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/).

For more information, visit https://www.dol.gov/whd/state/stateMinWageHis.htm

Data for "Resident Population" from 1900 to present are estimates as of July 1

Household data are collected as of March. Consumer Price Index research series using current methods (CPI-U-RS) presents an estimate of the CPI for all Urban Consumers (CPI-U) that incorporates most of the improvements made over that time span into the entire series. More information can be found at https://www.bls.gov/cpi/research-series/home.htm. As stated in the Census's "Source and Accuracy of Estimates for Income, Poverty, and Health Insurance Coverage in the United States: 2011" (http://www.census.gov/hhes/www/p60_243sa.pdf): Estimation of Median Incomes. The Census Bureau has changed the methodology for computing median income over time. The Census Bureau has computed medians using either Pareto interpolation or linear interpolation. Currently, we are using linear interpolation to estimate all medians. Pareto interpolation assumes a decreasing density of population within an income interval, whereas linear interpolation assumes a constant density of population within an income interval. The Census Bureau calculated estimates of median income and associated standard errors for 1979 through 1987 using Pareto interpolation if the estimate was larger than $20,000 for people or $40,000 for families and households. This is because the width of the income interval containing the estimate is greater than $2,500. We calculated estimates of median income and associated standard errors for 1976, 1977, and 1978 using Pareto interpolation if the estimate was larger than $12,000 for people or $18,000 for families and households. This is because the width of the income interval containing the estimate is greater than $1,000. All other estimates of median income and associated standard errors for 1976 through 2011 (2012 ASEC) and almost all of the estimates of median income and associated standard errors for 1975 and earlier were calculated using linear interpolation. Thus, use caution when comparing median incomes above $12,000 for people or $18,000 for families and households for different years. Median incomes below those levels are more comparable from year to year since they have always been calculated using linear interpolation. For an indication of the comparability of medians calculated using Pareto interpolation with medians calculated using linear interpolation, see Series P-60, Number 114, Money Income in 1976 of Families and Persons in the United States (www2.census.gov/prod2/popscan/p60-114.pdf).

These data come from the Current Population Survey (CPS), also known as the household survey. Civilian Labor Force includes all persons in the civilian noninstitutional population ages 16 and older classified as either employed or unemployed. Employed persons are all persons who, during the reference week (the week including the 12th day of the month), (a) did any work as paid employees, worked in their own business or profession or on their own farm, or worked 15 hours or more as unpaid workers in an enterprise operated by a member of their family, or (b) were not working but who had jobs from which they were temporarily absent because of vacation, illness, bad weather, childcare problems, maternity or paternity leave, labor-management dispute, job training, or other family or personal reasons, whether or not they were paid for the time off or were seeking other jobs. Each employed person is counted only once, even if he or she holds more than one job. Unemployed persons are all persons who had no employment during the reference week, were available for work, except for temporary illness, and had made specific efforts to find employment some time during the 4 week-period ending with the reference week. Persons who were waiting to be recalled to a job from which they had been laid off need not have been looking for work to be classified as unemployed. The unemployment rate is the unemployed percent of the civilian labor force [100 times (unemployed/civilian labor force)]. For more details, see the release's <a href=https://www.bls.gov/lau/laufaq.htm>frequently asked questions</a>.

Regional price parities (RPPs) are regional price levels expressed as a percentage of the overall national price level for a given year. The price levels are determined by the average prices paid by consumers for the mix of goods and services consumed in each region. Taking the ratio of RPPs shows the difference in price levels across regions. The term "all items" refers to all the detailed consumption goods and services used in the estimation of the RPPs. For more information about this release go to http://www.bea.gov/newsreleases/regional/rpp/rpp_newsrelease.htm or http://www.bea.gov/regional/methods.cfm.

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/).

Regional price parities (RPPs) are regional price levels expressed as a percentage of the overall national price level for a given year. The price levels are determined by the average prices paid by consumers for the mix of goods and services consumed in each region. Taking the ratio of RPPs shows the difference in price levels across regions. Rents RPPs are estimated only for observed tenants' rents and do not include imputed owner-occupied rent values. Metropolitan portion consists of all counties in a state that are parts of metropolitan statistical areas. For more information about this release go to http://www.bea.gov/newsreleases/regional/rpp/rpp_newsrelease.htm or http://www.bea.gov/regional/methods.cfm.

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.

Household data are collected as of March. As stated in the Census's "Source and Accuracy of Estimates for Income, Poverty, and Health Insurance Coverage in the United States: 2011" (http://www.census.gov/hhes/www/p60_243sa.pdf): Estimation of Median Incomes. The Census Bureau has changed the methodology for computing median income over time. The Census Bureau has computed medians using either Pareto interpolation or linear interpolation. Currently, we are using linear interpolation to estimate all medians. Pareto interpolation assumes a decreasing density of population within an income interval, whereas linear interpolation assumes a constant density of population within an income interval. The Census Bureau calculated estimates of median income and associated standard errors for 1979 through 1987 using Pareto interpolation if the estimate was larger than $20,000 for people or $40,000 for families and households. This is because the width of the income interval containing the estimate is greater than $2,500. We calculated estimates of median income and associated standard errors for 1976, 1977, and 1978 using Pareto interpolation if the estimate was larger than $12,000 for people or $18,000 for families and households. This is because the width of the income interval containing the estimate is greater than $1,000. All other estimates of median income and associated standard errors for 1976 through 2011 (2012 ASEC) and almost all of the estimates of median income and associated standard errors for 1975 and earlier were calculated using linear interpolation. Thus, use caution when comparing median incomes above $12,000 for people or $18,000 for families and households for different years. Median incomes below those levels are more comparable from year to year since they have always been calculated using linear interpolation. For an indication of the comparability of medians calculated using Pareto interpolation with medians calculated using linear interpolation, see Series P-60, Number 114, Money Income in 1976 of Families and Persons in the United States (www2.census.gov/prod2/popscan/p60-114.pdf).

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

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

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

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

A measure of spending on goods and services purchased by, and on behalf of, households based on households' state of residence in the fifty states and the District of Columbia. Spending on membership clubs, sports centers, parks, theaters, and museums, audio-video, photographic, and information processing equipment services, gambling, and other recreational services.For more information about this release go to http://www.bea.gov/newsreleases/regional/pce/pce_newsrelease.htm.

A measure of spending on goods and services purchased by, and on behalf of, households based on households' state of residence in the fifty states and the District of Columbia. Spending on outpatient services and hospital and nursing home services. Outpatient services consist of physician services, dental services, and paramedical services.For more information about this release go to http://www.bea.gov/newsreleases/regional/pce/pce_newsrelease.htm.

A measure of spending on goods and services purchased by, and on behalf of, households based on households' state of residence in the fifty states and the District of Columbia. Purchases of video, audio, photographic, and information processing equipment and media, sporting equipment supplies, guns, and ammunition, sports and recreational vehicles, recreational books, and musical instruments.For more information about this release go to http://www.bea.gov/newsreleases/regional/pce/pce_newsrelease.htm.

Personal consumption expenditures of a given area divided by the resident population of the area. Purchases of garments and other clothing materials and footwear. Garments consist of women's and girl's clothing, men's and boy's clothing, and children's and infant's clothing.For more information about this release go to http://www.bea.gov/newsreleases/regional/pce/pce_newsrelease.htm.

A measure of spending on goods and services purchased by, and on behalf of, households based on households' state of residence in the fifty states and the District of Columbia. Gross output is net of unrelated sales, secondary sales, and sales to business, government, and the rest of the world; excludes own-account investment (construction and software).For more information about this release go to http://www.bea.gov/newsreleases/regional/pce/pce_newsrelease.htm.

A measure of spending on goods and services purchased by, and on behalf of, households based on households' state of residence in the fifty states and the District of Columbia. Net expenses of NPISHs, defined as their gross operating expenses less primary sales to households.For more information about this release go to http://www.bea.gov/newsreleases/regional/pce/pce_newsrelease.htm.

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/).

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/).

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/).

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/).

Regional price parities (RPPs) are regional price levels expressed as a percentage of the overall national price level for a given year. The price levels are determined by the average prices paid by consumers for the mix of goods and services consumed in each region. Taking the ratio of RPPs shows the difference in price levels across regions. Goods refer to durable and nondurable consumption goods used in the estimation of the RPPs. Goods categories include apparel, education, food, housing, medical, recreation, transportation, and other goods. For more information about this release go to http://www.bea.gov/newsreleases/regional/rpp/rpp_newsrelease.htm or http://www.bea.gov/regional/methods.cfm.

The U.S. Census Bureau provides annual estimates of income and poverty statistics for all school districts, counties, and states through the Small Area Income and Poverty Estimates (https://www.census.gov/programs-surveys/saipe/about.html) (SAIPE) program. The bureau's main objective with this program is to provide estimates of income and poverty for the administration of federal programs and the allocation of federal funds to local jurisdictions. In addition to these federal programs, state and local programs use the income and poverty estimates for distributing funds and managing programs. SNAP benefits are one of the data sources used in producing SAIPE program estimates. The Supplemental Nutrition Assistance Program (SNAP) is the name for what was formerly known as the federal Food Stamp Program, as of October 1, 2008. The SNAP benefits data represent the number of participants in the Supplemental Nutrition Assistance Program for each county, state, and the District of Columbia from 1981 to the latest available year. See more details about SAIPE Model Input Data (https://www.census.gov/data/datasets/time-series/demo/saipe/model-tables.html).

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/).

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://www.bea.gov/data/gdp/gdp-state).

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/).

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.

These data come from the Current Population Survey (CPS), also known as the household survey. Civilian Labor Force includes all persons in the civilian noninstitutional population ages 16 and older classified as either employed or unemployed. Employed persons are all persons who, during the reference week (the week including the 12th day of the month), (a) did any work as paid employees, worked in their own business or profession or on their own farm, or worked 15 hours or more as unpaid workers in an enterprise operated by a member of their family, or (b) were not working but who had jobs from which they were temporarily absent because of vacation, illness, bad weather, childcare problems, maternity or paternity leave, labor-management dispute, job training, or other family or personal reasons, whether or not they were paid for the time off or were seeking other jobs. Each employed person is counted only once, even if he or she holds more than one job. Unemployed persons are all persons who had no employment during the reference week, were available for work, except for temporary illness, and had made specific efforts to find employment some time during the 4 week-period ending with the reference week. Persons who were waiting to be recalled to a job from which they had been laid off need not have been looking for work to be classified as unemployed. The unemployment rate is the unemployed percent of the civilian labor force [100 times (unemployed/civilian labor force)]. For more details, see the release's <a href=https://www.bls.gov/lau/laufaq.htm>frequently asked questions</a>.

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 U.S. Census Bureau provides annual estimates of income and poverty statistics for all school districts, counties, and states through the Small Area Income and Poverty Estimates (https://www.census.gov/programs-surveys/saipe/about.html) (SAIPE) program. The bureau's main objective with this program is to provide estimates of income and poverty for the administration of federal programs and the allocation of federal funds to local jurisdictions. In addition to these federal programs, state and local programs use the income and poverty estimates for distributing funds and managing programs. Poverty universe is one of the data sources used in producing SAIPE program estimates, it is made up of persons for whom the Census Bureau can determine poverty status (either "in poverty" or "not in poverty"). The definition of poverty universe for SAIPE estimates is the same for 2006 and beyond and conceptually matches the poverty universe of the American Community Survey (ACS). The poverty universe estimates are not the same as the population estimates from the Census Bureau's Population Estimates Program. Instead, they are derived estimates that differ from population estimates in the following ways: 1. The poverty universe does not include children under the age of 15 who are not related to a reference person within the household by way of birth, marriage or adoption (for example, foster children). The reason is that Census Bureau surveys typically ask income questions only of persons age 15 or older and those under 15 related to a reference person within the household. 2. Beginning with 2006, the poverty universe includes group quarters populations only for noninstitutionalized group quarters, not elsewhere classified. Residents of college dormitories, military housing, and all institutional group quarters populations are excluded. The 2005 poverty universe estimates excluded all group quarters' residents, matching the definition of the 2005 ACS. Prior to the estimates for 2005, the poverty universe data were derived from the Annual Social and Economic Supplement of the Current Population Survey. This marks a break in the data series due to a methodology change. See more details about SAIPE Model Input Data (https://www.census.gov/data/datasets/time-series/demo/saipe/model-tables.html).

GDP by county is a measure of the market value of final goods and services produced within a county area in a particular period. While other measures of county economies rely mainly on labor market data, these statistics incorporate multiple data sources that capture trends in labor, revenue, and value of production. As a result, the capital-intensive industries are captured more fully than when measured solely by labor data.

Data obtained from ACS Demographic and Housing Estimates, table DP05. Multiyear estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS 5-year estimate includes data collected over a 60-month period. The date of the data is the end of the 5-year period. For example, a value dated 2014 represents data from 2010 to 2014. However, they do not describe any specific day, month, or year within that time period. Multiyear estimates require some considerations that single-year estimates do not. For example, multiyear estimates released in consecutive years consist mostly of overlapping years and shared data. The 2010-2014 ACS 5-year estimates share sample data from 2011 through 2014 with the 2011-2015 ACS 5-year estimates. Because of this overlap, users should use extreme caution in making comparisons with consecutive years of multiyear estimates. Please see "Section 3: Understanding and Using ACS Single-Year and Multiyear Estimates" on publication page 13 (file page 19) of the 2018 ACS General Handbook (https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf) for a more thorough clarification.

Data obtained from ACS Demographic and Housing Estimates, table DP05. Multiyear estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS 5-year estimate includes data collected over a 60-month period. The date of the data is the end of the 5-year period. For example, a value dated 2014 represents data from 2010 to 2014. However, they do not describe any specific day, month, or year within that time period. Multiyear estimates require some considerations that single-year estimates do not. For example, multiyear estimates released in consecutive years consist mostly of overlapping years and shared data. The 2010-2014 ACS 5-year estimates share sample data from 2011 through 2014 with the 2011-2015 ACS 5-year estimates. Because of this overlap, users should use extreme caution in making comparisons with consecutive years of multiyear estimates. Please see "Section 3: Understanding and Using ACS Single-Year and Multiyear Estimates" on publication page 13 (file page 19) of the 2018 ACS General Handbook (https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf) for a more thorough clarification.

Data obtained from ACS Demographic and Housing Estimates, table DP05. Multiyear estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS 5-year estimate includes data collected over a 60-month period. The date of the data is the end of the 5-year period. For example, a value dated 2014 represents data from 2010 to 2014. However, they do not describe any specific day, month, or year within that time period. Multiyear estimates require some considerations that single-year estimates do not. For example, multiyear estimates released in consecutive years consist mostly of overlapping years and shared data. The 2010-2014 ACS 5-year estimates share sample data from 2011 through 2014 with the 2011-2015 ACS 5-year estimates. Because of this overlap, users should use extreme caution in making comparisons with consecutive years of multiyear estimates. Please see "Section 3: Understanding and Using ACS Single-Year and Multiyear Estimates" on publication page 13 (file page 19) of the 2018 ACS General Handbook (https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf) for a more thorough clarification.

Multiyear estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS 5-year estimate includes data collected over a 60-month period. The date of the data is the end of the 5-year period. For example, a value dated 2014 represents data from 2010 to 2014. However, they do not describe any specific day, month, or year within that time period. Multiyear estimates require some considerations that single-year estimates do not. For example, multiyear estimates released in consecutive years consist mostly of overlapping years and shared data. The 2010-2014 ACS 5-year estimates share sample data from 2011 through 2014 with the 2011-2015 ACS 5-year estimates. Because of this overlap, users should use extreme caution in making comparisons with consecutive years of multiyear estimates. Please see "Section 3: Understanding and Using ACS Single-Year and Multiyear Estimates" on publication page 13 (file page 19) of the 2018 ACS General Handbook (https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf) for a more thorough clarification.

Data obtained from ACS Demographic and Housing Estimates, table DP05. Multiyear estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS 5-year estimate includes data collected over a 60-month period. The date of the data is the end of the 5-year period. For example, a value dated 2014 represents data from 2010 to 2014. However, they do not describe any specific day, month, or year within that time period. Multiyear estimates require some considerations that single-year estimates do not. For example, multiyear estimates released in consecutive years consist mostly of overlapping years and shared data. The 2010-2014 ACS 5-year estimates share sample data from 2011 through 2014 with the 2011-2015 ACS 5-year estimates. Because of this overlap, users should use extreme caution in making comparisons with consecutive years of multiyear estimates. Please see "Section 3: Understanding and Using ACS Single-Year and Multiyear Estimates" on publication page 13 (file page 19) of the 2018 ACS General Handbook (https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf) for a more thorough clarification.

Estimate of educational attainment for population 18 years old and over whose highest degree was a bachelor’s, master’s, or professional or doctorate degree. (ACS variable S1501_C02_015E from table S1501.) For more information about the subject definitions, see: https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html. Multiyear estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS 5-year estimates include data collected over a 60-month period. The date associated with the data is the end of the 5-year period. For example, a value dated 2014 represents data from 2010 to 2014. However, the value does not describe any specific day, month, or year within that time period. Multiyear estimates require some additional considerations. For example, multiyear estimates released in consecutive years consist mostly of overlapping years and shared data. The 2010-2014 ACS 5-year estimates share sample data from 2011 through 2014 with the 2011-2015 ACS 5-year estimates. Because of this overlap, users should use extreme caution in making comparisons with consecutive years of multiyear estimates. Please see the ACS handbook (Section 3, "Understanding and Using ACS Single-Year and Multiyear Estimates," p. 13) for a comprehensive set of details and clarifications: https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf

Estimate of educational attainment for population 18 years old and over using 5 years of data. The percent of the population who is a High School Graduate or Higher includes people whose highest degree was a high school diploma or its equivalent, people who attended college but did not receive a degree, and people who received an associate's, bachelor's, master's, or professional or doctorate degree. People who reported completing 12th grade but not receiving a diploma are not included. (ACS variable S1501_C02_014E from table S1501.) For more information about the subject definitions, see: https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html. Multiyear estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS 5-year estimates include data collected over a 60-month period. The date associated with the data is the end of the 5-year period. For example, a value dated 2014 represents data from 2010 to 2014. However, the value does not describe any specific day, month, or year within that time period. Multiyear estimates require some additional considerations. For example, multiyear estimates released in consecutive years consist mostly of overlapping years and shared data. The 2010-2014 ACS 5-year estimates share sample data from 2011 through 2014 with the 2011-2015 ACS 5-year estimates. Because of this overlap, users should use extreme caution in making comparisons with consecutive years of multiyear estimates. Please see the ACS handbook (Section 3, "Understanding and Using ACS Single-Year and Multiyear Estimates," p. 13) for a comprehensive set of details and clarifications: https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf

Data obtained from ACS Demographic and Housing Estimates, table DP05. Multiyear estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS 5-year estimate includes data collected over a 60-month period. The date of the data is the end of the 5-year period. For example, a value dated 2014 represents data from 2010 to 2014. However, they do not describe any specific day, month, or year within that time period. Multiyear estimates require some considerations that single-year estimates do not. For example, multiyear estimates released in consecutive years consist mostly of overlapping years and shared data. The 2010-2014 ACS 5-year estimates share sample data from 2011 through 2014 with the 2011-2015 ACS 5-year estimates. Because of this overlap, users should use extreme caution in making comparisons with consecutive years of multiyear estimates. Please see "Section 3: Understanding and Using ACS Single-Year and Multiyear Estimates" on publication page 13 (file page 19) of the 2018 ACS General Handbook (https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf) for a more thorough clarification.

The single-parent household rate is calculated as the sum of male and female single-parent households with their own children who are younger than 18-years of age divided by total households with their own children who are younger than 18-years of age (ACS 5-year variables S1101_C03_005E, S1101_C04_005E, and S1101_C01_005E respectively from table S1101). Multiyear estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS 5-year estimate includes data collected over a 60-month period. The date of the data is the end of the 5-year period. For example, a value dated 2014 represents data from 2010 to 2014. However, they do not describe any specific day, month, or year within that time period. Multiyear estimates require some considerations that single-year estimates do not. For example, multiyear estimates released in consecutive years consist mostly of overlapping years and shared data. The 2010-2014 ACS 5-year estimates share sample data from 2011 through 2014 with the 2011-2015 ACS 5-year estimates. Because of this overlap, users should use extreme caution in making comparisons with consecutive years of multiyear estimates. Please see "Section 3: Understanding and Using ACS Single-Year and Multiyear Estimates" on publication page 13 (file page 19) of the 2018 ACS General Handbook (https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf) for a more thorough clarification.

The single-parent household rate is calculated as the sum of male and female single-parent households with their own children who are younger than 18-years of age divided by total households with their own children who are younger than 18-years of age (ACS 5-year variables S1101_C03_005E, S1101_C04_005E, and S1101_C01_005E respectively from table S1101). Multiyear estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS 5-year estimate includes data collected over a 60-month period. The date of the data is the end of the 5-year period. For example, a value dated 2014 represents data from 2010 to 2014. However, they do not describe any specific day, month, or year within that time period. Multiyear estimates require some considerations that single-year estimates do not. For example, multiyear estimates released in consecutive years consist mostly of overlapping years and shared data. The 2010-2014 ACS 5-year estimates share sample data from 2011 through 2014 with the 2011-2015 ACS 5-year estimates. Because of this overlap, users should use extreme caution in making comparisons with consecutive years of multiyear estimates. Please see "Section 3: Understanding and Using ACS Single-Year and Multiyear Estimates" on publication page 13 (file page 19) of the 2018 ACS General Handbook (https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf) for a more thorough clarification.

Multiyear estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS 5-year estimate includes data collected over a 60-month period. The date of the data is the end of the 5-year period. For example, a value dated 2014 represents data from 2010 to 2014. However, they do not describe any specific day, month, or year within that time period. Multiyear estimates require some considerations that single-year estimates do not. For example, multiyear estimates released in consecutive years consist mostly of overlapping years and shared data. The 2010-2014 ACS 5-year estimates share sample data from 2011 through 2014 with the 2011-2015 ACS 5-year estimates. Because of this overlap, users should use extreme caution in making comparisons with consecutive years of multiyear estimates. Please see "Section 3: Understanding and Using ACS Single-Year and Multiyear Estimates" on publication page 13 (file page 19) of the 2018 ACS General Handbook (https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf) for a more thorough clarification.

Data obtained from ACS Demographic and Housing Estimates, table DP05. Multiyear estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS 5-year estimate includes data collected over a 60-month period. The date of the data is the end of the 5-year period. For example, a value dated 2014 represents data from 2010 to 2014. However, they do not describe any specific day, month, or year within that time period. Multiyear estimates require some considerations that single-year estimates do not. For example, multiyear estimates released in consecutive years consist mostly of overlapping years and shared data. The 2010-2014 ACS 5-year estimates share sample data from 2011 through 2014 with the 2011-2015 ACS 5-year estimates. Because of this overlap, users should use extreme caution in making comparisons with consecutive years of multiyear estimates. Please see "Section 3: Understanding and Using ACS Single-Year and Multiyear Estimates" on publication page 13 (file page 19) of the 2018 ACS General Handbook (https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf) for a more thorough clarification.

Data obtained from ACS Demographic and Housing Estimates, table DP05. Multiyear estimates from the American Community Survey (ACS) are "period" estimates derived from a data sample collected over a period of time, as opposed to "point-in-time" estimates such as those from past decennial censuses. ACS 5-year estimate includes data collected over a 60-month period. The date of the data is the end of the 5-year period. For example, a value dated 2014 represents data from 2010 to 2014. However, they do not describe any specific day, month, or year within that time period. Multiyear estimates require some considerations that single-year estimates do not. For example, multiyear estimates released in consecutive years consist mostly of overlapping years and shared data. The 2010-2014 ACS 5-year estimates share sample data from 2011 through 2014 with the 2011-2015 ACS 5-year estimates. Because of this overlap, users should use extreme caution in making comparisons with consecutive years of multiyear estimates. Please see "Section 3: Understanding and Using ACS Single-Year and Multiyear Estimates" on publication page 13 (file page 19) of the 2018 ACS General Handbook (https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018.pdf) for a more thorough clarification.

GDP by county is a measure of the market value of final goods and services produced within a county area in a particular period. While other measures of county economies rely mainly on labor market data, these statistics incorporate multiple data sources that capture trends in labor, revenue, and value of production. As a result, the capital-intensive industries are captured more fully than when measured solely by labor data.

GDP by county is a measure of the market value of final goods and services produced within a county area in a particular period. While other measures of county economies rely mainly on labor market data, these statistics incorporate multiple data sources that capture trends in labor, revenue, and value of production. As a result, the capital-intensive industries are captured more fully than when measured solely by labor data.