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. For more details, see the release's <a href=https://www.bls.gov/lau/laufaq.htm>frequently asked questions</a>.

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. For more details, see the release's <a href=https://www.bls.gov/lau/laufaq.htm>frequently asked questions</a>.

Estimate of the percentage of the population with a credit score below 660. Counties with fewer than 20 people in the sample are not reported for privacy reasons. The estimate is based on the representative primary sample of the New York Fed Consumer Credit Panel, which includes only the primary sample member per household (about 5% of the U.S. credit report population, defined as all U.S. residents with a credit history). For more details about the data and sample, see "An Introduction to the Consumer Credit Panel" (https://www.newyorkfed.org/research/staff_reports/sr479.html). Source: Federal Reserve Bank of New York/Equifax Consumer Credit Panel Reprinted with permission. Copyright © 2016, Equifax. All rights reserved. Reproduction of median credit score per county in any form is prohibited except with the prior written permission of Equifax.

As stated by the source, these annual county indexes should be considered developmental. As with the standard FHFA HPIs, revisions to these indexes may reflect the impact of new data or technical adjustments. Indexes are calibrated using appraisal values and sales prices for mortgages bought or guaranteed by Fannie Mae and Freddie Mac. As discussed in the Working Paper 16-01, in cases where sample sizes are small for the county area, an index is either not reported if recording has not started or a missing value is reported with a period (.). Index values always reflect the native county index, i.e. they are not made with data from another area or year. For tracking and feedback purposes, please cite Working Paper 16-01 when using these data. A suggested form is: Bogin, A., Doerner, W. and Larson, W. (2016). Local House Price Dynamics: New Indices and Stylized Facts. Federal Housing Finance Agency, Working Paper 16-01. The working paper is accessible at http://www.fhfa.gov/papers/wp1601.aspx.

Data for "Resident Population" are estimates as of July 1. Data for 1970, 1980, 1990, and 2000 are annual census. Population estimates are updated annually using current data on births, deaths, and migration to calculate population change since the most recent decennial census. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. Each vintage of estimates includes all years since the most recent decennial census.

An establishment is an economic unit, such as a factory, mine, store, or office that produces goods or services. It generally is at a single location and is engaged predominantly in one type of economic activity. Where a single location encompasses two or more distinct activities, these are treated as separate establishments, if separate payroll records are available, and the various activities are classified under different industry codes.

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.

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.

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.

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.

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).

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. Household income includes income of the householder and all other people 15 years and older in the household, whether or not they are related to the householder. Median is the point that divides the household income distributions into two halves: one-half with income above the median and the other with income below the median. The median is based on the income distribution of all households, including those with no income. A confidence interval is a range of values, from the lower bound to the respective upper bound, that describes the uncertainty surrounding an estimate. A confidence interval is also itself an estimate. It is made using a model of how sampling, interviewing, measuring, and modeling contribute to uncertainty about the relation between the true value of the quantity we are estimating and our estimate of that value. The "90%" in the confidence interval listed above represents a level of certainty about our estimate. If we were to repeatedly make new estimates using exactly the same procedure (by drawing a new sample, conducting new interviews, calculating new estimates and new confidence intervals), the confidence intervals would contain the average of all the estimates 90% of the time. For more details about the confidence intervals and their interpretation, see this explanation (https://www.census.gov/programs-surveys/saipe/guidance/confidence-intervals.html).

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. Household income includes income of the householder and all other people 15 years and older in the household, whether or not they are related to the householder. Median is the point that divides the household income distributions into two halves: one-half with income above the median and the other with income below the median. The median is based on the income distribution of all households, including those with no income. A confidence interval is a range of values, from the lower bound to the respective upper bound, that describes the uncertainty surrounding an estimate. A confidence interval is also itself an estimate. It is made using a model of how sampling, interviewing, measuring, and modeling contribute to uncertainty about the relation between the true value of the quantity we are estimating and our estimate of that value. The "90%" in the confidence interval listed above represents a level of certainty about our estimate. If we were to repeatedly make new estimates using exactly the same procedure (by drawing a new sample, conducting new interviews, calculating new estimates and new confidence intervals), the confidence intervals would contain the average of all the estimates 90% of the time. For more details about the confidence intervals and their interpretation, see this explanation (https://www.census.gov/programs-surveys/saipe/guidance/confidence-intervals.html).

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. Household income includes income of the householder and all other people 15 years and older in the household, whether or not they are related to the householder. Median is the point that divides the household income distributions into two halves: one-half with income above the median and the other with income below the median. The median is based on the income distribution of all households, including those with no income.

For further information about this series go to https://www.census.gov/programs-surveys/saipe/about.html.

For further information about this series go to https://www.census.gov/programs-surveys/saipe/about.html.

For further information about this series go to https://www.census.gov/programs-surveys/saipe/about.html.

For further information about this series go to https://www.census.gov/programs-surveys/saipe/about.html.

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. Estimates of poverty by ages and families are not direct counts from enumerations or administrative records, nor direct estimates from sample surveys. Instead, for counties and states, the Census models income and poverty estimates by combining survey data with population estimates and administrative records. A confidence interval is a range of values, from the lower bound to the respective upper bound, that describes the uncertainty surrounding an estimate. A confidence interval is also itself an estimate. It is made using a model of how sampling, interviewing, measuring, and modeling contribute to uncertainty about the relation between the true value of the quantity we are estimating and our estimate of that value. The "90%" in the confidence interval listed above represents a level of certainty about our estimate. If we were to repeatedly make new estimates using exactly the same procedure (by drawing a new sample, conducting new interviews, calculating new estimates and new confidence intervals), the confidence intervals would contain the average of all the estimates 90% of the time. For more details about the confidence intervals and their interpretation, see this explanation (https://www.census.gov/programs-surveys/saipe/guidance/confidence-intervals.html).

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. Estimates of poverty by ages and families are not direct counts from enumerations or administrative records, nor direct estimates from sample surveys. Instead, for counties and states, the Census models income and poverty estimates by combining survey data with population estimates and administrative records. A confidence interval is a range of values, from the lower bound to the respective upper bound, that describes the uncertainty surrounding an estimate. A confidence interval is also itself an estimate. It is made using a model of how sampling, interviewing, measuring, and modeling contribute to uncertainty about the relation between the true value of the quantity we are estimating and our estimate of that value. The "90%" in the confidence interval listed above represents a level of certainty about our estimate. If we were to repeatedly make new estimates using exactly the same procedure (by drawing a new sample, conducting new interviews, calculating new estimates and new confidence intervals), the confidence intervals would contain the average of all the estimates 90% of the time. For more details about the confidence intervals and their interpretation, see this explanation (https://www.census.gov/programs-surveys/saipe/guidance/confidence-intervals.html).

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).

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).

For further information about this series go to https://www.census.gov/programs-surveys/saipe/about.html.

For further information about this series go to https://www.census.gov/programs-surveys/saipe/about.html.

For further information about this series go to https://www.census.gov/programs-surveys/saipe/about.html.

For further information about this series go to https://www.census.gov/programs-surveys/saipe/about.html.

For further information about this series go to https://www.census.gov/programs-surveys/saipe/about.html.

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. Estimates of poverty by ages and families are not direct counts from enumerations or administrative records, nor direct estimates from sample surveys. Instead, for counties and states, the Census models income and poverty estimates by combining survey data with population estimates and administrative records.

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. Estimates of poverty by ages and families are not direct counts from enumerations or administrative records, nor direct estimates from sample surveys. Instead, for counties and states, the Census models income and poverty estimates by combining survey data with population estimates and administrative records.

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. Estimates of poverty by ages and families are not direct counts from enumerations or administrative records, nor direct estimates from sample surveys. Instead, for counties and states, the Census models income and poverty estimates by combining survey data with population estimates and administrative records. A confidence interval is a range of values, from the lower bound to the respective upper bound, that describes the uncertainty surrounding an estimate. A confidence interval is also itself an estimate. It is made using a model of how sampling, interviewing, measuring, and modeling contribute to uncertainty about the relation between the true value of the quantity we are estimating and our estimate of that value. The "90%" in the confidence interval listed above represents a level of certainty about our estimate. If we were to repeatedly make new estimates using exactly the same procedure (by drawing a new sample, conducting new interviews, calculating new estimates and new confidence intervals), the confidence intervals would contain the average of all the estimates 90% of the time. For more details about the confidence intervals and their interpretation, see this explanation (https://www.census.gov/programs-surveys/saipe/guidance/confidence-intervals.html).

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. Estimates of poverty by ages and families are not direct counts from enumerations or administrative records, nor direct estimates from sample surveys. Instead, for counties and states, the Census models income and poverty estimates by combining survey data with population estimates and administrative records. A confidence interval is a range of values, from the lower bound to the respective upper bound, that describes the uncertainty surrounding an estimate. A confidence interval is also itself an estimate. It is made using a model of how sampling, interviewing, measuring, and modeling contribute to uncertainty about the relation between the true value of the quantity we are estimating and our estimate of that value. The "90%" in the confidence interval listed above represents a level of certainty about our estimate. If we were to repeatedly make new estimates using exactly the same procedure (by drawing a new sample, conducting new interviews, calculating new estimates and new confidence intervals), the confidence intervals would contain the average of all the estimates 90% of the time. For more details about the confidence intervals and their interpretation, see this explanation (https://www.census.gov/programs-surveys/saipe/guidance/confidence-intervals.html).

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. Estimates of poverty by ages and families are not direct counts from enumerations or administrative records, nor direct estimates from sample surveys. Instead, for counties and states, the Census models income and poverty estimates by combining survey data with population estimates and administrative records. A confidence interval is a range of values, from the lower bound to the respective upper bound, that describes the uncertainty surrounding an estimate. A confidence interval is also itself an estimate. It is made using a model of how sampling, interviewing, measuring, and modeling contribute to uncertainty about the relation between the true value of the quantity we are estimating and our estimate of that value. The "90%" in the confidence interval listed above represents a level of certainty about our estimate. If we were to repeatedly make new estimates using exactly the same procedure (by drawing a new sample, conducting new interviews, calculating new estimates and new confidence intervals), the confidence intervals would contain the average of all the estimates 90% of the time. For more details about the confidence intervals and their interpretation, see this explanation (https://www.census.gov/programs-surveys/saipe/guidance/confidence-intervals.html).

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. Estimates of poverty by ages and families are not direct counts from enumerations or administrative records, nor direct estimates from sample surveys. Instead, for counties and states, the Census models income and poverty estimates by combining survey data with population estimates and administrative records. A confidence interval is a range of values, from the lower bound to the respective upper bound, that describes the uncertainty surrounding an estimate. A confidence interval is also itself an estimate. It is made using a model of how sampling, interviewing, measuring, and modeling contribute to uncertainty about the relation between the true value of the quantity we are estimating and our estimate of that value. The "90%" in the confidence interval listed above represents a level of certainty about our estimate. If we were to repeatedly make new estimates using exactly the same procedure (by drawing a new sample, conducting new interviews, calculating new estimates and new confidence intervals), the confidence intervals would contain the average of all the estimates 90% of the time. For more details about the confidence intervals and their interpretation, see this explanation (https://www.census.gov/programs-surveys/saipe/guidance/confidence-intervals.html).

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. Estimates of poverty by ages and families are not direct counts from enumerations or administrative records, nor direct estimates from sample surveys. Instead, for counties and states, the Census models income and poverty estimates by combining survey data with population estimates and administrative records.

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).

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.

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.

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.

Mean commuting time is calculated by dividing the aggregate travel time to work for all workers (in minutes) by the total number of workers, 16-years old and older, who commute (ACS 5-year variables B08013_001E from table B08013 and B08012_001E from table B08012, respectively). 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 percentage of population below the poverty level comes from American Community Survey (ACS) variable S1701_C03_001E in table S1701. 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.

Estimate of educational attainment for population 25 years old and over using 5 years of data. The percent of the population who has completed an Associate's degree or higher is calculated by FRED by adding the following variables from the 5-year American Community Survey (ACS) percent of the population with an Associate's Degree, percent of the population with a Bachelor's degree, and percent of the population with a Graduate or Professional degree. (ACS variables S1501_C02_011E, S1501_C02_012E, S1501_C02_013E 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.

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.

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.

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.