Metropolitan Area Employment and Unemployment Technical Note

Technical Note

   This release presents labor force and unemployment data from the Local
Area Unemployment Statistics (LAUS) program (tables 1 and 2) for 387
metropolitan statistical areas and metropolitan New England City and
Town Areas (NECTAs), plus 7 areas in Puerto Rico. Estimates for 38
metropolitan and NECTA divisions also are presented. Nonfarm payroll
employment estimates from the Current Employment Statistics (CES)
program (tables 3 and 4) are provided for the same areas. State
estimates were previously published in the news release, Regional and
State Employment and Unemployment, and are republished in this release
for ease of reference. The LAUS and CES programs are both federal-state
cooperative endeavors.
 
Labor force and unemployment--from the LAUS program

   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 as a percent of the labor
force.

   Method of estimation. Estimates for states, the District of Columbia,
the Los Angeles-Long Beach-Glendale metropolitan division, and New York City
are produced using time-series models with real-time benchmarking to national
CPS totals. Model-based estimates are also produced for the following areas
and their respective balances: the Chicago-Naperville-Arlington Heights, IL
Metropolitan Division; Cleveland-Elyria, OH Metropolitan Statistical Area;
Detroit-Warren-Dearborn, MI Metropolitan Statistical Area; Miami-Miami
Beach-Kendall, FL Metropolitan Division; and Seattle-Bellevue-Everett, WA
Metropolitan Division. Modeling improves the statistical basis of the
estimation for these areas and provides important tools for analysis, such
as measures of errors and seasonally adjusted series. For all other substate
areas in this release, estimates are prepared through indirect estimation
procedures using a building-block approach. Employment estimates, which are
based largely on "place of work" estimates from the CES program, are adjusted
to refer to place of residence as used in the CPS. Unemployment estimates are
aggregates of persons previously employed in industries covered by state
unemployment insurance (UI) laws and entrants to the labor force data from
the CPS. The substate estimates of employment and unemployment, which
geographically exhaust the entire state, are adjusted proportionally to
ensure that they add to the independently estimated state or balance-of-
state totals. A detailed description of the estimation procedures is
available from BLS upon request.

   Annual revisions. Labor force and unemployment data shown for the prior
year reflect adjustments made at the beginning of each year, usually
implemented with the issuance of January estimates. The adjusted model-based
estimates typically reflect updated population data from the U.S. Census
Bureau, any revisions in other input data sources, and model re-estimation.
All substate estimates then are re-estimated using updated inputs and adjusted
to add to the revised model-based totals. In early 2015, a new generation of
time-series models was implemented, resulting in the replacement of data back
to the series  beginnings. At the same time, enhancements were made to the
substate estimation methodology, and more timely inputs from the American
Community Survey were incorporated.

Employment--from the CES program

   Definitions. Employment data refer to persons on establishment payrolls who
receive pay for any part of the pay period that includes the 12th of the month.
Persons are counted at their place of work rather than at their place of
residence; those appearing on more than one payroll are counted on each payroll.
Industries are classified on the basis of their principal activity in accordance
with the 2012 version of the North American Industry Classification System.

   Method of estimation. CES State and Area employment data are produced using
several estimation procedures. Where possible these data are produced using a 
"weighted link relative" estimation technique in which a ratio of current-month
weighted employment to that of the previous-month weighted employment is computed
from a sample of establishments reporting for both months. The estimates of
employment for the current month are then obtained by multiplying these ratios
by the previous month's employment estimates. The weighted link relative technique
is utilized for data series where the sample size meets certain statistical
criteria.

   For some employment series, the sample of establishments is very small or
highly variable. In these cases, a model-based approach is used in estimation.
These models use the direct sample estimates (described above), combined with
forecasts of historical (benchmarked) data to decrease volatility in estimation.
Two different models (Fay-Herriot Model and Small Domain Model) are used depending
on the industry level being estimated. For more detailed information about each
model, refer to the BLS Handbook of Methods.

   Annual revisions. Employment estimates are adjusted annually to a complete
count of jobs, called benchmarks, derived principally from tax reports that are
submitted by employers who are covered under state unemployment insurance (UI)
laws. The benchmark information is used to adjust the monthly estimates between the
new benchmark and the preceding one and also to establish the level of employment
for the new benchmark month. Thus, the benchmarking process establishes the level
of employment, and the sample is used to measure the month-to-month changes in the
level for the subsequent months.

   Seasonal adjustment. Payroll employment data are seasonally adjusted for states,
metropolitan areas, and metropolitan divisions at the total nonfarm level. For
states, data are seasonally adjusted at the supersector level as well. Revisions
of historical data for the most recent 5 years are made once a year, coincident
with annual benchmark adjustments.

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. The sums of
individual items may not always equal the totals shown in the same
tables because of rounding. Unemployment rates are computed from
unrounded data and thus may differ slightly from rates computed using
the rounded data displayed in the tables.

   Labor force and unemployment estimates. Measures of sampling error
are not available for metropolitan areas or metropolitan divisions.
Model-based error measures for states are available on the BLS website
at www.bls.gov/lau/lastderr.htm. Measures of nonsampling error are not
available for the areas contained in this release. Information on recent
data revisions for states and local areas is available online at
www.bls.gov/lau/launews1.htm.

   Employment estimates. Measures of sampling error are available for
metropolitan areas or metropolitan divisions upon request. Measures of 
sampling error for states down to the supersector level are available on
the BLS website at www.bls.gov/sae/790stderr.htm. Measures of nonsampling
error are not available for the areas contained in this release. Information
on recent benchmark revisions is available online at
www.bls.gov/sae/benchmark2016.pdf.

Area definitions

   The substate area data published in this release reflect the delineations
issued by the U.S. Office of Management and Budget on February 28, 2013. Data
reflect New England City and Town Area (NECTA) definitions, rather than
county-based definitions, in the six New England States. A detailed list of
the geographic definitions is available online at www.bls.gov/lau/lausmsa.htm.

Additional information
  
   Estimates of unadjusted and seasonally adjusted labor force and unemployment
data for states, census regions and divisions, and seven substate areas are
available in the news release, Regional and State Employment and Unemployment.
Estimates of labor force and unemployment for all states, metropolitan areas,
counties, cities with a population of 25,000 or more, and other areas used in
the administration of various federal economic assistance programs are available
on the Internet at www.bls.gov/lau/. Employment data from the CES program are
available on the BLS website at www.bls.gov/sae/.

   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.



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Last Modified Date: November 30, 2016