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UCATION

Policy Paper No. PP86-3-2
One Million Hours A Day:

Vocational Education in California
Public Secondary Schools

David Stern, E. Gareth Hoachlander,
Susan Choy, and Charles Benson

March 1986

 

Directors

James W. Guthrie
University of California
‘Berkeley

Michael W. Kirst
Stanford University

 

Policy Paper No. PP86-3-2
One Million Hours A Day:

Vocational Education in California
Public Secondary Schools

David Stern, E. Gareth Hoachlander,
Susan Choy, and Charles Benson

March 1986

David Stern is associate professor of education at the University of California, Berkeley.

E. Gareth Hoachlander is president, MPR Associates, Inc., a consulting firm specializing
in public sector management planning and research.

Susan Choy is vice president, MPR Associates, Inc.

Charles Benson is professor of education at the University of California, Berkeley.

Policy Analysis for California Education (PACE) is funded by the William and Flora
Hewlett Foundation and directed jointly by James W. Guthrie and Michael W° KirsL The
analyses and conclusions in this paper are those of the authors and are not necessarily
endorsed by the Hewlett Foundation.

Funding for this research was provided in major part by the
California Policy Seminar (University of California, Berkeley;
Karen Paget, Director). Additional funding was provided by
Policy Analysis for California Education (PACE).

Additional copies of this paper are available by sending $3.50 per copy to:

PACE
School of Education
University of California
Berkeley, California 94720

CHECKS PAYABLE TO THE REGENTS OF THE UNIVERSITY OF CALIFORNIA
(Residents of Alameda, Contra Costa, and San Francisco counties add 6 5 % sales tax.
Other California residents add 6%.)

Policy Analysis for California Education (PACE)
Berkeley, California
March 1986

ii

Contents

Page

List of Tables ............................................................................ v
Policy Analysis for California Education ........................................... vii
Executive Summary .................................................................... ix
I. Overview ................................................................................. 1
California's Hierarchical System of Vocational Education ....................... 1
II. Review of Previous Research on Effectiveness of Vocational Education ....... 7
Labor Market Outcomes ........................................................... 7
Dropout Prevention ................................................................ 12

III. Vocational Student Hours and Teacher Salaries in California Public
High Schools and ROC/ROPs ..................................................... 15
Definition of Vocational Programs ............................................... 15
Counting Teachers, Salaries, and Student Hours ............................... 17

Differences Between Vocational Teachers in High Schools

and ROC/ROPs .................................................................. 19
Segregation of Students by Sex .................................................. 21
Handicapped, Disadvantaged, and LEP Students ............................... 21
Explaining Variation in Teachers' Salary per Student Class Hour ............ 24
Summary ............................................................................. 29
IV. Outcomes of Vocational Education in California Public High Schools ......... 31
High Unemployment Among Recent Vocational Graduates ................... 31

Variation in Labor Market Outcomes by District and Instructional
Program: Does Anything Work Consistently? ............................... .33
Dropout Prevention ................................................................. 4 2
Summary ............................................................................ 44

V. Reconstituting Vocational Education in California Public

Secondary Schools .................................................................. 4 6

iii

Education and Work: Why Are They Separated? ............................... 46

Reintegrating Education and Work in the High Schools ........................ 4 8
Teaching Job-Specific Skills in ROC/ROPs ..................................... 51
Summary ............................................................................ 5 3
References ...................................................................................... 55

iv

10.

11.
12.

List of Tables

. Percentages of Students Taking (or Not Taking) Vocational Programs in

Specifc Areas Who Obtained a Job in an Occupation That Corresponded
to That Area, by Specific Area and Sex ............................................ 9

Dropout Rate by Grade Level, Probability of Dropping Out, and Amount
of Vocational Education ............................................................ 14

. Student Class Hours, Teachers, and Salaries in California Regional

Occupational Programs (ROP) and Public High School Vocational
Classes, by Instructional Program, October 1981 .............................. 18

Characteristics of California Teachers in Regional Occupational Programs
(ROP) and Public High School (HS) Vocational Classes, by
Instructional Program, October 1981 ............................................ 20

Number of Daily Student Class Hours, and Expenditure on Teachers'
Salaries Per Daily Student Class Hour, for Male Students in California
Regional Occupational Programs (ROP) and Public High School
Vocational Classes, by Instructional Program, October 1981 ................ .22

Number of Daily Student Class Hours, and Expenditure on Teachers’
Salaries per Daily Student Class Hour, for Handicapped, Educationally
Disadvantaged, and Limited English Proficient Students in California
Regional Occupational Programs (ROP) and Public High School (HS)
Vocational Classes, by Instructional Program, October 1981 ................ .23

Ratio of ROP to High School Annual Expenditure on Teachers' Salaries
per Daily Student Class Hour, by Type of Student and Instructional
Program, October 1981 ............................................................ 25

. Regression Coefficients Describing Variation in Annual Expenditure on

Teachers' Salaries per Daily Student Class Hour in California Regional
Occupational Programs (ROP) and Public High School Vocational
Classes, October 1981 ............................................................. 27

Characteristics of Selected Students in Vocational Programs in Selected
California Public High Schools, by Instructional Program, 1981 ............ 35

First-Year Follow-up Data on Selected Students from Vocational
Programs in Selected California Public High Schools, by 1981
Instructional Program, Spring 1982 ............. _ ................................ 3 6

Regression for First-Year Outcomes ............................................. 3 8-40
Percentage of 1980 California Sophomores Who Had Dropped Out in

1982, by Number of Vocational Courses Taken Through Grade 10 and
Whether in 1980 They Expected to Graduate .................................... 4 3

 

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Policy Analysis for California Education

Policy Analysis for California Education (PACE) is a university-based research center
focusing on issues of state educational policy and practice. PACE is located in the Schools
of Education at the University of California, Berkeley and Stanford University. It is
funded by the William and Flora Hewlett Foundation and directed jointly by James W.
Guthrie and Michael W. Kirst. PACE has expanded to include satellite centers in
Sacramento and Southern California. These are directed by Gerald C. Hayward
(Sacramento) and Allan R. Odden (University of Southern California).

PACE efforts center on five tasks: (1) collecting and distributing objective information
about the conditions of education in California, (2) analyzing state educational policy issues
and the policy environment, (3) evaluating school reforms and state educational practices,
(4) providing technical support to policy makers, and (5) facilitating discussion of
educational issues.

The PACE research agenda is developed in consultation with public officials and staff.
In this way, PACE endeavors to address policy issues of immediate concern and to fill the
short-term needs of decision makers for information and analysis.

PACE publications include Policy Papers, which report research findings; the Policy

Forum, which presents views of notable individuals; and Update, an annotated list of all
PACE papers completed and in progress.

Advisory Board

Mario Camara A. Alan Post
Partner California Legislative Analyst,
Cox, Castle & Nicholson Retired
Constance Carroll Sharon Schuster
President, Saddleback Executive Vice President
Community College American Association of University Women
Gerald Foster Eugene Webb
Region Vice President Associate Dean, Graduate School
Pacific Bell of Business, Stanford University
Robert Maynard Aaron Wildavsky
Editor and President Professor of Political Science

The Oakland Tribune University of California, Berkeley

Vii

 

 

Executive Summary

Eleventh and twelfth grade students in California comprehensive high schools and
Regional Occupational Centers/Programs (ROC/ROPs) spend about one million hours a
day on vocational education. This represents a large investment of student time and public
money; yet vocational education has a potential that far exceeds its present performance.

California students spend more than twice as much time in high school vocational
classes as in ROC/ROPs. Most vocational programs are offered in both places, though
possibly at a more advanced level in ROC/ROPS.

California students who took a concentrated sequence of high school vocational
subjects during 1981 had a 26 percent unemployment rate in the spring of 1982, compared
to 23 percent unemployment among all 16 to 19 year-olds and 27 percent unemployment
among high school dropouts. Evidently, high school vocational training did not give
students any relative advantage in finding jobs after they graduated. Available evidence
also did not reveal that vocational classes were effective in retaining would-be dropouts.

On the whole, vocational classes as currently offered in California comprehensive
high schools are not demonstrably effective in helping students find jobs after they
graduate, or in retaining would—be dropouts. Furthermore, there is no evident way in
which reallocating resources among existing high school vocational programs would bring
about much improvement in labor market outcomes for graduates.

We propose fundamental changes in vocational education at the secondary level.
Comprehensive high schools should stop trying to provide skill training for entry level
jobs-~a task they are not well situated to do--and instead should use vocational education to
prepare young people for a working life of continual learning, problem solving, and
communicating. To accomplish this broader purpose, vocational education in high schools
should include all students at some point in their high school career. It should be integrated
with the academic curriculum, but at the same time engage students in producing something
of real use. It should teach teamwork and encourage active inquiry. The success of
vocational education in high schools should be measured by improved performance in
academic subjects, lower dropout rates, and life long gains in productivity at work.

To provide training in specific job skills for high school students, ROC/ROP programs

should continue. We recommend, however, that evaluation of these programs put less
emphasis on job placement and more on students' attainment of measured competence.

ix

 

 

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In memory of

Charlotte Alhadeff

 

I. Overview

Vocational education, meaning preparation for jobs and careers in agriculture,
distributive trades, health occupations, technical and industrial trades, office occupations,
and occupational fields related to homemaking, is an activity of major proportions in
California. In 1983-84, 234,202 students took vocational courses in public comprehensive
high schools at the 11th and 12th grade levels.1 (An even larger number of 9th and 10th
graders took courses officially described as vocational education, mainly in subjects such
as typing, home economics, and the exploratory type of shop course called "industrial
arts"; however, in this report we are concerned with youth in the 11th grade and up.) For
high school students and adults, California also offers vocational programs in regionally-
administered agencies, called Regional Occupational Centers/Programs (ROC/ROPS).
ROC/ROPs enrolled 99,986 students in the 11th and 12th grades in 1983-84 and 39,277
adults. Lastly, the community colleges served approximately 700,000 students in
vocational subjects.

Our main concern in this report is the quality of vocational programs taken by 11th and
12th grade students in comprehensive high schools and ROC/ROPs. California students at
this level are spending about one million hours a day on vocational education. This
represents a large investment of student time--and public money.

We examine data to assess the usefulness of these investments. Our summary
conclusion is that vocational education has a potential that far exceeds its present
performance. We offer a number of proposals for reform of vocational education, the
strongest of these referring to the programs conducted in comprehensive high schools. Our
findings lead us to propose that the burden of preparing youth for entry-level jobs, i.e., the
inculcation of job-specific skills, should be lifted from the high schools and carried in full
by the ROC/ROPs. The comprehensive high schools would become providers of
"enterprise training," involving students in actual production of goods and services. The
basic point of our proposals, which we spell out in Section V, is to achieve two kinds of
integration: enterprise and skill training on the one hand, and academic and vocational study
on the other.

California's Hierarchical System of Vocational Education

With regard to depth or intensity of instruction, vocational education in California exists
in a hierarchical pattern. This is by design. At the top of the hierarchy stand the
community colleges. The size of most community college enrollments allows the colleges

 

1 This estimate, as well as others given in this paragraph, were provided by the California
State Department of Education.

to offer a considerable range of courses in a given occupational field; the large enrollment
of the whole campus serves to bring enough students into even highly specialized courses
to contain costs per student within tolerable limits. The permanent faculty of the campus is
available to treat theoretical topics in depth. At the same time, the colleges rely strongly
upon the services of part-time, untenured faculty, and the employment of such persons
enables the colleges to meet shifts in labor market demands rather easily. If employers
announce that they have openings for people to maintain numerically-controlled textile
looms, a college can add staff to serve such a demand for a set of new courses by hiring
textile machinery designers and mechanics part-time to serve as instructors. If the market
demand for the particular skill slacks off, the contracts of those hired to teach textile
machinery maintenance can be terminated At their best, community colleges offer a unique
combination of stability and flexibility in their approach to occupational training. This
helps to maintain rigor in instruction, but it also allows the colleges to keep their programs
timely. Among the institutions that provide vocational education in California, the
community colleges have the further advantage of enrolling the most mature students.

Next in the hierarchy of vocational institutions, ranked by depth or intensity of
instruction, stand the regionally-administered programs, the ROC/ROPs. Attendance in
ROC/ROPs is commonly on a half-day basis for high school students. In Northern
California (except San Francisco), the general pattern is to go to the home high school for
three to four hours in the morning and to the ROC/ROP for two or three hours in the
afternoon or vice versa. In Southern California, there is a strong practice to hold
ROC/ROP sessions after the full high school day has been completed, e.g., in the later
afternoon, evenings, or on weekends (San Francisco follows this latter practice in fair
part). In earlier years, many ROC/ROPs held extensive programs in the summer, but these
have been substantially curtailed.

ROC/ROPS are subject to rather detailed scrutiny by the State Department of Education,
working out of three regional ROC/ROP offices. Courses must be approved and reviewed
by the regional offices. New course applications are required to give documentary proof
(a) that the skills to be taught are in demand and (b) that other institutions are not meeting
that demand. Courses are also scrutinized by state offices with regard to duration and
intensity. ROC/ROPs are expected to meet standards of student completions of programs
and of placement of graduates in jobs.

The essence of the ROC/ROP program, however, is not to be found in its statewide
administrative structure but in the fact that enrollment of students in ROC/ROP programs is
strictly voluntary. Funding by the state-~standard practice--is based on attendance, which
is closely related to enrollment. Hence, the size of a given ROC/ROP's budget, from
which many good things flow, is a direct function of its ability to attract students.

This gives an entrepreneurial cast to ROC/ROP administration. Somehow, the director
and his or her faculty must convince an appropriate number of students (better yet, a

2

growing number of students) that enrollment in their ROC/ROP is "worthwhile." The most
compelling evidence to offer is a statement that graduates of the given ROC/ROP get good
jobs. The ROC/ROPs must sell their programs to students; in order to do this, they must
sell the skills of their students to local employers.

If ROC/ROP staff are to sell the skills of their students to local employers, they are
likely to have to meet a number of requirements of an entrepreneurial type.

1. They must keep in close touch with personnel officers, production managers,
foremen in large firms, and owners of small firms (a) to know what hiring will be
done in what fields in the near future and (b) to discover what specific skills,
competencies, and attitudes the firms are looking for and how job prerequisites are
changing, if they are.

2. They must be able to hire faculty who are completely up-to-date in methods of
production.

3. They must provide students with a learning environment that is well-stocked with
machinery or other implements of production; this equipment must be current and
of _a quality that is at least as good as what employees in local firms are presently
usrng.

To sum up, leadership of the ROC/ROP requires (1) information and contacts, (2) good
faculty, and (3) good equipment. In practice, these requirements are often dealt with
simultaneously. In pursuing contacts with employers, ROC/ROP leadership may take the
opportunity to hire outstanding skilled workers from local plants to serve as part-time
faculty, and it may also succeed in obtaining access to modern production equipment for
students' use, either as a gift, loan, or through establishment of a cooperative training
program or community classroom. In either case, part of the instructional program would
be conducted in employers' facilities.

The bottom rung of the vocational education hierarchy is occupied by programs that are
embedded in and administered by comprehensive high schools. Two qualifications should
be stated immediately. First, some vocational programs that are run by comprehensive
high schools are exceptional by whatever criteria one may choose to reach a judgment.
Second, we ourselves are postulating a hierarchy based on depth or intensity of instruction
toward the objective of equipping students with enu'y-level skills for employment. This is
not necessarily an appropriate objective for high schools, as we shall argue later.

In any case, the financial incentive structure does not establish any strong
entrepreneurial spirit toward vocational education in comprehensive high schools. Students
in the llth and 12th grades attend high school for a variety of reasons: to go to college, to
earn a diploma regardless of plans for further education, to please their parents, to have an
active social life, to avoid the physical dangers of street life, or to forego the stigma of
being a dropout. Whereas some vocational educators may believe otherwise, the evidence
that availability of vocational courses in high school prevents students from dropping out is

3

simply not strong (see Section 11). Hence, the amount of money received by a school
district for attendance generated in a particular high school is likely to be affected only
slightly by the scope and quality of the vocational program in that high school. Even if it
were so affected, there is no assurance that the district office would turn the money over to

the high school that earned it.

There are other problems for vocational education in comprehensive high schools.
Faculty are almost altogether full-time and tenured. This means that high schools, unlike
ROC/ROPs, lack flexibility in staffing to meet changes in labor market demand for skills.
Faculty ordinarily do not have expense accounts to go out and keep in close touch with
local employers; often they do not even have easy access to telephones to make placement
calls for their students--especially to respond to anyone returning a call. Compared to
ROC/ROPs and community colleges, the amount of money available to buy equipment for
vocational programs in comprehensive high schools is meager.2

Let us review the hierarchical structure of vocational education from the point of View
of a student who wants to prepare for a job in the field of electronics. A community college
in the Bay Area offered the following courses in 1984—85:

Fundamentals of Electronics

Electronics Soldering Techniques

Passive Circuits and Devices

Applied Electronic Mathematics

Active Electronic Devices and Circuits
Electrical/Mechanical Assembly Technology
Analysis of Linear Circuits
Modulation/Demodulation and Signal Processing Systems
Introduction to Microprocessors
Electrical/Mechanical Assembly Technology II
Advanced Circuit Applications
Micro-Computer Interfacing

Radio-Frequency Communication

Active Circuits and Devices

Applied Linear Amplified Analysis

Applied Electronics Circuit Analysis
Microwave Principles

In the same academic year, a nearby ROC/ROP offered four courses (only):

Electronics Design and Manufacturing I & II. This course is
designed to prepare students for employment in the electronics industry.

 

2 In one high school we visited, two classrooms for office occupations were side by side,
one run by a ROC/ROP and the other by the home high school itself. The ROC/ROP
classroom had a word processor at each student station. The classroom administered by

the comprehensive high school had manual typewriters-~two generations of equipment
behind.

4

Students will learn basic electronics theory and applications. This class
will emphasize robotics, computers, design, and construction of
projects.

Electronics Systems Analysis and Repair I & II. This course is
designed to prepare students for employment in the electronics industry.
Students will learn basic electronics theory and application. This course
will emphasize hands—on trouble-shooting, analysis of analog, digital,
operational amplifiers, and other related electronic technologies.

A large comprehensive high school in the Bay Area, commonly regarded as one of the
leading institutions in the state, had no course in electronics in its vocational program. For
the students seeking a job in the electronics field, possibly the course most closely related
would have been this one:

Machine Shop. A laboratory course in which students learn to use
the lathe, milling machine, drillpress, and other equipment commonly
employed in the manufacture of parts for industrial machinery. Projects
involve the cutting of various metals and plastics to specified shapes with
a high degree of precision. Students learn to work from blueprints and
to read micrometers and other measuring devices used in the machinist
trade. This course is designed for students seeking basic grounding in
an industrial trade or entry into mechanical engineering.

If vocational courses in the comprehensive high school improved the prospects of
students to enter post-secondary education, or really provided students with the skills to get
a well-paying job quickly upon graduation from high school, or even if the vocational
courses served as a dropout preventive, one might suggest that the courses be maintained in
their present form. Evidence we have seen, and new evidence presented in this report, do
not establish a case for preserving most of these courses in comprehensive high schools.

Indeed, we believe it is possible that there are some negative outcomes associated with
vocational courses administered by comprehensive high schools. These courses may be
used as a holding room for students who are judged to be slow or not highly motivated
toward academic studies. Moreover, because it is claimed that vocational courses lead to
jobs, students may become imbued with a false sense of security, to the point of drinking
that they are doing something worthwhile for their lives. But in the meantime, the amount
of academic work they do is reduced, resulting possibly in permanent damage to their
careers.

Vocational classes in comprehensive high schools often give students opportunity to do
something useful, concrete, and practical. A11 students-mot only those who are judged to
be slow in academic subjects--can benefit from this kind of opportunity. But such
experience should reinforce the academic curriculum, not exist in a separate curricular
track. The aim of vocational education in comprehensive high schools should not be to
provide students with specific skills for certain entry-level jobs; this is something the high

5

schools are not well-situated to do. Instead, the aim should be to prepare people to use
their heads at work: to acquire information, to communicate, to think. The next three
sections of this report provide evidence in support of these assertions, and the final section

explains our recommendations in more detail.

II. Review of Previous Research on
Effectiveness of Vocational Education

Labor Market Outcomes

In 1980 the National Center for Research in Vocational Education published a
systematic survey of studies on the outcomes of participation in vocational education
(Mertens and others, 1980). The survey covered more than two hundred published and
unpublished studies from 1968 to 1979. Outcomes of vocational education included
earnings, unemployment rates, basic and occupational skill attainments, postsecondary
training, and whether vocational education graduates were satisfied with their training. The
authors concluded (p. xiii):

1. No differences in unemployment rates were found for high school graduates of
vocational education programs as compared with nonvocational programs.
Postsecondary vocational graduates have somewhat lower unemployment rates than
nonvocational postsecondary graduates.

2. A majority of secondary and postsecondary vocational graduates did find jobs in
training-related areas.

3. The results of studies of earnings of vocational education graduates and
nonvocational graduates could not be generalized; however, trade and industry
graduates at the secondary level and technical graduates at the postsecondary level
have higher earnings than graduates of other vocational education programs.

4. Vocational students are below academic students but above or the same as general
curriculum students in terms of basic skills attainment and academic abilities.

The authors further reported, however, that there are serious methodological problems
with the research on the outcomes of vocational education: neither administrators nor
students' reports adequately describe the differences in programs or curriculum, and
earnings variables are highly affected by such factors as degree of unionization and state of
the economy. Isolation of the effects of participation in vocational education alone is
clearly made suspect by such factors. These factors will generally "...tend to obscure
rather than enhance differences among curricula. The differential effects of the separate
curricula must be fairly powerful to be detected with existing methods" (p. 17).

A different kind of research synthesis was commissioned in 1980 by the National
Institute of Education and conducted by the Huron Institute (U .S. Department of
Education, 1981, Chapter VII). This study reanalyzed data from three national longitudinal
surveys. It measured labor market success among young workers who had exactly 12
years of schooling, and compared graduates from high school vocational programs with
graduates from the general (neither vocational nor pre-college) cuniculum. It found that
young women who graduated from business and office programs worked more hours and

7

earned more money per week than women who graduated from the general curriculum.
Among young men, there were some advantages for white vocational graduates in their
first year out of school, but these diminished three years later. Most differences between
male vocational and male general curriculum graduates were small and inconsistent.

The Huron Institute study relied mainly on high school graduates’ own reports of
whether or not they were in vocational programs. However, these reports are not always
accurate. A more accurate classification can be obtained from students' high school
transcripts. Two studies have used high school transcript data, along with information
about labor market experience after graduation, to measure the consequences of vocational
education. One study was by Rumberger and Daymont (1982), the other by Meyer
(19 8 1 a).

Rumberger and Daymont (1982) used data from the new youth cohort of the National
Longitudinal Surveys (Center for Human Resource Research, 1983). Annual surveys of
this cohort began in 1979, with interviews of approximately 12,000 men and women
between the ages of 14 and 21. Complete high school transcripts were obtained for 6,591
of the 8,420 respondents who were 17 to 21 years old in 1979 and who had last attended
high school in the United States. Rumberger and Daymont further restricted their sample to
1,857 respondents who were not full-time students at the time of the 1980 interview and
who had completed 9 to 12 years of schooling. Another 500 cases had to be omitted due to
missing data, so the analysis finally included only about 10 percent of the full sample.

The advantage of being so selective is that Rumberger and Daymont were able to
identify students who had taken a whole program in one of six vocational areas:
agriculture; distributive education; health occupations; home economics; office occupations;
and technical, trades, and industrial occupations. They defined a program as three or more
credits in one of these fields. Their data (Table 1) show proportions of students in each
program who became employed in jobs related to their training. Programs in office
occupations, distributive education, health occupations (for women), and agriculture (for
men) appear most likely to lead to related employment.

Rumberger and Daymont show that the estimated payoff from vocational education
depends on how participation in vocational education is measured. Dependent variables are
the logarithm of hourly earnings in the 1980 survey week, the number of weeks
unemployed in the previous year, and the number of hours worked in the previous year.
Independent variables included as controls were race (black or HiSpanic), ninth grade
GPA, number of months since leaving school, whether married with spouse present,
number of children, parents' education, and a "cultural index" for the family of origin.
Their data show that the estimated payofi‘ from taking more vocational education credits is
always greater if those credits are part of a program and the student becomes employed in a
field related to that program. One finding is especially interesting: "used" vocational
program credits have a greater estimated effect on women's hourly pay than academic

8

Table 1

Percentages of Students Taking (or Not Taking) Vocational
Programs in Specific Areas Who Obtained a Job in an Occupation
That Corresponded to That Area, by Specific Area and Sex

 

 

 

 

Vocational and Vocational program status

occupational area Specific program Other program or

(# of students in program)* indicated no program
Men

Agriculture (40) 42 17

Distributive education (16) 38 27

Health occupation (O) - 5

Home Economics (13) 4 6

Office occupation (16) 54 19

Trade and industry (191) 65 59
Women

Agriculture (9) 3 7

Distributive education (16) 66 28

Health occupation (16) 4O 6

Home Economics (97) 15 13

Office occupation (248) 60 35

Trade and industry (34) 26 32

 

*The entries for the number of students in a program are unweighted while the main entries
are weighted percentages.

Source: Rumberger and Daymont (1982)°

credits, though academic credits in one equation have a bigger effect than vocational credits
in general. This makes it possible to say that, for women in this sample, vocational credits
have a bigger payoff than academic credits in terms of all three economic outcomes,
provided the vocational credits are part of a program and the women find employment in a
field related to that program.

For men, results are more mixed. Vocational credits in a program that is used do have
a bigger effect than academic credits on total hours worked, but not on raising hourly
earnings or reducing unemployment.

Rumberger and Daymont tried to estimate whether the effect of credits in vocational
programs depended on which program students took, but the numbers of students in most
programs were too small to make reliable comparisons between programs.

One drawback of this study is that it combines high school dropouts and graduates, and
the analysis does not control for that. Another is that unmeasured influences on whether
students have taken vocational courses may also affect labor market outcomes, so the
estimated "effects" of vocational education on employment may be biased.

In another study using course transcripts, Meyer (19813) estimated effects of vocational
education on the employment of high school graduates who had no postsecondary
schooling. His sample consisted of 2,431 women and 2,022 men, out of the original
sample of 23,000 high school seniors in the National Longitudinal Study of the Class of
'72. Data were available for eight years after graduation. Dependent variables were hours
and earnings for the first week of October and total weeks worked since the previous
October.

The base year survey included a Student Record Information Form addressed to the
school, which recorded the number of courses each student took in specific subjects. The
six vocational subjects were agricultural, business or commercial, distributive education,
health occupations, home economics, and trade or industrial occupations. For women,
Meyer looked at commercial, home economics, and "technical," which combined the
remaining four. For men, he looked at trade and industrial, commercial, and other. Meyer
did not try to determine whether courses were part of a "program"; his predictor variables
were simply the ratio of vocational education courses in the various fields to the total
number of courses taken in high school. Control variables were number of hours worked
per week while in high school (a strong positive predictor of successful employment after
high school), whether black or Hispanic, a test score, class rank, parents' income, whether
married and with dependents in 1973 and 1979, whether respondent lived in a
town/urban/rural area and in the south/east/west, and the local unemployment rates and
average wages in 1973 and 1979.

10

Meyer found that the estimated effects of commercial courses on employment and
earnings were consistently positive for women. The effects diminished after five years but
remained positive through the whole eight-year period for which data were available.
Women who took a lot of home economics, on the other hand, earned consistently lower
hourly wages. Other vocational courses appeared to have no consistent effect for women.

For men, courses in trades and industrial occupations had a slight positive effect on
hourly pay. Other vocational courses had no consistent effect.

In Meyer's sample, women had taken an average of 29 percent of their high school
course work in commercial subjects. The standard deviation was 19 percentage points.
Meyer computed the predicted gain from a 2-standard-deviation increase--that is, an
increase of 38 percentage points--in the proportion of total coursework taken in commercial
subjects for a woman with average pay and average hours and weeks worked. The
predicted gain in annual earnings would be 16 percent, of which 3.5 percent comes from
higher predicted hourly wages and the rest is due to a larger predicted number of hours and
weeks worked (Meyer implicitly assumes that the courses for which the extra commercial
courses are substituted had no effect on employment or earnings.)

A similar simulation for men, in which the proportion of courses in trades and
industrial occupations was increased by 34 percentage points (the mean is 19 percent),
resulted in a predicted gain in annual earnings of only 3 percent

A drawback of Meyer's study is that he did not determine whether or not students
found jobs in the fields for which they trained. Also, unmeasured variables may have
affected both vocational course enrollment and labor market outcomes, so that estimated
"effects" would be biased.

In at attempt to measure the payoff from vocational training over a longer period--up to
15 years after high school graduation—-the National Center for Research on Vocational
Education conducted a national survey in 1981 (Mertens and Gardner, 1983). The sample
consisted of individuals between the ages of 20 and 34, who were employed or looking for
work. Graduates of some vocational programs initially earned more money per week than
graduates from the general curriculum. However, the initial differences were not
statistically significant, and they also diminished over time.

To summarize, previous research has not found strong or consistent gains in
employment and earnings for graduates of high school vocational programs. Gains appear
to be greater for students who take a concentrated set of vocational courses in a particular
area and then find employment in that area. However, gains from vocational training
appear to diminish over time. For high school students who do not go on to college, an
academic course of study combined with part-time employment apparently provides better
preparation for the job market than taking vocational courses.

11

Dropout Prevention

Even though the evidence generally shows that vocational education gives high school
students little, if any, advantage in the labor market, vocational education might still have
important value for students who like their education to be practical, concrete, and useful.
To perform practical activities like raising crops or livestock, running a restaurant, or
repairing cars, students may have to exercise and improve their skills in reading,
computing, or problem solving. They may also have to acquire new information about
academic subjects. Vocational classes can thus provide academic education with a practical
theme. It is often said that a substantial number of students would quit high school if such
an alternative to the predominantly abstract approach of academic classes did not exit.

For example, former Secretary of Education--then Commissioner of Education--Terrell
H. Bell urged Congress to take this broader view of vocational education in 1975, after the
General Accounting Office (GAO) criticized the lack of labor market benefits from the
program. Bell stated:

Although we will make vocational education as responsive as possible to the
labor market, it would be erroneous to judge the success of all vocational
education by this criterion alone, as the GAO report does. We feel
vocational education programs should be perceived as an integral part of the
educational system of this country. As part of an educational system as
opposed to a trainee system, vocational education is responsible for
assisting in increasing and improving basic cognitive skill, heightening
career awareness, improving the understanding of a variety of work
environments, and in many instances, actually motivating students to remain
in school at the secondary or postsecondary levels as well as providing
special occupational skills. (Hearings before the House Subcommittee on
Elementary, Secondary, and Vocational Education, Feb. 19, 1975; pp. 308-
309. Emphasis added).

The best empirical study of whether vocational education actually does motivate some
students to remain in high school has been done by Mertens (1982), using data from The
National Longitudinal Survey of Youth Labor Market Experience (Center for Human
Resource Research, 1983). This study concentrated on individuals in the sample who were
18 years or older in 1980 and for whom there was information on highest grade completed,
enrollment status, and courses taken since 9th grade. In addition to school data, there was
a great deal of information about the personal characteristics of these youngsters, their
family backgrounds, and their personal experiences.

In order to test for the effect of participation in vocational education on persistence in
high school, Mertens analyzed dropping out at the end of the 9th, 10th, and 11th grades.
Participation in vocational courses was measured by credits reported on course transcripts.
The sample group was divided into two equal-sized subgroups: one with a low predicted
probability of dropping out, and one with a high predicted probability of dropping out.
This division was based on earlier studies that have shown an association between

12

dropping out and certain personal characteristics, family background factors, and
demographic variables.

Mertens's data (Table 2) show dropout rates for both the high and low probability
groups, for grades 10, ll, 12, and for three different levels of vocational education
participation. For vocational education between zero and less than three credits, dropout
rates increased; for more than three vocational education credits, however, the dropout rates
were below the level of those who had more than zero but less than three vocational
education credits.

In order to control more precisely for personal and background characteristics known to
be associated with dropping out, the authors analyzed the dropout rates for the high
probability group for each grade level using a linear regression model. The regression
equation includes race or ethnicity, sex, aspects of marriage and child-bearing, test scores,
and socio-economic status. When these control variables are included, increasing
participation in vocational education significantly reduced the probability of dropping out in
grades 10 and 12. The coefficient of vocational education credits for grade 11 was negative
but not significant. One vocational education credit in grade 9 reduced the probability of
dropping out of grade 10 by one—tenth of one percent, one vocational credit in grade 11
reduced the probability of dropping out of grade 12 by two-hundredths of one percent.
Thus, other things being equal, increased participation in vocational education decreased
the probability of dropping out, but only by a small amount. 1

This careful study suggests that the vocational education curriculum does increase
retention rates in high school, but that the effect is small. One reason for the small holding
power may be the absence in many high schools of opportunities to enroll in vocational
education programs until the 11th or 12th grades. Thus, increasing the number of
vocational education programs that are open to 9th and 10th graders might be one way to
reduce high school dropout rates.

Another study of the relation between vocational education and dropout rates surveyed
17 successful dropout prevention programs that incorporated either vocational education or
work-experience components (Lotto, 1982). Success was defined by empirical evidence
that the program either reduced the dropout rate or improved the attendance rate. The
survey found that 10 of the 17 successful programs emphasized individualized instruction
and close relationships between students and teachers. In 13 of the 17 cases, the program
matched vocational education with instruction in basic skills. In 12 of these 13 programs,
career counseling was also supplied In other programs, additional support services were
included. Thus, the common element in these successful vocational programs is a multi-
strategy approach that simultaneously addresses the many problems of dropouts.

l3

Table 2

Dropout Rate by Grade Level, Probability of propping Out,
and Amount of Vocational Education

Grade Level and Probability of Dropping Out

 

Amount of 1 0 1 1 Hr
Vocational . .
mm m Eight Igtal Lax High m lam .ngh Total

No Voc. % 0.1 3.9 1.6 0.0 6.6 2.5 0.7 11.0 4.0
n 50 2531 2581 23 2028 2051 187 1281 1568

Less than % 0.2 6.6 3.0 0.5 11.9 5.4 0.8 16.4 6.8
threecredits n 79 1971 2050 321 5478 5799 502 6248 6750

Three or % 0.0* -— 0.0* 0.0* 7.3 4.5 0.0 12.3 5.6
more credits n 0 - 0 0 56 5 6 0 827 827
Total % 0.1 4.7 2.0 0.3 9.7 4.1 0 7 14.7 6 0

n 129 4502 4631 344 7562 7906 689 8456 9145

Note: The frequencies (11) represent the weighted population in hundreds. Respondents with
no transcript data for a particular year were assigned to the mean for that grade level.
* Indicates that fewer than twenty-five actual cases were used as the base for calculation.

Source: Mertens (1982)

14

III. Vocational Student Hours and Teacher

Salaries in California Public High Schools and
ROC/ROPs

California students and teachers are spending about one million hours each day on
vocational education in public high schools and Regional Occupational Centers and
Programs (ROC/ROPs). This is about one—sixth of the total time spent in public secondary
schools. These numbers come from the Professional Assignment Information Forms filled
out by teachers in October 1981, for the California Basic Educational Data System
(CBEDS). We begin our analysis of this data by breaking it down between high schools
and ROC/ROPs, and among 18 instructional programs.

Definition of Vocational Programs

Instructional programs are defined as follows (numbers in parentheses are assignment
codes from the Professional Assignment Information Forms).

1. Aong' culture consists mainly of classes in agricultural production (4000), agricultural
mechanics (4002), ornamental horticulture (4004), introduction to agriculture
(4007), and "other" agricultural courses (4098). Each of these course titles
accounts for roughly one-fifth of the classes in this program.

2. Distributive education includes courses in various retail service areas. General
merchandise (4107) accounts for half the classes in this program. "Retail trade,
other" (4119) is the next most common assignment in this area, accounting for one-
sixth of the classes.

3. Practical nursing includes classes in nursing (4206), practical (vocational) nursing
(4207), and "other nursing" (4210). A practical nurse is trained to give direct
nursing care under the supervision of a nurse or physician.

4. Nurses” aide consists of nursing assistant (aide) courses (4208). An aide is trained
to perform simple tasks involved in the personal care of individuals receiving
nursing services.

5. Child care includes classes in child development (4311) and care and guidance of
children (4400).

6. Clothing consists mainly of courses in clothing and textiles (4302), with a few
classes in clothing management, production and services (4401), and textile
production/fabrication (4840).

15

10.

11.

12.

13.

14.

15.

16.

17.

Of the classes comprising the 13ml program, 70 percent are food and nutrition
(4305), and 19 percent are in food management, production, and serv1ces (4402).
The rest are scattered among food distribution (4105), food services (1106), and

quantity food occupations (4836).

. WW consists mainly (78 percent) of courses in accounting and

computing occupations (4600), with some classes for computer and console
operators (4601) or programmers (4602), and a few in other business data
processing (4603).

mm consists almost entirely (97 percent) of courses in "filing, office
machines, clerical occupations" (4604), with a few "other" office classes (4698).

Similarly, 97 percent of courses taught in the Warm program are in
stenographic, secretarial, and related occupations (4608). This instructional
program differs from the office occupations and typing programs because it
provides some training in written communications.

Mg is typing and related occupations (4610).
Auto mechanics includes automotive mechanics (4510) and auto mechanics (4803).
Drafting is drafting (4502) or drafting occupations (4817).

About two—thirds of the courses in mm are in metals (4508). Of the
rest, most are in welding and cutting (4829) or machine shop (4826). There are a
few classes in machine tool operation (4827), sheet metal (4828), tool and die
making (4830), other metalworking occupations (4831), and other industrial arts
(4598).

The most common courses in ggnsmggtign are carpentry (4811) and other
construction and maintenance trades (4848), each accounting for about one-fifth of
the total. The remaining are classes in construction (4500), electricity (4812),
masonry (4813), plumbing and pipefitting (4814), or other trades and industry
courses (4898).

Wood is woods (4512) or woodworking occupations (4843).

cher high tech is a grouping of courses not included in any of the preceding
programs, and which have titles that suggest relatively sophisticated equipment or
theory. Courses in electronics (4503, 4706, and 4819) account for 28 percent of
this group, and graphic arts courses (4505 and 4822) are 27 percent. Photography
(4506) is 11 percent. Power mechanics (4511) and automotive technology (4702)
are each another 4 percent. The rest are scattered among 39 course titles, none of
which by itself represents more than 3 percent of the courses in this group.

16

18. che; is a residual category in which "other office" courses (4698) account for 19
percent and "other health" courses account for 11 percent of the classes. Body and
fender repair (4802) is 8 percent. General industrial arts (4504) and barbering/
cosmetology/personal services (4833) are each 7 percent. Industrial crafts (4501)
and other industrial arts (4598) are each 6 percent. Other home economics-related
occupational preparation courses (4498) are 5 percent. Dental assistant (4200) and
public service occupations (4835) are each 4 percent. Forestry (4006) is 3 percent.
The remaining classes are scattered among 19 other course titles, none of which
accounts for more than 3 percent.

Counting Teachers, Salaries, and Student Hours

The list of course titles and assignment codes is part of the CBEDS Professional
Assignment Information Form. Teachers are asked to pick the course title that best
describes each of their classes. They are also asked to report the percentage of their time
spent teaching each class, and the numbers of male, female, handicapped, educationally
disadvantaged, and limited-English-proficient students in each class. Finally the form asks
for certain information about the teachers themselves, including annual salary, educational
attainment, year of birth, years of professional service, sex, and ethnicity. As with any
questionnaire, perfectly accurate reporting cannot be expected.

Table 3 shows the total reported numbers of full-time-equivalent (FI'E) teachers and
student class hours, and the numbers in each instructional program. Since the number of
class periods taught each day by a full-time teacher varies between districts and even
between schools in the same district, the number of FIE teachers is first computed in each
place, then added together for the whole state. In each district or school the number of FTE
teachers in each program is computed by dividing the number of daily class periods taught
by a full-time teacher into the total number of class periods taught in that program. (In most
places a full-time teacher has five or six class periods per day. In ROC/ROPS the number
was taken to be five. The statewide mean for vocational teachers is 5.4.) For example, if a
district offers 30 class periods of typing a day, and a full-time teacher in that district teaches
six class periods a day, then five PTE are counted as typing teachers, even if the actual
number of people teaching typing is more than five, with some teaching it part-time.

The amount of teachers' salaries allocated to each program is computed for each teacher
as the total salary multiplied by the fraction of time spent teaching in each program. This is
then added together for all teachers in the program.

The number of daily student class hours in each program is simply the total number of
students who each spend one class period a day in that program. If 25 students spend two
daily class periods in a drafting course, they are counted as 50 student class hours per day.
This is a much more precise way to count students' participation in specific programs than
trying to construct measures of "unduplicated enrollment."

17

81

Table 3

Student Class. Hours, Teachers, and Salaries in California Regional Occupational Programs
(ROP) and Public ngh School (HS) Vocational Classes, by Instructional Program, October 1981

Annual Expenditure on

 

 

 

 

Teachers' Salaries per Total Daily Student Estimated Total Num— Estimated Mean Teach-
Daily Student Class Hr. Class Hours ber of Teachers (FTE) er's Salary per FTE
EIBBIQE All All
Second- A11 Second-
ary Egg HS Secondarx ROP _ߤ__ arz 392 ߤ_ ROP HS
1. Agriculture $186 $201 $181 62,771 16,152 46,619 549 170 379 $19,219 $22,456
2. Distrib. Ed. 174 171 184 32,219 24,008 8,211 292 227 65 18,315 23,876
3. Practical Nursing 302 233 422 , 3,580 2,272 1,309 52 26 26 21,268 21,390
4. Nurse's Aide 188 180 229 11,380 9,590 1,791 121 104 17 16,668 24,490
5. Child Care 174 175 173 25,711 7,777 17,934 211 72 138 19,065 22,767
6. Clothing 181 158 183 28,068 1,354 26,714 228 15 214 14,738 23,024
7. Food 175 202 168 57,101 11,846 45,255 469 129 340 18,643 22,460
3. Accounting 8 Computer 148 121 164 69,057 25,260 43,797 499 209 290 14,749 24,825
9. Office Occupations 173 176 172 53,268 12,892 40,376 403 115 287 19,869 24,370
10. General Secretarial 186 175 189 28,155 6,367 21,789 227 S6 171 20,036 24,164
11 Typing 142 142 143 108,238 7,307 100,931 661 60 601 17,385 23,990
12 Auto Mechanics 177 175 178 77,958 20,894 57,064 631 184 446 20,013 23,035
13. Drafting 184 185 184 52,976 3,670 49,306 403 36 367 18,736 24,805
14. Machining & Metals 184 150 196 54,872 15,273 39,599 450 132 317 17,371 24,556
15. Construction 156 144 193 21,719 16,305 5,414 204 157 47 14,946 22,141
16. Wood 187 207 187 66,609 1,981 64,627 523 18 505 23,474 24,121
17. Other High Tech. 179 170 185 103,066 41,848 61,219 855 378 477 18,921 23,719
18. Other 154 131 170 126,307 51,257 75,050 1,014 453 561 14,918 22,809
Total $170 $160 $174 983,059 276,053 707,006 7.7925 2,541 5,248 $17,540 $23,625

Source: CBEDS

Finally, teachers' salaries per daily student class hour is just total teachers' salaries divided
by total daily student class hours, and teachers' salaries per FTE is total salary divided by
total FTE.

Table 3 shows that students spend more than twice as much time in high school
vocational classes than in ROC/ROPs. High schools occupy more than 90 percent of
student class hours in typing, drafting, clothing, and wood. These four programs account
for one-third of student hours in high school vocational classes. ROC/ROPs occupy more
than 75 percent of student hours in construction, distributive education, and the two
nursing-related programs. These programs account for just less than one-fifth of the
student time spent at ROC/ROPS. So to some extent the ROC/ROPs and high schools
specialize in different programs, but mostly they overlap.

Differences Between High School and' ROC/ROP Vocational Teachers

ROC/ROPs generally spend less than high schools on teachers' salaries per student
class hour. The reason for the difference appears to be lower salaries per FTE teacher in
the ROC/ROPS, though the estimated numbers of FTE and salaries per FTE in Table 3 may
exaggerate the actual diffference. As explained above, the number of daily class periods
taught by a full—time teacher was not reported in the data, and therefore had to be imputed.
For high schools, the imputed number was the modal number of class periods reported by
full-time teachers in each district or school. For the ROC/ROPs, the number was simply
taken to be five. If it were six, the estimated total number of FTE teachers in ROC/ROPS
would be only 2,118, and the mean salary per FTE would be $21,048. Even so, the mean
full-time salary in ROC/ROPs would be less than in high schools. ROC/ROP salaries per
FTE are sufficiently lower that they offset a higher ratio of FTE teachers to student class
hours, resulting in the lower expenditure on teachers' salaries per class hour reported in
Table 3.

Some of the reasons why ROC/ROPS pay lower salaries are suggested in Table 4.
Their teachers have fewer degrees and less seniority. This places them lower on the salary
ladder. ROC/ROPs also have larger proportions of blacks, Hispanics, and women, whose
alternatives in the labor market would on average pay less than those of white males. A
much larger percentage of ROC/ROP staff are teaching part—time. Some may teach part-
time in the ROC/ROP and part-time in the high school vocational program, but the fraction
of part-time high school teachers is so small that no more than a third of the part-time
ROC/ROP teachers could also be teaching in high schools, and the actual number is
probably much less. More likely, part—time ROC/ROP teachers are spending the rest of
their working time in an occupation related to what they teach, though we have only
anecdotal evidence of this. The fact that ROC/ROP teachers on average have about seven
years less seniority in their teaching positions but are only a year and a half younger than
their high school counterparts also suggests that they have more experience in occupations

19

OZ

Table 4

Characteristics of California Teachers in Regional Occupational Programs (ROP) and Public
High School (HS) Vocational Classes, by Instructional Program, October 1981

 

 

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B as as as ass as as as as E sat: E as as as: E

1. Agriculture 5.2 8.8 .29 .40 .18 .01 35.7 36.8 .04 .01 .02 .03 .40 .07 .22 .12
2. Distrib. Ed. 5.0 12.3 .20 .42 .50 .02 39.4 41.4 .11 .06 .04 .04 .32 .04 .57 .33
3. Practical Nursing 6.6 4.5 .18 .04 .20 .05 42.7 32.9 .20 .11 .11 0 .14 .02 .92 .74
4. Nurse's Aide 4.8 9.0 .13 .36 .52 .09 43.0 46.7 .13 .38 .03 .03 .30 .07 .99 .87
5. Child Care 5.5 10.4 .24 .36 .18 .01 39.5 41.3 .03 .04 .05 .03 .37 .07 .97 .98
6. Clothing 5.9 12.9 .09 .26 .54 .01 45.5 42.4 0 .07 .14 .03 .28 .06 .93 .99
7. Food 4.7 11.0 .09 .28 .47 .01 39.9 41.1 .04 .06 .04 .03 .22 .06 .53 .97
8. Accounting 5 Computer 5.6 14.7 .23 .51 .40 .01 39.3 44.9 .10 .04 .09 .01 .27 .03 .61 .35
9. Office Occupations 6.8 13.5 .30 .46 .23 .02 40.3 43.8 .13 .06 .06 .03 .32 .03 .77 .56
10. General Secretarial 6.0 13.7 .33 .43 .16 .01 44.0 45.2 .02 .05 .04 .04 .40 .06 .91 .82
ll. Typing 6.1 14.0 .25 .49 .20 .00 43.1 44.6 .08 .07 .06 .03 .21 .04 .87 .55
12. Auto Mechanics 6.4 11.6 .13 .34 .43 .06 40.6 41.3 .05 .03 .08 .06 .25 .05 .01 .01
13. Drafting 5.9 15.9 .26 .51 .30 .01 40.7 45.2 .14 .02 .15 .04 .32 .04 0 .01
14. lmchining & Metals 5.7 13.9 .16 .41 .56 .02 42.0 43.7 .03 .02 .15 .06 .22 .04 .00 .00
15. Construction 4.7 10.5 .13 .33 .61 .11 43.5 43.9 .06 .02 .04 .08 .21 .04 .01 .05
16. Wood 8.6 13.7 .24 .41 .23 .02 41.1 43.0 0 .02 .01 .05 .57 .02 0 .00
17. Other High Tech. 5.7 12.8 .18 .42 .51 .07 41.4 43.5 .09 .04 .08 .05 .32 .05 .19 .04
18. Other 5.6 11.7 .13 .34 .60 .02 42.8 41.0 .09 .05 .12 .05 .25 .04 .49 .60
All Programs 5.6 12.7 .18 .40 .45 .02 41.0 42.6 .08 .04 .07 .04 .29 .04 .42 .36

Source: CBEDS

other than teaching. The ROC/ROP staff are less established in the teaching profession but
probably more established in the occupations they teach.

Table 4 reveals a virtual absence of female teachers in the "trades and industry"
programs: auto mechanics, drafting, machining & metals, construction, and wood. Almost
the opposite is true in the nursing, child care, and clothing classes. In office-related
programs-—typing, general secretarial, accounting & computer, and office occupations-oa
large majority of teachers are female. Sex stereotyping of teachers in the office and nursing
programs is more extreme in the ROC/ROPs than in high schools.

Segregation of Students by Sex

Sex stereotyping of students in office-related courses is also more extreme in the
ROC/ROPs than in high schools, as shown in Table 5. This table also shows that most
programs, in either high school or ROC/ROPs, are predominantly male or female, not
mixed. Given the high degree of segregation by sex, is there any resulting disparity in
resources for males and females? Table 5 indicates there is not, at least in teachers' salaries
per daily student class hour. This was computed for male students by adding up the total
expenditure for male class hours and dividing by the total number of male class hours. The
total expenditure for male class hours is the sum over class periods of the males' fraction of
the teacher's salary allocated to each class period.

For example, suppose there are 12 males and 8 females in an agriculture class for one
period a day, and the annual expenditure for that class period is $3,500 (say, one-sixth of a
full-time teacher's salary of $21,000). Then, since 60 percent of the students in the class
are male, the annual expenditure for male class hours in that daily class period would be 60
percent of $3,500, or $2,100. This is added to the amounts computed from other classes
to derive total expenditure for male class hours. That total is then divided by the total
number of male daily class hours, to yield annual expenditure per daily student class hour
for males.

Comparing the figures in Table 5 with those in Table 3 reveals that per-class-hour
expenditure for males is generally very close to the expenditure for all students. In spite of
the fact that vocational classes are highly segregated by sex, there is apparently no
systematic sex disparity in expenditure on teachers' salaries per student class hour.

Handicapped, Disadvantaged, and LEP Students

Comparable figures for handicapped, educationally disadvantaged, and limited-English
proficient (LEP) students are presented in Table 6. Data on these students are subject to
more error than data on males and females because the CBEDS Professional Assignment
Form asks teachers to report actual numbers of males and females in each class, but for the

21

ZZ

Table 5
Number of Daily Student Class Hours, and Expenditure on Teachers' Salaries Per Daily

Student Class flour, for Male Students in California Regional Occupational Programs (ROP) and
Public High School (HS) Vocational Classes, by Instructional Program, October 1981

Annual Expenditure on

 

 

Program Teachers' Salaries per Daily Student Male Student Class—Hours
Daily Student Class Hr. Class—Hours as 3 Proportion of Total
All All
Secondarx Egg _ߤ_ Secondary 39: HS 39: _ߤ_
1. Agriculture 188 209 181 42,378 10,379 31,999 .64 .69
2. Distrib. Ed. 172 168 181 9,400 6,061 3,339 .25 .41
3. Practical Nursing 313 245 341 681 195 486 .09 .37
4. Nurse's Aide 195 193 207 8300 730 100 .08 .06
5 Child Care 164 156 168 1,824 603 1,221 .08 .07
6. Clothing 169 144 173 908 126 782 .09 .03
7 Food 177 202 168 20,536 5,661 14,876 .48 .33
8 Accounting 5 Computer 151 128 163 21,103 7,029 14,074 .28 .32
9. Office Occupations 172 180 171 9,379 1,038 8,341 .08 .21
10. General Secretarial 164 133 171 2,082 333 1,748 .05 .08
11. Typing 141 127 142 28,337 538 27,799 .07 .28
12. Auto Mechanics 176 175 177 73,550 20,096 53,455 .96 .94
13. Drafting 183 177 184 46,820 2,909 43,912 .79 .89
14. Machining & Metals 183 151 196 53,672 14,918 38,754 .98 .98
15. Construction 156 145 193 20,205 15,360 4,845 .94 .89
16. Wood 187 207 187 62,293 1,891 60,403 .95 .93
17. Other High Tech. 179 168 185 78,275 29,356 48,919 .70 .80
18. Other 153 130 170 54,363 23,092 31,270 .45 .42
Total 174 161 179 526,637 140,314 386,323 .51 .55

Source: CBEDS

{Z

Table 6

Number of Daily Student Class Hours, and Expenditure on Teachers’ Salaries per Daily
Student Class Hour, for Handicapped, Educationally Disadvantaged, and Limited English

Proficient Students in California Regional Occupational Programs (ROP) and Public
High School (HS) Vocational Classes, by Instructional Program, October 1981

Annual Expenditure
on Teachers'

Handicapped

Salaries

Educationally Disadvantaged

Annual Expenditure
on Tcachers' Salaries

Limited English Proficient

Annual Expenditure
on Teachers'

Salaries

 

EEQBEEE per Daily Student Daily Student per Daily Student Daily Student per Daily Student Daily Student
Class—Hour Class—Hours Class—Hour Class-Hours Class-Hour Class—Heurs
A11 A11 A11 A11 A11 A11
Second— Second- Second— Second- Second— Second-
ELL— Bflii w__§9£_£2£1_393_1£ ELL—BE JLLBOJJE. L103 .11
1. Agriculture 188 209 173 3,509 1,511 1,999 189 233 177 7,856 1,744 6,112 175 183 170 1,480 534 946
2. Distrib. Ed. 184 185 183 895 699 196 178 162 204 2,519 1,529 990 170 163 193 795 620 175
3. Practical Nursing 460 247 494 305 41 264 462 262 505 321 56 265 475 291 497 291 30 260
4. Nurse's Aide 254 257 205 236 223 13 194 186 217 964 716 248 193 189 206 365 281 84
5. Child Care 172 171 173 957 364 593 174 163 176 3,449 691 2,758 166 159 170 653 282 370
6. Clothing 183 129 185 1,045 28 1,017 183 220 182 3,939 107 3,832 182 183 181 2,335 373 1,961
7. Food 179 201 167 3,079 1,051 2,028 178 213 169 8,306 1,708 6,597 169 195 163 2,127 423 1,704
8. Accounting 6 Computer 153 130 168 1,190 471 719 159 157 160 4,101 773 3,328 147 120 165 2,069 801 1,267
9. Office Occupations 170 157 176 1,245 384 861 173 191 170 6,557 759 5,798 168 181 165 1,824 348 1,475
10. General Secretarial 190 164 206 352 133 219 190 178 194 1,753 435 1,318 162 137 184 905 419 486
11. Typing 146 170 144 2,299 228 2,071 149 244 146 12,284 453 11,831 142 157 141 6,691 465 6,226
12. Auto Mechanics 181 171 185 2,722 769 1,953 177 178 177 8,687 2,065 6,622 166 158 174 3,795 1,960 1,835
13. Drafting 176 118 193 1,405 331 1,074 187 180 187 4,099 129 3,970 169 122 181 2,189 458 1,731
14. Machining 6 Metals 181 141 197 2,399 676 1,723 196 171 202 7,085 1,433 5,652 156 130 195 3,980 2,410 1,569
15. Construction 205 191 265 1.081 875 206 217 223 207 1,803 1,120 683 125 112 166 610 457 153
16. Wood 188 220 187 3,071 91 2,981 192 222 191 9,098 269 8,828 186 214 185 2,188 39 2,149
17. Other High Tech. 172 160 187 4,118 2,297 1,822 185 191 182 9,191 3,190 6,001 160 145 180 5,451 3,246 2,205
18. Other 154 138 174 4,956 2,784 2,173 170 152 177 12,020 3:266 8,754 154 117 181 4,627 1,946 2,682
Total 177 168 182 34,867 12,954 21,912 179- 186 177 104,033 20,445 83,588 162 145 172 42,376 15,095 27,280

Source: CBEDS

other categories of students it asks only approximate numbers: none, 1-4, 4-9, 10-20, or
more than 20. In Table 6 and other analyses here, the midpoints of these intervals have
been used as estimates of the actual numbers. Another source of greater error is in
determining whether students should be classified as handicapped, educationally
disadvantaged, or limited English proficient. If students have been formally identified for
purposes of categorical programs, teachers must remember how many. Otherwise they just
have to use their own judgment.

With these cautions in mind, Table 6 does show some patterns. Comparing the
numbers of student class hour with the total in Table 3 reveals that handicapped students
account for 3.5 percent, educationally disadvantaged 10.6 percent, and limited English
proficient 4.3 percent of all daily student class hours. Some students may be in more than
one category, so the total fraction of all class hours accounted for by these identified
students is something less than 18.4 percent. ROC/ROPS have a lower fraction of
educationally disadvantaged student class hours than high schools, but: this is almost
entirely offset by somewhat higher percentages of handicapped and limited English
proficient students in ROC/ROPs.

Handicapped, educationally disadvantaged, and limited-English-proficient students are
all under—represented in distributive education, accounting & computer, general secretarial,
and drafting programs. These are programs directed toward jobs in offices and retail
enterprises where verbal and face-to—face communications are relatively important. In
contrast, the agriculture, machining and metals, food, and wood programs enroll
disproportionately large numbers of the handicapped and educationally disadvantaged.

On the whole, Table 6 shows expenditure on, teachers' salaries per daily student class
hour is somewhat higher than average for handicapped and educationally disadvantaged
students. But it is lower than average for students with limited English proficiency,
especially in ROC/ROPs.

Table 7 shows the ratio of ROC/ROP to high school expenditures on teachers' salaries
per daily student class hour in each instructional program and for each category of student
Differences in relative expenditures between categories of students are generally consistent
across instructional programs. In most instructional programs, ROC/ROPs spend
relatively more on educationally disadvantaged students, but less on the handicapped and
those with limited English proficiency. The reason for these differences is not apparent.

Explaining Variation in Teachers' Salaries per Student Class Hour

To gain a better understanding of expenditure differences, regression analysis was used
to measure the association between expenditures and several of these other variables. The
unit of analysis in the regressions is an instructional program offered in a particular district
or ROC/ROP. One such local program offering, for instance, is the agriculture program in

24

Table 7

Ratio of ROP to High School Annual Expenditure on Teachers’
Salaries per Daily Student Class Hour, by Type of Student
and Instructional Program, October 1981

 

A11 Educationally Limited English
Program Students Males Disadvantaged Handicapped Proficient
1. Agriculture 1.11 1.15 1.32 1.21 1.08
2. Distrib. Ed. .93 .93 .79 1.01 .aa
3. Practical Nursing .55 .72 .52 .50 .59
4. Nurse‘s Aide .79 .93 -86 1.25 .92
5. Child Care 1.01 .93 .93 .99 .96
6. Clothing .86 .83 1.21 .70 1.01
7. Food 1.20 1.20 1.26 1.20 1.20
8. Account and
Computer .74 .79 .98 .77 .73
9. Office Occupations 1.02 1.05 1.12 .89 1.10
10. General Secre- 0
tarial .93 .78 .92 .80 .74
11. Typing .99 .89 1.67 1.18 1.11
12. Auto Mechanics .98 .99 1.00 .92 .91
13. Drafting 1.01 .96 .96 .61 .67
14. Machining and
Metals .77 .77 .85 .72 .67
15. Construction .75 .75 1.08 .72 .67
16. Wood 1.11 1.11 1.16 1.18 1.16
17. Other High Tech. .92 .91 1.05 .86 .81
18. Other .77 .76 .86 .79 .65
All Programs .92 .90 1.05 .92 .84

Source: Tables 3, 5 6.

l

25

district A, another is the distributive education program in district A, a third would be the
agriculture program in district B, and so on. There were 3,974 such distinct local
vocational programs in high schools in 1981, and another 746 in ROC/ROPs.

Table 8 shows the coefficients from three different regression equations for expenditure
on teachers' salaries per daily student class hour. Each equation was run separately for
high schools and ROC/ROPs. All regressions were run with data weighted by the number
of student class hours in the local program.

Columns I and IV in Table 8 list the coefficients from the first equations, in which the
only predictor was the type of instructional program. The coefficients here simply repeat
the information from the second and third columns of Table 3. For instance, in ROC/
ROPs, agriculture programs on average cost $70 a year more than "other programs" per
daily student class hour. Distributive education programs cost $40 more, and so on. The
numbers in columns I and IV are not always identical to those in the second and third
columns of Table 3 because some local programs were excluded from the regressions in
Table 8 due to missing data.

The first item of new information in Table 8 is that type of instructional program alone
accounts for only 11 percent of the variance in expenditure by ROC/ROPs, 10 percent in
high schools. So even though some of the average differences between types of
instructional programs are substantial, there is almost as much variation in expenditure per
class hour among local programs of the same type as there is between types.

When size of local programs, measured by number of daily student class hours, is
added to the regression, results are as shown in columns H and V of Table 8. Now 30
percent of the variance among ROC/ROP programs is accounted for, but still only 12
percent in high schools. The coefficient on size is negative: a difference of 100 more
student class hours per day translates into $0.77 less annual expenditure per daily student
class hour in ROC/ROPS and $0.55 less in high schools. This is partly the result of
spreading teachers' salaries over a larger number of student class hours.

The average local program in ROC/ROP occupied 371 student class hours a day,
compared to 179 in high schools. Given the coefficient on size in Table 8, this difference of
almost 200 daily student class hours accounts for a difference of about $1 to $1.50 in
annual expenditure per class hour between high schools and ROC/ROPS, which is about
one-tenth of the total difference shown in Table 3. Larger size therefore does account for
some, but not most, of the difference in cost between ROC/ROP and high school
programs.

Size accounts for a larger part of the differences among types of instructional programs
in ROC/ROPS. Differences by type of instructional program in column II of Table 8 are
typically less than half as big as in column I. Evidently a large part of the reason why some

26

LZ

Table 8

Regression Coefficients Describing Variation in Annual Expenditure on Teachers' Salaries per Daily
Student Class Ilour in California Regional Occupational Programs (ROP) and Public High School
- Vocational Classes, October 1981
(Numbers 1n parentheses are absolute values of coefficients divided by estimated standard errors.)

 

 

ROP High School
I II . III IV V VI
Difference from ”other"
instructional program:
1. Agriculture 700.48) 250.09) “(2.20) 140.41) 100.41) 130-25)
2. Distrib. Ed. 400.58) -4( .36) 100.06) 210.67) 160.04) 170.22)
3. Practical Nursing 1110.58) 660.38) 880.33) 1440.66) 1390.46) 1630.05)
4. Nurse's Aide 490.08) 4( .27) 240.80) 590.67) 570.56) 650.23)
5. Child Care 45(2.60) -3( .16) 7( .43) 5( .07) I( .19) 6(l.05)
6. Clothing 26( .67) —22( .63) 7( .21) 150.06) 110.29) 110.41)
7. Food 700.86) 240.83) 420.30) 0( .11) -4( .94) 0( .14)
8. Accounting 6 Computer -10( .90) -25(2.54) -18(1.9l) -5(1.27) -6(1.41) -12(3.06)
9. Office Occupations 470.32) 7( .58) 170.35) 4( .88) 3( .78) 0( .07)
10. General Secretarial “(2.31) -2( .11) .z.( .21) 200.87) 170.31) 13(2-59)
11- Typing 12( .65) -25(1.52) -13( .85) -26(8.11) -28(8.7l) -33(10.48)
12. Auto Mechanic: 450.81) 6( .55) 150.41) 100.66) 90.54) 100.83)
13, Drafting 40(1.64) -3( .15) 18( .87) 15(3.95) 15(3.83) 5(1.36)
14. Machining 6 Metals 190.44) -8( .67) 8( .72) 270.48) 250.96) 210.25)
15. Construction 12( .97) -6( .55) 120.13) 220.29) 170.77) 200.18)
16. Wood 790.40) 29( .99) 300.07) 190.25) 170.82) 130.81)
17. Other 400.16) 220.56 200.52) 160.32) 180.88) 160.39)
Base value for "other" 13100.91) 18207.01) 18003.59) 16908.76) 17509.95) 15903.61)
pro ram ..
No. dilly student class haurs -.0077(13.52) --0049(6-51) -.0055(10.01) .0036(5.44)
No. disadvantaged student
class hours ,0397(1_68) —.0019( .31)
Region: SF Bay 140.43) -16(5.53)
Central Valley -29(3.37) -14(5.12)
LA/San Diego -54(6.33) “ —22(8.24)
Rest of state - _
Proportion teachers mas-
ter's or more 280.98) “(4.08)
Proportion teachers no
bachelor's -13(l.20) 2‘ .25)
Proportion teachers part
(131.: 290.29) 260.59)
Teacher mean years in
district 1.70.91) 2.102.40)
R2 .11 .10 .41 .10 .12 .18
N 723 721 723 ' 3880 3880 3878

Source: CREDS.

types of instructional programs cost more than others in ROC/ROPs is that they average
fewer student class hours. In high schools, the association between srze and cost accounts

for less of the difference among types of instructional program.

Finally, adding other features of local programs--region of the state, characteristics of
teachers, and number of class hours attributed to educationally disadvantaged students--
brings the proportion of variance explained up to .41 in ROC/ROPs and .18 in high
schools. (The remaining variance is due to variation in class size, the number of classes
taught per day, and the level of the salary schedule.) Coefficients are listed in columns 111
and VI of Table 8. More disadvantaged student class hours are associated with higher
expenditure per class hour in ROC/ROPs, but not in high schools. (But recall that
ROC/ROPs have proportionately fewer disadvantaged student class hours than high
schools.) The Los Angeles/San Diego region has lower costs than other parts of the state.
Reasons for these differences are not readily apparent.

Easier to understand are the differences in salary cost associated with teachers'
characteristics. If a full-time teacher had 125 student class hours a day, and if the salary
schedule paid $250 for each additional year of service in the district, then the average
difference in annual salary per daily student class hour associated with an additional year of
teacher's senority would be $2, which is about what Table 8 shows. Since ROC/ROP
teachers on average have about seven years less seniority than high school teachers, this
would account for virtually the whole $14 difference between high schools and ROC/ROPs
in annual salary per daily student class hour. Likewise, the standard features of teachers'
salary schedules explain the coefficients on proportions of teachers with master's or
without bachelor's degrees. Since ROC/ROP teachers on average have fewer degrees than
high school vocational teachers (see Table 4), these coefficients account for roughly $7
more of the difference between high schools and ROC/ROPs.

It would appear that we have accounted for more than the total actual difference in
expenditure between high schools and ROC/ROPs-—except that another factor, the
proportion of part-time teachers, is much larger in ROC/ROPs (see Table 4). The
coefficients in Table 8 imply that this difference adds about $7 a year per daily student class
hour to ROC/ROPs compared to high schools. This roughly offsets the cost difference due
to teachers' educational attainments.

But why should teachers' salaries per daily student class hour be higher in local
programs where more of the teachers are part-time? One possible explanation is that some
districts, as a cost-cutting measure, have encouraged teachers who are near retirement to
take partial retirement, that is, working part-time while drawing some retirement benefits.
Since these teachers would be at or near the top of the salary ladder, such part-time teachers
would tend to raise the average salary per student class hour. This practice may also be
more common in districts where enrollment has declined especially fast; partial retirement
for older teachers is an alternative to laying off younger teachers. Since declining

28

enrollment increases cost per class hour by reducing the number of class hours, this may
further explain why part—time teachers are associated with higher cost per class hour. In
this scenario, higher cost per student class hour causes districts to put more teachers on
part-time.

An alternative explanation is that districts hire more part-time teachers when strong local
demand for certain skills makes it hard to find people who have those skills and who want
to teach full time. Local program administrators then not only must accept a part-time
commitment but also must arrange to have these teachers placed high up on the salary
ladder--perhaps by counting years worked in the trade as years of previous teaching
experience. This could create a positive correlation between part-time teachers and salary
per class hour.

With the evidence at hand, neither of these explanations can be ruled out. But two
considerations make the second somewhat more plausible. One is that the second
explanation more readily accounts for the difference between ROC/ROPs and high schools.
It seems more likely that ROC/ROPs hire more part-time teachers because they are teaching
skills for which there is high demand than that they are relying more than high schools on
partial retirement as a cost-cutting measure. Second, the high-demand explanation is also
consistent with the findings reported in Section IV: that students who have been in
programs with part—time teachers are themselves more likely to find early success in the
labor market.

Summary

Students spend nearly one million hours a day in vocational classes offered by
California public high schools and ROC/ROPs (regional programs). They spend more
than twice as much time in high school vocational classes as in ROC/ROPs. Typing,
drafting, clothing, and wood shop are taught mainly in high schools; ROC/ROPs offer
most of the instruction in nursing, retailing, and construction. However, most vocational
programs are offered in both places, though possibly at a more advanced level in
ROC/ROPS.

Comparing instructors in the same subject, the average full-time teacher's salary is
greater in high schools ($23,625) than in ROC/ROPS ($17,540), in part because high
school teachers have more advanced degrees and more years of experience as teachers.
However, ROC/ROP teachers apparently have more experience outside teaching, and more
of them currently teach only part-time.

Students in ROC/ROPs have more access to teachers. The average full-time teacher in
ROC/ROPS is responsible for only 109 student class hours per day, compared to 135 in
high school vocational programs. Despite this difference, ROC/ROPs keep annual
teachers‘ salaries per daily student class hour at $160, which is below the $ 174 average in

29

high school vocational programs. Lower salaries per full-time teacher are what keep salary
costs per student class hour lower in ROC/ROPs. In both high schools and ROC/ROPs,
programs that enroll more students, or that occupy each student for more hours each day,
tend to have lower salary costs per daily student class hour.

Vocational programs remain highly segregated by sex in both high schools and
ROC/ROPs. However, there appear to be no systematic differences between
predominantly male and predominantly female programs in the annual amount Spent on
teachers' salaries per daily student class hour.

30

IV. Outcomes of Vocational Education in
California Public High Schools

High Unemployment Among Recent Vocational Graduates

Since spring 1981 the California State Department of Education has conducted an
annual survey of students who were enrolled in high school vocational programs during the
previous school year. The survey is called FUSE: Follow-Up of Students and Employers.
Each year it includes districts enrolling about one-fourth of the vocational students in the
state. In spring 1982, questionnaires were mailed to 20, 035 students who had been in
high school--notROC/ROP--vocationa1 programs the year before. Of the 12,304 students
who returned questionnaires, 59 percent reported that they were employed. The
unemployment rate (the number of unemployed divided by the sum of the number
employed and the number unemployed among those who returned questionnaires was 26
percent.

About 70 percent of the 1982 FUSE sample had been high school seniors during the
1981-82 school year, and about 30 percent had been juniors. By the time of the survey in
spring 1982, most would have been near their 19th birthday. In comparison, employment
and unemployment among the whole California population are reported separately for 16
through 19 year-olds. In this somewhat younger comparison group, unpublished data
from the California Employment Development Department show that 39 percent of the
population was employed during the FUSE survey months of February through April
1982. The unemployment rate among the 16 through 19 year-olds statewide was 23
percent. ;

These numbers imply that the former high school vocational students are much more
likely to be in the labor force than are 16-19 year-olds statewide. The labor force
participation rate (sum of employed and unemployed, divided by population) was almost
80 percent for the FUSE respondents and only 5 3 percent for all 16- 19 year-olds in the
state. This is understandable since most of the state's 16—19 year-olds would still be full-
time students in high school, while most of the FUSE respondents would have graduated.
What is not so readily understandable is that FUSE respondents appear to be somewhat
less successful when they enter the labor market--their unemployment rate is three points
higher.

Both the higher rate of labor force participation and the higher rate of unemployment of
FUSE students may be attributable to lower socio-economic backgrounds and lower
academic achievement. National studies have consistently shown that vocational students
on average come from families with lower socio-economic status, and score lower on
achievement tests, than other high school students (Oakes, 1983; Meyer, 1981b).
Differences in socio-economic background are especially pronounced when the comparison

31

involves students who concentrate intensively on a vocational program, rather than
including all students who enroll in vocational classes (Campbell and others, 1981). Only
a few students in high school vocational classes are taking a serious, concentrated program
including advanced specialized courses which impart entry-level skills in a specific
occupation or well-defmed group of related occupations. These are the stringent criteria by
which districts in the FUSE sample were instructed to select students (FUSE Operations
Handbook, 1981). Accordingly, students selected for FUSE accounted for only about 15
to 20 percent of student class hours in vocational programs in sample districts during 1981-
82. Although FUSE did not compile or collect data on these students' socio-economic
backgrounds or academic achievement, it is likely that these are both lower than average.

Comparing FUSE respondents' experiences in the labor market with that of all 16-19
year-olds in the state, without controlling for socio-economic status and academic
achievement, therefore, may not give an accurate measure of what vocational education
accomplishes. Without vocational education, would these students' experiences have been
even worse?

Data on high school dropouts shed some light on this question. Like vocational
students, dropouts tend to come from families with low socio—economic status, and tend to
score low on achievement tests. If vocational education is effective, then students who
concentrate in a vocational program should do better in the labor market than high school
dropouts. In fact, however, data on California high school dropouts from the same year
show no advantage for vocational students.

The data on dropouts come from the national High School and Beyond (HS&B) survey
(Jones and others, 1983). The California part of the HS&B sample included 2,863
sophomores in spring 1980, of whom 231 were not enrolled in high school and had not
graduated two years later. Using the appropriate sampling weights gives an estimated two-
year dropout rate of 16.8 percent among this cohort of California sophomores. The
pr0portion of students who had already taken at least a year of courses in a vocational
program by the end of sophomore year was the same-~about one-third--among those who
later did and did not drop out. '

The 1982 HS&B follow-up asked dropouts what they had been doing the first week of
February 1982. Seventy-one percent indicated they were in the labor force, either
"working for pay at a full-time or part-time job" or "looking for work." Of those in the
labor force, 27 percent were unemployed, i.e., looking for work (Stern and others, 1985).
This compares with the 5 9 percent labor force participation rate and 26 percent
unemployment rate for FUSE respondents.

On the face of it, high school juniors and seniors who had taken advanced, specialized
vocational courses in 1981-82 were somewhat less likely to be active in the labor force in
1982 than were 1980 sophomores who had dropped out of high school. Among those in

32

the labor force, the unemployment rate was virtually the same. Although dropouts cannot
be considered a control group in the strict, experimental sense, they generally do resemble
vocational students in low socio-economic background and low academic achievement.
The comparison, therefore, makes it plain that concentrating in high school vocational
education does not give such students any sure route to quick success in the labor market.

Ifdata for dropouts and vocational students had been collected in exactly the same way
and at exactly the same time, the comparison probably would have looked even worse for
the vocational students. In order to be counted as unemployed, they had to affirm that they
were "actively" seeking employment, while the HS&B questionnaire asked dropouts only
if they were looking for work, which could mean just waiting passively and hoping for a
job to turn up. The narrower FUSE definition would produce a lower unemployement
rate. Similarly, the timing of the two questionnaires probably produced a lower
unemployment rate for FUSE respondents. They were asked their "current" employment
status at the time they filled out questionnaires-~in February, March, or April 1982. But the
HS&B questionnaire referred specifically to the first week of February 1982; in California
the unemployment rate for 16-19 year-olds was lower in March and April (21 and 22
percent) than in February, (25 percent), according to unpublished figures from the
Employment Development Department.

Finally, both surveys were affected by some response bias. Blacks and Hispanics
were under-represented, and whites were over-represented, among dropouts in the 1982
HS&B follow—up. Similarly, former students who returned the FUSE questionnaire
included larger proportions of whites and Asians, and smaller proportions of Hispanics and
program leavers, than the whole sample of former students who were selected by the
districts. The unemployment rates computed from both surveys are therefore likely to be
lower than the true rates. Whether the bias is worse for one survey than the other is
unknown. However, it is not likely that any difference due to response bias would alter the
conclusion. High school students specializing in vocational education started out no better
in the labor market than high school dropouts.

Variation in Labor Market Outcomes by District and Instructional Program:
Does Anything Work Consistently?

Given this evident lack of any positive overall effect on students' early experience in the
labor market, are there nevertheless some particular programs or districts where vocational
students do get off to a good start? If so, are there any characteristics that successful local
programs share? To answer these questions, 1981-82 FUSE data were merged with 1981
CBEDS data. Information about students from each instructional program in each FUSE
sample district was combined with information about teachers who taught that program in
1981-82, when the FUSE students were enrolled. The unit of observation in this merged
file is the local instructional program; for instance, the typing program in district A as
opposed to the agriculture program in district A or the typing program in district B. The

33

file contains 473 such local programs, located in 83 secondary or unified districts. Since
the file is based on FUSE, it contains no data on ROC/ROPs.

Table 9 displays data on students from these 473 local programs. Proportions are
weighted by number of students in the local program. The mean number of students in
local programs exceeds the median in all but one instructional area, indicating that in all but
one area there are a few local programs that are much bigger than the rest.

The biggest differences among instructional areas are in the proportion of female
students. Females are concentrated in programs related to health, home economics, and
office occupations. Males predominate in the "trades and industry” group. The proportion
of females in male-dominated areas is bigger than the proportion of males in female—
dominated fields. This is exactly the same pattern of segregation by sex that is evident in
data for the whole state (Tables 4 and 5).

There is not a strong correlation across instructional areas between the sex and ethnic
composition of students. Asians are somewhat concentrated in fields that are integrated
by sex. Relatively high proportions of blacks and Hispanics can be found in some male-
dominated and some female-dominated fields, for example, drafting, wood, and clothing.
But other fields that are segregated by sex have relatively low proportions of blacks and
Hispanics, for instance, construction and general secretarial courses. Except for the very
small program to train nurse's aides, no instructional area as a whole is less than 50 percent
white.

Table 9 also shows some variation across instructional areas in the proportion of
students who were seniors at the time they were enrolled in the program in 1981. Almost
all students who were not in grade 12 were in grade 11, though there were a few adults
(over age 18). There are some small differences among programs in the proportion of
students who completed the program. Most 1981 seniors would have graduated from high
school by 1982 whether or not they completed their vocational program. Most 1981
juniors would still be in high school in 1982 whether or not they had completed their
program of vocational courses.

Table 9 shows the median number of hours in each instructional program. They range
from one year (at one hour a day for 180 days) to two years.

These differences might be expected to result in different degrees of success among
students from different programs. Table 10 shows outcomes by program. Means and
proportions are weighted by number of students in the local program. The proportions
currently employed and currently in school are computed from the whole group of students
in the FUSE sample, whether or not they returned the 1982 follow-up questionnaire. The
proportion who did not return questionnaires is shown in the third column of Table 10.
Overall, the nonresponse rate was 39 percent.

34

SE

Table 9

Characteristics of Selected Students in Vocational Programs in Selected California
Public High Schools, by Instructional Program, 1981

 

Hedian
Mean no. Median no. Proportion Propor— contact
No. of of students of students Asian or Proportion Proper" tion white Propor- Propor- Propor- Propor- hours of
districts per district per district Pac. Islander black (hat tion (not tion tion tion tion com- instruc—

Program with program in each program in each program (excl. Filipino) Hispanic) Hispanic Hispanic) handicapped female grade 12 pleters tion

1- Agriculture 39 13 11 .01 .01 .11 .84 .10 .40 .59 .89 360

2. Distrib. Ed. 16 21 18 .06 .06 .16 .70 .05 .63 .76 .88 270

4. Nurse's Aide 3 64 84 .02 .84 .07 .07 .70 .94 .54 .98 374

5. Child Care 10 31 21 .01 .09 .37 .52 .20 .95 .74 .97 181

6. Clothing 6 37 32 .02 .17 .23 .57 .12 .98 .77 .91 265

7. Food 8 34 29 .02 .08 .27 .59 .16 .56 .75 .83 239

8. Accounting 6

Computer 53 47 23 .06 .04 .10 .76 .09 .65 .68 .88 225

9. Office Occups. 57 66 29 .04 .07 .18 .69 .13 .77 .71 .86 296
10. Gen. Secretarial 53 34 23 .05 .05 .13 .74 .08 .06 .71 .87 343
11- Typing 57 so 20 .05 .04 .13 .74 .08 .80 .63 .83 270
12. Auto Mechanics 29 53 31 .03 .05 .14 .76 .10 .04 .74 .89 350
13. Drafting 20 32 20 .04 .09 .23 .61 .15 .09 .66 .92 350
14. Machining 6

Metals 24 20 13 .03 .07 .22 .65 .16 .02 .71 .91 350

15. Construction 5 15 13 .03 .01 .18 .78 .03 .03 .66 .84 303
16. Wood 15 20 18 .02 .08 .27 .60 .10 .10 .74 .87 346
17. Other High Tech. 29 37 27 .05 .15 .17 .59 .13 .31 .79 .91 214
18. Other 49 3o 14 .02 .12 . .17 .66 .18 .40 .62 .91 357
\11 programs 83 39 20 .04 .08 .16 .70 .12 .59 .69 .88 325

Source: Follow Up of Students and Employers

92

Table 10

First-Year Follow-up Data on Selected Students from Vocational Programs in
Selected California Public High Schools, by 1981 Instructional Program, Spring 1982

 

EEQEEEE Proportion Proportion Mean Expected hourly Proportion of
Proportion of whole sam- unknown Unem- hrs/wk wage (employment employed who
currently ple employed employment ployment for those rate X say job is
in school (incl. military) status rate employed mean wage) related to training

1. Agriculture .37 .33 .44 .19 31 $3.29 .34

2. Distrib. Ed. .38 .41 .38 .24 31 3.21 .46

4. Nurse's Aide .53 .20 .30 .60 33 1.65 .52

5. Child Care .28 .27 .46 .33 32 2.55 .25

6. Clothing .36 .39 .36 .22 27 3.22 .27

7. Food .27 .33 .45 .24 ‘ 29 2.98 .35

8. Accounting 8 Computer .50 .41 .31 .23 27 2.99 .27

9. Office Occupations .35 .33 .42 .29 29 2.77 .40

10. General Secretarial .43 .45 .33 .21 28 3.24 .43
11. Typing .42 .35 .38 .27. 27 2.69 .29
12. Auto Mechanics .29 .35 .46 .24 33 3.42 .33
13. Drafting .46 .34 .35 .31 30 2.96 .20
14. Machining 8 Metals .28 .41 .38 .22 34 3.62 .37
15. Construction .26 .33 .47 .25 35 3.15 .25
16. Wood .26 .34 .44. .30 34 3.09 .19
17. Other High Tech .39 .35 .37 .24 30 3.19 .26
18. Other .32 .36 .42 .24 32 3.21 .35
All programs .38 .36 .39 .26 30 3.00 .33

Source: FUSE

Unemployment rates in Table 10 are computed in the conventional way: among those
who returned questionnaires, the unemployment rate is the number seeking work divided
by the sum of those employed and those seeking work. The expected hourly wage, for
those in the labor force, is the probability of having a job, multiplied by the average wage
of those who have jobs. The probability of having a job is the proportion of the labor force
who are employed, i.e., one minus the unemployment rate.

Finally, Table 10° shows the proportion of employed respondents who said their present
jobs were "directly or closely related" to their field of vocational training.

Table 10 shows that these outcomes definitely do differ among instructional programs,
and some programs appear to have consistently better outcomes. Former students from
agriculture, accounting and computer, general secretarial, and machining & metals reported
relatively positive outcomes on the whole, while former students from nurse's aide and
child care training were doing poorly. But Table 9 shows that three of the four relatively
successful programs all have smaller than average proportions of black and Hispanic
students (the exception is machining & metals), while the two programs with especially
poor outcomes both have larger than average proportions of Hispanics or blacks.

Residential segregation and discrimination in labor markets continue to put black and
Hispanic youth at a disadvantage, apart from what schools do. It would be important to
know whether schools mitigate or exacerbate the disadvantage. If the enrollment pattern of
Hispanics and blacks were changed, would these students achieve more or less early
success in the labor market? Unfortunately, existing data cannot answer this question
definitively. Without some actual experimentation, there is no way to be sure whether
black and Hispanic students are systematically placed in poorer programs, whether they are
actually enrolled in relatively good programs but still do worse because of residential
segregation and discrimination in labor markets, or whether the pattern of enrollment has
no effect one way or the other.

Some suggestive evidence, however, can be obtained by regressions of the kind
reported in Tables llA- 1 1C. (For these regressions, data were grouped by local program
and weighted by the square root of the number of students in the local program, to correct
for heteroskedasticity.) Former students from local programs where a large proportion of
students were black reported high rates of unemployment, small proportions of all
respondents employed, and lower expected wages compared to former students from local
programs where a large proportion of students were white. A large proportion of Hispanic
students were also associated with negative outcomes compared to local programs with
large proportions of white-—but the black-white difference is much more pronounced than
the Hispanic-white difference.

37

Table 11A

Regression for First-Year Outcomes
(Numbers in Parentheses are Absolute Values of Coefficients Divided by

Estimated Standard Errors)

Unemployment Rate

Proportion of Whole
Sample Employed

I II
Difference from "other"
instructional program:
1. Agriculture .01 ( .20) -.01 ( .37) -.07
2. Distrib. Ed. .04 ( .73) .02 ( .42) .08
4. Nurse's Aide .15 (1.76) .14 (1.69) -.04
5. Child Care .09 (1.70) .09 (1.61) —.08
6. Clothing .01 ( .23) .01 ( .16) -.001
7. Food .05 ( .80) .03 ( .53) -.05
8. Accounting & Computer .05 (1.62) .05 (1.54) -.01
9. Office Occupations .08 (2.73) .07 (2.44) -.04
10. General Secretarial .02 ( .63) .01 ( .42) .05
ll. Typing .07 (2.43) .07 (2.36) -.03
12. Auto Mechanics .06 (1.75) .05 (1.40) v.02
13. Drafting .08 (1.94) .08 (2.00) “.03
14. Machining & Metals .02 ( .39) .01 ( .33) .04
15. Construction. .05 ( .55) .04 ( .49) -.07
16. Wood .07 (1.33) .06 (1.23) —.01
17. Other High Tech. .01 ( .35) .00 ( .03) -.02
Base value for "other" program .50 (4.92) .45 (4.50) .27
Proportion students black
(not Hispanic) .05 ( .44) .07 ( .67) -.24
Proportion students
Hispanic -.16 (1.50) —.14 (1.37) —.07
Proportion students white
(not Hispanic) -.25 (2.68) —.25 (2.68) .003
No. disadvantaged student
class hours .0002 (1.71) .0002 (1.77) —.00009
Region: SF Bay .01 ( .37) .00 ( .16) .02
Central Valley .10 (3.50) .09 (3.27) —.05
LA/San Diego .00 ( .06) .00 ( .03) —.01
Rest of state _ r
Proportion students grade 12 -.13 (4.42) -.13 (4.22) .06
No. contact hours in local
program —.00003 ( .93) —.00003 ( .76) .0001
Proportion teachers with
master's or more -.01 ( .39) — - 04
Proportion teachers with-
out bachelor's .04 ( .53) — .08
Proportion teachers part
time —.11 (3.04) <— —.03
Teacher mean years in
district .002 (1.11) - .0002
Mean full-time teacher's‘
salary -.000002 (1.00) — .000004
Annual expenditure on
teachers' salaries per
daily student class hr. — .00008 ( .77)
Proportion of employed who
say job related to train-
ing, minus proportion not
R2 .32 .30 .21
N 428 427 444

Source: Follow Up of Students and Employers
California Basic Educational Data System

38

III IV
(1.63) - 06 (1.59)
(1.33) .07 (1.32)
( .46) .01 ( .17)
(1.42) -.08 (1.43)
( .02) .01 ( .16)
( .83) -.05 ( .80)
( .21) .00 ( .04)
(1.21) - 03 (1.14)
(1.53) .06 (1.78)
(1.10) — 03 (1.11)
( .58) -.02 ( .44)
( .64) -.02 ( .51)
( .90) .06 (1.36)
( .86) _.04 ( .46)
( .11) - 00 ( .05)
( .52) -.01 ( .39)
(2.58) .37 (3.58)
(2.16) -.24 (2.21)
( .64) -.07 ( .62)
( .03) .009 ( .09)
( .65)-.00004 ( .30)
( .51) .02 ( .71)
(1.65) —.05 (1.84)
( .40) 4.01 ( .41)
(2 04) .06 (2 04)
(2.69) .00008 (2.26)
(1.70) -
(1.13) —

( .67) —

( .15) -
(1.97) -

- —.0001 ( .99)

.20
443

Table 1113

Regressions for First-Year Outcomes

Difference from “Other"
instructional program:

1.
2.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
l5.
l6.
17.

Agriculture
Distrib. Ed.
Nurse's Aide

Child Care

Clothing

Food

Accounting 8 Computer
Office Occupations
General Secretarial
Typing

Auto Mechanics
Drafting

Machining & Metals
Construction

Wood

Other High Tech

Base value for "other" program

Proportion students black
(not Hispanic)

Proportion students
Hispanic

Proportion students white
(not Hispanic)

No. disadvantaged student
class hours

Region: SF Bay
Central Valley
LA/San Diego
Rest of state
Proportion students grade 12

No. contact hours in local
program

Proportion teachers with

master's or more

Proportion teachers with—

out bachelor's

Proportion teachers
time

part

Teacher mean years '
district

Mean full-time teacher's
salary

Annual expenditure on teach—
ers' salaries per daily
student class hour

Proportion of employed who
say job related to train—
ing, minus proportion not

R2
N

(Cont‘d.)

Proportion of Employed Who Say
Job Related to Training. Minus

Proportion Currently

 

Source: Follow Up of Students and Employers
California Basic Educational Data System

39

Proportion Not in SCh°°1
I II III Iv
-.12 (1.14) -.12 (1.08) .06 (1.54) .05 (1.28)
.26 (1.72) .25 (1.67) .07 (1.20) .06 ( .97)
.00 ( .00) .10 ( .43) .20 (2.21) .24 (2.76)
-.21 (1.41) —.21 (1.39) .01 ( .18) .01 ( .11)
-.28 (1.54) -.24 (1.33) .02 ( .30) .02 ( .34)
.11 ( .66) .14 ( .82) ..04 ( .64) -.05 ( .85)
-.14 (1.59) -.16 (1.88) .13 (4.07) .14 (4.32)
.10 (1.22) .07 ( .83) .04 (1.21) .04 (1.24)
.07 ( .82) .07 ( .81) .09 (2.80) .09 (2.76)
—.11 (1.33) -.14 (1.66) .08 (2.44) .08 (2.44)
- 02 ( 16) -.01 ( .13) -.01 ( .29) -.01 (_.36)
—.36 (3.13) -.38 (3.28) .12 (2.87) .13 (3.03)
.05 ( .38) .09 ( .73) —.05 (1.09) -.03 ( .77)
—.37 (1.60) —.32 (1.40) -.10 (1.13) -.07 ( .80)
-.30 (2.20) —.31 (2.22) -.06 (1.08) -.05 (1.00)
—.24 (2.37) -.24 (2.35) .11 (2.87) .11 (2.83)
- 43 (1.55) -.42 (1.53) .39 (3.75) .46 (4.43)
.23 ( .78) .27 ( .90) -.14 (1.24) -.13 (1.20)
.05 ( .19) .11 ( .37) -.1o ( .91) -.10 ( .92)
.10 ( .38) .10 ( .38) -.04 ( .43) —.04 ( .46)
-.00007 ( .19) -.00006 ( .16) -.00004 ( .27) .00001 ( .07)
.11 (1.43) .07 ( .95) .07 (2.46) .08 (2.70)
.21 (2.74) .19 (2.45) .00 ( .02) —.01 ( .28)
.16 (1.99) .09 (1.16) .03 ( .92) .04 (1.28)
.01 ( .15) .005 ( .06) -.14 (4.65) -.14 (4.55)
.0002 (2.42) .0002 (2.03) .00 ( .19) .oo ( 12)
-.18 (2.88) - -.02 ( .67) -
.13 ( .69) - .11 (1.45) -
.08 ( .81) — -.11 (3.00) —
.00007 ( .02) — .009 ( .58) —
—.00 ( .03) - .000002 (1.17) —
- —.0001 ( .39) — — 00006 ( .54)
.19 .16 .25 .23
425 424 444 443

Table 11C

Regressions for First-Year Outcomes

Expected Hourly Wage

 

Difference from "other"
instructional program:

1. Agriculture -.20
2. Distrib. Ed. -.25
4. Nurse's Aide -.72
5. Child Care -.85
6. Clothing -.38
7. Food -.68
8. Accounting 8 Computer —.64
9. Office Occupations -.71
10. General Secretarial -.40
11. Typing -.76
12. Auto Mechanics -.18
13. Drafting -.44
14. Machining & Metals .11
15. Construction -.44
16. Wood -.22
17. other High Tech -.18

.Base value for "other" program 1.56

Proportion students black

(not Hispanic) -.28
Proportion students
Hispanic .75
Proportion students white
(not Hispanic) 1.03
No. disadvantaged student
class hours —.0017
Region: SF Bay .29
Central Valley —.33
LA/San Diego .24
Rest of state
Proportion students grade 12 .98
No. contact hours in local
program .0003
Proportion teachers with
master's or more .02
Proportion teachers with—
out bachelor's —.22
Proportion teachers part
time .50
Teacher mean years in
district -.011
Mean full-time teachers'
salary .00002

Annual expenditure on teach—
ers' salaries per daily
student class hour

Proportion of employed who
say job related to train-
ing, minus proportion not

R2 .35
N 416

Source: Follow Up of Students and Employers

(Cont’d.)
( .89) -.08
( .82) -.17
(1.49) -.63
(2.74) -.81
( .97) -.34
(1.99) -.59
(3.54) -.61
(4.22) —.66
(2.20) -.37
(4.43) -.74
( .91) —.12
(1.88) -.45
( .46) .13
( .95) -.39
( .76) -.20
( .89) ~.12
(2.75) 1.92
( 47) -.45
(1.27) .66
(1.97) 1.04
(2.27) —.0017
(1.76) .33
(2.06) ~.31
(1.47) 25
(5.87) .95
(1.62) .0003
( .14)
( .55)
(2.34)
(1.26)
(1.55)
-.0004
.33
415

California Basic Educational Data System

II

( .37)
( .54)
(1.36)
(2.62)
( .86)
(1.73)
(3.43)
(3.95)
(2.00)
(4.36)
( .61)
(1.92)
( .55)
( .85)

(3.43)

(.75)
(1.12)
(1.98)

(2.22)
(2.08)
(1.97)
(1.66)

(5.68)

(1.52)

III

-.35
-.72
-.78
-.31
~.73
—.58
-.75
-.43
-.72
-.18
-.31

.08
-.31
—.10

1.71

.77

-.0017

.24
-.41
.18

.96

.0002

.08

.47

-.011

.00002

.38

.37
416

(1.

(2.
(1.
(2.
(l.

(5.

(3.

.76)
.14)
.52)
.54)
.80)
.16)
.28)
.52)
.40)
.25)
.92)
.34)
.35)
.67)
.34)
.46)
.05)

.59)

.32)

96)

29)
49)
59)
ll)

86)

64)

When these racial-ethnic proportions and the other variables listed on the left side of
Tables llA-l 1C are statistically controlled, the instructional programs that appear relatively
effective or ineffective are not the same as in Table 10. Students from agriculture programs
still appear to do relatively well in the labor market, as do students from machining &
metals. Now, in addition, the "other high tech" and "other" programs are also associated
with relatively good labor market results. Students from the child care program again
appear to do especially poorly. But now accounting & computer programs seem also to be
associated with generally unfavorable labor market outcomes, as do office occupations,
typing, and drafting.

The four relatively successful programs together include a larger proportion of males
than the five relatively unsuccessful programs, but not all instructional programs in the
relatively successful group are predominantly male, and not all of the relatively
unsuccessful programs are predominantly female. (Because sex composition is so highly
correlated with instructional program, it was not included as a separate variable in the
regressions.) There is virtually no difference between the group of four relatively
successful and the group of five relatively unsuccessful programs in the proportion of black
or Hispanic students.

These results suggest that the existing distribution of males and females among
instructional programs may be helping males, but the enrollment pattern by race or ethnicity
appears neutral in its effect on early labor market outcomes. However, this finding is only
suggestive because the coefficients in Tables llA-l 1C were estimated for the existing
enrollment pattern, and if the enrollment pattern changed, the estimated coefficients might
change too.

Some definite variation by region of the state is apparent in Tables llA—l 1C. Students
in the Central Valley experienced a relatively high unemployment rate and obtained
relatively low expected wages. (The Central Valley here was defined as the counties of
Amador, Calaveras, Colusa, Fresno, Kern, Kings, Madera, Riverside, Sacramento, San
Bernardino, San Joaquin, Tulare, and Tuolonme. The San Francisco Bay region included
Alameda, Marin, N apa, San Mateo, Santa Clara, Solano, and Sonoma counties. Los
Angeles, Orange, San Diego, and Ventura counties made up the Los Angeles-San Diego
region. The rest of the state consisted of Butte, Imperial, Lake, Santa Barbara, Santa Cruz,
Shasta, Siskiyou, and Trinity counties. Not all counties in the state were included in the
FUSE sample.)

Students from local programs with larger proportions of seniors did generally better in
the labor market They were also less likely to be still enrolled in school during the follow-
up year. These are not surprising results.

Also not surprising—and possibly reassuring-are the coefficients on contact hours.
Students from local programs with more contact hours of instruction were more likely to

41

report that their jobs were related to their training. They also reported somewhat lower
unemployment rates and higher expected wages, but these coefficients are small relative to
their estimated standard errors.

In one regression, reported in column III of Table 11C, the proportion who said their
jobs were related to their training was included as a predictor of expected wage, and the
coefficient was positive. This is consistent with findings by Rumberger and Daymont
(1982). The immediate payoff from high school vocational education is greater for
students who find jobs related to their training. But as Table 8 showed, only one of three
employed respondents from the FUSE survey did have such jobs.

Finally, Tables llA-l 1C include several measures of teachers' characteristics from
CBEDS. Only one of these, the proportion of teachers who are part-time, is strongly
associated with first-year outcomes. Students from local programs with more part-time
teachers report lower unemployment rates and higher expected wages. They are also less
likely to be continuing in school. One possible reason for this pattern is that many part-
time teachers are spending the rest of their time in the occupations they teach, so they are
more effective in steering students into those occupations. However, the coefficient on
part-time teachers in column I of Table 11B does not indicate a strong association with
placement related to training. Another possible explanation, as mentioned in the section on
salaries, is that administrators hire more part-time teachers when generally strong demand
in the local labor market makes it difficult to recruit and retain fullutime teachers. The same
generally strong demand would also produce the relatively positive outcomes for students,
even if they found jobs outside their field of training.

Other characteristics of teachers are not strongly associated with first-year outcomes.
Tables llA-llC show coefficients on teacher characteristics that are small relative to their
standard errors. When expenditure on teachers' salaries per daily student class hour,
which depends on class size as well as teachers' salaries, is used instead of the teachers'
characteristics, the coefficients are again small relative to their standard errors. It is
possible that some errors occurred in merging these CBEDS data with the FUSE data,
since teachers may not have written assignment codes on their CBEDS forms that would
match them with the right students from FUSE. Whether for this reason or because there
really is no strong relationship between expenditures and outcomes, the evidence at hand
does not reveal that money for vocational education is being spent in a way that maximizes
students' early success in the labor market.

Dropout Prevention

In California, data from the High School and Beyond survey (Jones and others, 1983)
can be used to test whether students who take more vocational classes are less likely to
drop out. One test, which controls for sophomores' self-reported expectation of graduating
from high school, is reported in Table 12. Students who, as SOphomores, did not clearly

42

Table 12

Percentage of 1980 California Sophomores Who Had Dropped Out
in 1982, by Number of Vocational Courses Taken Through
Grade 10 and Whether in 1980 They Expected to Graduate

(Percentages are based on sample sizes in parentheses,
but computed from data weighted by sampling weights)

Combined years of course
work in business, trade,
technical, or other vocational

n f 1 D x utfrmhihch 19

yes m rn

none 10.4% 29.0%
(668) . (53)

1/2 or 1 year 6.5% 48.6%
(660) (49)

1 1/2 years or more 7.2% 50.0%
(385) (44)

Source: High School and Beyond data

43

affirm that they expected to graduate from high school were, in fact, more likely to have
dropped out two years later. This is clear from comparing the two columns of Table 12.
Among sophomores who expected to graduate, those who had taken more vocational
classes by the end of 10th grade were less likely to drop out. But among sophomores who
did not expect to graduate or were not sure, those who had taken more vocational classes
were more likely to drop out. Apparently, in this sample, vocational classes may have kept
some students interested in high school, but students who were already thinking of
dropping out when they were sophomores did not become more motivated to stay in school
by having taken more vocational classes. (Further analysis of vocational education and
dropouts in California is in Stern and others, 1985.)

We must repeat that nonrandom selection of students into vocational education makes it
difficult to measure the effect of vocational classes on dropping out. Students may be
selected into vocational classes for reasons that also make them more-~or less--likely to
drop out. Nevertheless, it seems safe to-conclude that, while some students find vocational
classes more interesting than other classes and are more likely to remain in high school
because those classes are offered, vocational education is not generally effective in retaining
California high school students who are most prone to drop out.

Summary

California high school students who concentrated in vocational subjects during 1981
had a 26 percent unemployment rate in Spring 1982. In that same spring, the
unemployment rate for all 16 to 19 year-olds in the state was 23 percent Also that spring,
a sample of California dropouts from the high school class of 1982 had an unemployment
rate of 27 percent. Evidently, high school vocational training did not give students any
relative advantage in finding jobs after they graduated.

Available evidence also did not reveal that vocational classes are effective in retaining
students who have doubts about finishing high school.

Controlling for geographic location and for racial or ethnic composition of students,
vocational programs in agriculture, metal work, and certain other subjects do appear
relatively successful in improving graduates' prospects for employment and earnings.
Generally, earnings tended to be higher if graduates found employment related to their
training.

Measured characteristics of vocational teachers were generally not related to labor
market outcomes for recent graduates. The only exception is that graduates from programs
with more part-time teachers did better in the labor market. The reason may be that
programs in high-demand areas have more success placing graduates and also more
difficulty hiring full-time teachers.

44

On the whole, vocational classes as currently offered in California comprehensive high
schools are not demonstrably effective in helping students find jobs after they graduate, or
in retaining would-be dropouts. Furthermore, there is no evident way in which
reallocating resources among existing high school vocational programs would bring about
much improvement in labor market outcomes for graduates. How high school vocational
education might be reconstituted in a more fundamental way is the subject of the next
section.

45

V. Reconstituting Vocational Education in
California Public Secondary Schools

Education and Work: Why Are They Separated?

A major paradox underlies high school vocational education. Schools are supposed to
help prepare students for work, yet they are designed to keep students out of the job
market. Vocational and other classes give students "school work," but virtually no chance
to produce anything of use or value to someone other than themselves. Of course, what
they learn from school work should be useful or valuable to students themselves, at least in
the future. But the enforced self-centeredness of the student role bothers some teenagers,
like the high school junior quoted in the Boyer (1983) report who said her classes were

pretty boring, but then I suppose that's the way school classes are supposed to
be... This year I've been working at McDonald's so I can buy some new clothes
and a stereo set. The work isn't all that hard or exciting, but still it makes me feel on
my own and that I'm an adult person, that I'm doing something useful. In school,
you never feel that way. Not ever. (p. 202)

This is not an unusual reaction. In 1980 the High School and Beyond survey estimated
that a majority of seniors nationwide considered their present or most recent job "more
enjoyable than school" (Jones and others, 1983b, p. 849).

As a method of preparing them for work roles, barring students from activity that
produces something useful for other people makes as little sense as training musicians
without ever letting them produce music. Separating education from production is also not
an effective method of academic education, as John Dewey persistently pointed out. The
lack of immediately useful applications reinforces teenagers' sense of separation between
school and the "real world." This could well be an important reason for the widespread
lack of interest in school work among high school students, which high school teachers
and principals consider their biggest problem (Goodlad 1984, p.72; Abramowitz and
Tenenbaum 1978, p. 86). The fact that high school students typically report spending as
much time watching television during one weekday as they Spend on homework during the
whole week (Jones and others 1983, pp. 8—18, 8-33) likewise suggests little motivation to
do school work.

In spite of these and other sad statistics, and in spite of all the ink spilled on the subject
of school reform, there has been little interest in confronting the basic paradox itself. For
instance, the otherwise excellent analysis of Hi h ch 1 and th ban in W r lac by
a committee of the National Academy of Sciences (1984) did not question the basic
institutional split between education and production. Where this separation has been
recognized as a problem, the remedy usually suggested has been to let students spend some
of their "school time" off campus getting "real world” experience (Boyer 1983, O'Toole

46

1977, President's Science Advisory Committee 1973). This is like allowing music
students to play their instruments only outside class. It is better than not letting them make
any music at all, but it would still leave them to wonder why no music in music class.

Why no practical applications in high school?

There is an obvious historical explanation. In the United States the transformation of
the high school from an elite to a "mass" institution, the enforcement of compulsory
schooling and child labor laws, and the passage of minimum wage laws all occurred with
the transition from a predominantly rural and agricultural to a predominantly urban and
industrial economy, in the period from roughly 1890 to 1935. As the hierarchy of jobs in
the industrial economy took shape, schools were seen as places to keep children safe from
the dangers of low-level work in factories and sweatshops, as well as to keep children from
competing for jobs against adult men, and also to nourish hope for able children to rise into
the ranks of managers and professionals. In spite of John Dewey and others, the high
school remained organized on the classical, subject-centered model that prevailed when it
was still an elite institution. It is still organized that way, in large part because most
colleges and universities are. So today, as Sizer (1984) put it:

"Taking subjects" in a systematized, conveyer-belt way is what one does in high
school. The adolescents are supervised, safely and constructively most of the
time, during the morning and afternoon hours, and they are off the labor market.
That is what high school is all about. (p.83)

Yet, around the edges of "school time," during summers or after school, most high
school students now do manage to get into the labor market. Recent surveys in the United
States have found that 50 to 60 percent of high school students are holding paid jobs at any
given time, and 80 or 90 percent of the seniors have held at least one paid job at some time
during their high school years (Lewis and others, 1983; Lewin-Epstein, 1981). In fact, a
careful analysis by Meyer and Wise (1982) of longitudinal data on high school graduates
found that the amount of part-time work experience while in high school was more strongly
correlated with later success in the labor market than was the number of vocational classes
taken.

However, we do not conclude, as Meyer and Wise do, that providing more part-time
jobs for high school students is a better way to prepare them for work than continuing to
provide vocational classes. For one thing, Meyer and Wise themselves note that their
finding might be attributable to self-selection of students--the more employable students
getting more part-time work, and the less employable taking more vocational classes--rather
than to any changes that work experience or vocational classes may actually cause in the
students' prospects for success. Furthermore, a series of studies by Ruggiero,
Greenberger, and Steinberg (1982; also Greenberger and Steinberg, 1981; Steinberg 1982)
has raised serious questions about whether adolescents' part-time work experience always
produces beneficial effects. Instead of developing productive skills and positive attitudes,
these studies found that some jobs give students practice in stealing and malingering, and

47

reinforce cynical attitudes toward work. Finally, vocational classes and work experience
are not necessarily alternatives. There are many exatnples of programs that combme both.

We do recommend that the state continue to sponsor vocational classes in high schools.
But we also recommend that the purpose of these classes should be to increase the
productivity of all students throughout their entire working lives-onot to train only the
less bookish students for specific entry-level jobs. The institutional. split between education
and production makes it difficult for high schools to train students for entry-level jobs, and
the evidence shows that, in fact, high school vocational classes have not been effective in
doing it. Rather than persist in trying to do what they are not well situated to do, we
recommend instead that high schools reaffirm the broader purpose of vocational education:
to prepare all young people for a working life during which they will continually have to
think, learn, and communicate. This implies integrating education and productive activity
within the school program.

Reintegrating Education and Work in the High Schools

Recommendation I : To prepare voung geogle for a working life of continual learningI
groblem solvingl and communicating, we recommend that high school vocational education
become what we call ”enterprise training."

This would incorporate the following five features:

I. combine production with education. Students should engage in producing goods

or providing services that have value or use to someone other than themselves. Some
vocational classes already do engage students in such productive activities as running a
restaurant, building a house, or operating a child care center. There is evidence that these
school-based enterprises can provide work experience of high quality (Stern 1984). We
recommend that the scope of these productive efforts be expanded beyond the current range
of activities, which ostensibly are designed to prepare students for entry-level jobs. For
example, students can run recycling centers, tutor younger children or students of their
own age, assist elderly shut=ins, rehabilitate houses, plant trees, clear streams, record oral
histories, publish books or periodicals, produce programs for radio or television, do
feasibility studies of proposals to conserve energy or water, help local communities prepare
against major earthquakes, conduct agricultural experiments, build and repair furniture for
schools and other public agencies, and many more things. The National Commission on
Resources for Youth, a nonprofit organization, has documented ”thousands" of projects
like this (Kohler 1981). According to Rosenfeld (1984), combining education with
production has been an important feature of traditional programs in vocational agriculture.

Not all such activities can be conducted on campus. Schools may choose to send
some students off campus, under supervision of school staff, or by arrangement with other

48

agencies. If students are placed off campus, they should be sent in groups of two or more
(see below).

2. Include all students in vocational education. There is a great deal of merit in

Boyer's (1983) call for a "new Carnegie unit" of service to be required of all high school
students. Participation in the new vocational education we are describing would
accomplish much the same thing. Either by providing strong incentives or by direct
mandate, the state should induce local districts to include a year of vocational education in
the requirements for high school graduation.

3. Wm. One of the absurdities that result from the separation of
education and production is this: in school, students are evaluated entirely on individual
performance, but in most workplaces success depends on people working together. In
small organizations, rigid separation of responsibilities is not possible. In large
bureaucracies it is increasingly recognized as undesirable.

A 1982 survey by the New York Stock Exchange found that, in US. corporations
employing at least 100 people, 54 percent of employees worked in companies that had
adopted some kind of program to encourage more sharing of responsibility, for instance,
quality circles, job rotation, or participatory goal-setting (New York Stock Exchange,
1982). Robert Reich (1983) has argued that more collaboration and sharing of
responsibility is inherent in the kind of "flexible-system production" on which the US.
economy will have to rely in order to remain competitive. Yankelovich and Immerwahr
(1983) present evidence that such changes are necessary to accommodate a new work ethic
among employees.

For these reasons the National Academy of Sciences (1984) report on High Schools
md the Changing Workplace. the Emplovers' View included skill in "interpersonal
relationships" among the "core competencies" that high school graduates need. This
includes the ability to "participate in reaching group decisions," "handle conflict maturely,"
"offer and accept criticism constructively," and "demonstrate respect for the opinions,
customs, and individual differences of others" (p.25).

Students cannot learn these things by instruction and example alone. They must
practice. To provide practice in teamwork, students should be assigned some tasks in
groups of two or more. In vocational classes that operate productive activities on campus,
this tends to happen naturally. But if students are placed off campus as part of their
vocational education, sending and supervising them in groups will represent a deliberate
departure from the usual procedure in work experience programs, where students have
individual placements.

4. Integrate vocational and academic education. Since we are proposing that the main
purpose of vocational education should not be to train for specific entry-level jobs, it

49

should become easier to break down the barriers between academic and vocational courses.
There are many conceivable combinations that might excite both students and teachers, for
example, agriculture and biology, chemistry and photography, drafting and geometry,
physics and auto mechanics, food services and foreign languages, distributive education
and English, child care and psychology, accounting and economics, carpentry and history,
machine shop and algebra, and more! Through practical application, theoretical ideas can
come alive for students. Vocational education should no longer be seen as another set of
subjects competing for students‘ time. It should be a set of activities that help students use,
understand, and appreciate what they are learning in other courses. This kind of vocational
education will increase students' long-term productivity as workers by encouraging them to
understand the theory underlying the work they do.

Such theoretical understanding has a very practical importance for people at work, as
illustrated by the 1983 strike of telephone workers during the scheduled breakup of AT&T.
The key issue in that strike was employment security. The union, Communications
Workers of America (CWA), eventually won a set of provisions to protect workers against
loss of employment due to organizational and technological change. One important clause
provides training that is " generic in nature as opposed to job specific" (World of Work
Remit, December 1983, p. 93). An example of such training was provided by a program
started in 1981 by CWA Local 7201 and the local telephone company in St. Paul,
Minnesota. That program offered 400 hours of training in such basics as the theory of
semiconductors and circuitry (Business Wgek, September 5, 1983, p. 33). Clearly,
telephone workers and their union have realized that employees need some theoretical
understanding of their work in order to remain productive and employable in the long run.

5. Encourage astive inquiry. Engaging students in productive applications of
theoretical information should imply that students conduct active inquiry. We want to
emphasize this by making it explicit. Schools often reward students for performance of
assigned tasks in which students exercise little discretion, initiative, or imagination.
Students are tested on retention of information selected by the teacher, and on solution of
problems formulated by the teacher or the textbook. All this is necessary, but it is not
sufficient to develop active learners. Vocational education should give students practice in
asking their own questions. This is an essential part of learning to learn.

The National Academy of Sciences committee

attempted to project the future of the American job market to determine the sort of
worker who will prosper in the future. It has asked its employer members to
describe the employees they will need, and be able to employ, in the years ahead.

A single answer comes from both sources: a person who is able and willing to learn
throughout a working lifetime. (p. 17)

The committee further specified the following required skills, among others, in reasoning
and problem solving: "identify problems. . .adjust to unanticipated situations. . .work out

50

new ways of handling recurring problems. . .determine what is needed to accomplish work
assignments" (pp. 20-21). If earning a living is increasingly going to require continual
learning, then vocational education should increasingly emphasize learning to learn.

Teaching Job-Specific Skills in ROC/ROPS

If high schools stop trying to teach specific skills for entry-level jobs, where will young
people get this kind of training?

Recommendation 2: §hitt training of specific skills for enm-level iobs in total to
RQQ/RQPS.

Demonstrably, ROC/ROPs have the greater capacity to provide training of specific
skills for entry-level jobs. It would also be good if more ROC/ROP courses in the northern
part of the state could be given in the late afternoon, evenings, weekends, and summers.
This would allow students who seek job-specific training to get it without having to
sacrifice part of their high school day.

Recommendation 3: With regard to skill traininty at the ROC/ROPs. we recommend

changing the basis of accountability to the creation of a iob-readv student.

Presently, the chief criterion of success for school programs that teach specific job
skills is placement of graduates in jobs. (Continuation of graduates into further education
also counts as a success.) There are several things wrong with job placement as a criterion:
(a) It emphasizes a result over which vocational educators have very imperfect control. A
good vocational program will produce skills up to a standard for employment, but it cannot
guarantee a job in a bad labor market. (b) The criterion places excessive emphasis on
getting a first job and not in building capacity for a good career. (c) Under the practice of
statistical discrimination, the criterion discourages vocational educators from enrolling low
income and minority youth, for it is assumed that those students will have difficulty in
entering the labor market. (d) The data requirements of the criterion are beyond the
capacity or inclination of vocational educators to reach.

 

Accordingly, we propose that the criterion of accountability become simply the creation
of a "job-ready" student. Some ROC/ROPs have already begun to adopt competency‘
based curricula, awarding students certificates that show exactly what skills they mastered.
Further development of competency-based instruction has occurred under the federal Job
Training Partnership Act (JTPA), which directs each local Private Industry Council (PIC)
to develop sets of competencies for its youth training programs. Programs are responsible
for developing competencies in at least one of four major areas:

Pre—Emplovment Competencies: basic awareness of the world of work, including
familiarity with a variety of career options, the level of education required to
pursue each, and the likely income that can be expected from each; an

51

understanding of one's own preferences, interests, and aptitudes; basic surVival
skills, including how to open a checking and savmgs account, rent an apartment,
obtain a social security card, make knowledgeable purchases of Mac consumer
items, and so forth; job search skills, including preparing .a resume, knowmg
where to look for job opportunities, filling out an application, and being
interviewed.

W: demonstrated abilities to meet employers'

expectations of basic responsibilities, such as regular and punctual attendance,
proper dress, ability to carry out instructions, ability to work With others, and so
forth.

Wm: skills in reading, writing, computing, and
communicating needed to function successfully in the workplace, with an
emphasis on demonstrated ability to apply these skills in real work Situations.

1912 Specific competencies: basic and advanced skills required to perform

effectively in a chosen occupation or cluster of occupations.

Each program must develop a set of specific, measurable competency statements along
with appropriate "benchmarks" that establish an acceptable level of performance for each
competency. Successfulcompletion of the program, therefore, depends entirely on
reaching the benchmarks that have been established for it, and the effectiveness of a
program can be assessed by the number of successful completions it achieves. To these
four, we also recommend adding a fifth "outcome" indicator, measures of programs'
accessibility by minorities, the handicapped, the disadvantaged, and men and women
enrolled in program areas not traditionally chosen by their sex.

Along with these major recommendations, we propose some more specific,
supplemental changes to enhance the entrepreneurial operation of ROC/ROPS.

Recommendation 4: The state should establish a fund to match glftsfrom emplovers

dollar-tor-dollar.

This would mean, of course, that gifts in kind would need to be appraised to establish a
dollar value, but this is not an insurmountable problem.

Recommendation 5: High school students should be allowed free passage across
ROC/ROP attendance boundaries. with the result that thev would have greater choice of

270 gram.

Presently, adults and out-of-school youth are allowed to do this but students enrolled in
high school are not.

52

Recgmmendation Q: :2 he state should establish a system of incentive awards: relative tg the
number at [ob-readv youth that a given RQQ /R§2P graduated eagh vear.

Indeed, the state could make one payment at the time of enrollment of a student and
another at the time that the student attained job readiness. An extra bonus could be granted
for the attainment of job readiness by low-income and minority youth.

Summary

We propose fundamental changes in vocational education at the secondary level.
Comprehensive high schools should stop trying to provide skill training for entry level
jobs-a task they are not well situated to do—and instead should use vocational education to
prepare young people for a working life of continual learning, problem solving, and
communicating. To accomplish this broader purpose, vocational education in high schools
should move toward what we call "enterprise training." This would combine education
with actual production, include all students at some point in their high school career, teach
teamwork, integrate vocational with academic curricular content, and encourage active
inquiry. The success of "enterprise training" would be measured by lower dropout rates,
improved performance in academic subjects, and lifelong gains in productivity at work.

To provide training in specific job skills for high school students, ROC/ROP programs
should continue. We recommend, however, that evaluation of these programs put less
emphasis on job placement and more on students' attainment of measured competence.

We have no illusions about the immediate feasibility of implementing these
recommendations. The first task is to build a consensus that such changes should occur.
We see this report as a step in that direction.

 

 

 

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