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All Employees: Goods Producing in New York-Newark-Jersey City, NY-NJ (MSA) (SMU36356200600000001SA)

Observations

Feb 2025: 707.87176
Updated: Mar 29, 2025 4:47 AM CDT
Feb 2025:  707.87176  
Jan 2025:  712.92685  
Dec 2024:  714.94046  
Nov 2024:  715.95249  
Oct 2024:  717.61132  

Units:

Thousands of Persons,
Seasonally Adjusted

Frequency:

Monthly
1Y5Y10YMax
to
Date:
Bar 1 - All Employees: Goods Producing in New York-Newark-Jersey City, NY-NJ (MSA) Vintage: 2025-03-17
Bar 1
(a) All Employees: Goods Producing in New York-Newark-Jersey City, NY-NJ (MSA), Thousands of Persons, Seasonally Adjusted (SMU36356200600000001SA)
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/SMU36356200600000001). 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.

Select a date that will equal 100 for your custom index:
  Enter date as YYYY-MM-DD
to

Write a custom formula to transform one or more series or combine two or more series.

You can begin by adding a series to combine with your existing series.

Type keywords to search for data

    Now create a custom formula to combine or transform the series.

    For example, invert an exchange rate by using formula 1/a, where “a” refers to the first FRED data series added to this line. Or calculate the spread between 2 interest rates, a and b, by using the formula a - b.

    Use the assigned data series variables (a, b, c, etc.) together with operators (+, -, *, /, ^, etc.), parentheses and constants (1, 1.5, 2, etc.) to create your own formula (e.g., 1/a, a-b, (a+b)/2, (a/(a+b+c))*100). As noted above, you may add other data series to this line before entering a formula.

    Finally, you can change the units of your new series.

    Select a date that will equal 100 for your custom index:
        Enter date as YYYY-MM-DD

    Bar 1 - All Employees: Goods Producing in New York-Newark-Jersey City, NY-NJ (MSA) Vintage: 2025-03-17
    Bar 2
    (a) All Employees: Goods Producing in New York-Newark-Jersey City, NY-NJ (MSA), Thousands of Persons, Seasonally Adjusted (SMU36356200600000001SA)
    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/SMU36356200600000001). 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.

    Select a date that will equal 100 for your custom index:
      Enter date as YYYY-MM-DD
    to

    Write a custom formula to transform one or more series or combine two or more series.

    You can begin by adding a series to combine with your existing series.

    Type keywords to search for data

      Now create a custom formula to combine or transform the series.

      For example, invert an exchange rate by using formula 1/a, where “a” refers to the first FRED data series added to this line. Or calculate the spread between 2 interest rates, a and b, by using the formula a - b.

      Use the assigned data series variables (a, b, c, etc.) together with operators (+, -, *, /, ^, etc.), parentheses and constants (1, 1.5, 2, etc.) to create your own formula (e.g., 1/a, a-b, (a+b)/2, (a/(a+b+c))*100). As noted above, you may add other data series to this line before entering a formula.

      Finally, you can change the units of your new series.

      Select a date that will equal 100 for your custom index:
          Enter date as YYYY-MM-DD

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      Bar 1
      All Employees: Goods Producing in New York-Newark-Jersey City, NY-NJ (MSA) Vintage: 2025-03-17
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      Bar 2
      All Employees: Goods Producing in New York-Newark-Jersey City, NY-NJ (MSA) Vintage: 2025-03-28
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      Notes

      Title Release Dates
      All Employees: Goods Producing in New York-Northern New Jersey-Long Island, NY-NJ-PA (MSA) 2014-01-28 2015-03-26
      All Employees: Goods Producing in New York-Newark-Jersey City, NY-NJ-PA (MSA) 2015-03-27 2025-03-16
      All Employees: Goods Producing in New York-Newark-Jersey City, NY-NJ (MSA) 2025-03-17 2025-03-28
       
      Source    
      U.S. Bureau of Labor Statistics 2014-01-28 2015-03-26
      Federal Reserve Bank of St. Louis 2014-01-28 2015-03-26
      U.S. Bureau of Labor Statistics 2015-03-27 2018-11-15
      Federal Reserve Bank of St. Louis 2015-03-27 2018-11-15
      U.S. Bureau of Labor Statistics 2018-11-16 2019-04-18
      Federal Reserve Bank of St. Louis 2018-11-16 2019-04-18
      U.S. Bureau of Labor Statistics 2019-04-19 2019-08-26
      Federal Reserve Bank of St. Louis 2019-04-19 2019-08-26
      U.S. Bureau of Labor Statistics 2019-08-27 2019-12-19
      Federal Reserve Bank of St. Louis 2019-08-27 2019-12-19
      U.S. Bureau of Labor Statistics 2019-12-20 2025-03-16
      Federal Reserve Bank of St. Louis 2019-12-20 2025-03-16
      U.S. Bureau of Labor Statistics 2025-03-17 2025-03-28
      Federal Reserve Bank of St. Louis 2025-03-17 2025-03-28
       
      Release    
      State and Metro Area Employment, Hours, and Earnings 2014-01-28 2025-03-28
       
      Units    
      Thousands of Persons 2014-01-28 2025-03-28
       
      Frequency    
      Monthly 2014-01-28 2025-03-28
       
      Seasonal Adjustment    
      Seasonally Adjusted 2014-01-28 2025-03-28
       
      Notes    
      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/.

      		2014-01-28 2018-11-15
      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/.

      		2018-11-16 2019-04-18
      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.

      		2019-04-19 2019-08-26
      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/SMU36356200600000001) 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.

      		2019-08-27 2019-12-19
      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/SMU36356200600000001).

      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.

      		2019-12-20 2025-03-28

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