Methodological Changes in the Job Openings and Labor Turnover 

Survey 

John Wohlford 

Supervisory Economist 

Job Openings and Labor Turnover Survey 

US Bureau of Labor Statistics 

I. Background 

The Job Openings and Labor Turnover Survey (JOLTS) conducted by the Bureau of Labor Statistics (BLS) provides valuable 

information on labor demand, hiring, and turnover for the US labor market. While the US has maintained a reliable measure 

of labor supply (the unemployment rate) for decades, measures of labor demand (job vacancies) have been more difficult to 

come by. Prior to the establishment of JOLTS, there was no measure of labor demand that was directly comparable to the 

unemployment rate as a measure of labor supply. Without this measure, it is more difficult to discern whether unemployment 

is due to micro factors such as job matching problems, or to macro-level factors such as deficient demand. 

The job openings rate can be compared to the unemployment rate to establish a rough aggregate measure of whether there are 

enough jobs available to absorb the existing excess supply of labor. If the job openings rate is higher than the unemployment 

rate, then there are enough jobs to go around, and better job matching efforts are needed to find work for the unemployed. 

(The author acknowledges that many of the vacant jobs may not be suitable for the available candidates, in that the 

vacancies may require specific skills, education, or certification that the available unemployed workers to not possess. 

Conversely, the available jobs along with their associated working conditions may not be attractive to the available workers 

at the prevailing wage. These issues fall under the heading of job matching problems.) If on the other hand the job openings 

rate is lower than the unemployment rate, then filling all available jobs will still result in unemployed workers left over. In 

this case an increase in demand for labor is needed to absorb the remaining unemployed. 

A. JOLTS Program History 

BLS has collected job openings and labor turnover data in the past, but most of these efforts were restricted to selected 

industries or to specific states. In 1954 BLS began the Monthly Report on Labor Turnover, which lasted until 1981. (This 

program was limited primarily to manufacturing industries.) In 1969 the collection of job openings data was added to the 

labor turnover survey, but this collection was dropped in 1973. In 1979 and 1980 and again in 1990 and 1991 the Bureau 

conducted pilot surveys to test the feasibility of collecting detailed job openings data by occupation, limited again to selected 

states (in the former) and to selected industries (in the latter). In FY1999 funding was made available to BLS to develop the 

existing program. 

B. Description of the JOLTS Survey 

In a monthly survey of 16,000 business establishments, data are collected for total employment, job openings, hires, and 

separations. Data collection methods include computer-assisted telephone interviewing (CATI), touchtone data entry (TDE), 

fax, e-mail, and mail. The JOLTS program covers private non-farm establishments as well as federal, state, and local 

government entities in the 50 states and the District of Columbia. JOLTS produces monthly estimates of rates and levels of 

job openings, hires, quits, layoffs and discharges, other separations, and total separations. 

JOLTS also produces estimates of monthly hires and separations of employees. JOLTS estimates of hires and separations 

can be used to approximately disaggregate the net monthly employment change measured in the BLS “Employment 

Situation” news release on the first Friday of each month. A hire is any addition to the payroll, including newly hired or 

rehired employees; full-time or part-time employees; permanent, short-term, or seasonal employees; employees who were 

recalled to a job at the reporting establishment following a layoff lasting more than seven days; on-call or intermittent 

employees who have returned to work after having been formally separated; workers who were hired and separated during 

the month; and transfers from other locations. The hires count does not include transfers or promotions within the reporting 

establishment; employees returning from strike; or employees of temporary help agencies, employee leasing companies, 

outside contractors, or consultants. A separation is any separation from the payroll, whether voluntary (quits), involuntary 

(layoffs and discharges) or other separations. The Quits count includes all employees who left voluntarily (except for 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

retirements and transfers to other locations). The Layoffs and Discharges count includes layoffs with no intent to rehire; 

layoffs (formal suspensions from pay status) lasting or expected to last more than seven days; discharges resulting from 

mergers, downsizing, or closings; firings or other discharges for cause; terminations of permanent or short-term employees; 

and terminations of seasonal employees (regardless of whether they are expected to return next season). The other 

separations count includes retirements; transfers to other locations; separations due to employee disability; and deaths. None 

of the separations categories include transfers within the reporting establishment; employees on strike; or employees of 

temporary help agencies, employee leasing companies, outside contractors, or consultants. (Please reference the JOLTS 

program home page at http://www.bls.gov/jlt/ for more detailed information on JOLTS data elements and collection 

methods.) 

C. Uses and Users 

Estimates of the job openings rate from JOLTS can be used as demand-side indicators of labor shortages. These nationallevel indicators of labor shortages can greatly enhance policy makers' understanding of imbalances between the demand for 

and supply of labor. At pesent there is no other comprehensive economic indicator of labor demand with which to assess the 

presence of labor shortages in the U.S. labor market. The number or rate of job vacancies is an important measure of 

tightness of job markets, parallel to existing measures of unemployment. JOLTS statistics reveal structural labor market 

conditions, such as the effectiveness of job matching and training processes, the implications of unemployment insurance and 

welfare, and deficient demand for labor. JOLTS statistics can be used as a potential indicator of business cycles. In addition, 

JOLTS statistics allow businesses to compare their turnover rates to national rates. JOLTS data have been used by nationallevel planners and policy makers, and by researchers in government and academia. They have also been used by numerous 

businesses, in assessing their individual turnover rates against national averages. 

II. Problem Statement 

Although not originally designed for this purpose, the JOLTS hires and separations data can be combined to produce an 

implied measure of monthly employment change. However, from the beginning of the JOLTS program in December 2000 

through December 2008 the change implied by the JOLTS data did not track well with the larger and better-known Current 

Employment Statistics (CES) Survey. JOLTS hires minus separations fairly consistently produced an overstatement of 

employment change when compared to the net change measured by the CES. 

Differing month-to-month measures of net employment change can be expected between two independent sample surveys. 

For example both surveys are subject to sampling error. The two programs will have differing levels of reliability, because 

the CES collects data from far more establishments (~400,000) than does JOLTS (~10,000). Also, CES was specifically 

designed to measure net monthly employment change, while JOLTS was not. In addition, differences in definitions and 

reference periods between the two surveys can cause month-to-month differences in measures of employment change. One 

would expect most of these issues to balance over long periods of time, resulting in two series that track relatively closely. 

However, this proved not to be the case. 

By early 2002, it was clear that in addition to incidental differences between the surveys as described above, a more serious 

upward bias was at work in the net JOLTS hires minus separations data. BLS took several steps to mitigate the bias, such as 

carefully reviewing and re-evaluating all micro-data reported to date, retraining all JOLTS telephone interviewers, 

interviewing selected survey respondents, re-contacting and re-educating all survey respondents, and making system 

modifications to identify the divergence at the micro-data level at the time the data are first reported.1

 In addition, JOLTS 

modified its sample design to begin taking an independent sample of the Employment Services industry (NAICS 5613). The 

re-evaluation of our reported data pointed to Temporary Help firms as one source of error in the reported data, and 

Temporary Help firms are included in the Employment Services industry. We modified the sample structure to isolate this 

industry so we could monitor it more closely. 

Over the remainder of 2002 and into 2003 the JOLTS/CES divergence was cut by two thirds. It appeared that our efforts to 

remove the overstatement of net employment change implied by the JOLTS hires and separations data had been largely 

1

 Werking, Clayton, Phillips, Wohlford, 2003 
 

 

 

 

 

successful. (See Chart 1) However, as JOLTS progressed through 2008, the divergence returned and grew larger. In 

addition, the effect of even small divergences in 2002 through 2005 began to accumulate. (See Chart 2) 

Chart 1 Chart 2 

III. Approach to Solving the Problem 

BLS embarked on a detailed research effort to understand the root causes of the divergence between CES monthly 

employment change and JOLTS hires minus separations data and to identify ways to permanently mitigate them. We 

conducted a thorough review of the data already collected, and we re-examined our sampling and estimation methodologies.. 

In studying the divergence on an industry basis, we knew that there were a number of industries that tracked fairly closely 

with the CES, and a few industries that overstated the CES by large amounts. Two industries in particular appeared to be 

driving most of the divergence. 

Chart 3 

The industries that overstated employment change by a significant amount were Professional and Business Services and State 

and Local Government. Within Professional and Business Services we were able to look at the Employment Services 

industry, since we had begun sampling that industry back in 2003. That industry is broken out into three components: 

Placement Services (NAICS 561310), Temporary Help Services (NAICS 561320), and Professional Employee Firms 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

(NAICS 561330). Since we did not sample below the four-digit NAICS level, we could only review micro-data to see which 

of these breakouts was likely to be contributing most to the divergence problem. Our review of the micro-data indicated that 

Temporary Help Services appeared to be contributing most to the problem. Within State and Local Government, our review 

of the micro-data indicated that State level education establishments were having the most impact on the divergence. 

A. Employment Services 

BLS conducted a Response Analysis Survey (RAS) in the Employment Services industry, concentrating on Temporary Help 

firms in our existing sample. We began by visiting a Temporary Help firm for a lengthy interview to help us narrow our 

search to specific areas that were most likely to be causing problems. We then designed our interview instrument, developed 

procedures, selected a sub-sample of Temporary Help respondents, and began telephone interviews. 

We sub-sampled 27 of the 197 Temporary Help firms in our sample, and learned that about half of these firms had difficulty 

reporting hires, but a great deal more than half experience trouble reporting separations. The respondents were split virtually 

evenly between units that could report hires (52%) and those that could not (48%). However, these same respondents had a 

great deal more difficulty reporting quits (70%), layoffs and discharges (59%), other separations (67%), and total separations 

(59%). 

B. State Education 

We also studied State Government Education. Most of the sample units from this industry were large state universities. 

Given the smaller number of these establishments, we began calling universities, beginning with the largest ones. In every 

case, the results were similar. State universities do a good job of identifying and reporting when people are hired, but not 

such a good job of reporting when people are separated from employment. This problem is especially pronounced with 

certain classes of workers, such as student workers and adjunct professors. As a result, thousands of hires were being 

reported each autumn, with no corresponding separations appearing the following spring. Universities with 10,000 

employees were appearing to double their employment over the brief history of the survey. 

C. Birth/Death Model 

We also explored the impact of new business units on the JOLTS estimates. From the initial conception of JOLTS, the need 

for a birth/death model was identified. The JOLTS sample is drawn from the Bureau’s Longitudinal Database (LDB), which 

is in turn built from the quarterly files submitted to BLS by the states as part of the Quarterly Census of Employment and 

Wages (QCEW). This sampling frame provides virtually complete national coverage of wage and salary employment since it 

includes all employees covered by state Unemployment Insurance programs as well as the Unemployment Compensation for 

Federal Employees (UCFE) program. Covered businesses file quarterly contribution reports covering their employees, the 

wages paid them, and the associated UI tax due the state. When a new business begins reporting, it will show up as a birth in 

the sampling frame within about one year. This time lag is of consequence for JOLTS, since new businesses are likely to 

have an initial burst of hiring and separating during their first year in existence. JOLTS is not able to capture this initial 

activity through the sample, because these new birth units are not yet represented on the sampling frame. 

We also compared JOLTS estimates to research using different data sources and gathered additional evidence that we may 

have been understating both hires and separations. Fallick and Fleischman2

 used CPS gross flows data to estimate that hires 

and separations rates averaged approximately 6.5% – 6.6% over a period of years spanning the last recession and recovery. 

Using State Unemployment Insurance wage records from eight states for the period 1978 - 1984, Anderson and Meyer 

concluded that total separations averaged at least 19.5% per quarter, or 6.5% per month.3

 In contrast, the JOLTS total 

separations measure averaged 3.3% from the beginning of the series in December 2000 through December 2008, while the 

hires rate averaged 3.4% over that same span. Even after allowing for coverage and definitional differences between these 

studies, it still appeared that JOLTS may be undercounting both hires and separations. 

2

 Fallick and Fleischman, 2004 

3

 Anderson and Meyer, 1994 
 

 

 

 

 

 

 

 

D. Sample Design 

We also re-examined the sample design to see whether there may be some subtle impact on the estimates not previously 

detected. The initial JOLTS design called for a set of sample panels including a certainty panel and a number of noncertainty panels. The certainty panel consisted of units whose probability of selection was virtually 100%. The non-certainty 

panels were each drawn as stand-alone samples representative of the entire (non-certainty) sampling frame. The certainty 

sample was initiated in at the beginning of the JOLTS data collection process, and one non-certainty panel was initiated each 

month after that. Each year a new sample was drawn. The certainty panel was adjusted based on changes in the LDB, and a 

new set of non-certainty panels (currently 24) was drawn. (Since only twelve of the non-certainty panels were initiated over 

the previous year, the unused panels were made available for sampling again.) Since one panel was initiated each month, the 

last panel to be initiated had been sitting on the shelf for an additional year between the time it was selected and the time it 

was initiated. This further exacerbates the problem described above, wherein younger establishments tend to show more 

hires and separations activity than more mature units. The longer the delay before a new unit begins reporting data, the more 

the early volatility escapes measure. 

E. Estimation Methodology 

We also conducted a review of our estimation methodology. Collected JOLTS data are reviewed and adjusted for outlier 

values, adjusted for item and unit non-response, and then “benchmarked” to the current universe employment level. This 

benchmarking consists of ratio-adjusting the JOLTS employment estimate to the current CES employment level, and then 

applying that same ratio to all JOLTS estimates. This process accounts for changes in the universe level of employment 

since the time the JOLTS sample was originally selected. However, while this process controls JOLTS weighted 

employment to the universe, JOLTS does not publish an employment estimate. The hires and separations estimates, used to 

derive the implied JOLTS employment change, were not controlled to the over-the-month CES change. When JOLTS was 

designed, minor divergences between JOLTS implied employment change and CES measured employment change were 

assumed, but it was assumed that these would be small and would balance out over time, explained by differences in 

reference periods and definitions of survey concepts. 

IV. Designing the Solution 

The resulting program improvements affected three distinct areas: sample design, data collection, and estimation. 

A. Sample Design 

The sample design was modified in several ways to increase our ability to capture hires and separations from newer 

(younger) establishments. First, the process used to roll in new sample panels was revised. Under the old process each panel

could stand alone as a representative sample of the (non-certainty) universe. One panel was initiated each month, while the 

remaining panels waited their turn. In order to more quickly capture the impact of newer units, the revised sample 

methodology examines all sample panels for “new” units (that is; units that appeared on the sampling frame for the first time 

during the current sampling process). These units are now moved to the front of the queue, and initiated immediately. Thus 

the individual sample panels no longer each constitute a representative sample of the universe. (Of course, the full JOLTS

sample is still representative.) 

In addition to the annual sampling process, the sampling frame is now examined at each quarterly update, and “new” units 

are identified. A sample of these new units is now drawn and sent to the Data Collection Center for initiation each quarter. 

These two steps (initiating new units first, and adding quarterly “birth” supplements) gives us as much as can be obtained 

from the sampling process given the timing limitations of the sampling frame. At the time each annual sample is drawn, outof-business units are now dropped from the existing sample, and all sample units are re-weighted to represent the current 

universe. 

B. Data Collection 

Changes have also been made to the collection and data review processes. Given the amount of the divergence that was 

being contributed by the Employment Services industry and the State Government Education industry, special emphasis is 

now placed on them. In the former industry, businesses have a difficult time reporting hires and separations of temporary 
 

 

 

 

 

 

 

 

 

 

 

 

 

help workers. In the latter industry, employers have a difficult time reporting hires and separations of student workers and 

other seasonal employees such as adjunct professors. The reported micro-data from these two industries are now reviewed 

by experienced senior economists. Establishments that show a large unexplained divergence are reviewed carefully against 

their own past history as well as current and historical trends in the industry. Interviewers re-contact these establishments

and work more closely with them to improve their reporting practices where possible. Units that are clearly and consistently 

incorrect but cannot be reconciled are dropped from the estimation process and imputed for using existing techniques. 

C. Estimation 

Improvements to estimation consist of the addition of a birth/death model and an alignment process. The birth/death model 

allows us to account for hires and separations at establishments too young to be represented on the sampling frame, and the 

alignment process allows us to control the JOLTS measure of hires minus separations to monthly CES employment change. 

1. Birth/Death Model 

The birth model provides estimates of the numbers of job openings, hires, and separations that are occurring in those units 

that have recently entered existence but have not yet appeared on the sampling frame. As with any sample survey, the 

JOLTS sample can only be as current as its sampling frame. Since new universe units cannot be reflected on the sampling 

frame immediately, the JOLTS sample cannot capture job openings, hires, and separations from these units during their early 

existence. To develop data for these units that cannot be measured through sampling, BLS has developed a model to estimate 

the contribution of these units to the current month estimates. The birth/death model estimates birth/death activity for the 

current month by examining the birth/death activity from previous years on the LDB and projecting forward to the present 

using X-12 ARIMA modeling. The birth/death model also uses historical JOLTS data to estimate the amount of “churn” 

(hires plus separations) that exists in establishments of various sizes. The model then combines the estimated churn with the 

projected employment change to estimate the number of hires and separations taking place in these units that cannot be 

measured through sampling. The model-based estimate of total separations is distributed to the three components: quits, 

layoffs, and other separations, in proportion to their contribution to the sample-based estimate of total separations. 

Additionally, job openings for the modeled units are estimated by computing the ratio of openings to hires in the collected 

data and applying that ratio to the modeled hires. The estimates of job openings, hires, and separations produced by the 

birth/death modeling process are then added to the sample-based estimates produced from the survey to arrive at the final 

estimates for hires, separations, and openings. Because JOLTS estimates did not previously include this step, addition of the 

birth/death model raised the levels and rates of the hires, separations, and openings measured by JOLTS, and helped the 

series to more accurately reflect the current labor market. 

2. Alignment Process 

We also implemented an alignment process that controls hires and separations to CES employment change on a monthly 

basis. There are some definitional differences between the series that can cause legitimate differences for individual months. 

The major reasons for these month-to-month divergences are: a) the reference periods of the two surveys are different. CES 

measures employment for the pay period including the 12th of the month, while JOLTS measures hires and separations for 

the entire month. b) CES counts those who worked or received pay for the reference pay period, while JOLTS counts those 

who were hired or separated during the reference month. It is possible for a person to miss being paid for a given pay period 

without having been separated. 

Both of these definitional differences can result in differing seasonal patterns between the two series, and therefore cause 

JOLTS to diverge from the CES in the short-term. Over time however, the computation of JOLTS hires minus separations 

should reflect employment changes that are consistent with the trends measured by the CES. The three changes to JOLTS 

that have been described above (sampling changes, special collection procedures for problem industries, and the addition of 

the birth/death model) produce JOLTS series’ that are much more consistent with the CES. The residual divergence is now 

controlled through the monthly alignment procedure, allowing JOLTS to vary from CES for the reasons listed above, while 

ensuring that the long-term trends in JOLTS hires-minus-separations match those of the CES net employment change. This 

method takes advantage of the availability of the CES employment series for the current reference month prior to the 

production of JOLTS estimates for that same reference month. 

Each month, the initially computed JOLTS estimates are seasonally adjusted using X-12 Arima. The JOLTS hires-minusseparations measure is then compared to that month’s CES employment change, and any difference is calculated. This 

difference is then used to proportionally adjust the seasonally adjusted hires and separations measures. This proportional 

adjustment is done by computing the total churn (hires plus separations) for the month, and then apportioning the adjustment 
 

 

 

 

 

 

 

 

 

 

according to the proportion of each to the total. For example, if CES employment changed by a negative 500,000 and JOLTS 

hires minus separations equaled negative 400,000, the difference would be negative 100,000. If hires made up 60% of total 

churn and separations made up the remaining 40%, then the 100,000 difference would be adjusted by subtracting 60,000 

hires and adding 40,000 separations to the seasonally adjusted JOLTS levels. 

Once the seasonally adjusted JOLTS levels have been proportionally adjusted, we reverse the application of the seasonal 

factors to “back out” the seasonal adjustment, and obtain aligned unadjusted level estimates. These aligned estimates are 

then passed back through X-12 Arima to produce a final seasonally adjusted set of estimates. This final JOLTS series will 

not precisely equal the CES seasonally adjusted net employment change but will be very similar. In this manner we can 

remove the trend difference between the two series, while maintaining the differing seasonal patterns of the two surveys. 

V. Implementing the Solution 

As part of the implementation of all of the changes discussed as well as the production of a revised time series, historical 

micro-data had to be revisited. At the time these changes were implemented, JOLTS micro-data had been reported over a 

period of eight years. Clearly it was not feasible to re-contact all respondents in the problem industries and expect to obtain 

corrected micro-data so long after the fact. Instead, JOLTS staff reviewed all reported micro-data in these two industries and 

manually adjusted the worst offenders based on their reported employment and the historical patterns in each industry. 

The birth model was used to calculate the contributions of births over the life of the JOLTS series, and then the entire time 

series was regenerated. The alignment process was run on the entire series, and the full set of historical estimates was 

replaced. As expected, the levels of hires and separations were slightly higher in the new series (See Table 1) but the overall 

pattern of the estimates over time was little changed. (See Charts 4 & 5) 

Table 1: Results of All Adjustments 

(in thousands) 

Hires Hires Level Percent Separations Separations Level Percent 

Year Before After Difference Difference Before After Difference Difference 

2001 54578 63768 9190 17% 54556 65611 11055 20% 

2002 49718 59800 10082 20% 49597 60410 10813 22% 

2003 49294 57788 8494 17% 48294 57849 9555 20% 

2004 54721 61615 6894 13% 51779 59671 7892 15% 

2005 57491 64502 7011 12% 54609 62087 7478 14% 

2006 59158 64911 5753 10% 55199 62626 7427 13% 

2007 57778 63381 5603 10% 54641 62104 7463 14% 

2008 51113 56496 5383 11% 52864 59343 6479 12% 

Total 433851 492261 58410 13% 421539 489701 68162 16% 
Chart 4, Before Adjutments: 

Chart 5, After Adjustments: 

The two industries that were significantly overstating employment growth now display results more consistent with 

employment change. For the eight year period from January 2001 through December 2008, the divergence between JOLTS 

and CES in the Professional & Business Services Industry went from 4,679,000 before the adjustments to 22,000 after. For 

State and Local Government, the divergence decreased from 4,035,000 to -38,000 over the same time period. 
 

 

 

 

Ratio of Unemployed Persons per Job Opening, 

Seasonally adjusted 

7.0 

6.5 

6.0 

5.5 

5.0 

4.5 

4.0 

3.5 

3.0 

2.5 

2.0 

1.5 

1.0 

0.5 

0.0 ~~~~,,,,~~,,,,,,,~Cb 

Most importantly, the trend divergence between the JOLTS hires and separations compared to CES net employment change 

was largely removed, while the independent seasonal patterns of the JOLTS series were maintained. (See Chart 6) 

Chart 6 

VI. Results 

After implementing these improvements, the entire historical JOLTS series was recalculated and re-published on March 10, 

2009. The revised data series now helps illustrate more clearly the factors underlying monthly net employment change, and 

also shows some additional interesting properties. For example, one can compare the levels of available supply of labor (the 

unemployment rate) with excess demand for labor (the job openings rate) and see that the current recession generated a ratio 

of unemployed persons per job opening that is well above the last recession. (See Chart 7) 

Chart 7 
 

 

5,000 

4,500 

4,000 

3,500 

3,000 

2.500 

!/) 

2Joo 

!/) 

1~00 

.c: 

1~00 

500 

0 

Layoffs & 

Discharges 

Another distinction between the last two recessions is illustrated by the behavior of quits (voluntary separations) when 

compared to layoffs & discharges (involuntary separations). (See Chart 8) In the last recession layoffs & discharges spiked 

upwards at the beginning of the recession and then quickly dropped back to a relatively stable level before moving upward 

again at the end of the recession. Over the entire previous recession and recovery, however, the level of layoffs & discharges 

remained well below the level of quits. In the current recession, layoffs & discharges remained relatively stable for several 

months after the recession began, only to move up dramatically as the recession entered its second year. In addition, the level 

of quits has fallen well below the low point of the previous recession, and well below the current level of layoffs & 

discharges. 

Chart 8 

JOLTS Quits and Layoffs & Discharges Levels, Total Nonfarm, 

Seasonally adjusted 

Another observation is that the labor market may have begun cooling prior to the beginning of the current recession. (See 

Chart 9) In the year prior to the beginning of the recession, both hires and separations were declining, while total nonfarm 

employment was continuing to grow. Even though hires were declining, total separations were also declining during this 

period. As long as hires remained greater than separations, employment continued to grow even though the level of activity 

was falling. 
 

 

 

C 

:::. 

7,000 

6,500 

6,000 

--------------------,- 139,000 

0 

133,000 

m 

w.i~rl'I.---A-ft.l#i~-.............,...,_--------------ll!lfF.\:------.;::---+ 131,000 

---~---------------+ 129,000 3 

(1) 

:::, ---------o=---a----------------1+-t- 121,000 ;: 

:::, 

... 

125,000 g 

C 

(h 

I» 

:::, 

Q. 

(h 

-

Chart 9 

JOLTS Hires and Total Separations and CES Employment, Total Nonfarm, 

seasonally adjusted 

VII. Conclusion 

JOLTS provides valuable information about the US labor market. BLS devoted significant time and effort to assessing the 

quality of the JOLTS data and making improvements in a number of areas. Improvements in sample design, data collection, 

and estimation have increased the value of the data to planners and researchers, and have allowed us all to better understand 

developments over the past business cycle.