Monthly Labor Review February 2005 33

Job Mobility and Wage Growth

Audrey Light is an

associate professor

in the Department of

Economics and

Center for Human

Resource Research,

Ohio State University.

E-mail:

light.20@osu.edu

Data from the 1979 National Longitudinal Survey of Youth

provide an unusually complete history of employment experiences;

analyses of why workers separate from their employers,

frequencies of these separations, and job mobility's impact

on earnings reveal that today's labor markets

are far more dynamic than previously realized

Job mobility and wage growth:

evidence from the NLSY79

Audrey Light Longitudinal data have contributed immeasurably to our understanding of individuals’ labor market activities, especially when

it comes to analyzing job mobility and wage

growth. Without the ability to “see” workers

move from employer to employer, we would know

very little about why workers separate from their

employers, how often separations occur, and how

job mobility affects earnings.1 Analyses of these

issues have revealed labor markets to be far more

dynamic than was previously realized.

One phenomenon that has received considerable scrutiny is the persistent, voluntary job mobility of young workers. In the mid 1970s,

economists began using search-theoretic models

to explain why information costs compel workers

to systematically “shop” for a better job.2 The

idea is that workers cannot immediately locate

firms where their skills are valued the most highly,

so upon accepting a job offer they continue to

search for an even better outside opportunity.

Workers might also learn over time that their current job is not as productive as they initially predicted. New information regarding outside offers

or the current job is predicted to lead to a workerinitiated job separation. Empirical researchers

have used longitudinal data to determine which

theoretical models are supported by the data and

to identify the contribution of “job shopping” to

life-cycle wage growth.

A related issue of long-standing concern is

the effect of job immobility on wage growth.

Human capital models predict that wages rise

with job seniority when workers “lock in” and

invest in firm-specific skills. Because these skills

cannot be transferred to a new job if a separation

occurs, workers and firms agree to share the

costs and benefits of the investment—and the

worker’s return on the shared investment takes

the form of within-job wage growth above and

beyond any gains due to the acquisition of general (transferable) skills. A variety of agency

models provide alternative explanations for upward sloping wage-tenure profiles. In these

models, employers defer wages as a means of

discouraging workers from quitting or shirking;

stated differently, they require workers to “post

a bond” as an incentive to sustain the employment relationship.3

 Longitudinal data have

proved to be essential for assessing the merits

of these theoretical models and identifying the

effect of tenure on wages.

Knowledge of the relative contributions of job

mobility and immobility to life-cycle wage growth

is fundamental to a number of important policy

issues. For example, the well-being of low-skill

labor market entrants is highly dependent on

whether they are consigned to a lifetime of lowwage jobs, or whether they can advance in the

wage distribution via life-cycle wage growth. As

a result, policymakers might ask what can be done

to enhance workers’ wage growth. If job-specific

skill investments are an important source of wage

growth, then policies that promote on-the-job

training might be useful to the low-wage population. If “job shopping” provides the lion’s share

of wage growth, then programs that provide jobsearch assistance might be warranted.
Job Mobility and Wage Growth

34 Monthly Labor Review February 2005

Of course, not all job separations are worker-initiated quits,

so it is equally important to focus attention on issues related

to involuntary job displacements. Researchers have relied on

longitudinal data to determine which workers are particularly

vulnerable to layoffs; which industries are the most volatile;

and how wages are affected in both the short run and the long

run when workers are displaced from their jobs.

Advantages of NLSY79 data

Analysts have been studying job mobility and wage growth

for decades, but they gained an important new data source

when the 1979 National Longitudinal Survey of Youth

(NLSY79) was launched. The NLSY79 plays a central role in

this type of research because it provides an unusually complete history of each respondent’s employment experiences,

including a record of virtually every job held. In this section,

the key attributes of these data are highlighted; additional

details can be found in the NLSY79 User’s Guide.

4

During each interview, NLSY79 respondents report information on every job currently in progress or held since the

last interview. When the first interview was conducted in

1979, respondents who were older than 18 retrospectively

identified each job held since age 18. (The 12,686 respondents

ranged in age from 14 to 22 at that time; 43 percent were older

than 18.) For the younger respondents, the job history begins between ages 15 and 17. As a result of this sampling and

data collection strategy, analysts can initialize respondents’

careers at a uniform point in the life cycle (the 18th birthday,

the first exit from school, and so forth) and obtain a remarkably complete record of jobs held from that point forward for a

large sample of individuals.

While the advantage of sampling young people is that complete histories (without left-censoring) are obtained, NLSY79-

based research has necessarily been limited to early-career

activities. The NLSY79 has taken a back seat to other longitudinal surveys—most notably, the Panel Study of Income Dynamics (PSID)—for the study of job mobility and wage growth

among prime-age workers. Now that the youngest respondents (those born in 1964) have entered their 40s, however,

the NLSY79 will be increasingly useful for the analysis of job

mobility in the mid-career.

The NLSY79 provides much more than a simple tally of

cumulative jobs held over the career. At each interview, respondents report the start date and stop date of any job that

began and/or “permanently” ended since the last interview.

Because the recall period is relatively short and respondents

report dates rather than time elapsed since the job began or

ended (which would invite them to “round” their responses),

analysts obtain high-quality data. Measurement error is inevitable in all survey data, but the NLSY79 is acknowledged to

identify job durations and job tenure more cleanly than other

surveys.5

In addition to start and stop dates, such job characteristics as industry, occupation, class of employer, rate of pay,

and weekly hours are identified for most jobs. These characteristics are usually known for as many as five unique jobs

held between each interview, although some characteristics

are identified only when the job lasts at least 9 weeks and the

respondent works at least 10 hours per week. When jobs last

long enough to span interviews, multiple reports of these

characteristics are recorded. For example, if a job begins 3

months before the 1980 interview and ends 3 months after the

1983 interview, the respondent reports his current wage, occupation, hours worked, and so forth during the 1980, 1981,

1982 and 1983 interviews; the stop date is then identified in

1984.

When respondents report that a job has ended, they are

asked to provide their reason for leaving and whether a new

job was lined up before they left. Analysts must contend

with missing data, ambiguous responses (especially when

reasons are recorded as “other”), and the possibility of

misclassification, but they can make considerable progress

in distinguishing between involuntary separations (layoffs,

firings) and voluntary “quits.” In combination with job start

and stop dates, these data also allow analysts to classify job

exits as “job to job” or “job to nonemployment.”

The survey also identifies temporary nonwork spells within

jobs—specifically, the start and stop date of each “within-job

gap” lasting at least 1 week, along with the reason for not

working. This information allows analysts to identify nonwork

spells due to strikes, temporary layoffs, health-related leaves

of absence, and so forth that do not lead to the permanent

termination of the employment relationship. Moreover, the

detailed information on work and nonwork spells collected at

each interview is used to create three weekly “work history”

arrays. One array identifies each respondent’s labor market

status (working, out of the labor force, active military service,

and so forth) during each week from January 1, 1978, onward.

Another array identifies the usual hours worked on all jobs

held during each week, and the third array identifies the number of jobs held during each week. These variables allow

analysts to construct extraordinarily detailed measures of

cumulative labor market experience and job tenure, and to

identify transitions between employment, unemployment, and

nonemployment spells, as well as transitions into and out of

jobs.

As discussed in other articles in this issue, the NLSY79

also contains detailed data on schooling attainment and enrollment, job training, geographic location, household composition, family formation, and much more. These data provide a rich set of controls for models of job durations, job exit

probabilities, and wages, and they allow researchers to study

the interdependence of job mobility and other events such as

school completion, migration, and marriage.
Monthly Labor Review February 2005 35

Overview of NLSY79-based research

Given the range of substantive issues that compel analysts to

study job mobility and the advantages of using NLSY79 data

for this purpose, it should come as no surprise that the existing NLSY79-based literature is very large. Rather than attempt

a comprehensive survey of the literature, this article describes

a dozen studies that, as a group, illustrate the ways in which

NLSY79 data have been used to explore mobility- and wagerelated issues.

In the first set of studies, analysts identify the determinants of job mobility by estimating models of job durations or

separation probabilities. Studies of this nature include those

by Henry S. Farber, Derek Neal, Anne Beeson Royalty, and

Madeline Zavodny.6 Farber focuses on the timing of job separations and the extent to which observationally equivalent

workers differ in their separation probabilities. He finds, among

other things, that the hazard rate rises with job tenure for about

3 months and declines thereafter—a pattern that is consistent

with the view that agents gather information before deciding

that a separation is optimal. Subsequent research has distinguished between different types of job separations. For example, Neal considers both “simple” job changes, where workers perform the same type of work on both jobs, and “complex” job changes that entail a change of career as well as a

change of employer. His analysis lends support to the idea

that workers first search for a suitable career and then concentrate on finding the best employer match within that career.

Royalty reconsiders the conventional wisdom that women are

more likely than men to leave their employers for nonemployment, but perhaps less likely to quit for a better job. By distinguishing between job-to-job and job-to-nonemployment transitions and estimating separation models for workers in distinct gender-schooling groups, she learns that this pattern

only applies to less educated workers; men and women with

more than 12 years of schooling prove to have similar separation patterns. Zavodny asks whether technology-intensive

industries (measured by computer usage, the fraction of workers in science and engineering, and so forth) have more or less

job stability than other industries. She finds that overall separation rates are lower in “high tech” industries than in “low

tech” industries, but that the difference is entirely due to lower

quit rates in the technology-intensive industries; among less

educated workers, involuntary separations may be more likely

in technology-intensive industries than in other sectors.

Wages are the outcome of interest in the next set of studies

discussed. To maintain the focus on mobility-related research,

studies that model wages as a function of past job mobility

and/or current tenure, among other factors, are considered.7

Pamela J. Loprest, Kristen Keith, and Abagail McWilliams conduct gender comparisons of the contemporaneous wage

change associated with a change of employer. Loprest finds

that men receive more wage growth than women over a 4-year

period, and that this premium is largely due to a higher return

to mobility. Keith and McWilliams find that between-job wage

gains are greater for workers (both men and women) who engage in formal job search prior to their separation, but that men

are more likely than women to conduct such activities. Audrey

Light and Kathleen McGarry ask how “overall” mobility (defined as the number of job separations in the first 8 years of the

career) affects both the level and slope of men’s wage paths.

They find that immobile workers have the highest and steepest wage paths, followed by moderately mobile men whose

mobility appears to conform to “job shopping,” while highly

mobile workers fare the worse in terms of both wage levels and

wage growth.

Turning to studies that focus on the wage-tenure relationship, Bernt Bratsberg and Dek Terrell assess race differences

in the returns to tenure, using various instrumental variables

to contend with the fact that tenure is endogenous to the

wage-generating process. They find that estimated tenure

slopes are sensitive to the estimation method, but are roughly

similar for black and nonblack workers (all of whom are terminal high school graduates in their sample). However, blacks

receive significantly lower returns than nonblacks to general

labor market experience. The human capital interpretation of

these findings is that blacks invest less intensively than whites

in skills that are transferable across jobs, but receive similar

returns to investments in firm-specific skills. The role of firmspecific skill investments is given a closer look by Daniel Parent, who estimates wage models that include measures of both

job tenure (time with the current employer) and industry tenure (time with the current industry). He finds that tenure effects virtually disappear when industry tenure is included as a

control, which suggests that workers are investing in skills

that are specific to their industry rather than their current job.

Randall J. Olsen’s study is distinguished by the fact that he

jointly estimates models of wages and job mobility. His unified, structural approach to assessing the relationship between

job mobility on wages suggests that cumulative work experience (general skill acquisition) and job mobility are more important sources of early-career wage growth than is tenure

(firm-specific skill acquisition).

Empirical patterns

In this section, some of the basic relationships between job

mobility and wage growth seen in the NLSY79 are highlighted—

specifically, the distribution of cumulative jobs held by NLSY79

respondents in the first 8 years of their careers, and the unconditional relationships between job mobility and both cumulative and year-to-year wage growth.

The first step of the analysis is to define a career start date—

that is, the date when individuals make a transition from school
Job Mobility and Wage Growth

36 Monthly Labor Review February 2005

to work. Many NLSY79 respondents are observed combining

school and work or cycling between the two activities, so a

judgment call is needed to determine when their work lives

begin. Therefore, careers are initialized at the start of the first

school exit that lasts at least 12 months.8 A total of 5,321

respondents, not enrolled in school at the time of their 1979

interview, are eliminated from the sample. Reported school

enrollment data are used to determine career start dates for the

7,365 remaining respondents. This date falls between April

1979 and June 1990 for all respondents, and precedes May

1983 for 75 percent of the sample.

In order to track job mobility and wages over a reasonably

long period of time (but not so long that right-censoring affects a significant number of careers), respondents are required

to be observed for 8 years beyond the start of the career. This

selection rule eliminates 51 individuals who drop out of the

survey before their 8-year window ends. To avoid having to

contend with missing data, respondents who miss one or more

interviews during the 8-year observation period are also eliminated. This leaves a final sample of 5,654 respondents.

Table 1 summarizes the number of jobs held by these 5,654

respondents between the beginning and end of the 8-year

observation period. The cumulative job count includes jobs

that are in progress at the start of the career, as well as any job

whose start date precedes the end of the 8-year window. Table

1 shows that men are slightly more mobile than women during

the first 8 years of their career: the mean job count is 4.8 for

men and only 4.3 for women, and a higher proportion of men

than women (25 percent versus 20 percent) hold seven or more

jobs. At the other extreme, 11-12 percent of women and men

hold no jobs or a single job during the period of observation.

In contrast to these relatively small gender differences, table 1

reveals that job mobility varies dramatically across schooling

levels. To assess the relationship between mobility and

schooling, the men are classified into a “high school”

subsample (those whose highest grade completed at the career start date is no greater than 12) and a “college” subsample.

The high school sample averages 5.2 jobs during the 8-year

window, which is almost one job more than the mean for the

college sample. Almost one-third of high school educated

men holds seven or more jobs, versus only 18 percent of the

college sample.

In table 2, the cumulative job count over the 8-year observation period is linked to cumulative wage growth. For this

exercise, attention is confined to 4,189 respondents for whom

a “valid” wage (an average, hourly wage between $1 and

$1,000) is reported to have been earned within 9 months of the

career start date and the career end date. Each average hourly

wage is divided by the gross domestic product (GDP) implicit

price deflator, and the 8-year difference in log-wages is used

as the measure of cumulative wage growth.

Table 2 reveals that, on average, overall wage growth declines with mobility for both men and women. Among women

who hold a single job in 8 years, the average change in logTable 1. Distribution of number of jobs held during first 8 years of career

Schooling less Schooling greater

All schooling levels All schooling levels than or equal to than grade 12

grade 12

Number Percent Number Percent Number Percent Number Percent

of of of of

sample sample sample sample

0 .......................... 87 3.0 58 2.1 42 2.5 16 1.4

1 .......................... 265 9.2 241 8.7 106 6.4 135 12.1

2 .......................... 423 14.7 353 12.7 180 10.9 173 15.5

3 .......................... 446 15.5 374 13.5 193 11.7 181 16.2

4 .......................... 442 15.4 408 14.7 240 14.5 168 15.0

5 .......................... 370 12.9 325 11.7 198 12.0 127 11.4

6 .......................... 283 9.8 313 11.3 194 11.7 119 10.6

7 .......................... 218 7.6 240 8.7 164 9.9 76 6.8

8 .......................... 132 4.6 173 6.2 132 8.0 41 3.7

9 .......................... 96 3.3 94 3.4 72 4.4 22 2.0

10 or more ............. 117 4.1 196 7.1 136 8.2 60 5.4

All ......................... 2,879 100.0 2,775 100.0 1,657 100.0 1,118 100.0

Mean ..................... 4.3 – 4.8 – 5.2 – 4.3 –

Standard deviation .. 2.6 – 2.9 – 2.9 – 2.7 –

Maximum ............... 17 – 19 – 19 – 15 –

Number

of

 jobs

Women Men
Monthly Labor Review February 2005 37

wage is 0.59. The average wage gain is virtually the same

among women who hold 1-2 jobs, but it is considerably smaller

(0.40–0.46) among the more mobile women. A similar pattern is

seen among the men, although their average wage growth is

markedly higher than the women’s. However, when the sample

of men is broken down by schooling attainment, the negative

correlation between overall mobility and overall wage growth

holds only for the more highly schooled men. Among the high

school sample, the average change in log-wage is 0.58 for those

who hold 2-3 jobs, but only 0.40–0.46 for men in any other

mobility category, including those who hold a single job in 8

years.

What are the explanations for the patterns seen in table 2?

To the extent that “job shopping” dominates early-career mobility (that is, to the extent that workers move to jobs where

their skills are more highly valued), it should be associated

with wage growth. However, high mobility can also go hand in

hand with a high frequency of involuntary discharges and/or

a tenuous attachment to the workforce. Workers who are frequently fired or have frequent nonwork spells are not expected

to receive substantial amounts of wage growth.

To explore some of these issues, the work history “status

array” is used to identify the number of weeks in which respondents are known to be working during the 8-year window. In table 3, sample means for this employment measure

(expressed as a percentage of total weeks) are reported for the

same sample of workers used for table 2. Table 3 reveals that

the average percentage of weeks worked declines with job

mobility for each group of workers. In the “all schooling

levels” groups, the average worker (male or female) who holds

only one job in 8 years works at least 90 percent of the time,

while average work effort falls to around 75 percent for workers who hold seven or more jobs. Clearly, the negative relationship between overall mobility and overall wage growth

seen in table 2 reflects the fact that highly mobile workers

tend to be nonemployed for a substantial portion of their

Table 2. Wage growth during first 8 years of career by number of jobs held

Schooling less than or equal Schooling greater than

to grade 12 grade 12

Number Standard Number Standard Number Standard Number Standard

of Mean devia- of Mean devia- of Mean devia- of Mean deviaobserva- tions observa- tion observa- tion observa- tion

tions tions tions tion

1 ............. 123 .59 .56 137 .66 .71 42 .40 .44 95 .77 .77

2–3 ......... 557 .57 .68 562 .68 .69 257 .58 .64 305 .77 .71

4–6 ......... 836 .46 .63 858 .51 .63 489 .43 .60 369 .63 .65

7 or more . 491 .40 .68 625 .47 .77 435 .45 .77 190 .51 .76

All ........... 2,007 .49 .66 2,182 .55 .70 1,223 .46 .67 959 .66 .71

Number

of

jobs

NOTE: Wage growth is defined as ln(W8)-ln(W1), where W1 and W8 are

average hourly wages reported at the beginning and end of the 8-year

observation period. Sample sizes are smaller than in table 1 because of

missing wages.

All schooling levels All schooling levels

Women Men

Table 3. Percent of weeks employed during first 8 years of career by number of jobs held

Schooling less than or Schooling greater than

 equal to grade 12 grade 12

Number Stand- Number Stand- Number Stand- Number Standof Mean ard of Mean ard of Mean ard of Mean ard

observa- devia- observa- devia- observa- devia- observa- deviations tion tions tion tions tion tions tion

1 ............. 123 90.0 22.0 137 94.4 16.2 42 87.6 22.2 95 97.3 11.7

2–3 .......... 557 83.6 23.5 562 87.1 23.0 257 78.9 28.7 305 94.0 13.5

4–6 .......... 836 77.4 23.1 858 80.4 21.6 489 74.4 23.5 369 83.3 15.6

7 or more .. 491 74.8 19.0 625 77.2 18.3 435 73.6 18.7 190 85.2 14.7

All ............ 2,007 79.2 22.7 2,182 82.0 21.4 1,223 75.5 23.4 959 90.4 14.9

Number

of

 jobs

All schooling levels All schooling levels

NOTE: The work history “status array” is used to identify the cumulative

number of weeks worked (excluding within-job employment gaps) during the

8-year observation period. Only respondents who report wages at the

beginning and end of this period are included in each sample.

Women Men
Job Mobility and Wage Growth

38 Monthly Labor Review February 2005

early careers. Workers who change jobs less often are much

more likely to work continuously—and, perhaps, to engage

in productive “job shopping.” Whether the above-average

wage growth of workers who undergo moderate mobility is

due to work continuity, job mobility, or a combination of the

two cannot be determined from tables 2 and 3.

Rather than look exclusively at cumulative wage growth,

the analysis concludes with an examination of year-to-year

changes in employers and log-wages. Beginning with each

worker’s entire sequence of reported wages during the 8-year

observation period, the change in log-wages for each successive pair of wages is computed and confined to those differences where the elapsed time between wages is approximately

1 year (10-14 months) or 2 years (20-28 months); the 2-year

differences are divided in half.9 Each year-to-year change in

log-wages is then classified according to whether the two

wages were earned on the same job or on different jobs.

Table 4 reports the mean log-wage changes for job stayers

and job movers. Among women, the average wage change is

quite a bit higher (0.048) for job stayers than for job movers

(0.027), while for each sample of men these two means are

almost identical. The bottom row of table 4 reports mean

changes in log-wages for a subsample of movers who make

voluntary job changes—defined as any change not reported

as a layoff, discharge, plant closing, or end of temporary work;

all quits and all separations for which the reason is “other” or

unknown are considered to be voluntary. By crudely narrowing the sample to job changes that might be voluntary, much

larger mean changes in log-wages are obtained. Women receive an average, annual boost in log-wages of 0.044–0.048

regardless of whether they maintain their current job or undergo a voluntary job transition. For men, the average wage

boost associated with a voluntary job change is about 0.01 log

points higher than the average wage change for job stayers,

although the difference in means is not always statistically

significant at conventional levels. Nonetheless, table 4 suggests that the average wage gain associated with a voluntary

job change is quite substantial for all groups of workers.

THIS BRIEF ANALYSIS HAS DEMONSTRATED that the typical worker

holds about five jobs in the first 8 years of the career, but that

workers vary considerably in their mobility rates. Highly mobile workers receive less cumulative wage growth, on average,

than their less mobile counterparts—a difference that is at

least partially attributable to the fact that employment continuity is negatively correlated with mobility. Finally, there is

cursory evidence that workers who change jobs voluntarily

receive significant contemporaneous wage boosts that, on

average, are at least as large as the wage gains received by job

stayers. Each of these patterns has been explored in greater

detail in other studies, and the NLSY79 will undoubtedly reveal

much more about these relationships in the future.

Table 4. Annual wage growth for job movers and job stayers

Schooling less than or Schooling greater than

equal to grade 12 grade 12

Number Stand- Number Stand- Number Stand- Number Stand-

 of ard of ard of ard of ard

observa- devia- observa- devia- observa- devia- observa- deviations tion tions tion tions tion tions tion

Job stayer .. 13,085 .048 .44 14,265 .050 .44 7,173 .045 .40 7,092 .054 .48

Job mover .. 3,637 .027 .62 3,755 .046 .69 2,277 .042 .60 1,478 .052 .81

Voluntary job

mover ..... 2,959 .044 .61 3,210 .060 .70 1,869 .055 .61 1,341 .068 .80

Mean Mean Mean Mean

NOTE: Wage growth is defined as the 1-year change in log average

hourly wages (or one-half the 2-year change) during the 8-year observation

period. Job movers change employers between wage reports; the subsample

of voluntary movers excludes those who report that they moved because

of a layoff, discharge, end of temporary job, or plant closing.

 Number

of

jobs

All schooling levels All schooling levels

Women Men

Notes

1 Throughout this article, job mobility refers to a change of employer, and not to an intra-firm change in position, rank, or work

assignment.

2 Examples of such models include Kenneth Burdett, “A Theory of

Employee Job Search and Quit Rates,” American Economic Review, Vol.

68, no. 1, 1978, pp. 212–220; and Boyan Jovanovic, “Job Matching

and the Theory of Turnover,” Journal of Political Economy, Vol. 87,

No. 5, part 1, 1979, pp. 972–990.

3 For a model of firm-specific human capital, see Gary Becker, “Investment in Human Capital: A Theoretical Analysis,” Journal of Politi-
Monthly Labor Review February 2005 39

cal Economy, Vol. 70, No. 1, pp. 9–49. Agency models include Edward

Lazear, “Why is There Mandatory Retirement?” Journal of Political

Economy, Vol. 87, No. 6, 1979, pp. 1261–84; and Joanne Salop and

Steven Salop, “Self-Selection and Turnover in the Labor Market,” Quarterly Journal of Economics, Vol. 90, No. 4, 1976, pp. 619–627.

4

 The NLSY79 User’s Guide is available at http://www.bls.gov/nls/

79guide/nls79usg.htm.

5

 In contrast, the PSID makes it considerably more difficult to identify unique jobs and to measure tenure on each job. See James N. Brown

and Audrey Light, “Interpreting Panel Data on Job Tenure,” Journal of

Labor Economics, Vol. 10, No. 3, 1992, pp. 219–257.

6

 See Henry S. Farber, “The Analysis of Interfirm Worker Mobility,”

Journal of Labor Economics, Vol. 12, No. 4, 1994, pp. 554–593; Derek

Neal, “The Complexity of Job Mobility Among Young Men,” Journal

of Labor Economics, Vol. 17, No. 2, 1999, pp. 237–261; Anne Beeson

Royalty, “Job-to-Job and Job-to-Nonemployment Turnover by Gender

and Education Level,” Journal of Labor Economics, Vol. 16, No. 2,

1998, pp. 392–443; and Madeline Zavodny, “Technology and Job Separation Among Young Adults,” Economic Inquiry, Vol. 41, No. 2, 2003,

pp. 264–278.

7

 Studies that control for past job separations include Kristen Keith

and Abagail McWilliams, “The Returns to Mobility and Job Search by

Gender,” Industrial and Labor Relations Review, Vol. 52, No. 3, 1999,

pp. 460–477; Audrey Light and Kathleen McGarry, “Job Change Patterns and the Wages of Young Men,” Review of Economics and Statistics, Vol. 80, No. 2, 1998, pp. 276–286; and Pamela J. Loprest, “Gender

Differences in Wage Growth and Job Mobility,” American Economic

Review, Vol. 82, No. 2, 1992, pp. 526–532. Studies that focus on the

wage-tenure relationship include Bernt Bratsberg and Dek Terrell, “Experience, Tenure, and Wage Growth of Young Black and White Men,”

The Journal of Human Resources, Vol. 33, No. 3, 1998, pp. 658–682;

Daniel Parent, “Industry-Specific Capital and the Wage Profile: Evidence from the National Longitudinal Survey of Youth and the Panel

Study of Income Dynamics,” Journal of Labor Economics, Vol. 18, No.

2, 2000, pp. 306–323; and Randall J. Olsen, “Job Switching, Earnings

Growth and the Rate of Return to Tenure,” Ohio State University working paper, November 2001.

8

 For a discussion of the ambiguity in career start dates, see Audrey

Light, “Estimating Returns to Schooling: When Does the Career Begin?” Economics of Education Review, Vol. 17, No. 1, pp. 31–45.

9

 Because interviews were conducted annually from 1979 to 1994,

and the 8-year observation period ends well before 1996 for most respondents, the majority of successive wage reports are approximately 1

year apart. One-half the 2-year differences is used to avoid discarding

wage data for those respondents whose 8-year period extends in the

mid-1990s; these are invariably younger respondents who stay in school

a relatively long time. For this exercise, the dependent variable is again

the average hourly wages divided by the GDP implicit price deflator.