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Title
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Release Dates
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All Employees: Professional, Scientific, and Technical Services in Wilmington, DE-MD-NJ (MD)
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2014-01-28
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2020-08-11
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All Employees: Professional and Business Services: Professional, Scientific, and Technical Services in Wilmington, DE-MD-NJ (MD)
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2020-08-12
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2025-05-21
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Source
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U.S. Bureau of Labor Statistics
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2014-01-28
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2018-11-15
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Federal Reserve Bank of St. Louis
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2014-01-28
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2018-11-15
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U.S. Bureau of Labor Statistics
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2018-11-16
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2019-04-18
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Federal Reserve Bank of St. Louis
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2018-11-16
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2019-04-18
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U.S. Bureau of Labor Statistics
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2019-04-19
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2019-08-26
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Federal Reserve Bank of St. Louis
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2019-04-19
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2019-08-26
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U.S. Bureau of Labor Statistics
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2019-08-27
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2019-12-19
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Federal Reserve Bank of St. Louis
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2019-08-27
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2019-12-19
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U.S. Bureau of Labor Statistics
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2019-12-20
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2020-08-11
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Federal Reserve Bank of St. Louis
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2019-12-20
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2020-08-11
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U.S. Bureau of Labor Statistics
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2020-08-12
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2025-05-21
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Federal Reserve Bank of St. Louis
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2020-08-12
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2025-05-21
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Release
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State and Metro Area Employment, Hours, and Earnings
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2014-01-28
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2025-05-21
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Units
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Thousands of Persons
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2014-01-28
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2025-05-21
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Frequency
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Monthly
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2014-01-28
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2025-05-21
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Seasonal Adjustment
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Seasonally Adjusted
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2014-01-28
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2025-05-21
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Notes
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The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on the 'x12' package can be found at http://cran.r-project.org/web/packages/x12/x12.pdf. More information on X-12-ARIMA can be found at http://www.census.gov/srd/www/x12a/.
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2014-01-28
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2018-11-15
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The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'x12' package from R with default parameter settings. The package uses the U.S. Bureau of the Census X13-ARIMA-SEATS Seasonal Adjustment Program. More information on the 'x12' package can be found at https://cran.r-project.org/web/packages/x12/index.html. More information on X13-ARIMA-SEATS can be found at https://www.census.gov/srd/www/x13as/.
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2018-11-16
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2019-04-18
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The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' 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 'statsmodel' X-13ARIMA-SEATS package can be found at https://www.statsmodels.org/dev/generated/statsmodels.tsa.x13.x13_arima_analysis.html. More information on X-13ARIMA-SEATS can be found at https://www.census.gov/srd/www/x13as/.
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.
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2019-04-19
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2019-08-26
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The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' 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 'statsmodel' 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/srd/www/x13as/).
Many series include both seasonally adjusted (SA) and not seasonally adjusted (NSA) data. In some cases, the NSA data will be updated but the SA data will not be updated. The reason is usually that the data series has not accumulated enough new seasonal factors to trigger an adjustment.The NSA series can be located here (https://fred.stlouisfed.org/series/SMU10488646054000001) The FRED team is currently working on a new procedure to replace SA data that has not yet be updated with NSA data that has been updated.
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.
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2019-08-27
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2019-12-19
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The Federal Reserve Bank of St. Louis seasonally adjusts this series by using the 'statsmodel' 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 'statsmodel' 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/srd/www/x13as/).
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/SMU10488646054000001).
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."
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2019-12-20
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2020-08-11
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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/SMU10488646054000001).
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.
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2020-08-12
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2025-05-21
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