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and do not necessarily represcm an official position of the Bureau of Laker Stadstics

or the U.S. Oe.pmment of Labor,
High School Employment: Consumption or Investment

Christopher J. Ruhm

University of North Carolina Greensboro

November 1994

TMs project was funded by the U.S. Department of Labnr, Bureau of Labor Statisticsundergmnt number

41USCZ52C3. C)phions statedin tbii paper do not necasarily reflect the official position or policy of tfte

U.S. Department of Labor.
Is High School Employment Consumption or Investment?

1. Introduction

During the middle 1970s, a number of prestigious commissions convened to study the

problems of adolescents (e.g. President’s Science Advisory Committee, 1974; National

Commission on the Reform of Secondary Education, 1973; National Panel on High Schools and

Adolescent Education, 1975) reached the common conclusion that additional early work

experience would foster the development of personal responsibility, smooth the transition from

youth to adulthood, and improve educational performance and occupational attainment. Shortly

thereafter, a number of federal initiatives (e.g. the Career Education Incentive Act of 1977) were

passed with the goal of increasing the employment experience of youths.

These recommendations were made in the absence of any hard empirical evidence that

increased job-holding causes or even is correlated with favorable outcomes. Economic theory

also fails to provide unambiguous predictions concerning the efficacy of youth employment. For

example, the human capital model identifies both potential benefits and costs of working. On

the one hand, time devoted to jobs could detract from potentially more productive educational

investments. On the other, the employment might provide skills and knowledge which increase

future productivity and complement in-class learning.’ Early work experience could also speed

the process by which youths obtain positions where there is a good match between job

requirements and worker qualifications.z

1 Similarly, sociologists have suggested zero-sum models whereby employment is a

diversion from academic pursuits and developmental models where work experiences fur&her

the total development of individuals.

2 Topel & Ward (1992) provide evidence of frequent job changing”for inexperienced workera

and argue that this is an important source of wage and productivity increases.
Page 2

Given these ambiguities, it is not surprising that a partial reappraisal of the benefits and

costs of student employment occurred during the 1980s. The seminal research of Greenberger

& Steinberg and their co-authors (Greenberger & Steinberg, 1980; Greenberger et. al. 1982;

Steinberg et. al. 1982a, 1982b) indicated mixed and generally negative outcomes, leading them

to conclude that “working is more likely to interfere with than enhance schooling; promotes

pseudomaturity rather than maturity is associated in certain circumstances with higher, not

lower, rates of delinquency and drug and alcohol use and fosters cynical rather than respectful

attitudes toward world (Greenberger & StSinberg, 1988: p. 235). These concerns are even

more salient in light of recent evidence suggesting that youths take jobs to finance short-term

consumption, rather than to enhance human capital investments.3 They also have provided a

justification for recent efforts to strengthen enforcement of the child labor provisions in the Fair

Labor Standards Act!

[t is important to better understand the effects of high school work experience. Rates of

employment by in-school youths are at historically high levels. If this job-holding has the

negative effects sometimes attributed to it and, in particular, if it reduces educational attainment

and academic performance, the increased work propensities could explain a portion of the

wage stagnation observed over the last two decades, especially among young workers without

college educations. Conversely, if early labor market experience has favorable impacts on

future economic outcomes, the relatively low employment rates of nonwhite youths could

contribute to racial earnings gaps observed later in life.

Previous research suffers from two fundamental shortcomings which make it difficult to

determine the net benefits or costs of job-holding by students. First, most studies treat youth

3

For example, 69% of working high school seniors, surveyed in 1982 by the High School

and Beyond Survey, report spending some of their earnings for car expenses, 97~0 to “buy

things”, but just 44% towards saving for college (Yeatts, 1994).

4

See Brooks (199’I ) for a description of these efforts. “““
Page 3

employment as an exogenous variable, ignoring the selection process determining which

students work and, conditional upon doing so, how many hours they are employed. Indeed,

much of the prior investigation has used unrepresentative samples and held constant few, if

any, individual characteristics. Second, analysts have focused upon educational achievement

and employment outcomes shortly after the completion of high school but have obtained little

information on long-run labor market success.

Using data from the National Longitudinal Swvey of Youth (NLSY), this paper improves

in both areas. Several strategies are used to account for difficult to observe differences

between workers and nonworkers. These entail controlling for an unusually comprehensive set

of background characteristics, examining whether reduced form estimates are biased by the

potential endogeneity of high school employment, and testing the robustness of key results to

changea in samples and specifications. The dependent variables are employment

consequences 6 to 9 yeare after the scheduled date of high school graduation, thus providing

the best available information on long-term effects of the student job-holding. In addition, this

study examines a wider variety of emnomic outcomes and utilizes better information on high

school employment status than has previously been available. The investigation focuses upon

the number of hours worked by high school students. Examining the role of job characteristics

or of employment by college students is beyond the scope of this analysis and is reserved for

future study.’

No evidence is uncovered of detrimental effects of low to moderate amounts of student

employment. To the contrary, job-holding in the senior year is associated with substantially

elevated future economic attainment, whether the latter is measured by earnings, wages,

occupational status, or the receipt of fringe benefits. These results are robust across a variety

5 See Greenberger & Steinberg (1988), Greenberger et. al. (1982) Stern&Nakata(1989),

and Stern et. al. (1990) for discussion of differences in job characteristics.
Page 4

of specifications and sample selection criteria and strongly suggest that employment plays an

important developmental role for students as they approach the end of high school

Interestingly, the economic benefits are obtained despite a small reduction in completed

schooling. This suggests that time spent on the job detracts slightly from educational human

capital investments but more than compensates for this Ioas through employment-related

2. Prior Research

The effects of high school employment have been widely studied since the late 1970s.

Most frequently, researchers have examined the impact of student work on academic

performance as measured by grades, test scores, or school completion rates. Employment

probabilities and wages, in the period shortly following high school completion, have also

received some attention.8 Samples, time periods, and study methodologies vary widely. The

key findings of previous research are briefly summarized in table 1.

There is currently no consensus whether student employment improves or worsens

school performance, although the data do suggest that any beneficial effects are maximized at

low or intermediate hours of work, while harmful impacts are most likely for heavy job

commitments. For example, Barone (1993), Greenbarger & Steinberg (1980), Greenberger, et.

al. (1982), Mortimer & Finch (1986), Steinberg & Dombusch (1991), and Steinberg, et. al.

(1993) find that high school employment is associated with lower grade point averages.

Conversely, Gade & Peterson (1980), Lillydahl (1990), Meyer& Wise (1982), and Schi[l, et. al.

(1985) detect either no effects or beneficial impacts at moderate work hours~ Interestingly,

6 Researchers have also studied the effects of youth unemo Iovmen t on future outcomes

(e.g. see Ellwood, 1982 or Smith, 1985).

7 A similar lack of consensus is found in research on employment by college students. For

instance, Paul (1982) uncovers negative effects of working during college, Hood, et. al. (1992)

find the highest GPAs among students working 7-14 hours/week, and Ehrenberg & Sherman

(1987) contrast positive effects of on-campus job holding with negative impacts of off-campus
Page 5

D’Amico (1984) finds that, despite reducing the amount of time spent on studying and school

activities, student employment correlates with higher class rank for white males (with no effect

for females or minorities) and with higher rates of school completion and college attendance.

This suggesta that working students may allocate their time more efficiently than their

counterparts.

The results pertaining to employment outcomes are more clearcut. Work during high

school is unambiguously associated with elevated rates of future job-holding and increased

earnings (DAmico, 198A Marsh, 1991; Meyer& Wise, 1982; Mortimer & Finch, 1986;

Stephenson, 1981; Stern & Nakata, 1989; and Stevenson, 1978). It is not obvious, however,

whether these represent permanent benefits or transitory gains which will disappear overtime.

Indeed, some researchers have argued that work by youths improves initial outcomes but

reduces human capital investments and so has a negative long-term impact. Unfortunately,

virtually all previous studies have focused on the period immediately following school

completion, making it difficult to infer Iifecycle effects.8

Correlations between student employment and future outcomes could result from

unobserved confounding factors, rather than being due to any causal effects of the work itself.

For example, Weiss (1988) has argued that the large earnings premium associated with high

school graduation occurs because graduates possess large amounts of unobservable traits he

groups under the rubric of “stick-to-itiveness”. Using the same analogy, if students with Iow

amounts of “stick-to-itiveness” are relatively likely to work (presumably because they do not like

school), than youth employment may be associated with unfavorable future outcomes, even in

the absence of a causal effect.’ Spurious correlation is likely to be particularly problematic

positions.

8 Exceptions include Mortimer & Finch (1986) and Stevenson (1978). However, data for

these studies is from the 1960s and earty 1970s and so provides limited information on recent

cohorts of high school students.
Page 6

when, as in many studies, only rudimentary controls for observable differences are included.

Even a more comprehensive set of covariates is unlikely to eliminate the selection bias, unless

it includes good proxies of the underlying attributes which determine the employment decision.

These methodological problems are further aggravated when (nonrepresentative)

convenience samples are used or when subsamples are selected in ways which introduce

unobserved differences between workers and nonworkers. To illustrate, consider Steinberg et.

al.’s (1982) influential longitudinal study of students at four high schools in Orange County

California. Their nubal sample included 3101 youths who were present on two testing days at

.

each school. From this group, they selected persons holding their first job and a random

sample of those who had never worked (n=l 000). 667 of the 1000 completed questionnaires in

March of 1979. For the longitudinal analysis, they deleted all respondents who were working at

the original survey date or who had ever been employed, reducing the sample sizeto319.

They were able to reinterview 228 of these individuals one year later, 52 of whom were

excluded because they had worked during the intervening period but were no longer doing so.

Thus, the subsample analyzed included 176 persons or 5.7% of the original (nonrepresentative)

sample. More damaging than the small sample size is the likelihood that biases (some of

known and some of unknown directions) were introduced at each stage.in the sampling

process, making it difficult to know how any findings should be interpreted.’”

9 Steinberg &Dornbusch(1991 ) and Steinberg, et. al. (1993) provide evidence showing that,

compared to nonworkers, employed high school students had lower grades and educational

expectations, spent less time studying, and were less engaged in school even before fhey

started working. Some researchers (e.g. Lillydahl, 1990, Meyer& VVse, 1982) have used

multi-equation models or analysis of the time structure of residuals in an effort to separate

causation from correlation. These attempts have met with limited success.

$0 For example, the restriction to students present at school during two testing days biases

the sample against individuals with high rates of absenteeism and the deletion of persons

having held but Ierl jobs eliminates students with histories of unstable employment.
Page ?

3. Trends in Student Employment

Much concern over student employment stems from the belief that this type of

job-holding has risen rapidly in recent years, in contrast to stable or falling rates earlier in the

century. For example, Greenberger & Steinberg (1986, p. 15) cite a 65°A increase in the labor

force participation rates of 16 and 17 year old school-going males (from 27% to 44%) occurring

between 1947 and 1960. The expansion in student employment is likely to be overstated by

these figures, however, for at least two reasons. First, youth unemployment increased

dramatically during this period, which implies larger increases in labor force participation than

employment probabilities.” Second, the calculated changes are quite sensitive to the

endpoints chosen. For instance, the increase in participation rates was less than half as large

(rising from 37% to 44%) between 1950 and 1960 as when the initial year is 1947 and there

was almost no change in participation between 1950 and 1970.’2

Unpublished Current Populafiofr Survey data on the employment-to-population (EP)

ratios of 16 to 18 year olds who were attending school are displayed in table 2. Prior to 1989,

individuals were classified according to the major activity (Le. “school” vs. “other”) they were

engaged in. [n 1989, the Bureau of Labor Statistics began separately reporting EP ratios by

school enrollment status. Since students with heavy work commitments may consider,

41 The unemployment rates of 16-19 year old males rose from 9.8% in 1948 to 18.3°A in

1980 (Economic Report of the President, 1992, p. 340).

!2 There is some evidence that the Current Population Survey statistics underestimate the

level of work involvement because the information on youths is typically provided by their

parents, who systematically understate their children’s labor ‘force attachments (see Freeman &

Medoff, 1982 for a careful analysis of this issue). As Flaim (1982) points out, however, much of

the difference between self-reports and proxy-responses relates to casual jobs (e.g. babysitting

or Iawnmowing), which are reported as employment by the youth but not by the parent.

Discrepancies behveen CPS and other swvey data often result from differences in what is

being measured. For example, much of the employment information in the High School and

Beyond survey refers to the current orrnosf recent job. Thus, many researchers (e.g. Marsh,

1991) report zero hours of work only for those students who have not held jobs at any point

during the survey year. This overstates the fraction employed at a given point in time.
Page 8

employment, rather than school, to be their major activity, they are excluded from the pre-1989

statistics. We therefore expect an increase in the reported EP ratios beginning in 1989.

Economic conditions were similar in 1968, 1978, and 1988, so there is no reason to anticipate

large biasea in the observed time trends over these yeare. By contrast, EP ratios are likely to

be depressed by the slack economic conditions of 1992.

The employment-to-population ratios of teenagers with school as a major activity rose

moderately between 1968 and 1978 (from 28% to 30%), with large increases for females and

little change for males. By contrast, no growth in job-holding is evident for the decade ending in

1988. Over this period, a slight contraction in male employment (from 31 Y. to 29%) more than

offsets the marginal increase for females. The underestimate of student employment

probabilities prior to 1989 is made evident by contrasting the EP ratios of teenagers with school

as a major activity in 1988, with those of enrolled youth one year later. The fraction holding

jobs is 7 percentage points or almost 25% larger in 1989 than a year earlier (37’% vs. 30’70).

Even more striking, is the large cyclical variability. By the 1992 recession year, the EP ratios of

students had declined from 37°A to 31‘A.

These data suggest that the frequency of student job-holding has changed relatively

little over time, particularly since the middle 1970s, with any trend being small when compared

to cyclical fluctuations induced by macroeconomic condtions. This finding is qualified by

possible changes in work intensity of employed students. We return to this issue after

describing the data set used in the analysis. A prelimina~ conclusion, however, is that

concerns that the employment levels of high school students have been rapidly rising, during

the last 25 yeara, may be exaggerated.
Page 9

4. Data

This study uses data from the National Longitudinal .Su!vey of Youth (NLSY), a sample

of 12,686 men and women aged 14 through 21 on January 1, 1979. Respondents have been

interviewed annually since 1979 and information through the 1991 intenriew is used below. The

subsample analyzed is restricted to respondents who: 1) were high school freshman or

sophomores in 1979, 2) remained enrolled in school through at least the interview date of their

senior year (two years if a sophomore in 1979 and three years if a freshman), and 3) were

members of the nationally representative cross-sectional sample of noninstitutionalized civilian

youths.” These exclusions reduce the sample size to 1,149 (566 males and 561 females),

1,067 of these respondents (545 men and 522 women) were interviewed in 1991, a

continuation rate of 9370.

The NLSY has several advantages for studying high school employment. First, it is the

only survey which has followed a recent cohort of students far a sufficient time period to allow

examination of the long-term effects of working. Second, it contains unusually rich information

on background variables which may jointly influence both the decision to obtain employment

during school and subsequent economic attainment. Third, it includes extensive retrospective

data on job-holding in the period since the previous interview, including a separate work history

file with week by week information on employment status.

Two types of information on high school employment are utilized in the below. The first

are questions indicating hours worked during the week prior to the survey date of the

respondent’s sophomore, junior, and senior year of high school. Second, the work history file is

used to construct measures of average work intensity during the junior and senior academic

13 The NLSY also includes a supplemental sample of Hispanic, black, and economically

disadvantaged white youths (n=5,295) and a sample of 17 to 21 year olds enlisted in the

military on September 30, 1978 (n=l ,280). See Center for Human Resources Research (1992)

for further information on the NLSY.
Page 10

years and the preceding summers. M By ~vera9ing over multiple weeks, the latter data has the

advantage of smoothing transitory variations in employment. On the other hand, individuals

may more reliably report work hours for the preceding week than for periods up to a year prior

to the survey date. It is therefore not obvious which employment measure is preferable and

results are presented for both below.

The dependent variables are outcomes averaged over the three year period 1988

through 1990, which is 6 to 9 years after the scheduled date of high school graduation. Using

information for multiple years period smoothes the effects of temporary fluctuations and

reduces the number of observations lost due to missing data.’5

The primary outcome is annual earnings from “wages, salary, commissions, or

tips... before deductions for taxes or anything else”. This total is then decomposed into wage

rates (hourly and weekly) and employment levels, where the former are measured as total

earnings divided by houra or weeks employed. Three additional measures of economic

attainment are also analyzed. The firat is the Duncan Socioeconomic Index, which is a widely

used measure of occupational status.’” The second and third indicate whether group health

insurance is provided by the current or most recent employer and if retirement benefits

(hereafter referred to as pensions) are supplied by the company. The Duncan score is included

14 Academic year hours were measured over a 26 week period during October, November,

February, March, April, and May. This time frame was chosen to eliminate potentially atypical

employment levels occurring during school weeks immediately surrounding the summer and

holiday seasona. Information on summer employment was obtained for an 8 week period

starting with the week which includes July 1 of the given year. Complete work histories were

unavailable for 14 and 15 year olds, which prevented construction of employment houra for

sophomores.

15 When data was missing for a single year, the averaging was done over the remaining two

years for which information was available.

16 See Duncan (1961) for information on the Duncan index and Mutchler & Poston (1983) for

a critique.
Page 11

to capture potential differences in occupational attainment, which are not yet reflected in the

relative incomes of persons in their middle to late twenties.

The regression analysis controls for MO sets of supplemental covariates. The first are

standard demographic variables indicating ethnic status (black, Hispanic, white), sex, marital

status (single vs. currently married), geographic region (northeast, northcentral, south, west),

residence in an SMSA and in an urban area, local unemployment rates (<30A, 3-6°Y0,6-90/&

9-1 2%, >12%), and high school class at the 1979 survey date (freshman vs. sophomore).

Schooling is excluded because student employment may havi? a strong impact on the level of

education, in which case the latter is endogenous. This will be directly tested for by examining

the relationship between student work hours and the highest grade completed.

The second set of attributes includes potentially important characteristics for which data

has typically been unavailable to previous researchers. These consist of dichotomous variables

indicating whether the respondent and his/her parents are foreign born (three covariates),

whether a foreign language was spoken at home, parent’s educational attainment (high school

dropout, high school graduate, college graduate), if magazines, newspapers, or library cards

were in the home at age 14 (three variables), if the respondent considered school boring,

unsafe, or was very dissatisfied with it, school type (public vs. private), whether hekshe had

smoked cigarettes or used drugs (marijuana or hashish) by the sophomore year of high school

(two regressors), and religion (Catholic, Jewish, Baptist, other). Expected yeare of education,

number of siblings, (log of) family incomes, and the score received on the Armed Forces

Qualifications Test (AFQT) are also included in this extended set of covariates.

Family income is averaged over the student’s sophomore through senior years of high

school, the AFQT score is determined in 1981, information on the age of first cigarette and drug
Page 12

use is obtained in 1984, and the time-vawing regressora are evaluated contemporaneously with

the outcome variables. All of the other covariates are evaluated at the 1979 interview date.

Mean values of key variables are presented in table 3. Column a) displays averages for

all respondents; column b) is restricted to persons interviewed in 1991. Roughly half the

sample are women, one-sixth are nonwhite, one-third are Catholic, and one-fifth are Southern

Baptists. The parents of over two-thirds of the sample graduated from high school. Fewer than

three percent of respondents are foreign born, compared to around twice as many of their

parents; however, a foreign language was spoken in the home of more than one in ten

individuals. Thetypical sample member has three siblings.

Three-quarters of the sample grew up in an urban area and two-thirds in a metropolitan

location. Aiargemajotity wereexposed tonewspubhcations andlived inhouseholds with

libraty privileges atage 14. 0ver90% ofrespondents atiended public schools and almost

one-sixth hadnegative attitudes towards their institution. Theaverage individual expected to

complete slightly overtwo years ofco[lege. More than 60°Ahad smoked acigarette by their

sophomore year but fewer than one in twelve admitted having tried marijuana or hashish by that

age. Respondents remaining inthesample through 1991 havevirtually identical characteristics

as the full sample, suggesting that attrition occurs fairly randomly.”

‘7 Sample means asafuntion ofsuwey weekemployment status inthe sophomore through

senior grades arepresented intabIe A.l. Thetable shows thatworking students are more

fikely, than their counte~atis, to bewhite andmale andaremore oflensmokem. They

disproportionately come from advantaged backgrounds, as measured by the educational level

of their parents andthepresence of magazines, newspapers, andlibary cards in the household.

Interestingly, there are no consistent differences across the type of school attended, highest

grade expected, orschool attitudes. Student workers do, however, have much higher AFQT

scores. Baptists woArelatively infrequently and Cathoti~dlspropotilonatelyoRen.

Respondents who are foreign born or have parents who come from other countries have high

employment probabilities in the junior and senior grades but not during the sophomore year.

Conversely, respondents living in homes where a foreign language is spoken work relatively

infrequently.
Page 13

5. High School Employment and Economic Outcomes

Descriptive infomation ontighschool workhours isprovided intable4. As previously,

column (a) refers to the full sample and column (b) to persons interviewed in 1991.

Employment rates are marginally higher for individuals remaining in the sample throughout the

period of investigation but there is again little evidence of attrition bias and the analysis in the

remainder of the paper is restricted to the 1067 respondents remaining in the sample through

1991. The first panel of thetable displays dataonwok houminthe weekpriorto thesuwey

date (hereafter referred to as the reference or interview week); the second presents

corresponding information for academic year and summer employment.

Workexpenen@ nsessteadily throughout thehigh school yearn. 28% of sophomores

are employed in the reference week, compared to 4YX0 of juniors and 51 YO of high school

seniors (seethe toppaneloftabIe4). Given thelarge fraction ofnonemployed students,

average work hours are modest, rising from slightly over 3 houra per week for sophomores to

around 10hours perweekforseniors. Conditional upon employment, sophomores, juniora,

andseniore workanaverageof 12, 16, and19hours perweek respectively. 0nly3°Aof

sophomores, 10%ofjuniora, and 19’% ofseniors work morethan 20houra intheintewiew

week and just 1Yo, 3Y0, and 5%!0are employed over 30 hours. Thus, only a small fraction of

students have the heavy job commitments which have raised particular concern in previous

research.

Work hours, measured over the 26 week academic year period (shown in the Iower

panel of the table), exceed those for the reference week by 1 hour for juniors (8.0 vs. 6.7 hours)

and almost 3 hours for seniors (12.3 hours vs. 9.6 houra). since there is no reason to expect

hours in any given week to differ systematically from those averaged over a longer time period,

the disparity is probably due to misreporting of the latter. Specifically, in the retrospective data,
Page 14

respondents appear to overstate work hours in weeks when they are employed.’a Conversely,

the percentage of weeks worked during the academic year corresponds closely to the

employment probabilities for the interview week (42.6Y0 vs. 43.3°A for juniors and 52.3°A vs.

50.8% for seniors).

The work history data further indicates that employment experience is the norm for high

school students, with almost two-thirds of juniors and threequarters of seniors holding jobs at

some point during the academic year. Employment hours are higher in the summer than during

the academic year but the differences are relatively small, suggesting that a large number of

students continue their academic year employment through the summer and vice versa.

Whites and males work more than nonwhites and females (see table 5). The gender

differential in survey date work hours is 57?6 for sophomores (4.1 vs. 2.6), 43% for juniors (7.9

vs. 5.5), and 12°A for seniors (10.1 vs. 9.0).’g White sophomores work 40% more houra their

minority peers (3.5 vs. 2.5), with still larger i’d~o and 54~o differentials for juniors (7.3 vs. 4.2)

and seniors (10.2 vs. 6,8). Conditional upon holding jobs, however, there no evidence that

whites have heavier employment commitments than nonwhites. If anything, the reverse is

true.zo This suggests that the race disparities may result from differences in opportunities rather

than tastes and, if student work is beneficial, could provide one reason why nonwhites receive

relatively low earnings later in life.

High school students who work generally have higher levels of future. economic

attainment than those who do not. ThLscorrelation holds across a variety of outcome

18 ThLs is consistent with other research finding that employment hours are inflated in

retrospective data. For instance, estimates from the Panel Study of Income Dynamics

validation study, reported by Duncan & Hill (1985), show that respondents claim to have worked

10% to 12% more hours during the previous year than were indicated by company records.

19 Similar gender differences in student employment have been uncovered by D’Amico

(1964); Gade & Peterson (1980); Michael& Tuma (1984); and Steinberg& Dornbusch (1991).

w Steele (1991 ) also finds that whites more often work than nonwhites but with no

differences in hours conditional on employment.
Page 15

measures, typically increases with grade level, is more pronounced for academic year than

reference week employment, and is strongest when considering earnings (see table 6). For

example, whereas sophomores working more than 20 hours in the interview week earn 9’%

more than their nonworking counterparts (6 to 9 years later), the differentials associated with

junior and senior employment are a much larger 31 % and 35% respectively. Individuals not

working at any point during the academic year do even worse – their counterparts averaging 20

hours of employment weekly in the junior and senior grades earn 39% and 55°A more per year.

These findings provide a first indication that high school employment has favorable effects on

future outcomes.

6. Econometric Estimates

The positive relationship between student employment and subsequent labor market

attainment could result from spurious correlation between youth work experience and

confounding factors which actually cause the favorable performance. For example, persons

with advantaged backgrounds may have superior access to jobs both in school and after

graduation. If so, socioeconomic differences, rather than high school employment, may explain

the disparity in economic achievement. Regression analysis is used to examine this possibility.

The basic equation estimated is:

(1) Yl=X@+y Hj+3H12+ c,,

where Yr is the outcome for individual i, X is a set of covariates, H a vector of high school work

hours, and e is the regression disturbance. Quadratic terms are included to allow for nonlinear

effects of student employment and the predicted effect of working H houra in a given high

school grade (compared to nonworkers) is ~H + ~H2, for ~ and ~ the regression mer%cients

obtained from estimating (1). Probability values (p-values) for the hypothesis that ~and 8 are

jointly equal to zero are also reported in the tables. These are obtained from For likelihood
Page 16

ratio tests, depending upon whether the equations are estimated by OLS or using maximum

likelihood techniques.

6.1 Earnings, Wages, and Work Hours

Table 7 presents coefficients on work hours and hours squared for a log earnings

equation estimated by OLS. Column (a) displays the results of regressions which separately

control for employment hours in a single high school year (i.e. the first panel reports coeticients

from three regressions) but which include no other covariates. Column (b) combines work

hours in the three high school grades into a single equation but again excludes other

regressors. Column (c) adds covariates for the respondent’s race, sex, marital status,

geographic region, urbanicity, residence in an SMSA, and high school grade in 1979. The full

set of attributes (described in section 4) are included in columns (d) and (e), with the difference

between the two being that only (e) contains the AFQT score.21 ~us, additional individual and

background characteristics are held constant when moving from the left of the table to the right.

To the extent that the association between high school employment and future incomes is due

to confounding factors, we therefore expect the hours coefficients to decline (in absolute value)

as covariates are added.

Work hours during the senior year of high school are positively and strongly correlated

with future incomes, even when holding constant an unusually large variety of observable.

Indeed, once the basic set of regressora included in column (c) is controlled for, additional

explanatory variables have little impact on the estimated effect For example, persons working

10 hours during the reference week of their senior year in high school are predicted to have

.

16% higher future earnings than their nonworking counterparts using specification (c) and 14’%

2’ The AFQT score is included separately since it may be endogenous. (It is measured in

1981 and therefore could be affected by sophomore and junior year employment.) Survey date

employment hours in the sophomore year are also included in the bottom panel, since a

corresponding academic year measure is not available from the work history file.
Page 17

greater incomes using model (e). Both results are highly significant and large earnings

differentials are also observed when controlling for academic year employment during the

senior year, using data from the work history file.

Conversely, there is no evidence of statistically significant employment effects for

sophomores and juniors, once senior work hours are held constant. The coefficients on junior

year employment are positive and significant when nothing else is controlled for (column a) but

become statistically insignificant when regressors for senior employment are added (column

b).z The inclusion of individual and background characteristics further reduces the predicted

sophomore and junior year effects and they never approach statistical significance (columns c

through e).” The remainder of the paper presents results using controls for the extended set of

characteristics included in column (e),

The estimates in columns (d) and (e) include a much broader set of covariates than

have been available to most previous researchers. ThLsreduces but does not completely

eliminate the possibility that the observed student employment effect is the result of

uncontrolled for individual differences. For example, exceptionally motivated individuals might

more often work in school and also receive relatively high incomes later in their careers. To the

extent that the explanatory variables fail to account for heterogeneity in motivation, the effect of

the latter may partially be captured by student work hours.

There are at least three reasons to doubt that the remaining bias is important. First, the

estimated impact of senior year employment falls only slightly when moving from a relatively

22 This is due to a moderately high correlation of work hours across grades. The correlation

between sophomore and junior work hours is 0.319, between junior and senior hours it is 0.447,

and between sophomore and senior employment it is 0.236.

23 Coefficients on the other covariates, which are displayed in table A.2, generally conform to

our expectations. In particular, subsequent earnings are relatively high for whites, men,

persons in areas with low local unemployment rates, and those with high educational

expectations and family incomes.
Page 18

parsimonious specification (column c) to one which controls for a broad array of potentially

important covariates (column e). Second, if high school employment is dlspropotilonately

obtained by persons with favorable unobserved characteristics, student job-holding should be

associated with high levels of academic achievement, rather than the opposite result observed

by some researchers. Third, there is no reason why the effects of confounding should be

limited to job-holding in the senior year. For instance, if differences in unobserved motivation

are of key importance, stronger effects might be expected for sophomore or junior (than senior)

year employment, since job-holding is less common in these grades and therefore presumably

occurs among a more selected group.

6.2 Alternative Specifications

To further reduce the possibility that the senior year employment effect is spurious, the

robustness of the earnings differential to changes in specifications and samples was tested for,

with results reported in table 8. For comparison purposes, column (a) repeats the findings from

specification (e) of table 7. Column (b) follows with estimates from a “treatment-effects” model

where the “treatment” ia the choice of whether or not to work in the senior year. For this model,

a probit equation was first estimated, with the dependent variable equal to 1 (0) for respondents

working positive houra (not working) in their senior year of high school. The inverse Mills ratio

from the probit was then added as an additional covariate in the second-stage earnings

equation.z’ The Mills coefficient indicates the selection effect into senior year employment, with

% The inverse Mills ratio is $/0 and -$/(14), respectively, for respondents who do and do

not work in their senior years, where @and @are the standard normal density and distribution

functions, evaluated at the inner-product of probit coefficients and individual attributes. It is

typically difticult to identify this type of model because it is hard to select covariates which can

justifiably be included in the probit equation but excluded tom the second-stage earnings

regression. In this case, however, it is reasonable to assume that geographic characteristics

(local unemployment rates, region of the country, SMSA and urbanicity) during the senior year

affect student employment (and so are included in the probt) but have no impact on future

outcomes (and so are excluded from the earnings equation) while the reverse is true for

geographic conditions averaged over the 1988-90 period.
Page 19

significantly positive (negative) coefficients implying that the employment estimates from

reduced form models (which do not control for the endogenous selection) are upwards

(downwards) biased35

Columns (c) and (d) exploit additional information available in the work history file. In

(c), controls for work hours during the summer between the junior and senior year of high

school are added. This allows comparison of the relative returns to academic year and summer

employment. Column (d) holds constant the number of weeks the student is employed during

the academic year, instead of the number of hours worked per week. Finally, columns (e) and

(f) return to the original specification but provide estimates for subsamples stratified by sex.

The finding that senior work hours are positively correlated with future earnings, but with

no statistically discernible impact for sophomore and junior employment, is robust across

specifications and samples. The treatment-effects estimates of column (b) fail to indicate any

significant “selection bias in the reduced form earnings equation. AHhough the estimated effect

of senior job-holding is slightly lowered when considering academic year work hours (and barely

misses statistical significance at the 10OAlevel), the correlation between reference week hours

and future earnings is stronger in the two-stage model than in the reduced form estimates.

Interestingly, column (c) suggests that academic year employment has a bigger payoff

than jobs held during the summer. This is somewhat surprising since summer work is less

likely to divert time away from educational pursuits. [t is possible, however, that the two types

of employment are qualitatively different. Moreover, school year jobs may require students to

develop time management skills whereas summer job holding does not?’ Column (d) indicates

25 See Greene (1993, pp. 713-4) for further discussion of the treatment-effects model.

28 Fairly high multicollinearity between summer and academic year employment makes it

dh%cultto separately identify the two effects, raising the standard errors of the estimates. The

correlation behveen work hours in the junior (senior) year and during the following (preceding)

summer is 0.452 (0.419).
Page 20

that the payoff to senior year employment experience holds when considering the number of

weeks worked per year, rather than hours per week, and suggests the need for future research

distinguishing between these hvo effects.

Finally, columns (e) and (f) suggest that the returns to job-holding by high school seniora

are larger for females than males, although small sample sizes imply that these differences

ahou[d be interpreted cautiously. One explanation is that since women have lower labor force

participation rates than men, high school girls may only obtain work if they anticipate strong

future workforce attachments, whereas a more random group of boys obtain positions. Even

for males, however, the returns to senior year employment are substantial and highly significant

when measured by interview week work hours. The issue of selection bias is considered

fur&herin section 6.4.

Table 9 presents econometric estimates for log wages and employment levels. Since

houra worked annually are left-censored at O and weeks employed are left and right-censored

at O and 52, OLS is inappropriate for these dependent variables and tobit models are

estimated.z’ Students holding jobs during their senior year of high school both earn higher

wages and work more in the future than do their nonemployed counterparts. For example, 10

hours per week of employment by seniors is associated with more than a 100 hour per year

increase in work, during the 1986-90 period, and a 5°A differential in hourly wages. The

difference in future work hours is highly significant, whereas the wage effect is not significant at

the .05 level and is only significant at the .10 level when measured by reference week

employment. There is again never any statistically significant impact of employment by

sophomores and juniors.

27 The table shows tobit coefficients. The effects of marginal changes in work hours can be

estimated by multiplying the relevant coe~lcients by 0(.), the predicted percentage of

noncensored observations.
Page 21

6.3 Occupational Status, Fringe Benefits, and Educational Attainment

Respondents lacking high school work experience may receive relatively low pay

because they have made substantial investments in education, which have not yet begun to

pay off by their middle to late twenties. Alternatively, nonworking students could subsequently

choose jobs with relatively low pay but which provide compensating differentials in the form of

fringe benefits. To explore these possibilities, we next consider the relationship behveen

student employment hours and four other outcomes. The Duncan score, a commonly used

index of occupational prestige, is the dependent variable in column (a) of table 10. Columns (b)

and (c) indicate whether the employer provides two important fringe benefits health insurance

and pension coverage. These dependent variables are trichotomous, indicating whether the

fringe benefit is provided in none, some, or all of the 3 years and so ordered probit models are

estimated. Finally, column (d) examines whether there are differences in educational

attainment, measured as the highest grade completed by the 1991 survey date.

Results for the Duncan index and the two fringe benefits are entirely analogous to those

for future incomes. Employment in the sophomore and junior year of high school is unrelated to

labor market outcomes, whereas a positive and strongly significant association is observed for

work hours in the senior year. Interestingly, the last effect is occurs despite a small negative

impact of senior grade employment on education levels (e.g. 10 hours per week of employment

is associated with a .05 to .07 year reduction in educational attainment). Thus, it appears that

working seniors sacrifice a small amount of formal education in exchange for substantially

larger job-related investments in human capital. These findings are mnsistent with previous

research indicating that youth employment has ambiguous impacts on educational achievement

but consistently positive effects on job-related outcomes.
Page 22

6.4 Deletion of Pati-Year Workers

The possibility that an unobsewed choice process causes the positive correlation

between senior year employment and future economic attainment can still not be entirely

dismissed. For example, work-oriented individuals might decide both to hold jobs while in high

school and to be employed relatively long hours later in life. They might also receive higher

rates-of-pay if wage offers and work hours are tied. Alternatively, some persons might have

better than average employment opportunities both during high school and as working adults.

As discussed above, a problem with these explanations is that a positive relationship between

future outcomes and student employment in all three high school grades would then be

expected, rather than just for the senior year.

One way of reducing the effect of potential selection mechanisms is to limit the analysis

to persons with more homogeneous future employment experiences. Towards this end, table

11 displays results for the subsamp[e of individuals averaging more than 26 weeks of

employment annually during the 1988-90 period. Although the returns to senior year

employment are somewhat smaller for this group than for the full sample, statistically significant

positive effects continue to be observed for incomes, Duncan scores, and the receipt of fringe

benefits. For example, 10 hours of work in the survey week of the senior year is predicted to

elevate annual earnings by 14% and the Duncan Index by 2.8 points, for the full sample, versus

a 1070 and 2.7 point increase among the subgroup. The wage effect continues to be positive,

but statistically insignificant, with the predicted differential declining slightly from 6% to 6%.

Student work experience is likely to improve subsequent economic attainment partly by

increasing future employment levels. Deleting persons with sporadic work experience

eliminates a large portion of this effect and so the results in table 11 understate the favorable
Page 23

effects of job-holding by youths. The finding of positive impacts, even for this subsample,

therefore furnishes powerful evidence that senior year employment provides genuine benefits.

6.5 Predicted Effects of Senior Year Employment

The expected changes in economic attainment associated with senior year employment

are presented in table 12. Predictions are obtained for the full sample and for equations which

control for the full set of covariates (i.e. specification (e) of table 7). The top panel summarizes

results for job-holding in the reference week prioL the bottom panel provides corresponding

estimates for work hours averaged over the academic year. The first three columns show

results for the log earnings, log wages, and the Duncan score. The last two columns refer to

predicted probabilities employer-provided health insurance and pension coverage at all three of

the 1988 through 1990 survey dates. The fourth row of each panel shows the number of hours

of senior year employment at which the outcome is predicted to reach a maximum.

Compared to seniors who do not hold jobs, working 10 hours during the interview week

is associated with 14% greater future earnings, an 8% rise in hourly wages, a 2.8 point higher

Duncan Score, and 8 and 9 percentage point increases in the probability of obtaining group

health insurance and pension coverage. Corresponding benefits for averaging 10 houra of

employment per week throughout the academic year are 11‘%0,5Y0, 2.2 points, 7 and 8

percentage points respectively. Substantially larger gains are obtained by those working 20

hours per week. For instance, 20 hours of employment in the week before the survey is

associated with 22’3!ohigher earnings, 11!/o greater hourly wages, a 4.1 point rise in the Duncan

score, and increases exceeding 11 percentage points in the probability of receiving each of the

two fringe benefits.

The predicted benefits of reference week employment are maximized at behveen 19

and27houm perweek, dependng upon the outcome considered. Bycontrast, the gains
Page 24

associated with academic year job-holding peak beyond 27 hours per week and, except for

pensions, increase steadily throughout the employment levels experienced by the vast majority

ofhigh school seniors. Apossible reason forthedifference isthatsin@ employment appears

to be overestimated in the retrospective information (see section 5), the number of hours at

which maximum benefits are received is likely to be overstated when using the Work History

file.

6.6 Noncollege Educated Respondence

‘ A question raised bytheprevious findings iswhystudent employment is beneficial

during the senior year of high school while having little or no corresponding positive impact in

thesophomore andjuniorgrades. Oneplausible hypothesis isthatstudentjob-holdng offem

advantages which, although substantial, depreciate rapidly over time if they are not quickly

utilized. Mostdredly, someworting seniommay beableto continue with the same company

arlerleaving high school. Alternatively, potential employe~may behevethat job-holdngduting

thesenioryear signals astrong wo~etticor provides sKllswhich the firm ~n use. Thus,

noncollege-bound employed seniors may have an easier school-to-work transition than their

counterparts whodonot hold jobs. This could translate into long-term benefits lfmltlal . .

(post-school) employment experien@s leadtomoretivomble future oppotiunities. Conversely,

the benefits of high school employment are likely to be smaller for individuals attending college,

since the senior year employment will have occurred several years before the end of schooling.

As an informal test of this hypothesis, the returns to student employment for

respondents with less than 13 years of education were compared to those, for the full sample

and forindividuals with morethan 12years of schooling. Thesubsample with no college is

expected to contain a relatively large proportion of individuals making immediate transitions into

the workforce after finishing high school, the group for whom senior year employment is
Page 25

hypothesized toconfer the largest benefits. Econometnc estimates forthe subsample,

summarized intable A.3, again show apositive relationship beWeenjob-holding in the senior

yearand future labor market outcomes, Theeffect ofworking inearliergrades continuesto

differ insignificantly from zero in most cases and the parameter estimates are sensitive to the

method of measuring student work houra.

Table 13 displays predicted senior year employment differentials for the subsample of

noncollege educated respondents. Contrasting these estimates tothe corresponding

prerhtions in table 12 reveals that working in the senior grade is more strongly related to future

earnings, hourty wages, and occupational attainment for persons with 12 or fewer years of

schooling than forthe full sample. Forexample, thepremium associated with worldng2Ohoure

in the interview week is 35°A for annual earnings and 18?L0for hourty wages among individuals

with less than 13yeamofeducation, versus 22Y0and llYoforthe all respondents. Similarly,

the gap in Duncan scores is 6.3 points for the former group as compared to 4.0 points among

the latter.

There is also less evidence of diminishing returns to senior year employment for the

noncollege educated. Among respondents without a college education, maximum benefits are

obtained at 29, 58, and 34 hours of work in the reference week for earnings, hourly wages, and

Duncan scores, respectively, versus 25, 22, and 24 hours for the full sample. These results

support the notion that persons working long hours in their senior year are frequently able to

continue with the same company after finishing school or to use the skills gained in related

positions.

Generally consistent, although less conclusive, findings are also obtained for fringe

benefits. In particular, heavy work mmmitments (i.e. 40 hours/week) in the senior grade

correlate with larger increases in probabilities of receiving future health insurance and pension
Page 26

coverage for respondents with 12 or fewer years of education than for the entire sample.

Among seniors working fewer hours, relative rates of fringe banefit attainment are sensitive to

whether student employment is measured in the week prior to the survey or averaged over the

academic year.

Table 14 presents corresponding information for respondents w“th more than 12 years

of schooling. As anticipated, the returns to working in the senior grade are lower than for their

counterparts never attending college. For instance, 10 hours of employment in the interview

week is associated with a 9% increase in annual earnings, a 4% rise in hourly wages, and a 2

point elevation in the Duncan index. None of these effects are statistically significant. Among

persons whose education stops with high school, the respective increases are 21%, 11%, and 4

points. Results for fringe benefits are more ambiguous, with more (less) beneficial effects for

low (high) employment houra for sample members attending college than for those who have

not. It is also noteworthy that the benefits of senior year employment are maximized at

relatively few hours for college-educated respondents (e.g. 19 hours in the reference week for

earnings and 13 hours for wages).

7. Conclusion

This study uses data from the National Longitudinal Survey Youth Cohort to examine

the effects of high school employment on future labor market outcomes. Contrary to some

previous research, the analysis fails to uncover any evidence of harmful effects of working

during high school. Instead, jobs held during the senior year yield substantial and lasting

benefits. For example, seniors employed 20 hours per week are expected to earn

approximately 20’% more annually and to receive 10% higher hourly wages, 6 to 9 years later,

than their counterparts who do not work at all. They are also more likely to receive

employer-provided fringe benefits and hold higher status occupations.
Page 27

The favorable effects of employment in the senior year persist after controlling for a

comprehensive set of background characteristics and the results are robust across a variety of

specifications, samples, and estimation techniques. For instance, gains continue to be

observed after implementing a method of accounting for selection bias and when limiting the

analysis to persons with strong (future) employment attachments. There is also some evidence

of larger advantages for females than males and for academic year versus summer

employment. The favorable impact of working in the senior grade is larger for persons without

college educations than for those completing one or more yeare of college. This suggests that

the benefits of student employment depreciate rapidly overtime, unless they immediately

precede the school-to-work transition, and may explain why the returns to job-holding in the

senior year of high school exceed those of working in the sophomore or junior grades.

Several caveats are worth noting. First, it is possible that controls for a still broader set

of covanates would reduce the advantages associated with job-holding by high school seniors.

Second, this study has focused exclusively on measurable economic outcomes. Third, the

analysis is restricted to individuals remaining in school through the normal age of high school

graduation. Finally, although this investigation covers a longer time period than previous

research, there may be deleterious impacts of student job-holding which do not show up until

later in life.

While these qualifications imply that the conclusions of this study should be interpreted

cautiously, it is doubtful that any of them acmunt for the key finding that benefits are associated

with senior year employment. The characteristics controlled for in this analysis are unusually

comprehensive and there is little evidence that the addition of covariates, beyond the basic set

available to previous researchers, substantially changes the results. The findings are unlikely

to be explained by spurious correlation between senior grade job-holding and important
Page 28

excluded characteristics, since most such factors would also be associated with employment in

the sophomore and junior years. For example, if unaccounted for differences in socioeconomic

status increase both the probability of working in high school and the level of future economic

attainment, student employment in all three grades would be positively correlated with

subsequent labor market status. Instead, strong benefits are observed only for working

seniors.

Deleterious effects of student employment on the social development of adolescents are

likely to be at least partially manifested in future labor market outcomes. The positive economic

impacts of working uncovered above therefore suggest that these problems either do not occur,

are transitory in nature, or are more than compensated for by beneficial investments in human

capital. Although the analysis does point to a negative relationship between senior year

employment and educational attainment, the predicted effect is extremely small – working 20

hours per week is associated with a reduction in schooling of less than two months. There

could be a larger impact on high school completion rates but this possibility is belied by the lack

of evidence that work in the sophomore or junior years has an impact on the highest grade

completed (among those remaining in school through their senior year) or on any of the other

outcome variables.

The likelihood that negative effects of high school work experience do not show up until

later ages than those studied is reduced by the strong positive correlation between senior year

employment and the Duncan occupational index. Working 20 hours per week in the senior year

of high school is associated with a 4 point increase in the Duncan Score.28 To the extent that

occupational attachments are established by the middle to late twenties, the Duncan index

should reveal differences in status which will be reflected in earnings at later ages.

28 For comparison purposes, the Duncan score of a welder is 6 points higher than that of an

assembler.
Page 29

Much of the alarm that the jobs held by high school students may hinder their long-term

development has been based on the analysis of nonrepresentative samples and using methods

which are unlikely to account for the selection process into student employment. The concerns

have also been magnified by a belief that job-holding among in-school youths has rapidly

increased since the end of World War II. This trend appears ‘tohave ended by the late 1970s,

with subsequent redutiions in the employment-to-population ratios of some groups (e.g. 16-18

year old boys). Moreover, the NLSY data indicates that relatively few students work the long

hours that have caused particular consternation.

Further research on the benefits and costs of student employment is needed. [n

particular, His important to better understand the mechanisms by which the early work

experience raises economic attainment, analyze the importance of characteristics of the jobs

held by high school students, and examine the types and sources of demographic group

differences in returns to student employment. Based upon the current state of knowledge,

however, concern that working during high school has extremely deleterious consequences

appears to be misplaced. A tentative but fairly strong conclusion is that light to moderate work

commitments provide important net human capital investments and so should be encouraged,

especially as students approach the end of their high school years.
Page 30

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(eds.) Adolescence and Work: Influences of Social Sfrucfure, Labor Markefs, & Culfure,

Hillsda[e, NJ: Lawrence Erlbaum Associates, pp. 189-233.

Stevenson, Wayne. 1978. “The Relationship Between Early Work Experience & Future

Employability” in Arvil V. Adams & Garth L. Mangum (Eds), The Lingering Crisis of
Page 33

Youth Unernp/oyment, Kalamazoo, Ml: W.E. Upjohn Institute for Employment Research,

pp. 93-124.

Topel, Robert H. and Michael P. Ward. 1992. “Job Mobility and the Careers of Young Men”

Quarterfy Journal of Economics, Vol. 107, May, pp. 441-79.

Tymms, P.B. & C.T. FitZ-Gibbon. 1992. “The Relationship Between Part-Time Employment and

A-level Results” Educafiona/ Research, Vol. 34, No. 3, Winter, pp. 193-9.

Weiss, Andrew. 1988. “High School Graduation, Performance, and Wages” Journal of Political

Economy, Vol. 96, No. 4,785-820.

Yeatts, James. 1994. %Vhich Students Work and Why?”, unpublished M.A. thesis, University of

North Carolina Greensboro.
Table 1: Results of Previous Studies Examining the Effects of High School Employment

Author Sample “Effects” of Employment and Comments

hrcraased Work Hours

arone (1993) 2000 students in 4 high Slightly lower GPAs, beyond a low Potentially

schools in upstate New work threshold. nonrepresentative sample,

York no covariates.

‘Amim (1984); National Longitudinal Reduced study time and time spent in Somewhat selected sample

‘Amico & Baker Survey Youth Cohort school acfivitiea. Positive effects on (e.g. clase rank only

984) (NLSY), 1979-82 class rank for white males, no impact available for respondents

interviews high school for other groups. Increased knowledge graduating high school by

students (in 1979). of work world for females. Increased January 1991).

(decreased) probability of dropping out

and lower (higher) rates of college

attendance above (below) 20

hourshveek of work. Lower

unemployment rates, higher wages,

and higher SEI in first year out of high

school (for noncollege bound).

iade & Peterson 351 tenth grade students Statistically insignificantly higher Small, possibly

1980) in two urban high schools grades. unrepresentative samples.

in upper midweat.

imenberger & 531 tenth and eleventh Greater absenteeism from school, Unrepresentative sample,

teinberg (1980); graders from 4 .%uthem lower GPAa, less time studying, lower selection procedure

~reenberger, et. California high schools. educational expectations, more introduces biases.

1.(1982) Sample includes 212 fraquent delinquency, greater

students currently “business knowledge”.

holding first jobs and 319

who had never worked.

il[ydahl (1990) 1987 National intermediate work levels (1-10 Simultaneous equation

Assessment of Economic hourslweek) associated with highest model is poorly describes

Education Suwey. levels of academic achievement. specifications vary acrosa

Sample includes juniors outcome measures.

snd seniom not attending

vocational schools.

Iarsh (1 991) High School and Beyond Reduction in a wide variety of Attrition reduces sample

Swvey (HSB), 1980-84 sducation outcome measures. size and could induce bias.

interviews. Decraase in probability of Effects of hours are

unemployment two yearsafternormal assumed to be linear

high school graduation date.

leyer & Wise National Longitudinal Increases in academic performance, Relatively good controls for

1982) Survey of the High weeks worked, and wage rates. background characteristics.

School Class of 1972

(NLS72), 1972-76

interviews, males only.

!Ortimer & Finch Youth i“ Transifio” Lower grades, academic self-esteem, Data available for 5 yea~

1986) Study, 1968-74 educational, and occupational after normal high school

interviews, tenth graders aspirations. Higher 1973 earnings and graduation date. Effects of

(in 1966). occupational attainment levels. dropping out of high school

Stronger effects at high work hours. not adequately accounted

for.
Table 1: (continued)

Author Sample “Effect# of Employment and Comments

Incrsased Work HOUR

Schill, et. al. 14-19 year old students in Higher GPAs, particularly at 1-20 No covariates controlled for,

(1985) Washington State taking hours OfWOdC Positive effecl of SEC on

classes raquirad for high work and GPA suggest

school graduation. confounding factors.

Steel (1991) NLSY, 1979-81 interviews, Future school enrollment rates raised Inclusion of out-of-school

17-18 year olds (in 1979). (lowered) by small to medium (high) youths in sample biases

work hours for whites. More negative analysis of future

effecfa for blacks. Subsequent weeks enrollment rates. Imprecise

worked increased for whit+ no effect estimatea for nontitea.

for blacks, hispanics.

Stephenson National Longitudinal Raises future wages, especially for Wages of nonworkers set to

(1981) Suwey (NLS) of Young full-time employment during high zero, rather than to

Men, 1966-71 intewiews. school. potential earnings levels.

Future wages could reflect

continuation of high school

jobs.

Stern & Nakata NLSY, 1979.82 interviews, Higher howfy earnings and less Relatively few covariates

(1989) high school seniors who unemployment after high school controlled for.

graduated high school but graduation, particularly when student

did not directly enroll in employment required complex

college. dealings with people, things, or data.

Stevenson (1978) NLS young men and High employment rates and earnings Few mvariates controlled

young women. 16-19 year in later years. for and some (e.g. labor

olds in initial survey year market knowledge) may be

and followed for 7 years. endogenous.

Steinberg & 10th-12th graders from 6 No effect for 1-10 hours of work. Few covariates controlled

Dornbusch high schools in Northern Negative effects on a wide variety of for. Some outcome

(1991); Steinberg, California and 3 in school performance, psychological, variables assessed 5

et. al. (1993) Wisconsin, interviewed in and psychosocial maturity variables months after employment

fall 1987 and spring 1988 for longer work hours. status was measured.

(and 1 year later in Potentially severe selection

Steinberg, et. al.) bias in longitudinal analysis,

Steinberg & Sophomores and juniors Greater work orientation: leas school Unrepresentative sample

Greenberger in 1979 tiom four involvement but nodifference in

(1982)

and selection procass

Southern Catiiornia high absenteeism or GPAx more introduces axtremely

schools reintewiewed in materialistic attitudea greater use of severe biases of unknown

1980. cigarettes and marijuana. direction.

Tymms & 1969 A-1evel [formation Small negative effect on A-1evel Few @variates controlled

Fitz-Gibbon Systems Data Set. grades, particularly above 9 for.

(1992) Sample inciudea U.K. hourskeek. No impact on study time.

students studying for the

A-level axams.
Table 2:

Employment-to-Population Ratios of 16-18 Year Old Students in Selected Yeare

Age in 1968 1978 1988 1989 1992

Yeara

(School is Major Activity) (Enrolled in School)

Males and Females

16

77

18

16-18

Males

16

77

18

16-18

18

17

18

18-18

23.5 %

30.7

30.0

27.6

27.0 ?4

35.8

38.3

32.4

19.9 %

25.4

22.8

22.5

25.5 %

35.2

31.8

30.4

26.2 %

36.3

32.8

31.4

24.9 %

34.0

30.3

29.3

23.8 % 28.8 %

33.9 40.0

33.3 44.3

29.8 37.2

23.4 “h 27.3 ‘h

32.8 39.2

32.5 44.2

29.1 38.3

23.8 y. 30.3 ‘%

35.2 40.9

34.1 44.3

30.6 38.0

22.4 ‘%

33.7

40.2

31.2

22.0 %

32.7

39.6

30.6

22.8 ?4

34.8

40.8

31.9

Note Table shows annual average employment-to-population ratios for 16 to 18 year olds for whom

school is the major activity (1 968, 1978, 1988) or who are enrolled in school (1989, 1992). Source:

unpublished Current Popu/afionSurvey data provided by the U.S. Department of Labor, Bureau of Labor

Statistics.
Table 3: Sample Means for Selected Characteristics

All Respondents

Respondents Interviewed

in 1991

Demographic Characteristics

Female

Black

Hispanic (Nonblack)

Foreign Language Spoken in Home

Foreign Bom

Number of Siblings

Education

Attends Public School

Negative Attitude Towards School

Highest Grade Expected

AFQT Score (1 981)

Residence

In Urban Area

[n SMSA

Household Resources at Age 14

Magazines

Newspapem

Library Card

Drug Use

Used Cigarettes By Sophomore Year

Used Marijuana or Hashish By Sophomore Year

Religion:

Baptist

Catholic

Jewish

Characteristics of Mother

Foreign Born

High School Graduate

College Graduate

Characteristics of Father

Foreign Born

High School Graduate

College Graduate

N

48.8 Y,

11.8

6.1

10.9

2.6

3.0

92.8

15.5

14,3 yrs

45.7

74.1 Y,

68.1

73.1

84.2

74.0

61.8

7,8

19.0

33.1

1.1

5.8

69.8

11.1

5.7

67.1

18.4

1,149

48.9 ‘h

11.5

6.3

11.2

2.7

3.0

92.8

15.3

14.3 yrs

45.9

74.6 ?6

66.4

73.9

84.3

74.1

62.4

7.9

18.2

33.6

1.0

6.2

69.7

10.8

5.8

67.5

18.4

1,067

Note Unless otherwise specified in the text of the paper, all variables are obtained from 1979 interview

and refer to 1979, Respondent is defined to have negative attitudes towards school if they respond that

they either of the statements “most of my classes are boring” or ‘“ldon’t feel safe at this school” ars “very

true” or if they say that they are “very dissatisfied with their school,
Table 4: Frequency and Amount of High School Employment

Sophomores Juniors Seniors

(a) (b) (a) (b) (a) (b)

Employment Status in Week Prior to Suwey Date

Percent Working 27.9 % 28.3 % 42.4 % 43.3 % 50.3 % 50.8 Y,

Ave. Hours/Week 3.3 hr 3.3 hr 6.6 hr 6.7 hr 9.4 hr 9.6 hr

Ave. HoursAfVeek if Employed 11.9hr 11.8hr 15.5 hr 16.5 hr 18.7 hr 18.9 hr

Hours Worked in Week Prior to Sunfey

o 72.2 % 71.8 Y. 57.6 % 56.6 “h 49.7 % 49.296

1-1o 16.3 16.4 15.4 15.8 11.3 11.3

11-20 8.1 8.6 17.1 17.5 20.5 20.5

21-30 2.2 2.3 7.6 7.7 13.4 13.8

31-40 0.7 0.7 2.0 2.1 4.1 4.2

>40 0.5 0.4 0.4 0.4 1.0 1.0

Academic Year Employment (From Work History File)

Percent Employed 1 Week 63.9 % 64.9 %

Percent of Weeks Employed 41.5 % 42.6 “k

Ave. Hours/Week 7.7 hr 8.0 hr

Ave. HoursAfVeek if Employed 18.6 hr 18.9 hr

Summer Employment (From Work History File)

Percent Employed 1 Week 56.2 Y. 57.2 %

Percent of Weeka Employed 44.1 % 45.0 %

Ave. HoursM/eek 10.2 hr 10.3 hr

Ave. Hours/Week if Employed 23.1 hr 23.0 hr

72.6 ‘%

51.5 %

12.1 hr

23.5 hr

59.6 %

48.5 %

13.7 hr

28.2 hr

73.4 %

52.3 !4

12.3 hr

23.5 hr

60.8 %

49.6 YO

14.0 hr

26.1 hr

Note Full sample included in column (a), n=l 149. Column (b) includes respondents interviewed in 1991,

n=1067, A~demic year employment status calculated for 26 week periods covering the months Of

October, November, February, March, April, and May of the relevant suwey years. Summer employment

status ia calculated for 8 week periods beginning with the week which includes July 1 of the summer

before the specified high school year.
Table 5:

Houre of High SchooI Employment by Race and Sex

Type of Full

Employment Sample Males Females Whites Nonwhites

Employment Hours in Week Prior to Survey Date

Sophomore Grade Employment

Ave. Hours 3.3 hr 4.1 hr 2.6 hr 3.5 hr 2.5 hr

Ave. Hours if Employed 11.8 i2.8 10.5 11.3 16.3

Junior Grade Employment

Ave. Hours 6.7 hr 7.9 hr 5.5 hr 7.3 hr 4.2 hr

Ave. Hours if Employed 15.5 16.8 13.9 15.6 15.0

Senior Grade Employment

Ave. Houra 9.6 hr 10.1 hr 9,0 hr 10.2 hr 6.6 hr

Ave. Hours if Employed 18.9 19.7 17,9 18.8 19.2

Academic Year Employment Houre (From Work History File)

Junior Grade Employment

Ave. Houra 8.0 hr 9.1 hr 6.9 hr 6.6 hr

Ave. Hours if Employed 78.9 20.1 17.4 18.7

Senior Grade Employment

Ave. Hours 12.3 hr 13.4 hr 11.2 hr 13.1 hr

Ave. Hours if Employed 23.6 25.3 21.8 23.5

Summer Employment Hours (From Work History FiIe)

Junior Grade Employment

Ave. Hours 10.3 hr 12.6 hr 8.0 hr 10.6 hr

Ave. Houra if Employed 23.0 24.8 20.5 22.2

Senior Grade Employment

Ave. Houra 14.0 hr 16.9 hr 10.9 hr 14.5 hr

Ave. Hours if Employed 28.1 30.9 24.6 27.9

5.4 hr

20.2

8.8 hr

24.3

9.4 hr

28.4

11.3hr

29.5

Note Sample includes respondents interviewed in 1991. Sample sizes are 1067, 545, 522,877, and 190

for the full sample, males, females, whites, and nonwhites respectively.
Table 6:

Economic Outcomes By High School Employment Houra in Week Prior to Survey

Outcome Measure (1988-90 Average)

High Scfrool Duncan Employer Employer

Employment Annual Occupation Health Pension

Hours N Earnings Index Insurance Plan

All Respondents 1,087 $16,513 42.4 75.6% 53.6%

Employment Ststus in Week Prior to Survey Date

Sophomore Work Houre

o 766 $16,012 42.1 7&2°h 54.1%

1-20 266 $17,846 44.3 77.5% 52.3%

>20 35 $17,441 34.0 71.1% 53.9%

Junior Work Hours

o

1-20

>20

Senior Work Hours

o

1-20

>20

Junior Work Houra

o

1-20

>20

Senior Work Hours

o

1-20

>20

604

355

108

$15,086 41.5

$17,969 44.1

$19,739 41.3

74.OY, 52.9’%

77.2% 54.5%

79.9% 55.0%?

525 $14,422 39.6 70.7%

339 $17,949 45.8 80.8%

203 $19,510 43.6 79.7%

Academic Year Work Hours (From Work History File)

370 $13,856 40.4 72.3%

553 $17,592 44.1 77.?%

139 $19,241 40.7 77.9%

282 $12,765 37.7 69.9%

494 $16,703 43.4 75.0%

289 $19,789 44.9 81.9%

49.6%

58.3?4

56.1’%

53.7%

52.2%

58.9%

51.7%

51.7%

58.3%

Note: Sample includes respondents interviewed in 1991. Table shows average values of outcome

variables for 1988-90 time period, If data is missing for one interview, the average is calculated for the

remaining two yeara.
Table 7:

Regression Estimates of Log Earnings on High School Employment Hours

Type Regression Specification

of

Employment (a) (b) (c) (d) (e)

Employment Hours in Week Prior to Survey Date

Sophomore Hours .0171 .0055 .0013 .0016 4.8E-4

Hours Squared -4.4E-4 -2.4&4 -1.5 E-4 -6.8E-5 -3.7E-5

P-Value [.193] [.683] [.642] [.966] [.980]

Junior Hours .0202 .0100 .0050 .0013 .0013

Hours Squared -3.8E-4 -1.IE4 -8.5&5 -1 .7E-5 1.6 E-5

P-Value [.005] [.173] [.701] [.878] [.877]

Senior Hours .0238 .0210 .0185 .0178 .0168

Hours Squared -4.6E-4 -4.5E-4 -3.7E-4 -3.7E-4 -3.4E-4

P-Value [.000] [.008] [.007] [.014] [.019]

Academic Year Employment Hours (From Work History File)

Junior Hours .0286 .0155 .0068 .0041 .0056

Hours Squared -6.0E4 -6.IE-4 -3.8E-4 -2.6E-4 -2.8E-4

P-Value [.004] [.243] [.371] [.601] [.610]

Senior Hours .0234 .0176 .0’151 .0138 .0120

Houra Squared -3.7E-4 -2.OE-4 -1 .9E-4 -2.OE-4 -1 .5E-4

P-Value [.000] [.002] [.006] [.036] [.062]

Notes

1. Sample includes respondents interviewed in 1991. Outcome measures are three-year averages for the

1988-90 period. Table displays regression coefficients on work hours and work houra squared (n=990).

P-value of the hypothesis Mat the coefficients on hours worked and hours worked aquared are jointly

equal to zero (obtained from F tests) is displayed in brackets.

2. Model (a) shows results from regressions which mntrol for work hours in a single high school class. In

model (b), hours in all high school grades are controlled for (sophomore, junior, and senior hours in the

top panel, junior and senior hours in the bottom panel). Model (c) adds regressors for the high school

grade in 1979, ethnic status (black, hispanic, white), sex, msrital status, geographic ragion (4 categories),

residence in an SMSA and Urban area, and the local unemployment rate (6 categories). Model (d)

includes the covariates in (c) plus whether the respondent and his/her parents are foreign born, if a

foreign language was spoken in the home when the respondent was a child, mother and father’s

educational attainment (4 categories each), whether magazines, newspapers, or library card were in the

home when the respondent was 14, number of siblings, religion (4 categories), educational attitudes (if the

respondent considered his school boring, unsafe, or was very dissatisfied with the school), educational

expectations, type of school at 1979 survey date (public vs. private), vhether the respondent had smoked

cigarettes or used marijuana or hashish by the sophomore year of high school, the log of average family

incomes during the respondent’s sophomore through senior years, and (in the lower panel) work hours

and hours squared in the weak prior to the sophomore year survey date. Model (e) includes these

variables, plus the (1981 ) AFQT score.
Table 8:

Additional Regression Estimates for Log Earnings on High School Employment

Type Full Sample Estimates Males FemaIes

of

Employment (a) (b) (c) (d) (e) (f)

Employment Houra in Week Prior to Survey Date

Sophomore Hours 4.8 E-4 4.3E4 .0134 -.0199

Hours Squared -3.7E-5 -3.8 E-5 -4.5E-4 9.7E-4

P-Value [.980] [.342] [.167] [.551]

Junior Hours .0013 .0013 7.8E-4 .0064

Hours Squared 1.6E-5 1.8E-5 4.IE-5 -2. IE-4

P-Value [.877] [.304] [.821] [.936]

Senior Hours .0168 .0198 .0146 .0290

Hours Squared -3.4E-4 -4.OE-4 -2.5E-4 -8.IE-4

P-Value [,019] [.088] [.038] [.110]

Inverse Mills Ratio -.0242

T-Statistic (.299)

Employment Hours or Weeks (From Work History File)

Junior HoursrWeeks

HrslWks Squared

P-Value

Senior Hours/Weeks

HrsNVks Squared

P-Value

Summer Hours

Hours Squared

P-Value

Inverse Mills Ratio

T.Statistic

.0056 .0059

-2.8E4 -3.OE-4

[.610] [.195]

.0120 .0078

-1 .5E-4 -7.3E-5

[.062] [.1 06]

.0065 .0010 .0031 4.IE-4

-3.OE-4 -5.3E4 -4.7E-5 -4.6E-4

[.646] [.386] [.886] [,248]

.0092 .0076 .0076 .0225

-9.1 E-5 3.5E-6 -4.7E-5 -4.3E-4

[.131] [.059] [.143] [.152]

.0049

-1.3 E-4

[.350]

.0413

(.717)

Note See notes on table 7, Table presents coehlcients on linear and quadratic terms for hours (or

weeka) worked. P-Values (obtained from F or likelihood ratio tests) are shown in brackets and t statistics

in parentheses. Additional covariates are the same as in specification (e) of table 7 and employment

coefficients from that specification are displayed in mlumn (a). Column (b) shows results of an equation

which corrects for selection bias by including the inverse Mills ratio from probit estimates of the probability

of working positive hours in the senior year of high school. Column (c) inc[udes controls for average hours

worked during an eight week period beginning with the week which includes July 1 of the summer before

the senior year of high school. In column (d), weeks rether than hours/week of academic year

employment, are controlled for. Column (e) and (f) present estimates for the same specification as

column (a), for subsamples of males (n=512) and females (n=471 ).
Table 9:

OLS and Tobit Estimates of Wages and Employment Leveis on

High School Employment Hours and Covariatea

Type Annual Annual

of Weekly Hourly Weeks Hours

Employment Wages Wages Worked Worked

Sophomore Hours

Hours Squared

P-Value

Junior Hours

Hours Squared

P-Value

Senior Hours

Hours Squared

P-Value

O(*)

Employment Hours in Week Prior to Suwey Date

.0031 .0013 .1925

-1 .6E-4 -9.4E-5 -.0056

[.616] [.747] [.627]

.0054 .0039 .0213

-1.2E-4 -5.6E-5 -.0022

[.576] [.535] [.640]

.0096 .0095 .4551

-2-OE-4 -2.2E-4 -.0061

[.088] [.067] [.000]

.547

1.172

-.013

[.967]

.869

-.034

[.988]

10.006

-.369

[.000]

.979

Academic Year Employment Hours (From Work History File)

Junior Hours .0054 .0030 -.0203 5.489

Hours Squared -Z6E-4 -1 .9E-4 -3.2E-4 -.i28

P-Value [.474] [.513] [.946] [.659]

Senior Houra .0058 .0053 .3516 12.223

Hours Squared 4.6E-5 -5.OE-5 -.0034 -.129

P-Value [.133] [.148] [.001] [.001]

@i”) .548 .979

Note See notes on table 7. The same covariates are included as in specification (e) of that table.

Estimation technique is OLS in columns (a) and (b) and Tobit in (c) and (d). Sample sizes are 979,960,

1048, and 1050 in columns (a) through (d) respectively. 44 observations are leftensored at zero hours

or weeke and 394 are right-censored at 52 weeks. 0(.) is the predicted percentage of noncensored

obsenrations (estimated as the average value of @(XfMs) in the single limit TO BIT case).
Table 10:

Regression snd Ordered Probit Estimates of Duncan Scores, Fringe Benefits and

Educational Attainment on High School Employment Houm and Covariates

Type Duncan Employer Employer Highest

of Occupation Health Pension Grade

Employment Index Insurance Plan Completed

Employment Hours in Week Prior to Survey Date

Sophomore Hours .0875 .0126 -.0017 -.0033

Hours Squared -,0058 -5.3 E-4 -I,8E-4 3.9 E-4

P-Value [,333] [.303] [.428] [.364]

Junior Hours .0263 -.0022 -.0077 -.0042

Hours Squared -.0023 9.6 E-5 3.1 E-4 -3.4E-5

P-Value [.804] [.973] [.735] [.678]

Senior Hours .3532 .0242 .0311 -..0041

Hours Squared -.0075 4.4E-4 -8.OE-4 -3.2E-4

P-Value [.012] [.009] [.002] [.006]

Academic Year Employment Hours (From Work History File)

Junior Hours .0074 -.0108 -.0161 -.0045

Hours Squared -.0045 1.3E-4 5.IE-4 -1 .8E-4

P-Value [.266] [.399] [.451] [.352]

Senior Hours .2426 .0216 .0183 -.0040

Houra Squared -.0020 -2.6 E-4 -3.4 E-4 -1 .2E-4

P-Value [.008] [.006] [.098] [.262]

Note See notes on table 7. The same covariates are included as in specification (e) of that table. The

Duncan score snd grade completion equations are estimated using OLS. Ordered probit models are

estimated for employer health insurance and pension coverage. The dependent variable in these

equations is equal to O, 1, and 2 if the fringe benefit is provided at none, some, or all or the three interview

dates, respectively. P-Values for these cases are obtained from likelihood ratio tests. Missing values on

the dependent variables reduce We sample sizes to 1000, 961, 961, 1045, respectively, for the Duncan

index, health insurance, pension coverage, and grade completion.
Table 11:

Regression and Ordered Probit Estimates of Log Earnings, Duncan Scores,and Fringe

Benefits for Persons Working Over 26 Weeks Per Year

_—

Type Duncan Employer Employer

of Annusl Hourly Occupation Health Pension

Employment Earnings Wages Index Insurance Plan

Employment Houra in Week Prior to Suwey Date

Sophomore Hours .0041 .0045 .1012 .0098 -.0049

Hours Squared -’1.9E-4 -1 .9E4 -.0068 -4.3E-4 -9.3E-5

P-Value [.521] [.438] [.233] [.462] [.409]

Junior Hours 3.5 E-4 .0039 .0642 -.0100 -.0116

Hours Squared 1.OE-4 -4.3E:5 -.oo4~ 3.8E-4 4.8E-4

P-Value [.390] [.374] [.651] [.722] [.507]

Senior Houre .0118 .0076 ,3498 .0282 .0362

Hours Squared -2.5 E-4 -1.8E4 -.0076 -5.2E4 -9.5E-4

P-Value [.023] [.135] [.021] [.004] [.000]

Academic Year Employment Hours (From Work History File)

Junior Hours -5.7E-4 6.9 E-4 -.0411 -.0119 -.0177

Hours Squared 5.8 E-5 -2.1 E-5 -.0036 2.6 E-4 6.4 E-4

P-Value [.915] [.993] [.197] [.622] [.408]

Senior Hours .0083 .0041 .2246 .0234 .0207

Hours Squared -8.2 E-5 -1.7E-5 -.0016 -3.0E4 =1.OE-4

P-Value [.023] [.111] [.014] [.008] [.083]

Note See notes on tables 7 and 10. Sample includes respondents interviewed in 1991 who average

more than 26 weeks of employment per year during the 1988 through 1990 period (n=876).
Table 12

Change in Economic Attainment Associated With Senior Year Employment

Hours of Duncan Employer Employer

Employment Annual Hourly Occupation Health Pension

in Senior Year Earnings Wages Index Insurance Plan

Differentials Associated with Employment in Week Prior to the Survey Date

10 Hours 14.3% 7.7% 2.8 pts. .075 .087

20 Hours 22.1 11.0 4.1 .114 .115

40 Hours 13.4 3.8 2.1 .097 -.015

Max. Difference 24.6 hr 22.2 hr 23.5 hr 27.3 hr 19.4 hr

Differentials Associated with Academic Year Employment (from Work History File)

10 Hours 11.0% 5.0% 2.2 pts. .074 .055

20 Hours 19.3 9.1 4.0 .125 .086

40 Hours 25.5 14.4 6.5 .168 .069

Max. Difference 37.9 hr 53.6 hr 59.8 hr 42.1 hr 26.8 hr

Note: Sample includes respondents interviewed in 1991. Table shows difference in predicted outcomes

compared tope~ons notworMng inthesenior yearofhigh school. Estimates areobtained from OLS and

ordered probit estimates of equations which include the same set of covariates aa in specification (e) of

tsble7. The foutihand fiffhcolumns refer to fiepmbabili~ thatthe respondent receives empIoyerhealth

insurance orpension coversge atthetime of the 1988, 1989, and1990 surveys, Sample averages forthe

Duncan Index and probabilities of receiving health insurance and pension coverage from the employer are

42.4 points, .623, and .375 respectively.
Table 13:

Change in Economic Attainment Associated With Senior Year Employment

for Respondents Never Atlending College

Houre of Duncan Employer EmpIoyer

Employment Annual HourIy Occupation Health Pension

in Senior Year Earnings Wages Index Insurance Plan

Differentials Associated with Employment in Week Prior to the Survey Date

10 Hours 21.1% 41.0% 3.8 ptS. .029 .054

20 Houra 35.4 18.0 6.3 .057 .075

40 Hours 32.9 16.8 7.4 .110 .011

Max. Difference 2&9 hr 57.8 hr 34,4 hr >100 hr 20.8 hr

Differentials Associated with Academic Year Employment (from Work History File)

10 Houra 10.2% 4.9% 4.4 pts. .070 .060

20 Hours 23.5 11.9 7.3 .124 .096

40 Hours 63.4 34.0 8.4 .192 .085

Max. Difference no maximum no maximum 33.8 hr 59.8 hr 28.2 hr

Note: See notes on table 12. Sample includes respondents interviewed in 1991 who have less than 13

years of education (n=478).
Table 14

Change in Economic Attainment Associated With Senior Year Employment

for Respondents With College Education

Houra of Duncan EmpIoyer Employer

Employment Annual Hourly Occupation Health Pension

in Senior Year Earnings Wages Index Insurance Plan

Differentials Associated with Employment in Week Prior to the Survey Date

10 Houra 9.070 4.1% 1.9 pts. .126 .129

20 Hours 11.5 3.3 2.3 .180 .166

40 Houra -3.1 -12.0 -1.7 .118 -.039

Max. Difference 18.7 hr 13.4 hr 17.3 hr 24.6 hr 18.9 hr

Differentials Associated with Academic Year Employment (from Work History File)

10 Hours 11.4% 3.3% -0.8 pte. .093 .049

20 Hours 17.5 5.7 0.4 .146 .077

40 Houre 11.4 7.1 9.0 .149 .064

Max. Difference 25.0 hr 36.5 hr no maximum 30.6 hr 27.4 hr

Note See notes on table 12. Sample includes respondents interviewed in 1991 who have more than 12

years of education (n=534).
Table A.1:

Sample Means By High School Employment Status in Week Prior to the Survey Date

~ore Hours Junior HOurs -r Hours

o >0 0 >0 0 =-0

Demographic Characteristics

Female

Black

Hispanic (Nonblack)

Foreign Language Spoken in Home

Foreign Bom

Number of Siblings

Education

Attends Public School

Negative Attitude Towards School

Highest Gmde Expected

AFQT Score (1991)

Household Resources at Age 14

Magazine

Newspapers

LibraV Card

Drug Use

Used Cigarettes By Sophomore Year

Used Marijuana By Sophomore Year

Religion

Baptist

Catholic

Jewish

Characteristics of Mother

Foreign Born

High School Graduate

College Graduate

Characteristics of Father

Foreign Born

High School Graduate

College Graduate

N

51 .4%

14.4

6.7

11.2

2.7

3.1

92.4

15.8

14.3

44.0

72.5

83.1

71.4

61,4

8.0

19.6

31.3

1.0

6.7

57.8

10.1

6.0

48.6

18.0

766

.

42.5%

4.3

5.3

11.3

2.7

2.9

93.7

14.0

14.3

50.9

77.7

67.4

81.1

65.1

7.6

14.6

39.5

1.0

5.0

61.5

12.6

5.3

50.5

19.3

52.2%

16.7

6.0

11,9

2.0

3.1

93.7

17.1

14.2

43.5

71,8

82.3

71.0

61,1

8.4

20.7

29.8

0.7

6.3

54.1

10.8

5.0

44.9

18.9

301 604

44.7%

4.8

6.7

10.4

3.7

2.9

91.6

13.0

14.5

49.1

76.9

87.0

76.2

64.2

7.1

14.9

36.7

1.5

6.0

65.0

10.8

6.9

54.6

A7.7

463

49.5%

17.3

6.5

12.2

2.5

3.1

93.1

17.5

14.3

42.7

69.7

82.3

67.8

59.8

7.8

21.7

29.9

1.0

5.5

54.5

10.3

5.3

43.0

17.9

525

48.3%

5.9

6.1

10.3

3.0

2.9

92.4

13.1

14.4

49.1

78.0

86.3

80.3

64.9

7.9

14.8

37.3

1.1

6.8

63.1

11.3

6.3

55.0

18.8

542

Note Sample includes respondents interviewed in 1991.
Table A.2:

Coer%cients from Regression of of Log Earnings on High

School Employment Hours and Covariates

Regression Absolute Value of

Regressor Coefficient T-Statistic

Sophomore in 1979

Ethnic Status Black

Hispanic

Female

Marned

Resides in: Northeast

Northcentral

South

SMSA

Urban Area

Local Unemployment Rate <3%

3-6%

6-9%

9-1 2%

>12Y0

Respondent is Foreign Bom

Mother is Foreign Born

Father is Foreign Bom

Foreign Language Spoken in the Home

Mother’s Education: High School Dropout

High School Graduate

College Graduste

Father’s Education: High School Dropout

High School Graduate

College Graduate

Magazines in Home (at 14)

Newspaper in Home (at 14)

l-ibra~ Card in Home (at 14)

Number of Siblings

Attends Public School (1979)

Educational Expectations

Negative Attitude Concerning School

Religion: Baptist

Catholic

Jewish

Has Smoked Cigarette (by Sophomore Year)

Marijuana/Hashish Use (by Sophomore Year)

Natural Log of Ave. Family Income

AFQT Score (1 981)

Note The dependent variable is the natural log of average annual earnings in 1988 through 1990. Work

hours and hours squared in the week prior to the sophomore, junior, and senior year (of high school)

interviews also controlled for, These coefficients are shown in specification (e) of the top panel of table 7.

.0478

-.2758

~.l 396

-.5398

-.0236

.7857

.0502

.1981

-.0916

.1423

2.0869

1.7228

1.4293

1.3110

1.0680

-.1240

.1902

.1119

.0305

-.1800

-.0978

-.1585

.2249

.1964

.1823

-.0057

.0278

-.0664

.0178

-.1571

.0348

-.1440

-.0168

-.0154

.0217

-.0482

-.0717

.1520

.0049

(0.81)

(2.37)

(0.85)

(9.06j

(0.36)

(1.81)

(0.54)

(2.11)

(0.95)

(1.43)

(6.82)

(7.33)

(6.10)

(5.15)

(2.82)

(0.57)

(1.15)

(0.69)

(0.23)

(1.18)

(0.66)

(0.92)

(1.74)

(1.56)

(1.27)

(0.08)

(0.32)

(0.92)

(1.15)

(1.29)

(2.09)

(1.74)

(0.20)

(0.21)

(0.08)

(0.78)

(0.65)

(2.44)

(3.40)
Table A.3:

Regression and Ordered Probit Estimates of Log Earnings, Duncan Scores,and Fringe

Benefits for Respondents Never Attending College

Type Duncan Employer Employer

of Annusl Hourly Occupation Health Pension

Employment Earnings Wages Index Insurance Plan

Employment Houre in Week Prior to Survey Date

Sophomore Hours -.0104 -.0049 -.0091 .0272 .0080

Hours Squared 2.3E-4 1.IE-4 -.0053 -8.OE-4 -3.8 E-4

P-Value [.755] [.860] [.121] [,325] [.668]

Junior Hours .0046 .0026 -.0682 -.0055 -.0121

Hours Squared 1.4E-4 -8.3E-5 .0042 3.8E-4 -2.IE-4

P-Value [.952] [.962] [.741] [.711] [.581]

Senior Houra ,0232 .1263 .4407 .0076 .0197

Hours Squared -4.OE-4 -2.1 E-4 -.0064 -7.3E-6 4.7E-4

P-Value [.033] [.077] [.001] [.443] [.271]

Academic Year Employment Hours (From Work History File)

Junior Hours .0170 .0120 .2180 -.0168 -.0131

Houra Squared -9.4E-4 -7.OE-4 -.0080 3.IE4 5.4E-4

P-Value [.089] [.025] [.606] [.507] [.680]

Senior Hours .0068 .0039 .5151 .0194 .0205

Hours Squared 8.7E-5 8.4 E-5 -.0076 -1 .6E-4 -3.6E-4

P-Value [.039] [.059] [.000] [.061] [.214]

Note See notes on tables 7 and 10. Sample includes respondents interviewed in 1991 who have less

than 13 years of education.
Numb.a

01

02

03

04

05

06

07

08

09

10

11

12

13

National Longitudinal Surveys (NLS)

Discussion Paper Series

Au.LhQI

Michael R. Pergnmit

Norman M. Bradbum

Mankr R. FrankeI

Reginald P. Baker

Michael R. Pergamit

Saul Schwartz

Robert Hutchens

George Jakubson

A. Cohn Cameron

R. Mark Gritz

Thomas MaCurdy

Henry S, Fnrber

Frank L, Mott

Paula Baker

Audrey Light

Manuelita Ureta

Lisa M. Lynch

Evangelos M. Falaris

H. Elizabeth Peters

Anne Hill

June E. ONeill

Arleen Leibowitz

Jacob Alex Klemmn

Linda Waite

Lee A. Lillard

Joseph G, Altonji

Thomas A. Dunn

IiLk

How the Federal Government Uses Data from

the National Longitudinal Surveys

A Comparison of Computer-Assisted Personal

Interviews (CAPI) with Paper-and Pencil interviews (PAPI) in the National Longitudinal Survey

of Youth

Dynamic Models of the Joint Determination of

Labor Supply and Family Structure

The effects of Unemployment Compensation

on the Unemployment of Youth

Evaluating Competing Theories of Worker

Mobili~

Evaluation of the 1989 Child-care Supplement

in the National Longitudinal Survey of Youth

G.e.nderDifferences in the Quit Behavior of

Young Workers

The Impact of Private Sector Training on Race

and Gender Wage Differentials and the Cnreer

Patterns of Young Workers

Responses of Female Labor Supply and Fertility

to the Demographic Cycle

A Study of Intercohort Change in Women’s

Work Patterns and Enmings

Women’s Employment During Pregnnncy

nnd Following Binh

Work Experience, Job Tenure, Job Separation,

and Wage Growth

Family Background and Labor Market Outcomes
14

15

16

17

18.

19.

20.

George J. Borjas

Stephen G. Bronnra

Stephen J, Trejo

James J. Heckman

Stephen V. Cameron

Peter Z. Schochet

R Mark Gritz

Thomas MaCurdy

Alan L. Gustman

Thomas L. Steinmeier

Audrey Light

Christopher J. Ruhm

Mark Lowenstein

James Spletzer

Self-Selection aud fntemal Migration in the

United States

The Detenninnnts and Consequences of Public

Sector md Private Sector Training

Participation in Low-Wage Labor Markets by

Young Men

Retirement in a Family Context A StmcturaI Model

for Husbands and Wives

Transitions from School to Work: A Survey of

Research Using the National Longitudinal Surveys

High School Employment Consumption or

Investment

Informal Training A Review of Existing Data and

Some New Evidence