The January 2023 report presents the scheduled annual revision of the ADP National Employment Report (NER), which updates the data series to be consistent with the annual Quarterly Census of Employment and Wages (QCEW) benchmark data through March 2022. This is a recurring process that happens every year, and is a common practice for reports of this nature. In addition to this regular, annual update, the NER weighting methodology was revised to facilitate an easier comparison of total employment estimates between the NER and QCEW; monthly aggregates now leverage weekly seasonal adjustments rather than a separate monthly seasonal adjustment; and the national aggregate is now constructed from industry aggregates. There was also a refinement in the labeling methodology which is used to determine how various employment sources fall into a particular industry and geography definitions. These changes were applied retroactively to the 13-year history of the NER.

The January 2023 report presents the scheduled annual revision of the ADP National Employment Report (NER), which updates the data series to be consistent with the annual Quarterly Census of Employment and Wages (QCEW) benchmark data through March 2022. This is a recurring process that happens every year, and is a common practice for reports of this nature. In addition to this regular, annual update, the NER weighting methodology was revised to facilitate an easier comparison of total employment estimates between the NER and QCEW; monthly aggregates now leverage weekly seasonal adjustments rather than a separate monthly seasonal adjustment; and the national aggregate is now constructed from industry aggregates. There was also a refinement in the labeling methodology which is used to determine how various employment sources fall into a particular industry and geography definitions. These changes were applied retroactively to the 13-year history of the NER.

The January 2023 report presents the scheduled annual revision of the ADP National Employment Report (NER), which updates the data series to be consistent with the annual Quarterly Census of Employment and Wages (QCEW) benchmark data through March 2022. This is a recurring process that happens every year, and is a common practice for reports of this nature. In addition to this regular, annual update, the NER weighting methodology was revised to facilitate an easier comparison of total employment estimates between the NER and QCEW; monthly aggregates now leverage weekly seasonal adjustments rather than a separate monthly seasonal adjustment; and the national aggregate is now constructed from industry aggregates. There was also a refinement in the labeling methodology which is used to determine how various employment sources fall into a particular industry and geography definitions. These changes were applied retroactively to the 13-year history of the NER.

The January 2023 report presents the scheduled annual revision of the ADP National Employment Report (NER), which updates the data series to be consistent with the annual Quarterly Census of Employment and Wages (QCEW) benchmark data through March 2022. This is a recurring process that happens every year, and is a common practice for reports of this nature. In addition to this regular, annual update, the NER weighting methodology was revised to facilitate an easier comparison of total employment estimates between the NER and QCEW; monthly aggregates now leverage weekly seasonal adjustments rather than a separate monthly seasonal adjustment; and the national aggregate is now constructed from industry aggregates. There was also a refinement in the labeling methodology which is used to determine how various employment sources fall into a particular industry and geography definitions. These changes were applied retroactively to the 13-year history of the NER.

The January 2023 report presents the scheduled annual revision of the ADP National Employment Report (NER), which updates the data series to be consistent with the annual Quarterly Census of Employment and Wages (QCEW) benchmark data through March 2022. This is a recurring process that happens every year, and is a common practice for reports of this nature. In addition to this regular, annual update, the NER weighting methodology was revised to facilitate an easier comparison of total employment estimates between the NER and QCEW; monthly aggregates now leverage weekly seasonal adjustments rather than a separate monthly seasonal adjustment; and the national aggregate is now constructed from industry aggregates. There was also a refinement in the labeling methodology which is used to determine how various employment sources fall into a particular industry and geography definitions. These changes were applied retroactively to the 13-year history of the NER.

The January 2023 report presents the scheduled annual revision of the ADP National Employment Report (NER), which updates the data series to be consistent with the annual Quarterly Census of Employment and Wages (QCEW) benchmark data through March 2022. This is a recurring process that happens every year, and is a common practice for reports of this nature. In addition to this regular, annual update, the NER weighting methodology was revised to facilitate an easier comparison of total employment estimates between the NER and QCEW; monthly aggregates now leverage weekly seasonal adjustments rather than a separate monthly seasonal adjustment; and the national aggregate is now constructed from industry aggregates. There was also a refinement in the labeling methodology which is used to determine how various employment sources fall into a particular industry and geography definitions. These changes were applied retroactively to the 13-year history of the NER.

The January 2023 report presents the scheduled annual revision of the ADP National Employment Report (NER), which updates the data series to be consistent with the annual Quarterly Census of Employment and Wages (QCEW) benchmark data through March 2022. This is a recurring process that happens every year, and is a common practice for reports of this nature. In addition to this regular, annual update, the NER weighting methodology was revised to facilitate an easier comparison of total employment estimates between the NER and QCEW; monthly aggregates now leverage weekly seasonal adjustments rather than a separate monthly seasonal adjustment; and the national aggregate is now constructed from industry aggregates. There was also a refinement in the labeling methodology which is used to determine how various employment sources fall into a particular industry and geography definitions. These changes were applied retroactively to the 13-year history of the NER.

The January 2023 report presents the scheduled annual revision of the ADP National Employment Report (NER), which updates the data series to be consistent with the annual Quarterly Census of Employment and Wages (QCEW) benchmark data through March 2022. This is a recurring process that happens every year, and is a common practice for reports of this nature. In addition to this regular, annual update, the NER weighting methodology was revised to facilitate an easier comparison of total employment estimates between the NER and QCEW; monthly aggregates now leverage weekly seasonal adjustments rather than a separate monthly seasonal adjustment; and the national aggregate is now constructed from industry aggregates. There was also a refinement in the labeling methodology which is used to determine how various employment sources fall into a particular industry and geography definitions. These changes were applied retroactively to the 13-year history of the NER.

The January 2023 report presents the scheduled annual revision of the ADP National Employment Report (NER), which updates the data series to be consistent with the annual Quarterly Census of Employment and Wages (QCEW) benchmark data through March 2022. This is a recurring process that happens every year, and is a common practice for reports of this nature. In addition to this regular, annual update, the NER weighting methodology was revised to facilitate an easier comparison of total employment estimates between the NER and QCEW; monthly aggregates now leverage weekly seasonal adjustments rather than a separate monthly seasonal adjustment; and the national aggregate is now constructed from industry aggregates. There was also a refinement in the labeling methodology which is used to determine how various employment sources fall into a particular industry and geography definitions. These changes were applied retroactively to the 13-year history of the NER.

The January 2023 report presents the scheduled annual revision of the ADP National Employment Report (NER), which updates the data series to be consistent with the annual Quarterly Census of Employment and Wages (QCEW) benchmark data through March 2022. This is a recurring process that happens every year, and is a common practice for reports of this nature. In addition to this regular, annual update, the NER weighting methodology was revised to facilitate an easier comparison of total employment estimates between the NER and QCEW; monthly aggregates now leverage weekly seasonal adjustments rather than a separate monthly seasonal adjustment; and the national aggregate is now constructed from industry aggregates. There was also a refinement in the labeling methodology which is used to determine how various employment sources fall into a particular industry and geography definitions. These changes were applied retroactively to the 13-year history of the NER.

The January 2023 report presents the scheduled annual revision of the ADP National Employment Report (NER), which updates the data series to be consistent with the annual Quarterly Census of Employment and Wages (QCEW) benchmark data through March 2022. This is a recurring process that happens every year, and is a common practice for reports of this nature. In addition to this regular, annual update, the NER weighting methodology was revised to facilitate an easier comparison of total employment estimates between the NER and QCEW; monthly aggregates now leverage weekly seasonal adjustments rather than a separate monthly seasonal adjustment; and the national aggregate is now constructed from industry aggregates. There was also a refinement in the labeling methodology which is used to determine how various employment sources fall into a particular industry and geography definitions. These changes were applied retroactively to the 13-year history of the NER.

The January 2023 report presents the scheduled annual revision of the ADP National Employment Report (NER), which updates the data series to be consistent with the annual Quarterly Census of Employment and Wages (QCEW) benchmark data through March 2022. This is a recurring process that happens every year, and is a common practice for reports of this nature. In addition to this regular, annual update, the NER weighting methodology was revised to facilitate an easier comparison of total employment estimates between the NER and QCEW; monthly aggregates now leverage weekly seasonal adjustments rather than a separate monthly seasonal adjustment; and the national aggregate is now constructed from industry aggregates. There was also a refinement in the labeling methodology which is used to determine how various employment sources fall into a particular industry and geography definitions. These changes were applied retroactively to the 13-year history of the NER.

The January 2023 report presents the scheduled annual revision of the ADP National Employment Report (NER), which updates the data series to be consistent with the annual Quarterly Census of Employment and Wages (QCEW) benchmark data through March 2022. This is a recurring process that happens every year, and is a common practice for reports of this nature. In addition to this regular, annual update, the NER weighting methodology was revised to facilitate an easier comparison of total employment estimates between the NER and QCEW; monthly aggregates now leverage weekly seasonal adjustments rather than a separate monthly seasonal adjustment; and the national aggregate is now constructed from industry aggregates. There was also a refinement in the labeling methodology which is used to determine how various employment sources fall into a particular industry and geography definitions. These changes were applied retroactively to the 13-year history of the NER.

The January 2023 report presents the scheduled annual revision of the ADP National Employment Report (NER), which updates the data series to be consistent with the annual Quarterly Census of Employment and Wages (QCEW) benchmark data through March 2022. This is a recurring process that happens every year, and is a common practice for reports of this nature. In addition to this regular, annual update, the NER weighting methodology was revised to facilitate an easier comparison of total employment estimates between the NER and QCEW; monthly aggregates now leverage weekly seasonal adjustments rather than a separate monthly seasonal adjustment; and the national aggregate is now constructed from industry aggregates. There was also a refinement in the labeling methodology which is used to determine how various employment sources fall into a particular industry and geography definitions. These changes were applied retroactively to the 13-year history of the NER.

The January 2023 report presents the scheduled annual revision of the ADP National Employment Report (NER), which updates the data series to be consistent with the annual Quarterly Census of Employment and Wages (QCEW) benchmark data through March 2022. This is a recurring process that happens every year, and is a common practice for reports of this nature. In addition to this regular, annual update, the NER weighting methodology was revised to facilitate an easier comparison of total employment estimates between the NER and QCEW; monthly aggregates now leverage weekly seasonal adjustments rather than a separate monthly seasonal adjustment; and the national aggregate is now constructed from industry aggregates. There was also a refinement in the labeling methodology which is used to determine how various employment sources fall into a particular industry and geography definitions. These changes were applied retroactively to the 13-year history of the NER.

The January 2023 report presents the scheduled annual revision of the ADP National Employment Report (NER), which updates the data series to be consistent with the annual Quarterly Census of Employment and Wages (QCEW) benchmark data through March 2022. This is a recurring process that happens every year, and is a common practice for reports of this nature. In addition to this regular, annual update, the NER weighting methodology was revised to facilitate an easier comparison of total employment estimates between the NER and QCEW; monthly aggregates now leverage weekly seasonal adjustments rather than a separate monthly seasonal adjustment; and the national aggregate is now constructed from industry aggregates. There was also a refinement in the labeling methodology which is used to determine how various employment sources fall into a particular industry and geography definitions. These changes were applied retroactively to the 13-year history of the NER.

The January 2023 report presents the scheduled annual revision of the ADP National Employment Report (NER), which updates the data series to be consistent with the annual Quarterly Census of Employment and Wages (QCEW) benchmark data through March 2022. This is a recurring process that happens every year, and is a common practice for reports of this nature. In addition to this regular, annual update, the NER weighting methodology was revised to facilitate an easier comparison of total employment estimates between the NER and QCEW; monthly aggregates now leverage weekly seasonal adjustments rather than a separate monthly seasonal adjustment; and the national aggregate is now constructed from industry aggregates. There was also a refinement in the labeling methodology which is used to determine how various employment sources fall into a particular industry and geography definitions. These changes were applied retroactively to the 13-year history of the NER.

The January 2023 report presents the scheduled annual revision of the ADP National Employment Report (NER), which updates the data series to be consistent with the annual Quarterly Census of Employment and Wages (QCEW) benchmark data through March 2022. This is a recurring process that happens every year, and is a common practice for reports of this nature. In addition to this regular, annual update, the NER weighting methodology was revised to facilitate an easier comparison of total employment estimates between the NER and QCEW; monthly aggregates now leverage weekly seasonal adjustments rather than a separate monthly seasonal adjustment; and the national aggregate is now constructed from industry aggregates. There was also a refinement in the labeling methodology which is used to determine how various employment sources fall into a particular industry and geography definitions. These changes were applied retroactively to the 13-year history of the NER.

The January 2023 report presents the scheduled annual revision of the ADP National Employment Report (NER), which updates the data series to be consistent with the annual Quarterly Census of Employment and Wages (QCEW) benchmark data through March 2022. This is a recurring process that happens every year, and is a common practice for reports of this nature. In addition to this regular, annual update, the NER weighting methodology was revised to facilitate an easier comparison of total employment estimates between the NER and QCEW; monthly aggregates now leverage weekly seasonal adjustments rather than a separate monthly seasonal adjustment; and the national aggregate is now constructed from industry aggregates. There was also a refinement in the labeling methodology which is used to determine how various employment sources fall into a particular industry and geography definitions. These changes were applied retroactively to the 13-year history of the NER.

The January 2023 report presents the scheduled annual revision of the ADP National Employment Report (NER), which updates the data series to be consistent with the annual Quarterly Census of Employment and Wages (QCEW) benchmark data through March 2022. This is a recurring process that happens every year, and is a common practice for reports of this nature. In addition to this regular, annual update, the NER weighting methodology was revised to facilitate an easier comparison of total employment estimates between the NER and QCEW; monthly aggregates now leverage weekly seasonal adjustments rather than a separate monthly seasonal adjustment; and the national aggregate is now constructed from industry aggregates. There was also a refinement in the labeling methodology which is used to determine how various employment sources fall into a particular industry and geography definitions. These changes were applied retroactively to the 13-year history of the NER.

The January 2023 report presents the scheduled annual revision of the ADP National Employment Report (NER), which updates the data series to be consistent with the annual Quarterly Census of Employment and Wages (QCEW) benchmark data through March 2022. This is a recurring process that happens every year, and is a common practice for reports of this nature. In addition to this regular, annual update, the NER weighting methodology was revised to facilitate an easier comparison of total employment estimates between the NER and QCEW; monthly aggregates now leverage weekly seasonal adjustments rather than a separate monthly seasonal adjustment; and the national aggregate is now constructed from industry aggregates. There was also a refinement in the labeling methodology which is used to determine how various employment sources fall into a particular industry and geography definitions. These changes were applied retroactively to the 13-year history of the NER.

The January 2023 report presents the scheduled annual revision of the ADP National Employment Report (NER), which updates the data series to be consistent with the annual Quarterly Census of Employment and Wages (QCEW) benchmark data through March 2022. This is a recurring process that happens every year, and is a common practice for reports of this nature. In addition to this regular, annual update, the NER weighting methodology was revised to facilitate an easier comparison of total employment estimates between the NER and QCEW; monthly aggregates now leverage weekly seasonal adjustments rather than a separate monthly seasonal adjustment; and the national aggregate is now constructed from industry aggregates. There was also a refinement in the labeling methodology which is used to determine how various employment sources fall into a particular industry and geography definitions. These changes were applied retroactively to the 13-year history of the NER.

Indexes of aggregate weekly payrolls are calculated by dividing the current month's aggregate by the average of the 12 monthly figures for the base year. Indexes are averages for production and nonsupervisory employees. For basic industries, the payroll aggregates are the product of average hourly earnings and aggregate weekly hours. At all higher levels of industry aggregation, payroll aggregates are the sum of the component aggregates. Production and related employees include working supervisors and all nonsupervisory employees (including group leaders and trainees) engaged in fabricating, processing, assembling, inspecting, receiving, storing, handling, packing, warehousing, shipping, trucking, hauling, maintenance, repair, janitorial, guard services, product development, auxiliary production for plant's own use (for example, power plant), recordkeeping, and other services closely associated with the above production operations. #Nonsupervisory employees include those individuals in private, service-providing industries who are not above the working-supervisor level. This group includes individuals such as office and clerical workers, repairers, salespersons, operators, drivers, physicians, lawyers, accountants, nurses, social workers, research aides, teachers, drafters, photographers, beauticians, musicians, restaurant workers, custodial workers, attendants, line installers and repairers, laborers, janitors, guards, and other employees at similar occupational levels whose services are closely associated with those of the employees listed. The series comes from the 'Current Employment Statistics (Establishment Survey).' The source code is: CEU4300000035

Indexes of aggregate weekly payrolls are calculated by dividing the current month's aggregate by the average of the 12 monthly figures for the base year. Indexes are averages for production and nonsupervisory employees. For basic industries, the payroll aggregates are the product of average hourly earnings and aggregate weekly hours. At all higher levels of industry aggregation, payroll aggregates are the sum of the component aggregates. The series comes from the 'Current Employment Statistics (Establishment Survey).' The source code is: CES4300000017

The series comes from the 'Current Employment Statistics (Establishment Survey).' The source code is: CES4349300001

Indexes of aggregate weekly payrolls are calculated by dividing the current month's aggregate by the average of the 12 monthly figures for the base year. Indexes are averages for production and nonsupervisory employees. For basic industries, the payroll aggregates are the product of average hourly earnings and aggregate weekly hours. At all higher levels of industry aggregation, payroll aggregates are the sum of the component aggregates. Production and related employees include working supervisors and all nonsupervisory employees (including group leaders and trainees) engaged in fabricating, processing, assembling, inspecting, receiving, storing, handling, packing, warehousing, shipping, trucking, hauling, maintenance, repair, janitorial, guard services, product development, auxiliary production for plant's own use (for example, power plant), recordkeeping, and other services closely associated with the above production operations. #Nonsupervisory employees include those individuals in private, service-providing industries who are not above the working-supervisor level. This group includes individuals such as office and clerical workers, repairers, salespersons, operators, drivers, physicians, lawyers, accountants, nurses, social workers, research aides, teachers, drafters, photographers, beauticians, musicians, restaurant workers, custodial workers, attendants, line installers and repairers, laborers, janitors, guards, and other employees at similar occupational levels whose services are closely associated with those of the employees listed. The series comes from the 'Current Employment Statistics (Establishment Survey).' The source code is: CES4300000035

The series comes from the 'Current Employment Statistics (Establishment Survey).' The source code is: CEU4349300001

Indexes of aggregate weekly payrolls are calculated by dividing the current month's aggregate by the average of the 12 monthly figures for the base year. Indexes are averages for production and nonsupervisory employees. For basic industries, the payroll aggregates are the product of average hourly earnings and aggregate weekly hours. At all higher levels of industry aggregation, payroll aggregates are the sum of the component aggregates. The series comes from the 'Current Employment Statistics (Establishment Survey).' The source code is: CEU4300000017

The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm.

The Dallas Fed has improved the quality of the payroll employment estimates for Metropolitan Areas of Texas using early benchmarking and two-step seasonal adjustment. More information regarding the early benchmarking technique can be found at http://www.dallasfed.org/research/basics/benchmark.cfm. More information pertaining to two-step seasonal adjustment can be found at http://www.dallasfed.org/research/basics/twostep.cfm.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU06112444349300001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU06112444300000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU06447004349300001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU09000004349300001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU06360844349300001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU12000004349300001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU42000004349300001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU37000004349300001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU41244204300000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU41105404300000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU36356144300000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU36000004349300001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU34935654300000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator." Middlesex-Monmouth-Ocean, NJ constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the New York-Wayne-White Plains, NY-NJ (MD)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU17169744349300001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU28250604300000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU28000004349300001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU53284204300000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU54265804300000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU45439004300000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU36205244300000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodels' library from Python with default parameter settings. The package uses the U.S. Bureau of the Census X-13ARIMA-SEATS Seasonal Adjustment Program. More information on the 'statsmodels' X-13ARIMA-SEATS package can be found here (https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html). More information on X-13ARIMA-SEATS can be found here (https://www.census.gov/data/software/x13as.html). Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. Occasionally, updates to the data will not include sufficient seasonal factors to trigger a seasonal adjustment. In these cases, the NSA series will be updated normally; but the SA series will also be updated with the NSA data. The NSA series can be located here here (https://fred.stlouisfed.org/series/SMU17209944300000001). Some seasonally adjusted series may exhibit negative values because they are created from a seasonal adjustment process regardless of the actual meaning or interpretation of the given indicator.

Middlesex-Monmouth-Ocean, NJ constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the New York-Wayne-White Plains, NY-NJ (MD)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics.

Delaware County, PA constitutes a significant percentage of the state's total statewide employment. This series is calculated in addition to the Philadelphia-Camden-Wilmington, PA-NJ-DE-MD (MSA)'s statistics because of it's importance to the state. For more details on non-standard area definitions, visit the additional resources (https://www.bls.gov/sae/additional-resources/non-standard-ces-areas.htm) of the Current Employment Statistics.