Economics of Education Review 18 1999 291–309
High school employment, high school curriculum, and post- school wages
Audrey Light
Department of Economics, The Ohio State University, 1945 North High Street, Columbus, OH 43210-1172, USA Received 19 July 1997; accepted 15 November 1998
Abstract
The direct, skill-enhancing effect of high school employment is difficult to identify because high school work effort is correlated with curricular choices, postsecondary schooling and work effort, and many other observed and unobserved
factors. This study uses data for male, high school graduates to estimate a wage model in which detailed measures of high school coursework and post-school work experience are included among the extensive array of covariates. Instru-
mental variable methods are used to contend with the correlation between high school employment and unobserved characteristics. The direct effect of high school employment on subsequent wages proves to be small and relatively
short-lived. Young men who work in high school gain additional, “indirect” wage benefits by taking vocational courses and gaining above-average work experience after graduation.
1999 Elsevier Science Ltd. All rights reserved.
Keywords: Work experience; Human capital; High school curriculum
1. Introduction
This study addresses a question that, despite its appar- ent simplicity, has yet to be answered satisfactorily by
social scientists: Does holding a job while enrolled in high school enhance future labor market productivity?
From a theoretical standpoint, high school employment has an ambiguous effect on career outcomes. It might
give students a “leg up” in their careers by providing them with marketable skills, good work habits, and
knowledge of the world of work. However, high school employment might indirectly hinder subsequent pro-
ductivity by preventing students from learning as much in high school as they otherwise would. In light of the
widely documented difficulties faced by many youth in transiting from school to a permanent, productive pos-
ition in the labor force, it is important to know which effect dominates. Public policy can then be directed
toward helping high school students gain employment
Tel: 001 614 292 6701; fax: 001 614 292 3906; e-mail: light.20osu.edu
0272-775799 - see front matter
1999 Elsevier Science Ltd. All rights reserved. PII: S 0 2 7 2 - 7 7 5 7 9 9 0 0 0 0 7 - 2
by providing job placement services, for example or, as appropriate, toward discouraging such activities.
Efforts to identify the “value added” of high school employment are confounded by the fact that observed
levels of work effort and scholastic achievement are cor- related with many factors. In deciding how to allocate
their time between work and study, high school students are likely to weigh such factors as parental input and
their own levels of ability and ambition. These same traits—as well as the choices made in high school—will
also influence postsecondary schooling and labor supply decisions. By the time workers are observed several
years after high school, their labor market productivity reflects the combined effects of innate factors, family
background, labor market characteristics, and their entire schooling and employment histories. As a result, we can-
not isolate the direct, skill-enhancing effects of high school employment on subsequent labor market out-
comes without “netting out” the influences of many other factors, not all of which are observed.
Until recently, analysts examining the link between high school employment and labor market outcomes
ignored the endogeneity problem described above. These
292 A. Light Economics of Education Review 18 1999 291–309
“first generation” studies estimate simple, single-equ- ation models in which a particular academic or labor
market outcome is expressed as a function of in-school work experience. Outcome measures that have been ana-
lyzed include high school grade point averages or class rank D’Amico, 1984; Greenberger Steinberg, 1986;
Lillydahl, 1990, high school completion or college attendance Meyer Wise, 1982; Marsh, 1991; Steel,
1991, post-high school employment or unemployment Stevenson, 1978; Meyer Wise, 1982; Marsh, 1991;
Steel, 1991, post-high school wages or earnings Stevenson, 1978; Stephenson, 1981; Meyer Wise,
1982; Coleman, 1984; Ruhm, 1995 and post-high school occupational attainment or job benefits Coleman,
1984; Ruhm, 1995. These studies typically control for a relatively small set of observed factors in addition to
high school employment, and none contends with the inevitable correlation between high school employment
and unobserved factors.
1
Two recent studies address the shortcomings of the earlier literature by controlling for observed and unob-
served sources of heterogeneity that, if ignored, might introduce a spurious correlation between high school
employment and career outcomes. Ruhm 1997 esti- mates a number of single-equation models to assess the
effect of high school employment on various outcomes e.g., annual earnings, hourly wages, schooling attain-
ment, and nonwage benefits measured 6–9 years after high school. His primary strategy is to absorb other
sources of observed heterogeneity that influence both high school employment decisions and subsequent career
outcomes; he accomplishes this by including among his covariates an extensive array of family background and
individual characteristics.
2
Hotz, Xu, Tienda and Ahituv 1998 abandon the single-equation approach in favor of
a dynamic, discrete choice model. Every year from age 13 until the first year of full-time employment, individ-
uals are assumed to choose one of six year-long states: school only, school plus part-time work, part-time work
only, military service, full-time work, and other activi-
1
See Ruhm 1997 for additional citations and a summary of findings. As Ruhm notes, several of these studies can also
be faulted for relying on nonrepresentative samples.
2
Ruhm 1997 also uses two strategies, with varying success, to control for the relationship between high school employment
and unobserved factors. A “treatment effects” model controls for unobserved factors affecting the decision to work while in
school, but not the choice of employment intensity. An instru- mental variables approach controls for unobservables that are
correlated with high school work intensity, but Ruhm’s IV esti- mators for the high school employment effect are imprecisely
estimated and implausibly large on the order of ten times larger than his other estimates, presumably because the instrumental
variables do not explain enough variation in the endogenous covariate.
ties; wages are observed for each state that includes employment. The authors jointly estimate a wage model
and the state choice models, allowing for correlations among the unobserved factors. This strategy enables
them to identify the wage benefits of choosing “school plus work” net of any correlation between that particular
choice and unobserved factors that also affect past choices andor wages.
In the current study, I expand on the efforts of Ruhm 1997 and Hotz et al. 1998 to identify the true “value
added” of high school employment on career outcomes. Using data for male respondents in the National Longi-
tudinal Survey of Youth NLSY—the same data source used by both Ruhm and Hotz et al.—I estimate a human
capital wage model in which log-wages earned on post- school jobs form the dependent variable and the covari-
ates include measures of high school employment, high school achievement, post-school work experience and a
host of labor market, job-related, family background, and personal characteristics. My goal is to determine whether
tradeoffs exist between high school employment and high school achievement, and to identify the effect of
each on estimated log-wage paths. I control for the con- founding effects of observed factors via my extensive
array of covariates, and I also contend with potential cor- relation between unobserved factors and my measures of
high school employment, high school achievement, and post-school work experience. I do this by using a gen-
eralized least squares, instrumental variables estimator similar to the one proposed by Hausman and Taylor
1981.
While Ruhm 1997, Hotz et al. 1998 and I use the same data source and share a common goal of overcom-
ing the endogeneity biases that plague earlier studies, our efforts differ in a number of important dimensions. First,
by estimating a dynamic, discrete choice model Hotz et al. take a more structural approach than do Ruhm and I.
While their strategy is intellectually appealing, it is not without costs. They must limit the number of choices
considered and, therefore, are unable to distinguish between the wage benefits of high school employment
and college employment. Moreover, they are constrained to treat student workers as a single, homogenous group
regardless of how many hours are devoted to in-school employment. As a result of these constraints, their find-
ings are not directly comparable to mine or to Ruhm’s.
Second, whereas Hotz et al. focus on very short-run career outcomes wages earned during the first year of
full-time employment and Ruhm focuses on long-term outcomes wages, earnings, and other characteristics
observed 6–9 years after high school graduation, I look at wages earned throughout the first 9 years after high
school. Thus, I am able to determine how the estimated wage effects of high school employment vary over time.
To my knowledge, mine is the first study to examine the
293 A. Light Economics of Education Review 18 1999 291–309
time-varying nature of the wage benefits associated with high school employment.
Third, I hold constant postsecondary schooling attain- ment and post-school work experience, which allows me
to interpret my estimates using a standard, human capital framework. Ruhm observes career outcomes 6–9 years
after high school, at which time respondents differ dra- matically with respect to their postsecondary schooling
and work histories. He omits measures of schooling attainment and post-high school work experience from
his set of covariates because they are endogenous, but as a result he cannot separate the productivity-enhancing
effects of high school employment from the effects of subsequent schooling attainment and on-the-job train-
ing.
3
I eliminate heterogeneity in postsecondary school- ing by limiting my sample to terminal high school gradu-
ates, and I control for heterogeneity in post-school, on- the-job training with measures of actual and potential
work experience. As noted above, I then contend with the endogeneity of work experience using an instrumen-
tal variables approach.
A final difference between this study and its prede- cessors is that I explicitly consider the potential trade-
off between high school employment and high school achievement. To control for high school achievement in
my wage models, I use data on subject-specific credit hours earned by each respondent—information that is
available because high school transcripts were collected and coded for a large number of NLSY respondents.
Detailed transcript data are indispensable if we wish to assess the effect of high school employment on wages
net of its effect on skills being learned contempor- aneously inside the classroom. In particular, these data
enable us to investigate the presumption that employed high school students shun academic subjects in favor of
such courses as typewriting, auto mechanics, and choir— curricula choices that may leave them with ample free
time and even high grade point averages, but with poorer post-school wage earning capabilities than their nonem-
ployed counterparts. This premise receives support in a recent study by Eckstein and Wolpin 1998, who use
NLSY data to find a small, negative relationship between
3
Ruhm’s approach is akin to identifying the returns to schooling with a wage model that omits post-school experience
from the covariates. Because schooling and work experience are strongly, positively correlated, such a model would imply
much larger returns to schooling than are found in conventional models. To circumvent this difficulty, Ruhm presents one speci-
fication in which the sample is confined to individuals who average at least 1000 h or 26 weeks of work during the period
in which earnings are observed 6–9 years after high school. This is only a partial solution, for it still allows considerable
variation in work effort within that window, as well as in the preceding years; in addition schooling attainment remains
uncontrolled for.
high school work effort and the accumulation of credit hours. Neither Ruhm nor Hotz et al. include measures
of high school achievement among their regressors, although a number of earlier studies use grade point
averages or class rank as outcome measures in assessing the value of high school employment.
In the next section I explain how I select the sample of male high school graduates used throughout the study,
and I provide a descriptive analysis of these respondents’ characteristics. In particular, I describe the extent of their
high school employment experiences and summarize the relationships between high school employment and
numerous other characteristics, including high school achievement. In Section 3 I describe the wage models
to be estimated, define the covariates, and discuss the estimation technique. Section 4 presents the estimates,
and in Section 5 I offer concluding remarks.
2. Data
