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Economic News Release
JOLTS JLT Program Links

State Job Openings and Labor Turnover Technical Note

Technical Note

This news release presents statistics from the Job Openings and Labor Turnover Survey (JOLTS). The JOLTS 
program provides information on labor demand and turnover. Additional information about the JOLTS program can 
be found at State estimates are published for job openings, hires, quits, layoffs and discharges, and 
total separations. The JOLTS program covers all private nonfarm establishments, as well as civilian federal, state, 
and local government entities in the 50 states and the District of Columbia. Starting with data for January 2023, 
industries are classified in accordance with the 2022 North American Industry Classification System.


Employment. Employment includes persons on the payroll who worked or received pay for the pay period that 
includes the 12th day of the reference month. Full-time, part-time, permanent, short-term, seasonal, salaried, and 
hourly employees are included, as are employees on paid vacation or other paid leave. Proprietors or partners of 
unincorporated businesses, unpaid family workers, or employees on strike for the entire pay period, and employees 
on leave without pay for the entire pay period are not counted as employed. Employees of temporary help agencies, 
employee leasing companies, outside contractors, and consultants are counted by their employer of record, not by 
the establishment where they are working. JOLTS does not publish employment estimates but uses the reported 
employment for validation of the other reported data elements.

Job Openings. Job openings include all positions that are open on the last business day of the reference month. 
A job is open only if it meets all three of these conditions: 
* A specific position exists and there is work available for that position. The position can be full-time or part-
time, and it can be permanent, short-term, or seasonal. 
* The job could start within 30 days, whether or not the employer can find a suitable candidate during that time. 
* The employer is actively recruiting workers from outside the establishment to fill the position. Active recruiting 
means that the establishment is taking steps to fill a position. It may include advertising in newspapers, on 
television, or on the radio; posting internet notices, posting "help wanted" signs, networking, or making "word-
of-mouth" announcements; accepting applications; interviewing candidates; contacting employment agencies; 
or soliciting employees at job fairs, state or local employment offices, or similar sources.

Excluded are positions open only to internal transfers, promotions or demotions, or recall from layoffs. Also 
excluded are openings for positions with start dates more than 30 days in the future; positions for which employees 
have been hired but the employees have not yet reported for work; and positions to be filled by employees of 
temporary help agencies, employee leasing companies, outside contractors, or consultants. The job openings rate is 
computed by dividing the number of job openings by the sum of employment and job openings and multiplying that 
quotient by 100.

Hires. Hires include all additions to the payroll during the entire reference month, including newly hired and 
rehired employees; full-time and part-time employees; permanent, short-term, and seasonal employees; employees 
who were recalled to a job at the location following a layoff (formal suspension from pay status) lasting more than 7 
days; on-call or intermittent employees who returned to work after having been formally separated; workers who 
were hired and separated during the month, and transfers from other locations. Excluded are transfers or promotions 
within the reporting location, employees returning from strike, employees of temporary help agencies, employee 
leasing companies, outside contractors, or consultants. The hires rate is computed by dividing the number of hires by 
employment and multiplying that quotient by 100.

Separations. Separations include all separations from the payroll during the entire reference month and is 
reported by type of separation:  quits, layoffs and discharges, and other separations. Quits include employees who 
left voluntarily, with the exception of retirements or transfers to other locations. Layoffs and discharges includes 
involuntary separations initiated by the employer, such as layoffs with no intent to rehire; layoffs (formal 
suspensions from pay status) lasting or expected to last more than 7 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 (whether or not they are expected to return the next season). Other 
separations include retirements, transfers to other locations, separations due to employee disability; and deaths. 
Other separations comprise less than 8 percent of total separations. Other separations rates are generally very low, 
and other separations variance estimates are relatively high. Consequently, the other separations component is not 
published for states. 

Excluded from separations are transfers within the same location; employees on strike; employees of temporary help 
agencies, employee leasing companies, outside contractors, or consultants. The separations rate is computed by 
dividing the number of separations by employment and multiplying that quotient by 100. The quits and layoffs and 
discharges rates are computed similarly.

State Estimation Method

The JOLTS survey design is a stratified random sample of approximately 21,000 nonfarm business and 
government establishments. The sample is stratified by ownership, region, industry sector, and establishment size 
class. The JOLTS sample of 21,000 establishments does not directly support the production of sample-based state 
estimates. However, state estimates have been produced by combining the available sample with model-based 

The state estimates consist of four major estimating models; the Composite Regional model (an unpublished 
intermediate model), the Synthetic model (an unpublished intermediate model), the Composite Synthetic model 
(published historical series through the most current benchmark year), and the Extended Composite Synthetic model 
(published current-year monthly series). The Composite Regional model uses JOLTS microdata, JOLTS regional 
published estimates, and Current Employment Statistics (CES) employment data. The Composite Synthetic model 
uses JOLTS microdata and Synthetic model estimates derived from monthly employment changes in microdata from 
the Quarterly Census of Employment and Wages (QCEW), and JOLTS published regional data. The Extended 
Composite Synthetic model extends the Composite Synthetic estimates by ratio-adjusting the Composite Synthetic 
model by the ratio of the current Composite Regional model estimate to the Composite Regional model estimate 
from the previous year.

The Extended Composite Synthetic model (and its major component-the Composite Regional model) is used 
to extend the Composite Synthetic estimates because all of the inputs required by this model are available at the time 
monthly estimate are produced. In contrast, the Composite Synthetic model (and its major component-the 
Synthetic model) can only be produced when the latest QCEW data are available. The Extended Composite 
Synthetic model estimates are used to extend the Composite Synthetic model estimates during the annual JOLTS
retabulation process. The extension of the Composite Synthetic model using current data-based Composite Regional 
model estimates ensures that the Composite Synthetic model estimates reflect current economic trends.

The Composite Regional approach calculates state-level JOLTS estimates from JOLTS microdata using sample 
weights and the adjustments for non-response. The Composite Regional estimate is then benchmarked to CES state-
supersector employment to produce state-supersector estimates. The JOLTS sample, by itself, cannot ensure a 
reasonably sized sample for each state-supersector cell. The small JOLTS sample results in several state-supersector 
cells that lack enough data to produce a reasonable estimate. To overcome this issue, the state-level estimates 
derived directly from the JOLTS sample are augmented using JOLTS regional estimates when the number of 
respondents is low (that is, less than 30). This approach is known as a composite estimate, which leverages the small 
JOLTS sample to the greatest extent possible and supplements that with a model-based estimate. Previous research 
has found that regional industry estimates are a good proxy at finer levels of geographical detail. That is, one can 
make a reliable prediction of JOLTS estimates at the regional-level using only national industry-level JOLTS rates. 
The assumption in this approach is that one can make a good prediction of JOLTS estimates at the state-level using 
only regional industry-level JOLTS rates.)

In this approach, the JOLTS microdata-based estimate is used, without model augmentation, in all state-
supersector cells that have 30 or more respondents. The JOLTS regional estimate will be used, without a sample-
based component, in all state-supersector cells that have fewer than five respondents. In all state-supersector cells 
with 5 to 30 respondents, an estimate is calculated that is a composition of a weighted estimate of the microdata-
based estimate and a weighted estimate of the JOLTS regional estimate. The weight assigned to the JOLTS data in 
those cells is proportional the number of JOLTS respondents in the cell (weight=n/30, where n is the number of 
respondents). The sum of state estimates within a region is made equal to the aligned regional JOLTS published 
regional estimates.

Seasonal adjustment. BLS uses the seasonal adjustment program (X-13ARIMA-SEATS) to seasonally adjust 
the JOLTS series. Each month, a concurrent seasonal adjustment methodology uses all relevant data, up to and 
including the current month, to calculate new seasonal adjustment factors. Moving averages are used as seasonal 
filters in seasonal adjustment. JOLTS seasonal adjustment includes both additive and multiplicative models, as well 
as regression with autocorrelated errors (REGARIMA) modeling, to improve the seasonal adjustment factors at the 
beginning and end of the series and to detect and adjust for outliers in the series. 

Annual estimates and benchmarking. The JOLTS state estimates utilize and leverage data from three BLS 
programs; JOLTS, CES, and QCEW. These state estimates are published as a historical series made up of a 
historical annually revised benchmark component ofthe Composite Synthetic model and a current component of the 
Extended Composite Synthetic model that provides monthly "real-time" estimates between lagged benchmarks.

The JOLTS employment levels are ratio-adjusted to the CES employment levels, and the resulting ratios are 
applied to all JOLTS data elements.

The seasonally adjusted estimates are recalculated for the most recent 5 years to reflect updated seasonal 
adjustment factors. These annual updates result in revisions to both the seasonally adjusted and not seasonally 
adjusted JOLTS data series for the period since the last benchmark was established.

Annual levels for hires, quits, layoffs and discharges, other separations, and total separations are the sum of the 
12 published monthly levels. 

Annual average levels for job openings are calculated by dividing the sum of the 12 published monthly levels 
by 12. 

Annual average rates for hires, total separations quits, and layoffs and discharges are calculated by dividing the 
sum of the 12 monthly JOLTS published levels for each data element by the sum of the 12 monthly CES published 
employment levels, and multiplying that quotient by 100. 

Annual average rates for job openings are calculated by dividing the sum of the 12 monthly JOLTS published 
levels by the sum of the 12 monthly CES published employment levels plus the sum of the 12 monthly job openings 
levels, and multiplying that quotient by 100.)

Reliability of the estimates

JOLTS estimates are subject to two types of error:  sampling error and nonsampling error.

Sampling error can result when a sample, rather than an entire population, is surveyed. There is a chance that 
the sample estimates may differ from the true population values they represent. The exact difference, or sampling 
error, varies with the sample selected, and this variability is measured by the standard error of the estimate. BLS 
analyses are generally conducted at the 90-percent level of confidence. This means that there is a 90-percent chance 
that the true population mean will fall into the interval created by the sample mean plus or minus 1.65 standard 
errors. Estimates of median standard errors are released monthly as part of the significant change tables on the 
JOLTS webpage. Standard errors are updated annually with the most recent 5 years of data. For sampling error 
estimates, see

Nonsampling error can occur for many reasons, including the failure to include a segment of the population, the 
inability to obtain data from all units in the sample, the inability or unwillingness of respondents to provide data on a 
timely basis, mistakes made by respondents, errors made in the collection or processing of the data, and errors from 
the employment benchmark data used in estimation. The JOLTS program uses quality control procedures to reduce 
nonsampling error in the survey's design. 

The JOLTS state variance estimates account for both sampling error and the error attributable to modeling. A 
small area domain model uses a Bayesian approach to develop estimates of JOLTS state variance. The small area 
model uses QCEW-based JOLTS synthetic model data to generate a Bayesian prior distribution, then updates the 
prior distribution using JOLTS microdata and sample-based variance estimates at the state and US Census regional 
level to generate a Bayesian posterior distribution. Once the Bayesian posterior distribution has been generated, 
estimates of JOLTS state variances are made by drawing 2,500 estimates from the Bayesian posterior distribution. 
This Bayesian approach thus indirectly accounts for sampling error and directly for model error.

Other information

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Last Modified Date: May 17, 2024