.

.

,.

NLS

Discussion

Paper
How the Federal Government Uses Data from the National

Longitudinal Surveys (NLS)

.

MichaelR. Pergamit

U.S. Bureau of Labor Statistics

November 1990 .

Revise&July 1991

. .

This paper was originally prepared for presentation at the conference on the Australian Longitudinal

Survey Social and economic Policy Research held in Canberra, Australia, December 10-1I, 1990 with “’

the ti~e, “SomeRcccm Goverirnen~l Uses of the National LongiurdinalSurveys (NLS) in the USA”. The

arrlhorwishes 10!hmk Marilyn Manser,John Ruser, and Jon Veum for helpful comments. Some of Ihc

tables and analyses presented in Ibis paper were prepared by staff al the Center for Human Resource Research at Ohio SImc University under contract to LheU.S. Department of Labor. The views expressed

here are those of [he mrthorand.do not necessarily represent the official position of the Bureau of Labor

Statistics.
This paper gives some recent”examples of

uses of how the U.S. Govenrnent uies NationaJ

Longitudinal Surveys QWS). These.smweyswere

begun in the mid 1960’swith the drawing of four

samples: Young men who were”14-24 years old in

+ 1966, young women who were 14-24 years old in

1968, older men who wefi 45-59 year’s old..in

1966, and mature wotrien who were 30-44 years

old in 1967. Each sample originally had about

5,000 individuals witi oversamples of blacks. In

the early 1980s, the young “men and older men

surveys were discmtdnrred.1 The two women’s

surveys continue and are currently on a birmnuaf

interview cycle. The interviews and retention

rates for each of these onginaJ cohorts arc found in

table.

In 1979, a new whort wti begun with a sample of over 12,~- young=rnen and women who

were 14-21 years of age on January 1, 1979. It included oversarnples of blacks, Hkpanics, economically disadvarrtaged whites, and youth in the

military.z This survey, which we call the Youth

Cohort, or “~SY, has-”been carried out by conducting interviews every year since it began. After twelve waves of interviewing, we had a @mttion rate of 89.9 percent of the originat sainple,

probably the highest retention rate of any longitudinal survey after such a long time. Retention

rates by year for the NLSY are found in table 2.

The NLS program”was originalitybegun by

the Office of Manp6wer Policy, Ev@uation,.Nd

Research of the United States Department of Labor. This agency was combued_yitQ others to

form”the “Employment and Training Administration in which the NLS was atilnistered through

1986. The NLSY was started in order to evahrate

the Comprehensive Employment fid Trahring

Act. Over time the NLS developed into a more

generaJ purpose data set for the study of labor

market behavior. It was determined that it fit better fito the mission of rhe Bureau”ofLabor Shit.k-.

tics (BLS) and was transferred to BLS in Octo&r

. . 1986. In the 4 years BLS has overseen the NLS

program, we have been developing a mutti-dimensional approach toward regutar use of the data.3

. — ,. ..

] in 1990theNational Jnstiun. onAgingfundedaresurvey of

hecolder men.

2 ‘hemilimv”overiwnple wasdiscciiimcd afterhe 19S4

Sumey, Presentptansare10 dismndme imewietigtie

ccmomicaltv disadvamo?ed whiteoversamde-lhy were last

‘In ill&rating governmental uses of the NLS

data in the United States, I wiUfocus primarily on

uses of the NLSY because it is most similar to the

Anstkdian Longhudti Survey (ALS), for which

it setved ma model. The examples discussed in

this paper come fmm a variety of uses made by the

U.S. Goveiient. Some were requested to help

prepare specific legislation some were used as

generfl backgmnnd for a body of legislation some

were.special Government reports; and others were

part of our extramural research progmm. In each

example I hope to illustrate different uses of fhe

data within the U.S. Excluding the extramural research, most of the uses do not involve sopftisticated econometrics but provide insight into specific questions. I have attempted to choose examples which fully exploit the Iongitudmsl mture of

rk!edata.

I have chosen six different areas of research

to demonstrate use of the NLSY and discusssome

of the ftidings. These areas are recent minimum

wage legislation, wage paths of young people, the

transition fmm school to work, work and the family, training, and the effects of military experience

on post service success of low-aptitude recmits.

Each of these areas is described in a separate section and discusses one or more studies.

1.Minimum wage i~isiation

The minimum wage in the United States was

.misedto $3.35 in.January 1981. It was held fixed

from that time until 1989.. Several times over the

intervening years, there were attempts to increase

the minimum wage. At the same time, there were

also attempts to create a speciaJ subminimmn

wage for youth.”The latter was proposed under the

premise that this made young people more attractive to businesses.. After acquiring a job a. subm@imurn wage, the youth wontd “have a foot in

the door” and learn job skills whfch would altow

him or her to advance in the labor market.

AU of these attempts failed until a new law

was passed “h November 1989, arnendhtg the Fair

Labor Standards Act. This law provided for an inc–tise in the minimum wage to $3.80 beginning

April 1, 1990 and then another increase to $4.25

beginning April 1, 1991. In addition the law proifidedfor a “training” wage of $3.85 or 85 percent

of the urevailirw minimum wage for any worker

-..

interviewed in1990. ““ ‘-.i@der~0 years ~ld. This “training” wage”contdbe

3 Formoreinfonmti.m oritieNM wk’eys,& theNL.3-’“Daidfor up to 6 months under certain conditions.

Handbook(1990)orivtmsiI”.iL al. (1990).
In response to specific requests regarding this

legislation, the NLSY was used in two diffemrt

ways. Fhst, NLS staff attempted to examine the

question, “Do minimum wage jobs provide a

means of entry into the labor market, or are they

essentiatfy dead-end jobs?” although c~ss-sec-..

tionat data fmm the Current Popntation Survey

(CPS) can trlus how many people earn the minimum wage (or less), longitudinrd data are required to see whetier or not people get out of

those minimum wagejobs.

Beginning with the 1981 NLSY survey, Rspondents were placed into one of three groups:

Those who were not working at the time they were

surveyem those who were employed and earning

the minimum wage or Ioweu and those who were

employed and earning above the mirdmmn wage.

We then looked at flws among the three groups

one year, 2 years, 5 years, and 6..yearslater. Table

3 presents the resufts. After 6 years, 60 p5rcent of

those who had been earning the minimum wage or

less were earning abwe the minimum wage. Tire

average wage of dris group rose from $2.84 in

1981 to $7.31 in 1987, representirigan increase of

157 percc$rrtcompared to an increase of 65 percent

over the same period for the group initiafly earning

abo~e the minimum wage- The generrdconclusion

of this simple analysis is that those who enter the

labor market in minimum wage jobs are not condemned to remain there.

The second set of NLSY information provided to those preparing the new minimum wage

legislation related to the tikely effects of a pmposed “training” wage, hr carty consideration of

the bfl, it was proposed that a “training” wage be

adopted but onty apply to workers with less than a

specified amount of cumulative work experience.

Even though one could use retrospective questions

in a cross-section survey to ascertain total lifetime

work experience, more reliable information comes

from a longitudmrd survey, particrdarly one like

the NLSY which collects job information in a

work history format. Each job’s starting and ending date is colfected over time, giving us a fairly

precise measure of total number of weeks worked

over any length of drne. The NLSY was appropriate to use for this exercise because people

with small amounts of cumidative work experience rend to be young, .

Using the entire NLSY sample in each year

from 1981 to 1987,the total number of weeks ever

worked was calculated. This wastabulated for the

categories of 0-13 weeks, 14-26 weeks, and 27

weeks or more. The resrdts are presented in table.4

As you would expect, the numbers in the lowest

category fall over dme and those in the highest

category rise as these young men and women gain

work experience. The two different cutoffsshown

he~ (13 weeks and 26 weeks) were under

.-consideration. In 1981 when tie youths were 16-

23, 6,757,000 had less than 26 weeks of work U

experience with all employerx. By the time they

were 22-29 in 1987, onfy 641,000, about 2 ptrcent -

of the age group, stilf had less than 26 weeks of -

work experience. This is a surprisingly smalf

number given that it includes women who began

families and never entered the labor force.

One final consideration was to place an extra

condition on receiving the “training”wagti A person coufd never have worked at a single job for

more than 4 weeks . This efiminatcd a significant

portion of those with little work experience. In

fact, everyone with over 13 weeks of total work

experience had held at least one job for over four

weeks.

The final law passed with a “training” wage

but without any restrictions concerning past cumulative work experience, perhaps because few

individuals would have been included under such

a provision. Undoubtedly, enforcement of such a

provision would also be problematic.

Il. Wage paths of young people

Considerable attention has been given recently to the question of whether the income distribution is becoming more dkparate. The premise

is that most newly created jobs have been lowwage jobs. Most analysts arc realizing that questions.dealing wifh the income distribution require

study using longitudinal data, at least as a corn- --

plement to cross-sectionafor aggregate data.

At a panel discussion on the topic, “What Are

the Real Trends in Wages and Employment,”

Manser (1987) investigated a variety of issues using dtiferent BLS data. Of interest here is her use .-

of three differwrt NLS cohorts to compare earningsin the first 5 years out of school. She compared the men in the NLSY to those in the young .

men’scohort and the women in the NLSY to those

in the young women’scohort. The earnings measure was taken as of the week preeedmg the interview. ‘fIre sample was reshicted to those not in

school over the 5 years studied who wem employed at the time of each survey. The earnings

patha for men are shown in figure 1 and for

women in figure 2. The data for young men refer
to the period 1967-71, Wd for young women,

1969-73. The data “forthe NLSY refer to l~8@

84,4 In Lr@.hcases the yorrth from the laffXcohort

fared worse. The earnings paths appear nearly

parattel, especially for womew the earnings of the

. later cohort are always Mow the earnings of the

earlier cohorts. We cannot conclude whether cohorf size or generat business conditions have

caused the downward shift in the earrdngs profiles

Nor can we generahze to the entire career paths of

these individuals or to those who stay in school

longer. The resrdfs hew, however, are consistent

with findings from other data.

In a related example, the Ways and MeWs

committee. of the U..%House of Representatives

was preparing a variety of welfare legislation and

sought information fm”rn the NLSY as. background. NM staff examiired”the wage paths in me

early years out of school (1980-1984) for the

NLSY sample by sex and graduation status. To be

included in the sample, the irtdividtratcould not be

in school during the yeam..st@@ and had to be

employed during the survey week in each year.

Tables 5a and 5b show the results for nominat and

rest weekly wages; fables 5.c and 5d show the

same for nominal and rest hourly wages. The

sample restrictions reduce sample sizes so that

caution must be used in brterpredng the resntts,

especially for femate dropouis. -”

Looking at table 5a_showingnominat weekly

wages, we see that mates always earn more than

females and graduates earn more “than dropouts

within each sex. The interesting finding is the

slope of the wage paths. Taking percent ,chr!nges

from 1980 to 1984””for each group shows that

wages grew sidlarly for mate and femate graduates (51.9 percent imd 509”@crmt, KX@ctively).

Wages grew more slowly for dropours but about

the same for each sex (38.8 percent for m~e

dropouts, 36.8 percent for fernsie dropours).s

There was no gain in female wages relative to

men, a result afso obtained in_tte _Marrsers.~dy.

-. Graduating from high school made an important

difference both in tetitts of stardng wages and

wage growth implying that completion of high

school creates”long term effects in relative wages.

Hourly wages show a simil?r story except

that femate dropouts lag behind all other groups

---

4 Earnings..= deflated using Lb. CE’I-Uwhichisknownto

havesomebiasm rbeMsmricatseries,

5 ~ M SK,TSCXCZptfematedmpu~,W%wmsem~okmicatty. Ihnvechosmtotreattie1983numberforfemalecbopxts

intable5aasanaberr&mcausalbythesnmlfsmnptesize

.&nsiderably. The other three groups show incfiases in rest hourly wages of between 12.1 and

19.2 percent over the period but femate dropouts

actuatly show a 3-percerrtdecline;

111.TransitIon from school to work

tn compiling part of the material on wage

paths described above, NLS staff atso investigated

oflreraspects of youth leaving school for the Ways

and Means committee. In partictdar, we presented

information on labor force characteristics in the

first.4 years after leaving school. Tables 6a, 6b,

and 7 were included to show various averages and

duratiops of employment and unemployment for

school leavers by graduation status, race, and sex.

Consider hig!r school dropouts first. Blacks

both mates and fernates had the highest average of

weeks unemployed and the lowest average of

weeks employed regardless of the duration since

leaving school (except for Hispanic mates in the

second year). Hkpanic and white males were

fairly shnitar with respect to both unemployment

and employment weeks. Hispanic females were

simitar to white females in terms of unemployment weeks, but they had significantly fewer

wee&i worked. Table 6b cmnutates these first 4

yearn since leaving school. It showsthat in the 4-

year period black mate high school dropouts spent

19.5 percent of those weeks unemployed, 54.5

“psrcentemployed, and 26.0”percent out of the latmr force. Compared wifh black mates, Hispanic

mates were less likely to be. unemployed (16.5

percent), more fikely to be employed (62.2 percent), and lesS_~gly to be out of the labor force

(21.3 percent).- white mates spent the least rime

unemployed (15.5 percent), the most time employed (68.8 percent), and the least time out of the

labor force (15.7 percent).

Black females dfiered even more dramatically from their Hispanic and white eocmterparts.

They were more than twice as tikely to experience

unemployment and worked a considerably smaller

percentage”of weeks than Hispanic or white femates.

As seen in both tables 6a and 6b, high school

graduates had a better employment experience.c

Looking at table 6a first, male high school graduates had about one-half as many unemployment

weeks as high school dropouts. Mate graduates

e ~ese areindividuals whoWmimredtheireducatimafter

SmduatisfrcmhighWhd.
also worked about 10more weekaeach year than

dropouts. Bofh the unemployment and employment differences between graduates and dmpmua

applied similarly to within-race comparisons. Female high school graduates also had less mremployment than dropouts, but the difference was

smallerthan for mates. The difference in weeks

worked, however, was substantiallylarger than for

males by over 15 weeks. Again, qualitatively the

results are similar witbin the race groups.

Table 611reveals a larger race differenfisJfor

high school graduates than for dropouts, particularly for males. Black mate graduates spent 1.7

times as many weeks unemployed as did white

mate graduates and 1.9 rimes as many weeks as

did Hispanic maIe graduates. They atso spent

considerably more time out of the labor force and

worked fewer weeka. Hispanic and white male

graduates had looked similar cumulative employment and unemployment experience. Black fe--

mate graduates also expxienced much greater unemployment, worked fewer weeka and spent more

time out of the labor force fh~ either Hkpanics or

whites. Unlike males, Hkpanic female graduates

worked less, had less unemployment, and were out

of the labor force more than white femate graduates.

Table 7 shows the distribution of mrerrrployment weeks for the same 4-year pxiod by graduation status and sex. In the first year after leaving

school, 63.2 percent of the males and 57.5 percent

of the females experienced no unemployment. An

additional 12.g percent of the males and 15.9 percent of the femates had between 1 and 4 weeks of

unemployment. Approximately three-quarters of

these youth thus had less than 4 weeks of

unemployment during the first year after ~eaving

school. At the other exlreme, 2.3 percent of the

mates and 1.9 percent of “thefemafes were unemployed moat of the year, between 40 and 52

weeks. The d~tribution remains relatively stable

in each of the subsequent 3 years,7 although for

femates there is a marked increase in the proportion experiencing no unemployment.s

The distribution of unemployment differs

significantly by high school graduation status. In

7 %S is founddespiretirebusiness cyclec+mngeswhich

cccurred duringtie19S0-1984Pried,

8T’&isnoIduetowomenspm+ingmoretieoutofrhelaker

forceandregiuaingztmweeksofunemploymemSummingthe

columnsinavemgeweeksunemployed andavengeweeksemployed

intable6ato@ averageIotalweeksintieInk forceshowsavery

stable rateovertie4yearPried.

the first year after leaving school, only 44.7 percent of mate high school dropnuts had zem weeks

of unemployment this figure was 68.5 percent for

the mate graduates. The difference wassomewhat

less pronounced for females, 46.8 percent for

dropouts and 60.4 percent for graduates. The dif- .

ference at the other extreme is observed onty for -

mates. Of the mate dropmm 4.6 percent were un- “ =

employed over 40 we&a, but of the mate gradu- -

ates onty 1.6 percent experienced such sustained .-

unemployment. For fematw the figures for

dropouts and graduates arc ahrtost identical (1.7

perwmtand 1.6 percent, respectively). As with the

overall population, the distribution is relatively

stable for dropouts and for graduates over the entire 4-year period. Ftiafes with zero reemployment weeks, however,increasrxirelatively for both

dropnuts and graduates.

In a study funded by BLS, Csmeroz Gntz,

and MaCurdy (1989) looked at the effects of

unemployment insurance benefits on the

unemployment of youths. Using the NLSY they

found that etigibllity of young men for

unemployment insurance increases with both age

and education and that use of unemployment

insurance increases with age but not necessarily

with more education. For young women eligibility

increases with education but not with respect to

age. Use patterns for young women are sirniiar to

tlrose of young men except for *e lowest

education group in which there is no apparent

relationship between the insured rate and age.

Overall, men have higher eligibtity and rates of

use than wnyten.

Using a variety of measures, Camemn, et. al.

esdrnate the effects rhat increasing the amount of V

weekty benefits and the number of weeka of eligibility has on the lengths of nonemployment apetls

and the amount of these apettsreported as spent in

unemployment. By looking across States, they

were able to use the differences in State Unemployment Insurance @JI)programs to look at behavioral differences. . .

Their findings imply that weekly benefit

amounts paid by programs have essentklly no effect on the durations of nonemployment spells. .-

On the ofher hand, the number of weeks of

eligibility offered does increase the length of time

spent between jobs. This effect, though, is not

uniform. The number of weeks of eligibility does

not inftuence the sborf durations of either nonemployment or unemployment, but it leads to an expansion of the longer duratiom with the Iongest

durations b@ngstretched out the most. In pardc-
-.

ulsr, an extension of weeks of.e~gibility from 26

to 39.generates only abwt a l-week Iengrhening

of unemployment duration for the median individual, but unemployment lengthens”by as much as 8

weeks for those persofis eX@kii6hig the 10ll&St

durations. --

The results for men and women are similar

with ordy slight exception Although weelw of

eligibility matter for both sexes, changes in the

amount of benefits have slight effect for men but

no effect for women. In other res~cts UI pelicies

influence “women’s experience between jobs in

nonemployment and unemployment in the same

pattern as with men, although magnitudes of tie

various effects differ.

IV. Work and the family

The relations~p between work and family

life has becomeo-neof the most important areas in

current policy planning. Considerable attention

has been given lately in the United States to a variety of legislation cleating with parental leave,

child care, and related subjects. It is therefore important to understand the behavior of women in

coordinating their work and family tife and how

policy changes wilf inffuence that behavior. To

better understand the issues involved, BLS has

sponsored several studies.

In one of the studies, Leibwitz, Waite, and

others (see Lcibowitz, Klermsn, Waite, and Wittsberger (1989b) and also_Desai, Leibowitz, and

Waite (1989)) invesrigatcd young women’s labor

force participation and employment during pregnancy “andfollowing birth. The objective of this

study was to analyze changes in women’s labor

force behavior during pregnancy and after the

birth of a first child, and in pardcuhm to examine

the timing of labor-force exit during pregnancy

the timing of return to work after childbirth; and

the effects of age, education, occupation, race, ahd

ethnicity on departure and reentry times. Because

the NLSY began as a sample of tcemgerx, the

mothers tend to be young and theteforv, the resuhs

may not be representative of women who do not

have their first child until later.

Using the NLSY, the authors co”tidcombine

the fertility histories with the work histories to

determine when a women left work during her

pregnancy and when she returned to work after

giving birth. Table 8 shows the unweigbted combinations of when young mothers left work and

when they returned to work. The rows of this

_table show the week during pregnancy that the

woman left work, divided by trimester. The last

row for the 39tlr week represents women who

work up until the week of the delivery. Most

women who left early during”the pregnancy did

not rrxum to work for a considerable period of

time, if ever. Over 28 percent of all women in the

sample did not leave work before the week of delivery. Women who worked this far into their

pregnancy tended to return to work very quickly.

Table 8 indicates that approximately one-fifth

of the sample took no more than l-week off from

work. This does not seem realistic and is probably

a resrdt of the way the questions are worded in the

survey. As in all labor force surveys, time on paid

vacation or paid sick leave is considered as time

employed, therefore, a woman is employed

though she is actually on maternity Ieave.p J.n

1983, the N“SY investigated this specifically.

Each Wornti was asked whether her employer offi~d maternity leave, the date at which she begart

maternity leave, and the age of her child when she

returned to work. Using these questions, L&.

tmwitz, et.al. were able to look at women who had

births in 1983 and appear to have worked continuously tbrougb childbirth. Only one-quarter of

these women report that their materrdty leave began wfrfrdelivery. Thrcequarrers of these women

appew to have worked into the week of delivery,

yet obviously began their maternity leave earlier.

Similm results are fo~d for work after delfvery in the 1983data. Only 9 percent of the women

who appared to be employed in the week before

defivery acrualry returned to work in tire week

following d~lvery as compared to nearly 74 percent found by merging the event histories. Nevertheless, the 1983 data show that these wornen do

return to work relatively quickly. Eighty-two percent returned to work within the first 3 months.

Using multivsriate hazard models, Leibowitz,

et. al. found that op@r@ty costs play the

stngest role in predicting labor supply near

childbirth. Women with more education and

higher wages remain in the labor force later into

pregnancy and return to work sooner after delivery.”

Women who were not married at the time of

the childs birth were more likely to withdraw from

rhe labor force in the first 6 months of pregnancy

and to return to employment more slowly after the

9Betig h 19SS,respondents wereaskedaboutpaidleave

rakembecauseofthebinhof a child. his willpmnit sepamingdme

atworkfmmdrneonpaidleaveforemployedwomen.
birth. This may be due to the receipt of Ald to

Famities with Dependent Children (AFDC).

AFDC acts as an income supportfor single women

in all States but for married women in only about

half of rhe States.

The age of the woman affects her withdrawal

from and return to tie labor force. For example,

teenagers are significantly less tikely to work until

fhe end of pregnancy or to return to work immediately after childbhth. Women age 20-27 are less

tikely to withdraw from the labor force in @e last

trimester of pregnancy and are more likely to return to work shortfy after giving birth.

Education plays an important role in this

story. College education reduces the chances that

work committed women leave theirjob in the first

two trimesters of pregnancy, but has m such effect

for women with low work-commitment.l” College-educated women are no more likely to return

to work during rhe first quarter after giving birffr

than are women with a high school diploma however, the college women are more Iiiely to Wum

to work in the 3-to 1l-month period after the bhth

of their firat child (given that they did not return

earlier).

M generat, the various covariates have effects

only in the first two timesters. By the third

trimester, only the wage effect is statistically significant. This atso holds true after the dehery.

Women who remain at work into the rhird

trimester are primarily influenced by their wage.

In the hazard model, Lelmwitz, et. al. again

find a strong correlation between late withdrawal

in pregnancy and early reentry after bhfh, controlling for obsetyable factors. This holds true even

when excluding the women who appear to have

continuous employment at the time of cbildblrrh.

To anafyze how occupational characteristics

influence labor supply during pregnancy and after

delivery, Desai, et. al. linked occupational characteristics“derivedfrom the Current Population Survey and the Dictionary of Occupational Thles to

the NLSY dati. Holding pemonal characteristics

constant, they found that women in jobs which require less training or mow physical strength were

more likely to quit work early. Women whose

occupations require higher levels of specific vocational preparation were more tiiely to continue

10 wok -UII.IIL is demmined by he answer 10 rhe

qu.sticuhWha would you Eke 10he doing when you ~ 35? 11.,e

whoreqmndal“workins” we~ deemedm havea hishdegreeof

workcommiunen<thosewhore.spcmded odm’wise, aIowdegreeof

workCCmmlimlml.

working during the first two trimesters of pregnancy and to return to work within the first quarter

following the birth.

Attributes of the occupation, such as larger

numbers of part-year workers and greater percentages of co-workers who are mothers, lead women *

with low work commitment to return to work earlier.]1 Women who are more work-oriented are

not infhrenced by these factors; wage rates and ed- ==-

ucation are the primary factors predicting their

timing for return to work.

In another study funded by BLS, Falaris and

Peters (1989) examined whether women are infhrenced in their choice of timing of their first child

by the size of their cohort. Their theoretical approach allows women to choose when to have

their firat birth and when to return to work to mitigate fire effects of the size of their own cohort on

their wage profiles. If large cohorts depress

wages, then a woman born into such a cohort can

choose to have a child early and enter the labor

force later with a smafler cohort. If she is born

into a smaff cohort, she is likely to work early in

her adrdt life, having her first child later. This sort

of adjumnent allows them to enter the labor market when wage profiles are to their greatest advantage.

Falaris and Peters estimated hazard rate models using data from the mature women, young

women, and Nf.SY. Pooling these three data sets

provided them with information on women born

during different phases of the demogmphic cycle

1918-37, 1942-53, and 1957-64. These periods

represent a baby boom, bust, and boom, respectively. Falans and Peters also contrcdktt for the

woman’s choice of schooling, which also can be

affected by the size of the cohort. frrsupport of

their theory, women who were born during the upswing of the demographic cycle were found to

have their tirat bh’fhearlier and to return to work

more quickly than wordd women who were born

during the downswing of tbe demographic cycle.

As part of a bigger study on support for ado- , .

leacent mothers, the Congressional Budget Office

(CBO) (1990), an agency of the U.S. Congress,

used the NLSY to study welfare patterns among -

adolescent mothers, focusing on the likelihood that

adolescent mothers will start and stop receiving

.

l] ~rn, ~m~ ~d w~e -= &n~&tics d fi~

cwwtion. 11wotid b more f+pmpnate10methemributes oftie

jcbwithagivenemployer. Akhmgh these data.= not available in

the NL3Y. il is not dear lhal ocqxzion is a usefid measure.
AFDCIZbmefits within the first few years after

giving birth .~ey attempted to investigate only

fhe characteristics associated with different patterns of welfare receipt, not the causes of diffeient

welfare experiences. Some of these characteristics

are no doubt correlated with the causes; but it is

important not to interpret them in this way. Because “orrtyteenagers are examined in this study,

the resrdrsmay not generahze to older mothers.

Table 9 from dre CBO report shows”the crrmulative entrance rates onto AFDC accounting for

tfte mother’s marital status, age at first birth, and

race. Twenty-eight percent of all adolescent

mothers receive AFDC.within the first year after

giving birth. Nearly one-half am receiving it

within 5 years after giving birth. Mothers aged

15-17 receive AFDC with much higher frequency

than mothers aged 18-19, and blacks receive

AFDC widr much bigher frequency than whites

however, it is marital statusthat makes the key difference. Unmarried women are three times as

liiely to receive AFDC as married women.13

When age and race are interactedwith marital

status,the differencesforthesetwo variablesdiminishor even disappear.Thisreflectsthedifferentmarriagepatternsin thesegroups.In particu-.

lar,young blackmothersaremuch lesslikelytobe

married than young white .mO@S. (11.qrcent

compared to67 percent);

Table 10 shows the cmnutative exit rates

from AFDC for teenage rnodrers by age of the

mother at first birth, marital status, and race. In

this table an exit is considered to be a period of 3

months or more not receiving -AFQC.Periods of 1

or 2 months were not considered as exits because

these often resntt simply tium dte family’sfailure

to comply with program rrdes. The sample used in

this table includes ordy those receiving AFDC and

thus reflects a smalter sample size than table 9.

Because some-of the cdl sizes get small, these

numbers shoutd be. considered more indicative

than precise.

Nearly one-drird of alt adolescent mothers

leave AFDC within 6 months after”they fht receive it three-quarters leave within 4 years. once

again rhepattem differs by age, marital StZtUS,~d

race. mrried women are more Iiiely tO exit_

AFDC than rinmarried womew mothers 18-19

more likely than those 15-IT, and whites more

]z A*~.F~~ titiDependent Chibhen (AFIX2 is one of

tie larsest we!fare programs in Lb. Unired Srares.

13 Mrnd .mm me eligibk for AFDC in appMXilllal~Y ‘XdY

half of rhe Sines.. .

likely d’tanblacks. In this case marital status does

not ftrltymitigate the differences forage or race.

‘ffteit are two possible explanations of why

“oldef teenage mothers tend to leave AFDC more

quickly. One is that fhey are mo~ tikely to find

jobs because they are older. The younger mothers

would find jobs as they grow older, inducing

longer welfare spells. The second explanation is

the difference in education leading to the difference in ability to become se~-sufficient. In the

NLSY sample used here, 21 percent of younger

single teenage mothers getdng AFDC had graduated fmm high school or had GEDs within

roughly 2 years after the birth of the child, compa@ with 58 percent of the group who were 18-

19when @eybecame mothers.

The racial difference appears to be somewhat

affected by the different marriage patterns of

blacks.snd.wbite$ however, drese patterns do not

explain the difference entirely. Artother possible

factor accounting for this difference may be the

birth of additional children. Ahhough white adolescent mothe~ wem.rnow likely than black adolescent modrersto have addltiorrsl chitdren within

a few years after first giving birth, they were also

more likely to get married before having those

chitdren. Among young mothers who remained

single during the survey, blacks were twice as

tikely as whites to have second children within 4

years after first giving bhth.

Table 11 shows rates of receipt of AFDC for

adolescent mothers in each l-year period for 4

years after giving birth. This eombmes the patterns of entry and exit fmm AFDC examined

previously. Though some differences appear by

age .arrdram, these are mosdy accounted for by

marital status.

There is a fairly constant proportion of adolescent mothers receiving AFDC over time. The

welfare population, however, is not static. These

is considerable mobUityonto and off of AFDC re-

“@ts. During any given period many young mothers leave the program and others enter or reenter.

Despite this mobfity, the proportion on -c

remains fairly stable. This result demonstrates the

greater usefltttess of lorrgimdinsl data as compared to cross-sectional data in understanding welfare receipt. A repeated cress-section showing

AFDC would reveat this mugfrly constant proportion and wotdd mask. the underlying movements

onto and off the program.

V. Training
Training the nation’s labor force has atways

been ar-importsnt issue. Recently it has risen to

the forefront as critical. Many people believe that

the entrants into today’s workforce are not

equipped with the skilfsrequired. BLS has formed

a task force m investigate what we know about

training and to make. recommendations on what

additional data are needed to better understand the

role training plays in the development of the workforce. The NLSY contributed to fhis task force in

several ways. The NLSY has been used to examine sepmately severat issues dealhg with private

sector training.

BLS has funded severat studies which are

currently in progress. One study by Lynch (1990)

has produced some interesting reaufts. She uses

the NLSY to anafyxcthe determinants of the probability of receiving different types of private sector

training and the effect of training on the wages and

wage growth of young workers who are not college graduates. Issues addressed _ticludethe relative importance of trainihg and tenure for wage

determination and the rate of return to companyprovided training programs compared to the rate

of”retumto training received ourside the firm fmm

private sector vendors and schoofirrg. Lynch also

investigated the Portabtlty of “company training

from one employer to another and the existence of

dMferentials in the rctmns to training by union

status, race, and sex.

Other studies of training have suffered from a

variety of data limitations. Some of these are the

lack of complete employment, training, and

schooling histories on individuals in the various

suwey~ difficulties in measuring the amount of

private sector training the individual received; and

dlfticidties in dktinguishing firm-specific from

generat training. Through 1987, the NLSY ordy

aLtowedfor measuring formal spetls of training of

1 month or moreld, but it does altow reconstmctio.nof individuals’ entire format training histories

(for programs of 1 month or more) from the moment they enter the labor market, including the occurrence and duration of each training apett. The

NLSY data arc also useful in distinguishing

among diffeieni sour@” of private secto~-tiaining

including company-provided training, training

from private sector vendom, and apprenticeships.

Lynch finds that private sector training plays

a significant role in determining wages and wage

growth for young workers in the United _States. —.

14~eg~ 19ss,M -g pmgmrn~ofinydumtimwere

c.mmred.

who do not gradnate from cottege (approximately

70 percent). Women and nonwhites are much less

tikely to receive training from their employer, either in a company training program or in an apprenticeship program. Workers with higher educational attainment have a trigher probability of -

acquiring off-the-job training and apprenticeships,

but there is lhde effect of post secondary school

education on receipt of firm provided, on-the-job “

mablirlg.

In estimating of the effect of training on

wagea, Lynch finds that all training increases

wages significantly, including off-the-job trabdng

from proprietary institutions. The impact of the

“E”ainingvariables is larger than the impact of

tenure on wages. Even though tenure stitf plays a

rule in wage determination, this implies that equa-

“fiofi estimated without training variables are allowing th~ tenure variable to capture some of the

effect of training.

Fitially,Lynch provides evidence that on-dtejob training fnmisbed by an employer is usnafly

specific to the firm. There appears to be no effect

of training “witha previous employer on current

wages.”

Leigh (1989), in a study for the U.S. General

Accounting Office, used the NLSY to examine

similar issues dealing with training. He atso investigated who received dlffercnt kinds of training

and how these types of training affected wages and

-s

Table 12 shows his unweighed tabulations of

receipt of at least one training program from 1979-

86, &aggregated by type of tr&ing, ~ce, and

sex. Frum this table we see that more people obtain training in pmpnetary schoolsthan fmm other

sources, ~d only a small percentage w in apprenticeship programs. Females of alt races receive more training fmm proprietary schools than

males, whereasthe differences by sex are not substantial for any orhersource. The patterns by race

are mixed. Whites generafly get more training of

any type than either blacks or f-WpaniCSexcept .-

that Hispanic mafes receive more training from

pmpnetary schools than either white or black

males.

BLS produced data for its task force on

maining which focused exclusively on company

training programs. These are presented as tables

13a-c. The data in these tables differ from Leigh’s

in that they come from different sample restrictions and are weighted, yielding somewhat higher

estimates of the amount of training received for all

groups except Hkpanic femates. These tables
.

show that over 9 percent of the sample completed

at least one company training program in the

1979-86 @nod. “Consistent with Leigh’s finding,

the BLS data show that whites receive the most

company training, foflowed by blacks and Hkpanics. Also consistent are rhe resrdts by sex.

Men receive more Cornprmy”training thao women

forall races. =

These tables atso present breakdownsfor education and job for which @wI. Resrdts for the

former show receipt of company training increases

manotoniixtty with amount of education for both

sexes. In examining job for which trained, different patterns emerge for men versus ivomen. “Clericalpositions are the jobs for which most trained

overall, and this type of trahdng is much more

prevalent for women. The second highest for

women is professional/tecfrnicat. Managerial and

skitled manual occupations are significantly lower.

Men, on the other hand, are more evenly represented in each type of rraining with professionaVtecfmicatand skitled manuat more prevafent

than managerial and clericaf. The highest category

is “other,”a conglomeration of aft occupational

categories not represented by the four shown.

In addition to table 12 presented above,Leigh

uses more.sophisticated econometric proceduresto

study various trainhig issues. He finds some Rsuhs that contrast with the study by Lynch. Consistent with Lynch (and orher studies), he finds

that women are less likely than men to gain access

to apprenticeship programs and more likely to

participate in proprietary school programs. In

contrast, however, he notes women are no less

likely than men to participate in company-sponsored training. He also does not find that Hkpanics or blacks are less likely to receive training.

Leigh’sstudy brings to a tight a correlation of

education with acquisition of training similar to

that discovered by Lunch but stronger. More educated workers are found to be more tikely to receive training from ail types of providers. At-

. . though Lynch found this relationship for company-provided training to “apply only to high

school graduates, Leigh finds the likelihood of receiving this type of training to increase even further with additional schooling. This is consistent

with the BLS data.

In his investigation of how training affects

wages and earnings, Leigh shows. Kat. cWmpW

training and apprenticeships both lead to an increase wages and earnings. Proprietary schools

appear to increase earnings but_not wages (Lynch

found an increase for wages). Leigh suggests that

this type of training works more to increase employment stabifitythan to raise wages.

The differences In findings for these two

studies cm”.be explained primarity by two differences in the measures and data used. First, Leigh

uses dummy variables for the presence of each

type of rrair@g received, and Lynch uses the actii~ number of weeks spent in each type of training. Second, Leigh uses_data through 1987, but

Lynch onty uses data through 1983. Because of

the ages crfthe sample at that time (18-25),the cell

sises for,coltege graduates are smalt. As a resutt,

Lynch does not include coflege graduates in her

study. Table 13a shows a strong correlation between the amount of education and the receipt of

company training. IOfact, adding additional years

of data to.Lynch’s data set and including college

graduatesmake her rcsutts more similar to Leigh’s.

The oriy exception is the effect of proprietary

training on wages which Lynch sdfl finds to be

strongly positive. More investigation woutd be

needed to understand why these results differ.

VI. Effects of militsry experience

The dectine in size of the cohorts reaching the

age of eligibithy for mititary service has created

considerable pressure to fill military personnel requirements by accepting lower-aptitude recruits.

“hr addition, there have been suggestions that the

Department of Defense (DOD) might help train

tomorrow’sworkforce by adrtWirrg disadvantaged

and low aptitude youth. Providing these youth

with training and discipline, it is suggested, wilf

Mter enable them to participate in the labor force.

hr a 1985 study sponsored by the Department

of DefeWe, the Human Resources Research Orgarrixation (HmnRRO) (1985) examined the demographic differences between low aptitude military

and nonmilitary youth. The DOD foltowed up on

this study by again sponsoring HmnRRO (1989)to

study the effects of military experience on the

post-service lives of low aptitude recruits.

The Armed Seiwices Vocational Aptitude

Battery (ASVAB) is administered to alt recruits

hefore entering military service. A subcomponent

of this exam is the Armed Forces Qu&ication

Test”(AFQT), w~-ch comprises verbat and mathematical sections. The percentiles are grouped into

AFQT categories I-V (with category IIt divided

into two parts, 111Aand IIIB). Category I is the

highest, covering tie 93rd-99th percentiles. Category V is the lowest, including the Lst-9th per-
centiles. Each of ffreAnned Services detemtines

ita own stamdmds. In general, indlviduids within

categories I-IIIA (50 percent and above) are actively sought. Category IV recmits are accepted

orrty sparingly (limits are actuaJly placed on their

enlistment), and those in category V are excluded

hy law.

Onty twice has the mitifary accepted a large

number of category IV recruits (l Ofh-30fhpercentiles). The first was in the 1960s during Project 100,000. In response to Lyndon Johnson’s

“War on Poverty” and because of the increasing

personnel requirements of the Vlemanr War, Project100,~” was laficfied.”-ffs prirnaiy goal was

to provide a means of upward mobility for the economically and educationally dkadvantaged by

admitting annually 100,000 low-aptitude and

medically remedkalmen into the military . More

than 320,01XImen enlisted under Project 100,000.

These men had scored in AFQTcatego~”IV.

The second time low-aptitude men were admitted into the Armed Forces in large numbers

came in the period fmm January 1976 “tiough

September 1980. Due to a misc&]bration in the

newly designed ASVAB exam, there was an inflating of scores for lower ability recruits. Many

individuals thought to be of average aptitude were,

in fact., iir catego~ IV. By the time the errors

were detected and new, corrected forms of the

ASVAB were introduced in October 1980, over

300,000 category IV’s had been incorrectly admitted into the military. LMke Project 100,OCH3

this more closely resembles a natural experiment.

After discovering the errors in the ASVAB

norms, the Deparunent of Defense needed a large,

nationtiy representative sample of military-aged.

youth to whom they could administer rite test to

create new norms. In the second wave of the

NLSY (1980), DOD funded “admbristering the

ASVAB to the entire NLSY sample (15-23 years

old in 1980). Respondents retiived $25 for their

participation, and local testing sitea were established. Approximately 94 percent of ““thetotal

baseline sample completed the ASVAB, providing

DOD the sample it required to renomr the test.

The results of that effort created the norms that

have been used by the Armed Services for fhe last

decade.ls

To determine the effect of sewing in the military, HrnnRRO located and surveyed samples of

the Project 100,000 youth “irtd the low-aptitude

Is~ dfi~, IZSW&~hnv.belledsmattYbythepresence

ofasmndardized IQLyFmeasurefornearlyticentire NLSYsnmple.

youth admitted during the period of use of the

fatity ASVAB. The NLS young men were used

as a comparison group for the Project lCO,OW

men of the 1960s, and the NLSY was used for

comparison with the gruup adfnhred in the late

1970s. Weighting schemes were derived to ac- -

count for demographic differem%sin the two vetemn samples and the two NLS nonveteran samples. Anatyses were carried out to determine if “-

having served in the military provided the veterans

advantages over their nonveterarr low aptitude

counterparts. Aptitude for the NLS young men

was determined by using school records and creating percen-tiesfor the respondents.

The comparison of the Project lfM,lXKlveterans, many of whom likely served in Vlemam,

with the nonveterans of the NLS young men

showed that these low-aptitude veterans did not

f~ better than their nonvetemfr munterparfs.

Those who never served in the military appear to

be better off in terms of employment status, educational attainment, and income. Veterans were

more likely to be unemployed and to have an average level of education sigrdficantfy below the

nonvetemns. Income dfiefences knween tJretwo

“groups showed nonveterans wifh irmmes of

$5000 to $7,020 higher, depending on the sources

included. Veterans were less liiely to be married

and mote Iiiely to be divorced than nonveterans.16

The lower-aptitude military group from the

1970s also did not fare belter than their nonveteran counterparts in the NLSY. fn this case, they

were similar in their employment status (see table

14), occupational category, and average income

(see table 15). The veterans had acquired significantly less forinal education, however. Veterans

had higher marriage rates than nonveterans but

also had higher divorce rates.

In conclusion, the HmnRRO report statex “Jn

terms of the cermd question of interest in this

study, therefore, the resrdfs are mrequivocaf.

These data provide no evidence to support the hypothesis that mifitary service offers a leg up’ to .-

low-aptitude and disadvantaged youth aa they seek

to overcome tJreir cognitive and skill deficits and

compete successfully in the civilian world.”

16F@& -t c1tiePmja 100,CCOvererans sewed in

Wimwm. ft is ma clear whm effects h service may have had cm

tie indiriduak Such an expsriencc may have .eSati the positive

effecti ofbemilitary
WI. Conclusion

~S paperhas demonsrratexiseveraldifferent.

ways inwhich the U:S. Goveriniient hfi used the

National Longinrdimd Surveys, focusing pnmsrily

on the youth cohort. These uses represent work on

a wide variety of topics. Although a large number

of findings have ken presented, the emphasis is

intended to be on the uses themselves.ney vary

from Srnill, quick-tumarormd tables presented in

support of specific legislation more complex ta

bles to provide backgrmrrtdmaterial for preparing

various Iegislatiom reports on specific topi~ and

long-term extramural studies on issues of eondnuing importance

The National Longitudinal Surveys have been

used extensively within and outside the govemmerrt. Over”time the data become even richer as

we follow people through different stages of the

life-cycle. Undoubtedly, the NLS will condrme to

prove valuable to researchers and policy-makers

aliie.
References

Cameron, A. Cohn, R. Mark Gritz and Thomas

MaCurdy. (1989) The Effects of Unemployment Compensation on the Unemployment of Youths. Chicago, II: NORC.

Final report submitted to the Department of

Labor, contractJ-9-J-7-0092.

Congress of “the United States, Congressional

Budget Office. (1990), “Servieea OfSUPPOrt

for AdolescentMothers.”

Desai, Sonalde, Arleen Leibowitz and Linda J.

Waite. (1989) “Women’sLabor For@ participation Before and After Their First Birtlx

The Effect ‘of Occupatiorraf characteristics

and Work Commitment.” Working Draft No.

WD-4460-NICHD/DOL. The Rand Corporation.

Falaris, Evengelos M. and H. Elizabeth Peters.

(1989) Responses of Fernale f.ubor Supply

and Fertiliy to the Demographic Cycle. FL

nal Report submitted to the Department of

Labor, grant E-9-J-8-009L

Laurence, Janice H., Jane G. Heisey, Barbara

Means, and Brian K. Waters. (1985).

Demographic Comparison @ Low-Aptitude

Military and Nonmilitmy Youth. Fbml report

No.W5-L Alexandria, VA.: Human Resourees Research Organization (HumRRO).

Laurence, Janice H., Peter F. Ramsbrager, and

Monica A. Gnbbcn. (1989). Effects of Miiitary Experience on the Past-Service Lives of

Luw-Aptitude Recruits: Project 100,000 and

the ASVAB Misnorming. ~tial reportNo. 89-

29. Alexandria, VA. Hmnan Resourees

Research Organization (HmnRRO).

Leibuwitz, Arfern and Linda Waite. (1989a)

Women>Employment During Pregnanq and

After Childbirth. Fd report submitted to

the Depamnent of Labor, eontraet J-9-J-7-

C091.

Leibowitz, Arleen, Jamb Klerman, Lti Waite,

and Christine WMberger. (1989b) “Women’s

Employment During Pregnaney and Following Birth.” Working Draft No. WD4451 -

DOL. The Rand Corporation.

IAgh, Duane E. (1989) What Kind of Training

Work’for Noncollege Bound Youth? Report

prepared for U.S. Generaf Accounting Office.

Lynch, Lisa M. (1990) “Private Sector Training

and the Earnings of Young Workers.” Unpublished paper. M. I.T.SloanSchool of Management.

Manser, Marilyn E. (1987) Remarks for a panel

on ‘What are the Real Trends in Wages and

Employment.” Unpublished paper. U.S. Bureau of Labor Statistics.

Manser, Marilyn, Michael Pergamit, and Wanda

Bland Peterson. (1990) “National Longitrsdinaf Surveyx Development and Uses”

Monthly f.abor Review (July).

NLS Handbook. (1991) Center for Human Resource.Research. The Ohio State University.

U.S. Bureau of Labor Statistics. (1990) “Education

and Training of American Workers.” Paper

prepared for the OECD National Experts

Gruup on Training statistics.
References

Cameron, A. Colin, R. Mark Grh.z and Thomas

MaCurdy. (1989) The Effecr.r of Unemployment Compensation on the Unemployment of Youths. Chicago, II: NORC.

FiriaI report submitted to the Department of

Labor, contract J-9-J-7-0C92.

Congress of the United Statea, Congr@sionaJ

Budget Office. (1990). “SeNices of “Suppmt

for Adolescent Mothera.”

Desai, Sonatde, Arleen Leibowitz and, Linda J.

Waite. (1989) “Wortien’sLabor ForcgPardcipation Before and After Ttteir First Birth:

The Effect of Occupational Characteristics

and Work Comrniiment.” Working Dr~ No.

WD-4460-NICHD/DOL. The Rand Corpo:

ration.

I+&@, Evefrgelos M. and 1+ Elizabeth pet?~.

(1989) Responses of Female Labor Supply

and Fertility to the Demographic Cycle. Finat Report submitted to the_Department of

Labor, grant E-9-J-8-CO$”I.

Laurence, Janice H., Jane G. Heisey, Barbara

Means, and Brian K. Watem. (1985).

Demographic Comparison of Low-Aptitude

Military and Nonmilitqy Youth. Hnat report

No.#85-l ~ Alexandria, VA.: Human Resources Research Organization (HmnRRO).

Laurence, Janice H., Peter F. Rarnsberger, and

Monica A. Gribben. (1989). Eflects of Military Expen”enceon the Past-Service Lives of

Low-Aptitude Recruits: Project 100,000 and

the ASVAB Misnorming. Firm!report No. 89-

29.”. Alexandria, VA. Human Resoumes

Research Organization (HrrrirRRO)..

Leitrowitz, Arleen and Linda Waite. (1989a)

Womenh Employment During Pregnancy and

After Childbirth. Final report submitted to

the Deparirrrent of Labor, Wtrtract J-9-J-7-

0091. ‘“

LMmwitz, Arleen, Jacob Klerrnan, Linda Waite,

and Christine Witsberger. (1989b) “Women’s

Employment Duiing Pregnancy and Following Birth.” Working Draft No. WD4451-

DOL. The Rand Corporation.

Leigh, Duwe E. (1989) What Kinak of Training

Wor// for Noncollege Bound Youth? Report

prepared for U.S. Generat Accounting OffIce.

Lfich, Lisa M. (1990) “Private Sector Training

and the Earnings of Young Workers.” Unpublished paper. M.I.T.Sloan School of Marragement.

Marrser,Marilyn E. (1987) Remarks for a panel

on “What are the Reaf Trends in Wages and

Employment.” Unpublished pap. U.S. Bureau of Labor Statistics.

Manser, Marilyn, Michael Pergamit, and Wanda

Bland Peterson. (1990) “Nationat Longitudinal Surveys: Development and Uses”

-MonthlyLabor Review (July).

NLS Handbook. (1991) center for Human Re-

““sorrreeResearch. The Ohio State University.

U.S. Bureau of Labor Statistics. (1990) “Education

and Training of American Workera.” Paper

prepared for Ore OECD National Experts

Group on Training Statistics.

-.

Table 2

InterviewSchedolesandRetentionRates NLSY

I NLSY Youth 14-12 on .lanuarr 1,1979 I

I I Ciw”IiinSamn 1e I MiIitarwSamn 1e I TotalSamD!e I

I I Type of I Retentinf I Retentio: I Retentio$ I

lYear I Interview I ‘rotaI Rate I ToM Rate I Total Rate 1

I I I I I

I I I I I I

11980 I Personal I 1C948 96.0 I 1193 93.21 12.141 95.7 I

I I I I I I

) 1981 I Personal 1 I 1(M3O 96.41 1195 93.4 I 12.195 96.1 1

I I I I i I

I I I I I I

~118 lPenalll 95,8 I 12,2 1

[ I I I I I

~1141 14 49 I 12 II

I I I I I I

J 1985 I Personal I 10708 93.9 I 18~ 92.5 I 10.89$ 93.9 [

I I I I I I

11986 I Personal 1 10472 91.8 I 183 91.1 I 10.655 91.8 I

1 I [ I I I

I1987 I TeIeuhone I 10306 90.4 I 179 89.1 I 10.485 90.3 I

I I I I 1 I

af I 10291 90.2 I 175 87,1 I 10.465 90.2 (

I I I I I

~ 10424 1.4 I 1 II 1.4 1

I I I I I I

~1 I 44

.

!Retentionmte is definedas thep&ent of thebaseyearresjmndentswithineachsample@ who were

interviewedin any givensurveyyear.

bAtotafof 201 militaryrqmodents were retainedfmm tfreoriginalmilitarysampleof 1,280.

. . cThe totalnumberof civilianandmiIitaryrespondentsin theNLSY atfheinitiationof the1985survey

was 11,607.

Source: Table b from NLS Handbook 1991
&
- %%f

, Jan. 1)

, 1981

1982

1983

1984

1985

1986

1987

Table 4

Number of Persons Age 14-21 as of January 1,1979

Age of Weeks Ever Worked Held Job

Sample

Never Held Job Total

t I Date 24 Weeks ? 4 Weeks

Members

16-23

17-24

18-25

19-26

20-27

21-28

22-29

0-13”

14-26

27 or more

0-13

14-26

27 or more

0-13

14-26

27 or more

0-13

14-26

27 or more

0-13

14-26”

27 or more

0-13

14-26

27 or more

0-13

14-26

27 or more

1,660,000

1,904,000

26,383,000

L223,000

1,405,000

28,822,000

913,000

1,030,000

30,198,000

639,000

777,000

31,142,000

452,000

521,000

31,827,000

325,000

349,000

32,246,000

257,000

2S7,000

32,500,000

3,193,000

--

--

1,692,000

--

--

l,ool,ocil

--

--

583,000

--

341,000

-.

220,000

--

--

97,000

--

--

4,853,000

1,904,000

26,383,000

1,914,000

1,030,000

30,198,000

1,222,000

777,000

31,142,000

793,000

521,000

31,827,000

545,000

349,000

32,246,000

354,000

287,000

32,500,000

*** Sourcti Na~onfl ~ngimdind Surveys of Labor Market Experience - Youtb Cohort, BU~U OfLabor

statistics.

.,
YBAR

1980

1981

1982

1983

1984

SAMPLE

SIZE

Table 5a

Nominal Average Wekly Uages for Youth*

Early Years Out of Efgh 8chool

By 8ex and Graduation Status

(’Jeighted)

MALE

GRADUATES

234.20

(86.06)

257.36

(100.83)

297.05

(119.67)

320.64

(124.59)

355.64

(136.59)

361

MALE

DROPOUTS

198.82

(72.23)

210.61

(77.31)

241.97

(90.99)

242.95

(87.02)

275.98

(92.75)

115

FEMALE

GRADUATES

156.76

(62.12)

185.38

(66.96)

207.89

(74.56)

224.29

(77.71)

236.56

(90.42)

374

FEMALE

DROPOUTS

114.69

(36.36)

128.05

(51.43J

152.01

(47.21)

215.02

(186.89)

156.86

(60.06)

29

*Notes: Wages are for the week in which the individual was surveyed.

Standard errors are in parentheses.

The ssmple includes (1) youths aged 14 to 21 at the time of the 1979

interview who were (a) not enrolled in school in 1979-1984; and (b) employed

during the survey week in each year 1979-1984; and (2) youths aged 15 to 22 ,.

at the time of the 19S0 interview who were (a) enrolled in school in 1979,

(b) not enrolled in school in 1980-1984, and (c) employed in the survey week .

in each year 1980-1984.

Source: Nat ional Longitudinal Survey of Labor Market Experience-Youth;

conducted by the Center for Human R6source Research at the

Ohio State University for the Bureau of Labor Statistic.
YEAR

1980

1981

1982

1983

1984

SAMPLE

SIZE

Table 5b

Real Average Weekly Wages for Youth*

Early Years Out of IKgh School

By Sex and Graduation Status

(I+?eighted)

(Deflated by Average Annual ‘.PI-U, 1967=100)

MALE

GRMUATES

94.89

94.48

102.75

lo7. f+5

114.32

361

MALE

DROPOUTS

80.56

77.32

83.70

81.42

8S.71

115

FEMALE

GRADUATES

63.52

68.05

71.91

75.16

76.04

374

FEMALE

DROPOUTS

46.47

47.01

52.58

72.06

50.42

29

●tJotes: Wages arid hours are for the week in which the individual was

surveyed.

The sample includes (1) youths aged 14 to 21 at the time of the 1979

interview who were (a) not enrolled in school in 1979-1984; and (b) employed

during the survey week in each year 1979-1984; and (2) youths aged 15 to 22

at the time of the 1980 interview who were (a) enrolled in school in 1979,

(b) not enrolled in school in 1980-1984, and (c) employed in the survey week

in each year 19 S0-1984.

Source: National Longitudinal Survey of Labor Market Experience-Youth;

conducted by the Center for Human Resource Research at the

Ohio State University for the Bureau of Labor Statistics.
—

—

—

s,

Table 5C

Nominal Average Iburly Wages for Youth*

Early Years Out of High School

By Sex and Graduation Status

(Weighted)

MALE MALE FEMALE

YZAR

FEMALE

GRADUATES DROPOUTS GRADUATES DROPOUTS

1980 5.64 4.68 4.17 3.30

(2.12) (1.45) (1.40) (0.91)

1981 6.29 5.00 4.8a 3.46

(2.53) (1.59) (1.60) (0.95)

1982 7.14 5.69 5.34 3.78

(2.78) (2.11) (1.79) (1.08)

1983 7.72 5.72 5.96 5.46

(2.97) (1.99) (2.24) (4.63)

1984 8.48 6.64 6.16 4.04

(3.79) (2.27) (2.19) (1.36)

MPLE 361 115 374 29

SIZE

*N6tea: Wages and hours are for the week in which the individual was

surveyed. Standard errors are in parentheses.

The sample includes (1) youths aged 14 to 21 at the time of the 1979

(b) not

in each

Source:

interview who were (a) not enrolied in school in 1979-1984; and (b) employed .

during the survey week in each year 1979-1984; and (2) youths aged 15 to 22

at the time of the 1980 interview who were (a) enrolled in school in 1979,

enrolled in school in 1980-1984, and (c) employed in the survey week .’

year 1980-1984.

National Longitudinal Survey of Labor Market Experience-Youth;

conducted by the Center for Human Resource Research at the

Ohio State University for the Bureau of Labor Statistics.

. .
YEAR

1980

1981

1982

1983

1984

SAMPLE

SIZE

Table 5d

Real Average Nnur.ly Wages for ‘Youth*

Esrly Years Out of High School

By 8ex and Graduation Status

(Weighted)

(Deflated by Average Annual CPI-U, 1967=100)

MALE

GRADUATES

2.29

2.31

2.47

2.59

2.73

361

MALE

DROPOUTS

1.90

1.84

1.97

1.92

2.13

115

FEMALE

GRADUATES

1.69

1.79

1.85

2.00

1.98

FEMALE

DROPOUTS

1.34

1.27

1.31

1.83

1.30

,

*Notes: Wages and hours are for the *e&k “in which the individual was

surveyed.

The sample includes (1) youths aged 14 to 21 at the time of the 1979

intervi~w who were (a)”not enrolied in school in 1979-1984; and (b) employed

during the survey week in each year 1979-1984; and (2) youths aged 15 to 22

at the time of the 1980 interview who were (a) enrolled in school in 1979,

(b) not enrolled in school in 1980-1984, and (c) employed in the survey week

in each year 1980-1984.

.,

Source: National Longitudinal Survey of Labor Market Experience-Youth;

conducted by the Center for Human Resource Research at the

Ohio” State University for the Bureau of Labor Statistics.
.

.
. . . .

-. ------
Table 6b

Percent of Ue@ks Suent Emlwed. IInemtowd. and Out-of-the-Labor Force in Flrat Four

Years After Leaving Schoo\ f& i6-19 ~ea; Oids Mho’ Left Schml Setueen 1977-1981 by

Race. Sex, and High Schoel Cqletion

Percent of Weeks Psrcent of Meets Percent of Weeks

Uneqloyed E8ployed Out of Labor Force

Kale Female Male Female Male Female

H.S. dropout

Hispanic 16.5 7.2 62.2 32.3 21.3 59.5

Black 19.5 17.2 5’4.5 18.6 26.0 64.2

Other 15. s 8.8 68.8 46.8 15.7 44.4

H.S. graduate

Hispanic 6.6 4.7 85.6 67.5 7.8 27.8

81ack 12.6 13.6 75.9 57.3 X1.5 29.1

Other 7.5 6.0 85.3 74.2 7.2 19.8

. .

SOURCE : Nat ional Longitudinal Survey of Labor Msrket Experience-Youth;

conducted by the Center for Human Resource Research at the

Ohio State University for the Bureau of Labor Statistics.
Table 7

Distribution of Unemplop?nt Ueeks in Each of the First Four Years Since Leaving School

for 16-19 Year Olds 16ho Left School, by Sex and by High ScbooI Graduation Status

(weighted)

I

Weeks of Unemployment

10 ,.4 ,-,3 ,4-26 *,3, 40-52

All

T%st yier

Male 63.2 12.8 10.4 7.8 3.5 2.3

Female 57.5 15.9 15.3 7.1 2.4 1.9

Second year

Male 62.4 12.1 12.0 6.5 4.8 2.2

Female 60.5 14.7 14.7 6.3 2.4 1.3

.

Thirdyear

Male 62.2 9.5 11.4 10.0 3.8 3.1

Female 69.0 13.1 9.8 5.3 1.4 1.4

Fourthyear

Male 65.1 8.9 11.5 9.2 2.4 2.7

Female 69.1 11.8 10.1 5.1 2.5 1.7

11.S.dropouts

Firstyear -

Male 44.7 16.6 14.8 12.3 7.0 4.6

Female 46.8 19.2 17.3 9.5 2.6 1.7

Secondyear

Male 50.2 10.4 18.4 6.9 8.1 6.0

Female 50.2 16.2 21.5 6.6 4.0 1.5

Third year

Male 47.5 11.0 16.0 15.2 6.4 3.9

Female 66.9 16.3 15.8 7.3 2.3 1.3

Fourthyear

Hale 49.2 18.8 12.6 15.7 6.0

Female 56.5 13:; 14.9 8.2 4.8 1.7

(continued .on naxc page)
Table 7 (continued)

I

Weeks of UnsRpI op?nt

o 1-4 5-13 14-26 27-39 40-52

KS. graduates

First year

Male 68.5 11.7 9.1 6.5 2.5 1.6

Fsnale 60.4 14.2 14.8 6.4 2.3 1.9

Second year

Male 67.1 11.4 10.2 6.4 3.8 1.1

Female 63.3 ,14.3 12.8 6.3 2.0 1.3

Thirdyear

Male “66.3 9.1” 10.1 8.5 3.1 2.9

Female 72.3 12.2 8.1 4.7 1.2 1.4

Fourthyear

Male 69.7 9.3 9.5 8.3 1.5 1.9

Female 72.5 11.2 8.8 3.9 1.7 1.7

Sample size Unweighed Ueighted

Male 1005 2633290

H.S. dronuh 308 627666

H.S~ gr~~uates 697 2205625

Female 1068 2978806

H.S. dropouts 289 6396o5

H.S. graduates 779 23392o1

SOURCE: Nat ional Longitudinal .Surviy of”“Labor Market Experience-Youth;

conducted by the Center for Human Resource Research at the

Ohio State University for the Bureau of Labor Statistics.
Table 8

Week of Leaving Work In Pregnancy and Week of Return to Work After Deliverly

.—

Week of Return to Work Following Birth

Week Left Within 2-13 .,14-52

in ~tijt

Pregnancy Week

N

1-13

. ..357-

.Row% :.00 14.34

Col%

._. ~~.38

0.00 ‘“ ‘“ 12.46” 24.76

N. ..O

31

14-26 Row% - 0.00 15.50. .. ;;.50

Cal% 0.00 11.03 24.76

N. “2

27-38 Row% 0.49

Cd% 0.79

la- 133

40.00 32.44

58.36 41.69

N 251 51 28

39 Row% 73.82 15.00

Col%

8.24

99.21 ----- 18.15 8.78

Tora.1 253 281 .. 319

21.19 2%53 ‘“. 26.72

100.00 100.00 100.00

Source: Table 2 from Lkbowitz, et. al. (1989b)

53-367

Total

130

53.28

38.12

90..

45.00

26.39

“111.

27.07

32.55

;;4

2.93

341

28,56.

100.00.

244

100.00

20.44

200

100.00

16.75

410

100.00

34.34

340

100.00

28.48

1194

100.00

100.00
Table 9

CUMULATIVE AFDC ENTRANCE RATES FOR .

ADOLESCENT MOTHERS, BY MOTHER’S MARITAL

STATUS AND AGE AT FIRST BIRTH, AND RACE

(In Percent&’

—

Cumulative Proportion WhoStarted ReceivhmAFDCa

ByBirth By 12Months By60Months

Characteristic ofFkst After Birth of Atter Birth of

of Mother Childb First Child First Child

All

Maxital Statua at

BitiMh&st Child

UnnMrried

Age at Birth of Fmt Child

All mothers

15 to 17

18 to 19

Mothers who were

unmarried when they

fmt gave birth

15to17

18 to 19

Race

AH fnothera

White

Black

Mothers who were

ummarried when thev

fmt gave birth -

White

Black

7

2

13

8

19

‘1

9

17

10

28

7

50

30

26

47

53

22

44

53

49

49

24

77

58

43

7-7

76

39

76

vi

SOURCE: Congressional BudgetCEticetabulations ofdatafromtheNationalL.mgitudinal Surveyof

Youth(1979-19S5).

NOTES EntranceratesfortheAidh FamilieswithDepmdentChildrenprogramrefertotheproportion

ofadolescent motherswho firststartedreceiting AFDC paymentsintbe specitied period.

Adoles.a?nt mothers nre de!inedu allwomenwhofirstgavebtihwhentheywerebetweenthe

agesof15 and 19. The results fw married adolescent motbem of different ages and races are not

included separately because ofthe muall sample size.

‘The findings are based on relatively small sampIw and therefore should be taken aEindkative

of general patterns of behavior rather than an preise edmat.%. partidarly for the pa-id

furtbeatfromthebirtk

a. Thea&eAimatas reflectthe total numberof adolescent mothers who entered the program for the

fimt time, regardle~ of their mk.quent exib from or reentries Ma the prog-zam. Thus the vakea

do not relate ta &e proportion receiving tenefita in any particular period.

b. States have the option of providing assistance t.a pregnant women, beginning in tbo ~ixth month of

mdically verified pregmmcien.

Sourc@ Table 13 from CongressionalBudget Office (1990)
Table 10

.

. CUMULATIVE AFDC EXIT RATES FOR ADOLESCENT

MOTHERS, BY MOTHER’S MARITAL STATUS AND AGE AT

FIRST BIRTH, AND RACE (In percent)

Cumulative Fh@oi+on Who Left AFDCa

Witbin 6 Months WMin 12 Months Within 48 Months

Characteristic After First After Fmt Mter First

of Mother AFDC Receipt AFDC Receipt AFDC Receipt

All

Marital Status at

Birth of First Child

Married

Unmarried

Age at Birth ofFirst Child

Allmothem

15t017

18to 19

Motherswhowere

umnarried when they

fmt gave birth

15t017

18 to 19. .

Race

All mothers

White

Black

Mothers who were

unmarried when they

first gave birth

White

Black

31

60

23

30

.32

23

24

. .

,.,, -

40

19

27

19

49

69

43

45

52

39

48

57

40

-48

40

76

94

71

70

82

66

76

77

66

SOURCE: Congres8ionsl BudgetCh5icekbulationa’ of data from the National Longitudinal Survey of

Youth (1979-19s5).

NOTE5 Exit ratea for-the Aid ta Families with Dependent Chil&”en program refer b the proportion of

adolescent mothem receiving AFDC payments who I@ the program for the tint time within the

se=fled wri~. AdOlef=ent mOthers =e d~n~ = ~ women who tit gave birth whenthey

werebebweentheage#d 15and19.Theresul~formarriedadolescent metiersof differentages

andracee=e notincluddsewatelytecemseofthesmallmmpleSk

Thesetindinga arebasedonrelatively small samples and therefore uhouldbe taken as indicative

of general patterns of behavior rather than an precise estimates, particukly far the F.3riod

furthest &om the btiL

a. ‘&se eatimntea rsfkt the total number of r=ipienta who leR the program for the fust time,

regsrdleea of sukquent reentries or reads. Thus the values do not relate to the proportion receitig benefits in 8ny psrticuk perk-i.

Source Table 16 horn Congressional Budget Office (1990)
Table 11

..

ADOLESCENT MOTHERS RECEIVING AFDC (In percent) .

Time Between Birth

and Receiut of AFDC (Months)

Characteristic of Mother o to 12 13 to24 25 to 36 37 to 48

All 27 28 29 30

Msrital Status at

Bir$&~st Child

7 8 14

Unmarried 48 49 :: 49

Age at B&h of First Chi~d

All mothers

15t017 29 32 39 38

18to19 26 25 23 24

Mothers who weie

unmarried when they

first gave birth

15 to 17 45 49 57 52

18to19 51 49 42 44

Race

All mothers

White 21 21 22 23

Black 42 46 50 47

Mothers who were

unmarried when they

first gave bti

White 49 45 47

Black :: 51 56 52

SOURCE Congressional Budget Wke tibulatior.$ of data fmm the NationsJ Longitudinal Sur?ey of

Youth (1979-19S5).

NCJTZS Adolescent mothers we defined us all women who Emt gave birth when they were between the

ages of 15 and 19.

These findinsn am based on relatively small samples and therefore should k taken as indim.

tive of seneral patterm of tebavior rather than as precise eatimaks, particularly for the pericd

fkthest from the birth ,.

Sourcfi Table 15 fiotn Con~ssimd Budget Office (1990)
F

_.

.- ———.

.-. c .

2“

●
Table 13a

..

Tml

F&kte

W&3

NL3 TABLBSONPRNA’IZ SE.(7OR TR41NiNCl

of5caofRe9adla.Qdlkblaiica

BRemefLabxs”wsdas

* 1~

Fectaf efNL-3YSmnk CuLdatim At L4asofJecum?=lY

k

Male

Femak

<12

12

13-15

M+

Tr8i& @l&, lh1986

9.12

9.81

6.S7

5.?8

2.37

1.34

6.35

1.67

264

10.50

7.70

3.05

8.51

11,15

1?+05

(030)

(039)

(0.51)

(05s)

(0,03)

(0,0s)

(0.21)

(0,06)

(0.09)

(a@

cm

(0.41)

(0.43)

(cm)

0s3

b

Source: Unpublished BLS data from the National Longitudinal Survey - Youth Cohort

3
Table 13b

-.

NM TABLESON PIUYATE SX713R ‘IRAIhm

CM5csofRscmh rmdEw&aiOJI

Bure+auafLabcrS!&itks

- 1~

Puwtu ofNLSY Ssqit CcmPJxingAtLt#! Cm C@xmy

Tdning- MS-1956

To@. . 1030 (0.4s)

I&e

Whi$$s 11.29 (JMs)

7.74 (0.76)

= 7.11 (0.92)

JcJJ’iY&edFm

Frc&aia@Tcr&&l 3.10 (0s4)

MaM@al 241 (0.11)

Ckaiui 2s5 (O.lO)

SkiIkd .Msmd 304 (0s4)

w 3.243 (0.15)

<12 3.39 (0.59)

12 10.69 (06s)

13-15 1271 (1.13)

16+ 13.10 (1.19)

Source Unpublished BLS data from the National Longitudinal Survey - Youth Cohort
Table 13c

Total

NLS TABLES ON HUVA’1%SECIQR TMININQ

OfaOofRe?aIchadEv@ti

Btmallc.mbxst$tklim

Futmmy lSW

m-cmtG?NLsY samn16cumbtinEAILcwono Cc?nlxny

Job Tmind Rx

Rdu&mwuhM

~

C-k&d

Skmed MRlwd

Cxbx

<12

12

13-15

16+

7.70

89

599

4.31

243

1.19

3.79

025

2m

259

6.47

9.n

10.%

.-

(0.40)

(032)

mm

iu.m

(0>13)

(Oi%)

(0.01)

(Ml)

(0,5s)

(0,s4)

(0,91)

(1,12)

b

Source Unpublished BLS data from the National LcmgitudinalSurvey - Youth Cohort

. .
Table 14

,

Empl >yment-”Status”’for _Potential ly .Inel igible and NLS 1979. Samples -.. . . .

●

Employment Status

Full-Time Part-Time Not Iforkinq Total

Saumle Na ~ Ha ~ Ha g Na ~

Veteran

PI Separated 218 77.0 26 9.3 39 13.8 283 100

PI Separated 243 78.8 26 8.5 39 12.6 308 100

and Active Duty

Nonveteran

NLS 1979 in 1985 670 77.2 45 5.2 153 17.6 868 100

Effective Sanmle

Full-Tim? Part-Time Not Workinq

<

PI Separated 165 ‘ “,’ 29

PI Separated and Active Outy 185 ::

NLS 1979 in 1985 “” “- 495 34 1:?

PI Separated vs. NLS

Work ing vs. Not Working

Full-Time vs. Part-Time vs. Not Working

Ful l-Time vs. Part-Time

Full-Time vs. Part-Time and Not Working

,, PI Separated and Active Duty vs. NLS

Working vs. Not Working

*.

. Full-Time vs. Part-Time vs. Not Working U*

Full-Time vs. Part-Time lm6ns

Fu 11-Time vs. Part-Time and Not Workinta Oosns

1.6ns

S,lns

2.6*

.Oens

a Weighted frequency produced by demographically equating the mi1itary and

civilian samples. The percentages may not sum to 100 due to the effects

of weighting and rounding.

b Includes those serving in the reserves.

* =p<.lo

ns = Not Significant. Source:Table 63 from Laurence, et. al. (1989)
Table 15

Adjusteci-Annua~.Income. l%xxiL Mages for. ,

Potentially Ineligible and NLS 1979 Samples

.*

...

Ad.iustedAnnual Income From Haaesa

Em@Olot

Samule

Standard

Nb Mean Hedian Deviation

Veteran

PI Separatedc Full-Timed 202 .14;564 13,000 9,229

Alle . 277 12,859 11,592 9,194

PI Separated and Full-Time 227 14,433 13,000 8,707

Active Duty All 301 12,899 11,760 8,796

Nonveteran

NLS 1979 in 1985 Full-Time 637 15,181 12,252 9,881

Al 1 833 12,862 10,124 9,920 —

t-Test Statisties

Effective Standard Degrees

Sanmle Deviation of Freedom t Value

FLIT1-Time Workers

.

PI Separated 153 9,183. 621 -0.7ns

NLS 1979 in ‘1985 -.470 9,334

PI Separated & Active 171 8,778 640 -O.9ns

NLS 1979 in 1985 470 9,629

All

PI Separated 209 9,100 822 -0.ons

NLS 1.979 in 1985 615 9,952

PI Separated & Active 228 8,797 454 -0.lns

NLS 1979 irl 1985 615 9,952 ,.

a In dollars.

.

b Weighted frquency produced by demographically equating the military and ‘

civilian Sarn>les.

c Includes those serving in the reserves.

d Includes only flJ1l-tim workers who,reported income.

e Includes full-time, part-time, and not working, excluding ,full-time and

part-time workers who did not report income.

ni = Not Significant. Source Table 70 from Laurence, et. al. (1989)
Figure 1

Mean Weekly Wage (1980$)

Two National Longitudinal Survey Cohorts

ME4N WEEKLY

E4RNINGS

(1980$]

330

300

270

240

21.0

180

. .

.

15“0

OUT–OF–SCHOO

/“’’’’’”’”4)

1,2 3 4 5

.-

SourccxFigure 11 horn Manser (1987)
Figlne2

Mean Weekly Wage (1980$)

Two National Longitudtid Survey Cohorts

300

1

270-

240- “.

;; ,~;i:j

.... -- -— ,,

150 - ‘“ I I I YE4R .

1 2 3. .4 5

. .

Source Figure 12 from Manscr (1987)
Figure 2

Mean Weekly Wage (1980$)

TWONational Longitudinal

.

330

300

270

240

210

180

● ,

G 150

Survey Cohorts

OUT–OF–SCHOOL

I

1 2 3 4 5

Source Figure 12 from Manser (1987)