BLS WORKING PAPERS 

 U.S. Department of Labor 

U.S. Bureau of Labor Statistics 

Office of Employment and Unemployment Statistics 

Studying the Labor Market with the Job Openings and 

Labor Turnover Survey

R. Jason Faberman, U.S. Bureau of Labor Statistics 

Working Paper 388

December 2005

All views expressed in this paper are those of the author and do not necessarily reflect the views or policies of the 

U.S. Bureau of Labor Statistics. 
Studying the Labor Market with the Job Openings and Labor Turnover Survey 

November 2005 

R. Jason Faberman 

U.S. Bureau of Labor Statistics 

Office of Employment and Unemployment Statistics 

2 Massachusetts Ave NE, Suite 4945 

Washington, DC 20212 

Faberman.Jason@bls.gov

Phone: (202) 691-5192 

Fax: (202) 691-7425 

Abstract: 

The Job Openings and Labor Turnover Survey (JOLTS) is a new data source of the 

Bureau of Labor Statistics that estimates monthly vacancies, hires, and separations. It has 

quickly become a useful tool for studying the labor market. This chapter summarizes its 

aggregate and micro-level evidence, including the relations of vacancies and worker 

flows to unemployment and other measures of labor market conditions. The chapter also 

discusses the implications of this evidence and the potential of the data for future 

research. 

Keywords: vacancies, Beveridge curve, labor turnover, labor market search 

JEL Codes: E24, E32, J63 

Acknowledgements 

I thank Eva Nagypal, Steve Davis, Mark Roberts, Tim Dunne, and Brad Jensen, as well 

as seminar participants at the NBER-CRIW Conference on Producer Dynamics for 

helpful comments on this paper. Any remaining errors are my own. The views expressed 

are solely those of the author and do not necessarily reflect the official positions or 

policies of the U.S. Bureau of Labor Statistics or the views of other staff members. 
1. Introduction 

In recent years, the Bureau of Labor Statistics (BLS) has released several new 

data products that describe the dynamics of the labor market. One of these is the Job 

Openings and Labor Turnover Survey (JOLTS). The survey is the only existing data 

source to measure vacancies, hires, and separations at the establishment level at a regular 

(monthly) frequency in the US. This survey already adds to the understanding of the role 

firm behavior plays in job posting and worker turnover. 

This chapter details the characteristics of the JOLTS data and provides descriptive 

evidence at both the aggregate and micro levels. The discussion is primarily for 

researchers wishing to use the data for their own studies. As such, it characterizes the 

data scope, composition, measurement and estimation, and the research potential these 

data have. The chapter also presents some basic evidence on the aggregate and microlevel relations of vacancy postings and labor turnover to unemployment and other labor 

market conditions. 

The survey is an evolution of earlier data series (notably the BLS Labor Turnover 

Survey1

), as well as research on vacancies, job turnover, and unemployment done by 

Abraham (1987), Blanchard and Diamond (1989, 1990), and others and theories of labor 

market search like those developed by Mortensen and Pissarides (1994). This work, and 

the wide literature that followed, underscores the importance of understanding the 

process of matching workers to firms and highlights the rich heterogeneity surrounding 

this process. As such, the BLS designed JOLTS to capture these facets. The result is a 

 1

 The Labor Turnover Survey measured vacancies, ascensions, and separations for the manufacturing 

industry; the BLS discontinued the survey in 1982. See Davis and Haltiwanger (1998) and Clark and Hyson 

(2001) for more on this survey. 

1
high-frequency, timely survey with several major advantages over previous data. The 

first is its reporting of hires and separations directly by the firm. Other sources (e.g., 

administrative wage records) forced researchers to infer these flows from observed 

changes in the employer of a worker. The second is its reporting of job openings, or 

vacancies, which are reported directly by the firm. Previously, researchers had to rely on 

indexes (such has the Help Wanted Index) for a measure of vacancies. These indexes, in 

addition to potential selection and measurement issues, did not lend themselves to 

studying vacancy postings at the micro level. This is particularly important since theories 

of labor market search and matching often model behavior at the level of workers and 

firms, not at the aggregate level. The final advantage is its distinction between quits and 

layoffs. The two types of separations have differing cyclical patterns, and separately 

measure voluntary and involuntary severances, respectively.

Existing research using JOLTS is sparse. Clark (2004) summarizes the aggregate 

evidence since the survey’s inception. Hall (2005) and Shimer (2005a) use the JOLTS 

data to study the volatility of vacancies over the business cycle and its relation to theories 

of labor market search. Besides this chapter, Davis, Faberman, and Haltiwanger (2005, 

2006) are the first to present analyses of the micro-level JOLTS data. The data have also 

become popular with the press and various industry and policy groups. Research into the 

theory and evidence of labor dynamics has ballooned in recent years, and the estimates 

JOLTS provides are related to several facets of this research. Davis and Haltiwanger 

(1999) and Mortensen and Pissarides (1999), provide complete review of the empirical 

and theoretical literature, respectively, related to worker and job flows and labor market 

search. In all, the JOLTS data complement existing data, such as matched employer2
employee data and workers’ gross flows, and can only add to our understanding of labor 

market dynamics. 

The following section defines the concepts and terminology used throughout the 

paper, discusses the data sample and estimation process, and highlights the survey’s 

research strengths and limitations. The next section explores the relation between 

vacancies and unemployment at both the aggregate and micro levels. An exploration of

the relations between labor turnover and aggregate and local labor market conditions 

comes next. The final section concludes and discusses potential avenues of future 

research. 

2. Data and Measurement 

2.A. Source Data 

The JOLTS program publishes monthly estimates of vacancies, hires, and 

separations, with separations broken out into quits, layoffs and discharges, and other 

separations (e.g., retirements). The data start in December 2000 and are updated monthly, 

with the latest estimates available within two months of a month’s end. The current time 

series spans over four years. The aggregate estimates are available nationally and for four 

major regions by 2-digit North American Industry Classification System (NAICS) 

sector.2

 The BLS reports JOLTS estimates in levels and as rates (which are percentages 

of employment). 

The primary unit of observation for the JOLTS survey is the establishment, which 

covers the operations of a firm at a single physical location. Firms can have one or more 

 2

 The NAICS replaces the older Standard Industrial Classification (SIC) system. The most notable change 

in NAICS is its classification of the service sector into several separate sectors, such as information, 

professional and business services, education and health, and travel and hospitality. In general, two-digit 

NAICS sectors correspond to major SIC industry sectors (e.g., manufacturing, services. etc.) 

3
establishments. The JOLTS data cover nonfarm payrolls, which implies that employment 

estimates generally exclude self-employed individuals and non-profit organizations not 

covered under a state unemployment insurance program. The JOLTS data are a sample of 

roughly 16,000 establishments surveyed each month. Establishments report their 

employment, hires, separations (broken out by type), and job openings for the month 

within the framework of the survey definitions. The survey is made up of overlapping 

panels that are each sampled for 18 months, and is weighted so that its employment 

estimates match those of the Current Employment Statistics (CES) survey.3

 The survey

also has each respondent’s state and industry codes, plus an identifier that allows a match 

to its corresponding record in the BLS Quarterly Census of Employment and Wages 

(QCEW, also known as ES-202) data. 

For the analyses in this paper, I use the sample of JOLTS establishments pooled 

over the December 2000 – January 2005 period. For most aggregate statistics, I use the 

unrestricted sample of all observations. For the micro-level analyses, I use a restricted 

JOLTS sample of all observations that have positive employment reported in two 

consecutive months. This minimizes the potential spurious effects of outliers and 

inconsistent data reporters. The resulting sample contains 372,288 observations, which 

represent 92.8 percent of the pooled observations (and 92.3 percent of the pooled 

employment) used in aggregate estimation and, due to the requirement of continuous 

reported employment, exclude the December 2000 observations.4

 The average month in 

the restricted sample has about 7,600 observations. Results in my analyses are all sample-

 3

 Consequently, if one wanted to replicate the JOLTS worker flow and vacancy rate estimates, they would 

use the appropriate CES employment estimate in the denominator. 4 Even with the noted restrictions, aggregate estimates from the unrestricted and restricted samples match

each other very closely. 

4
weighted, and often (where noted) also employment-weighted. Estimates are not 

seasonally adjusted, unless otherwise noted. 

2.B. Concepts and Definitions 

The JOLTS survey form has four major data elements: employment, hires,

separations, and job openings, with separations broken into three subcategories. Elements 

differ in their timing, and their definitions are succinct in what they do (and do not) 

capture. These definitions are created so that BLS can optimize its measurement of 

changes in the worker-firm match and to minimize respondent confusion in reporting.

(1) Employment. Establishments report their employment for the pay period that 

includes the 12th of the month. As such, it is a point-in-time measure of the employment

level. An individual is counted as employed if they are on payroll with an establishment. 

The reference period and definition are standard for all federal statistical establishment 

surveys and allows the BLS to benchmark the survey to the CES estimates. 

(2) Hires. Hires are new additions to the workforce of an establishment. They include 

new hires, re-hires, seasonal and short-term hires, recalls after a layoff, and transfers from

other worksites. Hires are a flow measure, and capture all occurrences between the first 

and end of a month. 

(3) Separations. Separations are removals from the workforce of an establishment. These 

removals include quits, layoffs lasting more than 7 days, firings and other discharges,

terminations of short-term and seasonal workers, retirements, and transfers to other 

worksites. Separations are also a flow measure, and capture all occurrences between the 

first and end of a month. 

(4) Quits. Quits are the subset of separations initiated by an employee. 

5
(5) Layoffs and Discharges. Layoffs and discharges are the subset of separations 

initiated by the employer, which include all layoffs lasting more than 7 days, firings and 

other discharges, and terminations of short-term and seasonal workers. 

(6) Other Separations. Other separations include retirements, transfers, and all other 

separations not covered by the previous two categories. 

(7) Job Openings (or Vacancies). These are all unfilled, posted positions available at an 

establishment on the last day of the month. The vacancy must be for a specific position 

that can start within 30 days, and an active recruiting process must be underway for the 

position. Vacancies are a point-in-time estimate, and its definition has two notable 

measurement implications. First, JOLTS does not capture vacancies for hires that start 

more than a month after their posting. Second, JOLTS does not capture vacancies that are 

both posted and filled within the month. Note that the unemployment measure from the 

Current Population Survey (CPS), which is also a point-in-time measure, has a similar 

feature, since it must deal with individuals who both enter and leave unemployment 

between survey periods. 

Unlike hires and separations rates, which use employment in the denominator, the 

vacancy rate uses the sum of vacancies and employment in its denominator, which is 

analogous to the sum of all available jobs. This is similar to the unemployment rate, 

which uses the labor force as its denominator (i.e., the sum of employed and unemployed 

labor). 

Note that, given the definitions of employment and labor turnover, an individual 

who stops receiving a paycheck may not count as part of employment, but also may not 

count as a separation. Examples of this occurrence include teachers, temporary help 

6
workers retained but not assigned to a particular job (i.e., “on call”), and layoffs of less

than 7 days. 

2.C. Some Notes on Research with the JOLTS Data 

The available JOLTS data already provide interesting evidence about the labor 

market, yet it remains a relatively new and evolving survey. The passage of time will 

lengthen the time series, making the survey even more useful in understanding the 

cyclical behavior of vacancies and labor turnover. Researchers should be aware that the 

JOLTS sample is only representative nationwide, by major industry, and by region. With 

a sample size of 16,000 establishments, exploiting the data at finer industrial or 

geographic detail will likely face issues of precision and selection. The multiple reference 

periods for different data elements can complicate some research studies. The survey 

does not have data on wages or other establishment characteristics, though the possibility 

exists for linking JOLTS data to other microdata sources (like the QCEW) to obtain this 

information. 

A significant issue for JOLTS is the measurement of hires and separations. 

Wohlford et al. (2003) and Faberman (2005) have BLS research aimed at understanding 

and improving their measurement. An important finding from their research is that the 

measurement of hires and separations is not as simple as theory would dictate. As noted 

earlier, the relation between hires, separations and the level of employment is 

complicated by the fact that employed workers can exist empirically in one of two states: 

employed and working, or employed but not working (where “working” is defined as on 

the payroll.) Other complications also exist—for instance, hires may occur months prior 

7
to the start of work.5

 These nuances make measuring hires and separations more difficult 

to measure than a point-in-time count of employees on payroll. 

Figure 1 illustrates the possible transitions a worker can undertake and the relative 

difficulty of measuring each, based on internal analysis by BLS program staff. As one 

might expect, the easiest flows to measure are those where an employed and working 

individual is either hired or separated. Flows that deal with employed individuals not 

currently on payroll are where measurement difficulties arise, with the greatest 

difficulties occurring where an individual separates from a job match during a period of

non-work. Wohlford et al. (2003) find that separations are disproportionately harder to 

measure, creating an asymmetry between the measurement issues of hires and 

separations. Faberman (2005) further finds that contracting establishments are less likely 

than other establishments to report any hires or separations. This asymmetry in turn 

results in a disparity between the CES employment trend and the cumulative difference 

between JOLTS hires and separations in the aggregate data. 

The BLS has taken steps (such as the creation of separate survey forms for 

schools and temporary help firms) to improve worker flow measurement. The BLS also 

continues research on JOLTS data measurement, which is obviously important for 

improving data quality, but can also prove useful in understanding the interaction 

between firms’ reporting of labor turnover and economists’ ability to measure such 

statistics. 

 5

 The JOLTS defines a hire when the work is actually started, and asks respondents to not to count a hire 

until that time.

8
3. Vacancies and the Beveridge Curve 

3.A. Aggregate Relations 

The publicly available JOLTS estimates present a wealth of new evidence for the 

aggregate labor market. While the times series is short, it spans a recession and slow 

labor market recovery, allowing researchers a glimpse of the cyclical behavior of 

vacancies and labor turnover. The National Bureau of Economic Research (NBER) dates 

a recession during this period as starting in March and ending in November of 2001, 

though losses in payroll employment (based on CES estimates) continue through July 

2003. Figure 2 illustrates the aggregate behavior of vacancies and unemployment 

between December 2000 and January 2005. The unemployment rate estimates come from

the CPS. Throughout the period, the two move in opposite directions. In 2001, 

unemployment rises while vacancies fall. Unemployment rates hover around 6 percent 

and vacancy rates remain near 2 percent for most of 2002 and 2003. Beginning in mid2003, the unemployment rate begins to fall while the vacancy rate starts to rise; these 

patterns continue into the beginning of 2005. These patterns are consistent with the 

behavior of employment growth during this period. 

An important relation in the theory of worker search and matching is the 

Beveridge Curve, which relates the cyclical movements of vacancies to those of 

unemployment. Figure 4 plots the aggregate Beveridge Curve with the vacancy rate on 

the vertical axis and the unemployment rate on the horizontal axis. The solid line 

represents the quadratic trend of the monthly vacancy-unemployment relation over the 

sample period. The dotted line charts the path of the vacancy-unemployment relation. 

The labor market begins the period relatively tight, with a ratio of vacancies to 

9
unemployment of 0.85. Vacancies then fall as unemployment rises, leading to a 

movement downward along the trend line. This pattern continues until mid-2003, when 

the unemployment rate peaks the vacancy rate reaches a trough. At this point, the ratio of

vacancies to unemployment is at a low of 0.38. The relation then “loops” around and 

moves back up along the trend line, with labor market tightness increasing as a result.

Given the economic downturn and recovery during this period, the evidence is consistent 

with the theoretical inverse relation of vacancies and unemployment predicted by the 

Beveridge Curve. 

The aggregate estimates also illustrate the more basic evidence on the magnitude, 

volatility, and comovement of these variables. Table 1 presents the aggregate means, 

standard deviations, and correlations (contemporaneous and dynamic) with relevant labor 

market variables of vacancies, hires and separations. Vacancy statistics are in the first 

column. The vacancy rate averages 2.4 percent, and exhibits relatively little volatility and 

high persistence. It is strongly negatively correlated with unemployment, strongly 

positively correlated with hires, and to a lesser extent, positively correlated with 

employment growth. The dynamic correlations of vacancies to unemployment remain 

persistently high for both lagging and leading values, with the contemporaneous 

correlation being the strongest. Theory would predict (as noted by Blanchard and 

Diamond, 1989) that vacancies should be more responsive than unemployment, which 

contrasts with this evidence. Faberman (2004) and others note, however, that the study 

period includes a recession that seems quite unlike previous recessions, particularly in 

terms of its labor dynamics. The dynamic correlations of vacancies to net growth are 

significant and positive for lagging values of net growth, but insignificant, and in some 

10
cases negative, for leading values of net growth, implying that high (low) growth is a 

good predictor of high (low) vacancies, but high (low) vacancies are not a good predictor 

of high (low) growth. 

Table 2 lists the summary statistics for vacancies, hires, and separations by

industry and region. Vacancy rates are again listed in the first column and vary 

considerably by industry; industries with high labor turnover are not necessarily the 

industries with the highest vacancy rates. Instead, vacancy rates tend to be highest in 

industries with considerable expansions during the sample period, such as professional 

and business services, and education and health services. Education and health has the

highest vacancy rate despite also having some of the lowest turnover rates.

Manufacturing, which underwent a large employment decline over this period, has one of 

the lowest vacancy rates (along with construction and resources). To a lesser extent, 

vacancies vary by region. In general, the South and West, which have relatively high 

employment growth, have higher rates of vacancies. 

3.B. Vacancy Postings and the Local Labor Market 

The JOLTS data are especially powerful in allowing a micro-level study of 

vacancies and their relation to the labor market. Most theories of labor market search 

model the relation of vacancies to unemployment as a firm-level decision whether to post 

a vacancy in response to current labor market conditions. Theory dictates that, controlling 

for outside factors, the negative aggregate relation should also hold at the micro level.

Consequently, I estimate the relation of establishment vacancy rates to local (i.e., state) 

unemployment rates.6

 This approach identifies the establishment response to local labor 

 6

 Note that there is a timing difference in the reporting of vacancies and unemployment for a given month. 

Reported vacancies are those posted at the end of the month, while the unemployed are those who actively

11
market conditions, illustrates whether a micro-level Beveridge Curve exists, and if it 

does, what shape the curve takes. 

It is useful to begin with the basic statistical properties of establishment-level 

vacancies, particularly since empirical evidence on them is sparse. Table 3 lists these 

properties for the pooled estimates of vacancy rates for establishment i in state j at month 

t (Vijt). The table lists separate vacancy rate statistics for all observations and for the 

subsample of observations with positive vacancies reported. Statistics are employmentweighted. Only 12 percent of establishment-month observations (representing 53 percent 

of employment, however) have a vacancy posted at the end of the month. This statistic is 

somewhat misleading, however, since at the monthly frequency many establishments 

have no net change in employment (79 percent) or hires (81 percent), and hence likely do 

not need a vacancy posting. Nevertheless, conditional on changing employment levels, 

only 34 percent of establishment-month observations (representing 67 percent of 

employment) have a vacancy posted at the end of the month. The vacancy rate for these 

observations is nearly double the rate for all observations. When looking at these 

statistics, remember that the JOLTS vacancy definition does not capture long-term

vacancy postings or vacancies that are posted and filled within the month. Nevertheless, 

the statistics may reflect the fact that establishments use less formal hiring practices than 

vacancies with some frequency, or that some establishments may have relatively short 

vacancy durations. Davis, Faberman, and Haltiwanger (2006) explore these conjectures. 

Table 3 also shows that state and month effects explain little of the vacancy 

variation. These effects account for less than 1 percent of the establishment vacancy 

 looked for work in the four weeks prior to the week of the 19th. This is true for both national and state-level 

unemployment. Thus, the vacancy rates used in this study will lead unemployment rates by about two

weeks. 

12
variation. Instead, establishment effects account for 41 percent of the variation of all 

vacancies and 66 percent of the variation conditional on an establishment’s posting of at 

least one vacancy. This suggests that much of the micro-level variation stems from

different vacancy-posting behaviors among establishments rather than varying behaviors 

within local labor markets or during certain points in the business cycle. 

I explore the relation between establishment vacancy postings and state 

unemployment by using my pooled sample to regress establishment vacancy rates on 

state unemployment rates. The unemployment rates come from the BLS Local Area 

Unemployment Statistics (LAUS) data, which use CPS and other data sources to produce 

estimates. In terms of magnitudes, unemployment rates for many states are similar to the 

national rate, though the average rates for several states are several percentage points 

higher or lower than the national rate. The cyclical volatility of unemployment for some

states also tends to be higher than volatility at the national level. I expect the state-level 

relation of vacancies to unemployment to be nonlinear, and use a fourth-order polynomial 

of unemployment in my regressions as a result. Nonparametric analyses of the data (not 

reported here) suggest that a polynomial of this order fits the data well.7

 I weight the 

regressions by employment and run separate regressions that include state and 

establishment fixed effects. 

I plot the predicted relations of vacancies to unemployment from these 

regressions in Figure 4. There are separate predicted trends for the unconditional relation, 

the relation with state effects removed, and the relation with establishment effects 

removed. As expected, vacancy postings are inversely related to the local unemployment 

 7

 These nonparametric tests include measures of average vacancies along the unemployment rate 

distribution, and local regression estimation of the same distribution. Both approaches give similar, albeit 

less smooth, trends in the data. 

13
rate. The polynomial coefficients for each regression are all jointly significant at the 5 

percent level. The relation is steeper once I control for state or establishment effects. This 

is likely due to the large variation in trend unemployment across states.8

 This suggests 

that not controlling for this trend variation tends to understate the responsiveness of 

vacancies to unemployment. It also suggests that the covariation of vacancies and 

unemployment occurs more from time-variation within states than from level differences 

across states. Controlling for establishment rather than state effects, however, makes little 

difference for the results. This suggests that much of the between-establishment variation 

in the relation is between states, and not necessarily between establishments within states. 

Overall, the results suggest that a Beveridge Curve relation in fact exists at both the micro 

and aggregate levels. 

4. Labor Turnover and the Labor Market 

4.A. Aggregate Evidence 

The JOLTS data also track labor turnover. Figure 5 plots the time series of 

aggregate hires and separations rates over the sample period. Their patterns reflect the 

downturn and recovery during this time. Hires decline during the recession and remain 

low through mid-2003. The hiring rate then begins a gradual, steady increase though the 

start of 2005. Separations are high throughout most of 2001. They then decrease in early 

2002, and reach a low in mid-2003. Separations then increase gradually through the end 

of the sample period, even though net growth is strong during this time; evidence not 

reported here shows that movements in the quits rate drive this increase. In addition, the 

 8

 Note that state fixed effects are a subset of establishment fixed effects, in the sense that establishments 

cannot change their location in the data. Consequently, establishment effects will identify both state trend 

variations and between-establishment variations within a state. 

14
patterns of hires and separations closely follow the patterns of gross job gains and gross 

job losses estimates, respectively, from the Business Employment Dynamics (BED) 

program.

9

As with vacancies, the aggregate time-series and industry estimates of labor 

turnover are summarized in Tables 1 and 2, respectively. Table 1 shows that over this 

period the hires rate averages 3.3 percent while the separations rate averages 3.2 percent. 

More than half (54 percent) of separations, on average, are quits. Hires and separations 

are both negatively correlated with unemployment—the latter correlation comes 

primarily from a negative correlation of quits with unemployment. Layoffs are 

uncorrelated with unemployment, but strongly negatively correlated with employment 

growth, leading to a negative correlation between growth and total separations. Hires are 

positively correlated with growth, but quits are essentially uncorrelated with growth. 

Hires, quits, and vacancies are all strongly positively correlated with each other. Hires 

and quits exhibit considerable persistence, while layoffs exhibit little to no persistence. 

The latter is consistent with the notion that layoffs tend to be episodic events rather than 

persistent dynamic processes. The dynamic correlations suggest that hires are a leading

factor for lower future unemployment. The contemporaneous correlation between quits 

and unemployment is stronger than either the lagging or leading dynamic correlations. 

The same can be said of the contemporaneous correlation between layoffs and 

employment growth and their dynamic correlations. 

Because of the short sample period, one should interpret the time-series 

correlations with caution. Nevertheless, the patterns illustrated, particularly by quits and 

 9

 For further aggregate evidence, see Spletzer et al. (2004), Clark (2004), and Davis, Faberman, and

Haltiwanger (2005). 

15
layoffs, shed some light on the cyclical behavior of labor turnover. Hires and quits are 

clearly procyclical, though the latter are more related to unemployment than job growth. 

Layoffs, on the other hand, are countercyclical, but only with respect to job growth—they 

seem to have little relation to the stock of unemployment. This evidence has implications 

for the recent debate on the cyclicality of separations and the job-finding rate of workers 

discussed by Hall (2006) and Shimer (2005b). They argue that the job-finding rate, and 

not necessarily the separations rate, drives cyclical movements in unemployment. The 

correlations in Table 1 support that claim, but only to the extent that movements in the 

quits rate drives the relationship between separations and unemployment. This suggests 

that a) separations and the job-finding rate are not mutually exclusive and b) the relative 

importance of separations versus the job-finding rate may depend critically on the 

cyclical behavior of employer-to-employer flows (described by Fallick and Fleishmann, 

2004), since quits tend to make up a large fraction of these flows. 

Table 2 illustrates that labor turnover patterns vary widely by industry and, to a 

lesser extent, by region. The industry evidence is consistent with the findings of 

Anderson and Meyer (1994) and Burgess, Lane, and Stevens (2000). Turnover is highest 

in seasonal industries, such as construction and leisure and hospitality, and low in other 

industries, such as manufacturing and government. Turnover is also slightly higher in the 

South and West than in the Northeast and Midwest. Industries and regions also vary 

widely in the share of their separations accounted for by quits. Goods-producing 

industries (resources, construction, manufacturing) tend to have most separations come

from layoffs, while service and retail industries tend to have most separations come from

16
quits. The Northeast and Midwest, which have relatively higher shares of goodsproducing industries, have relatively high shares of separations due to layoffs. 

The across-industry correlations suggest that both vacancies and growth are 

positively related to the share of separations made up by quits. Intuitively, expanding 

industries should have less layoffs, all else equal. The correlations also illustrate that 

high-turnover industries tend to have high rates of hires, quits, and layoffs. 

4.B. Labor Turnover and Establishment Growth 

Hires, quits, and layoffs are the result of continuous, dynamic interactions 

between workers and firms. In any period, a worker with a better job offer may choose to 

quit a successful, expanding firm at the same time a declining firm looks to hire new 

employees as it restructures its workforce. Anecdotal evidence of such occurrences is 

quite common. Yet, even with aggregate data on labor turnover, it is difficult to know 

what role, if any, such interactions play in the cyclical behavior of hires and separations. 

Luckily, the JOLTS microdata allow an analysis that can relate the behavior of 

labor turnover to the behavior of establishments. When, how, and to what extent 

establishments create or destroy jobs has been a topic of research for nearly two decades 

(e.g., Dunne, Roberts, and Samuelson, 1989a, 1989b; Davis and Haltiwanger, 1990, 

1992). Evidence from this research shows that each period, many establishments 

simultaneously expand, contract, start up, and shut down, but the evidence says little 

about the relation between these firm-level decisions and patterns of labor turnover. To 

explore this relation, I split the sample of JOLTS microdata into three groups: 

establishments with expanding employment (i.e., more hires than separations), those with 

contracting employment (i.e., more separations than hires), and those with constant 

17
employment (i.e., offsetting hires and separations). I then calculate the aggregate monthly 

labor turnover estimates for each group, using factors calculated from the public JOLTS 

estimates to seasonally adjust the data. 

Figures 6 and 7 show the patterns of hires and separations, respectively, by type 

of employment change. The figures show analogous pictures. Expanding establishments 

have high hires rates, while contracting establishments have high separations rates. These 

rates are also considerably more volatile than the other labor turnover series, with 

standard deviations that are between 1.5 and 3.6 times greater than those for the other 

groups. Second, separation rates at expanding establishments and hiring rates at 

contracting establishments are both higher than the rates at stable establishments. Stable 

establishments also have the least volatile turnover rates, suggesting that they are stable 

in both their employment levels and their within-establishment churning. This evidence 

suggests that the relation of establishment-level hires and separations to net growth is 

nonlinear and nonmonotonic—contracting establishments have more hires than those 

with no net growth and expanding establishments have more separations than those with 

no net growth. Finally, even though Figure 5 shows a long, persistent drop in hiring 

during the downturn and a mild pickup in separations during the recession, the series 

depicted in Figures 6 and 7 show little to no cyclical variation—the only exception is a 

moderate movement of the separations rate among contracting establishments during the 

2001 recession and during the 2003-04 recovery period. How can the evidence in the two 

figures be reconciled? As Davis, Faberman, and Haltiwanger (2005) illustrate, cyclical 

shifts in the distribution of establishment growth account for the differences between the 

18
figures.10 In other words, as the aggregate labor turnover rates change over the business 

cycle, it is not the case that all establishments are increasing or decreasing their own hires 

and separations. Rather, the shares of establishments that are expanding, contracting or 

remaining the same adjust over the business cycle, while labor turnover rates within each 

group remain essentially unchanged.

Figures 8 and 9 show two notable caveats for quits and layoffs. In Figure 8, the 

quits rate indeed mimics its aggregate behavior among contracting establishments and, to 

a lesser extent, expanding establishments. In Figure 9, layoffs among contracting 

establishments exhibit a mild spike in late 2001, but are otherwise acyclical. The cyclical 

behavior of employer-to-employer flows and episodic layoff events may account for 

these patterns, though further research is needed on the topic.

Table 4 summarizes the labor turnover rates for different growth rate intervals. 

The results underscore the patterns observed in the previous figures with several notable 

observations. First, quit rates exceed layoff rates for all but the largest contractions 

among establishments. Second, quit rates are relatively high for all contracting 

establishments—it is not the case that job losses at contracting establishments stem

primarily from layoffs. Finally, there is an asymmetry between the two extremes of the 

growth rate distribution: separations at expanding establishments are considerably higher 

than hires at contracting establishments. This may be suggestive of a shakeout process 

within the hiring patterns of expanding establishments, though further research on the 

topic is warranted. 

 10 Davis et al. also note that the patterns illustrated are robust to size, industry and establishment controls. 

19
4.C. Worker Flow Relations to the Local Labor Market 

Understanding how labor turnover relates to local labor market conditions can be 

an important aspect of understanding aggregate movements in labor turnover. With its 

ability to distinguish between quits and layoffs and its identification of an establishment’s 

state, the JOLTS data provide a unique opportunity to study local labor turnover. One 

basic yet important question is how local labor turnover relates to local unemployment. In 

particular, it would be useful to know whether hires, quits, and layoff rates have the same

relations to unemployment at the local level that they do at the aggregate level. 

Table 5 reports the basic relations of pooled establishment-month observations of 

hires (Hijt), quits (Qijt), and layoffs and discharges (Lijt) to state labor market statistics. 

These statistics include the state unemployment rate (obtained from LAUS data), its 

change from the previous month (∆Ujt), and the state employment growth rate (Njt). The 

last statistic uses estimates from the CES. Note that the reported correlations seem very 

weak, yet nearly all are significant at the 5 percent level. This is a consequence of using 

pooled establishment observations, which tend to have large idiosyncratic components to 

their variation regardless of the variable examined.11 Even establishment fixed effects 

only explain between 21 and 29 percent of the variations of these flows (state-month 

effects explain 1 to 2 percent). The most relevant characteristics of these correlations are 

their sign and their magnitudes relative to each other. The evidence suggests a procyclical 

pattern for establishment hires and quits and a countercyclical pattern for layoffs at the 

state level—higher growth, lower unemployment, and decreases in unemployment are 

related to more hires and less quits. Layoffs are negatively related to growth and 

 11 Ideally, I would calculate state level estimates of labor turnover and use them to estimate the reported 

correlations. Unfortunately, the JOLTS sample size and weighting structure do not allow for reliable 

estimates below the detail of its four geographic regions. 

20
positively related to increases in unemployment, but they are essentially uncorrelated 

with the unemployment level—this is no surprise given the national evidence. 

I also estimate the micro-level relations of hires, quits, and layoffs to the change 

in the state unemployment rate. I focus on the change in unemployment rather than the 

unemployment level since it is akin to a flow measure—the unemployment rate is a stock 

measure, which makes it comparable to the vacancy rate (which is a point-in-time 

estimate), but less comparable to the labor turnover measures (which are flow 

estimates).12 I regress each establishment-month observation on a fourth-order 

polynomial of ∆Ujt, weighting the regressions by employment, separately for each of the 

three labor turnover rates.13 As before, I perform separate regressions for the 

unconditional relation, and the relations with state and establishment effects removed. 

Figures 10, 11 and 12 plot the results for hires, quits, and layoffs, respectively. 

Figure 10 shows that establishments hire less when the local unemployment rate is rising. 

The relation is nonlinear, with hires most responsive to large decreases in unemployment. 

Figure 11 shows that quits also decrease as unemployment rises. This relationship is also 

nonlinear, with quits most responsive to large increases in unemployment. In Figure 12, 

layoffs increase with increases in local unemployment. The relationship is close to linear. 

This micro-level evidence parallels the patterns in the aggregate evidence. Controlling for 

state or establishment fixed effects does not alter these results. 

 12 Note that the change in unemployment is the net effect of the flows into unemployment and flows out of

unemployment. 13 As with the regressions of the previous section, the fourth-order polynomial results are consistent with 

similar nonparametric fits of the data (see note 7). 

21
5. Conclusions and Further Research Potential 

The JOLTS data provide a wealth of labor market information at both the 

aggregate and micro levels. The data are the most comprehensive data source for 

vacancies in the US, and have the timeliest, most frequent, and most direct measures of 

labor turnover. While the time series is short, the aggregate JOLTS estimates already 

present rich new evidence on the cyclical and secular behavior of its statistics. Vacancies, 

hires, and quits all exhibit persistent, procyclical behavior between 2001 and 2005, while 

layoffs exhibit an episodic, countercyclical pattern. Vacancies also exhibit a cyclical 

relation to unemployment consistent with the Beveridge Curve. The micro-level 

estimates provide several new insights into the behavior of vacancies and worker flows. 

Establishment-level vacancy postings are negatively related to local unemployment rates, 

suggesting that the Beveridge Curve relation holds even at the micro level. This result 

holds even though many establishments (even the ones who change employment levels) 

often do not post vacancies. Expanding establishments have high hiring rates while 

contracting establishments have high separation rates. Stable establishments, while 

exhibiting a steady pattern of worker churning, have the lowest rates of labor turnover. 

The evidence suggests nonlinear, nonmonotonic relations of hires and separations to 

establishment growth. Finally, the evidence suggests that hires are strongly related to 

changes in local unemployment rates, falling nonlinearly with increases in 

unemployment. Quits also fall with increases in the local unemployment rate, while 

layoffs rise with these increases. 

These findings barely scratch the surface of what the JOLTS data can say about 

the labor market. I highlight three areas where the aggregate estimates and microdata can 

22
aid labor market research. The first is vacancy postings. Earlier works, such as Abraham

(1987) and Blanchard and Diamond (1989, 1990), study vacancies and their relation to 

unemployment using estimates from the Help Wanted Index. The JOLTS vacancy data 

has a major advantage over this index (and others like it) in that it is reported directly by 

firms. This provides a representative, tangible measure of job openings and allows microlevel studies of firm vacancy posting behavior similar to previous work by Holzer (1994) 

and current work by Davis, Faberman, and Haltiwanger (2006). Evidence in this chapter 

already suggests that the micro-patterns of firms who post vacancies may differ from

existing theories of their behavior. 

The second area of potential research deals with separations. The JOLTS data can 

aid in further understanding separations since it differentiates between quits and layoffs. 

This is important for macroeconomic analyses of labor turnover, since quits tend to be 

procyclical, while layoffs are countercyclical. The distinction between quits in layoffs

and its importance for labor market movements is highlighted by the models of Akerlof, 

Rose and Yellen (1988) and McLaughlin (1991). The role of separations in business 

cycle fluctuations is also a topic of recent debate. Namely, do separations matter much 

for labor market fluctuations, or are movements primarily driven by changes in the jobfinding rate? This is a question recently addressed in research by Hall (2006) and Shimer 

(2005b). The evidence in this chapter suggests that the answer is not a straightforward 

“either-or”, as the quits that stem from the employer-to-employer flow of workers are 

closely intertwined with movements in the job-finding rate. 

A final area of potential research deals with labor turnover more broadly. The 

aggregate national, regional, and industry estimates already present many new findings. 

23
Future work with these and the micro-level estimates can build on the earlier work of

Anderson and Meyer (1994), Burgess, Lane, and Stevens (2000), and others. The 

existence of vacancy, employment, and worker flow data reported by each establishment 

allows a micro-level study of their interactions that was previously impossible, but 

essential for understanding labor market search and the matching of workers to firms. 

Research on the relation between worker flows and firm behavior also relates naturally to 

research on the relation between worker turnover and the patterns of job reallocation, as 

evidenced by research by Davis, Faberman, and Haltiwanger (2005). Overall, the JOLTS 

data provide many opportunities to increase our understanding of labor market dynamics. 

References

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unemployment.” Brookings Papers on Economic Activity, 207-43. 

Akerlof, George A., Rose, Andrew K., and Yellen, Janet L., 1988. “Job switching and job 

satisfaction in the U.S. labor market.” Brookings Papers on Economic Activity, 2, 495-

594. 

Anderson, Patricia and Meyer, Bruce R., 1994. “The extent and consequences of job 

turnover.” Brookings Papers on Economic Activity, Microeconomics: 177-249. 

Blanchard, Oliver J., and Diamond, Peter, 1989. “The Beveridge Curve.” Brookings 

Papers on Economic Activity, Vol. 2: 1-60. 

Blanchard, Oliver J., and Diamond, Peter, 1990. “The cyclical behavior of the gross 

flows of U.S. workers.” Brookings Papers on Economic Activity, Vol. 2: 85-143. 

Burgess, Simon, Lane, Julia I., and Stevens, David, 2000. “Job flows, worker flows, and 

churning.” Journal of Labor Economics 18(3): 473-502. 

Clark, Kelly A., 2004. “The Job Openings and Labor Turnover Survey: What initial data 

show.” Monthly Labor Review 127(11): 14-23. 

Clark, Kelly A., and Hyson, Rosemary, 2001. “New tools for labor market analysis: 

JOLTS.” Monthly Labor Review 124(12): 32-37. 

24
Davis, Steven J., Faberman, R. Jason, and Haltiwanger, John C., 2005. “The Flow 

Approach to Labor Markets: New Data Sources, Micro-Macro Links and the Recent 

Downturn.” IZA Working paper #1639. 

Davis, Steven J., Faberman, R. Jason, and Haltiwanger, John C., 2006. “Vacancies, 

Worker Flows and Job Flows: Market-Level and Micro-Level Relationships.” mimeo. 

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Microeconomic evidence and macroeconomic implications.” In NBER Macroeconomics 

Annual 5 (pp. 123-68). Cambridge, MA: National Bureau for Economic Research. 

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and employment reallocation.” Quarterly Journal of Economics 107(3): 819-63. 

Davis, Steven and Haltiwanger, John C., 1999. “Gross job flows.” In Orley Ashenfelter 

and David Card (eds.), Handbook of Labor Economics, Volume 3 (pp. 2711-2805). 

Amsterdam: Elsevier Science. 

Dunne, Timothy, Roberts, Mark J., and Samuelson, Larry, 1989a. “Plant turnover and 

gross employment flows in the U.S. manufacturing sector.” Journal of Labor Economics 

7(1): 48-71.

Dunne, Timothy, Roberts, Mark J., and Samuelson, Larry, 1989b. “The growth and 

failure of U.S. manufacturing plants.” Quarterly Journal of Economics 104(4): 671-98. 

Faberman, R. Jason, 2004. “Gross Job Flows over the Past Two Business Cycles: Not all 

‘Recoveries’ are Created Equal.” BLS Working Paper No. 372. 

Faberman, R. Jason, 2005. “Analyzing the JOLTS Hires and Separations Data.” 2005 

Proceedings of the Annual Statistical Association [CD-ROM]. Alexandria, VA: 

American Statistical Association. 

Fallick, Bruce and Charles A. Fleischman, 2004, “Employer-to-Employer Flows in the 

U.S. Labor Market: The Complete Picture of Gross Worker Flows,” mimeo. 

Hall, Robert E., 2005. “Employment Fluctuations with Equilibrium Wage Stickiness,”

American Economics Review, 95(1): 50-65. 

Hall, Robert E., 2006. “Job Loss, Job Finding, and Unemployment in the U.S. Economy 

over the Past Fifty Years,” forthcoming, 2005 NBER Macroeconomics Annual. 

Cambridge, MA: National Bureau for Economic Research. 

Holzer, Harry J., 1994. “Job vacancy rates in the firm: An empirical analysis.” 

Economica 61(1): 17-36. 

25
McLaughlin, Kenneth J., 1991. “A theory of quits and layoffs with efficient turnover.” 

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Mortensen, Dale T., and Pissarides, Christopher A., 1994. “Job creation and job 

destruction and the theory of unemployment.” Review of Economic Studies 61(3): 397-

415. 

Mortensen, Dale T., and Pissarides, Christopher A., 1999. “New Developments in 

Models of Search in the Labor Market.” in Orley Ashenfelter and David Card (eds.), 

Handbook of Labor Economics, Volume 3 (pp. 2567-2628). Amsterdam: Elsevier 

Science. 

Shimer, Robert, 2005a. “The Cyclical Behavior of Equilibrium Unemployment and 

Vacancies,” American Economic Review 95(1): 25-49. 

Shimer, Robert, 2005b. “Reassessing the Ins and Outs of Unemployment,” mimeo, 

University of Chicago. 

Spletzer, James R., R. Jason Faberman, Akbar Sadeghi, David M. Talan, and Richard L. 

Clayton, “Business Employment Dynamics: New Data on Gross Job Gains and Losses,” 

Monthly Labor Review 127:4 (2004), 29-42. 

Wohlford, John, Phillips, Mary Anne, Clayton, Richard, and Werking, George, 2003.

“Reconciling labor turnover and employment statistics.” 2003 Proceedings of the Annual 

Statistical Association [CD-ROM]. Alexandria, VA: American Statistical Association.

26
Figure 1.

Measurement Issues with Labor Turnover and Employment 

Figure 2. 

Vacancy and Unemployment Rates, December 2000 – January 2005 

0.015

0.020

0.025

0.030

0.035

0.040

0.045

0.050

0.055

0.060

0.065

0.070

20001

2

20010320010

6

20010

9

20011

2

20020320020

6

20020

9

20021

2

20030320030

6

20030

9

20031

2

20040320040

6

20040

9

20041

2

Vacancy Rate

Unemployment Rate

Source: Vacancies are from public JOLTS nonfarm estimates and unemployment is from the CPS. Both are 

seasonally adjusted. 

27
Figure 3. 

Vacancy vs. Unemployment Rates (Beveridge Curve), Dec. 2000 – Jan. 2005 

0.020

0.025

0.030

0.035

0.035 0.040 0.045 0.050 0.055 0.060 0.065

Unemployment

Vacancies

Dec. 00

Jan. 05

Source: Vacancies are from public JOLTS nonfarm estimates and unemployment is from the CPS. Both are 

seasonally adjusted. The dotted line represents the time-series path of the unemployment-vacancies

relation, while the solid line represents the quadratic trend of the relation. 

Figure 4. 

Establishment Vacancies and Their Relation to the Local Unemployment 

0.015

0.017

0.019

0.021

0.023

0.025

0.027

0.029

0.0350.0370.0390.0410.0430.0450.0470.0490.0510.0530.0550.0570.0590.0610.0630.0650.0670.069

Ujt

Vijt

Unconditional State Effects Removed Establishment Effects Removed

Source: Author’s estimation of establishment vacancy rates on a fourth order polynomial of the state 

unemployment rate using JOLTS establishment microdata and LAUS unemployment estimates. State and 

establishment fixed effects are used where noted. See text for details.

28
Figure 5. 

Hires and Separations Rates, December 2000 – January 2005 

0.025

0.030

0.035

0.040

20001

2

20010320010

6

20010

9

20011

2

20020320020

6

20020

9

20021

2

20030320030

6

20030

9

20031

2

20040320040

6

20040

9

20041

2

Hires Rate

Separations Rate

Source: Public JOLTS nonfarm estimates, seasonally adjusted. 

Figure 6. 

Hiring Rates by Type of Establishment-Level Employment Change 

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

200101200104200107200110200201200204200207200210200301200304200307200310200401200404200407200410200501

Expanding Establishments Stable Establishments Contracting Establishments

Source: Author’s tabulations of JOLTS microdata. Estimates are seasonally adjusted using factors from the 

aggregate public estimates.

29
 Figure 7. 

Separation Rates by Type of Establishment-Level Employment Change 

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

20010120010

4

20010720011020020120020

4

20020720021020030120030

4

20030720031020040120040

4

200407200410200501

Expanding Establishments Stable Establishments Contracting Establishments

Source: Author’s tabulations of JOLTS microdata. Estimates are seasonally adjusted using factors from the 

aggregate public estimates.

Figure 8. 

Quit Rates by Type of Establishment-Level Employment Change 

0.00

0.01

0.02

0.03

0.04

20010120010

4

20010720011020020120020

4

20020720021020030120030

4

20030720031020040120040

4

200407200410200501

Expanding Establishments Stable Establishments Contracting Establishments

Source: Author’s tabulations of JOLTS microdata. Estimates are seasonally adjusted using factors from the 

aggregate public estimates. 

30
Figure 9. 

Layoff Rates by Type of Establishment-Level Employment Change 

0.00

0.01

0.02

0.03

0.04

20010120010

4

20010720011020020120020

4

20020720021020030120030

4

20030720031020040120040

4

200407200410200501

Expanding Establishments Stable Establishments Contracting Establishments

Source: Author’s tabulations of JOLTS microdata. Estimates are seasonally adjusted using factors from the 

aggregate public estimates. 

Figure 10. 

Establishment Hirings and Their Relation to Changes in Local Unemployment 

0.030

0.031

0.032

0.033

0.034

0.035

0.036

-0.010-0.008-0.006-0.004-0.002

0.0000.0020.0040.0060.0080.010

∆Ujt

Hijt

Unconditional State Effects Removed Establishment Effects Removed

Source: Author’s estimation of establishment vacancy rates on a fourth order polynomial of the state 

unemployment rate using JOLTS establishment microdata and LAUS unemployment estimates. State and 

establishment fixed effects are used where noted. See text for details.

31
Figure 11.

Establishment Quits and Their Relation to Changes in Local Unemployment 

0.015

0.016

0.017

0.018

0.019

0.020

-0.010-0.0

0

8

-0.0

0

6

-0.0

0

4

-0.0

0

2

0.0000.0020.0040.0060.0080.010

∆Ujt

Qijt

Unconditional State Effects Removed Establishment Effects Removed

Source: Author’s estimation of establishment vacancy rates on a fourth order polynomial of the state 

unemployment rate using JOLTS establishment microdata and LAUS unemployment estimates. State and 

establishment fixed effects are used where noted. See text for details.

Figure 12. 

Establishment Layoffs and Their Relation to Changes in Local Unemployment 

0.010

0.011

0.012

0.013

-0.010-0.008-0.006-0.004-0.002

0.0000.0020.0040.0060.0080.010

∆Ujt

Lijt

Unconditional State Effects Removed Establishment Effects Removed

Source: Author’s estimation of establishment vacancy rates on a fourth order polynomial of the state 

unemployment rate using JOLTS establishment microdata and LAUS unemployment estimates. State and 

establishment fixed effects are used where noted. See text for details.

32
Table 1. 

Vacancy and Labor Turnover Aggregate Summary Statistics 

Vacancies 

(Vt) 

Hires 

(Ht)

Separations

(St)

Quits 

(Qt) 

Layoffs 

(Lt) 

Mean 

(Standard Deviation) 

0.024 

(0.003) 

0.033 

(0.002) 

0.032 

(0.002) 

0.018 

(0.002) 

0.014 

(0.001) 

Correlation with... 

 Unemployment (Ut) -0.97* -0.78* -0.77* -0.93* 0.05 

 Net Growth (Nt) 0.22 0.54* -0.29* 0.06 -0.75* 

 Vacancies (Vt) 1.00 0.82* 0.73* 0.92* -0.12 

 Hires (Ht) 1.00 0.68* 0.83* -0.13 

Autocorrelations 

 AR(1) 0.97* 0.77* 0.78* 0.93* 0.37* 

 AR(2) 0.94* 0.68* 0.79* 0.91* 0.37* 

 AR(3) 0.90* 0.63* 0.64* 0.84* 0.00 

Dynamic Correlations with Unemployment

 Ut−3 -0.86* -0.60* -0.88* -0.88* -0.50*

 Ut−2 -0.91* -0.67* -0.85* -0.91* -0.31*

 Ut−1 -0.95* -0.73* -0.84* -0.92* -0.21*

 Ut -0.97* -0.78* -0.77* -0.93* 0.05

 U t+1 -0.96* -0.84* -0.74* -0.92* 0.09

 Ut+2 -0.95* -0.85* -0.69* -0.90* 0.16

 Ut+3 -0.93* -0.89* -0.60* -0.84* 0.24

Dynamic Correlations with Net Growth

 Nt−3 0.43* 0.45* 0.13 0.31* -0.25 

 Nt−2 0.37* 0.38* -0.04 0.21 -0.49* 

 Nt−1 0.36* 0.37* 0.02 0.21 -0.37* 

 Nt 0.22 0.54* -0.29* 0.06 -0.75* 

 N t+1 0.05 0.14 -0.25 -0.09 -0.38* 

 Nt+2 -0.14 0.04 -0.42* -0.29* -0.38* 

 Nt+3 -0.28 -0.09 -0.41* -0.39* -0.18 

Source: Author’s calculations based on public JOLTS and CPS aggregate data (seasonally adjusted). Net 

growth rates are the difference between the hires and separations rates. Statistics are based on data from

December 2000 through January 2005. Asterisks (*) denote significance at the 5 percent level. 

33
Table 2. 

Vacancy and Labor Turnover Summary Statistics by Industry and Region 

Vacancies

(Vj)

Hires 

(Hj)

Separations

(Sj)

Quits 

(Qj)

Layoffs 

(Lj)

Quit Share

(Qj / Sj) 

Major Industry 

Resources 0.011 

(0.003) 

0.031 

(0.008) 

0.031 

(0.006) 

0.013 

(0.004) 

0.013 

(0.006) 0.421 

Construction 0.014 

(0.004) 

0.054 

(0.013) 

0.055 

(0.007) 

0.020 

(0.004) 

0.033 

(0.008) 0.370 

Manufacturing 0.014 

(0.003) 

0.022 

(0.004) 

0.027 

(0.004) 

0.012 

(0.002) 

0.012 

(0.003) 0.445 

Transportation & 

Utilities 

0.016 

(0.003) 

0.025 

(0.005) 

0.026 

(0.003) 

0.013 

(0.002) 

0.011 

(0.003) 0.500 

Retail Trade 0.019 

(0.004) 

0.044 

(0.009) 

0.043 

(0.007) 

0.027 

(0.005) 

0.013 

(0.005) 0.626 

Information 0.020 

(0.005) 

0.021 

(0.004) 

0.023 

(0.005) 

0.013 

(0.003) 

0.008 

(0.003) 0.577 

Financial Activities 0.021 

(0.002) 

0.022 

(0.004) 

0.023 

(0.004) 

0.013 

(0.003) 

0.007 

(0.002) 0.589 

Prof. & Business 

Services 

0.029 

(0.005) 

0.043 

(0.006) 

0.039 

(0.007) 

0.020 

(0.004) 

0.016 

(0.004) 0.512 

Education & Health 0.033 

(0.005) 

0.027 

(0.005) 

0.023 

(0.004) 

0.015 

(0.003) 

0.007 

(0.002) 0.638 

Leisure & 

Hospitality 

0.028 

(0.006) 

0.063 

(0.013) 

0.059 

(0.011) 

0.039 

(0.008) 

0.018 

(0.005) 0.661 

Other Services 0.019 

(0.004) 

0.032 

(0.007) 

0.032 

(0.009) 

0.019 

(0.004) 

0.011 

(0.006) 0.593 

Government 0.018 

(0.003) 

0.015 

(0.005) 

0.012 

(0.004) 

0.006 

(0.002) 

0.004 

(0.002) 0.488 

Region

Northeast 0.021 

(0.003) 

0.029 

(0.006) 

0.028 

(0.005) 

0.014 

(0.003) 

0.012 

(0.003) 0.498 

Midwest 0.020 

(0.003) 

0.032 

(0.006) 

0.031 

(0.005) 

0.017 

(0.004) 

0.012 

(0.002) 0.549 

South 0.023 

(0.003) 

0.035 

(0.005) 

0.034 

(0.004) 

0.020 

(0.003) 

0.012 

(0.002) 0.585 

West 0.022 

(0.004) 

0.033 

(0.005) 

0.033 

(0.004) 

0.018 

(0.003) 

0.013 

(0.002) 0.545 

Across-Industry Correlations with...

Net Growth (Nj) 0.74* 0.23 0.05 0.21 -0.20 0.47 

Vacancies (Vj) 1.00 0.33 0.21 0.38 -0.07 0.66* 

Hires (Hj) 1.00 0.98* 0.94* 0.80* 0.32 

Source: Author’s tabulations from JOLTS data. Net growth rates are the difference between the hires and

separations rates. Means are reported, with standard deviations in parentheses. Statistics are based on data

from December 2000 through January 2005. Asterisks (*) denote significance at the 5 percent level. 

 

34
Table 3. 

Local Unemployment and Establishment Vacancy Summary Statistics 

All Establishments Establishments with Positive 

Vacancies Only 

Mean 0.021 0.040

Standard Deviation 0.039 0.046 

Median 0.003 0.026

10th, 90th Percentiles 0.000, 0.063 0.005, 0.089 

Number of Observations 372,288 175,981 

Share of Employment 

[Estabs.] with Vijt > 0 

0.533 

[0.122] NA 

Share of Empl. [Estabs.] 

with Vijt > 0 | Net ≠ 0 

0.674 

[0.336] NA 

Percent of Variation Explained by... 

 Month Effects 0.5 0.8 

 State Effects 0.7 0.6 

 Establishment Effects 40.7 66.0 

Source: Author’s tabulations from pooled JOLTS microdata. Estimates are based on data from December 

2000 through January 2005. Estimates (except the share of establishments with positive vacancies) are 

weighted by employment.

Table 4. 

Labor Turnover Rates by Establishment Growth Rate Interval 

Net Growth Interval 

(Nijt) 

Hiring Rate

(Hijt)

Separations Rate

(Sijt)

Quits Rate

(Qijt)

Layoffs Rate

(Lijt) 

(-2, -0.3) 0.018 0.554 0.132 0.393

[-0.3, -0.1) 0.028 0.191 0.089 0.088

[-0.1, 0) 0.017 0.039 0.023 0.013

0 0.011 0.011 0.008 0.003

(0, 0.1) 0.042 0.019 0.013 0.005

[0.1, 0.3) 0.199 0.037 0.024 0.017

[0.3, 2) 0.541 0.034 0.020 0.013

Source: Author’s tabulations from pooled JOLTS microdata. Estimates are based on data from December 

2000 through January 2005. Estimates are weighted by employment. 

35
Table 5. 

Establishment Labor Turnover Variation and Local Labor Market Conditions 

Hiring Rate

(Hijt)

Quits Rate

(Qijt)

Layoffs Rate 

(Lijt) 

Pooled Correlation with...

Net Growth Rate (Njt) 0.026* 0.008* -0.009* 

Unemployment (Ujt) -0.025* -0.036* 0.001

Unemployment Change (∆Ujt) -0.012* -0.010* 0.009*

Percent of Variation Explained by... 

Establishment Effects 28.5 27.9 21.0 

State × Month Effects 1.9 2.2 1.1 

Source: Author’s tabulations from pooled JOLTS microdata (worker flows), supplemented by LAUS state 

data (unemployment), and CES state data (net growth). Estimates are based on data from December 2000

through January 2005. All estimates are weighted by employment. The variations explained are from the 

regression of each worker flow estimate on either 14,573 establishment effects or 1,887 state × month 

effects. Asterisks (*) denote significance at the 5 percent level.

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