The Natimaf LongitudinalSurveys(NM) programSUPPIS many studiesdcsigmd

to increasevndersmndkgof the fabormarketandmethodsof datacofkaion. The

DiscussionPapersseriesincmpmdes some of theI.searchcomingoutof theFgra.m.

Manyof thepapersinthisserieshavebeendefiveredes fmafrepxtscommissionedas

PWIof m CXUMIUUd @nt WOWIII, UII(k Whkh flimts are aWdd tbrcmgha

competitiveprocess.Theseriesisinlemfcdtocirwfatethefmdmgsto interestedreadezs

withinandoutsidetheBureauof LaborStatistic+..

Pem-onsinterestedinobtainingacqy of anyDiscussionPaper,otherNLSrepams,

mgenmf infonnztionabnutheNLSprogramandsurveyshoufdmmacttie BUImUof

b Statistics, Ufice of f?COIlOMiC Research,WashimSmm, ~ 20212-0+201, or Cdf

(202)606-7405.

MrIIeridinibispublicationis “mthepublicdomainand,witi appropriateCK&t,

maybereproducedwithout*sion.

Dpinionsandconclusionsexpressedinthisdccurnentarethoseof lb. author(s)md

donotnezes$arifyrepresentanofficialpositionof rheBureauof LaborStatisticsor the

Us. Depamnerdof L&r.
Transitions from School to Work:

A Suwey of Research Using the National Longitudinal Suweys

Audrey Light

The Ohio State University

April 1994

This paper was funded by the U.S. Department of Labor, Bureau of Labor Statistics

under Contract Number J-9-J-O-0065. Opinions stated in this paper do not necessarily

represent the officiaJ position or policy of the U.S. Department of Labor.
PREFACE

Data in this report are from the National Longitudinal Surveys (NLS) which are

sponsored by the Bureau of Labor Statistics (BLS). The Bureau contracts with the Center

for Human Resource Research of The Ohio State University to manage the surveys and

provided user services. The NLS were begun in the mid- 1960’s with the drawing of four

samples: Young Men who were 14-24 years old as of April 1, 1966, Young Women who

were 14-24 years old as of January 1, 1968, Older Men who were 45-59 years old as of

April 1, 1966. and Mature Women who were 30-44 years old as of April 1.1967. Each

sample originally had about 5,000 individuals with oversamples of blacks. In the early

1980s, the Young Men and Older Men surveys were discontinued. The two women’s

surveys continue and are currently collected every 2 years. The Bureau of the Census

collects the data for BLS.

In 1979, a new cohort was begun with a sample of over 12,000 young men and women

who were 14-21 years of age as of Janumy 1, 1979. It includes oversamples of blacks,

Hispanics, economically disadvantaged whites, and youth in the military. The military

oversimple was dkcontinued after the 1984 survey, and the economically disadvantaged

white oversimple was discontinued after the 1990 survey. This survey is called the

Youth cohort, and the cohort members have been interviewed every year since it began.

The data collection for the Youth cohort is undertaken by NORC (National Opinion

Research Center), a social science research center affiliated with the University of

Chicago.

For a detailed explanation of the NLS, see NLS Handbook 1994 (Center for Human

Resource Research, The Ohio State University). For information about the NLS, write to

National Longitudinal Surveys, Bureau of Labor Statistics, Office of Research and

Evaluation, 2 Massachusetts Ave., NE, Room 4915, Washington, DC 20212-0001, call

(202) 606-7405, or Internet JAIN_R@ORE.PSB.BLS.GOV
TABLE OF CONTENTS

1. JNTRODUC’ITON

2. WORK WHJLE IN SCHOOL

A. Prevalence of work while in school

B. Effects of work while in school on educational and

labor market outcomes

3. LEARNING ON AND OFF THE JOB

A. Human capital earnings functions

B. Formal private sector training

C. Reenrolling in school

D. High school equivalency exams

4. VOLUNTARY JOB SEARCH AND MOBILJTY

A. Determinants of job mobility

B. Effects of job mobility on wages

5. UNEMPLOYMENT AND NONEMPLOYMENT

A. Search methods

B. Effects of unemployment on wages

C. Persistence in unemployment

6. CONCLUDING REMARKS

Page Number

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4

4

5

10

12

17

20

23

25

26

28

31

32

34

35

39

REFERENCES 41
1. INTRODUCTION

Theperson who completes hisorher desired level ofschooling andimmediatdy begins a

career of continuous employment is not representative of the entire youth population. For many

young people, thetrmsition from schooIto work is far less “clean.” Consider the following aspects

of the school-tu-work transition:

● Students often hold jobs while they attend school. These jobs are not zJways confined

to vacation periods and, ss a result, many students gain a substarrtiaf amount of work

experience. fu-school work experience is likely to affect both educatiomd outcomes and

subsequent labor market behavior, so researchers who analyze school-to-work transitions

must acknowledge that work and school are not mutually exclusive.

● The distinction betweecr school and work is blurred further when young workers participate

in informal and formal job training programs, and especially when they return to degreegranting institutions foradditionz,l study. Wemustrecognize that school-to-work transitions

are not necessarily “once and for all” if weare to understand fully the relationship between

skill acquisition andtangible meazures of labor market success such as earnings.

● As a group, young people spend asurprisingarnount of time neither working nor attending

school. fn many cues, nonemployment reflects volunttiy and, presumably, beneficial job

search, but it can also reflect an inability to locate or maintain a satisfactory job. The

amount of time a young person spends in nonemployment influences labor market outcomes,

so it is important tounderst.md whya durable employment relationship is often so elusive.

This report surveys the literature that uses data from the National Longitudinal Surveys

of Labor Market Experience (NLS) to analyze transitions from school to work. The phenomena

described above-work while in school, participation in job training, reenrollment in school, job

searclr, endnonemployment-a.re given aconsiderable amount ofattention intbkliterature. However, these phenomena are intrinsically related to such broader issues as skill acquisition (including

thecosts ofandbenefits toschoofing), thedeterminants ofearnings, andjobmoMfity. As aresult,

the %hool-to-workli terature” encompasses allofthese areas.

1
The NLSisideally suited totheanalysis of these issues because it surveys alargemrmber

ofyoung people over unextended period of time. The NLS consists of five separate surveys, three

of which focus on young people. The Young Men cohort began in 1966 with asampleof 5,225

men between the ages of 14 and 24, and interviewed them annually or bianrmallythrongh 1981.

The Young Women cohort began in 1968 with a sample of 5,159 women who were 14-24 years old,

and is still in progress. The most recent interview occurred in 1993 and arrotheris scheduled for

1995. The Youth cohort (commody referred toasthe NLSY)began in1979with l2,686menmd

women who were 14-21 years old at the end of 1978. Annual interviews were conducted through

1993, with another scheduled for 1994 and biannuzd interviews planned forthefuture.1

During each annual or bimmual interview, respondents are questioned extensively about

their school, work, and nonemployment experiences. They report information on educational

attainment, school enrollment, current jobs (e.g., wages, hours worked, starting dates, industry,

and occupation), jobs that began and ended since the last interview, and the amount of time spent

unemployed zmd out of the labor force. The NLSYalso collects information on formal andinfornml

jobtraining andjobse.arch methods. Inaddition, itrelies heavily onan"event history" approa&,

which produces extremely detailed information. Forexample, the NLSY includes a week-by-week

report on respondents’ labor force status and hours of work and (starting in 1980) a month-bymonth report onschonl enrollment status. fnaddition, the NLSYcontains information on virtually

every job and nonemployment spell that occurs during theobservation period, including starting

and ending dates. When used in conjunction with the information collected on demographic

characteristics, family backgrcmnd, residentis,l location, and other factors, the data from allthree

cohorts permit any number of “school-t&work” issues to beanslyzed.

Another attractive feature of the NLS is that it permits racia.1comparisons. Blacks were

oversampled in the Young Men and Young Women cohorts so that they represent about 30% of

the origimd sample, with whites makhrgup the rest.2 The NLSY oversampled black, hispsaiThe remainingtwo cohoztsarethe OlderMensmdthe MatureWomen. The Older Men cohort began in 1966

with 5,020 men””agss 45-59 and ended ip 1983, with a follow-up interview in 1990. The Mature Women cohort began

in 1967 with 5,083 women ages 30-44 and is still in progress. The respondents were last interviewed in 1992 and

will be interviewed again in 1995 when the Mature Women and Young Women are combined into a single cohort. It

should also be noted that many of the children of the NLSY respondents have been s.rveysd biannually since 1966.

%spondents classh%d ss non-white, non-black represent a neghgible fmction of the original samples. Note that

the term ‘original sample” is used because attrition caused the composition of the awnples to change dightly over

2
ic, and economici-dly disadvantaged non-black, non-hispanic youth. The originzl sample is 25%

black, 16% hispardc, and 59% non-black, non-hispanic. Of the latter group, 27% are economically

disadvantaged.

The rest of the report is organized ss follows. Section 2 discusses the issue of work while

in school. More than a dozen studies have used the NLS to assess the amount of work experience

gained by students and relate it to various educational and labor market outcomes. The discussion

in Section 2 describes the samples, work experience measures and outcomes used by each study

and summarizes the general findings.

Section 3 summarizes the vwt literature that examines early-career skill acquisition and its

effect on labor market outcomes. The first subsection shows how NLS data have contributed to the

estimation of “human capital earuings functions< which are used to assess the effects on earnings of

schooling, work experience, and other indirect measures of skifl. The next three subsections focus

on sperific methods used by young people to enhance their skills: formal job training programs,

reenroflment in school, and the completion of high school equivalency exams. fn all three areas,

the literature examina young workers’ reasons for obtaining the additional training and its effect

on labor market outcomes.

Section 4 looks at the “good” type of job turnover that is often referred to as “job shopping”

or “job matching.” In recent years, researchers have devoted considerable attention to identifying

the causes and wage consequences of ezdy-career job turnover. The evidence largely supports the

notion that young workers’ labor market outcomes are ss much a function of their ability to locate

productive employment situations through search as their ability to invest in additional skilfs.

NLS data have been used for a sizeable share of the research in this area, so an exhaustive survey

is beyond the scope of this report. Instead, Section 4 summarizes a sample of studies that identify

the determinants of early-career job mobifity and assess its effects on wage growth.

Unfortunately, much of the job search that ch~acterizes the early career does not correspond

to “job shopping; but instmd reflects the inability to locate or retain a job. An overview of

this aspect of the school-to-work transition appears in Section 5. Section 6 contains concluding

comments.

time.

3
2. WORK WHILE IN SCHOOL

2.A. Prevalence of work while in school

A large number of studies use the NLS to document the extent to which students combine

work and school. This phenomenon can be measured a number of different ways: one can simply

enumerate the nnmber of students holding jobs at a point in time, or one cm measure the intensity

of work effort by looking at the hours worked per week or the weeks worked per year. Furthermore,

researchers zan focus on specific ages or grade levels, and on specific gender and/or race groups.

Table 1 summarizes the approaches taken in 13 studies and reports a selection of findings with

respect to the prevalence of work while in school. These findings can be summarized as followx

● Students are more likely to work as they age or, alternatively, ss they advance to higher

grade levels. D’Amico (1984) and D’Amico and Baker (1984) report that 50% of white, male

10~kgrade students work a positive number of weeks during the school year, while 75% of

12tk graders do so. Michael and Tuma (1984) report that 29% of 14 year old, white, male

students work during the week prior to the 1979 interview (which typically occured in April),

while 53% of 17 year olds-do S0.3 Light (1994) finds that 48% of white men hold jobs during

their last observed sxit from school, but the proportion falls to 38% for 12fk graders and

rises to 60% for those attending college or graduate school.

c Students are more likely to work part-time than full-time. Stephenson (1979, 198f5., 1982a)

reports that 33% of white men work part-time during their last year of school, while only 29%

work full-time. Stern and Nakata (1989) report that among high school seniors who work,

the average work intensity is about 16 hours per week. Ruhm (1994) reports an average

work intensity of 19 hours per week. Steel (1991) finds that 17 and 18 year olds average 25

hours a week, but about 30.% of her sample consists of non-students. D’Amico (1984, 1986)

and D’Amico and Baker (1984) measure work intensity in terms of weeks rather than hours

and find that 75% of white men work some amount during 12~hgrade, but only 57% work

more than half the school year. Ruhm (1994) finds that high school seniors work an average

of just over half of all weeks during the academic year.

3Table 1 shows statistics report .sd by M~chael and Tuma for all gender-race groups combined.

4
● In general, male students are slightly more likely than female students to work. M1cbael and

Tuma (1984) find that among 14 year olds (of all race groups), 28% of males and only 22%

of females work. Among 15 and 16 year olds, males continue to work in higher proportions

than do females, but among 17 year olds females are slightly more likely than males to work

(52% versus 49%).

● White students genersly work more than nonwhite students. Steel (1991) examines 17 and

18 year olds (not all of whom are enrolled) and finds that, at a point in time, jobs are held by

58% of whites, 41% of Hispanics, and 35% of blacks. D’Amico and Baker (1984) report that

among 12th grade males, whites work an average of 75~o of the school year, while nonwhites

average only 69%; similar statistics are reported for females. Furthermore, whites of both

genders are more likely than nonwhites to work more than 20 hours per week and less likely

to work only 1-10 hours.

2.B. Effects of work while in school on educational and labor market outcomes

fn addition to describing the amount of work experience gained by students, the studies listed

in Table 1 also exmuine the relationships between in-school work experience and both educational

aud labor market outcomes. The majority of researchers working in thk area are motivated by

concern ovar youth unemployment rates that are often alarmingly high. As a result, they seek to

learn whether students who work while in school have improved labor market outcomes, in whirl

case programs that help students to find jobs would be warranted. This particular policy issue

explains why the focus is more often on work experience gained by high school students than on

work by more advanced students.4

It is not immediately obvious whether in-school work experience should be encouraged, for

it bears both costs and benefits. Students benefit to the extent that they gain marketable skills

or learn interviewing and job search techniques. Early work experience can also foster feelings

of self reliance and instill good work habits. Furthermore, the money earned from student jobs

is nonneg!igible, and can be used for college or other worthwhile pursuits. However, holding

4Light(1994) asks whether estimated returns to experience and xhmiimg are sensitive to the “type” of schcding

and work experience controlled for, so her results are not d~ussed in this section.

5
a job while still in high school can also impose costs. Students who work must divert time and

energy toward their jobs and away from studying, extra-curricular activities, and socializing. Qnite

naturally, the primary concerns are that they will leave school premature y and/or suffer a decline

in academic performances

To learn whether in-school work experience is, in fact, related to either academic progress

or labor market success, researchers typically estimate simple, single-equation modefs in which a

particular labor market or educational outcome forms the dependent variable. Measures of inschool work experience are included as explanatory variables, afong with other controls. Among

the studies listed in Table 1, the folfowing outcomes are used as dependent variables:

SCHOOLING OUTCOMES

● Class rsmkinbigh school (D’Amico, 1984; D’Amico and Baker, 1984).

● Hours spent studying in high school (D’Amico, 1984; D’Amico and Baker, 1984).

● Free time in bighschcol (D’Amico, 1984; D’Amico and Baker, 1984).

● Whether rwpondent finisha tighschwl, college (D'Amico and Baker, l984).

● Whether enrolled twoyeam after age14-17(Michael and Tnma, 1984).

● Highest grade completed, 7-10 years later (Ruhm, 1994).

s Months enrolled 1-2 years after age17-18(Steel, 1991).

LABOR MARKET OUTCOMES

Earnings and wages:

●

●

●

✘

●

●

Honrlywage, reported lmd3yeam after tighschool (D'Amico, 1986; D'Aticoad B&er,

1984).

Hourly wage, reported in 1970, which is a varying number of years after high school and

college (Griliches, 1980).

Hourly wage, reported 2 years after age 14-17 (M,chael and Tnma, 1984).

Hourly wage, reported 1-2 years after high school, college (Stephenson, 1979; 1981~ 1981b;

1982a).

Hourly wage, reported 1-3 years after high school, averaged (Stern sad Nakata, 1989).

Hourly wage, weekly wage, annual earnings, reported 6-9 years after high school (Ruhm,

1994).

Other job characteristic.

● Duncauoccupation indW, land 3 years after bighschooI (D’Arnico and Baker, 1984).

5Thcscargumentsaremadeby D’Amico (1984,1986),D’Amico snd Baker(1984),Steel(1991),andSternand

Nskata(1989).

6
c Duncan occupation index, 6-9years after high schoo1 (Ruhm,1994).

● Whether employer provides health insursace, retirement benc&s,6-9 years after bigh school

(Ruhm, 1994).

Labor SUPPIW

● Weeks unemployed, 1 year after high school (D’Amico, 1984; D’Amico and Baker, 1984).

● Weeks unemployed, l-2years after high school wdcoffege (Stephenson, 1979, 1981b).

● Weeks mremployed, 1-3 years after high school (Stern aad Nakata,1989).

● Weeks employed, 1-2 years after age 17-18 (Steel, 1991).

● Weeks employed, 2 years after high school ($tephensorr, 1981b).

Q Whether employed, 2yearsafter age14-17(Michael and Tuma, 1984).

● Hours worked,2 yesm after age 14-17 (Mi&elandTu ma, 1984).

● Hours worked, 6-9 years after high school (Ruhm, 1994).

Asthe preceding list srrggests, Ruhm (1994 )looksat “longterm” effects of work while in

school, while his predecessors (Grifiches (1980) excepted) focus on labor market outcomes within

1-3 years ofleaving school. This isonlyone characteristic that distinguishes Ruhm’s work from

the earlier, “first generation” studies, sohk results are discussed separately at the end of this

subsection. First, the conclusions reached by the “first generation” studies are summarized as

follows:

Measures of educational outcomes or success are generally found to be unrelated to in-school

work experience. MIchaef and Tuma (1984) find that being employed at age 14-17is unrelated

toenrollrnent status twoyears later, while Steel (1991 ) finds that being employed at age 17-18is

unr+lated to enrollment status 1-2 years later. A statistically insignificant relationship between

working in high school and class rank is reported by D’Amico (1984) and D’Amico and Baker

(1984), regardless of the intensity of the work experience. However, they also find a negative

relationship between relatively intensive work experience and time spent studying by white high

school students (male and female), although not by blacks.

These analyses reveal either no relationship or a smafl, positive relationship between inschool work experience and subsequent wages. Michael and Tnma (1984) and D’Amico (1986)

fidthat worting in M@school isunrelatd towages earned ayearor twolater. Griliches (1980)

7
reports that work experience accumulated in Klgh school has no effect on subsequent wages, but

that experience gained iu college has a positive effect that is about half ss large aE the effect

of post-school experience. According to Stephenson (1979), both whites and blacks who work

full-time during their last year of high school earn higher hourly wages a year later than do their

counterparts who worked part-time or not at all. Estimates presented in D’Amico and Baker

(1984) suggest that the timing of the relationship between in-sch~l wOrk and subsequent wag=

maybe important: worhgrelatively intensivdy in Mghschool ispositively wsociatedwithwagw

earned one year later for females (white and nonwhite), while it is positively associated with wages

earned three years later for males.

The strongest relationships bet ween in-school work experience and labor market outcomes

emerge when measures of subsequent employment or unemployment are used as dependent variables. Michael and Tuma(1984) report that 14-17 yemoldhigh school strrdentswhoholdjobs are

more likely tham their nonemployed counterparts to hold jobs two years later. Furthermore, work

while in school is positively associated with the number of hours per week worked by those who

holdjobs twoyears later. Stephenson (1981b) finds apositive association among women between

working full-time while in high school and weeks worked immediately after leaving high schnol.

Stephenson (1979) finds that working either full-time or part-time iu school is associated with less

subsequent unemployment for whites, although not for blacks. D’Amico and Baker (1984) find

that working inhighschool isrregatively related tothetime spent unemployed the first year out

of high school for all gender-race groups.

These “first generation” studies reveal a number of interesting relationships, but the results

must reinterpreted with caution. For the most part, these analyses suffer from two shortcomings.

First, they ignore the fact that unobserved factors may influence the decision to work while in

school ww&=subsequent educationd andlabor market outcomes. Forexample, individuals who

live in communities with ample job opportunities for youth will be more likely than other students

to work while in school and also more likely to work shortly after leaving school. Similarly, arr

individual who must work for a year or two to save money for college is likely to begin working

while still in high school and continue for the entire period being observed by these researchers.

Inmanycaees, “tastes” oraptitndes might betheunobservable factors that drive both the school

8
and work decisions. Unless the effects of such unseen factors are controlled for, valid statements

about causafity cannot be made. That is, one cannot determine whether individuals have certain

labor market outcomes because they worked while in high school, or because of unobservable that

also influenced their decision to work while in high school.

Second, the studies discussed here typicall~ look at very short-term enrollment and labor

market outcomes (although Griliches (1980) makes some attempt to examine the wages of men who

are several years out of school). Asaresult, theynegfect theimportant question ofhow the effects

“ofin-school work experience changeover theesrly career. They also fail toprovide information on

how the relationships differ for individuals who receive several years of post-secondary schooling,

since they have generally been excluded from the analyses.

The study by Ruhm (1994) exploits additional information provided by the NLSY in an attempt toaddress these shortcomings. Hissample consists ofmdesand females from thenationdly

representative cross-sectional sample of the NLSY who arein grades 9-10in 1979. He measures

their in-school work experience during grades 10-12, but looks at their labor market outcomesin

1988-90 and their schooling attainment in 1991. For those who complete high school “on time,”

this information is reported 6or more years after graduation.

In addition to examining a broader horizon than his predecessors, Ruhm also addresses

the fact that in-school work experience is not exogeneously determined. First, he controfs for a

large array of personal and family characteristics in an attempt to hold constant factors that may

induce a correlation between in-school work experience and subsequent employment and enrollment

outcomes. Alternatively, he uses astatisticdmethod tocontrol forthe self-selection process that

compels some (but not afl) high school students to work. Upon comparing these strategies, he

argues that the first is an adequate means of controlling for the factors that may induce a spurious

correlation between in-school work experience and the various dependent variables.

A general finding that emergee in Ruhm’s analysis is that employment experience gained

during the senior year of Klgh school “matters: while experience gained earlier in life does not.

Specifically, he finds that an increase in hours worked during the senior year of high sdr.oolis

associated with (a) a higher Duncan index on jobs held 6-9 years later, (6) an increased likelihood of

holding ajob that provides health insurmce, and (c)an increased likelihood of receiving retirement

9
benefits. (Note thatonly the first ofthwenonmge benefits hwbeenandyzed bytis predecessors.)

He also finds that hours worked in high schcol are positively associated with subsequent earnings,

wages, and hours worked. In comparing a high school senior who works 10 hours during the

‘observation week” to a senior who does not work, Ruhm finds that, 6-9 yearr.later, the “worker”

will(a) have a 14.3% higher annrrz, lincome, (b) work an additional 100 hours per year, and (c)

have a5%kigher average hourIy wage. In-school work experience appears to be beneficial in that

it improves a variety of labor market outcomes several years later.

Ruhm addresses a number of issues that his predecessors neglect, but it is interesting to note

that many of thestudies summarized here reach a common conclusion: in-school work experience

is associated with asmallincrease in subsequent wages, andamuch larger increase insubseqrrent

labor supply. (This explains why Ruhm finds a bigger effect onannual income than on hourly

wages, forchanges inthe former reflect thecombined effect ofchanges inlabor supply and wages.)

This general finding ismnsistent with anargument made by Lazear (1977). Using data from the

Young Men cohort, Lazear looks at men who attend school in 1966 but not in 1968, and who work

in both years. This a,flowshimtoestimate awagechange model that identifies thesizeofthe wage

boost associated with leaving school. He finds that leaving school causes wages tojump roughly

15% &yondany w~egrowth duetoincreasea inschooling attainment, work experience, or hours

worked.s To justify tlds finding, Lazear argues that students do not necessarily choose jobs that

pay the highest wsges. Instead, they favor jobs that areconveniently located, tiowfl&ble hours,

and aregenerely compatible with thedemands ofschcol. One can conjecture that students lea.m

a great deal about find]ng and retaining employment from thk early work experience, but that

thw,ejobs teach them relatively few concrete skHfsthat arelater parlayed into higher wages.

3. LEARNING ON AND OFF THE JOB

Whereas the preceding section addresses the fact that students often work while they learn,

thk section focuses on the fact that young people invariably learn while they work. Innate ablity

and education received prior to entering the labor force are important determinants of earnings,

‘Evidence that, ceterw pam”bus, workers who are enrolled in school earn lower wages than nonstudents can also

be found in Light and Uretz (1993) a“d Light (1994),

10
but so too are the skills acquired once the career is underway. By studying early-career skill

acquisition in all its forms, researchers have learned a great deal about life cycle earnings growth

and earnings differences among “seemingly similar” youth.7

Most research in this area uses the human capitaJ model of wage determination to interpret

the economic role of learning. This model, as originally stated by Becker (1962, 1964) aud Mhcer

(1958, 1974), holds that a given worker’s wage is closely linked to his or her productivity. Therefore,

workers have an incentive to enhance their productivi~ by investing in their own ‘human capital.”

The cost of such humm capital investments often includes tuition and other out-of-pocket expenses.

Another component of the cost is foregone earnings, for workers who are learning new skills are less

productive than they otherwise would be and, therefore, lower pz.id. Consider an individud who

completes high school, attends four yesm of college, and then enters the labor force. The four years

of college (as well as the preceding years of school) are considered to be a period of “full-time”

investment during whidr the individual could have earned a substantial amount of money. Those

foregone earnings are part of the cost of the college investment. Foregone earnings also exist when

an individusJ undertakes the less formal, less intensive form of skill investment often referred to

m on-the-job training. The worker’s earnings are nonzero during the invsztment period, but they

are still lower than what they would be if all his or her efforts were devoted to working. Again,

the gap between actual and potential earnings is part of the investment costs

The reason individuals will accept lower (and sometimes zero) earnings while investing in

human capitsl is because they expect the investment to pay off in the form of higher future

wages. frr fact, individuals are b<leved to weigh the expected returns against the expected costs

when deciding whether to stay in school, return to school, or undertake on-the-job or off-the-job

trsining.g If the expected benefit exceeds the expected cost, then an optimizing individual will

choose to undertake the particular investment. This formulation of the decision process explains

why intensive human capital invest ment characterizes the early career, for young people generally

7SkUacquisitionhasZISOprovento be relatedto job twnoverbehavior.That subjectis takenup inSection4.

8The gap betwssnactualand potentialearningsis lessenedwhenthe employeragreesto bear part of the cost.

Thie. is likely to occur when the skills being acquired are highly specialized and cannot be transferred to a new

employer.

‘Of course, skilf acquisition >ays off in W*YSother khan higher future ea,rninss. It may lead to jobs that are

valnable along nonwage dimermions, md it may enhance one’s enjoyment of Iife for reasons unrelated to work and

pay. A comprehensive list of the nomvage retrims to education is provided in Haveman and Wolfe (1984).

11
face enormous berrefits and low costs relative to what they will face later in life.

This investment-based view of skill acquisition makes it clear that the early career is as

much a time for learning as a time for working. Furthermore, it is apparent that no evaluation of

young workers’ labor market outcomes can be complete without an understanding of their human

capital investment decisisous. The rest of this section summarizes the extensive literature that

uses the NLS to explore these issues. Because the topic is so broad, the discussion is divided into

several areas. The first subsection. selectively summarizes research on the estimation of ‘human

capital earnings functions” in which the estimated effects of general labor market experience, job

seniority, and schooling are interpreted as the returns to %M” Tbk core topic has received so

much scrutiny that, in lieu of a complete survey, attention is focused on the unique advantages of

using NLS data. The second subsection discusses research that utilizes the extraordinary amount

of job training information elicited by the NLS. The remaining two subsections examine two speciaf

aspects of early-career learning. the first is the phenomenon of recsiving one’s formal schooling

discontinuously, while the second concerns the increasingly common practice of taking a high

school equivalency examination rather tharr receiving a diploma.

3.A. Human capital earnings functions

Much of our empirical evidence of the importance of skill acquisition has been obtained via

the estimation of “human capital earnings functions: which are empirical analogues of the theoretical human capital model described above. In such earnings (or wage) functions, the logarithm

of annual earnings, weekly earnings, or average hourly wages servss as the dependent variable and

the independent variables include controls for a wide range of personal, family, job-related, aad

market characteristics that are likely to influence earnings. The precise specification variee from

study to study, but the models invariably include a number of “humaa capital variables” that

me intended to control for individuals’ skill levels. These variables include schooling attairrment

(e.g., years of school completed), which controls for the skills learned in school, and labor market

experience (e.g., cumulative years worked since first leaving school), which proxies the amount of

“general” skill gained in the labor market. For many applicat,ionq, it is also desirable to control

for job tenure (serriority) as a proxy for the ‘specific” skill acquired with the current employer.

12
(General skills are defied as those that can be transferred from one employer to another, while

job-specific .&ilk are particular to a given employer.) As will be discussed below, a measure of

innate ability such as IQ score is often a useful control in these functions as well.

One of the challenges facing researchers since. this empirical approach wss introduced by

Mincer (1958, 1974) is to obtain suitable measures of individuals’ work experience. Mincer performed his original empirical work using data from the decennial Census, which reports individuals’

ages and schooling completion levels but not the actual amount of work experience they have accumulated. As a result of this data deficiency, Mkcer and other researchers were forced to substitute

age for work experience or use a measure of potentird experience defined as “age minus years of

schooling minus six.”lo When individuals do not work continuously upon leaving school, potential

experience can become an extremely inaccurate approximation of actual work experience. It is

particularly inadequate for the analysis of women’s earnings because women often decrease their

labor supply during their child-bearing years. As a result, potential experience overstates women’s actual work experience (and their on-the-job skill investments) even more than it overstates

men’s, which leads to distorted inferences about the determinants of gender wage differentials.

In additimr, as Light (1994) shows, potential experience produces biased estimates of early-career

wage growth when youth begin workkg prior to leaving school and when they return to school

after beginning work.

The NLS Young Women, Young Men, and Youth cohorts have been used extensively to

“correct” these problems, for all three provide detailed information on actual work experience.

The Young Men and Young Women cohorts report the number of weeks worked during each year

of the observation period, while the Youth cohort provides weekly observations on individuals’

hours of work. information ou actual work wperience is also contained in the Mature Women

and Older Men cohorts of the NLS and in the Panel Study of fncome Dynamics (PSID), but they

do not focus exclusively on young people (although the PSID includes some youth). The Young

Women, Young Men, and Youth cohorts are the only large, nationally representative data sets

that allow researchers to control for all the work experience acquired during the important initial

10~searcher~ ~~ing the c~~ent Population survey face the same data constmint. The standardspecification

includes both the age or experience measure and its square, which is why controlling for age and schooling is mot

equivalent to controlling for ‘age minus schooling minus six,”

13
stages of the work life.

Garvey and Reimers (1980) and Piler (1993) use NLS data to construct measures of predicted

work experience. Thk is a particularly valuable application, for it provides users of Census and

Current Population Survey (CPS) data a prescription for improving their experience measures.

Using a sample drawn from the Young Women tohort, Garvey and Reimers model cumulative

actual work experience as a function of variables that are available in the Census and CPS—

for example, schooling attainment, health status, marital status, and age. The estimates from

their race-specific experience models can then be used to predict the amount of work experience

for individuals with given charaderistics. Garvey and Reimers estimate human capital wage

functions in which they alternately control for (a) actual work experience as reported in the NLS

(b) potential work experience, and (c) predicted work experience. When actual work experience

is used, the estimate for the linear experience term implies that a white woman’s wage will grow

13% during her first year in the labor force. The wage growth is estimated to be ouly 7.8% when

potential experience, but 14.1% when prd]cted experience is used. These estimates reveal that

the use of potentiaJ experience causes wmuen’s human capitaJ accumulation and wage growth to be

dramatically understated. They also show how information available in the NLS can aid users of

the Census and CPS to construct experience measures that are closer to the “truth” than potential

experience.11

Light and Ureta (1993) observe that the longitudinal nature of the NLS allows researchers to

not only construct detailed measures of cumulative work experience, but also to measure experience

noncumulatively. Instead of including “years of actual experience” and its square among the

independent variables, they cent ml for an array of experience variables that measure the amount

of time spent working during each year of the career. This specification (which Light and Ureta

refer to as the “work history” model) captures differences across workers in the patterns by which a

given amount of actual work experience is accumulated. Among a group of workers who accumulate

6 years of experience in the first 8 years after leaving school, for example, some might wait for

II Filer (1993) extend, the method ~rOposed by Garvey and Reimem by using a pOOled samPle frOm the young

Women and Mature cohorts to estimate work experience, znd estimating the pr.edkting functions within 3-digit

occupation categories. Ife, too, finds that a measure of pred]cted measure of work experience is geuemfly more

accurate than the crude measure of potential experience originally proposed by Mincer.

14
2 yews before beginning work, some might work for 6 years and then stop working, and others

fight wOrk 75~0 Of each Y-. The wOrk histOry mOdel Controk for these various work patterns

and, presumably, for unobserved heterogeneityy in human capital investment patterns.

Ueing data from the Young Men and Young Women cohorts, Light and Ureta compsre

estimates from three alternative wage models: the first includes potential experience and its square

among the regressors, the second controls for (cumrdative) actual experience and its square, and

the third controls for the fraction of time spent working during each year of the career. To compare

the estimates across specifications and genders, consider an individual who works continuously for

the first 8 years after leaving school. The potential, actual, and work history specifications imply

that a man’s wsge will rise 66%, 73%, and 210%, respectively, while a woman’s will rise 24%,

61%, and 78%. These estimates indicate that the use of potential experience causes the returns

to generaJ humsn capital to be understated for men and women ahke; furthermore, they indicate

that measuring actual experience cumulatively causes the returns to be understated as well. To

uuderscere the value of the work history model, Light and Ureta show that 1270of the observed

gender wage gap is attributable to the fact that workers differ in the patterns by which a given

amount of work experience is accumulated.

NLS data have also proven to be invaluable to researchers in estimating the returns to

schooling. When individuals’ inherent alilities are not controlled for in human capitsl earnings

functions, the estimated returns to schooling suffer from “ability tias.” That is, it is not possible to

determine whether the estimates reilect the “value added” of educational investments, or whether

they merely reflect the fact that more able individuals tend to receive more schooling and earn

higher wages. NLS data have a number of features that euable researchers to properly control for

ability bias, not the least of which is the inclusion of several ability measures.

Griliches (1976, ”1977) uses data from the Young Men cohort to aesaes the usefulness of

includ!ng ability measures in human capital earnings functions. He considers two s.fternative

measures of abHity: one is an IQ score collected in 1969 from respondents’ high schools, and one is

a score on a “knowledge of the world of work” test administered by the NLS in 1966. In Griliches

(1976), he estimates a simple model in which the log of wages reported in 1969 is regressed on years

of school completed, a work experience meaeure, and a small set of environments.1 and personal

15
characteristics. When no ability measure is included, the estimated coefficient for schooling is

0.065, which suggests that an additional year of education raises wages by 6.5%. When IQ scores

are included, the estimated return to schoofing fslls to 5.9%, and when “knowledge of the world of

work” scores are included instead of IQ, the estimated return falls to 5.3%. This simple experiment

documents the seriousness of ability bias and demonstrates one method of correcting for it with

NLS data.

Blackbrrrn aud Neumark (1993) use Griliches’s approach to determine whether changes in

the magnitude of ability bias are at least partially responsible for the fact that college graduates’

wages grew refative to high school gradnates’ during the 1980s. Using data for white, male

respondents in the NLSY, they select the first wage reported by each youth after the completion

of school es their dependent variable. Years of school completed is included among the regressors,

and this measnre is interacted with a time trend to capture secular changes in the estimated return

to schooling. Their ability measure is scores from the Armed Services Vocatiomd Aptitude Battery

(ASVAB), which was administered to 94% of NLSY respondents in 1980.

When the ability measure is omitted from the specification, Blackburn and Neumark obtain

a positive, statistically significant coefficient for the schooling-time interaction, which is consistent

with the widely held view that returns to schooling rose dnring this period of time. This effect

contiuues to hold when controls for ability are added to the wage model. That is, they find

no evidence in support of the notion that changes in ability bias are responsible for the secular

increase in the relative earnings of colfege graduates. However, by controlling for the interaction

between schooling, calendar time, and ability, they find that increases in the returns to schooling

were received primmily by high-abifity individuals. Simply put, schooling investments invariably

“pay off” for young people in the form of Klgher wages, but the 1980s was a period dnring which

academically able youths earned increasingly large returns relative to their less able counterparts.

Bishop (1990) uses the same NLSY data to gain additional insights into the mechanism by

which young people’s academic skills translate into rewards in the labor market. He estimates a

series of human capitaf functions (separately by calendar year) in which the dependent variables

are, in turn, hourly wages and annual earnings. In addition to controlling for a host of personal

and family background variables, he controls for scores on individual components of the ASVAB

16
test. In this way, he can determine how different types of narrowly defined skills afiect earlycareer wages and earnings. Bishop’s findings are somewhat counter-intuitive, although they are

consistent with results found with other data sets. For young men, measured achievement in such

academic areas zz mathematics, science, and verbal pursuits have no effect on wages and earnings,

while “low level” skills such as technical ability and computational speed have positive effects.

For young women, both mathematical and verbal abilities are positively correlated with wages

and earnings, sJthough achievement in science and technical areas is not.lz On the basis of these

findings, Bishop argues that U.S. students perform poorly on science and mathematics proficiency

tests because the lack of labor market rewards discourage them from concentrating their efforts

in these areas. His results undermine the widely-held belief that educational investments “pay,”

for they suggest that youth have difficulties signaling their academic achievements to employers

encountered early in their careers. Bishop’s conclusions are quite provocative, and suggest a need

for further exploration of the relationships between measurable skillz and labor market earnings.

3.B. Formal private sector job training

A useful feature of the NLS (particularly the NLSY) is that it asks respondents about

training received in the private sector. Thk training includes the type of on-the-job trzining

that is sufficiently formal to be quantified, off-the-job training received at businsss and vocational

schools (which are distinct from the “regular” high schools and colleges discussed in the next

subsection), and apprenticeships. The Young Men cuhort asks respondents about the longest

training spell experienced since the last interview, while the NLSY asks about the duration znd

type of up to four spells experienced per year. A number of researchers have used these data

to augment the indirect measures of on-the-job training contained in work experience and tenure

data.

LWmd and Tan (1992) analyze the training data in the Young Men cohort as part of an

extensive study that also examines the Older Men and Mature Women cohorts of the NLS, the

Current Population Surveys, and the Employment Opportunities Pilot Projects Survey (EOPP).

lz~umber~e~andD=ymont(19s2)look at & effwtson wagesof coursestakenin highschOOlratherthm ASVAB

scoRs. They find that creditsin academiccoursesha,wa positivebut statisticallyinsignificanteffect on DIen>e.

poz&bighschwl wages,anda small,positiveeffecton women’s,

17
Lillard and Tan categorize private sector training reported in the NLSY into four categories:

“regular school: company training, business and vocational school, and other types of trtining.

They first gToup these four types together and assess their effects on earnings by estimating an

earnings function. They find that workers who have received some training in the past year receive

a 9.5~0increase in esmdngs, on average, while training that occurred 2 years ago is associated with

a 10.8% increase in earnings. As the time since training increases beyond 2 years, the effect on

earnings falls steadily, so that training that took place 11 years ago has no impact on current

earnings. When they disaggregate the training measures by type and repeat the analysis, they

find that company training has the largest effect on earnings, followed by business and technical

school. Company training is associated with a 16.9% increase in earnings in the first year, with the

effect declining 1.3% for every year that has elapsed since the training. The effect of business and

technical school is only 1% the first year, but 10.6% the second yes,q this effect, too, dlmirrishes

at a rate of L3~o per year thereafter.

Lillard and Tan also estimate the probability of experiencing unemployment as a function of

having received private sector training. They find that young men who have received some training

in the recent past have significantly lower probabilities of experiencing unemployment. Just as the

effect of training on earnings diminishes over time, so does the effect on unemployment. When the

training experience is 12 years or more in the past, it has no discernible effect on unemployment

probabilities. However, the favorable effect of company training is longer-lived than the effects of

other types of private sector training.

In a series of papers, Lynch (1989a, 1991a, 1991b, 1992) analyzes the more recent and afso

more detailed training data contained in the NLSY. She focuses on three types of private sector

training company provided training (which she refers to as on-the-job training), training received

through business, vocational, and technical schools (off-the-job training), and apprenticeships.

In analyzing a sample of non-militwy men and women who have not graduated from college

(Lynch 1992), she identifies a number of observable characteristics that influence the probability

of receiving a certain type of training. For example, men are less likely than women to receive

off-the-job training, but more likely to receive on-the-job training or apprenticeships. The longer

individmds have been working for their current employers, the less fikely they are to receive off18
the-job trafning. IUgh schcml graduates are more likely than high school dropouts or individuals

with some college to serve as apprentices, and they are also the most likely to receive off-th~job

training. As the national unemployment rate rises, the probability of receiving on-the-job training

falls, but the likelihood of an apprenticeship rises. Fhmlly, Lynch finds that past participation in

on-the-job training is associated with an increased probability of current participation.

Using the same sample, Lynch examines the effects of the three types of training on wages.

In the spirit of Lillard and Tan (1989), she estimates wage fnnctions in which past and current

training is controlled for in various ways. Past training refers to training completed prior to joining

the current employer, while current training is tied to the current employer. She finds that a past

apprentices~p that lasted 10 weeks is associated with a 5% increase in current wages, while 10

weeks of past off-the-job training are associated with a 2% increase. On-the-job training with

a previous employer, however, appears to be unrelated to current wages. When she examines

the effect of current training (that is, training completed with the current employer), on-the-job

training has a pronounced effect on wages: 10 weeks of company-provided training is associated

with 2% higher wages. These results remain largely unchanged when she corrects for the underlying

selection process that sorts workers into the various training categories.

Based on these findings, Lynch argues that on-the-job training is primarily job-specific, for

workers receive a reward for their training only while they remain employed by the same company

that provided the training. Off-the-job training and apprenticeships appear to lack a job-specific

component, for the evidence suggests that workers who invest in these types of training receive

wage benefits orJy_when they switch to new jobs. These arguments receive additional support in

Lynch (1991a), where she finds that on-the-job training serves to lower the hazard rate out of first

jobs, while off-the-job training and apprenticeships increase job exit rates.

In summary, the research by Lynch and Llllard and Tan suggests that formal, quantifiable

types of job training area big factor in the early career. Lillard and Tan (1989) report that roughly

25% of the young men in their sample receive some form of private sector training (including

additional “regular” schooling), while Lynch (1992) reports that 21% of individuals in the NLSY

receive training. As the evidence summarized here suggests, young workers have an incentive

to continue to learn new skills by participating in job training programs, for it is a significant

19
determined of early-career wages and earninga

3.C. Reenrolling in school

A number of researchers have used data from the NLS to investigate the surprisingly common

practice of returning to “regular” school (as opposed to enroflhrg in job training programs) after

a period of nonenrollment. The simplest formulation of the human capitaf model predicts that

individuals will invest in schooling (as welf as out-of-school training) as early in fife az possible.

However, financial constraints keep some young people from completing the optimal levef of school

prior to entering the labor force. In addition, new information about the costs and benefits of

additional school investments may persuade young workers to reenroll. For example, an individual

who loses his or her job may find that additional schooling is less costly and, therefore, more

desirable than it once was. The studies summarized in thk section seek to determine what type

of individuals receive their schooling discontinuously, why they do so, and what impact it has on

thair subsequent earnings. Table 2 provides an overview of this research.

The fist NLS-related study to tackle this subject is by Griliches (1980). Using the Young

Men cohort, he selects a sample of men who were out of school and working in 1970, and determines

that 9% previously interrupted their schooling. He tinds that these “interrupters” tend to attain

higher schooling levels and have higher IQ scores than their continuously schooled counterparts,

which indicates that reenrolling in school is not a sign of dkadvantage. He then estimates a

simple wsge function in which the logarithm of the 1970 wage is regressed on a small number of

controls (including years of schooling and IQ scores) plus a dummy variable indicating whether

tbe indlviduaf hza interrupted his schooling. He finds that th]s indicator has a positive but

statistically insignificant effect on wages, aa doss an additional regressor measuring the length of

the interruption. To justify this finding he argues that individuals choose to return to school only

if it is beneficial for them to do sbthat is, if they will not be pen~lzed in the labor market. The

finding of ‘no effect: therefore, simply reflects the underlying decision-making processes of the

respondents in the sample.

Grifiches’ conclusions can only be viewed as suggestive, for his analysis suffers from two

problems. Fhst, he does not directly analyze the selection process that causes some men to return

20
to school, so he can only conjecture about its effects on the estimates. Second, hk, variables that

measnre schooling interruptions are based on retrospectively reported information, which is far

less accurate than data that subsequently became available.

Marcus (1984, 1986) also uses the Young Men cohort, but rather than relying on retrospective information he looks at the enrollment status reported by respondents during each interview

from 1966 to 1973. This annually reported enrollment information reveals that 28% of respondents

cease being enrolled and then return to school, with the average nonenrollment spell lasting 2.4

years. In his 1984 paper, Marcus estimates a was function in which the usual “years of schooling”

variable is replaced with two variables: years of school completed prior to the interruption, and

subsequent schooling. In addition, he centrols for the length of the interruption and the square

of total years of schooling. The estimates from thk model imply that, for example, the return

to completing the 14tfi year of school is 7.0% if there is no break between grades 13 and 14, and

7.6% if there is a 1 year interruption. In general, he finds that there is no penalty to interrupting

one’s schooling if the interruption lasts less than 2 years. He also finds that, holding constant the

length of the interruption, the wage penalty is smaller for those in high school than for those at

post-secondary levels.

Marcus does a better job tha Grifiches of exploiting the reenrollment data available in

the NLS, but he fails to make inroads into the selectivity issue. Both of these “first generation”

studies find that schooling interruptions have no negative effect on wages, but neither estimates

their effect net of the underlying selection process that dktinguishes “interruptors” from their

continuously schnded counterparts.

Ulght (in press) dHTersfrom her predecessors by addressing the selection issue and also by

using the NLSY rather than the Young Men cohort. Beginning in 1980, the NLSY has asked

respondents for a month-by-month report of their enrollment status, which allows very short

enrollment and noxenrollment spells to be identified. This feature of the data, coupled with the

fact that Light follows individuals for 11 years (versus 7 years for Marcus), suggests that higher

reenrollment rates shonld be found. In fact, Light (1993, in press) reports that slightly more than

one-third of white men who leave school between 1979 and 1988 reenroli at Ienst once by the 1989

21
interview. ‘3

By examining the relationship between unobserved wage shocks and transitions back into

school, Light (in press) finds evidence that the decision to reenroll is correlated with unobserved,

time-invariant factors (e.g., ability and family influences) but not with transitory unobservable.

She then estimates a discontinuous wage function in which individuals who reenroll in school

are allowed to follow different wage paths during the pre-return and post-return periods, and

she controls for the correlation between the independent variables and unobserved, time-invariant

factors. In this way, she obtains estimates of the effects of reenrollments that are unbiased by the

underlying selection process. She finds that individuals who will return to school proceed along the

same wage paths as those who will not, and then receive a sizeable wage boost upon completing

additional schooling. For example, a young man who finishes high school, works for 1 year, and

then attends (but does not complete) college receives a 10% wage boost, on average. A similar

individual who works for 2 years before reenrolling receives only an 8.7% return.

However, Light also finds that individuals who complete their schooling prior to beginning

their careers receive an even larger return. For example, a high school graduate who advances to

grades 13-15 without an interruption receives a 19.8% return to the additional schooling. Because

the returns to schooling among “non-interrupters” are so much higher than those for “intermptors,” Light shows that the estimated returns to continuous schooling investments are significantly

understated by a wage model that fails to distinguish between the two types of individuals.

In companion papers, Marcus (1986) and Light (1993) estimate reenrollment models rather

than wage models in order to identify the factors that explain the decision to return to school.

Marcus estimates a discrete choice model in which the probability of reenrolling between 1966

and 1973 is the dependent variable. He finds that increased schooling, increased schooLing attainment of the respondent’s mother, and past military service in.mease the likelihood of returning to

school. These are ‘positive” traits, and so thk finding is consistent with Griliches’s (1980) claim

that higher schooling and IQ levels characterize young men who return to school. Marcus also

l$The r~ndhnem figURS repOrted by Marcus (1984, 1986) and Light (1993, in Press) are in fine ~th thO* found

by Spletzer (1990) in the National Longitudinal Survey of the High School .Clas of 1972. According to Spletzer,

30% of individuals who graduated from college within 7 years of completing high school experienced a schooling

interruption.

22
finds, not surprisingly, that the probability of reenmlling declines as labor market experience accrues. Furthermore, he finds that “earnings luck” is negatively associated with the probability of

reenrolling that is, young men whose annual earnings are higher than the predicted lewd derived

from an earnings function are less likely to reenroll. However, he finds that the effect is very small

and that it falls to zero when average hourly wages are used rather than annual earnings.

Borus and Carpenter (1982) estimate a discrete choice model of the decision to return to

school that is similar to the one estimated by Marcus, but they cenfine the sample to high school

dropouts observed in the 1979-80 NLSY. In other words, they look only at the decision to return

to high school. They find that the probability of reenrollment fslls with age, and that family

background and school characteristics have virtually no effect. This suggests that the decision to

complete high school might differ from the decision to return at the post-secondary schooling.

Light (1993) treats the decision to reemoll W, a continuous-time problem by estimating a

proportional hazard model for the waiting time to reenrollment. In this way, she is able to examine

how changes in individuals’ labor market status influence the decision to invest in additional

schooling. If individuals postpone schooling because they cannot finance it, then we would expect

them to work intensively during the nonenrollment period. If they decide to return to school where

they find themselves unemployed or “under-employed; however, we would find just the opposite.

The descriptive analysis and hazard model estimates presented by Light provide support for both

hypotheses. Among at least half of the “returners” there is no evidence that poor labor market

outcomes (as measured by hours or weeks worked) drive the reenrollment decision. In particular,

she finds that 50% of the “returners” work an average of 40 hours per week or more during the

entire year prior to their reenrollment, and 60% experience no unemployment during that year.

However, the hazard model estimates reveal a definite relationship between leaving unemployment

and returning to school.

3.D. High school equivalency exams

In two recent papers, Cameron and Heckman (1990, 1993) use the NLSY to determine

whether individuals who receive a high school credential via an equivalency exam are “equivalent” to those who take the more traditional route of graduating from high school. According

23
to Cameron and Heckman, 14% of individuals receiving a high school credential in 1987 did so

via a certified exam, which is almost a three-fold increase over the 1968 percentage. Given the

increasing popularity of exam certification, it is worth asking whether young people choosing this

option differ from high school graduates in their educational and labOr m=ket OutcOmes. TMs

investigation is particularly important because many data sets, including the Current Population

Survey (CPS), do not distinguish between exam certified individuals and high school graduates.

Cameron and Heckmau compare the performance of high school dropouts, exam (GED)

certified individuals and high school graduates on the Armed Forces Qualifying Test (AFQT).

Approximate scores for this test can he constructed from the scores for the ASVAB, which was

administered to 94% of NLSY respondents in 1980; AFQT scores are widely regarded as usefuf

meaus of measuring various skills that are valued in the work place. Using a combined sample

of white, black, and hispanic men, Cameron and Heckman find the mean scores of high school

graduates, GED certified individuals, and dropouts to be 70.0,57.7, and 41.4, respectively. Using

other samples and a,fsolooking at other points of the distribution, they consistently find that this

ranking holds.

They also find that high school graduates have more favorable schooling and labor market

outcomes than do GED certified persons. Only 43% of GED certified young men attend a 2-year or

4-year college, while 63% of high school graduates do so. The mean hourly wage reported by high

school graduatea at age 25 is roughly a dollar more than the mean wage reported by GED certified

persons, while their average annuaf income is $3,500 more and they average about 4 additional

hours per year. In fact, these summary statistics show GED certified persons to be more similar

to high school dropouts than to high school graduates.

This summary of findings is drawn from the descriptive portion of the two studies, but these

results are also borne out by a more rigorous econometric analysis. Furthermore, the econometric

analysis reveala that inferences about the economic returns to a high school education are quite

sensitive to whether a distinction is made between exam certified individuals and high school

graduates. When the two groups are treated as one, Cameron and Heckman estimate the wages

of 4-year college graduates to be 21’% higher than the wages of high school graduates. When they

are identified separately, however, this estimated wage gap falls to 19.6%. In short, young men

24
who take the GED exam earn a smaller economic reward than do young men who graduate from

high school.

4. VOLUNTARY JOB SEARCH AND MOBILITY

In addition to investing in general and job-specific skills, young workers afso invest a considerable amount of time in job search. This section discusses the “good” type of job search that is

largely voluntay and that is typically associated with significant wage growth.14 The involuntary

search efforts of unemployed workers are discussed in Section 5.

In providing theoretical explanations for the propensity of young workers to change jobs,

researchers have emphasized two issues. The first is the interrefatedness of job turnover and

certain types of human capitaJ investment. As noted in Section 3, some of the on-the-job training

undertaken by young workers teaches them highly specialized skills that cannot be transported

to new firms. Such skill investments serve to deter job turnover because once the investment

cost is paid, there is a strong incentive to maintain the employer-employee relationship until the

investment “pays off.” In fact, it has besn argued (Becker, 1962; Hashimoto, 1981) that employers

and workers are likely to share the cost of job-specific skill investments in order to create a joint

incentive to prevent job separations. This human capitrd view of job turnover indicates that it

might be costly to young workers because it is incompatible with job-specific skill investments

that, in turn, lead to within-job wage growth.

The second issue is that young workers face a great deal of uncertainty about their job

prospects. This issue has led to a number of “job matching” models that justify how mobility can

be beneficial even when it occurs at the expense of job-specific human capital investments. Simply

put, a worker with a given stock of skills can increase his wage by moving to a job where his

skills are better matched with the employer’s needs. Underlying this view is the recognition that

workers are heterogeneous in the amount and type of skills they possess, while employers differ

in their technologies. As a result, workers face a dispersion of wage offers reflecting the manner

in which their productivity varies across firms. Incomplete information prevents workers from

34At tbi~ point, it ~ particularly important for readers to note that “job” is used throughout this report $0 refer

to a spell with a given employer, and not to a position, title, or occupation.

25
immediately locating their best match when they enter the labor market; moreover, the receipt of

new information drives mu& of their job mobility. Workers may change jobs upon learning that

their current match is less productive than they previously believed, or when they discover a new

OFFOrtunity that dOfinat~ their current match. wither way, their early-career job turnover can

be viewed as the procsss of “shopping” for increasingly better matches.ls

ResearrAers have frequently turned to NLS data to document the amount of early-career

job turnover that occurs and to investigate the empirical implications of these theoretical arguments. One strand of empirical research involves estimating job separation models to identify the

determinants of job mobility. Another approach is to estimate wage models which idemtify the

effects of job change on wage growth. Of course, wages and job turnover are not determined in

isolation, and many models estimate the two processes simnltaneously or take account of one in

estimating the other. The discussion that follows begins with the determinants of job mobility

and then turns to the subject of wage growth.

4.A. Determinants of job mobility

In estimating job turnover models, researchers have paid particular attention to the relationship between job seniority and the probability of separation. If job mobility is closely linked to the

absence of substantial investments in specialized skills, then current tenure should be negatively

correlated with the probabilityy of job separation. If job mobility reflects mat ddng behavior, there

conld be either a negative or positive relationship between job tenure and separation probabilities.

A negative relationship is generally predicted by models that assume match qudlty is observable

at the outset of the matclr (Burdett 1978, Jovanovic 1979b). fn such models, workers change jobs

when they discover a better one, but the probability of doing so is likely to fall over time. The

OPPOsitePrediction emerg= frOm the class Of matching models that argue that workers move on

to better matches when their current ones prove to be less productive than was initislly expected

(Johnson 1978, Jovanovic 1979a). These models assume that a job matck’s quality is revealed

only through “experience,” so separation probabilities should rise and then fall with tenure.

These issues have been investigated by a large number of researchers who, over time, have

MEx&mPl=~f~heore~icd~tudi&&t formi,ze theseviews include Burdett (1978), JOhmOn (1978), ~d JOWOtic

(1979a, 1979b).

26
used increasingly sophisticated econometric techniques. Table 3 lists several of these studies and

provides an overview of their methodology and findings.

The earliest investigations of these issues included Borjas and Rosen (1980), Mkcer and

Jovamovic (1981), and B1au and Kahn (1981). Each of these studies uses data from the early

years of the Young Men cohort (and Blau and Kahn use the Young Women cohort as well), amd

each estimates a simple, binary choice model in which the probability of a job separation between

adjacent NLS interviews is expressed as a function of tenure, labor market experience, and a

small set of additional controls. All three studies find a strong, negative relationship between

current tenure and the probability of separation. Blau and Kahn estimate the model separately

for four race-gender groups and find the tenure effect to be significantly more pronounced for

men than for women, but similar for black and whites. Taken at face value, the finding of a

negative relationship between job tenure and separation probabilities is consistent with human

capital theory, and inconsistent with a “learning” type of matchhrg model.

A shortcoming of these early studies is that they do not control for unobserved factors that

might make some workers “movers” and others “stayers?’ Thk type of population heterogeneity

could produce the estimated relationship between tenure and job mobility for reasons unrelated to

either from-specific human capital or job matching. In more recent resemch that takes advantage

of subsequent advances in econometric and computing techniques, Light and Ureta (1992), Farber

(1992), and Omori (1993) reexamine the relationship of interest after controlling for unobserved

heterogeneity.

Light and Ureta (1992) estimate proportional hazard models for job separations using data

from the Young Men and Young Women cnhorts. After controlling for unobserved, time-invariant

personal characteristics, they find evidence of negative duration dependence in the hazard rate

out of jobs—that is, the hazard rate falls as tenure accrues. This negative relationship is more

pronounced for men than for women. Comparing 24-25 year old men and women with a common

set of modal characteristics, Light and Ureta find that a man with no tenure has a 14% chance

of leaving his current employer in the next 6 months; after 12 months on the job this probability

falls to 11%, and after 24 months it falls to 9%. The corresponding figures for women are 34%,

30%, and 26%. These predictions, which do not utilize information on unobserved separation

27
propensities, indicate that men are also significantly less likely than women to separate from their

jobs. When the predictions are recomputed for workers whose unobserved characteristics make

them “stayers ~ however, Light and Ureta find that women are less likely than men to leave their

jobs.

Omori (1993) uses data for white, colfege-educated men from the 1979-87 NLSY. He, too,

astimates a proportional hazard model for job exits, but he looks exclusively at job-to-job transitions, which ate more likely to correspond to job matrhing than the job-to- “anything” transitions

examined by E]ght and Ureta. In addition, he allows the hazard rate to vary over the course of

an employment spell at l-month intervals. (Light and Ureta use 3-month intevafs after finding

that they yield the same results as l-month intervals.) Omori finds that the job-to-job hazazd

rate initially rises with tenure, and then falls after workers reach about 6 months of tenure. This

result is consistent with those job matching models in which workers quit upon receiving new (and

negative) information about their current productivity.

Evidence supporting “learning” models is also found by Flinn (1986) and Farber (1992).

Farber uses the 1979-88 NLSY to examine the monthly job separation probabilities of workers who

have made “long term” transitions to the labor force (defined as reporting work as the primary

activity for three consecutive interviews ). After carefully controlling for past turnover behavior

and other controls for heterogeneity, Farber finds that the hazard rate out of jobs rises withtenure

for the first 3 months and then decfine.s. f?linn uses 1966-71 data from the Young Men cohort to

estimate a highly stylized matcbhg model in which the underlying behavioral process is modelled

rather than observed mobility outcomes. The estimated parameters of his matchhg model are

largely consistent with the same theories that pred]ct the positive duration dependence found by

Omori and Farber.

4.B. Effects of job mobility on wages

A number of studies have estimated wage models that capture the effects of voluntary job

changes on wage growth. One of the goals of this research is to assess the tradeoff between withinjob wage growth which may correspond to investments in firm-specific human capital and the

between-job wage growth brought about by a job change.

28
Borjes and Rosen (1980] use data from the Young Men cohort to estimate the change in

wages between 1971 and 1975 for a sample of whites who are employed throughout that period

of time. They estimate the wage change model separately for the 35% of the sample who change

jobs between 1971 and 1973 and for those who do not, after applying a “selectivity correction”

to account for each man’s decision to be a “moved’ or “stayer.” They find that “movers” receive

significantly less wage growth the greater is their job tenure in 1971 (prior to the job change).

Taking 1971 tenure as a proxy for the amount of specific hnman capital acquired prior to the job

change, this finding indicates that specific investments do not completely deter job mobifity, but

do impose extra mobility costs. Workers who change jobs despite having built up considerable

skills at their old jobs benefit from doing so, but the wage gains they receive from the job change

are not as great ss they would have been had their tenure (and prior wages) been less.

Bartel and Borjas (1981) use a slightly different strategy to assess the relationship between

within-job wage growth that occurs prior to a move and the subsequent wage growth brought about

by a job change. Using 1967-73 data from the Young Men cohort, they identify those workers who

change jobs between the 1969 and 1971 interviews (and their reasons for changing) and those

who do not. They then estimate wage change equations for the 1967-69, 1969-71, and 1971-73

periods. This enables them to compare the wage growth received by “movers” and “stayers” prior

to any job change, at the time of the job change, and subsequent to it. They find that workers

who qnit their jobs for the self-reported reason that a “better” job wes found receive 18% more

wage growth than “stayers” during the period surrounding their job change. Prior to the move,

their wage growth is 3.8% higher than that of “stayers” and subsequent to the move it is 6%

higher, but neither of these estimated differences are statistically significant. Among workers who

quit because they are dissatisfied with their old jobs, however, the contemporaneous wage gain is

smaller (1If%) and the pm-move wage growth is 7’% lower than that of “stay ers.” It is difficult to

infer from these estimates which form of job matching is dominant in the data, but it certainly

appears that in~lviduals whO engage in “job shOpping” rewive considerable wage gains from doing

so.

Mincer and Jovanovic (1981) provide additional evidence that early-career job mobility

is beneficial-or, more accurately, that it does not merely reflect an inability to “settle down”
and undertake valuable skill investments. Using 1967-73 data from the Young Men cohort, they

estimate a simple wage change eqnation in which experience, job tennre, and the number of past

job changes are included among the controls. The measure of prior moves proves to have an

insignificant effect on wages, and the estimated returns to tenure and experience are invariamtto

its inclusion or exclusiou. On the basis of this finding, Mincer and Jovanovic argue that persistent,

early-career job mobility does not deter workers from eventually undertaking job-specific human

capital investments.

Thmreticaf job matchhrg models often assume that young workers locate new jobs while

stilf working for their old employers and, therefore, spend negligible amounts of time in nonemployment. Studies by Holzer (1987), Arrtel (1991), Parsons (1991), and Wolpin (1992) contradict

this notion by showing that job changes that seem to correspond to job shopping behavior often

entail intervening spells of unemployment. Wolpin uses the 1979-86 NLSY to develop a structural

job search model that produces estimates of the probability of receiving a job offer while employed

and unemployed. He finds these probabilities to be 0.24 (0.61) and 0.55 (0.65), respectively, for

white (black) men. Using 1969-71 data from the Young Men cohort, Antel finds that 4(1% of job

quitters have a spelf of unemployment between jobs. Parsons examines 1980-81 data from the

NLSY and finds that only half the workers wbo quit for voluntary, nonpersomd reasons have a job

lined up when they do so.

Parsons afso finds that wage levels on the “old” jobs are positively correlated with the

probability that a job-to-job (az opposed to job-to-unemployment) transition will be undertaken.

That is, workers with relatively low wages appear to be more willing to quit their jobs and engage

in full-time search, presumably because the cost of doing so is relatively low. Antel provides

corroborative evidence by estimating a simultaneous model of wages and job molility with the

Youth data. He finds that wage gains associated with job changes are twice as big for workers

who have an intervening spell of unemployment as for workers who move dkectly from one job

to another. Antel interprets this as evidence that it pays for “job shoppers” to undertake the

intensive search that is allowed when they are not working. Together, the evidence presented by

Antel ~d pZXE,OIMis consistent with the ‘learning” type of matching model in w~lcfr sePamtiOns ,

occur when workers receive negative signals about their current productivity and not necessarily

30
when they have an alternative job offer in hand.

5. UNEMPLOYMENT AND NONEMPLOYMENT

Sections 2and3discuss themany issues arising from the fact that work andschool (or,

more generally, “Iearning”) can and do occur simultaneously. For many youth, however, significant amounts of time are spent neither working nor attending school. Thsse young people may

besearchkg for a job (and not merely “shopping” for abetter job), or they may not even be

participating in the labor force. Nonparticipation sometimes corresponds to positive activities

such as child-rearingor travel, but all too often’ young people leave the labor force after growing

discouraged about their inability tolocate a job. Furthermore, individuals who are not working,

attending school, or actively searching for a job may be incarcerated or participating in illicit

activities.

fn a recent study, Klerman and Karoly (1993) use the NLSY to document the extent of

nonemployment among young men. Klerman and Karoly classify respondents into four labor

force states: worting full-time, working pint-time/not inschool, in school/not working frdl-time,

and not working/not in school. Classifications are based on respondents’ activities during the

week surrounding their birthdays for each year they are snrveyed, and are reported separately

byschooling attainment and age. Klerman and Karolyreport. that, among high school dropouts

between theages of17and 21,20-22% areneither working nor in school. Even when these men

areintheir rnid-20s, ronghly 15~oare not attending school or holding jobs. These percentages are

lower mongmen with Mgherschoofing completion levels, butstill alarmingly Mgh. Atages18 and

19,0ver 15% of high school graduates are neither working nor attending school; this percentage

falls steadily with age, but isstill in the double digits (10.l%)for22 year olds. At ages 23 and

24, 7-8% of young men who attended or completed college are nonemployed and nonenrolled.

Clearly, a colfege education does not provide a guarantee that a young man’s next activity will be

employment.

, There are many reasons to be concerned about youth nonemployment, particularly because

it may have long-lasting effects on individuals’ labor market prospects. Young people who are

31
not working or attending school are typically not acquiring humarr capital that will lead to higher

esmings in the futurq in fact, an individual’s stock of skills may depreciczteduring an extended

period ofnonemployment. Furthermore, extended periods of nonwork imelikely to have negative

effectson individuals’motivation and work habits. Even when nonemployment hae no real effect

on productivity, potential employers may usswnethat it does and decline tohireappficants with

extensive gaps in their work histories.16

Rwearchers have used the NLS extensively to identify the deterrrrinaats of early-career

nonemployment z,ud assess its costs. This section summarizes a sample of studkz that explore

search efforts undertaken by unemployed youth, theeffects of unemployment on subsequent wages,

and the determinants of unemployment. For amorewide-ranging review of this topic (that afso

focuses on NLS-based research), readers are referred to Howe and Frazis (1992).

ti.A. Search methods

The Youth cohort of the NLS has asked respondents hypothetical questions about jobs and

wages they might be offered to aid researchers in understanding their willingness to work. Borrrs

(1982) anafyzes the responses to a set of questions from the 1979 interview in which respondents

were asked whether they would accept jobs in seven different occupations if they were pzid, zJternatively, $2.50, $3.50, and $5.00 per hour. Theseven occupations aretypical’youth” jobsmd

included wzshing dishes, working in a factory, working as a supermarket czdier, and clearring

up neighborhoods. Borus regresses respondents’ yes/no answers on a set of demographic, family

background, and environmental characteristics and finds that increases in county-wide unemployment rates are associated with increases in yormgpeople’s reported willingness to work at fairly

low-level jobs forody$2.50 perhour (which is below theprevtihng titimum wage). Furthermore,

increases in county-wide per capita income are associated with decreases in the same dependent

variable. These relationships are consistent with search theory, which predicts that labor force

participants should lower their’’reservation wages” (and, insomesense, increase their willirrgnezs

to work) in response to poor economic conditions.

The questions anafyzed by Borus are hypothetical and, as a result, do not necess=ily reflect

lEThe*e Nguments ue mad, throughout the literature. See, for example, Becker and Hi~s (1983) ad Lynch

(1989).

32
thesearch behavior of unemployed youth. Holzer(1987, 1988) makes useofamore revealing set

ofquestionsin the 1981 NLSYin which job seekers .areasked toiderrti& the method or methods

actually used to find a job. Inadtition, they aremked whi&methods generated joboffers ad job

acceptances, and how long each method was used. Holzer (1988) finds that informal methods—

specifically, advice from friends/relatives and direct, unsolicited job applications-are far more

popdaraong youth than state employment agencies, newspapers, or other methods. More thar

80% of unemployed youth report rrsing these informal methods, with 18% of those using them

receiving an offer and 12-14% accepting an offer. Barely more than half the unemployed youth

use the remaining types of methods, with only 8-10~0 receiving and 4-5% accepting a job offer.

f?rom these fignre+ it appears that job seekers behave rationally by relying on those methods that

have lower costs and higher expected returns. This finding is borne out in Holza’s econometric

analysis.

In his 1987paper, Holzerfocuses onthespecid problems faced by black job seekers. He

shows that blacks are less likely than whites to use each of the five search methods listed above,

and are also less likely to receive a job offer from any given method. This is particrdarly true of

the direct application method 21% of whites using this method receive an offer, while only 9%

of blacks do so. In fact, Holzer finds that differences in the efficacy of the two informzJ search

methods account for rdmost 90% of the difference in employment probabilities between blacks and

whites. This is not due to an unwillingness among blacks to accept job offers, but rather to an

inability togenerate offers rrsinginformal search methods. Holzer’s finding puts the unemployment

problmn facing black youth in a rather grim light by suggesting that such environmental factors

asinadequate soddcontacts, ala& ofnearby jobvacanties, mdemployer discrimination maybe

important barriers to employment.

Wolpin (1987) and Stern (1989) both usethe NLSY toestimate structural semch models

which yield estimates of such parameters as reservation wages, application costs, and the probability of receiving an offer. With these estimatm in hand, they are able to predict the effects

of hypothetical government subsidies on expected unemployment durations. Wolpin finds that

a government prograru that helps to lower search costs (e.g., by transmitting information between employers amd job seekers) will actually increase expected search durations. Tlris occurs

33
because unemployed youth rtisetheir r@ervation wages andeflectively “holdout’’f orbette.rjobs

in response to the subsidy. A smaller, but qualitatively similar effect is found by Stern when he

introduces alO%incremein search costs and finds that expected search durations fall by 8%. A

government program that increases the probability of an offer being received in a given period of

time could have the same effect, but Wolpin finds that it serves to lower expected search durations. For example, raising the offer probability from 1% to 5% lowers expected unemployment

durations by 60%. Although these studies produce estimates that aretheoretically plausible, it

shonld be noted that anumberof restrictive assumptions are made in order to implement the estimation techniques. Asaresult, the findings should beviewed asmerely suggestive untfl further

refinements of the models are undertaken.

5.B. Effects of unemployment on wages

It is widely believed that unemployment is quite costly, particularly if it occurs frequently or

forprolonged periods of time. Notonly does unemployment often result inaloss occurrent income,

but it can cost workers additional human capital and, as a result, decrease future wages. fn addition, employers might assume that past unemployment experiences reflect slack of productivity,

and pay workers less than they otherwise would.

Becker and ENs (1980, 1983) investigate these propositions bycontrolling for past unemployment experiences ina wage function. Using data from the Young Men cohort of the NLS,

Becker and Hills (1983) select a sample of individuals who leave high school in 1966, 1967, or

1968. They observe each individual’s unemployment experiences during the year he leaves high

school and the following year, and then seehowit is related to wages earned in 1976. Theyfmd

that indlvidrrals who experience no unemployment during the first two years after high school

earn the~]ghest wages several years later, with individuals whormdergo two or more job chmrges

earning slightly more than thos.zwho stay with their initial employer. Early-career unemployment

experiences have a negative effect on 1976 wages, especially if the unemployment is long-lived.

White men whoexperience5 weeks of unemployment earn $6.01 perhour, onaverage, in 1976,

while those with 26 weeks of unemployment earn only $5.11. For black men, the corresponding ;

figures are$4.86and $3.97. Itisnot possible toteUwhether alukofhuman capital accumdation

..

34
irresponsible forthese long-run effects, but it is clear that prolonged early-career unemployment

is associated with fairlysnbstarrtisl wage loss.

Investigations of the type undertaken by Becker and IMls are inherently limited, for only

individuals who succeed in leaving nonemployment are selected into the sample. Any negative

effects of nonemployment are underestimated because the chronically nonemployed (who are likely

to=perience the~eatest Iosses) memissingfrom theandysis. Furthermore, only part of the story

is told by efforts to link nonemployment at one point in the career with wages in another. The

more pressing issue is whether the individual who succeeds in finding ajob will retain it, or whether

her paat nonemployment experiences increase the likelihood that she returns to nonemployment.

The determinants andcosts ofnonemployment are best assessedirr amore dynamic framework

than the one used by Becker and Hills.

5.C. Persistence in unemployment

Forthereasons stated above, researchers have focused more attention on learning whether

unemployment or nonemployment is a persistent event than on determining its effects on future

wages. The questions that have been posed in theliterature include the following

● DOW the fact that an individual experienced some amount of nonemployment in the past

increase the likelihood ofhk becoming nonemployed? This type of relationship is referred

to as accrmrence dependence by Heckman and Borjas (1980).

c Does the duration of past nonemployment spells have an effect on the dnrationof a current

spell? Heckman and Borjas (1980) refer to this as lagged duration dependence.

● Does theduration orintensity ofpastemployment experiences affect theduration ofacument

nonemployment spell? Thk issue is raised because controlling for past nonemployment

durations is not equivalent to controlling for the amount of employment experience that has

beengsined. Forexample, individuds whowere nonworkers fortwoofthe last fiveymrsmay

have all gained three years of work experience, but the number of hours worked could vary

considerably. There is even more ambiguity when the data allow for a distinction between

employment, unemployment,and other types ofnonwork.

35
● Does the current duration of a nonemployment spell affect the probability that it will end

in the next period? Throughout the literature, this type of relationship is referred to as

drmztion dependence (e.g., Heckmanand Borjas (1980), Flinnand Heckmrm (1982)). If

spells grow less likely to end the longer they last, the hazard rate out of nonemployment is

said toexhiblt negative duration dependence.

● If “dependence effects” are found, are they real or do they merely reflect unobserved heterogeneity? For example, occurrence dependence conld arise because nonemployment experiences cause individuals to be stigmatized or to lose valuable skills. These effects, in

turn, woutd contribute dkectly to the likelihood that they areltid offor firedin the future.

However, occurrence dependence could just as well reflect the fact that some people have

unobserved, time-invariaat qrmhties that make them chronic nonworkers, while others do

not. This type of population heterogeneity would lead to repeated nonemployment spells

for.somepeople, but it would not bethecase that nonemployment experience cause futnre

nonemployment. To distinguish between true and spurious dependence effects, researchers

must carefully control for unobserved, underlying heterogeneity.

In one of the first studies to define and examine these issues, Heckman and Borjzs (1980) use

data forwMte, high school graduates from the Young Men cohort. To deselected into thesz,rnple,

individuals had to complete high school by 1969 and be observed from that point through the 1971

interview. Asaresult, theanalysis isrestricted to the first 2-3 yeamafter high school graduation.

Heckman and Borjas estimate a continuous time model of heterogeneity and state dependence

which, operationally, amounts to using differences in durations between successive employment

and nonemployment spells as dependent variables in a linear regression model. They find no

evidence of occurrence dependents or lagged duration dependence in either type of spell, after

controlling for unobserved, in{lvidual heterogeneity. They find weak evidence of pcwitive duration

dependence for both nonemployment and employment spells, which means the probability of

leaving aspellincreazes as the spell wears on. While this latter result istheoreticslly plausible

(especially for nonemployment spells, since job seekers are entirely likely to lower their reservation

wages as their unemployment spell wears on), Heckmz,u and Borjas warn that this finding is not

36 ,’
1\,

vqrObust because it is based on untested distributional assumptions.

Lynch (1989b) uses data from the NLSY to reexamine the subject of duration dependence.

She selects a sample of men and women who experience an employment spell followed by a nonemployment speff, and estimates aproportiomdhazard model (separately by gender) for each type

of spell. k estimating the nonemployment hazard, she controls for a large array of observable

that are intended to proxy the probability of receiving a job offer and the probability of accepting

an offer. These controls include local unemployment rates, race, schooling, job training, health

and marital status, and the duration of the lsst nonemployment spell. She finds strong evidence

for negative duration dependence—that is, as a nonemployment spell wears on, the probability of

reemployment falls. This inference is made in the absence of a rigorous correction for unobserved

heterogeneity, but Lynch performs a number of experiments to assess the effect of unobserved

factors and argues that they do not bias her results.

Lynch also uses her hazard model estimate.s to predict expected nonemployment durations

forvarious gender-race groups. Herestimates imply that awhitemde with ``typical'' characteristics (including ordy llyears ofschoofing) can expect anonemployment spelltolast 7.2 weeks. ff

recompletes coUege, tMsduration fflsto2.6weeks; ifheisnonwfite, the duration is 14.1 weeks.

A “typicaJ” womai with only 11 years of schooling can expect her current nonemployment spell to

last 5.4weeks ifsheis white and24weeks ifshe is nonwhite. Forawhite, femafe, collegegraduate,

the expected nonemployment duration is 1.2 weeks. With these predictions, Lynch underscores

the fact that nonemployment durations depend strongly on demographic characteristics and other

personsJ, job-related, and market characteristics in addition to individuals’ past nonemployment

experiences.

Stephenson (1982), McCall (1989), and Omori (1994) focus on the relationships between past

employment experienceaud current nonemployment spells. Stephenson examines individuals from

the Young Women cohort who leave schcal in 1970, do not attend college, .and report complete

work histories between 1970 and 1973. He models the transition from nonwork to work m a

function of several personal and market-related characteristics, as well as employment experiences

gs.ined prior to becoming nonemployed. Hisresults, however, memixed he firrdsthat having held

a job in high school increases the rate of transition from nonemployment to employment, but that

37
having held a job at the time of leaving high school lowers the t ransition rate.

McCall (1989) uses tenure on the last job as his measure of past employment experience.

Using data from the 1979-86 NLSY, he focuses specifically on nonemployment spells following

the first job after leaving school. Furthermore, he looks only at individuals who left their first

jobs for reasons other than pregnancy and the end of temporary work, in order to eliminate those

nonemployment experience that are, in some sense, expected or desired. He models the log of

the nonemployment duration as a function of personal and environmental characteristics, plus the

log of tenure on the preceding job. Since “old” tenure and current joblessness are interrelated,

he also corrects for the endogeneity of the tenure measure by substituting predicted values. He

finds that tenure on the preceding job is associated with decreased nonemployment durations for

women, but not for men. For women, an increase in tenure on the last job from six months to one

year is associated with a seven week decline in the expected duration of nonemployment. In light

of this evidence, McCall argues that workers who accumulated more tenure on their last job also

accumulated more human capital, and so they have an added incentive to search intensively for a

new job in order to put their skills to use. Why this effect is gender-specific is left unanswered.

Omori (1994) estimates a proportional hazard model for nonemployment spells using data for

white, college educated males from the 1979.87 NLSY. He controls for scores of observable factors

includlng past employment and nonemployment experiences, and slso controk for unobserved,

individual heterogeneity. He finds that cumulative past nonemployment experience has a negative,

but statistically insignificant effect on the hazard rate out of nonemployment. However, he finds

that the hazard rate is higher for the first-time nonemployed than for individuals experiencing a

repeat nonemployment spell. Omori interprets this as evidence that young people ‘learn to search

through search’’—that is, the job search skills acquired during their previous nonemployment

spefls help them to leave subsequent nonemployment more quickly. Iu addition, he finds that

the months of employment gained prior to the current nonemployment spell and the number of

hours worked both have small, positive, statistically significant effects on the hazard rates. This is

consistent with McCalf’s argument that previous employment experience gives workers the skilfs

or motivation needed to find a job when they find themselves out of work.

38
6. CONCLUDING COMMENTS

As the research summarized in this report makes clear, a young person’s transition from

school to work does not necessarily occur at an easily identified point in time. Instead, many students acquire sizeable amounts of work experience before they ever leave school, while others gain

very little work experience by the time they are months or even years out of school. Furthermore,

many young people intersperse spells of formal schooling or job training throughout their early

careers, which indicates that the transition from school to work is not a “once and for all” event.

Because the transition from school to work is such a complex phenomenon, understanding

why young workers differ in their career outcomes in not an easy task. Young people experience

multiple and often overlapping spells of formal schooling, on-the-job training, off-the-job training,

employment, job search, and nonemployment. These transitions lead to frequent, unobserved

changes in their skill levels and, more generally, in their productivity levels, and also in the

perceptions that others (especially current and potential) employers have of them. To understand

why one young person earns more than another, or why one is continuously employed whHeanother

is not, researchers must unravel the relationships between all of these observed and unobserved

factors.

The data contained in the Young Men, Young Women, and Youth cohorts of the NLS are

unrivaled in their ability to sUow researchers to understand the various events that characterize

the early career. This report has reviewed a portion of the literature that uses the NLS to address

such topics as:

● How work while in school affects subsequent schooling decisions, labor supply, wages, and

other measures of labor market “success,” both in the short run and in the long run.

● Why work experience affects wages, and how to assess its effects.

● Which young workers receive job training and how it affects wages.

● How the quantity and quality of formal schooling affect wages, and how to assess the returns

to schooling.

39
● Which young people choose to make discontinuous schooling investments, and how it affects

wages.

● Which young people choose to substitute a high school equivalency exam for a reguIar diploma, and how it affects wages.

● Why job mobility is so common in the early career, and how it affects wages.

● Why nonemployment is so common in the early career, and how it affects future wages and

labor supply.

Because of the importance of the issues and the richness of the data, the body of research

that explores these and other aspects of school-to-work transitions will invariably grow in the years

to come.

40
REFERENCES

Antel, John J. “The Wage Effects of Voluntary Labor Mobility With and Without

Intervening Unemployment.” lndw-trial and Labor Relations Reuiew44(January l99l):

299-306.

Bartel, Ann P. and George J. Borjas. “WageGrowtha ndJobTnrnover: An Empirical

Analysis.” In Sherwin Rosen, editor, Studies in Labor Markets. Chicago: University of

Chicago Press, 1981.

Becker, Brian E. and Stephen M. Hills. <lTeenage Unemployment: Some Evidence Of

Long-Run Effects on Wages.” The Journal of Human Resource.15 (Summer 1980):

354-372.

and “The Long-Run Effects of Job Changes and Unemployment

Among Male Teenagers~” The Jotirnc[[ oflIuman Resouwes 17( Spring 1983): 197-212.

Becker, Gary S. “Investmentin Human Capital: A Theoretical Analysis.” Journal of

Political Economy 70( Oc.tober 1962 Supplement): 9-49.

— Human Capital: A Theowtical Analysis with Special Reference coeducation. .

New York: Columbia University Press, 1964.

Bishop, John H. “The Productivity Consequences”of What is Learned in Klgh School.”

Jaurnal of Cw-riculumStudie.s22( March/April1990): 101-126.

Blackburn, McKinley L. and David Neumark. “Omitted-Ability Bias and the Increase

in the Return to Schooling.” Journal of Labor Economics 11 (July 1993): 521-544.

Blau, Francine D. and Lawrence M. Kahn, “Race and Sex Differences in Quits by

Young Workers.” Industrial and Labor Relations Review 34 (July 1981): 563-577.

Borjas, George J. and Sherwin Rosen, ‘Income Prospects and Job Mobility of Younger

Men.” In Ronald G. Ehrenberg, editor, Research in Labor Economics, Volume 3. Greenwich, CT: JAI Press, 1980.

Borus, Michael E. “Willingness to Work Among Youth.” The Journal of Human

Resources 17 (Fall 1982): 581-593.

and Susan A. Carpenter. “Choices in Education.” In Mkhael E. Borus,

editor, Paihmays to the Future, Volume 2. Columbus, OH: Center for Human Resource

Research, The Ohio State University, 1982.

Burdett, Kenneth, “A Theory of Employee Job Search and Quit Rates.” American

Economic Review 68 (March 1978): 212-220.

Cameron, Stephen V. and James J. Iieckman. “Educational and Training Transitions:

Determinants of Youth Schooling and Training Transitions.” Chicago: University of

Chicago mimeo, March 1990.

— ad----- “The Nonequivalence of Klgh School Equivalents.” Journal of

41
Labor Economics 11 (January 1993, Part 1): 1-47.

D’Amico, Ronald. ‘Does Employment During High School Impair Academic

Progress?” Sociology of Education 57 (July 1984): 152-64.

“Why Does Work in High SChool Matter?” In Ronald D. D’Amico, et

al., Pathways to the Future, Volume 6. Columbus, OH: Center for Human Resource

Research, The Ohio State University, 1986.

and Paula Baker. “The Nature and Consequences of High School Employment.” In Paula Baker et al., Pathways to the Future, Volume 4. Columbus, OH: Center

for Human Resource Research, The Ohio State University, 1984.

Donohue, John J. “Determinants of Job Turnover of Young Men and Women in the

United States: A Hazard Rate AnaJysis.” In T.P. Schultz, editor, Resear-ch in Population Economics, Volume 6. Greenwich, CT: JAI Press, 1988.

Farber, Henry S. ‘Evaluating Competing Theories of Worker Mobility.” Washington,

D. C.: NLS Discussion Paper 92-5, March 1992.

Filer, Randall K. ‘The Usefulness of Predicted Values for Prior Work Experience in

Analyzing Labor Marekt Outcomes for Women.” The Jorwnal of Human Resources 28

(Summer 1993): 518-537.

Flinn, Christopher J. “Wages and Job Mobility of Young Workers.” .Jormnalof PoMical

Economy 94 (June 1986): 88-110.

and James J. Heckman. “New Methods for Analyzing Structural MOdek Of

Labor Force Dynamics.” Journat of Econometrics 18 (January 1982): 115-168.

Garvey, Nancy and Cordelia Reimers. “Predicted Vs. PotentisJ Work Experience in

an Earnings Fnnction for Young Women.” In Ronald Ehrenberg, editor, Research in

Labor Economics, Volume 3. Greenwich, CT: JAI Press, 1980.

Grilicbes, Zvi. “Schooling Interruptions, Work While in School and the Returns from

Schooling.” Scandinavian Journal of Economics 82 (1980): 291-303.

‘Wages of Very Young Men.” Journal of Political Economy 84 (August

l=; 2): S69-S85.

ttE~~imatingthe Returns to Schooling Some Econometric PrOblems.”

Eccmomeh-ica 45 (January 1977): 1-22.

Hashimoto, Masanori. “Fkm-Specific Human Capital as a Shared Investment.” American .Economic Reoiew 71 (June 1981): 475-482.

Haveman, Robert H. and Barbara L. Wolfe. “Schooling and Economic Well-Being

The Role of Nonmarket Effects.” The .louwud of Human Resources 19 (Summer 1984):

377-407,

Heckman, James J. and George J. Borjas. “Does Unemployment Cauze Fntnre Un42
employment? Definitions, Questions and Answers from a Continuous Time Model of

Heterogeneity and State Dependence-” EcOnOm’iCa47 (August 1980): 247-283.

Holzer, Harry J. “Informal Job Search and Black Youth Unemployment.” Ame~icmz

Economic Review 77 (June 1987a): 446-452.

“Job Search by Employed and Unemployed Youth.” Industrial and Labor

R-”Reoieru 40 (July 1987b): 601-611.

“Search Method Use by Unemployed Youth.” Journal of Labor Economics

6=; 1988): 1-20.

Howe, David K. and Harley J. Frazis. “What Researchers have Learned from the National Lorrgitudhml Surveys About Youtk Unemployment.” Washington, D. C.: Bureau

of Labor Statistics Report 828, August 1992.

Johnson, W]lfiam R. “A Theory of Job Shopping.” Quarterly Journal of Economics

92 (May 1978): 261-277.

Jovanovic, Boyan. “Job Matchhrg and the Theory of Turnover.” Journal of Political

Economy 87 (October 1979a): 972-990.

“Firm Specific Capital and Turnover.” Journal of Political Economy 87

(=e~ 1979b): 1246-1260.

Klermarr, Jacob Alex and Lynn A. Karoly. “The Transition to Stable Employment:

Milling Around?” Santa Monica, CA: RAND mimeo, December 1993.

Lazear, Edward P. “Schooling as a Wage Depressant.” The Journal of Human Resources 12 (Spring 1977): 164-176.

Light, Audrey, ~~TheEffects of Interrupted Schooling on Wages. ” The Jormd of

Human Resources (in press).

— , “Labor Supply Fluctuations and the Decision to Reenroll in School.” Columbus, OH: The Ohio State University mimeo, June 1993.

—. ‘{When Does the Career Begin?” Columbus, OH: The O’K1OState University

mimeo, February 1994.

and Kathleen McGarry. “Jolr Change Patterns and the Wages of Young

Men.” Los Angeles: UCLA rnimeo, March 1993.

and Manuelita Ureta. “Panel Estimates of Male and Female Job Turnover

Behavior: Can Female Nonquitters be Identified?” Journal of Labor Economics 10

(April 1992): 156-181.

— =d “Early Career Work Experience and Gender Wage DHerentials.” Columbus, OH: The Ohio State University mimeo, December 1993.

lllard, Lee A. and Hong W. Tan. “Private Sector Training: Who Gets it and What

Are Its Effects?” In Ronald Ehrenberg, editor, Research in Labor Economics, Volume

43
13. Greenwich, CT: JAI Press, 1992.

Lynch, Lisa. “The Role of the Private Sector in Training.” In F’raceedings of the 12nd

Annual Meeting of the Industrial Relations Research Association. Atlanta: Industrial

Relations Researck Association, 1989a.

“The Yonth Labor Market in the Eighties: Determinants of Reemployment

Probabilities for Yonng Men and Women.” Review of Economics and Statistics 71

(Febrnary 19891,) 37-45.

. “The Role of Off-the-Job vs. Ou-t.he-Job Training for the Mobility of Women

Workers.” .4rnericmr Economic Review 81 (May 1991a): 151-156.

. .“The Impact of Private Sector Training on Race arrd Gender Differentials

and the Career Patterns of Young Workers. ~~w=hin@,On, D. C.: NLS Discussion paper

92-8, July 1991b.

,$private Sector Trtining and the Earnings of Young Workers.” American

.EcorsornicReview 82 (March 1992): 299-312.

Marcus, Richard D. “Meaauring the Rate of Return to Interrupted Schooling.” Journal

of Educational Statistics 9 (Winter 1984): 295-310.

. Warnings and the Decision to Return to School.” Economics of Education

IZeoieru 5 (1986): 309-317.

Maxwell, Nan L. “The Effect on Black-White Wage Differences of Differences in the

Quantity and Quality of Education.” Industrial and Labor Relations Review 47 (January

1994): 249-264.

McCall, Brian P. “An Empirical Analysis of Youth Joblessness Durations.” In Proceedings of the 42nd Annual Meeting of ~h~ industrial Relations Research Association.

Atlanta Industrial Relations Research Association, 1989.

M]cbael, Robert T. and Nancy Brandon Tl[ma. “Youth Employment: Does Life Begin

at 16?” Journal of .LrzborEconomics 2 (October 1984): 464-476.

Mincar, Jacob. “Investment in Human Capital and Personal Income Distribution.”

Journal of Political Economy 66 (Augnst 1958): 281-302.

. ,%hooling, Experience and Earnings. New York: Columbia University Press,

1974.

and Boyan Jovarrovic. “Labor Mobility and Wages.” In Sherwin Rosen,

editor, Studies in Labor Markets. Chicagm University of Chicago Press, 1981.

Oi, Walter. “Labor as a Quasi-Fixed Factor.” Journal of Political Economy 70 (December 1962): 538-555.

Omori, Yoshiaki. “The Job-to-job Transition Rate Among Yonng College-Educated

Men.” Storrs, CT: University of Connecticut mimeo, June 1993.

44
C’The stigma Effects of NOn.Employment.” Storrs, CT: UniveI’sitY Of COn- —.

necticut mimeo, January 1994.

Parsons, Donald O. “The Job Search Behavior of Employed Youth.” Reoieco of Economic-s and Statistics 73 (November 1991): 597-604.

Ruhm, Christopher. ({Is High School Employment Consumption or Investment?”

Greensboro, NC: University of North Carolina at Greensboro mimeo, February 1994.

Rumberger, Russel and Thomas Daymont. ,~TheEffects of High School Curriculum on

Labor Market Success.” In Michael E. Borus, editor, Pathways to the Future, Vohune

2. Columbus, OH: Center for Human Resource Research, The Ohio State University,

1982.

Santos, Richard. “The Employment Status of Youths.” In Michael E. Borus, editor, Pathways to the Future, Volume 1. Columbus, OH: Center for Human Resource

Research, The Ohio State University, 1981.

— . ‘Employment Prospects of Unemployed Youth.” In Michael E. Borus, editor, Pathways to the Future, Volume 2. Colnmbus, OH: Center for Human Resource

Research, The Ohio State University, 1982.

Spletzer, James R. “Post-Secondary Education and Labor Supply in the Early Stages

of the Life Cycle.” Ph.D. Dissertation, Northwestern University, 1990.

Steel, Lauri. “Early Work Experience Among White and Non-White Youths: Implications for Subsequent Enrollment and Employment.” Youth and Society 22 (1991):

419-47.

Stephenson, Stanley P. “From School to Work.” Youth and Satiety 11 (September

1979): 114-132.

. “In-School Labour Force Status and Post-School Wage Rates of Young Men.”

Applied Economics 13 (1981a): 279-302.

“Young Women and Labor: In-School Labor Force Status and EarlY

Postschool Labor Market Outcomes.” Youth and Society 13 (December 1981b): 123-

155.

“Work in College and Subsequent Wage Rates.” Research in Higher Educat-982a): 165-178.

‘A Turnover Analysis of Joblessness for Young Women.” In Ronald Ehrenberg, editor, Research in Labor Economics, Volume 5. Greenwich, CT JAI Press,

1982b.

Stern, David and Yoshi-lluni Nakata. “Characteristics of High School Students’ Paid

Jobs and Employment Experience After Graduation.” In David Stern and Dorothy

Eichorn, editors, Adolescence and W’or!t: Influences of Social Structure, Labor Markets,

and culture. Hillsdale, NJ: Lawrence Erlbaum Associates, 1989.

45
Stem, Steven. “Estimating a Simultaneous Search Model.n Journal of Labor .Eco710W2iCS74 (July 1989): 348-369.

Wolpin, Kenneth I. “Estimating a Structure Search Model: The Transition From

School to Work.” I?conometrica 55 (July 1987): 801-817.

— . “The Determinants of Black-White Differences in Early Employment Careers: Search, Layoffs, Quits, and Endogenous Wage Growth.” Journal of Political

Economy 100 (June 1992): 535-560.

46
Table 1

Summary of Findings on the Extent to Which Work and School Are Combined

NLS Measure of

Study Cohort Sample In-School Work Selected Findings

D’Amico (1984, 1986)

D’Amico &, Baker (1984)

Grilichw (1980)

Light (1994)

Michael & Tuma (1984)

Ruhm (1994)

steel (1991)

Stephenson (1979, :1981a,

1982)

Stephenson (1981b)

Stern & Nakata(1989)

Y,

1979-82

WA,

1966-70

Y,

1979-91

Y,

1979

Y,

1979-91

Grades 8-12

Complete at least

grade 10 by 1970

Fkst leave school in

1978-91

Ages 14-17, in school

in 1979

Grade 9-10 in 1979

Y, Ages 17-18 in 1979

1979-81

YM, In school one year,

1966-71 out of school next;

1966-71

Yw, In schcol one year,

1968-78 out of SChCOlM.XU

Y, HS senior in 1979;

1979-82 terminal HS grads

Percent of weeks worked

during school year

Cumulative work experience while in school

Hold job at time of lmt

observed exit from school

Whether employed week

prior to 1979 interview

Whether employed interview

week, grada 10-12; houm/week

and weeks/academic year

Whether employed at

time of 1979 interview

Whether working FT or

PT or unemployed while

in school

Whether working F’T or

PT or unemployed wh)le

in school

Whether employed week

prior to 1979 interview

50% (75%) of white men work in grade

10 (12), 15% (57%) for more than half

the yea!; lmi for minorities, women.

48% work in high school, 63% in ccdlegq

24% average at least 20 hours a week.

48% of white men hold job, work 60% of

last year of school, on averagq coUege

students wmk more than others.

25% of 14 year olds work, 51% of 17

year olds; men more than women,

whites more than blacks or Hispanics,

28% of mphs, 43% of juniors, .50% of

seniors work, seniors work 19 hrs/week,

52% of academic yea, on average,

58% of whit= work, 41% of H]spancis,

35% of blacks; all average about 25

hours a week.

29% of whites work ITT, 33% PT,

7% unemployed; less FT, PT work

for blacks, more unemployment.

46% work FT, 11% PT, 9%

unemployed; (blacks and whites

combined),

51% work; average about 16 hours

a week in 1976-79.

Note: Y=Youth, YM=Young Men, YW=Young Women.

47
Table 2

Summary of Research on the Causes and Consequences of Reenrolling in School

Borus and Carpenter Y,

(1982) 1979-80

Griliche. (1980) YM,

1966-70

Light (1993) Y,

1979-89

Light (in press) Y,

1979-89

Marcus (1984) YM,

1968-73

Male and female high

school dropouts

Out of school and

workkgin 1970

White men who leave

school in 1978-89

White men who leave

school in 1978-89

Out of school and

workihgiu 1973

Probability that ’79 dropouts reenrollby ’80

1970 cross-sectional

wage function

Hazard for waiting

time to reenrollment

1979-89 wage function

1973 crosx-sectional

wage function

Marcus (1986) YM, Menwith usahle 1966-73 Probability ofreenrol.

,,

198~-73

Note: Y=Youth, YM=Young Men.

enrollment histories ling, 1966173

Probability falls with agq most family,

school characteristics insignificant.

Past school interruptions have pmitive

hut insignificant effect on log-wage.

Unemployment rtduces waiting

time to reenrollment

Schooling increments rak% wages, but

Iess than “early’’school

No wage penalty to interrupting

school if break is short.

Positive “earnings luck” has little

effect on reenrollment probability.

48

“ ,,,,,,

‘, ‘,,,,1

.,,
,-----

Table 3

Summary of Research on the Determinant of Job Mobility

NLS

Study Cohort Sample Model Selected Findings

Blau and Kahn

(1981)

Borjas and Rosen

(1980)

Farber (1992)

Mincer and Jovanovic

(1981)

Light and Ureta

(1992)

Omori (199S)

YM, 1969-71;

YW, 1970-2

YM, 1971-75

Y, 1979-88

YM, OM,

1967-73

YM, 1966-81;

YW, 1968-85

Y, 1979-87

Out of schml and

employed

Whites, out of school

and employed

Nonmilitary, out of

school

No selectioncriteria

given

Out qf schooland

employed,ages24-31

Men, out of school,

attendedcollege

Probability of quitting

job between surveys

Probability of changing

job bet ween snrveys

Hazard for job separations

Probability of changing

job batween smveys

Hazard for job separations

Hazard for job-to-job

separations

Tenure has strong negative effect,

especially for men.

Tenure has strong negative effech

indnstry is determinant of mobility

Hazard rate rises with tenure

foriirst3 months, then falls.

Tenure has strong negative effect

number of past jobs has no effect.

Hazard rate falls with tenure,

especially for men.

Hazard rate tiseswith tenure

for first 6 months, then falls.

Note: Y=Youth, YM=You,lg Men, YW=Young Women, OM=Older Men.

49
‘table 4

Summary of Research on the Persistence of Nonemployment

NLS

Study Cohort Sample Model Selected Findings

Heckmanand Borj~ YM, 1969-71 White high school gradu-

(1980)

Lynch (1989b) Y, 1982-83

McCall (1989) Y, 1979-86

Omori (1994) Y, 1979-87

Stephenson (1982) Yw, 1970-73

ates; leav; school in 1969

Not employed, in schcd

orin military in 1982

Leave school, leave first job

during observation period

White, college educa.ted

men

Leave schmd @ 1970,

no collegeattendance

Change in employment,

unemploymentdurations

Hazard for nommployrnent

durakiom

Duratiol] of first nonemployment spell

Hazard for nonemployment

durations

Transition rates betwen employment and nonemployment

No evidence that past unemployment incrm.sas duration of current

unemployment.

Probability of reemployment falls

with time, especially for men

Duration of past job decreases

nonemployment spell for women

only,

Length of past nonmnployrnent

increases duration of current

nonemployment.

White women leave ‘nonwork

faster than black women.

Note: Y=Youth, YM=Young Men, YW=Young Women.

50

, .,,”,

L,,

‘--’--%
National Longitudinal Surveys (NIX)

DEcussion Paper Series

01

02

03

04

05

M

07

08

09

10

11

12

13

Michael R. Pergamit

Nonnan M. Bradbum

MardnR. Fmnkel

ReginaldP. Baker

MicbaefR. Perganrit

Saul Schwartz

Robert Hutchens

George Jakubson

A. Colin Cameron

R. Mark Gritz

Thomas MeCurdy

Henry S. Farber

Frank L. Mott

Paula Baler

AudRy Light

Manuelita Ureta

Lks M. Lynch

EvangeIos M. Falaria

H. E1izaMb Peters

Anne Hill

June E. ONeill

Arlesm Leibewitz

Jacob Afex Kfsrnrsn

Linda Waite

Lee A. Lillsrd

Joseph G. Aftmrji

Thomas A. Dunn

TiL!e

How the Federal Government Uses Date tlom

the Nationnf Longitudinal Surveys

A Compmision of Compu@-Assisted Personal

Interviews (CAPI) with Paper-mrd Pencil Interviews (PAPI) in the Nationaf LongitudioaJ Survey

of Youth

Dynamic Medels of the Joint Determination of

Labor Supply end Family Structure

The effeus of Unemployment Compensation

on the Unemployment of Youth

Evaluating Competing Theories of Worker

Mobtity

Evaluation of the 1989 ChiId-eere Supplement

in the NationaJ Longitudinal Survey of Youth

Gender Differences in the Quit Behavior of

Young Workers

The Impact of Private Sezter Training on Race

and Gender Wage Differentials and the Career

Paaems of Young Workers

Responses of Femafe f.abnr Supply and FtiIty

re the Demographic Cycle

A Study of Intercohort Change in Women’s

Work Patterns and MIngs

Women’s Employment During Pregnancy

end Following Birth

Work Experience, Job Tenure, Job Sepmation,

and Wage Gfowth

Family Background and Labor Market Outcomes
14

15

16

17

Gemge J. Borjaa

Stephen G. Bronars

Stephen J. Trejo

JamesJ. Hedanmr

Stephen V. Cameron

Peter 2, Schochet

R. Mark GriLs

Thomas MaCtrrdy

Alan L. Gumman

Thomas L. Steinmeier

Self-SelectionandInternalMigradonin Ihe

UnitedStatea

The DerermieamsandConsequencesof PnbIic

Swter and Private Secror Training

participation in Low-Wage Labor Markets by

Young Men

Retirement in a Family ContexC A Strucmraf ModeI

for Husbamfa and Wives

/“. -

-——