Frequently Asked Questions (FAQs)
How can I receive a copy of the State and Area press release via e-mail?
To receive a copy of Regional and State Employment and Unemployment (LAUS) or Metropolitan Area Employment
and Unemployment (METRO) monthly press releases via e-mail, link to the BLS News Release Subscription page
at www.bls.gov/bls/list.htm and subscribe to the e-mail service.
Please explain the seasonal adjustment process.
Over the course of a year, the size of a state's employment
level undergoes sharp fluctuations due to such
seasonal events as changes in weather, reduced or expanded production, harvests, major holidays, and the
opening and closing of schools. Because these seasonal events follow a more or less regular pattern each year,
their influence on statistical trends can be eliminated by adjusting the statistics from month-to-month. These
adjustments make it easier to observe the cyclical and other nonseasonal movements in the series. In evaluating
seasonally adjusted series, it is important to note that seasonal adjustment is merely an
approximation based on past experience. Seasonally adjusted estimates have a broader margin of possible error
original data on which they are based, because they are subject not only to sampling and other errors
but are also affected by the uncertainties of the seasonal adjustment process itself. Employment data are
seasonally adjusted with a procedure called X-13 ARIMA-SEATS. State major industry division employment are
available electronically via the Internet and are published monthly in Employment and Earnings.
For a further examination of our seasonal adjustment process please consult the following resources:
Berger, Frank and Keith Phillips, The Disappearing January Blip and Other State Employment Mysteries,
Working Paper 94-03, Federal Reserve Bank of Dallas, February 1994.
Dagum, Estela Bee. The X-11 ARIMA Seasonal Adjustment Method. Ottawa, Statistics Canada, January
1983, Statistics Canada Catalogue No. 12-564E.
Shipp, Kenneth and Thomas J. Sullivan, “Using X-11 ARIMA to Seasonally Adjust State Level Industry
Employment Data in the Current Employment Statistics Program,” Proceedings of the ASA Business and
Statistics Section, 1992.
Stuart Scott, George Stamas, Thomas J. Sullivan, and Paul Chester, Seasonal Adjustment of Hybrid
Economic Time Series, American Statistical Association, Toronto, Canada, 1994.
What is the CES definition of employment?
Employment is the total number of persons on establishment payrolls
employed full or part time who received pay for any part of the pay period
which includes the 12th day of the month. Temporary and intermittent
employees are included, as are any workers who are on paid sick leave, on
paid holiday, or who work during only part of the specified pay period. A
striking worker who only works a small portion of the survey period, and
is paid, would be included as employed under the CES definitions. Persons
on the payroll of more than one establishment are counted in each
establishment. Data exclude proprietors, self-employed, unpaid family or
volunteer workers, farm workers, and domestic workers. Persons on layoff
the entire pay period, on leave without pay, on strike for the entire
period or who have not yet reported for work are not counted as employed.
Government employment covers only civilian workers.
On what basis are the industries in the CES Survey
A sample establishment in the CES survey is an
economic unit, such as a factory, which produces goods or services. It is
generally at a single location and engaged predominantly in one type of
economic activity. Establishments reporting on the schedule (form BLS 790)
are classified into industries based on their principal product or
activity determined from information on annual sales volume. This industry
classification, based on the 2012 North American Industry Classification
System (NAICS) Manual, is collected on a supplement to the quarterly unemployment
insurance tax reports filed by each employer. For an establishment making
more than one product, the entire employment is included under the
industry of the principal product or activity.
What kinds of hours and earnings data are
Geographic hours and earnings CES estimates are
available only for production workers primarily in manufacturing industries. Because
not all sample respondents report production worker, hours, and earnings
data, insufficient sample exists to make corresponding industry estimates
of average weekly hours and average hourly earnings outside of
manufacturing at the statewide level. National estimates of average
weekly hours and average hourly earnings are made for the private sector,
with detail for about 850 private industries as well as for overtime hours
Hours and earnings are derived from reports of gross payrolls and
corresponding paid hours for production workers, construction workers, or
nonsupervisory workers in the service sector. The payroll for workers
covered by the CES survey is reported before deductions of any kind, e.g.,
for old-age and unemployment insurance, withholding tax, union dues or
retirement plans. Included in the payroll reports is pay for overtime,
vacations, holidays and sick leave paid directly by the firm. Bonuses,
commissions, and other non-wage cash payments are excluded unless they are
earned and paid regularly—at least once a month. Employee benefits paid
by the employer, tips, and payments in kind also are excluded.
Total hours during the pay period include all hours worked (including
overtime hours) and hours paid for holidays, vacations, and sick leave.
Total hours differs from the concept of scheduled hours worked. The
average weekly hours reflects effects of numerous factors such as unpaid
absenteeism, labor turnover, part-time work, strikes, and fluctuations in
work schedules for economic reasons. Overtime hours in manufacturing are
collected where overtime premiums were paid if hours were in excess of the
number of straight time hours in a workday or workweek.
How are the data in the Current Employment
Statistics Survey collected?
Each month BLS collects
data on employment, hours, and earnings from a sample of about 634,000
nonfarm establishments which employ nearly 40 percent of the total nonfarm
population. All establishments with 1000 employees or more are asked to
participate in the survey along with a representative sample of smaller
establishments. Sample respondents extract the requested data from their
payroll records, which must be maintained for a variety of tax and
accounting purposes. Initially, data were collected primarily by mail until recent
BLS initiatives in collection methodology increased the use of
electronic media. Now, web collection, phone collection, touch-tone self response,
computer-assisted interviews, fax technology, and voice recognition are
also being used to obtain higher and faster response rates.
Data submitted on the schedules are used by BLS analysts in developing
statewide and major metropolitan area estimates. All states' samples are combined to form a collective sample
for developing national industry estimates. Statewide samples range from
nearly 71,000 sample units in California to about 1,900 units in smaller
states. It should be noted that BLS estimation procedures are designed
to produce accurate data for each individual state. BLS independently
develops the national employment series and does not force state estimates
to sum to national totals nor vice versa. Because each state series is
subject to larger sampling and non-sampling errors than the national
series, summing them cumulates individual state levels errors and can
cause significant distortions at an aggregate level. Due to these
statistical limitations, BLS does not compile a “sum of states” employment
series and cautions users that such a series is subject to a relatively
large and volatile error structure.
How are estimates in the CES Survey
Employment estimates are made at the publication cell level and aggregated upward to broader levels of industry detail.
A minimum guaranteed publication structure has been defined for all
States and MSAs. The structure consists of “expanded”
which break Manufacturing; Trade, Transportation, and Utilities; and
Government into further publication detail. The guaranteed publication
cells aggregate to the summary cells of goods-producing,
service-providing, total private, and total nonfarm employment. All other
published series had to pass a minimum sufficiency test of at least 30
unique unemployment insurance (UI) accounts in its sample, or a minimum
universe employment count of 3,000 with at least 50 percent covered by the
sample. The series were tested using employment data from the Covered
Employment and Wages program (CEW, or ES-202). See www.bls.gov/sae/saenaics.htm#guaranteed
for more information.
Guaranteed industries that do not pass the minimum sufficiency test are
estimated using a regression model. The CES Small Domain Model (SDM)
is a Weighted Least Squares model with three employment inputs: (1) an
estimate based on available CES sample for that series, (2) an ARIMA
projection based on trend from 10 years of historical data, and (3) an
estimate “borrowed” from the Statewide series for that industry.
In addition to the guaranteed industries, Sectors may be modeled at the
Statewide level. Approximately 44 percent of State and area CES series
For each non-summary cell a total level of benchmark employment is obtained
for a specific month (usually March). The sample data from reporters who
responded for consecutive months provides a link relative sample ratio. This ratio is applied to the benchmark employment month to produce an
April employment estimate. This process continues each month until the
next annual benchmark cycle when estimates are replaced with population
data. States also use a net birth/death factor to supplement the link
relative estimator in the monthly estimation process. Birth/death
factors are used to compensate for the inability to capture the entry of
new firms into the sample, as well as the exit of firms that went out of
business from the sample, on a timely basis.
For example, assume the benchmark level was 50,000 in March. The
sample, composed of 50 establishments which reported both months had
25,000 in March and 26,000 in April, a 4 percent increase. Also, there is
an April birth/death factor of 300. To derive the
April estimate, the change of these identical establishments reported is
applied to the March benchmark level in the form of a sample ratio, then
the birth/death factor is applied to this number: (50,000
x 26,000/25,000) + 300 = 52,300.
Why are estimates in the CES Survey benchmarked?
To control potential survey error, the estimates are benchmarked annually to universe counts derived from
administrative files of employees covered by unemployment insurance (UI). Original sample-based estimates are
replaced with benchmark data from the previous year through at least March of the benchmark year. In the
current 2017 benchmark, the estimates from April 2015 to September 2016 were replaced with UI-based universe counts.
For more info, see our Benchmark article at www.bls.gov/sae/benchmark2017.pdf.
Once the new September 2015 level was determined, the subsequent estimates were recalculated by applying the
appropriate sample links to the new levels. These links may differ slightly from those used to derive the original
estimates, because they account for late reporters. The entire period from October 2016 forward is referred to
as the post-benchmark projection period. This process was completed and the revised data were released with the
January 2017 estimates.
Are CES Estimates Revised? If so,
Yes, estimates are revised in the following manner.
Initial monthly estimates are calculated from an incomplete sample and are subject to revision in the subsequent month when more sample data are available.
s at the total nonfarm levels for preliminary statewide employment are generally small. For more info on preliminary-to-final revisions, please visit www.bls.gov/sae/saerevtxt.htm.
“Final” estimates are subject to annual benchmarks of universe counts of employment derived from
the unemployment insurance (UI) reports from employers. The average absolute benchmark revision at the state
total nonfarm level was 0.4 percent in March 2016. The average absolute revision from 2011 to 2015 was 0.5 percent.
The range of the percentage revision for the states at the total nonfarm level was from -1.6 to 0.9 percent in March 2016.
A positive revision indicates that the benchmark value is greater than the sample-based estimate and vice-versa.
Twenty-two states and the District of Columbia revised total nonfarm payroll employment upward, while 28 states
had downward revisions. Click here for more information on the benchmark.
Are CES data subject to administrative
As mentioned earlier, geographic data are subject to
changes in administrative mandates for revising NAICS and metropolitan area
definitions. The CES program has consistently attempted to maintain
industry and area time series, particularly at guaranteed publication levels,
where data and resources permit. Data are annotated where reconstruction
of time series is not possible.
What distinguishes the CES geographic data from
other economic data? Are there potential problems with using the data?
Advantages of CES Geographic Data
are a coincident economic indicator and are often cited in
national and local newspapers, magazines, and reports. This press
generates enthusiasm, curiosity and a wealth of outside material for
supplementary reading. The College of Business Administration at the
University of South Carolina uses seasonally adjusted employment as an
indicator of current employment trends in South Carolina. The regional
Federal Reserve Banks use CES data in easy-to-understand economic
applications. For example, the edition of the Southwest Economy
from the Federal Reserve Bank of Dallas used employment and unemployment
data in two different articles: one explaining the Phillips curve and
another describing the changing job market. Students and faculty can write
the regional FRBs to be placed on their mailing lists. The Philadelphia,
Dallas, Boston, Cleveland, and San Francisco FRBs provide excellent
articles for undergraduate students.
CES data are tangible and versatile. Employment,
hours, and earnings data can be used to study abstract economic concepts
which students can more easily comprehend with the use of data. Students
often need help in seeing how formal models can be used to explain the
real world economy. Business cycles, the effects of shocks in the economy,
and the impact of policy changes are examples of concepts that are more
readily understood when using CES data. Also, combined with data from
other sources, such as output data from the national accounts, they can be
used to compute productivity and other measures. Primarily, the concept of
employment is easy to comprehend, which permits a wide range of study and
understanding by graduate and undergraduate students, policy makers, and
business people. Data can be used for projects in labor economics, time
series analysis, business cycle theory, statistics, geography, urban
planning, and public policies.
CES data invite comparisons and analysis. CES data
provides complete coverage and consistently derived methodology at the state and area levels for employment in
major industries allowing for interstate and inter-area comparisons using
CES data alone or in conjunction with other economic data. They allow one
to compare growth patterns across states and regions. One can relate
cyclical changes to geographic employment changes. For example the 1990-91
recession did not affect states and regions equally or at the same time.
Employment declines started in the Northeast and spread along the Atlantic
and Pacific coasts. The Midwest was largely unaffected. These diverse
movements among states show how the mixture of industry, migration, and
public policies affect employment. For this type of study, CES data can be
combined with and compared to census migration data, immigration data, and
public policy data that affect economic activity.
CES data are affordable. They are collected,
tabulated, and distributed as part of the BLS and States' mission to
provide economic data to policy makers, business, labor, and the public.
Subscriptions are inexpensive and data on Internet are free. Since CES
data are time series data, forecasters are able to depend on a consistent
series to use in their modeling applications without incurring excessive
Users should be aware of the intricate
revision process which the CES estimates undergo. Preliminary monthly,
final monthly, post benchmark projection, and final benchmark data are
constructed for each monthly estimate. Analysis using estimates before
they are final benchmarked estimates is affected by subsequent revisions.
Users of time series CES data should also review the entire time-series
file to note any NAICS or MSA administrative breaks where reconstruction of
series was not possible. Breaks will only be noted on the month where the
time series break occurs. For example, a comparison of total nonfarm
employment for the Washington D.C. metropolitan area between 1980 and 2003
actually involves multiple definitions of the official metropolitan area.
As mentioned earlier, the CES national estimates are independently
produced and are not an aggregation of statewide data. Therefore users
cannot disaggregate or compare CES national economic movements to state,
regional, or metropolitan area CES estimates.
CES data are not to be confused with data from the Current Population
Survey (CPS) which is a household survey. The CES survey counts jobs; the
CPS counts people. A worker with two jobs is counted twice in the CES but
only once in the CPS.
Geographic hours and earnings data from the CES are limited in industry
coverage and scope. The only extensive industry coverage is in
manufacturing. CES hours and earnings data are also limited to money wages
of production workers in manufacturing. Researchers looking at total labor
costs and total compensation should be aware of these limitations.
I have a question, but it is not on this
If you have a question related to the Current Employment Statistics Survey dealing with State and area data, feel free to send an e-mail.
I want to learn more about your interesting survey;
can you provide me with some references so I can explore on my
Barth, Molly E., “Revisions to the Current Employment Statistics
State and Area Estimates Effective January 2003,” Employment and
Earnings, March 2003.
Barth, Molly E., “Recent changes in the
State and Metropolitan Area CES survey,” Monthly Labor Review,
BLS Handbook of Methods, April 1997.
Current Employment Statistics State Operating Manual, October
1989 (with annual updates).
Dahlin, Brian, “Revisions in State
Establishment-Based Employment Estimates Effective January 2003,” Employment
and Earnings, May 2003.
Dagum, Estela Bee. The X-11 ARIMA Seasonal Adjustment Method.
Ottawa, Statistics Canada, January 1983, Statistics Canada Catalogue No.
Employment and Earnings, monthly.
Employment, Hours, and Earnings, United States, 1909-90,
volumes I and II, Bulletin 3270, March 1991, annual supplement, August
Employment, Hours, and Earnings, States and Areas , 1987-94,
Green, Gloria P., “Comparing Employment Estimates from Household and
Payroll Surveys,” Monthly Labor Review, December 1969.
Kropf, Jurgen, Strifas, Sharon and Traetow, Monica, Accounting for
Business Births and Deaths in CES: Bias vs. Net Birth/Death Modeling,
Manual on Series Available and Estimating Methods, BLS Current
Employment Statistics Program, March 1994.
Morisi, Teresa L. “Recent changes in the National CES
survey,” Monthly Labor Review, June 2003.
National Commission on Employment and Unemployment Statistics.
Counting the Labor Force, 1979.
Last Modified Date: March 13, 2017