me National hngitudtial Sueys (NLS) pmgrm supporrs mmy studies designed

m tier- mdastmdrng of h. labor m%ket md mtiods of data collection. R. Dis.

cusion Pupers saies hw~rat% some of tie resach comtig OU1of the progrm.

Mmy of tie pa~m k his saics have bmn delivered ~ find rqorts .o_issioned u

pzt of m exummd pmt progra. mder wtich gms ~c awaded though a

com~tiuve proc~s. ~e series is tile”ded 10circtiate tic fmdi”gs LO klcreskd reders

witi md o“tsi& tie Buea” of Lakr Smtis~ics.

P-ins htffes~d in obtatimg a copy of my Disc&ion Paper, othm NLS repr~, or

gmerd kfomation about tie NM Fogm ad sweys should contact be Bweau of

Lakr Statitics, Office of Economic Rcsemch, Wmhtig~o”, DC 20212.0001 (202-606-

7m5).

Matid in WISpublication is ti tie public domin md, with appropriateadi[, may

b. repmd”ced Wi&OU[ p-ksioa

@tiom md conclusions ex~essti in his document z. hose of tie autior(s)

ad do not ne.=s%ily r~resml m o~cid position of tie Bucau of L* I S1alistim

or tie U.S. Depmtmcnt of Labor.
NLS

Discussion

Papers

.

.
Gender Differences in the Quit Behavior

of Young Workers

Audrey Ught and Manuelita Ureta .

Unicon Research Corporation

March 1990

~s find paper was funded by he U.S. Depatient of Labor, Bureau of Lahr Statistics under gmt numbr E9-J-8W3. Opinions stated in rbis pa~r do not necessarily represent tie offici~ ~sitinn or poficy of tie U.S.

Depament of Lahr.
.

FINAL REP ORT

Gender Differences in the Quit Behavior

of Young Workers

.

Audrey Light and LfanueEta Ureta

Unicon Research Corporation

Third Floor

10$01 National Boulevard

Los Angeles, California 90064

(213) 470-4466

hfard 30, 1990

This project \vas funded by the U.S. Department of Labor, Bureau of Labor Statistics under

Grant Number B9-J-8-0093. Opinions stated in this document do not nemssarily represent

the oficial position or policy of the U.S. Department of Labor.
Contents

Executive S u“mrnary

Section 1:

Section 2:

Section 3:

Section 4:

Section 5:

Section 6:

Introduction

The Statistical Model

Characteristics of the Data

Estimates for the Wll Sample

Estimates for Selected Sub-Samples

Conclusions

References

Appendix’

.

iv

1

4

10

18

32

43

45

47
.

4

List of Tables

1

2

3

4

5

6

s

10

11

6

12

Characteristics of Workers at First md Lmt hterview ,, . . . . . . . . . . .

Number of Jobs Held Per Ye= by Workers with Selected Personal ad Job Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Distribution of R-ens for Job Separations . . . . . . . . . . . . . . . . . . . . .

Job Duration and Prior Eqerience, by Remon for Leaving Current Job ad by

Re=onfor Leaving Lwt Job.., . . . . . . . . . . . . . . . . . . . . . . . . .

Characteristics at F]rst ad Lwt Interview . . . . .. . . . . . . . . . . . . . . .

Estimates for Gompertz, WeibuH, and Box.Cox H ~ard Models with Correction for

Unobserved Heterogeneity (SAMPLE: Women Whose First Jobs Begin or Are ill

Progress inthe First Yearof the Survey) . . . . . .. . . . . . . . . . . . . . . . . .

Estimates for Gompertz. W&bull, and BOX-COX H~ard hfodels \vit,h Correction]

for Unobserved Heterogeneity (SAhIPLE Men Whose F]rst Jobs Begin or Are in

Progr=s inthe First Yearofthe Survey) . . . . . . . . . . . . . . . . . . . . .

Sample \feans and Standard Deviations (SAhlPLE: Workers Whose Careers Have

Not Stinted or Whose First Jobs Begil~ or Are in Progress in the First Year of the

Survey ) . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Estin]ates fo? Gompertz Hazard hiodel witk Correction for ~Tnobserved Heteroge],e.

ity (SAMPLE: Workers Whose Cm~rs Have Not Started or Whose First Jobs Begin

or Are in Progress in the First Year of the Survey) . . . . . . . . .

Condition Probability of Job Separation in the Next Six Months, Given Current

Tenure of XMonths, for Modd Workers . . . . . . . . . . . . . . . . . . . . . . . .

Estimates for Gompertz Hazard Model with Comection for Unobserved Heterogeneity (SAMPLES: ‘Early- ad ‘Late” Birth Cohorts of Men and Women, from Ages

24t031) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Estimates for Gompertz H~ard Model with. Correction for Unobserved Heterogel]e.

ity (SAMPLES: Five Entry Cohorts of Men) . . . . .. . . .

ii

11

13

14

15

17

20

22

23

26

27

29

34
13

14

15

16

Al

A2

A3

A4

A5

Estimates for Gompertz H=ard Model writh Correction for Unobserved Hetirogeneity (SAMPLES: Five Entry Cohorts of Women) .’. . . . . . . . . . . . . . . .

Estimates for Gompertz Hazard Model with Correction for Unobserved Heterogeneity (SAMPLE: Jobs Ending in a Voluntary Trmsition) . . . . . . . . . . . . . . .

Estimat= for Gompertz Hazard Model with Correction for Unobserved Heterogeneity (SAMPLE: JOb-tO-JOb ~asitioni) . . . . . . . . . . . . i . . . .”. . . . . . . .

Estimates for Gompertz H=ard Lfodel with Correction for Unobserved Heterogeneity (SAM”PLE: Continuously Employed Workers) . . . . . . . . . . . . . . . . . . .

Estimates for Weibull Huard Model with Comection for Unobsemed Heterogeneity

(S.4NIPLE: IVorkers \Vhose Careers Have Not Started or \Vhose First Jobs Begin

or Are in Progress in the First Year of the Survey) . . . . . .“ . . . . . . . . . . .

Sample M=ns and Standard ,Deviations (SAMPLE: ‘Early” =d “Latem Cohorts

of24-31Year Old\Yomen). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Sample Means and Standard Deviations (SAMPLE “Early” and ‘Late” Cohorts

of24-31Year Old Men).... . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Sample Mess and Standmd Deviations for Women (SAMPLE: Voluntmy Transi-”

tions, Job-to-Job Transitions, and Continuously Employed Workers) . . . . . .

Smple Means and Standard Deviations for Men (SAMPLE: Volunt=y Tr=sitions,

Job-to-Job Transitions, md Continuously Employed Workers) . . . . . . . . .

36

37

39

41

47

4i

.4i

.

...

111
.

Executive Summary

Object ives

In this study, we examine the mhorts of young men and women in the National Lon~tudind

.

Surveys of LAOI M=ket Experience. Our broad objective is to tidress the following questions

about emly-career mobflity:

*

1.

2.

3.

4.

OverW, which gender undergoes the most turnover during the early career?

\Vhat observable factors influence the turnover of each gender? In particular, is there evidence that women, as well as men, quit their jobs because they are ‘shopping” for a durable

employment relationship? How is’ turnover influenced by such meaures of family respmlsibilities u marital status and the birth of a child? Do unemployment rates and other me~ures

of market conditions have differential effects on men and women?

Do unobservable or unmeaured factors account for a significant amount of turnover?” Are

,,

these factom relatively more important for women thm for “men? ,

1s the turnover behavior of men ad women chan@ng with succwsive birth cohorts or labor

m=ket entry cohorts? Do continuously employed workers exhibit a different pattern of

turnover than workers who intemupt their careers?

Are volunt~Y or job.t~job transitions caused by a different set of factors than other tYPes

of job separations?

MetllodoIogy

Although we begin with descriptive analyses of the samples, most of our conclusions =e based on

estimates of discrete time proportional h=ard models. A hward model describes the instantaneous

rate of job ‘Ytilure” at a particular level of tenure condition upon survival to that level. In a

proportional huard model, a vector of covariates is =sumed to =t multipficatively on a bwehlle
hward. After some experimentation, we settled on the ~sumption that the bm~ne h~ard mises

from a Gornpertz distribution.

By estimating a discrete time model, we mn readily flow for the. presence of time-varying CG

variates. That is, we estimate the effects on the h~ard rate of factors that et,olve over time. such

a wages, unemployment rates and mmitd status. We dso correct fo~ the presence of ~nObserved

heterogeneity. Nthough we control for a wide mray of’ pemon., job-, md mmket-spetific daacteristi~, there me Hkely to be unobserved or unme~ured fztors that sfso influence turnover.

By correcting for these factors, we ensure that our other wtimat~ ~e unbi=ed. h addition, we

can ~timate the proportion of men md women who ue ‘movers” md ‘stayers” for unobsemed

rewons.

Findings

1. W?hen we look at au workers u.hose careem had not started or whose first jobs began or x~,ere

in progress during the year of the first interview (the ‘fuU s~ple” ), we find that women

have a higher h~ard rate than men. Furthermore, we find that 58 permnt of the women

and only 46 percent of the men are movers for unobserved re~ons.

2. Nlen and women respond very differently to f~ily d~acteristics. Among the fu~ sample

of men, being maried, hemming mmried, and the b]tih of a ch]ld dl lower tbe h~ad of

job separation. The huard rate of women f~s when they get married, but it is otherwise

unaffected by their m~itd status. The h~ard rate incre== if they have a newborn tild,

but not if they have older &ildren.

3.

4.

Both men ~d women appeu to eng~e in job shopping. Among the fti saple, the h~ads

of both genders fd with incre~ed prior experience, incre%es in the proportion of time that

is spent working, incre~ed tenure, and incre~ed wages.

For women, there am lmge differences between an e=ly birth cohort md a late birth cohort.

The differences among men are considerably less pronounced. Unobserved heterogeneity’

becomes mr insigtifi~nt factor song the late cohort of women, ad the only important

“
.

.

.

determinmt of women’s turnover that may not be known at the time of hire =e the presence

of a newborn child and the act of becoming married. Overd, w,e find that w,omen appear

to be converging toward the turnover bebavior of men over time.

5. There me dso importmt differences among successive labor market entry cohorts. For men,

marital status, becoming” married, md becoming divorced appear to be growing increwingly

important. For women, the effect of a newborn child is becoming incremin~y important, =

are the effects of prior experience and wages.

6. Among continuously employed workers, family chuacteristics are less important in explaining turnover. Furthermore, only 16 percent of the men and 28 percent of the uromen are

movers for unobserved reaons.

7. M~y variables that are important determinmts of job separations do not explain voluntary,

and job-to-job trmsitions. h general, thsse transitions are less influenced by personal and

family characteristics such ss ed,ycational attainment,

ence.

Implications

The study provides a wealth of dettied information about

a][d !vomeI], but we wish to highlight four key implications.

becoming married, and prior experithe turnover behavior of young men

1. When we look at W trmsitimrs unde~one by the ftil s~ple of workers, we conclude that it

is more difficult for employers to identify non-quitters tiong a pool of women thm among

a pool of men. This is because a luger proportion of women are movers for r-ens that

cannot be observed, and because female turnover is influenced by two importmt factors that .

are generally not observed by employers at the time of hir+namely, becoming mmried and

the pr=ence of a newborn child.

2. TMs conclusion is reversed when we focus on separate birth cohorts, since unobserved heterogeneity is unimportant mong the younger women. In general, we conclude that younger

women look very much fike men in their turnover behavior.

vi
3.” Ftily r=ponsihfities (eipeciWy the birth of a chfid) are not primary causes of job ~parations among women. In fact, the h~ard rate of continuously employed women md of women

in the late birth cohort are the only ones that incre=e w,ith the birth of a child. Women me

more Ekely to leave their jobs when they get married and when they have a newborn find,

but both of these factors are short-fived by their very nature.

t

4. We find that both men ad women exhibit signs of job shopping. That is, they locate

increasin~y high quality job matches x they gain experience, and they lock into those jobs

by investing in job-specific skius.
Section 1: Introduction

The days when women automatically withdrew from the labor force upon marrying or ha>.ing

a child are long gone. Nthough it remains common for women with young chiltien to i=terrupt

I

their caee~, increaing numbers work continuoudy throughout their addt lives. There is concern,

however, that young women who are not planning career interruptions are unable to signal their

.

intentions to potential employers. Employers may simply equate ‘female” with “qtitter” because

women have higher average turnover rates than men. Such statistid discrimination would be

costly to women, since trtin’ing, promotions, and even the jobs themsel~,es ae often unavailable

to workers who ~e expected to quit.

At issue is whether young women ,me denied valuable opportunities because employers ~sume

they me quitters. Not only w,o”ld employers be wrong to take such a \,iew, hut they !i,o”ld be

\vsong to assume that young men a,e not quitters. Ear]y in their careers, men are extre”lcly

]ike]y to quit. their jobs—not necessarily to withdraw from the labor force, but because t]le~ are

‘shopping” for a durable employment rdationship. They may quit upon discovering a better job

or upon redlzing that their current job is not ~ good ~ had been hoped. While women are more

prone than men to leave the labor force, the f~t is that young workers are Ukely to quit their jobs

regardless of their gender.1

Although we can dismiss the notion that women quit ad men do not, a number of qu=tions

remtin. Do young women quit more often than men? Do young women quit primarily to have

, babies, or do they dso engage in joh shopping? Given that employers can only observe a handful

of char= teristics at the time of hire, is. it more difficult for them to identify the fem~e nonquittem thm the retie non-quitters? To answer these questions, we u= data from the National

Longitudinal Surveys of Labor hlarket Experience (NLS) to estimate proportional h=ard models

for all job sep=ations, regardless of whether the worker is subsequently employed, unemployed,

‘Of course, young men d= leave the labor f.,.., primarily m return to school or b{caux they are discouraged

about their employme”l prosp~ts.

1
or out of th,e labor force.z b other words, w,e view the data from the employer’s perspective.

We estimate separate models for men and women in order to compare each gender’s determinants

of turnover. We dso estimate separate models for an “early” ad a slate” birth cohort within

each gender. This enables us to identify whether later cohorts exhibit different turno~,er patterns,

and tvhether mhort effects are gender-specific. Flndly, we adyze the behavior of four additiond

sub-s~ples: five cohorts by their year of entW into the labor force, jobs that were left volunttily,

jobs left to start a new job, ad sub-sampl~ of continuously employed workers.

The notion that women comprise a homogeneous group char~terized by sporadic labor force

pmticipation h= b~n refuted by Heckman ad W]lfis (1977) w well ss others. While may women

continue to withdraw, from the labor force either temporarily or permanently to fulfi~ household

rmponsibifiti~, a l~ge number of women work continuously. Atilable data do not dmys pemit

us to distin~ish such women horn th,ose who experience short ad infrequent interruptions, but

the evidence suggests that even married women of child bearing age have grown incre=ingly

committed to the labor force. Smith and Ward (1985). for example, find that 25 to 34 year old

women incre~ed their participation rate by over 20 percent during the 1970s.

If women who plan to rem tin in the labor force dso pla to keep their jobs, then it is important

that employers be able to identify them ~ non-quitters. Women who plan.to interrupt their careers

me unwi~ng to invest in ski~s that will depreciate during their absence. Since skiU investments =e

=sotiated with w~e gowt h, these women me essentidy opting for relatively flat wage profiles

(SmdeU and Shapiro, 1978; Mincer ad Ofek, 1982; Cox, 1984). Job-specific inv=tments =e

beneficial to workers who do not pla to quit, but such inv=tments require that the firm shares .

the worker’s behef that the employment relationship will I=t (Becker, 1973 H=frimoto, 1981):

Women who plm to k~p thtir jobs will .be denied investment opportunities if their employers

=sume they me planning c=~r interruptions.

The question, of course, is whether young women who remtin in the labor force are =tudly

non-qtitters. If they ~e, then they differ dramatic~y from their mde counterparts. A number of

‘Although wc have in mind worker-initiated =parations, we realize that quih, fir=, and layoffs are no} cIearIy

d~tinguishable mnceptudIy or empirically. O“r only rKouw is to m*e u= of workem, reprtd re=ns for leaving

their jobs.

2
studl~ (Bmtel and Borj=, 1981; Topel and W7ard, 1985; Topel, 1986; L]ght, 1987, 1988) document

the rapid mobility that men typicdy undergo early in their careers as they search for a durable

job match. These workers dso enjoy tremendous wage grow,th, but the evidence suggests that

they ‘invest primarily in sk]lls that are portable across jobs (Light, 1988). If young women pro~re

to exhibit similar job matching beha.~,ior, then the quitter label cm be construed as an accurate

rderence to their age rather than a discriminatory inference about their gend~. Wtihermore,

accusations that women are denied trtining in job-specific skills would be unfounded, since SUA

“

investments would be nonoptimd for both the worker md the employer.

Efisting gender-related turnover studies (Banes md Jones, 1974; Vlscusi, 1980; Blau and

Iiahn, 1981; \Vtite md Berryman, 1985; Donohue, 1986a, 1986b; Lleitzen, 1986) prm,ide hints

that women who change jobs are indeed attempting to improve their match. For example, better

educated women me found to have higher quit probablfiti~, md quits are shown to improve the

wages of women as w,e~ as men. These studie~ do not shzre the focus of the proposed r-eirch,

howmer, so the issue of job shopping among women is left l~gely unexplored. Furthermore, many

conclusions about determinants of men’s and w,omen’s quit probabilities are suspect because the

studies pre-date or ftil to utifize state-of-the wt econometric techniquw.

In the next section, we discuss our econometric model. Section 3 describes the data set ad

summarizes differences in mde and female turnover behavior. Sections 4 and 5 present estimates

of huard models. and Section 6 conttins our conclusions.

.

3
Section 2: The Statistical Model

I

Most of the earher turnover studies cited in Section 1 estimated logit models. Ttre use the

apprOach that h= be~me standard for the analysis of turnover, which is to =timate h==d functions. Three recent studies (Meitzen, lg$& Donohue, lg86a, lg86b) &o ~~timate h=ard ~odel~,

but our approach differs from theirs in two important respects. First, we intimate a discrete time

model (obttined from a~egating mr underlying continuous time model) to allow for the pr~ence

of time-vmying re~essors. The theoretical literature on turnover describes workers’ efforts to

mtitize their hfetime earnings by searching for optimal ‘match=” of their skills with jobs. In

this context, a worker observe the stream of w~es paid to him or her on the current job and

the strem of outside wage offers; whenever either wage path chang~, the worker reetiuates the

benefit ,of continued employment. Not only cm’ioth n.ages &age many times within employment

spells, but those changes play a key role in determining the spe~’s duration.3 N[odels that do not

dlo~v for the presence of time x,arying regressors ftil to capture the essential ingredients of modern

theories of job matching. The second methodological difference is that we include corrections for

unobserved heterogeneity, since it is we~ know,n that ftilure to do so may yidd bl=ed ‘=timat~

of duration dependence. The type of correction that we pefiorm is described below.

The aggregation of a continuous time model to a discrete time model is retiiy performed—

md computations =e greatly simphfied-when the h~ard function is restricted to the fatily of

proportional h~ards.’ The propoitiond h’~ards model (in mrrtinuous time) can be expr=sed w

A(t; x) = A.(t) ap(xp)

where f denotes time, Ao(t) is m arbitruy b~efine h~~d function, x i3 a row vector of cowiates,

ad ~ is a column vector “of parmeters. As noted in Kdbfleisch ad Prentice (1980), a model

of discrete survid data is appropriate” when the surviti time is subject to interval Wouping—

that is, when we do not obswe the exxt survival time (mising from an underlying continuous

3Slnce we analyze job-to-”o”employ merit trmsitions u WCU= jo~t~job movement, we capture ~anges in the

vd”e of “unemployment by Ietti”g mmital and child-bearing status change tith time.

4
distribution) but rather the interval Aj inwhichitfds, whereAj = [aj-1,aj), ~ = 1,...,~ + 1

=e k + 1 disjoint intervals, md QO= O, and Qk+l = m. In the discrete time model, the hazud at

interval Aj is defined as

ew(x, -I 6) P(T c AjlT > aj_l) = I - ~~,

where T represents the completed duration of a employment speU,

~.j=~p(-~,~o(u)d~)

md xj - I is. the value of the vector x at time aj-1.

In printiple, we could atirnate the set of AA,, together with @ but it would involve an unusually

l=ge number of parameters. One alternative is to condense the likelihood function ad estimate

@ with no reference to the set of incidental parameters, AA, (a procedure made possible because

of the =sumption of proportional hazmds). There are two rewons why we chose not to proceed

along these Hnes. First, we want to examine the effect of current job tenure on the hkehhood

that a spe~ w.i~ end. By mtimizing a partial likelihood function we ignore the (possible) tenure

dependence of the underlying b~ehne b~ard. Second, we want to correct our estimates for the

potential presence of unobserved heterogeneity across individuals in the sample. This second

issue prevents us from perforating a simple condensation of the lk~hood function (W=d ad

Tan, 1985). Therefore, we chose to reduce the number of paraeters to be estimated by further

constraining the b-ehne h=md. We usume that the baefine h=ad aris= from a Gompatz

distribution with parameters 6 and 7:4

Ao(t) = 6eTt, 6 >’0. .“

The discrete time model dews us to hmdle time vmying covmiat= in a convenient way. By

=suting that tbe vector x is constmt within a time interv~ Aj,but thatitVari=from one

interval to another, time varying covmiates C= be readily incorporated into the model. That is,

at time aj_l the vector x takes on the vdu= Xj-1, XSumed to rem~n const~t Within the interv~

Aj.We define the intervals to be thr= months—a time period in which virtually dl =pects of a

‘This choice of h=ard is tiscu+ at length in Swtion 4

5
job mat& are Ekely to be constant.5

In duration models where unobserved individud heterogeneity is resumed to follo~v a known,

parametric distribution, estimates have been shown to be sensitive to the specified distribution

(Hechm and Singer, 1984a, 1984b; Trussell and Mchards, 1983). One alternative is to use

nonpmametric methods. Heckman and Singer ( 1984b) prove the insistency of a nmrpmaetric

mtimum hkefihood estimator (NPMLE) and find-from limited MOnte Carlo ~periments-that

the NPMLE estimates structural par~etcrs rather weU, but it does not yield acceptable estimates

of the underlying mifing distribution of unobserved individud heterogeneity components.

We have chosen to fo~ow the strate~ used in Lillmd and Wtite (1987) where a finite miting

distribution is specified to have two support points, We befiet,e ttis is a reasonable compromise.

Estimation of the model using the NPMLE estimator developed by Heckma and Singer—in

which the number of support points is determined during estimation—is computationdly qtite

burdensome. On the other hand, preliminary findings reported in Taubmm, Behrman, and Sickels

(1988) indicate that when a very rich set of memures of individual heterogeneity is a~.tilable, a

r~ative]y parsimonious specification for the finite mixture will do, the job. The h’LS prol,ides such

a wealth of information on observed individud heterogeneity that we expect that a finite mixture

with two support points shordd provide the reqtired flexibdity to avoid bi~~ in the estimat~ of

strrrcturd paaeters.

Consequently, we allow for the presence of unobserved indlvidud heterogeneity by specifying a

distribution of proportional shifts in the hazard function in the form of a finite mixture. The “shlft

factor” of the b~eltie h~ard is expmded to include m error term, Og, that takes Q vdu~. That

is, the shift factor is now equal to exp(x@ + @q), where 6g occurs with probablhty Pq,such that

~, Pq = 1: h particular, we set g = 1,2, ad sinm.a normtization” of the du- of the residuds

is required, we set 6, = O. Since P2 = 1 — PI, ~timation .of the puameters of the fitite mixture

involva the estimation of two parameters only, OZ and PI. To =sist numeric~ cOn?ergence, the

Hkehhood function is mtimized with r~pect to a, where P, = exp(- ~p(a)).

‘Mtho.gh the data permit “s to define intervals ~ short u one month, we found that on+month and thr~

month inkrvds yield virtually identicd ~timat~, We have optd to “se thr~month i“tervds b~ause they lower

computation cmti considerably.

6
We now describe the fikdihood function that emerges from this hazard model. To tinitize

the problem of initi~ conditions discussed by Heck man and Singer (1984a), we restrict the “ffl

sample” to individuals w,hose mreers ha~,e not started or whose first jobs begin or me in progress

during the year of the first interview. Because some workers’ first employment spells are in progress

at the time of the first interview., the data set has left truncation (i. e., wrorkers whose first. job

begins md ends before the first survey are =cluded from the sample, but those whose first job is

still in progress are included). The data set is not left censored, since u,e observe current tenure

,

for employment spells that are in progress at the time of the first survey. Consequently, the

contribution to the hkeUhood function of a spe~ that had b=fi in progress for m months at the

time of the first survey, ad’ that lasts to tinle t in interval A,, is

( )

~P, ‘fi’AA, [ ~,,. ) .

.xp(x, -, B+Eq) ~ _ ~exP(xr-, P+e, )

q=l , j>m,

The contribution to the hkefibood function of an employment spell that starts at the initial

survey date or later, and ends at time /’in interval A, is gi~,en b~

( )

:P, ‘fi’AA, ( ~, ) .

exp(x, _,5+o, ) ~ _ ~exP(xr-lo+ @q)

q=l j=l

Findy, the contribution to the Hkehhood of a employment spell that begins at the initial

survey date or later and is in progress at the time of tbe l~t survey ( i.e. of a censored spell). say

at time a~, is

M,hen an individ”d is included in the sample, W his or her observed employment 5Pe11s, in

tidition to the first job, contribute to the Ukehhoqd function. The contribution corresponds to

the second or the third c=e just discussed, depending on whether the ending time of the spe~ is

known or the spe~ is censored,

We specify the unobsewed individud heterogeneity component to be the same for each individud within and across spells, but independently distributed across individu~s. Alternatively,

we could ~sume that unobsemed heterogeneity components

i

are independent across spells for a
~ven individud (Tuma, Han?an, ad Groeneveld, 1979). The suitability of each approach depends on w,hat is being captured by the unobserved components. If the source of heterogeneity is

time-invariant factors that make some workers mo~.ers and others stayers (holding everything else

constant), then our formulation is appropriate. If, on the other hind, unobserved heterogeneity

reflects the quafity of the job match, then a specifimtion that dlo%.s for independent components

=ross spdls for a given work= is a better choice.

To test for the source of unobserved heterogeneity, we estimated hazard models (by gender)

for first employment spells, mating no corrections for unobserved heterogeneity ~d including the

length of future jobs w a covariate. Future job length w= me~ured ~ the length of the second

obser~,ed job and, alternatively, as tile average length of all subsequent jobs. If the hazard of job

sepmation for the first jobs is independent of the length of subsequent jobs, then we can infer that

mat& qutity is the source of unobserved heter~eneity (see Ffinn ad Heckman, 1982). On the .

other band, a significant coefficient on the length of future jobs Jvmdd suggest that the source of

heterogeneity is time-invariant factors that make some workers movers and others stayers.

We find the latter.c~e to apply to the u,omen: both me~ures of future job length have negatis,e

coefficients. Since longer future jobs result in a lower hward, we conclude that the smple of Women

consists of “movers” and “sta.vers’’-i. e., the same worke~ who have long future jobs ~so have

low h~ards of job sepamtion on their first jobs. For the men, the length of the second job h=

no effect on the hazud rate of the first job and the average length of dl subsequent jobs h= a

positive, but marginally significant effect .6 Cle=ly, a short pand introduces a bi= toward finding

that a longer than average first job must be followed by a shorter than average second job, and .

the mtimum panel length for men is two, jears shorter than it is for women. This may wpltin

why we find a positive coefficient on subsequent job duration for the men, which suggests that

tbe duration of first jobs and subsequent jobs me negatively correlated. Therefore, we conclude

that there is weak evidence (at best) that match qufllty is the source of unobserved heterogeneity

-ong the men. b fight of the very strong evidence that time-invariant f=tors are the source of

.,

‘The coefficient on “average length” is 0.019 and lhe norrnd statistic is 1.69; the coefficient on ?eng?h of the

_o”d jobn k 0.009 md the normal statistic is 0.899. For the wo”en, the mefficien~ (nomd statktic.) m= -0.OX

(-1.80) and -0.041 (-3.34 ), respectively.

8
unobserved heterogeneity among the women, however, we befieve that our choice of specification

of the unobserved compq~ents is justified.

9
Section 3: Characteristics of the Data

We adyze s21 a~rtildle years of data from the young men and young women cohorts of the

National Lmrgitudind Surveys. The men were fo~owed from 1966 to 1981; although the young

women’s survey is still in progress, only 1968 to 1985 data me mrdyzed in this study. We begfi by

identifying the date when =ch pmticipant entered the labor force. To avoid analyzing short-term

jobs that ~e foUow,ed by a return to school, we dld not start the cloti on a worker’s cmeer until

he or she begmr a period of labor force pmticipation that would lwt at le~t 18 months. H that

date occurred \vhile the suri,ey \vas in progress or no earlier than six.years prior to its inception,

the worker remtins in our sample.~ III addition, the worker must hold at le~t one job for which

a hourly wage is reported. These criteria. yield a sample of 17,361 jobs held by 4,600 men and a

sample of 15,372 jobs held by 4,490 women. +

In subsequent sections, we present h=ard model estimates for (a) all transitions undergone

by the workers in these two samples whose c=~n have not started or whose first jobs begin or

are in progress at the time of the first survey, (b) only those transitions which ae reported =

voluntary, (c) only job-to-job transitions and (d) ti trmsitimrs undergone by workers who have

b=n continuously employed. In this section, we summtize e~h of these samples.

Table 1, whl~ compares workers’ char=teristics at thtir first and I=t interviews, suggests that

men me more succ=sfti than women in locating high paying jobs during the ealy c=eer. When

tbe average mm is first interi,iewed, he is 20.6 years old, h= 1.7 ye=s of experience, and earns

$6.29 per hour (in 1982 dollars). When the typid ~vomu is first observed, she is almost a year

older than her mde counterpart, but she h=’ only 0.3 yeas of additiond experience and she earns

a lower w~e. Job seuiority is equal, so the differenw in ~perience mems that the worn- beg=

her current job with more prior experience than did the ma. On average, women we observed

over a sfightly longer period of time (9.7 versus 9.1 yems), but they receive substatidy 1=s wage

‘Although twmthirds of the partitipa”ts began their careers after the NLS begin, 622 mm and 61? women had

bwn worting for more than one year when they wre fimt intertiew~. We deleti the .mfll number of workers

who hti b-” worti”g for over six y-r. because it is difficult m determine when they first entered the labor force.

10
Table 1: Characteristics at First and Lmt Intemiew

MEN WOMEN

Beginning End of Beginning End of

of Panel Panel of PMel Panel

Age 20.6 29.6 21.4 31.0

POtentiaJ experience 1.7 10.8 2.0 11.7

Tenure 0.7 4.1 0.7 3.7

Red hourly wage a 6.29 9.77 4.86 6.32

Jobs per worker 3.8 3.4

Number of jobs li,361 15.3i2

Number of workers 4,600 4,490

a In 1982 dollus.

gowth: men’s wages increwe by an average annual rate of 6.1 percent, while women’s incre~e by

only 3.1 percent per year. Furthermore, the average man h= an additiond 0.4 yems of tenure at

the l~t inter~,iew. despite being a year younger md less experienced than his female counterpart.

The= numbers srr~est that while men move into durable, high paying jobs, women either ftil to

>.

locate equally good matfies or do so much later iu their carars.

Table 1 indicates that men recei$-e a higher return to their early labor force activitiw and

that they dso undergo more turnover. Men are observed holding an average of 3.8 jobs over a

nine yea paiod, while women average 3.4 jobs in 9.7 yems. As m alternative to this simple

comparison, Table 2 shows tlLe Ilunlber of jobs held per yea by workers in various demographic

SOUPS, occupations, and industri~. The first row indicates that the sample of 4,600 men holds

a average of 0.46 jobs per yeu, while the 4,490 wom,en hold 0.43 jobs per year; these numbers

refer to obsemed jobs divided by the sum of job durations, so they understate mtuaf turnovers

Sub-sapl= of married ad white men average fa fewer jobs per yem, but women in thew

mtegorier are indistin~ishable from the full s-pie. Among both men ad women, education

beyond the Mgh school level appems to reduce turnover =d Uving in the South or in an SMSA

h= no effect. Men who are uniotized dso hold fewer jobs per year while, surprisingly, uniorilzd

s.ob~=rv~. job ~e simply job that appear i“ the smpl-i. e., jobs for which starting ~d cn~ng dat= uc

reported or inferrd and =t le=t one wage is reportd. The me-r- of average jobs per year reported in Table 1

d= refer to okervd jok, m they understate turnover m well.

11
r

women hold fm more thsn average. Among the industries, the greated mount of turnover occurs

in construction and agriculture, dthmrgh women me rarely observed in these categories. This

finding is unsurprising, since the cychc and se~ond ntirrre of these industries leads to a great ded

of short-term employment. .The occupaticmd groupings reveal mother rrn~pected resrdt: both

mde mrd female professional and technid workers hold fewer jobs than average, yet. managerial

work hw opposite effects cm female ad mde turnover.

In addition to mamining the effects of observable characteristics cm turnover, we dso consider

sdf-reported rewons for job separations. The NLS repeatedly inked participants why they left their

I=t job. Mthough non-coding is unusrrdly prevalent, we cm ~sociate a response with roughly 27

percent of the jobs in each sample; 45 percent of the men and 40 percent of tke women provide a

rwpcmse for at least one job. Table 3 reports the frequency dlstributicnr of th=e responses. Men

ad women do not differ appreciably in the frequency with which they ae fired or Itid off, but they

differ draaticdly in their reported re~orrs for quitting. Women attribute 23.7 percent of thtir

sep~aticms to their f~ily or health, wfile this re~mr accounts for only four percent of separations

aorrg men.g By the same token, men ae f= more Ekely thm women to report the discovery

of a “better job x the reamn for their l~t job separation. Job dissatisfaction, which encomp=ses

wages, houm, location, working conditions, and c-workers, is the most common reported re=on

for both men ad women.

When men ad women change jobs for the same remon, we wish to 1- whether they are

at similw levels of aperience ad tenure, ad whether the transitions lead to more durable jobs.

The panel nature of our data enables us to dwsify a limited number Of jobs according to why they

w’12 end, in adtitiorr to why they begin (i.e., why the l=t job ended).l” “Table 4 pr=ents me~

completed durations and memr experience at the time of trasition for jobs dwsified in both these

ways. The first row of the top parrd indicat~ that the typical mm’s job that will end in a fire or

layoff began when be had five years of experience; Since it wi~ l~t 1.15 yea=, that is the worker’s

‘B=au= pregnancy is dtern.tively ~ded ~ YamUy” and “health”, thee categoriu are %gregat.d.

,OFor ~ job to be Agnd a ~e-n fo, i= evcntmd di~lution, thr~ titeria must be meti a worker must rePOrt

a re-n for leaving his or her l~t job, the l-t job must be in o“r swpl- i.e., its startinS md ending dat= ~e

how”, at l.ut on. hourly we k reportd, ud no other job intervend between the tw~and no mOre than 18

months c= have dapwd betwm” jobs.

12
Table 2: Number of Jobs Held Per Yea by lVorkers with Selected

PersOnd and Job Characteristics

Fdl Smple

M=ried

Konwhlte

< 12 years of school

> 12 years of school

five in South

five in SMSA

Wages set by union

Industry

Agricdture, for=try, fishing

Mining

Construction

Manufacturing

Transp., communication, utihties

Trade

Finance, insurance, red estate

Services

Pubfic administration

Occupation

Clerical

Sales

Prof=siond, technical

Managerial

Craftsmen, foremen

Operatives, laborers, service

“Zk/XDJ,, where k = 1 if job j is in ate

bZD,,fZD,,where D,~ i. duration if job

“ Includes mining and construction.

MEN WOMEN

Percent of Percent of

lobs Per Time Spent Jobs Per Time Spent

Ye=a in Categoryb Yeara in Categoryb

0.46 100 0.43 100

0.40 5? 0.45 56

0.53 25 0.44 30

0.49 61 0.46 66

0.42 3? 0.38 34

0.49 40 0.44 40

0.45 73 0.43 73

0.36 14 0.5i 14

O.iO

0.39

0.i5

0.38

0.34

0.57

0.42

0.50

0.28

2.5 0.61” 1.8”

1.4

9.0

35 0.41 20

8.7 0.27 5.5

16 0.64 15

3.7 0.34 8.6

17 ,0.43 42

6.4 0.29 6.5

0.42 9.1 0.39 40

0.45 5.4 0.71 3.8

0.34 17 0.34 19

0.38 7.5 0.44 2.5

0.46 ‘ 17 0.41 1.0

0.55 43, 0.54 31

Y k, Ootherwise.

s in cate”goryk, Ootherwk.

13
Table 3: Distribution of &wons

for Job Separations

Reported Rewon

Fired

Ltid off

Quit due to

Hedth/f-ily

Job d!ssatisfution

Found better job

School

Other

Number of .iobs

Number of ;,orkers

MEN WOL4EN

,3.2 2.8

26.1 22.6

4.3 23.7

29.9 26.8-

24.4 7.6

3.2 3.5

8.9 12.9

4,836 3,930

2,049 1.813

tenure when he is discharged. .kccordlng to the first rowofthe bottom panel, men \\,hohave been

fired or ltid off acquire new, jobs that lwt 0.96 years, on average.

A number of interesting contrasts emergefmm Table4. First, women tend to have stightly

Inore tenure thm men whel) they are fired Or ltid off (1.31 yeas versus 1.15 years), and their

subsequent jobs l~t longer (1.13’ years versus 0.96 j~ars). The former comparison su~~w that

wOmeninvwt less intensively injob.specific SkiRs, whllethe latter suggests that they firrd relativdy

better (i.e., 10nger)jObs after minvOluntary disch~ge. S~Ond, jObstha~are entered intObecause

they are abetter” are much longer thm average for both men and women. although women tend

tOmdesu& tr~sitiOns later intheircmers. Just theoppmite istruefor jobchanges caused by

hdthproblems and f=ilyobfigations. Menhave anaverage 0f.5.4yea= Ofexperience wh~they

make such traditions, while women have only four. Of murse, the Ukefihood of poor hedth—

which, for men, is Ekdy to be a more importmt factor th~ ftily obligations-incraa with

age, whale pre~mcy and family obligations are hkely to occur emly in tbe mreers of women.

The turnover described thus far includes transitions fmm jobs to new jobs, unemployment,

non-employment, ad spells that mnnot be identified. Since one of our gods is to compare the job

shopping behavior of men ad women, we wish to focus On tho~ transitions whI& =e job-to-job.

Given the spotty nature of the timelines, it is not always e~y to identify a job-t-job transition.

For each job, we me~ured the length Of time that elapsed between its termination date md the

14
,.

Table 4: Job Duration and Prior Experience, by Re~On for

Lea\,ing Current Job ad by Re=on for Leaving L-t Job

Reason for Leavin~

Cument Job -

Fired /laid Of

Quit due to

Health/family

Job dissatisfaction

Found better job

School

Other

AU quits

Retion for Leaving

L=t Job

Fired/ltid off

Quit due to

Health/family

Job dissatisfaction

Found better job

School

Other

AU quits

MEN \\TOhfER

{umber hleu htem Number Mean Nlean

of Jobs Duration Prior Exp. of Jobs Duration Prior Exp.

1,496 1.15 5.00 1,161 1.31 4.63

201 1.67 ,, 5.39 1,018 1.42 3.95 ,,

1,403 1.44 4.27 1,186 1.31 4.13

1,138 1.?2 4.42 351 1.96 4.66

185 1.06 2.73 159 1.31 2.96

476 1.61 5.21 551 1.82 4.20

3,403 1.55 4.43 3,418 1.51 6.80

Xumber b{ean Mem Number ~Mean Mean

of Jobs Duration Prior Exp. of Jobs Duration Prior Exp.

1,418 0.96 6.85 1,000 1.13 6.58

207 1.30 7.80 931 1.05 5.96

1,444 1.41 6.48, 1,054 1.28 6.13

1,179 1.82 6.98 299 3.76 7.32

157 0.91 4.62 138 1.13 4.89

431 1.28 7.68 508 2.06 6.95

3,265 1.50 4.09 2,930 1.59 6.28

15

,
stinting date of the next observed job. Fm men, the mem “gap” is 11.07 months, w,ith a stadmd

de~,iation of 14.47; for wmen, the mean is 15.70 months and the stmdard de~riation is 23.34. Jobs

that are fo~owed by a gap may in fact be immediately succeeded by a new job that is not in OU1

smple. However, in an effort to select jobs that ue tiown to be fo~owfed by another job, we

d

require that the gap be less thm three months.

This selection criterion yields a sample of 2,974 jobs for the men and 1,630 jobs for the women

that end with job-to-job transitions. The average rn~’s job ends when it is 0.87 years Old ad

the average w,oman’s ends after 1.03 years. In our subsequent andytis, we will detedne whether

the factors that cause these jobs to end differ born the f~tors that cause the full smple of jobs

to end.

The finrd sub-sample that u,e exmine consists of “workers who me ‘continuously employed.”

TO constmct these s~ples, we used information on the number of weks during the Imt ye= (or

since the l=t interview) that respondents spe;t working, unemployed, or out of the labor force.

Agtin, ye are, not able tO accorrnt for d] the weeks in each worker’s career. In particular, we

invmiably lose sight of a large number of w~ks when interviews are conducted bimnudly. h

order to SKIN this problem, we focus on those weeks that wn be accounted for ~d ‘calculate

the percentage of time that is known to be spent working. For the men, an average of 85.6

percent of the entire pael is accounted for, and mr avemge 81.1 percent of this time is known

to be spent working. The corresponding numbem for the women are 68.5 ad 62.1. We use the

media of the men’s time-spent-working distribution-89 percent—s the cutoff point in de fiting

continuously employed workers. That is, anyone who spends more than 88 percent of thek time

wOrK]ng is considered to be continuously employed. Gaps in the data, rather than unemployment

Or non-employment, account for most of the time spent not working.

Table 5 drrpficates the information contined in Table 1 for the saples of continuously employed workers. It revds that, among continuously employed workers, women appear to be more

successful tbm men at trmsiting into’ durable jobs. At the end of the panel, men md women

have rOu@y the sae ~onnt of labor market =perience, but women have almost 15 months of

tidltiond tenure. However, cmrtinuously employed women appea to be getting lower returns to

16
Table 5: Characteristics at First and L~t Intemiew

(SAhlPLE Continuously Employed Workers)

NfEN WOMEN

Beginnillg End of Beginning End of

of Panel Panel of Pmel Pael

Age 21.1 30.2 21.6 31.1

Potential experience 2.0 11.1 1.6 11.2

Tenure 1.0 5.3 1.0 6.5

Red hourly wage a 6.70 10.23 5.52 7,79

Jobs per worker 3.2 2.5

Number of jobs 7,433 2,812

Number of workers 2,343 1,142

“ In 198? doll.rs.

tenure than their male counterputs: the ratio of female to mde wages at the end of the panel is

0.76, whi& is exactly the same as the ratio for the full sample.
Section 4: Estimates for the Wll Sample

Prior to adopting a unique specification for the bm&lne h~md, we estimated models in w.hid

the b~efine hward v,= alternatively specified to be Gompertz, Weibrdl, and BOX-COX. The

bxeke h~ard under a GOmpertz specification w= described in Section 2. Under a Weibull

specification, it is

Ao(t) = pk(pt)~-1

with p, k > 0. The b~ehne huard u,nder a BOX.COX specificatimr ii defined ~

~vith A2 > Al z 0. .The Box- Cox hazard is quite general in that it conttins botb the Gompertz

md the WeibuH hmards ~ special cases. It is readily verified that setting Al = O and T2 = O in

the above wpre~ion yield$ a Weibull hazard, while setting A1 = 1 md 72 = O yields a GOmpertz,

h=ard.

The ~tra flexibility afforded by the BOX-COX h~~d comes at a price. To write the Ukefihood

function for our discrete time model we. foUow the stand=d appro=h of expressing the discrete

h=ad = a function of the integrated (continuous) b=&lne h==d. Since there is no =~ytid

solution for the integral of a BOX-COX hazard, numerical integration is required. Numerical inte-

~ation is dso required to compute the ~adient vector at each iteration. Of course this can be

avoided if numerical derivati~,es me used—a practice we avoided because of the large number of

pmameters md sample sizes involved,

In prehtinary tests, we found that numerical integmtion irrcre~ed estimation time by an order

of magnitude, so we obtdned estimates for’ a sample of workers ~hOse first jObs began or were

in progess in the first yea rrf the survey. This selection criterion, which efiminat- the prObIem

of initial conditions and fikely minimizes the problem of time inbomogeneity of the mvirontient,

yields ftirly large samples mryway. For botb men md women, about 35 percent of the ‘full

smples” described earher ~ conttined in the restricted smpl-: the sample of men ha 31,948

observations for 6,191 workers, and the fi~res for women me 27,025 observatimrs for 5,313 workers.

18
We report our estimates for the three models in Tabl= 6 and 7. “Tile BOX-COX h=md h=

four parameters to capture the tenure dependency of the hazard of job separation; the WeibuIl

ad Gompertz h=mds have only one parameter sch. Thus, it is unsuwrising that the BOX-COX

model yields the largest ldue of the hketihood function for the samples of men and women. The

estimates for women S11OV,that the BOX-COX hward fits the data only marginally better th~

the GOmpert~ the WeibuU hmmd results in a considerably worse fit. Comparing the parmeter

~timates that obttin from the Gompertz and from the BOX-COX models we find no sign reversals.

Indeed, the -timates are so close that there is no apparent benefit to be gained from using the

BOX-COX specification “in subsequent analysis.

The results for the sample of men parallel those for women, though in this c~e the BOX-COX

model yields a value of the UkeEhood function equal to -10011.8 versus a value of -10045.8 from the

Gompertz model. Agtin, we find no sign reversals in the parameter estimate, and the estimates ..

are very close in magnitude. The one exception is the estimate of the fraction of men who are

“movers” for unobserved rewons and, therefore, the estimate of 02 (the difference in the intercept

term for ‘stayers” md “moversn ). The fraction of stayers is estimated at 86 percent in the BOX-COX

model, compared with 74 percent in the GOmpertz model. Give’n that tbe remtining parameter

=timates appem to be unaffected by the choice of bm~ne bawd (ignoring the WtibuH model

=timates), md given the high cat of obtaining estimates for the BOX-COX model, we chose to

specify a GOmpertz h=~d for the remaining analysis.

We estimated a WeibuU model for the full samples of men and women to determine whether

our findings on the relative merits of alternati~,e bwetine haz=d specifications are sample specific.

We find the same pattern found for the restricted sampl=: the GOmpertz model fits the data

considerably better. The estimates are reported in Table Al in the AppendIx.

TO avoid the problem of initial conditions mentioned in Section 2, we begin by =timating

h=ard models for samples of workers whose car=rs had not started or whose first jobs began or

were in progres during the ye= of the first intemiew. The first interview for the men wu in 1966

(generWy in November) ad, for the women, it w= in Jmuq or Februa~, 1968. These selection

criteria field samples of 2,594 men ad 2,552 women.

19
Table 6: Estimates for GOmpertz, Weibull, and BOX-COX H=ard Models

Correction for UnObser\.ed Heterogeneity

(SAMPLE Women Whose First Jobs Begin Or Are “in Progrws in the Rrst Yem of the Surve},)

Variable

NOK!VHITE

MARRIED

WEDS

DWORCES

BIRTH

NEWBORN

PRESCHOOL

SCHOOL

PRIOREXP

TIMEWORKING

INVOLUNTARY

WAGE

‘P.4RTTIME

UNION

CONSTRUCTION

TRANSPORTATION

TRADE

PUBADhiIN

SMS.4

SOUTH

UNEMPLOYMENT

Y6869

Y7071

Y7273

Y7475

Y7677

Y7879

Y8081

Y8283

7

log(k)

71

Y2

10g(A, )

log(A~)

10g(6)”

a

o~

L% Hkelihood

. Forthe Weib”ll model, t}

_del.

-,

GOMPERTZ WEIBULL BOX-COX

Normal Normal

Coefficient Statistic Coefficient

Nomd

Statistic Coefficient Statistic

-.093 -1.68 -.065 -.9i -.084 -1.30

..182

.138

-.125

.243

.287

.238

.007

-.206

-.051

.388

-.511

.334

.341

.059

-.383

.426

-.481

.013

.059

.001

-.453

-.106

-.051

-.614

.278

-.123

.607

1.246

-.021

—

—

—

—

-2.220

-.286

1.193

3.09

1.50

-1.08

3.74

3.89

4.50

.41

-9.il

-,96

6:81

-9.42

i.06

5.i4

.38

-5.32

i.36

-5.94

.22

.1.15

.11

-4.57

-.76

-.33

-3.03

1.20

-.43

i.89

3.31

-12.4i

—

—

—

—

-9.00

-3.64

22.02

-8490.83i

is the -timateof k Iog(l

.124

.122

-.082

.267

.319

.204

.004

-.051

-.185

.409

-.545

.320

.309

.051

-.39i

.396

-.524

.026

.029

.014

-.700

-.672

-.871

-1.803

-1.209

-1.912

-1.562

-1.378

—

-.182

—

—

—

—

-1.948

-.182

1.316

2.16

1.2$

-.54

3.76

2.96

3.44

.29

-4.05

-2.02

6.24

-9.94

5.34

4.82

.29

-3.26

6.60

-4.10

.44

.52

1.13

-6.00

-4.94

-5.89

-9.77

-6.35

-9.17

-6.76

-5.45

—

-7.18

—

—

—

—

-8.44

-1.55

16.59

-8529.557

t k the~timateof~

.169

.144

-:114

.252

.288

.225

.008

-.202

-.089

.3i0

-.500

.325

.315

.067

-.376

.409

-.473

.023

.053

.002

-.437

-.098

-.033

-.598

.290

-.114

.605

1.220

—

—

-.114

-.009

-17.315

.150

-2.167

-.219

1.173

2.86

1.47

-.91

3.66 

2.85

3.93

.49

-8.54

-.99

5.01

-9.09

6.05

4.51

.46

-3.41

7.5i

-4,61

.38.

.88

.26

-4.41

-.72

-.20

-2.90

1.13

-.38 .

1.i6 .

3.06

—

—

-3.25

-1.86

-.27

1.41

-.8.75

-1.65

14.92

-8486.012

taut term in the Box-ax
Table 8 summmizes the variables we consider, which include characteristics of the individud,

the job match, ad the efivironment. In the first category are dummy nriables indicating race and

marit~ status; since changes in marital status are fikely to be important determinants of turnover,

we dso include dummy \rariabIw indicating whether the worker marries or divorces during the interval. To control for the effects of children, we include a dummy indicating whether a child is

born during the intend; for women, we dso include indicators of whether she has a newborn child

or preschool children. 11 In ~~tiOn, we include a measure of years of completed schOOling. ~’e

me=ure past job shopping activity by including years of potential prior experience (PRIOREXP),

the fraction of potential prior experience that is wmunted for by obserued jobs (TIMEWORKING), and a dummy \,ariable indicating whether the lmt job w= left involuntarily. TO memure

,.

&aracteristics of the current job match, we include the (log of the) hourly wage, dummy ~,ariables

indicating union status, part-time status, and industry of employment. Since most industries

.

proved to have no effect on the hazard, we include ordy construction, transportation, trtie~ and

public administration. 12 We ~ci~”nt for market chmacteristics with monthly unemployment rates

ad dummy variables indicating calendar years, residence in an SMSA, and residence in the South.

Table 9 reports estimates for a Gompertz hazard model with corrections for unobserved heterogeneity. The first tfing to note is that personal charmteristics do not effect the hwards w one

fight have expected. Being married lowers the h=ard rate for men but h= no effect for women.

BecOting married (WEDS) lowers the h=ard rate for men ad wmuen, but the effect of a divorce

is not statistidly significant for either gender. The birth of a &ld lowem the h=md rate for men

but, surprisin~y, it h= an insignificmt effect for women. Nevertheless, women are more fikdy to -

sepaate from thtir jobs if they have a newborn child, although the pr=ence of a preschooler h=

no effect on their h=ard. bce is not an important predictor of turnover behavior for either men

or women, while incremed education attinment lowers both h~mds, although the effect is far

more pronounced for men.

I, The ~“mbe, of chfidren ag, 18 0, “nd,r ~roved to have no effect on the huard for either gender. Sivce Mde

mDondents were not -kd their cbildre”’s az-, -. we cannot include the NEWBORN and PRESCHOOL dummies

in $htir b=ards.

XZPO,the wo~,”, the ~onstr”ction d“mmY dso i“cl.d~ agriculture nd mining. We formed th~ .OmPO.ite

industry becanse only a handful oi women appear in emh category.

21
Table 7: Estimates for Gompertz, Weibull, and BOX-COX Huard Modds

- Correction for Unobserved Heterogeneity

(SAMPLE Men Whose first “Jobs Begin 0. Are in Progress in the Pirst Yem of the Survey)

Vmiable

NONWHITE

MARRIED

WEDS

DIVORCES

BIRTH

SCHOOL

PRIOREXP

TIMEWORKING

INVOLUNTARY

WAGE

PARTTIME

UNION

CONSTRUCTION

TRANSPORTATION

TRADE

PUBADMIN

SbfSA

SOUTH

UNEMPLOYMENT

Y6869

Y7071

Y72?3

Y7475

Y7677

Y7879

Y80S1

:g(k)

71

72

10g(A, )

10g(A2 )

10g(6)-

a

e=

Log Uehhood

. FOrthe Weib”llmodel, th

GOLfPERTZ

Normal

Coefficient Statistic

.036 .66

-.142

-.295

.133

-.277

-.023

-.206

-.326

.423

-.124

.6i8

-.448

.419

-.399

.066

-.271

-.099

-.055

-.035

.748

1.396

1.004

1.417

2.124

2.387

2.502

-.030

—

—

—

—

—

-2.108

-1.209

.870

-2.76

-3.79

.76

-6.12

-2.2i

-8.66

-4.53

7.38

-2.64 .17:56.

-7.61

i.~1

-9.96

1.21

-3.05

-2.18

-1.10

-3.36

10.85

13.17

6.i4

7.54

8.99

8.85

i.85

.14.55

—

—

—

—

—

.14.87

-2.68

12.77

-10045.896

is the estimate of k l.g(p:

~,EIBuLL

Normal

Coefficient Statistic

.032 .55

-.237

-.337

.115

-.248

-.035

-.014

-.551

.333

-.203

.693

-.477

.443

-.293

.017

-.260

-.074

-.084

-.037

.517

.766

.056

.113

.373

.229

-.030

—

-.033

—

—

—

—

-1.997

.186

1.209

-4.68

-3.52

.63

-3.74

-3.88

-1.08

-6.43

4.96

-4.17

10.25

-6.50

6.52

-2.86

.30

-235

-1.41

-1.63

-2.66

7.25

7.17

.46

.71

2.18

i.33

-.13

—

-13.12

—

—

—

—

-9.45

1.26

8.79

BOX-COX ““”- 

Nomd

Coefficient Statistic

.031 .55

-.139

-.294

.143

-.262

-.017

-.199

-.397

.394

-.140

.658

-.428

.419

-.377

.047

-.275

-.092

-.064

-.035

.795

1.412

1.076

1.500

2.162

2.381

2.479

—

—

-.023

-.096

-16.866

-.387

-1.781

-1.903

.819

-10079.701 -10011.867

it & the estimate of the constant term in the BOX-GX

-2.46”

-3.21

.34

-3.92

-1.11

-7.01

-4.73

6.45

-2.10

7.77

-6.40

5.42

-3.44

.80

-2.55

-1.64

-1.26

-3.20

9.63

9.45

4.93

5.51

6.46

6.44

5.65

—

— 

-.52

-3.84

-.02

-4.95

-7.93

-.82

1.89

22
Table 8: Sample Means and Standwd Deviations

(SAhfPLE: Workers Whose Careers Ha\,e Not Started Or WhOse First

Jobs Begin or Are in Progress in the First Yem of the Survey)

Variable

NONWHITE

MARRIED

WEDS

DIVORCES

BIRTH

NEWBORN

PRESCHOOL

SCHOOL

AGE

PRIOREXP

TIMEWORKING

INVOLUNTARY

TEKURE

PARTTIhlE

~rAGE

UNION

CONSTRUCTION

TRANSPORTATION

TRADE

PUBADMIN

UNEkfPLOYMENT

SOUTH

SMSA

Y6869

Y?071

Y7273

Y7475

Y7677

Y7879

Y8081

Y8283

Number of individuals

MEN WOMEN

Std. Std.

Definition Niean De\,. Mean Dev.

1 if nonwtite 0.24i 0.431 0.274 ‘0.446

1 if mmied

1 if mamies during interval”

1 if divorces during interval”

1 if Aild is born during intervda

1 if &ild age one or under

1 if child age six or under

Years of schooling

Years of potential prior experience

Ratio of actual to potential experience

1 if left I=t job involuntarily

Years of tenure

1 if works less than 35 hO”rs/week

Log of real hourly wage6

1 if wages set by union

1 if industry is

Construction

Transp., communication, utilities

Trtie

Pubfic administration

Unemployment rated

1 if fiving in the South

1 if~ving in an SMSA

1 if year is

1968-69

1970-il

1972-73

1974-75

1976-77

19i8. 79

1980-81

1982-83

0.589 0.492 0.593

0.059 0.235 0.059

0.018 0.134 0.026

0.150 0.357 0.120

— — 0.037

— — 0.220

12.989 2.821 13.034

25.53i 4.611 26.694

3.644 3.604 4.031

0.488 0.348 0.415

0.094 0.292 0.085

2.542 3.010 2.651

0,061 0.240 0.161

2.015 0.500 1.702

0.175 0.380 0.195

0.101 0.301 0.019”

0.072 0.258 0.052

0.167 0.373 0.152

0.058 0.234 0.060

9.213 2.951 11.247

0.417 0.493 0.377

0.730 0.444 0.747

0.118 0.323 0.061

0.166 0.372 0.111

0.130 0.336 0.127

0.143 0.350 0.134

0.141 0.348 0.136

0.131 0,337 0.127

0.118 0.322 0.125

0.491

0.235

0.159

0.325

0.1s8

0.414

2.347

4.940

4.182

0.337

0.279

3.02G

0.367

0.476

0.396

0.137

0.222

0.359

0.237

3.399

0.485

0.435

0.239’

0.314

0.333

0.341

0.343

0.333

0.330

0.000 0.016 0.174 0.379

2594 2552

Number of obser}.ations 50,16i 48,570

a Since we O“IYknow that a change occur. betw~n s“cce=ive interviews, the vmi=ble ~“~s one for every

six-month i“tervd idling between the two interview date.

b In 1982 doUars,

‘ Includs mining md agric”lt”re.

‘ Unemployment rate d“ri”g first month of the i“tervd.

23
The effects of prior experience, tenure, and wag= suggest that both men ad women engage in

job shopping although, x we saw in Section 3, men appear to do so more vigorously. P~OREXP

md TIME!I’ORKIN”G (the m.tio of observed to potential experience) have a negative effect mr

the hazard rate for both genders. The h=ard rate dso dechnes in tenure for both genders, but

=pecidy for men (i. e., y is grmter in”“absolute value). This is consistent with the notion that

workers lock into good matches, presumably by investing in match-specific sWIS. We dso find that

m increase in the current wrage—whiti is considered to be a good indication of match qudity—

lowers the h~ud rate for both genders, but especially for women. The effect for men may be

d-pened by the fact that they are ,rLlatively more iucc=sful in parlaying a figh curr~t wage

into an even higher outside wage offer.

The coefficients On UNION and the industry dummies suggest that some of the observed,

unmndition~ difference in mde ad female turnover is attributable to the types of jobs favored

by each gender. About 19 perc~crt of the observatimrs for both men and women refer to rrdon

jobs, but UNIOhT lo~vers the hazard for men and rtises it for w,omen. .4pparently, uromen w,ho

are unionized are crmcentrated in service professions ( e.g., teaching) md do not gtin job stabi~ty

from their union status. The coefficients mr the industry dummies have the s-e si~s rmd me of

comparable magnitudes for men and women. The h=md of job separation is higher for work-s

in the mnstmction and trade industries, and is lower for workers in transportation ad pubfic

titinistration. R-idence in a SMSA and in the south lowers the h=md for mmr, but h= “no

&ect for women. Higher unemployment rates, on the other had, lower the h=ard for women,

.

but do not have a statistically significant effect on the h=ard rate for men.

The fist of regressors includes dummy variables for cdend~ yems, with the yeas prior to 1968

corresponding to the “omitted” period for both tien rmd women. The coefficients on the ydummies reverd a pronounced secula incre~e in tbe h~md for men and a secul= decre=e in the

h=ard for women. The smple period W= characterized by decfining labor force participation

rates of men and incre=ing rates for women. Mthough incre=ed pwticipation rates cm rtidt

from lager numbers of women in the labor force with no xcompanying change in behavior, our

-timat- suggest that an increwed commitment to the labor force may have dso been a fmtor.

24
TO summarize, the ~timates suggest that the early turnover behavior of men and women

differ, but not drmaticdly. k particular, job shopping appears to be an important determinant

of mobifity for both genders. Although we have identified a number of ~,ariables that employers

can use to predict turnover behavior for each gender, we have dso shown that female turnover is

increased by an important factor that is often unknon,n at the time of hire—namely, the presence

of a newborn child. For this reason done, it may be more difficult to scr=n for non-quitters among

women than among men.

Furthermore, we must take into account the effect of unobserved heterogeneity. As Table 9

reveals, it plays an important role in explaining turnover, esp’ecidly for women. The ~timate

of Oz is 1.02 for men and 1.22 for women. These numbers imply that unobserved heterogeneity

incre=es the h=ard rate by a factor of 2.8 for men (el 02 ) and by a factor of 3.4 for women. We

mn dso identify what proportion of the sample changes jobs’for re~ons that are not captured by

the observable. The value for a is -0.49 for tbe men and -0.13 for the women, which implies that

46 percent of the men and 58 percent of tbe women ~e ‘movers” for unobsert)ed retions.13 These

=timates indicate that there is a tremendous amount of unobserved heterogeneity within genders.

However, factors that employers cann~t control for are relatively more important for women tha

for men. This conclusion is reinforced by the results we obttined when we estimated the BOX-COX

TO detertine whether men ad women who =e deemed stayers differ in their quit behavior,

we comp”ute the imphed probabilities of job separation in the following six months (condition On

vmious levels of cnrrent tenure) for both mde and female stayers. These condition~ probabilities

were computed for the periods 1968-69, 1976.77, and 1980-81, for what roughly constitutes a modd

worker: sommne who is white and mmried, hm twe~~,e years of schoofing, and fives in = SMSA.

The pmbabihties were evaluated (separately for men and women) at the mean values of wages,

unemployment rates, P~OREXP, ad TIMEWORKING. The top pael in Table 10 reports the

pmbabitities of job separation for women and for men and the ratio of the former to the latter,

for the period 1968-69. For the remtining periods, we mrly report the probabfities for men ad

13p, = .zp(_e= p(=)) i. the ~.Tce”t that ue ‘stayers,m and Ps = 1 —PI percent are “movers.”

25
Table 9: Estimitei for Gompertz H~ard Model

Correction for Unobserved Heterogeneity

(SAMPLE: Workers Whose Careers Have Not Stated or W7h0se First

Jobs Begin or Are in PrWr:ss in tke First Ye= of the Survey)

Vaiable

NONWHITE

MARRIED

WEDS

DIVORCES

BIRTH

NEWBORN

PRESCHOOL

SCHOOL

PRIOREXP

TIME\\ ORKING

INVOLUNTARY

WAGE

PARTTIME

UNION

CONSTRUCTION

TRANSPORTATION

TRADE

PUBADMIN

SNfSA

SOUTH

UNEMPLOYMENT

Y6869

Y7071

Y7273

Y7475

Y7677

Y7879

Y8081

Y8283

Lg(6)

a

e~

Log fike~hood

MEN

,NomA

CO~cient Sbtistic

0.014 0.42

-0.205 -6.86

-0.372 -7.20

0.106 1.08

-0.334 -8.39

— —

—

-0.057 -10.39

.,0.147’ -20.33

-0.364 -8.34

0.272 i.19

-0.230 -8,00

0.534 14.58

-0.591 -14.42

0.398” :..10.72

-0.300 -5.23

0.128 4.12

-0.361 -5.39

-0.105 ..-:3.45

-0.099 -3.39

0.002 0.21

1,067 20.27

1.441 22.14

0.672 9.59

0.961 10.99

1.483 16.07

1.675 17.79

1.780 15.06

— —

-0.026 -33.99

-2.293 -2322

-0.492 “-4.47

1.019 28.31

-30275.431

WOMEN

Normal

Coefficient Statistic

-0.051 -1.58

0.037

-0.147

-0.121

0.046

0.379

0.047

-0.026

-0.105

-0.329

0.442

-0.58i

0.407

0.478

0.398

-0.575

0.334

-0.367

0.047

0.038

-0.021

-0,375

-0.190

-0.509

-1.2=

0.617

-1.313

-0.6.61

-0.220

-0.014

-1.538

-0.131

1.218

-271i6.867

1.2i

-2.83

-1.52

1.19

6.43

1.42

-3.95

-16.02

-7.11

11.03

-19.37

13.10

14.84

4.76

-7.74

10.58

-5.58

1.58

1.34

.3.49

-3.62

-1.81

-4.83

-10.94

-5.30

-10.86

-5.06

-1.52

-1 i.23

-10.81

-1.63

23.82

26
Table 10: Confitimrd Probability Of JOb Serrarationin the Next Six Months.

Given Current Tenure Of XMOnths, fOrkfOdal \VOrkers”.

By Gender, FO —

Current 1968-69

tenure

o

6

12

24

36

48

60

,VOmen blen WOmen/.Men

.107 .172 .621

.099 .149 .661

.091 .129 .706

.078 .097 .808

.067 .072 .927

.057 .054 1.067

.049 .040 1.146

—By Gene

Current

tenure

o

G

12

24

36

48

60

“A modd

jelected Periods.

1976-77

Men W’omen/Men

.249 .341

.217 .361

.190 .382

.143 .433

.107 .493

.080 .564

.059 .647

r, For Stayers anc

WOmen

Stayers MOvers

.107 .318

.099 .,.297

.091 .276

.078 .240

.067 .209

.05i .180

.049 .156

srker is whtte, marri

schooh”g, and hv- in an SMSA

1980-81

Men WOmen/Mm

.320 .254

.281 .267

.246 .282

.188 .316

.142 .358

.106 .407

.079 .465

~lovers, 1968-69.

Men ‘

3tayers Movers

.172 .407

.149 .361

.129 .318

.097 .246

.072 .187

.054 .143

.040 .107

h= 12 years OF

the femde-mde ratio.

Glancing at the ratios of women’s to men’s job separation probablliti=, it is apparent that

w,omen are generally l~s Ukely to leave their jobs in the next six months. The only exception is

=ong workers with more than three ye=s of cument tenure during the period 1968-69. Table 10

dso shows that men have undergone a rapid sectiar incre=e in the probability of job separation.

For example, men faced a 17 percent chance of separation in the first six mmrths of a new job

in 1968-69 and a 32 percent chance in 1980 -8.l—a tw~fold incr-e. For women, the probability

dropped from 11 percent to 8 percent during the same period .14 In 1968-69, women faced a

I{The ~c~ar d=re== imthe prObabifitiS fo, women k sbghtly undemtatd because the probabfliti= we~ CV~-

uated at the average unemployment rate for the entire period. The estimated coefiti. nton the unemployment rate

is -.o21 for women, implying that the hmard d=rem u the unemployment rate incre==, and the latter w=

about four percentage points below (above) average in 196&69 (1980-81). Thh k not a problem for men since their

coeffi.ie”t OD the unemployment rate is very CIO% to zero (0.002).

27
probability of job separation in the first six months of a new job that w= ody 62 percent m high

m that of men. This retie is 34 percent in 1976-77 md 25 percent in 1980-81.

TO compxe the behavior of movers to that of stayers, we compute analogous probabilitia of job

sepmation for movers for the period 1968.69. These me reported in the bottom panel of Table 10.

The estimate reveal that, in the first two years of a new job, mde movers face higher prObabiEties

of job separation than female movers. Although the discrete h~ard drops mnsiderably f~ta with

taure for men than far women, it is unhkely that movers’ jobs till sumive long enough for this

effect to t~e hold. For this reason, we conclude that jobs of mde movem are somewhat more

trmsitory thm those of female movers.

Since female stayers generdy ha~relower separation probabilities thm their male counterparts,

they may be a better bet from an employer’s standpoint. Of course, the stayer d~ignation refers

to unobserved qutities, so it is not ~t”dly possible to identify such workers. If employer: simply

fire workers On the basis of obser}.ables and Hope that they prove to be stayers, then they me

more hkely to be vindicated by their mde employees; w we lewned from Table 9, 54 perwnt

of the men me stayers compared with only 42 percent of the women. Although this news ,may

be discour~ing to female non-quitters, they a take mmfort in the knowledge that times =e

&mging. Not ody have succesdve cohorts of women incre~ed their education atttinrnent ~d

labor force participation rat-, but they have altered their turnover behavior M weU.

TO demonstrate this, we prwent ~timates of the h~ard model for m “early” ad a “late” birth

cohort within eafi gender. Our early cohort consists of women who were born in the period 1944-

46. The late cohort consists of women born in the period 1952-54. For the men, we use everyone

born in 1942-44 and 1950-52. Thre-yeu windows are used to mtinttin sufficieritly large saples;

for the sme re~on, we no longer require prier ~perience to be zero at the first ob=rntion. The

smples conttins observations on these workers while the r-pondents are betwen the ages of 24

md 31. There are 788 women in the e~ly cohort and 1,019 in the late cohort; the corr-pending

smples sizes for the men are 853 ad 1,438. Summmy statistics for the four samples are pr+ented

in Tables A2 and A3 in the Appendix.

28
Table 11: Estimates for Gompertz Hazmd Model

Correction for Unobserved Heterogeneity

(SAMPLE “Early” ad ‘Late” Birth COhOrts Of Men and WOmen, from Ages 24 to 31)

Variable

NONWHITE

MAR~ED

WEDS

DIVORCES

BIRTH

NEWBORN

PRESCHOOL

SCHOOL=12

scHooL=13-15

SCHOOL= 16+

PRIOREXP

TIMEWORKING

INVOLUNTARY

WAGE

PARTTIME

UNION

CONSTRUCTION

TRANSPORTATION

TRADE

PUBADMIN

SMS.4

SOUTH

UNEMPLOYMENT

Y6667

Y6869

Y7071

Y7273

Y7475

Y7677

Y7879

Y8081

Y8283

Y6485

L(6)

a

O*

Log Mkefihood

1944-42

Normal

COef. Stat.

.002 .02

-.022 -.34

-.192 -1,23

-.113 -.54

-.274 -3.65

——

——

-.002 -.02

-.061 -.65

-.391 -4.62

-.043 -2.91

-.401 -3.55

.006 .11

-.230 w3.34

.901 9.49

-.635 -7.05

.486 5.80

-.320 -2.84

‘.106 1.60

-.464 -3.18

-.056 -.84

-.085 -1.41

-.120 -3.57

-.613 -3.62

-.300 -2.01

.228 2.02

-.424 -3.S8

——

——

——

——

— —

— —

-.024 -12.58

.1.748 -5.64

-.777 -3.27

1.188 13.73

-6871.740

1950-52

Normal

20ef. stat.

..049 -.87

..077 -1.28

..191 -1.55

.119 .86

-.150 -2.17

— —

— —

-:204 -2.94

..178 -2.29

-,518 -5.31

-.018 -1.40

-.491 -5.37

.261 4.18

-.193 -2.84

.823 8.18

-.492 -7.00

.546 7.02

-.524 -4.76

.215 3.61

-.412 -3.54

-.074 -1.33

-.126 -2.41

-.220 -8.09

——

— —

——

— —

——

.535 7.52

.053 .62

.407 3.54

——

——

-.019 -13.00

-.437 -1.31

.586 1.22

-.837 -4.54

-7711.512

29

Wo

1944-46

Normal

COef. Stat.

-.111 -1.84

.190

-.313

-.249

.249

.040

.184

-.021

-.156

-.159

-.098

1.152

.451

-.432

.278

.549

.515

-.263

.146

-.372

.325

-.067

.242

—

.852

.762

.699

-.418

—

—

—

—

—

-.018

4.962

..054

1.587

2.68

-1.80

-1.33

2.72

.19

2.65

-.28

-1.71

-1.19

-8.44

-8.76

6.42

-5.64

3.60

5.88

3.10

-1.26

1.95

-2.58

5.57

-1.05

7.21

—

5.01

6.41

5.32

-3.87

—

—

—

—

—

-9.89

-14.71

-.41

11.42

-5481.353

EN

1952-54

Normal

COef. Stat.

-.023 -.42

-.061

-.020

.217

.316

.480

.028

-.239

-.371

-.421

-.059

-.28i

.098

-.351

.412

-.087

.218

-.127

.242

-.386

.004

.062

.216

—

—

—

—

—

—

-.555

-.192

.019

2.707

-.015

3.707

—

—

-1.05

-.14

1.31

4.32

3.30

.44

-3.77

-4.39

-4.24

-4.61

-3.24

1.18

-5.74

6.56

-1.17

.96

.-.69

3.61

-2.46

.08

1.27

4.92

—

—

—

—

—

—

-5.75

-2.43

.17,

-5.13

-10.85

-9.81

—

—

-5353.675
The cohort compmisons me presented in Table 11. Although the emly md late cohorts of

women ~e separated by mdy eight years, the difference between them is quite remarkable. For

the early cohort, the coefficients on NIARRIED, BIRTH and PRESCHOOL aTe ~ positive md

significant. For the late cohofi, BIRTH is the ody one of these thee that remtins si~ificant,

ad NEWBORN. dso registers a ve~ lmge, positive effect. Th=e ~timates reve~ that fmily obligations have not ce=ed to tiect women’s quit rates, but they am cofined to a mud

shorter period—titer ~, a pr~chOOIer is mound for six years, but the combined events of BIRTH

md NEWBORN l~t for only one year. The cohorts dso differ in thtir response to s&Oohng

the h~~d of the emly cohort is unflected by the s&OOfing dummies, but the late cohort’s

h=ard falls shmply u educational attainment increases. Interestingly, the coefficients on PRIOREXP and TIMEWORKING are less negative for the late cohort thm for the early cohort,

while UNION goes from positive to negative (ad insignific~t). With these &mgffi over time in

the turnover behavior of women, the coefficients On only four variables—BIRTH, SMSA, SOUTH,

ad UNEMPLOYMENT—have different signs for men and women. The inter-cohort tiffwenc=

-ong men are @nsiderably less pronounced. The bi~est changes are in schoofing and TIkfEWORKING, whiti have larger (negative) effects on the late cohort than the early cohort, md in

BIRTH ad PRIOREXP, both of which become l-s negative over time.

While fmily obligations have a diminished impmt on the ymmger women’s h~md rate, unobserved heterogeneity h= no impact whatsoever. This is in marked contr=t to the early cohort.

Among the= women, 61 percent are movem and the h=ard rate of the ,movers is 4.9 times =

high u for the stayers. The effect of unobserved heterogeneity dso virtu~y disappem over time

for the men. Among the early cohort, 37 percent are movers and their h==d rate increw- by a

fwtor of 3.3, while only 17 percent of the late cohort can be demed movers.

The message d~vemd by Table 11 is that employers shmdd have no difficulty predicting who

wi~ quit when they look at a sample similar to the late cohort of women. The importmt determinmts of these women’s h~ard rate are not only observable but, with the exception of BIRTH ad

NEWBORN, they are known at the time of hire. Furthermore, the two ‘unpredictable” factors

me, by their ve~ nature, short-~ved eventi. This is in mmked contr~t to the fmily chmxter30
istics that rtise the h~md of the emly mhort (MARRIED, BIRTH md PRESCHOOL), some of

which tend to endure for years at a time. From an employer’s point of \,iew, single women and

women who are likely to give birth in the near future appear to be ristier prospects than their mde

counterparts. HO\vever, women who me Ekely to have completed their desired fertifity app~r to

be safe bets, e~,en when their children are still quite young.

h addition to cltiming that non-quitters are identifiable in the late cohort of women, we cm

dso conclude that employers would be unwise to favor men over women w,hen attempting to pick

the non-qtitters from tkis pOOl of workers. A yourig worker of either gender is prone to future

turnover, of course, but the abifity to predict turnover do= riot incre~e when we confine our

*

sample to men. In fact. it may actua~y decre~e, since a smau proportion of the men are movers

for rewons that cannot be observed. Furthermore, we ~timated the hazard model for a combined

saple of men and women (using workers born in 1950-52) and controlled only for vmiabl= that

employers Obser\,e at the time of hire, i.e., we omitted \VEDS, DIVORCES, BIRTH, NEJVB ORN,

PRESCHOOL and the correction for unobserved heterogeneity. The estimated coefficient On a

dummy nriable equal to one for women is .0.19 with a normal statistic equal to .048. That is,

titer contro~lng for obsemables, the hazard rate of job separation is 17 percent lower for women

than for men. By contr=t, the gender effect is zero when we peflorm the same experiment on a

saple of men and women born in 1944-46.

31
Section 5: Estimates for Selected Sub-Samples

k the previous section, we exafined job separatimrs without regard to the type of trmsition

being undertaken. Jobs cotid end tither voluntarily or involuntarily, ad they cotid be fo~owed

by mother job, unemployment, non-employment, or an unidentified spe~. In addition, we i~ored

heterogeneity in individrrds’ commitments to the labor force. In order to augment our conclusions

about the comparative job shopping behavior of men and women, we now re-estimate our h~md

model for a vmiety of sub-smples. First, we 100k at five separate l~or market entry cohofis. We

then focus on a sample Of jobs that end voluntarily, regardless of the type of spe~ that is entered

into. We dso ex~ine a sample of jobs that are fo~owed by another job, regardless of whether the

trmrsition is voluntary or involuntary. Finally, we restrict oursdves to the job-twjob trmsitions

undergone by continuously employed workers,

Entry Cohorts

We focus on workers whose careers began in a specified calendar year in order to adtiess two

questions. The first is whether succe~i ve entry cohofis differ in their t~rnover behavior, md the

wend is whether the behavior of men ad women convwges wer time. TO accomphsh this, we

estimated our h~ard model for five successive entry cohorts for each gender. For the men, we

sdected workers whose meers begmr in 1966-67, 68-69, 70-71, 72-73 md i4-75; for the women,

the entry years me 1968.69, 70-71, 72-73, 74-75 and 75-76.1s Workers in each cohort are fo~owed

for the first six years of their careers: workers whose careers begin in 1966-67 ~e”followed rrntfl

1971, etc.

Estimates for the five entry cohorts are presented in Tables 12 ad 13. Unfortunately, the most

dramatic numbers in these tables are in the bottom two rows, whi~ indicate that the smple sizes

shink considerably with each sumessive cohort. The fourth and fifth mde cohorts conttin only 170

ad 63 workers (2,373 and 833 observations), r~pectively. The third female cohort—which entered

15T~= fir.t entry cohort for both ~ender$ d= incl.d~ workiri whose care= startd prior ~ the first”interview

but who sti wre in their fist jobs.

32
in 1972-73, the same years as the fourth male cohort—h- only 419 workers (7,433 obser~,ations),

while the next two cohorts conttin 161 and 109 w,orkers, respectively. These sma~ samples compel

us to discount the later cohorts—and, in fact, almost every secular pattern that we ca detect

stops abruptly with the fourth cohort,

Nevertheless, we do see that several variables become more important for each sucussive mde

cohort. The coefficients on MARRIED, WEDS and DIVORCE become incre~ingly negative for

the first thr- cohorts. The coefficients on PRIOREXP display an even more pronounced pattern:

they go from -0.023 for the 1966-67 cohort to -1.33 for the 1970-71 cohort. However, the effects of

WAGE ad TIMEWORKING evolve in a non-monotonic f=bion.

For the women, we find that the coefficients on PRIOREXP, TIhfE\VORKING, and }i’AGE

dl become more negative with ewh of the first three cohorts, although. the pattern is the most

pronounced in the c~e of WAGE. The only non-job-related variable that shows a pattern (and

one of the few with a non-zero effect) is h’EWk ORh’. Apparently, the presence of w infant raises

the hazard rate by incre=ing mounts with each successive entry cohort. Because we must restrict

our attention to the first three cohorts for each gender. there are only two ( 1968-69 and 1970-71)

with which we can m&e a v~d comparison across genders. Unfortunately, this information is too

fitited to allow us to draw inferences about the convergence of mde =d fem~e turnover behavior.

Another Utitation of these estimates is that the we ad schoofing distributions shift to the

right writh each succ=sive cohort. Since W the members of the full sample were 14”to 24 y-s old

in the first year, the workers who begs their car~rs later in the survey were relativdy younger

md/Or relatively better educated. k other w,ords, the five entry cohorts differ tigtificantly from

=ch other in two important demographic &mensiOns.

Voluntary ~ansitions

Table 14 presents wtimates b~ed on samples of voluntary transitions. Summ=y statistics for

thaae smple are reported in Tables A4 ad A5 in the AppendIx. As discussed in Section 3,

NLS respondents were repeatedly =ked why their lmt job ended. We are often able to fin~ thaae

r~pons~ to their l~t job, thereby Iearllillg why a subset of jobs will end. When the repofied

33
NOnwKlte

Mamied

Weds

Divorces

Birth

School

PriOrmp

TlmewrHng

Involunt=y

Wage

Pmttime

Union

COnstmctiOn

=asportation

made

Pubtitin

SMSA

South

Unemployment

Yea dummy I

Ye= dummy 2

7

10g(6)

Q

Oz

Log Ekelihood

Ye= dummy 1

Yew dummy 2

# of Workers

# of Ohs.

Table 12: Estimates for Gompertz H~=d Model

Correction for Unobserved Heterogeneity

(SAMPLES: F,ve Entry Cohorts of Men)

1966-67

P ~

.065 1.16

-.188 -3.59

-.395 -4.11

.4&7 .1.98

-.423 -5.39

-.033 -3.71

-.023 -1,07

-.815 -9.59

.331 4.27

-.038 -.79

.428 6.71

-.734 -8.16

.285 4.28

-.167 -1.64

.014 .24

-.351 -2.58

-.152 :2.90

-.003 -.07

-.057 -2.94

-1.282 -10.19

-.507 -5.20

-.041 -18.23

-.969 -3.86

-.154 -.70

1.019 8.84

-8904.015

Y6667

Y6869

1183

18,175

1968-69

bg

.031 .45

-.200 -2.71

-.381 -3.36

-.700 1.85

-.563 -5.37

-.014 -,94

-.482 -11.99

.723 8.37

.351 :.88

-.244 -3.92

.241 -3.44

-.576 -6.32

.255 .3.18

-.172 -131

.238 3.75

-.241 -1.51

-.227 -3.21

.014 .22

.027 1.52

-.029 -.29

-.160 -1.61

-.038 -9.64

-1.655 -6.31

-.888. -6.22

1.270 .17.67

-5814.019

Y6869

Y7273

741

10,507

1970-71

B&

.094 .90

-.509 -5.34

-.787 -4.48

.858 2.25

-.454 -2.93

-.069 -3.23

-1.330 -17.78

-1.392 -11.79

.301 2.28

-.192 -.2.45

.665 6.30

.1.083 -7.83

.485 3.40

-.413 -2.52

.033 .36

-.519 -2.09

.043 .41

-.367 -4.43

-.172. -7.47

-.059 -.48

2.142 9.67

-.083 -14.23

3.510 8.87

.1.110 -7.39

.2.157 -13.45

-2281.705

Y7273

Y7475

395

5,759

1972-73

-.069 %

B“

-.26

-.386 -2.05

-.289 -.88

-.271 -.50

-:370 -1.17

.027 .51

-.124 -.86

-2.034 -3.83

2.623 2.32

-.913 -3.56

-.069 -.26

-.400 -.93

.208 .71

.334 .76

.018 .08

-.502 -1.19

-.161 -.88

-.303 -1.68

-.095 -2.59

-2.918 -6.08

-.842 -3.20

-.026 -3.24

.11.839 .09

.320 2.39

13.391 .11

-706.247

Y7273

Y7475

170

2,372

1974-75

Bg

-.426 -.83

.374 .93

.429 .61

1.463 1.52

-2.342 -2.65

-.172 -1.56

-.526 -1.95

-2.291 -268

.056 .06

-.279 -.81

4.218 6.32

-1.003 -1.82

-1.377 -1.75

-3.417 -3.48

-2.078 -3.45

-.607 -.93

-.359 -.81

-.481 -1.18

-.046 -.55

-2.053 -3.42

.568 1.1s

-.016 -1.08

4.271 1.73

.026 .13

-3.866 -6.57

-243.233

Y7475

Y7879

63

.833

34
retion is health, f-ily, pregnancy, job dissatisfaction, found a better job, school, or other quit,

we view the job w ending in a \,oluntary transition. There are 3,403 such jobs for the men and

3,418 for the women.

Since Table 14 is hued on dl workers in the “full sample” who report a voluntary transition, it

should be compared to Table 9. For the men, the coefficients on PRIOREXP, TIkfE\VORKING,

ad WAGE me roughly the sme regardless of whether we look at all traditions or volunt~

trmsitions; the primary difference is that PRIOREXP is not ,X important a factor in deterring

voluntary transitions ~ it is in deterring other types of transitions. LooKlng at the demographic

vmiables, we see that MARRIED remtins negative, but h= a smaller magnitude than in the

all-transitions smple. AIso, SCHOOL is not m important determinant of voluntary transitions,

although it previously had a negative (and significant) coefficient.

Among the women, PWOREXP and WAGE continue to have the same negative effect mr

the hazard. TIM Etl’ORKIN G, however, is no longer a significant factor. Apparently, a woman’s

labor market history influences her chance of being laid Of or fired, but. not her propensity to quit.

.

Surprisingly, we see that the coefficients On WEDS and NEM’B ORN me no longer signifimnt.

Women who have pre-school aged children, on the other hand, are more fikely to quit their jobs.

Although this latter finding is unsu~rising, tbe fact that WEDS ad NEWBORN do not

increwe the h==d prompts us to =k whether our memure of voluntary transitions is refiable.

Not mdy might workers provide insincere responses to questions about the re~ons behind their job

sepmations, but the difference between a quit and a layoff is conceptu~ly indistinct. Furthermore,

because so few jobs can be *sociated with a re=on for their eventual dissolution, we may be left

tith a nonrepresentative smple. Although we do not entirely distrust the results in Table 14, the

dtia qutity is not high enough to wamant fmther analysis.

Job-to-Job ~ansitions

We cm view our sample of job-to-job transitions with considerably more confidence. As noted in

Section 3, this sample consists of dl job separations that are followed less than three months later

by a new job. That is, we elitinate virtually d job-t~unemployment and job-tenon-employment

35
Nonwhite

Mmied

Weds

Divorces

Birth

Newborn

Preschool

School

PtiOrexp

TlmeX\,Orking

Involuntuy

M7age

Parttime

Union

Construction

Transportation

Trade

Pubtitin

SMSA

South

Unemployment

Ye= dummy I

Y- dummy 2

7

10g(6)

o

82

LOS Hk&hood

Ye~ dummy 1

Ye= dummy 2

# of WOrkers

# of Ohs.

Table 13 Estimates for Gompertz H==d Model

Correction for Unobserved Heterogeneity

(SAMPLES: Rve Ent~ Cohorts of Women)

1968-69”

b +

.077 1.16

.280 4.50

.003 .03

-.556 -2.98

.263 3.43

.433 3.91

-.055 -.79

-.017 -1.18

-.259 -8.96

-.560 -5.32

.581 7.96

-.651 -10.48

.382 5.86

.835 12.85

.449 2.48

-.468 -3.42

.280 4.44

-.343 -2.44

.142 2.25

.102 1.69

-.03i -2.22

-.431 -4.60

.094 1.22

-.030 -14.55

.1.565 -5.75

-.137 -1.21

1.470 16.42

-6613.047

Y6667

Y6869

964

13,869

1970-il

P&

.086 1.16

.014 .20

-.399 -3.79

-.391 -1.64

-.385 -4.01

.680. 5.11

-.285 -3.14

.000 .03

-.439 -9.61

-.674 -5.71

.643 6.19

-:780 -10.80

.365 5.01

.823 12.66

.281 1.29

-.922 -5.60

.297 4.27

-.515 -2.76

.019 .25

.050 .72

.038 2.82

.142 1.71

-.044 -.26

-.030 -7.20

.2.693 -10.52

.103 1.35

2.107 17.42

-4795.413

Y6869

Yi2i3

725

10,811

36

1972-73

b $

.162 1.47

.164 1.73

-.176 -1.14

.436 1.57

-.203 -1.71

.902 4.79

-.364 -2.75

.027 1.02

-.359 .-6.04

-.622 -3.65

.304 1.86

-.805 -6.89

.482 4.67

.786 7.11

.597 2.08

1.150 -3.35

.539 4.89

.080 3.44

.054 .55

.017 .18

.076 4.09

-.389 -1.76

1.09i -7.78

-.016 -2.97

3.789 -6.90

.034 .24

2.058 9.64

-2640.479

Y7273

Y7475

419

7,433

1974-75

B&

-.li4 -.79

-.228 -1.11

.325 1.09

.419 .78

.071 .28

.688 1.72

.135 .42

-.042 -.72

-.275 -1.81

-.335 -.66

.753 1.75

.248 1.06

.776 3.72

-.142 -.44

.393 .39

.185 .36

.190 .71

-.114 -.30

.192 .9i

.205 1.06

-.150 -2.49

.221 .80

-.483 -1.53

-.030 -3.10

.1.374 -1.09

1.782 .13

.200 .50

-70i.034

Yi2i3

Yi4i5

161

2,647

1976-77

B &

.184 .59

.024

1.630

19.419

.239

-1.139

.278

.025

-.545

-2.159

-.902

-.33i

1.427

.093

1389

19.251

1.107

-.232

-.543

-.405

-.282

-.295

2.271

-.001

1.631

-.086

-3.845

.07

2.97

-7.75

.73

-1.4s

.53

.27

-2.85

-2.i5

-1.31

-1.20

4.65

.25

1.42

-8.li

1.77

-.39

.l.~~

-1.06

-4.09

-.81

4.39

-.15

1.16

-.55

-8.46

409.085

Y7475

Y7879

109

1,693
Table 14: Estimates for GOmpertz Haz~d Llodel

Correction for Unobserved Heterogeneity

(SAMPLE Jobs Ending in a Voluntary Transition)

Vmiable

NONWHITE

MARRIED

WEDS

DIVORCES

BIRTH

KE\YBORY

PRESCHOOL

SCHOOL

PRIOREXP

TIMEWORKING

INVOLUNTARY

WAGE

P.4RTTIhlE

UNION

CONSTRUCTION

TRANSPORTATION

TRADE

PUBADMIN

Sh$SA

souT1i

UNEMPLOYMENT

Y6869

Y7071

Y7273

Y7475

Y7677

Y7879

Y8081

Y8283

Lg(t)

a

62

Log Ekefihood

hI EN

Normal

Inefficient Statistic

.163 1.99

-.125

-.263

.212

-.336

—

—

-.006

-.059

.444

.310

-.182

.584

-.135

.331

-.165

.243

-.362

.173

.016

.053

.617

.853

-.083

-1.438

.046

.862

1.272

—

-.009

-1.390

.263

-1.930

-1.90

-2.22

.99

-3.99

—

—

-.48

-4.27

4.53

3.65

-2.85

6.89

-1.42

3.77

-1.14

3.71

-2.16

2.65

.25

3.48

3.83

4.75

-.42

-6.17

.21

4.02

4.82

—

-5.71

-5.82

3.20

-18.24

-6212.378

37

WIOMEN

Normal

~oefficient Statistic

-.009 -.14

,017

-.131

-.101

-.047

.161

.150

-.022

-.0i8

.072

.435

-.495

.296

.721

.472

-.566

.270

-.216

.088

-.004

-.023

.054

.053

-.547

-.639

,-.967

-.864

.153

.634

.002

-.705

-1.208

-1.533

.29

-1.33

-.75

-.58

1.28

2.18

-1.52

-5.68

.77

5.12

-7.34

4.74

10.43

2.57

-3.48

4.37

-1.37

1.36

-.08

-1,73

.17

,16

-1.68

-1.86

-2.75

-2.48

.41

1.51

1.12

-1.91

-7.89

-12.91

-6323.322
trmsitions.

The estimates for these. s-Pi=, which appear ii Table 15, reveal several contr~ts. First, the

coefficients On a number of key t.ariables go to zero. The coefficient on MA RMED, which W*

-0.205 for the men in the full (dl transitions) sample (Table 9), is now zero. Relati>,e to v,hat

we saw in Table 9, the coefficient on WEDS is now smaller for men and larger for women, but

neithw coefficient rem~ns si~ificmt. The effect of SCHOOL md TIMEWORKING goes to zero

for both genders, although. each had a negative effect on the huard rates of the full s~pls.

PRIOREXP no longer lowers the hazard for men, while UNION no longer rtisw the hward for

women. Apparently, W of th~e factors make workers more or l~s Hkely to leave the labor force

or become unemployed, but they haie no effect on a worker’s propensity to accept a new job.

Other variables prove to be more important in explaining job-to-job transitions than they were

in explaining d tmsitions. In Table, 9, we sa~v that BIRTH lowers the h=ud for men, but not

for women. h’ow w,e see a coefficient of roughly -0.i for both genders; that is, both men ~d women

are far less Ukdy to chmge jobs around the time of a new birth. We d~o see that DI\rORCE now.

h= a negative md significant coefficient for women, whi]e the effect of pRESCHOOL incre=e~

drmatic~y relatively to what we saw in Table 9. Both results ~e somewhat surprising, since we

mpect recently divorced women to be more Ekely to accept a new job, tihile women with young

tildren tight be constrained in their ablfity to undertake a joh hunt. Overall, however, we see

that job-to-job movements are generated by a smder set of covariates thm ae other types” of

traditions.

Continuously Employed, Workers

In focusing on job-tejob trmsitions, we effectively I.ooked at dl types of workers, regardless of

their overall commitment to the labor force”; in some sense, we were looking at workers who were

job shopping ‘at the moment.,, We now 100k at a smple Of w~rkeri””who we fikely to be ‘exnestjob shoppers by virtue of their demonstrated commitment to the labor force. Though, of course,

workers with a demonstrated commitment to the labor force need not be constatly shopping for

a new job. TO identify these workers, we me~ured the percent of their observed time that is

38

●
T~ble 15: Estimates for GOmpertz Hazmd Model

Correction for UnObser\.ed Heterogeneity

(SAMPLE Job-t&Job ~msitions)

Vmiable

NOXWHITE

MARRIED

WEDS

DIVORCES

BIRTII

NE\VBORN

PRESCHOOL

SCHOOL

PRIOREXP

TIhfE\!’ORKING

INVOLUNTARY

WAGE

PA RTTIME

UNIOY

CONSTRUCTION

TRANSPORTATION

TRADE

PUBADMIN

SMSA

SOUTII

UNEMPLOYMENT

Y6869

Y7071

Y7273

Y7475

Y7677

Y7879

Y8081

Y8283

70g(J)

a

e=

Log hkehhood

MEN

Normal

coefficient Statistic

-.101 -1.24

.091

-.271

-.121

-.733

—

—

-.002

-.003

.022

.160

-.244

.224

-.571

.214

-.018

.072

-.654

.013

-.Oli

-.035

1.253

2.193

.495

.437

1.041

1.366

2.353

—

-.024

-2.378

-1.120

2.317

1.27

-1.61

-.38

-6.13

—

—

-.13

-.18

.23

1.82

-3.41

2.31

-3.96

2.43

-.10

.92

-3.52

.17

-.24

-1.85

7,62

11.51

2.12

1.71

3.91

4.96

7.42

—

-9.66

-9.49

-9.67

13.26

-3709.703

39

~efficient Statistic

.125 1.03

.028

-.326

-1.662

-.710

-.144

.441.

.032

-.125

.027

1.225

-.539

.365

1.213

.708

-.337

.li4’

-.368

-.188

.183

.251

—

-.317

.003

-2.410

-1.489

-2.014

-2.512

.225

-.007

-4.213

-.167

1.988

-1848.164

.26

-1.59

-3.85

-3.60

-.64

3.24

1.29

-4.12

.15

9.26

-6.34

3.06

11.55

2.48

-1.09

1.55

-1.59

-1.81

1.58

10.30

—

-1.61

.01

-7.25

-4.90

-5.06

-5.68

.49

-2.34

-10.59

-1.34

18.41
known to be spent w,orkin~ anyone with a number abo~,e the median of the mde distribution (88

percent) is considered “c6ntinu0usly” mnpioyed. More accurately, these are workers who ~e more

comtitted to the work force thm is the medim man in our smnple..

Looking at Table 16, we agtin see the disappearance of several effects that ae evident in the

frdl s-pie. MARRIED lowers the hazard for men in the full s~ple, but it h~ an insignificant

&ect on continuously employed men. TO some de~ee it is likely that marital status b= no effect

because most of th~e men are married: 69 percent of the continuously employed men and 59

percent of the ffl sample of men are married. We saw a positive coefficient on PRESCHOOL in

Table 9, but now we see that it h= no effect on the turnov~ of continuously employed women.

tiong the full smple, becoting married deters turnoveq among continuously employed workers,

it h= nO effect On w~men and a smder effect on men.

~lven the criterion for selecting the s~pl~ of continuously employed workers, the vmiable

~MEWORKING is Ukely to have a value clo~e to one for most workem’ second ~d subsequent

jobs. Therefore, we expect that there is fittle mriace in TIMEWORKJNG across second and

higher numbered jobs. Since the vmiable is set equal to zero for the first job, most of the variance

in TIM EWORKING arises horn the difference in its value betwen first jobs ad the rest. We

find that, for the samples, of continuously employed workers, TIMEWORKING is no longer a

significant determinant of turnover. This is m inter~ting finding since it impli~ that first jobs

ue no shorter thm higher numbered jobs for the= workers.

Chief mong the vuiables that now show a more pronounced effwt is BIRTH. The coefficient

for men does not change relative to Table 9, but the coefficient for women incre=es horn zero

.,

to 0.199. Presumably, this effect wodd disappear if we cmdd 100k at workers who w~e trtiy

mntinuonsly employed, since it is fikely to be piting up the jOb-tw OLF movements that remtin

in our smple rather than job changes. An alternative explanation for this effect is that women

&OOse to chmge jobs upon giving birth to a child. Women may favor jobs that d-red fewer

hours, or jobs on a tied schedule, Or even part-time jobs.

The coefficient on SCHOOL dso inme~es for women, from -0.026 to 0.039. This may reflect

that we~ educated ‘career women” ~e succeaaful job shoppe~. We dso find that the coefficients

40
Table 16: Estimates for GOmpertz H==d Model

Correction for Unobserved Heterogeneity

(SAMPLE Continuously Employed Workers)

Vmiable

NONWHITE

MARRIED

WEDS

DIVORCES

BIRTH

NEtVBORX

PRESCHOOL

SCHOOL

PRIOREXP

TIM E\VORKING

INVOLUNTARY

WAGE

PARTTIME

UNION

CONSTRUCTION

TRANSPORTATION

TRADE

PUBADMIN

SMSA

SOUTH

UNEMPLOYMENT

Y6869

Y7071

Y?273

Y7475

Y?677

Y7879

Y8081

Y8283

7

log(d)

Q

82

Log Ukehhood

41

MEN

Nomd

:oeficient Statistic

-.083 -1.57

-.061

-.292

.211

-.376

—

—

-.025

-.102

.016

.420

-.207

.680

-.408

.326

-.436

.173

-.577

-.100

-.002

-.028

.507

.877

.s75

.315

:602

.802

.694

—

-.022

-2.335

-1.718

.942

-1.33

-3.67

1.49

-6.66

—

—

-3.15

-10.38

.27

6.82

-4.60

11.41

-6.40

5.71

-4.70

3.81

-5.29

-2.45

-.04

-2.58

6.23

8.99

1.70

2.57

4.58

6.26

4.17

—

-21.41

-16.54

-5.65

13.57

-13647.154

WOMEN

Normal

~oefficient Statistic

-.142 -1.66

-.088

-.109

-.099

.199

.286

-.149

.039

-.060

-.245

.334

-.649

.580

.263

.407

-.581

.296

-.410

.181

.015

.055

-1:178

-1.222

-1.116

-1.849

-1.805

-1.855

-1.891

-2.178

-.015

-2.440

-1.115

1.237

-5585.833

.21

3.53

-3.06

-3.21

-2.93

-4.64

-4.50

-4.60

-4.45

-4.77

-9.20

-5.54

-3.93

12.75

-1.20

-.93

-.46

1.77

1.46

1.50

1.88

-3.44

-1.88

3.05

-6.95

6.36

3.17

1.75

-3.77

3.36

-2.82

2.19
on PRIOREXP are 1=s negative for both genders. This may point to the fact that continummly

employed workers mtinttin a relatively high (and freneficid) rate of turno~,er for a longer period

of time than does the fuU smple.

The fial r~tit that emerges from Table 16 concerns unobserved heterogeneity. When we

=amined the fall. sample, we learned that unobserved heterogeneity rtises the h~md by a factor

of 2.8 for the men =d 3.4 for the women. TMs r~ult is virtu~y unchmged when we focus

on continuously employed workers: the corresponding sttiistics me 2.6 and 3.5. However, our

conclntions about movers and stayers are now significantly different. Among d workers, we found

that 46 percent “of the men arid 58 percent’ of the women are movers for unobserved re=ons.

tiong continuously employed workers, these percent~es are 16 and 28. Although ~ emPlOYer

is still more fikely to “draw” a mover from among a pool of women than from mong a pool of

men, -ch pOOl conttins very few movers. In other words, mployers cm readily use observable

to predict which continuously employed workers will quit.

,.

42
Section 6: Conclusions

In this study, we estimated hazard models for various samples of young men and women in

order to learn how they differ in their turnover behavior. We cm now pro}fide aswers to the three

questions posed in the introductory section.

DO young women separate from their jobs more often than men? When we estimate a haz=d

model for a combined sample of men and women, controlling only for characteristics that the

employer can obseme at the time of hire, the coefficient on FE~fALE is zero for the early cohort

ad negative for the late cohort. TIUS indicates that men and women in the early cohort have

roughly the sae overd quit rate, but that w,omen in the later cohort actua~y have lower quit

rates tha thtir mde counterpxts. These overall quit rates reflect the activities of both movers

md stayers. M’e ha~re dso found that, in a saple of combined birth cohorts, women are more

hkely thm men to be mo~,ers (for unobserf,ed reasons). We condudp, therefore, that women s

a grmrp have a lower quit rate than men, but that an indi~,idu~ woman is mor,e likely than an

individud man to be a quitter. For the late cohort, however, we conclude unequivocally that

women have lower quit rates thm men.

DO young women quit primarily to have b~ies, or do they dso engage in job shopping?

Mthough we do not put the job shopping hypothesis to a rigorous test, we control for match qutity

ad previous (potential) job matching by including such me=ures w prior experience and wages in

our hazard models. We find that increties in experience and wages lower the h=md rates of both

men and women.in our fu~ samples. E\,en though the estimates reveal that the h~ard for men h=

a more pronounced degrw of negative duration dependence, our estimates are consistent with the

hypothesis that the labor force consists of significmt numbers of ‘carwr-oriented” women who use

the early part of their careers to shop for a durable employment relationship. Furthermore, when

we looked at successi~,e labor m=ket entry cohorts, we found that the effects of prior experience

have become even more important mrer time; ~ong women, the same is true for the current wage.

When we focused on ~roluntary and job-twjob traditions, ,we found that the presence of a

43
newborn has no effect on the h=ard rate. Women with a pres&OOler are more Hkely to mde

dther type of transition, hov,ever. h addition, we learned that women who are continuously

~plOyed =e mOre ~kely tO l~ve their jOb ~~rhenthey give birth, but the turnover of these women

is not influenced by either ne~.born Or preschool-age children. Only among continuously .employed

women md the late cohort of women is there the su~estion that the birth of a baby induces

tmnover.

Given that employers can only observe a handful of chmacteristics at the time of hire, is it

more difficult for them to identify non-qtitters among the women than among the men? We have

learned (U did He&mm and wIIfis (1977) and others) that not d women me Ake. h p~ticul~,

our full smple reveals considerable unobserved heterogeneity among the women—in fact, much

more than among the men. If an employer were to hire a man and a womm with sitilar obsemable

&aracteristics,’ the woman would be more ~kely to emerge x a quitter. This fi.ndiig, coupled with

the fact that women quit for re~ons that cannot be observed ez ante, leads us to conclude that

it is harder to identify female non. quitt ers.

When we focus on the late cohort of wo~kers, ho,<.ever, our conclusion is dramatically Atered.

Unobserved hetemgentity becomes a insignificant factor ~ong women, ad the only impoti~t

detetinants of women’s turnover that may not be hewn at the time df hire ~e the presence of

a newborn child and the act of becoming married. Both factors mise the hazard rate of women,

but neitha represents a long-term deterrent tOjob st&llity. Among this cohort, we conclude that

non-qtitters can be identified equfly weH song the men and the “women.

We re~h a sitilar conclusion when we focus on continuously employed workes. k these -

smples, 16 percent of the men and 28 percent of the women cm be termed movers for unobservable

or unmeuured re~ons. Clearly, an employer who mndomly hires a employee of either gender

wmdd be unhkely to discover that the worker is a mover. Furthermore, continuously employed 

women who become married are not more hkely to leave their jobs, &though women who give

birth Me. We conclude that, among continuously employed workers, non-quitters - be readily

identified reg~dless of gender.

44
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Press (1981): 65-90

Becker, Gary S., Human Capital. New York: Columbia University Press, 1975.

Blau, Wancine D. and Lawrence M. Kahn, “Rxe and Sex Dlfferenc~ in Quits by Young

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COX, Donld , “Panel Estimates of the Effects of Career Interruptions on.tbe Earnings of Women?

Economic lnquir~ 22 (July, 1984): 386-403.

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HasKlmOtO, Masanori, “Firm-Specific Human Capital u a Shined ~vestment,” Amen”can Economic Reuieu 71 (June, 1981): 475.482.

Heckman, James J. and Burton Singer, ‘Econometric Duration An&ysis,n Jouma[ of EmnOmetn’cs 24 (January/February, 1984a): 63-132.

‘A Method for Minimizing the Imp-t of Distributional Assumptions in Econometric

~dels for Duration Data,” Economettica 52 (March, 1984b): 2il-320.

Heckman, James J. and Robert J. Willis, “A Beta-1ogistic Model for the Analysis of Sequential Labor Force Participation by Married Women ,“ Joumai of Political Emnomy 85 “

(February, 19i7):27-58.

Kalbfleisch, John D. and &ss L. Prentice, The Stattitiml Analysis of Failure Time Data.

New York: John Wiley and Sons, 1980.

Light, Audrey, “Job Shoppin and the Wage Growth of Young Men; unpublished Ph.D. ~ssertation, University of C3 Ifornla~ at LOS Angeles, 1987.

‘Job Shopping md the Effect of WWe Cuts On”Wage Grrrwth,” Stony Brook Working

~er No. 306, May, 1988.

Li11ar~~8~= A., and Linda J. Waite, ‘Children ad M=itd Disruption,” PAA Meetings, April,

Meitzen, Mark E., ‘Differences in Mde and Female Job-quitting Behavior: Joumal Of btir

Economics 4 (April, 1986): lS1-16i.

45
Mincer, Jacob and Haim Ofek, “Interrupted W70rk C=eers: Depreciation =d Restoration of

Human Capital?’ Journal of Human R=oumes 17 (Wint=, 1982): 1-24.

Sandell, Steven and David Shapiro, “Work Expectations, Human Capital Accumulation, and

the Wages of Young Women.m Journal of Human Raourms 15 (Summer, 1980): 536-53.

Smith, James P. and Michael P. Ward, ‘Time-Series Growth in the Female Labor Force,”

Journa/ of bbr Economics 3 (January, 1985): S59-S90.

~ubman, Paul J., Jere R. Behrman, and Robin C. Sickels, aAge Specific Death Wta:

Bld White Differences,” PAA Meetings, April, 1988.

Topel, Robert, ‘Job Mobility and Eartings Growth: A Reinte~retatiOn of Hmm Capital

Earnings Functionsfl in Ronald G. Ehrenberg, editor, R=eaxh in La&r Economi~ Volume

8, Part A. Greenwich: JAI Press, 1985.

Topel, Robert and Michael P. Ward, “Job hfobifity and the Carwrs of Young Men: unpubEshed paper, Febmuy, 1985.

fiussell, James and Toni Richards, “Correcting for Unmemured Heterogeneity in H==d

Models Usin the Heckmm-Singer Procedure:

University, 1883.

Office of Population Researti, Princetmr

fima, Nancy, M. Hannan, and L. Groeneveld, aDynmic Analysis of Event H1stOri~,n Amerimn Journal of Smiolqy 84, (1979): 820-854.

.

Viscusi, W. Kip, “sex Differences in Worker Qtitting,” Reuiew of Econom;cs and Statist~- 62

(August, 1980): 388-398.

Waite, Linda J. and Sue E. Berryman, ‘Women in Nontmditiond occupations,” Wnd Corporation, Report R-3106-FF, Santa Monica, California, March, 1985.

Ward, Michael P., and Hong W. Tan, “Tlle Retention of High- Qufllty Personnel in the U.S.

~;8~d Forces,” Rad Corporation, Report R-31 17-MIL, Stita Monica, California, February,

46
Appendk

,.,

47
Table Al: Estimates for Wtibull H=md MOdd

Correction for Unobserved Heterogeneity

(SAMPLE: Workers Whose Careers Have Not Started Or Whose First

Jobs Begin or Are in Pr~ress in tl~e First Ye= of the Sumey)

Vmiable

NONWHITE

MARRIED

~T~~s

DIVORCES

BIRTH

NEWBORN

PRESCHOOL

SCHOOL

PRIOREXP

TIMEWORKING

Involuntary;

WAGE

PARTTIME

UNION

Constriction

TRANSPORTATION

TRADE

PUBADMIN

SMSA

SOUTH

UN Elf PLO1-lfENT

Y6869

Y7071

Y7273

Y7475

Y7677

Y7879

Y8081

Y8283

log(k)

k log(p)

a

MEN

Nomd

~oefficient Statistic

-.000 -.00

-.258

-.353

.106

-.285

—

—

-.045

-.072

-.533

.245

-.290

.530

-.5il

.420

-.279

.120

-.348

-.080

-.087

.005

.974

1.184

.382

.510

.836

.830

.763

—

-.371

-2.023

.130

1.233

-8.94

-6.75

1.07

-7.19

—

—

-8.54

-11.83

-12.29

6.57

-10.2i

14.57

-14.21

11.59

-4.94

3.92

-5.30

-2.71

-3.15

.65

18.59

18.15

5.61

5.99

9.68

9.96

7.13

—

-28.60

-17.39

1.77

WOMEN

Normal

~oefficiint Statistic

-.052 -1.57

-.007

-.143

-.097

.046

.385

.022

-.017

-.069

-.440

.409

-.579

.398

.444

.359

-.584

.318

-.401

.049

.045

-.015

-.381

-.251

-.573

-1.371

-.817

-1.578

-1.051

-.781

-.200

-1.569

.125

-.22

-2.71

-1.11

1.19

6.53

.67””””

-2.60

-12.05

-9.25

10.11

-18.62

12.48

13.56

4.12

-7.86

9.90

-6.05

1.59

1.55

-2.46

-3.56

-2.29

-5.20

-11.34

-6.~2-

-12.62

-7.89

-5.42

-15.21

-10.48

2.12

19.68 1.461 24.62

-30351.003 -27201.189

9.

48
Table A2: Sample Means and Standard Deviations

(SAMPLE: “Early” and “Late” Cohorts of 24-31 Year Old Women)

Variable

NONWHITE

MARRIED

WEDS

DIVORCES

BIRTH

NER7BORK

PRESCHOOL

SCHOOL=12

SCHOOL= 13-15

SCHOOL= 16+

AGE

PRIOREXP

TIMEWORKING

INVOLUNTARY

TENURE

WAGE

UN1ON

P.4RTTIhfE

CONSTRUCTION

TRANSPORTATION

TRADE

PUBADMIN

UXEMPLOYMEKT

SOUTH

SMSA

Y6869

Y7071

Y7273

Yi475

Y7677

Y7879

Y8081

Y8283

Y8485

Number of individuals

Number of observation:

Note swes_ples= Tab

EARLY LATE

Std. Std.

Definition Mean Dev. Afean Dev.

1 if nonwhite .298 .457 .314 .464

1 ifmamied

I ifmarries during interval

1 ifdivorces during interval

lifchildis born during interval

1 ifcllild ageone or under

1 if child age six or under

high school graduate

college dropout

cOUege graduate

Years Of potential prior experience

btioofactuti topotentid experience

I ifleftl=t jobin~,nluntarily

Years of tenure

Lognf real hourly wage

1 ifwages set by union

1 if \vOrksless than 35 hours per week

1 ifilldustryis

Collslruction, ~riculture, mining

Trmsp., cml>muniatioll, utifities

Trtie

Public administration

Unemployment rate

1 if fivingin the SOuth

1 if hving in an SMSA

1 if year is

1968-69

1970-71

1972-73 ,

1974-75

1976-77

1978-79

1980-81

1982-83

1984-85

.6il

.040

.027

.114

.024

.244

.448

.154

.209

27.794

5.193

.281

.025

3.093

1.743

.151

.167

.013

.042

.149

.063

6.ii3

.428

.746

.105

.256

.235

.261

.143

—

—

—

—

788.

14.865

.4i0

.196

.163

.317

.153

.429

.497

.361

.407

2.340

3.ill

.302

.155

2.824

.49i

.358

.3i3

.115

.201

.356

.243

1.476

.495

.435

.307

.437

.424

.439

.350

—

—

—

—

.050

.028

.140

.031

.244

.434

.181

.219

27.479

4.568

.422

.056

3.764

1.707

.193

.140

.016

.044

.154

.053

7.703

.39i

.718

—

—’

—

—

.158

.272

.277

.230

.064

1,019

20.673

,493

.219

.165

.346

.li4

.429

.496

.385

.414

2.206

3.642

.319 ““

.223

3. Oil

.452

.395

.346

.126

.206

.361

.223

1.072

.489

.450

—

—

—

—

.365

.445

.447

.421

.244

1.

49
Table A3: Sample Means and Stmdard Deviations

(SAMPLE “Early” and “Late” Cohorts of 24-31 Year Old Lien)

Variable

NoNm7HITE

MARRIED

WEDS

DIVORCES

BIRTH

SCHOOL=12

SCHOOL= 13-15

SCH00L=16+

AGE

PRIOREXP

TIM EWORKING

1NV0LUNTAR%

TENURE”

WAGE

PARTTIkIE

UNION

CONSTRUCTION

TRANSPORTATION

TRADE

PUBADMIN

UNEkIPLOYMENT

SOUTH

SMSA

Y6667

Y6869

Y7071

Y72?3

Y7475

Y7677

Y7879

Y8081

Number of individuals

Number of observation!

Note: smes_ples= Tab

EARLY LATE

Std. Std.

Definition Mean Dev. Mean Dev.

1 if nonwhite .195 .396 .274 .446 

1 ifmamied .773 .419 .651 .477

1 if marries during interd .037 .190 .058 .235

1 ifdivorces during interval .0.21 .142 .030 .172

1 if child is born during, interval .177 .381 .172 .377

high school graduate .379 .485 .394 .489

college dropout .156 .363 .235 .424

co~ege graduate .238 ..426 .219 ..414

27.248 2201 26.644 2.074

Yems of potential prior experience 4.196 3.279 4.9~5 3.384

~atio of actual to potential experience .306 .336 .467 .302 ~~

I if left last job involuntarily .033 .178 .0.53 .223

Yeas of tenure 3.295 2.822 3.555 2.844

Log of red hourly wage 2.125 .457 2.046 .429

1 if works less than 35 hours per week .028 .165 .028 .165

1 if wages set by union .153 .360 .273 .445

I if industry is

Construction .085 .279 .101 .301

Trasp., communi~tion, utilities .081 .274 .106 .308

Trade .136 .343 .154 .361

Public administr~ion .062 .242 .075 .263

Unemployment rate 3.333 1.309 5.694 1.260

1 if fivingin the SOuth .350 .477 .438 .496

1 if fivinginan SMSA .709 .454 .717 .450

1 ifyearis

1966-67 .077 .266 — - -

1968-69 .269 .444 — —

1970-71 .287 .452 — -

1972-73 .242 .428 — —

1974-75 .126 .332 .147 .354

1976-77 — — .299 .458

19.78-79 — — .288 .453

1980-81 —— .265 .442

853 1,438

20,019 28,220

1.

50
Table A4: Smple Means and Stmdard Deviations for Women

(SAMPLE: Voluntary Transitions, Job-to-Job TrmsitiOns, md Continuously Employed Workers)

Mmried

Weds

D1vOrces

Birth

Newborn

Pr-chool

School

Age

PriOrexp

Timeu,nrking

Involuntary

Tenure

Parttime

Wage

Union

Construction

Transportation

Trde

Pubtitin

Unemployment

SOutb

SMSA

Y6869

Y7071

Y7273

Y7475

Y767i

Y7879

Y8081

Y82S3

Number of indi

.

Voluntary Job-t-job Continuously

Transitions Tmnsitions Employed

Std. Std. Std.

Definition Mean Dev. Mean De\r. Mean Dev.

1 if nonwhite .251 .434 .248 .432 .261 .439

1 if married

1 ifmmries during interval?

1 if di~,orces during interval”

1 if child is born during intert~”

1 if child ageone or under

1 if child age six or under

Years of sclloobng

{ears of potential prior ~perience

Ratio ofactud topotentialexp.

1 if left I=t j,ubinvol,untarily

Years of tenure .

if works less than 35 hOrrrs/week

Log of red hourly wage*

1 ifwrages set by union

1 if industry is

Const., a~iculture, mining

Trarrsp., communication, utifities

Trade

Public adrnirristratiorr

Unemployment rate’

1 iflivingin the SOuth

I if living in an SMSA

1 if ye= is

1968-69

1970-il

19i2-73

1974-75

1976-7i

1978-79

1980-81

1982-83

,540

.079

.040

.115

.042

.224

12.945

24.526

3.078

.391

.080

1.647

.178

1.628

.138

.015

.044

.188

.049

10.090

.397

.772

.075

.142

.234

.193

.116

.126

.078

.498

.270

.195

.319

.200

.417

2.168

3.901

3.439

.337

.271

1.890

.383

.448

.345

.122

.206

.391

.216

3.083

.489

.420

.263

.349

.424

.394

.320

.332

.269

.489

.068

.031

.095

.045

.200

12.61 i

24:247

2.858

.403

.0i8

1.913

.126

1.639

.143

.028

.024

‘ .196

.065

10.380

.329

.777

.113

.209

.167

.13i

.162

.084

.094

.500 .488

.252 .061

.172 .028

.294 .088

.207 .026

.400 .172

2.052 13.503

4.064 27.140

3.262 2.544

.347 .414

.268 .0i3

2.166 3.918

.332 .076

.453 1.865

.350 .216

.165 :016.

.154 .083

.397 .098

.246 .081

3.184 11.118

.470 .386

.417 .792

.316 .052

..407 .092

.3i3 .122

.343 .128

.368 .137

.278 .147

.292 .135

.239

.166

.283

.160

.378

2~8~

4.i3i

3.402

.391

.261

3.6s5

.264

.394

.412

.126

.277

.29i

.272

3.327

.48i

.406

.2?2

.289

.32i

.334

.343

.354

.341

.033 .178 .035 .183 .187 .390

Iuds 1105 653 789

Number of observations 11,394 3,559 .30,570

- Since we only know that a change occurs between successive interviews, the variable equals one ior every thre-montil

intervalftifing betw=n the two interview dat-.

b In 1982 doUars.

‘ Unemployment rati during first month of tbe interval.

51
Table ..45: Smple Means and Standard Deviations for Men

(SAMPLE: Voluntary TrasitiOns, Job-to-Job Trmsitions, ad Continuously Employed Workers)

Vuiable

Nonwhite

Mamied

Weds

Divorces

Birth

Sdool

Age

PriOrexp

T!meworking

Involuntuy

Tenure

Pxttime

Wage

Union

Construction

Transportation

Trade

Pubadmin

TJnemplOyment

South

SMSA

Y6869

Y7071

Y72?3

Y7475

Y7677

Y7879

Y8081

Number of indi

VOluntary- JOb-t@jOb Continuously

Transitions Transitions Employed

Std. Std. Std.

Definition M=n Dev. Mean Dev. Mea De>,. 

1 if nonwhite .169 .3?5 .233 .423 .200 .400

1 if married .603 .489 .540 .498 .689 .463

1 if m=ries during interval”

1 if divorws during intervfla

1 if ctid is born dufing interval”

Years of schoofing

Years of potential prior experience

Ratio of actual to potential exp.

1 if ldt lmt job involuntarily

Years of tenure

1 if ’works less tha 35 hOur~/week

Log of real hourly wageb

1 if wages set by union

1 if industry is

COnst., a~iculttiie, milling

Transp., communi~timr, utifities

Trade

Public administration

Unemployment rate’.

1 if Uving in the South

1 if fiving in an SMSA

1 if ye= is

1968-69

1970-71

1972-73

1974-75

1976-77

1978-79

.070

.017

.158

13.116

25.264

3.587

.483

.093

1.992

.069

1.987

.117

.108

.057

.209

.046

9.620

.402

.704

.109

.189

.108

.172

.220

.140

.255

.129

.365

3.034

4.003

3.340

.346

.290

2.230

.254

.511

.322

.311

.233

.407

.210

2.937

.490

.457

.311

.391

.310

.377

.415

.347

.014

.144

12.534

23.776

.2.925

.478

.111

1.564

.081

1.922

.082

.141

.043

.189

.050

8.739

.434

.706

.209

.276

.087

.127

.133

.068

.249

.117

.351

2.930

4.147

3.091

.362

.314

2.097

.273

.546

‘:2-75

.348

.204

.392.

.218

3.107

.496

.456

.406

.447

.281

.333

.340

.252

.060

.021

.174

13.089

26.222

3.022

.497

.060

3.316

.038

2.081

.176

.084,

.076

.158

.059

9.571

.431

.711

.109

.148

.134

.152

.150

.140

.237 

.142

.379

2.938

4.571

3.463

.390

.237

3.412

.192 

.4i7

.3$1

.278 

.265

.364

.236

3.010

.495

.454

.312

.355

.341

.359 

.357

.34i

1980-81 .031 .173 .037 .188 .127 .333 

iuds 1066 935 1353

Number of observations 11,841 5,615 49,406

- Since we only know that a cl, ange occur. between succesi,.e interviews, the vmiable equals one for every thre-month

interval fd~ng bctwen the two interview dates.

b In 198? do~ars.

= Unemployment rate during first month of the interval.

52