CES State and Area Benchmark Article

Revisions in State Establishment-based Employment Estimates Effective January 2026

• Introduction
  • Summary of benchmark revisions
• Benchmark methods
  • Changes to CES published series
    • Benchmark level adjustments to taxi and limousine services
    • Publication changes with the release of January 2026 data
  • Net business birth-death modeling
  • Seasonal adjustment
    • Variable survey intervals
    • Prior adjustments
    • Outlier detection in seasonal adjustment
    • Area updates
• Benchmark revisions
  • Revisions by industry
  • Revisions by state
  • Revisions by metropolitan statistical area
• Table of figures
  • Tables
  • Exhibits
  • Maps
• Additional information

Authored by Kristie Lee and Julianne Todd

Kristie Lee and Julianne Todd 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 2026 in April 2026, nonfarm payroll employment, hours, and earnings data for states and areas were revised to reflect the incorporation of the 2025 benchmarks and the recalculation of seasonal adjustment factors. The revisions affect all not seasonally adjusted data from April 2024 to December 2025, all seasonally adjusted data from January 2021 to December 2025, and select series subject to historical revisions before April 2024. This article provides background information on benchmarking methods, business birth-death modeling, seasonal adjustment of employment data, and details of the effects of the 2025 benchmark revisions on state and area payroll employment estimates.

Summary of benchmark revisions

The benchmark revision is the difference between the benchmark level for a given month and the published sample-based employment estimate for the same month.¹ The average absolute percentage revision across all states for total nonfarm payroll employment is 0.9 percent for September 2025. For September 2025, the range of the revision for total nonfarm payroll employment across all states is from -3.7 percent to 1.4 percent.

Differences in seasonality exist between the population data and the sample-based data in the nonfarm payroll series. These differences are significant enough that the Current Employment Statistics (CES) program must use a two-step seasonal adjustment process to develop its seasonally adjusted data for states and areas.

Given these differences, the benchmark revisions to the not seasonally adjusted September 2025 estimates are most appropriate to assess the reliability of the estimation process for states and areas since that month is 12 months after the latest population data used with the release of the 2024 benchmark. Over a 12-month period, the seasonal differences between the population and the sample-based data will largely be reconciled in the not seasonally adjusted data.

Benchmark methods

The CES survey, also known as the payroll or establishment survey, is a federal and state cooperative program that provides timely estimates of payroll employment, hours, and earnings for states and areas by sampling the population of employers. As with data from other sample surveys, CES payroll employment estimates are subject to both sampling and nonsampling errors. Sampling error is an unavoidable byproduct of forming an inference about a population based on a sample. A larger sample tends to reduce the size of sampling error, while high population variance and employment levels tend to increase it. These factors vary across states and industries.² Nonsampling error, by contrast, includes all other sources of statistical errors, including in reporting and processing.

To control for both sampling and non-sampling 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. As part of the benchmark process for benchmark year 2025, 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 430 metropolitan areas and divisions for the period from April 2024 to September 2025.

UI tax reports are not collected on a timely enough basis to replace CES payroll estimates for the fourth quarter, October 2025 to December 2025. For this period, estimates are revised using the new September 2025 series level derived from the census employment counts. From those levels, new sample-based estimates are developed that incorporate updated business birth-death factors and new or revised CES microdata.³

Changes to CES published series

Benchmark level adjustments to taxi and limousine services

During benchmark processing, the CES program found a substantial increase in first quarter of 2025 QCEW employment in the taxi and limousine services industry (NAICS 485300) within transit and ground passenger transportation (NAICS 485) in New York. CES concluded that additional employment for this industry should not be used in CES benchmarking at this time, pending further research.⁴

Publication changes with the release of January 2026 data

The Current Employment Statistics (CES) program reviews the set of published series each year as part of its annual benchmarking process. With the release of January 2026 data, the CES State and Area program discontinued approximately 900 employment, hours, and earnings data series for detailed industries. Working closely with our state partners, BLS identified series for elimination due to low or declining employment or sample coverage. Data series representing all employees (regardless of industry) in states and metropolitan areas will continue to be published.⁵

Net 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. Because new firm births generate a portion of employment growth each month, additional methods are 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 to offset the missing employment gains from business births. This is incorporated into the sample-based estimation procedure not by 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.⁶

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 5 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-13ARIMA-SEATS software. 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 will be used in monthly estimation of payroll employment in 2026. The updated birth-death factors are also used as inputs to produce the revised estimates of payroll employment for October 2025 to December 2025.

Effective with the release of January 2026 employment estimates, BLS modified the ARIMA-based component of the national level birth-death model by incorporating current sample information to inform the forecasts. More information on the impact of this modification is available in the CES National benchmark article. Also effective with the release of January 2026 estimates, the ratio-adjustment procedure used to reconcile the differences between nationwide birth-death forecasts and the sum of state-level forecasts will be performed on a monthly basis to ensure the state-level factors are informed by the most recent national-level data.

Seasonal adjustment

CES state and area payroll employment data are seasonally adjusted by a two-step process.⁷ BLS uses the X-13ARIMA-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 and then spliced together at the benchmark month (in this case September/October 2025).⁸ 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.⁹

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 except for goods producing and private service providing, which are 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 and logging; 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 calculated through aggregation but are derived directly by applying the seasonal adjustment procedure to the not seasonally adjusted total nonfarm level.¹⁰

BLS uses concurrent seasonal adjustment for CES state and area data. This method uses all available estimates, including those for the current month, in developing sample-based seasonal factors.¹¹ 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. Outlier detection is a regular part of the monthly seasonal adjustment process.

Variable survey intervals

BLS uses 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 reference period is always 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 affect the magnitude of seasonal hires or layoffs that have occurred at the time the survey is taken.¹²

Prior adjustments

BLS incorporates prior adjustments as part of the seasonal adjustment process. Unlike the use of seasonal outliers, prior adjustments remove the effect (rounded to hundreds) of a known nonseasonal event from the not seasonally adjusted data before running X-13ARIMA-SEATS. This is done to ensure that nonseasonal events, such as decennial census hiring or strikes, are not included in the calculation of the seasonal factors. Once the seasonal factors are calculated, they are applied to the not seasonally adjusted data used as inputs. Then the prior adjustments that were removed before running X‑13ARIMA‑SEATS are incorporated to create the seasonally adjusted estimates. Seasonal outliers will continue to be made where there is insufficient information to determine a prior adjustment.

Outlier detection in seasonal adjustment

Outlier detection is a regular part of the monthly seasonal adjustment process. When performing outlier detection, BLS uses a rule where, for all time series, data points over a certain critical value are designated as outliers.¹³

Area updates

As a result of (a) the BLS update in the 2024 benchmark to official 2020 area delineations and (b) limitations in data availability associated with the two-step process for seasonal adjustment, it was necessary for BLS to adjust its methodology for seasonally adjusting select areas.

Historically, when incorporating new area delineations, BLS has not been able to publish seasonally adjusted data for new areas or areas with large compositional changes due to an inability to produce historical sample-based estimates.¹⁴ For the 2024 benchmark, BLS researched the incorporation of historical simulations in conjunction with existing sample-based histories to allow for the publication of more seasonally adjusted series. In eight cases where these simulations were deemed inadequate, BLS suppressed data on a seasonally adjusted basis. This year, with an additional year of sample-based estimates, BLS will publish seasonally adjusted data for all metropolitan areas. The areas that were not published seasonally adjusted with benchmark year 2024, but will be published seasonally adjusted as of benchmark year 2025, are listed below in exhibit 1.

Exhibit 1. Areas where seasonally adjusted data were added to publication as of benchmark year 2025

Area FIPS code	Area Title
11180	Ames, IA
11200	Amherst Town-Northampton, MA
12700	Barnstable Town, MA
14580	Bozeman, MT
30500	Lexington Park, MD
31740	Manhattan, KS
41780	Sandusky, OH
45900	Traverse City, MI

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Benchmark revisions

Revisions by industry

As noted earlier, the average absolute percentage revision across all states for total nonfarm payroll employment is 0.9 percent for September 2025. For September 2025, the range of the revision for total nonfarm payroll employment across all states is from -3.7 percent to 1.4 percent. (See table 1.)

Table 1. Average absolute percentage differences between state employment estimates and benchmarks by industry, not seasonally adjusted, September 2020 - September 2025 (all values in percent)

Industry1	Sep. 2020	Sep. 2021	Sep. 2022	Sep. 2023	Sep. 2024	Sep. 2025
							Total nonfarm	1.1	0.9	0.7	0.7	0.7	0.9
Mining and logging	7.7	4.5	4.0	4.4	4.9	3.6
Construction	3.5	3.1	3.2	2.6	2.9	2.4
Manufacturing	2.8	1.8	1.7	1.7	1.9	1.9
Trade, transportation, and utilities	2.1	1.1	1.6	0.9	1.0	1.1
Information	4.1	5.0	3.8	4.9	3.6	3.6
Financial activities	2.5	1.9	2.6	2.2	1.8	1.8
Professional and business services	2.5	2.4	2.2	2.5	1.6	2.0
Education and health services	1.6	1.7	1.3	1.5	1.5	1.3
Leisure and hospitality	5.2	3.4	2.0	1.6	1.7	2.0
Other services	5.3	3.5	2.9	3.7	2.1	2.9
Government	1.5	1.0	0.8	0.9	1.1	1.2
Total nonfarm:
Range	-4.4 to 3.4	-1.2 to 3.4	-2.0 to 3.1	-1.8 to 1.8	-2.4 to 1.8	-3.7 to 1.4
Mean	-0.5	0.7	0.4	-0.1	-0.5	-0.8
Standard deviation	1.4	1.0	0.8	0.8	0.8	0.8
Footnotes: 1 Industry summary statistics are only representative of data for those states where the industry is published at the statewide level. Benchmark data for Puerto Rico and the U.S. Virgin Islands are not included in these summary statistics.

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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 September 2025. 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.

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 percent and 2.0 percent, respectively, for September 2025. However, for the same month, the average absolute level revision across all states for the information industry is 1,500, while the average absolute level revision across all states for the professional and business services industry is 6,400. (See table 2.) Relying on a single measure to characterize the magnitude of benchmark revisions in an industry can lead to an incomplete interpretation.

Table 2. Average absolute level differences between state employment estimates and benchmarks by industry, not seasonally adjusted, September 2020 - September 2025 (all values payroll employment)

Industry1	Sep. 2020	Sep. 2021	Sep. 2022	Sep. 2023	Sep. 2024	Sep. 2025
							Total nonfarm	27,400	24,700	16,600	20,200	19,900	21,900
Mining and logging	1,100	700	600	600	600	500
Construction	3,500	3,600	3,400	4,000	3,800	3,500
Manufacturing	4,400	3,100	3,600	2,700	3,600	3,100
Trade, transportation, and utilities	7,700	5,400	6,400	5,700	5,300	6,100
Information	1,600	2,200	1,700	2,500	1,700	1,500
Financial activities	3,100	3,200	3,500	4,200	2,400	2,300
Professional and business services	7,700	6,400	9,400	9,800	5,600	6,400
Education and health services	5,600	6,600	4,400	6,300	6,200	6,400
Leisure and hospitality	13,300	9,900	5,700	4,400	5,500	4,300
Other services	5,100	3,100	2,700	3,500	2,200	2,500
Government	4,600	3,900	3,400	3,800	3,700	5,400
Total nonfarm:
Range	-148,000 to 63,400	-31,600 to 221,300	-18,800 to 108,400	-273,000 to 34,200	-170,900 to 51,400	-80,600 to 22,800
Mean	-15,400	20,300	11,800	-11,600	-15,100	-20,200
Standard deviation	39,300	44,600	21,600	43,400	33,400	22,400
Footnotes: 1 Industry summary statistics are only representative of data for those states where the industry is published at the statewide level. Benchmark data for Puerto Rico and the U.S. Virgin Islands are not included in these summary statistics.

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Revisions by state

For September 2025, nonfarm payroll employment was revised downward in 44 states and the District of Columbia, was revised upward in 5 states, and was unchanged in 1 state. (See table 3 or map 1.)

Table 3. Percent differences between nonfarm payroll employment benchmarks and estimates by state, not seasonally adjusted, September 2020- September 2025 (all values in percent)

State	Sep. 2020	Sep. 2021	Sep. 2022	Sep. 2023	Sep. 2024	Sep. 2025
Alabama	-1.4	-0.2	1.3	0.7	-1.0	-0.9
Alaska	-1.2	1.8	0.1	1.8	-0.7	-0.2
Arizona	-1.1	0.2	0.4	1.1	-1.8	-0.1
Arkansas	0.8	1.3	1.8	-0.6	-0.4	-3.7
California	-0.9	1.3	0.6	-1.5	-1.0	0.0
Colorado	-1.2	0.9	-0.6	1.41	-1.32	-1.2
Connecticut	-1.0	0.7	0.2	0.0	-0.1	0.0
Delaware	3.4	0.0	2.6	-0.4	0.3	-0.2
District of Columbia	-2.0	0.3	-0.1	-1.8	-0.7	-1.7
Florida	-1.1	1.7	0.2	-0.1	-0.3	-0.8
Georgia	-2.0	0.4	0.1	-0.4	-0.4	-0.2
Hawaii	-4.4	2.8	1.2	-0.6	0.2	-1.7
Idaho	0.5	2.0	0.8	-0.9	-1.4	-0.3
Illinois	-0.9	0.4	-0.3	-0.7	-0.1	-0.2
Indiana	-1.5	0.9	0.4	-0.9	-1.3	-1.1
Iowa	0.1	-0.1	-0.7	0.4	-0.6	-1.1
Kansas	-0.8	-1.2	1.3	-0.3	-0.6	0.4
Kentucky	0.7	1.1	0.3	-0.3	-0.4	-1.6
Louisiana	-3.1	0.9	-0.3	-1.0	1.1	-0.9
Maine	2.1	1.5	-0.1	0.1	0.2	0.9
Maryland	-1.6	-0.4	-0.7	-0.6	1.8	-1.5
Massachusetts	-0.2	0.6	-0.4	-1.8	-0.9	-0.4
Michigan	1.5	0.9	0.3	0.6	-0.1	-1.1
Minnesota	-0.4	-0.9	0.3	-0.1	-0.4	-1.0
Mississippi	-1.0	0.4	1.7	0.9	0.0	-0.9
Missouri	-0.2	0.1	0.5	-0.1	-2.4	-1.9
Montana	0.8	2.8	1.2	0.3	-2.2	-1.1
Nebraska	-1.0	-1.2	-0.5	0.7	-1.5	-0.1
Nevada	-3.0	3.4	3.1	-0.8	-0.3	1.4
New Hampshire	2.0	0.9	0.9	-0.1	-1.5	-1.5
New Jersey	-0.6	1.4	0.4	-0.2	-0.3	-0.5
New Mexico	-2.1	1.0	0.2	0.5	0.1	-1.3
New York	-0.5	1.7	0.6	0.1	0.0	-0.4
North Carolina	1.2	1.7	0.4	0.2	-0.4	-1.1
North Dakota	-0.2	0.4	-0.1	0.1	-0.4	-0.7
Ohio	1.2	0.1	0.8	-0.6	-0.4	-1.0
Oklahoma	-0.8	-0.2	1.2	1.6	-0.4	-1.0
Oregon	0.0	0.4	-0.9	-1.3	0.2	-1.8
Pennsylvania	0.0	0.6	0.4	-1.0	-0.9	-1.3
Rhode Island	-1.0	0.7	-0.1	1.8	-0.2	-0.2
South Carolina	-1.5	-0.1	0.8	0.4	-1.0	-1.1
South Dakota	0.2	1.4	0.1	-0.4	0.2	-0.8
Tennessee	-0.2	0.8	0.4	-0.6	1.2	-1.4
Texas	-1.1	0.0	0.4	-0.5	-0.8	-0.3
Utah	-1.2	-0.1	0.9	0.4	-0.8	-0.4
Vermont	0.8	0.5	0.5	0.5	-1.4	-1.5
Virginia	-0.4	0.4	0.3	0.5	0.0	-0.1
Washington	-0.7	-0.9	0.6	-0.9	-0.2	-0.7
West Virginia	0.3	-0.2	-2.0	1.0	-0.3	0.1
Wisconsin	1.7	0.3	0.9	-0.1	0.0	-0.8
Wyoming	-0.6	1.7	-0.2	-0.3	-0.2	-1.0
Footnotes: NOTE: Values of 0.0 indicate percent revisions within +/- 0.05 percent. 1 Revisions for Colorado are included in this table. Users are cautioned given the unusual movements in the Colorado QCEW data. See the changes to CES published series section in the 2023 benchmark article for more information. 2 Revisions for Colorado are included in this table. Users are cautioned given the unusual movements in the Colorado QCEW data. See the changes to CES published series section in the 2024 benchmark article for more information.

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The distribution of percent revisions for September 2025 can be found in exhibit 2. Quintiles are representative of 20 percent of the range of state benchmark revisions. For example, 20 percent of the revisions are -1.4 percent or less for September 2025 while 100 percent of the revisions are equal to or less than 1.4 percent.

Exhibit 2. Distribution of state percent revisions for September 2025 (all values in percent)

Percentiles of Percent Revisions	September 2025
		20th percentile	-1.4
40th percentile	-1.1
60th percentile	-0.7
80th percentile	-0.2
100th percentile	1.4

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Revisions by metropolitan statistical area

For all MSAs published by the CES program, the total nonfarm percentage revision for September 2025 ranged from -5.8 percent to 5.1 percent, with an average absolute percentage revision of 1.2 percent across all published MSAs. (See table 4.) For comparison, at the statewide level, the range was from -3.7 percent to 1.4 percent, with an average absolute revision of 0.9 percent for September 2025. (See table 1.) In general, both the range of percentage revisions and the average absolute percentage revision increase as the amount of employment in an MSA decreases. Metropolitan areas with 1 million or more employees during September 2025 had an average absolute revision of 0.9 percent, while metropolitan areas with fewer than 100,000 employees had an average absolute revision of 1.4 percent. (See table 4.)

Table 4. Benchmark revisions for nonfarm employment in metropolitan areas for September 2025, 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	387					175	160	16	36
Average absolute percentage revision	1.2	1.4	1.2	0.9	0.9
Range	-5.8 to 5.1	-5.2 to 5.1	-5.8 to 4.3	-2.3 to 1.7	-2.6 to 2.2
Mean	-0.4	-0.5	-0.4	-0.3	-0.5
Standard deviation	1.6	1.8	1.5	1.1	1.0

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End Notes (click on any end note to return to its location in the article)

¹ BLS uses unrounded values when calculating benchmark revisions.

² Further information on the sample size for each state is available at https://www.bls.gov/sae/additional-resources/current-employment-statistics-sample-by-state.htm.

³ Further information on the monthly estimation methods of the CES program can be found in the BLS Handbook of Methods at https://www.bls.gov/opub/hom/sae/.

⁴ More information about benchmark level adjustments to taxi and limousine services is available at https://www.bls.gov/ces/publications/benchmark/ces-benchmark-revision-2025.pdf.

⁵ More information about publication changes, including a list of discontinued series, is available at https://www.bls.gov/sae/notices/2026/notice-of-publication-changes-with-the-release-of-january-2026-data.htm.

⁶ 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.

⁷ Research from the Dallas Federal Reserve has shown that CES benchmarked population data exhibits a seasonal pattern different from the sample-based estimates.  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 https://www.dallasfed.org/~/media/documents/research/er/1994/er9402d.pdf.

⁸ 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,” available at https://www.bls.gov/osmr/research-papers/1994/pdf/st940350.pdf.

⁹ A list of all seasonally adjusted employment series is available at https://www.bls.gov/sae/additional-resources/list-of-published-state-and-metropolitan-area-series/home.htm.

¹⁰ A list of BLS-published areas is available at https://download.bls.gov/pub/time.series/sm/sm.area.

¹¹ Technical information on concurrent seasonal adjustment for CES state and area data can be found at https://www.bls.gov/sae/seasonal-adjustment/implementation-of-concurrent-seasonal-adjustment-for-ces-state-and-area-estimates.htm.

¹² More information on the presence and treatment of calendar effects in CES data is explained by Cano, Stephanie; Getz, Patricia; Kropf, Jurgen; Scott, Stuart; and George Stamas (1996), “Adjusting for a Calendar Effect in Employment Time Series,” available at https://www.bls.gov/osmr/research-papers/1996/pdf/st960190.pdf.

¹³ For a list of outliers identified during the concurrent seasonal adjustment process, see https://www.bls.gov/sae/seasonal-adjustment/#outliers.

¹⁴ The X-13ARIMA-SEATS software used by BLS requires a minimum of 3 years of data to process a time series.

Table of figures

Tables

• Table 1. Average absolute percentage differences between state employment estimates and benchmarks by industry, not seasonally adjusted, September 2020 - September 2025 (all values in percent)
• Table 2. Average absolute level differences between state employment estimates and benchmarks by industry, not seasonally adjusted, September 2020 - September 2025 (all values payroll employment)
• Table 3. Percent differences between nonfarm payroll employment benchmarks and estimates by state, not seasonally adjusted, September 2020 - September 2025 (all values in percent)
• Table 4. Benchmark revisions for nonfarm employment in metropolitan areas for September 2025, not seasonally adjusted

Exhibits

• Exhibit 1. Areas where seasonally adjusted data were added to publication as of benchmark year 2025
• Exhibit 2. Distribution of state percent revisions, September 2025 (all values in percent)

Maps

• Map 1. Percent differences between nonfarm payroll employment benchmarks and estimates by state, September 2025

Additional information

Historical state and area employment, hours, and earnings data are available on the BLS website at https://www.bls.gov/sae. Previously released benchmark articles for CES state and area data are available at https://www.bls.gov/sae/publications/benchmark-article/home.htm.

 

Last modified date: April 8, 2026