LEARNING AND LIFE CYCLE PATTERNS OF OCCUPATIONAL TRANSITIONS

• Date: 01 May 2019
• Author: Aspen Gorry,Devon Gorry,Nicholas Trachter
• Published date: 01 May 2019
• DOI: http://doi.org/10.1111/iere.12371

INTERNATIONAL ECONOMIC REVIEW

Vol. 60, No. 2, May 2019 DOI: 10.1111/iere.12371

LEARNING AND LIFE CYCLE PATTERNS OF OCCUPATIONAL TRANSITIONS∗

BYASPEN GORRY,DEVON GORRY,AND NICHOLAS TRACHTER1

Clemson University, U.S.A.; Clemson University, U.S.A.; Federal Reserve Bank of Richmond,

U.S.A .

Individuals experience frequent occupational switches during their lifetime, and initial worker characteristics

are predictive of future patterns of occupational switching. We construct a quantitative model of occupational

choices with worker learning and occupation-specific productivity shocks to match life cycle patterns of occu-

pational transitions and quantify the value of occupational mobility and learning. For the average 18-year-old

worker, the value of being able to switch occupations is about 67 months of the maximum wage he or she could

earn in the model, and the value of a worker learning his or her type is about 32 months of this maximum wage.

1. INTRODUCTION

About 20% of workers from ages 18 to 28 years change broad occupational categories

(between blue and white collar jobs) each year.2Although it has long been known that job

mobility plays a crucial role in the wage growth of young workers (see Topel and Ward, 1992),

occupational choices are also important for human capital development during a worker’s

first 10 years in the labor market.3Therefore, workers’ occupational choices are crucial for

explaining their human capital accumulation, patterns of job switching, and worker turnover.

Leading explanations of why workers switch occupations are based on career ladders as in

Jovanovic and Nyarko (1997) and worker learning about their type as in Johnson (1978).

Although occupational choices are an important factor in understanding workers’ early ca-

reer decisions, most empirical studies of occupational mobility have focused on cross-sectional

patterns of switches over time instead of following individuals over their life cycle. To illustrate

the prevalence of occupational switches over one’s life cycle, Figure 1 shows a histogram for the

number of years that individuals with a high school education switch between blue and white

collar jobs in data from the National Longitudinal Survey of Youth 1979 (NLSY79) during

workers’ first 10 years after high school. The mean number of years with an occupational switch

∗Manuscript received July 2014; revised March 2018.

1The views expressed herein are those of the authors and not necessarily those of the Federal Reserve Bank of

Richmond or the Federal Reserve System. We would like to thank Guido Menzio (the editor) and three anonymous

referees for helpful feedback. We would also like to thank Ludo Visschers, Christian vom Lehn, and seminar participants

at the 2012 QSPS Workshop, Midwest Macro Workshop, the Federal Reserve Bank of Chicago, and UC Santa Cruz

for helpful comments and useful discussions. We greatly appreciate research assistance from Timothy Elser, Jackson

Evert, and Matthew Pecenco. All mistakes are our own. Please address correspondence to: Nicholas Trachter, Research

Department, Federal Reserve Bank of Richmond, 701 E. Byrd St., Richmond, VA 23219, U.S.A. Phone: 804–405–0266.

E-mail: trachter@gmail.com.

2Trends in the level of occupational mobility have been changing over time. Kambourov and Manovskii (2008) show

that occupational mobility has been increasing in the United States between 1968 and 1997, whereas Moscarini and

Thomsson (2007) find that mobility begins to decline after 1995.

3Recent research shows that much of the human capital gained through experience occurs at the level of a worker’s

occupation or industry. Papers by Neal (1995) and Parent (2000) argue that human capital is largely based at the industry

level instead of being firm specific, whereas more recent research by Kambourov and Manovskii (2009a, 2009b) argues

that human capital is based at the occupational level and can account for a large amount of observed wage inequality.

Poletaev and Robinson (2008) and Gathmann and Sch¨

onberg (2010) have shown skills to be task specific. Even under

this view skills show strong correlation within occupational categories. See Carrillo-Tudela et al. (2016) for recent

evidence on the wage growth associated with occupational transitions.

905

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(2018) by the Economics Department of the University of Pennsylvania and the Osaka University Institute of Social

and Economic Research Association

906 GORRY,GORRY,AND TRACHTER

FIGURE 1

HISTOGRAM OF THE NUMBER OF YEARS WITH AN OCCUPATIONAL SWITCH BETWEEN THE AGE OF 18 AND 28 FROM THE NLSY79

[COLOR FIGURE CAN BE VIEWED AT WILEYONLINELIBRARY.COM]

is 1.5. Whereas about 36% of individuals do not switch occupations, 18% switch occupations

exactly once, and despite only having two broad occupational categories, a large portion of

workers switch occupations more than once, with 23% switching twice and another 23% of

the population switching more than twice. The rates of transition between finer occupational

categories are even higher.

Motivated by recent papers such as Groes et al. (2015) and Papageorgiou (2014) that find

learning to be important in understanding worker occupational patterns, we first develop a

quantitative life cycle model of occupational mobility. A goal of the model is to be able to

measure the option value of being able to switch occupations and of an individual learning his

or her type. Our life cycle framework builds on the classic job shopping framework developed

by Johnson (1978) where worker learning generates occupational mobility. In our model, risk-

averse workers have an unknown type and must choose their occupation in each period to

maximize their expected utility. Risk aversion is included to properly measure the gains from

occupational choices, as in learning models workers can potentially trade off current wages

to learn faster; see, for example, Miller (1984). We also explicitly model the worker’s initial

occupational choice. Prior to entering the labor market the worker receives a signal about his

or her type. The worker then chooses an initial occupation based on his or her belief about

his or her type that is formed prior to labor market entry. This belief is updated over time by

observing his or her output in an occupation each period. In addition to the learning process,

the model also includes observable individual demand shocks that shift the worker’s relative

productivity in each occupation.

Next, we document life cycle patterns of occupational transitions in the NLSY79 for workers’

first 10 years after high school. Using panel data to study occupational choices over the life cycle

generates several findings that are consistent with optimal behavior predicted by the model.

These life cycle implications are novel relative to the literature that uses cross-sectional data to

study time trends in the level of occupational mobility.4First, we construct initial probabilities of

4All results in the main text are constructed using broad occupational categories of white and blue collar jobs as

in Keane and Wolpin (1997). We focus on workers with exactly high school education. One possible concern with

this approach is that the high frequency of switching between occupations is due to this particular classification. In

Appendix A.1, we show that our results also arise in both two-category classifications of occupations used by Jaimovich

and Siu (2015). In particular, our main results also hold when occupations are classified as routine versus nonroutine and

cognitive versus noncognitive. For a broader summary of the literature on job polarization that uses these occupational

categories see Acemoglu and Autor (2011). Additionally, Appendix A.2 replicates our main results using single-

digit occupational categories. Finally, we also show that results hold for the sample of college-educated workers in

Appendix A.3.

LEARNING AND OCCUPATIONAL TRANSITIONS 907

each individual choosing a job in a white collar occupation based on observable characteristics

before starting work and show that these initial probabilities are informative about future

switching behavior. Workers whose characteristics make them more likely to be in the initial

occupation that they are observed to choose are less likely to ever switch, and for those who do

switch the number of switches is smaller and the average time until their first switch is longer.5

Additionally, we find that for workers who switch more than once, the average time to their

first switch is longer than the average time to the second switch. This result is interesting in light

of cross-sectional evidence that the rate of occupational switches declines with age.

After documenting these life cycle patterns of occupational choices, the model is calibrated

to match moments about worker’s occupational mobility and wages. We find that the model

with learning and productivity shocks is consistent with patterns of occupational mobility, wage

growth, and a reduction in time to second switch for individuals who have more than two occu-

pational switches in the sample. An individual who just switched occupations will be relatively

indifferent between the two occupations although the distribution of initial beliefs from our

empirical specification implies that workers entering the labor force are not as concentrated

around the switching threshold. This result is consistent with mechanisms such as learning or

productivity fluctuations across occupations where individuals switch occupations when they

cross a threshold, but is not consistent with other models of occupational transitions such as job

ladders unless workers gain skills more rapidly as they age. The productivity shocks are included

in the model to understand the relative importance of learning and productivity fluctuations in

generating this shifting behavior.

Finally, the calibrated model is used to measure how much workers value the ability to switch

occupations and how much they would be willing to pay to learn their type. Measuring the value

to workers for being able to switch occupations and learn is interesting, as it provides evidence

about the importance of workers’ ability to efficiently match with well-suited occupations.

Moreover, it is interesting to quantify the value of switching occupations for workers given

the concern that the recent decline in occupational transitions is associated with a reduction in

dynamism in the United States as discussed in Davis et al. (2012), Hyatt and Spletzer (2013),

and Molloy et al. (2014). Although we do not deal with time series patterns of these values,

our measurement highlights how option values change for workers as they age. For the average

18-year-old workers, the value of being able to switch occupations is about 67 months of

the maximum wage they could earn in the model (if they knew their type), and the value of

the workers learning their type is about 32 months of the maximum wage they could earn.

These values decline to nearly zero by the time the worker is 50, and much of the decline is

due to learning in the model instead of mechanical horizon effects. Although both learning and

productivity shocks are important to generate switches in the model, we find that the magnitude

of these option values are robust to changes in risk aversion, the magnitude of the productivity

shocks, and the inclusion of switching costs in the model.

This article contributes to an ongoing literature to understand the selection process of workers

across occupations. Early studies such as Johnson (1978) and Miller (1984) emphasize uncer-

tainty and imperfect information to explain workers’ occupational sequencing decisions. The

implication of these models is that young workers would initially choose riskier professions

for higher expected returns. Jovanovic and Nyarko (1997) question the role of learning about

ability in labor markets, presenting evidence that workers are learning skills that allow them to

transfer to new jobs, higher in an occupational ladder. This method of learning argues that in

observed occupational sequences people learn by completing simple tasks first. Learning should

5This finding contributes to our understanding of the pattern of occupational transitions. It is well established

that one’s current occupation is predictive of future occupational patterns. For example, simple transition matrices in

McCall (1990) and Kambourov and Manovskii (2008) show that some occupational transitions are much more likely

than others. Gathmann and Sch¨

onberg (2010) provide additional evidence with a task-based approach. More recently,

Guvenen et al. (2015) show that current human capital measured by innate skills and past experience helps to predict

future movements. We add to this literature by showing that initial information known to the worker when choosing

his or her first job is predictive both of initial choices and future patterns of occupational movement.