32 Monthly Labor Review December 2001

Job Openings and Labor Turnover Job Openings and Labor Turnover

New tools for labor market

analysis: JOLTS

As a single, direct source for data

on job openings, hires, and separations,

the Job Openings and Labor Turnover Survey (JOLTS)

should be a useful indicator of the demand for labor

in the U.S. labor market and of other economic conditions

Kelly A. Clark

and

Rosemary Hyson

Kelly A. Clark is an

economist and

Rosemary Hyson is a

research economist in

the Office of

Employment and

Unemployment

Statistics, Bureau of

Labor Statistics.

Analysis of the U.S. labor market is a difficult and challenging effort. A number ofexisting economic indicators, including

the unemployment rate, payroll employment, and

others, serve as useful measures of labor market

activity, general economic conditions, and labor

supply. However, to facilitate a more comprehensive analysis of the U.S. labor market and to show

how changes in labor supply and demand affect

the overall economy, the Bureau of Labor Statistics will introduce a new data series measuring

labor demand and turnover: the Job Openings

and Labor Turnover Survey (JOLTS).1

The availability of unfilled jobs—the number

of job openings or the openings rate—is an important measure of the tightness of labor markets. JOLTS calculates the number of job openings

from a nationwide sample of establishments and

computes a job openings, or vacancy, rate. This

new survey also collects data on separations by

type and hires, providing a single source for

these data that will enhance empirical analyses

of the economy and the labor market. This article

briefly describes the survey and then discusses

how JOLTS data will help enrich analysis of the

U.S. labor market and the economy as a whole.

The survey

BLS collects and analyzes monthly data on many

aspects of the U.S. labor market. One BLS survey,

the Current Employment Statistics (CES) survey,

collects data from businesses and produces employment estimates. Another survey, the Current

Population Survey (CPS), conducted by the Census Bureau, collects employment status data from

households for BLS to determine the unemployment rate, which measures excess labor supply.

The BLS Job Openings and Labor Turnover Survey, completes the labor market picture by collecting monthly data from businesses to measure

unmet labor demand and job turnover.

This new program involves the collection, processing, and dissemination of job openings and

labor turnover data from a sample of 16,000 business establishments. The universe frame for the

JOLTS sample consists of approximately 8 million

establishments compiled as part of the operations

of the BLS Covered Employment and Wages, or

ES-202, program. This frame includes all employers subject to State Unemployment Insurance (UI)

laws and Federal agencies subject to the Unemployment Compensation for Federal Employees

(UCFE) program. The sampling frame is stratified

by ownership (public or private), geographic region, major industry division, and size class. The

JOLTS sample is representative of private nonfarm

establishments as well as Federal, State, and local government entities in the 50 States and the

District of Columbia. The sample is rotated so

that most establishments participate in the survey for 18 consecutive months. Total employment

estimates from JOLTS are controlled to the current

month CES employment estimates, and this is used
Monthly Labor Review December 2001 33

to adjust the levels for all other JOLTS data elements.

The data elements collected monthly from each establishment include employment for the pay period that includes the

12th of the month; the number of job openings on the last

business day of the month; and hires, quits, layoffs and discharges, and other separations for the entire month. To encourage consistent and accurate reporting, respondents are

given detailed definitions for each data element. For example,

the definition of a job opening requires that a specific position exists, the job could start within 30 days, and that the

employer is actively recruiting from outside the establishment

to fill the position. Hires are all additions to the payroll during

the month, and a layoff should be counted if it lasts or is

expected to last more than 7 days.

BLS anticipates releasing monthly estimates of job openings,

hires, and separation rates and levels beginning in early 2002.

The JOLTS data series will be considered a developmental series

for the first 2 years of publication. Estimates will be released for

the Nation as a whole and for four geographic regions. The

national estimates for the private sector will be divided into nine

industry divisions based on the Standard Industrial Classification (SIC) system. Additional estimates will be published for the

Federal Government and for State and local governments combined. JOLTS estimates will be converted to the North American

Industry Classification System (NAICS) in 2003.2

Anticipated uses of the data

The JOLTS data series on job openings, hires, and separations

will assist policymakers and researchers in addressing some

fundamental issues concerning labor demand and movements

in the labor market. The JOLTS data will provide a basis for

improved understanding of the factors driving fluctuations in

unemployment and the overall economy, determining appropriate approaches for reducing unemployment, and studying

how workers flow in and out of establishments, are matched

with jobs, and are distributed across sectors.

Of particular interest to researchers will be the study of the

relationship of vacancies and unemployment. The JOLTS job

openings rate is expected to have a negative relationship to

the unemployment rate. For example, as the economy expands,

the unemployment rate generally falls, reflecting a decreased

pool of excess workers. Simultaneously, the job openings rate

is expected to increase as businesses seek workers to fill new

and existing jobs. Alternatively, in a weak economy, the unemployment rate typically increases as businesses shed workers

in response to weak demand, and the job openings rate is

expected to fall as employers cut back their hiring plans.

The empirical relationship linking vacancies with unemployment and the overall economy is called the Beveridge

curve. A stylized version of the Beveridge curve is shown in

exhibit 1. First described by William Beveridge in the 1940s,

the curve reflects the negative relationship between vacancies and unemployment.3 Changes in the business cycle generate movement along the curve, while reallocation of labor

and capital between sectors moves the curve further away

from or closer to the origin. These forces are not independent

of one another, and changes in labor force composition and

job search behavior also affect where the Beveridge curve is

relative to the origin. Economic researchers can use the JOLTS

data to test and refine models that show how such factors

affect labor market dynamics and the distribution of workers

across sectors.

Another topic of importance to economists is the relationship between changes in wages and changes in employment.

Primarily, economists have only been able to examine this in

the context of the Phillips curve, a relationship linking deviations from the natural rate of unemployment—or excess labor supply—to wage changes. The JOLTS series on job openings will finally provide a consistent series on excess labor

demand that will extend economists’ ability to study the relationship between wage and employment changes.

To study the Beveridge curve and its implications, researchers have had to use a variety of proxies for the vacancy rate.

Much work in this area has been done using a vacancy series

constructed from employment, as measured by BLS, and the Conference Board’s Help-Wanted Advertising Index. This proxy has

served as an imperfect measure, because the index does not

cover the entire country, job openings are not directly counted,

there are no precise definitions or adjustments for what constitutes actual job openings, and there are other factors independent of labor demand that can increase the volume of want ads.

JOLTS will provide a directly measured vacancy series that can

be used for this type of analysis.

The Bureau of Labor Statistics has conducted various surveys to measure job openings in the past, but these were

either short lived or were limited to the manufacturing industry. JOLTS was developed to consistently record monthly job

openings for all nonagricultural industries, including both

public and private businesses, and is representative of the

national economy.

Researchers have used the available proxies to study

movements in the unemployment-vacancy relationship over

the past few decades because a consistent set of vacancy

data has not been available. Generally, this work has documented upward movement along the Beveridge curve during

the 1960s, and a combination of movement along the curve

and outward shifts in the relationship for various periods

during the 1970s and 1980s.4 Recent work by Hoyt Bleakley

and Jeffrey C. Fuhrer documents an inward shift of the curve

in the early 1990s.5

Uses of JOLTS data in economic analysis. The analysis of

the co-movement of the unemployment and vacancy rates
34 Monthly Labor Review December 2001

Job Openings and Labor Turnover

can provide more information about business cycles and the

type of unemployment that is prevalent in the economy. When

the structure of the economy is fixed, the position on the

Beveridge curve reflects whether an economy is expanding or

contracting, as shown in exhibit 1. In times of economic expansion, the unemployment-vacancy combination will be high

on the curve (low unemployment rate, high vacancy rate), and

in contractions it will be low (high unemployment rate, low

vacancy rate). The changes in unemployment generated by

this movement will mostly reflect cyclical fluctuations in labor

demand. The Beveridge curve will shift with changes in the

probability that jobseekers and job openings will be matched,

as shown in exhibit 2. The curve will shift away from the origin

if various factors make it harder for unemployed workers to fill

vacancies and shift back when matching efficiency improves.

These shifts reflect changes in structural unemployment or

frictional unemployment, or both, that result from changes in

the composition of the labor force, from reallocation of jobs

across sectors or geographic areas, or from other changes in

the way workers match with jobs. In considering the curve’s

shape and movement of the unemployment and vacancy rates

over time, economists can determine the differences between

unemployment driven by deficient demand and unemployment generated by reduced matching.

For example, suppose the unemployment rate begins to

rise substantially. If no corresponding decrease in the vacancy rate is observed, this points to an outward shift in the

Beveridge curve and a reduction in matching efficiency. The

matching inefficiency could reflect structural unemployment

if the individuals who are without a job do not have the right

skills to fill the vacancies. To reduce unemployment, training

could be provided to workers who lack needed skills. If aggregate shocks are driving the increase in unemployment—that

is, there is movement along the Beveridge curve—efforts to

train workers will produce little improvement. Rather, macroeconomic efforts to spur job creation may be more effective.

The models that distinguish between the amount of a

change in the unemployment rate attributable to cyclical fluctuations and the amount generated by factors that reduce

matching efficiency are well described by a number of authors. Katharine G. Abraham and Lawrence F. Katz and Olivier

Blanchard and Peter Diamond demonstrate how data on vacancies can differentiate between deficient demand and unemployment generated by reduced matching.6 An article by

Barbara Petrongolo and Christopher A. Pissarides discusses

issues regarding the matching function—a relationship that

describes how workers match with jobs and which relates

changes in hires to changes in vacancies and unemployment.7 A later section of this article outlines some of the basic

ideas behind these models.

JOLTS elements as indicators of the business cycle. As described earlier, the job openings rate, in conjunction with the

unemployment rate, provides information about the state of

the economy. In addition, hires, quits, and layoffs and discharges data can be useful in analyzing business cycles. Direct measures of all these data series have not been available

in the past. Many hypotheses about how these data elements

trend against one another and how they relate to the movements of the business cycle over time can be tested once the

JOLTS data are available. Economists will be able to examine

how the data elements move with business cycles directly. In

addition, economists can use the JOLTS series to enhance analyses of how gross employment flows and job creation and job

destruction relate to the business cycle.

Movement in the job openings (or vacancy) rate leads

economic activity at business cycle peaks and lags at troughs,

so that the number of vacancies tends to decrease before the

economy begins a downturn and increases after the economy

begins a recovery. Because cutting job openings is less costly,

employers tend to reduce the number of job postings for new

hires or replacement workers before decreasing the firm’s current employment level when sensing an economic downturn.

When conditions begin to improve, firms will tend to increase

hours of work and recall workers from layoff before searching

for new employees.

Hires, as well as establishment growth rates, tend to be

procyclical, moving in the same direction as general economic

Vacancy rate

Unemployment rate

Expansion

Contraction

U , V L H

U , V H L

Exhibit 1. Movement along the Beveridge curve 
Monthly Labor Review December 2001 35

activity. Under good economic conditions, firms replace workers who separate, whereas during downturns, employers may

delay hiring until the economic situation improves.

Intuitively, economists expect quits to be procyclical if

workers feel safe leaving their jobs when economic conditions are favorable. It also is thought that layoffs and discharges are countercyclical, moving in the opposite direction

as economic activity, because employers tend to shed workers when business conditions are unfavorable. Patricia M.

Anderson and Bruce Meyer found total separations, which

includes quits and layoffs and discharges, to be procyclical.8

If quits are procyclical, they will decrease during downturns and

recessions. Therefore, if total separations are also procyclical, in

order for both quits and separations to fall during a downturn,

the decrease in quits must more than offset the increase in layoffs and discharges during those periods. However, this aspect

may vary by industry, because sensitivities to business cycles

can vary. Hoyt Bleakley, Ann E. Ferris, and Jeffrey C. Fuhrer

provide evidence that flows between employment and unemployment in manufacturing vary much more with the business

cycle than in the nonmanufacturing sector.9

Economists have examined how business cycles affect

workers moving between jobs, becoming unemployed, and

dropping out of the labor force. JOLTS does not measure such

flows, but its turnover data should enhance estimates of these

flows using other data sources. For example, if quits and hires

are procyclical, then employer-to-employer flows should be

procyclical. Recent work by Bruce C. Fallick and Charles A.

Fleischman, however, found no evidence that such employerto-employer flows are procyclical using State-level variation

in economic conditions during 1994–2000.10 The evidence on

the procyclicality of hires and quits tends to come from data

collected during the 1970s and 1980s; the new JOLTS series will

be able to help economists understand whether these relationships still hold or if other components need to be incorporated into models of how job turnover and labor market flows

relate to the business cycle.

Many of the conclusions that economists have reached

about the relationship between the business cycle and job

openings, hires, and separations have been based on data

series that are not direct measures of these data elements. The

JOLTS program’s data series will provide a better source for

economists to examine these relationships over time.

Uses of JOLTS in economic research. In a simple model in

which an economy has fixed structural characteristics, fluctuations in aggregate demand generate movements along the

Beveridge curve as the economy expands and contracts. During contractions, there are few vacancies and high unemployment rates; as the economy expands and demand for labor

increases, unemployed workers will find jobs and growing

firms will be creating new jobs, generating a higher vacancy

rate and lower unemployment rate.

The reallocation of economic activity across industries

and geographic areas also generates distinct movements in

unemployment and vacancies that will be reflected in shifts of

the Beveridge curve. Expanding sectors have greater employment growth and shrinking sectors experience reductions in

employment. If workers who lose their jobs in the shrinking

sectors are perfectly mobile and have the skills to work in the

expanding sectors, then reallocation across sectors would not

affect the position of the Beveridge curve. Such seamless

transitions are rarely the case; impediments such as the lack

of skills or geographic immobility affect jobseekers’ ability to

match with job openings. As a result, concurrent increases in

both vacancies and unemployment can be observed and shift

the Beveridge curve further away from the origin.

Demographic changes in the labor force also will affect the

degree to which workers match with jobs. Workers have much

higher turnover early in their career, so a change in the age

distribution of workers towards younger workers can generate shorter job durations. Shorter job durations are usually

associated with greater turnover and more openings at any

one time. Concurrent increases in both unemployment and

vacancies could result, shifting the Beveridge curve further

away from the origin. Researchers have found some evidence

that the Beveridge curve shifted out when the baby-boom

Vacancy rate

Unemployment rate

Reduced 

matching efficiency

Improved

matching 

efficiency

Exhibit 2. A shift in the Beveridge curve 
36 Monthly Labor Review December 2001

Job Openings and Labor Turnover

generation entered the labor market and shifted the distribution of the labor force towards younger workers. An anticipated question that economists may be able to examine with

the JOLTS data is: What effect will the retirement of the babyboom generation have on the unemployment-vacancy locus?

In addition, the entry of their children into the labor force is

also likely to be currently affecting the age distribution of the

work force and possibly shifting the Beveridge curve as well.

Others have noted how changes in family structure, work

disincentives, and changes in recruiting practices on the part

of employers can influence the number of vacancies relative

to unemployment.11 In addition, the increase in the flow of

information on job openings, made possible by electronic communication and the Internet, may reduce both structural and

frictional unemployment, reduce the duration of job vacancies, and thus shift the Beveridge curve inwards. Such questions cannot be directly examined using the JOLTS data, but its

job openings, hires, and separations data could be combined

with other microdata to investigate such questions.

Aggregate demand shocks, changes in the distribution of

jobs across industries and regions, shifts in the demographic

characteristics of the work force, and other changes in the

way labor markets operate all can affect the combination of job

openings and unemployment that are observed at any one

time. Economists are interested in the extent to which each of

these factors matter, as well as how long their impact persists.

Several researchers used the job vacancy series from the 1970s

to estimate the relative importance of each of these on the

movement of unemployment and job openings.

In looking at the evidence using earlier vacancy data series, researchers find that aggregate demand shocks generate

larger short-run movements in the vacancy-unemployment relationship than reallocation or changes in labor supply.12 The

impact of aggregate demand does not persist in the long run,

while the effects of changes in the labor force composition

and increases in the intensity of reallocation do.

A number of authors have found that the Beveridge curve

shifted out in the 1970s and 1980s. Changes in the composition of the labor force appear to have played at least a small

part in this shift.13 Reallocation of jobs across industries has

played a role, but these are not entirely of a consistent timing

and magnitude to explain the remainder of the shifts between

the 1970s and 1990s. One study provides evidence that increased geographic dispersion of job creation and destruction are more consistent with the shifts; all the researchers cite

additional factors that also were likely to have affected matching and shifted the Beveridge curve.14

JOLTS will be the first data series in which macroeconomic

analyses can be performed without constructing at least one,

if not more, of the main components of these models from

proxies based on manufacturing data. The studies from the

1980s had to rely on relationships between employment

changes, job openings, quits and layoffs constructed from

turnover, and vacancy data collected in 1981 and earlier, all of

which were limited to the manufacturing industry. In addition,

these relationships were used in conjunction with household

data to approximate the number of persons moving from one

employer to another to generate measures of job separations

and hires that did not come from nonemployment. These constructed series on vacancies, quits, and hires seemed to fit

labor market data well through the 1980s.15 However, several

changes in the labor market over the past 30 years, including

the shift in the concentration of jobs from manufacturing to

services and changes in the way workers match with job openings, limit the extent to which one can hope to “update” these

earlier series. Moreover, it is always preferable to have direct

measures of a model’s components rather than having to construct some components. The hires series from JOLTS will provide data on the number of workers who match with jobs

during each month. The separations series and breakouts of

quits, layoffs and discharges, and other separations will provide the necessary turnover data and job openings will provide a direct measure of vacancies for these models.

The JOLTS data also will enhance economists’ understanding of matching models—the process by which employed and

unemployed jobseekers match with available jobs. The hires

series provides a monthly estimate of the number of such

matches, and the job openings series provides a measure of

the number of unfilled jobs. In analyses to date, economists

have had to rely on less than ideal measures of these two

critical elements. Often, exits from nonemployment were used

to proxy for job matches even though recent evidence indicates that a substantial number of workers go from one job to

the next without a spell of nonemployment. Also, these models have had to rely on proxies for a vacancy series or gross

employment changes to control for labor demand. The proxies for vacancies and job matches have been sufficient for

certain analyses, but their being imperfect measures of the

true values can confound testing of other hypotheses.

An example of how measurement error interferes with analyzing matching models can be seen in economists’ examination of the effect that increasing the number of jobseekers has

on the rate at which vacancies are filled. Some models imply

that at the aggregate level, the matching function should exhibit constant returns to scale—that is, the overall match rate

will equal the sum of the probabilities that any given jobseeker

and firm will match. In regression models that estimate the

elasticity of job matches or new hires with respect to vacancies and unemployment, constant returns to scale implies that

the elasticity of matches with respect to vacancies and the

elasticity of matches with respect to unemployment should

sum to one. Other models suggest that there might be increasing returns to scale in the matching function—that is, the

total number of matches will increase by more than the amount
Monthly Labor Review December 2001 37

1

 For a brief introduction to the Job Openings and Labor Turnover

Survey see, “New survey measures demand for labor,” Monthly Labor

Review, September 2000, pp. 37–39.

2

 See James A. Walker and John B. Murphy, “Implementing the

North American Industry Classification System at BLS,” also in this issue.

3

 See Hoyt Bleakley and Jeffrey C. Fuhrer, “Shifts in the Beveridge

Curve, Job Matching, and Labor Market Dynamics,” New England

Economic Review, September/October 1997, pp. 3–19. Also a derivation of the Beveridge curve can be found in Bent Hansen, “Excess

Demand, Unemployment, Vacancies and Wages,” Quarterly Journal of

Economics, 1970, vol. 84, no. 1, pp. 1–23.

4

 See Katharine G. Abraham, “Structural/Frictional vs. Deficient

Demand Unemployment: Some New Evidence,” American Economic

Review, 1983 vol. 73, no. 4, pp. 708–24; and Katharine G. Abraham,

“Help-Wanted Advertising, Job Vacancies, and Unemployment,”

Brookings Papers on Economic Activity, no. 1, June 1987, pp. 207–48;

Olivier Blanchard and Peter Diamond, “The Beveridge Curve,”

Brookings Papers on Economic Activity, no. 1, 1989, pp. 1–76; and

Bleakley and Fuhrer, “Shifts in the Beveridge Curve” 1997, pp. 3–19.

5

 Bleakley and Fuhrer, “Shifts in the Beveridge Curve,” 1997,

pp. 3–19.

6

 Katharine G. Abraham and Lawrence F. Katz, “Cyclical Unemployment: Sectoral Shifts or Aggregate Disturbances?” Journal of Political Economy, 1986, vol. 94, no. 3, pp. 507–22; and Blanchard and

Diamond, “The Beveridge Curve,” 1989, pp. 1–76.

7

 Barbara Petrongolo and Christopher A. Pissarides, “Looking into

the Black Box: A Survey of the Matching Function,” Journal of Economic Literature, June 2001, pp. 390–431.

that the number of jobseekers and job openings has increased.

Increasing returns to scale would imply that the estimated sum

of the unemployment and vacancy elasticities is greater than

one. Measurement error in either unemployment or vacancies

can result in the estimated elasticities being biased towards zero.

Therefore, imperfect measures of job vacancies might suggest

constant returns to scale simply because the estimated effect of

job vacancies is biased downward.

Matching models have been used to examine the extent to

which employers prefer employed jobseekers to unemployed

jobseekers. Such ranking of types of workers has implications for jobless durations and unemployment durations.

Recent evidence indicates that a substantial proportion of

new employees come from other jobs rather than unemployment.16 Although JOLTS does not collect data on whether hires

come from another job, unemployment, or out of the labor

force, its information on matches (hires) and job openings

will provide better information than existing proxies created

from other surveys.

With data from a single consistent source, there is less

concern that measurement error in the key components will

interfere with estimating the true relationship between vacancies, unemployment, and other economic indicators.

Notes

These series will improve the ability to test how well current

models describe the evolution of labor demand and labor

market dynamics in the United States. Also, the time series

on hires and quits, in combination with data on gross employment flows, may enable researchers to back out other

turnover series, such as employment-to-employment transitions, and further enhance such research.

These topics are part of economists’ research agendas

and extend beyond JOLTS use as an indicator of labor demand

in the United States. The full scope of the data’s usefulness

cannot be anticipated; however, as the data series accumulates and models of labor dynamics are further developed,

they may substantially deepen economists’ understanding

of labor markets and the economy.

AS A SINGLE SOURCE for directly measured data on job openings, hires, and separations, JOLTS statistics can be used as

indicators of general economic conditions, and are important

tools for considering the implications of economic policies

on unemployment and the labor market. These data series, in

conjunction with other micro- and macroeconomic data, are

also likely to enhance researchers’ understanding of labor

market dynamics and their relation to the economy as a whole.

Consequences of Job Turnover,” Brookings Papers: Microeconomics,

1994, pp. 177–248.

9

 Hoyt Bleakley, Ann E. Ferris, and Jeffrey C. Fuhrer, “New Data

on Worker Flows During Business Cycles,” New England Economic

Review, July/August 1999, pp. 49–76.

10 Bruce C. Fallick and Charles A. Fleischman, “The Importance of

Employer-to-Employer Flows in the U.S. Labor Market,” Finance and

Economics Discussion Paper Series, no. 2001-18 (The Federal Reserve

Board, April 2001).

11 For more details, see Abraham, “Help-Wanted Advertising,”

1987, pp. 207–48; Bleakley and Fuhrer, “Shifts in the Beveridge Curve,”

1997, pp. 3–19; and Petrongolo and Pissarides, “Looking into the Black

Box,” 2001, pp. 390–431.

12 Abraham and Katz, “Cyclical Unemployment,” 1986, pp. 507–

22; and Blanchard and Diamond, “The Beveridge Curve,” 1989, pp. 1–

76.

13 For more details, see Abraham, “Help-Wanted Advertising,”

1987, pp. 207–48; Blanchard and Diamond “The Beveridge Curve,”

1989, pp. 1–76; and Bleakley and Fuhrer, “Shifts in the Beveridge

Curve,” 1997, pp. 3–19.

14 Abraham, “Help-Wanted Advertising,” 1987, pp. 207–48.

15 Abraham, “Help-Wanted Advertising,” 1987; and Blanchard and

Diamond, “The Cyclical Behavior of Gross Flows of Workers in the

U.S.,” Brookings Papers on Economic Activity, no. 2, 1990, pp. 85–155.

16 Fallick and Fleischman, “The Importance of Employer-to-Employer Flows,” 2001.

8

 Patricia M. Anderson and Bruce D. Meyer, “The Extent and