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Discussion

Papers

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-..

FINAL REPORT

EVALUATING COMPETINGTWORIES OF WORKERMOBILITY

Henry S. Farber

National Bureau of Economic Research

1050 Massachusetts Avenue

Cambridge, MA 02138

(617) 868-3900

and

Department of Economics

Princeton University

Princeton, New Jersey 08544

(609) 258-4044

e-mail: farber@princeton.edu

March 5, 1992

.

Th?s project was funded by the U.S. Department of -bor, Bureau

of -bor Statistics under Grant Number E-9-J-9-0050. Opinions

stated in this document do not necessarily represent the official

position or policy of the U.S. Department of tibor.
. .

tiecutive Summary

Section 1: Introduction

Section 2: The Data

Es-1

..1

Section 3: How Important is Heterogeneity in Mobility

Section 4: Hov Mobility Varies with Tenure:

The Shape of the Hazard

Section 5: Concluding Remarks

References

Tables

4

13

33

44

47

50

following page 69

i

. .
1 Disposition of Sample

2, Sumary Statistics for

3 Monthly Probability of

Jobs

Job Change

by Experience and Tenure

4 Fre~ency pistributisn: N@er of Previous Jobs

by Years of E~erience

5 Average Number of Previous Jobs by Selected Worker

Characteristics by Year of Experience

6 Ordered Probit Analysis of Ntier of Previous Jobs

7 tigit Analysis of Monthly Turnover Rates

Selected Coefficients on Prior Mobility and Worker

Characteristics

8 Logit Analysis of Monthly TU~OVer Rates

Selected Coefficients on Prior Mobility and Worker

Characteristics - First Six Months on Job

9 .Logit ~alysis of Monthly Turnover Rates

Selected Coefficients on Prior Mobility and Worker

Characteristics - After First Six Months on Job

io Fre~ency Distribution: N@er of Prior Jobs Started

in Each Year Prior to Start of Current Job

11 Analysis of E~ality”of Prior Mobility Effects

All Periods

50 ,

51

52

53

54

55

56

58

60

-.

62

63

ii
12

13

14

15

16

17

Analysis of E~ality of Prior.Mobility Effects 64

First Six Months

Analysis of Eguality of Prior Mobility Effects 65

After First Six Months

Illustration of One-Year Survival Probabilities 66

Logit Analysis

Coefficients

Logit Analysis

Coefficients

Logit Analysis

Coefficients

of

on

of

on

of

on

Monthly Turnover Rates

Tenure

Monthly Turnover Rates

Tenure - First Six Months

Monthly Turnover Rates

67

68

on Job

69

Tenure - After First six Months on Job
1 survivor Function for Jobs: Product Limit Esttiate

~.

Hazard Rate for Job Ending (At Different Fre~encies)

3 Hazard Rate by Ntier of Previous Jobs

For Each Year of Ewerience

4 Monthly Hazard Rate for Job Ending

By Year of Prior Eqerience

iv
ObJectives

. . .

In this study I use a

EXECUTIVESUMMARY

sample of over fourteen thousand full-time jobs

held by workers in the National Louitudinal Survey of Youth (NLSY) to

examine mobility patterns and to evaluate theories of labor mobility (defined

as change of employer) . In particular, I investigate the followi~

questio~:

1) How important is heterogeneity in deternlni~ mobility rates for you

workers?

2) en heterogeneity in mobility rates be characterized as fixed

differences across” workers or as var”table with workers changing t~es

over time (either systematically or otherwise)?

3) HOW important ‘is state dependence in mobility rates? In other words,

does mobility vary importantly with how low a worker has held his or

her Job?

4) Doe: mobility decline systematically with how long a worker has held his

or her job, or are there periods where likelihood of mdility increases?

51 mat do the facts discovered about the mture of the relationships

between mobi 11ty and both heterogeneity and state dependence tel1 w

about what actually causes mobility? Specifically, how important is the

accumulation of specific capital, how im~rtant is the the ~ality of

particular matches between workers md firms, and how importat is the

mderlyi~ variation in the stability of workers?
Methods

The methods used in this study exploit two features of the NLSY that

differentiate this study

new insight:

1) The NLSY contains a

longi tud.inal survey

from earlier work and allow for the possibll ity of

more complete

of comparable

employment history than any other

le~th. Virtually all Jobs since

entry to

21 The NLSY

dates of

The first use

the labor market are reported.

allows precise determination of how long Jobs are. The actual

the begiming and the end of all Jobs are reported.

of the complete emplowent history is to determine when workers

initial ly m&e a substantial comitment to the labor force defined by me as

three consecutive years working on full-time Jobs for at least half of each

year. A complete Job history from this point forward for 3776 workers who

made this comitment to the labor force between 1979 and 1985 covers 14160

ful”l-time job”s-””andserves–as the.basis for the aalysis. The precise

information on duration is used to compute monthly probabilities (hazards) of

jobs endiu, and this yields findings not possible with 10wer fr@qu@ncY

measures of mobility (annual or @rterly).

The first part of the analysis uses the complete emplowent histories

and the monthly hazards of Jobs ending to determine the import-ce of

heterogeneity. This is accomplished using two statistical models. First, an

ordered -probi t analysis of the number of jobs held by workers in a given

period of time since entry 1s estimted in order to examine how mobility

varies with observable worker characteristics such as sex, race, age, and

education. The ordered probi t mode 1 is appropriate given the ordered-y.etdiscrete nature of the tout of mber of jobs held- Second, a discrete-time

logit model of the monthly hazard of a Job ending is estimated conditioning

on the detailed history of mobility prior to the start of the job along with
the observable characteristics of workers. This 1S done in

how weasured differences in mobility rates are related to

and whether the relationships are fixed over time.

order to examine

future mob LliLy

The second part of the analysis uses the same discrete-time Iogit model

of the monthly hazard of a job endi~ In order to investigate how the hazard

varies with how 10U workers have held their jobs. It is particularly

important that prior mobility is accomted for when carryl~ out this

analysis in order to minimize the usual heterogeneity bias in estimati~ the

role of duration in hazard models. The discrete-time logit model is an

appropriate tec~lque fOr this analYsis because it allows for a completely

flex’iblebase 1ine hazard. The specification used allows each month early in

the job to have a dlfferen~ baseline hazard rate.

Findings

There are five main findings. me first

workers in mobility rates. The last two

three relate to heterogeneity across

are about state-dependence or how

mobility rates vary with how low a worker has held his or her Job.

. .

1) Mobility in a new Job is stro~ly positively related t; the fre~ency of

job change prior to the start of the job. There is substantial

heterogeneity in mobility rates, and this persists throughout subsequent

jobs .

2) Job change in the most recent year prior to the start of a new job is

more stro~ly related thm earlier job cha~e to mobility on the new

job. Thus, heterogeneity in mobility rates is not fixed over time, and

workers seem to ch~e, be;omi~” fundamentally mire or less mobl le ove”f

time.

3) Females hold significantly fewer jobs than do males in a fix~ period of

time early in their careers. Thus, females who have comi tted to the
4)

5)

labor force exhibit less mobility than otherwise e~ivalent males. mis

result seems to be driven by a lower exit rate for’females from the

first Job after entry. Potential li~s between the fact that the sex

differential in wages rises with ewerience and the lower mobility of

yo~ females needs to be investigated further.

Mobility rates are very high early in Jobs. me-third of jobs end.

within the first SIX months, and one-half are over within the first

year. Mobility rates are much lower later” in Jobs. ~ese findings

demonstrate the importance of detailed analysis of mobility early in

Jobs, and they suggest the tiportance of accumulation of specific

capital (in the form of both Job-specific skills and Job-specific

information) on the Job.

me monthly hazard of Job endi~ is not mono tonlcal Iy decreasing in

tenure as most earner work usiw anual data has feud, but it

increas- to a maximm at three months and dec Iines thereafter. nis

findi~ 1S

appears in

consistent

~lity of

Job.

Conclusions

robust to controlling for worker heterogeneity, and it

Jobs starting at ._anypoint after entry. ~is finding is

with a situation where workers md employers learn about the

the worker-f irm mat ch over the flrst several months on the

—

me flndl~s s~est that heterogeneity is a very important determinant

of mobility rates. However, this heterogeneity is not fixed over time, and

this suggests that public policies designed to help yomg workers be mor@

stable could have a real effect. Workers do change types. Important work

remains to be

heterogeneity

done e~licitly m’odeli~ the stochastic process mderlying the

and examini~ the extent to which there is systematic
maturation of workers from less stable types into more stable types.

me findi~ that the hazard actually increases early in Jobs before

declining is consistent with models of heterogeneous match quality that

c-et be observed ex,ante. In this type of model, workers and employers

learn over tlme whether the worker-job match 1s a good one. If the match

revealed to be good it persists. Othewise it ends. The general decline

mobillty rates after the first few months on the Job 1s consistent with

continued investment in’specific capital on the job, but this investment

is

in

occurs only after an initial perl~d when it is determined that the emplowent

match is llkely to survive. To the extent that ex ante tinom match quality

is an important cause of turnover early in jobs, publlC policies designed to

provide better information ex ante to workers md employers about the quality

of matches has the potential to reduce the very high mobility rates early in

Jobs.

me more general (and not new) finding is that half of all Jobs end in

the first year. This mandates a focus on the first year on the job in order

to uderstmd labor mobility. However, it also highlights a potential

measurement problem in the NLSY: The measured low hazard

remit simply from mder-reporti~ of very short Jobs.

There are three comments on the desi~ of the NLSY

early in Jobs could

that would improve

its usefulness for the purposes of the analysis of worker mobility. First, a

special effort should be made to be sme that that the respondents report all

Jobs, regardless of their duration. Second, complete Job itiormatlon should

be collected and reported on Jobs of all durations. In the current survey,

detailed information is collected only about jobs which last at least eight

weeks. Since over twenty percent of Jobs are over by the eighth week,

Important information is missed about a substantial fraction of jobs.

Finally, wage data .atmore regular points would be very useful in the
analysis of moblllty. ~1 le the.most recent waves of the NLSY tive

information on startl~ and endi.~ wages on jobs alo~ with wages at

interview dates, data at monthly..intervals in the first half-year would also

be ‘very useful.

In conclusion, the aalysis in this

itiormatlon on mobility patterns that are

variable worker differences in mderlyi~

study provides important new

consistent with 1) important tho~h

mobility rates and 2) ex ante

mobservable match ~al 1ty that workers and flrms learn about over a

relatively short period during the first six months to one y@ar on the job.
Section 1: Introduction

In this study I use a smple of over fourteen thousand full-time jobs

held by workers in the National Longitudinal Survey of Youth .(NLSY) to

.’

examine mobility patterns and to evaluate theories of labor mobility (defined

as cknge of employer). In partlcular, I investigate the importance of both

heterogeneity and..state dependence i.ndetermining mobility...ra.t.e.s. for youg

workers. One question tht has implications for the determimts of labor

turnover is whether worker heterogeneity in mobility rates can be

characterized as fixed differences across workers or as variable with workers

changing types over time (either systematically or otherwise). bother

important question is whether state dependence in

the mobility rates decline monotonically with the

cha~e (tenue ).

The answers to these questions not only can

mobility rates 1s such that

time since the last Job

help shed light on

competing theories of labor mobility, but they can also help in the design

and evaluation of publlc policies to help YOW workers have a stable

emplo~erit history. For example, to the extent tkt heterogeneity is

important in determini~ mobility rates and this heterogeneity is not fixed

over time, there may be scow ‘for training and supported-work programs to

help workers become more stable. However, if worker types are fixed over

time with little or no evidence of ch~e, these sorts of programs may be

.

less useful in reduci~ turnover.

Investigation of the role of state dependence in lahr turnover can be

eqmlly itiormative. First, the relationship between mobility rates ~d

tenure, by helping to disti~ish “standard” specific-capital models (Becker,

1962; Oi, 1962; Mortensen, 19781 from models based on information about match

Wality (JOvanovic, 1979a), can provide information on the sorts of programs

that would be most effective in promoti~ stable matches. Second, a clear
mderstandi~ of how mobility rates vary with tenure is important in the

evaluation of public policy in this area. For example, it may be that the

effects of a one PO1icy to reduce.turnover might affect long-term survival

probabilities of Jobs while another policy might be most effective in

reducing turnover early in Jobs.=.

There are two features of the empirical analysis that differentiate it

from earlier studies and allow for the possibility of new insight. First,

the NLSY contains a more complete emplo~ent history than any other

longitudinal survey of comparable.length. This allows me to develop a‘

virtually compiete history of pa~”tmobility that can be used to control for

heterogeneity ‘ac”rossworkers i“n~derly”ing mobility rates. Worker

heterogeneity is an important-confomdi~ factor when investigating the

relationship between the hazard of a Job endiw and tenure, and very good

measures of past mobility have the potential to limit the difficulties this

-–

poses.

The second new feature of the empirIca1 analysis is that the NLSY

allows precise determination (tothe day!1 of how long Jobs are. mile I

assregate job durations to the monthly level in most of my analysis, even

that level is far finer than has been reliably u;ed in the past.1 The

empirical importance of this is clear from figure 1 which contains the

product-limit estimate of the monthly survivor fmction for the Jobs in the

NLSY sample I use in MY analysis..(describedin detail in the next section).

The exit rate is clearly very high early in Jobs. About one-third of Jobs

are over within six months, and fully one-half of jobs are over by the end of

the first year. Clearly, much of the important information about state

‘See Brom and Light (1989) for zfianalysis of the cliffiml.ties in

determini~ Job durations in the Panel Study of Income D~amics (PSIDI
dependence in mobility refolds very early on the Job, and data on Job

durations that can be calibrated only atiually”’or’even qtiarte”riy” is not

likely to be very informative.

me next section contains both a detailed description of the data that

lie at the heart of the analysis and a simple analysis of state-dependence.in

the raw data that shows a surprisi~ regularity. In section 3, I present an

analysis of heterogeneity in nobility rates that focuses on the relationship

between prior mobility and mobility on the current job. Section 4 contains

an analysis of state

on prior mobility to

dependence in mobiIity rates that uses the information

control for heterogeneity. Section 5 concludes.
Section 2: The Wta

The National Longitudinal Survey of Youth (NLSY) has a nmber of

advantages for the tialysis of tu_rkover. First, by focusing on yomg

workers, the NLSY allows us to use longitudinal information to determine

relatively precisely when workers make their first long-term transition to

the labor force. Second and as mentloned in the introduction, the detalled

emplo~ent histories l,ncludedin~he NLSY allow me both to determine job

durations with more’than usual precision and to use previous mobility to

accout for heterogeneity.in mobility rates.

This is not to say that the NLSY is perfeCt. There are at least two

important drawbacks. First, information on wages is only collected for one

(ambiguous) point in time at each.interview mtil the most recent interview

years. It would have been very useful to have additionally at least aJstarting wage and an ending wage for each Job. In fact, the sparseness of

the wage data precludes it use at this point. “me second drawback is that

detailed information on jobs is only collected for jobs that last more than

eight weeks. There is itiormation on the duration of the shprt jobs and on

why these Jobs ended, but.there L.sno itiormation on industry or occupation.

Since what happens early in Jobs Is central to the analysis here, this means

that industrial and occupational variation in mobility patterns camot be

examined. mile this is a serious limitation, there is still much to be

learned about mobl1ity from these.data.

Individuals in the NLSY were between the ages of fourteen and

twenty-one on Jauary 1, 1979. We eliminate from our amlysis the 1280

workers in the military sample. The rernaini~ smple is’comprised of 11406

workers, includi~ 6111 workers from a representative cross-section sample

and 52?5 individuals from a

minorities and economically

suppl”ementa1 sample of

disadvantaged workers.

uder-represented

At the time I carried out
my analysis, there were data avaiIable for the 1979 through 1988 interview

years.

In order to focus on nobility frOm the time wOrkers first make a

primary comitment to the labor market, I limit our sample to individuals who

make their first lo~-term transition from non-work to work during the sample

period. I define a 10U term-transition to occur when an individual spends

three consecutive years (i.e., intervals between interviews) primariIY

worki~ titer at least a“year spent not primarily working. h individual is

classified (by me) as primarily working lf he/she worked in at least half of

the weeks since the last interview and averaged at least thirty hours per

week in the working weeks,2 Only Individuals aged 16 or older were asked the

relevant questions on emplo~ent history. Thus, we could not classify the

yowgest cohorts (aged 14 and 15 in 1979) in the earliest years of the

survey.

There are 2587 individuals whom we classify as primarily working at the

first interview for which there Is valid data to classify them. We dropped

these individuals from the analysis because we could not determine whether

the first observation for these workers was their first year’primarily

workl~. There were also 14 individuals who were classified as primarily

working in all three years from 1975-1977 based on responses to retrospective

,, ~estions asked in 1979. These individuals were also droppsd from the sample

because they had already made a long-term transition to the labor force by my

definition. On this basis, the first year individuals could make their first

low-term transition to the labor force was between the 1979 and 1980

interviews.

2At the 1979 interview date, the last interview was asswed ..ttbe January 1,

1978.
Individuals

primarily worki’~

19S8, I camot be

worki~ for three

,.

were dropp~ from the sample if they were.not classified as

in three consecutive Interviews. Because the data end in

sure that workers who enter after 1985 were primarily

years. @ this basis, the last year individuals could make

their first long-term transition to the labor force was between the 1985 and

1986 interviews. I dropped 4114 individuals who never made a long-term

transition to the labor force by this definition as well as 468 individuals

with miski~ data on key variables.

In order to focus on full-~ime jobs, I then dropped 5486 jobs which

were never reported as full time [usual weekly hours greater than or equal to.

thirty). Two individuals.had no jobs that Walified as full-time by my

definition so that only 4235 individuals remained in the sample at this

point. Next, I dropped 411 jobs where the worker was either self-employed or

upaid and 17 jobs that started before tie worker was sixteen. This f“urther

reduced the sample of workers by 24. Finally, all 2070 jobs for 421

individuals whose first qualifying job started before 1979 or after 1985 were

dropped from the sample.

The final sample consists af 14160 jobs for 3776 individuals who made

their initial lo~-term transitl.on’to the labor force (by our definition)

between 1979 and 1985. Table 1 s-arize= the disposition of the original

sample of.12686 individuals to yield the final smple of 3776.

My definition of a worker’s initial low-term transition to the labor

force is arbitrary. Redefining our criteria with regard to minimm weekly

hours or mlnimu weeks worked had very little effect on the fiml sample

size. Chawi~ the three-year consecutive history re~irement had a

predictably larger effect on the final sample size. Some information is

available to evaluate how sharply we have defined the transition into the

labor force. Some workers were classifled as primarily working for some
years prior to their first lo~-term transition: 2870 were never classified

as primarily worki~ prior to their first lo~-term transition, but 5S2 were

primarily working for one year, 280 for two years, and 44 for three or more

years. Overall, our rule captures what seems to be a reasonably sharp

transition from not worki~ to working.

Table 2 contains sample average characterlsti= at the time of

transition to primarily working (at the start of the first qualifying job]

Most of these jobs (84.1

characteristics are also

year since entry and for

percent) end duri~ the sample period. Average

presentd for the subset of jobs starting in each

all 14160 Jobs in the sample. For example, there

are 2039 jobs (other tha first jobs) that started in the year (year 1) that

workers made their transition to primarily working and 2224 jobs that”started

in the next year (year 2). tily 12.3 percent of jobs starting in the first

year are censored (last interview.is.held before the job ends) while 49.3

percent of jobs started in the seventh or later year are censored. .Thisis

due in large part to the fact that the jobs started in later years are closer

to the list interview date. It could also be due to the fact that jobs

started when workers are older and/or have more experience may be

duration. This will examined’in more detail in the next section.

Figure 2 contains empirical hazard fmctions for job ending

of longer

at four

frequencies usi~ the sample of 14160 jobs fr~m the NLSY. The upper-left

panel contains the amual hazard fmct ion usi~ 29387 amual observations on

the 14160 jobs. This hazard is monotonically declinl~ in tenure and shows

the 0.5 hazard in the first year that was apparent from the survivor fuct ion

in figure 1. The hazard falls to 0.3 by year 2,.and it is less than O.1 by

year 8. The upper-right panel contains the quarterly hazard fmction using

93675 quarterly observations on the same jobs. This hazard is also

monotonically declintig. The decline is very sharp in the first year, with
“the”hazard falli~ from greater than 0.2 in the first quarter to about O.1 by

the fourth quarter.

Both the amual and qmrterly hazards are monotonically declining, and

it 1s evidence on the hazards at roughly these frequencies that bs driven

the stylized fact that the probability Of Job Chawe is. mOnOtOnicallY

declining with tenure. However,a different picture emerges when the hazard

is computed at greater frequenciti. fie lower-left panel of figure 2

contains the monthly haza”rdfmct.!on USiW 266449 mOnthly ObservatiOns On the

14160 jobs. mat is most striki~ about the hazard fmcbion in figure 2 is

..

that the hazard is actually relatively low in the first month at 0.06, rising

to a peak of almost O.10 at three months and declining sharply thereafter

before leveling off at less than .0.02. ne high period-to-period volatility

of the hzard fmction at the longer durations (> 48 months) is due to the

relatively small nmber of observations on jobs that long, and it should not

be taken seriously due to sampliw error.

Given the new findiu of an incfeasl~ hazard early in the job, the

lower-right panel of figure 2 contains an even finer breakdo.m of the hazard

early in jobs. This panel contains the weekly hazard fuct ion using 287882

weekly observations on the first 26 weeks on the 14160 jobs. This weekly ‘

hazard shows sn increase from a low of less than 0.01 in the first week to a

peak of about 0.025 in the third month. It is not surprisi~ that the weekly

hazard seems more variable week-to-week despite the

the low probability of separation in a “givenweek.

data show a more pronomced peak in the hazard witb

hazard to the first week’s hazard bel~ about 2.5 (

the peak monthly hazard to the first month’s hazard

large sample size given

If anything, the weekly .

the ratio of the peak

025/.01). The ratio of.

iS 1.57 (.0967/.0615).

In the analysis that follows, I use monthly data on the hazard of a job

ending. ~is fre~ency seems an.~appropriatecompromise between 1) the
crudeness of amul or Warterly data which will miss important variation in

the hazard and 2) the computational and expositlonal burden of weekly data.

The basic data then become the 266,449 months observed for the 14,160

Jobs in my sample. The maximm nmber of months observed for my single job

is 121.

Table 3 contains tabulations of mean nonthly rates of turnover by

experience ,md by tenure. The breakdom by ever ience, measured in years

since the first transition to primarily working, In the left-hand COIW shows

a sharp decline in mobility with experience. Workers in their first year

have a 6 percent monthly protibility of job cha~e while workers in their

eighth year have only a 2.5 percent monthly probability of job change. Of

course it is true that workers with more experience are also.likely to have

more tenure, and the right-hand COIU of table 3 shows a br~kdom of the

monthly hazard of job cha~e. 3 This is a smary of the information in the

monthly hazard plotted in fi~re 2, and it shows the peak in the hazard at 3

mont,hsfollowed by a decline In subseqent periods.

,

Given that investigation of the non-monotoniclty of the hazard will be

= important part of the the analysis in subsequent sections, it is worth

investigating to the extent possible how likely it 1s that the lower observed

hazard early in jobs is simply.an artifact of waler-reporting problems in the

KSY. In particular, if the NEY is less likely to code information on short

jobs or workers are more likely to”fail to r“eportvery short jobs to the

interviewer then the hazard ear1ler in jobs wi11 be measured to be lower than

it actually is.

It is interesti~ to ask how serious wder-repor

%ultlvariate analyses in sections 3 and 4 will be use<

1X of short jobs

to determine the

extent to which th-eWard declines with experience after control1i~ for

tenure.
would have to ba in order to elimimte the lower measured failure rate in the

first month, In the raw data graphed in fi~re 2 for the monthly hazard and

tabulated in table 3, the failure rate is 6.2 percent in the first month and

9.7 percent in the peak (third) month. Assmiw (unreasonably)that al1 jobs

that fail in the third month are-reported, there would have to be 557

additional jobs tkt failed in the first month but went mreported in our

sample in order to equalize the bzards in these two periods. This compares

.

with 871 reported Jobs in our smple that failed in the first month.

Is 557 missed Jobs .anwrea.sonably large n~ber? The NGy ~urveY

instrment 1s design+ to pick up all Jobs held since the previOus interview.

though ltiormation on only a rnaximm of five jobs are reported on the public

distribution of the data. It is wclear how these five are selected when

more than five are reported, but the survey instrment asks about Jobs in

reverse chronological order with no reference to duration. The distribution

does contain informatlon on how many jobs each worker reported at each

interview. For the 3776 individuals in my smple only 88 Jobs after entry

for 51 workers are reported but not coded in the public distribution, and

some of these are likely to not Walify because they are part-time. Thus,

omission of jobs in excess of five per worker per year camot accomt for the

problem.

If individuals simply forg-t short Jobs, it is reasonable to expect

that short Jobs held immediately prior to the interview date would be more

1ikely to be remembered.snd rewrtd th= short Jobs held low before the

interview date. I~oriM seasonally in Job durations and assming that the

probability of a Job starti~ is_.miforrnover the year, “oneC= investigate

the distribution of short Jobs 8.sa fmction of time mtil the next
interview.4 There were

Fully 113 of these Jobs

ye accept 113 as a full

871 Jobs in the sample that ended in the first month.

were started in the month prior to the interview. If

comt of the ve~ short jobs started in a given month

then there ought to be 12.113 = 1356 very short jobs in my sample since there

twelve months on average ktween interviews. Since I only observe 871, there

is a ,shortfallof 485 one-month JQbs. This is quite close to the 557

one-month Jobs it would t~e to equalize the hazards for the first and third

months.

The,ca~culation in the preceding paragraph may be too extreme because

it ass~es that no jobs longer than one month went ureported. If I assume

that there is mder-report ing of Jobs held two or fewer months, the

conclusions are ~ite different. Of the 2036 Jobs in the sample that ended

in the first two months, 360 of these were started in the two months prior to

the interview. If this is accepted as a full tout of the very short Jobs

started within two months of the interview In my sample, then there ought to

be 6.360 = 2160 jobs with completed duration less than two months. The

shortfall here is only 124 = 2160 - 2036 Jobs. This is less than twenty-five

percent of the jobs required to equalize the hazards for the first and third

months.

The indirect evidence is mixed regardi~ whether the finding of an

increasing hazard in the first few months on the Job is real or a statistical

artifact. There is some possibility that part of it is due to uderreporti~ of short Jobs, but there is no obvious way to get more direct

evidence without a survey (1ike the Survey of Income arid program

41n fact, the Jobs in my sample are disproportionately likely to start in

Jme and disproportionately 1ikely to end in A~ust. However, this does not

accout for the peak in the hazard since a multivariate analysis of the sort

carried out in the next section that includes a complete set of day

variables for calendar-months shows the same peak in the hazard.
Participation) that is conductedat more frequent intervals than one year. I

will proceed assmi~ the increasi~ hazard feud early in the job is a real

phenomenon.
Section 3: How Import-t is Heterogeneity in Mobility?

In this section I abstract from variation in the probability of job

. cha~e With tenure for a given worker (true durat10n .deP$ndencein the

hazard) in order to .facuson heterogeneity in mobillty rates across workers.

This is important for two reasons. First, the mders tandi~ the nature of

heterogeneity.in worker mobility iS impOrtac! in its..OW right. Second,

consistent estimates of the role of state dependence in the probability of

job change cao t b.e..invgs.ti.gat.e.d. Withog!....coE!.Ko.12J.w fOr heterOgeneitY

(e.g., Lancaster, 1979; Heckman and Singer, 19821. Thus, heterogeneity in

mobility must be considered very carefully in order to evaluate even models

of mobility that do not incorporate heterogeneity directly.

A. Are the Data Cotiistent with a fire Heterogeneity Model?

It is easy do dismiss the possibility that labor turncver is strictly

the result of fixed worker heterogeneity without any state dependence. Two

pieces of evidence at odds with a pure-heterogeneity explamtion for mobility

have already been presented. First, the breakdow of mobil;ty rates by

experience, contained in table“3, shows that the probabi1ity of job cha~e

declines with experience. Heterogeneity implies that the probability of job

cha~e will be mcorrelated with experience. Second, the monthly &zard

fmction, plotted in fi~re 2 and tabulated.ih table 3, rises to a peak

after three months of tenure md declines subse~ent ly. Heterogeneity

implies that the hazard will decline monotonically with tenure.

A simple model suffices to demonstrate that heterogeneity in mobility

rates implies no relationship between mobility rates ~d e~erience and a

wlformly negative relationship between mobility rates and tenure. I asswe

that there are two types of workers, but the analysis generalizes

straightforwardly to k types with an arbitrary distribution. The two t~es
of workers are differentiated only by their turnover probabilities, Al and

A2. This sort of mover-stayer model was first ussd for the analysis of job

mobility by Blmen, Kogen, and Mc&rthy (1955). Type 1 workers are

relatively more mobile so that Al > A2, and these “turnoverprobabilities are

fixed over time for each worker. ““”Theproportion of the population that is of

type i is e.

The overal1 turnover rate at

average of the individual turnover

(3.1) PI,-=eal + (1-e]a2.

- This implies that the average =t:

=Y POint in t~ne is simply the e weighted

probabilities,

.

of job change does not vary with

labor-market experience since the composition of the sample does not vary

with experience. This property is clearly independent of the nmber of..

types, the distribution of the types (e1“,or the turnover propenslties of the

types (the A’s).

The same model can be used -to derive the result that pure heterogeneity

implies that the hazard declines monotonically with tenue. 5 ~ simple

intuition is tbt the sample of workers observed in the same job in multiple

periods is disproportionately composed of low-turpover workers. The average

mobility rate for workers these workers is lower. More formally, consider.

first Pl:o,

‘conditional

(3.2)

where Cl is

the probabi1ity that a worker eh~es jobs in the second period

on not having chmged.,jobs in the first period. This is

P = AIPr[Typei I CI=OI + X2Pr[Type2 I C1=OI 1.0

a binary variable such that CI=O if the worker did not change

‘To be precise, the proof here of the proposition that pure heterogeneity

implies that mobility declines monotonically with tenure is strictly valid

only for the first Jobs workers hold. However, the proposition holds

generally, and the proof here illustrates the selection process that

generates the result. I show later that the non-monotonicity of the hazard

with illustrated in figure 2 for all jobs in my sample also holds for first

jobs alone. See figure 4 and.tab~es 15..and. 16.
jobs in peri~ one and Ci=l if the worker did change jobs. The conditional

probability that a worker who did not cha~e jobs ia type 1”is

(3.3)

(l-al)e

Pr[Typel I CI=OI =

(l-Ai)e + (1-a2)(l-e)

which is less than e as long as Al > AZ so that the sample of stayers is

disproportionately composed of low-turnover workers. Substitution of

eqution 3.3 into ~ation 3.2 yields

second period for workers who did not

(3.51 P = AIPr[Typel I Ci=Ol 1.0

the probabi1ity of turnover in the

ch~e jobs. This ..is...

+ A2Pr[Typ& I CI=O]

al(I-alle + a2(l-a2)(l-e]

(I-xl)e + (1-a2)(l-e)

which 1s less tbn both PI and P1.l as low as Al > A2.

This generalizes easily to n periods of tenure where

(3,61 Pr(Change I no previous cha~es in n periods)

; Ai(l-kl)n e + a2(l-a2)n(l-e]

= ,-

(l-A1)ne + (1-A21n(1-e)

ad the derivative of this probabillty with respect to n is

(3.7) aPr(Cha~e I no previous ch~ea in n periods)/an

e(A1-A2)(l-e)d.ln[al

=

(ea + (l-e))z ‘

where

.

[ 1

n

(3.8) 8 = (1-s1)/(1-s2) .

This derivative never wsltive, ad it is strictly negative for all but three

special caaes where there 1s no heterogeneity (AI=A2,9=0, or 8=1). Thus,

the probability of job cha~e will decline monotonically with tenure in the

presence of pure heterogeneity

.. . . .
B. The Rela tio~hip of Obsemabl e Worker Characteristics with Hobi 1ity

Altho~h the results presented so far s~est tha”tother factors are

also likely to be important, how important is heterogeneity in turnover

rates? There are two manifestations of heterogeneity that I look for in the

data. First, I -amine the extent to which the nmber of earlier jobs a

worker with a given amomt of tine since labor-market entry has held Ls

related to observable characteristicsof workers includi~ age, education,

race, sex, and the year of entry. This provides some evidence on the

relationship between turnover rates and observable characteristics. Second,

I examine the extent to which the hazard of a job ending is related to

earlier mobility after control11~ for observed characteristi~. This

provides some evidence on the relationshlp between turnover rates and

mobserved characteristics.

Table 4 contains a breakdon of nmber of jobs held since entry by year

since entry. There Is considerable variation in this quantity. Naturally,

it is the case that the nmber of previous jobs held is positively related to

the nuber of years since entry. For this reason, separate analyses are

carried out for each year of experience. The sample for each year of

experience consists of those individuals in my sample

smple at that point.

Table 5 contains the average nmber of previous

dlmensIons of the data separately for each experience

dimension is sex. At all levels of experience, males

who are observed in

jobs for different

level. The first

have held more jobs

the

than females s~est lng that men ch~e jobs W1th higher frequency than

women.6 On average, the 1878 men in the sample held 3.79 jobs while the 1898

6See Loprest (1991) for a detailed analysis of male-female differences in

turnover rates in the NLSY and the relationship of turnover rates with

male-female differences in wage growth.

.
women held 3.21 jobs.’ The difference of 0.58 (se. = .063) is statisti~lly

different from zero at COnVentiOnal levels. b average, the men did have O.I

years more total experience than women,..b_utEhis .d.l.ffere.zcS.__ls_no !..

statistically signifi~nt (p-value = .154). The smal1 difference in average

experience camot accomt for the difference in the nmber of jobs held.

There is only a a weak relationship between race md the nmber of Jobs

held. ti average, the 2239 whites in the sample held 3.54 jobs while the

1537 nonwhites held 3.43”jobs. The difference of 0.11 (se. = .065) is only

marginally statistically different from zero at conventional levels (p-value

= .101. There 1s a sizable difference in the total e~erience of whites and

nonwhites with nonwhites bviw O.2 years less experience on average th~

whites. This difference is statistically significant (p-value = .003), and

it can accomt for the small difference in nmber of jobs held. On balance

there seems to be no difference in tunover rates by race,

Finally and with regard to education, the mems In table 5 suggest that

workers with sixteen or more years of education hold significantly fewer Jobs

than workers w1th twelve years education at every level of experience. The

difference is small shortly after entry (less than 0.2 jobs in year two) but

rises to about 0.5 Jobs by year six. The differences are statistically

sigtiicant at conventional levels for all years through year nine.

In order to measure the relationship of mobility rates “ith observable

characteristics in Z multlv”ariatecontext, separate ordered-probit models of

the n~ber of previous jobs were estimated at each

These models included controls for bducatIon (four

age at entry, md d-y variables for the calendar

level of experience.

categories), sex, race,

year at entry. Ordered

‘This comt and the aalogous comts of total jobs

Job, while the tabulations.of previous jobs in the

current job.

held include the cur~ent

table do not include the
probits were ussd because the nwber of jobs held is ordinal ad takes on

relatively few values so that stadard linear regression tectii~es are not

appropriate.8 mile there are up to 22 earlier jobs for a single worker, the

distribution of observations with more than five earl”ierjobs is rather

sparse, particularly at the low and middle experience levels. For this

reason, the ordered probit analysis is carried out USIW seven categories for

the dependent variable: six categories for zero through five

a single category.for six or mor%.earl~?r jObs. This is the

in table 4. —

Table 6 contains estimates of ordered probit models of

earlier jobs and

breakdom used

the nuber of

previous jobs as a fuct ion of fixed observable worker characteristics. A

separate model is estimated for each experience level from one through six

years. ne results are fairly consistent across experience level%, with

females showing less mobility (hCvix fewer PreviOus jObs) than males at

every experience level. After t~e first year, nonwhites show significantly

less mobllity than whites tho~h the difference by race is much smaller than

the sex difference. The findi~~ in these two dimeniioni are consistent with

the wlvarlate mean differences in table 5. However, the ordered-probit

results with regard to education cliffer somewhat from the uivar iate means

in table 5. The rnultivariatemalysis yields the result that workers with

less than twelve years education have significmtly lower mobility (fewer

previow Jobs1 at most experience levels than workers with exactly twelve

years education. Workers with thirteen to fifteen years education have

mobility rates that are indistin~ishable from workers with twelve years

education. Workers with at least sixteen years education have significantly

fewer previous jobs in the first year than do workers with twelve years

._

.

‘See Maddala (1983) for a discussion of the ordered probit techique.
education, but after the first year the difference, while estimated to be

negative, is not si~ificant at conventioml le”vels.

., Age at entry has a different relationship with prior mobility depending

on the experience level. In the first year, workers who were older when they

entered have had significantly more prior mobility. However, by the time

workers attain five years experience, workers who were older upon entry have

tid significantly less prior mobility. Note ttit these results are fomd

after controlling for education so that older workers are

took longer to enter the labor force perhaps because they

complete a given course of schoollng.

Finally, calendar year of entry seems to be related

those workers who

took longer to

to mobility with

entrants in later years showi~ less mobility than earlier entrants other

things eqml. ~is is true at every experience level.

A likelihood-ratio test statistic is presented for each probit model.

~ls statistic refers to a test of a constrained model with only the six

ordered-probit thresholds against the mconstralned model presented in table

6. me constrained model can be rejetted at conventions1 leve1s in every

case. ~is suggests that the observable characteristics of workers are

si~ific~tly related to mobility and that there i.ssignificant heterogeneity

across workers in nobility rates.

me last COIW of table 6 contains estimates of a pooled model that

includes al1 observations on years one thro~h six. ~is model also includes

. a set of five d-y variables (not shorn) for experience level in order to

acmmt for the natural phenomenon that workers with more e~erlence wi11

have had more prior jobs on average. mile the pooled model is not strictly

appropriate because workers (and jobs) are inclded multiple times (up to a

maximm of SIX), it does give a ro~h smary of the overall relationships of

worker characteristics with prior mobility. However, a 1ikelihood-ratio test
of this constrained model against the mconstralnd model implicit in the

first six COIWS of the table reso~dingly reJects the constrained model.9

C. The Relatiomhip of the Hazard Rate with Prior” Mobility

One way to investigate the relationship of the hazard rate to prior

mobility is to examine turnover probabilities conditional on previous

turnover in the context of the simple two-t~e mode1 used above. As W3S

discussed .in section 3A, turnover probabilities differ by turnover history

<

because the sub~population with =Y particular turnover “historyis not

distributed as type 1 with probability e ad type 2 with probability l-e.

I already derived.Pl.o,the probability that a worker chmges jobs in

the second period conditional on not havl~ changed Jobs in the first period,

in equation 3.S. The analogous want ity, PI.~, iS the probabiLity that a

worker changes jobs in the second period condltional

in the first period. This is –

(3.9) P

1.1

= AIPr[Typel I Cl=ll“+ A2Pr[Type2 I

Applying Bayes’s rule yields the result that

aze + a2(l-e]

[3.10) P = 1....... ..2 _. . .-:. 1.1 Ale + X2(1-9)

on having changed Jobs

Cl=l1

It is st,,raightforwardto show that P1.l must be greater than both PI and

‘Pi.o. The intuition 1s the sme as that used earlier: the sub-population

that cha~ed jobs last period is composed of a higher fraction of workers

with high turnover probabilities (type 1) tha either the entire population

or the sub-population that did not ch~e jobs last period. A

history of ‘priorturnover has a higher probabi1ity of turnover

.

worker with a

than a worker

‘The unconstrained Iog-llkelihod. is -32280.S. The 1ikelihood-ratio t=t

statisiic 1s 2365.6 with S2 degrees of freedom. The constrained model is

reJected at any reasonable leveL .of sl~ificmce.
without such a his,tory.

Fl&e 3 contains separate plots for each year of experience of the

hazard of job endi~ by nmber of previous jobs. ~ls figure shows that the

hazard increases with’the .nmber.of.previous jobs at al1 experience levels.10

It also shows the general decline in the hazard with experience.

It is straightforward to show that all that matters for the

“probabilitiesof turnover conditional on previous turnover history in a pure

heterogeneity model is the n~ber of prior periods with job changes (c) and

the nmber of prior periods without Job cha~es (n-c). me order in which

prior turnover took place is irrelevant. me general formula isll

(3.11) Pr(C”hi~e I c previous changes in n periods) =

A;+i(l-ll)n-”e + A:+l(l-X2)n-c(l-e)

=

A:(l-al)‘-cc + a~(i-a2)n-”(1-e) .

Clealy, c a“ndn-c are sufficient statistics for the sample information on

heterogeneity. ~is sufficiency is what uderlies ~amberlain’s (1984)

fixed-effect Iogit model that “incorpbra”tes“het&rogeneltyof this sort. “It

also fiderlies efforts by Mincer ad Jovanqvic (1981) and others to control

for heterogeneity by includi~ the nuber of previous jobs as a control

variable in mobility models.

me relatiomhip in g~ation 3.11 provides an additional prediction of

the pure heterogeneity model: if prior mobility history in.the..formof c and

10~e relationship is not monotonic in the first year and the ninth year

because of the smal1 nmber of workers in the first year who have had more

thm two previous Jobs ad the relatively small total sample size in the

ninth year. me P1Ot for the tenth year 1s not contained in figure 3 because

it is not very ltiormative due to the very small nmber of observations. See

table 4 for a detailed bre~do-.

llEqmtion 3.6 is the special case of this relationship where there have been

no previous cha~es in n periods (c=O).
n-c is appropriately controlled for, there will be no partial correlation of

mobility with tenure. Only when prior mobility and experience are not

controlled for appropriately will..anegative negative relationship between

curren”tmobl11ty and tenure & fo~d. 12

I now turn to zn analysis of hazard rates that controls e~licitly for

prior mobility along with experience and observable characteristics. Table 7

contains

In order

separate

entry at

estimates of a logit model of the monthly probabillty of Job ending.

to focus on the role of Worker characteristicsand prior mobility,

logit models are estimated and presented for jobs by years since

the start of the Job. The first colm of table 8 contrains

estimates of a logit model with all monthly observations on the first job

workers hold. The second COIW of the table contains estimates of a logit

model with all monthly observations on jobs (after the first Job) that

started in the first year in the labor market, the third colm contains

estimates of a logit model with all monthly observations on all jobs that

started in the second year in the labor market, and so on. Al1 mode1s

include measures of:

1) sex,

2) marital status (measurd at the start of the Job),

3) the interaction of sex and marital status,

41 race,

5 ) age at the start of the Job,

6) education,

7) months of nonemplo~ent immediately prior to the start of

the job,

121n tha model worked out in section 3A, tenure and experience are

indisti~ishable so that there i? no prior history
81

91

pkior mobility history measursd

prior jobs started in each year

the start of the current job,

tenure (measured by six monthly

by a set of variables for

(12 month interval) preceding

dwies for the first half

year, one semi-aual dwy for the second half of the first

year, and up to four mual variables for years two through

five),

10) dmy variables for each calendar year, and

111 a dmy variable ,forresidence in an urban area.

Table 7 contains the estimates of the parameters only on the first eight sets

of Variables (the demographic characteristics other than urban residence,

months of nonemplowent, md prior mobility history). The tenure effects

(duration dependence in the hazard) are presented in table 15 and discu=sed

in the next section.

The first COIW of table 7 contains the estimates of the turnover

model for the.first Job workers hold after entry. Of course, there is no

prior history in this Job. Of the 3776 lndlvidwls in the smple, 633 hold

only one Job for the entire period they are observed. The hazard of the

first.Job ending is significantly relatd. to the set of demographic

characteristlcs (p-value <1.e-5).13 The maJor differences across groups are

that women and nonwhites are less likely to leave their first job than men or

whites. Because the probability of mobility in any month is small, the

coefficient estimate in the logit model 1s approximately the average

proportional mrginal effect of the relevmt variable on the probability of

13Thls and later p-values related to restricted models are derived from

likelihod ratio tests computed from estimates of the restricted model and

the unrestricted estimtes contained in table 7.
Job ch~e. 14 Thus, women have about a twenty percent lower probabi1ity than

men of leaving their first Job wh~le nonwhites have about a ten percent lower

probability than whites of leavi~ their first Job. There seems to be no

systematic relationship between

marital status, or education.

The spec~ication used in

demographic varIables to be the

mobility on the first Job and age at start,

table 7 constrains the effect of the

same thro~hout the Job. It may be that

-—

particular demographic characteristics are more Important early In a Job than

later or vice-versa. In order to examine this possibility, I reestimated the

mobility fmction separately for “the first six months on the Job and for al1

months after the first six months.1s ~ese estimates are In the first COIWS

of tables 8 and 9 respectively. It is of course true that all first Jobs are

represented in the hazard for the first six months in table 8 while only

those first Jobs

for later months

the estimates in

that last more m= six months are represented in the hazard

In table 9, To the extent that heterogeneity is important,

table 9 are based on a smple of workers who have

demonstrated that they are less mobile while the estimates in table 8 are

based on the full saple. The relationships I find between the hazard later

in the Job and the demographic variables will be driven in part by this

selection mechmism.

The estimates for the first six months, contained in the flrst COIW

14T0 be precise, the pro~rtional effect is computed by multiplying the

relevant parameter by one minus the average probability of Job cha~e. Since

the probability of mobility in any month 1s small (<.1), this is well

approximated by the coefficient itself151 also split the Jobs at three months ad twelve months. The substantive

results on early-late contrasts are not affected by the precise split, and I

use the six-month split as a convenient rule. Fully 38 percent of first Jobs

(1448 Of 3776) end in the first six months.
of table 8, are somewhat different th= those for later in the job, contained

in the first COIW of table 9. Women have about a one-third lower monthly

probability of mobility in the first six months of the first j“ob,while there

seems to be no significant difference by sex after the first six months for

those jobs that survive. There is an approximately ten percent &f ference by

race that seems to persist throughout the job. The least educated workers

(<12 years education) have a fifte$n perC~nt 10Wer.PrObabil_i_t_y ....ofmobility

early in their first Job than do workers with exactly 12 years education

This does not persist after six months. @ the other hand, while there is no

significant difference in mobility early in jobs for college graduates

relative to workers with 12 years education, CO1lege graduates on jobs that

last more than six nonths have significantly lower mobility after six months.

The remaini~ COIQS in tables 7 tho~h 9 contain..estima.tesof the

same basic specification of mobility fuctions for jobs starting in the first

through sixth years after entry. The jobs starti~ in the first year exclude

the first jobs held after entry. Each of tbe s=ples of jobs starti~

sometime after entry are subject to systematic selection in that a previous

job bs to end in order for a new job to start In one of these periods.

Since the probability of job endl~ 1s correlated with observable

characteristics of workers to the extent that heterogeneity is important, the

results must be interpreted with this selection process in mind.

There are some interestl~ contrasts between the hszard on first jobs

ad hazards on Jobs starti~ later. There 1s no si~ificmt male-female

differential in mobillty rates after the first job while the white-nonwhite

differential persists for jobs starti~ tko~ year two. More educatd

workers generally have less mobility from jobs after the first job than do

less educated workers. The contrasts between these estimates and the

estimates for the first job may be partly driven by the facts that there 1s
sample selectlon in who starts a job in a given year and that there are

controls for prior mobility. Reestimation of the model without the prior

mobility variables (not presented here) yields results rouhly similar to

those in the tables.

The stratification of the saple by year-of-start is very useful in

controlli~ appropriately fir the relationship between current mobility and

prior mobillty. In the remainder of this section, I investigate the extent

to which current mobility”is related both to prior mobility and to worker

demographics. I leave for the next section an examination of the shap$ of

the hazard [how the probability of a job endi~ is related to tenure] when

prior mobility is controlled for.

Two sets of measures in the mobillty fmctions (for jobs other than the

first] are meant to mntrol for prior history. First I include a measure of

the length of any spell of nonemplo~ent lmediately prior to the start of

the Job.16 This is romded to the nearest month. By this measure, there is

no nonemployment spell prior to 58 percent of the jobs (not comting first

jobs), and there is a one month gap prior to 14 percent of the Jobs. Only

seven percent of jobs are preceded by a gap of more than six months. Second,

I include as measures of the prior mobility history a set of variables fo~

the ntier of prior Jobs started..in each year (12 month interval or fraction

thereof) precedi~ the start ofdhe current job. For Jobs starti~ in the

first year, there is only one such variable; for Jobs starti~ in the second

year, there are two such variables; and so on. Table 10 contains the

frewency distributions of the set of prior mobility variables.

The ‘eskimates”in table 7 show that mobility 1s psitively related to

161 also investigated models that used the accumulated tine not emplc,.edsince

entry. Nothi~ lnteresti~ was revealed wlw this variable.
the emplopent gap only on jobs starti~ five or six years after entry. One

month of nonemplowent is related to an increase in subsequent mobi11ty of

about five percent

from

each

Prior mobility is fomd to be a very

the current job. For example, in the

prior Job is related to a ten percent

import=t lndlcator of mobility

jobs that start in the first year,

increase in the subse~ent

probability of mobility. In later years, mobility in the year immediately

prior to the start of the job is related to = even larger increase in

mobi1ity from the subsequent Job. This Is on the order of twenty percent.

Given an average monthly mobility rate of about five percent in the

first year on a job, one job change in the.imedlately. preceding year raises

the mobillty rate to six percent. This has a substantial effect on the

survival probability. As a crude approximation, if the monthly mobility rate

is five percent, the one-year survival probability is 0.54. Contrast this

with a SIX percent monthly mobility rate where the one-year survival

probability is 0.48.

;

h importmt Testion to ask is if the timl~ of earner job changes

matters holdlw the total nwber of earlier’job cha~es fixed. If workers

cha~e over time (&rhaps maturing) one would expect that the more recent

mobility history is more important than the part of the history that is

further removed fron the cmrent Job. me simple model of “fixed”.

heterogeneity outl.i.ned. ..abo~ve. irnplies that the”Coefficiehtk On lagged “MobiiitY

in each year wil1 all be ewal.

The estimates in table 7 provide mixed evidence on this point. The

Jobs starti~ in years two thro~h six all have multiple years of prior

history,

starting

mobility

and I test the hypothesis that within each COIW (the set of Jobs

at a particular year of exprience) the coefficients on lagged

are e~al. Inspection of the estimates in table 7 shows 1) that
prior mobility 1s a very lm~rtmt determinant of

that in every category the largest coefficient 1s

most recent year. ‘Thiss~ests that more recent

current mobility and 2)

on prior mobility in the

history is the most

import=t, and this 1s supported..by formal statistical tests.

The first panel in Table 11 contains the maximized log-likelihood

values for ttiee specifications of prior mobility in the logit model of

monthly mobility. Model #l 1s the unconstrained mde 1, presented in table 7,

where the nmber of Job cha~es Ln each year prior to the start of the

current Job is entered separately. Model *2 constrains the prior mobility to

enter thro~h a si~le variable measuri~ the total nuber of Jobs held prior

to the start of the current Job. Model #3 1s an intermediate specification

where prior mobillty is measured.by two variables: 11 the total nmber of

previous jobs and 21 the nuber of Job cha~es in the year immediately prior

to the start of the current job. This model allows job cha~es in the most

recent year prior to the job to have a differential relationship with

mobility than Job cha~es in earlier years, but it constrains the

relationship to be the ssme for all earlier years.

The first p-cl of table 11 also.contains results of likelihood-ratio

tests of models #2 and #3 versus the mconstrained model #1. The fulIy

constrained mdel #3 can be reJected only for Jobs starti~ in year 3. In

all other years, there is not a significant difference in the relationship of

prior mobility with the probability of current mobility by when the prior

mobility occurred. The intermediate model #3 is never reJected against the

mconstrained model.

me second panel of table 1.1contains estimates md the results of

statisti-1 tests of constrained model #3 against the intermediate model

These results show two thl~s. First, mobility on the current Job is

#2.

strongly and signlficantly related to mobility in earlier years. Second,
there is a marginally statistically si~ificant difference in the

relationships for noblllty in the most recent prior year and for mobility in

ear1ier years. Recent mobility seems to have a marginally stro~er positive

relationship with current mobility than earlier mobility.

It is interesting to how whether prior mobility has the same

relationship with current mobility thro~hout the Job. For example, it may

be that a history of much prior mobility implies a higher probability of job

cha~e early in Jobs but no difference later. In order to.investigate this,

I once again split the emplo~ent spel1s at six months, separately analYzing

mobility in the first six months and mobility after six months (on Jobs that

last that low). Tables 12 and 13 contain the results of statistical tests

for the subsamples analogous to the tests in table 11.

The results in table 12 s~est that the relationship between mobility

early in a job and prior mobllity does depend significantly on the timing of

earlier mobility. Constrained model *2 can be reJected against the

unconstrained model #l except for jobs starti~ in year 6. However,

intermediate model #3 camot be reJected against the wconstrained model #1.

Taken together, the results s~est that mobility in the year immediately

prior to the start of a Job bears a special relationship with mobility early

in the Job. The

directly, and it

start of the Job

mobility earlier

analysis in the’bottom panel of table 12 tests this

indicates that mobility in the year immediately prior to

has a si~ificantly larger effect on mobility than does

in workers’ careers.

the

Table 13 contains the sane ~alysis of mobility for later in the job

(after the sixth month). The results here are very different fram what was

fomd early in the Job. The constrained model #2 C- be reJected against the

wconstralned model #1 only for Jobs starti~ in year 2, and the intermediate

model #3 canot be reJected against the wconstrained model #1 in for any set
of jobs.

later in

there is

start of

the most

The =alysis In the bottom psnel of table 13 shows tkt mobility

a job has a significmt relationship with prior mobillty but that

no special relationship with mobility in the first year prior to the

the Job. Three of the five estImated coefflcients on mobi1ity in

recent year are actually negative.17

Overall, the aalysis of the relationship

prior mobility in tables 11 thro~h 13 provides

of current mobility with

a clear message that workers

who have ch~~ Jobs relatively frequently have a higher pribability of

mobility on their current job. fi exaple serves to lllwtrate this.

Consider a worker in the base group (white, male, not married, 12 years

education, not livi~ in an urban..area, no prior spell of nonemplo~ent) who

is 23 years old. -Supposehe starts a Job in year 4 and but has not changed

Jobs in the last year. The six-month -d one-year survival probabi1ities of

this Job depend on the nmber of prior jobs. Simple calculation using the

estimates of the intermediate model #3 in tables 12 and 13 yields the

survival probabilities contained in the top panel of table 14. These show

the large effect that prior mobility has on the smvival probabilities.

Workers with no prior jobs have a one-year survival probability of 0.6 while

those with six prior Jobs have a one-year survival probability of only 0.4.

I noted from tables U snd 13 that the relationship of mobility with

prior mobility is not wiform thro~hout the job. The mobility history in

the year immediately prior to the start of a Job is relatively more important

early in the current job than later (for Jobs that survive the early stages).

h extension of the example serves to illustrate this. Consider a worker in

170f course, the net effect of recent mobillty is not estimated to be negative

because the net effect is the sw of the coefficient on the total nmber of

prior jobs and the coefficient on the the nmber of jobs in the most recent

year.
the base group who starts a job in year 4 and has had four prior jobs (the

medi~ nwti.r for this_tfl.e... of worker). me. six-month and o.n.e-yearsurvival

probabilities of this Job depends on the temporal distribution of the four

prior Job,s. me second pmel in table 14 contains the calculations of these

survival.probabilities usi~ the estimtes of the intermediate model #3 in

tables 12 ad 13. ~ese show the large effect thst the distribution of prior

Jobs has on the probability that a Job lasts the first six months or the

first full year. If the four Jobs were all in the last year, the Job has a

probability of less than 30 percent of lasting the first full year while if

all prior

of almost

Is due to

months.

jobs were earlier.in.the.wor~er’.s..careex,the Job has a probability

50 percent of lasting the first full year. All of this difference

differences in the probability of the job lasting the first six

D. &ervi ew of the Role of Heterogeneity

I presented clear evidence that heterogeneity alone c~ot accowt for

the mobility patterns seen the data. First, mobility decllnes with labor

market experience whlle a pure heterogeneity model suggests that they would

be ucorrelated. Additiom}ly, mobility does not decline monotonically with

tenure. me hazard rises to a peak at three months after which it declines.

However, I did find that heterogeneity plays a stro~ role in mobility.

Mobility is significmtly related to certain observable characteristics of

workers lncludi~, most prominently, sex md race. Females and nonwhites

have held fewer jobs since entry at my point In the first six years in the

labor

rates

force.

me most Important evidence for the role of heterogene1ty in turnover

is that mobillty on the curent Job is stro~ly relatd to prior

mobility. Workers who have ch~ed Jobs fr~ent ly in the past are more
likely to ch~e Jobs in the future. However, this heterogeneity does not

seem to be fixed over the low periods. While it is true that even mobility

several years prior to the start of a job is related to tmnover

probabilityies, the relationship is stro~est for mobility that occurs in the

year prior to the start of a Job. Workers who have &en relatively mobile in

the recent past have higher turnover rates earlY in a new job than do workers

with the same amout of total mobility who moved in the more distmt past.

mis

over time,

model, the

last findi~ s~ests a model where a worker’s type evolve slowly

.

either as a mndom walk or as systematic change.1s In this sort of

.,.

most recent mobillty history would be more closely related to

current mobility than to the more distant history. However, it is not really

possible to determine whether pattern I feud reflects changes over time in

workers’ nderlyi~ propensities to move or in the types of jobs they hold.

Of course, these are not independent, and it is mf ort-te that the NEY

does not have the information on industry and occupation in very short Jobs

that would be an important part of the investigation of the role of types of

Jobs. Nonethele.s.s,the .results L? rule out a model where the only form of

heterogeneity comes from flxed worker types because that would suggest that

all parts of the mobility history was equally itiormative about current .

mobility..

laFor example, Osterman (1980) presents an -Iysls of the youth labor market

with a fecus on the maturation of workers as they get older.
Section 4: How Mobility Vmies with Tenure: fie Shape of the =z=d

In the previous section I examined the evidence on inter-firm mobility

with regard to the predictions of a pure heterogeneity model of turnover, and

I concluded that while heterogeneity in mobility is important, there are

clearly other factors that determine moblllty. One factor that 1s 1ikely to

be important in determini~ mobility is the accumulation of firm-specific

‘caPitalof ,vario”stypes, and the central evidence on this comes from

investigation of the variation in mobility rates with tenure.

A. Some Further Empirical Results

Figure 4 contains separate plots of the empirical monthly hazard

fmction for the first 48 months on Jobs starti~ in each of the first five

years since entry. This is a disaggrega.tlonof.the overall empirical hazard

in figure.2,.and it s.ho.ws. the .5.Zrn?_.ba?l.?.._PZt_k? ~.Q..?s.tigure 2. For each

subset of Jobs, the hazard first rises.to a pe+k at abOut three mOnths then

de~l.inessteadily. Table 15 contains the estimates of the set of tenue

d-y variables for the Iogit mobility model whose coefficients on worker

characteristics ad heterogeneity are presented in table 7. The base level

of tenure is more than five years; and the coefficients can be interpreted as

the approximate proportional difference in mobility rates between the

indicated tenure group and otherwise e~ivalent workers with more than five

.

years tenure. These are estimates of the shape of the hazard after

centrolliw for heterogeneity uslW observable.worker ..charaGt?ristics and

--—...... .——.— — ——..—.—————-

prior mobility.,

me results in table 15 reinforce the general impression from the raw

empirical hazards in figure 4. Even after controlli~ for heterogeneity

through worker characteristics md and. prior mobility, the hazard firs: rises

to a peak at about three months and subse~ent ly dec1ines. The proportional
difference batween the hazard at the peak and the hazard in the first month

is computed approximately as the difference in the relev=t coefficients. On

the first job this difference is,=O.71 s~est i~ that jobs are Over ~3 again

as likely to.end in month three than in month 1. For the subsets of jobs

starti~ in years 1, 2, and 3, the differences are 0.41, 0.46, and 0.65

respectively. For jobs startiu in later years, the difference is less

precisely estimated but of the s=e general ma~itude.

Table 16 presents the resdts on the shape of the hazard for the mode1

of mobility estimated USIU only the first six months on the job.19 The base

tenure group here has six months of tenure, and the coefficients C- be

interpreted as the approximate proportional difference in mobility rates

between the indicated tenure group and otherwise equivalent workers with six

months tenure. Recall that I fowd that the relationship of current mobility

with prior mobility was different early in the job relative to late in the

job, and it is possible that constraini~ the effect to be the same could

yield misleadi~ estimates of the shape of the hazard. However. the

estimates in table i6 yield approximately the same results as the estimates

in table 15. The hazard peaks at about three months before decllni~. The

proportional differences between the hazard at the peak and the hazard in the

first month are very close to what was computed from table 15: 0.72 for first

jobs, 0.46 for other Jobs starti~ in the first year, 0.41 for Jobs startiw

in the second year, ad 0.67 for.jobs starti~ in the third Year.

For completeness, table i7 contains estimates of the shape of the

hazard for the Iogit model of no~lllty estimated usi~ only observations

.

19The coefficients on worker characteristics md heterogeneity for this

model are contained in table 8.
after the first six months on the Job.= The b2se tenure level is more than

five years. Of course, this does not yield estimates of the shape of the

hanrd early in the job, but it does verify that the hazard is declining

after the first six months.

The task of a theoretical framework will be to accomt not only for the

general decline of the hazard but also for the pe* early in the hazard.

B. A tire Specific Capital Hdei

The defining characteristic of specific capital is that it is the

result of m investment that makes a particular m?tch more valuable and that

is not useful in any other match. Turnover of the sort aalyzed here simply

destroys the value of this capital. Efficiency implies that the gains from

the match will be shared in such a way as to reduce the probability of

turnover. Since the gains from the match increase with the length of the

match as more is invested in specific capital, it is e~ected that turnover

will decrease with tenure due to the accumulation of speci.fic capital.

Models developed by Becker (1962) ad Oi (1962) are amo~ the early efforts

to incorporate specific capital into our wderstmdi~ of wages and

turnover.21 Mortensen (1~8) ad Jovanovic (1979b) present a theoretical

analysis of specific capital accumulation and turnover where optimal

investment, search, and turnover behavior are derived. Parsons (1986)

‘The coefficients on worker characteristics ad heterogeneity for this model

are presented in table 9.

*iWork by Mincer and Jovsnovic (1981) ar~es that apportioni~ earnings growth

into components correlated with general lahr-market experience ad with

employer-specific tenure can provide evidence on the relative importance of

general versus specific hman capital. Topel (1991) and Law (1988) argue

persuasively that one ks to be extremely careful about apportioni~ earnings

growth into components due to experience and tenure due to the fact that

turnover is endogenous.
presents a recent survey of the 11terature on speclfic capltal and turnover.

tinsider the followl~ ~ simple statistical model of the

relationships between the probabi1ity of Job ch~e and labor-market

experience and firm-speclfic tenure in”the presence of investment in

firm-specific hman capital. All workers and jobs are assmed to be

ldentical, md workers have m ex.~t e turnover probabi1ity of PI that

perhaps comes from firm-speci.ficXemand shocks. Firms and workers invest in

specific

adJusted

does not

capital at some optiml rate, and the rate of compensation is

so that the probability of turnover the next period for a worker who

change Jobs is reduced to some value PI.0 < PI. me 1.0 notation

refers to this peqiod’s probabllity of turnover (event 1) conditional on last

period’s event (stay with employer, event O). Similarly, the probability of

turnover after two periods for a worker ‘who does not cha~e jobs is red~ced

by further investment in specific capital to some value PI.W ~ PI.0 < PI.

If the worker does chawe jobs after the first period, he starts from fresh

with a new employer, and investment in specific capital starts again. Thus,

the probability of turnover IS wchanged at PL.l.= PI. Repeated turnover

does not cha~e the probability of turnover so that P1.li= pl.1 = pi. OnlY

the le~th of the most recent job affects the turnover prObabi.1itY. Thus,

ths relative turnover’probabilities for the complete set of two year

emploment histories are:

(4.1) P = P = P1.ll= PI > Pi.o = P1.ol > P*.w. 1.1 1.10

Clearly, a pure specific h-n capital model without any heterogeneity.

implies that mobility will decline monotonically with tenure.22

.-

2%obility will”also decline wlt~ labor-market exper~ericesimply due to the

fact that workers camot, accwu~ate tenure Without a.cc~ulatiW exPerience.

However, once tenure is centro1led for, the probabi1j.ty of turnover wi11 not

be correlated with labor-marketexperience.
~e heterogeneity in mobility rates that I fomd is likely to have

important effects on the accwulation of specific capital. If firms can

obseme who the stable (type 2) workers are then they will invest more in

these workers, =d any heterogeneity in the likelihood that a worker wi11

remain with the firm will be reitiorced through variation in investment in

specific capital (Jovanovlc, 1979b). Even If firms camot observe who the

type 2 workers are but they learn this over time, there wi11 be more

investment in specific capital for workers who are revealed to be more llkely

to be type 2. ~us, the turnover probability of stable workers wllI be

further reduced in a way that is correlated with tenure, and mobility will be

negatively related to tenure even after controlli~ for previous mobility.

~is specific capital model implies that mobility rates decline

monotonically with tenure, and it does not support the initial increase in”

the hazard fomd in the empirical analysis. I now examine whether

heterogeneity in Job and/or match ~ality can accomt for ttis finding.

C. Ex tite Obsentile Job ati Hatch Heterogeneity

Suppose now that al1 workers are identical but that Jobs and/or matches

are heterogeneous and that there is no specific h~an capital of the usual

sort. Jobs may be heterogeneous in the sense that there are Jobs which have

lower turnover rates than others, perhaps because some firms pay higher wages

than others precisely to reduce turnover. mis is implied by some efficiency,

wage theories (Katz, 1986). Matches may be heterogeneous in the sense thst

some worker-firm matches may be more productive thsn others. Efficiency

implies that the gains from such matches will & shared (at least In part in

the form of.higher wages] in a way that lowers turnover rates.

Consider a very simple model with two types of Jobs or matches. me

notation here is very similar to the case of individual heterogeneity with
two t~s of wo~kers, but the empirical implications are quite different.

The match types are differentiated by their exogenous turnover probabilities,

Al and A2. Type 1 matches are relatively less stable so that Al’> AZ, and

these turnover probabi1ities are fixed over time for each match. I wi11 make

a pair of assumptions for the present in order to simplify the analysis.

First, it is ass~ed that the type of match is knon by both the worker snd

the firm from the start (match eality 1s = inspection good). Second, 1t is

assued that the probability that a worker draws a type 1 match is fixed at e

and that this probability does not change over ‘timeas workers are sorted

into good matches.

In this model, the probabl..~ty

period is

‘(4.2) pi = exi + fl-e)12

because the fraction e of the Smple

that a worker changes Jobs in the first

is in type 1 matches md the fraction

l-e of the sample is in type 2 matches. The

Elrst period draw new matches of types 1 -d

l-e respectively. Since the 1-e workers who

workers who cha~e Jobs in the

2, again with probability s and

were in good matches the

first period only chawe jobs with probability 12. the fraction Of wOrkers

who are in bad matches in the second perl.odis

(4.3) ez = (I-al)e + aiez + x2(l-e)e.

The first term represents the fraction of workers in bad first period matches

who do not cha~e Jobs. The second term represents the’fraction of wOrkers

In bad first period matches who cha~e jobs and wind up in a bad match again.

The final term represents the fraction of workers in good first-period

matches who chsnge Jobs and wind up in a bad match. E~ation 10 can be

rewritten as

(4.4) e2 = e[l - (1-e)(al-a2)]< e.

Thus, the turnover rate in the second period, which 1s
(4.51 P2 = e2A1 + rl-e2)aa,

is lower than the initial tunov.er...rate,, and.the.f<r:,tempirical.implication

is that turnover rates fall with experience as the saple is progressively

sorted into better matches.23

mat I have outllned here is a simple Markov trasltion process between

type 1 and type 2 jobs. This process does not govern the probability of job

cha~e, only the probabi1ity of cha~e of job type. It is straightforward to

derive the steady state fraction of bad matches, (A2e/[X2e+Ai(l-e.)]), and the

steady state turnover rate, (AiA2/[A2e+Al(l-e)]). It is a“lsostraightforward

to show that average match quality is lmprovi~ oxer time and that turno~er

rates are falli~.

mat of the relationship between mobility rates and tenure? A worker

observed to have a large amomt of tenure is more likely to be in a type 2

match. Thus, mobility rates will fall with tenure. The general formula for

the turnover

(4.6)

probability of a worker with T years of tenure is

Pr(ChaMe I tenure = T) =

al(l-al)=e + a2(l-a2)T(1-el

=

(l-ai)=e + (1-a2)T(l-e) .

Thus, pure match ~al 1ty considerateions s-es t that mobi1ity rates depend

only on tenure and not on e~erience or turnover prior to the current job.

Tenure is a stificient statistic for the probability of job change. This iS

in marked contrast to the pure heterogeneity model where past mobility

history [nmber of job cha~es and nwber of years of experience) is a

sufficient statistic for the probability of job ch~e.

..-

23Note that the progressive sorti~ of the smple over time into better jobs

and/or matches is what mderlies the decline in mobility with experience in

the search model developed by Burdett (197S). This has also been emphasized

by Topel (19861 and Topel and Ward (1988).
~e intrduct ion of endogenous investment in speclfic capital again

reitiorces these re”sults, MOre will & invested in specific capital where

match ~ality is knon to be high. me dec1ine in turnover rates with tenure

will be reitiorced by investment in sp@clfic capital, and current tenure is

remains a sufficient statistic for the likelihood of mobility.

me implications of this simple match ~allty model are identical to

those of a pure speciflc capital.,model. In fact, Mincer and Jovanovic (1981)

consider match quality to be a form of specific capital. ~is Is consistent

with the view that anythi~ having value within the firm but not having value

(or havi~ less value) outside is..specific :apital.24 But it also implies

that the ex ante observable match job/match heterogeneity model camot

supper’tthe positive relationship between tenure and mobility early in jobs.

C. Ex ht e Umbservabl e Job & Hatch Heterogeneity

A richer version of the match quality model does yield different

implications from the stadard specific hu~ capital model and cm imply an

initial increase in the hazard with tenure followed by a decline. ~is model

starts with the asswption that the type of match is not kom ex ante by

either the worker or the firm but that the firm md the worker get noisy

signals over.tirne.about the..~alL$y of the match (psrhaps by observi~

output) ~US, match quality is a experience good. Maintain the assmption

that the probability that a worker draws a t~e 1 match is fixsd at e and

that this probabi1ity does not cha~e over time as workers are sorted into

good matches. Further asswe that it 1s costly to cha~e jobs md that

24F1im (1986) develops ‘atiodel~ turnover based on match heterogeneity. He

draws out the implications for w~e d~aics, snd estimates a wage

determination model in order to test this model. He finds that the wage

dflamics are quite consistent with his matchi~ mdel, but he does not

est lmate a mode1 of turnover.
workers are infinitely lived.25 ~is is essentially the m~el worked out by

Jovanovic (1979a) who asswes that workers are paid their expected output

each period, titput, which is assmed to be a noisy signal of match ~ality,

is observed every period, and the worker md f1rm use this itiormation to

u~ate their beliefs about the ~ality of the match.

Jovanovic (1979a) derives a reservation match quality such that workers

will decide to sample a new match if the posterior distribution on the

current match has mem less than the reservation match ~allty. me

reservation match quality is inversely related to the variance of the

posterior distribution because there is option value for workers in sampling

further from a Job with large up-side potential. Jovanovic uses a normal

learni~ model (DeGroot, 1970) which has the reasonable property that the

vari~ce of the posterior distribution falls with the arrival of new

information. Since a new si~al arrives each .perlodon the Job, this

suggests that the reservation match quality increases with tenure.

Since Job cha~e is costly and the variance is high early in jobs,

workers will be relatively mlikely to cha~e jobs early &cause there is

still much option value. Even a Job about which the first piece of

ltiormation is negative may be worth keepi~ in order to get more itiormatlon

on match quality. Note that havi~ a positive cost to Job change early in

Jobs 1s required for this result. If Job ch~e was costless, as soon as the

posterior mea fell below the initial prior e~ected value the worker would

cha~e Jobs be~use Jobs are freely available at the initial prior value.

.

Using this framework, Jovanovic (1979a) concludes that the hazard of a Job

ending will first be quite low as workers ad firms learn abut the match

‘s~e latter assuption 1s surely no problem in the smple of yomg workers in

the KSY.
~ality and then wil1 fall as matches revealed to bs bad end =d the

continuiw matches are disproportionatelyof high Wality.

Thus, a matthing model where match qual1ty is an experience god ad

job cha~e is costly vi11 generate a hazard that initially increases with

tenure and then declines, as I find in the data here.

find another class of model that has the sl~le-peaked

this version of the matching model.

It iS difficult to

hazard predicted by

D. & Itilrect Test of Matchi~ vs. “Stadard” Specific Capital

A less direct test of matchiw models (of either type) cm be derived

from the recognition that speci~:c capital that derives from the Wality of

the match is a different sort of–specific capital than, say, knowledge of a

production process that is derived over”a relatively low period of time.

The test requires some a priori .assuptions regardi~ the relative rates of

ofi.accwulation of match capital and other specific capital. Two assumptions

tht seen reasonable are that 1)–most learni~ about match quality occws

soon after the start of a job (very likely within one year) and 2) specific

capital in the form of task specific howledge Is accumulated more gradually

over a longer period of time. To the extent that these restrictions are

-,

accept?d it becomes possible to disti~ish between these two variations of

the “genera1” specific-capital mdel.

Mile it is true that thesk restrictions are arbitrary, I find the

OppOsite set of assumptions, thst learning about match quslity occurs slowly

over time and that most investment in specific capital occurs relatively

quickly, to be less plausible. This is because learni~ that a match is high

quality is likely to cause more_investment in specific capital. Firms and

workers wil1 be unwl11ing to invest very much wtil they have some

information that a match is a good one. This is not to deny either that
there are important specific investments made at the time of hiring (e.g.,

basic traini~ and orientation) or that learni~ about match ~al ity can

occur later

position).

evidence in

early while

in the job (e.g., learni~ about msuitability for a higher

However, w1th these caveats, it seems reasonable to interpret the

the context of a model where learniu about match ~a 11ty occurs”

investment in spcif 1c capltal occurs on a more centinuous basis.

The tremendous smowt of turnover fowd early in a job may be due to

individual heterogeneity or it may be due to match heterogeneity.

Controlling for heterogeneity,usiw prior nobillty, the estimates in tables

15 and 16 show very high turnover rates in the first year on jobs. ~e

turnover rates deGline relatively slowly after the first year on the job

(tables 15 and 17). This 1s the expected pattern where learni~ about mat~h

quality Is importmt .
Section 5: tincludi~ Rewks

me analysis of inter-firm worker mobility here shows the .import=ce of

both heterogene1ty and state dependence. me most basic findi~ with regard

to heterogeneity is that the probability of exit from the current Job is

stro~ly related to the fre~ency of Job ch~e prior to the start of the

Job. ~is relationship is quite stro~, md it persists thro~hout the

current Job. Workers with a history of fre~ent job cha~e are more 1ikely

to leave their current job within the first SIX months, but even if they

survive the first six months, they are also more likely to leave at some

later point.

A more subtJe but equally importat findi~ is that, while all previous

Job change is related .to the probability of exit from the curent Job, Job

cha~e in the most recent year prior to the start of the Job bears a

significantly stronger relationship with mobility on the current Job. In

particular, the hazard of the current Job endi~ in the first six months is

higher where there has been mobi1.1ty in the year immediately prior to the

start of the current Job. ~is s~ests that while workers vary

substantially in their mderlyi~ mobility, these differences in mobility

not ffxed over time. Important work remains to be done in examining the

movements individual workers’ propenslties to move.

~e most striki~ differeric”ein turnover rates by.observable

are

characteristics Is between males =d females. Femles hold significantly

fewer Jobs thm do males in a fixed period of time early in their careers.

~us, females who have comitted to the labor force exhibit less mobility

than otherwise equivalent males. ~is result seems to be driven by a lower

exit rate for females early in the first Job after entry, and it rms

contrary to results uii~ earlier data from the National Longitudinal Surveys

of Yo~ Men md Yo~ Women which show higher turnover rates for women (Blau
-d K*, 1981). Recent work by Topel and Ward (1988) and Loprest (1991)

.

which examines links between wage growth -d Job change for YOW workers

sWgests important directions for further work usi~ more detailed Job

..

characteristics.

With regard to state dependence, the.key flndi~ is that the monthly

hazard of Job endi~ is not monotonically decreasing in tenme as most

earlier work usiw amual data has fomd. The hazard increases to a maximm

at three months and declines thereafter. The finding of an initially

increasing hazard folloue=d”” by a monotonic decline is new evidence consistent

with a model of heterogeneous match q~lity that camot be observed ex ante.

More work, usiw data on hth wage d~amics md mobility, remains to be done

in evaluatl% the importace of the role played by matchi~ and ‘in

determini~ where (which Jobs and sectors) matchi~ most important.

To the extent ttit learni~ about match qmlity is an important cause

of turnover early in jobs, policies designed to give workers and firms

realistic previews of how good a match is likely to be have the potential to

reduce mobi1ity. For example, recent theoretical work by Montgomery (1988)

and empirical work by Staiger (19901 usim the NLSY focuses on the role of

referrals as a means of findi~ a Job in this context.26 mile the

theoretical results s~est tkt there should & less mobility from Jobs

feud through personal contacts (where there is likely to be better

itiormation about match Wality), Staiger’s empirical results do not support

this view.

A more general [md not new) findi~ is that half af all Jobs end in

the first year. This mandates a focus on the first year on the Job in order

‘sEarlier work by Rees and Schultz (1970) md Granovetter (1974) also

addresses the role of referrals and information about match quality.
to wderstad labor mobility, -d it highlights the imprtance af using data

at a greater freqency than one year. Indeed, if I used data on job

durations at a lower frequency, the hazard would appear to be monotonically

declini~ thro~hout. However, it also highlights a potential measurement

probLem in the NLSY: If ~ery short Jobs are mder-reported, the measured low

hazard early in Jobs could simply k a statistical artifact of this

nder-reporti~.

mere are three cements on the design of the NLSY that would improve

.

its usefulness for the purposes of the analysis of worker mobility. First, a

special effort should be made to be sure that that the respondents report all

jobs, regardless of their duration. Second, complete job information should

be coilected“and reported on jobs of al1 durations. In the current survey,

detailed information 1s collected only about jobs which last at least eight

weeks. Since over twenty percent of jobs are over by the eighth week,

important information is missed about a substantial fraction of jobs. mis

precluded me from Investigating industrial and occupational differences in

mobility. Finally, wage data at more regular points would be very useful in

the analysis of mobility. ~ile.the most recent waves of the NLSY have

itiormatio,non starti~ and endi~ wages on jobs alo~ with wages at

interview dates, data at monthly intervals in the first year would also be

very useful. ..

In conclusion, the amlysis in this study provides important new

information on mobility patterns that are consistent with 1) importat tho~h

variable worker differences in mderlying mobility rates ~d with ~ ~te

uobsenabl.e retch ~al ity that workers ad firms learn about over a

relatively short period duri~ the first six months to one year on the job.
RzPsRswcxs

.

Bartel, ti P. and George J. Borjas. “Wage Growth ad Job Turnover: k

fipirical &alysls, ” in Shemin Rosen, ed. Studies in Labor Markets,

Chicago, The University of Chicago Press, 1981.

Becker, Gary S. “Investment in Hman &pital: A Theoretical halysls, “

Journal of Political Economy 70 (Supplement:October 1962): S9-S49

Blau, Francine D. and Lawrence M. K&. “Race -d Sex Differences in Quits by

YOW Workers,“ Itistrial d Labor Relatio= Revim 34 (July 1981):

563-S77.

Blwen, 1., Kogen, M. and McCarthy, P. The Itistrial Mobility of Labor as a

Probability Process. Ithaca, XY: Cornell Studies in Industrial and

Labor Relations, vol. 6, 1955.

Bro-, James N. and Audrey Light. “Interpreti~ Panel Data on Job Tenure,“

State University of New York at Stony Brook, mimeo, September 1989.

Burdett, K. “~p I&yee Search and Quits,“ &ric= Economic Review 68 (1978): “

212-220.

Chamberlain, Gary. “Panel Data,” in Zvi Griliches and Michael D.

Intriligator, eds. Htibook of Econometrics, bsterdam, Elsevier

Science Publishers B.V., 1984.

DeGroot, Morris H. Optiml Statistical. Decisio~. New York: McGraw Hill,

1984.

Film, Christopher J. ‘“Wagesad Job Mobility of Yo~ Workers,“ Journal of

Political Economy 94 (Supplement:Jme 1986): S88-S11O.

Granovetter, Mark S. ~tti~ a Job: A Sttiy of Contracts d Career,

Cambridge: Harvard University Press, 1974.

Heckman, James J. “Heterogeneity and State Dependence,” in Sherwin Rosen, ed.

Studies in Labor Markets, Chicago, The University of Chicago Press,

1981.

Heckman, James J. and Burton Si~er. “The Identification Problem in

Econometric Models For ~ration Data,“ in Werner Hildenbrand, ed.

Adv=ces in Econo=t rics, Cambrldge, Cabr idge University Press, 1982.

Jovanovic, Boy=. ‘iJob Matchl~ -d the Theory of Turnover,“ Journal of

Poii tica~ Economy 87 {October 1979): 972-990. (a)

Jovanovic, Boyan. “Firm Specific Capital and Turnover,” JOUM1 of Poli tikal

Economy 87 (December 1979): 1246-1260. (b)

Katz, hwrence F. “Efficiency wage Theories: A Partial Evaluation,” in

Stanley Fischer, ed. NBER Macroeconomics tial 1986, Cambridge, MA.

MIT Press, 1986.
Lancaster, Tony. “Econometric Methods for the ~rat ion of Unemplopent, “

Econowt rica, 47 (1979): 939-956.

Lmg, Kevin,

>

“Reinterpreti~ the..Return to Seniority,” Boston University,

mimeo, Wrch 1988. .

Loprest, Pamela. “Wage Growth and Job Mobi1ity of Youg Workers,“

Massachusetts Institute of Tectiology, mimeo, 1991.

Maddala, G. S. Limi ted-De~tient & @alitative Variables in Econometrics,

&mbridge, Cabr idge Unive.rsity Press, 1983.

Mincer, Jacob. “On-the-Job-Traini~: Costs, Returns, and Some Imp1ications,”

Journal of Political Economy 70 (supplement:October 1962]: S50-S79.

Mincer, Jacob and Boyan Jovanovi_c.“Labor Mobility and wages,“ In Sherwin

Rosen, ed. Sttiies in Labor Markets, Chicago, The University of Chicago

Press, 1981.

Mincer, Jacob. ‘aWage Cha~es and Job Changes,“ In Research in L&or Economi m

Vol. 8 Parb A, Greenwich, CT: JAI ,Press,Inc. (1986):171-197.

Montgomery, James. “Social Networks and Labr Market Outcomes: Toward an

Economic tialysist’, mimeo (1988].

Mortenson, Dale T. ‘aSpecific &pi tal and Labor Turnover,“ Bel 1 Journal 9

(1978): 572-586.

Oi, Walter Y. “Labor as a Quasi-Fixed Factor,“ Jourml of Political Ecommy

70 (1962): 538-555. ::.

Osterman, Paul. Gstti~ Started: The Youth Labor Market, &mbridge, MA., MIT

Press, 1980.

.

Parsons, Donald O. “Specific Hman Capital: h Application to Quit Rates and

Layoff Rates,“ Jourml of Political EconoW 80 (1972): 1120-1143.

-.

Parsons, Donald O. “Models of hbor &rket Tmnover: A Theoretical and

Empirical Survey,” in R. Ehrenberg,ed. Research in Labor Ecommics,

Vome 1, Greenwich,’CT. JAI Press Inc., 1977.

Parsons, Donald O. “The fiplo~eht Relationship: Job Attactient, Work Effont,

-d the Nature of Contracts,” in Orley Ashetielter nd Richard Layard,

eds. H&book of Ldor Economics, ksterdm., Elsevier Science

Publishers B.V., 1986.

Rees, Albert and korge Schultz. Workers in a Urbm Lsbor Harket, Chicago:

university Of Chicago Press, (1970)..

Topel, Robert. “Job Mobility, Search, nd, Earni~s Growth: A

Reinterpretation of H~an &pltal Earni~s Factions, ” in R.Ehretierg

ed. Research in Labor Econmics, vol 8, pt. A. Greewich CT: JAI Press,

1986. .
Topel, Robert. “Specific Gpital, Mobility, and Wages: Wages Rise With Job

,

Seniority.“ Journal of Pol itlcal Economy 99 (February 1991): 145-176.

Topel, Robert H. and Michael P. Ward. “Job Mobi1ity and the Careers of Young

Men,“ nimeo, May 19S8, @arterly Jourml of Economics, in press.

Willis, Robert J. ‘SUageDetermination: A Survey and Reinterpretation of Hwan

=pltal Earni~s Factions, ” in Orley Ashefie1ter and Richard Layard,

eds. H&book of Labor Economics, hsterd=, Elsevier Science

Publishers B.V., 19S6.
Table 1

Disposition of Smple

Initial Sample: 126S6 Individuals

Deletions:

Ml11tary subsample 1280

Primarily worki~ in first year 2601

Without 3 years primarily worki~ 4114

Missing data on key variables . 468

Never held full-time job 2

Always self-employed or wpaid 22

Had ful1-time Job before age 1.6 2

First qulified Job before 1979 or 421

after 1985

Total Deletions: 8910 Individuals

Final Sample: 3776 Individuals

.

Mote: See Section 2 of text for details
Table 2

Smary Statistics for Jobs

N=Y

(Means and Standard Deviations]

At Time of Start of Job

by Years Since ~try at Start of Job, for First Job, md for All.Jobs

Year N age Educ- female non- censor duration censoriw Nmber

of Start ation

(since

white in months time of

(not In months Prev

entry) censored) (censored) Jobs

First 3776 20.6 12.7 .503 .407 .1s9 17.5 69.5 --- .—.

Job (2.34) (2.17) (19.9) (23.0)

1 2039 .21.0 12.6.. .424

(2.28) (2.12)

2 2224 21,8 12.8 .441.

(2.31) (2.17)

3 1641 22.7 12.8 .449

(2.32) (2.18)

4 1412 23.5.... -.12.7 .448

(2.26) [2.08)

5 1.112.24.2 12..7 .418

[2.13) (2.121

6 845 25.0 .......12.6 .406

[2.00) (2.14)

>=7 11.11....26..2..12.7 .410

(1.98) (2.19)

.408

.391

.389

.382

.401

.388

.386

,123 14.4 59.4 1.30

(17.0) (23.8) (.586)

.164 14.1 50.6 2.04

(15.2) (22.6) (1.14)

.204 12.7 34.6 2.92

(13.31 (21.9) (1.62)

.270 10.4 ..25..2 3..70

(10.6) (20.0) (2.19)

.296 9.37 20.1 4.12

(9.481 [16.6) (2.38)

.369 9.60 17.6” 4.81

(9.07) .(14.71 (2.911

.493 7.53 11.9 5.66

(6.90) [9.46) [3.47)

All 14160 22.3 12.7 .450 .397 .220 13.7 36.8 2.27

Jobs (2.87) (2.15) (1s.9) (28.9) (2.50)

Notes: The nwbers in parentheses are stmdard deviations. me Female,

Nonwhite, and Censored variables are d-y variables. The sample consists of

Jobs started after transitlon to primarily worki~ that were ever ful1 time

(>30 hours per week). The sample in year 1 row consists of ...Jobsother than

the first Job started within one year of labor market entry. See Section 2

of text for details of sample selection criteria.
Table 3

Monthly Pr’obabi11ty of Job m-e by fiperience ad by Tenure

Experience

Year 1

Year 2

Year 3

Year 4

Year 5

Year 6

Year. 7

Year 8

Year 9

year 10

Total

Monthly

Mobility Nmber of

Rate Job-Months

.0639

.0416

.0389

.0393

.0368

.0348

.03Q0

.0251

.0235

.0167

43720

46405

45891

40592

32569

24131

16907

10422

4856

956

.0414 266449

Tenwe

Month 1

Month 2

Month 3

Month 4

Month 5

Month 6

Quarter 3

Qurter 4

Year 2

Year 3

Year 4

Year 5

zYear 6

;onthly

iobi1ity Nuber of .

*te Job-Months

.0615

.0883

.0967

.0823

.0636

.0606

.0456

.0369

.0329

.0250

.0219

.0192

.0145

14160

13198

11937

10690

9746

9064

23991

20632

59417

37307

23589

14661

18057

.0414 266449

Note: Based on the sample of 14160 jobs for 3776 individuals smarized in

table 2 and described in the text.
Years

Experience

1

2

3

4

5

6

7

8

9

10

All

Table 4

Fre~ency Distribution

Nmber of Previous Jobs by Years of ~er ience

lorkers Distribution of Nmber of Previous Jobs

o 1 2 3 4 5 >=6

3776““”--.168

3751 .166

3706 .165

3W8 .161

3089 .146

2447. .137

1804 .133

1273 .123

796 .111

339. .0796

.667

.372

.281

.243

.211

.192

.171

.167

.157

.153

.1“34

‘.280

..252

.224

.21.0

.188

.181

.170

.177

.171

.0252 .0053

.125 .0413

.151.........0882

.155 ..0984

..154 :.110

.150 .116

.142 .119

.143 .11.3

.138 .108

.136 .118

.0003

.0101

.0359

.0598

.0715

.0793

.0898

.101

.0842

.0649

.0003

.0064

,0259

.0590

.0978

.138

.164

.185

.225

.277

4559 \.152 .311 .– .209 .127 :0800 .0480 .0723

Total

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

Note: The sample consists of amual observations on the 3776 workers in the

sample. mere is one observation for each year sInce entry that each ~~rker

is observed in the sample.
Table 5

Average Nmber of Previous Jobs by Selected Worker ~aracteristics

Years

Experience

1

2

3

4

5

6

7

8

9

10

Al1

by Year of ~perience

(stmdard deviation of mean In parentheses)

3776 1.10 .970

(.016) (.015)

3751 1,70 1.42

(.028) (.025)

3706 2.16 1.73

. (.038) (.0331

3578 2.54 1.99

(.048) (.0401

3089 2.93 2.29

(.059) (.050)

2447 3.27 2,56

(.074) (.065)

1804 3.55 2.83

(.096) (.084)

1273 3.87 2.97

(.1291 (.102)

796 4.15 3.18

(.177”1( 135)

339 4.53 3.44

(.266) (.216)

:4559 2.49 1.97

(.021) [.017)

? Nmhr of Pr

%ce

fiite Nonwhite

1.04 1.02

(.014) ‘(.018)

1.59 1.52

[.024) (.030)

1.99 1.88

[.033) (.039)

Z.34 2.15

[.042).(.047)

2.68 2.50

[.052) (.060)

3.01 2.79

(.065) (.077)

3.29 3.06

(.083) (.102)

3.47 3.37

(.106) (.136)

3.68 3:“60

(.140) (.185)

!.02 3.93

(.2101 (.308)

2.29 2.13

(.018) [.020)

JiOUS Jobs

Years of Education

<12 12 13-15 216

1.02 1.06 1.03 ,997

(.0361 (.017) (.024) (.021)

1.63 1.61 1..60 1.48

(.062) (.029) (.039) (.036)

z.10 2..00 1.91 1.78

(.0881 (.038) (.052) (.046)

2.49 2.34 2.23 2.02

(.111) (.047) (.065) [.056)

2.84 2.71 2.58 2..29

(.138) (.059) (.083) (.068)

3.07 3.05 2.95 2.54

(.176) [.075) (.103) (.085)

3.37 3.30 3.24 2.78

(.2241 (.096) (.134) (.1111

3.64 3.50 3.55 2..96

(.291) (.123) (.1791 (.141) -

.,

3.s7 3.74 3.88 3.14

(.372) (.165) (.245) [.291)

4.34 3.94 4.13 3.65

(.599) (.249) (.392) (.287)

2.370 2.31 2.22 1.96

(.”046)(.021) (.029) (.023)

Note: The sample conslsts of mual obsewat ions on the 3776 workers In the

saple. There is one observation for each year since entry that each worker

is observed in the sample.
Table.6

Ordered Probit kalysls of Nmber of Previous Jobs

Year of 2xperlence

1 2 3 4 5 6 Pooled

.

Variable

Female

Nonwhite

fitry &e

Education

<12 yrs

13-15 yrs

>=16 yrs

Entry Year

1980

1981

1982

1983

1984

1985

, N

Log L

. x= stat.

X2 D.F.

-.232

( 0378)

-,264 -.290

(.0348) (.03461

-,295

(.0352)

-.29.7

(.0379)

-.312

(.0428)

-.277

(.0149)

-.0469

(.0389)

-.0741 -.0870

(.03591 (.0357)

-.105

(.0362)

-.0938)

(.0391)

-.111

(.0441)

-.0858

(.0153)

.00986 -.00578

(.00987) (.00985:

-.01s0.

(.0101)

.0287

(.0106)

-.0229

(.0112)

-.0316

[.0130)

-.00663

(.00429)

-.136

(.0595)

-.0727 -.0509

(.05501 (.05481

-.0712

(.0557)

-..100.

(,0597)

-..194

(.0676)

-.111

(.0235)

-.0441

(,0487)

-.0698 -.0271

(.0450) (.0448)

-.0260

(.0455)

-.0253

(.0493)

.0148

(.0553)

-.0301

(.0193)

-.124 -.0995

(.0562) (.0519)

-.0831

(.0517:

-.0862

(.0524)

-.0728 -.0417 -.0772

(.0578) [.0678) (.0224)

-.0939 -.159 -.105 “!

(.0618) (.0645) (.0248)

-.0469 -.109

(.0646) (.0594)

-.111

(.0591

-.0846

(.0599)

-.151 -.196

(.0658) (.0602)

-.165

t.0597

-.117

(.0606)

-.1.05.. ..-....166 -.150

(.0629) (.0652)’ (.0251)

-.186 “-.”192

(.0669.) (.0616)

-.106

(.0611

.-.0807

(.0620)

-.107 -.196 -.146

(.0644) (.0674) (.0257)

-.171 -.183 -.115 .-.126 -.167 -.261 -.175

(.0692) (.06371 (.0634) (.0643) .0667) (.0735) (.0268)

-.267 -.217 -.198 -.243

(.0741) (.0683) (.0681) (.0692)

-.582 -.527 -.510 -.588

(.101) (.0945) (.0942) (.0985)

.283 --- -.240

.0759) (.0303)

--- --- -.507

(.0456)

3776 3751 3706 3578 3089 2447 20347

-3599.2 .-5544.7 -6293.7 -6451.3 -5763.3 -4628.3 -33463.3

80.2 105.0 130.2 160.0 112.0 100.0 633.2

12 12 12 12 11 ..10 12

Note: The nmbers in parentheses are as~ptot ic stadard errors. The base

group consists of white male workers WIth twelve years education who entered

the labor market in 1979. Each model includes as parameters a set of six

threshold values separati~ the seven ordered categories for nwber of

previous jobs (O, 1, 2, 3, 4, 5, ><). The poolsd godel includes a complete

set of d-y variables for e~erience level. me x statistic is for a

1ikelihood ratio test of the relevmt model against a constrained model with

only the six ordered-probit thresholds. The constrained specification for

the pooled model additionally includes the set of dwy variables for

experience 1evel.
Table 7

Logit halysls,of Monthly Turnover Rates

Selected Coefficients on Prior Mobility and WOrker ~aract?ristics

123 Year of ~erience at Start of Job

.irstJob 1 4 5 6

.

Variable

Constant

Female

Married

(at start

Female*

Married

Nonwhite

Age

(at start

Education

<12 yrs

13-15 y

>=16 yr

Nonemploy

Spell pri

Prior Job

l..year

prior

2 years

prior

3 years

prior

4 years

prior

5 years

prior

6 years

prior

I

.3.79 -3.42 -x 89 -X.84 -4.13 -3.S6

(.223) (.332)

-3.58

(.330) (.446) [.5181 (.580) (.727)

-.0190

(.121)

-.202

(.04011

-.0373

(.0562)

70189

(.0550)

--0567

(.06951

-.139.

(.0813)

-.0169

(.0967)

.121

(.09211

.=-.0s79

(.0838)

-..127

(.0900)

-.208

(.0978)

-.161

[.107)

.0348

(.12s)

.03S2

(.08s8)

(:::)

-.0232

(.08061

-.122

(.1081

--326

(.133)

<:0379

(.119)

.2s1

(.145)

-.06S3

(.192)

-.,0130

(.0616)

-.0244

(.0707)

-.0236

(.09881

-.101

(.0387)

-..0930

(.0524)

-.121

[.0s15)

-.0113

(.0235)

-.00106

(.01141

-.0228

(,0161)

.Z-0239

(.0149)

-:0346

(.017s)

-.0086s

(.0197)

-.0264

(.0285)

.2S4

(.0844)

...131...

(.0979)

...11s

(.110)

..-.0603

(.13s)

-.086s

(.0s13)

-.0.978

[ 0673)

-.262

(.070s)

-.118

[.0990)

-.0189

(.0489)

.-.0766

( 0683)

-.0329

..(.0642)

.0678

(.07641

--0504

(.08561

-.129

[.123)

-.240

(.114)

-.0799

(.167)

-.104

(.0649)

-.207

(.0881)

-.209

(.0780)

-.233

(.0967)

-. 19s

(..138)

.00988 --0184 -.0123 .00481

(.00987) (.00828) [.01651 (.0141]

.106 .207 .249 .186

(.04191 .f.0273) (.03081 (.0327)

.0429 .0541

.0129) (.01301

---

---

---

---

---

---

---

.152 -215

.0434) ( 0573)

--- -.137 .111 ..101

(.0419) (.0317) (.0338)

..---— —.. .0997 .0974

(.049S) (.0361)

--- .. --- —. .184

(.0533)

.0975

(.04141

.144

(.0s43) .

.0416

(.0413)

.071s

(.04931

.121 .109

(.0434) (.0s13) “,

.0640

(.0636)

.130

(.0487)

— -—

.0721

(.0746)

.—. — —- —- —-

(continued on next page)
.

Table 7

(centinued)

LOglt halysis of Monthly Turnover Rates

Selected Coefficients on Prior Mobility and Worker ~aracteristics

Monthly Obs

Jobs

Individuals

Ending Rate

Log L

x= stat.

X2 D.F.

Year of .Sxperienceat Start of Job

?irst Job 1 2 ..3 4 5 6

97112 40629 44722 28085 20317 13963 10619

3776 2039 2224 1641 1412 1112 845

3776 1541 1612 1237 1057 861 681

.0327 .0441 .0416 .0465 .0507 .0561 .0502

-13235.8 -6980.0 -7423.2 -5090.8 -3898.9 -2915.8 -2030.3

1500.8 711.6

29 31

Note: The nmbers in Parentheses

618.0 389.1 351.7 201.2 167.6

31 31 30 29 29

are asmptotic stmdar~ errors. All models

also include up to el~ven categorical v~r~ables for tenure [six monthly

variables for the first &lf year, one semiamual variable for the second

half of the first year, and up to four mual variables for the next four

years1, d-y variables for.each calendar year, ~d a d-y variable fOr

residence in an urban area. The base group consists of warried white male

workers wi”thtwelve years education in 1979 and who live outside an urban

area. The model for jobs t~at start in the first year excludes the first job ;,

held by each worker. The x statistic is for a likelihood ratio test of the

relevant model against a constrained model with only a constant. The tenure

coefficients are contained in table 15, ad they are discussed in section 4.
Table 8

,Logithalysis of Monthly Turnover fites

Selected Coefficients on Prior Mobility and Worker ~racteristics

First Six Months on Job

Year of ~erience at Start of Job

Variable “irstJob 1 .2 3 4 5 6

COnst~t

(.476) (.479) (.584) (.661) (.775) (.9941

Female .-.0860

(.0829)

= 0920

(.0852)

-.0980

(.106)

-.0418

(.116)

-.0412

(.130)

-.0738

(.167)

.132

[.1291

.= 153

~ 1261

-.0908

-(.134)

-.128

(.139)

-.216

(.148)

-.0428

(.176)

Married

(at start)

Fema1e●

Married

.-.290

(.195)

=.. 0729

[.1941

-.0916

(..207)

.0565

(.208)

.00289

(.227)

-.222

(.284)

-.0300

(.111)

.0293

(.143)

Nonwhite -.00035

(.0760)

-..0219

(.07941

-.116

(.0949)

-.0325

(.101)

-.0237

(.0232)

-,0404

(.0228)

-.0529

(.0265)

-..0523

(.0288)

-.0318

(.0323)

-.0392

(.0403)

&e

(at start)

Education

<12.yrs -.186

(.193)

.321

-[.100)

.377

(.120)

.00479

(.0983)

-.119

S.102)

-.0695

(.123)

-.174

(.137)

-.0933

(.1741

13-15 yr$

>=16 yTS -.233

(.133)

.=.262

(.1271

-,274

(.157)

-.172

(.169)

-.0873

(.192)

-.0955

(.242)

-.00011

(.0148)

-.0623

(.0154)

-.0475

( 0274)

.00215

[.02081

.0524

(.0170)

.0634

(.0171)

Nonemploy

Spell priol

Prior Jobs

1 year

prior

.266.

(:”%5) 7%3) (:%34) (.0450)

.251

(.0581)

..272

(.0761)

--- .0614 ..0878 .0705

(.0615) (.0461) (.0464)

.0816

(.0570)

.0563

(.0743)

2 years

prior

.,

--- --- .168 .0559

(.0700) (.0504)

-.0689

(.0584)

.162

(.0653)

3 years

prior

4 years

prior

--- ,210

(.0719)

.0881

(.0571)

...0653

(.0687)

--- .174 157

(.0843) (:0657)

5 years

prior

--- --- ---

--- -— .0113

[.102)

6 years

prior

----- — --- ---

(continued on next page
Table 8

(continued)

Logit halysis of Monthly Turnover Rates

Selected Coefficients on Prior Mobility =d Worker ~aracteristics

First Six Months on Job

Year of Experience at Start of Job

First Job 1 2 3 4 5 6

Monthly Obs 18404 9729 11067 8306 6842 5262 4038

Jobs I

3776 2039 .2224 1641 1412 1112 845

Individuals 3776 1541 1612 1237 1057 861 681

Endi~ Rate .0787 .0893 .0733 .0693 .07a .0796 .0664

Log L I -4956.0 .-2887.3 -2808.5 -2014.5’ -1770.0 -1430.5 -943.3

X2 stat. I 229.3 74.7 183.1 156.4 .144.5 .63.1 85.2

X2 D.F. I

23 25 25 25 25 25 25

Note: The nmbers in parentheses are as~ptotic stadard errors. All models

also include five monthly d-y variables for tenure, dwy variables for

each calendar year, and a dwy variable for residence in an urban area. The

base group consists of married white male workers with twelve years

education In 1979 and who live outside an urba area. The model for jobs

t~at start in the first year excludes the first job held by each worker. The

x statistic is for a likelihood ratio test of the relev~t model against a

constrained model with only a constant. me tenure coefflcients are

contained In table 16, and they are discussed in section 4.
Table 9

Logit halysis of Monthly Turnover ~tes

Selected Coefficients on Prior Mobilitv =d Worker ~aracteristics

After Fi=t Six Mon;hs on Job

Year of &perience at Start of Job

Variable ‘irstJob 1 – 2 3 4 5. 6

4.288 -2.34 -4.29 -4.22 -5.89 ....-.3.58

(.379)

-4.11

(.6411 (.649) (.536) (1.19) (.955) (1.21)

Constant

Female

Married

(at start)

Female*

Married

Nonwhite

Age

[at start)

Education

<12 yrs

13-15 yrs

>=16 yrs

Nonemp10Y

Spell prior

Prior Jobs

1 year

prior

2 years

prior

3 years

prior

4 years

prior

5 years

prior

6 years

prior

-.0764

(.0536)

.00586 ::.-106 -.0272 -.215 -.113 .0127

(.0767) (.07271 (.0924) (.115) (.146) (.178)

.116 .0195 -.118 -.250 -.136

(.132) (.113) (.123) [.139) (.1s7) (::::1

-.0342

(.123)

-.0197

(.148)

-.3S8 .013s .239 .373 .264

(.183) (.1541

.0S08

(.170) (.202) (.229) (.268)

-.17s -,188 .0711 -.0707 -.0321 -.0943

[.0726) [.0684) (.0814) (.0999) (.120) (.139)

-.018S -.0110 -.0131 -.0309 .00492 -.00969

[.0226) [.0199), (.0237) (.0275) (.03S0) (.0411)

-. 094s

(.os~4)

.00489

(.01s0)

-.0105

(.0674)

.0667

(.09ss)

.0652 .0379 .0974

(.147) (.169) (.191)

-.0449

(.06S2)

-..151

( 09s41

—0316 .0127

(.08321 (.103)

-.0679 -.0659 -.187

(.121) (.14s) (.177)

-.247

(.0886)

-.208

(.119)

--.184 .-_239 ;

[.0998) (.1241

--334.

(.156)

..293

.2021

.0293

.0“200)

.0309

.0672)

.111

.0649

.233)

.0411

.0203]

.139

.0882)

.2ss

.0177

(.0133)

..00286 .“0124

“C00988) (,0209)

. 00S98

(.0192)

---

(:::4) (:=s) (::HS) (::%7)

--- . 20S .128 .130 ---

(.0s77) (.0441) (.0499) (.0616.) (. 0829)

--- --- .0224

(.0711) (::LI

.164

(.0S961

-.0316

[.07781

--- =--- ---

(:::71

.166

(.0684)

.1s3

(.0796)

---

-.0S67

(.100)

. 09ss

(.0743)

--- --- ,-—--- ---- ---

.144

(.112)

--- --- --- --- ---- -—

(continued on next page)
.

Table 9

(centinued)

Logit balysis of Monthly Turnover %tes

Selected Coefficients on Prior Mobility md Worker ~aract@ristlcs

After First Six Months on Job

Year of tiperience at Start of Job

First Job 1 2 3 4 s 6

Monthly Obs I 78708 30900 33655 19794 13475 8701 6581

Jobs ( 2328 1171 1413 1046 820 606 483

Individuals 2328 1143 1339 1007 780 591 462

Ending Rate .0220 .....0298 .0311 .0369 .-.0377 .0418 .0403

Log L -8254.7 -4082..7 -4587.9 -3060.5 -2117.7 -1468.7 -1078.7

X2 stat. 106.0 119.9 158.8 131.7 9%.3“ 85.9 64.2

X2 D.F. 23 25 25 25 24 23 23:

Note: The nmbers in parentheses are as~ptotlc standard errors. All models

also include up to 5 dmy variables for tenure (one semimual variable for

the second half of the first year, and up to four mual variables.for the

next four years), duy variables for each calendar year, and a dmy

variable for residence in m urban area. The base group consists of

married white male workers with twelve years education in 1979 and who live

outside - urban area. The model for Jobs that st$rt in the first year

excludes the first Job held by each worker. me X statistic is for a

likelihood ratio test of the relevat model against a constrained model with

only a constant. The tenure coefflcients are contained in table 17, and they

are discussed in section 4.

.
Year Prior

to Start

of Job

1

2

3

4

5

6

Al1

Table 10

FreqWncy Distribution

Nmber of Previous Jobs Start&

in Each.Year Prior to the Start of the Current Job

(row percentages)

Distribution of Nuber of Previous Jobs

o 1. 2 3 4 5 6 7

4367 4170 1375 349

:42.1) [40.2) (13.2) (3.36) (072) (0~9] (0.~6”)”(0.”ii)

2993 “.38.08...1122 — 307 84 29 2“0

:35.9) (45.6) (13.4)..(3.6S)(1.01) (0.35) (0.02) (0.0)

2251 2704 862

:36.S) (44.2) (13.4)

15s1 2097 612

:34.6) (46.8) (13.7)

1003 1459 459

:32.7) (47.61 (15.0)

579 1029 244

:29.61 (52.6) (12.5)

232

(3.791 (0s:3)

170

(3.79) (0%8)

- 118 17

(3.851 (0.551

(4.::) (0!:7)

(0.;0)

10

(0.32)

(0.16)

(0.:9) (0.:4)

(0.86) (O.:)

(0.:5) (0.:)

12744 15267 4674 1255 300 91 20. 3

:37.1) (44.4) (13.6).-.[3.65)(0.87) (0.26) (0.06)‘(0.011

Total

“

10384

(1OTAI

8345

(1OWL)

6121

(100%),

4480

(100%)

3068

(1OWA)

1956

[1OUL)

34354

(1OVAI

Note: The samule consists of observations on prior mobillty for the 10384

jobs after th; first job for th$._3776workers in the S~Ple.

,
.

Year of

fiperience

Table 11

tialysis of Equality of Prior Mobillty ~fects

All Periods

Equlity of Coefficients of Lagged Mobility Variables

Likellhod-btio Tests

Mode1 #1 Model #2 Model #3 ~ test

Log L Log L Log L #2 v. #l

DF p-value

R test

#3 v. #1

DF p-value

2

3

4

5

6

All

-7423.2 -7424.1 -7423.2 1 .180

-5090.8 -5096.5 -5090.8 2 .0033

-3898.9 -3901.5 -3900.0 3 .158

-2915.8 -2917.5 -2916.7 4 .493

-2030.3 -2032.2 -2031.0 5 .579

-21359.0 -21371.8 -21351.71 15 .0424

0 ---

1 .888

2 .333

3 .615

4 .751

10 .156.

Model #l - unconstrained specification in table 7

Model #2 - constrained with sl~le prior mobility variable (total prior jobs)

Model #3 -.constrained with two prior mobility variables (total prior jobs

ad nmber of job cha~es in most recent year)

Note: ~e “All” row 1s computed as the sum of the specific-year rows.

Difference in Coefficient of First Prior Year Mobility

Year of Coeff. of Total Coeff. of # Jobs p-value

~per ience # Prior Jobs in 1 year prior (#3 v. #2)

2 .0692 .185

(:M9) (.0522)

3 .107 ...o.o.oa..

(.0251) (::87)

4

(:::9)

5 ..0829

(.0186)

6 .10s

.0755 .0802

.04311

.0662 .-19s.

.0514)

.106 ..-105

(.0192) (.0652)

Note: The nmbers in parentheses are as~ptotic standard errors. The

coefficient estimates are from a loglt m~el with a specif.lcatlonidentical

to those used.In table 7 with the exception that the set of prior history

variables is replaced by two variables: 11 the total n~ber of prior jobs and

2) the nmber of job cha~es in the most recent prior year. ~is is model #3.

above. The p-value 1s for a test of the hypothesis that the coefficient of

the nmber of jobs in the most recent year is zero (model #3 v. model #2).
Table 12

halysis of E~ality of Prior Mobility Effects

First Six Months

EWality of Coefficients of Laggad Mobility Variables

Likelihood-htio Tests

Year of Model #1 Model #2 Mdel #3 ~ test ~ test

=perience Log L Log L Log L #2 v. #1 #3 v. #i

DF p-value DF p-value

2 -2S08.5 -2811.6 -2808.5 1 .0128 0 ---

3 -2014.5 -2018.2 -20i4.9 2 .0247 1 .371

4 -1770.0 -1776.2 -1771.6 3 .00613 2 ,.202

5 -1430.5 -1437.8 -1433.8 4 .00561 3 .0858

6 -943.3 -946.3 -944.6 5, .306 4 .627

I I I

Al1 -8966i8 -8990.1 -8973.4 15 .587 10 .i56

Model #1 - mconstrained “specificationin table 8

Model #2 - constrained with si~le prior mobility variable (total prior jobs]

Model #3 - constrained with two prior mobility variables (total prior Jobs

ad nmber of job cba~es in most recent year)

Note: The “All” row is computed as the s= of the specific-year rows.

Difference in Coefficient of First Prior Year Mobility

Year of. –Coeff- of Total Coeff..of# Jobs p-value

Exprience # Prior Jobs in 1 year prior (#3 v. #2)

2 .0614 .0148

(.06i5.) (:%11

3 -.li4 .153 - .0101

(“.0358) (.0595)

4 .0892 .00250

(.0285) (:Ei9)

5 ..0510 .196 .00388

(.0254) (.0679)

.

6 .ioo .0594

(.0250) (:=0)

Note: The nmbers in parentheses are as~ptotic st-dard errors. The

coefficient estimates are from a logit m~el with a specification identical

to those used in table 8 with the exception that the set of prior history

variables is replaced by two variables: 1) the total nmber of prior jobs =d

2) the nukr of job ch~es in tbe most recent prior year. This is model #3

above. The p-value is for a test of the hypothesis that the coefficient of

the nmber of jobs in the most recent year is zero (mdel #3 v. model #2).

..
4

. .

.

Table 13

halysls of E~ality of Prior Mobillty Effects

After First Six Months

Equality of Coefficients of Lagged Mobility Variables

Likelihood-ktio Tests

Year of Mdel #1 Model #2 Model #3 ~ test

Experience Log L Log L Log L #2 v. #l

DF p-value

LS test

#3 v. #l

DF p-value

2

3

4

5

6

Al1

-4587.9 -45ss.1 -45s7.9 1 .527

-3060.5 -3063.3 -3061.2 2 .0608

-2117.7 -2118.0 .-2117.7 3 .896

-1468.7 -1471.7 -1470.8 4 .199

-1078.7 -1082.1 -10s2.0 5 ..236

-1231~.5 -12352.3 -12319,61’15 .196

0 ---

1 .237

2 .995

3 .241

4 .159

10 .272

Model #l - mconstrained specification in table 9

Model #2 - constrained with slwle prior mobility variable (total prior jobs)

Model #3 - constrained with two prior mobility variables (total prior jobs

-d nmber of job cha~es in most recent year)

Note: The “Al1“ row is computed as the su of the specifit-year rows.

Difference in Coefficient of First Prior Year Mobility

Year of Caeff. of Total Coeff of # Jobs

Experience

p-value

# Prior Jobs in 1 year prior (#3 v. #2)

2 .205 -.0408 .575

(.0578) (.0727)

3 .0956 .122 .–-.040.0.

(.0355) (.0594)

4 -.0524 .416

(:M7) (.0645)

5 -.104 .1964

(:%79) (.0805)

6 .119 .0320 .748

(.0305) (.100)

Note: The nmbers in parentheses are aspptotic standard errors. The

coefficient estimates are from a Iogit model with a specification identical

to those used in table 9 with the exception that the set of prior history

variables is replaced by two variables: 1) the total nmber of prior jobs and

2 ) the nmber of Job cti~es in the most recent prior year. This is model #3

above. The p-value is for a test of the hypothesis that the coefficient

of the nwber of jobs in the most recent year is zero (model #3 v. model #2).
Table 14

Illustration of he Year Survival Probabilities

by Nmber of Prior Jobs

Jobs Since Prob Survive Prob Survive Prob Survive

fitry 6 months 2ti 6 months one year

(conditional1

0 .675 .896 .603

1 .652 .878 .573

2 .627 .861 .S40

3 .602 L .843 .507

4 .575 .823 .473

5 .548 .800 .438

b .519. .775 .402

Note: These probabilities were calculated USIM monthly mobility rates

predicted by the estimates of intermediate model #3 s~ari.zd in tables 12

and 13. The probabilities refer”””toa base group worker [white, male, not

married, 12 years education, not .livi~ in sn urbm area, no priOr SPell Of

nonemplo~ent ) who is 23 years old =d who starts a Job in year 4. None of

the prior Jobs are in the most recent y@ar. The six-month survival –.

probabi1ity is computed as the product of one minus the hazard at each of the

first six months. The conditional probability of survival for the seand six

months is computed as the product for the second six months of one minus the

monthly hazard. The one-year survival probabi1ity is computd as the product

of the two six-month survival probabi11ties.

by “Distributionof Prior Jobs

Jobs in Most Prob Survive Prob Survive Prob Survive

Recent Year 6 months 2nd b months one year

(conditional)

o .575 .823 .473 -

1 , .Sis .831 .431

2 .-... -.-459 .838 .385

3 .398 .846 .337

4 .336 .853 .287 -

Note: These probabilities were ~lculated usi~ monthly mobility rates ,

predicted by the estimates of intermediate model #3 s~ariz~ in tables 12

ad 13. The probabilities refer to a base group worker (white, male, not

married, 12 years education, not livi~ in ~ urb~ area, no prior spell of

nonemplo~ent) who is 23 years old and who starts a Job in year 4 with four

previous Jobs. The six-month survival probabi1ity is computed as the product

of one minus the hazard at each of the first six months. The conditional

probabi11ty of survival for tbe second slx months is’computed as the product

of one minus the hazard for the second six months. The one-year survival.

probability is computed as the product of the two six-month survival

probabilities.
Tenure

Month 1

Month 2

Month 3

Month 4

Month 5

Month 6

Months 7-12

Year 2

Year 3

Year 4

Year 5

Table 15

Logit halys is of Monthly Turnover %tes

Coefficients on Tenure

Year of *er ience

‘irstJob 1 2 3 4 5 6

1.45

(.127)

1.83

(.122)

2.16

[.120)

1.85

(.125)

1.30.

(.137)

1.27

(.1391

.650

(.116)

.563

(.109)

.406

(.109)

.%4

(.110)

.183

1.60

(.190.)

1.99

(.186)

2.01

(.189)

2...0.0

(.189)

1.66

(.198)

1.49

[.204)

1.02

(.179)

.895

(.172)

.481

(.177)

.448

(.177)

.510

(.195)

1.39

(.204)

1-.85

(.198)

1.78

(.200)

1...74

(.203)

1.57

[.207)

1...40

(.213)

.14

.1881

-891

.183)

.670

.187)

.435

(.1991

..174

(.2271

1.10

(.330)

,1.45

(.300)

1.75. .

(.297)

1..56

(.301)

1.42

(.305)

1.55

(.304)

1.23

(.286)

.923

(.284)

.604

(.293)

.488

[.306)

.387

(.341)

1..20

(.337)

1.90

(.329)

1...7.1

(,333)

1.33

(.341)

1.62

(.337)

1.54

(.341)

1.22

(.321)

.9.66.”..

(.320)

.843

(.330)

.409

(.367)

---

1.19

(.276)

.1.39.

[.274)

1-53..

(.274)

1.25

(.283)

1.37

(.284)

1.30

(.289)

.973

(.259)

......778

(.259)

-342

(.2S6)

---

---

.460

(.336)

1.11

(.322)

.792

(.334)

1.17

(.326)

.507

.354)

,779 ,~’

346)

.636

(.307)

.356

(.308)

-.114

(.342)

---

---

Note: The nubers in Parentheses are aswDtotic st=dard errors. All models

.

also include dwy variables for each cal~ndar year, a dummy variable’for

residence in a urban area, ad the controls for worker characteristics and

prior mobility in table 7. The base group consists of -arried white male

workers with twelve years sducation in 1979 who live outside an urbm area

ad who have been on the Job more than the maximu tenure level in the

relevant colm. The mode1 for Jobs tbat2start in the first year excludes

the first Job held by each worker. The x statistic is for a likelihood

ratio test of the relevant model against a constrained model “withonly a

constant. The worker characteristic and prior mobility coefficients are

contained in table 7 alo~ with the smary statistics for the estimations.

The results are discussed in section 3.

~. .
MOnth 1

‘Month2

Month 3

Month 4

Month 5

Table 16

Logit halysis of Monthly Turnover %tes

Coefficients on Tenure

First Six Months on Job

123456 Year of tiperlenirst Job 1

.120

(::%) (.144)

.526 .505

(.109) (.139)

.S65 .526

(.107) (.1411

.564 J.13

(.113) .(.143)

.028 .168

I (.127) (.155)

=.0360

(.145)

–.435

(.137)

; 371

.(.140)

_..332

.(.144)

.170

(.151)

-.481 -.363

(.164) (.177)

..129. ..-353.

.155) (.161)

188

:149) (:::)

.00320 -.210

.158) (.186)

.138 .0841

.166) (.179)

-.110

[.190)

.0966

(.187)

.240

[.187)

-.0383

(.201)

.0737

(.201)

-.336

(.246)

.322

(.226)

.OD740

(.242)

.392

(.232:

-.269

(.271’

Note: The nubers in parentheses are aspptotic standard errors. All models

also include dwy variables for each calendar year, a dmy variable for

residence in an urban area, and the controls for worker characteristics and

nrior mobilltv in table 8. The base flroupconsists of -arriti white male

~orkers with ~welve years education i; 19+9 who live outside - urban area

and who have been on the job for.”six months. The model for jobs tha~ start

in the first year excludes the first job held by each worker. The X

statistic is for a likelihood ratio test of the relevant model against a

constrained model with only a constant. The worker characteristic and prior

mobility coefficients are contained in table 8 along with the smary

statistics for the estimations. The results are discussed in section 3.
Table 17

Logit ha 1YS15 of Monthly Turnover Rates

Coefficients on Tenure

After First Six Months on Job

Year of fiperlence

Tenure First Job 1 2 3 4 5 6

Months 7-12 .751 1.00 1.12 1.19 1.23 .910

(.121)

.591

(.185) (.192) (.291) (.328) (.267) (.319)

Year 2 .639 .890 .878....... ..894

(.113) (.177) [.186) [.288)

Year 3 .458 .481 .666 :585

(.111) (.180) (.189) (.295)

Year 4 .385 .442 .431. .495

.978

.325)

.853

.3331

.417

I (.lli) (.186) (.2001 (.307) (.36S)

Year 5

I

.496 .394 ---

(:::) (.196) (:=) (.341)

.770 .318

.263) (.315)

.377 -.136

.287) (.344)

--- ---

--- ---

Note: The nmbers in parentheses are asmptotlc standard errors. All models

also include dmy variables for each calendar year, a dmy variable for

residence in an urban area, and the controls for worker characteristics and

prior mobility in table 9. The base group consists of married white male

workers wfth twelve years sducatlon in 1979 who live outside an urban area

and who have been on the Job more thm the maxfmw tenure level in the

relevant colm, The model for jobs that2start in the first year excludes “

the first job held by each worker. me x statistic is for a likelihood

ratio test of the relev~t model against a constrained model with only a

constant. The worker characteristic and prior mobility coefficients are

contained in table 9 alo~ with the smry statistics for the estimations.

The results are discussed in section 3.

.

,
,,:

I I I I

o, 12 24 36

. .

I I 1, I

60 72 84 96 1

Montps

I

‘:

——

, w
rI —/—————

.0

7

——————7

m
■

,,6 ~.,4,

,,,, ‘11:<. .,,

I ‘m 1’ ,,, ‘ ,,4

.,2

i.

‘;gi

F:

,., ,