The 10-20-30 Provision: Defining Persistent Poverty Counties

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The 10-20-30 Provision: Defining Persistent
~~February 24, 2021~~April 14, 2022
Poverty Counties
Joseph Dalaker
Research has suggested that areas for which the Research has suggested that areas for which the poverty rate (the percentage of the (the percentage of the
population Analyst in Social Policy Analyst in Social Policy
~~population~~ that is below that is below poverty, or economic hardship as measured by comparing , or economic hardship as measured by comparing

income against a dollar income against a dollar amount that represents a low level of need) reaches 20% amount that represents a low level of need) reaches 20%
experience more acute systemic problems than in lower-poverty areas. Recent experience more acute systemic problems than in lower-poverty areas. Recent

congresses have enacted antipoverty policy congresses have enacted antipoverty policy interventions that target resources on local interventions that target resources on local
communities based on the characteristics of those communities, rather than solely on those of individuals or communities based on the characteristics of those communities, rather than solely on those of individuals or
families. One such policy, dubbed the families. One such policy, dubbed the 10-20-30 provision, was first implemented in the American Recovery and , was first implemented in the American Recovery and
Reinvestment Act of 2009 (ARRA, P.L. 111-5). Title I, Section 105 of ARRA required the Secretary of Reinvestment Act of 2009 (ARRA, P.L. 111-5). Title I, Section 105 of ARRA required the Secretary of
Agriculture to Agriculture to ~~al ocate~~allocate at least 10% of funds from three rural development program accounts to at least 10% of funds from three rural development program accounts to persistent poverty
counties—counties that maintained poverty rates of 20% or more for the past 30 years, as measured by the 1980, —counties that maintained poverty rates of 20% or more for the past 30 years, as measured by the 1980,
1990, and 2000 decennial censuses.1990, and 2000 decennial censuses.
One notable characteristic of this provision is that it did not increase spending for the rural development programs One notable characteristic of this provision is that it did not increase spending for the rural development programs
addressed in ARRA, but rather targeted existing funds differently. Since ARRA, Congress has applied the 10-20-addressed in ARRA, but rather targeted existing funds differently. Since ARRA, Congress has applied the 10-20-
30 provision for other programs in addition to rural development programs, and may continue to do so, using 30 provision for other programs in addition to rural development programs, and may continue to do so, using
more recent estimates of poverty rates. Doing this, however, requires updating the list of counties with persistent more recent estimates of poverty rates. Doing this, however, requires updating the list of counties with persistent
poverty, and that requires making certain decisions about the data that poverty, and that requires making certain decisions about the data that ~~wil~~ will be used to compile the list.be used to compile the list.
Poverty rates are computed using data from household surveys fielded by the U.S. Census Bureau. The list of Poverty rates are computed using data from household surveys fielded by the U.S. Census Bureau. The list of
counties identified as persistently poor may differ by roughly 60 to 100 counties in a particular year, depending on counties identified as persistently poor may differ by roughly 60 to 100 counties in a particular year, depending on
the surveys selected to compile the list and the rounding method used for the poverty rate estimates. In the past, the surveys selected to compile the list and the rounding method used for the poverty rate estimates. In the past,
the decennial census was the only source of county poverty estimates across the entire country. After 2000, the decennial census was the only source of county poverty estimates across the entire country. After 2000,
however, the decennial census is no longer used to collect income data. However, there are two newer data however, the decennial census is no longer used to collect income data. However, there are two newer data
sources that may be used to provide poverty estimates for sources that may be used to provide poverty estimates for al all U.S. counties: the American Community Survey U.S. counties: the American Community Survey
(ACS) and the (ACS) and the ~~Smal~~ Small Area Income and Poverty Estimates program (SAIPE). The Census Bureau implemented Area Income and Poverty Estimates program (SAIPE). The Census Bureau implemented
both the ACS and SAIPE in the mid-1990s. Therefore, to determine whether an area is both the ACS and SAIPE in the mid-1990s. Therefore, to determine whether an area is persistently poor in a time poor in a time
span that ends after the year 2000, policymakers and researchers must first decide whether ACS or SAIPE poverty span that ends after the year 2000, policymakers and researchers must first decide whether ACS or SAIPE poverty
estimates estimates ~~wil~~ will be used for the later part of that time span. Which of these surveys is the best data source to use for be used for the later part of that time span. Which of these surveys is the best data source to use for
compiling an updated list of counties with persistent poverty may differ based on the specific area or policy for compiling an updated list of counties with persistent poverty may differ based on the specific area or policy for
which the antipoverty intervention is intended. which the antipoverty intervention is intended.
When defining When defining persistent poverty counties in order to target funds for programs or services, the following factors in order to target funds for programs or services, the following factors
may be relevant: may be relevant:
 Characteristics of interest: SAIPE is suited for analysis focused solely on poverty or median  Characteristics of interest: SAIPE is suited for analysis focused solely on poverty or median
income; ACS for poverty and income and other topics (e.g., housing characteristics, disability, income; ACS for poverty and income and other topics (e.g., housing characteristics, disability,
education level, occupation, veteran status). education level, occupation, veteran status).
 Geographic areas of interest: SAIPE is recommended for counties and school districts only; ACS  Geographic areas of interest: SAIPE is recommended for counties and school districts only; ACS
may be used to produce estimates for other may be used to produce estimates for other ~~smal~~ small geographic areas as geographic areas as ~~wel~~ well (such as cities, towns, (such as cities, towns,
and census tracts). and census tracts).
 Reference period of estimate: Both data sources produce annual estimates. However, the SAIPE  Reference period of estimate: Both data sources produce annual estimates. However, the SAIPE
estimate is based on one prior year of data while ACS estimates draw on data from the past five estimate is based on one prior year of data while ACS estimates draw on data from the past five
years.years.
 Rounding method for poverty rates: Rounding to 20.0% (one decimal place) yields a shorter list  Rounding method for poverty rates: Rounding to 20.0% (one decimal place) yields a shorter list
of counties with persistent poverty than rounding to 20% (whole number). of counties with persistent poverty than rounding to 20% (whole number).
 Special populations: Poverty status is not defined for  Special populations: Poverty status is not defined for al all persons. This includes unrelated persons. This includes unrelated
individuals individuals under age 15 (e.g., children in foster care), institutionalized persons, and residents of under age 15 (e.g., children in foster care), institutionalized persons, and residents of
college dormitories; the homeless are not explicitly targeted by household surveys; and areas with college dormitories; the homeless are not explicitly targeted by household surveys; and areas with
~~Congressional Research Service~~

large numbers of students living off-campus may have higher poverty rates than might be large numbers of students living off-campus may have higher poverty rates than might be
expected, because poverty is measured using cash income and does not include student loans. expected, because poverty is measured using cash income and does not include student loans.
Congressional Research Service Congressional Research Service

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Contacts
Author Information ........................................................................................................................ 28 26

Congressional Research Service Congressional Research Service

The 10-20-30 Provision: Defining Persistent Poverty Counties

Introduction
Antipoverty interventions that provide resources to local communities, based on the Antipoverty interventions that provide resources to local communities, based on the
characteristics of those communities, have been of interest to Congress. One such policy, dubbed characteristics of those communities, have been of interest to Congress. One such policy, dubbed
the the 10-20-30 provision, was implemented in the American Recovery and Reinvestment Act of , was implemented in the American Recovery and Reinvestment Act of
2009 (ARRA, P.L. 111-5). Title I, Section 105 of ARRA required the Secretary of Agriculture to 2009 (ARRA, P.L. 111-5). Title I, Section 105 of ARRA required the Secretary of Agriculture to
~~al ocate~~allocate at least 10% of funds provided in that act from three rural development program accounts at least 10% of funds provided in that act from three rural development program accounts
to persistent poverty counties; that is, to counties that have had poverty rates of 20% or more for to persistent poverty counties; that is, to counties that have had poverty rates of 20% or more for
the past 30 years, as measured by the 1980, 1990, and 2000 decennial censuses.1the past 30 years, as measured by the 1980, 1990, and 2000 decennial censuses.1
One notable characteristic of this provision is that it did not increase spending for the rural One notable characteristic of this provision is that it did not increase spending for the rural
development programs addressed in ARRA, but rather targeted existing funds differently. Given development programs addressed in ARRA, but rather targeted existing funds differently. Given
Congress’s interest both in addressing Congress’s interest both in addressing poverty (economic hardship as measured by comparing (economic hardship as measured by comparing
income against a dollar amount that represents a low level of need)2 and being mindful about income against a dollar amount that represents a low level of need)2 and being mindful about
levels of federal spending, the levels of federal spending, the ~~past four~~113th through the 117th Congresses included 10-20-30 language in multiple Congresses included 10-20-30 language in multiple
appropriations appropriations ~~bil s~~bills, some of which were enacted into law., some of which were enacted into law.3 However, the original language used in However, the original language used in
~~ARRA~~ ARRA could not be re-used verbatim, because the decennial census—the data source used by could not be re-used verbatim, because the decennial census—the data source used by
~~ARRA~~ ARRA to define persistent poverty—stopped collecting income information. As a consequence, to define persistent poverty—stopped collecting income information. As a consequence,
the appropriations the appropriations ~~bil s~~bills varied slightly in their definitions of varied slightly in their definitions of persistent poverty counties as it was as it was
applied to various programs and departments. This variation occurred even within different applied to various programs and departments. This variation occurred even within different
sections of the same sections of the same ~~bil~~ bill if the if the ~~bil~~ bill included language on different programs. In turn, because the included language on different programs. In turn, because the
definitions of definitions of persistent poverty differed, so did the lists of counties identified as persistently poor differed, so did the lists of counties identified as persistently poor
and subject to the 10-20-30 provision. The and subject to the 10-20-30 provision. The ~~bil s~~bills included legislation included legislation for rural development, public for rural development, public
works and economic development, technological innovation, and brownfields site assessment and works and economic development, technological innovation, and brownfields site assessment and
~~remediation.~~ remediation. Most recently, in the Most recently, in the ~~116th~~117th Congress, much of the language used in these previous Congress, much of the language used in these previous
~~bil s~~bills was was included in P.L. 117-103 (the Consolidated Appropriations Act, 2022).4 References to persistent poverty counties, with provisions other than a 10% set-aside, also appeared in P.L. 117-58 (the Infrastructure Investment and Jobs Act). Additionally, more than 40 other bills introduced but not ~~included in P.L. 116-6 (Consolidated Appropriations Act, 2019), P.L. 116-93~~
~~(Consolidated Appropriations Act, 2020), and P.L. 116-94 (Further Consolidated Appropriations~~
~~Act, 2020).3~~

1 While the 1980-2000 period is actually 20 years, local communities have traditionally relied upon the decennial 1 While the 1980-2000 period is actually 20 years, local communities have traditionally relied upon the decennial
censuscensus data for small areas up to 10 years after their publication, hence the reference to “30 years.” However, since the data for small areas up to 10 years after their publication, hence the reference to “30 years.” However, since the
late 1990s newer data sources have become available for smalllate 1990s newer data sources have become available for small communities at intervals shorter than 10 years, which communities at intervals shorter than 10 years, which
has implications that will behas implications that will be ~~discussed~~ discussed in this report. in this report.
2 For a more thorough discussion 2 For a more thorough discussion of how poverty is definedof how poverty is defined and measured, see CRSand measured, see CRS Report R44780, Report R44780, An Introduction to
Poverty ~~Measurem ent~~Measurement, by Joseph Dalaker. , by Joseph Dalaker.
3 Additionally, in the 112th Congress, the 10-20-30 provision was proposed as an amendment to H.R. 1 but was not adopted. 4 3 In the In the ~~116th~~117th Congress, the Consolidated Appropriations Act, Congress, the Consolidated Appropriations Act, ~~2019~~2022 (P.L. (P.L. ~~116-6~~117-103) included 10-20-30 language in ) included 10-20-30 language in
numerous sections: Section numerous sections: Section ~~752~~736, in reference to loans and grants for rural housing, business, in reference to loans and grants for rural housing, business and economic and economic
development, and utilities; Section development, and utilities; Section ~~539~~533, in reference to grants authorized by the Public Works and Economic , in reference to grants authorized by the Public Works and Economic
Development Act of 1965 and grants authorized by Development Act of 1965 and grants authorized by ~~Section~~section 27 of the Stevenson 27 of the Stevenson -Wydler -Wydler ~~T echnology~~Technology Innovation Act of Innovation Act of
1980; Division 1980; Division ~~D, T itle~~E, Title I, in reference to the Community Development Financial Institutions (CDFI) Fund Program I, in reference to the Community Development Financial Institutions (CDFI) Fund Program
Account; and Division Account; and Division ~~E, T itle~~G, Title II, in reference to the Comprehensive Environmental Response, Compensation, and II, in reference to the Comprehensive Environmental Response, Compensation, and
Liability Act (CERCLA) of 1980 and its role in authorizing fundingLiability Act (CERCLA) of 1980 and its role in authorizing funding for brownfieldsfor brownfields site assessment and remediation. Further, Division L, Title I of the act refers to persistent poverty counties, though without specifying a figure of 10% to be set aside. That portion of the act set aside $20 million for National Infrastructure Investment grants for “projects in historically disadvantaged communities or areas of persistent poverty,” and $20 million for Transit Infrastructure Grants for areas of persistent poverty; both of these programs include persistent poverty counties in their definitions. It also enabled the Secretary of Transportation to prioritize persistent poverty counties to receive technical assistance under the Thriving Communities Initiative. Congressional Research Service 1 The 10-20-30 Provision: Defining Persistent Poverty Counties enacted as of the cover date of this report also referred to persistent poverty counties, with or without requiring a 10% set-aside specifically. ~~site assessment and remediation.~~
~~T hese same programs, with the addition of T ransit Infrastructure Grants, were included in two appropriations acts for~~
~~FY2020: the Consolidated Appropriations Act, 2020 ( P.L. 116-93; public works grants in Division B, T itle V, Section~~
~~533, and CDFI in Division C, T itle I), and the Further Consolidation Appropriations Act, 2020 ( P.L. 116-94; rural~~
~~programs in Division B, T itle VII, Section 740; CERCLA in Division D, T itle II; and T ransit Infrastructure Grants in~~
~~Division H, T itle I). Additionally, more than a dozen bills referencing 10 -20-30 or persistent poverty counties had been~~
~~introduced in the 116th Congress but not enacted. T hese bills covered a wide range of topics, such as rural jobs,~~
~~restructuring of rural development loans, hospitals in rural areas, veterans’ job opportunities, internet accessibility, the~~
~~donation of federal electronic equipment to schools, programs to prevent or eliminate discrimination in h ousing,~~
~~programs to support victims of trafficking, programs to ameliorate opioid abuse and various other Department of~~
~~Congressional Research Service~~

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~~The 10-20-30 Provision: Defining Persistent Poverty Counties~~

This report discusses how data source selection, and the rounding of poverty estimates, can affect This report discusses how data source selection, and the rounding of poverty estimates, can affect
the list of counties identified as persistently poor. After briefly explaining why targeting funds to the list of counties identified as persistently poor. After briefly explaining why targeting funds to
persistent poverty counties might be of interest, this report explores how persistent poverty counties might be of interest, this report explores how persistent poverty is is
defined and measured, and how different interpretations of the definition and different data source defined and measured, and how different interpretations of the definition and different data source
selections could yield different lists of counties identified as persistently poor. This report does selections could yield different lists of counties identified as persistently poor. This report does
not compare the 10-20-30 provision’s advantages and disadvantages against other policy options not compare the 10-20-30 provision’s advantages and disadvantages against other policy options
for addressing poverty, nor does it examine the range of programs or policy goals for which the for addressing poverty, nor does it examine the range of programs or policy goals for which the
10-20-30 provision might be an appropriate policy tool. 10-20-30 provision might be an appropriate policy tool.
Motivation for Targeting Funds to Persistent Poverty
Counties
Research has suggested that areas for which the Research has suggested that areas for which the poverty rate (the percentage of the population (the percentage of the population
that is below poverty) reaches 20% experience systemic problems that are more acute than in that is below poverty) reaches 20% experience systemic problems that are more acute than in
lower-poverty areas. The poverty rate of 20% as a critical point has been discussed in academic lower-poverty areas. The poverty rate of 20% as a critical point has been discussed in academic
literature as relevant for examining social characteristics of high-poverty versus low-poverty literature as relevant for examining social characteristics of high-poverty versus low-poverty
areas.areas.45 For instance, property values in high-poverty areas do not yield as high a return on For instance, property values in high-poverty areas do not yield as high a return on
investment as in low-poverty areas, and that low return provides a financial disincentive for investment as in low-poverty areas, and that low return provides a financial disincentive for
property owners to spend money on maintaining and improving property.property owners to spend money on maintaining and improving property.56 The The il ill effects of high effects of high
poverty rates have been documented both for urban and rural areas.poverty rates have been documented both for urban and rural areas.67 Depending on the years in Depending on the years in
which poverty is measured and the data sources used, between 360 and 500 counties have been which poverty is measured and the data sources used, between 360 and 500 counties have been
~~identified as persistent poverty counties, out of a total of 3,143 counties or county-equivalent~~

~~Justice programs, and a number of regional authorities and commissions.~~
4 5 For instance, George For instance, George Galster of Wayne State University Galster of Wayne State University ~~conduct ed~~conducted a literature review that suggested a literature review that suggested “that the “that the
independent impacts of neighborhood poverty rates in encouraging negative outcomes for individualsindependent impacts of neighborhood poverty rates in encouraging negative outcomes for individuals like crime, like crime,
school leaving, and duration of poverty spells appear to be nil unless the neighborhood exceedsschool leaving, and duration of poverty spells appear to be nil unless the neighborhood exceeds ~~a bout~~ about 20 percent 20 percent
poverty.” Galster distinguishespoverty.” Galster distinguishes the effects of living in a poor neighborhood from the effects of being poor oneself but the effects of living in a poor neighborhood from the effects of being poor oneself but
not necessarily in a poor neighborhood. Cited in Georgenot necessarily in a poor neighborhood. Cited in George C. Galster, “C. Galster, “~~T he~~The Mechanism(s) of Neighborhood Effects: Mechanism(s) of Neighborhood Effects:
~~T heory~~Theory, Evidence, and Policy Implications,” presented at the Economic and Social Research Council Seminar, , Evidence, and Policy Implications,” presented at the Economic and Social Research Council Seminar,
“Neighbourhood Effects: “Neighbourhood Effects: ~~T heory~~Theory & Evidence,” St. Andrews & Evidence,” St. Andrews University, Scotland, UK, February 2010. University, Scotland, UK, February 2010.
Additionally, the Census Additionally, the Census ~~Bureau~~ Bureau has publishedhas published a series of reports examining local areas (censusa series of reports examining local areas (census tracts) with poverty tracts) with poverty
rates of 20% or greater. See,rates of 20% or greater. See, for instance, Alemayehu Bishaw,for instance, Alemayehu Bishaw, ~~Craig~~ Craig Benson, Emily Shrider, and Brian Glassman, Benson, Emily Shrider, and Brian Glassman,
“Changes in Poverty Rates and Poverty Areas Over “Changes in Poverty Rates and Poverty Areas Over ~~T ime~~Time: 2005 to 2019,” American Community: 2005 to 2019,” American Community Survey Brief 20-08, Survey Brief 20-08,
December 2020; Alemayehu Bishaw,December 2020; Alemayehu Bishaw, “Changes in Areas With Concentrated Poverty: 2000 to 2010,” U.S. Census “Changes in Areas With Concentrated Poverty: 2000 to 2010,” U.S. Census
Bureau,Bureau, American Community SurveyAmerican Community Survey Reports ACS-27, June 2014; and Leatha Lamison-White, “Poverty Areas,” U.S. Reports ACS-27, June 2014; and Leatha Lamison-White, “Poverty Areas,” U.S.
CensusCensus ~~Bureau Statist ical~~ Bureau Statistical Brief, June 1995. Brief, June 1995.
~~5 T he~~6 The effects of poverty rates on property values are explored by George effects of poverty rates on property values are explored by George C. Galster, Jackie M. Cutsinger,C. Galster, Jackie M. Cutsinger, and Ron and Ron
MalegaMalega in “in “~~T he~~The Costs of Concentrated Poverty: Neighborhood Property Markets and the Dynamics of Decline,” pp. 93 Costs of Concentrated Poverty: Neighborhood Property Markets and the Dynamics of Decline,” pp. 93 --
113 in N. Retsinas and E. Belsky, eds.,113 in N. Retsinas and E. Belsky, eds., Revisiting Rental Housing: Policies, ~~Program s~~Programs, and Priorities (Washington, (Washington,
~~DC:~~ DC: Brookings Institution Press, 2008). They indicate that “the relationship between changes in a neighborhood’s Brookings Institution Press, 2008). They indicate that “the relationship between changes in a neighborhood’s
poverty rate and maintenance choices by local residential property owners will be lumpy and nonpoverty rate and maintenance choices by local residential property owners will be lumpy and non -linear. Substantial -linear. Substantial
variations in poverty rates in the low-moderate range yield no deviations in the owner’s decision to highly maintain the variations in poverty rates in the low-moderate range yield no deviations in the owner’s decision to highly maintain the
building....building.... Past some percentage of poverty, however, the owner willPast some percentage of poverty, however, the owner will switch to an undermaintenance mode whereby switch to an undermaintenance mode whereby
net depreciation will occur.” net depreciation will occur.”
67 See, See, for instance, a 2008 report issued jointly by the Federal Reserve System and the Brookings Institution, “The for instance, a 2008 report issued jointly by the Federal Reserve System and the Brookings Institution, “The
EnduringEnduring Challenge of Concentrated Poverty in Challenge of Concentrated Poverty in ~~Am erica~~America: Case Studies: Case Studies from Communities Across the U.S.,” David from Communities Across the U.S.,” David
Erickson et al., eds.,Erickson et al., eds., 2008. Additional research into concentrated poverty in both rural and urban2008. Additional research into concentrated poverty in both rural and urban areas has been areas has been
undertaken for decades;undertaken for decades; for example, educational attainment and health disabilityfor example, educational attainment and health disability ~~were discussed~~ were discussed in a rural context by in a rural context by
Calvin Beale in “Income and Poverty,” chapter 11 in Glenn V.Calvin Beale in “Income and Poverty,” chapter 11 in Glenn V. Fuguitt, David L. Brown, and Calvin L. Beale, eds., Fuguitt, David L. Brown, and Calvin L. Beale, eds.,
Rural and ~~Sm all Town Am erica~~Small Town America, Russell Russell ~~Sage~~ Sage Foundation, 1988. Foundation, 1988.
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identified as persistent poverty counties, out of a total of 3,143 counties or county-equivalent areas nationwide. Therefore, policy interventions at the community level, and not only at the areas nationwide. Therefore, policy interventions at the community level, and not only at the
~~individual~~ individual or family level, have been and may continue to be of interest to Congress.or family level, have been and may continue to be of interest to Congress.7
8 Defining Persistent Poverty Counties
Persistent poverty counties are counties that have had poverty rates of 20% or greater for at least Persistent poverty counties are counties that have had poverty rates of 20% or greater for at least
30 years. The county poverty rates for 1999 and previous years are measured using decennial 30 years. The county poverty rates for 1999 and previous years are measured using decennial
census data. For more recent years, either the census data. For more recent years, either the ~~Smal~~ Small Area Income and Poverty Estimates (SAIPE) Area Income and Poverty Estimates (SAIPE)
or the American Community Survey (ACS) are used. Both of these Census Bureau data sources or the American Community Survey (ACS) are used. Both of these Census Bureau data sources
were first implemented in the mid-1990s and both provide poverty estimates no longer available were first implemented in the mid-1990s and both provide poverty estimates no longer available
from the decennial census.from the decennial census.89 The data sources used, and the level of precision of rounding for the The data sources used, and the level of precision of rounding for the
poverty rate, affects the list of counties identified as persistent poverty counties, as poverty rate, affects the list of counties identified as persistent poverty counties, as ~~wil be~~
will be described below. described below.
Computing the Poverty Rate for an Area
Poverty rates are computed by the Census Bureau for the nation, states, and Poverty rates are computed by the Census Bureau for the nation, states, and ~~smal er~~smaller geographic geographic
areas such as counties.areas such as counties.910 The official definition of poverty in the United States is based on the The official definition of poverty in the United States is based on the
money income of families and unrelated individuals. Income from each family member (if family money income of families and unrelated individuals. Income from each family member (if family
members are present) is added together and compared against a dollar amount members are present) is added together and compared against a dollar amount ~~cal ed~~called a a poverty
threshold, which represents a level of economic hardship and varies according to the size and which represents a level of economic hardship and varies according to the size and
characteristics of the family (ranging from one person to nine persons or more). Families (or characteristics of the family (ranging from one person to nine persons or more). Families (or
unrelated individuals) whose income is less than their respective poverty threshold are considered unrelated individuals) whose income is less than their respective poverty threshold are considered
to be in poverty (sometimes also described as to be in poverty (sometimes also described as below poverty).).10
~~Every person in a family has the same poverty status. Thus, it is possible to compute a poverty~~
~~rate based on counts of persons. This is done by dividing the number of persons below poverty~~

~~7 In~~11 8 In the 117th Congress, P.L. 117-103 (Consolidated Appropriations Act, 2022) used 10-20-30 provisions in multiple sections (see footnote 4 for details), and P.L. 117-58 (Infrastructure Investment and Jobs Act) referred to persistent poverty counties without specifically using a figure of 10% for a set-aside. Of the public laws passed by the 116th Congress, P.L. 116-6 (Consolidated Appropriations Act, 2019), P.L. 116-93 (Consolidated Appropriations the 116th Congress, P.L. 116-6 (Consolidated Appropriations Act, 2019), P.L. 116-93 (Consolidated Appropriations
Act, 2020), and P.L. 116-94 (Further Consolidated Appropriations Act, 2020) used the 10Act, 2020), and P.L. 116-94 (Further Consolidated Appropriations Act, 2020) used the 10 -20-30 provision; -20-30 provision; ~~see footnote~~
3 ~~for details. Of the public laws passed by the 115 th~~multiple other bills with the provision were introduced but not enacted into public law. Of the public laws passed by the 115th Congress, 10-20-30 language was Congress, 10-20-30 language was ~~included~~ included in P.L. 115-31 in P.L. 115-31
(Consolidated Appropriations Act, 2017), P.L. 115-141 (Consolidated Appropriations Act, 2018), and P.L. 115-334 (Consolidated Appropriations Act, 2017), P.L. 115-141 (Consolidated Appropriations Act, 2018), and P.L. 115-334
(Agricultural(Agricultural Improvement Act of 2018)Improvement Act of 2018)~~. Multiple other bills were~~ , as well as multiple bills introduced butintroduced but not enactednot enacted. ~~into public law.~~ In the In the
114th Congress, no bills114th Congress, no bills containing 10-20-30 language werecontaining 10-20-30 language were enacted into public law,enacted into public law, but 10-20-30 language was but 10-20-30 language was
~~included~~ included in H.R. 1360 (America’s FOCUSin H.R. 1360 (America’s FOCUS Act of 2015), H.R. 5393 (Commerce, Justice, Science,Act of 2015), H.R. 5393 (Commerce, Justice, Science, and Related and Related
~~Agencies~~ Agencies Appropriations Act, 2017), H.R. 5054 (Agriculture, Rural Development, Food and DrugAppropriations Act, 2017), H.R. 5054 (Agriculture, Rural Development, Food and Drug Administration, and Administration, and
Related AgenciesRelated Agencies Appropriations Act, 2017), H.R. 5538 (Department of the Interior, Environment, and Related Appropriations Act, 2017), H.R. 5538 (Department of the Interior, Environment, and Related
~~Agencies~~ Agencies Appropriations Act, 2017), and S. 3067 and H.R. 5485 (Financial Services and GeneralAppropriations Act, 2017), and S. 3067 and H.R. 5485 (Financial Services and General ~~Government~~
Government Appropriations Act, 2017). However, the Consolidated Appropriations Acts for 2017, 2018, and 2019 used language Appropriations Act, 2017). However, the Consolidated Appropriations Acts for 2017, 2018, and 2019 used language
analogous to the billsanalogous to the bills introduced in the 114th Congress, with some modification. Additionally, in the 113th Congress, introduced in the 114th Congress, with some modification. Additionally, in the 113th Congress,
H.R. 5571 (H.R. 5571 (~~T he~~The 10-20-30 Act of 2014) was introduced and referred to committee but not passed. 10-20-30 Act of 2014) was introduced and referred to committee but not passed.
~~8 T he~~ 9 The decennial census decennial census does not collect income information in the 50 states, the District of Columbia, and Puerto Rico, does not collect income information in the 50 states, the District of Columbia, and Puerto Rico,
but still asks for income information in American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, but still asks for income information in American Samoa, the Commonwealth of the Northern Mariana Islands, Guam,
and the U.S. Virginand the U.S. Virgin Islands. Islands. ~~Neit her ACS~~ Neither ACS nor SAIPE poverty estimates are currently available for these island areas.nor SAIPE poverty estimates are currently available for these island areas.
~~9 T here~~ 10 There are two definitions of poverty used in the United States: one for statistical purposes, which is are two definitions of poverty used in the United States: one for statistical purposes, which is ~~used~~ used by the by the
CensusCensus ~~Bureau~~ Bureau and describedand described in Statistical Policy Directive 14 by the Office of Management and Budget;in Statistical Policy Directive 14 by the Office of Management and Budget; and the other and the other
for program administration purposes, which is usedfor program administration purposes, which is used by the Department of Health and Human Servicesby the Department of Health and Human Services and is referred to and is referred to
in the Omnibus Budgetin the Omnibus Budget Reconciliation Act of 1981. Measuring the poverty rates of counties, which are in turn usedReconciliation Act of 1981. Measuring the poverty rates of counties, which are in turn used in in
the 10-20-30 plan, is a statistical use of poverty data; thus, the statistical definition of poverty (used by the Census the 10-20-30 plan, is a statistical use of poverty data; thus, the statistical definition of poverty (used by the Census
Bureau)Bureau) applies. applies.
1011 For further details about the official definition of poverty, see CRS For further details about the official definition of poverty, see CRS Report R44780, Report R44780, An Introduction to Poverty
~~Measurem ent, by Joseph Dalaker.~~
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Every person in a family has the same poverty status. Thus, it is possible to compute a poverty rate based on counts of persons. This is done by dividing the number of persons below poverty within a county by the county’s total population,within a county by the county’s total population,1112 and multiplying by 100 to express the rate as a and multiplying by 100 to express the rate as a
percentage. percentage.
Data Sources Used in Identifying Persistent Poverty Counties
Poverty rates are computed using data from household surveys. Currently, the only data sources Poverty rates are computed using data from household surveys. Currently, the only data sources
that provide poverty estimates for that provide poverty estimates for al all U.S. counties are the ACS and SAIPE. Before the mid-U.S. counties are the ACS and SAIPE. Before the mid-
1990s, the only poverty data available1990s, the only poverty data available at the county level came from the Decennial Census of at the county level came from the Decennial Census of
Population and Housing, which is collected once every 10 years. In the past, these data were the Population and Housing, which is collected once every 10 years. In the past, these data were the
only source of estimates that could determine whether a county had persistently high poverty only source of estimates that could determine whether a county had persistently high poverty
rates (ARRA referred explicitly to decennial census poverty estimates for that purpose). However, rates (ARRA referred explicitly to decennial census poverty estimates for that purpose). However,
after Census 2000, the decennial census has no longer collected income information in the 50 after Census 2000, the decennial census has no longer collected income information in the 50
states, the District of Columbia, and Puerto Rico, and as a result cannot be used to compute states, the District of Columbia, and Puerto Rico, and as a result cannot be used to compute
poverty estimates.poverty estimates.1213 Therefore, to determine whether an area is persistently poor in a time span Therefore, to determine whether an area is persistently poor in a time span
that ends after 2000, it must first be decided whether ACS or SAIPE poverty estimates that ends after 2000, it must first be decided whether ACS or SAIPE poverty estimates ~~wil be~~
will be used for the later part of that time span. used for the later part of that time span.
The ACS and the SAIPE program serve different purposes. The ACS was developed to provide The ACS and the SAIPE program serve different purposes. The ACS was developed to provide
continuous measurement of a wide range of topics similar to that formerly provided by the continuous measurement of a wide range of topics similar to that formerly provided by the
decennial census long form, available down to the local community level. ACS data for decennial census long form, available down to the local community level. ACS data for al
all counties are availablecounties are available ~~annual y~~ annually, but are based on responses over the previous five-year time span , but are based on responses over the previous five-year time span
(e.g., (e.g., ~~2015-2019~~2016-2020). The SAIPE program was developed ). The SAIPE program was developed ~~specifical y~~specifically for estimating poverty at the for estimating poverty at the
county level for school-age children and for the county level for school-age children and for the ~~overal~~ overall population, for use in funding population, for use in funding ~~al ocations~~allocations
for the Improving America’s Schools Act of 1994 (P.L. 103-382). SAIPE data are also available for the Improving America’s Schools Act of 1994 (P.L. 103-382). SAIPE data are also available
~~annual y~~annually, and reflect one calendar year, not five. However, unlike the ACS, SAIPE does not , and reflect one calendar year, not five. However, unlike the ACS, SAIPE does not
provide estimates for a wide array of topics. For further details about the data sources for county provide estimates for a wide array of topics. For further details about the data sources for county
poverty estimates, see tpoverty estimates, see the Appendix.

11 Measurement, by Joseph Dalaker. 12 Poverty rates are computed using Poverty rates are computed using ~~adjusted~~ adjusted population totals because there are some individualspopulation totals because there are some individuals whose poverty status whose poverty status
is not determined. is not determined. ~~T hese~~These include unrelated individuals include unrelated individuals under ageunder age 15, such as foster children, who are not asked income 15, such as foster children, who are not asked income
questions and whoquestions and who are not related to anyone else in their residence by birth, marriage, or adoption; persons living in are not related to anyone else in their residence by birth, marriage, or adoption; persons living in
military barracks; andmilitary barracks; and persons in institutions such as nursing homes or prisons. Some surveys (suchpersons in institutions such as nursing homes or prisons. Some surveys (such as those described as those described
in this report) do not compute poverty status for persons living in collegein this report) do not compute poverty status for persons living in college dormitories. dormitories. ~~T hese~~These persons are excluded persons are excluded from from
the total population when computing poverty rates. Furthermore, people who have no traditional housing and who do the total population when computing poverty rates. Furthermore, people who have no traditional housing and who do
not live in shelters are typically not sampled in household surveys. not live in shelters are typically not sampled in household surveys.
~~12 T he~~13 The decennial census decennial census still collects income information in American Samoa, the Commonwealth of the Northern still collects income information in American Samoa, the Commonwealth of the Northern
Mariana Islands,Mariana Islands, Guam,Guam, and the U.S. Virginand the U.S. Virgin Islands. Neither the ACSIslands. Neither the ACS nor the SAIPE program is conductednor the SAIPE program is conducted for these for these
islandisland areas; decennial census data are the only small-area poverty data available for them. areas; decennial census data are the only small-area poverty data available for them. ~~T he~~The 2020 Census 2020 Census
questionnaire for these islandquestionnaire for these island areas covered the same topics as the ACS;areas covered the same topics as the ACS; see the Island Areas Censusessee the Island Areas Censuses Operation Operation
Detailed Operational Plan at https://www.census.gov/programs-surveys/decennial-census/2020-census/planning-Detailed Operational Plan at https://www.census.gov/programs-surveys/decennial-census/2020-census/planning-
management/planning-docs/IAC-detailed-op-plan.html. For Puerto Rico, ACSmanagement/planning-docs/IAC-detailed-op-plan.html. For Puerto Rico, ACS estimates are still produced,estimates are still produced, but SAIPE but SAIPE
estimates stopped being producedestimates stopped being produced after 2003. For details see https://www.census.gov/programs-surveys/saipe/technical-after 2003. For details see https://www.census.gov/programs-surveys/saipe/technical-
documentation/methodology/puerto-rico.html. documentation/methodology/puerto-rico.html.
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Considerations When Identifying and Targeting
Persistent Poverty Counties
Selecting the Data Source: Strengths and Limitations of ACS and
SAIPE Poverty Data
Because poverty estimates can be obtained from multiple data sources, the Census Bureau has Because poverty estimates can be obtained from multiple data sources, the Census Bureau has
provided guidance on the most suitable data source to use for various purposes.provided guidance on the most suitable data source to use for various purposes.13
14 Characteristics of Interest: SAIPE for Poverty Alone; ACS for Other Topics in
Addition to Poverty
The Census Bureau recommends using SAIPE poverty estimates when estimates are needed at The Census Bureau recommends using SAIPE poverty estimates when estimates are needed at
the county level, the county level, ~~especial y~~especially for counties with for counties with ~~smal~~ small populations, and when additional populations, and when additional
demographic and economic detail is not needed at that level.demographic and economic detail is not needed at that level.1415 When additional When additional detail is required, detail is required,
such as for county-level poverty estimates by race and Hispanic origin, detailed age groups (aside such as for county-level poverty estimates by race and Hispanic origin, detailed age groups (aside
from the elementary and secondary school-age population), housing characteristics, or education from the elementary and secondary school-age population), housing characteristics, or education
level, the ACS is the data source recommended by the Census Bureau. level, the ACS is the data source recommended by the Census Bureau.
Geographic Area of Interest: SAIPE for Counties and School Districts Only;
ACS for Other Small Areas
For counties (and school districts) of For counties (and school districts) of ~~smal~~ small population size, SAIPE data have an advantage over population size, SAIPE data have an advantage over
ACS data in that the SAIPE model uses administrative data to help reduce the uncertainty of the ACS data in that the SAIPE model uses administrative data to help reduce the uncertainty of the
estimates. However, ACS estimates are availableestimates. However, ACS estimates are available for a wider array of geographic levels, such as for a wider array of geographic levels, such as
ZIP code tabulation areas, census tracts (subcounty areas of roughly 1,200 to 8,000 people), cities ZIP code tabulation areas, census tracts (subcounty areas of roughly 1,200 to 8,000 people), cities
and towns, and greater metropolitan areas.and towns, and greater metropolitan areas.16
Reference Period of Estimate: SAIPE for One Year, ACS for a Five-Year Span
While the ACS has greater flexibility While the ACS has greater flexibility in the topics measured and the geographic areas provided, it in the topics measured and the geographic areas provided, it
can only provide estimates in five-year ranges for the can only provide estimates in five-year ranges for the ~~smal est~~smallest geographic areas. Five years of geographic areas. Five years of
survey responses are needed to obtain a sample large enough to produce meaningful estimates for survey responses are needed to obtain a sample large enough to produce meaningful estimates for
populations below 65,000 persons. In this sense the SAIPE data, because they are based on a populations below 65,000 persons. In this sense the SAIPE data, because they are based on a
single year, are more current than the data of the ACS. The distinction has to do with the single year, are more current than the data of the ACS. The distinction has to do with the
reference period of the data—both data sources release data on an annual basis; the ACS reference period of the data—both data sources release data on an annual basis; the ACS
estimates for estimates for ~~smal~~ small areas are based on the prior five years, not the prior year alone. areas are based on the prior five years, not the prior year alone.

~~13 T his guidance~~ 14 This guidance is posted on the Census Bureau’sis posted on the Census Bureau’s ~~website~~ website at https://www.census.gov/topics/income-poverty/poverty/at https://www.census.gov/topics/income-poverty/poverty/
guidance/data-sources.html, and is reproducedguidance/data-sources.html, and is reproduced in thin the ~~Appe ndix~~Appendix. .
14 15 SAIPE county-level estimates are available for the poverty status of the total population, persons under age 18, and SAIPE county-level estimates are available for the poverty status of the total population, persons under age 18, and
related children agesrelated children ages 5 to 17 living in families, and for median household income. 5 to 17 living in families, and for median household income.
16 Some bills, including Division L, Title I of P.L. 117-103 (see footnote 3) define areas of persistent poverty to include census tracts with poverty rates “not less than 20 percent” along with persistent poverty counties and “any territory or possession of the United States” per 49 U.S.C. §6702(a)(1). Congressional Research Service Congressional Research Service

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Other Considerations
Treatment of Special Populations in the Official Poverty Definition
Regardless of the data source used to measure it, poverty status is not defined for persons in Regardless of the data source used to measure it, poverty status is not defined for persons in
institutions, such as nursing homes or prisons, nor for persons residing in military barracks. These institutions, such as nursing homes or prisons, nor for persons residing in military barracks. These
populations are excluded from totals when computing poverty statistics. Furthermore, the populations are excluded from totals when computing poverty statistics. Furthermore, the
homeless population is not counted explicitly in poverty statistics. The ACS is a household homeless population is not counted explicitly in poverty statistics. The ACS is a household
survey, thus homeless individuals who are not in shelters are not counted. SAIPE estimates are survey, thus homeless individuals who are not in shelters are not counted. SAIPE estimates are
~~partial y~~partially based on Supplemental Nutrition Assistance Program (SNAP) administrative data and based on Supplemental Nutrition Assistance Program (SNAP) administrative data and
tax data, so the part of the homeless population that either filed tax returns or tax data, so the part of the homeless population that either filed tax returns or ~~rec eived~~received SNAP SNAP
benefits might be reflected in the estimates, but only implicitly.benefits might be reflected in the estimates, but only implicitly.
In the decennial census, ACS, and SAIPE estimates, poverty status also is not defined for persons In the decennial census, ACS, and SAIPE estimates, poverty status also is not defined for persons
livingliving in college dormitories.in college dormitories.1517 However, students who live in off-campus housing are included. However, students who live in off-campus housing are included.
Because college students tend to have lower money income (which does not include school loans) Because college students tend to have lower money income (which does not include school loans)
than average, counties that have large populations of students living off-campus may exhibit than average, counties that have large populations of students living off-campus may exhibit
higher poverty rates than one might expect given other economic measures for the area, such as higher poverty rates than one might expect given other economic measures for the area, such as
the unemployment rate.the unemployment rate.16
18 Given the ways that the special populations above either are or are not reflected in poverty Given the ways that the special populations above either are or are not reflected in poverty
statistics, it may be worthwhile to consider whether counties that have large numbers of people in statistics, it may be worthwhile to consider whether counties that have large numbers of people in
those populations would receive an equitable those populations would receive an equitable ~~al ocation~~allocation of funds. Other economic measures may of funds. Other economic measures may
be of use, depending on the type of program for which funds are being targeted. be of use, depending on the type of program for which funds are being targeted.
Persistence Versus Flexibility to Recent Situations
The 10-20-30 provision was developed to identify counties with persistently high poverty rates. The 10-20-30 provision was developed to identify counties with persistently high poverty rates.
Therefore, using that funding approach by itself would not Therefore, using that funding approach by itself would not ~~al ow flexibility~~ allow flexibility to target counties that to target counties that
have recently experienced economic hardship, such as counties that had a large manufacturing have recently experienced economic hardship, such as counties that had a large manufacturing
plant close within the past three years. Other interventions besides the 10-20-30 provision may be plant close within the past three years. Other interventions besides the 10-20-30 provision may be
more appropriate for counties that have had a recent spike in the poverty rate. more appropriate for counties that have had a recent spike in the poverty rate.
Effects of Rounding and Data Source Selection on Lists of Counties
In ARRA, persistent poverty counties were defined as “any county that has had 20 percent or In ARRA, persistent poverty counties were defined as “any county that has had 20 percent or
more of its population livingmore of its population living in poverty over the past 30 years, as measured by the 1980, 1990, in poverty over the past 30 years, as measured by the 1980, 1990,
and 2000 decennial censuses.”and 2000 decennial censuses.”1719 Poverty rates published by the Census Bureau are Poverty rates published by the Census Bureau are ~~typical y~~
typically reported to one decimal place. The numeral used in the ARRAreported to one decimal place. The numeral used in the ARRA language was the whole number language was the whole number
20. Thus, for any 20. Thus, for any ~~col ection~~collection of poverty data, there are two reasonable approaches to compiling a of poverty data, there are two reasonable approaches to compiling a
list of persistent poverty counties: using poverty rates of at least 20.0% in list of persistent poverty counties: using poverty rates of at least 20.0% in al all three years, or using three years, or using

15 17 Details on the poverty universe in the ACS Details on the poverty universe in the ACS are available at https://www2.census.gov/programs-surveys/acs/are available at https://www2.census.gov/programs-surveys/acs/
tech_docs/subject_definitions/tech_docs/subject_definitions/~~2018_ACSSubjectDefinitions.pdf?#page=107~~2020_ACSSubjectDefinitions.pdf#page=112 and for the SAIPE estimates at and for the SAIPE estimates at
https://www.census.gov/programs-surveys/saipe/guidance/model-inputhttps://www.census.gov/programs-surveys/saipe/guidance/model-input -data/denominators/poverty.html. -data/denominators/poverty.html.
1618 For some counties, the percentage-point difference could be For some counties, the percentage-point difference could be large when off-campus students are excluded.large when off-campus students are excluded. Using Using
ACSACS data for 2009-2011, Whitman County, WA, experienced the largest poverty rate difference among all counties data for 2009-2011, Whitman County, WA, experienced the largest poverty rate difference among all counties
when off-campus students werewhen off-campus students were excluded—itsexcluded—its poverty rate fell by 16.5 percentage points. For the United States as a poverty rate fell by 16.5 percentage points. For the United States as a
whole, the poverty rate fell from 15.2% to 14.5% when offwhole, the poverty rate fell from 15.2% to 14.5% when off -campus students were-campus students were ~~excluded (based~~ excluded (based on the same dataset). on the same dataset).
For details, seeFor details, see Alemayehu Bishaw,Alemayehu Bishaw, “Examining the Effect of Off-Campus College Students on Poverty Rates,” “Examining the Effect of Off-Campus College Students on Poverty Rates,”
Working Paper SEHSDWorking Paper SEHSD 2013-17, U.S. Census2013-17, U.S. Census Bureau,Bureau, May 1, 2013. May 1, 2013.
17 19 P.L. 111-5, Section 105. P.L. 111-5, Section 105.
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poverty rates that poverty rates that round up to the whole number 20% or greater in to the whole number 20% or greater in al all three years (i.e., poverty three years (i.e., poverty
rates of 19.5% or more in rates of 19.5% or more in al all three years). The former approach is more restrictive and results in a three years). The former approach is more restrictive and results in a
shorter list of counties; the latter approach is more inclusive. shorter list of counties; the latter approach is more inclusive.
Table 1 ~~il ustrates~~illustrates the number of counties identified as persistent poverty counties using the 1990 the number of counties identified as persistent poverty counties using the 1990
and 2000 decennial censuses, and various ACS and SAIPE datasets for the last data point, under and 2000 decennial censuses, and various ACS and SAIPE datasets for the last data point, under
both rounding schemes. The rounding method and data source selection can each have large both rounding schemes. The rounding method and data source selection can each have large
impacts on the number of counties listed. impacts on the number of counties listed. ~~Approximately 25 to 30 more counties appear in~~
~~SAIPE-based lists compared to ACS-based lists using the same rounding method~~From 2011 to 2018, using SAIPE for the latest year resulted in more counties being identified as persistently poor than were identified by using the ACS; since then, the reverse has been true. Compared to ~~. Compared to~~
using 20.0% as the cutoff (rounded to one decimal place), rounding up to 20% from 19.5% adds using 20.0% as the cutoff (rounded to one decimal place), rounding up to 20% from 19.5% adds
approximately approximately 5040 to 60 counties to the list. Taking both the data source and the rounding method to 60 counties to the list. Taking both the data source and the rounding method
together, the list of persistent poverty counties could vary by roughly 60 to 100 counties in a together, the list of persistent poverty counties could vary by roughly 60 to 100 counties in a
given year depending on the method used. given year depending on the method used.
Table 1. Number of Counties Identified as Persistently Poor,
Using Different Datasets and Rounding Methods
Counties identified as having poverty rates of 20% or more (applying rounding methods as indicated Counties identified as having poverty rates of 20% or more (applying rounding methods as indicated
below) in 1989 (from 1990 Census), 1999 (from Census 2000), and latest year from below) in 1989 (from 1990 Census), 1999 (from Census 2000), and latest year from
datasets indicated below. datasets indicated below.
Rounded to
to One Rounded ~~to One~~
~~Whole~~ to Difference
Decimal Place
~~Number~~
~~Difference~~Whole Between
(20.0% or
Number (19.5% or
~~Between~~ Rounding
Dataset
Greater)
or Greater)
Methods
ACS, 2007-2011 ACS, 2007-2011
397 397
445 445
48 48
ACS, 2008-2012 ACS, 2008-2012
404 404
456 456
52 52
ACS, 2009-2013 ACS, 2009-2013
402 402
458 458
56 56
ACS, 2010-2014 ACS, 2010-2014
401 401
456 456
55 55
ACS, 2011-2015 ACS, 2011-2015
397 397
453 453
56 56
ACS, 2012-2016 ACS, 2012-2016
392 392
446 446
54 54
ACS, 2013- ACS, 2013-~~2017a~~2017 a
386 386
436 436
50 50
ACS, 2014- ACS, 2014-~~2018a~~2018 a
384 384
430 430
46 46
ACS, 2015-2019 ACS, 2015-2019
375 375
418 418
43 43 ACS, 2016-2020 355 397 42

Mean difference: Mean difference: ~~51.11~~50.2

SAIPE, 2011 SAIPE, 2011
433 433
495 495
62 62
SAIPE, 2012 SAIPE, 2012
435 435
491 491
56 56
SAIPE, 2013 SAIPE, 2013
427 427
490 490
63 63
SAIPE, 2014 SAIPE, 2014
427 427
486 486
59 59
SAIPE, 2015 SAIPE, 2015
419 419
476 476
57 57
SAIPE, 2016 SAIPE, 2016
420 420
469 469
49 49
SAIPE, 2017 SAIPE, 2017
411 411
460 460
49 49 SAIPE, 2018 395 443 48
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Rounded to
to One Rounded ~~to One~~
~~Whole~~ to Difference
Decimal Place
~~Number~~
~~Difference~~Whole Between
(20.0% or
Number (19.5% or
~~Between~~ Rounding
Dataset
Greater)
or Greater)
Methods
SAIPE, SAIPE, ~~2018~~
~~395~~
~~443~~
482019 361 407 46
SAIPE, SAIPE, ~~2019~~
~~361~~
~~407~~
462020 306 354 48

Mean difference: Mean difference: ~~54.33~~53.7

~~Differences between~~ Differences between datasets released in same year

Difference, Difference, SAIPE 2011 minus ACS 2007-2011 SAIPE 2011 minus ACS 2007-2011
36 36
50 50

Difference, Difference, SAIPE 2012 minus ACS 2008-2012 SAIPE 2012 minus ACS 2008-2012
31 31
35 35

Difference, Difference, SAIPE 2013 minus ACS 2009-2013 SAIPE 2013 minus ACS 2009-2013
25 25
32 32

Difference, Difference, SAIPE 2014 minus ACS 2010-2014 SAIPE 2014 minus ACS 2010-2014
26 26
30 30

Difference, Difference, SAIPE 2015 minus ACS 2011-2015 SAIPE 2015 minus ACS 2011-2015
22 22
23 23
Difference, Difference, SAIPE 2016 minus ACS 2012-2016 SAIPE 2016 minus ACS 2012-2016
28 28
23 23

Difference, Difference, SAIPE 2017 minus ACS 2013-2017 SAIPE 2017 minus ACS 2013-2017
25 25
24 24

Difference, Difference, SAIPE 2018 minus ACS 2014-2018 SAIPE 2018 minus ACS 2014-2018
11 11
13 13

Difference, Difference, ACS 2015-2019 minus SAIPE 2019 ACS 2015-2019 minus SAIPE 2019
14 14
11 11 Difference, ACS 2016-2020 minus SAIPE 2020 49 43

Mean difference: Mean difference:
~~24.22~~
~~26.78~~26.7 28.4
Source: Congressional Congressional Research ServiceResearch Service (CRS) tabulation of data from(CRS) tabulation of data from U.S. Census Bureau, 1990 Census, U.S. Census Bureau, 1990 Census,
Census 2000, 2012-Census 2000, 2012-~~2019 Smal~~ 2020 Small Area Income and Poverty Estimates, and AmericanArea Income and Poverty Estimates, and American Community Survey 5-Year Community Survey 5-Year
Estimates for 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, 2014-2018, Estimates for 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, 2014-2018, ~~and~~
2015-20192015-2019.
, and 2016-2020. Notes: ACS: American ACS: American Community Survey. SAIPE: Community Survey. SAIPE: ~~Smal Area~~ Small Area Income and Poverty Estimates.Income and Poverty Estimates. Comparisons Comparisons
between ACS and SAIPE estimatesbetween ACS and SAIPE estimates are between datasets releasedare between datasets released in the same year (both are typical y releasedin the same year (both are typical y released in in
DecemberDecember of the year fol owing the referenceof the year fol owing the reference period). There are 3,143 county-type areas in the United States. period). There are 3,143 county-type areas in the United States.
The selectionThe selection of the data source and rounding method has a large effect on the number of counties identified as of the data source and rounding method has a large effect on the number of counties identified as
being in persistent poverty. The longest list of persistent poverty counties (SAIPE, 19.5% or greater,being in persistent poverty. The longest list of persistent poverty counties (SAIPE, 19.5% or greater, that is, that is,
rounded up to the whole number 20%) minus the shortest list of persistent poverty counties (ACS, 20.0% or rounded up to the whole number 20%) minus the shortest list of persistent poverty counties (ACS, 20.0% or
greater) yields the maximumgreater) yields the maximum difference. Comparing datasets that weredifference. Comparing datasets that were ~~released~~ released in the same year, the maximum in the same year, the maximum
differences in the lists of counties were: differences in the lists of counties were:
SAIPE 2011, whole number - ACS, 2007-2011, one decimal SAIPE 2011, whole number - ACS, 2007-2011, one decimal = 98 counties = 98 counties
SAIPE 2012, whole number - ACS, 2008-2012, one decimalSAIPE 2012, whole number - ACS, 2008-2012, one decimal = 87 = 87
SAIPE 2013, whole number - ACS, 2009-2013, one decimalSAIPE 2013, whole number - ACS, 2009-2013, one decimal = 88 = 88
SAIPE 2014, whole number - ACS, 2010-2014, one decimalSAIPE 2014, whole number - ACS, 2010-2014, one decimal = 85 = 85
SAIPE 2015, whole number - ACS, 2011-2015, one decimalSAIPE 2015, whole number - ACS, 2011-2015, one decimal = 79 = 79
SAIPE 2016, whole number - ACS, 2012-2016, one decimalSAIPE 2016, whole number - ACS, 2012-2016, one decimal = 77 = 77
SAIPE 2017, whole number - ACS, 2013-2017, one decimalSAIPE 2017, whole number - ACS, 2013-2017, one decimal = 74 = 74
SAIPE 2018, whole number - ACS, 2014-2018, one decimalSAIPE 2018, whole number - ACS, 2014-2018, one decimal = 59 = 59
ACS, 2015-2019, whole number - SAIPE 2019, one decimalACS, 2015-2019, whole number - SAIPE 2019, one decimal = 57 ACS, 2016-2020, whole number - SAIPE 2020, one decimal = 91~~= 57~~

The lists of persistent poverty counties vary by about The lists of persistent poverty counties vary by about 7880 counties on average (mean: counties on average (mean: ~~78.22~~79.5), depending on which ), depending on which
data source is used for the most recent poverty rate estimate,data source is used for the most recent poverty rate estimate, and which rounding method is applied to identify and which rounding method is applied to identify
persistent poverty. persistent poverty.
Congressional Research Service 8 link to page 13 link to page 28 The 10-20-30 Provision: Defining Persistent Poverty Counties a. These counts include Rio Arribaa. These counts include Rio Arriba County, County, NMNew Mexico, despite an ACS data , despite an ACS data ~~col ection error~~ collection error that occurred that occurred in that in that
county in both 2017 and 2018. The Census Bureau detected the errorcounty in both 2017 and 2018. The Census Bureau detected the error after the five-year data for 2013-after the five-year data for 2013-
2017 had been released,2017 had been released, but before the 2014-2018 data had been released.but before the 2014-2018 data had been released. As a result,As a result, the 2014-2018 the 2014-2018
poverty rate for Rio Arribapoverty rate for Rio Arriba County was not published, and the 2013-2017 poverty rate (formerlyCounty was not published, and the 2013-2017 poverty rate (formerly ~~reported~~
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reported as 26.4%) was removedas 26.4%) was removed from the Census Bureau website.from the Census Bureau website. The 2012-2016 ACS poverty rate for Rio Arriba The 2012-2016 ACS poverty rate for Rio Arriba
County was 23.4%, and the 2018 SAIPE poverty rate was 22.0%. Because the ACS poverty rate immediately County was 23.4%, and the 2018 SAIPE poverty rate was 22.0%. Because the ACS poverty rate immediately
before the errorbefore the error (2012-2016) and the SAIPE poverty rate were(2012-2016) and the SAIPE poverty rate were both above 20.0%, Rio Arriba County is both above 20.0%, Rio Arriba County is
included in this table’s counts of persistentincluded in this table’s counts of persistent poverty counties. For details seepoverty counties. For details see https://www.census.gov/https://www.census.gov/
programs-surveys/acs/technical-documentation/errata/125.html.programs-surveys/acs/technical-documentation/errata/125.html.
Example List of Persistent Poverty Counties
The list of persistent poverty counties belowThe list of persistent poverty counties below (Table 2) is based on data from the 1990 Census, is based on data from the 1990 Census,
Census 2000, and the Census 2000, and the ~~2019~~2020 SAIPE estimates, and includes the SAIPE estimates, and includes the ~~407~~354 counties with poverty rates of counties with poverty rates of
19.5% or greater (that is, counties with poverty rates that were at least 20% with rounding applied 19.5% or greater (that is, counties with poverty rates that were at least 20% with rounding applied
to the whole number). These same counties are mapped ito the whole number). These same counties are mapped in Figure 1..
This list of This list of ~~407 counties~~ 354 counties (out of a total of 3,143 nationwide) is similar but not identical to a list that would be compiled if ACS is similar but not identical to a list that would be compiled if ACS ~~(2015-~~
~~2019)~~ data were used with 1990 and 2000 Census data to determine counties with persistent data were used with 1990 and 2000 Census data to determine counties with persistent
poverty. poverty.
Table 2. List of Persistent Poverty Counties, Based on 1990 Census, Census 2000, and
~~2019~~2020 Small Area Income and Poverty Estimates (SAIPE), Using Poverty Rates of
19.5% or Greater
Poverty ~~Poverty~~ Poverty Rate, Rate, Poverty
FIPS
Congressional
~~Rate~~
~~Rate~~1989 1999
Rate,
Geographic
District(s)
~~1989~~
~~1999~~
~~2018,~~(from (from 2020
Identification
Representing
(1990
(Census
(from
Count
Code
State
County
the ~~Count~~yaCountya
Census)
2000)
SAIPE)
1 1
~~1005~~01005
Alabama Alabama
Barbour Barbour
2 2
25.2 25.2
26.8 26.8
~~27.1~~25.5
2 2
~~1007~~01011
Alabama Alabama
~~Bibb~~
6
~~21.2~~
~~20.6~~
~~20.3~~
3
~~1011~~
~~Alabama~~
~~Bul ock~~
2
~~36.5~~
~~33.5~~
30
4
~~1013~~
~~Alabama~~
~~Butler~~
2
~~31.5~~
~~24.6~~
~~21.6~~
5
~~1023~~
~~Alabama~~
~~Choctaw~~
7
~~30.2~~
~~24.5~~
~~22.6~~
6
~~1035~~Bul ock 2 36.5 33.5 30.8 3 01013 Alabama Butler 2 31.5 24.6 20.6 4 01023 Alabama Choctaw 7 30.2 24.5 20.4 5 01025 Alabama Clarke 1,7 25.9 22.6 19.5 6 01035
Alabama Alabama
Conecuh Conecuh
2 2
29.7 29.7
26.6 26.6
22. 22.29
7 7
~~1047~~01047
Alabama Alabama
~~Dal as~~Dallas
7 7
36.2 36.2
31.1 31.1
26 26.7
8 8
~~1053~~01053
Alabama Alabama
Escambia Escambia
1 1
28.1 28.1
20.9 20.9
20. 20.5
9
~~1063~~4 9 01061 Alabama Geneva 2 19.5 19.6 21.0 10 01063
Alabama Alabama
Greene Greene
7 7
45.6 45.6
34.3 34.3
~~31.7~~
10
~~1065~~27.9 11 01065
Alabama Alabama
Hale Hale
7 7
35.6 35.6
26.9 26.9
~~20.5~~
11
~~1085~~21.9 12 01085
Alabama Alabama
Lowndes Lowndes
7 7
38.6 38.6
31.4 31.4
~~26.6~~
12
~~1087~~
~~Alabama~~
~~Macon~~
3
~~34.5~~
~~32.8~~
~~29.3~~
13
~~1091~~
~~Alabama~~
~~Marengo~~
7
30
~~25.9~~
~~24.8~~
14
~~1099~~
~~Alabama~~
~~Monroe~~
1
~~22.7~~
~~21.3~~
~~23.3~~
15
~~1105~~
~~Alabama~~
~~Perry~~
7
~~42.6~~
~~35.4~~
~~33.9~~
16
~~1107~~
~~Alabama~~
~~Pickens~~
7
~~28.9~~
~~24.9~~
~~24.3~~
17
~~1109~~
~~Alabama~~
~~Pike~~
2
~~27.2~~
~~23.1~~
~~21.8~~
18
~~1119~~
~~Alabama~~
~~Sumter~~
7
~~39.7~~
~~38.7~~
~~36.4~~
19
~~1131~~
~~Alabama~~
~~Wilcox~~
7
~~45.2~~
~~39.9~~
~~32.5~~
20
~~2050~~
~~Alaska~~
~~Bethel Census Area~~
~~at large~~
30
~~20.6~~
~~23.5~~
~~Congressional Research Service~~

9

~~link to page 26~~ ~~The 10-20-30 Provision: Defining Persistent Poverty Counties~~

~~Poverty Poverty Poverty~~
~~FIPS~~
~~Congressional~~
~~Rate~~
~~Rate~~
~~Rate~~
~~Geographic~~
~~District(s)~~
~~1989~~
~~1999~~
~~2018,~~
~~Identification~~
~~Representing~~
~~(1990~~
~~(Census~~
~~from~~
~~Count~~
~~Code~~
~~State~~
~~County~~
~~the Countya~~
~~Census)~~
~~2000)~~
~~SAIPE~~
~~404~~
~~54099~~
~~West Virginia~~
~~Wayne~~
3
~~21.8~~
~~19.6~~
~~19.6~~
~~405~~
~~54101~~
~~West Virginia~~
~~Webster~~
3
~~34.8~~
~~31.8~~
~~21.8~~
~~406~~
~~54109~~
~~West Virginia~~
~~Wyoming~~
3
~~27.9~~
~~25.1~~
~~22.9~~
~~407~~
~~55078~~
~~Wisconsin~~
~~Menominee~~
8
~~48.7~~
~~28.8~~
~~25.3~~
Source: Congressional Research Service (CRS) tabulation of data from U.S. Census Bureau , 1990 Census,
~~Census 2000, 2019 Smal Area Income and Poverty Estimates, and Nation-Based Relationship File for~~
~~Congressional Districts and Counties (116th Congress).~~
Notes: FIPS: Federal Information Processing Standard.
~~a. Numbers are ordinal, referring to the name of the congressional district(s) present in the county. For~~
~~example, Barbour County, AL, is represented by Alabama’s 2nd Congressional District (indicated by the 2).~~
~~A congressional district may span multiple counties; conversely,~~ 21.9 Congressional Research Service 9 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, Poverty FIPS Congressional 1989 1999 Rate, Geographic District(s) (from (from 2020 Identification Representing 1990 Census (from Count Code State County the Countya Census) 2000) SAIPE) 13 01087 Alabama Macon 3 34.5 32.8 27.9 14 01099 Alabama Monroe 1 22.7 21.3 22.5 15 01105 Alabama Perry 7 42.6 35.4 30.7 16 01107 Alabama Pickens 7 28.9 24.9 22.7 17 01109 Alabama Pike 2 27.2 23.1 19.7 18 01113 Alabama Russell 3 20.4 19.9 20.3 19 01119 Alabama Sumter 7 39.7 38.7 29.2 20 01131 Alabama Wilcox 7 45.2 39.9 22.2 21 02050 Alaska Bethel Census Area at large 30.0 20.6 25.3 22 02158 Alaska Kusilvak Census Areab at large 31.0 26.2 27.9 23 02290 Alaska Yukon-Koyukuk at large 26.0 23.8 20.5 Census Area 24 04001 Arizona Apache 1 47.1 37.8 32.4 25 04012 Arizona La Paz 4 28.2 19.6 20.8 26 04017 Arizona Navajo 1 34.7 29.5 23.3 27 05011 Arkansas Bradley 4 24.9 26.3 20.6 28 05017 Arkansas Chicot 1 40.4 28.6 26.8 29 05035 Arkansas Crittenden 1 27.1 25.3 22.9 30 05037 Arkansas Cross 1 25.4 19.9 21.2 31 05041 Arkansas Desha 1 34.0 28.9 22.8 32 05073 Arkansas Lafayette 4 34.7 23.2 19.8 33 05077 Arkansas Lee 1 47.3 29.9 36.8 34 05079 Arkansas Lincoln 1 26.2 19.5 23.6 35 05093 Arkansas Mississippi 1 26.2 23.0 21.0 36 05095 Arkansas Monroe 1 35.9 27.5 23.8 37 05103 Arkansas Ouachita 4 21.2 19.5 20.5 38 05107 Arkansas Phil ips 1 43.0 32.7 22.1 Congressional Research Service 10 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, Poverty FIPS Congressional 1989 1999 Rate, Geographic District(s) (from (from 2020 Identification Representing 1990 Census (from Count Code State County the Countya Census) 2000) SAIPE) 39 05111 Arkansas Poinsett 1 25.6 21.2 21.2 40 05123 Arkansas St. Francis 1 36.6 27.5 30.1 41 05129 Arkansas Searcy 1,3 29.9 23.8 22.3 42 05147 Arkansas Woodruff 1 34.5 27.0 22.6 43 08011 Colorado Bent 4 20.4 19.5 26.6 44 08023 Colorado Costil a 3 34.6 26.8 21.8 45 12039 Florida Gadsden 5 28.0 19.9 21.9 46 12047 Florida Hamilton 5 27.8 26.0 24.2 47 12049 Florida Hardee 17 22.8 24.6 21.2 48 12079 Florida Madison 5 25.9 23.1 23.8 49 12107 Florida Putnam 3 20.0 20.9 24.3 50 13003 Georgia Atkinson 8 26.0 23.0 21.6 51 13005 Georgia Bacon 1 24.1 23.7 21.1 52 13007 Georgia Baker 2 24.8 23.4 23.7 53 13017 Georgia Ben Hil 8 22.0 22.3 22.3 54 13027 Georgia Brooks 8 25.9 23.4 20.6 55 13031 Georgia Bul och 12 27.5 24.5 20.7 56 13033 Georgia Burke 12 30.3 28.7 20.0 57 13037 Georgia Calhoun 2 31.8 26.5 34.4 58 13043 Georgia Candler 12 24.1 26.1 20.1 59 13059 Georgia Clarke 9,10 27.0 28.3 24.6 60 13061 Georgia Clay 2 35.7 31.3 24.1 61 13065 Georgia Clinch 1 26.4 23.4 20.4 62 13071 Georgia Colquitt 8 22.8 19.8 20.4 63 13075 Georgia Cook 8 22.4 20.7 19.5 64 13081 Georgia Crisp 2 29.0 29.3 24.5 Congressional Research Service 11 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, Poverty FIPS Congressional 1989 1999 Rate, Geographic District(s) (from (from 2020 Identification Representing 1990 Census (from Count Code State County the Countya Census) 2000) SAIPE) 65 13087 Georgia Decatur 2 23.3 22.7 25.6 66 13093 Georgia Dooly 2 32.9 22.1 27.0 67 13095 Georgia Dougherty 2 24.4 24.8 27.4 68 13099 Georgia Early 2 31.4 25.7 24.0 69 13107 Georgia Emanuel 12 25.7 27.4 26.4 70 13109 Georgia Evans 12 25.4 27.0 19.6 71 13141 Georgia Hancock 10 30.1 29.4 30.7 72 13163 Georgia Jefferson 10 31.3 23.0 21.1 73 13165 Georgia Jenkins 12 27.8 28.4 28.0 74 13167 Georgia Johnson 10 22.2 22.6 25.9 75 13193 Georgia Macon 2 29.2 25.8 31.1 76 13197 Georgia Marion 2 28.2 22.4 20.6 77 13201 Georgia Mil er 2 22.1 21.2 19.8 78 13205 Georgia Mitchell 2 28.7 26.4 38.2 79 13239 Georgia Quitman 2 33.0 21.9 23.1 80 13243 Georgia Randolph 2 35.9 27.7 27.4 81 13251 Georgia Screven 12 22.9 20.1 20.7 82 13253 Georgia Seminole 2 29.1 23.2 22.9 83 13259 Georgia Stewart 2 31.4 22.2 31.3 84 13261 Georgia Sumter 2 24.8 21.4 24.3 85 13263 Georgia Talbot 2 24.9 24.2 20.8 86 13265 Georgia Taliaferro 10 31.9 23.4 23.2 87 13267 Georgia Tattnall 12 21.9 23.9 20.7 88 13269 Georgia Taylor 2 29.5 26.0 23.2 89 13271 Georgia Telfair 8 27.3 21.2 29.9 90 13273 Georgia Terrell 2 29.1 28.6 27.8 Congressional Research Service 12 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, Poverty FIPS Congressional 1989 1999 Rate, Geographic District(s) (from (from 2020 Identification Representing 1990 Census (from Count Code State County the Countya Census) 2000) SAIPE) 91 13277 Georgia Tift 8 22.9 19.9 20.5 92 13279 Georgia Toombs 12 24.0 23.9 22.2 93 13283 Georgia Treutlen 12 27.1 26.3 23.4 94 13287 Georgia Turner 8 31.3 26.7 22.2 95 13289 Georgia Twiggs 8 26.0 19.7 20.0 96 13299 Georgia Ware 1 21.1 20.5 26.0 97 13301 Georgia Warren 10 32.6 27.0 23.4 98 13303 Georgia Washington 10 21.6 22.9 21.3 99 13309 Georgia Wheeler 12 30.3 25.3 35.6 100 13315 Georgia Wilcox 8 28.6 21.0 27.9 101 17003 Il inois Alexander 12 32.2 26.1 24.2 102 17153 Il inois Pulaski 12 30.2 24.7 20.4 103 21001 Kentucky Adair 1 25.1 24.0 22.1 104 21011 Kentucky Bath 6 27.3 21.9 22.5 105 21013 Kentucky Bell 5 36.2 31.1 29.8 106 21025 Kentucky Breathitt 5 39.5 33.2 27.9 107 21045 Kentucky Casey 1 29.4 25.5 22.7 108 21051 Kentucky Clay 5 40.2 39.7 37.3 109 21053 Kentucky Clinton 1 38.1 25.8 21.5 110 21057 Kentucky Cumberland 1 31.6 23.8 21.0 111 21063 Kentucky El iott 5 38.0 25.9 28.8 112 21065 Kentucky Estil 6 29.0 26.4 20.6 113 21071 Kentucky Floyd 5 31.2 30.3 28.3 114 21075 Kentucky Fulton 1 30.3 23.1 25.2 115 21095 Kentucky Harlan 5 33.1 32.5 28.0 116 21099 Kentucky Hart 2 27.1 22.4 22.1 Congressional Research Service 13 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, Poverty FIPS Congressional 1989 1999 Rate, Geographic District(s) (from (from 2020 Identification Representing 1990 Census (from Count Code State County the Countya Census) 2000) SAIPE) 117 21109 Kentucky Jackson 5 38.2 30.2 24.0 118 21115 Kentucky Johnson 5 28.7 26.6 22.5 119 21119 Kentucky Knott 5 40.4 31.1 27.7 120 21121 Kentucky Knox 5 38.9 34.8 27.8 121 21127 Kentucky Lawrence 5 36.0 30.7 22.3 122 21129 Kentucky Lee 5 37.4 30.4 32.1 123 21131 Kentucky Leslie 5 35.6 32.7 25.8 124 21133 Kentucky Letcher 5 31.8 27.1 24.4 125 21135 Kentucky Lewis 4 30.7 28.5 22.2 126 21147 Kentucky McCreary 5 45.5 32.2 36.2 127 21153 Kentucky Magoffin 5 42.5 36.6 30.9 128 21159 Kentucky Martin 5 35.4 37.0 31.9 129 21165 Kentucky Menifee 6 35.0 29.6 22.7 130 21169 Kentucky Metcalfe 1 27.9 23.6 21.4 131 21171 Kentucky Monroe 1 26.9 23.4 22.5 132 21175 Kentucky Morgan 5 38.8 27.2 24.5 133 21189 Kentucky Owsley 5 52.1 45.4 30.6 134 21193 Kentucky Perry 5 32.1 29.1 22.0 135 21195 Kentucky Pike 5 25.4 23.4 23.7 136 21197 Kentucky Powell 6 26.2 23.5 20.5 137 21203 Kentucky Rockcastle 5 30.7 23.1 22.4 138 21205 Kentucky Rowan 5 28.9 21.3 24.4 139 21231 Kentucky Wayne 5 37.3 29.4 23.6 140 21235 Kentucky Whitley 5 33.0 26.4 21.7 141 21237 Kentucky Wolfe 6 44.3 35.9 29.7 142 22001 Louisiana Acadia Parish 3 30.5 24.5 20.7 Congressional Research Service 14 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, Poverty FIPS Congressional 1989 1999 Rate, Geographic District(s) (from (from 2020 Identification Representing 1990 Census (from Count Code State County the Countya Census) 2000) SAIPE) 143 22003 Louisiana Allen Parish 4 29.9 19.9 21.1 144 22009 Louisiana Avoyelles Parish 5 37.1 25.9 21.6 145 22013 Louisiana Bienvil e Parish 4 31.2 26.1 22.8 146 22017 Louisiana Caddo Parish 4 24.0 21.1 20.9 147 22021 Louisiana Caldwell Parish 5 28.8 21.2 21.4 148 22025 Louisiana Catahoula Parish 5 36.8 28.1 28.4 149 22027 Louisiana Claiborne Parish 4 32.0 26.5 31.9 150 22029 Louisiana Concordia Parish 5 30.6 29.1 28.6 151 22031 Louisiana De Soto Parish 4 29.8 25.1 19.8 152 22035 Louisiana East Carrol Parish 5 56.8 40.5 37.6 153 22037 Louisiana East Feliciana Parish 5,6 25.0 23.0 19.9 154 22039 Louisiana Evangeline Parish 4 35.1 32.2 24.5 155 22041 Louisiana Franklin Parish 5 34.5 28.4 24.1 156 22045 Louisiana Iberia Parish 3 25.8 23.6 22.5 157 22047 Louisiana Ibervil e Parish 2,6 28.0 23.1 23.7 158 22049 Louisiana Jackson Parish 5 23.9 19.8 20.9 159 22061 Louisiana Lincoln Parish 5 26.6 26.5 21.7 160 22065 Louisiana Madison Parish 5 44.6 36.7 33.6 161 22067 Louisiana Morehouse Parish 5 31.0 26.8 23.3 162 22069 Louisiana Natchitoches Parish 4 33.9 26.5 21.7 163 22071 Louisiana Orleans Parish 1,2 31.6 27.9 21.1 164 22073 Louisiana Ouachita Parish 5 24.7 20.7 23.7 165 22081 Louisiana Red River Parish 4 35.1 29.9 23.7 166 22083 Louisiana Richland Parish 5 33.2 27.9 22.5 167 22085 Louisiana Sabine Parish 4 27.1 21.5 22.1 168 22091 Louisiana St. Helena Parish 5,6 34.4 26.8 22.7 Congressional Research Service 15 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, Poverty FIPS Congressional 1989 1999 Rate, Geographic District(s) (from (from 2020 Identification Representing 1990 Census (from Count Code State County the Countya Census) 2000) SAIPE) 169 22097 Louisiana St. Landry Parish 3,4,5 36.3 29.3 22.6 170 22101 Louisiana St. Mary Parish 3 27.0 23.6 19.8 171 22105 Louisiana Tangipahoa Parish 1,5 31.5 22.7 20.1 172 22107 Louisiana Tensas Parish 5 46.3 36.3 30.8 173 22117 Louisiana Washington Parish 5 31.6 24.7 22.5 174 22119 Louisiana Webster Parish 4 25.1 20.2 19.7 175 22123 Louisiana West Carrol Parish 5 27.4 23.4 20.8 176 22125 Louisiana West Feliciana Parish 5 33.8 19.9 21.9 177 22127 Louisiana Winn Parish 5 27.5 21.5 22.6 178 24510 Maryland Baltimore city 2,3,7 21.9 22.9 20.0 179 28001 Mississippi Adams 3 30.5 25.9 27.2 180 28005 Mississippi Amite 3 30.9 22.6 21.7 181 28009 Mississippi Benton 1 29.7 23.2 19.8 182 28011 Mississippi Bolivar 2 42.9 33.3 28.1 183 28017 Mississippi Chickasaw 1 21.3 20.0 24.8 184 28021 Mississippi Claiborne 2 43.6 32.4 34.1 185 28025 Mississippi Clay 1 25.9 23.5 21.5 186 28027 Mississippi Coahoma 2 45.5 35.9 39.6 187 28029 Mississippi Copiah 2 32.0 25.1 22.5 188 28031 Mississippi Covington 3 31.2 23.5 20.3 189 28035 Mississippi Forrest 4 27.5 22.5 24.9 190 28037 Mississippi Franklin 3 33.3 24.1 21.4 191 28041 Mississippi Greene 4 26.8 19.6 21.4 192 28043 Mississippi Grenada 2 22.3 20.9 21.4 193 28049 Mississippi Hinds 2,3 21.2 19.9 25.9 194 28051 Mississippi Holmes 2 53.2 41.1 34.5 Congressional Research Service 16 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, Poverty FIPS Congressional 1989 1999 Rate, Geographic District(s) (from (from 2020 Identification Representing 1990 Census (from Count Code State County the Countya Census) 2000) SAIPE) 195 28053 Mississippi Humphreys 2 45.9 38.2 33.3 196 28055 Mississippi Issaquena 2 49.3 33.2 43.3 197 28063 Mississippi Jefferson 2 46.9 36.0 30.8 198 28065 Mississippi Jefferson Davis 3 33.3 28.2 25.2 199 28069 Mississippi Kemper 3 35.1 26.0 25.2 200 28075 Mississippi Lauderdale 3 22.8 20.8 22.5 201 28079 Mississippi Leake 2 29.6 23.3 21.0 202 28083 Mississippi Leflore 2 38.9 34.8 25.3 203 28093 Mississippi Marshall 1 30.0 21.9 22.7 204 28097 Mississippi Montgomery 2 34.0 24.3 21.2 205 28103 Mississippi Noxubee 3 41.4 32.8 26.2 206 28105 Mississippi Oktibbeha 1,3 30.1 28.2 23.5 207 28107 Mississippi Panola 2 33.8 25.3 21.0 208 28113 Mississippi Pike 3 32.9 25.3 26.5 209 28119 Mississippi Quitman 2 41.6 33.1 29.9 210 28125 Mississippi Sharkey 2 47.5 38.3 30.3 211 28127 Mississippi Simpson 3 22.7 21.6 21.2 212 28133 Mississippi Sunflower 2 41.8 30.0 34.8 213 28135 Mississippi Tallahatchie 2 41.9 32.2 32.0 214 28143 Mississippi Tunica 2 56.8 33.1 26.7 215 28147 Mississippi Walthall 3 35.9 27.8 23.5 216 28151 Mississippi Washington 2 33.8 29.2 27.7 217 28153 Mississippi Wayne 4 29.5 25.4 22.1 218 28157 Mississippi Wilkinson 3 42.2 37.7 28.4 219 28159 Mississippi Winston 1 26.6 23.7 21.8 220 28161 Mississippi Yalobusha 2 26.4 21.8 21.2 Congressional Research Service 17 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, Poverty FIPS Congressional 1989 1999 Rate, Geographic District(s) (from (from 2020 Identification Representing 1990 Census (from Count Code State County the Countya Census) 2000) SAIPE) 221 28163 Mississippi Yazoo 2 39.2 31.9 31.0 222 29035 Missouri Carter 8 27.6 25.2 20.3 223 29069 Missouri Dunklin 8 29.9 24.5 20.2 224 29085 Missouri Hickory 4 21.9 19.7 19.6 225 29149 Missouri Oregon 8 27.4 22.0 22.0 226 29153 Missouri Ozark 8 22.1 21.6 20.3 227 29155 Missouri Pemiscot 8 35.8 30.4 35.3 228 29181 Missouri Ripley 8 31.5 22.0 21.3 229 29203 Missouri Shannon 8 24.1 26.9 21.8 230 29215 Missouri Texas 8 22.9 21.4 20.3 231 29223 Missouri Wayne 8 29.0 21.9 23.2 232 29510 Missouri St. Louis city 1 24.6 24.6 20.8 233 30003 Montana Big Horn at large 35.3 29.2 28.9 234 30005 Montana Blaine at large 27.7 28.1 20.9 235 30035 Montana Glacier at large 35.7 27.3 24.3 236 30085 Montana Roosevelt at large 27.7 32.4 23.8 237 30107 Montana Wheatland at large 21.3 20.4 20.9 238 35003 New Mexico Catron 2 25.6 24.5 22.8 239 35006 New Mexico Cibola 2 33.6 24.8 25.1 240 35013 New Mexico Doña Ana 2 26.5 25.4 20.5 241 35019 New Mexico Guadalupe 2 38.5 21.6 22.8 242 35023 New Mexico Hidalgo 2 20.7 27.3 19.8 243 35029 New Mexico Luna 2 31.5 32.9 22.3 244 35031 New Mexico McKinley 2,3 43.5 36.1 32.0 245 35033 New Mexico Mora 3 36.2 25.4 19.8 246 35037 New Mexico Quay 3 25.1 20.9 22.0 Congressional Research Service 18 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, Poverty FIPS Congressional 1989 1999 Rate, Geographic District(s) (from (from 2020 Identification Representing 1990 Census (from Count Code State County the Countya Census) 2000) SAIPE) 247 35039 New Mexico Rio Arriba 3 27.5 20.3 19.7 248 35045 New Mexico San Juan 3 28.3 21.5 21.5 249 35047 New Mexico San Miguel 3 30.2 24.4 20.9 250 35051 New Mexico Sierra 2 19.6 20.9 22.1 251 35053 New Mexico Socorro 2 29.9 31.7 25.1 252 36005 New York Bronx 13,14,15,16 28.7 30.7 24.4 253 37015 North Carolina Bertie 1 25.9 23.5 22.8 254 37017 North Carolina Bladen 7,9 21.9 21.0 21.6 255 37047 North Carolina Columbus 7 24.0 22.7 21.3 256 37065 North Carolina Edgecombe 1 20.9 19.6 24.1 257 37083 North Carolina Halifax 1 25.6 23.9 23.9 258 37117 North Carolina Martin 1 22.3 20.2 20.1 259 37131 North Carolina Northampton 1 23.6 21.3 21.7 260 37155 North Carolina Robeson 9 24.1 22.8 26.6 261 37177 North Carolina Tyrrell 3 25.0 23.3 20.8 262 37181 North Carolina Vance 1 19.6 20.5 21.3 263 37187 North Carolina Washington 1 20.4 21.8 24.3 264 38005 North Dakota Benson at large 31.7 29.1 24.2 265 38079 North Dakota Rolette at large 40.7 31.0 21.3 266 38085 North Dakota Sioux at large 47.4 39.2 28.3 267 39009 Ohio Athens 6,15 28.7 27.4 22.0 268 40001 Oklahoma Adair 2 26.7 23.2 22.3 269 40021 Oklahoma Cherokee 2 28.8 22.9 19.6 270 40023 Oklahoma Choctaw 2 32.7 24.3 19.5 271 40055 Oklahoma Greer 3 23.4 19.6 22.9 272 40057 Oklahoma Harmon 3 34.2 29.7 23.3 Congressional Research Service 19 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, Poverty FIPS Congressional 1989 1999 Rate, Geographic District(s) (from (from 2020 Identification Representing 1990 Census (from Count Code State County the Countya Census) 2000) SAIPE) 273 40063 Oklahoma Hughes 2 26.9 21.9 21.4 274 40107 Oklahoma Okfuskee 2 29.4 23.0 21.7 275 40119 Oklahoma Payne 3 21.7 20.3 20.1 276 40133 Oklahoma Seminole 5 24.0 20.8 21.2 277 40141 Oklahoma Til man 4 22.9 21.9 21.9 278 45005 South Carolina Allendale 6 35.8 34.5 31.6 279 45009 South Carolina Bamberg 6 28.2 27.8 21.0 280 45011 South Carolina Barnwell 2 21.8 20.9 21.6 281 45029 South Carolina Col eton 1,6 23.4 21.1 20.1 282 45033 South Carolina Dil on 7 28.1 24.2 22.2 283 45049 South Carolina Hampton 6 27.7 21.8 19.6 284 45061 South Carolina Lee 5 29.6 21.8 23.0 285 45067 South Carolina Marion 7 28.6 23.2 21.8 286 45069 South Carolina Marlboro 7 26.6 21.7 26.0 287 45089 South Carolina Wil iamsburg 6 28.7 27.9 25.4 288 46007 South Dakota Bennett at large 37.6 39.2 28.3 289 46017 South Dakota Buffalo at large 45.1 56.9 32.8 290 46023 South Dakota Charles Mix at large 31.4 26.9 24.0 291 46031 South Dakota Corson at large 42.5 41.0 37.1 292 46041 South Dakota Dewey at large 44.4 33.6 24.9 293 46071 South Dakota Jackson at large 38.8 36.5 28.7 294 46085 South Dakota Lyman at large 24.7 24.3 23.9 295 46095 South Dakota Mellette at large 41.3 35.8 29.9 296 46102 South Dakota Oglala Lakotac at large 63.1 52.3 38.1 297 46121 South Dakota Todd at large 50.2 48.3 42.5 298 46137 South Dakota Ziebach at large 51.1 49.9 43.9 Congressional Research Service 20 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, Poverty FIPS Congressional 1989 1999 Rate, Geographic District(s) (from (from 2020 Identification Representing 1990 Census (from Count Code State County the Countya Census) 2000) SAIPE) 299 47013 Tennessee Campbell 2,3 26.8 22.8 20.0 300 47029 Tennessee Cocke 1 25.3 22.5 19.7 301 47067 Tennessee Hancock 1 40.0 29.4 28.6 302 47069 Tennessee Hardeman 7 23.3 19.7 22.0 303 47095 Tennessee Lake 8 27.5 23.6 36.4 304 47151 Tennessee Scott 3 27.8 20.2 19.8 305 48025 Texas Bee 34 27.4 24.0 23.1 306 48041 Texas Brazos 17 26.7 26.9 22.3 307 48047 Texas Brooks 15 36.8 40.2 28.7 308 48061 Texas Cameron 34 39.7 33.1 24.4 309 48107 Texas Crosby 19 29.5 28.1 19.8 310 48123 Texas DeWitt 34 25.3 19.6 20.5 311 48127 Texas Dimmit 23 48.9 33.2 25.5 312 48131 Texas Duval 15 39.0 27.2 20.0 313 48145 Texas Falls 17 27.5 22.6 20.0 314 48163 Texas Frio 23 39.1 29.0 22.3 315 48169 Texas Garza 19 23.1 22.3 20.6 316 48207 Texas Haskel 19 20.8 22.8 20.0 317 48215 Texas Hidalgo 15,28,34 41.9 35.9 23.9 318 48225 Texas Houston 8 25.6 21.0 20.0 319 48229 Texas Hudspeth 23 38.9 35.8 24.2 320 48247 Texas Jim Hogg 15 35.3 25.9 20.1 321 48249 Texas Jim Wells 34 30.3 24.1 20.1 322 48273 Texas Kleberg 34 27.4 26.7 20.8 323 48283 Texas La Sal e 23,28 37.0 29.8 24.0 324 48315 Texas Marion 4 60.6 22.4 20.0 Congressional Research Service 21 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, Poverty FIPS Congressional 1989 1999 Rate, Geographic District(s) (from (from 2020 Identification Representing 1990 Census (from Count Code State County the Countya Census) 2000) SAIPE) 325 48323 Texas Maverick 23 50.4 34.8 20.0 326 48347 Texas Nacogdoches 1 25.2 23.3 19.5 327 48405 Texas San Augustine 1 29.7 21.2 19.6 328 48427 Texas Starr 28 60.0 50.9 25.2 329 48479 Texas Webb 28 38.2 31.2 19.9 330 48489 Texas Wil acy 34 44.5 33.2 24.7 331 48505 Texas Zapata 28 41.0 35.8 24.6 332 48507 Texas Zavala 23 50.4 41.8 27.2 333 51027 Virginia Buchanan 9 21.9 23.2 23.7 334 51105 Virginia Lee 9 28.7 23.9 26.0 335 51195 Virginia Wise 9 21.6 20.0 20.3 336 51660 Virginia Harrisonburg city 6 21.5 30.1 22.2 337 51720 Virginia Norton city 9 26.7 22.8 20.5 338 51730 Virginia Petersburg city 4 20.3 19.6 20.8 339 51750 Virginia Radford city 9 32.2 31.4 24.6 340 53047 Washington Okanogan 4 21.5 21.3 19.8 341 54001 West Virginia Barbour 1 28.5 22.6 20.8 342 54013 West Virginia Calhoun 2 32.0 25.1 20.0 343 54015 West Virginia Clay 2 39.2 27.5 23.3 344 54019 West Virginia Fayette 3 24.4 21.7 20.8 345 54021 West Virginia Gilmer 1 33.5 25.9 23.0 346 54043 West Virginia Lincoln 3 33.8 27.9 20.6 347 54045 West Virginia Logan 3 27.7 24.1 22.3 348 54047 West Virginia McDowell 3 37.7 37.7 31.8 349 54059 West Virginia Mingo 3 30.9 29.7 24.9 350 54087 West Virginia Roane 2 28.1 22.6 20.7 Congressional Research Service 22 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, Poverty FIPS Congressional 1989 1999 Rate, Geographic District(s) (from (from 2020 Identification Representing 1990 Census (from Count Code State County the Countya Census) 2000) SAIPE) 351 54089 West Virginia Summers 3 24.5 24.4 21.1 352 54101 West Virginia Webster 3 34.8 31.8 23.7 353 54109 West Virginia Wyoming 3 27.9 25.1 21.3 354 55078 Wisconsin Menominee 8 48.7 28.8 22.6 Source: Congressional Research Service (CRS) tabulation of data from U.S. Census Bureau, 1990 Census, Census 2000, 2020 Small Area Income and Poverty Estimates, and Nation-Based Relationship File for Congressional Districts and Counties (116th Congress, the latest available as of the cover date of this report). Notes: FIPS: Federal Information Processing Standard. a. Numbers are ordinal, referring to the name of the congressional district(s) present in the county. For example, Barbour County, Alabama, is represented by Alabama’s 2nd Congressional District (indicated by the 2). A congressional district may span multiple counties; conversely, a single county may be split among multiple a single county may be split among multiple
~~congressional~~ congressional districts.districts. Part of Clarke County, Part of Clarke County, ALAlabama, for example,, for example, is represented by Alabama’s is represented by Alabama’s 1st Congressional ~~1st~~
~~Congressional~~ District (indicated by the 1) and part by the 7th Congressional DistrictDistrict (indicated by the 1) and part by the 7th Congressional District (indicated by the 7). (indicated by the 7).
Counties labeled “at large” are located in states that have one memberCounties labeled “at large” are located in states that have one member of the House of Representativesof the House of Representatives for for
the entire state. the entire state.
b. Changed name and geographic code effective July 1, 2015, from Wade Hampton Census Area b. Changed name and geographic code effective July 1, 2015, from Wade Hampton Census Area (02270) to (02270) to
Kusilvak Kusilvak Census Area (02158). Census Area (02158).
c. Changed name and geographic code effective May 1, 2015, from Shannon County (46113) to Oglala Lakota c. Changed name and geographic code effective May 1, 2015, from Shannon County (46113) to Oglala Lakota
County (46102). County (46102).

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Figure 1. Persistent Poverty Counties Using Two Rounding Methods, Based on
1990 Census, Census 2000, and ~~2019~~2020 Small Area Income and Poverty Estimates

Source: Created by Congressional Created by Congressional Research ServiceResearch Service (CRS) using data from(CRS) using data from U.S. Census Bureau, 1990 Census, Census 2000, and U.S. Census Bureau, 1990 Census, Census 2000, and ~~2019 Smal~~ 2020 Small Area Income and Poverty Area Income and Poverty
Estimates. Estimates.

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Appendix. Details on the Data Sources
Decennial Census of Population and Housing, Long Form
Poverty estimates are computed using data from household surveys, which are based on a sample Poverty estimates are computed using data from household surveys, which are based on a sample
of households. In order to obtain meaningful estimates for any geographic area, the sample has to of households. In order to obtain meaningful estimates for any geographic area, the sample has to
include enough responses from that area so that selecting a different sample of households from include enough responses from that area so that selecting a different sample of households from
that area would not likelythat area would not likely result in a result in a ~~dramatical y~~dramatically different estimate. If estimates for different estimate. If estimates for ~~smal er~~smaller
geographic areas are desired, a larger sample size is needed. A national-levelgeographic areas are desired, a larger sample size is needed. A national-level survey, for instance, survey, for instance,
could produce reliable estimates for the United States without obtaining any responses from many could produce reliable estimates for the United States without obtaining any responses from many
counties, particularly counties with counties, particularly counties with ~~smal~~ small populations. In order to produce estimates for populations. In order to produce estimates for al all 3,143 3,143
county areas in the nation, however, not only are responses needed from every county, but those county areas in the nation, however, not only are responses needed from every county, but those
responses have to be plentiful enough from each county so that the estimates are meaningful (i.e., responses have to be plentiful enough from each county so that the estimates are meaningful (i.e.,
their margins of error are not unhelpfully wide). their margins of error are not unhelpfully wide).
Before the mid-1990s, the only data source with a sample size large enough to provide Before the mid-1990s, the only data source with a sample size large enough to provide
meaningful estimates at the county level (and for other meaningful estimates at the county level (and for other ~~smal~~ small geographic areas) was the decennial geographic areas) was the decennial
census. The other household surveys available prior to that time did not have a sample size large census. The other household surveys available prior to that time did not have a sample size large
enough to produce meaningful estimates for enough to produce meaningful estimates for ~~smal~~ small areas such as counties. Income questions were areas such as counties. Income questions were
asked on the census long form, which was sent to one-sixth of asked on the census long form, which was sent to one-sixth of al all U.S. households; the rest U.S. households; the rest
received the census short form, which did not ask about income. While technically received the census short form, which did not ask about income. While technically ~~stil~~ still a sample, a sample,
one-sixth of one-sixth of al all households was a large enough sample to provide poverty estimates for every households was a large enough sample to provide poverty estimates for every
county in the nation, and even for county in the nation, and even for ~~smal er~~smaller areas such as areas such as ~~smal~~ small towns. The long form was towns. The long form was
discontinued after Census 2000, and therefore poverty data are no longer available from the discontinued after Census 2000, and therefore poverty data are no longer available from the
decennial census for the 50 states, the District of Columbia, and Puerto Rico.decennial census for the 50 states, the District of Columbia, and Puerto Rico.1820 Beginning in the Beginning in the
mid-1990s, however, two additional data sources were developed to ensure that poverty estimates mid-1990s, however, two additional data sources were developed to ensure that poverty estimates
~~for smal~~ for small areas such as counties would areas such as counties would ~~stil~~ still be available:be available: the American Community Survey the American Community Survey
(ACS), and the (ACS), and the ~~Smal~~ Small Area Income and Poverty Estimates program (SAIPE). Area Income and Poverty Estimates program (SAIPE).
American Community Survey (ACS)
The ACS replaced the decennial census long form. It was developed to accommodate the needs of The ACS replaced the decennial census long form. It was developed to accommodate the needs of
local government officials and other stakeholders who needed detailed information on local government officials and other stakeholders who needed detailed information on ~~smal~~
small communities on a more frequent basis than once every 10 years. To that end, the ACS communities on a more frequent basis than once every 10 years. To that end, the ACS
questionnaire was designed to reflect the same topics asked in the census long form. questionnaire was designed to reflect the same topics asked in the census long form.
In order to produce meaningful estimates for In order to produce meaningful estimates for ~~smal~~ small communities, however, the ACS needs to communities, however, the ACS needs to
collect a number of responses comparable to what was collected in the decennial census.collect a number of responses comparable to what was collected in the decennial census.1921 In In
order to collect that many responses while providing information more currently than once every order to collect that many responses while providing information more currently than once every
10 years, the ACS collects information from respondents continuously, in every month, as 10 years, the ACS collects information from respondents continuously, in every month, as
opposed to at one time of the year, and responses over time are pooled to provide estimates at opposed to at one time of the year, and responses over time are pooled to provide estimates at
varying geographic levels. To obtain estimates for geographic areas of 65,000 or more persons, varying geographic levels. To obtain estimates for geographic areas of 65,000 or more persons,

18 20 Poverty estimates from the decennial census continue to be produced for American Samoa, the Commonwealth of Poverty estimates from the decennial census continue to be produced for American Samoa, the Commonwealth of
the Northern Mariana Islands, Guam,the Northern Mariana Islands, Guam, and the U.S. Virginand the U.S. Virgin Islands. SAIPE and ACSIslands. SAIPE and ACS estimates are not. See estimates are not. See ~~footnote 12.~~
19footnote 13. 21 A sample of approximately 18.3 million households received the Census 2000 long form. Scott Boggess A sample of approximately 18.3 million households received the Census 2000 long form. Scott Boggess and Nikki L. and Nikki L.
Graf, “Measuring Education: A Comparison of the Decennial CensusGraf, “Measuring Education: A Comparison of the Decennial Census and the American Community Survey,” and the American Community Survey,” ~~prese nted~~presented
at Joint Statistical Meetings, Sanat Joint Statistical Meetings, San Francisco, CA,Francisco, CA, ~~August~~ August 7, 2003. http://census.gov/content/dam/Census/library/7, 2003. http://census.gov/content/dam/Census/library/
working-papers/2003/acs/2003_Boggess_01_doc.pdf. working-papers/2003/acs/2003_Boggess_01_doc.pdf.
From 2014 to 2018, 17.7 million housing unit addressesFrom 2014 to 2018, 17.7 million housing unit addresses were sampledwere sampled in the ACS.in the ACS. http://www.census.gov/acs/www/http://www.census.gov/acs/www/
methodology/sample-size-and-data-quality/sample-size/index.php. methodology/sample-size-and-data-quality/sample-size/index.php.
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one year’s worth of responses are pooled—these are the ACS one-year estimates. For the one year’s worth of responses are pooled—these are the ACS one-year estimates. For the ~~smal est~~smallest
geographic levels, which include the complete set of U.S. counties, five years of monthly geographic levels, which include the complete set of U.S. counties, five years of monthly
responses are needed: these are the ACS five-year estimates. Even though data collection is responses are needed: these are the ACS five-year estimates. Even though data collection is
ongoing, the publication of the data takes place only once every year, both for the one-year ongoing, the publication of the data takes place only once every year, both for the one-year
estimates and the estimates that represent the previous five-year span. estimates and the estimates that represent the previous five-year span.
Small Area Income and Poverty Estimates (SAIPE)
The SAIPE program was developed in the 1990s in order to provide state and local government The SAIPE program was developed in the 1990s in order to provide state and local government
officials with poverty estimates for local areas in between the decennial census years. In the officials with poverty estimates for local areas in between the decennial census years. In the
Improving America’s Schools Act of 1994 (IASA, P.L. 103-382), which amended the Elementary Improving America’s Schools Act of 1994 (IASA, P.L. 103-382), which amended the Elementary
and Secondary Education Act of 1965 (ESEA), Congress recognized that providing funding for and Secondary Education Act of 1965 (ESEA), Congress recognized that providing funding for
children in disadvantaged communities created a need for poverty data for those communities that children in disadvantaged communities created a need for poverty data for those communities that
were more current than the once-a-decade census. In the IASA, Congress provided for the were more current than the once-a-decade census. In the IASA, Congress provided for the
development and evaluation of the SAIPE program for its use in Title I-A funding development and evaluation of the SAIPE program for its use in Title I-A funding ~~al ocations.20~~
allocations.22 SAIPE estimates are model-based, meaning they use a mathematical procedure to compute SAIPE estimates are model-based, meaning they use a mathematical procedure to compute
estimates using both survey data (ACS one-year data) and administrative data (from tax returns estimates using both survey data (ACS one-year data) and administrative data (from tax returns
and numbers of participants in the Supplemental Nutrition Assistance Program, or SNAP). The and numbers of participants in the Supplemental Nutrition Assistance Program, or SNAP). The
modeling procedure produces estimates with less variability than estimates computed from survey modeling procedure produces estimates with less variability than estimates computed from survey
data alone, data alone, ~~especial y~~especially for counties with for counties with ~~smal~~ small populations. populations.
Guidance from the U.S. Census Bureau,
“Which Data Source to Use”21
23 The CPS The CPS ~~ASEC22~~ASEC24 provides the most timely and accurate national data on income and is the provides the most timely and accurate national data on income and is the
source of official national poverty estimates, hence it is the preferred source for national source of official national poverty estimates, hence it is the preferred source for national
analysis.analysis. ~~Because of its large sample size, the ACS is preferred for subnational data~~ Because of its large sample size, the ACS is preferred for subnational data on on
income and poverty by detailed demographic characteristics. The Census Bureau income and poverty by detailed demographic characteristics. The Census Bureau
recommends using the ACS for 1-year estimates of income and poverty at the state level. recommends using the ACS for 1-year estimates of income and poverty at the state level.
Users looking for consistent, state-level trends should use CPS ASEC 2-year averages and Users looking for consistent, state-level trends should use CPS ASEC 2-year averages and
CPS ASEC 3-year averages for state to state comparisons. CPS ASEC 3-year averages for state to state comparisons.

For substate areas, like counties, users should consider their specific needs when picking For substate areas, like counties, users should consider their specific needs when picking
the appropriate data source. The SAIPE program produces overall poverty and household the appropriate data source. The SAIPE program produces overall poverty and household
income 1-year estimates with standard errors usually smaller than direct survey estimates. income 1-year estimates with standard errors usually smaller than direct survey estimates.
Users looking to compare estimates of the number and percentage of people in poverty for Users looking to compare estimates of the number and percentage of people in poverty for
counties or school districts or the median household income for counties should use SAIPE, counties or school districts or the median household income for counties should use SAIPE,
especially if the population is less than 65,000. Users who need other characteristics such especially if the population is less than 65,000. Users who need other characteristics such
asas ~~poverty among Hispanics or median earnings, should use the ACS, where and when~~
~~available.~~

20 poverty among Hispanics or median earnings, should use the ACS, where and when available. 22 Details about the origins of the SAIPE project are available on the Census Details about the origins of the SAIPE project are available on the Census Bureau’sBureau’s ~~website~~ website at at
https://www.census.gov/programs-surveys/saipe/about/origins.html.https://www.census.gov/programs-surveys/saipe/about/origins.html.
2123 Downloaded Downloaded from http://www.census.gov/topics/income-poverty/poverty/guidance/data-sources.html, from http://www.census.gov/topics/income-poverty/poverty/guidance/data-sources.html, ~~November 29,~~
~~2016.~~
22March 24, 2022. 24 Author’s note: CPS CPS ASEC:ASEC: Current Population Survey Annual SocialCurrent Population Survey Annual Social and Economic Supplement. and Economic Supplement.
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The The ~~SIPP23~~SIPP25 is the only Census Bureau source of longitudinal poverty data. As SIPP collects is the only Census Bureau source of longitudinal poverty data. As SIPP collects
monthly income over 2.5 to 5 year panels, it is also a source of poverty estimates for time monthly income over 2.5 to 5 year panels, it is also a source of poverty estimates for time
periods more or less than one year, including monthly poverty rates. periods more or less than one year, including monthly poverty rates.
Table A-1 below reproduces the Census Bureau’s recommendations, summarized for various below reproduces the Census Bureau’s recommendations, summarized for various
geographic levels: geographic levels:
Table A-1. U.S. Census Bureau’s Guidance on Poverty Data Sources by Geographic
Level and Type of Estimate

Cross-Sectional Estimates

Income/Poverty Detailed Year-to-Year
~~Geographic~~
~~Detailed~~
Longitudinal
Geographic Level ~~Level~~
~~Income/Poverty~~ Rate
Characteristics
~~Year-to-Year~~ Change
Estimates

CPS ASEC/ CPS ASEC/
United States United States
CPS ASEC CPS ASEC
ACS 1-year ACS 1-year ~~estimates for~~ CPS ASEC CPS ASEC
SIPP SIPP
estimates for detailed race groups detailed race groups
ACS 1-year estimates ACS 1-year ACS 1-year States CPS ASEC 3-year estimates ~~ACS 1-year estimates~~
~~States~~
~~CPS ASEC 3-year~~
~~ACS 1-year estimates~~
~~ACS 1-year~~ estimates estimates

averages averages
~~Substate (areas ACS 1-year estimates/~~
~~with populations~~ACS 1-year Substate (areas with ACS 1-year estimates/ ACS 1-year estimates / SAIPE populations of estimates for counties and None 65,000 or more) SAIPE for counties and school districts school districts
~~ACS 1-year estimates /~~
~~of 65,000 or~~
~~SAIPE for counties and ACS 1-year estimates~~
~~SAIPE for counties and~~
~~None~~
~~school districts~~
~~more)~~
~~school districts~~
SAIPE for counties and SAIPE for counties and
school districts/ school districts/
SAIPE for counties and SAIPE for counties and
~~Substate (areas~~ACS using 5-year
ACS 5-year ACS 5-year ~~estimates/~~ Substate (areas with school districts/ estimates/ populations less period estimates for ACS using 5-year None than 20,000) all other geographic a Decennial Census entities/ 2000 and prior period estimates for all other geographic Decennial Census entities 2000 and prior State-to-Nation
~~school districts/~~
~~with populations ACS using 5-year period~~
~~less than~~
~~estimates for al other~~
~~Decennial Census 2000~~
~~ACS using 5-year period None~~
~~geographic entities/~~
~~20,000)a~~
~~and prior~~
~~estimates for al other~~
~~Decennial Census 2000~~
~~geographic entitiesb~~
~~and prior~~
~~State-to-Nation~~
comparison comparison
CPS ASEC CPS ASEC
CPS ASEC CPS ASEC
CPS ASEC CPS ASEC

Source: Congressional Congressional Research ServiceResearch Service (CRS) formatted reproduction of table by U.S. Census Bureau, with an (CRS) formatted reproduction of table by U.S. Census Bureau, with an
expansion to the notes. Original table downloaded fromexpansion to the notes. Original table downloaded from http://www.census.gov/topics/income-poverty/poverty/http://www.census.gov/topics/income-poverty/poverty/
guidance/data-sources.html, guidance/data-sources.html, ~~January 16, 2020~~March 24, 2022. .
Notes:
ACS: AmericanACS: American Community Survey.Community Survey.
CPS ASEC: Current Population Survey, Annual Social and Economic Supplement.CPS ASEC: Current Population Survey, Annual Social and Economic Supplement.
SAIPE: SAIPE: ~~Smal~~ Small Area Income and Poverty Estimates.Area Income and Poverty Estimates.
SIPP: Survey of Income and Program Participation. SIPP: Survey of Income and Program Participation.
a. a. Author’s note: Data for areas with populations of 20,000 to 65,000 persons previously had been produced : Data for areas with populations of 20,000 to 65,000 persons previously had been produced
using ACS three-year estimates, using ACS three-year estimates, but are now only produced using the ACS five-year estimates.but are now only produced using the ACS five-year estimates. ACS three-ACS three-
year estimatesyear estimates are no longer produced (with 2011-2013 data as the last in the series).are no longer produced (with 2011-2013 data as the last in the series). For details,For details, see see
https://www.census.gov/programs-surveys/acs/guidance/estimates.html.https://www.census.gov/programs-surveys/acs/guidance/estimates.html.
~~b. Use non-overlapping periods for ACS trend analysis with multiyear estimates. For example, comparing~~
~~2006-2010 ACS five-year estimates with 2011-2015 ACS five-year estimates is preferred for identifying~~
~~change.~~

23 25 Author’s note: SIPP: Survey of Income and Program Participation; mentioned here only as part of a quotation. : SIPP: Survey of Income and Program Participation; mentioned here only as part of a quotation.
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b. Use non-overlapping periods for ACS trend analysis with multiyear estimates. For example, comparing 2006-2010 ACS five-year estimates with 2011-2015 ACS five-year estimates is preferred for identifying change.
Author Information

Joseph Dalaker Joseph Dalaker

Analyst in Social Policy Analyst in Social Policy

Acknowledgments
The author is grateful for the assistance of Sarah Caldwell, CRS Senior Research Librarian, for assistance The author is grateful for the assistance of Sarah Caldwell, CRS Senior Research Librarian, for assistance
with legislative research, and Calvin DeSouza, CRS GISwith legislative research, and Calvin DeSouza, CRS GIS Analyst, in creating the county map. Analyst, in creating the county map.

Disclaimer
This document was prepared by the Congressional Research Service (CRS). CRS serves as nonpartisan This document was prepared by the Congressional Research Service (CRS). CRS serves as nonpartisan
shared staff to congressional committees and Members of Congress. It operates solely at the behest of and shared staff to congressional committees and Members of Congress. It operates solely at the behest of and
under the direction of Congress. Information in a CRS Report should under the direction of Congress. Information in a CRS Report should ~~n ot~~not be relied upon for purposes other be relied upon for purposes other
than public understanding of information that has been provided by CRS to Members of Congress in than public understanding of information that has been provided by CRS to Members of Congress in
connection with CRS’s institutional role. CRS Reports, as a work of the United States Government, are not connection with CRS’s institutional role. CRS Reports, as a work of the United States Government, are not
subject to copyright protection in the United States. Any CRS Report may be reproduced and distributed in subject to copyright protection in the United States. Any CRS Report may be reproduced and distributed in
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