Skip to main content

Home > Releases > State Employment and Unemployment > All Employees: Education and Health Services: Private Education and Health Services in St. George, UT (MSA)

All Employees: Education and Health Services: Private Education and Health Services in St. George, UT (MSA) (STGE149EDUH)

Observations

Apr 2026: 15.78561
Updated: May 26, 2026 2:47 PM CDT
Apr 2026:  15.78561  
Mar 2026:  15.78719  
Feb 2026:  15.71203  
Jan 2026:  15.70791  
Dec 2025:  15.74734  

Units:

Thousands of Persons,
Seasonally Adjusted

Frequency:

Monthly

Fullscreen

Notes

Title Release Dates
All Employees: Education and Health Services in St. George, UT (MSA) 2012-01-24 2023-03-12
All Employees: Education and Health Services: Private Education and Health Services in St. George, UT (MSA) 2023-03-13 2026-05-22
 
Source    
Federal Reserve Bank of St. Louis 2012-01-24 2017-05-18
U.S. Bureau of Labor Statistics 2017-05-19 2026-05-22
 
Release    
Regional and State Employment and Unemployment (Seasonally Adjusted) 2012-01-24 2017-03-12
State Employment and Unemployment (Seasonally Adjusted) 2017-03-13 2017-05-18
State Employment and Unemployment 2017-05-19 2026-05-22
 
Units    
Thousands of Persons 2012-01-24 2026-05-22
 
Frequency    
Monthly 2012-01-24 2026-05-22
 
Seasonal Adjustment    
Seasonally Adjusted 2012-01-24 2026-05-22
 
Notes    
The data services of the Federal Reserve Bank of St. Louis include series that are seasonally adjusted. To make these adjustments, we use the X-12 Procedure of SAS to remove the seasonal component of the series so that non-seasonal trends can be analyzed. This procedure is based on the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on this program can be found at http://www.census.gov/srd/www/x12a/.
The seasonal moving average function used is that of the Census Bureau’s X-11-ARIMA program. This includes a 3x3 moving average for the initial seasonal factors and a 3x5 moving average to calculate the final seasonal factors. The D11 function is also used to output the entire seasonally adjusted series that is displayed. For specific information on the SAS X-12 procedure, please visit their website: http://support.sas.com/documentation/cdl/en/etsug/60372/HTML/default/viewer.htm#etsug_x12_sect001.htm.

2012-01-24 2017-05-18
The data services of the Federal Reserve Bank of St. Louis include series that are seasonally adjusted. To make these adjustments, we use the X-12 Procedure of SAS to remove the seasonal component of the series so that non-seasonal trends can be analyzed. This procedure is based on the U.S. Bureau of the Census X-12-ARIMA Seasonal Adjustment Program. More information on this program can be found at https://www.census.gov/srd/www/x13as/.
The seasonal moving average function used is that of the Census Bureau’s X-11-ARIMA program. This includes a 3x3 moving average for the initial seasonal factors and a 3x5 moving average to calculate the final seasonal factors. The D11 function is also used to output the entire seasonally adjusted series that is displayed. For specific information on the SAS X-12 procedure, please visit their website: http://support.sas.com/documentation/cdl/en/etsug/60372/HTML/default/viewer.htm#etsug_x12_sect001.htm.

2017-05-19 2019-03-05
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 2019-08-26
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/STGE149EDUHN) 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/STGE149EDUHN).

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 2026-05-22

Release Tables

Related Data and Content

Data Suggestions Based On Your Search

Content Suggestions

Other Formats

Monthly, Not Seasonally Adjusted

Related Categories

St. George, UT MSAs Utah States U.S. Regional Data Categories

Releases

More Series from State Employment and Unemployment

Tags

St. George Utah Health Education Services St. Louis Fed Metropolitan Statistical Area Employment Seasonally Adjusted Bureau of Labor Statistics Monthly Public Domain: Citation Requested United States of America