This series is discontinued and will no longer be updated. The Federal Reserve Bank of St. Louis previously calculated this seasonally adjusted (SA) series based on the not seasonally adjusted (NSA) version available here (https://fred.stlouisfed.org/series/SMU32000000800000011). However, most of the earnings-related series do not have a significant seasonal component, so the values for both the SA and the NSA series are very similar. See the NSA series (https://fred.stlouisfed.org/series/SMU32000000800000011) for updated values. The Federal Reserve Bank of St. Louis previously used to seasonally adjust 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/SMU32000000800000011). 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.

This series is discontinued and will no longer be updated. The Federal Reserve Bank of St. Louis previously calculated this seasonally adjusted (SA) series based on the not seasonally adjusted (NSA) version available here (https://fred.stlouisfed.org/series/SMU32000002000000002). However, most of the earnings-related series do not have a significant seasonal component, so the values for both the SA and the NSA series are very similar. See the NSA series (https://fred.stlouisfed.org/series/SMU32000002000000002) for updated values. The Federal Reserve Bank of St. Louis previously used to seasonally adjust 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/SMU32000002000000002). 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.

This series is discontinued and will no longer be updated. The Federal Reserve Bank of St. Louis previously calculated this seasonally adjusted (SA) series based on the not seasonally adjusted (NSA) version available here (https://fred.stlouisfed.org/series/SMU32000002000000003). However, most of the earnings-related series do not have a significant seasonal component, so the values for both the SA and the NSA series are very similar. See the NSA series (https://fred.stlouisfed.org/series/SMU32000002000000003) for updated values. The Federal Reserve Bank of St. Louis previously used to seasonally adjust 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/SMU32000002000000003). 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.

This series is discontinued and will no longer be updated. The Federal Reserve Bank of St. Louis previously calculated this seasonally adjusted (SA) series based on the not seasonally adjusted (NSA) version available here (https://fred.stlouisfed.org/series/SMU32000002000000007). However, most of the earnings-related series do not have a significant seasonal component, so the values for both the SA and the NSA series are very similar. See the NSA series (https://fred.stlouisfed.org/series/SMU32000002000000007) for updated values. The Federal Reserve Bank of St. Louis previously used to seasonally adjust 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/SMU32000002000000007). 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.

This series is discontinued and will no longer be updated. The Federal Reserve Bank of St. Louis previously calculated this seasonally adjusted (SA) series based on the not seasonally adjusted (NSA) version available here (https://fred.stlouisfed.org/series/SMU32000002000000008). However, most of the earnings-related series do not have a significant seasonal component, so the values for both the SA and the NSA series are very similar. See the NSA series (https://fred.stlouisfed.org/series/SMU32000002000000008) for updated values. The Federal Reserve Bank of St. Louis previously used to seasonally adjust 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/SMU32000002000000008). 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.

This series is discontinued and will no longer be updated. The Federal Reserve Bank of St. Louis previously calculated this seasonally adjusted (SA) series based on the not seasonally adjusted (NSA) version available here (https://fred.stlouisfed.org/series/SMU32000002000000011). However, most of the earnings-related series do not have a significant seasonal component, so the values for both the SA and the NSA series are very similar. See the NSA series (https://fred.stlouisfed.org/series/SMU32000002000000011) for updated values. The Federal Reserve Bank of St. Louis previously used to seasonally adjust 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/SMU32000002000000011). 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.

This series is discontinued and will no longer be updated. The Federal Reserve Bank of St. Louis previously calculated this seasonally adjusted (SA) series based on the not seasonally adjusted (NSA) version available here (https://fred.stlouisfed.org/series/SMU32000002000000030). However, most of the earnings-related series do not have a significant seasonal component, so the values for both the SA and the NSA series are very similar. See the NSA series (https://fred.stlouisfed.org/series/SMU32000002000000030) for updated values. The Federal Reserve Bank of St. Louis previously used to seasonally adjust 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/SMU32000002000000030). 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.

This series is discontinued and will no longer be updated. The Federal Reserve Bank of St. Louis previously calculated this seasonally adjusted (SA) series based on the not seasonally adjusted (NSA) version available here (https://fred.stlouisfed.org/series/SMU32000003000000007). However, most of the earnings-related series do not have a significant seasonal component, so the values for both the SA and the NSA series are very similar. See the NSA series (https://fred.stlouisfed.org/series/SMU32000003000000007) for updated values. The Federal Reserve Bank of St. Louis previously used to seasonally adjust 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/SMU32000003000000007). 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.

This series may have irregularities or important features that are not disclosed here. To see whether this is the case, please consult Part 1, Section 1, Table 8 in the original source at https://fraser.stlouisfed.org/scribd/?item_id=6408&filepath=%2Fdocs%2Fpublications%2Fbms%2F1914-1941%2FBMS14-41_complete.pdf&start_page=1 Relevant details can be found in the footnotes of each table as well as the introductory material for Section 1.

This data series illustrates the movement of exports from the given state to the given country. A missing observation can mean two things. First, missing observations can appear because no exports were made to the listed country that year. Secondly, it could signify a low number of exporters thus giving away proprietary data away.

This data series illustrates the movement of exports from the given state to the given country. A missing observation can mean two things. First, missing observations can appear because no exports were made to the listed country that year. Secondly, it could signify a low number of exporters thus giving away proprietary data away.

This data series illustrates the movement of exports from the given state to the given country. A missing observation can mean two things. First, missing observations can appear because no exports were made to the listed country that year. Secondly, it could signify a low number of exporters thus giving away proprietary data away.

The Business Formation Statistics are now released on a monthly basis, and the quarterly series will no longer be updated. See BACBATOTALSANV (https://fred.stlouisfed.org/series/BACBATOTALSANV) for an alternative. High-Propensity Business Applications (HBA) from a corporation or personal service corporation, based on the legal form of organization stated in the IRS Form SS-4. Similar to the WBA series, this series is important primarily because it consists of a set of applications that have a high rate of transitioning into businesses with payroll.

See the EIA's report on Energy-Related Carbon Dioxide Emissions by State (http://www.eia.gov/environment/emissions/state/analysis/) for technical notes and documentation.

See the EIA's report on Energy-Related Carbon Dioxide Emissions by State (http://www.eia.gov/environment/emissions/state/analysis/) for technical notes and documentation.

The Quarterly Summary of State and Local Government Tax Revenue provides quarterly estimates of state and local government tax revenue at a national level, as well as detailed tax revenue data for individual states. This quarterly survey has been conducted continuously since 1962. The information contained in this survey is the most current information available on a nationwide basis for government tax collections. For more information about the survey, visit the U.S. Census Bureau (https://www.census.gov/programs-surveys/qtax/about.html). A Special Note Concerning Revised Data: Revisions reflect tax collection amounts obtained from three general sources. State and local government respondents have submitted revisions to amounts as originally reported. In other cases, governments have reported data, which we used to replace data that were previously imputed or estimated. Finally, some of the revisions were compiled from government sources, both published and unpublished. For more information, see the survey's methodology (https://www.census.gov/programs-surveys/qtax/technical-documentation/methodology.html).

This series is discontinued and will no longer be updated. Additional consolidated data is available on the FDIC's Bank Data and Statistics (https://www.fdic.gov/bank/statistical/). This series represents Quarterly Average of Total Assets call item RCFD3368. Geographic location is determined by Abbreviated State Name call item RSSD9200 = 'NV' representing two character state abbreviation of Nevada. Users are advised to use the Federal Reserve Board of Governors' data dictionary (https://www.federalreserve.gov/apps/mdrm/data-dictionary) to retrieve detailed information for specific call items. This series is calculated by the Federal Reserve Bank of St. Louis using raw data that are collected by the FFIEC. Raw data can be found at https://cdr.ffiec.gov/public/.

This series is discontinued and will no longer be updated. Additional consolidated data is available on the FDIC's Bank Data and Statistics (https://www.fdic.gov/bank/statistical/). This series represents Total Equity Capital call item RCFD3210 that has been weighted each quarter to smooth out the fluctuations. Geographic location is determined by Abbreviated State Name call item RSSD9200 = 'NV' representing two character state abbreviation of Nevada. Users are advised to use the Federal Reserve Board of Governors' data dictionary (https://www.federalreserve.gov/apps/mdrm/data-dictionary) to retrieve detailed information for specific call items. This series is calculated by the Federal Reserve Bank of St. Louis using raw data that are collected by the FFIEC. Raw data can be found at https://cdr.ffiec.gov/public/.

This series is discontinued and will no longer be updated. Additional consolidated data is available on the FDIC's Bank Data and Statistics (https://www.fdic.gov/bank/statistical/). This series represents Total Loans and Leases, Net of Unearned Income call item RCFD2122 that has been weighted each quarter to smooth out the fluctuations. Geographic location is determined by Abbreviated State Name call item RSSD9200 = 'NV' representing two character state abbreviation of Nevada. Users are advised to use the Federal Reserve Board of Governors' data dictionary (https://www.federalreserve.gov/apps/mdrm/data-dictionary) to retrieve detailed information for specific call items. This series is calculated by the Federal Reserve Bank of St. Louis using raw data that are collected by the FFIEC. Raw data can be found at https://cdr.ffiec.gov/public/.

This series is discontinued and will no longer be updated. Additional consolidated data is available on the FDIC's Bank Data and Statistics (https://www.fdic.gov/bank/statistical/). This series is a ratio of Net Income call item RIAD4340 to Quarterly Average of Total Assets call item RCFD3368. Geographic location is determined by Abbreviated State Name call item RSSD9200 = 'NV' representing two character state abbreviation of Nevada. Data are annualized. Users are advised to use the Federal Reserve Board of Governors' data dictionary (https://www.federalreserve.gov/apps/mdrm/data-dictionary) to retrieve detailed information for specific call items. This series is calculated by the Federal Reserve Bank of St. Louis using raw data that are collected by the FFIEC. Raw data can be found at https://cdr.ffiec.gov/public/.

This series is discontinued and will no longer be updated. Additional consolidated data is available on the FDIC's Bank Data and Statistics (https://www.fdic.gov/bank/statistical/). This series is a ratio of Net Income call item RIAD4340 to Average of Total Equity Capital call item RCFD3210. Geographic location is determined by Abbreviated State Name call item RSSD9200 = 'NV' representing two character state abbreviation of Nevada. Data are annualized. Users are advised to use the Federal Reserve Board of Governors' data dictionary (https://www.federalreserve.gov/apps/mdrm/data-dictionary) to retrieve detailed information for specific call items. This series is calculated by the Federal Reserve Bank of St. Louis using raw data that are collected by the FFIEC. Raw data can be found at https://cdr.ffiec.gov/public/.

For more information, see https://www.census.gov/programs-surveys/qtax.html.

This series is discontinued and will no longer be updated. Additional consolidated data is available on the FDIC's Bank Data and Statistics (https://www.fdic.gov/bank/statistical/). This series represents Allowance for Loan and Lease Losses call item RCFD3123. Geographic location is determined by Abbreviated State Name call item RSSD9200 = 'NV' representing two character state abbreviation of Nevada. Users are advised to use the Federal Reserve Board of Governors' data dictionary (https://www.federalreserve.gov/apps/mdrm/data-dictionary) to retrieve detailed information for specific call items. This series is calculated by the Federal Reserve Bank of St. Louis using raw data that are collected by the FFIEC. Raw data can be found at https://cdr.ffiec.gov/public/.

This data series illustrates the movement of exports from the given state to the given country. A missing observation can mean two things. First, missing observations can appear because no exports were made to the listed country that year. Secondly, it could signify a low number of exporters thus giving away proprietary data away.

This data series illustrates the movement of exports from the given state to the given country. A missing observation can mean two things. First, missing observations can appear because no exports were made to the listed country that year. Secondly, it could signify a low number of exporters thus giving away proprietary data away.

This data series illustrates the movement of exports from the given state to the given country. A missing observation can mean two things. First, missing observations can appear because no exports were made to the listed country that year. Secondly, it could signify a low number of exporters thus giving away proprietary data away.

This data series illustrates the movement of exports from the given state to the given country. A missing observation can mean two things. First, missing observations can appear because no exports were made to the listed country that year. Secondly, it could signify a low number of exporters thus giving away proprietary data away.

The Quarterly Summary of State and Local Government Tax Revenue provides quarterly estimates of state and local government tax revenue at a national level, as well as detailed tax revenue data for individual states. This quarterly survey has been conducted continuously since 1962. The information contained in this survey is the most current information available on a nationwide basis for government tax collections. For more information about the survey, visit the U.S. Census Bureau (https://www.census.gov/programs-surveys/qtax/about.html). A Special Note Concerning Revised Data: Revisions reflect tax collection amounts obtained from three general sources. State and local government respondents have submitted revisions to amounts as originally reported. In other cases, governments have reported data, which we used to replace data that were previously imputed or estimated. Finally, some of the revisions were compiled from government sources, both published and unpublished. For more information, see the survey's methodology (https://www.census.gov/programs-surveys/qtax/technical-documentation/methodology.html).

This series may have irregularities or important features that are not disclosed here. To see whether this is the case, please consult Part 1, Section 1, Table 8 in the original source at https://fraser.stlouisfed.org/scribd/?item_id=6408&filepath=%2Fdocs%2Fpublications%2Fbms%2F1914-1941%2FBMS14-41_complete.pdf&start_page=1 Relevant details can be found in the footnotes of each table as well as the introductory material for Section 1.

Patent origin is determined by the residence of the first-named inventor. Total is the sum of utility, plant, design, and reissue patents granted. See PTMT Products and Services Brochure (https://www.uspto.gov/web/offices/ac/ido/oeip/taf/brochure.htm) and Types of patent applications and proceedings (https://www.uspto.gov/patents/basics/types-patent-applications) for more information.

Patent origin is determined by the residence of the first-named inventor. Utility patents are patents for invention. See PTMT Products and Services Brochure (https://www.uspto.gov/web/offices/ac/ido/oeip/taf/brochure.htm) and Types of patent applications and proceedings (https://www.uspto.gov/patents/basics/types-patent-applications) for more information.

This series may have irregularities or important features that are not disclosed here. To see whether this is the case, please consult Part 1, Section 1, Table 8 in the original source at https://fraser.stlouisfed.org/scribd/?item_id=6408&filepath=%2Fdocs%2Fpublications%2Fbms%2F1914-1941%2FBMS14-41_complete.pdf&start_page=1 Relevant details can be found in the footnotes of each table as well as the introductory material for Section 1.

This series may have irregularities or important features that are not disclosed here. To see whether this is the case, please consult Part 1, Section 1, Table 8 in the original source at https://fraser.stlouisfed.org/scribd/?item_id=6408&filepath=%2Fdocs%2Fpublications%2Fbms%2F1914-1941%2FBMS14-41_complete.pdf&start_page=1 Relevant details can be found in the footnotes of each table as well as the introductory material for Section 1.

This series may have irregularities or important features that are not disclosed here. To see whether this is the case, please consult Part 1, Section 1, Table 8 in the original source at https://fraser.stlouisfed.org/scribd/?item_id=6408&filepath=%2Fdocs%2Fpublications%2Fbms%2F1914-1941%2FBMS14-41_complete.pdf&start_page=1 Relevant details can be found in the footnotes of each table as well as the introductory material for Section 1.

This series may have irregularities or important features that are not disclosed here. To see whether this is the case, please consult Part 1, Section 1, Table 8 in the original source at https://fraser.stlouisfed.org/scribd/?item_id=6408&filepath=%2Fdocs%2Fpublications%2Fbms%2F1914-1941%2FBMS14-41_complete.pdf&start_page=1 Relevant details can be found in the footnotes of each table as well as the introductory material for Section 1.

Information about this release can be found here (https://www.bea.gov/data/gdp/gdp-state). For information about BEA industries and other regional definitions, visit their Regional Economic Accounts: Regional Definitions website (https://apps.bea.gov/regional/definitions/).

Information about this release can be found here (https://www.bea.gov/data/gdp/gdp-state). For information about BEA industries and other regional definitions, visit their Regional Economic Accounts: Regional Definitions website (https://apps.bea.gov/regional/definitions/).

This series may have irregularities or important features that are not disclosed here. To see whether this is the case, please consult Part 1, Section 1, Table 8 in the original source at https://fraser.stlouisfed.org/scribd/?item_id=6408&filepath=%2Fdocs%2Fpublications%2Fbms%2F1914-1941%2FBMS14-41_complete.pdf&start_page=1 Relevant details can be found in the footnotes of each table as well as the introductory material for Section 1.

This series may have irregularities or important features that are not disclosed here. To see whether this is the case, please consult Part 1, Section 1, Table 8 in the original source at https://fraser.stlouisfed.org/scribd/?item_id=6408&filepath=%2Fdocs%2Fpublications%2Fbms%2F1914-1941%2FBMS14-41_complete.pdf&start_page=1 Relevant details can be found in the footnotes of each table as well as the introductory material for Section 1.

The Business Formation Statistics are now released on a monthly basis, and the quarterly series will no longer be updated. See BFSBF8QTOTALSANV (https://fred.stlouisfed.org/series/BFSBF8QTOTALSANV) for an alternative. This series combines (splices) https://fred.stlouisfed.org/series/BF8QSANV and https://fred.stlouisfed.org/series/PBF8QSANV to provide the entire time series for the actual and projected business formations within 8 quarters.

This series may have irregularities or important features that are not disclosed here. To see whether this is the case, please consult Part 1, Section 1, Table 8 in the original source at https://fraser.stlouisfed.org/scribd/?item_id=6408&filepath=%2Fdocs%2Fpublications%2Fbms%2F1914-1941%2FBMS14-41_complete.pdf&start_page=1 Relevant details can be found in the footnotes of each table as well as the introductory material for Section 1.

Real hourly compensation is the average payments and benefits made to individuals for an hour of labor, adjusted for inflation.

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/SMU32000004244600001). 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.

Overview of the Index The Index is a quarterly comprehensive picture of the average American household’s financial condition. Built by assessing the key elements of financial health and distress, it converts a complex set of factors into a single, easy to understand number. Scope and History The index measures the U.S., all 50 states and more than 70 MSAs. The national and state versions date back to 1980 and the MSA versions date back to 1990. Public Data and Proprietary Methodology We use more than 65 data points from government, public and private data and a proprietary methodology for compiling, combining and evaluating data. With nearly 50 years of experience and insight into helping consumers in financial distress, we know the biggest causes of distress, how people react to financial challenges and proven strategies for regaining control. (Note: Our client data is not a data source for the Index) Measured on a 100 Point Scale Financial distress is measured on a 100 point scale and a score under 70 indicates financial distress. The lower the score equals more distress, a weaker financial position, more urgency to act, takes longer and is harder to resolve, and increases the probability of needing a third party help to resolve. The Index score is tied to one of 5 general rating categories, which reflect the strength and stability of the consumer’s position. Less than 60 Emergency / Crisis 60 – 69 Distressed / Unstable 70 – 79 Weakening / At-Risk 80 – 89 Good / Stable 90 and Above Excellent / Secure What Does the Index Measure? We measure the 5 categories of personal finance that reflect or lead to a secure, stable financial life—Employment, Housing, Credit, Household Budget and Net Worth. All are equally important, so have given each category equal weighting. Employment. Stable income is the foundation of any family’s finances. This category measures the impact of unemployment and underemployment on financial health. Key Measures: Unemployment, Underemployment Sample Data Source: Department of Labor, Bureau of Labor Statistics Housing. Safe, affordable housing is a priority for all families. This category measures how consumers are paying their mortgage/rent and the impact of housing costs on their finances. Key Measures: Mortgage and Rental Delinquencies, Housing as Percent of Budget Sample Data Source: National Delinquency Survey Credit. Responsible use of credit creates more borrowing options and lower costs. This category assesses the strength of credit scores and how well families manage their credit. Key Measures: Credit Scores, Trade Line Utilization, Credit Delinquencies, Per Capita Bankruptcies Sample Data Source: National Credit Bureau Household Budget. Spending less than you make is the daily choice that leads to long-term success. This category measures families’ spending patterns and saving for emergencies. Key Measures: Disposable Income, Savings, Consumer Confidence Sample Data Source: Department of Commerce, Bureau of Economic Analysis Net Worth. Strong, positive net worth creates options and independence. This category measures how well consumers are strengthening their personal balance sheets. Key Measures: Household Net Worth, Net Worth versus Funds Required for Long-Term Needs (e.g. retirement) Sample Data Source: Federal Reserve Flow of Funds, Survey of Consumer Finances

This series is discontinued and will no longer be updated. The Federal Reserve Bank of St. Louis previously calculated this seasonally adjusted (SA) series based on the not seasonally adjusted (NSA) version available here (https://fred.stlouisfed.org/series/SMU32000003000000008). However, most of the earnings-related series do not have a significant seasonal component, so the values for both the SA and the NSA series are very similar. See the NSA series (https://fred.stlouisfed.org/series/SMU32000003000000008) for updated values. The Federal Reserve Bank of St. Louis previously used to seasonally adjust 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/SMU32000003000000008). 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.

This series is discontinued and will no longer be updated. The Federal Reserve Bank of St. Louis previously calculated this seasonally adjusted (SA) series based on the not seasonally adjusted (NSA) version available here (https://fred.stlouisfed.org/series/SMU32000003000000030). However, most of the earnings-related series do not have a significant seasonal component, so the values for both the SA and the NSA series are very similar. See the NSA series (https://fred.stlouisfed.org/series/SMU32000003000000030) for updated values. The Federal Reserve Bank of St. Louis previously used to seasonally adjust 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/SMU32000003000000030). 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 '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/.

See the EIA's report on Energy-Related Carbon Dioxide Emissions by State (http://www.eia.gov/environment/emissions/state/analysis/) for technical notes and documentation.

This series is discontinued and will no longer be updated. The Federal Reserve Bank of St. Louis previously calculated this seasonally adjusted (SA) series based on the not seasonally adjusted (NSA) version available here (https://fred.stlouisfed.org/series/SMU32000000500000002). However, most of the earnings-related series do not have a significant seasonal component, so the values for both the SA and the NSA series are very similar. See the NSA series (https://fred.stlouisfed.org/series/SMU32000000500000002) for updated values. The Federal Reserve Bank of St. Louis previously used to seasonally adjust 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/SMU32000000500000002). 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.

Labor hours are measured as annual hours worked by all workers, including wage and salary workers, unincorporated self-employed workers, and unpaid family workers, in the production of goods and services.

Output per worker is ratio of the amount of goods and services produced relative to the number of workers who produced that output for a given period of time.

Information about this release can be found here (https://www.bea.gov/data/gdp/gdp-state). For information about BEA industries and other regional definitions, visit their Regional Economic Accounts: Regional Definitions website (https://apps.bea.gov/regional/definitions/).

The Business Formation Statistics are now released on a monthly basis, and the quarterly series will no longer be updated. See BAHBATOTALSANV (https://fred.stlouisfed.org/series/BAHBATOTALSANV) for an alternative. Business Applications (BA) that have a high propensity of turning into businesses with payroll. The identification of high-propensity applications is based on the characteristics of applications revealed on the IRS Form SS-4 that are associated with a high rate of business formation. High-propensity applications include applications: (a) for a corporate entity, (b) that indicate they are hiring employees, purchasing a business or changing organizational type, (c) that provide a first wages-paid date (planned wages); or (d) that have a NAICS industry code in manufacturing (31-33), retail stores (44), health care (62), or restaurants/food service (72).

This data series illustrates the movement of exports from the given state to the given country. A missing observation can mean two things. First, missing observations can appear because no exports were made to the listed country that year. Secondly, it could signify a low number of exporters thus giving away proprietary data away.

This data series illustrates the movement of exports from the given state to the given country. A missing observation can mean two things. First, missing observations can appear because no exports were made to the listed country that year. Secondly, it could signify a low number of exporters thus giving away proprietary data away.

The leading index for each state predicts the six-month growth rate of the state's coincident index. In addition to the coincident index, the models include other variables that lead the economy: state-level housing permits (1 to 4 units), state initial unemployment insurance claims, delivery times from the Institute for Supply Management (ISM) manufacturing survey, and the interest rate spread between the 10-year Treasury bond and the 3-month Treasury bill.

This series is discontinued and will no longer be updated. The Federal Reserve Bank of St. Louis previously calculated this seasonally adjusted (SA) series based on the not seasonally adjusted (NSA) version available here (https://fred.stlouisfed.org/series/SMU32000007000000011). However, most of the earnings-related series do not have a significant seasonal component, so the values for both the SA and the NSA series are very similar. See the NSA series (https://fred.stlouisfed.org/series/SMU32000007000000011) for updated values. The Federal Reserve Bank of St. Louis previously used to seasonally adjust 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/SMU32000007000000011). 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.

This series is discontinued and will no longer be updated. Additional consolidated data is available on the FDIC's Bank Data and Statistics (https://www.fdic.gov/bank/statistical/). This series is constructed as a sum of Total Loan and Lease Finance Receivables, Nonaccrual call item RCFD1403 and Total Loan and Lease Finance Receivables, Past Due 90 Days or More and Still Accruing call item RCFD1407 to the Total Loans and Leases, Net of Unearned Income call item RCFD2122. Geographic location is determined by Abbreviated State Name call item RSSD9200 = 'NV' representing two character state abbreviation of Nevada. Users are advised to use the Federal Reserve Board of Governors' data dictionary (https://www.federalreserve.gov/apps/mdrm/data-dictionary) to retrieve detailed information for specific call items. This series is calculated by the Federal Reserve Bank of St. Louis using raw data that are collected by the FFIEC. Raw data can be found at https://cdr.ffiec.gov/public/.

This series is discontinued and will no longer be updated. Additional consolidated data is available on the FDIC's Bank Data and Statistics (https://www.fdic.gov/bank/statistical/). This series includes institutions with the Charter Type call item RSSD9048 = 200 and Entity Type call item RSSD9331 = 1. Charter Type call item RSSD9048 = 200 represents Commercial Bank (including depository trust companies, credit card companies with commercial bank charters, private banks, development banks, limited charter banks, and foreign banks) Entity Type call item RSSD9331 = 1 represents Commercial Bank. Geographic location is determined by Abbreviated State Name call item RSSD9200 = 'NV' representing two character state abbreviation of Nevada. Users are advised to use the Federal Reserve Board of Governors' data dictionary (https://www.federalreserve.gov/apps/mdrm/data-dictionary) to retrieve detailed information for specific call items. This series is calculated by the Federal Reserve Bank of St. Louis using raw data that are collected by the FFIEC. Raw data can be found at https://cdr.ffiec.gov/public/.

Please visit http://research.stlouisfed.org/fred2/personalincome for documentation on the derivation of personal income.