Economic News Release

Regional and State Unemployment, 2015 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 employment and unemployment 
on a place-of-residence basis. The universe for each is the civilian 
noninstitutional population 16 years of age and older. Employed persons 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 persons 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; persons 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 persons. The unemployment rate is the number of unemployed 
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 
utilizes 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. A detailed 
description of the estimation procedures is available from BLS upon 

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 extrapolation from the 2010 Census. 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 estimates are revised back to the decennial estimates base 
(April 2010).

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 1.

Use of error measures. The introductory section of this release preserves 
the long-time practice of highlighting the direction of the movements in 
regional and state unemployment rates and employment-population ratios 
regardless of their statistical significance. The remainder of the 
analysis in the release--other than historical highs and lows--takes 
statistical significance into consideration. 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 0.6 percentage point. Measures 
of nonsampling error are not available.

Additional information

Information in this release will be made available to sensory impaired 
individuals upon request. Voice phone: (202) 691-5200; Federal Relay 
Service: (800) 877-8339.

Table of Contents

Last Modified Date: February 26, 2016
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