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Economic News Release
LAU LAU Program Links

Regional and State Unemployment, 2023 Annual Average Technical Note

Technical Note

This release presents labor force and unemployment data for census regions and divisions and states
from the Local Area Unemployment Statistics (LAUS) program. The LAUS program is a federal-state
cooperative endeavor.


Definitions. The labor force and unemployment data are based on the same concepts and definitions
as those used for the official national estimates obtained from the Current Population Survey (CPS),
a sample survey of households that is conducted for the Bureau of Labor Statistics (BLS) by the U.S.
Census Bureau. The LAUS program measures employed and unemployed people on a place-of-residence
basis. The universe for each is the civilian noninstitutional population 16 years of age and older.
Employed people are those who did any work at all for pay or profit in the reference week (the week
including the 12th of the month) or worked 15 hours or more without pay in a family business or farm,
plus those not working who had a job from which they were temporarily absent, whether or not paid,
for such reasons as labor management dispute, illness, or vacation. Unemployed people are those who
were not employed during the reference week (based on the definition above), had actively looked for
a job sometime in the 4-week period ending with the reference week, and were currently available for
work; people on layoff expecting recall need not be looking for work to be counted as unemployed.
The labor force is the sum of employed and unemployed people. The unemployment rate is the number 
of unemployed people expressed as a percent of the labor force. The employment-population ratio is
the proportion of the civilian noninstitutional population 16 years of age and older that is employed.

Method of estimation. Estimates for 48 of the 50 states, the District of Columbia, the Los Angeles-Long
Beach-Glendale metropolitan division, New York City, and the balances of California and New York State
are produced using estimating equations based on regression techniques. This method uses data from 
several sources, including the CPS, the Current Employment Statistics (CES) survey of nonfarm payroll
employment, and state unemployment insurance (UI) programs. Estimates for the state of California are
derived by summing the estimates for the Los Angeles-Long Beach-Glendale metropolitan division and 
the balance of California. Similarly, estimates for New York State are derived by summing the estimates
for New York City and the balance of New York State. Estimates for all nine census divisions are 
based on a similar regression approach that does not incorporate CES or UI data. Estimates for census
regions are obtained by summing the model-based estimates for the component divisions and then 
calculating the unemployment rate. Each month, census division estimates are controlled to national
totals; state estimates are then controlled to their respective division totals. Estimates for Puerto
Rico are derived from a monthly household survey similar to the CPS. A detailed description of the
estimation procedures is available from BLS upon request. 

Annual revisions. Labor force and unemployment data for prior years reflect adjustments made at the
beginning of each year. The adjusted estimates incorporate updated population controls from the U.S.
Census Bureau, any revisions in the other data sources, and model re-estimation. The population 
controls reflect a "blended base," with elements from three independent data sources for April 1, 2020.
In most years, historical data for the most recent 5 years (both seasonally adjusted and not seasonally
adjusted) are revised near the beginning of each calendar year, prior to the release of January 
estimates. Though the labor force estimates typically are updated for 5 years, the population controls
are revised back to the most recent decennial estimates base (April 2020).

Reliability of the estimates

The estimates presented in this release are based on sample surveys, administrative data, and modeling
and, thus, are subject to sampling and other types of errors. Sampling error is a measure of sampling
variability—that is, variation that occurs by chance because a sample rather than the entire population
is surveyed. Survey data also are subject to nonsampling errors, such as those which can be introduced
into the data collection and processing operations. Estimates not directly derived from sample surveys
are subject to additional errors resulting from the specific estimation processes used. In table 1,
level estimates for states may not sum to level estimates for regions and divisions because of rounding.
Unemployment rates and employment-population ratios are computed from unrounded levels and, thus,
may differ slightly from rates and ratios computed using the rounded level estimates displayed in table

Use of error measures. Changes in unemployment rates and employment-population ratios are cited in the
analysis of this release only if they have been determined to be statistically significant. Furthermore,
unemployment rates and employment-population ratios for the latest year generally are cited only if 
they have been determined to be significantly different from the corresponding U.S. measure. The 
underlying model-based error measures are available online at BLS uses
90-percent confidence levels in determining whether changes in LAUS unemployment rates and employment-
population ratios are statistically significant. The average magnitude of the over-the-year change in
an annual state unemployment rate that is required in order to be statistically significant at the 90-
percent confidence level is about 0.4 percentage point. The average magnitude of the over-the-year 
change in an annual state employment-population ratio that is required in order to be statistically 
significant at the 90-percent confidence level is about 0.8 percentage point. Measures of nonsampling
error are not available.

Additional information

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Last Modified Date: March 01, 2024