Total Quarterly Wages in Aguadilla-Isabela, PR (MSA)

Observations

Q3 2024: 419,091,622.53685

Updated: Mar 5, 2025 11:21 AM CST

Q3 2024:	419,091,622.53685
Q2 2024:	417,605,077.23681
Q1 2024:	420,823,281.90760
Q4 2023:	442,451,860.64005
Q3 2023:	439,101,165.03682

Units:

Dollars,

Seasonally Adjusted

Frequency:

Quarterly

Notes

Title	Release Dates

Total Quarterly Wages in Aguadilla-Isabela, PR (MSA)	2015-06-17	2025-03-05

Source

U.S. Bureau of Labor Statistics	2015-06-17	2019-03-05
Federal Reserve Bank of St. Louis	2015-06-17	2019-03-05
U.S. Bureau of Labor Statistics	2019-03-06	2019-06-04
Federal Reserve Bank of St. Louis	2019-03-06	2019-06-04
U.S. Bureau of Labor Statistics	2019-06-05	2025-03-05
Federal Reserve Bank of St. Louis	2019-06-05	2025-03-05

Release

Quarterly Census of Employment and Wages	2015-06-17	2025-03-05

Units

Dollars	2015-06-17	2025-03-05

Frequency

Quarterly	2015-06-17	2025-03-05

Seasonal Adjustment

Seasonally Adjusted	2015-06-17	2025-03-05

Notes

Total wages are the wages paid by Unemployment Insurance covered employers during the calendar quarter, regardless of when the services were performed. 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 https://www.census.gov/srd/www/x13as/.	2015-06-17	2019-03-05
Total wages are the wages paid by Unemployment Insurance covered employers during the calendar quarter, regardless of when the services were performed. 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.	2019-03-06	2019-06-04
Total wages are the wages paid by Unemployment Insurance covered employers during the calendar quarter, regardless of when the services were performed. Included in wages are pay for vacation and other paid leave, bonuses, stock options, tips, the cash value of meals and lodging, and in some States, contributions to deferred compensation plans (such as 401(k) plans). 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. 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/ENUC103830010) 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. The data were retrieved from the BLS API on the "Updated" date referenced above the graph. BLS.gov cannot vouch for the data or analyses derived from these data after the data have been retrieved from BLS.gov. "	2019-06-05	2025-03-05