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 www.bls.gov/jlt/. 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.
Definitions
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 estimates.
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 nonresponse. 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 of the 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
www.bls.gov/jlt/jolts_median_standard_errors.htm.
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 samplebased
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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