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Journal of Business & Economic Statistics
ISSN: 0735-0015 (Print) 1537-2707 (Online) Journal homepage: http://www.tandfonline.com/loi/ubes20
Employer-to-Employer Flows in the United States:
Estimates Using Linked Employer-Employee Data
Melissa Bjelland, Bruce Fallick, John Haltiwanger & Erika McEntarfer
To cite this article: Melissa Bjelland, Bruce Fallick, John Haltiwanger & Erika McEntarfer (2011)
Employer-to-Employer Flows in the United States: Estimates Using Linked Employer-Employee
Data, Journal of Business & Economic Statistics, 29:4, 493-505, DOI: 10.1198/jbes.2011.08053
To link to this article: http://dx.doi.org/10.1198/jbes.2011.08053
Published online: 24 Jan 2012.
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Date: 11 January 2016, At: 23:19
Employer-to-Employer Flows in the United
States: Estimates Using Linked
Employer-Employee Data
Melissa B JELLAND
Employment and Disability Institute, School of Industrial and Labor Relations, Cornell University, New York,
NY 10016 ([email protected])
Bruce FALLICK
Federal Reserve Board, Washington, DC 20551 ([email protected])
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John H ALTIWANGER
Department of Economics, University of Maryland, College Park, MD 20742 and U.S. Census Bureau
([email protected])
Erika M C E NTARFER
Center for Economic Studies, U.S. Census Bureau, Washington, DC 20233-7400 ([email protected])
We use administrative data linking workers and firms to study employer-to-employer (E-to-E) flows. After
discussing how to identify such flows in quarterly data, we investigate their basic empirical patterns. We
find that the pace of E-to-E flows is high, representing approximately 4% of employment and 30% of separations each quarter. The pace of E-to-E flows appears to be highly procyclical and varies systematically
across worker, job, and employer characteristics. There are rich patterns in terms of origin and destination
of industries. Somewhat surprisingly, we find that more than half of the workers making E-to-E transitions
switch even broadly defined industries (i.e., NAICS supersectors).
KEY WORDS: Employer-to-employer flows; Job flows; Turnover; Worker flows.
1. INTRODUCTION
An enormous amount of worker and job reallocation continually takes place in the U.S. economy. For example, Davis,
Faberman, and Haltiwanger (2006; DFH hereinafter) report that
the quarterly job creation and destruction rates each average
approximately 6%–8% of total employment, and that accession and separation rates are two to three times as large (see
also Davis, Haltiwanger, and Schuh 1996; Acs, Armington, and
Robb 1999; Burgess, Lane, and Stevens 2000). Job reallocation
induces worker reallocation, with less profitable firms shedding jobs and thus workers and more productive firms adding
them. Much of this reallocation occurs within narrowly defined
sectors, so the primary drivers of the continual reallocation of
jobs evidently include idiosyncratic shocks to technology, demand, and costs. The pace of job reallocation does fluctuate
both secularly and cyclically, however, and as such is related
both to the overall pace of technological change and to macroeconomic shocks. In addition, workers also continuously sort
themselves over a given distribution of jobs. Workers switch
jobs and change employment status because of “supply-side”
events such as labor force entry, family relocation, and retirement or for reasons of necessity, career development, better pay,
or preferable working conditions.
There is a large and growing literature on quantifying and
studying these job and worker flows and the associated outcomes for firms and workers. The U.S. statistical agencies now
have various data products, including the Business Employment
Dynamics (BED) and the Job Openings and Labor Turnover
Survey (JOLTS) programs at the Bureau of Labor Statistics and
the Longitudinal Business Database (LBD) and Longitudinal
Employer Household Dynamics program (LEHD) programs at
the Census Bureau, that tabulate these flows by a variety of
worker and firm characteristics. Given the huge volume of these
flows and the inherent frictions involved in the reallocation of
jobs and workers, there is considerable interest in understanding the nature and implications of these frictions at the micro
and aggregate levels. One important element largely missing
from the expanding measurement of worker and job flows is
the flows of workers directly from one employer to another. In
conjunction with extant information on other worker flows, the
measurement and analysis of E-to-E flows sheds light on how
the frictions in the labor market are resolved over time, across
sectors, and across different types of firms and workers. Such
direct E-to-E flows are not without friction, but by construction they reflect flows that do not involve a period of frictional
unemployment.
In interpreting the patterns of the E-to-E flows, it is important
to emphasize that this flow is only one component of the worker
and job flows, but it has been not well measured to date. Put
differently, measuring E-to-E flows allows the decomposition
of separations into those that yield an E-to-E flow and those
493
© 2011 American Statistical Association
Journal of Business & Economic Statistics
October 2011, Vol. 29, No. 4
DOI: 10.1198/jbes.2011.08053
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494
that do not. In that respect, measuring E-to-E flows permits an
important decomposition of separations (and accessions) that
heretofore was not feasible.
Quantifying and studying E-to-E flows is of interest not only
because of what it tells us about frictions in the labor market,
but also because E-to-E flows include information about the origin and destination employers. It is of interest to know about the
origin and destination of transitions for workers with different
characteristics (i.e., age, gender, education, ethnicity, and foreign born status). It also is of interest to know how earnings
change with such transitions; the origin–destination information permits measurement of earnings changes, which we have
pursued in other work (Fallick, Haltiwanger, and McEntarfer
2007).
In this article we use the recently developed longitudinal
linked employer and employee data from the Census Bureau’s
LEHD program to measure and study E-to-E flows. These data
are exceptionally well suited to an examination of quarterly
worker flows because they trace the job histories of a large proportion of employed individuals over several years. As we discuss in Section 2, the long histories of workers, as well as the
rich set of employer characteristics, yield advantages over measurement of E-to-E flows from the Current Population Survey
(CPS) (see Fallick and Fleishman 2001, 2004). The CPS-based
flows have alternative advantages, as we also discuss in Section 2, and thus we regard these measurement efforts as complementary.
Our main findings are as follows. First, the flow of workers from employer to employer is large and an important component of overall worker flows. Using our preferred measurement method, we find that quarterly E-to-E flows account for
approximately 4% of employment on average and 29% of main
job separations. These patterns vary systematically over time,
with interesting secular, seasonal, and cyclical variations. Eto-E flows appear to be procyclical, rising throughout the late
1990s and falling sharply in 2001. We also find that the pace
of E-to-E flows as a fraction of employment and separations
remained low at least through 2003. This finding is consistent
with related findings in the recent literature of a declining pace
of worker and job flows in the U.S. economy in the post-2000
period (see DFH and Davis et al. 2010).
The pace of E-to-E flows varies systematically by worker,
job, and firm characteristics. For workers, E-to-E flows as a
fraction of employment or of separations decline with age
through the mid-60s and then flatten out. The decline with age
as a fraction of separations is especially rapid in the age range
50–65, when retirements presumably become a more important
form of separation than E-to-E transitions. We find that lesseducated workers have a higher pace of E-to-E flows as a fraction of employment, reflecting the higher overall rate of worker
flows of the less educated; however, conditional on a separation,
education is not an important factor.
E-to-E flows are somewhat concentrated among frequent job
changers, and even more so among high-turnover firms. Firms
in the highest quintile of turnover—those in which >6.6% of
workers experience an E-to-E flow in an average quarter—
account for >60% of total E-to-E flows. The concentration of
E-to-E flows among highly mobile workers is less pronounced
but still in evidence; >50% of total E-to-E flows in a 3-year
Journal of Business & Economic Statistics, October 2011
period are accounted for by workers who changed jobs at least
twice in that period. Correspondingly, almost 50% of E-to-E
flows are workers with only one E-to-E flow over a 3-year
period. Whereas E-to-E flows are higher among workers with
short job tenure, the fraction of longer jobs (more than 2 years
of job tenure) that result in an E-to-E flow is almost 2%, compared with nearly 4% of all jobs.
There are rich patterns in terms of origin and destination of
industries. To our surprise, we find that a considerable fraction
of the workers making E-to-E transitions switch even broadly
defined industries; more than half of workers making an E-toE transition switch NAICS super-sectors. This finding can be
explained in part by examining the details of the origin and
destination sectors. For example, for workers employed in the
software publishing industry, we find that just over one-quarter
of the workers making an E-to-E transition remain in software
publishing; however, the other most common destination industries reflect closely related activities, such as computer systems
design services and semiconductor manufacturing.
The article is organized as follows. Section 2 discusses the
LEHD data infrastructure and its suitability for measuring Eto-E flows. Section 3 describes methods for measuring E-to-E
flows in the LEHD data. Section 4 presents the main empirical
findings, and Section 5 presents concluding remarks.
2. ADVANTAGES AND DISADVANTAGES OF
USING LEHD DATA FOR ESTIMATING
EMPLOYER–TO–EMPLOYER FLOWS
Recent research on E-to-E flows in the United States has exploited the dependent interviewing techniques used in the CPS
to provide nationally representative estimates of high-frequency
flows of workers between employers. Fallick and Fleischman
(2004) estimated that 2.6% of employed persons change employers each month, and that E-to-E flows fell markedly during
the recession that began in 2001. The CPS has several advantages for estimating E-to-E flows of workers. The CPS is already the primary source of the data on flows of workers across
labor market states, so the estimated E-to-E flows can be joined
consistently with the other flows in generating a more complete
picture of labor market dynamics. The CPS sample is representative of the entire civilian noninstitutional population in that it
is not limited to particular cohorts, household members, or sectors. The data measure flows at a monthly frequency and so are
likely to capture movements from one employer to another that
do not involve a significant period of nonemployment. And of
course, the CPS contains a rich set of demographic and other
information on individuals.
The CPS also has several important limitations, however. The
size of its sample, although large for a survey of households,
is small for some purposes. The representativeness of the CPS
is compromised by significant attrition, especially because, by
design, it does not follow respondents when they change residences. The CPS follows individuals for only 4 consecutive
months, so long employment histories cannot be constructed.
As a household survey, it relies on the responses of individuals
or other members of their households. Moreover, the survey design is such that it can be used to measure flows only between a
worker’s main jobs. The CPS also measures worker flows from
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Bjelland et al.: Employer-to-Employer Flows in the United States
only one side of the market: the worker’s side. As such, quantifying E-to-E flows on the basis of employer characteristics is
not feasible.
The LEHD data offer several advantages for estimating the
magnitude of E-to-E flows. First, the LEHD data are based on
comprehensive administrative records covering a large fraction
of the U.S. workforce. This enables analysis of flows of workers
across detailed industries, across detailed demographic groups,
and even after specific regional economic shocks. The universe
of the LEHD data is employment covered by the state unemployment insurance (UI) system. Longitudinal business identifiers permit accurate tracking of employers over time, including the entry and exit of businesses. State UI system coverage
is broad and basically comparable among states. UI and associated data are provided to the Census Bureau by more than
40 participating state partners. These data are supplemented
with Census demographic data, providing worker characteristics such as age, race, and gender.
Second, the longitudinal nature of the LEHD data means
that individual work histories can be followed continuously for
years. This is important because it allows us to examine the extent to which E-to-E flows in the U.S. economy are driven by
a small but mobile segment of the workforce. The LEHD data
follow workers across employers as long as they continue to
collect UI-covered wages, and follow workers who change residence as long as they continue to work in one of the more than
40 states participating in the program. Although we hope to take
advantage of this broad geographic coverage in future work, in
the current study we did not follow workers across state lines
for the most part.
Finally, the most valuable aspect of the LEHD data is the
presence of the worker–employer link. The longitudinal nature
of the linked data on both the individual and business sides allow us to track the flows of workers between a large sample of
firms in the U.S. economy while also identifying workers’ employment histories and the dynamics of the firms across which
workers are being redistributed. This link allows us to determine the extent to which flows between employers are driven
by the dynamics of the employers themselves. For instance, we
can examine to the extent to which E-to-E flows are concentrated among employees in high-turnover firms, or the extent
to which flows are driven by job creation at destination firms
versus job destruction at separating firms.
The LEHD data do have several disadvantages in identifying E-to-E flows. There is the potential to miss E-to-E
transitions that involve cross-state migrations in employment.
Employment in the federal government, unincorporated selfemployment, religious and charitable organizations, and certain agricultural enterprises is not covered. [For detailed information on UI-covered employment see Stevens 2007 and the
BLS Handbook of Methods, U.S. Department of Labor, Bureau of Labor Statistics (1997).] The LEHD program is currently adding administrative data on federal workers and selfemployed workers, but these data were not yet available at the
time of this study. Most fundamentally, the LEHD data do not
measure employment at a point in time. The UI records underlying the data report only quarterly earnings; they contain no
information regarding how many hours or weeks were worked
within the quarter, or which weeks were worked if the worker
495
was at the firm for less than a full quarter. In addition to limiting
the analysis to quarterly frequency, this poses some problems
in distinguishing between true E-to-E flows and multiple-job
holding, and also in determining the direction of some E-to-E
flows in the presence of short-duration jobs. These are not insignificant problems, given that flows between short-term jobs
account for a substantial fraction of E-to-E flows. In the next
section we discuss in detail how we deal with this feature of the
data.
Golan, Lane, and McEntarfer (2007) used LEHD data to examine annual flows of significantly attached workers between
employers within and across industries. They found that 25%
of workers who demonstrated some attachment to their employer changed jobs in a given year, split almost evenly between
industry-stayers and industry-changers. Given that the focus of
this article is on estimating the quarterly frequency of E-to-E
flows in both main and secondary jobs, our methodology differs
from theirs. In the next section we explain in greater detail how
we estimate the quarterly flows of workers between employers
in the LEHD data.
Before proceeding to the measurement methodology and the
estimates of the E-to-E flows, we note that the underlying
source data for all of the statistics reported in this article are
administrative data that provide universal coverage of workers
in covered sectors. With approximately 6–7 million jobs and
200,000–360,000 E-to-E flows observed each quarter, there is
no significant sampling error in our estimates, and we report no
sampling error statistics. Of course, there may be measurement
error from a variety of sources (e.g., measurement error in longitudinal identifiers, industrial classification for employers, or
reported earnings for workers) that affect the reliability of these
estimates, but this represents nonsampling error. A comprehensive description of the LEHD data infrastructure files, the measurement methodology, and possible sources of nonsampling
error has been provided by Abowd et al. (2009). In this article, we provide only descriptive statistics of the E-to-E flows,
with little or no statistical analysis of hypotheses regarding the
causes and consequences of E-to-E flows. Our contribution is to
develop and implement the statistical methodology for measuring E-to-E flows using the LEHD data infrastructure and then
provide basic information on the magnitude and nature of the
variation in the E-to-E flows.
3. DEFINING E–TO–E FLOWS IN LEHD DATA
The quarterly nature of the LEHD data poses some unique
methodological problems with regard to measuring E-to-E
flows. As mentioned in the previous section, the UI records
that underlie the data do not measure employment at a point in
time, but instead report quarterly earnings. They contain no information regarding which weeks during the quarter the worker
was employed or how many hours he or she worked within the
quarter. Table 1 provides a hypothetical example of wage history data, in this case the quarterly wage histories for two individuals who were each employed with two different employers
during the year.
Our goal is to characterize quarterly job transitions, including those involving shorter jobs or secondary jobs. We begin
by defining the existence of a job match in quarter t if worker i
496
Journal of Business & Economic Statistics, October 2011
Table 1. Example of quarterly earnings histories
Person/Employer Match (Job)
Person 1 Firm A
Person 1 Firm Q
Person 2 Firm B
Person 2 Firm Z
Q1
Q2
Q3
Q4
$0
$0
$2500
$0
$500
$1000
$1500
$750
$0
$0
$0
$3000
$0
$0
$0
$3000
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NOTE: All persons, firms, and wages in examples are fictionalized and do not reflect any
individual’s wage records.
has positive earnings from employer j in that period, denoted by
mijt = 1 (mijt = 0 otherwise). Matches in only one of two consecutive quarters indicate that either an accession or separation
has occurred; that is, workers with positive earnings in one period but none from the same employer in the subsequent period
(i.e., mijt = 1 & mijt+1 = 0) are assumed to have separated from
their employers at some point during the quarter with positive
earnings. Similarly, individuals who are observed to move from
receiving no earnings from a firm to earning a positive amount,
(i.e., mijt−1 = 0 & mijt = 1) are assumed to have acceded to the
employer sometime during the quarter with positive earnings.
In Table 1, we would say that person 2 separated from employer
B and acceded to employer Z during quarter 2.
If we define an E-to-E flow simply as an accession to a new
employer k that occurs in the same quarter as that same worker
separated from employer j, then we have the following definition of an E-to-E flow:
Flow E-to-E transition (EEmijkt ): individual i moves
from firm j to firm k (j = k) during t:
⎧
⎨ 1, if {mijt = 1 & mijt+1 = 0} and
{mikt−1 = 0 & mikt = 1}
EEmijkt =
⎩
0, otherwise.
(1)
Although valid E-to-E flows might include situations in which
a person separates from one employer in quarter t and accedes
to a different employer in quarter t + 1, we choose not to count
these transitions. For the purpose of our current study, we are
interested in flows that do not involve a significant period of
intervening nonemployment. As noted earlier, we know only
each individual’s earnings from a given employer for the entire quarter. Thus, we observe whether an individual worked
for a given employer during the quarter, but we do not know
which weeks during the quarter the match was in effect. A typical quarter contains 13 weeks. If the timings of the separations
in one quarter and of the accessions in the subsequent quarter
are independent of each other, and each is uniformly distributed
within the quarter, then the mean duration between the two employers is 13 weeks, which is longer than we want to count for
an E-to-E transition. Therefore, we restrict our present analysis to E-to-E flows that occur within a single quarter, that is, to
flows where the separation and accession, as defined earlier, occur in the same quarter. Although it is possible to have a short
nonemployment spell during an E-to-E flow that occurs within
the quarter, it is unlikely to be a spell of more than 1 month in
duration. (Again assuming uniform distributions, the mean duration is approximately 4 weeks.) We leave an analysis of flows
into and out of longer spells of nonemployment for future work.
A limitation of the definition in (1) is that in the case of
matches that appear in only one quarter, we cannot distinguish
between an E-to-E flow and multiple job holding with any confidence. For instance, imagine a work history such as that of
person 1 in Table 1. Although person 1 would be defined as
experiencing an E-to-E flow in the second quarter according to
the definition (EEm) in eq. (1), it is also possible that person
1 held jobs at firm A and firm Q simultaneously. The inclusion of multiple job holding in this measure is likely to result
in an overcounting of E-to-E flows. Preliminary estimates of Eto-E rates using the EEm measure in LEHD data found rates
as high as 16%. This seems to be an improbably high rate of
E-to-E flows, and thus this measure is likely overcounting the
true number of flows. Moreover, even if we knew that person 1
did not hold both jobs simultaneously, it would be difficult to
decide which was the origin job and which was the destination
job.
For these reasons, for our analysis we use a more restrictive definition of E-to-E flows that requires observation of two
quarters of positive earnings at the separating and the acceding
employer. Formally,
Consecutive-quarter E-to-E transition (EEbijkt ): individual i transitions from firm j to firm k (j = k) during t:
(2)
⎧
⎨ 1, if {mijt−1 = 1 & mijt = 1 & mijt+1 = 0}
and {mikt−1 = 0 & mikt = 1 & mikt+1 = 1}
EEbijkt =
⎩
0, otherwise.
Here we use consecutive quarters of earnings to construct
an estimate of point-in-time employment. If a worker has positive earnings at an employer in both quarter t − 1 and quarter
t, then we can say with reasonable confidence that he was an
employee of that firm on the first day of quarter t as well as on
the last day of quarter t − 1. Thus the restrictions that we add
here amount to (a) the worker must be employed at the separating employer on the first day of the relevant quarter, and (b) the
worker must be employed at the acceding employer on the last
day of the relevant quarter, with both the separation and the accession occurring during the quarter. In the example in Table 1,
person 2 experiences a consecutive-quarter E-to-E flow in the
second quarter from firm B to firm Z. The additional attachment restriction in EEb compared with EEm provides greater
confidence that the transition we have identified is in fact an Eto-E flow, and also clarifies the direction of this flow. Of course,
the greater clarity comes at the cost of excluding some E-to-E
flows that involve very short jobs, so this definition may underestimate the true magnitude of E-to-E flows.
Applying these definitions is straightforward if a person experiences no more than one separation and no more than one
accession each quarter. However, the application is more complicated if a person experiences multiple separations or accessions in a quarter such that more than one pair of jobs would fit
the definition of an E-to-E flow. For the purpose of counting the
number of E-to-E transitions, we impose the rule that a given
separation or accession can contribute to only one E-to-E transition. Even so, for some purposes there remains the problem
of pairing the separations with the accessions so as to identify
the origin and destination job in each transition. We would like
to pair origin and destination jobs that play similar roles in the
worker’s career, those that the worker herself would identify as
replacing one another. To take an extreme example, we would
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Bjelland et al.: Employer-to-Employer Flows in the United States
not like to pair a separation from a full-time job with an accession to a part-time weekend job if more equal pairings were
possible. (Of course, even with only one separation and one accession in a quarter, we may observe E-to-E flows that have this
unequal nature.)
Given the limited information at our disposal, we do this by
pairing the jobs with the most similar relative earnings. Lacking
data on the number of hours or weeks worked during a quarter, we recognize that the relative earnings of jobs in a single
quarter can be influenced by when during the quarter each job
began or ended, as well as by transitory fluctuations in hours
worked. To minimize these influences, we compare earningsweighted average quarterly earnings. Specifically, we rank all
separating jobs by the earnings-weighted
average earnings over
the period t − 3 to t, ( tt−3 w2ijt )/4. Similarly, we rank all accessions by earnings-weighted
average earnings over the pe
riod t to t + 3, ( t+3
w2ijt )/4. We then pair the highest-ranked
t
separating job with the highest-ranked acceding job. That is,
when there exist multiple job separations or job accessions for
a worker in a particular quarter, we take the separating job with
the greatest average earnings over the previous four quarters
and match it to the accession with the greatest average earnings in the subsequent four quarters. We refer to this match as
the “primary” E-to-E flow in a quarter. If after pairing the separation and accession with the greatest earnings there remains
another unmatched separation/accession pair, we match the separation and accession with the next highest earnings. We repeat
this process until no potential accession/separation pair matches
the job attachment criteria for the particular flow measure. As
it happens, multiple E-to-E flows in a quarter are fairly rare,
generally 80% of separating workers from a particular firm had an E-to-E flow to the
same new firm. Because this rule would overidentify reorganization events when there was only a small number of separating workers, we also require that the separating firm have a
minimum of 10 separations in the quarter. Using this definition,
we find that approximately 4.5% of E-to-E flows in our sample
are in fact caused by such reorganization events (approximately
0.17% of jobs). Although this is not a large component of our Eto-E flows, such E-to-E flows should be quantified in any public
domain statistics on E-to-E flows, because they likely are quite
different from E-to-E flows generally.
Another relevant issue in the use of administrative UI data is
the presence of measurement error in the worker flows induced
by errors in the personal identifiers in the UI wage records.
Abowd and Vilhuber (2005) studied this issue extensively and
found that transitory (typically one quarter) miscoding of the
SSN generates small biases in the worker flows; for example,
they found an average bias of approximately 2% at the industry
level. The measurement error identified by Abowd and Vilhuber (2005) generates a potential upward bias in our measure of
separations, but not in our measures of E-to-E flows. The errors
that they identified are based on cases in which a worker has a
temporary hole in the data for the same employer; this induces
a spurious separation, followed by a spurious accession with
the same employer. Such holes would not influence the counts
of E-to-E flows given our methodology. However, because they
would cause us to overstate counts of separations, such measurement error implies that we may be slightly understating the
fraction of separations that result in E-to-E flows.
Finally, consistent with the literature, we focus on E-to-E
flows that involve a separation from one employer and an accession to a different employer. Worker reallocation that involves
changing positions within the same firm by individual workers
is not captured by design. Such within-firm worker reallocation
is of interest, but by its very nature involves different costs and
frictions compared with reallocation across firms.
4. EMPIRICAL PATTERNS OF E–TO–E FLOWS
For the current analysis, we construct a 13-year panel of
linked employer–employee data by pooling the wage histories
of three large LEHD states that have data for 1991–2003: Wisconsin, North Carolina, and Oregon. Our sample includes both
male and female workers age 15 years and older, although we
break out much of our subsequent analysis by age groups. We
impose no earnings restrictions other than those discussed in
the previous section requiring positive earnings to identify employment at the firm.
We have calculated transitions only within each state; transitions from an employer located in one state to an employer
located in another state are lost. Thus we may undercount the
true number of E-to-E flows in our sample. However, none of
the selected states has a large metropolitan area at the border of the state that would be conducive to a large number of
cross-state transitions. As a check, although LEHD coverage
is not yet national, from 1998 forward we are able to follow
workers across 30 states (Alabama, Arkansas, California, Colorado, Delaware, Florida, Idaho, Indiana, Iowa, Kansas, Kentucky, Maine, Maryland, Minnesota, Missouri, Montana, North
498
Journal of Business & Economic Statistics, October 2011
Table 2. E-to-E flow rates by employment and separations
Consecutive-quarter
E-to-E flows (EEb)
E-to-E flow rate
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E-to-E flows/jobs
Persons experiencing E-to-E/employment
E-to-E flows/job separations
Persons experiencing E-to-E/persons
separating from jobs
0.0387
0.0410
0.2647
0.2727
Carolina, North Dakota, New Jersey, New Mexico, Oklahoma,
Oregon, Pennsylvania, South Carolina, Texas, Vermont, Virginia, Washington, West Virginia, and Wisconsin). Using these
data, we can perform a limited examination of how severely we
are undercounting flows by not looking out of state for the new
job. Between 1998q1 and 2003q1, within-state flows in our
three-state sample represent 26% of quarterly job separations.
If we look to the other 29 states for the new job, an additional
3% of quarterly job separations are E-to-E flows to an out-ofstate job. These findings suggest that neglecting E-to-E flows
involving moving across state lines has a modest, but not overwhelming, impact on the measures on which we focus in this
study.
We limited the current study to three illustrative states and
to within-state movements to reduce the substantial computational burden of the analysis. The long-run objective is to create
new public use E-to-E flow statistics that incorporate cross-state
movements yielding E-to-E flows.
4.1 The Overall Size of E-to-E Flows
Table 2 summarizes the magnitude of E-to-E flows in LEHD
data using the EEb measure of E-to-E flows. Overall, we find
that 3.9% of jobs held on the first day of the quarter experience a separation from that employer and an accession to a
new employer during the quarter. Using employment as a denominator instead of jobs, the figure is similar: 4.1% of persons
employed on the first day of the quarter separate from an employer during the quarter and are employed at a new employer
on the last day of the quarter. As a percentage of separations,
27% of workers separating from employers during the quarter
have a new employer on the last day of that quarter. This is a
significant amount of job reallocation occurring in the economy
in any given quarter. Note that although our estimate of a 4%
quarterly E-to-E rate is somewhat lower than the quarterly Eto-E rate implied by the 2.6% monthly E-to-E rate that Fallick
and Fleischman (2004) found in the CPS, it is much closer than
was the very high rate that we found in our preliminary analysis
of EEm flows, which likely included instances of multiple job
holding. This gives us greater confidence in using EEb as our
primary E-to-E flow measure.
Table 3 breaks out E-to-E transitions by whether the flows
involve main jobs or secondary jobs. Most jobs in our data are
main jobs, with only 5.9% of workers in our data holding multiple jobs on the first day of a given quarter. Not surprisingly,
the overwhelming majority of our E-to-E flows are flows from
one main job to another (96%), and our estimate of the E-toE rate for main jobs of 3.9% is similar to that for all jobs. We
would expect most workers to be less likely to leave their main
jobs voluntarily without another job already lined up or particularly good prospects than they would be to leave a secondary
job under such circumstances, and our results confirm this expectation. We find that separations are less common from main
jobs (13%) than from secondary jobs (31%), and that separations from main jobs are much more likely to result in a flow to
Table 3. E-to-E flows by main and secondary jobs
Description
Rate
Proportion of workers with only a main job in a quarter
Proportion of workers with secondary jobs in a quarter
Separation rate from main jobs
Separation rate from secondary jobs
E-to-E flows/jobs: main job to main job flows only
E-to-E flows/jobs: secondary job flows only
0.9407
0.0593
0.1345
0.3145
0.0393
0.0288
Percentage of flows
Main job-to-Main Job
Main job-to-Secondary job
Secondary-to-Main job
Secondary-to-Secondary job
0.9558
0.0057
0.0102
0.0283
Percentage of separations
Separation from main job into new main job
Separation from main job into new secondary job
Separations from main job that do not involve a E-to-E flow
Separation from secondary job into new main job
Separation from secondary job into new secondary job
Separations from secondary jobs that do not involve a E-to-E flow
0.2899
0.0017
0.7084
0.0219
0.0587
0.9194
NOTE: All E-to-E flows are EEb flows. Separations from main jobs are defined as separations from jobs with
positive earnings in t and t − 1 that have the greatest earnings-weighted average earnings over the last four
quarters (i.e., max[( tt−3 w2ijt )/4]). Accessions to main jobs are defined as accessions to jobs with positive
earnings in t and t + 1 that have the greatest earnings-weighted average earnings in the subsequent four quarters
w2ijt )/4]).
(i.e., max[( t+3
t
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Bjelland et al.: Employer-to-Employer Flows in the United States
another employer (29%) than are separations from secondary
jobs (8%).
The E-to-E flow rates derived from the CPS by Fallick and
Fleischman (2004) are based on comparisons of main jobs from
one month to the next. Thus the main job measures presented
in Table 3 should be more comparable to the CPS definition.
Our estimate of a 3.9% quarterly E-to-E rate is less than three
times the 2.6% monthly E-to-E rate that Fallick and Fleischman
found in the CPS. There are several reasons that estimates of Eto-E flows may differ between the LEHD and the CPS. First,
as noted earlier, our calculations do not include E-to-E flows
involving crossing state lines. Including cross-state transitions
to 30 states from our robustness check discussed earlier would
imply an increase in the overall E-to-E rate to 4.3%. Assuming
that the 20 missing states are drawn at random, a back of the
envelope calculation suggests that the overall rate extended to
all 50 states would be approximately 4.5%. This rate is still
lower than that implied by the CPS, but cross-state transitions
account for a nontrivial fraction of the difference.
Second, our definition of the main job, based on an explicit
ranking by earnings, need not match the subjective concept of
a main job used in the CPS. However, the similarity of our estimates of E-to-E flow with and without the restriction to main
jobs suggests that this does not explain much of the discrepancy.
Probably more important are differences between a monthly
point-in-time measure of employment as in the CPS and the
quarterly earnings measures developed with the LEHD. In particular, the requirement in our preferred definition that separating jobs are held on the first day of the quarter and acceding
jobs are held on the last day of the quarter eliminates many Eto-E flows involving very short-duration jobs. One issue here
is multiple transitions within the quarter. Suppose that both the
monthly and quarterly data were point-in-time measured at the
same point in the month. Say that the monthly rate of E-toE transitions (CPS definition) is X, and that a fraction Y of
all persons who make a monthly E-to-E transition makes another monthly E-to-E transition during that quarter. Then the
rate of quarterly E-to-E transitions is not 3 ∗ X; rather, it is
(3 − 2 ∗ Y) ∗ X. If this were the only issue, then the figures from
the CPS and from the LEHD would be perfectly reconciled if
three-quarters of all monthly E-to-E transitions were made by
individuals who make another monthly E-to-E transition within
the quarter. In fact, the evidence from the CPS suggests that the
fraction Y is only approximately 8%. In contrast, we show that
approximately one-half of quarterly E-to-E transitions are made
by individuals who make another E-to-E move within the year.
Other differences between the CPS and our LEHD sample
may be important as well. For example, the two sources differ in scope (e.g., the CPS includes the unincorporated selfemployed), and the sample periods do not match exactly (1992–
2002 vs. 1994–2003). Nevertheless, despite these differences
in data and methodology, the two sources yield estimates that
are roughly comparable in magnitude when it comes to the importance of E-to-E flows in separations, with E-to-E transitions
constituting 39% of main job separations in the data of Fallick
and Fleischman (2004), compared with 29% in our data.
499
Figure 1. Time series of EEb flows: fraction of beginning-of-quarter jobs that experience a flow to another employer by year and quarter.
4.2 Fluctuations and Trends in E-to-E Transitions
Figures 1 and 2 show our estimates of how the quarterly E-toE rate has changed over time between 1992 and 2003. The popular perception is that United States has become an ever more
transient labor market in which workers change jobs more often, both from choice and out of necessity, and contingent work
arrangements have become more common. As background, in
the states in our study, overall labor market transitions—the
rates of both separation and accession—rose slightly during the
1990s and fell starting in 2001.
E-to-E flow provides a unique measure of churning, because this flow inherently reflects both sides of the separation/accession accounts. Whether considering E-to-E transitions as a fraction of employment or as a fraction of separations,
we see that E-to-E rates rose from 1992 to 2000, fell dramatically in 2001, and 2002 and 2003 remained at a lower level than
in the 1990s. We find that the correlation between the underlying net growth rate of employment (for the states that we use
for this analysis) and the E-to-E flows is strongly positive; at an
annual frequency, the correlation is 0.82 (with a standard error
of 0.006). Although there is just one major aggregate cyclical
episode in our sample period, it is nevertheless striking that Eto-E transitions are so cyclically sensitive. Unfortunately, our
data stop just as the labor market began to seriously recover,
and thus we cannot infer a secular trend from our study period.
Figure 2. Time series of EEb flows: fraction of separations that experience a flow to another employer by year and quarter.
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500
Journal of Business & Economic Statistics, October 2011
However, our finding that the pace of the E-to-E flows remained
low in the post-2001 period is consistent with the declining
pattern of job and worker flows found in DFH, Davis et al.
(2010), and Konigsberg, Spletzer, and Talan (2009) in the BED
data, with the declining trend in unemployment inflows and outflows (measured from the CPS) shown by DFH and Davis et
al. (2010), and with the apparent downtrend in E-to-E flows
found in the CPS data by Fallick and Fleischman (2004) and by
Nagypal (2008). A common finding in those studies is that the
trend of declining worker and job flows continued through the
2007 period.
There is also a pronounced seasonal cycle in the pattern of Eto-E flows. In our sample, E-to-E flows as a fraction of jobs are
most common in the third quarter. On average, over our sample
period, the E-to-E rate as a fraction of jobs was 4.0 % in the
third quarter, compared with a low of 3.5% in the first quarter.
Interestingly, the seasonal pattern is different as a fraction of
separations, where the most important finding is the low rate
at which separators move to new employers in the fourth quarter of the year. Not surprisingly, however, these patterns are not
entirely uniform either across states or across industries. (Datasharing agreements between the states and the Census Bureau
prohibit us from reporting state-specific results.) The seasonality of E-to-E flows raises many interesting questions and merits
further exploration. We leave such exploration for future work.
Figure 3. E-to-E (EEb) rates by age.
4.3 The Distribution of E-to-E Flows
We have documented that E-to-E flows are common, in the
sense that their magnitude is large. We would like to know
how common such flows are in the sense of being experienced
widely across workers and firms. In terms of the limited demographic characteristics currently available to us, E-to-E flows
are fairly widespread. Age is the major source of concentration, although education also contributes. The difference between genders is minimal.
It is well known from sources like the NLSY that turnover
(including E-to-E rates) drops rapidly with age from the early
post-school years through early middle age. Less is known
about E-to-E flows at older ages. Fallick and Fleischman (2004)
found that E-to-E rates (relative to employment) were fairly stable from about the mid-40s on, despite an increase in separation rates that becomes quite steep around retirement age. The
LEHD data tell a somewhat different story. The upper panel of
Figure 3 shows that E-to-E rates fall continuously with age before flattening out in the mid-60s (although the pattern at these
older ages does vary by state). The lower panel of Figure 3
shows that, not surprisingly, the fraction of separations that are
E-to-E transitions drops rapidly in the early 60s, because retirements presumably take up a large share of separations.
Figure 4 shows that differences by education are not as pronounced. Relative to employment, E-to-E rates are lower for
workers with more education; however, much of this simply
reflects greater general turnover among less-educated workers.
These patterns are consistent with the findings of Fallick and
Fleischman (2004) and Nagypal (2008) from the CPS. When
viewed relative to separations, E-to-E rates fall more gently
as education rises. This latter pattern differs from the CPSbased findings of Fallick and Fleichman (2004) and Nagypal
(2008), who found that more-educated workers exhibited a
greater share of separations that yield E-to-E flows. Some caution is required when making these comparisons, because the
patterns by education vary by state in the LEHD data.
These demographic differences show that although E-to-E
flows are quite widespread, some workers are more likely than
others to change employers. But of course, there is a great deal
of heterogeneity not accounted for by the crude demographic
characteristics available, and many workers change employers
repeatedly. Table 4 examines the extent to which frequent job
changers dominate the overall rate of flows. The upper panel
shows the frequency of E-to-E transitions (conditional on being employed) in the first quarter of 1997, broken down by the
number of E-to-E transitions a person made in the 3-year period
Figure 4. E-to-E (EEb) rates by educational attainment.
Bjelland et al.: Employer-to-Employer Flows in the United States
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Table 4. E-to-E flow rates conditional on flow history and
concentration of E-to-E flows among highly mobile workers
Proportion of
workers in this
group having an
E-to-E flow in
1997q1
Proportion of
separating workers
in this group
having an E-to-E
flow in 1997q1
Number of E-to-E flows in
3 years (1994–1996)
0
1
2
3
4+
0.0265
0.0583
0.0840
0.1084
0.1452
0.2488
0.3514
0.3916
0.4185
0.4479
Number of separations in
3 years (1994–1996)
0
1
2
3
4+
0.0193
0.0365
0.0561
0.0759
0.1119
0.2285
0.2891
0.3222
0.3516
0.3953
Percent of total E-to-E flows during 1994q1–1996q4 accounted
for by the top X% of persons ranked by number of E-to-E
flows during the interval
% of total E-to-E flows
Top 1% (5+ flows)
Top 5% (3+ flows)
Top 25% (2+ flows)
4.13%
24.70%
53.46%
1994–1996. Even among those who have made no E-to-E transitions during the 1994–1996 period, 2.6% changed employers
in 1997q1. Not surprisingly, however, the likelihood of an Eto-E transition (conditional on being employed) is greater for
those who have made such a transition recently and rises with
the number of recent E-to-E transitions. For the most part, this
is because workers who changed employers more frequently in
the previous few years are more likely to separate from their
current employers; however, as shown in the right-most column, even as a fraction of separations, E-to-E rates are higher
for those who recently changed employers more often.
This pattern of occurrence–dependence is reflected in the
concentration of E-to-E flows among workers. As shown in
lower part of Table 4, approximately 4% o
ISSN: 0735-0015 (Print) 1537-2707 (Online) Journal homepage: http://www.tandfonline.com/loi/ubes20
Employer-to-Employer Flows in the United States:
Estimates Using Linked Employer-Employee Data
Melissa Bjelland, Bruce Fallick, John Haltiwanger & Erika McEntarfer
To cite this article: Melissa Bjelland, Bruce Fallick, John Haltiwanger & Erika McEntarfer (2011)
Employer-to-Employer Flows in the United States: Estimates Using Linked Employer-Employee
Data, Journal of Business & Economic Statistics, 29:4, 493-505, DOI: 10.1198/jbes.2011.08053
To link to this article: http://dx.doi.org/10.1198/jbes.2011.08053
Published online: 24 Jan 2012.
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Date: 11 January 2016, At: 23:19
Employer-to-Employer Flows in the United
States: Estimates Using Linked
Employer-Employee Data
Melissa B JELLAND
Employment and Disability Institute, School of Industrial and Labor Relations, Cornell University, New York,
NY 10016 ([email protected])
Bruce FALLICK
Federal Reserve Board, Washington, DC 20551 ([email protected])
Downloaded by [Universitas Maritim Raja Ali Haji] at 23:19 11 January 2016
John H ALTIWANGER
Department of Economics, University of Maryland, College Park, MD 20742 and U.S. Census Bureau
([email protected])
Erika M C E NTARFER
Center for Economic Studies, U.S. Census Bureau, Washington, DC 20233-7400 ([email protected])
We use administrative data linking workers and firms to study employer-to-employer (E-to-E) flows. After
discussing how to identify such flows in quarterly data, we investigate their basic empirical patterns. We
find that the pace of E-to-E flows is high, representing approximately 4% of employment and 30% of separations each quarter. The pace of E-to-E flows appears to be highly procyclical and varies systematically
across worker, job, and employer characteristics. There are rich patterns in terms of origin and destination
of industries. Somewhat surprisingly, we find that more than half of the workers making E-to-E transitions
switch even broadly defined industries (i.e., NAICS supersectors).
KEY WORDS: Employer-to-employer flows; Job flows; Turnover; Worker flows.
1. INTRODUCTION
An enormous amount of worker and job reallocation continually takes place in the U.S. economy. For example, Davis,
Faberman, and Haltiwanger (2006; DFH hereinafter) report that
the quarterly job creation and destruction rates each average
approximately 6%–8% of total employment, and that accession and separation rates are two to three times as large (see
also Davis, Haltiwanger, and Schuh 1996; Acs, Armington, and
Robb 1999; Burgess, Lane, and Stevens 2000). Job reallocation
induces worker reallocation, with less profitable firms shedding jobs and thus workers and more productive firms adding
them. Much of this reallocation occurs within narrowly defined
sectors, so the primary drivers of the continual reallocation of
jobs evidently include idiosyncratic shocks to technology, demand, and costs. The pace of job reallocation does fluctuate
both secularly and cyclically, however, and as such is related
both to the overall pace of technological change and to macroeconomic shocks. In addition, workers also continuously sort
themselves over a given distribution of jobs. Workers switch
jobs and change employment status because of “supply-side”
events such as labor force entry, family relocation, and retirement or for reasons of necessity, career development, better pay,
or preferable working conditions.
There is a large and growing literature on quantifying and
studying these job and worker flows and the associated outcomes for firms and workers. The U.S. statistical agencies now
have various data products, including the Business Employment
Dynamics (BED) and the Job Openings and Labor Turnover
Survey (JOLTS) programs at the Bureau of Labor Statistics and
the Longitudinal Business Database (LBD) and Longitudinal
Employer Household Dynamics program (LEHD) programs at
the Census Bureau, that tabulate these flows by a variety of
worker and firm characteristics. Given the huge volume of these
flows and the inherent frictions involved in the reallocation of
jobs and workers, there is considerable interest in understanding the nature and implications of these frictions at the micro
and aggregate levels. One important element largely missing
from the expanding measurement of worker and job flows is
the flows of workers directly from one employer to another. In
conjunction with extant information on other worker flows, the
measurement and analysis of E-to-E flows sheds light on how
the frictions in the labor market are resolved over time, across
sectors, and across different types of firms and workers. Such
direct E-to-E flows are not without friction, but by construction they reflect flows that do not involve a period of frictional
unemployment.
In interpreting the patterns of the E-to-E flows, it is important
to emphasize that this flow is only one component of the worker
and job flows, but it has been not well measured to date. Put
differently, measuring E-to-E flows allows the decomposition
of separations into those that yield an E-to-E flow and those
493
© 2011 American Statistical Association
Journal of Business & Economic Statistics
October 2011, Vol. 29, No. 4
DOI: 10.1198/jbes.2011.08053
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494
that do not. In that respect, measuring E-to-E flows permits an
important decomposition of separations (and accessions) that
heretofore was not feasible.
Quantifying and studying E-to-E flows is of interest not only
because of what it tells us about frictions in the labor market,
but also because E-to-E flows include information about the origin and destination employers. It is of interest to know about the
origin and destination of transitions for workers with different
characteristics (i.e., age, gender, education, ethnicity, and foreign born status). It also is of interest to know how earnings
change with such transitions; the origin–destination information permits measurement of earnings changes, which we have
pursued in other work (Fallick, Haltiwanger, and McEntarfer
2007).
In this article we use the recently developed longitudinal
linked employer and employee data from the Census Bureau’s
LEHD program to measure and study E-to-E flows. These data
are exceptionally well suited to an examination of quarterly
worker flows because they trace the job histories of a large proportion of employed individuals over several years. As we discuss in Section 2, the long histories of workers, as well as the
rich set of employer characteristics, yield advantages over measurement of E-to-E flows from the Current Population Survey
(CPS) (see Fallick and Fleishman 2001, 2004). The CPS-based
flows have alternative advantages, as we also discuss in Section 2, and thus we regard these measurement efforts as complementary.
Our main findings are as follows. First, the flow of workers from employer to employer is large and an important component of overall worker flows. Using our preferred measurement method, we find that quarterly E-to-E flows account for
approximately 4% of employment on average and 29% of main
job separations. These patterns vary systematically over time,
with interesting secular, seasonal, and cyclical variations. Eto-E flows appear to be procyclical, rising throughout the late
1990s and falling sharply in 2001. We also find that the pace
of E-to-E flows as a fraction of employment and separations
remained low at least through 2003. This finding is consistent
with related findings in the recent literature of a declining pace
of worker and job flows in the U.S. economy in the post-2000
period (see DFH and Davis et al. 2010).
The pace of E-to-E flows varies systematically by worker,
job, and firm characteristics. For workers, E-to-E flows as a
fraction of employment or of separations decline with age
through the mid-60s and then flatten out. The decline with age
as a fraction of separations is especially rapid in the age range
50–65, when retirements presumably become a more important
form of separation than E-to-E transitions. We find that lesseducated workers have a higher pace of E-to-E flows as a fraction of employment, reflecting the higher overall rate of worker
flows of the less educated; however, conditional on a separation,
education is not an important factor.
E-to-E flows are somewhat concentrated among frequent job
changers, and even more so among high-turnover firms. Firms
in the highest quintile of turnover—those in which >6.6% of
workers experience an E-to-E flow in an average quarter—
account for >60% of total E-to-E flows. The concentration of
E-to-E flows among highly mobile workers is less pronounced
but still in evidence; >50% of total E-to-E flows in a 3-year
Journal of Business & Economic Statistics, October 2011
period are accounted for by workers who changed jobs at least
twice in that period. Correspondingly, almost 50% of E-to-E
flows are workers with only one E-to-E flow over a 3-year
period. Whereas E-to-E flows are higher among workers with
short job tenure, the fraction of longer jobs (more than 2 years
of job tenure) that result in an E-to-E flow is almost 2%, compared with nearly 4% of all jobs.
There are rich patterns in terms of origin and destination of
industries. To our surprise, we find that a considerable fraction
of the workers making E-to-E transitions switch even broadly
defined industries; more than half of workers making an E-toE transition switch NAICS super-sectors. This finding can be
explained in part by examining the details of the origin and
destination sectors. For example, for workers employed in the
software publishing industry, we find that just over one-quarter
of the workers making an E-to-E transition remain in software
publishing; however, the other most common destination industries reflect closely related activities, such as computer systems
design services and semiconductor manufacturing.
The article is organized as follows. Section 2 discusses the
LEHD data infrastructure and its suitability for measuring Eto-E flows. Section 3 describes methods for measuring E-to-E
flows in the LEHD data. Section 4 presents the main empirical
findings, and Section 5 presents concluding remarks.
2. ADVANTAGES AND DISADVANTAGES OF
USING LEHD DATA FOR ESTIMATING
EMPLOYER–TO–EMPLOYER FLOWS
Recent research on E-to-E flows in the United States has exploited the dependent interviewing techniques used in the CPS
to provide nationally representative estimates of high-frequency
flows of workers between employers. Fallick and Fleischman
(2004) estimated that 2.6% of employed persons change employers each month, and that E-to-E flows fell markedly during
the recession that began in 2001. The CPS has several advantages for estimating E-to-E flows of workers. The CPS is already the primary source of the data on flows of workers across
labor market states, so the estimated E-to-E flows can be joined
consistently with the other flows in generating a more complete
picture of labor market dynamics. The CPS sample is representative of the entire civilian noninstitutional population in that it
is not limited to particular cohorts, household members, or sectors. The data measure flows at a monthly frequency and so are
likely to capture movements from one employer to another that
do not involve a significant period of nonemployment. And of
course, the CPS contains a rich set of demographic and other
information on individuals.
The CPS also has several important limitations, however. The
size of its sample, although large for a survey of households,
is small for some purposes. The representativeness of the CPS
is compromised by significant attrition, especially because, by
design, it does not follow respondents when they change residences. The CPS follows individuals for only 4 consecutive
months, so long employment histories cannot be constructed.
As a household survey, it relies on the responses of individuals
or other members of their households. Moreover, the survey design is such that it can be used to measure flows only between a
worker’s main jobs. The CPS also measures worker flows from
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Bjelland et al.: Employer-to-Employer Flows in the United States
only one side of the market: the worker’s side. As such, quantifying E-to-E flows on the basis of employer characteristics is
not feasible.
The LEHD data offer several advantages for estimating the
magnitude of E-to-E flows. First, the LEHD data are based on
comprehensive administrative records covering a large fraction
of the U.S. workforce. This enables analysis of flows of workers
across detailed industries, across detailed demographic groups,
and even after specific regional economic shocks. The universe
of the LEHD data is employment covered by the state unemployment insurance (UI) system. Longitudinal business identifiers permit accurate tracking of employers over time, including the entry and exit of businesses. State UI system coverage
is broad and basically comparable among states. UI and associated data are provided to the Census Bureau by more than
40 participating state partners. These data are supplemented
with Census demographic data, providing worker characteristics such as age, race, and gender.
Second, the longitudinal nature of the LEHD data means
that individual work histories can be followed continuously for
years. This is important because it allows us to examine the extent to which E-to-E flows in the U.S. economy are driven by
a small but mobile segment of the workforce. The LEHD data
follow workers across employers as long as they continue to
collect UI-covered wages, and follow workers who change residence as long as they continue to work in one of the more than
40 states participating in the program. Although we hope to take
advantage of this broad geographic coverage in future work, in
the current study we did not follow workers across state lines
for the most part.
Finally, the most valuable aspect of the LEHD data is the
presence of the worker–employer link. The longitudinal nature
of the linked data on both the individual and business sides allow us to track the flows of workers between a large sample of
firms in the U.S. economy while also identifying workers’ employment histories and the dynamics of the firms across which
workers are being redistributed. This link allows us to determine the extent to which flows between employers are driven
by the dynamics of the employers themselves. For instance, we
can examine to the extent to which E-to-E flows are concentrated among employees in high-turnover firms, or the extent
to which flows are driven by job creation at destination firms
versus job destruction at separating firms.
The LEHD data do have several disadvantages in identifying E-to-E flows. There is the potential to miss E-to-E
transitions that involve cross-state migrations in employment.
Employment in the federal government, unincorporated selfemployment, religious and charitable organizations, and certain agricultural enterprises is not covered. [For detailed information on UI-covered employment see Stevens 2007 and the
BLS Handbook of Methods, U.S. Department of Labor, Bureau of Labor Statistics (1997).] The LEHD program is currently adding administrative data on federal workers and selfemployed workers, but these data were not yet available at the
time of this study. Most fundamentally, the LEHD data do not
measure employment at a point in time. The UI records underlying the data report only quarterly earnings; they contain no
information regarding how many hours or weeks were worked
within the quarter, or which weeks were worked if the worker
495
was at the firm for less than a full quarter. In addition to limiting
the analysis to quarterly frequency, this poses some problems
in distinguishing between true E-to-E flows and multiple-job
holding, and also in determining the direction of some E-to-E
flows in the presence of short-duration jobs. These are not insignificant problems, given that flows between short-term jobs
account for a substantial fraction of E-to-E flows. In the next
section we discuss in detail how we deal with this feature of the
data.
Golan, Lane, and McEntarfer (2007) used LEHD data to examine annual flows of significantly attached workers between
employers within and across industries. They found that 25%
of workers who demonstrated some attachment to their employer changed jobs in a given year, split almost evenly between
industry-stayers and industry-changers. Given that the focus of
this article is on estimating the quarterly frequency of E-to-E
flows in both main and secondary jobs, our methodology differs
from theirs. In the next section we explain in greater detail how
we estimate the quarterly flows of workers between employers
in the LEHD data.
Before proceeding to the measurement methodology and the
estimates of the E-to-E flows, we note that the underlying
source data for all of the statistics reported in this article are
administrative data that provide universal coverage of workers
in covered sectors. With approximately 6–7 million jobs and
200,000–360,000 E-to-E flows observed each quarter, there is
no significant sampling error in our estimates, and we report no
sampling error statistics. Of course, there may be measurement
error from a variety of sources (e.g., measurement error in longitudinal identifiers, industrial classification for employers, or
reported earnings for workers) that affect the reliability of these
estimates, but this represents nonsampling error. A comprehensive description of the LEHD data infrastructure files, the measurement methodology, and possible sources of nonsampling
error has been provided by Abowd et al. (2009). In this article, we provide only descriptive statistics of the E-to-E flows,
with little or no statistical analysis of hypotheses regarding the
causes and consequences of E-to-E flows. Our contribution is to
develop and implement the statistical methodology for measuring E-to-E flows using the LEHD data infrastructure and then
provide basic information on the magnitude and nature of the
variation in the E-to-E flows.
3. DEFINING E–TO–E FLOWS IN LEHD DATA
The quarterly nature of the LEHD data poses some unique
methodological problems with regard to measuring E-to-E
flows. As mentioned in the previous section, the UI records
that underlie the data do not measure employment at a point in
time, but instead report quarterly earnings. They contain no information regarding which weeks during the quarter the worker
was employed or how many hours he or she worked within the
quarter. Table 1 provides a hypothetical example of wage history data, in this case the quarterly wage histories for two individuals who were each employed with two different employers
during the year.
Our goal is to characterize quarterly job transitions, including those involving shorter jobs or secondary jobs. We begin
by defining the existence of a job match in quarter t if worker i
496
Journal of Business & Economic Statistics, October 2011
Table 1. Example of quarterly earnings histories
Person/Employer Match (Job)
Person 1 Firm A
Person 1 Firm Q
Person 2 Firm B
Person 2 Firm Z
Q1
Q2
Q3
Q4
$0
$0
$2500
$0
$500
$1000
$1500
$750
$0
$0
$0
$3000
$0
$0
$0
$3000
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NOTE: All persons, firms, and wages in examples are fictionalized and do not reflect any
individual’s wage records.
has positive earnings from employer j in that period, denoted by
mijt = 1 (mijt = 0 otherwise). Matches in only one of two consecutive quarters indicate that either an accession or separation
has occurred; that is, workers with positive earnings in one period but none from the same employer in the subsequent period
(i.e., mijt = 1 & mijt+1 = 0) are assumed to have separated from
their employers at some point during the quarter with positive
earnings. Similarly, individuals who are observed to move from
receiving no earnings from a firm to earning a positive amount,
(i.e., mijt−1 = 0 & mijt = 1) are assumed to have acceded to the
employer sometime during the quarter with positive earnings.
In Table 1, we would say that person 2 separated from employer
B and acceded to employer Z during quarter 2.
If we define an E-to-E flow simply as an accession to a new
employer k that occurs in the same quarter as that same worker
separated from employer j, then we have the following definition of an E-to-E flow:
Flow E-to-E transition (EEmijkt ): individual i moves
from firm j to firm k (j = k) during t:
⎧
⎨ 1, if {mijt = 1 & mijt+1 = 0} and
{mikt−1 = 0 & mikt = 1}
EEmijkt =
⎩
0, otherwise.
(1)
Although valid E-to-E flows might include situations in which
a person separates from one employer in quarter t and accedes
to a different employer in quarter t + 1, we choose not to count
these transitions. For the purpose of our current study, we are
interested in flows that do not involve a significant period of
intervening nonemployment. As noted earlier, we know only
each individual’s earnings from a given employer for the entire quarter. Thus, we observe whether an individual worked
for a given employer during the quarter, but we do not know
which weeks during the quarter the match was in effect. A typical quarter contains 13 weeks. If the timings of the separations
in one quarter and of the accessions in the subsequent quarter
are independent of each other, and each is uniformly distributed
within the quarter, then the mean duration between the two employers is 13 weeks, which is longer than we want to count for
an E-to-E transition. Therefore, we restrict our present analysis to E-to-E flows that occur within a single quarter, that is, to
flows where the separation and accession, as defined earlier, occur in the same quarter. Although it is possible to have a short
nonemployment spell during an E-to-E flow that occurs within
the quarter, it is unlikely to be a spell of more than 1 month in
duration. (Again assuming uniform distributions, the mean duration is approximately 4 weeks.) We leave an analysis of flows
into and out of longer spells of nonemployment for future work.
A limitation of the definition in (1) is that in the case of
matches that appear in only one quarter, we cannot distinguish
between an E-to-E flow and multiple job holding with any confidence. For instance, imagine a work history such as that of
person 1 in Table 1. Although person 1 would be defined as
experiencing an E-to-E flow in the second quarter according to
the definition (EEm) in eq. (1), it is also possible that person
1 held jobs at firm A and firm Q simultaneously. The inclusion of multiple job holding in this measure is likely to result
in an overcounting of E-to-E flows. Preliminary estimates of Eto-E rates using the EEm measure in LEHD data found rates
as high as 16%. This seems to be an improbably high rate of
E-to-E flows, and thus this measure is likely overcounting the
true number of flows. Moreover, even if we knew that person 1
did not hold both jobs simultaneously, it would be difficult to
decide which was the origin job and which was the destination
job.
For these reasons, for our analysis we use a more restrictive definition of E-to-E flows that requires observation of two
quarters of positive earnings at the separating and the acceding
employer. Formally,
Consecutive-quarter E-to-E transition (EEbijkt ): individual i transitions from firm j to firm k (j = k) during t:
(2)
⎧
⎨ 1, if {mijt−1 = 1 & mijt = 1 & mijt+1 = 0}
and {mikt−1 = 0 & mikt = 1 & mikt+1 = 1}
EEbijkt =
⎩
0, otherwise.
Here we use consecutive quarters of earnings to construct
an estimate of point-in-time employment. If a worker has positive earnings at an employer in both quarter t − 1 and quarter
t, then we can say with reasonable confidence that he was an
employee of that firm on the first day of quarter t as well as on
the last day of quarter t − 1. Thus the restrictions that we add
here amount to (a) the worker must be employed at the separating employer on the first day of the relevant quarter, and (b) the
worker must be employed at the acceding employer on the last
day of the relevant quarter, with both the separation and the accession occurring during the quarter. In the example in Table 1,
person 2 experiences a consecutive-quarter E-to-E flow in the
second quarter from firm B to firm Z. The additional attachment restriction in EEb compared with EEm provides greater
confidence that the transition we have identified is in fact an Eto-E flow, and also clarifies the direction of this flow. Of course,
the greater clarity comes at the cost of excluding some E-to-E
flows that involve very short jobs, so this definition may underestimate the true magnitude of E-to-E flows.
Applying these definitions is straightforward if a person experiences no more than one separation and no more than one
accession each quarter. However, the application is more complicated if a person experiences multiple separations or accessions in a quarter such that more than one pair of jobs would fit
the definition of an E-to-E flow. For the purpose of counting the
number of E-to-E transitions, we impose the rule that a given
separation or accession can contribute to only one E-to-E transition. Even so, for some purposes there remains the problem
of pairing the separations with the accessions so as to identify
the origin and destination job in each transition. We would like
to pair origin and destination jobs that play similar roles in the
worker’s career, those that the worker herself would identify as
replacing one another. To take an extreme example, we would
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Bjelland et al.: Employer-to-Employer Flows in the United States
not like to pair a separation from a full-time job with an accession to a part-time weekend job if more equal pairings were
possible. (Of course, even with only one separation and one accession in a quarter, we may observe E-to-E flows that have this
unequal nature.)
Given the limited information at our disposal, we do this by
pairing the jobs with the most similar relative earnings. Lacking
data on the number of hours or weeks worked during a quarter, we recognize that the relative earnings of jobs in a single
quarter can be influenced by when during the quarter each job
began or ended, as well as by transitory fluctuations in hours
worked. To minimize these influences, we compare earningsweighted average quarterly earnings. Specifically, we rank all
separating jobs by the earnings-weighted
average earnings over
the period t − 3 to t, ( tt−3 w2ijt )/4. Similarly, we rank all accessions by earnings-weighted
average earnings over the pe
riod t to t + 3, ( t+3
w2ijt )/4. We then pair the highest-ranked
t
separating job with the highest-ranked acceding job. That is,
when there exist multiple job separations or job accessions for
a worker in a particular quarter, we take the separating job with
the greatest average earnings over the previous four quarters
and match it to the accession with the greatest average earnings in the subsequent four quarters. We refer to this match as
the “primary” E-to-E flow in a quarter. If after pairing the separation and accession with the greatest earnings there remains
another unmatched separation/accession pair, we match the separation and accession with the next highest earnings. We repeat
this process until no potential accession/separation pair matches
the job attachment criteria for the particular flow measure. As
it happens, multiple E-to-E flows in a quarter are fairly rare,
generally 80% of separating workers from a particular firm had an E-to-E flow to the
same new firm. Because this rule would overidentify reorganization events when there was only a small number of separating workers, we also require that the separating firm have a
minimum of 10 separations in the quarter. Using this definition,
we find that approximately 4.5% of E-to-E flows in our sample
are in fact caused by such reorganization events (approximately
0.17% of jobs). Although this is not a large component of our Eto-E flows, such E-to-E flows should be quantified in any public
domain statistics on E-to-E flows, because they likely are quite
different from E-to-E flows generally.
Another relevant issue in the use of administrative UI data is
the presence of measurement error in the worker flows induced
by errors in the personal identifiers in the UI wage records.
Abowd and Vilhuber (2005) studied this issue extensively and
found that transitory (typically one quarter) miscoding of the
SSN generates small biases in the worker flows; for example,
they found an average bias of approximately 2% at the industry
level. The measurement error identified by Abowd and Vilhuber (2005) generates a potential upward bias in our measure of
separations, but not in our measures of E-to-E flows. The errors
that they identified are based on cases in which a worker has a
temporary hole in the data for the same employer; this induces
a spurious separation, followed by a spurious accession with
the same employer. Such holes would not influence the counts
of E-to-E flows given our methodology. However, because they
would cause us to overstate counts of separations, such measurement error implies that we may be slightly understating the
fraction of separations that result in E-to-E flows.
Finally, consistent with the literature, we focus on E-to-E
flows that involve a separation from one employer and an accession to a different employer. Worker reallocation that involves
changing positions within the same firm by individual workers
is not captured by design. Such within-firm worker reallocation
is of interest, but by its very nature involves different costs and
frictions compared with reallocation across firms.
4. EMPIRICAL PATTERNS OF E–TO–E FLOWS
For the current analysis, we construct a 13-year panel of
linked employer–employee data by pooling the wage histories
of three large LEHD states that have data for 1991–2003: Wisconsin, North Carolina, and Oregon. Our sample includes both
male and female workers age 15 years and older, although we
break out much of our subsequent analysis by age groups. We
impose no earnings restrictions other than those discussed in
the previous section requiring positive earnings to identify employment at the firm.
We have calculated transitions only within each state; transitions from an employer located in one state to an employer
located in another state are lost. Thus we may undercount the
true number of E-to-E flows in our sample. However, none of
the selected states has a large metropolitan area at the border of the state that would be conducive to a large number of
cross-state transitions. As a check, although LEHD coverage
is not yet national, from 1998 forward we are able to follow
workers across 30 states (Alabama, Arkansas, California, Colorado, Delaware, Florida, Idaho, Indiana, Iowa, Kansas, Kentucky, Maine, Maryland, Minnesota, Missouri, Montana, North
498
Journal of Business & Economic Statistics, October 2011
Table 2. E-to-E flow rates by employment and separations
Consecutive-quarter
E-to-E flows (EEb)
E-to-E flow rate
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E-to-E flows/jobs
Persons experiencing E-to-E/employment
E-to-E flows/job separations
Persons experiencing E-to-E/persons
separating from jobs
0.0387
0.0410
0.2647
0.2727
Carolina, North Dakota, New Jersey, New Mexico, Oklahoma,
Oregon, Pennsylvania, South Carolina, Texas, Vermont, Virginia, Washington, West Virginia, and Wisconsin). Using these
data, we can perform a limited examination of how severely we
are undercounting flows by not looking out of state for the new
job. Between 1998q1 and 2003q1, within-state flows in our
three-state sample represent 26% of quarterly job separations.
If we look to the other 29 states for the new job, an additional
3% of quarterly job separations are E-to-E flows to an out-ofstate job. These findings suggest that neglecting E-to-E flows
involving moving across state lines has a modest, but not overwhelming, impact on the measures on which we focus in this
study.
We limited the current study to three illustrative states and
to within-state movements to reduce the substantial computational burden of the analysis. The long-run objective is to create
new public use E-to-E flow statistics that incorporate cross-state
movements yielding E-to-E flows.
4.1 The Overall Size of E-to-E Flows
Table 2 summarizes the magnitude of E-to-E flows in LEHD
data using the EEb measure of E-to-E flows. Overall, we find
that 3.9% of jobs held on the first day of the quarter experience a separation from that employer and an accession to a
new employer during the quarter. Using employment as a denominator instead of jobs, the figure is similar: 4.1% of persons
employed on the first day of the quarter separate from an employer during the quarter and are employed at a new employer
on the last day of the quarter. As a percentage of separations,
27% of workers separating from employers during the quarter
have a new employer on the last day of that quarter. This is a
significant amount of job reallocation occurring in the economy
in any given quarter. Note that although our estimate of a 4%
quarterly E-to-E rate is somewhat lower than the quarterly Eto-E rate implied by the 2.6% monthly E-to-E rate that Fallick
and Fleischman (2004) found in the CPS, it is much closer than
was the very high rate that we found in our preliminary analysis
of EEm flows, which likely included instances of multiple job
holding. This gives us greater confidence in using EEb as our
primary E-to-E flow measure.
Table 3 breaks out E-to-E transitions by whether the flows
involve main jobs or secondary jobs. Most jobs in our data are
main jobs, with only 5.9% of workers in our data holding multiple jobs on the first day of a given quarter. Not surprisingly,
the overwhelming majority of our E-to-E flows are flows from
one main job to another (96%), and our estimate of the E-toE rate for main jobs of 3.9% is similar to that for all jobs. We
would expect most workers to be less likely to leave their main
jobs voluntarily without another job already lined up or particularly good prospects than they would be to leave a secondary
job under such circumstances, and our results confirm this expectation. We find that separations are less common from main
jobs (13%) than from secondary jobs (31%), and that separations from main jobs are much more likely to result in a flow to
Table 3. E-to-E flows by main and secondary jobs
Description
Rate
Proportion of workers with only a main job in a quarter
Proportion of workers with secondary jobs in a quarter
Separation rate from main jobs
Separation rate from secondary jobs
E-to-E flows/jobs: main job to main job flows only
E-to-E flows/jobs: secondary job flows only
0.9407
0.0593
0.1345
0.3145
0.0393
0.0288
Percentage of flows
Main job-to-Main Job
Main job-to-Secondary job
Secondary-to-Main job
Secondary-to-Secondary job
0.9558
0.0057
0.0102
0.0283
Percentage of separations
Separation from main job into new main job
Separation from main job into new secondary job
Separations from main job that do not involve a E-to-E flow
Separation from secondary job into new main job
Separation from secondary job into new secondary job
Separations from secondary jobs that do not involve a E-to-E flow
0.2899
0.0017
0.7084
0.0219
0.0587
0.9194
NOTE: All E-to-E flows are EEb flows. Separations from main jobs are defined as separations from jobs with
positive earnings in t and t − 1 that have the greatest earnings-weighted average earnings over the last four
quarters (i.e., max[( tt−3 w2ijt )/4]). Accessions to main jobs are defined as accessions to jobs with positive
earnings in t and t + 1 that have the greatest earnings-weighted average earnings in the subsequent four quarters
w2ijt )/4]).
(i.e., max[( t+3
t
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Bjelland et al.: Employer-to-Employer Flows in the United States
another employer (29%) than are separations from secondary
jobs (8%).
The E-to-E flow rates derived from the CPS by Fallick and
Fleischman (2004) are based on comparisons of main jobs from
one month to the next. Thus the main job measures presented
in Table 3 should be more comparable to the CPS definition.
Our estimate of a 3.9% quarterly E-to-E rate is less than three
times the 2.6% monthly E-to-E rate that Fallick and Fleischman
found in the CPS. There are several reasons that estimates of Eto-E flows may differ between the LEHD and the CPS. First,
as noted earlier, our calculations do not include E-to-E flows
involving crossing state lines. Including cross-state transitions
to 30 states from our robustness check discussed earlier would
imply an increase in the overall E-to-E rate to 4.3%. Assuming
that the 20 missing states are drawn at random, a back of the
envelope calculation suggests that the overall rate extended to
all 50 states would be approximately 4.5%. This rate is still
lower than that implied by the CPS, but cross-state transitions
account for a nontrivial fraction of the difference.
Second, our definition of the main job, based on an explicit
ranking by earnings, need not match the subjective concept of
a main job used in the CPS. However, the similarity of our estimates of E-to-E flow with and without the restriction to main
jobs suggests that this does not explain much of the discrepancy.
Probably more important are differences between a monthly
point-in-time measure of employment as in the CPS and the
quarterly earnings measures developed with the LEHD. In particular, the requirement in our preferred definition that separating jobs are held on the first day of the quarter and acceding
jobs are held on the last day of the quarter eliminates many Eto-E flows involving very short-duration jobs. One issue here
is multiple transitions within the quarter. Suppose that both the
monthly and quarterly data were point-in-time measured at the
same point in the month. Say that the monthly rate of E-toE transitions (CPS definition) is X, and that a fraction Y of
all persons who make a monthly E-to-E transition makes another monthly E-to-E transition during that quarter. Then the
rate of quarterly E-to-E transitions is not 3 ∗ X; rather, it is
(3 − 2 ∗ Y) ∗ X. If this were the only issue, then the figures from
the CPS and from the LEHD would be perfectly reconciled if
three-quarters of all monthly E-to-E transitions were made by
individuals who make another monthly E-to-E transition within
the quarter. In fact, the evidence from the CPS suggests that the
fraction Y is only approximately 8%. In contrast, we show that
approximately one-half of quarterly E-to-E transitions are made
by individuals who make another E-to-E move within the year.
Other differences between the CPS and our LEHD sample
may be important as well. For example, the two sources differ in scope (e.g., the CPS includes the unincorporated selfemployed), and the sample periods do not match exactly (1992–
2002 vs. 1994–2003). Nevertheless, despite these differences
in data and methodology, the two sources yield estimates that
are roughly comparable in magnitude when it comes to the importance of E-to-E flows in separations, with E-to-E transitions
constituting 39% of main job separations in the data of Fallick
and Fleischman (2004), compared with 29% in our data.
499
Figure 1. Time series of EEb flows: fraction of beginning-of-quarter jobs that experience a flow to another employer by year and quarter.
4.2 Fluctuations and Trends in E-to-E Transitions
Figures 1 and 2 show our estimates of how the quarterly E-toE rate has changed over time between 1992 and 2003. The popular perception is that United States has become an ever more
transient labor market in which workers change jobs more often, both from choice and out of necessity, and contingent work
arrangements have become more common. As background, in
the states in our study, overall labor market transitions—the
rates of both separation and accession—rose slightly during the
1990s and fell starting in 2001.
E-to-E flow provides a unique measure of churning, because this flow inherently reflects both sides of the separation/accession accounts. Whether considering E-to-E transitions as a fraction of employment or as a fraction of separations,
we see that E-to-E rates rose from 1992 to 2000, fell dramatically in 2001, and 2002 and 2003 remained at a lower level than
in the 1990s. We find that the correlation between the underlying net growth rate of employment (for the states that we use
for this analysis) and the E-to-E flows is strongly positive; at an
annual frequency, the correlation is 0.82 (with a standard error
of 0.006). Although there is just one major aggregate cyclical
episode in our sample period, it is nevertheless striking that Eto-E transitions are so cyclically sensitive. Unfortunately, our
data stop just as the labor market began to seriously recover,
and thus we cannot infer a secular trend from our study period.
Figure 2. Time series of EEb flows: fraction of separations that experience a flow to another employer by year and quarter.
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500
Journal of Business & Economic Statistics, October 2011
However, our finding that the pace of the E-to-E flows remained
low in the post-2001 period is consistent with the declining
pattern of job and worker flows found in DFH, Davis et al.
(2010), and Konigsberg, Spletzer, and Talan (2009) in the BED
data, with the declining trend in unemployment inflows and outflows (measured from the CPS) shown by DFH and Davis et
al. (2010), and with the apparent downtrend in E-to-E flows
found in the CPS data by Fallick and Fleischman (2004) and by
Nagypal (2008). A common finding in those studies is that the
trend of declining worker and job flows continued through the
2007 period.
There is also a pronounced seasonal cycle in the pattern of Eto-E flows. In our sample, E-to-E flows as a fraction of jobs are
most common in the third quarter. On average, over our sample
period, the E-to-E rate as a fraction of jobs was 4.0 % in the
third quarter, compared with a low of 3.5% in the first quarter.
Interestingly, the seasonal pattern is different as a fraction of
separations, where the most important finding is the low rate
at which separators move to new employers in the fourth quarter of the year. Not surprisingly, however, these patterns are not
entirely uniform either across states or across industries. (Datasharing agreements between the states and the Census Bureau
prohibit us from reporting state-specific results.) The seasonality of E-to-E flows raises many interesting questions and merits
further exploration. We leave such exploration for future work.
Figure 3. E-to-E (EEb) rates by age.
4.3 The Distribution of E-to-E Flows
We have documented that E-to-E flows are common, in the
sense that their magnitude is large. We would like to know
how common such flows are in the sense of being experienced
widely across workers and firms. In terms of the limited demographic characteristics currently available to us, E-to-E flows
are fairly widespread. Age is the major source of concentration, although education also contributes. The difference between genders is minimal.
It is well known from sources like the NLSY that turnover
(including E-to-E rates) drops rapidly with age from the early
post-school years through early middle age. Less is known
about E-to-E flows at older ages. Fallick and Fleischman (2004)
found that E-to-E rates (relative to employment) were fairly stable from about the mid-40s on, despite an increase in separation rates that becomes quite steep around retirement age. The
LEHD data tell a somewhat different story. The upper panel of
Figure 3 shows that E-to-E rates fall continuously with age before flattening out in the mid-60s (although the pattern at these
older ages does vary by state). The lower panel of Figure 3
shows that, not surprisingly, the fraction of separations that are
E-to-E transitions drops rapidly in the early 60s, because retirements presumably take up a large share of separations.
Figure 4 shows that differences by education are not as pronounced. Relative to employment, E-to-E rates are lower for
workers with more education; however, much of this simply
reflects greater general turnover among less-educated workers.
These patterns are consistent with the findings of Fallick and
Fleischman (2004) and Nagypal (2008) from the CPS. When
viewed relative to separations, E-to-E rates fall more gently
as education rises. This latter pattern differs from the CPSbased findings of Fallick and Fleichman (2004) and Nagypal
(2008), who found that more-educated workers exhibited a
greater share of separations that yield E-to-E flows. Some caution is required when making these comparisons, because the
patterns by education vary by state in the LEHD data.
These demographic differences show that although E-to-E
flows are quite widespread, some workers are more likely than
others to change employers. But of course, there is a great deal
of heterogeneity not accounted for by the crude demographic
characteristics available, and many workers change employers
repeatedly. Table 4 examines the extent to which frequent job
changers dominate the overall rate of flows. The upper panel
shows the frequency of E-to-E transitions (conditional on being employed) in the first quarter of 1997, broken down by the
number of E-to-E transitions a person made in the 3-year period
Figure 4. E-to-E (EEb) rates by educational attainment.
Bjelland et al.: Employer-to-Employer Flows in the United States
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Table 4. E-to-E flow rates conditional on flow history and
concentration of E-to-E flows among highly mobile workers
Proportion of
workers in this
group having an
E-to-E flow in
1997q1
Proportion of
separating workers
in this group
having an E-to-E
flow in 1997q1
Number of E-to-E flows in
3 years (1994–1996)
0
1
2
3
4+
0.0265
0.0583
0.0840
0.1084
0.1452
0.2488
0.3514
0.3916
0.4185
0.4479
Number of separations in
3 years (1994–1996)
0
1
2
3
4+
0.0193
0.0365
0.0561
0.0759
0.1119
0.2285
0.2891
0.3222
0.3516
0.3953
Percent of total E-to-E flows during 1994q1–1996q4 accounted
for by the top X% of persons ranked by number of E-to-E
flows during the interval
% of total E-to-E flows
Top 1% (5+ flows)
Top 5% (3+ flows)
Top 25% (2+ flows)
4.13%
24.70%
53.46%
1994–1996. Even among those who have made no E-to-E transitions during the 1994–1996 period, 2.6% changed employers
in 1997q1. Not surprisingly, however, the likelihood of an Eto-E transition (conditional on being employed) is greater for
those who have made such a transition recently and rises with
the number of recent E-to-E transitions. For the most part, this
is because workers who changed employers more frequently in
the previous few years are more likely to separate from their
current employers; however, as shown in the right-most column, even as a fraction of separations, E-to-E rates are higher
for those who recently changed employers more often.
This pattern of occurrence–dependence is reflected in the
concentration of E-to-E flows among workers. As shown in
lower part of Table 4, approximately 4% o