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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 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

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