CES State and Area Benchmark Article (PDF)
Revisions in State Establishment-based Employment Estimates Effective January 2018
Authored by Lee Baker, TJ Lepoutre, and Anna Grace Rutledge
Lee Baker, TJ Lepoutre, and Anna Grace Rutledge are economists in the Division of Current Employment Statistics –
State and Area, Office of Employment and Unemployment Statistics, Bureau of Labor Statistics.
Telephone: (202) 691‑6559; email: Contact CES-SA
Introduction
With the release of the payroll employment estimates for January 2018, nonfarm payroll employment, hours, and
earnings data for states and areas were revised to reflect the incorporation of the 2017 benchmarks and the
recalculation of seasonal adjustment factors. The revisions affect all not seasonally adjusted data from April
2016 to December 2017, all seasonally adjusted data from January 2013 to December
20171, and select series subject to historical
revisions before April 2016. This article provides background information on benchmarking methods, business
birth/death modeling, seasonal adjustment of employment data, and details of the effects of the 2017 benchmark
revisions on state and area payroll employment estimates.
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Summary of benchmark revisions
The average absolute percentage revision across all states for total nonfarm payroll employment is
0.4 percent for March 2017. This compares to the average of 0.5 percent for the same measure during
the five prior benchmark years of 2012 to 2016. For March 2017, the range of the percentage revision
for total nonfarm payroll employment across all states is from −1.0 to 1.2 percent.
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Benchmark methods
The Current Employment Statistics (CES) program, also known as the payroll survey, is a federal
and state cooperative program that provides, on a timely basis, estimates of payroll employment,
hours, and earnings for states and areas by sampling the population of employers. Each month
the CES program surveys about 149,000 businesses and government agencies, representing
approximately 651,000 individual worksites, in order to provide detailed industry level data on
employment and the hours and earnings of employees on nonfarm payrolls for all 50 states, the
District of Columbia, Puerto Rico, the U.S. Virgin Islands, and about 450 metropolitan areas and
divisions.2
As with data from other sample surveys, CES payroll employment estimates are subject to both
sampling and nonsampling error. Sampling error is an unavoidable byproduct of forming an inference
about a population based on a sample. The larger the sample is, relative to the population
size and variance, the smaller the sampling error. The sample-to-population ratio varies across
states and industries. Nonsampling error, by contrast, generally refers to errors in reporting and
processing.3
In order to control for both sampling and nonsampling error, CES payroll employment estimates are
benchmarked annually to employment counts from a census of the employer population. These counts
are derived primarily from employment data provided in unemployment insurance (UI) tax reports that
nearly all employers are required to file with state workforce agencies. The UI tax reports are
collected, reviewed, and edited as part of the Bureau of Labor Statistics (BLS) Quarterly Census
of Employment and Wages (QCEW) program.4 As part
of the benchmark process for benchmark year 2017, census-derived employment counts replace CES
payroll employment estimates for all 50 states and the District of Columbia, Puerto Rico, the U.S.
Virgin Islands, and about 450 metropolitan areas and divisions for the period from April 2016 to
September 2017.
UI tax reports are not collected on a timely enough basis to allow for replacement of CES payroll
estimates for the fourth quarter, October 2017 to December 2017. For this period, estimates based
on sample information are revised using the new September 2017 series level derived from
census employment counts and incorporate updated business birth/death
factors.5
Changes to CES published series
Conversion to the 2017 North American Industry Classification System (NAICS)
With the release of January 2018 data on March 12, 2018, the CES survey updated the
basis for industry classification to the 2017 North American Industry Classification
System (NAICS) from the 2012 NAICS basis.6
This conversion resulted in minor revisions reflecting content and coding changes within
retail trade and information sectors for CES State and Area. All CES series affected by
the revisions remain in-scope; thus, total nonfarm employment is not impacted in any state
or metropolitan area. The majority of the changes associated with the 2017 NAICS update
impacted levels of detail not published by CES State and Area; therefore, only the cases
in which CES State and Area industries were impacted are discussed in detail
here.7
The conversion from the 2012 NAICS to the 2017 NAICS affected CES industry codes in several ways.
Some CES series were converted as a whole from their 2012 NAICS industry code to their new
2017 NAICS industry code. Other 2012 NAICS industry codes were partially distributed to
multiple new 2017 NAICS industry codes. The changes resulting from the reclassification
from the 2012 NAICS to the 2017 NAICS for CES State and Area can be seen below
in exhibit 1.8
Exhibit 1. Reclassifications from the 2012 NAICS to the 2017 NAICS
2012
NAICS |
2012
Series Code |
2012
CES Series Title |
2017
NAICS |
2017
Series Code |
2017
CES Series Title |
| 452111 |
42452100 |
Department Stores |
452210 |
42452200 |
Department Stores |
| 452112* |
42452100 |
Department Stores |
452210* |
42452200 |
Department Stores |
| 452112* |
42452100 |
Department Stores |
452311* |
42452300 |
General Merchandise Stores, including Warehouse Clubs and Supercenters |
| 452910 |
42452900 |
Other General Merchandise Stores |
452311 |
42452300 |
General Merchandise Stores, including Warehouse Clubs and Supercenters |
| 452990 |
42452900 |
Other General Merchandise Stores |
452319 |
42452300 |
General Merchandise Stores, including Warehouse Clubs and Supercenters |
| 517110 |
50517100 |
Wired Telecommunications Carriers |
517311 |
50517311 |
Wired Telecommunications Carriers |
| 517210 |
50517200 |
Wireless Telecommunications Carriers (except Satellite) |
517312 |
50517312 |
Wireless Telecommunications Carriers (except Satellite) |
To Table of Figures
Special notice regarding the impact of Hurricanes Harvey, Irma, and Maria on CES re-estimation
A series of hurricanes struck Florida, Texas, Puerto Rico, and the U.S. Virgin Islands in
August and September 2017, complicating the post-benchmark (October-December 2017)
re-estimation process for these areas. Hurricane Harvey made landfall on the Texas Gulf Coast
in late August. The population counts obtained from QCEW indicated that the September CES
sample-based estimates suitably captured the employment drops associated with this event.
No modifications were made for Texas October or November re-estimates, which saw corresponding
returns in employment.
Hurricane Irma hit Florida just prior to the September reference period for many establishments.
The benchmark data showed a much larger decline in September than the CES estimates did. A large
part of this discrepancy was attributed to a spike in employment loss associated with business
deaths, although there was information that many of these establishments returned to normal
operation prior to October. Consequently, establishments which reported positive employment to
the CES survey in August and October, and zero in September, were used in the Florida October
re-estimates matched sample, a modification of the standard handling of business births and
deaths. In the same fashion, a small number of establishments that reported they were shut down
through the October reference period but reported positive employment in November were used in
November’s matched sample.
Hurricane Irma also struck Puerto Rico, although job losses there were more evident in October
following the destruction caused by Hurricane Maria. Modifications were made to the birth/death
procedure for re-estimates in Puerto Rico as well, to use reported zero employment for
establishment deaths attributed to the storm in the October matched sample, and to use returning
units in November and December. This procedure was also used when making the initial sample-based
estimates for October through December.
The U.S. Virgin Islands showed large job losses in the aftermath of both Irma and Maria. No
modifications were made to the re-estimation procedures for the U.S. Virgin Islands, which uses
a quota-based sample design that differs from the rest of the CES program.
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Business birth/death modeling
Sample-based estimates are adjusted each month by a statistical model designed to
reduce a primary source of nonsampling error: the inability of the sample to capture
employment growth generated by new business formations on a timely basis. There is
an unavoidable lag between an establishment opening for business and its appearance
in the sample frame, making it unavailable for sampling. Because new firm births generate
a portion of employment growth each month, nonsampling methods must be used to estimate
this growth.
Earlier research indicated that, while both the business birth and death
portions of total employment are generally significant, the net contribution is relatively
small and stable. To account for this net birth/death portion of total employment, BLS
uses an estimation procedure with two components. The first component excludes employment
losses due to business deaths from sample-based estimation in order to offset the missing
employment gains from business births. This is incorporated into the sample-based estimate
procedure by simply not reflecting sample units going out of business, but rather imputing
to them the same employment trend as the other continuing firms in the sample. This step
accounts for most of the birth and death changes to
employment.9
The second component is an autoregressive integrated moving average (ARIMA) time series
model designed to estimate the residual birth/death change to employment not accounted for
by the imputation. To develop the history for modeling, the same handling of business deaths
as described for the CES monthly estimation is applied to the population data. Establishments
that go out of business have employment imputed for them based on the rate of change of the
continuing units. The employment associated with continuing units and the employment imputed
from deaths are aggregated and compared to actual population levels. The differences between
the two series reflect the actual residual of births and deaths over the past five years. The
historical residuals are converted to month-to-month differences and used as input series to
the modeling process. Models for the residual series are then fit and forecasted using X-13
ARIMA-SEATS software.10
The residuals exhibit a seasonal pattern and may be negative for some months. This process is
performed at the national level and for each individual state. Finally, differences between
forecasts of the nationwide birth/death factors and the sum of the states’ birth/death factors
are reconciled through a ratio-adjustment procedure, and the factors are used in monthly
estimation of payroll employment in 2018. The updated birth/death factors are also used as
inputs to produce the revised estimates of payroll employment for October 2017 to December 2017.
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Seasonal adjustment
CES State and Area payroll employment data are seasonally adjusted by a two-step
process.11 BLS uses the X-13 ARIMA-SEATS
program to remove the seasonal component of employment time series. This process uses
the seasonal trends found in census-derived employment counts to adjust historical benchmark
employment data while also incorporating sample-based seasonal trends to adjust sample-based
employment estimates. These two series are independently adjusted then spliced together at the
benchmark month (in this case September 2017).12
By accounting for the differing seasonal patterns found in historical benchmark employment data
and the sample-based employment estimates, this technique yields improved seasonally adjusted series
with respect to analysis of month-to-month employment
change.13
Seasonally adjusted employment data for the most recent 13 months are published regularly in table
D-1.14
The aggregation method of seasonally adjusted data is based upon the availability of underlying
industry data. For all 50 states, the District of Columbia, and Puerto Rico, the following series
are sums of underlying industry data: total private, goods-producing, service-providing, and
private service-providing. The same method is applied for the U.S. Virgin Islands with the exception of
goods-producing, which is independently seasonally adjusted because of data limitations. For all 50
states, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands, data for manufacturing, trade,
transportation, and utilities, financial activities, education and health services, leisure and
hospitality, and government are aggregates wherever exhaustive industry components are available;
otherwise these industries’ employment data are directly seasonally adjusted. In a very limited number
of cases, the not seasonally adjusted data for mining, construction, manufacturing, trade, transportation,
and utilities, financial activities, education and health services, leisure and hospitality, and
government do not exhibit enough seasonality to be adjusted; in those cases the not seasonally adjusted
data are used to sum to higher level industries. The seasonally adjusted total nonfarm data for all
metropolitan statistical areas (MSAs) and metropolitan divisions are not an aggregation but are derived
directly by applying the seasonal adjustment procedure to the not seasonally adjusted total nonfarm
level.15
Implementation of concurrent seasonal adjustment
With the release of January 2018 data, CES State and Area converted to concurrent
seasonal adjustment which uses all available estimates, including those for the current month,
in developing sample-based seasonal factors.16
Concurrent sample-based seasonal factors are created every month for the current month’s
preliminary estimates as well as the previous month’s final estimates in order to incorporate
the real-time estimates. Previously, CES State and Area forecasted the sample-based seasonal
factors once annually and applied these factors to the sample estimates for the remainder of
the year. CES State and Area research shows that concurrent seasonal adjustment will reduce the
revisions of the seasonally adjusted estimates compared to seasonally adjusted benchmark data
as well as reduce the month-to-month variability of the seasonally adjusted time
series.17
Variable survey intervals
BLS utilizes special model adjustments to control for survey interval variations, sometimes
referred to as the 4 vs. 5 week effect, for all nonfarm seasonally adjusted series. Although
the CES survey is referenced to a consistent concept, the pay period including the 12th
day of each month, inconsistencies arise because there are sometimes 4 and sometimes 5 weeks
between the weeks including the 12th day in a given pair of months. In highly seasonal
industries, these variations can be an important determinant of the magnitude of seasonal hires
or layoffs that have occurred at the time the survey is
taken.18
Metropolitan statistical area (MSA) updates
Beginning in early 2015 with the release of the 2014 benchmark, CES updated its area definitions
to reflect new area delineations announced by the Office of Management and Budget (OMB) based
on the application of new data standards from the 2010
Census.19
New areas resulting from the BLS update in the 2014 benchmark to official 2010 area
delineations now meet the minimum requirement of three years of sample history to reliably
forecast seasonal factors.20 Therefore, all
redelineated areas are now published seasonally adjusted at the total nonfarm
level.21
CES updated its list of covered areas to include the Enid, OK MSA (FIPS 21420) beginning in
early 2017 with the release of the 2016 benchmark. This was formerly a micropolitan statistical
area that now meets the Office of Management and Budget (OMB) criteria to qualify as a
metropolitan statistical area (MSA).22
Due to the availability of only one year of sample history, BLS will not be publishing any
seasonally adjusted data for this area for at least two more years.
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Benchmark revisions
Revisions by industry
The magnitude of benchmark revisions is commonly gauged by the percentage difference
between the sample-based estimates of payroll employment and the revised benchmark
payroll employment levels for March of the benchmark year, presently March 2017. As
noted earlier, the average absolute percentage revision across all states for total
nonfarm payroll employment is 0.4 percent for March 2017. This compares to the
average of 0.5 percent for the same measure during the five prior benchmark years
of 2012 to 2016. For March 2017, the range of the percentage revision for total nonfarm
payroll employment across all states is from −1.0 to 1.2 percent. (See
table 1a.)
For December 2017, the average absolute percentage revision for state total nonfarm
payroll employment is 0.6 percent. This compares to the average of 0.6 percent
for the same measure during the five prior benchmark years of 2012 to 2016. The range
of the percentage revision for state total nonfarm payroll employment is from −2.0
to 1.9 percent for December 2017. (See table 1a.)
Absolute level revisions provide further insight on the magnitude of benchmark revisions.
Absolute level revisions are measured as the absolute difference between the sample-based
estimates of payroll employment and the benchmark levels of payroll employment for March 2017.
A relatively large benchmark revision in terms of percentage can correspond to a relatively
small benchmark revision in terms of level due to the amount of employment in the industry.
Table 1a. Absolute percentage differences between state employment
estimates and benchmarks by industry, not seasonally adjusted, March
2012 – March 2017 and December 2017 (all values in percent)
| Industry |
Mar.
2012 |
Mar.
2013 |
Mar.
2014 |
Mar.
2015 |
Mar.
2016 |
Mar.
2017 |
Dec.
2017 |
Total nonfarm |
0.7 |
0.4 |
0.5 |
0.4 |
0.4 |
0.4 |
0.6 |
Mining and logging |
4.7 |
3.7 |
2.8 |
4.2 |
4.5 |
3.7 |
6.1 |
Construction |
4.4 |
3.1 |
3.0 |
2.6 |
2.3 |
2.5 |
3.8 |
Manufacturing |
1.5 |
1.4 |
1.2 |
1.3 |
1.3 |
1.3 |
2.1 |
Trade, transportation, and utilities |
1.1 |
1.0 |
0.7 |
0.6 |
0.8 |
0.7 |
0.9 |
Information |
3.2 |
2.2 |
2.0 |
2.6 |
3.0 |
2.7 |
3.6 |
Financial activities |
2.2 |
1.6 |
2.0 |
1.9 |
2.3 |
1.6 |
1.9 |
Professional and business services |
1.9 |
1.8 |
1.6 |
1.6 |
1.4 |
1.5 |
2.1 |
Education and health services |
1.4 |
1.6 |
0.9 |
0.9 |
0.8 |
0.8 |
0.9 |
Leisure and hospitality |
2.3 |
1.4 |
1.4 |
1.4 |
1.5 |
1.6 |
1.7 |
Other services |
2.7 |
2.1 |
2.4 |
2.1 |
2.4 |
2.7 |
3.3 |
Government |
1.0 |
0.7 |
0.9 |
0.7 |
0.5 |
0.8 |
0.9 |
|
|
|
|
|
|
|
|
Total nonfarm: |
|
|
|
|
|
|
|
Range |
-1.5
to
2.2 |
-0.7
to
2.9 |
-1.5
to
2.0 |
-1.8
to
1.3 |
-1.6
to
0.9 |
-1.0
to
1.2 |
-2.0
to
1.9 |
Mean |
0.6 |
0.3 |
0.1 |
|
-0.1 |
-0.1 |
-0.3 |
Standard deviation |
0.7 |
0.6 |
0.6 |
0.5 |
0.6 |
0.5 |
0.7 |
To Table of Figures
The following example demonstrates the necessity of considering both percentage revision and level revision
when evaluating the magnitude of a benchmark revision in an industry. The average absolute percentage benchmark
revisions across all states for information and for professional and business services are 3.6 and 2.1 percent, respectively,
for December 2017. However, for December 2017, the absolute level revision across all states for the information
industry is 1,400, while the absolute level revision across all states for the professional and business services industry
is 6,000. (See table 1b.) Relying on a single measure to characterize the magnitude of
benchmark revisions in an industry can potentially lead to an incomplete interpretation.
Table 1b. Absolute level differences between state employment
estimates and benchmarks by industry, not seasonally adjusted, March
2012 – March 2017 and December 2017 (all values payroll employment)
| Industry |
Mar.
2012 |
Mar.
2013 |
Mar.
2014 |
Mar.
2015 |
Mar.
2016 |
Mar.
2017 |
Dec.
2017 |
Total nonfarm |
14,800 |
16,900 |
11,500 |
9,200 |
7,700 |
7,100 |
13,300 |
Mining and logging |
600 |
600 |
400 |
800 |
500 |
500 |
1,000 |
Construction |
4,200 |
2,700 |
2,800 |
2,500 |
2,700 |
2,200 |
3,500 |
Manufacturing |
2,200 |
1,500 |
1,700 |
2,200 |
2,200 |
2,200 |
3,400 |
Trade, transportation, and utilities |
3,900 |
3,900 |
2,600 |
2,700 |
3,300 |
2,600 |
3,900 |
Information |
1,500 |
800 |
900 |
1,100 |
1,400 |
1,000 |
1,400 |
Financial activities |
2,500 |
2,000 |
2,100 |
1,900 |
2,300 |
1,600 |
2,200 |
Professional and business services |
5,500 |
4,100 |
3,900 |
5,100 |
4,400 |
3,300 |
6,000 |
Education and health services |
4,600 |
12,000 |
3,400 |
3,700 |
3,000 |
3,200 |
3,800 |
Leisure and hospitality |
5,200 |
2,900 |
3,500 |
2,600 |
2,900 |
3,400 |
3,700 |
Other services |
2,300 |
2,000 |
2,000 |
1,800 |
1,800 |
2,200 |
3,000 |
Government |
4,100 |
2,500 |
3,900 |
2,600 |
2,300 |
3,000 |
3,900 |
|
|
|
|
|
|
|
|
Total nonfarm: |
|
|
|
|
|
|
|
Range |
-28,900
to
59,400 |
-13,700
to
428,200 |
-40,800
to
103,800 |
-103,600
to
21,200 |
-26,500
to
40,400 |
-44,900
to
16,400 |
-99,000
to
30,800 |
Mean |
13,100 |
13,800 |
5,500 |
-2,400 |
200 |
-2,300 |
-7,200 |
Standard deviation |
16,200 |
60,800 |
20,200 |
17,400 |
11,600 |
11,000 |
20,500 |
To Table of Figures
Back to Top
Revisions by state
For March 2017, 23 states and the District of Columbia revised nonfarm payroll employment upward,
while 27 states revised payroll employment downward. (See table 2 or
map 1.)
For December 2017, 17 states revised nonfarm payroll employment upward, while 33 states and the
District of Columbia revised payroll employment downward. (See table 2
or map 2.) The distribution of percent revisions for March 2017 and December
2017 can be found in exhibit 2.
Table 2. Percent differences between nonfarm payroll employment benchmarks
and estimates by state, not seasonally adjusted, March 2012 – March
2017 and December 2017 (all values in percent)
| State |
Mar.
2012 |
Mar.
2013 |
Mar.
2014 |
Mar.
2015 |
Mar.
2016 |
Mar.
2017 |
Dec.
2017 |
| Alabama |
0.6 |
0.4 |
-0.1 |
-0.3 |
0.4 |
0.8 |
0.2 |
| Alaska |
0.8 |
0.1 |
-0.2 |
0.2 |
-1.1 |
0.2 |
-0.7 |
| Arizona |
0.3 |
0.3 |
0.1 |
-0.2 |
-0.3 |
0.5 |
0.8 |
| Arkansas |
1.2 |
-0.5 |
-0.7 |
-0.6 |
(1) |
-0.2 |
-1.0 |
| California |
0.3 |
2.9 |
0.7 |
-0.7 |
(1) |
(1) |
0.2 |
| Colorado |
0.2 |
0.5 |
0.5 |
0.7 |
-0.5 |
0.4 |
0.6 |
| Connecticut |
0.6 |
0.2 |
-0.1 |
-1.0 |
-0.2 |
-0.2 |
-0.1 |
| Delaware |
0.1 |
0.2 |
0.3 |
0.4 |
-1.1 |
0.1 |
0.4 |
| District of Columbia |
-0.8 |
1.1 |
0.3 |
0.4 |
0.9 |
0.3 |
(1) |
| Florida |
0.5 |
0.3 |
-0.1 |
-0.2 |
0.5 |
-0.1 |
-0.5 |
| Georgia |
0.7 |
(1) |
0.7 |
-0.3 |
-0.6 |
-0.8 |
-0.7 |
| Hawaii |
0.5 |
1.0 |
0.6 |
0.7 |
-0.7 |
0.4 |
-0.4 |
| Idaho |
0.3 |
0.2 |
2.0 |
-0.4 |
(1) |
0.4 |
0.6 |
| Illinois |
0.7 |
0.1 |
0.5 |
0.2 |
0.1 |
0.3 |
0.4 |
| Indiana |
0.7 |
-0.2 |
-0.1 |
-0.1 |
0.6 |
-0.3 |
-0.6 |
| Iowa |
0.8 |
-0.1 |
(1) |
-0.5 |
-0.3 |
-0.5 |
-1.1 |
| Kansas |
0.9 |
-0.2 |
0.5 |
-0.2 |
0.9 |
-0.4 |
-0.3 |
| Kentucky |
-0.1 |
-0.3 |
0.3 |
-0.6 |
-0.2 |
-0.9 |
-1.4 |
| Louisiana |
-1.5 |
-0.1 |
0.5 |
0.3 |
(1) |
0.1 |
-0.4 |
| Maine |
0.3 |
(1) |
-0.7 |
0.3 |
0.6 |
0.2 |
0.1 |
| Maryland |
-0.2 |
-0.4 |
-0.3 |
-0.2 |
-0.1 |
-1.0 |
-1.9 |
| Massachusetts |
1.3 |
1.2 |
0.1 |
0.5 |
0.5 |
-0.2 |
-0.6 |
| Michigan |
1.1 |
0.9 |
1.1 |
-0.6 |
-0.5 |
-0.2 |
-0.7 |
| Minnesota |
0.8 |
(1) |
-0.6 |
-0.1 |
0.1 |
(1) |
-0.6 |
| Mississippi |
1.1 |
-0.7 |
(1) |
0.2 |
0.1 |
0.5 |
0.1 |
| Missouri |
0.4 |
1.1 |
-1.5 |
0.4 |
0.7 |
-0.3 |
-0.5 |
| Montana |
2.1 |
0.6 |
0.2 |
1.3 |
0.8 |
-0.8 |
-0.7 |
| Nebraska |
1.5 |
1.3 |
0.7 |
(1) |
-0.2 |
-0.2 |
-1.2 |
| Nevada |
0.4 |
0.5 |
-0.6 |
0.7 |
0.2 |
0.8 |
0.2 |
| New Hampshire |
0.8 |
(1) |
-0.3 |
-0.1 |
(1) |
-0.3 |
-0.3 |
| New Jersey |
0.3 |
-0.1 |
0.5 |
(1) |
-0.2 |
(1) |
0.5 |
| New Mexico |
-0.2 |
0.2 |
0.5 |
-0.4 |
0.2 |
-0.8 |
-1.3 |
| New York |
(1) |
(1) |
0.6 |
0.1 |
0.4 |
0.1 |
0.1 |
| North Carolina |
0.3 |
-0.3 |
-0.1 |
-0.5 |
0.1 |
(1) |
-0.2 |
| North Dakota |
2.0 |
-0.2 |
-1.4 |
-1.8 |
-1.6 |
-1.0 |
-2.0 |
| Ohio |
0.6 |
0.9 |
0.4 |
0.1 |
-0.2 |
(1) |
-0.2 |
| Oklahoma |
1.5 |
0.4 |
-0.3 |
0.5 |
-0.5 |
-0.1 |
0.5 |
| Oregon |
0.7 |
0.2 |
-0.4 |
(1) |
0.1 |
0.2 |
-0.3 |
| Pennsylvania |
0.4 |
(1) |
0.2 |
-0.1 |
-0.2 |
(1) |
(1) |
| Rhode Island |
1.7 |
0.4 |
-0.2 |
0.1 |
(1) |
-0.7 |
(1) |
| South Carolina |
0.3 |
0.2 |
0.5 |
-0.2 |
-0.1 |
0.5 |
-0.3 |
| South Dakota |
1.4 |
-0.1 |
0.8 |
(1) |
-0.1 |
-0.6 |
-0.2 |
| Tennessee |
0.8 |
-0.2 |
0.4 |
0.4 |
(1) |
-0.5 |
(1) |
| Texas |
0.5 |
(1) |
0.1 |
0.1 |
0.1 |
-0.4 |
-0.8 |
| Utah |
0.9 |
-0.2 |
-0.1 |
-0.2 |
0.3 |
-0.1 |
0.2 |
| Vermont |
0.5 |
0.1 |
(1) |
-0.8 |
-1.5 |
-0.7 |
-1.0 |
| Virginia |
0.1 |
0.3 |
-0.3 |
0.6 |
-0.3 |
-0.2 |
-0.4 |
| Washington |
0.1 |
1.9 |
0.6 |
-0.6 |
-0.4 |
-0.2 |
0.3 |
| West Virginia |
1.0 |
-0.7 |
-0.9 |
1.3 |
-1.2 |
0.2 |
0.1 |
| Wisconsin |
2.2 |
0.6 |
-0.3 |
0.2 |
-0.2 |
(1) |
-0.8 |
| Wyoming |
1.0 |
0.4 |
-0.7 |
-0.4 |
0.4 |
1.2 |
1.9 |
To Table of Figures
Exhibit 2. Distribution of percent revisions, March 2017 and December 2017
(all values in percent)
| Percentiles of Percent Revisions |
March
2017 |
December
2017 |
20th percentile |
-0.5 |
-0.8 |
40th percentile |
-0.2 |
-0.4 |
60th percentile |
(1) |
-0.1 |
80th percentile |
0.3 |
0.2 |
100th percentile |
1.2 |
1.9 |
To Table of Figures
Back to Top
Revisions by MSA
For all metropolitan statistical areas (MSAs) published by the CES program, the percentage revisions ranged
from −7.1 to 3.3 percent, with an average absolute percentage revision of 0.9 percent across all MSAs
for March 2017. (See table 3a.) Comparatively, at the statewide level the range was
−1.0 to 1.2 percent, with an average absolute percentage revision of 0.4 percent for March 2017. (See
table 1a.) As MSA size decreases so does the sample size, resulting in larger relative
standard errors and therefore increasing both the range of percentage revisions and the average absolute percentage
revision. Metropolitan areas with 1 million or more employees during March 2017 had an average absolute
revision of 0.4 percent, while metropolitan areas with fewer than 100,000 employees had an average absolute
revision of 1.1 percent. (See table 3a.)
For December 2017, the percentage revisions ranged from −12.0 to 7.8 percent, with an average absolute
percentage revision of 1.2 percent across all published MSAs. (See table 3b.)
Comparatively, at the statewide level the range was −2.0 to 1.9 percent, with an average absolute
percentage revision of 0.6 percent for December 2017. (See table 1a.) As noted
previously, both the range of percentage revisions and the average absolute percentage revision generally
increase as the amount of employment in an MSA decreases. Metropolitan areas with 1 million or more employees
during December 2017 had an average absolute revision of 0.6 percent, while metropolitan areas with fewer
than 100,000 employees had an average absolute revision of 1.5 percent. (See table 3b.)
Table 3a. Benchmark revisions for nonfarm employment
in metropolitan areas for March 2017, not seasonally adjusted
| Measure |
All MSAs |
MSAs grouped by level of total nonfarm employment |
Less than
100,000 |
100,000 to
499,999 |
500,000 to
999,999 |
1 million
or more |
| Number of MSAs |
388 |
186 |
150 |
19 |
33 |
| |
|
|
|
|
|
Average absolute percentage
revision |
0.9 |
1.1 |
0.8 |
0.5 |
0.4 |
| |
|
|
|
|
|
| Range |
-7.1
to
3.3 |
-7.1
to
3.3 |
-2.7
to
2.5 |
-1.1
to
1.1 |
-1.3
to
0.8 |
| Mean |
-0.1 |
-0.2 |
-0.1 |
(1) |
-0.1 |
| Standard deviation |
1.2 |
1.5 |
1.0 |
0.6 |
0.5 |
To Table of Figures
Table 3b. Benchmark revisions for nonfarm employment
in metropolitan areas for December 2017, not seasonally adjusted
| Measure |
All MSAs |
MSAs grouped by level of total nonfarm employment |
Less than
100,000 |
100,000 to
499,999 |
500,000 to
999,999 |
1 million
or more |
| Number of MSAs |
388 |
186 |
150 |
19 |
33 |
| |
|
|
|
|
|
Average absolute percentage
revision |
1.2 |
1.5 |
1.1 |
0.7 |
0.6 |
| |
|
|
|
|
|
| Range |
-12.0
to
7.8 |
-12.0
to
7.8 |
-4.3
to
3.2 |
-1.3
to
1.9 |
-1.4
to
1.0 |
| Mean |
-0.3 |
-0.3 |
-0.3 |
0.2 |
-0.2 |
| Standard deviation |
1.7 |
2.2 |
1.3 |
0.9 |
0.6 |
To Table of Figures
Back to Top
Map 1. Percent differences between nonfarm payroll employment
benchmarks and estimates by State, March 2017
|
To Table of Figures
Map 2. Percent differences between nonfarm payroll employment
benchmarks and estimates by State, December 2017
|
To Table of Figures
Back to Top
Appendix
Table A1. Redelineated areas with CES publication in 2015 published
seasonally adjusted beginning in 2018
| Area Code |
Area Title |
31740 |
Manhattan, KS |
37964 |
Philadelphia, PA Metropolitan Division |
41540 |
Salisbury, MD-DE |
74204 |
Lawrence-Methuen Town-Salem, MA-NH NECTA Division |
78254 |
Taunton-Middleborough-Norton, MA NECTA Division |
To Table of Figures
Table A2. New areas added to CES publication in 2015 published seasonally adjusted
beginning in 2018
| Area Code |
Area Title |
10540 |
Albany, OR |
11640 |
Arecibo, PR |
13220 |
Beckley, WV |
14100 |
Bloomsburg-Berwick, PA |
15680 |
California-Lexington Park, MD |
16060 |
Carbondale-Marion, IL |
16540 |
Chambersburg-Waynesboro, PA |
19300 |
Daphne-Fairhope-Foley, AL |
20524 |
Dutchess County-Putnam County, NY |
20700 |
East Stroudsburg, PA |
20994 |
Elgin, IL Metropolitan Division |
23900 |
Gettysburg, PA |
24260 |
Grand Island, NE |
24420 |
Grants Pass, OR |
25220 |
Hammond, LA |
25940 |
Hilton Head Island-Bluffton-Beaufort, SC |
26140 |
Homosassa Springs, FL |
27980 |
Kahului-Wailuku-Lahaina, HI |
33220 |
Midland, MI |
33874 |
Montgomery County-Bucks County-Chester County, PA |
35100 |
New Bern, NC |
42034 |
San Rafael, CA |
42700 |
Sebring, FL |
43420 |
Sierra Vista-Douglas, AZ |
44420 |
Staunton-Waynesboro, VA |
45540 |
The Villages, FL |
47460 |
Walla Walla, WA |
48060 |
Watertown-Fort Drum, NY |
74854 |
Lynn-Saugus-Marblehead, MA |
93565 |
Middlesex-Monmouth-Ocean, NJ |
97962 |
Delaware County, PA |
To Table of Figures
Table A3. New area added to CES publication in 2017 not published seasonally adjusted in 2018
| Area Code |
Area Title |
21420 |
Enid, OK |
To Table of Figures
Back to Top
End Notes (click on any end note to return to its location in the article)
1
Further information regarding the difference in historical reconstruction between not
seasonally adjusted data and seasonally adjusted data is available in the
seasonal adjustment section of this article and at
https://www.bls.gov/sae/790over.htm.
2
Further information on the sample size for each state is available at
https://www.bls.gov/sae/sample.htm.
3
Further information on the reliability of CES estimates is contained in the Technical
Note of the latest State Employment and Unemployment news release and is available
at https://www.bls.gov/sae/news.htm.
4
Further information on the BLS Quarterly Census of Employment and Wages program is
available at https://www.bls.gov/cew/.
5
Further information on the monthly estimation methods of the CES program can be found
in Chapter 2 of the BLS Handbook of Methods and is available at
https://www.bls.gov/opub/hom/pdf/homch2.pdf.
6
Further information about the 2012 NAICS and the 2017 NAICS classifications can be found at
the Census Bureau's NAICS page at
https://www.census.gov/eos/www/naics.
7
Further information on NAICS codes and CES industry codes, as well as previous NAICS
conversions, is available at
https://www.bls.gov/sae/saenaics2017.htm.
8
Further information on the impact of the 2017 NAICS update to CES National can be found in the
CES National Benchmark Article at
https://www.bls.gov/web/empsit/cesbmart.htm.
9
Technical information on the estimation methods used to account for employment in business
births and deaths is available at
https://www.bls.gov/web/empsit/cesbd.htm.
10
Further information on X-13 ARIMA-SEATS is available on the Census Bureau website at
https://www.census.gov/srd/www/x13as/.
11
Research from the Dallas Federal Reserve has shown that CES benchmarked population data
exhibits a seasonal pattern different from the sample-based estimates. Please see Berger,
Franklin D. and Keith R. Phillips (1994) "Solving the Mystery of the Disappearing January
Blip in State Employment Data," Federal Reserve Bank of Dallas, Economic Review, April,
53-62, available at
http://www.dallasfed.org/assets/documents/research/er/1994/er9402d.pdf.
12
The two-step seasonal adjustment process is explained in detail by Scott, Stuart; Stamas,
George; Sullivan, Thomas; and Paul Chester (1994), "Seasonal Adjustment of Hybrid Economic
Time Series," Proceedings of the Section on Survey Research Methods, American
Statistical Association, available at
https://www.bls.gov/osmr/abstract/st/st940350.htm.
13
A list of all seasonally adjusted employment series is available at
https://www.bls.gov/sae/saeseries.htm.
14
Table D-1 can be viewed at
https://www.bls.gov/sae/tables.htm.
15
A list of BLS MSAs is available at
https://download.bls.gov/pub/time.series/sm/sm.area.
16
Technical information on concurrent seasonal adjustment for CES State and Area can be found at
https://www.bls.gov/sae/saeconcurrent.htm.
17
Mance, S. Concurrent Seasonal Adjustment of State and Metro Payroll Employment Series October 2015.
Available at
https://www.bls.gov/osmr/pdf/st150110.pdf.
18
For more information on the presence and treatment of calendar effects in CES data, see
https://www.bls.gov/ore/pdf/st960190.pdf.
19
For a summary of changes to statistical areas made with the 2014 benchmark, see
https://www.bls.gov/sae/benchmark2015.pdf.
20
The X-13 ARIMA-SEATS software used by BLS requires a minimum of 3 years of data to
reliably forecast seasonal factors.
21
Lists of redelineated and new areas added in 2015 now published seasonally adjusted are
available in tables A1 and A2 of the
Appendix.
22
MSA delineations may be found at
https://www.bls.gov/sae/saemsa.htm.
Back to Top
Table of figures
Tables
Exhibits
Maps
Back to Top
Additional information
Historical state and area employment, hours, and earnings data are available on the BLS website at
https://www.bls.gov/sae. Inquiries for additional information on
the methods or estimates derived from the CES survey should be sent by email to
sminfo@bls.gov.
Assistance and response to inquiries by telephone is available Monday through Friday, during the hours
of 8:30 am to 4:30 pm EST by dialing (202) 691‑6559.
Previously released CES State and Area benchmark articles are available
at https://www.bls.gov/sae/saebmk.htm.
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Last Modified Date: March 23, 2018
