Job Mobility and Wage Dynamics

Job Mobility and Wage Dynamics

Dean R Hyslop
Statistics New Zealand and
New Zealand Department of LabourandDavid C Maré
Statistics New Zealand andMotu

April 2009

Job Mobility and Wage Dynamics

Acknowledgements and notes on the data
This report was undertaken while the authors were on secondment to Statistics New
Zealand, and was funded by the Department of Labour. We thank Sylvia Dixon andMichael Slyuzberg for constructive comments on an earlier draft. The opinions, findings,
recommendations and conclusions expressed in this report are those of the authors.
They do not purport to represent those of Statistics New Zealand, the Department ofLabour, or Motu, who take no responsibility for any omissions or errors in theinformation contained here.
Access to the data used in this study was provided by Statistics New Zealand underconditions designed to give effect to the security and confidentiality provisions of theStatistics Act 1975. Only people authorised by the Statistics Act 1975 are allowed to see
data about a particular person or firm. The tables in this paper contain information aboutgroups of people so that the confidentiality of individuals is protected.
The results are based in part on tax data supplied by Inland Revenue to Statistics New
Zealand under the Tax Administration Act 1994. This tax data must be used only forstatistical purposes, and no individual information is published or disclosed in any otherform, or provided back to Inland Revenue for administrative or regulatory purposes.
Any person who had access to the unit-record data has certified that they have beenshown, have read and have understood section 81 of the Tax Administration Act 1994,
which relates to privacy and confidentiality. Any discussion of data limitations or
weaknesses is in the context of using the Linked Employer-Employee Database (LEED)
for statistical purposes, and is not related to the ability of the data to support Inland
Revenue's core operational requirements.
Careful consideration has been given to the privacy, security and confidentiality issues
associated with using tax data in this project. A full discussion can be found in the LEED
Project Privacy Impact Assessment paper (Statistics New Zealand, 2003).
Any table or other material published in this report may be reproduced and publishedwithout further licence, provided that it does not purport to be published under
government authority and that acknowledgement is made of this source.

Citation
Maré, D.C., & Hyslop, D.R., (2009). Job mobility and wage dynamics. Wellington:
Statistics New ZealandPublished in April 2009 by
Statistics New ZealandTatauranga Aotearoa
Wellington, New Zealand
www.stats.govt.nz

ISBN 978-0-478-31580-6 (online)

Job Mobility and Wage Dynamics

Abstract
Matched employer-employee data research has found that workers’ wages are affectedby the characteristics of the firms they work in, and that higher skilled workers tend tobe employed by higher paying firms. This paper examines the contribution of workers’
job mobility to their wage dynamics. We focus on the possible trade-off between moving
to a better paying firm and losing a firm-tenure specific component of earnings, andexamine what types of workers benefit from changing firms, rather than staying with
their existing employer.
Our analysis provides four main findings. First, although the raw earnings gains to jobmovers and stayers are about the same, we find that, after controlling for observabledifferences, job-movers have about 1.3 percent lower annual earnings growth than nonmovers. Second, we estimate that job-movers gain 0.3 percent per year on average
from moving to higher paying firms, but lose 1.6 percent in transitory earnings
associated with changing jobs. The gains from moving to better firms are larger for bothyounger and new entrant workers, while the transitory earnings losses are smaller. We
interpret these findings as being due to an earnings growth trade-off for workers
between moving to a higher paying firm and losing their tenure-related earnings at their
existing firm.
Third, we estimate that, on average, workers gain (almost) all of the change in firm
earnings premiums when they change jobs. However, such gains are not equallyshared by all workers. In particular, our estimates suggest that it is the higher ability
workers (as measured by the estimated worker earnings premiums) whose earnings
gain (or lose) the most from moving to a firm with higher (or lower) earnings premiums.
Finally, we find that workers’ earnings also benefit on average from a change in the
average earnings of their co-workers. Controlling for other factors, we estimate that a 1
standard deviation change in the estimated average peer earnings is associated with
about 0.25 percent change in a worker’s earnings on average.

Keywords
Earnings, Linked Employer-Employee Data, worker mobility, job turnover.
__________________________________________________________________

Job Mobility and Wage Dynamics

Contents
1.
2.
3.

Introduction ............................................................................................................... 1
A brief literature review ............................................................................................. 2
Empirical approach ................................................................................................... 4
3.1. Components of earnings rates and change....................................................... 4
3.2. Estimation issues............................................................................................... 5
4. Data description ........................................................................................................ 6
5. Results ...................................................................................................................... 8
5.1. Descriptive analysis ........................................................................................... 8
5.2. Regression analysis .......................................................................................... 9
6. Concluding discussion ............................................................................................ 14
7. References ............................................................................................................. 16
8. Appendix 1 .............................................................................................................. 18

TablesTable 1 ........................................................................................................................... 19
Table 2 ........................................................................................................................... 20
Table 3 ........................................................................................................................... 21
Table 4 ........................................................................................................................... 22
Table 5 ........................................................................................................................... 23
Table 6 ........................................................................................................................... 24

FiguresFigure 1 .......................................................................................................................... 25
Figure 2 .......................................................................................................................... 26
Figure 3 .......................................................................................................................... 27
Figure 4 .......................................................................................................................... 28

Appendix tables and figures
Appendix table A1 ......................................................................................................... 29
Appendix table A2 ......................................................................................................... 30
Appendix table A3 ......................................................................................................... 30
Appendix table A4 ......................................................................................................... 31
Appendix table A5 ......................................................................................................... 32
Appendix table A6 ......................................................................................................... 33
Appendix figure A1 ....................................................................................................... 34

Job Mobility and Wage Dynamics

1. Introduction
Workers can expect wage changes to be related to their experience, for a given set ofhuman capital characteristics. In addition, workers who remain in their jobs may expect
wage gains related to their job-tenure at the firm. In contrast, workers who change jobs
will lose their accumulated tenure premium but may increase their wage if they move toa higher paying firm or find a better firm-match.
In this paper, we provide a preliminary analysis of the relationship between workers’ jobmobility and wage dynamics in New Zealand, and investigate the influence of firm
characteristics on the size of wage gains experienced by job-movers.1 Employers differ
in their human resource practices and pay structures, yet much of the literature on wage
dynamics relies largely on panel studies of workers, and has been unable to identify
whether wage changes for job-movers depend on or are related to the change incharacteristics of their employer.
Our analysis uses Statistics New Zealand’s Linked Employer-Employee Database(LEED), which provides longitudinal employment and earnings data on individual
workers, together with information on the firms that they work for. Such linked data
facilitate the analysis of the contribution of firm characteristics to workers’ wages andwage variability. Applying methods developed by Abowd, Kramarz and Margolis (1999)
and Abowd, Creecy and Kramarz (2002), research has found that workers’ wages areaffected by their firms’ characteristics, and that higher skilled workers tend to be
employed by higher paying firms.2 In previous research (Maré and Hyslop, 2006), we
estimated that permanent firm differences account for between 10 and 25 percent of thevariation in workers’ earnings and, also that permanent worker and firm components ofjob earnings are positively correlated.
We extend our previous analysis of the relationship between worker and firm earnings
premiums and workers’ earnings levels, and focus on the contribution of workers’ jobmobility to their earnings dynamics. We begin by estimating indexes of workers’ andfirms’ earnings premiums, and then examine the evolution of worker earnings as they
move between firms offering different average premiums. In particular, we examine thetrade-offs to individual workers between staying with their current employer, and moving
to a firm with a different earnings premium. We analyse the contribution to earnings
growth of changes in the estimated firm earnings’ premiums when workers change
firms. We examine what types of workers benefit more or less from changing firms
versus staying with their existing employer.3 However, we also analyse the effects of achange in workers’ average peer earnings premiums on workers’ earnings changes.
In the next section, we briefly discuss some related literature to help place the analysis
here in context. In section 3, we present our empirical approach and discuss some of1

Our analysis is also related to an earlier study by Maloney (2006), who examined the job
mobility and earnings patterns of workers in New Zealand using LEED.

2

For example, Abowd, Kramarz, Lengermann and Pérez-Duarte (2004) estimate a negative
correlation using French data and a small positive correlation in US data. Subsequentstudies have confirmed and elaborated on their finding that estimated correlations can be
negatively biased due to low turnover (Andrews et al., 2008; Maré and Hyslop, 2006).

3

Andersson, Holzer and Lane (2003, 2005) examine earnings dynamics for low-wage
workers. They analyse the relationship between wage changes and the pay premium of firms
where they are subsequently employed, and find that “low earners were much more likely toincrease their earnings if they gained employment at a higher-wage firm”, implying aninteraction between wage growth and changing firm effects.

1Job Mobility and Wage Dynamics

the estimation issues, and section 4 contains a discussion of the LEED data. We
present and discuss the results in section 5, and the paper concludes with a discussion.

2. A brief literature review
There is an extensive international literature both on job mobility and the related issue ofthe economic returns to firm-specific tenure (or seniority).4 Job mobility can have twooffsetting effects on workers’ wages. First, via active job-search by workers and firms, it
can result in a better match between workers and firms, in terms of job-specificproductivity, and result in wage increases. Second, any tenure-related productivity andwage gains will be destroyed with any job-move. In addition, the wage implications
associated with a job change depend very much on whether the move is voluntary or
involuntary on the part of the worker (Gottschalk and Maloney, 1985). On average,
voluntary “quits” are associated with wage increases, while involuntary “layoffs” areassociated with wage cuts. Light (2005) reports that workers who change jobs
experience smaller wage gains than those who stay with their employer, with voluntary
movers gaining 1.4 percent (men) to 1.7 percent (women) more than movers generally.
Men who change jobs voluntarily have higher wage growth than non-movers.5
Thus, there is a potentially complex relationship between workers’ job mobility and theirwage dynamics. This is further complicated by the possible endogeneity of workers’
decisions to change jobs, and also the empirical magnitude(s) associated with matchquality and tenure-productivity effects. A number of US studies have used alternative
methods and approaches to control for the possible endogeneity of job-moves, inmeasuring the contribution of firm-specific tenure to workers’ wages. For example,
Topel (1986), Abraham and Farber (1987), Altonji and Shakotko (1987), and Altonji andWilliams (2005) each conclude that the returns to tenure are relatively small; while
Topel (1991) finds large and significant returns to tenure. Buchinsky et al. (2008)
provides a recent attempt to unify the literature, using a structural approach that
explicitly models the individual’s decision to change jobs and their decision to work, aswell as their wage equation, and conclude that the returns to tenure are significantly
positive and larger even than Topel’s (1991) estimates. This suggests that there may besmall to large loss-of-tenure related wage losses associated with changing jobs.
Empirically, most jobs have short-tenure, but long-tenure jobs are also common (Farber,
1999).6 In addition, the job separation hazard rate exhibits negative duration
dependence, so that longer jobs have a lower probability of ending. While most job
moves are associated with wage gains, a substantial fraction (20–40 percent) of moves
is associated with wage cuts (Jolivet, Postel-Vinay and Robin, 2005). Declining hazard
rates could be due to either heterogeneity in individual workers’ propensity to change
jobs, and/or due to state dependence effects, whereby past job-mobility (or lack of it)
directly affects the probability of separating in the future.
Linked employer-employee data have been used in a number of recent studies to shedfurther light on the issue of wage dynamics and turnover. Such data enable a clearer

4

Farber (1999) provides a useful summary.

5

Light (2005) reports that 80 to 90 percent of moves are voluntary. New Zealand estimates
are scarce. Herzog (1996) reports a lower proportion of voluntary moves (half to two-thirds)
during 1985 to 1994; a period of substantial job loss and structural change. The proportion of
moves that are voluntary is likely to be higher than this during our study period, whenemployment growth was strong.

6

Statistics New Zealand (2007) estimates from LEED that 42 percent of jobs at 31 March
2006 had been ongoing for less than 1 year, while 12 percent had been ongoing for at least 7
years.

2

Job Mobility and Wage Dynamics

identification of the interaction of worker turnover and firms’ pay policies, analysing thecontributions of heterogeneous firms’ pay policies (Abowd, Kramarz and Roux, 2006);
export opportunities (Kaplan and Verhoogen, 2006); and the process of endogenous
mobility and matching (Postel-Vinay and Robin, 2006, Cornellissen and Hübler, 2007,
Gruetter and Lalive, 2009). At the core of each of these studies is a two-way fixed effectmodel of wage determination, in some cases with additional explicit modeling of tenure
effects or mobility. There is consistent evidence of a high degree of heterogeneity
across firms – not only in wage premiums paid, but also in starting wages and tenure
profiles, with starting wage and within-firm wage growth being inversely related.
Workers are more likely to leave low-paying firms, consistent with self-selective job
mobility. Gruetter and Lalive (2009) find that the identity of the firm is a more importantfactor in wage determination when workers are entering from unemployment than when
they are making more self-selected job-to-job moves. Andersson et al (2003, 2005)
document the significant influence that firm pay policies have on patterns of wage
growth for low-earning workers. They find that low earning workers are much more likely
to increase their pay if they gain employment with a higher-wage firm.
The only empirical analysis of the relationship between job mobility and wages in New
Zealand that we are aware of is a recent study using LEED data by Maloney (2006).
Maloney uses 5 years of monthly data from April 1999 – March 2004 and focuses onprime aged (aged 25–54 in April 1999) male workers. He further restricts the analyticalsample of job-movers to those workers who have at least one year of continuous
employment with a firm prior to a job move and at least one year of employment with afirm after the move. As a comparison group, he selects a sample of job-stayers who
have at least two years of continuous employment with the same firm over the period.
Maloney’s analysis then compares the patterns of monthly earnings trends of the jobmovers over the two years around their move and the job-stayers. He first shows that
job-movers experience, on average, about 0.2 percent higher earnings growth thanstayers. However, after controlling for age differences of movers and stayers, anddifferences in characteristics such as firm size, average monthly earnings and theworker’s tenure and earnings at the start of the first (12-month) period, Maloney findsthat job-movers earnings fall by 0.3–0.5 percent on average relative to the wages of jobstayers over the two year period. He also estimates that about one-third of the change
in the average earnings of workers in the firm(s) a worker is employed by is reflected in
the worker’s earnings change over the period.7
Our analysis in this paper differs from Maloney’s in several dimensions. First, we have alonger period of LEED data (9 versus 5 years), focus on annual earnings, and consider
all workers who have employment in pairs of consecutive years, rather than restricting
attention to movers who make a single move between stable (one-year or more) jobs.8
Second, our earnings measure is an annualised full-time equivalent (FTE) earnings
rate, which we estimate from monthly data using an algorithm discussed in detail in
Maré and Hyslop (2006). Third, we incorporate explicit measures of the change in firm’s
earnings premiums, based on estimates from a two-way (worker and firm) fixed effects
regression specification, and the worker’s average peer earnings, and examine their

7

We interpret that this largely reflects the effect of job-changers moving between firms withdifferent pay levels; however, it may be capturing average wage changes within firms, due,for example, to worker mobility and/or general earnings adjustments.

8

We examine the potential influence of impact of the selection criterion by examining a
subsample of workers who have at most a single job change over our study period. This
comparison is presented in Table 6 and discussed in section 5.2.

3

Job Mobility and Wage Dynamics

contributions to worker’s wage changes.9 We believe this is akin to Maloney’s approachof examining the contribution of the change in the firm-average earnings on worker’s
earnings change, but separated and modelled more explicitly.
Notwithstanding these differences in sample selection and methodology, our results are
somewhat larger than those of Maloney. In particular, we also find that job-movers raw
average earnings gains are slightly larger than those of job-stayers, but that regressionadjusted movers’ earnings gains are lower than those of stayers. Beyond these results,
we believe our analysis provides some useful preliminary evidence on the average
earnings change associated job-match quality versus tenure loss, and what types ofworkers do make earnings gains from moving.

3. Empirical approach
We use a two-stage procedure to estimate the relationship between workers’ earnings
growth and changing firm characteristics. Following Maré and Hyslop (2006, 2008), we
first estimate the permanent firm and worker components of log(FTE earnings) acrossall jobs, using two-way fixed effects estimation. Second, we analyse earnings growth forworkers according to whether they are movers or stayers. In particular, we examine
variation in the degree to which workers’ earnings growth is related to the changing
characteristics of the firms in which they are employed and the changing mix of workers
with whom they work.
3.1.

Components of earnings rates and change

In the first stage of our analysis, we estimate an additive log-linear two-way fixed effects
model for the log(FTE annual earnings) of worker-i, employed in firm-j, in year-t (yijt). We
regress yijt on a vector of worker-level observable characteristics (Xit), time-invariant
fixed worker (θi) and firm (ψj) effects, and an idiosyncratic earnings component (εijt):

y ijt = X it′ β + θ i + ψ j + ε ijt .

(1)

The vector Xit consists of sex-specific age-quartics and time-effects; the worker effect θirepresents the portable earnings premium of worker-i and reflects factors such as theirability and motivation; similarly, the firm effect ψj represents the earnings premium paidby firm-j to each of their workers and reflects the firm’s pay structure; and the residual
term εijt = mijt + τijt + υijt, where mijt is a, possibly time-varying, component capturingmatch-quality, τijt captures tenure effects, and υijt is a random idiosyncratic component.
In the second stage of our analysis we focus on individual worker level earnings and,more specifically, year-to-year earnings changes. To facilitate this the data are
aggregated to worker-year observations, by taking the FTE employment weighted
average of the workers earnings, and the estimate components from equation (1),across all the jobs the worker held during a year. Based on the estimates of equation(1), we can express worker-i's earnings rate as,y it = X it′ βˆ + θˆi + ψˆ j (i ), t + εˆit(1′)

where a caret (^) denotes an estimate obtained from estimating equation (1), and ψˆ j (i ),tis the average estimated firm effect across all firms that worker-i worked for in year-t.
Based on equation (1′), the implied year-to-year change in worker-i's earnings rate is

9Note, being a time-invariant ‘fixed-effect’, the change in the firm-effect of job-stayers is, byconstruction, zero.

4

Job Mobility and Wage Dynamics

Δy it = ΔX it′ βˆ + Δψˆ j (i ), t + Δεˆit .

(2)

The contributions and interpretations of earnings change are different for movers andstayers.10 The first component captures the effect of changes in individual
characteristics, in particular, changes in work experience, and is common to both
movers and stayers. However, the second term, the change in the firm premium( Δψˆ j (i ),t ) is zero for workers who do not change firms, and reflects the impact of moving
to a higher- or lower-paying firm for movers. The final term in equation (2), Δεˆit , also
captures different effects for movers and stayers. Assuming match-quality effects aretime-invariant (mijt = mij) and ignoring idiosyncratic components, for workers who staywith the same firm, Δεˆit captures the additional wage growth associated with anadditional year’s tenure at the firm (Δτijt) plus any idiosyncratic change (Δυijt). In
contrast, for workers who move from firm-j to firm-k, Δεˆit captures the change in matchquality associated with the move (mik- mij) less any tenure premium that is lost as aresult of the move (τijt-1) plus any idiosyncratic change (υikt -υijt). If workers choose to
move only if the move leads to a higher wage, the sum of these terms is likely to bepositive for voluntary moves.
3.2.

Estimation issues

Equation (1) is estimated on job-level information, with one observation for eachobserved combination of worker, firm and year. This regression is estimated by ordinary
least squares (OLS) by adapting Abowd, Creecy, Kramarz’s (2002) conjugate gradientalgorithm using all job-year observations in LEED, weighted by contemporaneous FTE
employment (see Maré and Hyslop, 2006, 2008 for further details). The OLS estimates
are unbiased estimates under the assumption of exogenous matching.
In the presence of tenure and match effects that are correlated with worker and firmcomponents, θˆi and ψˆ j will reflect the average match quality and tenure for eachworker and firm respectively, in addition to the underlying productivity-relatedcharacteristics.11 If match and tenure effects are positively correlated with both worker
and firm components, they will induce a positive correlation between these components.
We follow the approach of Andersson, Holzer and Lane (2003) and interpret theestimated firm and worker components from equation (3) as summary measures ofworker earning ability and firm pay structures (including average match quality andtenure). An alternative approach is to formally model the wage dynamics associatedwith tenure and the process of matching.1210Note that, because our second stage analysis focuses on changes in FTE earnings, we thus
use only the balanced sample of workers who work in each pair of consecutive years.

11

Similarly, the observable demographic earnings profiles (X′itβ) will also reflect the average
match quality and tenure effects at different stages of the life cycle. Woodcock (2008) derives
formulae for the ‘bias’, and implements a random effects identification strategy to obtainseparate estimates of match effects.

12

Abowd, Kramarz and Roux (2006) develop an econometric model to study the simultaneous
determination of worker mobility and wage rates. Postel-Vinay and Robin (2006) advocate
the use of matched employer-employee data for the estimation of structural job searchmodels.

5

Job Mobility and Wage Dynamics

Assuming the first-stage regression model (1) is correctly specified, the implied
earnings-change regression, equation (2), has unitary coefficients on ΔX it′ β̂ andΔψˆ j (i ),t . However, either non-random selection of the balanced sample of workers withconsecutive year employment, or lagged FTE employment weighting of our secondstage change analysis (while the first-stage levels regression is estimated weighted bycontemporaneous FTE employment), may result in non-unitary coefficients. For thesereasons, we estimate change regressions of the following form:()

Δy it = λ X .Δ X it′ βˆ + λψ .Δψˆ j ( i ), t + u it

(3)

Also, in our subsequent analysis we examine the relative contribution of the change infirm-effects to a worker’s earnings change across different groups of workers, and wewill also include other covariates, such as the change in the worker-i's peer average
earnings, that may affect their earnings change. For instance, it’s possible that thecoefficients of this equation vary according to the worker’s initial wage level. For
example, new entrant workers may benefit most from moving to a high-wage firm,alternatively they may benefit least if there is segmentation along occupational lines sothat they do not receive the full firm-premium.

4. Data description
The data that we use for the analysis are from the Statistics New Zealand Linked
Employer-Employee Database (LEED), which uses information from tax and statisticalsources to construct a record of paid jobs. Since April 1999, all employers in New
Zealand are required to file a monthly record with Inland Revenue (IRD) called an
Employer Monthly Schedule (EMS), which lists all paid employees at that firm during themonth, the earnings they received and the amount of tax that was deducted at source.
Two types of recipients are covered by EMS: those who have Pay-As-You-Earn (PAYE)
tax deducted, who are employees; and those who pay withholding tax, who are a subsetof the self-employed. Because the selection and coverage of which self-employedworkers have tax withheld is unknown, we use only information on PAYE-deducted(employee) jobs.13 We use all the available data on PAYE employee jobs in New
Zealand during the nine March-years from April 1999 to March 2008.
Firms (employers) and workers (employees) are identified by unique, confidential
identifiers based on their IRD tax numbers. For workers, this represents a single
identifier over time, enabling workers to be tracked longitudinally and across the firmsthat they work for. In the IRD data, employers are identified as the legal oradministrative unit to which the EMS return relates, and do not equate to any consistentconception of a firm. That is, legal and/or other administrative changes can trigger achange in an employer’s IRD identifier, with no effective change in the economic
structure of the firm. Statistics New Zealand has used a range of administrative data toidentify continuing enterprises even when IRD identifiers change. We use continuingenterprises, as defined in the Longitudinal Business Frame (LBF) (Seyb, 2003) as ourdefinition of firms.

13

In addition to regular firm-worker employment jobs being identified in the LEED, several otherrelationships involving PAYE tax deductions can also be identified by particular “employer”
identifiers. These are working-age social welfare taxable benefits; earnings-related accident
compensation payments from the Accident Compensation Corporation (ACC); Student
Allowance payments (SA); Paid Parental Leave (PPL) payments; and New ZealandSuperannuation (NZS) retirement pensions. In what follows, we make a distinction between
LEED earnings from employment-jobs and other LEED income from these other (nonemployment) sources.

6

Job Mobility and Wage Dynamics

Conceptually, the LEED covers the universe of PAYE employment relationships andearnings in New Zealand over the period. In addition, there is limited information on thecharacteristics of workers and firms: age, sex, and location of workers; and industry andlocation of firms. One limitation of the LEED data for the current project is that it
contains only monthly earnings information, without any measure of monthly hours ofwork. As a result, we cannot accurately distinguish between hourly wages hours worked
in terms of the variation in earnings. For example, low monthly earnings may be due toeither a low hourly wage and/or low hours worked. In order to create a proxy for thehourly wage rate, we estimate a ‘full-time-equivalent’ (FTE) annual employment
measure, using information on multiple jobs, monthly earnings, and receipt of income
from non-employment sources. The algorithm is more fully described in Maré andHyslop (2006, 2008). Annual earnings are divided by FTE to create a FTE earnings ratemeasure, which we convert to constant (2008) dollars, using the CPI.
Table 1 presents descriptive statistics of the balanced sample of worker-year
observations that is used in the analysis.14 The analysis is weighted by each worker’s
previous year’s (lagged) FTE employment.15 The first row presents the summary
statistics for the full sample, and subsequent blocks of rows summarise various
subsamples. The mean log(FTE earnings) is 10.70 (approximately $45,000), and theaverage change in log(FTE earnings) is 0.036. The average age of workers is 38.4
years, and 46 percent are female.
We characterise workers as job-stayers if their (FTE-weighted) average firm fixed effectis the same in both years, and as job-movers if their average firm effect changes. To bea job-stayer essentially requires that they work for a single firm in both the current andprevious year, while movers generally have some different combination of employers ineach year.16 Owing to the way that we measure moves, a single job change will
generally appear as a two-year job-change episode and such job changes will thus
entail gains that are twice as large as indicated by the annual changes. A little over onehalf (53 percent) of workers are characterised as year-to-year job-stayers. In rows 2 and
3 of Table 1 we describe the characteristics of the subsamples of movers and stayers.
On average, stayers’ FTE earnings are about 20 percent higher than movers, andstayers are more likely to be male and are about 5 years older, while the log(FTEearnings) growth rates are about the same for movers and stayers.
The next pair of rows presents the characteristics of males and females. Males earnmore on average (the difference in average log(FTE earnings) is 0.27, about 30
percent), but have about the same average earnings growth, are slightly younger (0.9
years), and are more likely to stay in the same job. Table 1 next describes outcomes by
age group. Not surprisingly, younger workers (aged 20–29) have 25–30 percent (0.25
log points) lower average earnings than prime aged or older (30–44 or 45–59) workers,
14

That is, the sample of paired observations for which a worker worked in consecutive years.

15

A description of the effects of the balanced sample selection and weighting scheme isprovided in Appendix 1.

16

However, there are several ways in which a worker may be classed as a mover, including thepossibility that they have the same set of (multiple) employers in each year but with therelative earnings from each changing over time. A further consequence of defining a worker
as a job mover based on changing average firm effect between the last and this year is thatjob changes that occur during a year will result in the worker being classified as a mover bothin that year and the subsequent year. Appendix Table A2 summarises the number of jobs
held by job-movers and stayers. For job-movers, about one-third have a single job in thecurrent year and, of these, two-thirds had 2 jobs in the previous year while 10 percent had
just 1 job; and nearly one-half (44 percent) had 2 jobs in the current year, of which one-half
had a single job in the previous year and one-third had 2 jobs.

7

Job Mobility and Wage Dynamics

but substantially stronger growth (over 6 percent versus 2.8 and 1.8 percent for 30–44
and 45–59 year olds respectively). Younger workers are also more likely to change jobs
(62 percent change jobs compared to 54 and 38 percent of the prime aged and older
workers). While this latter finding may reflect the benefits of moving to better jobs, it may
also simply reflect different employment patterns over the life cycle.
The next pair of rows presents the average characteristics of four subsamples definedby the quartiles of the estimated worker fixed-effects from the first stage of estimation.
Unsurprisingly, there is a strong positive earnings gradient across these skill-quartiles.
In addition, workers in the lower quartiles are substantially more likely to be female andmovers than in higher quartiles. However, there are not systematic earnings growthdifferences across quartiles or between movers and stayers.
The final sample stratification in Table 1 is by the pattern of employment transitions
observed in the LEED data over the nine year sample period. In particular, we define“Continuing” workers as those who work in each of the nine years. We define “Entering”
workers as those who do not work in the first year and make a single transition into
LEED employment during the period. “Exiting” workers are those who work in the firstyear and make a single transition out of LEED employment during the period, and“Miscellaneous” workers are all other workers, who have miscellaneous LEED transition
experiences. The composition of these groups reflects various life cycle patterns. For
example, Entrants are predominantly young, with an average age about 6 years
younger than the overall average, and are more likely to be job-movers; in contrast,
Continuers are primarily prime aged and less likely to change jobs; the Miscellaneousgroup is also younger and has a greater fraction of females, reflecting the intermittent
work patterns of women during child-rearing ages.

5. Results
We begin our analysis of the effects of a worker’s job-mobility on their earnings bysummarising the results of the first-stage estimation.
5.1.

Descriptive analysis

Table 2 presents a summary for the full balanced sample, and the same sets of
subsamples described in Table 1.17 For each sample, we describe the average log(FTEearnings), and estimated firm fixed effects and residual earnings from the first-stage
regression, the average change in each of these, and the average change from moveyears relative to stay-years over the sample period. As outlined above, residual
earnings changes reflect tenure-related in-job wage growth (net of life-cycle growth) forjob-stayers, and a combination of the loss of tenure-related wage premiums and thepotential for mobility-related wage gains for movers.
For the full sample, presented in row 1, the contribution to wage growth from changes inthe estimated firm fixed-effect is 0.002 (0.2 percent), while the average residual change
is -0.001 (-0.1 percent). Firm-effect changes are associated only with job-moves. Thus,
column 5 shows that the average firm-effect change associated with job changes is 0.3
percent per year. The final column in Table 2 shows the average residual change
associated with annual job-moves versus job-stays. For the full sample, this is -1.6
percent per year. These latter two results suggest that, on average, job-moves are17

To describe the effects of selection on year-to-year continuing workers, and weighting bylagged FTE employment, Appendix table A1 presents summary statistics for all worker-year
observations, years 2–9 observations, and the balanced sample weighted bycontemporaneous FTE employment.

8

Job Mobility and Wage Dynamics

associated with workers moving to better paying firms, but at the cost of losing timevarying tenure and other residual earnings associated with staying with a firm.18
The subsequent rows present analogous results for subsamples. The next two rows (formovers and stayers) show that the average -0.016 annual difference in residual
earnings growth for job-changing versus staying is due to a 0.010 (1.0 percent) fall inresidual earnings for movers and a 0.006 (0.6 percent) gain in residual earnings forstayers. In addition, this panel also shows that movers work for firms with lower fixed
effects and have negative residual earnings, on average, than job-stayers.
These patterns of positive firm-effect changes, and negative residual earnings changes,
for job-movers versus stayers are broadly true across almost all the subgroups
considered in Table 2.19 Average firm-effect gains associated with moving areparticularly strong (1–1.2 percent per annum) for younger (20–29 year old) and“Entering” workers, suggesting the importance of “job-shopping” early in a worker’s
career. These two groups of workers also have smaller residual earnings loss
associated with moving versus staying relative to the overall sample, which is consistent
with such workers not having built up much firm-specific tenure.
One concern with interpreting the changes in firm-effects and residual earnings asreflecting causal effects of moving versus staying, is that such changes may simply
reflect heterogeneity in which workers change jobs. One partial control for this issue isto condition on both the number of paired-years observed and the number of moveyears, and compare the changes during years in which workers change jobs withchanges during years when they don’t change jobs. We have done this for each of the
LEED sample transition groups, and the results are qualitatively the same as the moreparsimonious summary presented in the final panel of Table 2.
In summary, the patterns described here are consistent with workers changing jobs tobetter paying firms, but at the cost of the loss of firm-specific returns to tenure relative toworkers who stay with the firm. The results imply the average annual returns to an
additional year of tenure for job-stayers is about 0.6 percent, while the accumulated
average tenure loss is about 1.6 percent for job-movers.
5.2.

Regression analysis

We wish to identify the contribution to wage growth of changes in which firm workers
work in. Table 3 summarises the relationship between the level of wages and theestimated first-stage components, as in equation (1’), as well as the relationshipbetween wage changes and changes in components, as in equation (3).
The first column reports a level regression, weighted using the same (FTE) weights as
used in the first stage regression. If this regression were run on exactly the same
sample as the first stage regression, the coefficients on each covariate would be

18

These changes for movers may result from one or more job changes. For movers who are
observed in each year and who make a single job change, the annual change in firm-effectassociated with moving is 0.4 percent and the corresponding change in transitory earnings is-1.4 percent. For this highly selective group, the gains from firm upgrading are larger, and theloss in transitory earnings is smaller, than for movers generally. For this group, the overall
gains from moving accrue for two years, giving gains of 0.8 percent from a higher firm effectand a transitory loss of 2.8 percent for the job change episode.

19

The only exceptions are that the groups of 45–59 year old and “Exiting” workers who movejobs lose 0.002 (0.2 percent) in terms of the estimated firm effects. Exiting workers also havelarge (4.4 percent) residual earnings losses from moving versus staying. These patterns may
reflect aspects of later-career employment changes over the life cycle.

9Job Mobility and Wage Dynamics

precisely 1. Thus, the deviations from 1 reflect the non-randomly selected nature of thebalanced sample.20 The R-squared for this regression shows the same degree of fit asin our first stage estimated regression, with worker demographics, and worker and firm
fixed effects, and time effects accounting for 91 percent of the cross-sectional variation
in log wages.
Next, to provide a sense of the effect of using lagged FTE employment weights, thesecond column contains the results of the regression using these weights. The
estimated coefficients from this regression are further from 1, except on the
demographics variable, suggesting that the lagged FTE weights tends to accentuate theeffects of non-randomness associated with the balanced sample selection.
The third and fourth columns provide analogous estimates for the regression of annual
wage change on the change in wage components weighted, respectively, by current
and lagged FTE employment. The estimated coefficients on the three components(estimated firm fixed effects, worker demographics and time effects) are now all lessthan 1, and vary somewhat between the two sets of estimates. The coefficients on thechange in firm effects are 0.94 (weighted by current FTE employment) and 0.97
(weighted by lagged FTE employment): the latter implying that workers moving between
firms gain 97 percent of the difference in firm wage premiums in the year that they
move. The estimated coefficients on the change in worker demographics and change intime effects are both much lower than 1 and more variable across the two columns.
In our subsequent analysis, in order to provide a more flexible specification when we
include additional variables, we replace the change in the estimated year effects in theearnings-change regressions with separate year dummy variables. Column 5 showsthat replacing the estimated time effects components with explicit year dummies makes
no appreciable difference in estimates for the contribution of firm fixed effects ordemographic components, and the R-squared is also unchanged.
In the final specification reported in Table 3, we add a dummy variable for whether ajob-change occurred. For observations where workers move between jobs, wage
growth is estimated to be 0.013 log points (1.3 percent) lower. This contrasts with thehigher raw wage growth for movers shown in Table 2. By including the demographic
effects, the regression estimates control for the fact that younger workers have both
higher rates of job change and higher rates of wage growth. The estimated coefficientson the firm and demographic effects are slightly higher in this specification than those incolumn (5).
We treat the specification reported in column (6) of Table 3 as the base specification for
our analysis, and report results that extend this specification in Table 4. These
extensions examine the possible influence of changing peers (co-workers) on wage
growth, and whether workers with different abilities benefit differentially from gaining
jobs with high-paying firms or high-ability peers. We repeat the results for this baseline
specification in the first column of Table 4.
For the first extension, which we report in column (2), we add measures of changing
peer demographics and peer fixed effects, and also the level of the worker’s estimated
fixed effect. The peer variables are calculated as FTE-weighted averages of thedemographic and worker fixed effects estimates for co-workers with whom each worker

20

Panel B of Appendix table A1 shows the effects on the average estimated components oflosing the first year due to differencing (column 2) plus non-random balanced sample
selection (column 3) relative to the full sample used in the first-stage estimation (column 1).
Column 4 presents the means for the balanced sample weighted by lagged FTE
employment.

10

Job Mobility and Wage Dynamics

works in a particular year, using estimates from the first stage regression.21 Earnings
growth is higher for workers who have an increase in their average peer fixed effects;
while the change in peer demographics has a small negative impact on earnings
growth. We also estimate a positive coefficient on the worker fixed effect, whichsuggests that workers with higher permanent earnings premiums also have higher
earnings growth on average. For example, a one standard deviation increase in worker
effect (about 0.35) is associated with about 0.4 percent stronger earnings growth per
year. The inclusion of these additional covariates also reduces the estimatedcontribution of changing firm fixed effects. Although not reported separately, thisreduction is largely due to the inclusion of the peer effects covariates rather than theworker effect, suggesting that the advantage of moving to a higher paying firm includes
a benefit of working with ‘better’ co-workers.
In columns (3) – (6) we report the results of specifications that interact the worker effect(and its square in columns (4) and (6)) with the job mover dummy, the change in thefirm effect, and the change in average peer fixed effects (in columns (5) and (6)). These
interaction terms generally have significant coefficients, but are individually difficult tointerpret. For this reason, in Figure 1 we present the estimated coefficients for each of
the job-mover effect, change in firm fixed effects, and change in peer effects acrossworker effect profiles (ranging from two standard deviations below the mean worker
effect to two standard deviations above).22 These profiles are based on the final
specification reported in Table 4, which includes linear and quadratic worker effectinteractions with each of these three variables.
First, the job-mover (mobility) profile for workers is mildly non-linear and negatively
sloped across the worker effect, implying an increasing earnings penalty associatedwith changing jobs for workers with higher fixed effects. That is, for workers with belowaverage to average earnings premiums, the earnings penalty associated with moving
jobs is around -1 percent; and this penalty increases gradually to about -1.8 percent forworkers with high earnings premiums.
Second, the estimated coefficient-profile associated with changing firm effects isapproximately linear and positively sloped over the range of worker effects presented in
Figure 1. This is centred on 0.99 for workers with fixed effect zero, and has a slope ofabout 0.3. The estimated relative gain associated with a change in firm effect is 0.6 for aworker effect of -0.7 (2 standard deviations below the average), and nearly double that(1.19) for a worker effect of 0.7 (2 standard deviations above the average). The
contribution to a worker’s earnings growth also depends on the size of the change in thefirm fixed effect. For example, if a worker moves to a firm with a 1 standard deviation
higher effect (0.09), the contribution to their wage growth is estimated to range fromabout 5.5 percent for low-effect workers to 10.9 percent for high-effect workers.
Third, the estimated profile associated with the change in average peer fixed effects is
concave, but mostly increasing over the range of worker effects. The relative
contribution ranges from -0.02 for low-effect (-0.7) workers to about 0.035 – 0.045 for21

To contribute to the estimation of the impact of peers, a worker must work with at least 1 FTEof other workers in the year. The regressions include dummies to absorb the cases where
the change in peer demographics or peer fixed effects is due to having no peers in either
year.

22

In Appendix figure A1(a) we present the fractions of workers who change jobs and theaverage change in peer fixed effects across the range of worker fixed effects; and in
Appendix figure A1(b) we show the average change in firm fixed effects of movers, andaverage change in peer fixed effects of movers and stayers across the range of worker fixed
effects.

11

Job Mobility and Wage Dynamics

average to high-effect (0 – 0.7) workers. Again, expressing these effects in terms of thescale of the change in peer effects, the earnings growth for a worker who experiences a
1 standard deviation (0.08) increase in average peer effects is estimated to range from 0.2 percent for low-effect workers to 0.3 percent for average to high-effect workers. The
magnitude of these effects is considerably smaller than those for changes in firm fixed
effects.
We estimate the specification reported in column (6) of Table 4, separately for males
and females, and also separately for 20–29, 30–44 and 45–59 year olds. The
regression estimates and presented in Table 5, and the analogous coefficient profiles
across worker effects are shown in Figure 2 for males and females, and in Figure 3 byage groups. The estimated mobility and change in firm effects profiles in Figure 2 arevery similar for males and females; while the estimated peer effects profiles are about
0.025 higher for males than females.
The estimated profiles for the three age groups, presented in Figure 3, show somewhat
different patterns. For mobility, shown in Figure 3(a), the effects are approximately
linear and declining for prime aged workers (30-44 year olds), are steeper for older (45–59 year olds), and are weaker and convex for young workers (20–29 year olds) acrossthe worker effect range. For older workers the worker effect is likely to be capturing astronger average tenure effect, particularly for higher-effect workers, and the stronger
negative effect of job change on workers’ earnings likely reflect the loss in such a tenurecomponent of their earnings.
We describe the patterns of the change in firm effects in Figure 3(b). The profiles acrossworker effects are both higher and (slightly) steeper for older workers than younger
workers, while the profile for prime aged workers lies between the other two but isflatter. Thus, for a given change in firm effect associated with a job move, older workers
tend to capture more of the change in their earnings, while the relative gradient acrossworker effects is similar for older and younger workers. In Figure 3(c) we present the
profiles of the estimated change peer effect coefficients. The profile for young workers is
steeper than for prime-aged and older workers.
All of the results presented above have been from analysis based on the full sample ofpaired-year worker observations. This data potentially confounds the experiences ofworkers who move voluntarily with a view to improving their earnings with theexperiences of workers who make less systematic moves. In order to examine therobustness of our results to these effects, we now select a sample of more stable job
movers and stayers. In particular, we select a sample of workers with the following
characteristics: (i) they have at most a single transition into or out of LEED during theobservation period (that is, they are either “Continuers”, “Entrants”, or “Exiters”, asdefined above); (ii) they hold multiple jobs in at most one year; and (iii) their firm FE(ψj(i)t) either changes at most once if they have a single job in each year, or changes in
two consecutive years if they have multiple jobs in one year.23 These criteria selectworkers who have either stable employment with a single firm (job-stayers), or
experience a single job change. For an additional robustness check, we also select thebalanced subsample of workers who work in each year of the sample (that is, theContinuers).
In Table 6 we compare the regression results based on all worker-year observations(presented in column (1)), with those based on the more selective sample of single job23

Recall that, as discussed above, a typical job change (occurring during a year) will result in achange in the Firm FE both in the year of the change and the following year. Workers whochange jobs between years (so that they hold a single job in each) will have a single change
in their Firm FE.

12

Job Mobility and Wage Dynamics

movers and stayers (in column (3)), and the Continuers’ subsample (in column (5)). For
this exercise, we use the regression specification reported in column (2) of Table 4, andour main focus is the estimated effect of moving on workers’ earnings growth. The
estimated coefficient on the “Mover” dummy variable is similar across the threesamples, suggesting that moving is associated with an annual fall in earnings of
between 1.1 percent (for the sample of single movers and stayers) and 1.5 percent (forthe subsample of Continuing workers).
To describe how the selected samples differ, we have also reported the means of thevariables in the even numbered columns of Table 6. By construction, the two selected
samples have much lower fractions of job-movers (14.9 and 10.0 percent versus 47.3
percent for the full sample of worker-year observations).24 In addition, workers in thesesamples also have lower average earnings growth and Firm FE growth, and higher
average Worker FE (θi) than the overall sample. Each of these differences reflects therelative selectivity of prime aged workers with higher worker-specific earnings
premiums, who are more likely in stable career employment. We suspect this helps
explain the larger loss in earnings associated with changing jobs for workers among theContinuers’ subsample of single movers and stayers: that is, such workers are likely tohave built up more firm-specific tenure over time, which is lost when changing jobs.
We also describe the residual earnings change trends over the period for the selected
sample of single-movers and stayers. First, Figure 4(a) shows the average residual
earnings change of the subsamples of job-staying Continuers, Entrants, and Exiters. In
this figure we have aligned the years such that year-0 refers to the first year-change
observed for Continuers (that is, 1999/2000–2000/01) and for Entrants, and the final
year-change for Exiters. For Continuers, the average residual change is very small (lessthan 0.7 percent in magnitude in each year), and shows no particular trend. However,for Entrants, the average residual change is positive and quite large (3.3 percent) in thefirst year, and declines gradually to about zero in year-4, and then negative in years 5
and 6. This pattern is consistent with positive tenure effects during the early years of
employment in a job, although the first year’s change may be confounded by unusual
LEED-entry effects. For Exiters, somewhat symmetrically, the average residual earnings
change is small and positive 5 and 6 years before exit, turning negative in the 4 years
leading up to exit, and a large (9.6 percent) positive change in the exit year. We suspect
this large final-year change is due to a combination of accumulated leave, redundancy
and other retirement payments to such workers.
Next, in Figure 4(b) we present analogous trends in average residual earnings changes
for single job-moving Continuers, Entrants, and Exiters. In this figure, we have aligned
the years such that year-0 refers to the year of observed job-change for each worker.
There are two patterns we want to emphasise from this figure. First, Entrants havegenerally positive but declining residual changes in both their first-job (before year-0)
and second-job (after year-0), again consistent with positive and declining tenurerelated earnings growth during the early years of a job. The residual changes for
Entrants are also generally larger in magnitude than those of either Continuers or
Exiters, except during the final year of employment for Exiters where, again, there is alarge (8.0 percent) average change. Continuing workers also have positive average
residual earnings change in the years after a job change (years 2–5), consistent withpositive but declining tenure-related earnings growth.
24

We have also estimated regressions that control for worker fixed effects (rather than theestimated Worker FE from the first-stage regression) for these two samples. The estimatedjob-moving effects on workers’ earnings remain negative and are somewhat larger (-1.5 and 1.8 percent per year for the two samples, or -3.0 percent and -3.6 percent respectively permove) than those reported in Table 6.

13

Job Mobility and Wage Dynamics

Second, there are quite striking residual-change patterns in the year of change and thefollowing year. For Entrants who change jobs, the average residual change increases inyear-0, suggesting newer (and younger) workers benefit from changing jobs. However,for Continuers and Exiters, the residual change falls in years-0 and 1 (to 0.0 and -2.1
percent for Continuers and -2.9 and -2.8 percent for Exiters). These patterns are also
consistent with the loss of tenure-related earnings premiums when workers change
jobs. Curiously, for both of these groups, the residual earnings change is, on average,
positive (1.3 percent) in the year prior to a job change. We have no satisfactory
explanation of this effect.
Finally, to describe the residual earnings changes around the point of a job-change
further, in Figure 4(c) we show the pattern of average residual earnings change for all
single-mover workers around the year of job-change on a smaller scale, and repeat thepatterns for the job-moving Continuers shown in Figure 4(b). We believe this highlights
the patterns shown in Figure 4(b). In particular, this shows the declining, and generally
positive, average residual changes in the years prior to job-change; the saw-tooth
positive-negative-postive average changes in the three years around a job-change; andthe declining, and generally positive, changes in the later years. We suspect the year-0
zero average residual changes may be due to negative tenure-related effects
associated with job change offsetting positive accumulated leave and redundancy
payments in the final-year with a firm.

6. Concluding discussion
The paper provides a preliminary analysis of the relationship between workers’ jobmobility and their annual wage changes using LEED data. Our primary focus has been
on to what extent workers upgrade their jobs over time by moving to higher paying
firms, and the extent of the tradeoff between making wage gains from such upgrading
and the loss of tenure-related earnings associated with changing firms.
The analysis provides the following tentative contributions. First, we conclude that,
although job-movers have slightly higher raw annual earnings growth than job-stayers,
when we control for other factors job-movers have on average lower earnings growth.
Our estimated penalty associated with job moves depends on the particular regression
specification: from our base specification the estimate is 1.3 percent per year. These
results are somewhat larger than Maloney’s (2006) LEED results, which were based ona more selective sample of job-movers and stayers, and a somewhat different
methodology.
Second, workers who change jobs, on average, gain 0.3 percent per year from moving
to a firm with a higher firm earnings premium, but lose 1.6 percent per year transitory
earnings relative to workers who don’t change jobs. For younger workers (aged 20–29)
and those who enter LEED employment during the period, the extent of moving to better
firms is larger (1.0 – 1.2 percent on average), while the associated transitory earnings
losses are smaller (1.2 – 1.3 percent on average). We interpret these findings as being
due to an earnings growth tradeoff for workers between moving to a higher paying firmand losing their tenure-related earnings at their existing firm. Our regression estimatesalso show that, on average, higher ability workers (as measured by higher worker fixed
effects) and older workers experience greater earnings loss associated with a job move.
Third, consistent with our first-stage estimation, we estimate that, on average, workers
gain (almost) all of the change in firm earnings premiums when they change jobs.
However, such relative gains are not equally shared by all workers. In particular, ourestimates suggest that it is the higher ability workers who make the greatest income
gain (or loss) from moving to a firm with a higher (or lower) earnings premium.
Andersson et al., (2005) emphasise the importance to low-paid workers of moving to ahigher paying firm. We find that, while all workers appear to gain from a move to better

14

Job Mobility and Wage Dynamics

paying firms, higher ability workers benefit relatively more. We also estimate that older
workers tend to receive more of the change in firm effects than younger workers.
Finally, we find that workers’ earnings also benefit on average from a change in the
average earnings premium of their co-workers. Controlling for other factors, we estimatethat a 1 standard deviation change in the average peer effect provides about 0.25
percent change in a worker’s earnings on average.

15

Job Mobility and Wage Dynamics

7. References
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Jolivet, G., Postel-Vinay, F. & Robin, J.M., (2005). The empirical content of the job
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Job Mobility and Wage Dynamics

8. Appendix 1
In this appendix we describe the selection and weighting effects in terms of the sample
characteristics of the analysis sample versus wider population samples in the data. The
first stage estimation of the log(FTE earnings) equations are weighted bycontemporaneous FTE employment, while our second stage analysis examines year-toyear wage dynamics and is weighted by workers’ lagged FTE employment. Appendix
table A1 compares descriptive statistics of the balanced sample weighted by eachworker’s previous year’s (lagged) FTE employment (column 4) as used in the analysis,
and three samples weighted by workers’ contemporaneous FTE employment (columns
1–3). These samples are the full sample of worker-year observations in all years(column 1) and the full sample of worker-year observations in years 2–9 (column 2), andthe balanced subsample of observations for workers who also worked in the previous
year (column 3).
Over the 9 years of the sample period, there are 18.4 million total worker-year
observations (a little over 2 million per year on average) and FTE employment of 12.6
million workers (about 1.4 million per year). Dropping the first year observations (forcomparability with the balanced sample), there are 16.6 million observations and FTE
employment of 11.4 million. Of these, 14.5 million (FTE employment of 10.9 million) also
had observations in the previous year, which represents a match rate of 88 percent ofworkers, and 95 percent of FTE employment.
A comparison of columns (2) and (3) shows that workers observed in consecutive years
on average have higher earnings and are older than other workers in the sample. The
year-to-year growth in the average log(FTE earnings) over the period for the full sample
is 0.015 (1.5 percent) per year. In contrast, the average balanced sample log(FTEearnings) growth shown in Appendix table A1 is 0.056. The difference between these
two averages is due partly to experience related wage-growth associated with ageing in
the balanced sample, and partly to a selection effect of workers with lower than average
initial year log(FTE earnings) and stronger wage growth being over represented in thebalanced sample. For example, older workers, who have higher-earnings but lower
earnings growth on average, will contribute to this latter selection effect because they
are more likely to retire and so not be in the balanced sample.
The mean log(FTE earnings) of the balanced sample weighted by each worker’s lagged
FTE employment in column (4) are 1–3 percent higher than in column (3), while theaverage change in log(FTE earnings) is lower (0.036 versus 0.056) implying thatworkers with higher earnings and lower growth on average worked more in the previous
year than current year.
Second, in panel B of Appendix table A1, we compare the averages of the various
components from the first-stage two-way fixed effects regression of log(FTE earnings).

18

Job Mobility and Wage Dynamics

Table 1
Sample characteristics(5)

Number of
Observations
(6)

Total
FTE
(7)

38.40

0.527

14,506,400

10,602,400

0.488
0.439

35.75
40.78

0.000
1.000

7,702,700
6,803,700

5,016,600
5,585,800

0.037
0.036

0.000
1.000

37.99
38.88

0.550
0.500

7,379,400
7,127,000

5,705,100
4,897,300

10.53510.78910.785

0.064
0.028
0.018

0.450
0.451
0.496

Age group:
24.70
37.09
51.17

0.378
0.540
0.618

3,368,200
5,125,800
3,767,300

2,282,200
4,063,600
3,159,100

1 (Lowest)2
3
4 (Highest)

10.276
10.527
10.73311.245

0.025
0.038
0.044
0.038

Worker fixed-effects Quartiles:
0.722
41.46
0.499
35.92
0.361
35.65
0.266
40.56

0.474
0.463
0.524
0.646

4,449,900
3,761,600
3,385,600
2,909,200

2,650,600
2,650,600
2,650,600
2,650,600

Continuers
Entrants
Exiters
Miscellaneous

10.757
10.54510.653
10.539

0.030
0.073
0.024
0.037

LEED Sample Transitions:
0.459
40.48
0.459
32.11
0.467
37.99
0.487
33.96

0.559
0.430
0.529
0.447

8,141,000
2,937,300
1,714,200
1,713,900

6,909,600
1,614,900
1,234,400
843,500

Log(FTE Earn)(1)

Change in log(FTE Earn)
(2)

All

10.695MoversStayers

FemaleAge

Stayer

(3)

(4)

0.036

0.462

10.58610.793

0.037
0.036

Males
Females

10.81810.552

20-29
30-44
45-59

SampleNotes: Based on LEED data from 1999/2000 – 2007/08. All means are weighted by lagged FTE employment. Earnings in constant (2008) $-values.

19Job Mobility and Wage Dynamics

Table 2
Summary of Mover versus Stayer Earnings and Firm-effects changes
Log(FTE Earn)(1)

LevelsFirm
effect(2)

Residual(3)

Log(FTE Earn)(4)

ChangesFirm
effect(5)

ResidualAll

10.6950.003-0.002

0.036

0.002

-0.001

0.001

0.003

-0.016

MoversStayers

10.58610.793-0.0140.018-0.013
0.008

0.037
0.036

0.003
0.000

-0.010
0.006

…
…

…
…

…
…

Males
Females

10.81810.5520.020-0.018-0.001-0.003

0.037
0.036

0.001
0.002

0.000
-0.003

0.002
0.002

0.003
0.004

-0.016-0.015

20-29
30-44
45-59

10.53510.78910.785-0.0060.020
0.003-0.004-0.0030.001

Age group:
0.064
0.006
0.028
0.000
0.018
-0.001-0.009-0.001-0.0010.001-0.011-0.0190.010
0.001-0.002-0.013-0.012-0.0171 (Lowest)2
3
4 (Highest)

10.276
10.527
10.73311.245-0.037-0.0050.009
0.045-0.006-0.006-0.001
0.007

Worker fixed-effects Quartiles:
0.025
0.001
0.038
0.003
0.044
0.002
0.038
0.000

-0.004-0.008-0.0020.008-0.0050.008
0.012-0.008

0.002
0.005
0.005
0.000

-0.013-0.015-0.012-0.017

Continuers
Entrants
Exiters
Miscellaneous

10.757
10.54510.653
10.5390.012-0.0230.002-0.021-0.001
0.002-0.005-0.009

LEED Sample Transitions:
0.030
0.001
0.073
0.007
0.024
-0.001
0.037
0.002-0.0020.007-0.005-0.009-0.0020.025-0.030-0.0070.002
0.012-0.0020.003-0.012-0.008-0.044-0.023Sample(6)Move - Stay Changes
Log
Firm
Residual(FTE Earn)effect(7)(8)(9)

Notes: All estimates are weighted by lagged FTE employment. The difference between a “Move” and “Stay” change is calculated as the difference between the averagechange of move-year observations and the average of stay-year observations. “Continuing” workers work in each of the 9 years; “Entering” workers make a single transition
into LEED employment; “Exiting” workers make a single transition out of LEED employment; “Miscellaneous” workers are all other workers. Changes are annual changes. A
consequence of our definition of job change is that a job change is generally captured as a two-year job change episode (see text). The change associated with a job change
is therefore obtained by doubling the change entries in the table.

20

Job Mobility and Wage Dynamics

Table 3
Regressions of log(FTE Earnings) change, all workers
LevelsFTE
Lag(FTE)
Weights
Weights(1)(2)
Firm FE
Demographics
Time effects
Worker FE
Mover

Year controls
Young/old controls
R-squared

Changes
FTE
Weights
(3)

(4)

(5)

(6)

Lag(FTE) weights

1.016(.0003)0.987(.0003)0.998(.001)
1.007(.0003)…

1.035(.0002)0.995(.0002)0.978(.001)
1.012(.0002)…

0.938(.001)
0.916(.001)
0.814(.004)…

0.965(.001)
0.781(.001)
0.863(.005)…

0.965(.001)
0.779(.001)…

0.967(.001)
0.814(.001)
…

…

…

…

…

…

-0.013(.0001)

N
N

N
N

N
Y

N
Y

Y
Y

Y
Y

0.910

0.880

0.144

0.116

0.116

0.117

Notes: Robust standard errors are in parentheses. Regressions in columns 1 and 3 are estimated using
FTE employment weights; all other regressions are estimated using lag(FTE employment) weights.

21

Job Mobility and Wage Dynamics

Table 4
Regressions of log(FTE Earnings) change

Firm FE
Demographics
Mover
Peer demographics

1

2

3

4

5

6

0.967
(.001)
0.814(.001)-0.013(.0001)…

0.958(.001)
0.811(.001)-0.012(.0001)-0.002(.001)
0.026(.001)
0.011(.0002)0.964(.002)
0.811(.001)-0.012(.0001)0.003(.001)
0.027(.001)
0.006(.0003)0.991(.002)
0.817(.001)-0.011(.0001)0.002(.001)
0.026(.001)
0.005(.0003)0.963(.002)
0.812(.001)-0.012(.0001)0.002(.001)
0.026(.001)
0.007(.0003)0.988(.002)
0.817(.001)-0.011(.0001)0.001(.001)
0.035(.001)
0.006(.0003)-0.004(.0003)0.410(.005)…

-0.008(.0004)0.300(.007)
0.009(.004)…

-0.005(.0003)0.395(.005)
0.041(.003)
0.005(.001)…

-0.005(.001)-0.171(.008)-0.048(.005)

Peer FE…

Worker FE…

Worker FE *
Mover

…

…

Firm FE…

…

Peer FE…

…

-0.008(.0004)0.307(.007)…

Worker FE-squared

…

…

…

0.005
(.001)

Worker FE squared *
Mover

…

…

…

Firm FE

…

…

…

…

Peer FE…

…

…

-0.005(.001)-0.180(.008)
…

Y
N
Y

Y
Y
Y

Y
Y
Y

Y
Y
Y

Y
Y
Y

Y
Y
Y

0.117

0.117

0.119

0.119

0.119

0.119

Year controls
Peer controls
Young/old controls
R-squared…

Notes: All regressions are estimated using lag(FTE employment) weights. All covariates are in changes,
except for the Mover dummy variable and worker fixed effects.

22

Job Mobility and Wage Dynamics

Table 5Regressions of log(FTE Earnings) change, by Sex and Age
Male
Firm FE
Demographics
Mover
Peer demographics
Peer FE
Worker FE
Worker FE *Mover
Firm FE
Peer FE
WFE-squared
WFE-squared *MoverFirm FE
Peer FE

Year dummies
Peer controls
Young/old dummy
R-squared

By SexFemale20-29

By Age
30-44

45-59

0.999
(.002)
0.866(.002)-0.011(.0002)0.000(.002)
0.046(.002)
0.017(.0005)0.980(.004)
0.772(.002)-0.013(.0002)0.005(.002)
0.026(.002)
0.003(.0005)0.910(.003)
1.006(.006)-0.011(.0003)-0.019(.002)
0.034(.002)
0.063(.0007)1.000(.003)
0.392(.016)-0.011(.0002)0.000(.002)
0.024(.003)
0.008(.0004)1.120(.005)
0.949(.021)-0.017(.0003)0.022(.003)
0.034(.003)-0.014(.0005)-0.002(.0007)0.323(.008)
0.030(.006)-0.005(.001)-0.007(.0006)0.398(.010)
0.029(.004)
0.013(.001)

0.001(.0010)0.388(.010)
0.067(.007)
0.004(.002)-0.006(.0005)0.303(.007)
0.016(.005)
0.016(.001)-0.015(.0006)0.485(.013)
0.027(.005)
0.014(.001)-0.009(.001)-0.112(.009)-0.044(.008)-0.002(.001)-0.276(.027)-0.054(.007)

0.032(.003)-0.166(.038)-0.062(.018)

0.004(.001)-0.195(.014)-0.035(.011)-0.001(.001)-0.246(.017)-0.043(.007)

Y
Y
Y

Y
Y
Y

Y
Y
Y

Y
Y
Y

Y
Y
Y

0.131

0.105

0.139

0.098

0.093

Notes: All regressions are estimated using lag(FTE employment) weights. All covariates are in changes,
except for the Mover dummy variable and worker fixed effects.

23

Job Mobility and Wage Dynamics

Table 6Regressions of log(FTE Earnings) change
All Observations
Regression Means
Log(FTE earnings)
changeFirm FE
Demographics
Mover
Peer demographics
Peer FE
Worker FE

Year controls
Peer controls
Young/old controls
R-squared
No. Observations
Total lag(FTE emp)

Maximum 1 job change
Regression
Means

Balanced sample
Regression Means…

0.0363…

0.0315…

0.02330.958(.001)
0.811(.001)-0.012(.0001)-0.002(.001)
0.026(.001)
0.011(.0002)0.00161.061(.001)
0.735(.003)-0.011(.0003)-0.018(.003)
0.025(.002)
0.011(.0003)0.00061.110(.008)
0.835(.006)-0.015(.0004)-0.062(.004)-0.006(.003)
0.007(.0003)-0.0001

YYY

0.01320.47320.0018-0.00400.0059

…
…
…

Y
Y
Y

0.117
…
14,506,400
10,602,400

0.00450.14850.0007-0.00650.0842

…
…
…

0.066
…
4,804,500
3,918,600

YY
Y

0.00070.10070.0003-0.0072
0.1131

…
…
…

0.063
…
2,806,200
2,614,400

Notes: All regressions are estimated using lag(FTE employment) weights. All covariates are in changes,
except for the Mover dummy variable and worker fixed effects.

24Job Mobility and Wage Dynamics

Figure 1

0.06

1.2

0.04

0.8

Mobility (LH Scale)
0.02

Peer FE (LH Scale)

0.4

Firm FE (RH Scale)

0.00
-0.74

-0.55

-0.37

-0.18

0.01

0.19

-0.02

0.38

0.56

0.0
0.75

-0.4

-0.04

-0.8
Worker fixed effect25

Impact on log(FTE Earnings)

Impact on log(FTE Earnings)

Impacts of Mobility, changing Firm effects and Peer effects on log(FTE earnings)

Job Mobility and Wage Dynamics

Figure 2

0.06

1.2

0.04

0.8

0.02

0.4

0.00
-0.74

-0.02

-0.04

-0.55

-0.37

-0.18

0.01

0.19

Mobility (LH Scale) (Male)
Mobility (LH Scale) (Female)Peer FE (LH Scale) (Male)
Peer FE (LH Scale) (Female)
Firm FE (RH Scale) (Male)
Firm FE (RH Scale) (Female)
Worker fixed effect

26

0.38

0.56

0.0
0.75

-0.4

-0.8

Impact on log(FTE Earnings)

Impact on log(FTE Earnings)

Impacts of Mobility, changing Firm effects and Peer effects, by Sex

Job Mobility and Wage Dynamics

Figure 3
Impacts of Mobility, changing Firm effects and Peer effects, by Age(a) Mobility
0.02
20-29
30-44

Impact on log(FTE Earnings)

0.01

45-59
0.00
-0.74

-0.55

-0.37

-0.18

0.01

0.19

0.38

0.56

0.75

0.38

0.56

0.75

0.38

0.56

0.75

-0.01

-0.02

-0.03

-0.04
Worker fixed effect(b) Change in Firm effects
1.4

Impact on log(FTE Earnings)

20-29
1.2

30-44
45-59

1.0

0.8

0.6

0.4
-0.74

-0.55

-0.37

-0.18

0.01

0.19

Worker fixed effect(c) Change in Peer effects
0.06
20-29
Impact on log(FTE Earnings)

0.04

30-44
45-59

0.02

0.00
-0.74

-0.55

-0.37

-0.18

0.01

0.19

-0.02

-0.04

-0.06
Worker fixed effect27

Job Mobility and Wage Dynamics

Figure 4
Residual log(FTE earnings) dynamics(a) Job-stayers

Residual log(FTE earnings) change

0.1
Continuers
Entrants
Exiters

0.08

0.06

0.04

0.02

0
-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

7

-0.02
Relative to first (or last) year employed(b) Job-movers

Residual log(FTE earnings) change

0.1
Continuers
Entrants
Exiters

0.08
0.06
0.04
0.02
0
-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

7

3

4

5

6

7

-0.02
-0.04
Relative to year of job change(c) Job-movers
0.015

Residual log(FTE earnings) change

0.01
0.005
0
-7

-6

-5

-4

-3

-2

-1

0

1

2

-0.005-0.01

All single job-movers
Balanced sample-0.015-0.02-0.025
Relative to year of job change

28

Job Mobility and Wage Dynamics

Appendix table A1
Sample characteristics

FTE Employment
weighted
All years
samplelog(FTE Earn)lag log(FTE Earn)log(FTE Earn) change Δyit
FemaleAge(2)
Job moverYears 2-9
sampleBalancedsampleLaggedFTE Employment
weighted
Balancedsample

A: Summary of raw characteristics
10.66510.67010.684(1)(1)
10.62810.62810.6280.056(1)0.056(1)
0.056
0.462
0.462
0.463
38.78
38.89
39.08
…
…
0.487

B: Summary of first stage two way fixed effects estimation(3)Demographics (X itβ)
0.000
0.000
0.005
Time effects (τt)
0.000
0.009
0.009
Worker effects (θi)
0.000
-0.0030.001
Firm effects (ψj)
0.000
-0.001
0.002
Residual (εit)
0.000
0.000
0.002
Peer demographics
0.002
0.002
0.004
Peer worker effects
0.006
0.003
0.005
′

Worker-year Obs
Worker-year FTE

18,377,700
12,614,800

16,553,800
11,375,800

14,506,400
10,862,600

10.69510.659
0.036
0.462
39.43
0.473

0.015
0.009
0.001
0.003
-0.0020.005
0.006
14,506,400
10,602,400

Notes: Based on LEED data from 1999/2000 – 2007/08. Earnings in constant (2008) $-values.
(1)
Mean over balanced sample of workers with previous year jobs.
(2)
Average age of those aged 18-64 years.
(3)
All estimates expressed relative to full sample (column 1) means for each component.

29

Job Mobility and Wage Dynamics

Appendix table A2
Number of Jobs by Mover / Stayer Characteristic
No.
Jobs

Full sample
FTE weighted
Movers Stayers Total

Balanced sample
FTE weighted
lag_FTE weighted
Movers Stayers Total Movers Stayers Total

1
2
3
4
5
6+

42.64
38.58
12.17
4.06
1.43
1.12

99.98
0.02
0
0
0
0

67.98
21.54
6.79
2.26
0.8
0.62

34.54
44.06
13.89
4.63
1.63
1.26

99.98
0.02
0
0
0
0

68.13
21.46
6.76
2.25
0.79
0.61

35.52
43.89
13.44
4.41
1.54
1.19

99.98
0.02
0
0
0
0

69.48
20.78
6.36
2.09
0.73
0.56

All

100

100

100

100

100

100

100

100

100

Notes: Total FTE employment of the full sample is 12.6 million, the balanced sample is 10.9 million; andlag(FTE) weighted employment of the balanced sample is 10.6 million.

Appendix table A3Distributions of Movers and Stayers by year

Year

Full sample
FTE weighted
MoversStayers

Balanced sample
FTE weighted
MoversStayers

Balanced samplelag(FTE) weighted
MoversStayers

1
2
3
4
5
6
7
8
9

17.60
9.47
9.67
9.90
10.09
10.47
10.89
10.92
10.99

0
10.80
11.18
11.74
12.39
12.87
13.19
13.63
14.21

0
11.53
11.76
12.01
12.22
12.69
13.23
13.29
13.27

0
10.80
11.18
11.74
12.39
12.87
13.19
13.63
14.21

0
11.57
11.77
11.96
12.16
12.57
13.20
13.45
13.31

0
10.82
11.15
11.69
12.35
12.84
13.16
13.71
14.28

Total

55.8

44.2

48.67

51.33

47.32

52.68

Notes: Total FTE employment of the full sample is 12.6 million, the balanced sample is 10.9 million; andlag(FTE) weighted employment of the balanced sample is 10.6 million.

30

Job Mobility and Wage Dynamics

Appendix table A4
Sample characteristics
By Worker FE quartile
Q2
Q3

Mean

Q1

log(FTE Earn)log(FTE Earn) change
FemaleAge
Job stayer
Worker-year Obs
Worker-year FTE

10.695
0.036
0.462
39.43
0.527
14,506,400
10,602,400

10.272
0.025
0.726
42.33
0.474
4,358,300
2,587,800

10.522
0.038
0.503
36.96
0.463
3,750,100
2,639,000

10.726
0.044
0.364
36.87
0.522
3,415,000
2,661,600

11.237
0.038
0.267
41.55
0.644
2,983,100
2,714,000log(FTE Earn)log(FTE Earn) change
FemaleAge
Worker-year Obs
Worker-year FTE

10.586
0.037
0.488
36.70
7,702,700
5,016,600

10.255
0.023
0.718
40.65
2,506,100
1,360,600

Movers10.462
0.041
0.501
34.03
2,258,300
1,418,400

10.648
0.051
0.371
33.67
1,815,500
1,272,400

11.153
0.033
0.298
38.94
1,122,800
965,300

10.290
0.028
0.734
44.23
1,852,100
1,227,200

Stayers
10.592
0.034
0.505
40.37
1,491,800
1,220,600

10.797
0.038
0.357
39.76
1,599,500
1,389,300

11.284
0.040
0.250
42.99
1,860,300
1,748,700

log(FTE Earn)log(FTE Earn) change
FemaleAge
Worker-year Obs
Worker-year FTE

10.793
0.036
0.439
41.89
6,803,700
5,585,800

Q4

Notes: Based on LEED data from 1999/2000 – 2007/08. All means are weighted by lagged FTE
employment. Earnings in constant (2008) $-values.

31Job Mobility and Wage Dynamics

Appendix table A5
Summary of first-stage two-way fixed effects estimation

SampleBalanced
MoversStayers
All workers
MoversStayers
All workers
MoversStayers
All workers
MoversStayers
All workers
MoversStayers

Log(Earnings)(yit)

Workersex &age(X′itβ)Time
effects(τt)

Workereffects(θi)

Firm
effects(ψj)

Lag(FTE employment) weighted
10.695
0.015
0.009
0.006
0.003
Expressed relative to row 1 means-0.109-0.029-0.001-0.052-0.017
0.098
0.026
0.001
0.046
0.015
Quartile 1 Worker fixed-effects-0.4230.049
0.003-0.430-0.041-0.4400.044
0.002-0.423-0.053-0.4050.054
0.004-0.437-0.028
Quartile 2 Worker fixed-effects-0.173-0.0280.002-0.134-0.008-0.233-0.0620.001-0.134-0.023-0.1030.011
0.003-0.1330.009
Quartile 3 Worker fixed-effects
0.031-0.043
0.000
0.067
0.006
-0.047-0.090-0.001
0.064-0.009
0.102
0.000
0.001
0.070
0.018
Quartile 4 Worker fixed-effects
0.542
0.023
-0.005
0.474
0.042
0.457
-0.003-0.008
0.442
0.032
0.588
0.037
-0.0040.492
0.047

Notes: Earnings quartiles are defined based on worker fixed effects.

32

Residual(εit)-0.002-0.0110.010-0.005-0.0100.001-0.005-0.0150.007
0.001-0.0120.012
0.008-0.0060.016

Job Mobility and Wage Dynamics

Appendix table A6Distributions of first-stage regression estimates by year
Log
(Earnings)(yijt)

Workersex & age
(X′itβ)Time effects(τt)

Worker effects(θi)

Firm effects(ψj)

Residual(εijt)

Fraction
FemaleAverage
Age

No. ofWorkers

Total FTE
Emp’ment

All years

10.66510.665

0.000

0.000

0.000

0.000

0.462

37.41

18,377,700(1)12,614,800(1)1999/2000
2000/01
2001/02
2002/03
2003/04
2004/05
2005/06
2006/07
2007/08

10.61410.612
10.627
10.63410.661
10.67610.68910.70310.736-0.002-0.001
0.001
0.002
0.002
0.000
-0.0010.000
0.000-0.080-0.078-0.055-0.040-0.004
0.020
0.040
0.058
0.098

Relative to full sample average:
0.026
0.006
0.000
0.022
0.005
0.000
0.014
0.003
0.000
0.007
0.000
0.000
0.000
-0.0020.000-0.005-0.0020.000-0.011-0.0020.000-0.016-0.0020.000-0.024-0.003
0.000

0.462
0.462
0.462
0.462
0.462
0.461
0.462
0.463
0.463

36.58
36.85
37.19
37.38
37.50
37.54
37.64
37.79
37.91

1,823,900
1,863,300
1,910,000
1,971,700
2,038,200
2,112,300
2,176,400
2,219,100
2,262,800

1,239,100
1,268,600
1,304,100
1,351,700
1,401,100
1,454,200
1,501,500
1,528,800
1,565,700

0.122

0.002

0.179

1999/2000 – 2007/08 Change:-0.050-0.008
0.000

0.001

1.33

24.1%

26.4%

Year

Notes: Based on LEED data from 1999/2000 – 2007/08. Earnings in constant (2008) $-values. Analysis weighted by contemporaneous FTE employment.
(1)
Total worker-year observations, and annual FTE employment over the period.

33

Job Mobility and Wage Dynamics

Appendix figure A1
Job mobility and changes in Firm and Peer effects across workers(a) Job mobility and average change in Peer fixed effects
0.7

0.000

0.6

-0.0020.5-0.0040.4-0.0060.3-0.0080.2-0.0100.1-0.0120.0

Average change in Peer FE

Fraction Mover

Mover
Peer FE-0.014
-0.74

-0.55

-0.37

-0.18

0.01

0.19

0.38

0.56

0.75

Worker fixed effect(b) Average changes in Firm and Peer fixed effects
0.008

0.004
Firm FE (Movers)

0.006

0.002

Peer FE (Movers)
Peer FE (Stayers)

0.000

0.002

-0.0020.000-0.004-0.002-0.006-0.004-0.008-0.006-0.010-0.008-0.012-0.010-0.014
-0.74

-0.55

-0.37

-0.18

0.01

0.19

Worker fixed effect

34

0.38

0.56

0.75

Average change in Peer FE

Average change in Firm FE

0.004