMENT TOTAL 2,320 9,560 48,480 114,370 46,950 181,570 42,230 138,060 24,610 608,150 19 7 1 4,100 13,500 48,900 123,200 47,700 191,300 42,500 145,000 24.700 640.900 1.921 13.518 38.750 111.359 43,941 167,230 35,997 100,064 45,251 558,031 2.427 14,282 44,552 119,024 43,476 183,513 42,844 131,112 566 1.208 .956 708 .707 .669 991 1.251 1.088 928 1.027 .961 984 1.068 1.08n O'.Q 1.086 .989 OV<, 1.173 .986 952 1.380 1.053 996 .544 — - 1 9 7 3 AG, FORESTRY (, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM 6 P UTILS TRADE FIN, INS, f. REAL EST SERVICES GOVERNMENT TOTAL 3,380 9,430 66,410 119,690 47,010 202,390 50,210 155,740 31,070 685,330 5.500 12.300 68.400 120.300 46.100 207,700 49,500 156,600 29.400 695,800 3.169 13.722 66.053 137.196 50,926 218,169 53.476 149.277 71.675 763,663 2,782 14,288 69,831 138,976 51,554 221,032 54,160 155,603 .615 1.067 1.215 .767 .687 .660 .971 1.005 .951 .995 .872 .861 1.020 .923 .912 .97*, .928 .916 1.014 .939 .927 .995 1.043 1.001 1.057 .433 D-l-3 connecticut injjstry A3, FORESTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM £, P UTILS TRADE FIN, INS. S REAL EST SERVICES GOVERNMENT TOTAL 'VPPENDIX TABLE 0-1 EMPLOYMENT BY MAJOR INDUSTRY GROUP. BY STATE. -- 1 9 7 1 1971 AND 1971 4.180 5,300 4,509 780 1,000 572 49,980 48,800 47,408 419,250 431,000 406,497 44,420 44.300 49.682 227,420 240,200 219,601 69,410 80,600 72.782 187,170 194,100 166.631 69,680 71,500 81.328 1,072,290 1 ,116,800 1,049,010 2,540 824 44,890 399,965 48.466 223,977 73,527 180,680 CWHS 10*/ CWHS 1* UI .789 .927 1.646 .780 1.364 .947 1.024 1.054 1.113 .973 1.031 1.048 1.003 • 894 .917 .947 1.036 1.015 .861 .954 .944 .964 1.123 1.036 .975 .857 — 19 7 3" AG, FORESTRY f, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM {, P UTILS TRADE FIN, INS, ;, REAL EST SERVICES GOVERNMENT TOTAL 3,890 5,100 4,579 2.838 630 600 663 952 ^0,320 52,700 50,266 49.556 386,510 393,700 415,683 413.902 44,040 41,400 51.256 48.730 231,200 237,400 238.001 240,341 68,360 68,000 79.803 80,853 193,570 191.200 183.879 192,061 72,180 74.600 162.709 1,050,700 1 ,064,700 1.186.839 1,029,233 .763 1.050 .950 .662 .955 1.001 1.015 .982 .930 .934 1.064 .859 .904 .974 .971 .962 1.005 .857 .845 1.012 1.053 1.008 .968 .444 DELAWARE AG. FORESTRY (, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. CO"M r, P UTIL TRADE FIN, INS. 6 REAL EST SERVICFS GOVERNMENT TOTAL 1,070 30 12,100 72,220 10,470 52,140 10,050 30,970 28,470 217,520 19 7 1 — 1 ,400 12.500 76.800 10,600 52.300 10,300 28,700 ?8 .700 575 80 13.021 69.314 9.439 43.372 9.296 16,976 16.401 178.474 963 98 11.698 66.160 8,972 44,136 9,888 28,438 .375 .306 .968 .929 1.034 .940 1.042 1.092 .988 1.109 1.167 .997 1.202 1.181 .976 1.081 1.016 1.079 1.824 1.089 .992 1.736 .983 — 19 7 3 AS. FORESTRY 6 FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM £, P UTILS TRADE FIN, INS, £, REAL EST SERVICES GOVERNMENT TOTAL 1,600 120 14,210 68,340 10,330 54,290 22,660 33,120 30,940 235,610 2.500 15,000 69,600 10,900 53,100 22,500 32.200 29.600 235,400 786 95 15,539 73,493 10,189 49.120 10,741 32.265 22.341 1.090 104 15.988 71,912 9,829 50,619 11,910 33,456 .640 2.036 1.468 1.263 1.154 .947 .914 .889 .982 .930 .950 .948 1.014 1.051 1.022 1.105 1.073 1.007 2.110 1.903 1.029 1.026 .990 1.045 1.385 D-l-4 DISTRICT OF COLUMBIA INDUSTRY APPENDIX TABLE D-l EMPLOYMENT RY MAJOR INDUSTRY GROUP. BY STATE. 1971 AND 1973 CWHS 108 CRP CWHS 10V CWHS 1H UI AG, FORESTRY £, FISH MINING CON. CONSTRUCTION' MANUFACTURING TRANS. COMM 6 P UTIL5 TRADE FIN, INS. 6 REAL EST SERVICES GOVERNMENT TOTAL 190 200 18,660 23,200 27,920 99,8*0 33.400 137,950 40,030 380,590 100 200 16,900 23,000 29,300 103,100 35,100 151,200 52,400 155 46 18.081 18.093 25.883 75.726 30.891 115.867 262.650 194 146 16,798 22,968 29,347 78,672 36,079 131 ,567 1.900 1.226 .979 1.000 4.348 1.37" 1.116 1.043 1.123 1.009 1.282 1.010 .953 1.079 .951 .959 1.305 1.256 .952 1.081 .926 .912 1.191 1.049 .764 .152 .925 — - 1 9 7 3 A3, FORESTRY & FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COM"! 6 P UTIL TRADE FIN, INS, 6 REAL EST SERVICES GOVERNMENT TOTAL 290 150 19,540 22,980 24,190 98,510 35,610 140,970 40,050 382,290 800 100 18,600 21,200 23,400 100.500 34,200 145,100 40,500 150 43 20,751 16,666 26,491 71.750 29,940 132,572 271 ,992 2?n 89 20,334 20,455 28,717 76,175 38,594 138.825 .363 1.933 1.318 1.500 3.488 1.685 1.051 .942 .961 1.084 1.379 1.123 1.034 .913 .842 .980 1.373 1.293 1.041 1.189 .923 .972 1.063 1.015 .989 .147 FLORIDA A3, FORESTRY '_, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, C0 ; 1M 6 P UTILS TRADE FIN, INS, 6, REAL EST SERVICES GOVERNMENT 18,540 5,390 162,720 313,920 138,080 613,630 142,450 520,970 147,460 — 19 7 1 TOTAL 21,000 5,600 169,300 333.800 139,500 643,900 157,800 549,900 143.600 2,164.400 9,102 9,292 201,362 309,763 144,115 549,025 122,914 283,467 76,149 15.118 5,937 161,292 313,574 143,702 587,566 144,986 414,585 .963 .580 .908 .961 .808 1.009 .940 1.01 3 1.001 .990 .958 .961 .953 1.118 1.044 .903 1.159 .983 .947 1.838 1.257 1.027 1.936 ---1973 AG. FORESTRY r, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM 6 P UTILS TRADE FIN, INS, c REAL EST SERVICES GOVERNMENT TOTAL 21,980 3,410 227,690 324,040 148,200 673,690 173,610 598,720 168,250 32,100 7,100 235,600 334,400 148,500 689,800 186,900 608,900 163,800 2,407,100 16,116 8,583 255,403 372.220 170.647 700,155 172,310 474.731 170,101 18,915 6,917 257,634 372,614 171,116 705,718 197,868 513,198 480 .397 .493 966 .891 .884 969 .871 .870 998 .868 .866 11 .962 .955 929 1.008 .877 983 1.261 1.167 .972 D-l -5 GEORGIA INDUSTRY AG. FORESTRY & FISH MININ3 CON. COVSTRUCTION MANUFACTURING TRANS. COMM & o IITILS TRADE FIN, PIS. 6 REAL EST SERVICES GOVERNMENT APPENDIX TABLE D-l EMPLOYMENT BY MAJOR INDUSTRY GROUP. BY STATE. 1971 AND 1973 CBP 7,830 6,650 94,130 502,730 89,580 333,470 78,120 327,500 64,490 — 19 7 1 TOTAL 10,100 7,800 101.300 542,700 95,600 358,200 80.600 332.100 67,900 1 .596.300 5,137 6,851 71.577 450.087 90.464 309,455 68,988 113,506 80,461 1,196,526 6,262 6,365 84,913 444,713 87,237 402,761 78,735 185.812 CWHS 10%/ CWHS 1% UI 775 1.524 1.250 853 .971 1.045 929 1.315 1.109 926 1.117 1.130 937 .990 I .027 931 1.078 .828 969 1.132 .992 986 2.885 1.763 950 .802 19 7 3 A3, FORESTRY J, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COM'! t P UTILS TRADE FIN, INS, S REAL EST SERVICES GOVERNMENT TOTAL 8,590 6,360 100,750 481,970 83,510 346,540 88,760 354,370 39,200 14,500 7.800 105,700 495,300 88,100 360,200 91,100 348,300 40,600 1,551,600 7,410 7,076 104,395 492,580 103,382 385.083 86.416 199,556 1 11 ,842 6,579 6,726 110,831 482,360 100,544 403,360 93,117 222,791 .592 1.159 1.306 .815 .899 .946 .953 .965 .909 .973 .978 .999 .948 .808 .831 .962 .900 .859 ,974 1.027 .953 .017 1.776 1.591 .966 .350 - — 19 7 1 A3. FORESTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM lj P UTILS TRADE FIN. INS. 6 REAL EST SERVICFS GOVERNMENT TOTAL 1,590 22,590 28,010 16,140 64,530 16,880 52,320 36,090 238,150 li ,400 600 20,700 31,100 21,400 65,700 17,600 54,700 36,40n 3.681 31 23.608 24.486 23.807 70.508 18.032 56.963 122.412 343.528 957 475 22.587 27,996 22,151 80,723 17,994 52,623 1.136 1.091 .957 1.000 .901 1.144 1.001 .754 .678 .729 .982 .915 .799 .959 .936 .938 .956 .918 .994 .991 .295 19 7 3 A3. FORESTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM & P UTILS TRADE FIN, INS, 6 REAL EST SERVICFS GOVERNMENT TOTAL 1,930 22,680 26,040 19,480 70,570 20,380 57,810 36,680 255,570 2,200 700 18,900 27,000 21,400 69,000 20,300 62,100 38,900 10,067 25 25.601 22,935 24,843 79,621 20,201 68,005 79,753 331.0M 1,133 472 24,046 24,521 23,711 77,484 21 ,957 63,640 .877 1.200 .886 .943 .964 1.135 1.062 .910 .784 .822 1.023 .886 .911 1.004 1.009 .928 .931 .850 .908 .943 .460 D-l -6 IDAHO APPENDIX TABLE D-l EMPLOYMENT BY MAJOR INDUSTRY GROUP, BY STATE. 1971 AND 1973 CWH5 10t/ CWHS 1* UI A3. FORESTRY £, FISH MINING CON. CONSTRUCTION ' MANUFACTURING TRANS, COMM 6 p UTILS TRADE FIN, INS, 6 REAL EST SERVICES GOVERNMENT TOTAL 1,300 2,800 10,720 35,340 10,460 53,960 8,690 52,920 12,110 188,320 1,500 2,500 12,100 41,700 11.400 52,100 10,300 55,200 12,400 199,200 1 ,156 3,320 9,255 38,837 10,393 49,020 8,027 22,297 18,265 160,570 1,125 2,930 9,672 40,124 10,574 52,153 8,659 29,434 .867 1.125 1.156 1.120 .843 .956 .886 1.158 1.108 .847 • 910 .881 .919 1.008 .991 1.036 1.101 1.035 .844 1.083 1.004 .959 2.373 1.798 .977 .663 -—1973 A5,F0RFSTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM £, P UTILS TRADE FIN, INS, 6 REAL EST SERVICES GOVERNMENT TOTAL 1,330 2,000 12,860 39,650 10,840 54,410 9,890 57,570 13,570 3,200 2,200 12,500 43,000 11,000 51,600 9,900 58,700 14,500 206,600 1,505 2,654 12.248 44,691 11,618 58,926 9,491 33,948 25,702 200.783 1.171 2,947 12,813 45,979 11.276 59,352 10,317 33,102 .416 .884 1 .136 ,909 .754 .679 1.029 1.050 1.004 .927 .892 .867 .985 .933 .961 1.054 .923 .917 .999 1.042 .959 .981 1.696 1.739 .936 .528 .979 ILLINOIS A3.F0RFSTRY 6 FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM 6 P UTILS TRADE FIN, INS, 6 REAL EST SERVICES GOVERNMENT TOTAL 19 7 1 8,360 14,190 166,680 1,288,620 234,020 920,890 225,240 693,110 108,590 3.659,700 10,500 18,400 177,600 1 ,348,700 245,800 951,700 237,500 713,200 114,000 3,817,400 6,497 21,872 151,792 1 ,255,856 218,919 854,072 202.996 345.374 110.640 3.168.018 6.050 20,468 157,048 1,274,441 228,878 944,161 234,098 633.829 3,498.973 796 1.287 1.382 771 .649 .693 q iq 1.098 1 .061 953 1.026 1.011 952 1.069 1.022 96a 1.078 .975 04 6 1.110 .962 972 2.007 1 .094 953 .961 - — 19 7 3 A3, FORESTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM £, P UTILS TRADE FIN, INS, 6 REAL EST SERVICES GOVERNMENT TOTAL 9,420 13,530 169,390 1,254,050 231,760 910,080 224,260 718,480 76,710 3,607,680 13,500 17,300 175,900 1,269,000 233,900 926,800 226,800 722,500 77,600 3,663,300 9,295 22,232 167,173 1 ,340,440 228,114 934,529 235,166 616,570 244,757 3,798,276 7,193 20,621 173,084 1 ,359,601 238,196 1 ,003.287 249,713 675,816 698 1.013 1.310 7>?? .609 .656 963 1.013 .979 988 .936 .922 Qt)l 1.016 .973 982 .974 .907 989 .954 .898 994 1.165 1.063 989 .313 D-l -7 INDIANA INDUSTRY A3, FORESTRY £. FISH MINING CON. CONSTRUCTION M4NJFACTURING TRANS. COMM (, P UTILS TRADE FIN, INS. 6 REAL EST SERVICES GOVERNMENT APPENDIX TABLE D-l EMPLOYMENT BY MAJOR INDUSTRY GROUP, BY STATE, 1971 AND 1973 CWHS 10?S 3,540 3,970 70, 040 687,070 82,990 369,600 75,620 386,720 66,550 — 19 7 1 4,800 7,300 77,200 715.900 81,200 384,500 77,500 377,200 71.100 2.144 6.618 59.066 674.547 77.492 332.829 67.552 106.683 54.153 TOTAL 2.975 6.162 67,246 668,491 77,167 371,133 75,921 202,376 1,471,471 CWHS 10*/ CWHS 1* UI .738 1.651 1.190 .544 .600 .644 .907 1.186 1.042 ,960 1.019 1.028 1.022 1.071 1.075 .961 1.110 .996 .976 1.119 .996 1.025 3.625 1.911 .936 1.229 .972 — I q 7 3 A3. FORESTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM 6 P UTIL TRADE FIN, INS, £, SERVICES GOVERNMENT TOTAL REAL EST 3,650 2,810 74,310 717,440 82,780 386,310 79,690 412,660 68,010 6,300 9,900 77,200 721,000 80, 100 386.100 76.600 403.100 69,300 3,709 6,589 76,369 746,966 84,201 399,876 80,507 211,876 88,826 3,055 6,401 82,557 742,786 83,247 408.477 83.308 231.140 1,640,971 .579 .984 1.195 .284 • 426 .439 .963 .973 • 900 .995 .960 .966 1.033 .983 .994 1.001 .966 .946 1.040 .990 .957 1.024 1.948 1.785 .981 .766 19 7 1 AG.FORFSTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM 6 P UTILS TRADE FIN. INS. 6 REAL EST SERVICES GOVERNMENT TOTAL 3,340 1,500 36,800 190,480 41,640 205,500 43.470 231,840 43,700 798,270 5.100 3.200 35,900 203,800 40,800 216,100 39,500 243,100 42,800 830,300 3,797 2,561 28.558 203.209 38.312 186.798 36.928 58.461 20.630 579,254 3.184 2.582 30,823 199,511 39,341 210,312 43,108 125.210 .655 .880 1.049 .469 .586 .581 1.025 1.289 1.194 .935 .937 .955 1.021 1.087 1.058 .951 1.100 .977 1.101 1.177 1.008 .954 3.966 1.852 1.021 2.118 .961 — 19 7 3 — AG, FORESTRY 6 FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM 6 P UTILS TRADE FIN, INS, J, RFAL EST SERVICES GOVERNMENT TOTAL 4,420 1.350 38,760 219,810 40,640 208,250 42,330 239,440 44,470 839,470 7,700 2,700 35,800 215,900 39,500 208,300 38,700 245.500 42.200 836.300 5.176 2.609 35.017 234,784 43,100 225,915 43,475 127,203 56,928 774,207 2,944 2,776 34,293 227,710 42,516 227,129 46,640 138,084 .574 .854 1.501 .500 .517 .486 1.083 1.107 1.130 1.018 .936 .965 1.029 .943 .956 1.000 .922 .917 1.094 .974 .908 .975 1.882 1.734 1.054 .781 1.004 D-l -8 KANSAS INDUSTRY APPENDIX TABLE D-l EMPLOYMENT BY MAJOR INDUSTRY GROUP. BY STATF, 1971 AND 1973 TRp cwhs io»/ CWHS l« 1J I A3. FORESTRY £, FISH MINING , CON. CONSTRUCTION MANUFACTURING TRANS, COMM f, P UTIL TRADE FIN. INS. 6 REAL EST SERVICES GOVERNMENT TOTAL 2,510 8.220 31,830 US, SOP 35,720 165,880 29.950 162,870 55,900 61 1 ,680 2.900 8.300 32.900 123.000 39,200 167.800 30,200 165,500 58,300 628,100 3,792 9,328 24,765 126.563 33.375 142.029 26.204 48.839 25.726 440,621 2.453 9,043 27,519 123,851 34,259 160,678 30,376 96,138 866 .662 1.023 990 .881 .909 967 1.285 1.157 Qnf, .939 .959 911 1.070 1.043 989 1.168 1.032 992 1.143 .986 984 3.335 1.694 959 2.173 19 7 3 — AG, FORESTRY £. FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM 6 P UTIL TRADE FIN, INS, 6 REAL EST SERVICES GOVERNMENT TOTAL 2.760 7.860 33,670 129,670 36,380 174,940 32,060 170,350 57,760 645,450 3.800 9.100 35,200 125,500 39,400 179,500 30,700 168.500 59,400 651.100 4.812 9.022 31,635 156,162 37,015 175,288 32,294 96,703 44.335 587,266 2,916 8,535 33,051 153,400 36,253 182,082 33,928 105,804 .726 .574 .947 .864 .871 .921 .957 1.064 1.019 1.033 .830 .845 .923 .983 1.004 .975 .998 .961 1.044 .993 .945 1.011 1.762 1.610 .972 1.303 KENTUCKY AG, FORESTRY (, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM 6 P UTILS TRADE FIN, IMS. 6 SERVICES GOVERNMENT RFAL EST 3.080 24,580 53,370 251 .880 40,330 182,470 35,010 182,430 34,210 807,360 4,100 29,200 49,600 277,400 46,800 190,700 36,300 184,500 35,500 854,100 1 ,997 29,934 39,117 246,303 41 ,654 166,146 31.104 59,318 42,540 658,113 2,613 29,472 39,716 234,508 40,354 186,047 35,704 123,466 .751 1.542 1 .179 .842 .821 .834 1.076 1.364 1.344 .908 1.023 1.074 .862 .968 .999 .957 1.098 .981 .964 1.126 .981 .989 3.075 1.478 ,964 • 804 .945 A3, FORESTRY J, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM £, P UTIL TRADE FIN, INS, (, SERVICES GOVERNMENT TOTAL REAL EST 2,920 21,130 55,050 271,790 55,730 199,550 38,000 207,580 38,550 890,300 5.400 23.600 53.500 280.900 57,700 201,400 33,000 207,400 40,100 908,000 3,021 30,271 51.383 279.581 46.222 202.327 37.178 122.772 64,306 837,061 3,089 28,112 49.176 268,364 43,611 211,782 40,400 135,669 .541 .967 .945 .895 .698 .752 1.029 1.071 1.119 .968 .972 1.013 .966 1.206 1.278 .991 .986 .942 1.000 1.022 .941 1.001 1.691 1.530 D-l -9 LOUISIANA ;-.:.5 T - • AS, FORESTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM f, P UTILS TRADE FIN. INS. 6 REAL EST SERVICES GOVERNMENT APPENDIX TABLE 0-1 EMPLOYMENT BY MAJOR INDUSTRY GROUP. BY STATEi 1R71 AND 1971 TOTAL 4.650 45,710 78,400 154,420 75.1R0 226,370 47,030 108,700 44,130 874,600 19 7 1 5,700 45,100 78,600 165,000 78.200 237.900 52.300 202.700 45.600 911.100 3.185 48.788 69.243 167.134 83.131 214.772 41.526 88.045 29,699 745,523 4,313 46,000 75,776 169,039 75,290 232.887 49,898 149,533 CWHS 10*/ CWHS 1% UI .816 1.460 1.078 1.014 .937 .994 .997 1.132 1.035 .936 .024 .914 .962 .904 .999 .952 1.054 .972 .899 1.133 .943 .980 2.257 1.329 .968 1.486 .960 AG, FORESTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM TRADE FIN, INS, 6 SERVICES GOVERNMENT TOTAL & P UTILS REAL EST 4,830 42,310 77,740 156,380 74,950 240,580 51,840 198,330 44,640 891 ,600 8,400 43,000 75,600 160,600 75,300 242,500 53,100 201,800 45,000 905,300 4,974 52,690 80,115 180,989 86,409 263, 7«U 52,320 151.438 95.572 968.258 4,681 49,663 83,063 177,881 87,750 260,035 56,811 166,609 .575 .971 1.032 .984 .803 .852 1.028 .970 .936 .974 • 864 .879 .995 .867 .854 .992 .012 .925 .976 .991 .912 .983 1.310 1 .190 .992 .467 .985 MAINE AG, FORESTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM 6 P UTIL TRADE FIN. INS. & PFAL FST SERVICES GOVERNMENT TOTAL 19 7 1 1,410 90 15,640 101 ,330 14,430 66,170 11,960 51 ,140 8,430 270,600 1,400 800 13,000 106,400 16,600 69,200 13,600 51,000 8,300 280,300 547 155 12,324 100.265 13.148 57.322 11.099 16.386 8.958 220.204 1.122 273 13.804 9R,871 13,257 63,632 12,684 42,69-; 1.007 2.578 1.257 .113. .581 .330 1.203 1.269 1.133 .952 1.011 1.025 .869 1.098 1.088 .956 1.154 1.040 .879 1.078 .943 1.003 3.121 1 .198 1.016 .941 19 7 3 AG. FORESTRY o FISH MINING CON. CONSTRUCTION MANUFACTUR ING TRANS. COMM r, P UTIL TRADE FIN, INS, £, REAL EST SERVICES GOVERNMENT TOTAL 1.650 170 16,390 97,000 14,920 70,380 13,290 55,240 9,320 278,360 2,100 900 16,800 95,500 17,600 72,400 14,700 52,300 10,000 1 .274 192 15.416 102.986 14.423 69.725 13.244 45,111 15.823 278.104 1.183 275 16,157 99,868 14,038 71,183 13,867 48,208 .786 1.295 1.395 .189 .885 .618 .976 1.063 1.014 1.016 .942 .971 .848 1.034 1.063 .972 1.009 .989 .904 1.003 .958 1.056 1.225 1 .146 .932 .589 .986 D-l-10 MARYLAND AG, FORESTRY £, FISH MINING , CON. CONSTRUCTION MANUFACTURING TRANS.COMM 6 P UTILS TPADE FIN, INS. 6 REAL EST SERVICES GOVERNMENT APPENDIX TABLE D-l EMPLOYMENT RY MAJOR INDUSTRY GROUP, BY STATF, 1971 AND 1971 IOTAL 5,860 930 79,430 265,330 66,280 287,140 65,870 333,380 65,720 1 ,169. 940 19 7 1 6,000 900 81,500 298,400 67,400 297,700 67,100 339.400 66.700 4,288 1,800 84,650 252,834 69,238 305,311 67,242 144,331 119,712 4,063 1 ,685 82,798 259,724 64,057 288,755 68,886 201,369 CWHS 1096/ CWHS 1% U I CBP .977 1.367 1 .442 1.033 .517 .552 .975 .938 .959 .889 1.049 1.022 .983 .957 1 .035 .965 .940 .994 .982 .980 .956 .982 2.310 1.656 .985 .549 — 19 7 3 AG.FORFSTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM {, P UTILS TRADE FIN, INS, 6 REAL EST SERVICES GOVERNMENT TOTAL 7,460 1 ,000 84,140 234,050 52,260 291,120 71 ,990 361,670 65,630 1 .169,320 9,500 1,200 87,200 246,000 54,200 291,700 72,500 360,600 66.300 4.895 2,014 97,513 252,169 69,507 336,106 75.009 230.908 163,736 1 ,231,857 5,222 1,711 91,443 260,442 66,433 322,438 78,126 229,592 .785 1.524 1.429 .833 .497 .584 .965 .863 .920 .951 .928 .899 .964 .752 .787 .998 .866 .903 .993 .960 .921 1.003 1.566 1.575 .990 .401 MASSACHUSETTS AG. FORESTRY 6 FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS.COMM 6 P UTILS TRADE FIN, INS, £, REAL EST SERVICFS GOVERNMENT TOTAL 6,350 1,720 87,610 626,830 111,940 482,650 133,290 412,680 2,560 7,300 1.500 89,800 632,600 113,100 489,300 137,100 421,800 2,900 6,502 801 86,163 595,004 1 10,864 483,569 125,381 233,474 76.155 6,312 1 ,439 80,011 622,038 111,346 510,210 129,899 404,631 1 ,865,886 .870 .977 1.006 .1*7 2.147 1.195 .976 1.017 1 .095 .991 1.053 1.008 .990 1.010 1.005 .986 .998 .946 .972 1.063 1.026 .978 1.768 1.020 .883 .034 .984 1.000 — 19 7 3 AG. FORESTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS.COMM 6 P UTILS TRADE FIN. INS, 6. REAL EST SERVICES GOVERNMENT TOTAL 7.120 1,530 91 ,450 611 ,900 101,310 488,800 124,400 427,430 3,020 1 ,856,960 9,800 1,500 91 ,900 604,500 100,600 490.800 125,200 427,500 2,400 7,186 691 95,147 618.424 115,314 505,689 129,500 429,148 104,599 2,005,698 6,665 1,434 91,734 644,889 116,544 515,034 136,501 440,822 .727 .991 1.068 1.020 2.214 1.067 .995 .961 .997 1.012 .989 ,949 1.007 .879 .869 .996 .967 .04° .994 .961 .911 1.000 .996 .970 1.258 .029 1.001 D-l-11 MICHIGAN A3. FORESTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM 6 P UTILS TRADE FIN, INS. 6 REAL EST SERVICES GOVERNMENT APPENDIX TABLE D-l EMPLOYMENT PY MAJOR INDUSTRY GROUP. BY STATE. 1971 AND 1973 TOTAL 5,490 5,740 110,440 1 .129,040 95,740 623,000 121,980 664,770 143,020 2,899,220 19 7 1 6,200 10,900 116,300 1,135,700 124,300 647,600 122,200 684,000 142,700 2,989,900 4,358 11,097 97,337 1 ,042.903 126,454 592,760 115,268 256,760 159.083 2.406.020 4,287 10,217 100,589 1 ,063,665 127,944 602,652 120,369 395,827 2,425,550 CWHS 10*/ CWHS \% UI ,885 1.260 1.281 .527 .517 .562 .950 1.135 1.098 .994 1.083 1.061 .770 .757 .748 .962 1.051 1.034 .998 1.058 1.013 .972 2.589 1.679 1.002 .899 .970 19 7 3 AG, FORESTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM 6 P UTILS TRADE FIN, INS. 6 REAL EST SERVICES GOVERNMENT TOTAL 5,900 5,920 114,110 1,151,590 96,910 630,770 125,330 696,080 153,470 2,980,080 7,900 9,600 123,500 1,142,200 99, 100 635,100 123,800 701,700 155,000 2,997,900 5,120 1 1 ,954 111.535 1 ,141,535 131,385 641 ,898 123,893 420,918 163.893 2,752,131 4,673 10,815 117,722 1.165.142 133.459 655,960 131,536 446,818 2,666,125 .747 1.152 1.263 .617 .495 .547 .924 1.023 .969 1.008 1.009 .988 .978 .738 .726 .993 .983 .962 1.012 1.012 .953 .992 1.654 1.558 .990 .936 MINNESOTA A3. FORESTRY I FISH MINING CON.. CONSTRUCTION MANUFACTURING TRANS, COMM {, P UTILS TRADE FIN, INS, £, REAL EST SERVICES GOVERNMENT 2,430 11,360 59,840 343,790 62,330 304,260 64,000 288,550 41,930 19 7 1 — 3,800 14,000 58,600 •360,700 66,300 306,700 73,700 289,700 47,700 TOTAL 1,776 13.118 47,606 287,395 62.920 295.244 60.562 115.229 112,955 996,805 2,756 12,842 50,199 292,107 65,820 309,969 65,363 207,182 .639 1.368 .882 .811 .866 .885 .021 1.257 1.192 .953 1.196 1.177 .940 .991 .947 .992 1.031 .982 .868 1.057 .979 .996 2.504 1.393 .879 .371 A3. FORESTRY 6 FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM 6 P UTILS TRADE FIN, INS, 6 REAL EST SERVICF5 GOVERNMENT TOTAL 2,830 11,140 56,870 336,000 67,780 317,020 69,030 316,420 49,150 5.900 13,400 54,000 336,000 70,300 303.800 75,500 315,200 49,800 3, 067 12,962 52,557 318.722 70.744 336.281 68.072 215.317 81.331 3.311 12.910 56,035 313,862 68,995 338,471 73,263 235,042 1,101,889 ,480 .831 1.053 1.000 .964 1.044 .914 1.004 .987 1.002 .923 .859 1.082 1.054 .958 .943 1.014 1.470 .604 1.058 .855 .863 1.015 1.071 .982 .937 .942 1.346 D-l-12 MISSISSIPPI INDUSTRY AG.FORFSTRY f, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM 6 P UTILS TRADE FIN, INS. 6 REAL EST SERVICFS GOVERNMENT APPENDIX TABLE D-l EMPLOYMENT BY MAJOR INDUSTRY GROUP. BY STATF. 1971 AND 1973 TOTAL 1 .980 4.770 28,260 165.170 25,930 113,630 20,930 150,570 34,660 545,900 19 7 1 3,200 5,400 30,200 174, BOO 29,000 1 18,900 23,500 153.800 39,400 578,200 1, .880 6.480 25.940 182,025 24,703 93,545 18,167 32,508 22,100 407,348 2,056 5,470 28,061 176,107 25,176 110,352 22,178 66,541 CWHS 10*/ CWHS 1* UI 619 1.053 .963 883 .736 .872 936 1.089 1.007 945 .907 .938 894 1.050 1.030 956 1.215 1 .030 891 1.152 .944 979 4.632 2.263 880 1.568 - — 19 7 3 AG, FORESTRY r, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM 6 P UTILS TRADE FIN, INS. 6 RFAL EST SFRVICFS GOVERNMENT TOTAL 2,510 4,020 34,150 177,540 28,610 120,130 23,860 155,140 38,200 584,160 4,900 4,600 36,100 185,400 29,800 127,100 22,700 152,100 40,000 602,700 3.051 6.411 38.647 216.503 30.635 124.927 23.483 65.716 38.303 547,676 2.444 5.436 36.990 208.094 29,977 128.519 26.072 73.599 .512 .823 1 .027 .874 .627 .740 .946 .884 .923 .958 .820 .853 .960 .934 .954 .945 .962 .935 1.051 1.016 .915 1.020 2.361 2.108 .955 .997 MISSOURI AG. FORESTRY 6 FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM 6 P UTILS T3ADF FIN, INS, 6 RFAL EST SERVICES GOVERNMENT 3,460 4,070 73,570 403,190 97,590 387,700 90,130 348,370 85,540 19 7 1 TOTAL 5,100 11,200 76.200 426.700 106.000 396.300 87,600 359,600 82,500 1,551,200 3,061 12.537 61 .839 417.124 103.154 374.514 79.379 130.020 69.121 3.499 9,202 67,625 427,782 98,298 378,286 91 ,296 255.224 .678 1.130 .989 .363 .325 .442 .965 1.190 1.088 .945 .967 .943 .921 .946 .993 .978 1.035 1.025 1.029 1.135 .987 .969 2.679 1.365 1.037 1.238 19 7 3 AG. FORESTRY f, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM J, P UTILS TRADE FIN, INS, (, REAL EST SFRVICFS GOVERNMENT TOTAL 4,160 5,150 69,230 398,990 101,890 380,620 97,920 372,050 87,920 7,600 8,300 71,600 408,500 102,000 379,200 92,800 376,800 83,700 1 ,530,500 5,066 7,917 67,715 452,093 105,829 397,893 89,633 250,176 100.003 3.748 9,205 70,467 458,906 103,024 407,648 99,654 283,078 .547 .821 1.110 .620 .650 .559 .967 1.022 .982 .977 .883 .869 .999 .963 .989 1.004 .957 .934 1.055 1.092 .983 .987 1.487 1.314 1.050 .879 D-l-13 MONTANA INDUSTRY AG. FORESTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURIMG TRANS. COMM f. P UTILS TRADE FIN, INS, £, REAL EST SERVICES GOVERNMENT APPENDIX TABLE 0-1 EMPLOYMENT BY MAJOR INDUSTRY GROUP, BY STATE, 1971 AND 1973 "'•>_ 350 ".,600 8,100 21,550 10, BIO 45, 800 8,150 c 0,130 17,980 167, (,70 19 7 1 1 . • 100 4,900 A, 800 25,700 10,700 49,400 8,800 52,600 18,600 178,600 807 5,931 8,080 22,412 10,546 46,068 7,571 18,300 10,762 130,477 550 5 .434 7 .929 22 ,517 10 .393 46 ,278 8 ,282 29 ,368 130 ,751 CWHS 10*/ CWHS 1% UI .318 .434 .636 .939 .776 .847 1.191 1.002 1.022 .839 .962 .957 1.010 1.025 1.040 .927 .994 .990 .926 1.076 .984 .953 2.739 1.707 .967 1.671 1 p 7 3 A3, FORESTRY r, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, CP"M TRADE FIN, INS, 6 SERVICES GOVERNMENT TOTAL £, P UTRS REAL EST 550 1,260 9,770 18,920 11,250 49,450 9,240 53,980 19,050 173,470 2.300 2,000 9,400 17,900 10,600 56,100 9,100 53,000 20,300 180,700 947 6,077 11,086 23,421 11.627 52.893 8.726 33.131 17.051 164.959 1.002 5.830 10.891 22,337 11,839 54,135 9,777 34,033 .239 .581 .549 .630 .207 .216 1.039 .881 .897 1.057 .808 .847 1.061 .968 .950 .881 .935 .913 1.015 1.059 .94* l.oia 1.629 1.586 .938 1.117 NEBRASKA AG, FORESTRY & FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, CP«M 6 P UTIL TRADE FIN, INS. C REAL EST SERVICES GOVERNMENT 19 7 1 TOTAL 1 .620 960 22,880 78,860 27,680 121,540 29,380 122,790 38,850 444,560 1 .700 2,000 21,500 88,800 29.600 127.400 31,200 122,300 40,100 464,600 804 1.236 17.564 103.924 22.915 109,421 25,188 37.950 17.169 336.171 1 .686 1 .399 19.963 81 .545 23.952 123.735 29,448 76,595 .953. 2.015 .961 .480 .777 .686 1.064 1.303 1.146 .888 .759 .967 .935 1.20B 1.156 .954 1.111 .982 .942 1.166 .998 1.004 3.236 1.603 .969 2.263 19 7 3 AG.FORFSTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM 6 P UTILS TRADE FIN, INS, f, REAL EST SERVICFS GOVERNMENT TOTAL 2,000 800 25,510 84,470 27,240 123,110 27,060 130,410 36,540 457,160 3,200 1 .400 24,000 84,800 27,800 124,800 28,500 131,400 35,700 461 ,600 1.636 1.349 24.560 90.203 26.173 133.602 29,894 77,753 41,215 426.385 1 .884 1,448 " 25,417 91 ,016 26,397 135,036 32,150 85,120 .625 1.222 1.062 .571 .593 .552 1.063 1.039 1.004 .996 .936 .928 .980 1.041 1.032 .986 .921 .912 .950 .906 .842 .992 1.677 1.532 1.024 .887 D-l-14 NEVADA INDU5TPY APPENDIX TABLF 0-1 EMPLOYMENT BY MAJOR INDUSTRY GROUP. BY ST4TF, l420 49 ,100 12 ,820 1- 1 ,730 19 7 1 400 3;500 7,300 8.100 10,100 44.900 7,100 50,700 13,800 145.900 818 1.367 5.958 9.400 7.905 38.199 5.747 11.446 11.098 91 .918 357 1 .760 7,566 8,883 8,145 45,220 7.394 27,832 CWHS 10*/ CWHS 1% UI .775 .379 .868 .826 2.114 1 .642 .941 1.153 .908 .967 .833 .88! .844 1.07B 1.046 1.024 1.203 1.017 1.045 1.291 1.004 .968 4.290 1.764 .929 1.155 .971 19 7 3 A3. FORESTRY (, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM £, P UTILS TRADE FIN, INS. 6 REAL EST SERVICES GOVERNMFNT TOTAL 350 3,040 7,950 9,230 8,610 46,540 8,140 52,020 7,520 143,400 700 3.700 8.300 9.900 9.600 47.500 8.400 53,900 7,900 149,900 1»124 1.391 8,235 11,087 8.955 49.055 7.068 28.820 16.174 131 .909 357 1.961 7,818 10,262 9,378 49.865 7,901 30,605 500 .311 .980 822 2.185 1.550 958 .965 1.017 932 .833 .899 897 .961 .918 980 .949 .933 969 1.152 1.030 965 1.805 1.700 952 .465 19 7 1 A3. FORESTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM £, P UTILS TRADE FIN, INS. 6 REAL EST SERVICES GOVERNMFNT TOTAL 6,020 19,820 142,050 1,391,310 185,980 771,990 154,810 545.850 4,370 3,222,200 8,300 23.600 143.700 1 ,458,000 194,400 823,100 157,100 570,600 7,900 3.386,700 5.279 20,492 129,707 1.339,518 179.886 725,274 143,971 273,757 97,589 2,915.473 5,932 19,902 128,733 1,346.078 181 ,244 833,155 157,229 523.712 3,19^.985 840 .967 .996 989 1.095 1.103 954 1.039 1.034 957 1.034 1.026 938 1.064 .927 985 1.075 .985 957 1.994 1.042 553 .045 19 7 3 A3.F0PESTRY £, FISH MINING CDN. CONSTRUCTION MANUFACTURING TRANS, COMM 6 P UTILS TRADE FIN, INS, 6 REAL EST SERVICES GOVERNMENT TOTAL 8,130 21 ,650 152,460 1,362,170 178,430 791,350 163,970 573,100 3,680 3,254,940 10.400 24,300 148,700 1,372,800 177,000 801,000 165,100 587,400 4,500 3,291,200 8,074 22.507 149.034 1 .400.361 187.376 828.979 165,010 534,829 207,928 3.504,098 6,792 22,564 156,376 1 ,408,482 186,508 841,244 173,380 569,028 .782 1.007 1.197 .891 .962 .959 1.025 1.023 .975 .992 .973 .967 1.008 .952 .957 .988 .955 .941 .993 .994 .946 .976 1.072 1.007 .818 .018 D-l-18 OKLAHOMA A3. FORESTRY 6 FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM £, P UTILS TRADE FIN, INS. f, REAL EST SERVICES GOVERNMENT TOTAL APPENDIX TABLE D-l EMPLOYMENT BY MAJOR INDUSTRY GROUP. BY STATE. 1971 AND 1973 UI 2.070 30,370 43,980 123,470 48,940 180,200 38,310 188,520 40,870 696,730 19 7 1 — 2,800 32,000 48,000 139,400 53,500 191 ,300 39,100 195,700 44,400 746,200 3,584 35,167 34,035 128,451 46,354 150,473 31,964 60,036 55,124 545,188 2,491 27,901 39,807 128.490 47,097 174,705 39,019 113,530 CWHS 10*/ CWHS 1% UI 739 .578 .831 949 .864 1 .088 9,16 1.292 1.105 88 6 .961 .961 915 1.056 1.039 942 1.198 1 .031 980 1.199 .982 963 3.140 I .661 920 .741 19 7 3 AG, FORESTRY 6 FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM £, P UTILS TRADE FIN, INS. f, REAL EST SERVICFS GOVERNMFNT TOTAL 3.480 24,720 44,39" 150,660 48,240 183,460 40,440 199,570 45,580 740,540 5,200 25,300 47,300 151,700 49,100 189,400 40,000 204,800 46,800 759,600 4,707 35,538 42,099 151,369 49,532 190,132 40,872 107,555 107,499 729,303 2,779 27,495 45,515 146,040 49,393 198,309 44,410 121,851 .669 .739 1.252 .977 .696 .899 .938 1.054 .975 .993 .995 1.032 .982 .974 .977 .969 .965 .925 1.011 .989 .911 ,974 1.856 1.638 .974 .424 OREGON AG, FORESTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM f, P UTILS TRADE FIN, INS, 6 REAL EST .SERVICES GOVERNMENT TOTAL 4,560 870 33,130 149,340 42,080 164,790 33,720 164,110 51,500 644, 100 5,400 500 32,000 160,300 41,200 171.400 34,500 169,500 51,900 666,700 2,830 1,241 26,151 162,830 38,209 157,366 33,213 74,304 58,723 554.867 3.244 1.318 26,704 158,235 39,582 161,193 34,671 104,851 .8 44 1.611 1.406 1.740 .701 • 660 1.035 1.267 1.241 .932 .917 .944 1.021 1.101 1.063 .961 1.047 1.022 .977 1.015 .973 .968 2.209 1.565 .992 .877 1 9 7 3 AG, FORESTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM £, P UTILS TRADE FIN, INS, £, REAL EST SFRVICFS GOVERNMENT TOTAL 4,840 710 39,680 178,060 41 ,670 180,110 32,820 179,670 56,270 713,830 6,800 1,400 38,300 184,500 40,000 192,200 31,200 176,900 58,500 729,800 3,492 1,542 36,344 185,890 42,419 182,607 38,353 113,732 69,577 673,956 4,025 1,716 36,310 184,123 42,236 185,078 39,943 120,516 .712 1.386 1.202 .507 .460 .414 1.036 1.092 1.093 .965 .958 .967 1.042 .982 .987 .937 .986 .973 1.052 .856 .822 1.016 1.580 1.491 .962 .809 .978 D-l-19 PENNSYLVANIA APPENDIX TABLF D-l EMPLOYMENT BY MAJOR INDUSTRY GROUP. BY STATE. 1971 AND 1973 AS. FORESTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM (, p UTILS trade fin. ins. & real est services governmfnt TOTAL 9. 2nd 41,790 176,240 1.445,750 235,790 864,580 189,330 879,090 194,190 4,036,040 19 7 1 9,000 49,400 184,500 1 .504,700 242,700 891 ,900 199,500 910,100 196.900 4,188,700 7.506 39.547 170.426 1 ,445.549 214.397 815.527 186.721 352.653 250.139 3.482.465 CBP 7,306 42,078 ■170,675 1,434,608 214,425 839,897 195.668 624.220 CWHS 1 056/ CWHS 1% UI .031 1.236 1.270 .846 1.057 .993 .955 1.034 1.033 .961 1.000 1.008 .972 1.100 1.100 .969 1.060 1.029 .949 1.014 .968 .966 2.493 1.403 .986 .776 19 7 3 AG. FORESTRY (, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM r, P UTILS TRADE FIN, INS. £, REAL EST SERVICES GOVERNMFNT TOTAL 10,95^ 40,380 187,630 1.393,160 208,180 850,420 196,040 684,500 10B,7S0 3,680,040 14,300 44,100 189,500 1,385,800 216,000 863,600 192,700 694,400 109,000 3,709,400 9.652 39,259 189.112 1.462.349 221.519 872,270 200,109 645,916 292,394 3,932,580 9,048 42,052 189,465 1,456,455 224,757 891,252 211.392 676.432 1.210 ,916 1.029 .960 .990 • 992 • °90 1,005 .953 .957 .964 .940 .926 .985 .975 .954 1.017 .980 .927 .986 1.060 1.012 .998 .372 RHODE ISLAND AG. FORESTRY (, F I SH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM f, P UTILS TRADE FIN, INS, I REAL EST SERVICES GOVERNMENT TOTAL 19 7 1 930 150 14,660 119,890 14,840 69,630 16,120 53,930 35,120 325,270 1,000 500 16,500 126,300 13,700 78,500 17,000 51,600 33,100 338,200 946 106 11 .779 112.694 14.628 69.000 15.692 28.159 31.843 284.849 675 214 12.276 113,004 14,601 69,980 16,563 52,660 .930 .983 1.378 .300 1.389 .701 .888 1.245 1.194 .949 1.064 1.061 1.083 1.014 1.016 .887 1.009 .995 .948 1.027 .973 1.045 1.915 1.024 1.061 1.103 19 7 3 FISH AG. FORESTRY £, MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM ;, P UTI TRADE FIN, INS. £, REAL ES SERVICES GOVERNMFNT TOTAL 900 110 13,690 123,750 14,720 73,890 16,740 57,190 39,720 340,710 900 100 15,500 121,800 14.100 74,800 18,300 56,300 37,400 339,200 1.264 130 13.137 124.042 14.641 74.143 17.101 56.616 33.532 334,606 917 225 12,957 127,911 15.620 74,803 18,158 57,027 1.000 .712 .981 1.100 .846 .489 .883 1.042 1.057 1.016 .998 .967 1.044 1.005 .942 .988 .997 .988 .915 .979 .922 1.016 1.010 1.003 1.062 1.185 D-l -20 SOUTH CAROLINA APPENDIX TABLE 0-1 EMPLOYMENT BY MAJOR INDUSTRY GROUP. BY STATE, 1971 AND 1971 UI CWHS 10*/ CWHS 1* HI 19 7 1 — AG. FORESTRY 6 FISH MINING , CON. CONSTRUCTION MANUFACTURING TRANS. COMM lj P UTILS TRADE FIN, INS. £, REAL EST SERVICES GOVERNMENT TOTAL 3,800 470 53,630 330,1,60 31 ,870 147,860 29,800 176,310 29,970 804,370 5,000 1,400 55,600 346,700 36,100 145,300 31,100 182.500 31.500 835,200 2,185 1.571 46,661 330,861 32,669 129.124 25.100 47.350 33,835 649,356 2,645 1.232 47,626 320,849 31,464 145.781 32.938 81.509 .336 .299 .381 .968 1.154 1.130 .953 .999 1.030 .883 .976 1 .013 1.018 1.145 1.014 .958 1.187 .905 .966 3.724 2.163 .951 .886 — 19 7 3 A3. FORESTRY & FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM £ P UTIL TRADE FIN, INS, I, SERVICES GOVERNMENT TOTAL REAL EST 3,450 540 54,090 333,770 32,390 149,530 33,890 187,170 21,500 816,330 6,300 1,500 54,500 338,800 35,700 152,400 32,400 188,300 23,000 832,900 3.048 1.738 64.954 369.798 36.964 164.853 32.716 85.471 52.806 812.348 3.096 1.352 62,561 357,462 34,190 167,930 36,904 95,950 .548 1.132 1.114 .360 .311 .399 .992 .833 .865 .985 .903 .934 .907 .876 .947 .981 .907 .890 1.046 1.036 .918 .994 2.190 1 .951 .935 .407 SOUTH DAKOTA — 19 7 1 — AG, FORESTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM 6 P UTIL TRADE FIN, INS, 6 REAL EST 5ERVICFS GOVERNMENT 520 200 6,760 14,090 8,990 46,420 7,320 43,490 19,610 1,400 2,100 5,400 17,100 11,200 49,900 7,500 46,200 20,100 1,699 2.357 4,852 15.112 8.746 40.829 6.031 12.500 10.239 879 2,642 6.353 15,750 8,936 45,793 7,451 28,669 .371 .306 .592 .095 .085 .076 1.252 1.393 1.064 .824 .932 .895 .803 1.028 1 .006 .930 1.137 1.014 .976 1.214 .982 .941 3.479 1.517 .976 1.915 TOTAL 147,400 160,900 102,365 19 7 3 AG, FORESTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM 6 P UTILS TRADE FIN, INS, 6 REAL EST SERVICES GOVERNMENT TOTAL 620 100 8,630 17,090 9,800 49,680 6,770 47,960 21,110 161,760 1,200 2,400 7,100 18,200 8,500 51,500 7,000 51,100 20,600 167,600 1.875 2.189 8.026 18.725 10.481 49.648 7.468 31.190 16.282 145,884 1.072 2,392 8,292 18,910 9,917 49,800 8,355 33,058 .517 .331 .578 .042 .046 .042 1.215 1.075 1.041 .939 .913 .904 1.153 .935 .988 .965 1.001 .998 .967 .907 .810 .939 1.538 1.451 1.025 1.297 D-l-21 TENNESSEE : \ - . f - - - APPENDIX TABLE D-l EMPLOYMENT BY MAJOR INDUSTRY GROUP. BY STATE. 1971 AND 1973 CWHS 1 0*/ CWHS IX UI - — 19 7 1 A3, FORESTRY (, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM 6 P UTIL5 TRADE FIN. INS. 6 REAL EST 5ERVICES GOVERNMENT TOTAL 3,000 6,110 74,290 436,820 80,620 277,300 56,540 271,940 52,220 3,500 7,500 76,200 470,400 82.000 292.100 60.300 274.900 56.200 2 .743 2 .704 6 .326 7 .166 56 ■ 369 68 .924 449 >390 438 .713 52 .681 51 .263 234 ■ 558 281 .068 49 i622 58 .060 88 .601 175 .637 48 .257 988 .547 1,083 .535 857 1.094 1.109 815 .966 .853 975 1.318 1 .078 929 .972 .996 983 1.530 1.573 949 1.182 .987 938 1.139 .974 989 3.069 1.548 929 1.082 A3, FORESTRY f, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM TRADE FIN, INS. f, SERVICES GOVERNMENT TOTAL 6 P UTILS RFAL EST 3.230 5,280 77,610 449,300 82,480 293,320 61,430 290,940 49,180 5,700 7,400 79,700 462,700 80,100 298,300 64,000 281,700 51,100 4,491 6,534 75,678 513.154 60.027 301.602 62.390 175.637 77.632 1,277,145 3,023 7,424 81,745 493,270 59,185 314,574 65.093 200.739 .567 .719 1.06B .714 .808 .711 .974 1.026 .949 .971 .876 .911 1.030 1.374 1.394 .983 .973 .932 .960 .985 .944 1.033 1.656 1.449 .962 .634 TEXAS A3. FORESTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM 6 P UTIL TRADE FIN. INS, 6 REAL EST SERVICES GOVERNMENT 19,030 92,590 284,750 685,930 201,550 928,860 197,090 719,450 153,550 19 7 1 TOTAL 21.200 94,300 287,900 759,800 217,000 971,800 205,600 734,000 153.100 3.444,700 7,029 98,142 208.322 703.608 220.729 813.608 170.730 342.440 159.150 14,554 100,556 250,675 720,202 223,963 914,560 202,209 554,156 2,980,875 .898 2.707 1.308 .982 .943 .921 .989 1.367 1.136 .903 .975 .952 .929 .913 .900 .956 1.142 1.016 .959 1.154 .975 .980 2.101 1.298 1.003 .965 — - 1 9 7 3 A3. FORESTRY (, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM t, P UTIL TRADE FIN, INS, 6 SERVICES GOVERNMENT TOTAL REAL EST 23,260 88,530 292,940 699,730 216,690 975,550 209,460 765,380 173,060 3,444,600 31,600 90,400 288.200 711.900 219,100 987,700 218,300 767,100 165,800 3,480,100 12.306 104,987 260,784 780.605 244.839 987.906 218,513 579,512 313.610 17.986 103,513 286,912 786,105 239,876 1,021,748 235,465 622,662 .736 1.890 1.293 ,979 .843 .855 1.016 1.123 1.021 .983 .896 .890 .989 .885 .903 .988 .987 .955 .960 .959 .890 .998 1.321 1.229 1.044 .552 .990 D-l -22 UTAH INDUSTRY A3, FORESTRY (, FISH MINING CON. CONSTRUCTION ' MANUFACTURING TRANS, COMM f, P UTILS TRADE FIN, INS. 6 REAL EST SERVICFS GOVERNMENT APPFNDIX TABLE D-l EMPLOYMENT BY MAJOR INDUSTRY GROUP. BY STATF. 1971 AND 1973 TOTAL 480 11.930 14,300 46,050 16,880 77,940 14,290 94,200 20,190 296,260 19 7 1 — 300 12,900 16,100 48,900 16,900 82,900 15,500 97,000 22.200 312.700 563 12.331 13.387 52W44 17.852 79.533 15,105 34.459 43,000 268,974 506 12,173 14,725 50,332 16,800 77,775 15.653 52.969 CWHS 10*/ CWHS 1* UI 1.600 .853 .940 .925 .967 .980 .888 1.068 .971 .942 .873 .915 .999 .946 1.005 .940 .980 1.002 .922 .946 .913 .971 2.734 1.778 .909 .470 19 7 3 A5» FORESTRY f, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM 6 P UTIL TRADE FIN, INS, 6 REAL EST SERVICES GOVERNMENT TOTAL 660 10,950 18,010 52,100 17,370 90,990 16,390 103,370 22,180 332,020 1 ,400 11,200 17,900 54,200 17,300 93,500 18,300 105,200 24,400 343,400 762 12,222 20,034 61,345 19,566 93,688 17,878 53,591 62,210 341.296 618 12.060 21.612 58.351 19.531 94,058 19,239 61,231 .471 .866 1.068 .978 .896 .908 1.006 .899 .833 .961 .849 .893 1.004 .888 .889 .973 .971 .967 .896 .917 .852 .983 1.929 1.688 .909 .357 VERMONT AG, FORESTRY & FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM 6 P UTILS TRADE FIN, INS, 6 REAL EST SERVICES GOVERNMENT TOTAL 460 910 6,380 34,810 6,810 29,680 5,550 41,990 11,590 138,180 700 l ,000 6,800 40,400 8,500 29,400 4,300 39,200 11,100 141 ,400 331 929 6.667 37,508 6,530 25,987 5,266 13,722 3,869 100,809 691 950 6,269 36.388 6,358 30,251 5,661 29,957 .657 1.390 .666 .910 • 9P0 .958 .938 .957 l.oie .862 .928 .957 .801 1.043 1.071 1.010 1.142 .981 1.291 1.054 .980 1.071 3.060 1.402 1.044 2.996 .977 19 7 3 AG. FORESTRY t FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM 6 P UTILS TRADE FIN, INS, £, REAL EST SERVICFS GOVERNMENT TOTAL 720 850 7,190 30,790 6,880 30,180 5,070 44,390 12,260 138,330 I . ilOO 600 7,500 31,800 7,500 28,900 5,100 42,500 12,600 137.600 455 879 8 .010 40 .307 7 .302 32 .006 6 .373 28 .692 7 .847 131 .871 683 717 7 .546 39 .534 7 .164 32 835 6 ,454 33 ,003 127 ,936 .655 1.582 1.054 1.417 .067 1.185 .959 .898 .953 .968 .764 .779 .917 .942 .960 1.044 .943 .919 .994 • 796 .786 1.044 1.547 ! .345 .973 1.562 D-l-23 VIRGINIA IN?. S T =1 AG.F3RFSTRY & FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. CP V '1 £. P UTILS TRADE FIN. INS. £, REAL EST SERVICFS GOVERNMENT APPENDIX TABLF D-l EMPLOYMENT BY MAJOR INDUSTRY GROUP. BY STATE. 1971 AND 1973 CBP 5,560 15.100 104,670 334.980 75,790 287.640 66,610 345,480 69,400 19 7 1 5,000 19,000 107.000 362.000 82.200 297,700 71,600 360.900 66.700 3,504 15,557 85.137 354.428 77.120 285.669 61 .994 120.247 146.752 TOTAL 4,193 16,959 90,649 348,414 77,937 300,788 69,518 199,497 1,107,95? CWHS 10*/ CWHS 1% UI 1.112 1.587 1.326 .795 .971 .890 .978 1.229 1.155 .925 .945 .961 .922 .983 .972 .967 I. 008 .957 .930 1.074 .958 .957 2.873 1.732 1.040 .473 19 7 3 AG. FORESTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM 6 P UTILS TRADE FIN, INS, £, REAL EST SERVICFS GOVERNMENT TOTAL 5,830 13,930 122,100 352,580 69,070 298,03" 75,090 377,710 78,170 9,300 14,200 120,900 362,700 74,200 311,700 78,500 378, 2P0 71,500 5.813 15.827 115.741 397.777 86.558 347,388 80,203 217,302 188,017 5,189 17,721 1 15,686 390,515 82,259 348,362 83.350 234,739 .981 .880 .786 1.010 1.055 1.055 .972 .886 .903 .931 .796 .840 .956 .858 .856 .957 .936 .901 .999 1.738 1.609 1.093 .416 WASHINGTON AG, FORESTRY (, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM 6 P UTILS TRADE FIN, INS, 6 REAL EST SERVICFS GOVERNMFNT TOTAL 6,250 1,060 57,330 217,030 59,110 245,120 56,380 284,170 58,450 9R4.900 — 19 7 1 7,000 2,100 58.300 229.000 61 .600 242.000 56.300 288.600 57.900 4.429 1.534 46.760 212.002 57.260 230.087 55.738 118.785 117.143 843.738 4,482 1,554 47,749 210,368 56,844 228,989 56,951 160,186 .893 1.411 1 .394 .505 .691 .682 .983 1.226 1.201 .948 1.024 1.032 .960 1.032 1.040 1.013 1.065 1.070 1.001 1.012 .990 .985 2.392 1 .774 1.009 .499 .982 1.167 — - 1 9 7 3 AG. FORESTRY & FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM TRADE FIN, INS. 6 SERVICFS GOVERNMENT TOTAL 6 P UTILS 7,050 1,330 58,800 187,110 57,950 253,040 56,290 297,430 66,150 985,150 8,400 2,100 55,300 193.600 59.500 251.000 58.400 295,300 67,500 991.100 5.889 1.733 51.893 234,990 59,019 251.763 61 .025 170,243 123.964 960,519 5,822 1,481 55,932 235.138 59,278 256,039 61.613 180,072 .839 1.197 1.211 .633 .767 .898 1.063 1.133 1.051 .966 .796 .796 .974 .982 .978 1.008 1.005 .988 .964 .922 .914 1.007 1.747 1.652 .980 .534 ,994 D-l -24 WESTVIRGINIA INDUSTRY A3,F0RFSTRY £, FISH MINING CON. CONSTRUCTION ' MANUFACTURING TRANS. COMM 6 P UTILS TRADE FIN, INS. 6 REAL EST SERVICES GOVERNMENT APPENDIX TABLE D-l EMPLOYMENT RY MAJOR INDUSTRY GROUP. BY STATE. 1971 AND 1973 TOTAL 840 46, 340 31,200 115,490 29,210 96,090 15,650 114,240 19,250 468,310 ---1971 1 ,000 53.900 33.500 123.000 28.300 102.800 16,100 110,800 21,500 490.900 8R6 52,457 24,729 124,110 27,956 84,385 13,347 28,850 13,514 370,234 719 49,562 23,409 119,735 29,140 96,290 16,745 61,051 CWHS 10«/ CWHS 1* HI ,840 .948 1 .168 ,860 .883 .935 • 931 1.262 1.333 .939 .931 .965 1.032 1.045 1.00? .935 1.139 .998 .972 1.173 .935 1.031 3.960 1.871 .895 1.424 AS.FORFSTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM £, P UTILS TRADE FIN. INS, f, REAL FST SERVICFS GOVERNMENT TOTAL 830 40,600 30,590 104,900 25,980 97,840 16,880 119,220 21,000 457,840 1,900 46,700 32,200 103.800 21,800 100,500 19,000 120,000 22,600 473,500 908 51,447 31.065 127.222 30,9i,8 104,350 16,741 59,994 27.356 450,031 755 49,817 27,434 124,118 28,755 104,404 18,868 66,720 .437 .914 1.099 .869 .789 .815 .950 .985 1.115 .964 .825 .845 1.192 .839 .903 .974 .938 .937 .888 1.008 .895 .994 1.987 1.787 .929 .768 WISCONSIN AG, FORESTRY £, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM 6 P UTILS TRADE FIN, INS, !, REAL EST SERVICES GOVERNMENT 3,540 1,600 52,160 492,460 53,450 310,190 60,550 289,070 120,570 — 19 7 1 4,500 2,100 50,900 501,100 66,500 317,000 67,700 287,100 124,300 2,389 1.767 43,941 463,060 65,727 291,731 54,485 106,346 164,561 TOTAL 3,410 2,001 50,057 471,048 65,192 323,318 63,920 213.131 1,192,077 .787 1.482 1.038 .762 .905 .800 1.025 1.187 1.042 .983 1.063 1.045 .804 .813 .820 .979 1.063 .959 .894 1.111 .947 1.007 2.718 1.356 .970 .733 AG, FORESTRY {, FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS, COMM {, P UTILS TRADE FIN, INS, 5 REAL EST SERVICES GOVERNMENT TOTAL 4,540 1,340 57,470 494,680 64,600 321,110 63,480 317,760 119,830 6,300 1 ,500 57,300 492,600 64,000 330,500 65,300 317,500 117,400 1 ,452,400 3,575 1.936 56,589 517,456 71,883 349,703 66,416 221,641 121.633 3,830 2,045 57,692 514,652 67,979 349,566 70,988 241,001 .721 1.270 1.185 .893 .692 .655 1.003 1.016 .996 1.004 .956 .961 1.009 .899 .950 .972 .918 .919 .972 .956 .894 1.001 1.434 1.319 1.021 .985 D-l -25 WYOMING INDUSTRY A3, FORESTRY £, FISH FINING CON. CONSTRUCTION' MANUFACTURING TRANS. COMM {• P UTILS TRADE FIN. INS. £. REAL EST SERVICES GOVERNMENT APPENDIX TABLE D-l EMPLOYMENT BY MAJOR INDUSTRY GROUP. BY STATE* 1971 AND 1973 UI 19 7 1 — TOTAL 300 500 337 295 9,0*0 9,200 10,120 9,233 5,220 7,600 5,973 5,285 6.920 6.000 6.800 6.780 6,650 6.100 6,805 6,512 20,830 25.900 22.409 23.275 3,480 3.100 4.386 3,587 28,120 29.800 11.012 13,917 6,960 10.200 5.888 89,520 98.400 73.730 68.884 CWHS 10*/ CWHS 1* UI CBP .600 .890 1.017 .983 .893 .979 .687 .874 .988 1.153 1.01B 1.021 1.090 .977 1.021 .804 .930 .895 1.123 .793 .970 .944 2.554 2.021 .878 1.522 .910 — 19 7 3 — A3. FORESTRY & FISH MINING CON. CONSTRUCTION MANUFACTURING TRANS. COMM £, P UTILS TRADE FIN, INS. 6 REAL EST SERVICES GOVERNMENT TOTAL 310 900 485 319 9,590 9,700 12.092 10.969 5,850 7,900 8.750 9,027 8,170 7,800 7.660 7,990 6,290 5,700 7.293 7,545 21,590 26,400 25.005 25,971 3,860 3,500 3,788 4,063 27,780 26,300 13,995 15,981 9,020 9.900 9.644 92,460 98.100 88.712 81.865 .344 .639 .972 .989 .793 .874 .741 .669 .648 1.047 1.067 1.023 1.104 .862 .834 .818 .863 .831 1.103 1.019 .950 1.056 1.985 1.738 .911 .935 D-l -26 o a CL *+ a. >■ sr — i/l o Cj • •••••••• ^H*c^r-cc.- CM ir\ r- *fr r-.J Csj >o in o o r- r- a ' H «-» ITt (M C (M h- -t ^ f^ co n P rocrm m ir< <\j o m ^ oo ■— i r- r- h c in «nm o h <\j if> eg a CN. rsj rn — ' %0 (Ni m r*- -ooorg h- o c o rr, o -h •-< ir\ <\i co r\j *t oo «-trof\jccm^fSjm>3- • •••••••• <-• o o o ^ tr. o m ^ tf (E .-. 1^1 O D h- o ■z in ■1 UJ Z> t- r > z: a u. _j u a 2 D: UJ c LU I- < V- io Q LU Z D I C CO CC ^j G >J i N-CMCNCOrHrH r- IT- N^H m m ^ o (M ^ O -H *T r- m a o a. rr, o- CN, cc a o ^ «o f\j r- O r- rsj t> -o . o r\j m r\j .— < i o o r> r- m •i. 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SEX, AND AGE GRCUP, 1960 AND 1970 CWHS 1960 CENSUS CWH5/CEN CWHS 1970 CENSUS CWHb/CEN ("ALES 15 - 19 22,000 44 ,437 .495 20 - 24 60,100 70,095 .857 25 - 29 53,500 7 7, (X'7 .760 30 - 34 61 ,600 84,276 .731 35 - 39 59,600 6 5 , 6 8 1 .696 40 - 44 63,400 H2,076 .772 45 - 4y 56,300 81,295 .693 50 - 54 45,200 69,412 .651 55 - 59 32,000 53,962 .593 60 - 64 20,600 33,876 .60b 65 6 OVER 13,400 3 0,77 h .435 TOTAL 492,700 712 ,895 .691 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 4 ( ;ver TOTAL 12,900 33,700 29,100 31,500 34,700 28.700 30,500 24,400 15,000 9,300 4,700 254,500 24,044 40,254 34 ,863 39,432 43,814 43,355 41,719 33,424 24,170 13,352 10,777 349,224 .537 .837 .83<» .799 .792 .662 .731 .730 .621 .697 .436 .729 FtMALtS 42,700 83,300 79,900 7 5,800 59,000 62,500 62,300 58,500 50,400 35,600 19,600 629,600 19,400 59,000 46,800 41 ,800 41 ,300 45,600 43,100 36,800 30,900 2 0, 100 10,000 394,800 49,089 84,512 90,490 80,620 79,476 81 ,907 79,547 72,368 62,441 44,802 28,33 7 753, 5R9 25,682 63,501 47,776 44,554 47,152 50,917 48,313 42,564 36,150 23,661 14,436 444,706 .870 .986 .683 .940 .742 .763 .783 .808 .807 .795 .692 ,835 .755 .929 .980 .938 .876 .696 .692 .865 .655 .849 .693 .888 &RAND TOTAL 747,200 1 ,062,119 .703 1 ,024,%00 1 ,198,295 .855 ALASKA 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 £ OVER TOTAL 700 1,500 3,100 3,300 3,400 2,400 2,700 2,500 2,000 900 700 23,200 1 .866 ,375 2,655 .565 4,41b .702 5,714 .57,) 5,355 .635 4,786 .501 4,302 .626- 3,287 .761 2,228 • 89-i 1,18 5 .759 693 • 70<, MALES 36,t-91 .632 3,300 6,000 8,800 7,400 7,000 6,600 7,700 4,800 4,200 3,000 1 ,000 59,800 3,289 1.003 4,810 1.247 7,867 1.119 7,574 .9 n 6,427 1.089 7,075 .933 6,245 1.233 4, 7b7 1.0C7 3,528 1.190 1,824 1.645 1,189 .841 1.095 15 - 19 20 - 24 25 - 2 9 30 - 34 35 - 39 40 - 44 45 - 4 ) 50 - 54 55 - 59 60 - 64 65 £ 3VEK TCT4L 900 2,400 1,400 1,700 1,400 1,100 1,800 1,500 800 40 200 13,600 FEMALES 1,731 .520 2.500 2,473 .9 70 10.700 2,771 .505 5,400 2,791 .609 5,100 2,971 .471 2,900 2,702 .407 3,600 2 ,144 .840 3,900 1 ,630 .920 2,700 1,11b .716 2,600 429 .932 1,500 287 .697 1,100 3,061 .817 6,291 1.701 5,114 1.056 4,259 1.197 4,096 .708 3,642 .988 3,391 1.150 2,422 1.115 1,850 1.405 873 1.716 512 2.148 21 ,047 .646 42 ,000 35,511 1.183 GRAND TOTAL 36,800 5 7,738 .637 101 ,800 90,106 1.130 D-4-1 APPENDIX TABLE D-4 CWHS AMD CENSUS EMPLOYMENT , LY STATE, SEX, AND AGE GRCUP, 1960 AND 1970 ARIZONA AGE GROLP 15 - 19 20 - 2- 25 - 2<* 3 3 - 34 35 - 3 9 1,0 - hm 45 - 49 5C - 5m 55 - 59 60 - 64 65 t , :ver CWHS t:t^ l 8,300 14,400 11 ,400 12,600 14,300 14,500 11,100 9,100 5,700 4,900 2,500 108,800 1960 CENSUS 10,948 14,140 11 ,867 L 3 » 7 3 fc 16,842 17,207 16,302 12,739 9,058 5,211 3,990 132,042 CWHS/CEN 15 - 19 12,100 U ,705 • 647 20 - 24 24,200 2 9,951 .808 25 - 29 27,400 34,554 .793 30 - 3m 29,500 36,620 .806 35 - 3- 30,400 38,970 .780 40 - 44 26,400 35,674 .736 45 - 49 24,500 32,900 .745 50 - 54 19,700 26,521 .743 55 - 59 14,900 19,661 .758 60 - 64 7,800 1 1 .999 .650 65 6 :vek 7,800 9,727 .802 TCTAL 224,700 245,482 .760 .758 1.018 .961 .917 .849 .843 .681 .714 .629 .940 .627 .824 MALES FEMALES CWHS 3 0,500 44,400 46,000 40,000 35,100 33,200 31,600 26,200 24,700 15,200 10,200 337,100 16,800 37,600 28,300 21,700 20,800 22,500 21,900 19,300 14,900 9,200 6,400 219,400 1970 CENSUS 30,635 42,350 46,694 42,416 40,909 42,365 41,690 37,318 30,250 20,556 13,636 388,819 20,794 34,669 24,273 21,167 22,115 23,663 25,143 21,711 17,316 11,386 7,528 229,765 CWHS/CEN .996 1.048 .9e5 .943 .858 ,784 .758 .702 .817 .739 .748 .867 .808 1.085 1.166 1.025 .941 .951 .871 .889 .860 .808 .850 .955 GKAI X TAbLt D-4 CWHS .aND CENSUS EMPLOYMENT, tY STATE, SEX, AND AGE GRCUPi 1960 AND 1970 CALIFORNIA AGE GROUP CWHS 1 9 (i CENSUS C.vHS/CEM CWHS MALES 1970 CENSUS CWHS/CtN 15 - 19 115,400 187 ,749 .615 245,300 300,546 .816 20 - 24 281 , 70" 317,841 ,886 485,500 485,537 1.000 25 - 29 351,400 421 ,943 .833 539,600 580,202 .930 30 - 34 389,700 4 7 9, 2 (.9 .013 455,300 522,910 .871 35 - 39 385,200 523,271 .736 395,200 498,856 .792 40 - 44 347,200 475,694 .730 403,900 527,883 .765 45 - 49 316,600 4 3 5,680 .72 7 395,700 535,691 .739 50 - 54 266,900 3 ',9, 824 .742 329,100 460,850 .714 55 - 59 187,200 290,331 .645 269,600 369,600 .729 60 - 64 133,100 196 ,682 .677 196,000 244,314 .802 65 6 OVER 102,500 15 3,852 .666 108,600 150,487 .722 TCTAL 2,876,90" 3,842, 136 .749 3,823,800 4,676,876 .818 FEMALES 15 - 19 84,7nn 122 ,641 .691 156,500 216,106 .724 20 _ 24 192,900 195,265 .988 459,600 445,589 1.031 25 - 29 149,600 168,995 .885 310,900 327,585 .949 30 - 34 161 ,100 19 5,114 .826 226,400 261,596 .865 35 - 39 203,200 245,32? .828 219,900 265,732 .828 40 - 44 194,000 243,72? .796 251 ,700 304, 194 .827 45 - 49 175,100 229,640 .762 264,900 318,493 ,e32 50 - 54 139,400 187,232 .745 221 ,100 270,826 .816 55 - 59 101,900 144,311 .706 173,900 214,913 .809 60 - 64 72,900 92,597 .737 114,100 134,599 .848 65 6 OVER 51 ,4 71,381 .720 72,800 86,768 .839 TCTAL 1 ,526,200 1,896,227 .805 2,471 ,800 2,846,401 .866 GUAM) TOTAL 4,403,100 5,738,363 .767 6,295,600 7,523,277 .837 COLORADO MALES 15 - 19 16,400 25,446 .645 35,200 39,515 .891 20 - 24 31,40-. 36,637 .852 54,900 54,018 1.016 25 - 29 36,20" 4 5,698 .792 53,300 62, 788 .849 30 - 34 40,800 5 3,110 .768 46,200 57,198 .808 35 - 39 35,900 5 3,657 .669 3 9,900 55, 759 .716 40 - 44 31,500 49,3 15 .639 38,900 5 7,235 .680 45 - 49 26,100 45,033 .560 35,500 55,249 .643 50 - 54 22.800 38,139 .598 30,700 48,007 .639 5 5 - 5 9 16,200 31,159 .520 23,800 38,580 .617 60 - 64 12,400 21 ,64( .573 19,300 27,064 .713 65 (, CVER 8,80 J 20,504 .429 12,600 18,930 .666 TOTAL 278,500 4 2 C ,544 .662 390,300 514,343 .759 FEMALES 15 - 19 20 - 24 2 5 - 2 9 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 6 OVER TCTAL 9,700 20,800 14,400 14,200 14,900 1 7,700 15,200 12,300 7,700 6,100 4,000 137,000 17,406 22,365 18,351 20,018 23,708 24,320 2 3,001 19,950 15,330 9,666 P, 001 202,116 .557 .930 .785 .709 .628 .728 .661 .617 .502 .631 .500 .678 20,500 48,200 30,600 20,800 20,900 23,700 22,100 21,100 15,000 10,200 5,900 239,000 29,499 .695 49,934 .965 35,375 .865 28,637 .726 31,261 .669 33,749 • 7C2 32,811 ,674 28,271 .746 23,551 .637 14,621 .698 9,717 .607 317,426 .753 GRAND TCTAL 4 15,500 622,660 .667 629,300 831 ,769 .757 D-4-3 CONNECTICUT AGE GROUP APPENDIX TABLE D-4 CWHS AIVJ CENSUS EKPLOYrtKT, tY bTATE, SEX, AND AGE GROUP, 1960 AND 1970 CWHS 1960 CENSUS CwHS/CtN CWHS MALES 1970 CENSUS CWHS/CEN 15 - 19 24.400 31,365 .778 48,800 50,449 .967 20 - 24 43,000 47,371 1.013 80, 700 72,7 71 1.109 25 - 2 9 62,100 64,593 .961 88,900 8 1 ,548 1.015 30 - 34 67,200 79,856 .842 72,300 76,984 .939 35 - 39 72,900 87,930 .829 64,200 76,628 .838 40 - 44 74,100 85,749 .864 66,800 86,735 .770 45 - 49 65,900 76,921 .835 76,700 89,804 .854 50 - 54 47,700 64,179 .743 74,100 82,728 ,896 55 - 59 40,300 52,590 .766 61 ,900 68,288 .906 60 - 64 33,800 38,910 .869 42,100 46,612 .9C3 65 6 3VER 28,800 31,595 .912 29,400 31,768 .925 TOTAL 565,200 663,055 .852 FE 705,900 •1ALES 770,315 .916 15 - 19 19,200 26,434 .726 34,500 43,237 .798 20 - 2^ 35,200 34,317 1.026 70,400 67,841 1.038 25 - 29 26,100 25,813 1.011 41 ,500 43,195 .961 30 - 3h 26,800 29,257 .916 32,900 35,474 .927 35 - 39 34,500 38,534 .895 36,800 40,622 .906 40 - -.., 43,500 45,635 .953 43,700 52,023 .840 45 - 49 38,200 44,B42 .852 53,200 58,130 .915 50 - 54 35,300 37,006 .954 53,400 55,101 .969 55 - 59 25,300 29,669 .853 41,200 44,104 .934 60 - 64 17,100 18,640 .917 26,200 29,0OB ,9C3 65 6 :ver 10,900 14,257 .765 17,600 19,207 .927 TCT4L 312,100 344 ,404 ,90b 451 ,600 487,942 ,926 GRAND TOTAL 877,300 1 ,007,463 .871 1,157,500 1,258,257 .920 DELAWARE MALES 15 - 19 20 - 24 25 - 29 30 - 34 3b - 39 40 - 44 45 - 4 3 50 - 54 55 - 59 60 - 64 65 I :ver TOTAL 3,600 7,800 11,200 12,000 13,100 13,000 11 ,600 8,900 7,400 4,400 4,600 97,600 15 - \i 2,700 20 - 24 7,800 25 - 29 5,300 30 - 54 6,200 35 - 39 5,600 -.0 - 44 7,500 -.5 - 4 3 5,900 50 - 54 4,600 55 - 5 3 3,000 60 - 64 1,900 65 6 ;ver 2,100 4,772 .754 8,560 .911 12,219 .917 14,136 .849 14,669 .893 13,943 .932 11,693 .992 10,033 .887 6,102 .913 5,592 .787 4,67b .983 108,397 .900 TO"AL 52,800 3,693 5,744 5,121 5,829 fc.433 6,989 6,336 5,134 4,043 2,560 2,120 54,002 .731 1.358 1.035 1.064 .871 1.073 .931 .935 .742 .742 .991 .978 FEMALES 8,300 14,900 16,400 11,900 12,000 12,400 12,700 11,100 10,700 6,200 4,200 120,800 5,300 11,700 9,300 7,800 6,000 7,700 9,300 8,500 5,700 3,700 2,300 77,300 8,552 13,550 16,307 14,665 14,421 14,743 15,195 13,321 10,171 7,344 4,165 132,434 .971 1.100 1.006 .811 .832 .841 .836 .833 1.052 .844 1.008 .912 6,329 .837 12,410 .943 6,381 1.110 7,266 1.073 7,745 .775 6,153 .944 8,845 1.C51 7,757 1.096 5,952 .958 4,114 .899 2,680 .658 79,632 .971 GRAuD TOTAL 150,400 162,395 .926 196,100 212,066 .934 D-4-4 APPENDIX TABLE D-4 CWHS AND CENSUS EMPLOYMENT. LY STATE» SEX. AND AGE GRCUP. 1960 AND 1970 DISTRICT OF COLUMBIA AGE GROUP 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 £ DVER TOTAL GRAND TOTAL CVvHS ;5 - i9 6.200 20 - 24 20,500 25 - 29 20,600 30 - 34 24,600 35 - 39 26,700 40 - 44 24,800 45 - 49 19,400 50 - 54 16,900 55 - 59 13,800 60 - 64 10,700 65 6 OVER 6,400 TOTAL 190,600 5,200 19,700 14,500 13,200 16,600 15,200 15,700 13,700 10,200 5,500 4,700 134,200 324,800 1960 CENSUS 6,765 16,678 15,112 16,352 18,283 18,784 17,919 17,084 13,160 8,647 6,859 155,863 341 ,035 CWHS/CEN 6,489 .955 18,492 1.109 22,569 .913 22,258 1.105 21,599 1.236 20,036 1.238 19,766 .981 18,613 .908 15,234 .906 10,936 .978 9,180 .697 185,172 1.029 .766 1.167 .960 .807 .908 .809 .876 .802 .775 .636 .685 .861 .952 MALES FEMALES CWHS 11,100 36,100 37,400 28,800 21,300 19,600 20,200 18,300 14,600 14,200 11,600 233,200 12,700 42,700 25,400 16,600 12,600 13,200 13,700 14,200 10,900 7,600 5,900 175,500 408,700 1970 CENSUS CWHb/CEN 8,205 1.353 20,797 1.736 25,403 1.472 20,388 1.413 17,917 1.189 17,406 1.126 16,659 1.213 15,356 1.192 12,599 1.159 9,875 1.438 7,369 1.574 171,974 1.356 8,262 1.537 27,459 1.555 22,789 1.115 15,847 1.048 14,260 .884 15,174 .870 16,385 .836 14,728 .964 12,629 .863 9,234 .823 7,244 .814 164,011 1,070 335,985 1.216 FLORIDA 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 6 OVER TOTAL MALES lb - 19 20 - 24 25 - 29 3 - 34 55 - 39 4 - 44 45 - 4^ 50 - 5h 55 - 59 60 - 64 65 I DVER TOTAL 42,7oC 101,700 100,900 107,200 107,900 97,400 93,200 76,000 5fc,300 3'8,400 34,100 855,800 27,900 62,600 46,30C 49,600 62,3o0 59,600 52,500 48,50C' 31,600 19.000 13,000 473,30') 59,616 100,521 121,201 138,464 142,052 133,737 123,848 105,357 84,621 56,249 49,066 1 ,114,752 38,023 60,392 57,735 67,549 77,903 75,511 72,722 59,147 43,615 26,329 20,264 599,390 .716 1.012 .833 .774 .760 .728 .753 .721 .665 .683 .695 .768 .734 1.037 .802 .737 .800 .792 .722 .820 .721 .722 .642 .790 FEMALES 96,400 151,000 145,300 133,100 117,600 128,500 125,500 109,400 93,900 65,800 63,900 1,230,400 60,400 140,200 91,600 78,200 77,200 89,000 95,600 84,400 65,200 48,600 31,100 861,700 10 3,049 142,667 162,240 149,389 150,895 166,527 163,505 148,123 121,583 89,463 74,826 1,472,267 72,368 131,687 96,595 89,102 92,383 109,994 108,458 94,641 78,508 54,250 39,285 967,271 .935 1.058 .896 .891 .779 .772 .768 .739 .772 .735 .854 .836 .835 1.065 .950 .878 .836 .809 .881 .692 .830 .896 .792 .891 GRAND TOTAL 1,329,100 1 ,714,142 .775 2,092,100 2,439,538 .858 D-4-5 GEORGIA APPENDIX TABLE D-4 CWHS AND CENSUS EMPLOYMENT. BY STATE* SEX. AND AGE GRCUP, I960 AND 1970 AGE GROtP CWHS 1960 CENSUS 1970 CWHS/CEN 15 - 19 39,400 62,387 .632 20 - 24 81.300 90,036 .903 25 - 29 8 5.90 99,e41 .860 30 - 34 91.500 105,123 .870 35 - 39 79.300 109,228 .726 40 - 44 78,700 103.806 .758 45 - 49 69,300 98,306 .705 50 - 54 55,800 80,895 .69'0 55 - 59 34,400 61,436 .560 60 - 64 22,500 38,362 .586 5 6 OVER 16.600 35,902 .462 CTAL 654,700 885,344 .739 15 - IS 20 - 24 25 - 29 30 - 3 m 35 - 39 40 - 4h ■.5 - 49 5J - 5 m 55 - c m 60 - 64 65 6 1 3VER TOTAL 21.000 51,500 41,800 47,100 47,300 50,000 44,800 37,100 23,900 12,500 9,700 386,700 35,201 56,779 51,793 57,037 62,901 60,905 57,278 46,130 32,892 18,647 14,496 494,059 .597 .907 .807 .826 .752 .821 .782 .804 .727 .670 .669 .783 MALES FEMALES CWHS 76,200 133,200 136,000 113,100 99,400 99,700 86,500 83,000 69,100 47,200 27,000 970,400 40,700 109,700 87,300 65,500 64,500 67,400 64,000 58,700 48,200 32,000 19,500 657,500 CENSUS 76,989 127,874 139,033 117,921 110,193 110,138 106,818 95,362 79,446 53,777 37,530 1,055,081 49,938 109,007 83,850 71,068 71,874 74,086 70,522 62,869 51,264 33,744 22,000 700,222 CWHS/CEN .990 1.042 .978 .959 .902 .905 .810 .870 .870 .878 .719 .920 .815 1.0C6 1.041 .922 ,897 .910 .9C8 .934 .940 .948 .886 .939 GRAND TCTAL 1.041,400 1,379,403 .755 1,627,900 1,755,303 .927 HAWAII 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 h3 - 49 5 - 54 5 5 - 59 60 - 64 65 ( , CVER MALES TOTAL 3,800 9,400 10,400 9,400 13,500 12,800 9,800 9,500 7,100 4,300 2,900 92,900 15 - 19 2,300 20 - 24 9,700 25 - 29 7,200 30 - 34 9,900 35 - 39 10,300 40 - 44 6,900 45 - 49 4,800 50 - 54 3,300 55 - 59 2,300 60 - 64 1,700 65 6 OVER 600 TOTAL 59,0o0 6,5 86 10,159 13,450 17,099 19,353 17,899 16,908 13,924 11,196 6,265 3,341 136,200 4,196 6,449 8,265 10,937 11 ,226 10,012 7,367 4,957 3,565 2,050 1,214 72,238 .577 .925 .773 .550 .698 .715 ,580 .682 .634 .684 .868 .682 .548 1.148 .871 .905 .918 .689 .652 .666 .645 .829 .494 .817 FEMALES 9,800 18,200 19,600 14,000 12,400 12.700 15,100 13,000 8,600 7,700 4,900 136,000 7,200 19,400 12,700 11,900 11,200 14,000 13,600 9,800 6,400 3,600 1,200 111,000 10,073 .973 17,991 1.012 20,896 .938 16,885 .829 17,004 .729 19,778 .642 20,496 .737 17,431 .746 14,903 .577 9,557 .806 4,758 1.030 169,772 .801 8,681 18,867 14,488 12,010 12,437 15,104 13,693 10,238 6,556 3,172 1.815 117,061 .829 1.028 .877 .991 .901 .927 .993 .957 .976 1.135 .661 .948 GRAND TCTAt 151,900 208,436 .729 247.000 286,833 .661 D-4-6 IDAHO AGE GROUP APPENDIX TABLE D-4 CWHS AND CENSUb EMPLOYMENT, bY STATE. SEX, AND AGE GROUP, 1960 AND 1970 CWHS 15 - 19 4,600 20 - 24 12,400 25 - 29 12,000 30 - 34 12,400 35 - 39 10,200 40 - 44 13,500 45 - 49 12,900 50 - 54 8,700 55 - 59 5,600 60 - 64 5,200 65 6 OVER 3,500 TOTAL 101,200 15 - 19 3,000 20 - 24 7,000 25 - 29 4,100 30 - 34 4,700 35 - 39 6,400 40 - 44 5,200 45 - 49 5,400 50 - 54 6,700 55 - 59 4,400 60 - 64 1,600 65 6 OVER 1,800 1960 CENSUS TOTAL 50,3o0 6,968 6,282 4,814 5,956 7,650 8,335 8,264 6,824 4,931 3,036 2,995 66,075 CwHS/CEN 12,739 .361 14,365 .863 15,923 .754 18,083 .686 19,114 .534 18,963 .711 18,771 .687 16,03e .542 12,101 .479 8,895 .585 9,591 .365 164,603 .615 .429 1.114 .852 .789 .837 .624 .653 .982 .892 .527 .601 .761 MALES FEMALES CWHS 8,300 15,900 15,900 10,200 10,900 10,400 9,600 9,800 9,500 7,100 3,300 110,900 6,900 11,800 9,100 7,700 7,200 6,700 8,800 5,600 6,400 5,200 3,200 78,600 1970 CENSUS CWhS/CEN 14,739 .563 16,728 .951 18,770 .847 17,601 .580 16,111 .677 17,200 .605 17,038 .563 16,816 .583 15,424 .616 11,148 .637 7,937 .416 69,512 .654 9,712 .710 11,846 .996 8,448 1.077 7,788 .989 8,163 .682 9,617 .697 9,687 .908 9,386 .597 7,777 .823 5,023 1.035 3,275 .977 90,722 GRAND TOTAL 151,500 230,678 .657 189,500 260,234 .728 ILLINOIS MALES 15 - 19 86,100 129,067 .667 165,000 185,455 .890 20 - 24 198,800 212,767 .934 284,000 274,629 1.034 25 - 29 229,900 268,106 .857 298,800 325,624 ,918 30 - 34 265,600 306,556 .866 250,200 287,127 .871 35 - 39 265,300 320,216 .828 238,600 278,605 .856 40 - 44 233,000 303,303 .768 251,700 299,899 .839 45 - 49 234,800 291,915 .804 256,800 300,108 .856 50 - 54 189,100 255,237 .741 219,100 274,527 .798 55 - 59 152,200 220,781 .689 200,700 240,652 .834 60 - 64 117,900 163,222 .722 144,800 172,886 .838 65 6 :ver 104,300 136,900 .762 104,200 120,783 .863 TOTAL 2,077,000 2,608,092 .796 2,413,900 2,760,295 .875 FEMALES 15 - 19 77,500 106,854 .725 138,700 162,254 .855 20 - 24 145,100 136,727 1.061 249,600 247,229 1.010 25 - 29 94,700 101 ,368 .9 34 159,300 163,792 .973 30 - 34 102,800 112,685 .912 121,300 133,578 .908 35 - 39 118,200 139,131 .850 123,400 143,703 .859 40 - 44 133,300 154,130 .865 148,800 170,390 .873 45 - 49 134,500 156,969 .857 160,700 182,012 .883 50 - 54 118,900 135,196 .879 153,200 171,301 .694 55 - 59 86,200 107,746 .800 128,000 144,207 .888 60 - 64 57,600 69,101 .834 84,600 97,360 .869 65 6 3VER 47,800 56,857 .812 64,300 70,185 .916 TOTAL 1 ,116,600 1 ,278,784 .873 1,531,900 1,686,011 .909 GRAND TOTAL 3,193,600 3,886,876 .822 3,945,800 4,446,306 .887 D-4-7 INDIANA AGE GROi^P IOWA APPENDIX TABLE D-4 CWHS AND CENSUS EMPLOYMENT. bY STATE. 5EX, AND AGE GRCUP, 1960 AND 1970 OHS 1960 CENSUS CWHS/CEN CWHS MALES MALES 1970 CENSUS CWHS/CEN 15 - 19 43.000 69,044 .623 91.000 96,062 ,947 20 - 24 91.900 107,695 .853 142.400 143,832 .990 25 - 29 106. 400 125,851 .845 147,700 155,089 .952 30 - 34 119.200 141 ,746 .841 120,600 134,543 .896 35 - 39 120,100 146,055 .822 112,300 128,442 .874 40 - 4H 113.200 132,393 .855 119,300 140,907 .647 45 - 49 95.400 125,100 .763 117,000 137,680 .850 50 - 54 83,500 109,188 .765 103,500 121,534 .852 55 - 59 64,500 92.568 .697 85,500 104,908 .815 60 - 64 48,600 69.096 .703 61 ,400 73,221 .839 65 6 I 3VER 37,600 58,145 .647 39,100 50,662 .772 TOTAL 923,400 1 ,176,881 .785 1,139,800 1,286,880 .886 FEMALES 15 - 19 27,700 45,433 .610 57,700 67,132 .860 20 - 24 56,800 57,044 .996 116,400 108,783 1.070 25 - 29 42,800 44,525 .961 72,500 71,459 1.015 30 - 34 45.000 50,565 .890 60,300 64,390 .936 35 - 39 58.800 61,494 .956 65,400 68,602 .953 40 - 4h 58,500 63,694 .918 75,300 79,631 .946 45 - 49 53,900 60,596 .889 78,500 82,076 .956 50 - 54 49,400 53,402 .925 68,300 72,288 ,945 55 - bS 35,900 43,387 .827 58,800 59,563 .987 60 - 64 28,300 27,500 1.029 39,400 39,595 .995 65 6 1 3VER 18,800 24,912 .755 27,200 29,045 .936 TOTAL 475,900 532,552 .894 719,800 742,564 .969 GRAND TOTAL 1,399.300 1,709,433 .819 1,859,600 2,029,444 .916 15 - 19 24.700 49,872 .495 39,000 58,555 ,666 20 - 2m 44.900 60,211 .746 61 ,800 71,533 .864 25 - 29 51,700 66,308 .757 62,600 76,510 .616 30 - 34 48,200 77,150 .625 50,800 69,438 .732 35 - 39 47,000 79,664 ,590 48,100 65,727 .732 40 - 44 44,100 75,890 .581 45,800 71,404 .641 45 - 4 9 40,700 74,485 .546 45,200 72,058 .627 50 - 54 34,200 66,807 .512 40,000 68,259 .586 55 - 59 28,100 56,966 .477 39,400 60,578 ,650 60 - 64 20,600 46,450 .443 26,600 46,925 .609 65 6 1 3VER 22,300 47.344 .471 23,200 40,985 .566 TOTAL 406,500 705,147 ,576 484,500 701,972 ,690 FEMALES 15 - 19 20*900 31 ,931 .655 31,100 44,037 ,7C6 20 - 24 35,800 33,451 1.070 63,300 54,207 1,166 25 - 29 19,400 21 ,281 .912 34,000 33,234 1.023 30 - 3^ 19,900 24,047 .828 30,900 30,400 1.016 35 - 39 24,100 30,006 .803 29,300 32,638 .698 40 - 44 27,800 32,762 .849 30,600 38,718 .790 45 - 4 9 26,800 33,952 .789 35,600 40,596 ,877 50 - 54 27,700 32,157 .861 36,700 39,243 .935 55 - 59 21,600 28,000 .771 30,800 34,943 ,881 60 - 64 15,600 ie,893 .826 24,400 25,634 .952 65 6 ( 3VER 14,500 19,990 .725 17,900 22,656 .790 TOTAL 254,100 306,470 .829 364,600 396,306 .920 GRAND TOTAL 660,600 1,011 ,617 .653 849,100 1,098,278 .773 D-4-8 APPENDIX TABLE D-4 CWHS AND CENSUS EMPLOYMENT* bY STATE* SEX, AMD AGE GROUP, I960 AND 1970 KANSAS AGE GROUP 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 6 OVER TOTAL OHS 13.500 22.300 15.100 17,300 19.500 23,600 19,400 21,600 19,800 11,500 11,200 194,800 1960 CENSUS 22,323 24,614 18,370 20,78V 25,606 27,653 27,992 25,581 21,552 14,246 14,308 243,034 OHS/CEN 15 - 19 19,100 32,985 .579 20 - 24 34,000 43,590 .780 25 - 29 40,800 53,381 .764 30 - 34 43.200 60,939 .709 35 - 39 41,900 64,121 .653 40 - 44 37,300 57,671 .647 45 - 49 32,600 56,625 .576 50 - 54 25,900 51 ,172 .506 55 - 59 24,300 44,503 .546 60 - 64 19,600 34,182 .573 65 6 OVER 18,000 37,186 .484 TOTAL 336,700 536,355 .628 .605 .906 .822 .832 .762 .853 .693 .844 .919 .807 .783 .802 MALES FEMALES CWHS 30,500 48,300 45,600 41,500 35,200 36,500 38,000 35,000 30,300 18,200 18,800 377,900 23,000 44,400 26,100 24,000 21,900 27,400 26,300 28,400 20,200 18,100 14,400 274,200 1970 CENSUS 43,248 56,281 58,521 52,352 51,155 55,996 5?, 546 52,057 46,613 36,217 32,624 542,610 31 ,084 45,804 28,203 25,151 26,666 32,293 32,666 30,153 27,361 19,967 17,300 316,648 CKHb/ctN .7C5 .858 .779 .793 .688 .652 .660 .672 .650 .503 .576 .696 .740 .969 .925 .954 .821 .848 .805 .942 .738 .906 .832 .866 GRAND TOTAL 531,500 779,389 .682 652,100 859,258 .759 KENTUCKY 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 6 OVER MALES TOTAL 15 - 19 20 - 24 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 - 64 65 6 OVER TOTAL 14,100 41,900 47,000 49,800 51,000 47,000 44,800 34,700 27.900 15,700 17,400 391,300 10,900 30,100 21,300 24,600 25,700 24,400 25,600 19,900 12,500 8,900 6,900 210,800 36,673 59,487 68,871 76,791 81,405 73,761 72,430 62,901 53,242 36,709 36,128 658,598 19,360 30,971 25,598 27,840 31 ,983 31,715 31,576 27,565 21,822 13,730 12,423 274,583 .382 .704 .682 .649 .626 .637 .619 .552 .524 .428 .482 .594 .563 .972 .832 .884 .804 .769 .811 .722 .573 .648 .555 .768 FEMALES 34,000 73,400 68,500 59,100 51,600 50,100 49,800 46,400 41,100 25,800 17,200 517,000 20,000 54,800 35,700 29,500 30,800 33,800 36,000 30,600 23,400 17,800 9,500 321,900 45,140 79,374 85,147 75, WO 71,170 76,345 74,660 65,691 56,652 40,785 30,817 700,951 28,965 60,019 41,058 37,068 36,682 41,391 41,211 36,757 31 ,707 21,328 16,170 392,356 .753 .925 .804 .786 .725 .656 .667 .706 ,725 .633 .558 .738 .690 .913 .870 .796 .840 .817 .874 .832 .738 .835 .588 .820 GRAND TOTAL 602,100 933,181 .645 838,900 1,093,307 ,767 D-4-9 APPENDIX TABLE D-4 CWHS AMD CENSUS EMPLOYMENT. BY STATE. SEX. AND AGE GROUP, 1960 AND 1970 LOUISIANA AGE GROtP 15 - 19 20 - 24 25 - Z-> 30 - 34 35 - 39 40 - 4s 45 - t) 50 - 54 55 - 59 60 - 64 65 { , l :ver CWHS TOTAL 10,800 34,100 20,700 24,700 25,100 23,600 22,600 19,000 13,500 7,100 4,400 205,600 1960 CtNSUS 21 ,693 36,599 31 ,288 35,703 39,481 38,348 37,600 31,265 23,017 12,682 8,333 316,009 CWHS/CEN 15 - 19 19,100 35,977 .531 20 - 24 57,300 67,029 .855 25 - 29 58,800 76,130 .753 30 - 34 69,200 88,696 .780 35 - 39 66,000 87,790 .752 40 - 44 57,700 79,491 .726 45 - 49 53,000 77,804 .681 50 - 54 43,500 66,095 .658 55 - 59 31,800 52,624 .604 60 - 64 18,500 32,636 .567 65 6 OVER 14,900 22,555 .661 TOTAL 489,800 688,827 .711 ,498 .932 ,662 .692 ,636 .615 .601 .608 .587 ,560 .528 .651 MALES FEMALES CWHS 3 7,100 86,500 82,800 70,200 61 ,600 62,400 61,500 50,700 44,400 28,200 19,900 605,300 17,600 55,700 35,700 32,100 29,200 34,300 35,100 31,100 23,900 14,200 11,300 320,200 1970 CENSUS 46,243 85,406 94,306 83,814 79,453 85,080 80,344 71,172 60,378 41,550 25,492 753,238 26,278 60,055 44,429 39,383 42,419 46,899 44,033 38,215 32,246 21,550 14,170 409,677 CWHS/CtM .802 1.013 .878 .838 .775 .733 .765 .712 .735 .679 .781 .804 .670 .927 .804 .815 .688 .731 .797 .814 .741 .659 .797 .782 GRAND TOTAL 695,400 1,004,836 ,692 925,500 1,162,915 .796 MAINE MALES 15 - : 5 6,400 12,270 .522 20 - 24 15,900 17,944 .886 25 - 29 16,800 22,487 .747 30 - 3 4 18,400 25,515 .721 35 - 39 19,100 26,232 .728 40 - 44 16,500 24,440 .675 45 - 49 16,100 23,680 .680 50 - 54 15,100 21 ,923 .689 55 - 59 10,800 18,092 .597 60 - 64 9,500 14,417 .659 65 6 ( 3VER 7,300 12,106 .603 TOTAL 151,900 219,106 .693 15 - 19 20 - 24 25 - 2 9 3 J - 34 35 - 3 9 4 - '-4 4 b - 49 50 - 54 55 - 59 60 - 64 65 6 OVER TOTAL 5,000 9,800 7,200 7,400 9,500 9,600 9,700 10,200 7,700 5,200 3,500 84,800 6,718 10,477 8,166 9,717 11,600 12,738 12,917 12,226 10,010 6,921 6,368 109,858 ,574 .935 .882 .762 .819 .754 ,751 ,834 ,769 .751 ,550 ,772 FEMALES 12,700 20,400 21,600 16,800 17,800 18,700 16,000 15,300 13,600 11,200 8,500 172,600 8,300 16,100 9,700 9,400 11,100 12,100 13,000 10,300 10,500 9,200 5,300 115,000 15,182 23,597 24,862 22,378 23,235 24,786 24,598 23,057 19,283 15,350 10,772 227,100 12,032 18,415 12,075 10,862 12,598 15,406 16,030 14,661 12,691 9,251 7,091 141,112 .837 .865 .869 .751 .766 .754 .650 .664 .705 .730 .789 .760 .690 .874 .803 .865 .861 .785 .811 .703 .827 .994 .747 .815 GRAf - 54 55 - 59 60 6 OVER TOTAL 5,800 106,309 .055 5,000 7,258 .689 3,900 7,052 .553 3,000 7,339 .409 3,800 7,986 .476 4,100 8,808 .465 3.400 8,662 .303 3,800 8,515 .446 3,600 26,296 .137 6,400 188,225 .193 12,600 97,412 .129 4,600 6,862 .670 4,700 5.803 .810 5.000 5,687 .879 3.200 6,068 .527 3,100 6,311 .491 3,600 6,757 .533 4,400 7,174 .613 5,400 27,254 .198 46.600 169,328 .275 WHITE FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - c 4 55 - 59 60 £, OVER TOTAL 22,400 299,542 .075 8,500 38,997 .218 9,300 42,313 .220 11,500 45,089 .255 10,300 44,539 .231 10,500 44,064 .238 10,900 41,807 .261 8,000 37,598 .213 6,700 110,992 .060 98,100 704,941 .139 40.400 331,823 .122 22.800 50,955 .447 17,700 43,538 .407 14,600 42,991 .340 19.200 45,073 .426 18.400 46,389 .397 15.600 45,840 .340 15,300 44,613 .343 19,400 151,711 .128 183,400 802,933 .228 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - A^ 45 - 4 50 - 54 55 - 59 60 6 DVER TOTAL 2,100 106,795 .020 1,600 9,018 .177 1,400 9,687 .145 2,100 9,791 .214 1,700 10,015 .170 2,700 10,640 .254 2,600 9,825 .265 2,100 9,544 .220 1,600 27,029 .059 17,900 202,344 .088 5,600 98,907 .057 4,300 8,270 .520 3,600 7,557 .476 3,300 7,548 .437 3,100 8,392 .369 3,600 8,365 .430 2,400 8,383 .286 2,600 8,645 .301 3.800 31,985 .119 32,300 188,052 .172 GRAND TOTAL 335.900 1,786,272 .188 509,300 1.923.295 .265 n-5-8 CALIFORNIA APPENDIX TAISLE 0-5 cwhs r" p LnYvr'-T p m p cr KI ?U5 population, by sfx, pacf, and agf coup I960 AND 1970 A3E GROUP CWHS WORK FORCE 1960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCE 1970 CENSUS POPULATION CWHS/CEN WHITE MALES UNDER 25 377,200 3,184,331 .118 25 - 29 327,100 466,142 .702 30 - 34 367,600 505,766 .727 35 - 39 363,500 545,647 .666 40 - 44 329,500 494,448 .666 45 - 49 299,700 450,108 .666 50 - 54 255,400 380,333 .672 55 - 59 178,200 325,717 .547 60 £, OVER 230,000 840,602 .274 TOTAL 2,728,200 7,193,094 .379 687,700 4 ,045,126 .170 501,800 645,765 .777 422,700 536,738 .788 367,000 508,686 .721 377,800 530,472 .712 373,000 549,393 .670 312,600 484,121 .646 256,100 413,105 .620 294,600 1 ,017,961 .289 3,593,300 8 ,731,367 .412 BLACK MALFS UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER TOTAL 22,100 309,105 .071 24,300 49,676 .489 22,100 49,857 .443 21,700 51,015 .425 17,700 41,512 .426 16,900 37,458 .451 11,500 31,783 .362 9,000 26,431 .341 6,800 46,776 .145 52,100 643,613 .236 46,700 569,834 .082 37,800 81,209 .465 32,600 74,494 .438 28,200 66,540 .424 26,100 61 ,858 .422 22,700 57,767 .393 16,500 45,814 .360 13,500 38,154 .354 11,000 89,648 .123 235,100 1,085,318 .217 WHITE FEMALES UNDFR 25 267, non 3,045,56 4 .088 25 - 29 139,100 450,232 .309 30 - 34 149,400 505,066 .296 35 - 39 191,700 557,721 .344 40 - 44 181,200 496,276 .365 45 - 49 164,800 451,642 .365 50 - 54 130,700 384,796 .340 55 - 59 96,900 334,409 .290 60 £, OVER 118,700 1,036,430 .115 TOTAL 1 ,439,500 7,262,136 .198 585,800 3 ,904,877 .150 284,300 643,338 .442 201,500 529,649 .380 200,900 501,423 .401 233,400 546,451 .427 247,400 577,990 .428 207,500 506,217 .410 163,700 444,492 .368 177,200 1 ,375,228 .129 2,301,700 9 ,029,665 .255 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 & OVER TOTAL 11,300 304,377 .037 10,500 50,033 .210 11,700 53,448 .219 11,500 50,978 .226 12,800 40,401 .317 10,300 32,813 .314 8,700 24,358 .357 5,000 20,365 .246 4,200 41,588 .101 86,000 618,361 .139 32,200 562,999 .057 26,600 87,302 .305 24,900 78,508 .317 19,000 73,350 .259 18,300 69,769 .262 17,500 60,753 .288 13,600 47,700 .285 10,200 37,493 .272 9,800 88,910 .110 172,100 1,106,784 .155 GRAND TOTAL 4,405,800 15,717,204 .280 6,302,200 19,953,134 ,316 D-5-9 COLORADO APPENDIX TABLE D-5 CWHS EMPLOYMENT AMD CENSUS POPULATION, BY SEX, RACE, AND AGE GROUP 1060 AND 1970 5R0UP CWHS WORK FORCE 1960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCF 1970 CENSUS POPULATION :whs/cen WHITE MALES UNDER 25 25 - :° 30 - 'h 35 - 39 40 - 44 45 - ^ 50 - 54 55 - 59 60 I DVEK TOTAL 46.700 35,400 39,300 34,800 30.R00 25,200 22,600 15,800 20,600 271,200 397,417 53,200 56,520 58,009 53,326 48,153 42,025 35,041 99,884 843,575 .118 .665 .695 .600 .578 .523 .538 .451 .206 .321 87,900 514,895 .171 52,400 73,694 .711 44,600 62,882 .709 39,100 60,165 .650 37,600 60,585 .621 33,900 58,751 .577 30,100 52,450 .574 23,100 43,769 .528 30,900 114,173 .271 379,600 1,041,364 .365 BLACK MALES UNDER 25 25 - 29 30 - 34 35 - 39 <, n _ l, t, 45 - 49 50 - 54 55 - 59 60 6 OVER TOTAL 1,200 800 1,500 1,100 ?nn 900 200 400 600 7,400 13,670 .088 2,320 .345 2,204 .681 1,996 .551 1 ,559 .449 1,235 .729 961 .208 775 .516 2,172 .276 26,892 .275 2,700 900 1,600 800 1,300 1,600 600 700 1,000 11,200 27,656 .098 3,720 .242 3.143 .509 2,863 .279 2,487 .523 2,210 .724 1,713 .350 1,211 .578 3.010 .332 48,013 .233 WHITE FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 I OVER TOTAL 30,100 387,795 .078 14,100 54,860 .257 13,600 58,215 .234 14,300 59,986 .238 17,200 53,602 .321 14,900 48,604 .307 11,500 41,933 .274 7,600 36,226 .210 9,900 115,904 .085 133,200 857,125 .155 67,400 498,518 .135 29,200 76,309 .383 20,100 64,482 .312 19,900 61,013 .326 22,900 62,608 .366 21,200 61,638 .344 20,400 53,412 .382 14,600 46,894 .311 15,000 146,114 .103 230, ion 1,070,988 .215 BLACK FEMALES UNDER 25 25 - 29 3n _ ■M. 3 5 - i n 40 - 44 45 - 49 50 - 54 55 - 59 60 £, DVER TOTAL 400 300 60n 600 500 300 800 100 200 3,800 3,099 .031 2,298 .131 2,236 .268 ? , OP" • ?Q9 1,529 .32 7 1,173 .256 857 .933 833 .120 2,322 .086 26,355 .144 1,400 1,400 700 1 ,000 900 700 400 1,100 8,400 25,108 .056 3,697 .379 3,277 .214 3,075 .325 2 ,R2fi . ? p 3 2,306 .390 1,720 .407 1,290 .310 3,593 .306 46,894 .179 GRAND TOTAL 415,600 1,753,947 .237 629,900 2,207,259 .285 D-5-10 APPFNOIX TABLE D-5 CWHS EMPLOYMENT AND CENSUS POPULATION, BY SEX, RACE, AND AGE GROUP 1960 AND 1970 CONNECTICUT AGE GROUP CWHS WORK FORCE 1960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCF 1970 CENSUS POPULATION :whs/cen WHITE MALES UNDER 25 69,000 503,259 .137 25 - 29 58,700 68,773 .854 30 - 34 64,000 81,907 .781 35 - 39 69,000 89,158 .774 40 - 44 71 ,100 88,583 .803 45 - 49 63,100 80,657 .782 50 - 54 45,700 67,275 ,679 55 - SQ 39,3^0 56,78° .692 60 6 DVER 60,800 153,252 .397 TOTAL 540,700 1,189,653 .455 120,700 622,521 .194 82,200 94,167 .873 66,800 76,458 .874 59,400 76,330 .778 62,800 86,980 .722 72,300 90,636 .798 71,200 86,298 .825 59,700 73,451 .813 ^8,50^ 170,358 .40? 663,600 1,377,199 .482 BLACK MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER 3,400 3,400 3,200 3,900 3,000 2,800 2,000 1,000 1,800 27,720 .123 4,719 .720 4,660 .687 4,389 .889 3,250 .923 2,714 1.032 2,003 .999 1,759 .569 3,362 .535 9,100 6,700 5,500 4,800 4,000 4,400 2,900 2,200 3,500 53,150 .171 7,385 .907 6,446 .853 5,319 .902 5,004 .799 4,451 .989 3,364 .862 2,634 .835 5,535 .632 TOTAL 24,500 54,576 ,449 43,100 93,288 .462 WHITE FEMALES UNDER 25 51,900 493,005 .105 25 - 29 24,500 72,005 .340 30 - 34 24,900 84,630 .294 35 - 39 32,800 93,767 .350 40 - 44 41,400 92,101 .450 45 - 40 35,900 81,825 .439 5 - R 4 3 a . 1 po 69,277 .478 55 - 59 24,600 60,21) . 4 C 60 6 OVER 26,700 187,34? .143 TOTAL 295,800 1.234,163 .240 96,100 615,894 .156 36,900 95,481 .386 28,700 78,297 .367 33,300 79,555 .419 40,300 90,957 .443 50,400 96,340 .523 50,600 91,828 .551 3P ,500 77,917 .4° 4 42,200 231,990 .182 417,000 1,458,259 .286 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER TOTAL 2.600 1,600 1,900 1,700 2,100 2,300 2,200 700 1,300 16,400 28,e32 .090 4,900 .327 4,626 .411 4,415 .385 3,406 .617 2,93P .783 2,215 .993 1 ,838 .381 3,672 .354 56,842 ,289 8.800 4,600 4,200 3,500 3,400 2,800 2,800 2,700 1,900 34,700 55,721 .158 8,978 .512 7,553 .556 6,224 .562 5,578 .610 4,925 .569 3,849 .727 3,027 .892 7,108 .267 102,963 .337 GRAND TOTAL 877,400 2,535,234 .346 1 ,158,400 3,031,709 .382 D-5- 11 DELAWARE APPENDIX TABLE D-5 CWHS FMPLOYMENT AND CENSUS POPULATION, BY SEX, RACE, AND AGE GROUP I960 AND 1970 AGE GRDUR CWHS WORK FORCE 1960 CENSUS POPULATION CWHS/CFN CWHS WORK FORCE 1970 CENSUS POPULATION CWHS/CEN WHITE MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 I OVER TOTAL 9,500 85,611 .111 10,300 12,885 .799 10,000 14,006 .714 11,500 14,624 .786 11,100 13,495 .823 10,000 11,357 .881 7,600 9,718 .782 7,000 7,917 .884 7,900 20,573 .384 84,900 190,186 .446 20,000 108,522 .184 13,400 16,232 .826 10,000 13,564 .737 10,900 13,964 .781 9,100 14,211 .640 10,900 14,360 .759 9,700 13,055 .743 8,800 10,339 .851 9,400 23,731 .396 02,200 227,978 .448 BLACK MALES UNDER : c 25 - 29 30 - 34 35 - 39 40 - «.4 45 - 4? 50 - ^ 55 - 59 60 i, ! 1VFR 1,900 900 2,000 1,600 1,900 1 ,600 1,300 400 1,100 15,569 .122 1,984 .454 2,006 .997 2,046 .782 1,872 1.015 1,744 .917 1,522 .854 1,330 .301 2,877 .382 3,200 3,000 1,900 1,100 3,300 1,800 1,400 1,900 1,000 22.031 .145 2,390 1.255 2,222 .855 2,128 .517 2,002 1.648 1,911 .942 1,690 .828 1,487 1.278 3,493 .286 TOTAL l?,70n ?0,9?0 .4]0 18,600 39,354 .473 WHITE FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 S OVER TOTAL 9,700 3,900 5,500 4,500 6,400 4,700 3,700 2,700 3,200 44,300 83,934 .116 12,861 .303 14,298 .385 14,944 .301 13,413 .477 11,284 .417 9,798 .378 8,314 .325 25,295 .127 194,141 .228 14,500 107,128 .135 7,300 16,762 .436 6,400 13,943 .459 4,000 13,724 .291 6,500 14,837 .438 7,800 15,135 .515 7,400 13,378 .553 4,700 11,011 .427 5,000 32,563 .154 63,600 238,481 .267 BLACK FEMALES UNDE :r 25 25 - 29 30 - 34 3 5 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER TOTAL 800 1,400 700 1,100 1,100 1,200 1,100 300 800 8,500 15,657 .051 2,115 .662 2,165 .323 2,175 .506 1,887 .583 1,740 .690 1,417 .776 1,161 .258 2,698 .297 31,015 .274 2,500 2,000 1,400 2,000 1,200 1,500 1.100 1,000 1,100 13,800 22,818 .110 2,809 .712 2,518 .556 2,409 .830 2,257 .532 2,143 .700 1,750 .629 1,559 .641 4,028 .273 42,291 .326 GRAND TOTAL 150,400 446,292 .337 198,200 548,104 .362 D-5- 12 APPENDIX TABLE D-5 CWHS EMPLOYMENT AND CENSUS POPULATION, BY SEX, RACE, AND AGE GROUP 1960 AND 1970 DISTRICT OF COLUMBIA AGE GROUP CWHS WORK FOPCE 1960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCE 1970 CENSUS POPULATION :whs/cen WHITE MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER TOTAL 18,600 14,200 18,200 19,300 18,800 13,100 11,800 10,100 13,700 137,800 52,449 ,355 12,995 1.093 10,978 1.658 10,156 1.900 9,686 1.941 10,784 1.215 11,731 1.006 10,993 .919 28,352 .483 58,124 .871 29,900 26.600 21,200 15,300 14,400 14,200 12,800 10,200 18,800 163,400 29,155 1.026 10,181 2.613 6,548 3.238 5,405 2.831 5,150 2.796 5,405 2.627 5,795 2.209 6,267 1.628 20,182 .932 94,088 1.737 BLACK MAL r S UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 DVEK TOTAL 8,100 95,326 .085 6,400 15,143 .423 6,400 15,593 .410 7,400 15,792 .469 6,000 13,474 .445 6,300 12,502 .504 5,100 10,194 .500 3,700 7,973 .464 3,400 14,050 .242 2,800 200,047 .264 17,300 131,528 .132 10,800 20,904 .517 7,600 16,965 .448 6,000 15,119 .397 5,200 15,025 .346 6,000 14,373 .417 5,500 12,267 .448 4,400 10,711 .411 7,000 20,511 .341 69,800 257,403 .271 WHITE FEMALES UNDER 2^ 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER TOTAL 19,900 9,900 9,000 11,200 10,800 11 ,200 10,500 8,000 8,000 98,500 ^4,272 .367 10,308 .960 10,020 .898 11,520 .972 12,558 .860 14,607 .767 15,738 .667 14,372 .557 43,744 .183 187,139 .526 35,300 16,800 9,700 7,500 8,900 10,200 10,500 7,700 1 1,000 1 17,600 31,509 1.120 9,826 1.710 5,570 1.741 4,434 1.691 5,282 1.685 6,689 1.525 7,589 1.384 8,657 .889 35,628 .309 15,184 1.021 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER TOTAL 5,000 100,776 .050 4,600 16,752 .275 4,200 17,662 .238 5,400 1 7,891 .302 4,400 15,506 .284 4,500 13,619 .330 3,200 10,777 .297 2,200 8,220 .268 2,200 17,443 .126 5,700 218,646 .163 20,100 141,436 .142 8,600 23,989 .358 6,900 18,542 .372 5,100 17,379 .293 4,300 17,180 .250 3,500 16,831 .208 3,700 14,727 .251 3,200 12,359 .259 2,500 27,392 .091 57,900 289,835 .200 GRAND TOTAL 324,800 763,956 .425 408,700 756,510 ,540 D-5-13 FLORIDA APPENDIX TABLE D-5 CWH5 FMPLOYMFNT AMD CENSUS POPULATION, BY SEX, RACE, AND AGE GROUP 1960 AND 1970 AGE GROUP CWHS WORK FORCE 1960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCE 1970 CENSUS POPULATION :whs/cen WHITE MALES UMPE^ 25 114,300 828,024 .138 2 5 - 2 9 79,80° 1 16, 95^ .682 30 - 34 87,700 130,485 .672 35 - 39 89,600 137,029 .654 40 - 44 78,700 128,660 .612 45 - 49 77,500 118,786 .652 50 - 54 62,300 106,324 .586 55 - 59 48,200 96,346 .500 60 6 OVER 62,900 337,989 .186 TOTAL 701,000 2,000,593 .350 209,500 1,137,173 .184 12 2,900 165,806 .741 1 1 1 ,500 144,567 .771 97,800 145,279 .673 107,800 161,267 .668 109,200 164,111 .665 93,000 151,803 .613 81,500 136,606 .597 115,300 556,167 .207 1,048,500 2,762,779 .380 BLACK MALES UNDER 25 30,300 232,514 .130 25 - 29 21 ,100 30,280 .697 30 - 34 19,500 28,513 .684 35 - 39 18,300 26,853 .681 40 - 44 18,700 24,495 .763 45 - 49 15,700 23,635 .664 50 - 54 13,700 19,940 .687 55 - 59 8,100 17,145 .472 60 & OVER 9,600 32,815 .293 TOTAL 155,000 436,190 .355 39,700 290,862 .136 22,400 29,637 .756 21,600 28,604 .755 19,800 26,320 .752 20,700 25,573 .809 16,300 23,006 ,709 16,400 21,723 .755 12.400 19,450 .638 14,600 47,617 .307 83,900 512,792 .359 WHITE FEMALES UNDER 25 76,200 803,415 .095 25 - 29 36,000 119,972 .300 30 - 34 38,200 137,516 .278 35 - 39 52,100 146,267 .356 40 - 44 50,200 133,762 .375 45 - 49 45,400 126,543 .359 50 - 54 41,000 116,876 .351 55 - 59 27,200 110,684 .246 60 E, OVER 28,000 368,253 .076 TOTAL 394,300 2,063,288 .191 168,900 1,097,655 .154 71,800 172,604 .416 59,300 152,208 .390 62,200 152,799 .407 74,600 174,317 .428 81 ,200 181,059 .448 72,000 169,673 .424 58,300 169,313 .344 70,900 686,936 .103 719,200 2,956,564 .243 BLACK FEMALES UNDER ? c 25 - 29 30 - 34 35 - 39 40 - 44 45 - 4Q 50 - H 55 - 59 60 6 OVER TOTAL 14,500 236,525 .061 10,300 31,036 .332 11,600 31,216 .372 10,200 28,976 .352 9,600 26,621 .361 7,100 24,656 .288 7,500 20,019 .375 4,400 16,911 .260 4,000 35,529 .113 79,200 451,489 .175 32,200 297,510 .108 20,000 35,049 .571 18,900 33,286 .568 15,000 31,014 .484 14,400 30,298 .475 14,400 26,264 .548 12,400 24,348 .509 6,900 21,968 .314 8,800 57,571 .153 143,000 557,308 .257 GRAND TOTAL 1,329,500 4,951,560 .269 2,094,600 6,789,443 ,309 D-5-14 GEORGIA APPENDIX TABLE D-5 CWHS F v PLOYMFNT A«D CENSUS POPULATION, BY SEX. RACE, ANp AGE GROUP 1960 AND 1970 AGE GROUP CWHS WORK FORCE 1960 CENSUS POPULATION CWHS/CFN CWHS WORK FORCE 1970 CENSUS POPULATION CWHS/CEN WHITE MALES UNDER 25 25 - 29 30 - 34 35 - 19 40 - 44 45 - 49 50 - 54 55 - 59 60 6 DVER TOTAL 88,500 66,600 73,700 62,500 59,300 51,600 41,700 26,800 29,300 500,000 664,731 93,582 95,878 97,182 92,020 84,247 72,136 57,114 134,845 1,391 ,73* .133 .712 .769 ,643 .644 .612 ,578 .469 .217 .359 158,700 799,396 .199 107,100 129,208 .829 88,300 105,895 .834 81,000 99,160 .817 81,900 100,564 .814 70,000 97,456 .718 67,600 88,447 .764 56,100 75,924 .739 56,900 170,607 .334 767,600 1,666,657 .461 BLACK MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 . 49 50 - S4 55 - 59 60 £ 3VER TOTAL 32,300 308,822 .105 19,300 28,948 .667 17,800 27,584 .645 16,800 27,451 .612 19,400 27,293 .711 17,700 27,281 .649 14, ion 22,813 .618 7,600 18,742 .406 9,800 45,244 .217 154,800 534,178 .290 51,800 330,546 .157 28,900 34,217 .845 24,800 28,074 .883 18,400 25,446 .723 17,800 24,497 .727 16,500 23,376 .706 15,400 23,066 .668 13,000 21,687 .599 17,800 53,130 .335 204,400 564,039 .362 WHITE FEMALES UNDER 25 60,900 639,291 .095 25 - 29 33,800 94,729 .357 30 - 34 36,800 98,474 .374 35 - 39 35,700 101,559 .352 40 - 44 39,300 92,690 .424 45 - 49 34,000 85,459 .398 50 - 54 28,100 74,930 .375 55 - 59 17,700 63,050 .281 60 £, OVER 15,800 175,306 .090 TOTAL 302,100 1 ,425,488 .212 1 14,800 765,752 .150 64,700 128,342 .504 48,100 106,434 .452 47,700 100,595 .474 51,100 103,278 .495 50,300 102,352 .491 44,400 92,828 .478 36,800 82,894 .444 38,500 242,110 .159 496,400 1,724,585 .288 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 19 40 - 44 45 - 49 50 - 54 55 - 59 60 £, OVER TOTAL 11,700 313,670 .037 8,000 34,511 .232 10,300 34,415 .299 11 ,600 33,871 .342 in.70n 32.97R .324 10,800 32,410 .333 9,000 26,325 .342 6,200 22,601 .274 6,400 60,934 .105 84,700 591,715 .143 35,900 336,770 .107 22,600 39,023 .579 17,400 33,592 .518 16,800 31,733 .529 16,300 31,801 .513 13,700 29,094 .471 14,300 28,484 .502 11,400 26,621 .428 13,200 77,176 .171 161 ,600 634,294 .255 GRAND TOTAL 1 .041,600 3,943,116 1,630,000 4,589,575 .355 D-5- 15 HAWAII UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - (,g 50 - 54 55 - 59 60 & I DVER APPENDIX TABLE D-5 CWHS EMPLOYMENT AND CENSUS POPULATION, BY SEX, PACF, AND AGE GROUP I960 AND 1970 1960 1970 CWHS CENSUS CWHS CENSUS AGE GROUP WORK FORCE POPULATION CWHS/CEN WORK FORCE POPULATION CWHS/CEN UNDER 25 13,200 25 - 29 10,400 30 - 34 9,400 35 - 39 13,500 40 - 44 12,800 45 - 49 9,700 50 - 54 9,500 55 - 59 7,100 60 6 OVER 7,200 TOTAL 92,800 100 TOTAL UNDER 25 11,900 25 - 29 7,100 30 - 34 9,800 35 - 39 10,300 40 - 44 6,900 45 - 49 4,800 50 - 54 3,300 55 - 59 2,300 WHITE MALES 61,515 .215 9,713 1.071 8,641 1.088 8,723 1.548 7,353 1.741 5,210 1.862 3,717 2.556 2,812 2.525 5,231 1.376 12,915 .822 27,800 88,300 .315 19,300 14,462 1.335 13,900 10,840 1.282 12,400 10,093 1.229 12,700 8,398 1.512 15,100 7,812 1.933 13,000 6,544 1.987 8,500 4,824 1.762 12,600 8,911 1.414 135,300 160,184 .845 BLACK MALES 110,630 .000 12,698 .000 15,536 .000 16,444 .000 14,090 .000 14,357 .007 12,159 .000 10,291 .000 19,053 .000 225,258 .000 200 113,819 .002 300 15,896 .019 100 13,006 .008 12,991 .000 15,351 .000 15,535 .000 13,076 .000 100 12.213 .008 27,134 .000 700 239,021 .003 WHITE FEMALES 44,463 .268 7,697 .922 7,477 1.311 7,651 1.346 5,648 1.222 4,356 1.102 3,418 .965 2,538 .906 6,067 .379 60 & OVER 2,300 TOTAL 58,700 89,315 .657 26.600 71 ,499 .372 12,500 13,314 .939 11,900 9,883 1.204 11,100 8,321 1.334 13,900 7,573 1.835 13,600 7,014 1.939 9,800 5,689 1.723 6,400 4,448 1.439 4,800 10,235 .469 110,600 137,976 .802 BLACK FEMALES UNDER 25 100 25 - 29 100 30 - 34 100 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 & OVER TOTAL 300 GRAND TOTAL 151,900 632,772 .240 247,100 768,561 .322 106,577 .001 14,445 .007 17,282 .006 16,427 .000 13,267 .000 9,562 .000 6,771 .000 6,371 .000 14,582 .000 205,284 .001 100 110,239 .001 200 15,787 .013 13,904 .000 100 15,280 .007 100 17,522 .006 15,838 .000 12,744 .000 8,858 .000 21.208 .000 500 231,380 .002 D-5- 16 IDAHO APPENDIX TABLE 13-5 CWHS EMPLOYMENT AND CFNSUS POPULATION, BY SEX, RACE, AND AGE GROUP 1Q60 AND 1970 I960 1970 UNDER 25 25 - 29 30 - 34 35 - 39 4 - 44 45 - 49 50 - 54 55 - 59 60 6 3VER 9,900 157,636 .063 4,100 18,478 .222 4,700 19,629 .239 6,400 21,018 .305 5,200 20,519 .253 5,400 18,567 .291 ft, 600 15,764 .419 4,400 12,796 .344 3,400 39,678 .086 0,100 324,085 .155 lfl .800 9 ,100 7 .700 7 .200 6 .700 P .800 5 .600 6 ,400 8 .400 7fi .700 2.66R .037 327 .000 310 .000 287 .000 236 .000 184 .000 114 .877 149 .000 410 .000 170,163 .142 21,727 .727 18,800 .532 17,723 .615 18,639 .553 18,805 .511 18,906 .513 17,569 .541 46,238 .225 348,570 .316 CWHS CENSUS CWHS CENSUS AGE GROUP WORK FORCF POPULATION CWH5/CEN WORK FORCE POPULATION CWHS/CEN WHITE MALES UNDER 25 17,200 162,734 .106 24,100 25 - 29 12,000 18,055 .665 15,800 30 - 34 12,400 19,633 .632 10,000 35 - 39 10,200 20,351 .501 10,900 40 - 44 13,400 20,52? .653 10,300 45 - 49 12,900 19,883 .649 9,600. 50 - 54 8,700 17,451 .499 9.700 55 - 59 5.800 14,211 .408 9,500 60 6 OVER 8.700 40,457 .215 10,400 TOTAL 101,300 333,298 .304 110,300 BLACK MALES UNDER 25 2,745 .000 200 25 - 29 351 .000 100 30 - 34 311 .000 200 35 - 39 354 .000 40 - 44 100 251 .398 100 45 - 49 236 .000 50 - 54 174 .000 100 55 - 59 176 .000 60 5 OVER 525 .000 TOTAL 100 5,123 .020 700 7,180 .097 WHITE FEMALES 4,235 .047 489 .204 407 .491 377 .000 329 .304 353 .000 26] .383 217 .000 512 .000 165,485 .114 21.688 .420 19,026 .405 18,303 .393 18,841 .356 19,703 .447 19,280 .290 17,184 .372 50,722 .166 350,232 .225 TOTAL BLACK FEMALES UNDER 25 100 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 100 55 - 59 60 E, OVER 410 .000 491 .000 TOTAL 200 4,685 .043 00 6,585 .000 GRAND TOTAL 151,700 667,191 .227 189,700 712,567 .266 3,806 .000 456 .000 376 .000 386 .000 335 .000 297 .000 249 .000 189 .000 D-5-17 ILLINOIS UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 £, OVER TOTAL APPENDIX TABLE D-5 CWHS EMPLOYMENT AND CENSUS POPULATION, BY SEX, RACE, AND AGE GROUP 1960 AND 1970 1960 1970 CWHS CENSUS CWHS CENSUS A3E GROUP WORK FORCE POPULATION CWHS/CEN WORK FORCE POPULATION CWHS/CEN WHITE MALES UNDER 25 259,700 1,880,921 .138 25 - 29 200,600 262,802 .763 30 - 34 239,100 296,805 .806 35 - ^9 237,800 310,359 .766 40 - 44 210,500 298,690 .705 45 - 49 212,900 288,865 .737 50 - 54 173,200 258,533 .670 55 - 59 140,700 231,527 .608 60 6 OVEN 206,400 607,185 .340 TOTAL 1 ,880,900 4,435,687 .424 397,000 2,119,720 .187 260,000 315,688 .824 222,500 268,200 .830 205,300 257,577 .797 224,500 283,519 .792 229,500 290,053 .791 199,500 272,122 .733 181,400 248,162 .731 228,600 619.858 .369 2,148,300 4,674,899 .460 BLACK MALES 26,500 257,958 .103 29,300 36,86" .795 26,500 39,168 .677 27,500 37,751 .728 22,500 31,201 .721 21,900 27,693 .791 15,900 23,682 .671 11,500 21,949 .524 15,800 40,909 .386 197,400 517,179 .382 55,000 397,553 .138 38,800 49,140 .790 27,700 43,804 .632 33,300 41,439 .804 27,200 39,604 .687 27,300 35,914 .760 19,600 29,101 .674 19,300 23,825 .810 20,700 56,557 .366 268,900 716,937 .375 WHITE FEMALES UNDER 25 204,000 1,858,384 .110 25 - 29 80,700 266,344 .303 30 - 34 87,000 299,239 .291 35 - 39 103,200 319,970 .323 40 - 44 119,100 312,653 .381 45 - 49 123,700 298,070 .415 50 - 54 106,800 265,448 .402 55 - 59 80,500 238,882 .337 60 & OVER 98,700 715,575 .138 TOTAL 1 ,003,700 4,574,565 .219 340,600 2,089,652 .163 132,500 322,355 .411 98,500 270,618 .364 103,400 260,126 .397 127,200 291,091 .437 141,900 303,969 .467 138,100 292,858 .472 118,000 269,506 .438 137,800 825,307 .167 1,338,000 4,925,482 .272 BLACK FEMALFS UNDER 25 18.700 272,466 .069 25 - 29 14,000 42,878 .327 30 - 34 15,800 44,290 .357 35 - 39 15,000 41 ,084 .365 40 - 44 14,200 33,727 .421 45 - 49 10,800 29,004 .372 50 - 54 12,100 24,357 .497 55 - 59 5,700 21,398 .266 60 I OVER 6,700 44,523 .150 TOTAL 113,000 553,727 .204 48«700 418.011 .117 26,800 60,237 .445 22,800 53,822 .424 20,000 49,355 .405 21,600 46,221 .467 18,800 39,874 .471 15,100 32,663 .462 10,000 26,700 .375 11.100 69,775 .159 194,900 796,658 .245 GRAND TOTAL 3»195»000 10,081,158 .317 3.950,100 11.113.976 .355 D-5- II INDIANA APPENDIX TABLE D-5 CWHS EMPLOYMENT AND CENSUS POPULATION, BY SEX, RACE, AND AGE GROUP 1960 AND 1970 AGE GROUP CWHS WORK FORCE 1960 CENSUS POPULATION CWHS/CFN CWHS WORK FORCF 1970 CENSUS POPULATION :whs/cen WHITE MALES UNDER 25 128,700 994,899 .129 25 - 29 99,400 130,360 .763 30 - 34 111, BOO 143,258 .780 35 - 39 111,600 146,726 .761 40 - 44 106,400 135,072 .788 45 - 49 90,500 127,413 .710 50 - 54 78,800 112,441 .701 55 - 59 60,400 98,888 .611 60 £, OVER 81,700 276,452 .296 TOTAL 869,300 2,165,509 .401 206,200 1,128,367 .183 139,500 160,311 .870 114,200 135,273 .844 105,200 127,140 .827 110,500 138,518 .798 108,200 138,696 .780 97,100 125,228 .775 80,800 111,532 .724 94,600 286,475 .330 1,056,300 2,351,540 .449 BLACK MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER TOTAL 6,300 7,000 7,400 8,500 6,800 4,900 4,700 4,100 4,500 54,200 67,422 .093 8,628 .811 9,413 .786 9,249 .919 7,650 .889 7,201 .680 5,942 .791 5,503 .745 12,221 .368 133,229 .407 28,000 8,200 6,400 7,100 8,800 8,800 6,400 4,700 6,200 84,600 100,586 .278 11,138 .736 9,561 .669 9,184 .773 9,713 .906 9,156 .961 7,424 .862 6,675 .704 16,193 .383 179,630 .471 WHITE FEMALES UNDER 25 81 ,500 977,709 .083 25 - 29 40,400 134,550 .300 30 - 34 41,200 147,023 .280 35 - 39 54,200 153,317 .354 40 - 44 54,300 139,760 .389 45 - 49 50,900 128,162 .397 50 - 54 45,500 113,823 .400 55 - 59 33,700 102,431 .329 60 £, OVFR 44,500 326,270 .136 TOTAL 446,200 2,223,045 .201 158,900 1,115,562 .142 65,400 161,700 .404 54,200 137,630 .394 60,000 131,915 .455 69,800 143,414 .487 73,900 146,729 .504 63,900 133,454 .479 55,900 118,682 .471 62,100 379,698 .164 664,100 2,468,784 .269 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 J, OVER TOTAL 3,000 2,400 3,800 4,600 4,200 3,000 3,900 2,200 2,600 29,700 70,311 .043 10,099 .238 10,550 .360 9,931 .463 8,288 .507 7,513 .399 6,199 .629 5,427 .405 12,397 .210 140,715 .211 15,500 7,100 6,100 5,400 5,500 4,600 4,400 2,900 4,600 56,100 103,052 .150 12.713 .558 11,145 .547 11,226 .481 11,289 .487 9,955 .462 8,215 .536 7,093 .409 19,027 .242 193,715 .290 GRAND TOTAL 1 ,399,400 4,662,498 .300 1,861,100 5,193,669 .35? D-5-19 JOWA APPENDIX TABLE D-5 CWHS EMPLOYMENT AND CENSUS POPULATION, BY SEX, RACE, AND AGE GROUP 1960 AND 1970 AGE GROUP CWHS WORK EORCE 1960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCE 1970 CENSUS POPULATION :whs/cen WHITE MALES UNDER 25 68,100 605,750 .112 25 - 29 51,200 73,185 .700 30 - 34 47,900 81,024 .591 35 - 39 46,300 82,766 .559 40 - 44 43,700 80,355 .544 45 - 49 40,600 77,396 .525 50 - 54 34,000 71,589 .475 55 - 59 27,500 65,294 .421 60 £, OVER 42,400 207,574 .204 TOTAL 401,700 1,344,933 .299 99,200 633,587 .157 61,300 81,557 .752 49,500 71,407 .693 47,200 67,974 .694 44,400 75,531 .588 44,200 76,306 .579 39,800 73,381 .542 39,200 67,562 .580 51,500 205,262 .251 476,300 1,352,567 .352 BLACK MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER 1,600 500 300 700 400 100 200 600 500 ,148 .224 944 .530 92P .323 898 .780 726 .551 669 .149 581 .344 610 .984 ,610 .311 1,700 1.300 1,300 900 1.400 1,000 200 200 400 11,702 .145 1,397 .931 1,207 1.077 983 .916 960 1.458 864 1.157 710 .282 635 .315 1,842 .217 TOTAL 4,900 14,114 .347 8,400 20,300 .414 WHITE FEMALES UNDER 25 56,200 595,807 .094 25 - 29 19,000 76,630 .248 30 - 34 19,800 82,081 .241 35 - 39 23,800 85,603 .278 40 - 44 27,200 83,079 .327 45 - 49 26,500 78,590 .337 50 - 54 27,300 72,330 .377 55 - 59 21,400 68,451 .313 60 & OVER 30,100 241,205 .125 TOTAL 251,300 1,383,776 .182 93,100 626,845 .149 33,400 83,084 .402 30,100 73,315 .411 28,900 71,808 .402 30,300 77,432 .391 34,900 80,023 .436 36,000 77,850 .462 30,700 71,643 .429 41,700 268,195 .155 359,100 1,430,195 .251 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 & OVER 500 400 100 300 600 300 400 200 7,422 .067 1,045 .383 1,019 .098 926 .324 752 .798 737 .407 611 .655 563 .355 1,639 .000 1,400 600 800 400 300 700 700 100 600 11,785 .119 1,467 .409 1,334 .600 1.176 .340 1,032 .291 933 .750 785 .892 709 .141 2,093 .287 TOTAL 2,800 14,714 .190 5,600 21,314 .263 GRAND TOTAL 660,700 2,757,537 ,240 849,400 2»824,376 .301 D-5-20 KANSAS APPENDIX TABLE D-5 CWHS EMPLOYMENT AND CENSUS POPULATION, BY SEX, RACF, AND AGF GROUP 1960 AND 1970 AGE GROUP CWHS WORK FORCE 1960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCE 1970 CENSUS POPULATION :whs/cen WHITE MALES UNDER 25 50,700 464,112 .109 25 - 29 38,700 61 ,764 .627 30 - 34 41 ,700 66,374 .628 35 - 39 40,100 69,158 .580 40 - 44 36,200 62,014 .584 45 - 49 30.900 58,631 .527 50 - 54 24,800 54,188 .458 55 - 59 23,700 48,016 .494 60 6 OVER 36,600 147,152 .249 TOTAL 323,400 1,031,409 .314 74,900 485,385 .154 43,500 66,035 .659 39,000 55,329 .705 33,500 54,812 .611 35.200 59,297 .59*. 36,400 60,687 .600 33,900 55,202 ,614 28,800 50,919 .566 35,500 152,154 .233 360,700 1,039,820 .347 BLACK MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER TOTAL 2,600 2,100 1,500 1,800 1.100 1,700 1,100 600 1,000 13,500 25,257 .103 3,547 .592 3,213 .467 2,988 .602 2,540 .433 2,356 .722 2,047 .537 2,043 .294 5,977 .167 49,968 .270 4,100 2,100 2,500 1,700 1,300 1 ,600 1,100 1,500 1,700 17,600 5,307 .116 4,043 .519 3,119 .802 3,027 .562 2,783 .467 2,592 .617 2,329 .472 2,127 .705 6,426 .265 61,753 ,285 WHITE FEMALE5 UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 £, DVER TOTAL 34,900 448,346 .078 14,200 62,013 .229 16,300 66,392 .246 18,500 68,887 .269 22,300 63,109 .353 18,300 59,584 .307 21,200 55,581 .381 18,800 50,782 .370 22,000 172,563 .127 186,500 1 ,047,257 .178 64,600 466,856 .138 24,300 64,938 .374 22,100 56,350 .392 20,700 56,492 .366 26,100 60,681 .430 25,000 62,146 .402 26,900 58,808 .457 19,000 54,839 .346 31,000 201,138 .154 259,700 1,082,248 .240 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 £, OVER TOTAL 900 900 1,000 1,000 1,300 1,100 400 1,000 700 8,300 24,519 .037 3,461 .260 3,402 .294 3.035 .329 2,623 .496 2,422 .454 2,158 .185 2,185 .458 6,172 .113 49,977 .166 3.200 1,800 1,900 1,200 1,300 1,300 1,500 1,200 1,500 14,900 3,064 .097 4,013 .449 3,439 .552 3,439 .349 3,241 .401 2,883 .451 2,525 .594 2,326 .516 7,827 .192 62,757 .237 GRAND TOTAL 531,700 2,178,611 .244 652,900 2,246,578 .291 D-5-21 KENTUCKY UNDER 25 25 - 29 30 - 34 35 - 3Q 40 - 44 45 - 49 50 - 54 55 - 59 60 (, OVER TOTAL UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER APPENDIX TABLE 0-5 CWH5 EMPLOYMFNT AND CENSUS POPULATION, BY SEX, RACE, AND AGE GROUP 1960 AND 1970 1960 1970 CWHS CENSUS CWHS CENSUS AGE GROUP WORK FORCE POPULATION CWHS/CEN WORK FORCE POPULATION CWHS/CEN WHITE MALES UNDER 25 51,800 676,672 .077 25 - 29 43,500 79,573 .547 30 - 34 45,500 85,156 .534 35 - 39 47,700 88,883 .537 40 - 44 43,200 82,072 .526 45 - 49 40,100 78,853 .509 50 - 54 30,400 70,314 .432 55 - 59 26,000 62,705 .415 60 £, OVER 29,100 177,676 .164 TOTAL 357,300 1,401,904 .255 98,500 706,685 .139 63,400 94,426 .671 56,000 81 ,065 .691 47,100 76,702 .614 46*400 82,341 .564 47,200 82,902 .569 42,400 75,456 .562 37,300 69,288 .538 38,600 195,534 .197 476,900 1,464,399 .326 BLACK MALES UNDER 25 4,400 51,693 .085 9,200 25 - 29 3,500 5,774 .606 5.100 30 - 34 4,300 5,958 .722 . 3,100 35 - 39 3,300 6,198 .532 4,500 40 - 44 3,800 5,761 .660 3,700 45 - 49 4,700 5,832 .806 2,600 50 - 54 4,300 5,327 .807 4,000 55 - 59 1,900 5,210 .365 3,800 60 & OVER 4,000 14,791 .270 4,500 TOTAL 34,200 106,544 .321 40,500 WHITE FEMALES 38,900 647,434 .060 19,500 83,697 .233 21,200 89,649 .236 22,300 92,731 .240 22,100 84,682 .261 22,300 80,671 .276 17,500 72,247 .242 11,300 65,793 .172 12,900 201,275 .064 188,000 1,418,179 .133 BLACK FEMALES 2,100 50,830 .041 1,800 6,426 .280 3,400 6,861 .496 3,400 7,027 .484 2,300 6,388 .360 3,300 6,403 .515 2,400 5,839 .411 1,200 5,586 .215 2,900 16,169 .179 2,800 111,529 .204 63,266 .145 5,810 .878 4,877 .636 5,019 .897 5,294 .699 5,392 .482 5,022 .796 4,817 .789 15,140 .297 14,637 .353 68,800 676,220 .102 33,400 97,143 .344 26,900 84,698 .318 27,200 81,666 .333 29,900 86,547 .345 31,500 87,588 .360 27,100 80,335 .337 21,400 75,890 .282 23,400 247,280 .095 289,600 1,517,367 .191 6,000 59,601 .101 2,300 6,818 .337 2,600 6,153 .423 3,600 6,214 .579 3.900 6,557 .595 4,500 6,418 .701 3.500 5,940 .589 2,000 5,649 .354 3,900 18,953 .206 32,300 122,303 .264 TOTAL GRAND TOTAL 602.300 3.038,156 .198 839,300 3.218.706 .261 D-5-22 LOUISIANA APPENDIX TABLE 0-5 CWHS EMPLOYMENT AND CFNSUS POPULATION, BY SEX, RACE, AND AGE GROUP 1960 AND 1970 AGE GROUP CWHS WORK FORCE 1960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCE 1970 CENSUS POPULATION CWHS/CEN WHITE MALES UNDER 25 53,200 519,552 .102 25 - 29 44,100 69,549 .634 30 - 34 52,500 75,774 .693 35 - 39 50,800 75,722 .671 40 - 44 43,400 69,397 .625 45 - 49 37,800 65,271 .579 50 - 54 30,40n 57,630 .528 55 - 59 23,200 48,637 .477 60 £, OVER 26,600 108,774 .245 TOTAL 362,000 1,090,306 .332 91,500 616,537 .148 65,600 85,884 .764 53,300 72,466 .736 45,800 68,938 .664 49,400 74,588 .662 48,300 72,317 .668 39,900 64,715 .617 32,600 57,701 .565 35,800 136,486 .262 462,200 1,249,632 .370 BLACK MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 DVEK TOTAL 23,300 288,525 .081 14,700 25,590 .574 16,700 25,051 .667 15,200 25,103 .606 14,300 23,237 .615 15,200 23,667 .642 13,100 21,377 .613 8,600 19,480 .441 6,800 49,918 .136 27,900 501,948 .255 32,500 308,485 • .105 17,200 27,710 .621 16,900 23,430 .721 15,800 21,819 .724 13,000 22,455 .579 13.200 21,533 .613 10,800 20,135 .536 11,800 19,334 .610 12,400 56,951 .218 143,600 521,852 .275 WHITE FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 & OVER TOTAL 36,900 513,790 .072 15,000 72,167 .208 17,000 77,954 .218 17,200 78,187 .220 16,600 70,256 .236 13,600 65,741 .207 14,000 58,981 .237 9,500 50,870 .187 8,000 133,463 .060 147,800 1,121,409 .132 58,100 592,413 .098 26,300 86,920 .303 19,900 73,793 .270 21,200 71,876 .295 23,200 77,300 .300 25,000 75,875 .329 22,200 68,077 .326 15,700 62,049 .253 19,100 183,563 .104 230,700 1,291,866 .179 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER TOTAL 8,000 293,214 .027 5,700 30,814 .185 7,700 31,491 .245 7,900 30,317 .261 7,000 27,496 .255 9,000 26,795 .336 5,000 24,168 .207 4,000 21,050 .190 3,500 58,014 .060 7,800 543,359 .106 15,200 314,271 .048 9,400 33,116 .284 12,200 29,275 .417 8,000 28,030 .285 11,100 28,540 .389 10,100 26,203 .385 8,900 24,266 .367 8,200 22,424 .366 6,400 71,831 .089 89,500 577,956 .155 GRAND TOTAL 695,500 3,257,022 .214 926,000 3«64l ,306 ,254 D-5-23 MAINE UNDER 25 25 - 29 30 - 34 35 - 39 40 - 4 A 45 - 49 50 - c 4 55 - 59 60 & 1 3VER TOTAL UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 & 1 3VER APPENDIX TABLE D-5 CWHS FMPLOYMENT AND CENSUS POPULATION, BY SEX, RACE, AND AGE GROUP 1960 AND 1970 I960 1970 CWH5 CENSUS CWHS CENSUS AGE GROUP WORK FORCE POPULATION CWHS/CFN WORK FORCE POPULATION CWHS/CEN WHITE MALES UNDE* 25 22,300 218,958 .102 25 - 29 16,800 27,644 .608 30 - 34 18,400 28,987 .635 35 - 39 19,100 29,908 .639 40 - 44 16,400 28,323 .579 45 - 49 16,100 26,876 .599 50 - 54 15,100 25,034 .603 55 - 59 10,800 22,173 .487 60 £, OVER 16,700 67,779 .246 TOTAL 151,700 475,682 .319 33,200 227,691 .146 21,500 28,758 ,748 16,800 24,987 .672 17,800 25,333 .703 18,600 27,286 .682 16,000 27,352 .585 15,100 25,718 .587 13,600 23,493 .579 19,600 68,623 .286 172,200 479,241 .359 BLACK MALES UNDER 25 1,971 .000 25 - 29 459 .000 100 30 - 34 260 .000 35 - 39 154 .000 40-44 1 IIP .847 100 45 - 49 89 .000 50 - 54 69 .000 200 55 - 59 73 .000 60 & OVER 100 179 .559 100 TOTAL 200 3,372 .059 500 3,624 .138 WHITE FEMALES 14,900 21 1,996 .070 7,200 27,762 .259 7,300 30,189 .242 9,500 31,130 .305 9,600 29,078 .330 9,500 27,450 .346 10,200 26,176 .390 7,700 23,679 .325 8,700 80,149 .109 84,600 487,609 .173 BLACK FEMALES 1,452 .000 300 .000 238 .420 156 .000 91 .000 SR 3.448 60 .000 57 .000 188 .000 200 100 100 300 200 100 2,093 .000 313 .319 255 .000 251 .000 159 .629 104 .000 102 1.961 87 .000 260 .385 24,600 222,903 .110 9,500 29,169 .326 9,400 25,698 .366 11,100 26,699 .416 12,000 28,920 .415 12,900 29,017 .445 10,000 27,220 .367 10,300 25,437 .405 14,400 90,972 .158 114,200 506,035 .226 685 .000 262 .763 268 .000 214 .000 165 .606 129 .775 78 3.846 78 2.564 269 .372 TOTAL 300 2,602 .115 1,000 3,148 .318 G3AND TOTAL 236,800 969,265 .244 287,900 992,048 .290 D-5-24 MARYLAND APPENDIX TABLE D-5 CWHS EMPLOYMENT AND CENSUS POPULATION, BY SEX, RACE, AND AGE GROUP 1960 AND 1970 AGE GRHUP CWHS WORK FORCE 1960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCE 1970 CENSUS POPULATION CWHS/CEN WHITE MALES UNDER 25 58,300 576,731 .101 25 - 29 52,900 80,064 .661 30 - 34 56,500 92,881 .608 35 - 39 57,600 99,469 .579 40 - 44 55,800 92,075 .606 45 - 49 45,800 80,954 .566 50 - 54 40,900 67,876 .603 55 - 59 29,900 55,353 .540 60 I OVER 40,000 128,041 .312 TOTAL 437,700 1 ,273,444 .344 113,500 732,294 .155 74,800 116,471 .642 62,500 96,609 .647 58,000 92,773 .625 58.500 100,484 .582 58.100 101,472 .573 55,700 90,453 .616 42,200 74,238 .568 51.500 160,687 .320 574,800 1,565.481 .367 BLACK MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 & OVER TOTAL 17,200 133,682 .129 14,100 16,414 .859 13,200 17,916 .737 14,800 18,122 .817 13,000 16,157 .805 12,900 14,545 .887 10,200 11 ,866 .860 6,800 10,473 .649 8,200 20,581 .398 110.400 259,756 .425 28,100 192,514 .146 17,100 23,611 .724 19,200 20,906 .918 13.100 19,809 .661 15.100 19,532 .773 14,100 18,013 .783 12,300 15,318 .803 11,600 12,777 .908 13.300 28,360 .469 143.900 350,840 .410 WHITE FEMALES UNDER 25 45,300 560,013 .081 25 - 29 20,900 81,087 . .258 30 - 34 23,300 94,810 .246 35 - 39 29,000 105,123 .276 40 - 44 32,400 92,024 .352 45 - 49 27,200 79,329 .343 50 - 54 25,800 66,968 . .385 55 - 59 15,300 56,905 .269 60 & 3VER 21,700 164,216 .132 TOTAL 240,900 1,300,475 .185 88,100 717,332 .123 45,100 118,882 .379 29.800 96,360 .309 31.800 92,314 .344 40,200 102,564 .392 41,100 108,899 .377 36.600 93,638 .391 29.400 77,922 .377 32.500 221,496 .147 374,600 1,629,407 .230 BLACK FEMALES NDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 30 - 54 55 - 59 60 & OVER TOTAL 9,300 136,637 .068 9,100 18,649 .488 7,700 19,343 .398 11,200 19,399 .577 7,800 16,225 .481 8,700 14,295 .609 6,300 11,357 .555 3,900 9,801 .398 4,800 21,308 .225 68,800 267,014 .258 23,900 198,141 .121 12,500 27,461 .455 12.700 24,130 .526 11.500 22.828 .504 12,300 21,583 .570 12,500 19,770 .632 10,700 16,330 .655 8,900 13,532 .658 10,000 32,896 .304 115,000 376,671 .305 GRAND TOTAL 857,800 3.100,689 .277 1,208,300 3.922.399 ,308 D-5-25 APPENDIX TABLE 0-5 CWHS EMPLOYMENT AND CENSUS POPULATION. BY SEX, RACE' AND AGE GROUP 1960 AND 1970 MASSACHUSETTS AGE GROUP CWHS WORK FORCE 1960 CENSUS POPULATION CWHS/CFN CWHS WORK FORCE 1970 CENSUS POPULATION IWHS/CEN WHITE MALES J^OER 2 5 143,500 1,046,619 .137 25 - 29 107.700 142,564 .755 30 - 34 118,300 159,060 .744 35 - 39 119,900 167,222 .717 40 - 44 115,000 161,291 .713 45 - 49 106,600 150,047 .710 50 - 54 96,400 135,119 .713 55 - 59 78,300 120,989 .647 60 f, OVER 125,200 341 ,036 .367 TOTAL 1,010,900 2,423,947 .417 238.100 1.217,047 .196 137.100 175,103 .783 115.300 138,371 .833 100,500 139,736 .719 107.600 155.328 .693 107,700 159,212 .676 98,800 150,954 .655 88,800 132,280 .671 121,800 350,899 .347 1,115.700 2,618,930 .426 BLACK MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER TOTAL 3,300 3,200 2,900 3,300 1,800 3,000 1,200 1,000 1,800 21,500 0,332 .109 5,151 .621 4,995 • 5R1 4,837 .682 3,748 .480 3,000 1.000 2,291 .524 2,078 .481 5,856 .307 62, 28? ,345 7,900 56,034 .141 7,400 7,995 .926 3,800 6,563 .579 3,800 5,834 .651 3,300 5,161 .639 3,900 4,861 .802 2,600 3,901 .666 2,100 2,752 .763 2,400 7,367 .326 7,200 100,468 .370 WHITE FEMALES UNDER 25 123,700 1 ,029,374 .120 25 - 29 51,800 144,923 .357 30 - 34 50,700 165,203 .307 35 - 39 64,100 1 78,362 .359 40 - 44 71,900 173,830 .414 45 - 49 71,200 163,095 .437 50 - 54 69,200 150,077 .461 55 - 59 57,800 137,318 .421 60 OVER 73,500 457,015 .161 TOTAL 633,900 2,599,197 .244 215,900 1,215,089 .178 73,300 178,948 .410 51,300 142,754 .359 59,100 144,598 .409 71,800 164,238 .437 83,600 173,211 .483 82,600 167,149 .494 69,000 151,768 .455 92,200 520,939 .177 798,800 2,858,694 .279 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 63 t OVER TOTAL 2,200 2,400 2,400 2,000 2,300 1,700 2,300 700 1,300 17,300 30,595 .072 5,121 .469 5,228 .459 4,765 .420 3,715 .619 3,013 .563 2,481 .927 2,160 .324 6,063 .214 63,146 .274 10,100 58,614 .172 4,400 9,551 .461 3,300 7,562 .436 3,000 6,655 .451 2,800 6,132 .457 2,700 5,293 .510 1,600 4,176 .383 1,400 3,328 .421 2,100 9,767 .215 31,400 111,078 .283 GRAND TOTAL 1 ,683,600 5,148,578 .327 1,983.100 5,689,170 ,349 D-5-26 MICHIGAN APPENDIX TABLE D-5 CWHS EMPLOYMENT AND CENSUS POPULATION, BY SEX, RACE, AND AGp GROUP 1960 AND 1970 AGE GROUP CWHS WORK FORCF 1960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCE 1970 CENSUS POPULATION CWHS/CEN WHITE MALES UNDER 25 191 ,500 1,626,070 .118 25 - 29 162,600 207,455 .784 30 - 34 189,500 238,620 .794 35 - 39 200,700 243,685 .824 40 - 44 182,400 225,267 .810 45 - 49 171,500 208,896 .821 50 - 54 134,900 183,702 .734 55 - 59 119,700 165,182 .725 60 6 OVFR 140,300 421,545 .333 TOTAL 1,493,100 3,520,422 .424 329,500 1,869,576 .176 238,000 259,518 .917 185.300 213,457 .868 170,200 204,458 .832 182.600 230,737 .791 187,700 229,805 .817 166,500 208,050 .800 146,200 181,566 .805 145,200 447,339 .325 1,751,200 3,844,506 .456 BLACK MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVFR TOTAL 11,400 179,027 .064 14,400 22,922 .628 20,700 27,363 .756 20,600 28,618 .720 20,600 24,541 .839 15,500 21,869 .709 13,800 17,026 .811 9,700 14,979 .648 10,200 26,101 .391 136,900 362,446 .378 40,900 275,887 .148 28,700 33,985 .844 19,800 26,033 .761 18,900 26,035 .726 22,900 28,617 .800 22,300 28,009 .796 19,700 23,517 .838 12,000 19,762 .607 13,000 42,297 .307 198,200 504,142 .393 WHITE FEMALES UNDER 25 138*100 1 ,611,649 .086 25 - 29 63,700 216,112 .295 30 - 34 67,700 242,725 .279 35 - 39 78,300 253,878 .308 40 - 44 84,600 233,752 .362 45 - 49 89,200 209,366 .426 50 - 54 66,200 181,624 .364 55 - 59 49,700 159,119 .312 60 6 OVER 50,600 457,218 .111 TOTAL 688,100 3,565,443 .193 265,700 1,853,150 .143 100,900 262,580 .384 75,200 218,250 .345 83,100 212,546 .391 101,900 236,748 .430 110,100 242,015 .455 101,000 221,528 .456 84,900 190,003 .447 75,500 552,148 .137 998,300 3,988,968 .250 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER TOTAL 6,800 186,311 .036 7,700 27,299 .282 10,000 30,154 .332 9,400 29,417 .320 9,400 25,215 .373 7,100 21,066 .337 5,500 15,947 .345 3,200 13,307 .240 2,300 26,167 .088 61,400 374,883 .164 32,200 284,299 .113 18.700 37,515 .498 15.100 31.025 .487 14,100 31,033 .454 16,600 32,148 .516 15,100 29,392 .514 10,700 24,692 .433 9,100 19,922 .457 7,000 47,441 .143 138,600 537,467 .258 GRAND TOTAL 2»379,50O 7,823,194 .304 3,086,300 8,875.083 .348 D-5-27 MINNESOTA APPENDIX TABLE D-5 CWHS EMPLOYMENT AND CFNSUS POPULATION, BY 5EX» RACE. AND AGE GROUP 1960 AND 1970 AGE GROUP CWHS WORK FOPCE 1960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCE 1970 CENSUS POPULATION !WHS/CEN WHITE MALES UNDER 25 85.100 773,206 .110 25 - 29 65.000 94,185 .690 30 - 34 70.700 101,191 .699 35 - 39 66.300 103,615 .640 40 - 44 58,300 99,959 .583 45 - 49 55.900 95,806 .583 50 - 54 43.300 87,010 .498 55 - 59 36.500 78,062 .468 60 £, OVER 51,100 238,459 .214 TOTAL 532,200 1,671,493 .318 151,500 886.784 .171 107,800 121,213 .889 86,400 100,890 .856 71,600 94,072 .761 74,400 99,055 .751 68,400 99,097 .690 58.800 93,459 .629 51.100 85,167 .600 63.300 249,680 .254 733.300 1,829,417 .401 BLACK MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 i, OVER TOTAL 800 700 800 1,200 900 500 600 300 400 6.200 1,298 .071 1,577 .444 1,545 .518 1,432 .838 1,197 .752 1,068 .468 824 .728 681 .441 1,847 .217 21,469 .289 2.400 1.100 1.500 1.800 800 1.300 900 900 500 11,200 19,784 .121 2,635 .417 2,224 .674 1,778 1.012 1,693 .473 1,495 .870 1,159 .777 1,014 .888 2,611 .191 34,393 .326 WHITE FEMALES UNDER 25 70,800 771,709 .092 25 - 29 21,700 95,786 .227 30 - 34 24,400 102,275 .239 35 - 39 26,800 104,799 .256 40 - 44 31,600 102,680 .308 45 - 49 32,800 96,359 .340 50 - 54 31,200 87,648 .356 55 - 59 23,800 80,453 .296 60 £, OVER 32,900 258,401 .127 TOTAL 296,000 1,700,110 .174 147,900 888,428 .166 54,600 122,896 .444 38.500 101,486 .379 37,800 95,120 .397 45,300 100,390 .451 44,100 100,834 .437 44,600 98,213 .454 39,400 89,849 .439 46,900 309,405 .152 499,100 1,906,621 .262 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER 1,000 300 700 200 400 300 100 100 300 1,387 .088 1,612 .186 1,476 .474 1,317 .152 1,032 .388 916 .328 708 .141 644 .155 1,700 .176 1,300 900 1.400 500 800 500 400 200 200 19,873 .065 2,772 .325 2,169 .645 1,893 .264 1,572 .509 1,478 .338 1,125 .356 981 .204 2,677 .075 TOTAL 3.400 20,792 .164 6.200 34.540 ,180 GRAND TOTAu 837,800 3,413,864 .245 1,249,800 3,804,971 ,328 D-5-28 MISSISSIPPI UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 DVFK TOTAL UMDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 DVER TOTAL UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 (, OVER TOTAL UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 £. OVEK APPENDIX TABLE D-5 CWHS EMPLOYMENT AND CENSUS POPULATION, BY SEX, PACE» AND AGE GROUP 1960 AND 1970 1960 1970 CWHS CENSUS CWHS CENSUS AGE GROUP WORK FORCF POPULATION CWHS/CEN WORK FORCE POPULATION CWHS/CEN WHITE MALES 26,700 294,464 .091 24,300 37,132 .654 22,100 39,146 .565 23,500 39,598 .593 20,900 37,490 .557 20,100 37,542 .535 15,100 34,773 .434 11,700 29,032 .403 11,800 75,834 .156 176,200 625,011 .282 42,400 320,800 .132 28,800 45,200 .637 24,900 40,281 .618 22,700 37,880 .599 24,000 40,058 .599 22,600 38,677 .584 18,500 35,983 .514 18,500 34,106 .542 22,700 90,762 .250 25.100 683,747 .329 BLACK MALES 13,500 268,881 .050 8,100 1 f,659 .459 9,300 16,861 .552 7,200 17,072 .422 8,300 17,889 .464 7,600 20,060 .379 5,900 18,622 .317 5,700 17,296 .330 4,200 48,582 .086 69,800 442,922 .158 20,800 237,574 .088 10,800 16,573 .652 12,400 14,315 .866 6,500 13,195 .493 7,800 13,754 .567 7,900 13,687 .577 7,900 14,820 .533 7,500 15,376 .488 12,500 51,176 .244 94,100 390,470 .241 WHITE FEMALES 23,200 278,720 .083 14,200 38,406 .370 13,800 41,213 .335 16,100 41 ,647 .387 14.200 39,314 .361 14,300 38,729 .369 8,500 35,413 .240 5,800 30,637 .189 7,800 88,456 .088 117,900 632,535 .186 39,400 306,216 .129 20,000 45,935 .435 19,000 41,109 .462 17,100 39,742 .430 19,100 41,987 .455 18,900 41,090 .460 16,300 38,781 .420 17,000 37,366 .455 14,200 117,310 .121 181,000 709,536 .255 BLACK FEMALES 4,600 269,650 .017 3,800 22,581 .168 4,500 23,203 .194 4,800 22,973 .209 3,800 22,755 .167 3,500 23,424 .149 4,900 20,849 .235 3,500 18,866 .186 3,300 53,363 .062 12.900 241,518 .053 8,300 20,449 .406 7,500 18,784 .399 6,900 17,827 .387 8,000 19,154 .418 7,300 18,207 .401 5,300 18,194 .291 4,900 17,938 .273 8,700 61,088 .142 69,800 433,159 .161 TOTAL 36,700 477,673 .077 GRAND TOTAL 400,600 2,178, Ul ,184 570,000 2,216,912 .257 D-5-29 MISSOURI UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 £, OVER TOTAL APPENDIX TABLE D-5 CWHS EMPLOYMENT AND CENSUS POPULATION, BY SEX, RACE, AND AGE GROUP 1960 AND 1970 1960 1970 CWHS CENSUS CWHS CENSUS AGE GROUP WORK FORCE POPULATION CWHS/CEN WORK FORCE POPULATION CWHS/CEN WHITE MALES UNDER 25 109,900 826,271 .133 25 - 29 86,100 109,917 .783 30 - 34 93,700 117,449 .798 35 - 39 92,500 122,922 .753 40 - 44 80,500 116,031 .694 45 - 49 81,100 117,336 .691 50 - 54 68,700 110,893 .620 55 - 59 58,300 100,996 .577 60 J, OVER 73,900 296,563 .249 TOTAL 744,700 1,918,378 .388 167,900 913,799 .184 110,100 131,317 .838 98,200 110,676 .887 84,600 108,092 .783 83,600 115,654 .723 84,300 118,431 .712 71,200 108,698 .655 67,400 104,250 .647 89,200 309,360 .288 856,500 2.020,277 .424 BLACK MALES 9,500 95,208 .100 6,800 10,932 .622 8,600 11,551 .745 11,700 11,437 1.023 7,900 10,187 .775 6,500 10,319 .630 5,700 9,429 .605 4,500 8,784 .512 5,700 22,054 .258 66,900 189,901 .352 20,900 130,335 .160 12,200 14,266 .855 10,300 12,145 .848 8,500 11,293 .753 8,600 11,537 .745 10,300 11,049 .932 6,500 9,514 .683 5,900 9,075 .650 7,700 26,461 .291 90,900 235,675 .386 WHITE FEMALES UNDER 25 89,000 805,607 .110 25 - 29 38,400 113,392 .337 30 - 34 37,000 122,671 .302 35 - 39 44,800 130,032 .345 40 - 44 48,400 124,503 .389 45 - 49 43,900 122,857 .357 50 - 54 42,600 117,996 .361 55 - 59 34,000 109,408 .311 60 £, OVER 40,900 357,623 .114 TOTAL 419,000 2,004,589 .209 143,800 896,245 .160 59,600 135,877 .439 43,900 114,247 .384 45,400 112,678 .403 55,700 121,339 .459 57,000 125,945 .453 57,300 119,579 .479 43,900 115,790 .379 59,800 415,518 .144 566,400 2,157,218 .263 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 £, OVER TOTAL G3AND TOTAL 1,276,600 4,319,813 .296 1,592,300 4,676,501 .340 6,300 98,533 .064 5,700 13,582 .420 5,500 14,134 .389 6,200 13,689 .453 5,900 12,145 .486 4,300 11,518 .373 5,400 10,287 .525 3,700 9,516 .389 3,000 23,541 .127 6,000 206,945 .222 18,300 134,398 .136 10,700 16,866 .634 8,200 14,930 .549 6,700 14,781 .453 8,600 14,440 .596 8,400 13,369 .628 6,500 11,549 .563 4,100 10,705 .383 7,000 32,293 .217 78,500 263,331 .298 D-5-30 MONTANA APPENDIX TABLE D-5 CWHS EMPLOYMENT AND CENSUS POPULATION. BY SEX. RACE. AND AGE GROUP 1960 AND 1970 AGE GROUP CWHS WORK FORCF I960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCE 1970 CENSUS POPULATION :whs/cen WHITE MALES UNDE R 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 5 OVER TOTAL 13,000 152,652 .035 13,000 18,666 .696 13,300 20,531 .648 12,500 21,159 .591 10,200 21,437 .476 11,500 20,180 .570 10,000 17,333 .577 6.900 14,727 .469 8,200 44,689 .183 98,600 331,374 .298 17,400 156,898 .111 12,700 20,216 .628 9,900 17,751 .558 11,900 17,267 .689 10,000 18,730 .534 9,700 18.961 .512 9,600 19.123 .502 8.900 17,003 .523 9.700 45,262 .214 99,800 331,211 .301 BLACK MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - S4 55 - 59 60 & !)VER TOTAL 100 100 200 ,533 .000 762 .000 710 .000 635 .000 519 .000 490 .204 402 .000 352 .000 966 .104 12,369 .016 100 100 200 9,950 .000 955 .105 875 .000 691 .000 676 .000 574 .000 515 .000 440 .227 1,118 .000 15.794 .013 WHITE FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER TOTAL 10.700 150,075 .071 4,200 18,637 .225 3,800 19,854 .191 6,700 20,392 .329 7,200 20,736 .347 6,200 18,823 .329 6,900 15,293 .451 3,700 13,112 .282 5,100 42,442 .120 54,500 319,364 .171 16,800 153,269 .110 7,400 20,050 .369 6,600 18,149 .364 6,800 17,469 .389 6,200 18,635 .333 8,200 18,569 .442 8,900 19,026 .468 6.300 16,870 .373 7,500 49,795 .151 74,700 331,832 .225 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 & OVER 100 100 ,277 .000 817 .122 634 .000 587 .000 510 .196 446 .000 307 .000 301 .000 781 .000 100 9,681 .010 981 .000 902 .000 836 .000 694 .000 558 .000 511 .000 413 .000 996 .000 TOTAL 200 1 1,660 ,017 ion 15,572 .006 GRAND TOTAL 153,500 674,767 .227 174,800 694,409 ,252 D-5- 31 NEBRASKA UNDER : c 25 - 29 30 - 3 A ^5 - 3Q *0 - u 45 - 4'. 50 - 54 5 3 - 59 60 £ , 1 3VER APPENDIX TABLE D-5 CWHS EMPLOYMENT AND CENSUS POPULATION, BY SEX, RACE, AND AGE GROUP 1960 AND 1970 1960 1970 CWHS CENSUS CWHS CENSUS ASE 3R0UR WORK FORCE POPULATION CWHS/CEN WORK FORCE POPULATION CWHS/CEN WHITE MALES UNDER 25 35,fiO0 302,690 .118 25 - 29 25,000 39,697 .630 30 - 34 23,800 42,244 .563 35 - 39 25,600 42,971 .596 40 - 44 21,600 40,02"^ .540 45 - 49 18,800 38,717 .486 50 - 54 18,800 36,687 .512 55 - 59 13,600 33, 14^ .410 60 £, OVER 23,600 105,427 .224 TOTAL 206,600 681,603 .303 59,800 326,319 .183 29,000 42,826 .677 26,100 37,140 .703 24,300 36,914 .658 24,800 39,233 .632 25,400 39,432 .644 21,500 36,158 .595 16,200 33,892 .478 28,600 107,928 .265 255,700 699,842 .365 BLACK MALES UNDER 25 1,000 9,897 .101 1,900 25 - 29 1,100 1,41? .777 1,200 30 - 34 1,000 1,285 .778 900 35 - 39 800 1,117 .716 500 40 - 44 800 904 .885 300 45 - 49 800 801 .999 1,000 50 - 54 300 691 .434 1,000 55 - 59 300 623 .482 100 60 <, OVER 800 1,690 .473 500 TOTAL 6.900 18,423 .375 7,400 WHITE FEMALES 14,586 .130 1,476 .813 1,443 .624 1,310 .382 1,206 .249 1,012 .988 809 1.236 713 .140 2,058 .243 24,613 .301 UNDER 25 27,200 297,103 .092 25 - 29 11,200 40,119 .279 30 - 34 11,200 41,936 .267 35 - 39 13,600 42,586 .319 40 - 44 12,900 40,921 .315 45 - 49 14,000 39,337 .356 50 - 54 13,500 37,664 .358 55 - 59 13,900 34,444 ,404 60 6 OVER 16,300 119,051 .137 TOTAL 133,800 693,161 .193 52,500 322,837 .163 18,000 42,975 .419 14,300 37,707 .379 16,100 37,992 .424 19,600 39,467 .497 18,800 39,461 .476 16,000 38,106 .420 16,300 36,405 .448 24,400 138,075 .177 196,000 733,025 .267 BLACK FEMALES 600 9,392 .064 1,900 500 1,394 .359 700 600 1,354 .443 900 200 1,165 .172 600 300 954 .314 800 600 839 .715 600 200 694 .288 300 300 621 .483 400 1,730 .000 300 TOTAL 3,300 18,143 .182 6,500 26,013 .250 GRAND TOTAL 350,600 1,411,330 .248 465,600 1,483,493 .314 14,539 .131 1,776 .394 1,641 .548 1,558 .385 1,401 .571 1,097 .547 888 .338 778 .514 2,335 .128 D-5-32 NEVADA APPENDIX TABLE 0-5 CWHS EMPLOYMENT ANO CENSUS POPULATION, BY SEX, PACE, AND AGE GROUP I960 AND 1970 AGE GROUP CWHS WORK EORCE 1960 CENSUS POPULATION CWHS/CFN CWHS WORK FOKCF 1970 CFNSUS POPULATION :whs/cen WHITE MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 (, OVER TOTAL 8,900 57,352 .155 5,900 8,925 .661 6,400 9,620 .665 7,000 10,403 .673 6,600 10,215 .646 7,400 9,938 .745 4,400 8,194 .537 4,600 6,960 .661 5,400 14,691 .368 56,600 136,298 .415 16,500 101,283 .163 13,200 16,966 .778 12.000 15,974 .751 8,400 14,900 .564 8,900 14,856 .599 10,700 14,798 .723 9,100 13,671 .666 7,900 11,687 .676 9.100 23,369 .389 95,800 227,504 .421 BLACK MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 E, OVER 600 300 400 200 300 100 6,091 .000 923 .650 807 .372 709 .564 609 .328 546 .000 438 .000 417 .719 683 .146 1.100 1,200 400 800 800 400 200 300 400 11.379 .097 1,648 .728 1,434 .279 1,170 .684 1,061 .754 831 .481 803 .249 618 .485 1,350 .296 TOTAL 1 ,900 11,223 ,169 5,600 20,294 ,276 WHITE FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 & OVFR TOTAL 4,600 3,100 2,800 3,300 3,900 2,800 3,300 1,300 1,800 26,900 55,564 .083 8,758 .354 9,464 .296 10,497 .314 9,754 .400 8,740 .320 7,069 .467 5,366 .242 11,933 .151 27,145 .212 14,300 99,248 .144 8.400 17,206 .488 5, 500 15,236 .361 6,800 13,987 .486 6.400 14,050 .456 6.400 14,348 .446 4,900 12,812 .382 3.300 10,761 .307 4.000 23,025 .174 60,000 220,673 .272 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 & OVER 100 200 700 200 100 100 200 6. 058 .017 892 .000 833 .240 601 1.165 56 1 .357 519 .193 310 .323 297 .673 541 .000 1.200 600 800 100 500 600 300 100 300 11.406 .105 1,662 .361 1,476 .542 1,339 .075 1,120 .446 841 .713 718 .418 605 .165 1,100 .273 TOTAL 1,600 10,612 .151 4.500 20,267 .222 GRAND TOTAL 87,000 285,278 165.900 488,738 D-5-33 APPENDIX TABLE D-5 CWHS EMPLOYMENT AND CENSUS POPULATION. BY SEX, RACE* AND AGE GROUP 1960 AND 1970 NEW HAMPSHIRE A3E GROUP CWHS WORK FORCE 1960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCE 1970 CENSUS POPULATION :whs/cen WHITE MALES UNDER 25 17,100 132, 448 .129 25 - 29 13,400 16,881 .794 30 - 34 14,700 18,337 .802 35 - 39 14,300 19,493 .734 40 - 44 11,400 19,004 .600 45 - 49 11,900 17,678 .673 50 - 54 11,700 15,973 .732 55 - 59 9,500 14,436 .658 60 <, OVER 16,200 42,412 .382 TOTAL 120,200 296,662 .405 31,100 171,165 .182 17.900 23,857 .750 15,400 20,227 .761 13,900 19,462 .714 15,300 20,444 .748 14,600 20,446 .714 10,800 19,214 .562 11,600 16,743 .693 18,300 46,703 .392 148,900 358,261 .416 BLACK MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER 100 805 .000 219 .000 124 .806 8 C .000 5? .000 3? .000 ^7 .000 30 .000 61 .000 100 100 100 200 1.397 .072 213 .469 175 .000 178 .562 111 1.802 88 .000 66 .000 41 .000 142 .000 TOTAL no 1,445 .069 500 2.411 .207 WHITE FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 £, OVER TOTAL ]? ,500 6 i400 5 .900 7 ,400 9 .700 10 ,000 7 .100 6 ,700 7 ,900 73 ,600 127,646 17,227 19,212 20,558 19,387 18,435 17,202 15,421 52,584 307,672 .098 .372 .307 .360 .500 .542 .413 .434 .150 .239 26,600 166,357 .160 11,100 24,159 .459 9,200 20,269 .454 8.500 19,903 .427 9,800 21,007 .467 10,300 21,399 .481 11,100 19,986 .555 10,100 18,493 .546 11,500 63,272 .182 08,200 374,845 .289 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER 100 100 641 .000 1M .000 105 .000 68 1.471 36 .000 21 .000 22 4.545 20 .000 6? .000 100 200 100 100 100 100 1,143 .087 206 .971 164 .000 192 .521 139 .719 73 1.370 44 .000 48 .000 155 .645 TOTAL 200 1,142 .175 700 2,164 .323 GRAND TOTAL 194,100 606,921 ,320 258,300 737,681 ,350 D-5-34 NEWJERSEY APPENDIX TABLE D-5 CWHS E M PLOYMENT AND CENSUS POPULATION, BY SEX, RACE, AND AGE GROUP 1960 AND 1970 AGE GROUP CWHS WORK FORCE 1960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCE 1970 CENSUS POPULATION :whs/cen WHITE MALES UNDER 25 135, soo 1,120,165 .121 25 - 29 121,100 155,466 .779 30 - 34 133,900 190,796 .702 35 - 39 160,400 209,240 .767 40 - 44 149,000 201,258 .740 45 - 49 133,500 187,125 .713 50 - 54 107,700 162,354 .663 55 - 59 92,700 139,710 .664 60 & OVER 126,600 351,398 .360 TOTAL 1,160,700 2,717,512 .427 228,400 1,353,935 .169 156,600 194,516 .805 134,400 169,937 .791 133,600 177,088 .754 143,000 203,968 .701 155,300 210,381 .738 144,700 195,260 .741 114,400 170,557 .671 153.500 404,573 .379 1,363,900 3,080,215 .443 BLACK MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - S9 60 & OVER TOTAL 18,700 124,947 .150 18,200 19,491 .934 17,500 20,033 .874 17,700 19,392 .913 13,600 15,818 .860 11,600 13,650 .850 10,100 11,287 .895 6,600 10,217 .646 6,700 19,644 .341 120,700 254,479 .474 31, roo 214,428 .148 22.800 27,011 .844 20,800 24,721 .841 17,900 23,223 .771 15,800 21,964 .719 17,200 19,441 .885 13,100 15,580 .841 10,600 12,351 .858 11.700 28,439 .411 161 ,600 387,158 .417 WHITE FEMALES UNDER 25 101,200 1,090,576 .093 25 - 29 47,600 164,724 .289 30 - 34 52,800 201,805 .262 35 - 39 70,700 222,996 .317 40 - 44 82,400 212,019 .389 45 - 49 81,400 1Q1,166 .426 50 - 54 70,000 164,676 .425 55 - 59 49,700 143,573 .346 60 I OVER 54,300 429,956 .126 TOTA' 610,100 2,821,491 .216 212,200 1.323,979 .160 83,100 207,223 .401 60,800 178,042 .341 71,900 185,500 .388 91,200 214,068 .426 112,900 226,117 .499 102,600 210,565 .487 82,200 182,972 .449 83,800 541,227 .155 900,700 3,269,693 .275 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER TOTAL 10,600 130,518 .081 10,400 22,692 .458 8,200 22,446 .365 10,600 20,801 .510 9,300 17,044 .546 8,600 14,780 .582 6,600 12,214 .540 3,700 10,612 .349 4,800 22,193 .216 72,800 273,300 .266 28,600 220,970 .129 15,800 34,414 ,459 15,700 30,775 .510 13,800 28,118 .491 12,000 25,492 .471 14,000 22,039 .635 10,000 17,698 .565 9,500 14,797 .642 9,500 36,795 .258 128,900 431,098 .299 GRAND TOTAL 1 ,964,300 6,066,782 .324 2,555,100 7,168,164 ,356 D-5-35 NEWMEXICO APPENDIX TABLE D-5 CWHS EMPLOYMENT AND CENSUS POPULATION, BY SEX, RACE, AND AGE GROUP 1Q60 AND 1970 A3E GROUP CWHS WORK FORCE 1960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCE 1970 CENSUS POPULATION :whs/cen WHITE MALES UNDER 25 24,100 232,186 .104 25 - 29 19,900 30,870 .645 30 - 3* 18,500 30,719 .602 35 - 39 15,600 30,030 .519 (.0 - 44 12,900 26,380 .489 45 - 49 14,400 23,123 .623 50 - 54 9,400 18,714 .502 55 - 59 7,400 15,147 .489 60 6 OVER 8,700 35,183 .247 TOTAL 130,900 442,352 .296 35,800 234,205 .153 18,500 28,699 .645 16,600 25,890 .641 14,800 25,992 .569 16,300 26,111 .624 16,400 24,752 .663 10,100 21,699 .465 11,400 19,150 .595 10,300 45,622 .226 150,200 452,120 .332 BLACK MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 S OVER TOTAL 500 800 200 200 400 200 100 2,400 23,123 .022 2,615 .306 2,189 ,091 1,955 .102 1,631 .245 1,410 .000 1,113 .180 1,115 .000 2,267 .044 37,411 ,064 300 200 500 400 200 200 100 400 2,300 30,622 .010 3,054 .065 2,675 .187 2,423 .165 2,041 .098 1,743 .115 1,473 .068 1,251 .000 3,422 .117 48,704 .047 WHITE FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 s OVER TOTAL 15,300 225,566 .068 7,300 30,246 .241 7,000 30,529 .229 8,000 30,135 .265 6,900 25,332 .272 6,000 22,485 .267 5,300 18,251 .290 3,000 14,891 .201 2,900 35,976 .081 61,700 433,411 .142 23,100 230,014 .100 13.000 29,996 .433 9,900 27,865 .355 10,900 27,087 .402 11,400 27,098 .421 10,000 26,222 .381 7,400 22,559 .328 6,000 19,946 .301 5,900 52,908 .112 97,600 463,695 .210 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 f, OVER TOTAL ion 200 200 100 300 100 1,000 23,937 .000 2,807 .036 2,437 .000 1,968 .000 1,530 .131 1,414 .141 1,015 .099 970 .309 1,764 .057 37,842 ,026 400 300 300 400 200 300 300 2,200 31,713 .013 3,408 .088 3,139 .096 2,913 .137 2,399 .000 1,819 .110 1,503 .200 1,381 .000 3,206 .094 51,481 ,043 GRAND TOTAL 196,000 951,023 ,206 252,300 1,016,000 ,248 D-5-36 NEWYORK APPENDIX TABLE D-5 CWHS EMPLOYMENT AND CENSUS POPULATION, BY SEX, RACE, AND AGE GROUP 1960 AND 1970 AGE GROUP CWHS WORK EOPCF 1960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCF 1970 CENSUS POPULATION :whs/cen WHITE MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 £, OVER TOTAL 397,800 365,300 423,500 421,100 405,400 3S4,200 381,400 319,000 462,200 3,559,900 2,995,305 440,606 503,989 522,061 497,085 490,900 466,265 423,522 1,081,631 7,421,364 .133 .829 .840 .807 .816 .783 .818 .753 .427 .480 614,800 3,311,685 ,1«6 467,300 499,462 .936 409,000 421,081 .971 374,700 423,188 .885 406,500 471,899 .861 400,100 478,720 .836 370,100 448,836 .825 331,800 419,550 .791 487,800 1,121,650 .435 ,862,100 7,596,071 .508 BLACK MALES UNDER 25 53,400 323,001 .165 25 - 29 46,100 53,962 .854 30 - 34 44,300 57,895 .765 35 - 39 45,900 58,992 .778 40 - 44 36,400 48,445 .751 45 - 49 33,000 42,108 .784 50 - 54 26,000 34,668 .750 55 - 59 18,600 30,304 .614 60 & OVER 22.100 52,500 .421 TOTAL 325,800 701,875 .464 89,800 589,203 .152 67,600 85,313 .792 63,500 79,005 .804 55.100 71,582 .770 46,200 66,282 .697 44,200 60,281 .733 33,800 47,392 .713 29,000 37,482 .774 32,900 82,728 .398 462.100 1,119,268 .413 WHITE FEMALES UNDER 25 354,300 2 ,989,327 .119 25 - 29 173,900 463,450 .375 30 - 34 162,800 531,058 .307 35 - 39 194.000 563,616 .344 40 - 44 227,200 543,714 .418 45 - 49 253,700 534,178 .475 50 - 54 248,800 493,704 .504 55 - 59 189,400 446,004 .425 60 6 OVER 229,200 1 ,300,656 .176 TOTAL 2,033,300 7 ,865,707 .259 599,200 3 ,306,797 .181 266,100 531,659 .501 169,200 441,039 .384 187,300 445,468 .420 232.100 505,069 .460 273,400 525,601 .520 270,900 502,598 .539 247,700 474,116 .522 311.000 1 ,505,672 .207 2,556,900 8 .2™. 019 .310 BLACK FEMALES UNDER 25 44,200 347,360 .127 25 - 29 33,500 66,03 c .507 30 - 34 33,500 70,689 .474 35 - 39 37,600 68,497 .549 40 - 44 32,800 57,853 .567 45 - 49 26,600 49,436 .538 50 - 54 21,400 39,337 .544 55 - 59 14,800 32,892 .450 60 6 OVER 15,400 61,259 .251 TOTAL 259,800 793,358 .327 88,000 615,292 .143 56,900 109.775 .518 50,900 97,239 .523 42,900 87,301 .491 41,200 81,703 .504 41,800 71,531 .584 33,400 59,430 .562 22,900 48,474 .472 27,000 112,864 .239 405,000 1,283,609 .316 GRAND TOTAL 6.178,800 16.782,304 .368 7,286,100 18,236,967 .400 D-5-37 APPENDIX TABLE D-5 CWHS EMPLOYMENT AND CENSUS POPULATION, BY SEX, RACE, AND AGE GROUP 1960 AND 1970 NORTH CAROLINA AGE GROUP CWHS WOPK FORCF 1960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCE 1970 CENSUS POPULATION CWHS/CEN WHITE MALES UNDER 25 100,300 811,362 .124 25 - 29 75,R0O 113,396 .668 30 - 34 81,400 118,782 .685 35 - 39 81,400 120,138 .678 40 - 44 65,300 108,495 .602 45 - 49 58,500 98,733 .593 50 - 54 47,100 83,475 .564 55 - 59 31,200 67,976 .459 60 £, OVER 40,500 162,440 .249 TOTAL 581,500 1 ,684,797 .345 162,500 914,570 .178 112,100 139,896 .801 93,700 118,416 .791 79,700 113,996 .699 83,100 118,671 .700 85,700 116,232 .737 63,400 102,562 .618 56,100 88,940 .631 63,600 207,559 .306 799,900 1,920,842 .416 BLACK MALES UNDER : c 25 - 29 30 - 3" 35 - 39 40 - 44 i c - 49 50 - 54 55 - 59 60 f , 1 DVER TOTAL 24,300 19,200 18,800 20,400 17,700 17,600 14,000 10.800 8,800 151,600 331,346 29,280 29,658 30,919 29,431 28,152 22,427 19,114 41,945 562,272 ,073 .656 .634 ,660 ,601 ,625 ,624 ,565 ,210 ,270 46,000 332,223 .138 23,300 32,077 .726 17,100 26,177 .653 18,700 24,728 .756 17,100 26,287 .651 19,400 26,253 .739 18,000 24,789 .726 16,600 22,352 .743 17,600 52,639 .334 193,800 567,525 .341 WHITE FEMALES UNDER 25 78,200 773,234 .101 25 - 29 52,000 116,483 .446 30 - 34 47,500 122,087 .389 35 - 39 53,800 124,320 .433 40 - 44 46,500 111,296 .418 45 - 49 43,100 102,835 .419 50 - 54 36,800 89,067 .413 55 - 59 26,700 74,602 .358 60 £, OVER 20,100 200,564 .100 TOTAL 404,700 1,714,48ft .236 142,800 858,789 .166 81,900 140,335 .584 66,400 122,008 .544 64,200 118,774 .541 66,000 123,385 .535 72,000 122,535 .588 54,500 110,452 .493 44,900 100,013 .449 46,500 284,634 .163 639.200 1,980,925 .323 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 (, OVER TOTAL 7,600 330,910 .023 8,400 33,73R .249 11,000 35,754 .308 11,800 35,55P .332 11,000 32,784 .336 10,800 30,460 .355 8,400 24,345 .345 5,500 20,922 .263 6,000 50,127 .120 80,500 594,598 .135 32,300 330,667 .098 16,100 34,397 .468 17,000 30,600 .556 14,500 30,577 ,474 17,000 32,669 .520 14,900 30,812 .484 13,600 28,413 .479 11,300 25,808 .438 11,300 68,824 .164 148,000 612,767 .242 GRAND TOTAL 1,218,300 4,556,155 .267 1,780,900 5,082,059 .350 D-5-38 APPFNDIX TABLE D-5 CWHS EMPLOYMENT AND CENSUS POPULATION, BY SEX, RACE, AND AGE GROUP 1960 AND 1970 NORTH DAKOTA CWHS AGE GROUP WORK FORCE 1960 CENSUS POPULATION CWHS/CFN CWHS WORK FORCE 1970 CENSUS POPULATION :WH5/CEN WHITE MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 I OVER TOTAL 9,100 151,908 .060 8,500 17,696 .480 9,300 18,84 c .493 7,500 18,334 .409 6.500 18,477 .352 6,700 18,374 .365 4,600 16,663 .276 3,600 14,374 .250 7,300 41,966 .174 3,100 316,637 .199 16,900 147,304 .115 12,600 17,227 .731 8,700 14,679 .593 7,200 15,149 .475 8,900 16,177 .550 7,700 15,969 .482 6,800 15,954 .426 6,700 15,479 .433 8,100 44,400 .182 83,600 302,338 .277 BLACK MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 & DVER TOTAL 200 100 100 400 4,253 .000 399 .000 360 .000 285 .000 263 .760 213 .469 233 .000 176 .000 389 .257 6,571 .061 200 100 100 400 6,062 .033 596 .000 523 .000 518 .000 366 .273 271 .369 215 .000 173 .000 547 .000 9,271 ,043 WHITE FEMALES UNDER 25 25 - 29 30 35 40 45 50 55 34 39 44 49 54 59 60 & OVER TOTAL 12,100 147,505 3,200 17,421 3,600 17,804 3,100 17,365 3,800 17,608 4,100 17,100 4,500 15,240 4,600 13,140 3,300 39,718 42,300 302,901 ,082 ,184 .202 .179 .216 ,240 ,295 .350 ,083 ,140 15,300 6,800 4,600 5,900 6,100 5,600 4,300 5,000 7,500 61,100 140,327 .109 16,195 .420 15.059 .305 15,178 .389 15,939 .383 15,360 .365 15,665 .274 15,061 .332 48,363 .155 297,147 ,206 BLACK FEMALES U«JDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER 100 4,046 .000 416 .000 353 .000 309 .000 267 .000 222 .450 185 .000 164 .000 375 .000 100 5,735 .000 585 .000 588 .000 526 .190 345 .000 268 .000 245 .000 210 .000 503 .000 TOTAL 100 6,337 ,016 100 9,005 ,011 GRAND TOTAL 105,900 632,446 .167 145,200 617,761 ,235 D-5-39 OHIO APPFNDIX TABLE D-5 CWHS EMPLOYMENT AND CENSUS POPULATION, BY SEX, RACE, AND AGE GROUP 1960 AND 1970 AGE GROUP CWHS WORK FORCE 1960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCF 1970 CENSUS POPULATION CWHS/CEN WHITE MALFS UNDER 25 239,700 1,971,982 .122 25 - 29 207,500 262,423 .791 30 - 34 237,900 299,268 .795 35 - 39 240,700 310,300 .776 40 - 44 217,500 286,804 .758 45 - 49 199,000 262,448 .758 50 - 54 169,000 231,016 .732 55 - 59 126,300 198,758 .635 60 & OVER 162,500 553,127 .294 TOTAL 1 ,800, 100 4,376,126 .411 395,500 2,219,877 .178 264,100 309,170 .854 223,700 265,769 .842 201,500 255,973 .787 222,200 285,527 .778 222,900 289,363 .770 196,200 262,855 .746 164,800 227,491 .724 182,600 569,660 .321 2,073,500 4,685,685 .443 BLACK MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 I OVER TOTAL 16,600 189,863 .087 17,900 25,906 .691 23,100 29,104 .794 20,400 28,90^ .706 18,000 23,949 .752 14,500 21,764 .666 14,100 17,875 .789 9,900 16,624 .596 13,600 34,112 .399 148,100 388,102 .382 38.900 255,681 .152 23,100 28,565 .809 18,400 25,805 .713 17,400 26,078 .667 23,600 28,029 .842 20,000 26,800 .746 16.100 22,287 .722 12,300 18,818 .654 ' 15,000 45,625 .329 184,800 477,688 .387 WHITE FEMALES UNDER 25 25 - 29 30 - 3186 100,100 718,422 .139 53,500 115,719 .462 43,300 100,878 .429 43.300 98,192 .441 46,000 102,619 .448 50,000 103,801 .482 37,500 94,878 .395 31,000 87,519 .354 33.500 270,095 .124 438,200 1,692,123 .259 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 & OVER TOTAL 5,100 153,792 .033 4 ,90^ 17,909 .274 5,600 18,542 .302 5,600 18,273 .306 6,300 17,310 .364 7,500 17,712 .423 5,300 15,406 .344 3,300 13,755 .240 3,800 35,455 .107 47,400 308,154 .154 18,300 172,292 .106 10,200 18,722 .545 8,900 16,949 .525 9,200 16,729 .550 8,100 17,139 .473 7,500 16,113 .465 6,700 15,317 .437 6,800 14,891 .457 6,900 45,738 .151 82,600 333,890 .247 GRAND TOTAL 906,800 3,567,089 .254 1,323,100 3,923,687 .337 D-5-47 TEXAS APPFNDIX TABLE D-5 CWHS EMPLOYMENT AND CENSUS POPULATION, BY SEX, RACF, AND AGE GROUP 1960 AND 1970 AGE GROUP CWHS WORK FORCE 1960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCE 1970 CENSUS POPULATION :whs/cen WHITE MALES UNDER 25 238,700 1,994,628 .120 25 - 29 175,200 271 ,449 .645 30 - 34 189,300 283,251 .668 35 - 39 196,300 284,850 .689 40 - 44 159,800 252,240 .634 45 - 49 152,200 242,109 .629 50 - 54 123,300 213,911 .576 55 - 59 91,800 181,495 .506 60 I OVER 103,900 435,577 .239 TOTAL 1 ,430,500 4,159,510 .344 412,200 2,322,392 .177 255,200 331,907 .769 219,200 277,487 .790 190,300 270,086 .705 194,500 280,638 .693 192,400 274,217 .702 155,100 240,491 .645 136,900 217,940 .628 162,400 552,472 .294 ,918,200 4,767,630 .402 BLACK MALES UNDER 2 5 25 - 29 30 - '4 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 S OVER TOTAL 32,100 307,215 .104 26,000 35,720 .728 21 ,400 35,537 .602 25,600 33,991 .753 21,700 30,439 .713 20,200 ^0,21P .668 17,700 27,235 .650 13,100 24,59? .533 14,500 60,524 .240 192,300 585,471 .328 56,500 399,501 .141 32,200 44,683 .721 30,100 36,937 .815 26,300 35,330 .744 24,300 34,792 .698 26,000 31,963 .813 18,700 28,111 .665 17,000 26,372 .645 21,200 75,850 .279 252,300 713,539 .354 WHITE FEMALES UNDER 25 158,000 1,933,141 .082 25 - 29 75,500 276,495 ,273 30 - 34 73,700 290,939 .253 35 - 39 87,900 292,500 .301 40 - 44 75,100 255,569 .294 45 - 49 71,200 247,017 .288 50 - C 'H 68,800 215,879 .319 55 - 59 45,600 188,358 .242 60 6 DVER 45,600 515,423 .088 TOTAL 701 ,400 4,215,321 .166 297,300 2,257,026 .132 132,100 336,810 .392 99,000 286,954 .345 109,000 280,367 .389 110,000 292,713 .376 115,200 288,148 .400 89,700 252,079 .356 76,600 236,174 .324 101,100 719,227 .141 1,130,000 4,949,498 .228 BLACK FEMALES UNDER 21 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER TOTAL 13,700 309,337 ,044 13,500 40,958 .330 13,200 41,717 .316 13,300 39,008 .341 11,700 34,298 .341 11,800 33,634 .351 9,400 29,272 .321 7,500 25,709 .292 6,900 65,442 .105 101 ,000 619,375 .163 40,100 400,808 .100 26,300 49,241 .534 20,000 43,077 .464 22,800 42,061 .542 20,000 41,598 .481 17,800 36,638 .486 15,000 31,R51 .471 12,100 29,990 .403 14,600 90,799 .161 188,700 766,063 .246 GRAND TOTAL 2,425,200 9,579,677 .253 3,489,200 11,196,730 ,312 D-5-48 UTAH APPFNDIX TABLE 0-5 CWHS EMPLOYMENT AND CENSUS POPULATION. BY SEX. RACE. AND AGE GROUP 1960 AND 1970 i960 1970 CWHS CENSUS CWHS CENSUS AGE GROUP, WORK FORCF POPULATION CWHS/CEN WORK FORCE POPULATION CWHS/CEN WHITE MALES NDER 25 25 - 29 30 - M, 35 - 39 40 - 44 45 - 4Q 50 - 54 55 - 59 60 6 OVER TOTAL 29.400 231,813 .127 21,900 28,098 .779 18,300 27,425 .667 18,400 26,528 .694 14,900 24,788 .601 15,300 22,293 .686 13,900 19,124 .727 9,900 15,791 .627 12,600 40,338 .312 54,600 436,198 .354 42,600 275,457 .155 26,900 34,964 .769 23,900 27,899 .857 19,400 25,721 .754 16,500 26,318 .627 14,900 25,233 .590 14,100 23,422 .602 11,500 20,018 .574 16,500 49,965 .330 186,300 508,997 .366 BLACK MALES 20,300 229,823 .088 7,100 27,725 .256 8,400 27,053 .311 10,700 26,773 .400 8,200 24,527 .334 8,800 21,791 .404 8,200 18,870 .435 5,500 15,887 .346 4,800 45,181 .106 82,000 437,630 .187 8,835 .011 1,002 .000 753 .133 599 .000 619 .162 659 .000 482 .207 360 .278 959 .000 14,268 .035 UNDER 25 4,708 .000 100 25 - 29 624 .000 30 - 34 200 621 .322 100 35 - 39 677 .000 40 - 44 100 467 .214 100 45 - 49 200 347 .576 50 - 54 265 .000 100 55 - 59 272 .000 100 60 5 OVER 745 .000 TOTAL 500 8,726 .057 500- WHITE FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 & OVER TOTAL BLACK FEMALES UNDER 25 500 4,694 .107 25 - 29 200 564 .355 30 - 34 100 632 .158 100 35 - 39 200 557 .359 100 40 - 44 100 416 .240 100 45 - 49 200 287 .697 50 - 54 100 199 .503 100 55 - 59 100 210 .476 100 60 6 OVER 514 .000 TOTAL 1,500 8,073 .186 500 13.079 .038 GRAND TOTAL 238,600 890,627 «268 311,100 1,059,273 .294 38,100 274,805 .139 13,600 35,309 .385 7,800 28,430 .274 9,300 26,589 .350 13,000 26,367 .493 12,500 26,055 .480 10,500 24,119 .435 8,500 20,534 .414 10,500 60,721 .173 123,800 522,929 .237 7,918 .000 892 .000 712 .140 649 .154 686 .146 564 .000 449 .223 314 .318 895 .000 D-5-49 VERMONT APPFNOIX TABLE D-5 CWH5 EMPLOYMFNT AND CENSUS POPULATION. BY SEX. RACE. AND AGE GROUP 1060 AND 1970 AGE GROUP CWHS WORK FORCE I960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCE 1970 CENSUS POPULATION CWHS/CEN WHITE MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 S OVER TOTAL 9.300 89,490 .104 7,300 10,330 .707 7,300 11,154 .654 6,700 11,594 .578 8.000 11,314 .707 8.200 11,063 .741 6.000 10,398 .577 5.200 9,124 .570 5.600 26,854 .209 63.600 191,321 .332 16,800 106,391 .158 11,500 14,350 .801 9,000 11,885 .757 9,000 11,264 .799 8,300 11,683 .710 7,300 11,476 .636 7,200 10,904 .660 7,700 9,884 .779 7,300 28,393 .257 84,100 216,230 .389 BLACK MALES UNDER 25 25 - re 30 - 34 35 - 39 40 - 44 45 - 49 50 - H 55 - 59 60 £ DVER 207 .000 39 .000 23 .000 27 .000 24 .000 18 .000 16 .000 20 .000 4P .000 100 100 477 .210 102 .000 94 .000 51 .000 48 .000 34 2.941 30 .000 22 .000 78 .000 TOTAL 00 422 .000 200 936 .214 WHITE FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER TOTAL 5,500 86,874 .063 2,500 10,635 .235 2,100 11,697 .180 4,100 12,235 .335 4,100 11,781 .348 2,900 11,132 .261 3,400 10,509 .324 2,700 9,590 .282 3,500 33,318 .105 30,800 197,771 .156 14,200 104,352 .136 6,500 14,368 .452 4,000 11,709 .342 5,000 11,331 .441 5,900 12,193 .484 6,000 12,235 .490 6,000 11,558 .519 2,900 10,667 .272 5,200 37,910 .137 55,700 226,323 .246 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 I OVER 100 100 100 160 .625 39 .000 29 .000 2 6 .000 1? 8.333 12 .000 15 6.667 14 .000 6 .000 100 100 401 .000 104 .000 60 .000 67 .000 45 .000 25 4.000 31 .000 26 .000 82 1.220 TOTAL 300 367 .817 200 841 .238 GRAND TOTAL 94,700 389,881 .243 140,200 444.330 .316 D-5-50 VIRGINIA APPENDIX TABLE D-5 CWHS EMPLOYMENT AND CENSUS POPULATION. BY SEX. RACE. AND AGE GROUP 1960 AND 1970 AGE GROUP CWHS WORK FORCF 1960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCE 1970 CENSUS POPULATION :whs/cen WHITE MALES UNDER 25 76.100 742,270 .103 25 - 29 58.900 103, 528 .569 30 - 34 63,400 110,059 .576 35 - 39 65,100 115,538 .563 40 - 44 57,000 105,994 .538 45 - 49 49,900 93,778 .532 50 - 54 42,600 77,996 .546 55 - 59 27,900 63,380 .440 60 6 OVER 36.300 150,740 .244 TOTAL 477,700 1,563,283 .306 118,900 894,411 .133 90.700 141,752 .640 72.600 117,076 .620 61.100 111,332 .549 66.700 115,094 .580 66.000 113,937 .579 53.700 100,889 .532 46.000 83,542 .551 56.600 186,683 .303 632.300 1.864,716 .339 BLACK MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER TOTAL 22,900 214,475 .107 17,100 23,764 .720 17,500 24,827 .705 14,800 26,528 .558 13,400 24,301 .551 13,200 22,616 .584 13,600 17,768 .765 8,100 15,257 .531 9.700 37,572 .258 130,300 407,108 .320 30,800 235,428 .131 20,200 25,886 .780 17,100 22,289 .767 18,700 21,554 .868 16,500 22,223 .742 15,400 22,940 .671 13,300 20,925 .636 11,600 18,353 .632 16,100 42,807 .376 159,700 432,405 .369 WHITE FEMALES UNDER 25 54,900 699,335 .079 25 - 29 31,200 102,894 .303 30 - 34 30,800 111,53? .276 35 - 39 32.400 120,369 .269 40 - 44 36.300 105,381 .344 45 - 49 32,600 93,356 .349 50 - 54 28,100 79,735 .352 55 - 59 19,000 67,073 .283 60 & OVER 17,400 188,661 .092 TOTAL 282,700 1,568,337 .180 105,300 840,264 .125 62,700 140,113 .447 42,200 116,221 .363 41,900 111,324 .376 45,600 117,767 .387 46,200 120,924 .382 40,900 104,164 .393 33,200 89,600 .371 36,900 256,421 .144 454,900 1,896,798 .240 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 3VER TOTAL 9,000 213,658 .042 9,100 25,624 .355 10,100 27,022 .374 11,400 27,620 .413 11,000 24,578 .448 10,200 22,358 .456 9,300 17,707 .525 5,100 15,930 .320 5,800 41,204 .141 81,000 415,701 .195 23.800 233,920 .102 16.300 27,294 .597 13.800 24,816 .556 11.600 25,086 .462 14.000 25,582 .547 14,000 24,684 .567 11,900 21,925 .543 9,400 19,125 .492 11,200 52,143 .215 126.000 454,575 .277 GRAND TOTAL 971,700 3,954,429 .246 1,372,900 4,648,494 .295 D-5-51 WASHINGTON APPENDIX TABLE 0-5 CWHS EMPLOYMENT AND CENSUS POPULATION, BY SEX. RACE. AND AGF GROUP 1960 AND 1970 AGE GROUP CWHS WORK FORCE 1960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCE 1970 CENSUS POPULATION :whs/cen WHITE MALES UNDER 25 63,200 622,024 .102 25 - 29 52.600 80,639 .652 30 - 34 57,300 85,449 .671 35 - 39 58,000 93,780 .618 40 - 44 59,400 91,761 .647 45 - 49 57,800 86,446 .669 50 - 54 44,900 74,547 .602 55 - 59 37,200 63,691 .584 60 I OVER 47,300 132,924 .259 TOTAL 477,700 1,381,261 .346 114,300 762,106 .150 93,000 114,858 .810 72,200 92,505 .780 63,200 86.200 .733 62,000 91, 016 .681 61,100 95,807 .638 63,600 90,526 .703 52,500 79,819 .658 55,100 199,965 .276 637,000 1,612,802 .395 BLACK MALE5 UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER TOTAL 1,400 500 700 800 1,400 1,100 500 400 6,800 26,846 .052 3,960 .126 3,741 .187 4,037 .198 3,371 .415 2,943 .374 2,655 .188 2,091 .000 4,132 .097 53,776 ,126 2,800 2,700 1,600 1,700 1,000 1,200 1,900 1,100 400 14,400 44,647 .063 5,871 .460 4,857 .329 4,285 .397 4,291 .233 4,239 .283 3,417 .556 2,907 .378 6,431 .062 80,945 .178 WHITE FEMALES UNDER 2? 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 S, \ OVER TOTAL 47,500 23,400 22,500 31,600 33,800 30,300 27,400 21,100 27,100 264,700 600,276 77,961 86,659 96,782 91,285 84,647 71,834 61,999 198,971 1,370,414 ,079 .300 ,260 .327 ,370 ,358 ,381 ,340 ,136 ,193 93,100 735,345 .127 50,100 112,282 .446 33,900 91,073 .372 32,400 85,551 .379 38,500 92,539 .416 44,700 99,468 .449 39,100 91,784 .426 32,300 81,905 .394 36,600 248,306 .147 400,700 1,638,253 .245 BLACK FEMALES UNDER ? c 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 1 OVER 300 400 400 300 900 800 300 500 100 25,427 .012 3,816 .105 4,050 .099 3,896 .205 2,774 .324 2,035 .393 1,459 .206 1,222 .409 3,084 .032 2,900 1,100 700 1,300 800 800 1,000 600 300 41,500 .070 5,693 .193 4,963 .141 4,984 .261 4,982 .161 4,366 .183 3,047 .328 2,247 .267 5,387 .056 TOTAL 4,500 47,763 .094 9,500 77,169 ,123 GRAND TOTAL 753,700 2,853,214 .264 1,061,600 3,409,169 .311 D-5-52 APPENDIX TABLE D-5 CWHS EMPLOYMENT AND CENSUS POPULATION, BY SEX, RACE, AND AGE GROUP 1960 AND 1970 WEST VIRGINIA AGE GROUP CWHS WORK FORCE 1960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCE 1970 CENSUS POPULATION CWHS/CEN WHITE MALES UNDER 25 34,100 410,188 .083 25 - 29 28,900 45,169 .640 30 - 34 34,400 53,248 .646 35 - 39 40,500 56,375 .718 40 - 44 37,700 53,614 .703 45 - 49 33,700 52,797 .638 50 - 54 28,200 46,726 .604 55 - 59 20,700 39,974 .518 60 £, OVER 22,900 113,087 .202 TOTAL 281,100 871,178 .323 50,900 372,157 .137 34,800 46,889 .742 32,700 42,033 .778 27,800 40,489 .687 32,200 48,436 .665 37,100 50,368 .737 30,300 47,090 .643 28,100 44,527 .631 30,000 119,420 .251 03,900 811,409 .375 BLACK MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 I OVER TOTAL 1,300 1,000 1,000 1,400 1,100 1,800 2,000 1,700 2,500 13,800 21,719 .060 1,408 .710 1,798 .556 2,054 .682 2,146 .513 2,361 .762 2,460 .813 2,558 .665 7,353 .340 43,857 .315 1 ,900 700 1 ,300 900 800 1 ,300 1 ,000 1 ,000 1 ,900 10 ,800 16,861 .113 1,226 .571 1.074 1.210 1,056 .852 1,458 .549 1,599 .813 1,631 .613 1,761 .568 6,594 .288 33,260 .325 WHITE FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 3VER TOTAL 25,000 406, 22B .062 10,800 51,509 .210 12,600 58,879 .214 14,900 61,344 .243 13,400 57,803 .232 15,600 54,236 .288 13,500 47,577 .284 8,400 41,629 .202 12,600 119,750 .105 26,800 898,955 .141 32,800 368,716 .089 15,000 48,474 .309 11,400 45,491 .251 13,300 46,516 .286 17,700 53,299 .332 17,200 54,401 .316 16,200 51,548 .314 17,000 47,872 .355 18,300 145,754 .126 158,900 862,071 .184 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER 900 400 700 400 600 800 .300 600 600 1,979 .041 2,005 .200 2,733 .256 2,758 .145 2,641 .227 2,777 .288 2,697 .482 2,618 .229 6,223 .096 800 500 600 800 1,400 1,100 600 800 1.000 17,063 .047 1,623 .308 1,507 .398 1,559 .513 2,097 .668 2,085 .528 2,044 .294 2,109 .379 7,410 .135 TOTAL 6,300 46,431 .136 7,600 37,497 .203 GRAND TOTAL 428,000 1,860,421 .230 481 ,200 1.744,237 ,276 D-5-53 WISCONSIN APPENDIX TABLE D-5 CWHS EMPLOYMENT AND CENSUS POPULATION, BY SEX, RACF, AND AGE GROUP I960 AND 1970 ASE GROUP CWHS WORK FORCE 1960 CENSUS POPULATION CWHS/CEN CWHS WORK FORCE 1970 CENSUS POPULATION :whs/cen WHITE MALES UNDER 25 102,100 868,228 .118 25 - 29 85,000 109,331 .777 30 - 34 85,100 119,028 .715 35 - 39 83,700 120,796 .693 40 - 44 77,400 117,309 .660 45 - 49 78,700 114,297 .689 50 - S4 63,900 104,248 .613 55 - 59 59,200 94,276 .628 60 & 3VER 84,700 270,686 .313 TOTAL 719,800 1,918,199 .375 161,200 1 ,002,043 .161 105,000 129,838 .809 90,300 111,301 .811 81,900 107,466 .762 88,200 117,327 .752 82,100 116,266 .706 76,300 110,336 .692 69,700 102,864 .678 92,500 292,785 .316 847,200 2,090,226 .405 BLACK MALES UNDER 25 25 . TO 30 - ^4 35 . 39 40 - 44 *5 - 49 RO - 54 55 - 59 60 > DVER 2,600 1,900 3,800 2,500 1,700 1,200 1,000 1,000 800 25,946 .100 3,702 .513 3,769 1.008 3,197 .782 2.379 .715 1,977 .607 1,552 .644 1,282 .780 2,509 .319 5,900 3,800 3,600 2,600 3,100 2,600 1,200 1,300 1,500 47,198 .125 5,288 .719 4,544 .792 4,153 .626 3,908 .793 3,279 .793 2,408 .498 1,972 .659 4,397 .341 TOTAL 16,500 46,313 .356 25,600 77,147 .332 WHITE FEMALES UNDER 25 25 - 29 30 - 3 4 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 £, 3VER TOTAL 75.000 34,000 33,000 38,200 43.000 43,100 36,300 31,800 37,400 371,800 854,849 109,995 119,392 124,493 120,907 115,689 103,872 94,284 297,223 1,940,704 .088 .309 .276 .307 .356 .373 .349 .337 .126 .192 147,200 990,250 .149 58,100 131,518 .442 38,000 112,491 .338 43,400 109,416 .397 50,400 118,719 .425 55,000 120,976 .455 56,100 • 116,986 .480 46,500 109,013 .427 56,800 359,364 .158 551,500 2,168,733 .254 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 3Q 40 - I.U 45 - AT 50 - 54 55 - 59 60 6 OVER TOTAL 1,300 2,000 1,700 1,300 1,100 800 500 100 400 9,200 26,788 .049 4,150 .482 3,785 .449 3,074 .423 2,221 .495 1,841 .435 1,376 .363 1,121 .089 2,205 .181 46,561 .198 3,800 3,000 2,800 2,600 2,800 1,700 1,200 700 900 19,500 48,349 .079 6,102 .492 5,365 .522 4,986 .521 4,271 .656 3,202 .531 2,478 .484 2,069 .338 4,803 .187 81,625 .239 GRAND TOTAL 1,117,300 3,951,777 ,283 1,443,800 4.417,731 .327 D-5-54 APPENDIX TABLE D-5 CWHS F^PLOYMfNT AND CENSUS POPULATION, BY SEX, RACE, AND AGE GROUP 1960 AND 1970 WYOMING 1960 1970 CWHS CENSUS CWHS CENSUS AGE GROUP WORK FORCE POPULATION CWHS/CFN WORK FORCE POPULATION CWHS/CEN WHITE male: UNDER 25 6,900 76,696 .090 10,800 76,964 .140 25 - 29 6.800 10,220 .665 7,300 10,434 .700 30 - 34 6,500 11,514 .565 6,800 8,892 .765 35 - 39 7,700 11,306 .681 6,000 8,802 .682 40 - 44 4,700 10,517 .447 6,200 9,916 .625 45 - 49 6,100 9,871 .618 4,700 9,513 .494 50 - 54 4,100 8,673 .473 5,100 8,797 .580 55 - 59 3,200 7,059 .453 4,300 8,001 .537 60 6 5VER 4,300 19,493 .221 5,000 20,642 .242 TOTAL 50,300 165,349 .304 56,200 161,961 .347 BLACK MALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 6 OVER 100 100 100 2,132 .000 218 .459 234 .000 204 .000 185 .541 145 .690 134 .000 120 .000 294 .000 100 2,840 .000 335 .299 295 .000 249 .000 216 .000 175 .000 193 .000 137 .000 374 .000 TOTAL 300 3,666 .082 100 4,814 ,021 WHITE FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 & DVER TOTAL 6 ,200 2 .300 2 .600 3 .ROO ? .800 3 ,500 2 ,400 1 ,900 2 ,100 27 ,600 74,679 .083 10,253 .224 10,728 .242 11,035 .344 9,864 .284 9,303 .376 7,645 .314 6,349 .299 17,717 .119 157,573 .175 8,900 74,364 .120 4,600 10,426 .441 4,000 9,148 .437 3,600 9,127 .394 3,800 9,447 .402 4,600 9,467 .486 4,300 8,740 .492 3,100 7,942 ,390 4,700 22,402 .210 41 ,600 161,063 .258 BLACK FEMALES UNDER 25 25 - 29 30 - 34 35 - 39 40 - 44 45 - 49 50 - 54 55 - 59 60 & OVER 2,042 .000 250 .000 242 .000 202 .000 188 .000 120 .000 103 .000 106 .000 225 .000 100 100 100 2,662 .000 303 .330 295 .339 279 .000 258 .388 183 .000 169 .000 117 .000 312 .000 TOTAL on 3,478 .000 300 4,57£ ,066 GRAND TOTAL 78,200 330,066 .237 98,200 332,416 .295 D-5-55 •& U.S. GOVERNMENT PRINTING OFFICE : 1977 0-227-698 To get the answer . . . read the monthly SURVEY OF CURRENT BUSINESS published by the Bureau of Economic Anal- ysis, Social and Economic Statistics Admin- istration, U.S. Department of Commerce. 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