LIBRARY OF CONGRESS QD0DS17flS51 Hi in 8^H Hi SHI mm HhHBh mHH! ■1 H mm wKKSKRWSSB c "of r ^0 x «5°^ r • < • ^°a ^ -•■ VV A 1> * A^ - ,,• v- ^V*^' a°° *«>/*•"•' a* °°*" +* y •'fife*- <$h ^ •^%PA % ** ^ '^fef' <^ <£ ^v !^i!M; ^ :^«^t V4* refill: ^ V c' 1 ^ *©- < • o, *> ^°^ v O 'O.J, A* .. "v*. ** v ^ : » i S^^ ^"^ °oWc^" A* y % -.^ r*° ^> *> A** • • A V ^ : ^ - A % c ° " ° * ^ •& A^5SiW* "^ c V "•" , A^ °^ * A <* *'..«* A v v ..i^% cv 0^ »;•»'*> V K £A£* <% A*? , • » " A <» *-7vT* a*" ^ '• • • * < s <* • %../ .-ia&fcfc ^** /^fe\ \/ .-ijSfefc 4.^ «^ % ' " ' ' > ' °*. *° *!**£.. *> o « o «, /.•^L-.V .%. Wm; ^>„ -. •*^, %. - ''A *S» A v ♦MA' A* c$ 'bV * **' * 4; "" i\ <&,£ /, • "^ a"^ 4 o ^ *1^L'.» o, * * rt A ** v % VV ^.!iiik-.V :'G^iSX>>o ^\c^.V ><• 0^\^,,A ^ ^ ^ '^o % .. V*"- e ° y °^ '- f~ ^ ^ 'a^^ ,'„ *P- A> l 'V A^ J.°v <>* A ^0^ a " ° A ^- ' « i * ^ ' in parentheses refer to items in the list of references preceding the appendixes at the end oi this report. 4 The Standard Industrial Classification (SIO was re\ised in 1987; the tndustrs group numbers used here are those in effect at the time of the MIPS. the employees covered by this survey is the same as that of the data collected by MSHA form 7000-1 for mine accidents, injuries, illnesses, and fatalities, and MSHA form 7000-2 for quarterly mine employment. The collection of the fundamental statistics reported on these two forms is required by law (30 U.S.C. 813; 30 CFR 50). SAMPLE The principal feature of the survey sample design was its use of two-stage stratified random sampling. The primary sampling units (first stage) were the mine establishments; the secondary sampling units were employees within each of the chosen mine establishments. The characteristics used to stratify the primary units were the in- dustry (anthracite coal, bituminous coal, metal, stone, sand and gravel, nonmetal); mine type (underground, surface, plant or mill); employment size class (1-19, 20-49, 50-99, 100-249, 250-499, 500-999, 1,000 and above); and status code (active, intermittent). Since the first three stratification characteristics are highly correlated with the characteristics that the survey was to measure, use of stratified sampling increased the efficiency of the sample design and thus resulted in a smaller required sample size. The fourth characteristic, status code, was chosen so that nonresponse adjust- ment could be made within more homogenous groups. This is desirable because proportionately higher numbers of nonmailable, out-of-business, refusal, etc., responses are reported from inter- mittent mine establishments than from active mine establishments. The sampling frame used for this survey was the 1985 preliminary address and employment file maintained by MSHA. A probability sample of 297 nonmetallic mining establishments from a universe of 1,043 nonmetallic mining establishments was selected by stratifying the frame as previously described and using a systematic sampling procedure with a random start for each stratum. The employees within an establishment were selected by using a systematic sampling procedure with a common random start for each employment size class. A brief description of the sample allocation is as follows. For larger employment size classes, the allocation procedure placed all of the establishments on the frame in the sample as primary sampling units from which the employees were subsampled at a low frequency rate. As employment size class decreased, smaller and smaller proportions of the establishments were included as primary sampling units, but the employees within the establishments were subsampled at a higher frequency rate. The use of this procedure gave each employee, to the extent possible, about the same probability of inclusion in the sample, thus reducing the sampling variability. In order to limit the response burden for any one establishment, a maximum sample of 50 employees per establishment was selected. DATA COLLECTION The MIPS was conducted from March through September 1986 by mail questionnaire through the Bureau's Twin Cities (MN) Research Center. A reproduction of the original letter, followup letter, and the questionnaire bearing the Office of Management and Budget clearance number authorizing collection of the data are in- cluded in appendix F. The response status for the nonmetallic mining sector from the original and followup mailings, as well as from telephone calls to the nonrespondents, is summarized here. From a total population of 1,043 nonmetallic mining establishments, the survey sampled 297 operations. The overall response and rate were 282 and 95 pet, respectively. There were 41 out-of-scope returns (i.e., out of businesses, nonmailables, duplicates, temporary inactives, and new businesses under construction); the remaining 256 returns were within the scope of the survey (i.e., nonrespondents, usables, refusals, and unusables). Of the 256 in-scope records, 219 were usables. Thus, the survey achieved a usable response rate of 86 pet. A brief description of the response terms follows: Response code Description Nonrespondent Received no response from the establishment. Usable Establishment provided usable data. Refusal Establishment refused to provide any data. Unusable Establishment provided data that were not in usable format. Nonmailable Establishment's address was either insufficient or wrong. Duplicate Data were combined with another establishment's data. Out-of-business .... Establishment was permanently closed. New business Establishment was in development stage. Temporary inactive . Establishment was temporarily not operating. As part of the data collection phase, all the returns were reviewed and edited for completeness and reasonableness of the data. Whenever there were inconsistencies, the respondents were called for reconciliation. Also, almost all of the respondents that had initially refused to participate in the survey were contacted by phone. Approximately 80 pet of these respondents ultimately sup- plied data. Adjustments for those mine establishments that did not supply the data, or supplied partial data, are explained in the "Estimation Procedures" section and in appendix C. DATA CODING, ENTERING, AND EDITING The returns underwent a very comprehensive review and editing process in order to (1) minimize the reporting differences among the respondents (establishments), (2) ensure consistency of coding among the individual worker entries, (3) ensure the accuracy of the data entry, and (4) ensure compatibility of occupation and equip- ment coding with the MSHA injury data base. ESTIMATION PROCEDURES In a simple random sampling plan, all units are sampled with the same sampling ratio. To derive the population estimates, the sample units are weighted (replicated) by the inverse of the sampling ratio. Because of efficiency consideration, the data for this demographics study were collected using a complex survey design. Hence, the data for each worker, the ultimate sampling unit, were not equally weighted. Instead, the population estimates were derived by weighting data for each worker with the appropriate final weight which of the data, was the product of the following three factors: (1) the inverse of the sampling ratio with which the primary sampling unit (establishment) was sampled; (2) a nonresponse adjustment factor that was computed separately for each sampling stratum and assigned to all responding establishments in a stratum to account for those establishments in that stratum that did not respond; and (3) the inverse of the sampling ratio with which the secondary sampling units (workers) were selected. A detailed discussion of the different weights and estimation formulas are given in appendix C. In statistical terms, the survey's estimates of the popula- tion total were based on a Horvitz-Thompson estimator (6). No adjustment was made for partial nonresponse. That is, the characteristics that were left blank by the respondents were coded as unspecified and were, naturally, weighted by their appropriate final weight in computing the population estimates. The percentage unspecified for a particular characteristic gives the user an indica- tion of the completeness of the schedules. GROUPING OF CHARACTERISTICS The original data base has detailed data for the characteristics mentioned below. For purposes of publication, the detailed data were combined into groups. Please contact the authors to obtain detailed data or a different grouping of the data for any or all of the characteristics. Job Title and Principal Equipment Operated Since the original data base has about 100 codes for each of these two categories (see appendixes A and B), the entries were combined into 20 to 25 groups. Similarities of the job title or prin- cipal equipment operated and number of workers in each entry were two of the main criteria used in forming the groups. Employment Size Class The classes used for this characteristic are the standard size class definition used by MSHA. Because there were very few mines for the size class having 1,000 or more employees, the estimates for this class were computed separately and then were combined with the estimates for employment size class 500 through 999 in order to protect the confidentiality of the mines as well as the workers. The combined size class is labeled as 500 + . Present Job, Present Company, and Total Mining Experience The data for all three of these characteristics were coded only as the number of years. It was felt that data were not reliable enough to be accurate to the month. The groupings were formed to be as compatible as possible to the groupings used by MSHA for its injury statistics. Job-Related Training During the Last 2 Years The grouping for this characteristic was formed to reflect the definite and logical intervals that various mine operators employ and that meets the need of the mine safety personnel. The most frequently reported number was 16 h for training during the last 2 years; this is because MSHA requires a minimum training of 8 h/yr. Also, MSHA and safety personnel are interested in knowing the percent of workers who receive no training. Hence, both and 16 h were categorized separately. Age The groupings for age were formed to be about the same as what MSHA uses for its injury statistics. RELIABILITY OF ESTIMATES As stated in reference 7: All estimates derived from a sample survey are subject to sampling and nonsampling errors. Sampling errors occur because observations are made on a sample, not on the entire population. Estimates based on the different possible samples of the same size and sample design could differ. Nonsampling errors in the estimates can be attributed to many sources, e.g., inability to obtain information about all cases in the sample, mistakes in recording or coding the data, definitional difficulties, etc. Nonsampling errors occur in a census as well as in a sample survey. As mentioned earlier, the completed forms underwent a very comprehensive review and edit process. This was primarily done to minimize the nonsampling errors. In a probability sample, the coefficients of variation (CV's), which are a measure of the sampling errors in the estimates, can be estimated from the survey data. CV's were calculated for the basic characteristics as part of the survey estimation process; these CV's as well as the corresponding estimates for number of workers are given in tables E-41 through E-48. The CV's for other estimates can also be derived if requested. The methodology used to com- pute the estimated CV's is given below. By definition, the CV of any sample estimate is equal to the standard error of the estimate divided by the value of the estimate (5). In other words, it is a measure of relative variation. Because the survey data will be used by numerous researchers to measure different statistics (e.g., totals, means, medians, percentages) by various cross-classification categories, it was not feasible to use the exact formula for the standard error estimates. Hence, a generalized formula that approximated the exact formula and that was easy to implement for computing all the standard error estimates was developed. It should be noted that since the survey uses a com- plex sampling design, the usual variance, standard deviation, and standard error estimates computed by the software packages are no longer valid because they are based on simple random sample design. The reliability measures for this survey were computed by employing a random group variance technique. A brief descrip- tion of it is given in appendix D and a detailed discussion is given in reference 9. The purpose of producing a reliability measure for this report is to define the confidence interval or range that would include the comparable complete coverage value. For example, the total number of estimated truck drivers for the 1986 nonmetallic mining industry was 1,565 (table E-l and E-42) with a CV of 12.0 pet (table E-42). Based on this information, the standard error on the total number of truck drivers is 188 (estimate x CV = 1,565 x 0.120) and the 95-pct confidence interval is 1.189 to 1.941 (1.565 ± 2 x 188). This means that with 95 pet confidence, it can be said that the interval 1.189 to 1.941 includes the total number of truck drivers in the nonmetallic mining industry that would have been obtained from a census of the frame. In general, the smaller the subpopulation size, the larger the variability in the estimates. Additionally, the larger the nonresponse. the less reliable the estimate may be. As mentioned earlier, nonresponse error is considered a nonsampling error. This error occurred more frequently for estimates of job-related training dur- ing the last 2 years and total mine experience than for other variables because conceptually these variables are harder to report. Moreover, it is possible that the training estimates might be somewhat biased because many respondents filled in 16 h. the minimum number of hours required by MSHA over a 2-year period. VALIDATION OF ESTIMATES Once the estimates were produced, they were validated for accuracy and reasonableness by several mining industry specialists. Additionally, the total employment for each industry was compared to an independent census conducted by MSHA, the results of which are reported in references 10 through 14. The injury experience reports tabulate the injury-illness-fatality data reported to MSHA on form 7000-1 and employment data reported on form 7000-2. While the data base used to compile the statistics for these reports contains detailed information for the injured victims, it does not contain similar information for the entire workforce. The breakdown of total employment is available only by type of ore mined, employ- ment size class, and work location. Hence, the MIPS was conducted so that MSHA injury data could be analyzed in greater detail. The data show that the overall employment figures from the two sources differed about 1 pet for the nonmetallic mining industry, with the MSHA figures being higher than those of the demographic survey. The difference in the estimates is caused in part by dif- ferences in reporting, coverage period, definitions, and methodology as explained below for data comparison by employment size class and by work location. When comparing distribution of workers by employment size class, the differences between the data of the total row of table E-l of this report and MSHA data as stated in table 4 of reference 13 are substantial. This is mainly due to the differences in definition and methodology. The MIPS classification is based on total employ- ment of an establishment as it existed when the respondents filled out the questionnaire. MSHA collects employment on a quarterly basis, and for each quarter it is possible for the employment to be broken into a maximum of four different work locations; hence, each establishment may have up to 16 different employment figures. Per MSHA's methodology, the size groups are classified according to the lowest numbered (primary) subunit's average employment of four quarters and not on the total employment of an establishment, as is the case with the MIPS. For example, if an establishment's annual average employment is 60, but the employment for the primary subunit, say underground, is 15, then the establishment per MSHA's methodology is classified in size class 1 through 19, whereas according to the MIPS procedure it is in size class 50 through 99. It is for this reason the average employment per operation as stated in table 4 of reference 13 is 4. 1 for size class 1-4. It should be noted that MSHA classification overestimates the employment in smaller size classes. In view of the above, the injury data as published in reference 13 by size class should not be analyzed against the MIPS employ- ment size class data. Instead, the analyst needs to retabulate the MSHA injury data from the original data tapes so that the size class definition corresponds to the MIPS. Also, a large difference existed between MIPS and MSHA figures for employment distribution by work location. This is primarily due to differences in reporting. The employment reported to MSHA every quarter is in aggregate numbers for each work loca- tion (maximum of four). Generally, this type of reporting results in gross approximations in the breakdown of variables such as employment. For the MIPS data, the work location was reported for each worker in the sample, in the same manner as it is reported to MSHA on form 7000-1 for each injured worker. It should be noted that the data on work location for individual workers is known with more specificity than for the whole population. Hence, it is appropriate to analyze the survey work location data with MSHA injury statistics. Additionally, a small portion of the difference in the two estimates is due to the job title category of office workers. The MIPS underestimated the number of employees in this category because many respondents assumed that these workers very seldom incur injuries and therefore were not to be reported. For the purposes of injury analysis, the office workers are to be excluded because of some of the obvious difference in the injury risk. Hence, the difference in counts of office workers does not make any difference. SUMMARY OF MAJOR FINDINGS The findings of the survey by various cross-classifications are given as estimates in tables E-l through E-40; tables E-41 through E-48 give reliability estimates for the basic characteristics and a detailed discussion of their use is given in the "Reliability of Estimates" section. If desired, the estimates by some other classification criteria including more detailed estimates (e.g., distribution of workers by age and experience at present company working at the plant or mill location) can be derived from the original data base. The following findings are based on the data for the entire 1986 nonmetallic mining workforce. • The total estimated workforce for 1986 was approximately 33,400 (table E-l). The data in table E-l also indicate that 24 pet of the workforce was employed in establishments with 49 or less employees, 40 pet in establishments with 50-249 employees, and 36 pet in establishments with 250 or more employees. • The largest category of workers was plant operator-ware- houseman with 20 pet of the employment; followed closely by mechanic-welder-oiler-machinist with 18 pet, and laborer- miner-utility man with 12 pet (table E-l). Each of the remaining occupation groupings had fewer than 10 pet of the employees. • The distribution of workers by job title varied greatly according to the employment size class (table E-l): For example, the front-end loader-forklift operator, and truck driver category constituted 11 and 18 pet, respectively, of the workforce in employment size class 1-19, however, in the size class 500 + they each represented only 1 pet of the workforce. Mechanic-welder-oiler-machinist, on the other hand, made up only 7 pet of the employment in size class 1-19 and 25 pet in size class 500 + . • The distribution of workers by work location was underground mine, 11 pet; surface at underground mine, 5 pet; surface mine, 34 pet; plant or mill, 41 pet; office, 9 pet (table E-3). The data in table E-3 also show that four distinct distributions exist by size class 1-19, 20-99, 100-499, and 500+ . • Of the female employees, 54 pet had the job title category office worker, compared with 4 pet of the males (table E-l 5). The following findings are based on nonmetallic mining data that exclude the job title category of office worker. • The largest category of equipment operated was handtools (powered and nonpowered) with 21 pet of the employment, followed closely by the category none with 19 pet, and plant equipment with 17 pet. • The median experience at present job, present company, and total mining were 5,9, and 10 years, respectively (table EA). The data also show that workers employed in establishments with 250 or more employees had higher median experience in all three categories than those employed in establishments with less than 250 employees. Mean job-related training during the last 2 years was 44 h and it varied greatly by size class (table E-5). Mean age was about 39 years across all size classes (table E-6). The age group 40-49 had the largest number of workers (6,926), followed closely by the 50 and over age group (6,632); these two groups made up about 43 pet of the workforce. Males made up 97 pet of the workforce (table E-7). Note that the 97-pct figure excludes the unspecified category. Whites, blacks, and Hispanics made up 75, 15, and 8 pet, respectively, of the workforce (table E-7). The remaining 2 pet workers belonged either to another race or were unspecified. Of those workers whose education was specified, 74 pet had a high school or better education (table E-7). Note that this figure is obtained by (1) summing the workers in the categories high school diploma, vocational diploma, some college, and college degree, and (2) dividing this sum by the total number of workers minus the workers in the unspecified category. In this case, it is 21,308 divided by 28,633. The distribution of males and females by principal equip- ment operated is shown in table E-21. Handtools (powered and nonpowered) was the principal equipment operated category for males (22 pet) compared with 6 pet for females; scale-lab equipment-controls, on the other hand, were operated by 13 pet of the females and by 5 pet of the males. 82 pet 85 pet 87 pet 80 pet 82 pet 68 pet 59 pet 15-23 24-26 27-29 30-34 35-39 AGE, yr 40-49 50 + Figure 1 .—Percentage of 1986 nonmetallic mining workforce with at least a high school diploma, by age (excluding job title category of office worker, as well as workers whose education was unspecified. Education and median experience at the present company were inversely related (table E-37); that is, on the average, the less educated the person was, the longer he or she was employed with the company. There was a higher percentage of employees with at least a high school education under the age of 40 than there were of age 40 and over (table E-38 and figure 1); education, categorized by sex (table E-39), and race (table E-40) are shown in figures 2 and 3, respectively. 75 pet 79 n/-f MALE -EMALE Figure 2.— Percentage of 1986 nonmetallic mining workforce with at least a high school diploma, by sex (excluding job title category of office worker, as well as workers whose education was unspecified. 80 pet 65 pc: 52 pet - 1 s ; - ; c Figure 3.— Percentage of 1986 nonmetallic mining workforce with at least a high school diploma, by race (excluding job title category of office worker, as well as workers whose education was unspecified. APPLICATION OF DATA FOR INJURY ANALYSES The ultimate objective of this study is to provide a basis for— 1. Analyzing the 1986 MSHA nonmetallic mining injury statistics and identifying those subpopulations exhibiting higher or lower than average injury rates. 2. Producing some selected estimates by geographic location such as regions (east, central, west), MSHA districts, or States, and performing injury data analyses. 3. Developing an easy to use computerized data base that would be available to the researchers to do their own analyses especially in the area of targeting injury prevention and training efforts. The results from these analyses, which encompass all facets of mining operations, can help identify areas where research efforts should be devoted to achieve the greatest safety improvements, thus preventing creation of unnecessary regulations or crash research programs that tend to waste funds. RECOMMENDATIONS FOR FUTURE WORK 1 . After the injury analyses are performed, and the hazardous areas or subpopulations have been identified, it would be desirable to further investigate their problems and needs. This can be accomplished by conducting some special surveys such as an equip- ment use survey, maintenance related work survey, small mines survey, etc. 2. Repeat the MIPS and perform the injury analyses period- ically, say every 3 to 5 years, in order to study the changing min- ing environment and its impact on mining safety and productivity. When the survey is repeated, it is recommended that modifications be made to the questionnaire to reflect new needs. It is also recom- mended that the collection of total mine experience and job-related training data be eliminated, since these variables are conceptually very hard to measure. Also, the variables experience on the job and experience with the company should be measured in years only. REFERENCES 1. Butani, S. J., and A. M. Bartholomew. Characterization of the 1986 Metallic Mining Workforce. BuMines IC 9201, 1988, in press. 2. . Characterization of the 1986 Stone Mining Workforce. BuMines IC 9202, 1988, in press. 3. . Characterization of the 1986 Sand and Gravel Mining Workforce. BuMines IC 9203, 1988, in press. 4. . Characterization of the 1986 Metal and Nonmetal Mining Workforce. BuMines IC 9193, 1988, 60 pp. 5. . Characterization of the 1986 Coal Mining Workforce. BuMines IC 9192, 1988, 67 pp. 6. Cochran, W. G. Sampling Techniques. Wiley, 3d ed., 1977, 429 pp. 7. U.S. Bureau of Labor Statistics. Occupational Illnesses in the United States by Industry, 1985. May 1987, 81 pp. 8. Hansen, M. H., W. N. Hurwitz, and W. G. Madow. Sample Survey Methods and Theory. Wiley, v. 1, 1953, 638 pp. 9. Wolter, K. M. Introduction to Variance Estimation. Springer- Verlag, 1985, 440 pp. 10. U.S. Mine Safety and Health Administration. Injury Experience in Metallic Mining, 1986. Inf. Rep. 1158, 1987, 276 pp. 11. . Injury Experience in Stone Mining, 1986. Inf. Rep. 1160, 1987, 450 pp. 12. . Injury Experience in Sand and Gravel Mining, 1986. Inf. Rep. 1161, 1987, 111 pp. 13. . Injury Experience in Nonmetallic Mining, 1986. Inf. Rep. 1159, 1987, 291 pp. 14. . Injury Experience in Coal Mining, 1986. Inf. Rep. 1157, 1987, 390 pp. APPENDIX A.— NONMETALLIC MINING INDUSTRY JOB TITLE GROUPING Description Job title code Backhoe-crane-dragline-shovel operator 367, 378, 778, 387 Beltman-belt repairman 601, 1012, 996 Blaster 807 Deckhand-barge and dredge operator 372 Dozer-heavy and mobile equipment operator 368, 768, 985 Driller-rock bolter 33, 34, 333, 334, 1056, 46 Electrician-lampman 402, 602, 603, 385 Front-end loader-forklift operator 382, 782, 825, 389 Grader-scraper operator 375, 775, 957 Laborer-miner-utility man 616, 53, 316, 36, 38, 39, 45, 57, 58, 59, 158, 216, 224, 327, 386, 395, 609, 624, 663, 710, 716, 874, 997, 1013, 1055 Manager-foreman-supervisor: General 430, 449, 481, 489, 494 Manager-foreman-supervisor: Maintenance 418 Manager-foreman-supervisor: Working 749 Mechanic-welder-oiler-machinist 404, 604, 605, 1019, 1018, 1060, 394, 608 Mine technical support 320, 393, 396, 414, 423, 456, 464, 495, 593, 594, 920, 921, 930, 965, 998, 1014 Office worker 497 Plant operator-warehouseman 374, 379, 380, 388, 390, 392, 1022 Shuttle car-tram operator 850, 28, 29, 269, 373, 728, 962, 969 Stone cutter-finisher 398, 399 Truck driver 376, 776 Code Description 28 Scoop tram operator 29 Mucking machine operator 33 Driller helper, underground 34 Exploration driller, underground Longhole driller, underground Prospect driller, underground Diamond driller, underground 36 Continuous miner operator 38 Cutting machine operator 39 Hand loader Trammer 45 Hangup man Rockman Raise blaster Chute blaster Rock handler 46 Pinner Truss bolter Rock bolter Roof trimmer Roof man Scaler operator Roof bolter 53 Nipper Utility man 57 Stope miner 58 DXC miner Drift miner 59 Raise miner 158 Rock machine operator, underground 216 Trackman 224 Trainees, underground Code Description 269 Chute puller, underground Locomotive operator Car loader underground Whistle punk, underground 316 Service truck operator Laborer Track gang, surface Surface worker Utility man, surface Pumper, surface Tamping machine operator 320 Cage attendant, surface Aerial tram— outside only 327 Surface miner 333 Driller helper 334 Carriage-mounted drill operator, surface Wagon drill operator, surface Churn driller, surface Rotary drill operator JP drill operator, surface Air-track driller, outside only 367 Backhoe operator Power shovel operator Pitman 368 Dozer operator Track operator helper, surface Tractor operator, surface 372 Deckhand Dredge operator Barge attendant Barge loader Boat operator Code Description 373 Car dropper 374 Storekeeper Blunger Process operator Sandbox operator Mill operator Reagent operator Car loader, surface Warehouseman Shipping Media operator Breakerman Crusher operator Sewing machine operator Boney preparation plant operator Packaging Cleaning plant operator Truck loader Bagger-baler Preparation plant operator Cobber 375 Grader operator, surface 376 Truck driver, surface 378 Dragline operator Dropball operator Crane operator, surface 379 Kiln operator Calciner Dryer operator 380 Fine coal plant operator 382 Loader operator Front-end loader operator, surface Pan operator Scraper operator Highlift operator Payloader operator 385 Lampman 386 Refuse truck driver 387 Rotary bucket excavator operator 388 Separator operator Scalper Shaker operator Screen operator 389 Forklift operator 390 Silo operator 392 Washery operator Topman Skip dumper Binman Scrubber operator Tipple operator-attendant 393 Scaleperson Weighman-weighmaster 394 Carpenter 395 Water truck operator 396 Watchman Security guard 398 Sawyer Stone finishing 399 Dimension stone cutter-polisher 402 Master electrician 404 Master mechanic Code Description 414 Laboratory assistant Analyst Laboratory technician Laboratory supervisor Quality control Dust sampler Emission control specialist 418 Maintenance supervisor Maintenance foreman 423 Surveyor 430 Assistant mine manager Assistant mine foreman-vice president 449 Mine owner Assayers President General foreman Mine manager Mine foreman 456 Engineer Metallurgist-geologist Chemist 464 Inspector 481 Superintendents Project managers Coordinators Supervisors 489 Outside foreman 494 Plant manager Mill manager Plant foreman Mill foreman 495 Safety coordinator Safety manager Safety director Environmental coordinator Safety engineer 497 Office help Computer operator Controller Clerk 593 Nurse 594 Training specialist 601 Conveyor man Belt walker Belt installer Tunnel worker Tailpiece man Belt mover Mobile bridge carrierman Beltman 602 Lineman Electrician 603 Electrician helper 604 Fueler Boilermaker Plumber Pipefitter Boiler operator Pipe man Boiler trainee Mechanic Repairman Mill wright 10 Code Description Code Description 605 Mechanic helper 608 Mason 609 Supplyman Material man 616 Rock picker Parts runner Groundman Unit helper Bathhouse attendant Pointman Laborer Slate picker Roustabout Extra man 624 Trainees Apprentice 663 Ledgeman Quarry man Miner, not elsewhere classified Shaft miner Probeman 710 Propman Timberman 716 Cement man Form man Grizzly tender 728 Gizmo operator Load-haul-dump operator, underground 749 Shift boss Foreman-leadman Bullgang foreman Labor foreman Section boss-foreman 768 Heavy equipment operator 775 Grader operator, underground 776 Truck driver, underground 778 Cherry picker Crane operator, underground Dragline operator, underground Backhoe operator, underground Gradall operator Front-end loader operator, underground 807 Chargeman Shot firer Powder man Blaster Airdox operator Loading hole shooter Powder monkey 825 Bobcat operator 850 Ramcar operator Shuttle car operator Buggy operator 874 Mine equipment operator 920 Cager 92 1 Hoist operator Hoist engineer Shaftman 930 Skip tender 957 Scraper operator 962 Car runner, surface Trip rider Brakeman Flagman Car rider Conductor 965 Dispatcher 969 Swamper Motorman Switchman 985 Heavy equipment operator, surface Mobile equipment operator, surface 996 Feeder man 997 General or many equipment operator 998 Janitor Bag stenciler Prospector Painter 1012. . . Belt repairman Belt vulcanizer 1013. . . Cleanup man 1014. . . .Sampler 1018. . . Lube man Greaser-oiler 1019... Welder 1022. . . Dump man Dump operator 1055. . . . Chainman 1056... Rock driller 1060. . . .Machinist Shopman Shop foreman Bit sharpener 11 APPENDIX B.— NONMETALLIC MINING INDUSTRY EQUIPMENT OPERATED GROUPING Description Equipment code Backhoe-crane-dragline-shovel 16, 14 Belt 13, 96 Dozer-heavy and mobile equipment 8, 85 Drill (underground)-rock bolter 53, 54, 49 Drill (surface) 9 Explosives 47 Front-end loader-forklift 24, 23 Grader-scraper 52, 57 Handtools (powered and non-powered) 28 Hoist-elevator 30, 19, 38 Many equipment 97 Miscellaneous utility equipment 95, 12, 16 Plant equipment 40, 7, 10, 11, 15, 18, 22, 26, 32, 39, 46, 51, 58, 69, 82, 83 Pump 48 Scale-lab equipment-controls 92, 80, 91 Shuttle car-locomotive 61, 34, 33, 41, 42, 43, 65 Stone cutter-finishing machine 17 Truck (haulage) 44, 45 Truck (utility)-personnel carrier . .' 67, 37, 66 Welding machine-lathe 70, 5 None Not elsewhere classified 98, 68, 71, 81, 88 Unspecified 99 Code Description None 5 Drill press Bench grinder Lathe 7 Boats Barges Water transportation 8 Bulldozer Dozer Crawler tractor 9 Carriage .mounted drill Jumbo drill Churn drill Rotary drill Jet piercing drill Airtrack compressor drill 10 Chute Airslide 11 Classifier Cyclones 12 Continuous miner Dosco miner 13 Belt feeder Mobile bridge carrier Conveyor All types belts 14 Cherry picker Basket scaler Scaling machine Rock or dropball Boom hoist Derrick Crane Gantry Code Description 15 Breaker Crusher 16 Cutting machines Undercutter Chain cutter 17 Polishing machinery Dimension stone cutting 18 Dredge 19 Elevator Buckets Cage Skip 22 Precipitator heavy media bath Filters Flotation machines 23 Forklift 24 Highlift Skip tender Front-end loader Pay loader 26 Grizzlies 28 Handtools (powered and nonpowered) Ram jack 30 Hoist Car dropper Hydraulic jack 32 Impactor 33 Scoop tram Unitrac Load-haul-dump Teletram car Bobcat, underground 12 Code Description 34 Locomotive Trammer Tow-motor Lorry car Rail-mounted locomotive 37 Porta bus Mancar Golf cart Mantrip Rail runner Rail rover Personnel carrier Boss buggy Jeep 38 Man lift Scaling ring 39 Grinding mills Ball or rod mills 40 Milling machinery Block press General plant equipment 41 Nipper truck, underground Mine car, underground Underground flatcar Timber truck, underground 42 Mine car, surface Ore-coal car, surface Boxcar, surface Hopper car, surface 43 Mucking machine Overshot loader 44 Ore haulage trucks, offhighway 45 Payloader ore haulage, onhighway 46 • Bagger Sewing machine Packaging machine 47 Pneumatic blast agent loader Pop shooter Driller loader Prill loader Powder buggy Explosives 48 . Pump 49 Raise borer 51 Raw coal storage Tipple Dump bins 52 Roadgrader Motor grader Motor patrol 53 Jackleg Drifter drill Airleg Diamond drill Track drill Jumbo drill Rock drill Buzzy drill Jackhammer Hydraulic drill Stoper drill Code Description 54 Pinner Roof bolting machine 57 Pan scraper Scoop, surface Self-loading scraper Tractor scraper Scraper loader 58 Shaker Vibrator Screen 60 Dragline Dragline bucket Backhoe Power shovel Clamshell 61 Buggy Shuttle car Ram car 65 Track maintenance Track repair equipment 66 Tractor, underground Elkhorn Supply car 67 Trash truck Service truck Utility truck Water truck Dump truck Pickup truck 68 Tugger Air winch 69 Washers 70 Welding machine Torch 71 Machines, not elsewhere classified Rock rake Drilling rigs Impact roller 80 Lab equipment 81 Rigs, not elsewhere classified 82 Boilers 83 Furnaces Calciners Kilns Dryers 85 Heavy equipment Mobile equipment 88 Diesels 91 Controls Consoles 92 Scales 95 Miscellaneous utility equipment 96 Feeders 97 Many-all types of equipment 98 Not elsewhere classified 99 Not specified 13 APPENDIX C— ESTIMATION PROCEDURES Establishment weight. —Suppose one out of every five mine establishments in a sampling stratum (industry-mine type-employ- ment size class-status) was selected. Then, the sampling ratio is 1-5, and the establishment weight (EWT) is 5.00, the inverse of the sampling ratio. Nonresponse adjustment factor.— Also suppose in a given sampling stratum, 80 pet of the establishments that were within the scope of the survey responded. Then, the nonresponse adjustment factor (NRAF) is 1.25 (i.e., 100/80). Worker weight.— Additionally, there was the sampling ratio with which the workers in the establishment were sampled; the worker weight (WWT) ranged from 1.00 to 30.00 (see the first page of the MIPS questionnaire in appendix F). Theoretically, all the workers in a sampling stratum should have had the same weight. Hence, there would have been no need to assign weight at the worker level, as the worker weight could have been incorporated into the establishment weight. In practice, however, this is seldom the case because for a few establishments the employment level changes from what it was on the sampling frame to the time of the survey data collection. Since all the establishments did not report in the same employment size class that they were sampled in, it was necessary to also assign each worker a weight. Final weight.— For the purpose of computing the estimates, each worker was assigned a final weight (FWT) which was the product of establishment weight (EWT), nonresponse adjustment factor (NRAF), and the worker weight (WWT). That is, FWT = EWT X NRAF x WWT. Estimates of number of workers. —The estimates of the total number of workers were computed by (1) summing the final weights over the appropriate domain, and (2) rounding the sum to the nearest integer. Example: To estimate the total number of truck drivers: 1. Compute x I FWTj kD Where the domain, D, was the set of all records (workers) that had an occupation code of truck driver. 2. Compute y = round (x). Estimates of mean. —The estimates of mean age (training) were computed by summing over the appropriate domain (1) the product of age (training) and final weight, (2) the final weights, and then (3) dividing the sum of the products by the sum of the weights and rounding the result to the nearest whole number. It should be noted that for each domain only those entries where age (training) was specified were included in the computation. Example: To estimate the mean age of the truck drivers: 1. Compute x 2. Compute y = I (A gei * FWT,). itD I FWT,, kD Where domain, D, is the set of all records that had an occupation code of truck driver with age being specified. 3. Compute z = round (x/y). Estimates of median. — The estimates of median job, company, and mining experience were derived by (1) sorting over the domain the records in ascending order of the experience for which the median statistic was desired, (2) computing the total number of workers (NW) in the domain by summing the final weights, and (3) selecting the experience corresponding to the middle worker(s) in the ordering. That is, if NW is an odd number, then the median experience is the experience corresponding to the (NW/2 + l)th worker in the ordering; if NW is an even number, then the median experience is the midpoint (rounded to the nearest integer) of the experience corresponding to the (NW/2)th and (NW/2 + l)th worker in the ordering. As with the mean estimates, the median estimates also excluded those entries in the domain with unspecified experience. 14 APPENDIX D.— RELIABILITY OF ESTIMATES: RANDOM GROUP VARIANCE TECHNIQUE The random group method of variance estimation employed in this study consisted of selecting eight samples using the same sampling scheme for each sample as the parent sample. The primary sampling units (establishments) were divided into two sets. The first set consisted of noncertainty (probability of selection less than 1 .00) primary sampling units sorted by their original industry-mine type- employment size class-status. A random integer, say j, between 1 and 8 was generated. The first primary unit in the ordering was assigned to the random group j, the second to the random group j + 1, and so forth in a modulo 8 fashion. Then, the secondary sampling units (workers) were assigned the same random group number as the primary unit to which they belonged. The second set consisted of all secondary sampling units belonging to the cer- tainty (probability of selection equal to 1 .00) primary sampling units. The secondary sampling units were sorted by the same scheme as above, and a random integer, say k, between 1 and 8 was generated. Then, the first secondary unit in the ordering was assigned to the random group k, the second to the random group k + 1, and so forth in a modulo 8 fashion. Hence, each worker belonged to a random group. For a more detailed discussion of the random group technique, the reader is referred to reference 9 of the main text. The following procedure was followed in computing the estimated variance (var), standard error (s), and the coefficient of variation (CV) for the estimated number of workers belonging to a particular category. 1. The domain (i.e., category) was defined. 2. A separate estimate for total number of workers, 0^ for each of the eight random groups was computed. If any random group was empty, then a zero was assigned to that random group. 3. Total number of workers, 0, for all eight groups was computed as A = 0, + 2 + . . . + 8 . 4. The mean number of workers per group was computed as , i = 0/8. 5. The variance for was computed as 8 , var (0) = 8 I (0, - 0) 2 . i = l 7 6. The standard error of was computed as s(0) = ^ var (0). 7. The CV for was computed as CV(0) = s(fl) X 100.0. 15 APPENDIX E.— NONMETALLIC MINING 1986 WORKFORCE ESTIMATES Table E-1.— Nonmetallic mining 1986 workforce estimates: job title, by employment size class 1 Tl9 20-49 50-99 100-249 250-499 500+ Total Workers pet Workers pet Workers pet Workers pet Workers pet Workers pet Workers pet Backhoe-crane-dragline-shovel operator. . 142 4 190 5 68 2 184 2 241 4 158 2 983 3 Beltman-belt repairman 22 1 38 27 60 1 146 Blaster 15 12 9 12 30 77 Deckhand-barge and dredge operator ...50 00 00 63 1 00 00 68 Dozer-heavy and mobile equipment operator 265 7 114 3 149 3 299 3 109 2 156 2 1,092 3 Driller-rock bolter 95 2 85 2 73 2 168 2 76 1 60 1 558 2 Electrician-lampman 37 1 35 1 29 1 245 3 136 2 126 2 608 2 Front-end loader-forklift operator 408 11 190 5 132 3 371 4 145 3 40 1 1,286 4 Grader-scraper operator 89 2 95 2 158 4 19 361 1 Laborer-miner-utility man 356 9 332 8 545 13 1,199 13 709 13 904 14 4,046 12 Manager-foreman-supervisor: General 425 11 318 8 335 8 454 5 222 4 398 6 2,152 6 Maintenance 27 1 52 1 49 1 174 2 145 3 90 1 537 2 Working 78 2 175 4 183 4 426 5 264 5 392 6 1,519 5 Mechanic-welder-oiler-machinist 253 7 574 14 715 16 1,520 17 1,194 22 1,673 25 5,929 18 Mine technical support 133 3 271 7 329 8 883 10 521 9 862 13 3,000 9 Office worker 321 8 264 7 197 5 659 7 381 7 534 8 2,356 7 Plant operator-warehouseman 464 12 979 24 1,136 26 1,947 22 1,253 23 962 15 6,742 20 Shuttle car-tram operator 15 33 1 65 1 120 1 42 1 120 2 394 1 Stonecutter-finisher 15 00 00 00 00 00 15 Truck driver 681 18 320 8 188 4 266 3 51 1 58 1 1 ,565 5 Total ' 3,825 100 4,062 TOO 4,360 100 9,049 100 5,514 100 6,625 100 33,434 100 1 MSHA size groups are based on the annual average employment of the primary subunit and not on the total employment; hence, MSHA published injury statistics by size groups should not be analyzed against these data. 2 As defined by MSHA; see appendix A for detailed explanation of job title grouping. NOTE— Owing to independent rounding, data may not add to totals shown. Table E-2.— Nonmetallic mining 1986 workforce estimates: 1 principal equipment operated, by employment size class 2 ~ ' " '. " 1^19 20-49 50-99 100-249 250-499 500+ Total Equipment operated grouping 2 Workers pet Workers pet Workers pet Workers pet Workers pet Workers pet Workers pet Backhoe-crane-dragline-shovel 157 4 190 5 68 2 205 2 168 3 98 2 887 3 Belt 5 22 1 43 1 27 1 60 1 157 1 Dozer-heavy and mobile equipment 255 7 128 3 162 4 233 3 109 2 216 4 1,102 4 Drill (underground)-rock bolter 5 38 1 5 111 1 56 1 60 1 275 1 Drill (surface) 80 2 48 1 68 2 86 1 32 1 313 1 Explosives 15 12 9 12 30 77 Front-end loader-forklift 539 15 318 8 230 6 563 7 145 3 120 2 1,915 6 Grader-scraper 116 3 136 4 172 4 83 1 506 2 Handtools (powered and nenpowered) .. . 289 8 585 15 778 19 1,803 21 1,290 25 1,819 30 6,564 21 Hoist-elevator 25 1 5 60 1 47 1 28 1 38 1 203 1 Many equipment 110 3 11 109 3 70 1 540 9 840 3 Miscellaneous utility equipment 168 5 230 6 165 4 549 7 595 12 208 3 1,915 6 Plant equipment 452 13 717 19 876 21 1,547 18 1,061 21 652 11 5,304 17 Pump 11 9 91 1 48 1 20 179 1 Scale-lab equipment-controls 67 2 143 4 185 4 497 6 287 6 328 5 1,506 5 Shuttle car-locomotive 15 38 1 36 1 178 2 62 1 60 1 389 1 Stone cutting-finishing machine 15 15 Truck (haulage) 686 20 320 8 188 5 266 3 51 1 58 1 1,570 5 Truck (utility)-personnel carrier 25 1 34 1 221 3 84 2 240 4 604 2 Welding machine-lathe 22 1 34 1 181 2 126 2 80 1 443 1 None 443 13 694 18 980 24 1,512 18 825 16 1,383 23 5,837 19 Not elsewhere classified 17 9 39 60 1 20 145 Unspecified 16 101 3 24 1 52 1 81 2 60 1 333 1 Total 3,504 100 3,798 100 4,163 100 8,389 100 5,133 100 6,091 100 31,078 100 'Excluding job title category of office workers. 2 MSHA size groups are based on the annual average employment of the primary subunit and not on the total employment; hence, MSHA published injury statistics by size groups should not be analyzed against these data. 3 See appendix B for detailed explanation of equipment operated grouping. NOTE — Owing to independent rounding, data may not add to totals shown. 16 Table E-3.— Nonmetallic mining 1986 workforce estimates: work location at mine, by employment size class 1 ... , . " Vl9 20-49 50-99 100-249 250-499 500+ Total Work location — — - — — — Workers pet Workers pet Workers pet Workers pet Workers pet Workers pet Workers pet Underground mine 165 4 138 3 220 5 1,119 12 742 13 1,260 19 3,643 11 Surface at underground mine 30 1 15 65 1 411 5 392 7 870 13 1,783 5 Surface mine 2,275 59 1,483 37 1,583 36 2,087 23 1,538 28 2,242 34 11,208 34 Plant or mill 962 25 2,073 51 2,187 50 4,504 50 2,317 42 1,591 24 13,634 41 Office 393 10 354 9 304 7 929 10 525 10 662 10 3,167 9 Total 3,825 100 4,062 100 4,360 100 9,049 100 5,514 100 6,625 100 33,434 100 'MSHA size groups are based on the annual average employment of the primary subunit and not on the total employment; hence, MSHA published injury statistics by size groups should not be analyzed against these data. 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