High School Employment

Audrey tight

The Ohio State University

June 1995

W paper w tidd ~ tie U.S. Dq~ent of tir, Bwau of Labr Sbtistics wdw mdl pwchse order.

me tiws qressd here ~e those of tie authors md do not nwemtily r~a the tiem of tie U.S.

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Executive Summary

This study addr~= a qu=tion that, dwpite its apparent simplicity, h- yet to be satisfactorily answered by social scientkts DOW hoIding a job while enrolled in high school enhanm, detrwt from, or have

no effect on subsequent cmeer outcomes? from a theoretical pempective, high school employment h= an

ambiguous effect on ca~r outcoms. On one hind, it ,may give students a “leg up,’ in their subs~uent

careers by providtig them with maketable skills, good work habits, ad knowledge of the world of work.

On the other had, high school employment may indirectly hinder subsequent employment opportunities by

preventing students from performing m well in high school m they otherwise would. In light of the wideIy

documented dlfficulti- fawd by many youth in trasiting from schml to a permaent, productive position

in the labor force, it is important to know whi~ effect dominat=. After all, pubfic policy cm readily be

directed towmd helpi~g hlgb school students gati emplo~ent (by providing job platiment services, for

mample) or, m appropriatee, toward discouraging such wtivit ies.

The rewon social scientists have ftiled to rech a consensus on the role of high schml employment is

be=uw it is mtremely difficult empirically to identify the net effect of high school work experience on measures of subsequent cweer outcom- such m wages, earnin~, weeks worked, or weeks unemployed. Comider

a situation where 24y--old workem who held jobs while in high school me fomd to wu higher hourly

wages, on avera~, than stilarly aged workers who did not hold jobs in high school. Before concluding that

high school aployment enhances subsequent labor market productivity, one rntist acbowledge that the

two “typ~’, of workers may differ in many dimensions b=id= high school employment statm. There may

be signific=t differences in their family backgrounds, the quality of their K,gh schools (md tbe intensity

of their school effort), their levels of post-secondary education, the amount of post-school work experience

they gtined, and even their innate ability. Ud-s each of th-e factors is “held constant: we =nnot te~

whether high school employment h= a direct, skill-enhmcing effect on subsequent wag- or whether one or

more of th~e other fztors explains the observed difference in average wages. Moreover, a simple comparison of average wag- earned at a point in time cannot reveal whether the reloiionship between high school

employment ad subsequent mges chmg= over time.

The current study focusm ticlusively on the relationship between high school employment md subsequent average hourly wages rather than considering a broad arr~ of career outcomes. However, it contends

with the complexity of this single relationship in ways that previous r-each does not. Specific featur- of
the current study include:

● The data ae from the National Longitudinal Survey of Youth, which began in 1979 with a smple

of 12,686 individu~ between the ag= of 14 and 22. The survey, whifi remains in progr=s, h=

trzked the employment and schooling experiencti of the= indlvidu~ over the pmt 16 ye-, md

h= also recorded numerous other -pects of their lives. The current study uses data from su~ey

years 1979-91.

. The aalysis is breed on a racially het.emgeneous s-pie of ~,897 male and female high school grduat= who, along with their h]gh schools, participated in a special Klgh schml survey md high schml

trmscript collection effort. As a rwult, we know what courses these indlviduab twk in each yew of

high school, .W well = the. gzadw and. credit: received for each COELS!.We also know a gr%t ded

Aout the characteristic of their high schools and fellow high school $tudents.

. The first part of the analysis involvw wefully segmenting the sample by gender and the mount

of work =perience gtined while in high school (whether the average kours worked per w-k during

~adw 11 md 12 equals O, 1-10, 11-20, or 21+) arid providing a comprehensive view Of what students

in each category ‘look Eken in term of their personal, ftiy, and schw[ “ch~ac$eris!i=, ~lgh =h~l

wuss and grad-, schoofing attGnment, and post-high school emplopent.

. The second part of the analYsis involv~ ~timating a seri=” of wage models “d=lgned to@6nfifY” the”net

effat of high school employment on iubsquent wages by aefully controuing for a hmt of ob=rvd

~d unobserved factors that may confound this relationship. This malyticil method dm reve~

whether the relationship betwwn high school employment and wage chang= u workers age.

The analysis reveals new information about the relationship between high schml emplo~ent ad

post-school wages, = well m a wide range of factors that are fikely to be” finked to botb tie dwision to

work wh~le in school and subsequent wage. The key findings of the analysis are

...

111

● The majority of”students (8070 of males and 73.70.of females) work at some point during their junior or

senior year of high school. Male students average 10.5 hou~ of work per ~veekover the murse of the

academic years ‘(12.8 hours per week among those who work a positive number of houm) and femd~

average 8.3 hours per week (11.1 hours per week among the worker~),
● Wgh school students who work unmudly intensively (over 20 hours per week, on average, during

grad= 11 =d 12) differ from their 1-s employed counterparts in a number of dimension=

– The overwhelming majority of the= students (72Y0 of md- ad 74% of femda) are white.

– They typicauy score below-average on the Armed For.= Qudifytig T&t (AFQT), which is used

by the mihtary to -*SS trainabihty. The relationship betwen high school employment md

AFQT scor- is particularly strong for males.

– M&= in this category have significantly higher family inmmm, on average, thm their less employed counterparts, although the same is not true for females.

– They tend to receive substantially less post-secondary schoofing than other high school graduat=.

Only 467. of males ad 39% of femd- in this category report any enrollment in the Sk yews

titer high school graduation.

‘– They work during 80% of dl weeks, on average, in the first sk Y= titer high school graduation and average about 30 hours of work per week, which is fm more work effort than their

counterparts typically exhibit.

– While in grad- 11 ad 12, they generally t~e fewer credits and receive lower gad- than their

counterparts in academic mbjects, while concentrating their efforts and receiving relatively high

grad- in vocational subjecti.

● High s&ool students who do not work at all during grad= 11 and 12 can be characterized m foloww

– Most (51% of real- and 58% of females) me nonwhite.

– They tend to receive below-average AFQT scor=, &pecially if they are female.

— They have below-awrage famfly incom-.

– While in high school, they tend to live in are= with unemployment rate that are shghtly higher

thm the unemployment rat- typically faced by students who work while in high school.

– They work far lew than their counterpmts during the sk yearn immedidely followtig high school.

They work in barely more than hdf of all weeks, on average, ~d average abOut 20 hO~rsOf wOrk

per week.

iv
● Inferences about the “neb” effect of high school employment on pos&high schml w~w me ~tr~ely

sensitive to how the w~e modd is specified and estimted. One a obttin & very tisleding view

of the relationship of inter-t by

– relying on a cro=-smtional sample in which w~es are exatid at a single point in the life cycle.

- ftiltig to control fully for confounding factors such w schooling attainment and pint-high schml

emplopent.

constraining the relationship between high sch~l aployment md log w~w to be line= or

qudratic.

. Models that e~e the r~trictions ~ited dove reveal that there is no significant wage pretium ~

sociated with high school employment ezcept among individuals who work more than 20 hours per

week, on average, while in high school. Among th~e individuals, the “mlue adde& of high schwI

employment ris- and then falk u post-high schml work tiperienm b gained. When they have am

cumul~ed 3-5 years of pos~high school tiperience, they are typically earning $10% more than theti

(pre~Ously) 1= employed counterparts.

. There is no evidence that the esttiated effect on wag= of high school empIoywnt k bi=d by

correlation bet ween high school employment and unobserved factors. However, the t=k U4 to

look for this hi= are not very powerfd because the proxy variable used to control for unobserved

ability (pr-high sch~!. test scores) is mising for more than half the r=pondents and the imtmmentd

nriables u=d me ofly weakly correlated with high school employment.

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1 Introduction

The relationships between high school emplo~ent md subsequent labor market outcom~ have been extmsively mdyzed w part of a broader literature that seeks to identify the determinants of succ~sful

school-t-work transitions. from a theoretical stmdpoint, high school employment h- = ablguous effect on cmeer outmmes. It tight facilitate the trmsition from school to work by providing students with

mmketable skilk, good work habits, and information about job opportunitiw =d employers’ apectations.

Such beneficial eff-ts would reveal themelve via a pmitive relationship between in-school work mperience wd post-school wag- and employment. However, high .schml employment tight do little mom thm

divert students horn a~etic p“muits, in wh,ch cme it would be ~egatively ~ociated with ~~efic

achievement, schookg attainment, ad suhequent wag~.

To &&s the relative metits of th- mmpeting hypotheses, malysts tWicdly etimate singl~equation

modek h which a particulm acadetic or labor m=ket outcome k wpr-ed = a function of in-school work

e~erience. Outcome rnemurw ticlude high schooI grade point averaga or cl&s rmk (D’Atico, 1984;

Greenberger md Steinberg, 1986; Lillydahl, 1990), high school mmpletion or college attendace (Meyer

and Wffie, 1982; Mu&, 1991; Steel, 1991), poskhigh schooI employment or unemplo~ent (Stevemon,

1978; Meyer md Wi=, 1982; Stmn =d N&ata, 1989; Mush, 1991; Steel, 1991), pos~high s&ool wag=

or emnin~ (Stevenson, 1978; Stephenson, 1981; Meyer and Wise, 1982; Coleman, 1984; Stem ad Nakta,

1989; Ruhm, 1995), and post-high school occupational attainment or job benefits (Colemm, 1984; Ruhm,

1995).1 In each study cited, the outcome memme is regr~sed on a ntier of ~omiat= in ~d{ltion to

the me~ure(s) of high school employment but, in each cm, the list of controls is fw from complete.

Colem= (1984), for =-pie, models post-school wag- = a function of. the nutier of months worked

while in school (Klgh school or co~ege) titer controlling for nothing more than r~pondents,, fathers’ ~d

mothers’ schoofing levels and fathers’ occupational status. Ruhm (1995) regrew= post-school wages on

in-school work experience ad m intensive set of personal and fady characteristic=, but does not control

completely for post-school work mperience or schooling attainment—two key detertinats of wwu that

are ~so highly correlated with the mount of work experience acquired in high school.

The studies cited ii the preceding paragraph have revealed a nufier of iitera”ting relationships, but

they cm be faulted for taking a piecemeal approach to the problem. Uther than examining the separate,

1R* (1995) provide a V.V mefti s— of theEteratwe.

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unconditioned relationtips between high school emplo~ent and v~ious outcomes, we shotid addr~ the

foIlowing quwtion: What is the effect of high school employment on posbschool ww& wndifional on

high schml achievement, pmt-schooI emplo~ent, and other factors that ae correlatd with in-schml job

holding and wag=? Suppose, for exaple, thti individuals who work intensively while in high schml me

lW likely than their nonemployed or moderately employed counterpmts to t&e college preparatory cou~

=d attend mllege but, w a rault, tend to gain more work experience in the first few yems after high

school. To deterfine whether high school employment is beneficial to th-e individual in the xnse of

being “skill enhancing: we must -timate its effect on wag= titer mntro.ung for the other factom that

differ systematically betwen high schooI job holders and non-holders. More gener~ly, we m~t identify

the various direct md indirect paths through which high school employment influenm subsequent Itior

m=ket outmmm.

h the current study, I use data from the National Longitudind Survey of Youth (NLSY) to ~tine

the relationship betw=n high school employment and post-school wagm, = well w a lmge number of

additional factors that me likely to be linked to both the decision to work while in school ud subq”ent

wages. Chief among thee additiond futors me d?ttiIed me=ur+ .of high achml achievement md ..schml

quality. A high school survey ad transcript collection effoti were undert&en w part of the NLSY, m I have

data on each coume t~en in high school and the corresponding grade and gredits, along with such school

chmactetitics = average t~&er salties, attendance rat&, ad student-ttiteacher ratios. Previous studi~

have used overall grade point average or CIWSrank = a me-ure of high school achievement, but dettiled

transcript data =e indispensable if we wish to msess the effect of high school employment on wag- net of

its effect on skilb being learned mntemporanmusly inside the clwsrmm. b pmticular, th- data mable

us to investigate the presumption that employed high school students shu academic subjects in favor of

such tours- ~ typewriting, auto “mechanics, and choir=urricula choic= th”atmay leave them tith aple

free time and even high grde point aveiag=, but with poorer pos~school “w~& earning capatihti~ thu

their nonemployed counterparts.

To US-S the various relationships, I begin by cl~ifying individuals by gender and the amount of

\vork experience ga~n~d i“ high _schml_pecifically, whether the average hours worked per week during

grad- 11 ad 12 equals O, 1-10, 11-20, or 21+. I.then cmmpare the distributions of a l~ge nufier of

observable characteristics across these gender-work effort categoti~. The obsexvAIes include personal,

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fdIy, md labor mmket ch=acteristim (e.g., race, net fatiy income, IOC4 unemployment rat-), high

shool cbmacteristi- (e. g., student-teacher ratios, graduation rata, te=her s~=i-), memur- of high

school ahkvement (credits tden and grade point averages b each of four subject =e=), and muures of

schooHng attaiment and posbhigh school employment. After wing this prelitin=y dmcriptive andysk to

get a seine of what employed Klgh s&ool students “look like; I wthate a series of wa~ models that control

for high schml employmnt along with vatious combination of tbe obwrvable charwtertitiw. Th-e models

reveal the eff%t of high school employment on subsequent wages before and after the effec~ of mnfounding

factors are taken into account.

It can be =gued that even after I control for an unusudy wideranging set of obsermble factors in tbe

wage models, highschool workexperience remains endogenous. That is, theesttiated effect onw~m of high

school aployment tight reflect the relationship betwem high school employment md unobservd factors

that influence wag-. For emmple, a positive -timated effect of high school employment on subsequent

wag~tight reflect the fact that, ceiem's pan'bus, individu& with relatively high levels ofunobsmved (to the

research~) ability, ambition, etc. me the on= whos~kand are offered employment. It is these unobservd

qualities, andnothlgh school employment per se, that leads toabov~average w~-titerhighschml. In

m attempt to contend with this type of potential endogeneity, I =periment with the inclusion ofprofi=

for aabiht< (prebigh school abifity t=t scores) md with imtrummts for”the high school employment

memures.z My experiment (the r-dts of which are reported in =ction 4) sugget that “abifity biwn is

not veu severe or, dternativeIy, that I lack suitable proxiw md instruments. I therefore opt to put =ide

issu- of endogeneity for most of the an~ysis. After ~, it is importwt to produce a dettiled picture of

the relatiomhips between bigh school employment, wages, and ahostof observable factom—adecription

that the etisting literature faik to provid~ven ifucertainty remtins about whether the inferences are

influenced by heterogeneity in factors that camot be observed.

In addition to exploiting transcript and school-specific information to help isolde the “valu&adde&

of high school employment, the current study introduw other innovation. Efiting r-arch on the relationship between bigb schooI employment md wages focuses on post-school wagesat astigle point in time,

with thepmticulm “point in time” varying from study to study. Stevenson (1978 )?ndyzes.wagar epotied

2Fixedeffe@ meth&_oftmmdto pwgemodekof thetype oftim-kvmimt, mobserved heterowneity with wtich I

-cone-ed. Th-metho& =etiamroptiteti the —t appfi-tionbe-me I wodd beu=ble toidemtifyp-etm

for tk-~vtimt reG&om, kcludng the mex-s of M@ s&ool emplommt.

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during the l-t year of the panel survey used in his study, at which time r~ondents range in age from. 23

to 26. Stephenson (1981) =amines wages reported one year after hlgb school, md Ruhm (1995) models

wag= emned 6-9 years titer high school. I use a 14 year panel that follows rupondents from grade 11 until

they are w many m 12 ye=s beyond high school graduation, and I analyze all wage reported cluing the

pint-graduation periods As a r=dt, I am able to see how the relatiomhlp between K,gh school employment

and subsequent wagw chmge over tim=omethlng that analysw bwed on crm-sectional data cmnot do.

I m also able to me modeb that exploit both the within-person and cr~person v=iation in the data,

thereby obttinhg more efficient -timat= thti my ““predecesmrs. In tidition, I mntrol for the mount of

work experience gained in high school more cmefully thm may premding studi=. As I demonstrate, it k

important to Alow the relationship between high school work experience -d log wag- to he nonhne~, m

Meyer md Wise (1982) also do. A nu~er of analysts may have &torted th~ rdationshlp by constraining

it to be Eneif [e.g., Coleman, 1984; Stern and Nakta, 1989) or quadratic (e.g., Ruhm, 1995).

k the next section, I describe tbe manner in which I comtructed the data sets wed throughout the

malysis. In section 3, I cl=sify ample members by gender and the mount of work flperience &quird

dining high school and pres=t summ statistl”=-”d=cri~ng diff:,wnces~:~eir persOnal ~~d labOr m=ket

&mwteristim, fafily~ackgro.nd, schoohng atttirnent, school quahty, high schml achievement, md pmh

school employment. The wage models are d=cribed and thei estimate disc~=d in section 4, which begins

tith a detailed list of the qu=tions that the regression analysis is d=igned to aswer. ”A su-m of r-ulb

appears in section 5. 

2 Data Set Construction

The data me from the National Longitudin~ Survey of Youth (NLS~, which began in 1979 with a ample

of 12,686 males and femal= born in 1957-64. bpondents were interviewed annua~y from 1979 to 1994,

with the next interview scheduled. for 1996. I u= data from the interview years 197%1991. ~d I r=trict

the analysti to a subsample of 1,897 males and females. This reduction in simple size is @umd pfimmily

by my tequiremen~ that high school transcript data be avtilable for ewh respondent and that their labor

market experience be “observedm from the start of grade 11 onward. In the remainder of this =ction I

elaborate on the selection criteria md also dacribe the transcript and employment data.

3My description of the pad lmgk -d endpoints appiies to my $“bsmple, but not to the“NLS’?”’kY&eA=Det& on

..=. ....!. :-r --- -:., .

th data ~e provided in i~tion 2.

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b 1980, 1981, md 1983 an attempt w= made to collect high school transcripts for efigible NLSY

r=pondents. Respondents were ehgible if they had consentd to rele=e their high school records, dld

not attend high schmh outside the Utited States, and were not in the tihtay subsmple.4 In addition,

r-p ondents hti to graduate from or otherwise efit high school before their tr-cripts were co~ect ed;

given the seven-year age rmge of the sample, the collection effort w= forced to spm a four Y= period.

Although more tb~ 10,000 NLSY respondents were eligible for the transmipt collection in one of the thrss

years, mmpleted trasmipts were acquird md coded for only 9,010 r~pondents, generally because thek

s&ools did not coop~ate with the collection effort. A Table 1 indicates, I delete 3,676 rapondents from

the original sample because tramcript data we unavailable due to ineb~bdlty or tb- other probIem.

Table 1 shows that I delete an additional 2,552 individuals because they did not graduate horn high

sdool. Although the relatiombip between tigh s&ool emplo~ent ad the likeWood of graduation is a

worthy subjwt for analysk “(see D,Atim (1984), or Ehrenberg and Sherm~ (1987) for an ~tination of

employment in college md college completion), I chwse to .~clude high schml dropouts m I can look at al

respondents’ b-schml employment ~periences for two entire calend~ YWS prior to their schml tit. M

dropouts were included in the mdysis, I would have to contend with the f~t that they ae legally barred

from holding a job for mmt (or perhWs d) of their lmt two yems of school.

The NLSY mks respondents about their work mperiences with virtutily every empIoyer ever encowtered from J~”ary 1978 onw~d (or, for r~pondents who were not yet age 16 at that date, from age 16

onward). The reported Mormation is Wed to create wek.by-wwk wriabIss on labor force statw and

hours worked on aUjobs in prog~s during the given weeks To ensure that e~h r~pondent’s employment

experiences ~e recorded tom the stint of grade 11 onwazd, I delete r~pondents fzom the sample if they

graduate from high school before January 1980 or prior to their skteenth birthday. As indicated by Table

1, this Ieads to an additiond 3,345 deletions.

Most of the remaining smple deletions su-arized in Table 1 are nec~sitated by me~urement error.

I e~n~e 74 individuals from the sample became their high school graduation dates cmnot be determined.

During several of the annual interviews respondents Me inked whether they have a high s&ool diploma

4The ori~d NLSY s-pie of 12,=6 mspondems cotited of a natiody repr~enhtiw s~s-ple (n=6,111 ), m owr.

s-pie of Mspti=, bla~ md wonofidy &sdvmaged wtites (.=5,295), md a titw s“b-ple of inditid”~ who

were dwed in the tifit~ on or befo~ Sept*w 30, 1978 (n=l ,280).

3Th-e meahd wee~y vtia~es _ atihble h tie Work History File. S= Cata for H— Reso_ ~mh (1995)

for detdk.

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and, if so, when it wm received. They are also ~ked a host of additiond quwtions about theti schooling

attainment and the dat- of theti school enrollments. This seM-ceported information cm be verified for

internal consktency and cm also be checked against the date of high school departure reported in the

trmscripk sumey, In the vut majority of c~es, I w= able to reconcile my inconsistencies ~ the= vaious

pieces of itiormation and @me up with a seemingly mcurate date of high school etit. In 74 u=, thwe

were dramatic inmnstit encies tkt could not be reolved, so I drop thow r=p ondents from the saple.

In addition, I eliminate 1,103 individual because their trmscripts do not reveal m adequate mout

of information about coumes t&en in grades 11 ~d 12. . I. require that each r~pondent have “complete”

data for at Iemt three co=est&en ti both grades. Data are complete if(a) avdld, thrm-digit title code

is usociated with the course, (b) the student received betw=n zem andsk Canegie crediti.for the mume,

with one Carnegie credit reprwentbg ayear-longm”rse, and (c) agrade of A, B, C, D,or Fis ~i~ed to

the murm.6

The find selwtion rule hvolva elitiating 39 respondents who ftil to report a vafid wage dwing the

poskhigh school period. The post-high school period begins when the rapondent graduatu horn Klgh

stiool md ends at the date of the lmt interview (the 199.1 interviewer for non-attriters md any interview

between 1981md1990 forattriters); itrang- inlength fromonetol2ye-. FoIthe39individuds with no

post-school wage, the average pint-school panel length is2.4ym (stand~d deviation= l.1), while for the

overall smple it is 9.9 ye=s (standmd deviation= O.93). Clearly, the 39 individuals are dropped from the

smple because they attrit from thesurvey relatively early, and not nec~mfiy because they ~echronic~y

nonemployed.

Theremtining sample of l,897rwpondents is heterogeneous with r=pect to gender and race Table2

reveak that the sample consists of 926 males ad 971 females. OverW, 24% of the sample is bluk, 14%

is Hispanic and the remaining 62% is non-bla&, non-Hkpaic, heredter referred to m wh]k. It would

be interwtingto undertaken ageider andricial comparison ofhighsch~l ernplo~ent ~erieic~ ~d

subsequent labor market outcomes, especially in light oftbestriklng racial mntrmtsreveddby Michwl

and~ma(1984), Ahit”v, Tienda, Xuad Hotz (1994) and Hotz, Xu, Tiendaand Ahituv (1995). However,

‘Co-e titles ad C_e~e cre&ts were m~~d u ~t of the d~ta coUtiion/pFtiion efloti totiem am

. . .—. -- — -. 

of Afofity Sc,om Mb schook. The Cent= for H— Rewwce Re.se_h at me Oh. St=te UN.v@tJ di=etiati m

mdti do-cut entitled ,,NLSY figh S&d Tr_tiPt S_ey, Ovewiew md D_en~tion,> tht p-id- (titd)

ifio-tion onhowthecodng w~done. The doc-ent wmprod”cdby the Cem&for H—R=o_&emdmd the

now-&fmct Nmtiod C*tifir.R_M&ti Vomtiond Ed.~tionat The Otio State UtiveAty, the two .Wtimti- that

co~abormed in mnd”dhg the tr-~t swvey.

6
my sample k not large enough to support a racial decomposition, so I cotine my attention to a md~female

compmkon in the ensuing analysis.

3 Sample Characteristics

h this section, I d~cribe the amount of employment experience acquired by the 1,897 smple members

during grades 11 md 12. I then segment the sample by gender and high school work intensity md extine

difference amom groups in a l~ge number of chmmteristiw. This simple, descriptive ~dysk reveals a

nuder of interesting mntr~ts between individ”ak who choose not to work in high school, i~divid”~ who

work a mod~t amount, ad individual who work very intensively, ~d it sets the stage for the parametric

malysis that appea~ in section 4.

Tablm 3-M (for males) and 3-F (for females) d~cribe the distrihutiom of average houm worked per

WA during the two years preceding high school graduation. The two ye= period b broka down into four

contiguow segments the summer before grade 11, the grade 11 acadetic year, the sumer before grade

12, and the grade 12 acadefic year. To detertie when each segmmt begti ad ends, I first remnciled

inmnsistenci- in the reported date of high school etit to come up with the “true,, graduation date (s=

the discussion on pages 5-6). I then worked backwmds md inferred the stint date for the setior ye= of

high school and the start and stop dates of the preudlng three segments by using the school enrollment

dates reported by each rmpondent or, in the absence of such itiormatbn, by msuting that acadetic years

l=t tine months and s-er v~tions account for the remaining three months of the cdend~ yea.

Table 3-M and 3-F reveal that many high school juniors and setiors work a substatid nufier of

houm during the academic year. Focustig on the coIumn in table 3-M titled “Grade 12p we see that only

26.8% of real= fi the sampIe do not work my hours during their senior year of high school. The r~tining

73% of the male smple is fairly evenly divided mong the= who average 1-10 hours per w=k during the

academic year (22.270 of the saple), those who average 11-20 hours per week (24.2Yo), and those who

average 21 or more hours per week (26.8Yo). Among the workers, the typical young man averages 17.1

houm per week during his senior year of high school; among the full sample of 926 mala, the mea is 12.5

houm per week.

There are two key d,fferenc- betw~n the employment intensities of mde and fede high school

students. First, the males average more hours of work than the females tkoughout the two year period

7
under consideration. For maple, real= who work during their junior yea; of high sdml awrage 13.0

hours per week, while femal~ average only 11.3 hours per week. During the. summer prior to the junior

year, male workers average 17.4 hours per week and female workers average only 14.3 hems per wink.

Second, females ~e more likely than males to not work at all, but they are also more Wely to work vv

intensively. Focusing on the ‘Grade 1in colu~.s .of tables 3-M and 3-F, for =ample, we see that 35.770

of males do not work at all during the junior yea, “while 13.370 average more thm 20 houm per w~k.

Relative to these ntiers, a larger proportion of the female smple (45.7%) does not work at W, and a

larger proportion (24.8%) averages 21 or more hours of work per week. The” gender difference in meaw

~PlOYment intensiti= seen here is widely repofied in the literature (Stevenmn, lg7& Mich=l and ha,

1984; Llllydahl, 1990), but the gender differences in the shapes of the d~tributions swn in tahlw 3-M wd

3-F =e not well documented.

Tahl- 3-M ~d 3-F dso reveal the temporal patterns that one ~pects to see in the aployment of

young people. Among both male ad females, there is a pronounced rightwmd shift in the distribution of

average hours = one moves from grtie 11 to grtie 12 or from the sumer before Gtie 11 to the aumer

before ~ade 12. That ti, young people me more likely to work—red, condition on working, me more

likely to work more hous—~ they age. However, there is not a monotonic increwe in work effort over

time, for young people frequently work intensively during the su~ers and then cut back on thek hours

(or quit their jobs altogether) during the subsequent academic ye~.

Became of the intrapersonal mriation in work effort over time seen in tables 3-M ad 3-F, it h not

obviom how I should me~ure high school employment in the ensuing mdysis. It is highly dwirable to m

a singIe statktic to summarize high school employment rather than, for example, a twmwy cldfimtion

of grade 11 employmnt by grade 12 employment. furthermore, it smm d~irable to ignore employment

during the sumer month hemme I am interested in mting the relationship betw~n Klgh schml

employment and the course work being undertaken contemporanmusly. In light of these contideratiom

(and after experimenting at length with alternative me~ures), I opt to use the average nufier of howorked per week in grades 11 ad 12 = my me=ure of high school employment. That is, I cOunt the total

number of hours worked over the coume of the two wademic ye- and divide by the combined length of

the two academic years.

The right-most columns of tabl~ 3-M ad 3-F show tbe distribution of average houm worked per wmk

8
in grad- 11 md 12. For su~ary purposes, I group the respondents wcordlng to whethm they average O,

1-10, 11-20, or 21+ hours per week during tti (roughly) 18 month period of time.7 Tables 3-M md *F

show that 20.4% of males md 27.2% of females do not work at all during either their junior or senior “yea

of high school, wh]le 16.570 of males md 10.9% of females average more thm 20 hours per wek throughout

the two acadetic years. It is worth noting that 80% of all rwpondenb would be clmsified the sme if I

were to me average hours worked per week in grade 12 & the memure of high schml emplo~ent.

k tabl= 4-M (red=) and 4F (femal@), I categorize each smple maber by gerider ad high schml

employment category (O, 1-10, 11-20, or 21+ hours per week), and report mess and standard deviations of

a nuder of tiarxteristim for each of the 8 categori=. Tble 5 reports the number of observations used to

compute the statistics within e=h gender-employment intensity category.s The charactetistiw sumarized

h tabl= 4M and 4F are gouped according to whether they repr=ent personal, fdy, or labor m=ket

factom, tigh school fmto=, me=ures of schoohng atthment or ewectations, or memures of post-high

s&ool labor m=ket outcom= (wag- ad employment). I continue the analysis in tables 6-M and 6-F,

whine information from the r~pondents’ high school transcripts is sumarized. To orgtize the &Jscustion

of tables 4-M, 4-F, 6-M, md 6-F, I begin by focusing on real- and then summmize the key differencbetween md- and fe~es.

Looking first at personal, fmfly, and labor mmket factors, the top thre rows of table 4-M reveal

a striking difference in the racial .mmpmition of the four subs-pies defied by intensity of Klgh school

employment. bong the 189 md= who do not work at all during grades 11 md 12, 49% me white, 38%

am black, =d 14% are Klspaic. Reading acres table 4-M, we see a sizeable increme in the proportion of

the sample that is white and a decre= in the proportion that k black = high school work effort incremes:

among real= averaging more thti 10 hours of work per w~k (combinbg the two right-most categori-),

72% me white md only about 1570 me black. The l~t two rows in the top pmel of table 4-M reveal that

nonemployed high school real- are [ess likely thm their employed counterpmts to five in urban are= (68Y0

versus 7477Yo), but are mom likely to five in are= tith high loc~ unemployment rates (9.4~o, on a~rage,

V=SUS 8.&8.9Yo). ThB evidence is only patilally consistent with an often cited explmation br the fmt

7AgA, I tri~ tiow dtemdi- bdore .ett@ on ties. fom =t%ories. L-s &m 690 of the ._ple .v=%8 mom thm

30 hem/week so there is Uttle to be g-d by distin@M fmther _ong rewondems in the upper td of the distrihtion.

8TaMe 5 shows that them is a subst-id _omt of tissi.g data for tie s~ml &xt*tim. S&ool-spcific tits come

from tie s-l s-.Y, which w- conhcted on a onettie bmis in 1979. As a resdt, -y respo”dmte’ Mgb @cbok were

nev- -eyed. Mhaom, m q“=ti-dr= wet. O* ptii~y completd, pree-bIy became the s&wl titietr~or

did nothave retiy accees to the mec=sq ifi-tion.

9
that black men have higher unemplo~ent rat- than white men—titz., that blacks are concentrated in

econofica~y deprmsed urban me= where jobs are smrce.

Two of the remaining personal and family char=teristim reveal p=ticularly interesting contrwts. F1~t,

there k a clem relationship between the intensity of high school employment and famfly income where=

mala who do not work in ~adw 11 and 12 come from fafihes with an average income of lW than $21,000

per year, those who average over 20 hours per week have family incomes of almost $32,000 per yem, One

(pafiial) explanation for this discrepancy is that the money e=ned on jobs held in high school mntribut~

to ftiy income. Because fm~y incomes tend to be higher when one or more parents work continuomly

throughout the year, another explanation is that students may Ie=n about employment opportunitia from

their employed parentz or even obttin jobs at their p~ents, work plac~.

Semnd, md~ who do not work at ali or who work very intensively (21 or more houm/w@k) tend

to score significantly lower than their “intermediate’, counterparts on the Armed Forc~ Qutiyhg Tat

(AFQT)-a ttit that is used by the tibtary to &sew trainablhty.’ & table 4M reveals, percmtile smr~

average uound 43 mong males who work zero hours or, at the other mtreme, who average more th= 20

hours of work per week. kong males who average 1-20 hours per wwk, the average percentile AFQT score

rmges from 49 to 55. .Itis worth positionhg this systematic pattern in AFQT smr- agtinst the racial

difference noted above, for the relatively low AFQT scor& mong m~= who work 21+ hours per wek,

727. of whom are white, mntrmts &amaticdly with the fact that whit= tend to smre mu& higher thm

blacks on the AFQT. My calculations show that among the roWhly 11,900 NLSY respondent for whom

AFQT SOI- are amilable, the average percentile score for whit= is 48.4 (standard deviation=28.4) and

the average smre for bla&s is 22.9 (standard deviation=20.6).10 Hence, the relationship between AFQT

scores and high school emplo~ent points to the etistence of (acquired) ablfity-b~ed sorting into high

school employment status above md beyond any rwial sort~ that t~es place.

I turn now to the h,gh school characteristics, which were reported directly by school adtinktratom

during the 1979 survey of respondents, secondmy institutions. “Of the seven wriables summtized” in table

gNLSY re~omdetis wem not titistied the AFQT dkedly, but W= ~timd the Amed Semi- V_tiod

Aptitude BatteW (ASVAB) in the stiw of 1980. About 94% of the ori~ 12,666 ~SY _ondmts a~d to t~ the

ASVAB in .x-e for a pammt of $50. Approxi_te SCO.S for CheAFQT _ comp”td hy comb- raw m.r~ &om

the mithetic reMhg, n-rn~ op=ations, p~~aph comp~etioq md wed bowledge portiom of the ASVAB. Note

tbt I W. Pem.nti/c rati~ thm ram AFQT scores.

10R~pondmts w- I&24 p- .Id when they took the ASVAB, so titir scores mflcd ..& fwtOm = the ~ti md

q“dw of s&ooEng remived u weU m their tesbtting stiti -d even th.ir level of cmperatio”. As a rmtit, the AFQT is

dhly to provide ~ WCWU. me-me of imae inte~gmce.

10
4M, the one that reveals the most extreme contrwt across the four “typm” of students k the percent

of students k the school who are clmfied u economically dlsdvmta~d.11 Table 4M reveals a strong,

negative relationship between the nufier of houm worked by the student =d the percent of studmts k

his schml cl-sified u disadvmtaged. Mae students who work intensively not only belong to fatifia with

relatively high famfiy incomes, m seen e=fier, but they dso attend schooIs with studenk from relatively

tiuent fatifi=.

The means for the remaining school chmacteristics suggest a negative relationship betwen hours

worked in high school and schwl qu~lty. Students in tbe 21+ category attend schmb with higher studentt~tewher ratios, lower attendance and graduation rat-, md higher te=hm turnover rak than do students

who do not work at dl. The difference k mems between the right-most ad left-most COIUDS ~e not

Iage, but they are often statistidly significant. It is unsurprising that students who concentrate theh

efforts on emplo~ent attend schools that appear to be weak in a number of dimensiom, but ttis evidence

is mmewhat at odds with the finding that these “weak” schmls have disproportionately few econoticdly

disadmntaged students. Moreover table 4-M shows that thek teacher ean salaries that ue roughly equal

to the sdmi= reported by schools in the other =tegories.

A strong relatiomhip &sts between high school employment and s&mfing attainment, u the next

section of table 4-M revds. Wading across the first three colums, it is” app=ent that incmaed work

intensity is =sociated with a slight increme k expected schooling ad the amount of schooling actually

received. Students who average 11-20 hours of work per week spect to complete 14.9 years of school,

on average, compared to 14.5 yea~ for students who do not work in Klgh school. Students in the 11-20

category end up mmpleting 0.3 more years of schwl, on average, than their nonemployed counte~~ti md

they spend slightly more time enrolled in school during the fi~t several years alter Klgh school. However,

the righkmost colum reveals a sh=p break in this trend when it comes to students who average 21 or

more ho~s of work a w~k dwtig high school: th-e students have average schooling expectations md

average schoohng completion levels that me markedly lower th= their lew employed counterpmts, and

they spend f= 1=s tfie emoUed in school after graduating from high school. Table 4-M shows that 54% of

these students report no pos~high school enrollment, versus 40-44% for everyone else in the sample. Mmh

(1991) md Steel (1991) have 4s0 found that intensive high school work effoti is =sociated with a decre11= -we~~ ~~ p=ti- ~ue~tiou,.~~ra~oti were~k~ to qor~ the cl~sfi~tion bxed O. ESEA @d&ne. or,

dt-tively, the @dch- wed by thtir s&ml.

11
in postiwcondary schmIing attainment.

The bottom section of ttile &M illustrat- the enormous dlffermces zross the four categoria in posh

high school employment. Specifically, it shows that high school employment intensity k highly correlatd

tith post-high school employment. The typical mde who averag= more than 20 hours of work per w=k

dining high schml is employed for 77.7% of the first year after graduation md averag~ 28.8 hours of work

per week (36.8 hours per week if the denominator is confined to wrk weeks). This is substatiaUy more

work effort than is seen song the non-workem, who are employed for m average of 35.770 of the first yem

titer graduation and average only 11.9 (25.3) ho”,s of work per week. Reg~dl~ of how p~s~bigh s&wl

employment is memured, table 4 M shows a steep rise m high school employment incremes.

It is tempting to infer that the= differences in poskhigh school work effort reflect the fad that individu~ who work relatively little (or not at ,W) during high school tend to be enrolled in school, rather

thm employed, in the petiod immediately folloting ~aduation. In fret, the employment gap seen in table

4M prova to petist even after differences in post-high school enrollment ~e t~~ into acmunt. Panel a

of figure 1-M plots average hours workd per” week over the 24 quatem followtig high schml graduation

for the four “typm” of individu~. This figure shows, x does table 4M, that md~ who average 21+

hems/w&k of work during high school dso work far more houm dining the Sk ytis titer Mgh schml

tha th&r less employed counterparts. Figure I-M alm shows that post-high schml work effort is bighIy

cyclical, particularly among the O,.1-10, and 11-20 categories, pr=umably becau= th~e indltidu~ tmd to

be emolled h school. Pmel b of figure I-M plots the average w=ks enrolled in school for each of the fom

“tYP=” Over the same 24-qu=ter period; this plot underscores the nomonototic relationship between high

school employment and pat-secondary enrollment seen in table &M. The fial panel of figure I-M nets out

dlfferenca in emohent propensities across the four “typ w’, by focusing only on thow indiriduds who do

not enroll in school at all during the first 24 quaters after high school. Pmel c reveals that the cychc~ty

aU but d~appears and, more importantly, tbe gap in employment intensity between the 21+ category ~d

the others persists. Males who work long hours during high school conthue to do so upon leaving schml,

md they work_more intensively tha other non-enrolled high school graduate.

To sumarize the differences between mdffl in the 21+ category wd those in the remaining three

categori=, members of the form= sample are far more fikely to be white thm the others. h aH ~ielihood,

they also have lower AFQT scores, attend weaker high schwis (despite hating relatively high fatily income),

12
receive 1- post-secondary schooling, md work f~ more hours upon graduation from high school than their

1-s employed counterparts. How do th-e dlfferenc~ trmdate into wag-? The bottom few rows oft able

4-M suggest that males in the 21+ category ewn substantia~y higher WW- than the others one ye= aftm

high school graduation (average log wage of 1.49 versus 1.37-1.42, which translata into hourIy wag= of

$4.50/hour vem~ $3.95-4.15/hour) and even six yem titer graduation (log wages of 1.94 versus 1.6%1.88,

or $7.00 fiour versus $5.40-6.55/hour). However, thk wage gap all but disappears when I mntrol for the

sizeable dflerence in the accumulation of post-high schwl work mperienm. When I mmpute mean wages

at the point where one year of work experience h= been gtined (or, alternatively, at the sti ye= mark),

there is virtually no difference in wa~s among the four “types?’ A more rigoro~ analysis of wages appems

in =ction 4, but the statistics shown here underscore the dficulty of w-sing the relationship betwen

high school employment and wa&s, given the mmy confounding factors that etist.

The final ch=acteristim that I sumartie =e intended to memure high schooI achievement. Table

6-M shows me= credits =d grade point averages for the same four subsamples =alyzed in table 4M. The

NLSY transaipt data tidude the titl= of ewh course taken in ea& grade of high school, along with the

nuder of credits received (coded in Cmnegie uni”ti for uniformity) “md the grades receivd. I confine my

attention to the couw t&en in grades 11 and 12, ad I group the comses kto four aggregate categori~

hummiti= md mcid studies, mathematics and science, vocational, and dl other tours=. The five most

frequently reported course titles in euh category are fisted at the bottom of table 6.M.

Table 6-M reveab that the four ‘types” of high school students dlffm fairly-predictably in theti curricda

choices ad grad-. Just = the Oand 21+ samples were seen in table 4-M to ha- the low=t average AFQT

smres and the lowest schoohg completion levels, they also appear to receive the weak-t acadetic training

while in high school. The top panel oft able 6-M shows that the typical male in the righ&most employment

group completes about 0.35 fewer credits h grades 11 and 12 than do hk munterparts who work only 1-10

or 11-20”hours per week. A slightly smaller gap etists betwmn male who do not work in high school md

the intermediate groups. One Carnegie credit repre~nts a ye~-long course (see footnote 6), so the gap of

0.35 credits repraents something on the order of 60 fewer hours in tbe clmsroom over a two ye= period.

Moreover, the gap in credits completed widens when we focus on courses in hwmiti=, sotid studl-, math,

and science. Comparing” students in the 21+ mtegory to three in the 1-10 category, the former average

almost 0.5 fewer credits in both hunnitie/sociaI studies md math/stience, while averaging one credit
more (that is, one additiond yem-Iong course) in vocational subjects. In fact, students in the 21+ category

devote 31% of their cl%~oom time to vocationti subjects, on average, which b far more tha any of the

other groups.

The bottom panel of table 6-M shows that students in the 21+ category tend to receive ~ad- that me

significmtly lower than the grades received by their le= employed munterparts in eve~ subject mea ~cept

vocational tours-. Interestingly, in the two academic are= (hummiti=/ao cial studia =d math/science),

difference ti the m= GPAs among the O, 1-10, and 11-20 categori- me statktically insiqificmt, whiIe

the 21+ category h= a significantly lower mean GPA than any other group. In vocational subjec~, however,

there is a strong, positive correlation between the nufier of hours worked in high school ud the”~ada

received. Some students may remive high school credit for holdtig a pat-time job @particularly three taking

courses titled “general work ~periencen or wmething sitilar), in which c=e they tight actually receive a

boost to their grade point average from worktig long hours.

With few ficeptions, data for female high school students reveal the sme relationstilps that tabl~ 4M

and 6-M ud fi~re 1-M show for tbe maim. In fact, my primmy re=on for prmnting sepaate table of

summy statisti~ for males md females is to be consistent with the regr=skn mdysis in section 4, for the

data reject the poohng acre= genders when I ~timate the wage models. b mmparing tabl- 4M and &F,

a notable ~erence between males and females k men in the relationship bet wmn high school employment

and AFQT scores. Maies in the two extreme categorie (O and 21+ hours/week) have mean AFQT smr~

that =e roughly equal to each other and significantly lower than the me- for the intermediate categories.

Among female, individuals who do not work in grades 11 md 12 have the low-t mem percentile AFQT

smre (35.5), but there is little difference between the meam for the remaining three subsampla. Comidering

that individual in “the 21+ category have the lowest probability of attending college, reg~dl=s of gender,

one would expect them to have below-average AFQT score. .(In m&g this statement, I =mme that the

“trainability’, memured by AFQT scor- reflects adetic ability, and that individuals select into coUege

on the b=is of this ab”ihty.) Therefore, the pattern seen for the femd= is somewhat anomalous.

Two additional gender differences emerge in tabl- 4M .md 4F,.Fimt, the weak, negative relationship

between hours worked in Klgh school and school quality seen for the real- is even weaker for the femd~.

Among femalw, for ~ample, it ii riot the c~e that students in the 21+ category attend schmls with tbe

low=t average high school graduation rates and the highest avaage teacher turnover rates. Semnd, figure

14
—

I-F shows that femal~ k the 21+ category work more hours per week than their counterp~ts in the period

following Klgh s&ool graduation, which is also seen for the males in figure l-M. However, figure 1-F shows

a stronger convergence in the work effort of the four “types” of students than what is smn for the male.

Mormver, figure 1-F shows that females in the O category me outliers in the opposite direction—that is,

females who do not work in high school work far fewer hours than their counterparts after graduating, even

after vmiation in post-secondary enrollment is taken into account.

4 Wage Models

The sumary statistim shown in the preceding section demonstrate that the. number of hours high school

students choose to work are systematically related to a Ixge number of characteristi- that are likely to

be correlated with subsequent wages. Mwy of these chmacteristics, such M the me=ur= of schooling

att~nment, pos~hlgh school employment, and high school achievemat, we reco~ized to be importmt

determinants of wag= because they reflect workers’ p=t hum= capital invmtmmts.lz The remati~ng

chuacteristi= (=pecially the person- and famfly-specific factors and the memm- of school quafity) may

be proxi= for human capital inv~tments w well, and they may also be related to such intm@bla & peer

=d fatily influence and labor m~ket opportunitia, all of which =e likely to infiuenm wag-.

Because high school employment is correlated with so many wage detertinats, it is appment that

one mmt proceed c~efully in identifying the mmgind, skill-efianctig effect of high school employment

on sub=quent wages. E waga were re~~sed on high school employment without proper controls for

nriation in schoohng attainment, for exmple, the estimated effect of high schml employment would be

bl=ed downwad due to the negative correlation between hours worked in high school md subsequent

school enrollment. If the wage model failed to control for variation in subsequent work =petimce, an

upward bim might r~ult, patic”larly if the wages were reported in the first few ye- after high school

when high school “workem” have typica fly acc”m”lated far more work aperience than their nonemployed

counterparts. Clearly, model specification is of paramount importance in the current apphetion.

In -timating the relatiomhlp between high schml employment and wages, I experiment not only
with various sets of regre~ors, but with a number of alternative smpl~ and ~timation techtiqu~. The

misting literature provid= a multitude of estimates of the value of high school employment without rwching

a consensus, yet r=emchers have made little effort to justify their model specifications ad =timation

, 13 Therefore, I present what I believe to be the techniques, let alone reconcile their results with others

“right” answer, but 1 dso attempt to demonstrate how one might obttin a very different conclusion by

pursuing a suboptimd estimation strategy. I devote tbe ret of thw section to summarizing the reulti

obttined from estbating a lmge nuder of wage models deigned to mswer the followhg qumtions:

● Do me=ure of bfgh school employment contribute significantly to explaining the mriation in posh

high school wagss? To what =tent d-. the explanatory power of these me=w- depend on the

inclusion or aclusion of other controls h the model?

. Are our inferenm about the relationship between high school employment wd subsquent wagsensitive to whether the sample ticludes witbin- permn m well = interpersonal variation in wage?

. Does the atimated relationship between high school employment and subsequent employment chage

= workers ags? If so, how?

. IS it nec~swy (and possible) to control for conflation betwen high school employment and unob

served wage detertinmts?

● Is the relationship between high school employment and log waga linear and, if not,what is the

nsture of the nonlinearity?

. After mamining the sensitivity of our inferences to n-erous issues of model specification and =ti.

mation, can the “true,” skill enhancing effect of high school emploflent on wages be identified? If

so, what is the effect?

. Do the mswers to the prewding questions differ for male and female workers?

4.1 Variables, Samples, and Estimation Methods

Much of my experimentation consists of =timating wage models that combine alternative sets of regrmors.

Table 7 provides a comprehensive hit of the regr=sors tied in atlemt one specification; there is considerable

13~ ~~e ~oup .f ,eS,m&CrS Wh... work is cited h section 1, Rti (1995) i. the O~Y One to P~,~t .?!!~ts .~~ ‘n

dte-tive model specifiwiom ad .sti_tion tehq”es.
overlap between the vmiables hted in table 7 and the on- s-arized in tabl- 46. The group headin~ in

table 7 =e p=ticularly important because I use them tkoughout this section to refer to =ts of regr=ors.

When I say a wage model includes high school employment, for exaple, I mea it includw the three duy

variablw AH_lO, AH_20, and AH_21+. fisted k table 7. A model .th.at kclud- school charwteristics

cent rols for all seven variabIes Iisted in table 7 along with the seven comesponding “mksingn indicators.

(The b=eline v=iabl- are given that heading because they are included h every wage modeI.)

The dependent v=iable used throughout the analysti is the loguithm of the CPI-deflated: average

hourly wage, apr=sed in 1982 dollars. During each unud NLSY interview, information is obttined on

the cument eanings for jobs (employer spells) in progress and both the ending md ‘usual” earnings for jobs

left subsequent to the previous interview. I U* the information on current and usual earning. Respondents

are pertitted to report their earnings in any units they choose (e.g., annual, monthly, bi-weeHy, or weekly

income), md an average hourly rate of pay is computed for the public rele=e of the NLSY data using

the ewtigs informtiion along with rapondent-reported information on weeks worked per year and hours

workd per week. This created rate of pay variable is the one that I use to form my dependent vmiable.

To msociate a memure of work mperience (X) with ea& wage, I count the cumulative nuder of

hours worked from high school graduation to the date when the wwe is reported. The tenme wriabie

(T) is me=ured sitilmly, but the count begins at the reported starting date for the pmticulm job. The

information on hours worked comes from the week-by-w=k hours array mentioned in section 2. For X and

T, w well= their higher order term, I divide the cumulative nuder of hou~ worked by 2,000 (40 houm per

week tire- 50 w-h) to convert to units of “full-the, year long” work. Note that by me=uring labor market

=perience frOm high schOOlgraduation Onward, I am =suming that every individual begins h~ cmeer when

he leaves high school. This is not an innocuous mumption, for it impfies that the labor market experience

of terminal high school graduates and college students =e treated identically. However, my eafier malysis

of alt-native meer starttig dat= (Light, 1995) leads me to believe that this carer ititiahzation rule

is prefemed to a rule b-d on, for exaple, person-specific school completion data. Because I include

wages reported by post-secondav students, I define a variable called INSCHOOL, which equals one if the

individual is enrolled when the wage is earned and zero otherwise. The PARTTIME vmiable, which equals

one if the individual works less than 30 hours/week, also helps to control for differences betw=n students

and non-students.
In addition to”~timating wage models that dlffm only in the choice of regressors, I dso comp=e r~dti

b=ed on Merent samplw.. For each gender, I use five alternative sampl=. The largest s=ple conttins data

for eve~ wage reported by every r=pondent during the post-graduation period. Th=e ‘dl obserntions”

sapl- contti 10,763 observations for 926 male workers and 10,899 ob=rvations for 971 female workem.

Thae are my preferred samples becawe they m&es full use of the ~ormation provided in the NLSY, but

for illustrative purpos- I abo form four additiond sampl~ for each gender by taking cross-sections of the

“all ob=rvatiomn samples. One subsample is formed by selecting only thox wages earned one y= titer

high school graduation or, more accurately, at the on~year mark plus or Anus 3 months. The “1 ye= alter

high school” subs=pl- contti 992 observation for 925 male workrs and 957 for 945 female workem.

I form the remtining subsamples by selecting wage earned three, sk, ad nine yem titer high school.

These cross-sectional sampl= needl-sly disc~d information, but I use them to show what the relationship

of inter=t looks We when one extines wag- at a single point in time u the authors of previom work k

this =ea have done. Tabl- 8-M md 8-F report means and stand=d deviatiom for euh v~iable defined in

table 7 for the five subsmpl=. (The proxy and instrummtd mriable will be dacribed in ~ction 4.2.)

The “41 observatiomm sample contains multiple wage observations for most r-pondents. There ~e

between 1 ad 28 observations per person for the males, with a mem of 11.6 (standd deviation=4.4).

For tbe fem~es, the rmge is 4s0 1-28 and the mem is 11.2 (standmd deviation=4.3). It is._@apprOpriate

to view the residuds for th- sfiples m independently distributed am= observation, so I -ume m

erw structure consisting of a pemon-specific (mean zero) random effect PIUSwhite nOise. Except where

otherwise stated, I wsume that both componenh of the r=idual are uncorrelated with the regr-sors.

The fow seeded cross-sectional samples also contati multiple wage obsemations for a small numb~

of respondents because I me all wages reported within the relevmt sti-month window rather than rwtrict

the sample to one observation per person. However, tbe am~unt of within-person variation& very smdl—

e.g., the “1 yea’, mde smple h= 925 individuals with one observation and 67 with two, and the “6 yeti’

mde smple h= 651 indlviduak with one observation and 275 with two observations. Even though the

-umption of independence of the error term is not met, I use ordinary Ieut squarm (OLS) to etimate

models b-d on th-e sapie. I do so because the r-ults me indistingui~ able horn what I obtah using

generalized lemt squares (GLS) or, alternatively, limiting the sample to a true cross-section by efitiating

observations.

18
4.2 Estimates

Tables 9-M and 9-F sumarize the -timat= from a series of wage models in which I use different comblnatiom of regr-sors tith each of the five sampla. To obtsin the statisti= k the fist row of table 9-M, I

we the wages reported one year after high school md estimate a model via OLS that controls for nothing

but the b~eline characteristics defined in table 7. I then r=tkate the same model aftm adding the thr~

high school employment variablm to the small fist of regessom. The first three columns of statistics report

the r~ults of u F tet for the null hypoth~is that the addition of the high school employment variablffl

contributes signific~tly to the prediction of the dependent variable. A “no” mder the “reject at 95Yo”

cola means that I fail to reject the null hypothesis” at a 95% confidence level. The remaining columns

report the coefficients and standmd errom for the three employment vtiables.

In subsequent rows of the top panel of table 9-M, I re~timate the two regrasions dwcribed above

after adding the regr~som listed. In row 2, for example, I control for the bssefine variables Pius the two

schooling attainment du-y variablm, with and without the high sch~l emplo~ent vmiabl-. In row 3,

I control for the b~he variablm, schooling attainment, and the sh wpetience ad tenure variabI@. The

Sk =ts of regr-siom me repeated for each of the four remtining smpl- to obtati the ~tfiat- in the

othm panels. The andysk summarized in tables 9-M md 9-F involves the intimation of 60 wage models for

ea& gender, with u many ss 63 co.wriat~ per model, so I present these succtict tabulation of tbe r~tits

rather than the full set of parameter atimat~ for ~ch model.

In a sense, the models underlying tables 9-M md 9-F ae “straw men” that demonstrate that almost

mything cm be inferred about the relationship bet wen high school employment and subsequent wagss.

For males, 1 can at Ie=t cltim that the mrious estkat~ form a consistent ad seemingIy sensible pattern.

One year after Klgh school, males who worked 21+ houm/week in grades 11-12 earn signifi-tly more than

their (previously) nonemployed or Iew employed counterparts: the ~ttited wage premium for thee malw

is about 1270, regud~s of which controls are included. Schooling attainment ad pmt-high school employment are not expected to exert much itiuence because they show little variation this soon after graduation,

but it is somewhat surprising that the AH_21+ coe~cient is hmrimt to the add~tion of other controls.

The coeffici~ts for AH_10 and AH_20 are smaller than three for ~_2.1+—hinting at the Astence of

smaller rewards for males who worked less intensively in high school—but statistic~ly imignifimt at a

19
5% significmce level. The nmt few paels of table 9-M reveal that the w~e premium enjoyed by md~

who workd 21+ hours/week in high school decremes over time, fd~ig to zero mmewhere betmen sti

and nine years titer ~aduation. Thew p=els Am reveal that the addition of mmt regr~mrs (epe<ldly

post-high school employment) to the model caus~ a substantial decre~e in the AH_21+ cmfficient. At

the thre-year mark, for mample, the coefficient is 0.160 when the b-efine varitilm ~e the ody addltiond

controls md 0.096 when Al remaining regrwmrs are added to the model.

T*le 9-F telk a very different stow for female. High school employment appeam to have no (stat&

ticdly signficat) effect on the wages of female workers =cept when they are Sk y= p=t high sch~l

gaduation. At this point—red after the entire list of regressors is mntrolled for—femal~ who worked

11-20 hews a week in grd~ 11-12 appear to earn &bout 9% more tha dl other female workem. Quite

ine~limbly, high school employment does not appear to tipltin wag= b my saple except the ‘6 ye=

titer Klgh s&ool” crm~ection.

The patterns k tables 9-M md 9-F protide partial answem to some of the qu-tions posed emlier in

this sect ion. Fimt, it appears that the =planatory power of high schml mployment erod= “& tidtiond

regresso~ me added to the model. The addltioi of regasors typically cau- the mefficients for Mgh schml

employment to shrink, the standard errors to incre~e, and the F statistic to deche drmtiicdly in due.

This tendency is seen most clemly in the “6 y- after Mgh schml,’ saple for real-, where high schml

employment m&es a statkticia~y sigtifimnt contribution to the prediction OTlog wag= in rom 1 ~d 2,

but no contribution when additional regr~sors are added. Among women, however, the nu~ hypothtis

that high school employment contributes to the model is rejected more often khm not.14

Second, = noted above, the fact that the estfiat~ &ffer across the four crm5sectional smpl= suwe&

that the relationship betw~n high school employment md wages chang~ over time. I explore the time

mrying effeck of high school employment further in tables 1O-M md 1O-F, where I work excl~ively with the

“dl obxrvatiom” sampl-. The longitudinal samples allow me to lwk at inter~tions betwen high schml

employment and ac~ual experience (recall that actual experience vti- within each crosssectiond sample),

and they also produce far more efficient ~timates of the time-mrying effects of high schoo~ernployment

14The..effitimti for the pmt-tigh saool employment -iabl- _ jotily Siet at a 57. Sidmce I.wl h CVew

model ~~te~ md the coefficimts for the s&ooUng attkmt tiabl~ _ joitily titifi-t for e=v mdd -ttitd

with the exception of those mkg the ,L1ye= dtm Mgh s-,, mos~wction. With few =x~tions, the c~tients for the

f~y -d Mgh s&ool +evement v~inbl- =e jointly sififi-t m .wcU. me s~d tiac:rnstim rm~ P.- @ +Ve a

statistidy sificmt effect on wage, singly or joitily, pomibly be~~e iti-tion is timing for a relatimly lug. propotiom

of the s-PI.. These titi~ continue tohold when I re—ge the order ti whi& I tid sets of re~esom.

20
than do- a -ies of mos~sections. h fact, the primmy value of the series of crm-sections used for tabl~

9-M and 9-F is that they demomtrate that resear~ers are taking a unec~sazily nmrow tiew of the role

of high school employment when they use single, point-in-time mtimatw. Depending on which ‘point” is

chmen, the inferences chmga drmaticdly.

A find set of estimates is summmized in tables lQ-M and lQ-F, where I u= the “aU ohserwtiom”

smple to e~lore the remaining questions of interest. Specifically, I consider (a) the tim+v~ying effects

of high school employment on wages (columns 1-2), (b) nonIine= ities in the relation~ip between high

school employment and log wages (columns 4-6), and (c) methods for contending with potential correlation

between high school employment and unobserved factors (mlums 2, 3, 5, and 6). Ea& modd estkated

for tables 1O-M and 1O-F is a modified vmsion of the specification prmented in the bottom row of table

9-M ad 9-F.

Wcti tht tabl- 9-M md 9-F sugg-t that the effect of high school employment on wage faW over time

for mal~, but peaks at mound 6 yeas of potential wperience (time elapsed since high school grtiuation)

for femda. To obttin the etimates in colum 1 of tables 1O-M md 1O-F, I use a specification that is

better able to idmtify tim~mrying effects thm ae the crow-sectional” ettiitm tined ember. While

the seti- of cros-sectiom shows how the relatiomhlp betw~n high school aploymmt ad wag= &=ga

over time, the models used for tables IO-M and 1O-F -k how it chmges u workers g~ work ezpetience,

which I beheve k the more relevmt qu-tion. furthermore, modeb b-d on the “dl observation” smple

cm identify continuow changw in the effect of high school emplo~ent, rather thm identifying chag= at

discrete intemls. The estimat- are also f= more efficient, for more data are used ad the mefitients for

BLACK, HISPANIC, etc. =e not rwstimated at every point in time.

After =perimenting with interactions between the thr~ high s&wl oployment variablw and X, X2,

md X3, m well x other specifications, I determined that the optimal way to identifi tirn&tiying effects

is to use m unrestricted spline function. I divide the X-tis into six segments—less than or equal to one

year of actual eqerience, two years, three years, four ye=, five yearn, md Sk or more yea~—=d defie a

dumy variables for every one of the 24 AH-X combinations. When a person who averaged 15 hours of work

per week in high school reports a wage at three ye- of experience, for &impIe, the variable representing

this particular AH-X combtiation (AH40 1X=3) equals one and all other mmbinations equal zero.

Column 1 of table IO-M indicates that the returns to high schml employment for males =e concentrated

21
mong individual who wmk relatively intemively in high school, ad that th=e returns tie ad then fd

u experience is gained. The interactions betwen X and both AH_O md AH_10 tend to yield meficients

that me small in absolute wlue (ad somethes negative), with extremely l=ge stand=d aors. At no

point in time is there a wage benefit =sociated with working Oor 1-10 hours per w-k in grad- 11-12. The

coefficients for the interact ions bet ween AH_20 ad X are statistidly insi~ficant except at ~perienw

leveIs of five yem and higher. These estimatw sugg-t that mala who averaged 11-20 houm of work per

week in high schooI earn more than their nonemployed (and lew employed) munterparts onIy titer gtining

several years of post-high school work experience. The pattern seen -ong the AH_21+ inter~tions

indicates that males who worked 21 or more hems/week in high school receiw a subst=tid wage premium

in the years that follow graduation. k contr-t to what table 9-M reveak, however, the estimate in table

1O-M *OW that this w~e pretium rkes w work MP erience is gtined and then fals titer about five yeof experience. (Itt~a vhtudIy 41 workers fm more than five years of dendar time to accumulate five

ye= of work mperience, given that X is me=ured in units of 2,000 houm.) ~ble 1O-F shows a sitilm

pattern for femala.

Throughout the analysis, I have controlled for high school emplo~ent with three categorical duu

v~iables to dwtingtish between mean wages e=ned by individuals who averaged O, 1-10, 11-20, ad 21+

hours of work per w-k h gad- 11-12. A moze comon pr=tim in the literature is to u= a shgle,

continuous memure of high school employment or, alternatively, a continuous memure md its squm=that

k, to resume a lin~ or quadratic relatiomtip between high school employment md log WWU. Th=

=sumptions are testable, of course, and I have chosen the categoti- med thus fm because the data prefer

them to any simple alternative, includlng alternativ- that group hours worked into different cakgoriw.

In COIUD 4 of tablw 1O-M and IO-F, I show =timates obtained ustig one of the dternativ- to help

substantive my claim. I control for the average number of hous per wek worked in high schml (0-51 for

males, O-44 for females, with the means ad studard deviations reported in tables 3-M and >F) ad the

squme of that nriable. The issue of inter~tions bet weeu high school employment and work aperience is

set =ide, so the colun 4 esiimat= should be compared to th~e appearing in the bottom row of tabl=

9-M and 9-F.

Column 4 of table 1O-M reveals that, for real-, the coefficient for the conthuous me=ure of high

stiool emplowent (AH) is 0.005 ad the inefficient for AH2 is -0.00009. Th-e coefficients imply that a

22
young man who worked 8 hou~/week in grad= 11-12 earm 3.3% more than someone who dld not work at

dl, a mde who worked 15 hours/week earns 5.3% more, md a male who worked 25 hou~/w&k earns 6.6%

more. The latter -tkate is very close to the wage premium of 6.370 bwed on the categorical specification

(see table 9-M). However, the quadratic specifimtion imph- that ma]= who worked 1- htensively in high

school receive wage premia m well, where= the more fletible specification reveab no su& effect. In table

1O-F, tbe quadratic speci6cation imphes wage boosts of 3.5%, 5.2%, and 5.4% for females who workd 8, 15,

ud 25 hours/we& h high school. Agtin, the quadratic specification sefiously over-timatw the returns to

hght-t~moderate Klgh school employment.15

Next, I -k whether the =timat= of the wage benefits of high school employment are b,-ed by the

correlation between high school employment and unobserved f=tors such u abfiity, dition, and peer

itiumce, 41 of which may tiect poskbigh school w~es. To addr=s th~ iwue, I begin by using a small

set of pray vaiables intended to control for unobserved ablhty. The ideal prow would be scor= horn

a inte~gence t=t administered prior to the start of high school, for such a proxy would control for any

“pretesting” ability differences that influence the decision to work while in high schml. As pat of the

high school tr-ctipt cotiection, info~ation on aptitude and intelligence tests given to the r-pondent -d

contained b his or ha school r=ord W= collected, so I have such “ided” proties for a nuder of saple

members. The information coded for e=h tat consists of the total and percenttie smre and the month,

yem, ad gade level in which it w= taken. The specific tests for which information is avddle (for ~ng

subsets of rmpondents) include the California Test of Mental Maturity, the Otti-Lennon Mmtal Ablfity

T-t, the Differential Aptitude Test, and the Stanford-Blnet InteWgence Scale. ”

I form a single wiable (PRETEST) defined = the percentile score for whichever intelligence tat is

reported for the given respondent. When more than one test smre is avtil~le for a respondent, I use the

tat t&en in grade 9 or m soon before grade 9 w possible. When no test score k amilable for a r-pondent,

I set the t-t score equal to zero and set a miwing indlmtor equal to onq when a tet score is reported,

the mksing indicator equals zero. Summmy statistics for the PRETEST variable and =ociated mistig

indicator appem in tables &M md 8-F. These tabl- show that, unfortunately, information is misting for

6l% of males and 64% of femal-. Becauw this “idea~’ prov suffers from a severe tissing data problem, I

15~=n . ~e= ,pe~=~ion i, ~e~ (t~ i,, ~~a A~z is &.ppa, the .oefiuent. for AH _ 0.0021 (.t~& ~~.

m=O.00~) for den md 0.C019 (0.W09) for fdes. Tfis spe~cmion does not ovemtat. the ret- to low Ieveh of E&

stiol empl-ent u Seri-ly - d- the q“=htic mdel, but it understates tie ret- to 21+ ho_/week of Mgh a-l

empi.yment.

23
also qeriment with percentile AFQT scores u a proxy for ablhty. As discussed ~ footnoti 9, th-e scor~

are taken from a t-t adminktered in 1980, at which point r=pondenk’ schoobg attainment raged from

grade 9 to the completion of college. Hence, AFQT scoresdo not control for the skills that ead r~pondent

embodied at the time that the high schooI employment deckion wu made.

Colums 2,3, and 5 of table 1O-Mmd IO-F report the results from adding proxy variabl~ to alternative

specifications” of the wage model. One point to be mde about thee ~ttiate is that the mefficients for

the prow vwiabl- do not differ significantly from zero fin isolation or jointly) in any of the th~ modeb.

The =cond point is that, comparing column 2 to CO1.D 1, colum 3 to the bottom row of tabl~ %M ad

9-F, md mlumn 5 to colum 4, it is apparent that adding proties for abtity does not change my infer=cabout the relationship between high school employment ad wages. Thae two conclusions mntinue to hoId

when I use AFQT SCOESor PRETEST scores in isolation.

The inclusion of proxy variables may have no &ect on the high schml empIoymmt cm fficienti becaw

unobserved heterogeneity does not biw the =timates, or because the tat scores me we~ profi= for

unob=ved factors that are correlated with Klgh school employment. Therefore, = m dtemative to the

me of protiu, I attempted to find instmmental variabl= for the high schml employment me-ur=. The

instrwents that I use =e the unemploywnt rate prevtiIbg in the r-pondent’s region of r=idence at the

time he or she Ieav- high school =d a dummy vmidle indicating “wbethm the r=pondent’s Klgh school

off~ vomtionfl and tetical coumes; the latter variable k tisshg for numerom r~pondents, so I agab

form a tisskg variable indicator. Un=ployment rat- are shown in tablw AM and 4F to be systematically

related to h]gh schooI unemployment, presumably because they reflect the job opportunity= atikble to

high schooI students. The amilablhty of vocational couses is also hkeIy to influence students’ pmpensitia

to s-k employment: students may substitute a job for the opportunity to acquire vocation &ilk within the

clmsroom or, = tabl- 6-M and 6-F =em to suggest, they may be inched to combine employment with

a vocational cumiculum. At the same time, it is remonable to ssume that these labor mxket and school

opportunities are unrelated to indlvidud students’ unobserved charactertitia, so they appem to be suitable

choices for fistruments. Tables 8-M and 8-F pr=ent suuw statistim for the instrumental vmiabl=.

1 cannot &timate the column 1 model in tablffi 1O-M and 1O-F using instrumental variabl= became

I do not have enough instruments for identification, given that there are 18 endOgenOus variabI=. When

I estimate the column 3 model (or, more accurately, tbe model =mciatd with the bottom row of tabla

24
9-M md 9-F, for the proxy vtiables are dropped from the instrumental variabl- specification), I obtain

nonsensical results. For the real=, for example, I obtain coefficients for AH_lO, AH_20, and AH_21+

of -3.58, 0.88, and 5.29. An examination of the first stage =tbata reveals why this problem ak-: the

R2S ~e aromd 0.009. Apparently, the ‘cautionary tale” told by Bound, J=ger, and Baker (1993) about

tbe bi~= that radt when wed instruments are used apphw to my particular application. However,

mperi=ce hw suggested that it is often e=i~ to use instruments fOr cOntinuOusv=iables than fOr du~y

vtiablm, so I apply the instrumental mriables method to the specification shown in OIUD 4 of tables

1O-M and 1O-F. The =tfiates shown in column 6 are not quite = nonsensical m the ones alluded to above,

but a severe bi~ still appea~ to ~st for both males and females, the use of instrumental variabl= cam=”

the AH coefficient to inire~ by a factor of ten md the AH2 @efficient to increwe in absolute value by a

factor of 33.

5 Concluding Comments

In this study, I identify the “net” effect of high school employment on pmt-schml WW- by wtimattig a

wage model that mntrols for a broad array of personal, f-ly, ad high school characteristic, along with

dettiled meuur~ of high school whievement, schoohng attainment, and post-school employment. I &o

attmpt to control for unobserved factom that may influence both wages =d the decision to work while

in high school, but my proxy and instrumental vaiables me demomtrably flawed. Nonethelas, I conclude

that tbe net effect of high school employment on wages is generally not significantly different than zero.

Indlviduds who average more than 20 hours of work per week in Klgh school do receiye a return to their Klgh

school experience, but the effect is shor& lived. Focusing on males, for &le, tbe wage boost -ociated

with 21+ houm per week of high schml emplovent rise horn 6% immediately titer high school to about

10% when 4-5 years of post-high school work ~perience h= been accrued, and then declin-. Femal- who

work 21+ hours per week in high school receive a similm return, but the post-school WW= of those who

xe 1-s intensively employed in high school =e roughly the same m those who =e nonemployed.

One interpretation of the resulh reported here is that the transitory wage boost enjoyed by individu~

wbo work very intensively in high school is not a return to marketable skilk per se, but instead reflects jobs-king skills obtained via high school employment. That is, high school employment might benefit young

workers by teaching them how to locate good employment opport uniti= and comuticate eff&cfiVely with

25
potential employers, and perhaps by providing a valuable signal (of reliablhty, for =aple) to potential

employers. Such benefits would cause high school workers to proceed ‘up tbe job Iaddef, at a different

rate than their counterparts, and perhaps explain the nonlhear wage benefit that they rective. I sugg=t

this interpretation because if high school empIoymnt were to benefit workers by enhancing their stock

of marketable skills, there is no rewon to believe the wage effect wodd be time-mrying or short-hved.

These notions can be explored further by examinhg the relationship between high school employmmt =d

patterns of job mobifity and wage growth, but I will leave such andyse for future work.

In closing, it is worth noting that by focusing -elusively on average hourly wag- I m, in dl likelihood, undemtating my career-enh=cing benefits of high schml employment. After dI, even if high school

employment h= no effect on average hourly wag=, it will signifimntly incre=e Hietime eain~ through

its positive effec& on post-school employment. As I show in section 3, individual wbo avmage 21+ houm

of work per w~k in high SAOOI continue to work very intensively after graduation relative to their counterpmts who gained l-s work experience while in high school. ~rthermore, high school employment may

have a positive impact on nonwage components of c=eer “SUCWS,” such m occupational status ad fringe

benefits. Ruhm (1995) analyzes the relationship betwem Mgh schwl employment and alternative memma

of cz=r outcomes, so I have opted to forego such m extension here.

26
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Meyer, Robert H. md David A. Wise. “High School Preparation and Early Labor Force

27
Experience.” b Ri&ard B. Freemari and David A. Wise, (editors), The Youth &&r

Market Problem: Its Nature, Cawes and Consequences. Cficagm Utiversi~ of Chicago

Press (for the National Bureau of Econotic Res~rch), 1982.. ““.

Mchad, Robert T. and Nancy Br=don ~ma. ~~youth Employment: Does fife Be@n at

16?. Jouma[ of Labor Econom~cS 2 (October 1984): 464.476.

Miner, Jamb. Schwling, Experience, and Earnings. New York: Columbia University

Pr+s (for National BurQu of Economic Resemch), 1974,

Ruhm, Christopher. “1s Wlgh School Employment Consumption or kvestment?” Greensboro, NC: University of Notih Cmoha at Greensboro timeo, revised June 1995.

Steel, Lauri. “Early Work Experience Among White and Non-WMte Youth: bptimtions

for Subsequent EnroHment md Employment.” Youth Ond Society 22 (June 1991): 419-

447.

Stephenson, Sttiey P. “h-School Labour Force Status ad Pos&S&ool Wage Rates of

Young Men.n Applied Emnomics 13 (1981): 279-302.

Stevenson, Wayne. “The Relationship Bet ween Early Work E~erience ad fiture EmploWbiIity.” h Arvil V. Adams md Gwth L. Mmgum (editors), The Lingen”ng Cn.sis

of Youth Unemployment, Kdmazoo; Mfi W.E. Upjohn kstititute for Emplo~ent

Research, 1978.

Stern, David and Yosbi-Futi Nahta. “Chaact=istics of High Stiml Students’ Ptid Jobs

and Employment Experienms titer Klgh School.” k David Stern md Dorothy Eichom

(editom), Adolesmnce and Work: Influences of Smial Stmct.re, Labr Markets, and

Culture. HiUsdde, NJ: Lawrence Erlbaum Assotiatw, 1989.

28
Number of

bpondents

12,686

3,676

9,010

-2,552

6,458

- 74

6,384

-1,103

5,281

-3,299

1,982

- 46

1,936

-39

1,897

Table I

Number of Saple Deletions by Re~on

Re=on for Deletion

Original NLSY saple

~amcript data not collected

Did not graduate from high school

Date l-t enrolled in high school indeterminate

~anscript missing course information for grada 11 md 12

Graduate from high school before Jmuaiy:i980

Grtiuate horn tigh school before stit~nth birthday

No post-high school wages

Smple used for analysis (926 males, 971 females)

Table 2

Gender md mm Composition of Sample

Males I Females

Percent Percent

Rze Number of Sample Number of Sample

White I

606 65.4 I

574 59.1

Black 207 22.4

Hispanic

241 24.8

113 12.2 156 16.1 I

All \ 926 \ 971 I

29
Table 3-M (Males)

Distribution of Average Hours Worked Per Week

in Grades 11 and 12 ad Preceding Summers

Average Houm

Worked Per Week

o

1-10

11-20

21+

Meu avg. hours/w=k

(stmdard deviation)

Mem among workers

(standmd deviation)

Number of individual

Percent of Intividu*

Su-er Before Summer Before

Grade 11 Grade 11 Grade 12 Grade 12

36.8 35.7 25.4 26.8.

18.8 29.0 17.0 22.2

22.5 21.9 24.8 24.2

21.9 13.3 32.8 26.8

11.0 8.4 14.9 12.5

(12.1) (10.0) (~.;) (;~)

17.4 13.0

(11.0) (9.7) (11:8) (10.8)

921 926 926 926

~

Grdm

11 and 12

20.4

35.2

27.9

16.5

4

10.5

yz~

(9.5)

926

Table 3-F (Femal=)

Distribution of Average Hours Worked Per Week

in Grad= 11 md 12 and Preceding S-ers

.—

Percent of InditiduA

Avmage Hours Summer Before Summw Before Grad=

Worked Per Week Grade 11 Gradell Grade 12 Grtie 12 11 ad 12

0 51.5 43.7 37.7 30.7 27.2

1-1o 9.9 15.0 12.2 16.4 38.9

11-20 17.3 14.4 19.1 15.6 23.0

21+ 21.3 24.8 31.1 37.4 10.9

Mean avg. hoursfwek 6.9 6.1 9.9 10.6 8.3

(studard deviation) (10.0) (8.3) (:::) (:::;) yi::

Mean song workers 14.3 11.3

(standad deviation) (10.0) (! ’4) .

(10:9) . (:0.4) (8.3)

Number ofindividuak 962 971 971 971 971

30
Table 4M (Malw)

Characteristics of Smple by Average Hours Worked Per Week in Grad- 1l-I2

I .A. ve,ase ..- ------ Eo,,rs W“vkeA . . ..- .Pev.. Week -----i“.- Crn.d- ----- --11-12--

0- 1-10 11-20 21+

Mem S.D. Me~ S.D. Mew S.D. Mem S.D.

Pmsond, f-fly, bbor maket cbacteristics

I if white 0.49 0.67 0.72 0.72

1 if black 0.38

1 if Hispmic

0.22 0.16 0.14

0.14 0.11 0.12

AFQTscore

0.14

42.98 30.38 48.85 28.49 54.67 27.41 43.3o 25.14

1 if foreign born 0.04 0.05 0.07 0.06

Net fhy income fin 1000s of 1982 dollars)” 20.59 14.69 26.17 17.30 29.49 15.41 31.80 18.22

Number of sibfings 3.71 2.78 3.18 2.24 2.98 1.94 3.33 2.63

Father’s highest grade completed 11.09 “3.74 12.32 3.52 12.41 3.47 12.04 3.26

Area unemployment rate 9.42 3.90 8S4 3.22 8.55 3.21 8.60 3.46

1 if five in urhu area 0.68 0.77 0.77 0.74

Wlgh sdool ctiacteristics

Total emohent k high school (1000s) 1.23 0.77 1.30 0.81 1.32 0.72 1.40 0.80

Studmt-teacher ratio 18.96 4.42 18.97 3.96 18.98 4.04 19.01 3.72

Percent of students clmsified m disadvantaged 26.33 23.57 21.50 19.52 19.16 18.66 15.80 17.54

Avemge dtily percent attendance 90.34 12.89 89.57..14.34 89.49 14.16 88.88 16.81

Permnt of 10th ~aders who ftil to graduate 12.68 14.67 14.91 20.81 15.56 22.98 15.35 21.19

Average salary for firs&year teachers (1000s) 10.68 1.32 10.87 1.17 10.87 1.00 10.80 1.49

One ye= turnover rate song full-time tethers 6.75 7.46 6.57 8.14 6.78 8.95 7.21 8.12

Schoolkg ewectations -d atthment

Highest grade mpect to completeh 14.45 2.12 14.65 2.17 14.86 2.01 14.22 2.03

Highest wade completed by 1991 13.70 2.00 13.79 2.05 14.09 2.17 13.28 1.85

Winks enrolled inschoolk fist y- after HS 6.42 5.45 6.38 5.34 6.86 5.45 5.34 5.33

W&ks enroUed in s&ool h 1st 6 years after HS 21.17 24.64 20.96 25.35 22.91 24.88 15.61 22.26

1 if no emollment titer HS 0.44 0.40 0.44 0.54

Pmt-h]gh s&ool employ m-t

Percent of weeks worked in fimt year after HS 35..74 34.05 51.24 34.23 64.04 33.44 77.68 29.93

Percent of weeks worked in 1st 6 ye~. titer HS 56.44 27.81 60.29 27.93 67.97 28.44 79.71 25.05

Average houm worked per week

In fimt y= after HSC 11.91 12.85 15.68 12.33 20.15 13.39 28.80 15.02

In 1st 6 years after HSC 21.66 12.63 22..82 12.62 25.97 13.23 33.48 13.03

During work w=k in first YW titer HSd 25.32 18.17. 28.82 14.78 30.55 12.69 36.76 12.02

During work winks in Ist 6 years titer HSd 37.10 9.35 37.20 9.55 37.54 8.70 41.70 8.90

Log of average hourly w.= (1982 doll=.)

1 yen titer HS 1.37 0.32 1.42 0.38 1.42 0.28 1.49 0.34

6 yeas after ES 1.69 0.48 1.80 0.48 1.88 0.43 1.94 0.49

After gaining 1 y- of post-HS work experience 1.45 0.40 1.49 0.40 1.50 0.34 1.50 0.37

After gaiting 6 yem of POSGHSwork wperience 1.87 0.52 1.90 0.40 2.00 0.43 1.96 0.43

Number of observations [percent of 926]e 189 [20.4] 326 [35.2] 258 [27.9] 153 [16.5]

.-. . ,.- .. ,.. . . . . .... . ,,, +” -uousekoka lncOme lrom al sourc- net 01 rmponaenc .s wage eznlngs, IOrye= In wnl:n r:sponaenc leaves ma.

bfiported in 1979, 1981, or 1982, whichever is closest to date of etit from high school.

“Total weeks workd in one (six] year(s) divided by 52 (312) weeks.

‘Total weeks worked in one (sk) year(s) divided by number of work weeh.

‘Sumary statistim =e computed for nonfissing observations only. See Table 5 for sample size within each cell.

31
Table 4-F (Femal-)

Cb=acterktics of Sample by Average Hours Worked Per W-kin Grad= 11-12

I A.,. . ..- u-,... WA.L.A Da. W..L :“ P-.. A- , ?–1 9 -, ..- - -vu. ” ..-. n.” . . . ,. ..= .“ “Am=== -----

0 1-1o 11-20 21+

Ma S.D. Mean S.D. Mea S.D. M- S.D.

P=sond, fdy, kbor market cbacteristics

1 if white 0.42 0.59 0.72 0.74

1 if black 0.41 0.24 0.14

1 if Hispanic

0.12

0.17 0.17 0.14 0.14

AFQTscore 35.54 25.65 44.25 26.49 48.07 23.33 43.82 24.51

1 if foreign born 0.03 0.04 0.07 0.04

Net fmfiyinmme fin 1000. 0f1982d011ars)a 19.34 14.29 26.44 17.27 27.57 15.58 27.82 14.45

Number of siblings 3.70 2.73 3.37 2.37 3.22 2.19 “- 3.38 1.97

Father,s high-t grade mmpleted 10.67 3.58 11.84 3.66 12.11 2.94 11.05 2.79

Area unemployment rate 9.21 3.29 8.45 3.00 8.71 3.56 8.08 3.12

1 iflivein urban area 0.67 0.79 0.80 0.71

High school cbacteristics

Total enrollment in high school (1000s) 1.20 0.75 1.38 0.78 1.42 0.79 1.44 0.70

Student-teacher ratio ~~~~ 19.50 5.06 19.22 4.17 18.99 3.76 19.56 4.09

Percmt of students d=sified ~ dlstivantaged 28.52 24.36 19.20 18.34 17.25 18.11 15.21 15.91

Average daily percent attendace 89.59 13.67 89.39 14.03 89.12 15.69 87.49 18.21

Percent of 10tb graders who fafl to graduate 18.19 23.05 13.79 17.78 15.75 23.62 12.24 13.83

Average sdq for fit-year teachers (1000s) 10.61 1.31 10.72. 1.22 10.69 1.11 10.89 0.82

One year turnover rate song full-time te=hers 7.23 9.04 7.34 8.47 6.50 5.67 7.04 8.04

SchOOltig e~ectatiOns andatt~ment

Eighestgrade~ect tommpleteb 14.31 2.04 14.61 2.02 14.54 2.01 14.14 1.93

High=t grade completed by 1991 13.52 1.96 13.86 2.01 13.55 1.97 12.86 1.64

Weeks enmUed k school in fist yem after HS 6.41 5.41 6.44 5.48 6.05 5.41 3.75 4.83

Weeks enmffed h schml in 1st 6 years titer HS 20.17 24.50 21.52 24.37 17.79 22.14 8.58 16.57

1 if no enrollmmt aft= HS 0.44 0.41 0.48 0.61

P-t-high s&ooI employment

Percent of weeks worked in first y= after HS 25.86 30.53 51.94 36.33 66.64 33.98 80.50 26.72

Percmt of weeks worked in 1st 6 ye- titer HS 50.79 26.32 64.50 25.98 71.03 25.31 76.96 22.44

Average hours worked per week

In first ye= after ~c 7.30 9.60 13.82 11.99 19.64 13.00 28.20 11.91

In 1st 6 y- titer HSC 17.43 10.31 21.66 10.53 25.04 10.98 28.90 10.71

During work weks in first year titer HSd 18.93 17.72 22.98 13.99 27.29 12.96 35.00 9.60

During work wwks in 1st 6 yeus aftw HSd 33.66 9.35 33.09 8.12 34.55 8.02 37.05 6.98

Log of average homly wage (1982 doll=s)

1 year titer HS 1.24 0.34 1.31 0.39 1.32 0.28 1.33 0.31

6 years after HS 1.53 0.53 1.66 0.48 1.72 0.43 1.72 0.44

After gaining 1 year of pos&HS work mperience 1.37 0.43 1.37 0.42 1.39 0.35 1.40 0.34

After gaining 6 years of pos&HS work mperience 1.70 0.41 1.80 0.50 1.79 0.45 1.79 0.44

Number of obsermtions [percent of 971]’ 264 [27.2] 378 [38.9] 223 [230] 106 [103]

----- . . . . . . . . . ,., ,,, -- ‘HOu=hOla Income irom ail SOUICSnet oi respondents wage ealngs, Ior year In wnlcn respondent Ieav- ma.

bRepOrted in 1979, 1981, or 1982,”whichever is closest to date of ait from high school.

“Total weks worked in one (six) year(s) divided by 52 (312) w-ks.

‘Total weeks worked in one (six) year(s) divided by riumber of work weeks.

‘Summ~ statistics are mmputed for nonmissing observation only. See Table 5 for sample size within each ceU.

32
Table 5

Number of Nontising Observations Used to Compute Sumary Statistim in Tables 4M and 4F

I N,,_ka, fir ohao,-~.;-n.

. .U... --- -. - ---- .-”.--”

MALES FEMALES I

o 1-1o 11-20 21+ o 1-1o 11-20 21+

Personal, fmfiy, labor m=ket cbactmistics

1 if white 189 326 258 153 264 378 223 106

1 if bld 189 326

1 if Hispanic

258 153.. 264 3?8 223 106

189 326 258 153... 264 378 223 106

AFQT score 184 323 253 148 257 370

1 if foreign born

220 102

189 326 258 153 264 378 223 106

Net fdy inmme 151 261 209 109 201 293 175 74

Number of siblin~ 189 326 258 153 264 378 223 106

Father’s high=t grad: completed 155 287 240 139 229 329 203 99

Area unemplowent rate 185 324 257 148 261 375 220 100

1 if hve in urban area 189 326 258 153 264 378 223 106

High school &aracterfitics

Total enrollment in high school 147 253 201 116 197 277 171 81

Studmt-teacher ratio 144 245 196 113 193 270 162 79

Percent of studenti clxsified m disadvantaged 129 219 180 96 172 243 144 72

Average daily percent attendance 148 2.56 213 121. 199 278 172 83

Pmcent of 10th gradem who fail to graduate 146 251 205 113 201 271 169 78

Average sdmy for fifst:year teachers (1000s) 139 252 208 113. 188 267 165 82

One ye= turnover rate among full-time teachem 146 258 208 1.19. 202 279 174 80

Sdooling expectations -d attainment

Highest grade expect to complete 183 325 248 147 255 363 213 95

High-t grade completed by 1991 189 326 258 153 264 378 223 106

Weeks rerolled in schml in first year after ES 189 326 258 153 264 378 223 106

W&ks enrolled in school ii 1st 6 years after HS 189 326 258 153 264 378 223 106

1 if DOenrollment after HS 189 326 258 153 264 378 223 106

Post-high school empl~mwt

Percent of weeks worked in first year after HS 189 326 258 153 264 378 223 106

P=cent of weeh worked in Ist 6 years aft= HS 189 326 258. L5_3. 264 378 223 106

Average hours worked per week

In first year titer HS 189 326 258 153 264 378 223 106

In 1st 6 ye=s titer HS 189 326 258 153 264 378 223 106

During work week in first year after HS 189 326 258 153 264 378 223 106

During work weks in 1st 6 yeus after HS 189 326 258 ..153 264 378 223 106

Log of average hourly wage

1 yem titer ES 114 252 209 123 130 291 177 95

6 years after HS 161 260 216 137 197 308 185 87

After gaining 1 yea of pOst-HS work experience 180 301 242 143 240 355 211 100

After gtining 6 years of”popt-HS work mperience 103 182 169 .9.5. 116 230 147 67

33
Table 6-M (Mal-)

Credits Taken md Grade Point Avaage by Subject Area

ad Average Hours Workd Per Week in Grad= 11-12

Subject Arean

Humanitia and social studies

Mathemati- and natmal science

Vocational subjects

Other subjects

All coursm

Hummiti- and social studies

Mathematics and natural science

Vocational subjects

Other subjecti

Humaniti- and social studi=

Mathematics and natural science

Vocational subjects

Other subjects

All COUrS_

Number of observations [percent of 926]

‘The five most fiequen tly repor ted cou~e

Hummitimfsocid studies: berican Hi

Literature.

4.3$ 1.30

1.91 1.40

1.99 1.73

2.25 1.80

10.49 1.58

D

42.05 12.08

18.00 12.70

21.38 16.39

18.58 11.66

2.34 0.84

1.90 1.16

2.25 1.17

2.50 1.24

2.47 0.70

189 [20.4]

ties in each cat

Dry, English 111

Number oi

4.36 1.36

1.99 1.50

2.37 2.01

2.03 1.45

10.75 1.49

cent S eoft

40.77 12.16

18.35 13.53

22.18 18.35

18.71 12.96

Grade pa

2.36 0.81

1.97 1.16

2.23 1.29

2.62 1.19

2.56 0.67

326 [35.2]

W~k in Graa-- 11-12

11-20 21+

Wean S.D. ~ Mem S.D.

?dlts twen

4.23 1.39

1.87 1.42

2.70 2.04

1.92 1.46

10.72 1.57

3.88 1.29

1.55 1.50

3.30 2.16

1.65 1.38

10.38 1.95

1

J credit.tb

m

average

2.37 0.79

1.98 1.15

2.40 1.20

2.58 1.24

2.57 0.64

258 [27.9]

2.21 0.71

1.66 1.19

2.53 1.05

2.33 1.33

2,47 0.63

153 [16.5]

~ory, in aecen ]g oraer, ae

Ameticm Govmnmeht, Engbd IV, POPU1=

Mathematiw/science: Chemistry I, Algebra II, Physi= I, Gmmetry I, Biology.

VOmtiOnal: General Work Experience, Typewriting 1, Accounting, Automobile Mechmim, Auto Shop I

Other: Physical Education, Health, Band/Orch-tra, Driver Eaumtion, Art I.

34
Table 6-F (Females)

Cre&ts T&en and Grade Pofit Average by Subj@ Area

and Average Hours Worked Per Week in Grades 11-12

I .A.“e.a.re .. . . . .Hm,,, . . . .. Worked . . ..— Pe, Week . ....—;“ Grad,.. ------ --11-12 I

0 I 1-10 I 11-20 I

ii+

Subject Areaa Mean S.D. Mean S.D. Mean S.D. Mea S.D.

Number of credits t~en

Humaiti- and social studia 4.53 1.24 4.47 1.31 4.28 1.37 3.85 1.10

Mathematia and natural science 1.49 1.30 1.54 1.35 1.42 1.34 1.12 1.11

VacatiOnA subjects 2.33 1.92 2.22 1.96 2.82 1.99 3.19 1.94

Other subjects 2.46 1.64 2,32 1.41 2.05 1.57 2.22 1.60

All COUIS= 10.81 1.52 10.56 1.69 10.56 1.67 10.37 1.81

Pe rcentage of to td credits th n

Humaniti- and social studlw 42.42 11.83 42.74 11.51 40.96 12.85 37.81 11.23

Mathematiu ad natural science 13.59 11.57 14.26 12.11 13.37 12.40 10.85 10.67

Vocational subjecti 21.30 16.66 20.65 17.06 26.51 17.77 30.14 17.12

Other subjech 22.69 14.55 22.35 13.85 19.16 14.01 21.20 14.29

Grade PO.mt average

Humanitks md mcial studies 2.55 0.80 2.60 0.79 2.65 0.72 2.52 0.76 ~

Mathematim and natural science 1.93 S.32 1.91 1.34 1.80 1.35 1.67 1.30

Vocational subjects 2.32 1.30 2.25 1.35 2.54 1.21 2.62 1.13

Other subjects 2.79 1.14 2.84 1.12 2.66 1.19 2.56 1.25

All COUISeS 2.71 0.67 2.71 0.69 2.76 0.61 2.67 0.65

Number of observtiions [percent of 971] 264 [27.2] 378 [38.9] 223 [23.0] 106 [10.9]

. . . .. .

“‘~he tive most frequently reported course tltl- In each category, In a=cenalng Oraer, am

Humanities/wcid studies: berican History, Englbh III, Amdcan Government, English IV, PsychOlO~.

Mathemati~/science: Chemistry I, Algebra 11, Gmmetry I, Biology, Physics. 1.

Vocational General Work Experienc% Typewriting I, Accounting, Shorthand, Office Practice.

Other Physical Education, Choir, Health, Bmd/Orch-tra, Driver Education.

35
Table 7

Nma and Definitions of Variabl~ Used h Regr~siOn Analysis

V=iable Name Definition

Dependent wtible

WAGEHigh s&OOlemplOyment

AH_10

Log of average hourly wage (1982 doffam)

Iifaverage hours wOrked/w&ktigrad~ 11-12~

1.10

AH_20

AH_21+

Bme~e ~~actetisticsa

BLACK

HISPANIC

KIDS*

MARMED*

DIVORCED”

INSCHOOL*

PA~IME.

UNION*#

cmY.

SOUTH*

GOVT”

3&00tig attainment

HS*

COLLEGE*

Post-high schol emplo~entb

x*

T*

F-fly chmact~istics

FOREIGN

NUMSIBS

FATHELED#

INCOME#

SAool ctiacteristics

SCHOOLSIZE#

RATIO#

DBADVANTAGED#

A~ENDANCE~

DROPOUT#

TURNOVER#

SALARY#

High shool achievement

CRED2UMSOC

CRED_MATHSCI

CRED_VOC

CRED OTHER

GPA_~UMSOC

GPA~MATHSCI

GPA_VOC

11-20

21+

1 if respondent.is bld

1 if r=pondent is H&panic

1 if child under the age of 6 is prment

Iifrespondmtk martied

1 if respondent is divorced

1 if cumently ~rolled in school

1 if work less th 30 hOum/week

1 if union job

1 if live h urban aea

1 if five in South

1 if pubfic sector job

Iifhigh-t grade completed is 12

1 if high-t grade completed is 16+

Yem of post-high school work mperience

Yem of tenure with current employer

1 if foreign born

Number of sibfings

Fath~’s highest grade completed

Net fatily income (1000s of 1982 dollars)’

Total enrollment in high school (1000s)

Student-teaAer ratio

Percent of students clustied ~ dwdvutaged

Average dtily percent attendmce

percent ~flOthgradem whOftilt Ogrduate .

One year turnover rate mong ftill-ttie teachem

Average salary for first-ye= temhers (1000s)

Credits t&en bh_ities/sOcid science

Credits taknin mathematics/natural stimce

Credits takm k vomtional subjects

..Credi.ts taken in other subjec@

GiadepOint average (GPA) inhummities/mci& scienct

GPA in mathematics/natur& scien~

GPA in vocational subjects

GPLOTEER GPA in other subiects

“B~ebne chmacteristics also include dummy variables indicating the calendaryem in which

the wage W- emned.

6P0st-high school employment chmacteristics also incIude X2, X9, T2, and ~.

‘For the year in which the respondent l~ves high s&ool.

#DenOta reg~~Or~ fOr ~~1~ ~~me ~b~ervatiOn~have fi~i~g v~~~, A fissi~gvaluein(]~~t~~

k defined for th=e vmiabl~; see the note to tables 8-M and 8.F.

*Denot@ regressors that wry over time afta the respondent hm left high school.

36
Table &M [Males)

lariable

)ependent variable

WAGE

~lgh school employment

AH_10

AH20

AH_21t

3aseEne cbaracterktics

BLACK

HISPANIC

KIDS

MARRIED

DIVOWED

INSCHOOL

PARTTIME

UN1ON

CITY

SOUTH

GOVT

:&Oofing attdnment

HS

COLLEGE

BOst-HS employment

x

X2

X3

T

T2

Ts

?~ily chacteristics

FOREIGN

NUMSIBS

FATHER>D

INCOME

;onthued on next page.

1 year after HS

Nean S,D. Mi=.

Summary Statistics for Samples Uwd in Wgr=sion Analysis

3 years after HS

1.41 .33

’37

.31

.19

,17

,13

.02

.03

—

.48

.42

.12

,74

,32

.10

.96

—

.57 .35

,45 .58

.44 1,01

.41 .32

,27 .47

.24 :81

‘.06

3.12 2.32

10.87 4.95

22,47 18,76

.02

.10

.20

Mean S.D. Miss

1.51

.36

.30

.19

.18

,12

.07

.09

,02

!39

,33

.12

,73

.34

.09

,75

.00

1,90

4“49

12.13

,91

1,63

3.95

,05

3.11

11,16

22.22

.40

.94

4,00

15.76

.8?

2.92

10,20

2.30

4,81

18,85

.01

.09

.20

6 yearn after HS

Mean S.D. M16S.

1,79

.37

.26

.18

.22

.13

,21

.28

.03

,16

.19

!14

.75

.35

,08

,52

.21

4:24

21,86

.49

,05

1.97

17.49

[26,06 139.88

1,65 1.69

5.58 lo,d9

26.37 76.76

.05

3,22 2.29

10.79 5.01 .11

21.73 18.41 :19.

9 yearn after HS

Mean S.D. Miss.

1.95 .51

.34

.28

.19

,23

,13

.29

’47

,08

.07

,15

,24

<80

.34

,10

.44

.,32

7;43 2.72

62;68 42.19

j77,18 579.82

2,.74 2.69

14.76 28,29

109,85 360.88

,05

3.18 2.28

11.00 5.08

21<82 18.59

.05

.11

.21

All obwrvtiimm

Mean S.D. MISS.

1.70 .49

.36

.29

.19

,20

,13

.17

.25

,03

,24

,26

.13

.76

,34

.09

.63

.16

4.04 3.12

26.62 36,34

16.70 430.17

1,61 2,00

6,58 17.37

41,82 195.37

.05

3,18 2.31

10.89 4,99

21.69 18.45

,04

11

.20

37
Variable

5&001 clmracteristics

SCHOOLSIZE

RATIO

DISADVANTAGED

A~ENDANCE

DROPOUT

TURNOVER

SALARY

Kighs&ool achievement

CRED2UMSOC

CRED_MATHSCI

CRED_VOC

CRED_OTHER

GPA2UMSOC

GPAMATHSCI

GPA_VOC

GPA_OTHER

Proxy ad instrumental vmiables

AFQTSCORE

PRETEST SCORE

UNEMPLOYMENT RATE

VOC/TECHPROGRAM

~umber of observations

Yote: l’Mk” refers tothefrutionof

~ble 8-M (Male)

CONTINUED

Sumary Statistics for Samplm Used in Regr~iOn Analysis

1 ywr aftec HS

lean S.D. Mi~.

1.05 .88 .22

.4.54 8.83 ,24

:3.76 18.84 .32

‘1.88 38.15 ,20

:1.72 19.62 .22

5.20 7.51 .20

8>37 4,65 .23

4,23 1.29

1.81 1,42

2,64 2.10

1,94 1.46

2,32 ,17 ~

1,87 1,16

2.33 1.23

2,55 1.26

992

ervtiions in the sam

data. the vwiable k *t eaual b ze~o and a ti=in~” indicator

3yearsaf~~HS

Wean S.D. Miw.

1,01 .86 .22

14.16 8.90 .25

13.70 18.95 .32

71.65 38.25 .20

11!68 19.93 .23

5.51 7.95 .20

8,24 4,67 .24

4.20 1,31

1.88 1,43

2.69 2.07

1,87 1.40”

,2.36 ,79

,1,92 1.15’

2.41 1.18

2,51 1,28

I

974

with mi=ingdatw n

6 years after HS

Mean S.D. Miss.

.96 .68 .25

13.86 9.18 .27

13.66 19.36 ,35

69.22 39.87 ,23

11.48 20.04 <26

5.17 7.86 .24

7.98 4.88 .26

4,27 1.35

1.80 1,46

2.63 2.04

1.95 1.39

2,31 ,78

1.88 1.17

2.32 1.20

2<54 1.23

1,201

]utier means there t

9ymnafterHS

lean S.D. Miss.

1.05 .87 .21

14.62 8.82 .23

13.96 19.09 .33

72.83 37.44 .19

11.07 17,81 .22

5.72 8.30 .19

8,52 4.62 .22

4,24 1.36

1.83 1.43

2.60 2.05

1.96 1.40

2.34 .79

1.89 1.15

2.32 1.22

2,59 1.23

977

nOmiwingobxrvatio

All observdio~

Mean S.D. Mire,

1.02 .87 ,22

14.37 8,92 .25

13.72 18<82 .33

71.80 38.05 ,20

11.34 18.89 .23

5.42 7.91 .21

8,29 4.68 .23

4.24 1,35

1.84 “1,44

2:62 2.05

1,92 1.39

2,34 .79

1.91 1.16’

2,34 1.20

2.56 1.24

48.70 28.50 .01

12.97 27.11 .61

11.33 2.51’ \

.77 .18

10,763

. Inc=ofmting

setequaltoonq fornontilngc~w, tbeindlcatirismt qudtowro. Memswe for timing

andnonmiming obervations.

38

‘1 I

.,.

:,

I

I j
Table 8-F (Femal=)

Summary Statistim for Samplw U*d in Regr=ion Analysis

1 year after HS 3 years after HS 6 years after HS 9 years after HS All obwrvatiom

Variable Mean S.D. Miss. Mean S.D. Mi=. Mean S.D. Mi=. Mean S.D. Miss. Mean S.D. Miw.

Dependent mriable

WAGE 1.30 .34 1.36 .37 1.61 .40 1.72 ,52 1.53 .47

High SCIIOO1employment

AH_10 .39 ,39 .39 .39 ,40

AHJO .30 .26 .26 .26 .26

AH_21+ .16 .13 ,11 .13 .13

Basefine &aracterffitics

BLACK .20 .21 .25 ,26 .24

HISPANIC ,16 .17 .16 .15 .15

KIDS ,05 .14 .23 .24 .19

MARKIED .08 .21 ,37 .50 .32

DIVORCED !00 .02 .06 .10 ,06

INSCIIOOL ,40 !86 ,13 .07 ,21

PARTTIME ,53 .46 ,28 .26 .36

UNION .09 .03 .07 .02 .10 .06 .11 .03 .09 .04

CITY .77 .76 .79

SOUTH

.80 ,77

,36 ,40 .40 ’41 .40

GOVT ,11 .12 .10 .11 .11

S&OOfing attainment

Hs ,97 .79 :49 ’45 ,62

COLLEGE .00 ,25 !31 ,16

POst-HS employment

x ‘>50 .31 1.58 .83 3.77 1.59 6.48 2.18 3.51 2.69

X2 .35 .44 3,17 2.93 16.77 12.90 46.69 27.69 19.55 25,38

X3 /30 .64 7(35 9.49 83,16 92.46 361.04 303.27 133.25 244.13

T .37 .29 ,64 .80 1.51 1.54 2.16 2.18 1!37 1.67

T2 .22 .32 1735 2.23 4,63 8.71 : 9,41 17.05

Ts

4,67 11.70

.17 .37 ~ 2.82 6,39 : 19.61 51.04 i 55.14 142.73 24.25 99.09

Fmfiy &macteristics

FOREIGN ,04 .04 .05 .04 .04

NUMSIBS 3.35 2.22 3.47 2.35 3.40 2.46 3,26 2.26 3.84 2.33

FATHERED 10.34 4.86 ,11 10.37 4.77 .09 10.54 4.92 .10 ! 10.37 4.83 .11 10.40 4.86 .11

INCOME 20.32 17.67 .22 19,96 17.87 .23 : 20.26 18.12 .23 ~ 20,32 18.18 .23 20.16 18.02 .23

n._.: _..., -“ ..-. -“”. U“,,b,,, ”.u “u lL-. P05..

39
Variable

Schml clmracteristics

SCHOOLSIZE

RATIO

DISADVANTAGED

ATTENDANCE

DROPOUT

TURNOVER

SALARY

High schoal achievement

CREDfiUMSOC

CREDNATHSCI

CRED_VOC

CRED_OTHER

GPAJUMSOC

GPANATHSCI’ ~

GPA_VOC

GPA_OTHER

Proxy md tistrmentd v=iables

AFQT SCORE

P~EST SCORE

UNEMPLOYMENT RATE

VOC/TECH PROGRAM

Number of observations

Note ‘<Miss” refe7st0the fr=ti0n0f

Table 8-F (Femal=)

CONTINUED

Summary Sktistics fm Sampl= Used in Wgr-ion AnaIysk

1 year after HS

imn SD. MN.

1.04 .90 .25

[4.23 9.49 ,27

12,71 18.41 .35

;8.43 39.90 .23

[0,91 17.64 .26

5.43 7.32 ,23

7.99 4,84 .26

4.35 1.33

1,44 1.31

2.53 1.98

2,30 1,60

2,58 ,78

1.86 1.32

2.24 1,28

2,72 1,17

957

erv~ions in the sam

data, the v~iable is set equal w zero md a “tiing” indicator

and nOnm&lng Observations.

3 yearn after HS

tean S.D. MIS.

.97 .88 .28

.3.40 9.44 .30

.1.81 17,61 ,37

i4.42 41.93 .28

.0.98 19.14 :28

5.26 7,36 .26

7.47 4,98 .30

4.37 1.30

1,42 1.30

2<59 1.97

2.26 1.53

2.61 ,77

1,87 1.35

2.42 1,26

2.76 1,16

1)039

with miming datw n

6ymmafterHS

Mean S.D. MM.

.97 .87 .27

13.49 9.31 .29

11.82 17,94 .38

66.06 40.99 .28

11.15 19,46 .27

5,51 7.93 ,26

7,53 5.00 .30

4,40 1,29

1!54 1.31

2,46 1,98

2,29 l,M

2,64 :78

1,93 1,30

2.43 1329

2.76 1.16

1)168

]ufier means there i

9 years after ES

bean S.D. Mire.

1.03 .87 .24

!4.26 9.12 .26

;3.15 18.54 .34

i7.85 40.22 .23

10.25 16.40 .24

5.53 7.63 .22

7,80 4.87 .27

4.39 1.31

1.49 1.35

2.44 1,93

2.24 1!51

2.56 >76

1.82 1.32

2.33 1;29

2,71 1.15

1,035

uo mi~ingobwrvati

All observdions

Jean S.D. Miw.

1.01 .88 .25

13.92 9,26 .27

12,41 18.14 .35

57.06 40.63 .25

10.75 18.06 ,26

5.42 7.63 .24

7’73 4.91 .28

4.38 1,30

1.48 1,33

2.47 1,97 ~

2.28 1.64 ~

2.60 .77

1,87 1.32”

2,37 1.29

2.77 1,15

13.56 26.07 .02

10,55 25.08 .64

11,33 2.52

.74 ,2J

10,899

Incmsofmi=ing

etequaltionq fornontiwing x,thehdimtorkmt qualtomo. Means are for miming

40

,,,
Tale 9-M (Malw)

Coridltional Effects of High School Em]

Other %gresom

Included in Model

1 ye- titer HS [OLS]

1. B=eline characteristic

2. 1 + schooling attainment

3. 2 + pOst-HS employment

4. 3 + fm~y char~terktia

5. 4 + school ch=acteristi~

6. 5 + HS achievement

3 y-s alter HS [OLS]

1. B-efine &macteristiw

2. 1 + schoobg attainment

3. 2 + pmt-HS employment

4. 3 + fatiy charactmisti~

5. 4 + school chuactetistics

6. 5 + HS achievement

6 ye=. titer HS [OLS]

1. Bdine &macterktiw

2. 1 + schooltig attainment

3. 2 + pos&HS employment

4, 3 + fatiy &=acteristim

5. 4 + school chmacteristia

6. 5 + HS achievement

9 ye=. alter HS [OLS]

1. B=eline chmacteristics

2. 1 + sdoohg attainment

3. 2 + POSGHSemployment

4. 3 + fady chmacteristim

5. 4 + school characteristic

6. 5 + HS whievement

AU obser=tions [GLS]

1. Bmhe characterktics

2. 1 + schooling attainment

3. 2 + POSGHSemployment

4. 3 + famfiy characteristics

5. 4 + school &aracteristi@

6. 5 + HS achievement

F test for null hypothesis

that high school employment contributes to

prediction of log wage

F Wject

;tatktic df at 95%

4.86 (3,973) no

4.28 (3,972) no

3.61 (3,966) no

3.76 (3,960) no

3.94 (3,946) nO

3.72 [3.938) no

5.75 (3,954) no

5.79 (3,952) no

3.81 (3,966) nO

3.52 (3,946) no

2.63 (3,926) no

2.70 (3,918) no

4.17 (3,1181) nO

4.71 (3,1179)

2.28 (3,1173) ;:

1.35 (3,1167) yes

1.15 (3,1153) yes

1.03 (3,1145) yes

.49 (3,958) yes

.87 (3,956) yes

.52 (3,950) yes

.56 (3,944) yes

.52 (3,930) yes

.45 (3.928) yes

1.75 (3,10735) yes

2.09 (3,10732) yes

1.89 (3,10727) y=

1.33 (3,10721) y=

1.26 (3,10707) yes

1.22 (3,10699) yes

,yment on Log Wag=

~

Coefficient (S.E.) for average hours

worked per week in grades 11-12

1-10 11-20 21+

.063 (.032)

.060 (.032)

.059 (.032)

.057 (.032)

.057 (.032)

.056 (.032)

.034 (.038)

.031 (.038)

.016 (.038)

.006 (:039)

..010 (.039)

..009 “(.040)

.076 (.039)

.071 (.039)

.056 (.039)

.035 (.039)

.035 (.039)

.030 (.039)

..011 (.046)

..021 (.045)

..040 (.ozq

..051 (.044)

..049 (.044)

..051 (.044)

.032 (.024)

.026 (.024)

.025” (.023)

.008 (.023)

.008 (.023)

.009 (.023)

.055 (.033)

.054 (.033)

.052 (.034)

.053 (.034)

.046 (.035)

,050 (.035)

.059 (.039)

.057 (.039)

.033 (.040)

.021 (.041)

.011 (.042)

.012 (.042)

.116 (.042)

.103 (.042)

.079 (.042)

.052 (.043)

.047 (.043)

.052 (.043)

.025 (.048)

.017 (.047)

-.008 (.046)

-.025 (.046)

-.019 (.047)

-.028 (.047)

.095 (.026)

.087 (.025)

.067 (.025)

.044 (.025)

.045 (.025)

.048 (.025)

,132 (.036)

.124 (.036)

.118 (.037)

,123 (,038)

,124 (.039)

,125 (.039)

.160 (.043)

.159 (.043)

.128 (.045)

.118 (.046)

.092 (.047)

.096 (.047)

.152 (.046)

.165 (.045)

.119 (.047)

.093 (.047)

.088 (.048)

.081 (.049)

.036 (.051)

.047 (.051)

.007 (.052)

.012 (.052)

.009 (.053)

..034 (.054)

.115 (.115)

.122 (.028)

.081 (.027)

.060 (.028)

.058 (.028)

.063 (.028)

I

41
I

Table 9-F (Females)

Conditional Effects of–—High —–e–—. School .—. .Em~lOvment .... .. . ... .–—. on —.- Lo~ Wazw . . .

F tat for null hypothesis

that high school employment contribut- to COefficienE(S.E.) for average houm

prediction of log w~e worked per week in qada 11-12

Other Re~esmrs F Reject

Included in Model statistic df at 9570 1-1o 11-20 21+

1 ye= titer HS [OLS]

1. B=efine char~t~fitia 1.10 (3,936) y= .050 (.032) .058 (.034) .056 (.039)

2. 1 + schoofing attainment 1.10 (3,935) y- .050 (.032) .058 .(.034) .055 (.039)

3. 2 + pos&HS employment .70 (3,929) y= .044 (.033) .045 (.035) .043 (.042)

4. 3 + fatiy chm~tetitics .53 (3,923) y- .040 (.033) .039 (.036) .037 (.042)

5. 4 + shool daracteristi= .27 (3,909) Y= .029 ~033) .025 (.036) .018 (.042)

6. 5 + HS achievement .32 (3,901) y- .029 (.033) .021 (.037) .008 (.043)

3 ye=s alter HS [OLS]

1. B-ltie chmacteristia 1.54 (3,1019) yes .019 (.031) .066 (.034) .050 (.041)

2. 1 + schoofing attatient 1.54 (3,1018) yes .019 (.031) .066 (.034) .050 (.041)

3. 2 + pOst-HS emplopent .56 (3,1012) y= .002 (.031) .036 (.036) .001 (.043)

4. 3 + ftiy chmacteristi~ .61 (3,1006) y- -.010 (.031) .028 (.036) -.006 (.044)

5. 4 + schwl ch=acteristi- .49 (3,992) y- -.008 (.031) .025 (.036) -.010 (.044)

6. 5 + HS =hlevement .56 (3,984) yes -.004 (.032) .032 (.036) -.001 (.044)

6 yems dta HS [OLS]

1. B=efine characteristi- 5.06 (3,1148) no .058 (.037) .151 (.041) .123 (.052)

2. 1 + schooling attainment 5.67 (3,1146) nO .051 (.036) .150 (.041) .138 (.051)

3. 2 + pOst-HS aplowent 3.85 (3,1140) nO .036 (.037) .128 (.041) .105 (.053)

4. 3 + fatiy daractiristi= 3.01 (3,1134) no .008 (.037) .098 (.041) .084 (.053)

5. 4 + school A=actflisti= 2.81 (3,1120) nO .000 (.03q .090 (.042) .081 (.053)

6. 5 + HS achievement 2.63 (3,1112) uo -.004 (.038) .085 (.043) .077 (.054)

9 ye=. titer HS [OLS]

1. B~eline daractertitiw 1.93 (3,1016) y= .087 (.041) .067 (.046) .105 (.055)

2. 1 + schmhg attainment 1.95 (3,1014) ye -.021 (.045) .017 (.047) .047 (.051)

3. 2 + POSGW mplOPent 1.15 (3,1008) ._Y= .071 (.040) .029 (.045) .035 (.055)

4. 3 + fatiy chaacteristim .59 (:i::ay Y= .036 (.041) -.007 (.045) -.004 (.055)

5. 4 + school ch=acteristi= .77 yes .032 (.041) -.020 (.045) -.011 (.056)

6. 5 + HS >chievemmt 1.03 (3;980) Y= .035 (.041) -.027 (.045) -.017 (.056)

AU observations [GLS]

1. B~ebe characteristic= 2.89 (3,10871) ““no ,084 (.022) .102 (.024) .118 (.030)

2. 1 + schoofing attainment 1.01 (3,10868) Y= .059 (.u21)- .094 (.024) .122 (.030)

3. 2 + pOst-HS employment 1.29 (3,10863) y- .048 (.020) .064 (.023) .070 (.028)

4. 3 + fatiy Amacteristics .83 (3,ioa57) Y- .030 (.020) .044 (.023) .054 (.02s)

5. 4 + school ch=acteristim .58 (3,10843) y= .025 (.020) .033 (.023)

6. 5 + ES achievement

.046 (.028)

.96 (3,10835) y= .028 (.020) .036 (.023) .053 (.02s)

42
Table 1O-M (Mala)

Mternative GLS and IV/GLS Estimata

of the Conditional Effect of High School Employment On Log Wag=

Using the “All Observations Sample”

I 1 I 2 I 3 I 4 I 5 1 6

, I

GLS GLS GLS GLS GLS IV/GLSa

Vmiable Coeff. S.E. COeff. S.E. Coeff. S.E. Coeff. S.E. Coeff. S.E. COeff. S.E,

AH_OIX = 2 .017 .033 .017 .033

AH_OIX = 3 .050 .040 .051 .041

AH_OIX = 4 .050 .048 .052 .048

ALOIX = 5 .048 .054 .050. .054

AH_OIX >= 6 .038 .057 .041 .057

AmlO\X <= 1 .028 .028 .028 :028

AH_lOIX = 2 -.016 .035 -.016 .035

AH_lOIX = 3 -.022 .037 -.022 .037

AH_lOIX = 4 -.021 .039 -.021 .039

AH_lOIX = 5 .003 .041 .003 .041

A~lOIX >= 6 .034 .031 .034 .031

AH_20[x <= 1 .024 .030 .017 .030

AH_20[x = 2 .031 .037 .025 .037

AH_201x = 3 .030 .040 .024 .040

AHJOIX = 4 .039 .041 .033 .041

AH_201x = 5 .092 .044 .08% .044

AH_201X >= 6 .093 .032 .086 .032

AH_21+[x <=1 .056 .035 .059 .035

AH_21+lx = 2 .077 .036 .080 .042

AH_21+]x = 3 .088 .045 .081 .045

AK21+IX = 4 .096 .046 .099 .046

AH_21+lx = 5 .101 .047 .105 .047

Am21+[X >=.6 .063 ,034 .066 .035

AH_10 .009 .023

AH_20 .042 .025

AH_21+ .066 .028

AH .005 .002 .004 .002 .054 .092

AH2/loo -.009” .007 -.007 .007 -.299 .451

AFQT SCORE .001 .000 .001 .000 .001 .000

MISSING .051 .032 .050 .032 .051 .032

PRETEST SCORE/100 -.018 .026 -.018 .026 -.017 .026

MISSING -,016 .016 -.016 .016 -.016 .016

.-. .,, . . . . . . . . . . -. . . .. .... ,7 -Instruments are mea unemployment rat- at time 01 nlgn scnool graauaclon, a aumy mrlaDle Inalcaclng wnecner

the respondmt ,s high school offers vOcatiOnd/technicd coumes, and a dumy variable indicating whether the vocw

tiOnd/tectilc4 vmiable k missing.

Note Each model d= controls for the b-line vtiabla, schoohng attainment, post-tigh s&ool employmnt, fafily

chmacteristics, school characteristi~, and high school achievement.

43
Variable

AH_OIX = 2

ALO\X = 8

AH_OIX = 4

AH_OIX = 5

AH_OIX >= 6

AuO]X <=-1

AH_lOIX = 2

AH_lOIX = 3

AH_lOIX = 4

AH_lOIX = 5

AH_lOIX >= 6

AH_201x <= 1

AH_20[x = 2

AH_201x = 3

AH201X = 4

AH_201x = 5

AH_201X >= 6..

AH_21+lx <= 1

AH_21+lx = 2

AH_21+lx = 3

Am21+lX = 4

AH_21+]x = 5

AH_21+jX >= 6

AEIO

AHQO

A&21+

AH

AH2/loo

AFQT” SCORE

MISSING

PRETEST SCORE/100

MISSING

Table l@F (Femal-)

Alternative GLS and IV/GLS Estimatof the Condition Effect of Hixh School EmplO~ment on LOZ Wag=

1

GLS

COeff. S.E.

-.047 .029

-.066 .036

-.062 .044

-.052 .051

-.049 .057

-.005 .024

.040 .030

.071 .031

.035 .033

.052 .036

.041 .029

-.002 .027

.040 “.034

.108 .035

.078 .037

.085 .041

.022 .031

-.005 .035

.079 .041

.098 :043

.116 .045

.086 .048

.052 :037

Usinz the “All ~bservatiom Sa]-

2’

GLS

;oeff. S.E.

-.046 .029

-.063 .036

-.058 .044

-.048 .051

-.045 .057

-.013 .024

.032 .030

.062 .031

.026 .034

.043 .037

.032 .029

-.012 .028

.030 .034

.097 .035

.068 .037

.074 .041

.012 .032

-.013 .035

.072 .041

.090 .043

.107 .046

.078 .048

.042 .037

.001 .000

-.014 .026

.010 .028

.002 .017

3

GLS

gmff. S.E.

.020 .020

.026 .023

.045 .028

.001 .000

-.015 .026

.008 .027

-.000 .000

h school gra(

?le”

4

GLS

Coeff. S.E.

.006. .002

-.013 .008

5

GLS

COeE. S.E.

.005 .002

-.011 .008

.001 .000

-.013 .026

.009 .028

.000 .017

6“ 1

IV/GLSa

;oeff. S.E.

.065 .056

-.373 .318

;ing whether

.er the vOca-

‘Instruments are area un nployment rat= at time of h ~tion, a dummy vmiahle indi

the r~pondent’s high sck o! offers vOcatiOnd/technical coum@, and a Lmmy variable indicating wh!

tiOnti/technical variable &sing.

Note: Each model also mntrols for the b~eline vtiables, schooling attainment, post-high school employ-t, faily

char=terkti=, school characteristics, md high school achievement.

44
figure 1-M (Males)

Work Effort and School EnrOUment in Mrst 6 Yars After High Sch~l

by Average Hours Worked Per Week in Grad= 11-12

a. All Males (n=926)

I

1 d

1 ! I

9 17 2;

Oui;ter

c. Males with no pOst-HS enrollment (n=410)

45

b. AH Males (n=926)

I , 1 I

1 4 9 17 21

Ou::ter

Average hnurs worked per week

in gradaa 11-12

0 hnure

0 a l-fO hourg

11-20 hourg

Q o 2it hours
m
02

03

04

05

06

07

08

09

10

11

12

13

National Lon@tidinalSumeys~LS)

Discussion Paper Series

No- M. Brdum

Mn R. Ftiel

Read P. BAer

Mcbel R Per-t

SauJ Schwti

Rnben Hutchem

George JWsOn

Heq S. Fmkr

Fr@ L. Mott

Pada BA=

Mey Light

-ueJim Ureti

Emgelos M. FMs

H. Etikth Petem

ke~

Jme E. O’NeM

Arl- Leibotiti

Jacob Mex ~eLInb Wtite

Lee A. Lfllmd

Jo-h G. Aftonji

Thorns A. Dm

~

How the Faded Gov-ent Uses Dam from

the Natioti Longimditi Suqs

A Com@son of Computer-Astiaed Pasod

ktetiws (CAPI) titb Papr-ad Pentil Intertiws &API) h the Natioti Longitnditi Suey

of Youth

-C Mdels of the Joint Destination of

Lsbor Supply md Ftily Stictie

The effa of Unempl~ent Compe=tion

on the Unemplopent of Youth

Evdm@ Com~dng Theories of Worker

Mobility

EvaJmtion of the 1989 Child+= Supplement

in the Natioti Longitnditi Suey of Youti

Gender DMerencea k“the @t Bektior of

Young Workers

The Impact of Priwte Sector Tting on Mce

ad Gender Wage Differentials md the -r

Pattas of Youg Workms

Reapo~s of Fetie hbor Supply ad FetiliW

to the Demo~pJric &cle

A Smdy of Intarcohofi Ctige in Women’s

Work Patterns md Etings

Women’s Empl~ent Dting Pmqancy

ad Follotig B1fi

Work E~rience, Job Tenwe, Job Sepmation,

md Wage Goti

Ftily Backgoud md Labor Mket @tmm=
14

15

16

17

18

19

20

21

22

23

24

25

26

27

George J. Bo~u

Skphm G. Broms

Stephen J. Trejo

S~-Sele&on md Intefi Mgration in the

Utitd Shti

Jaes J. Hec&

Stephen V. Cmeron

Peter Z. Schochet

The Detetimta md Comquaces of hblic

S-or ad Private Sec@rTMfing

Pticipation in Low-Wage Labor Wketa by

Ymmg Men

AJmrL. Gumm

Them L. Sttieier

Wtiremnt in a Ftily ConteW A S-cti Mdd

for Hub& md Wives

Atiq Light Trtitiom from Schml to Work A SWcy of

Wsach Using the Natioti Longi@di@ SWWS

Ctiatophm J. Rti

&k LOwNein

Jaes Splemer

Jamb AJw Uem

Jamb Mex Wemm

&leen Motie

Stephen G. Bro~s

Dodd 0, PwsOm

DmmJd O. Pwm

- k P. Btiel

Nachw Sich_

Au&ey Light

High School Emplo~ent &_ption or

hveswmt

Irrfoti Trtitig: A Rtiew”of Etiating Dam md

Some New Etidmce

C*cterifig kve for MatetiW Mtiehng the

NLSY Dati

Emplopent Continuity hong Nw Mofias

~ Evoltig Stmties of Fe~e Work Atititiw:

Etidmw from the Natioti hngimditi SWqs of

~me Women SW~, 1967-1989

Pwe~ m= bong &tie Women Etidence

horn the Natioti bn~mtiti SWcys, 1967-1989

Twhnrdogid Cfmnge md the SW Acqtisitimr of

Yomg Workers

High khml Emplo~ent

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