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

Changes from April 14, 2022 to February 27, 2023

This page shows textual changes in the document between the two versions indicated in the dates above. Textual matter removed in the later version is indicated with ~~red strikethrough~~ and textual matter added in the later version is indicated with blue.

The 10-20-30 Provision: Defining Persistent
~~April 14, 2022~~February 27, 2023
Poverty Counties
Joseph Dalaker
Research has suggested that areas Research has suggested that areas ~~for which the poverty rate (the percentage of the population~~
~~Analyst in Social Policy~~
with a poverty rate 20% or greater experience more acute Analyst in Social Policy systemic problems than do lower-poverty areas. The poverty rate is the percentage of the population that is below that is below poverty, or economic hardship as measured by comparing income against a dollar , or economic hardship as measured by comparing income against a dollar

amount that represents a low level of needamount that represents a low level of need~~) reaches 20% experience more acute systemic~~
~~problems than in lower-poverty areas~~. Recent congresses have enacted . Recent congresses have enacted ~~antipoverty policy~~

antipoverty policy interventions that target resources on local communities based on the characteristics of those interventions that target resources on local communities based on the characteristics of those
communities, rather than solely on those of individuals or families. One such policy, dubbed the communities, rather than solely on those of individuals or families. One such policy, dubbed the 10-20-30 provision, was first , was first
implemented in the American Recovery and Reinvestment Act of 2009 (ARRA, P.L. 111-5). Title I, Section 105 of ARRA 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 allocate at least 10% of funds from three rural development program accounts to required the Secretary of Agriculture to allocate 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 —counties that maintained poverty rates of 20% or more for the past 30 years, as measured by the
1980, 1990, and 2000 decennial censuses. 1980, 1990, and 2000 decennial censuses.
One notable characteristic of this provision is that it did not increase spending for the rural development programs addressed 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 10-20-30 provision for other 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 more recent estimates of poverty rates. 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 poverty, and that requires making certain decisions Doing this, however, requires updating the list of counties with persistent poverty, and that requires making certain decisions
about the data that will be used to compile the list. about the data that will 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 counties 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 the surveys 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 decennial census was 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, however, the decennial census is no longer 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 sources that may be used to provide poverty estimates for all used to collect income data. However, there are two newer data sources that may be used to provide poverty estimates for all
U.S. counties: the American Community Survey (ACS) and the Small Area Income and Poverty Estimates program (SAIPE). U.S. counties: the American Community Survey (ACS) and the Small 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 The Census Bureau implemented both the ACS and SAIPE in the mid-1990s. Therefore, to determine whether an area is
persistently poor in a time span that ends after the year 2000, policymakers and researchers must first decide whether ACS or poor in a time span that ends after the year 2000, policymakers and researchers must first decide whether ACS or
SAIPE poverty estimates will be used for the later part of that time span. Which of these surveys is the best data source to use SAIPE poverty estimates will 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 which the 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. antipoverty intervention is intended.
When defining When defining persistent poverty counties in order to target funds for programs or services, the following factors may be in order to target funds for programs or services, the following factors may be
relevant: 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 income; ACS for poverty and income and other topics (e.g., housing characteristics, disability, 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 may be used to produce estimates for other small geographic areas as well (such as cities, towns, used to produce estimates for other small geographic areas as well (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 estimate is based on one prior year of data while ACS estimates draw on data from the past five 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  Rounding method for poverty rates: Rounding to ~~20.0% (one decimal place~~one decimal place (e.g., not including a county with a poverty rate of 19.9% because it is less than 20.0%) yields a shorter list ) yields a shorter list
of counties with persistent poverty than rounding to of counties with persistent poverty than rounding to ~~20% (whole number~~a whole number (e.g., including a county with a poverty rate of 19.9% because it rounds up to 20%). ).
 Special populations:  Special populations:  Poverty status is not defined for all persons. This includes unrelated Poverty status is not defined for all persons. This includes unrelated
~~individuals~~household members under age 15 under age 15 (e.g., children in foster care), institutionalized persons, and residents of (e.g., children in foster care), institutionalized persons, and residents of
college dormitoriescollege dormitories~~; the homeless are not explicitly targeted by household surveys; and areas~~.  Persons without housing are not included in household surveys.  Areas with with
large numbers of large numbers of college students living off-campus may have higher poverty rates than students living off-campus may have higher poverty rates than might be 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

link to page link to page 3231 The 10-20-30 Provision: Defining Persistent Poverty Counties

Contacts
Author Information ........................................................................................................................ 2827

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
allocate at least 10% of funds provided in that act from three rural development program accounts allocate 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.1 the 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 113th through the 117th Congresses included 10-20-30 language in levels of federal spending, the 113th through the 117th Congresses included 10-20-30 language in
multiple appropriations bills, some of which were enacted into law.3 However, the original multiple appropriations bills, some of which were enacted into law.3 However, the original
language used in ARRA could not be re-used verbatim, because the decennial census—the data language used in ARRA could not be re-used verbatim, because the decennial census—the data
source used by ARRA to define persistent poverty—stopped collecting income information. As a source used by ARRA to define persistent poverty—stopped collecting income information. As a
consequence, the appropriations bills varied slightly in their definitions of consequence, the appropriations bills varied slightly in their definitions of persistent poverty
counties as as ~~it was~~ applied to various programs and departments. This variation occurred even applied to various programs and departments. This variation occurred even
within different sections of the same bill if the bill included language within different sections of the same bill if the bill included language onrelating to different programs. In different programs. In
turn, because the definitions of turn, because the definitions of persistent poverty differed, so did the lists of counties identified as differed, so did the lists of counties identified as
persistently poor and subject to the 10-20-30 provision. The bills included legislation for rural persistently poor and subject to the 10-20-30 provision. The bills included legislation for rural
development, public works and economic development, technological innovation, and development, public works and economic development, technological innovation, and
brownfields site assessment and remediation. brownfields site assessment and remediation.
~~Most~~More recently, recently, inup through the end of the 117th Congress, much of the language used in these previous bills was the 117th Congress, much of the language used in these previous bills was
included in P.L. 117-included in P.L. 117-328 (the Consolidated Appropriations Act, 2023)4 and P.L. 117-103 (the Consolidated Appropriations Act, 2022).103 (the Consolidated Appropriations Act, 2022).4 References to persistent References to persistent
poverty counties, with provisions other than a 10% set-aside, also appeared in P.L. 117-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~~169 (commonly referred to as the Inflation Reduction Act of 2022), and P.L. 117-58 (the Infrastructure Investment and Jobs

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
census data for small areas up to 10 years after their publication, hence the reference to “30 years.” However, since the census 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 small communities at intervals shorter than 10 years, which late 1990s newer data sources have become available for small communities at intervals shorter than 10 years, which
has implications that will be discussed in this report. has implications that will be discussed in this report.
2 For a more thorough discussion of how poverty is defined and measured, see CRS Report R44780, 2 For a more thorough discussion of how poverty is defined and measured, see CRS Report R44780, An Introduction to
Poverty 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 3 Additionally, in the 112th Congress, the 10-20-30 provision was proposed as an amendment to H.R. 1 ~~but~~that was not was not
adopted. adopted.
4 In the 117th Congress, the Consolidated Appropriations Act, 4 In the 117th Congress, the Consolidated Appropriations Act, ~~2022~~2023 (P.L. 117- (P.L. 117-~~103~~328) included 10-20-30 language in ) included 10-20-30 language in
numerous sections: Section 736, in reference to loans and grants for rural housing, business and economic numerous sections: Section 736, in reference to loans and grants for rural housing, business and economic
development, and utilities; Section 533, in reference to grants authorized by the Public Works and Economic development, and utilities; Section 533, in reference to grants authorized by the Public Works and Economic
Development Act of 1965 and grants authorized by section 27 of the Stevenson-Wydler Technology Innovation Act of Development Act of 1965 and grants authorized by section 27 of the Stevenson-Wydler Technology Innovation Act of
1980; Division E, Title I, in reference to the Community Development Financial Institutions (CDFI) Fund Program 1980; Division E, Title I, in reference to the Community Development Financial Institutions (CDFI) Fund Program
Account; and Division G, Title II, in reference to the Comprehensive Environmental Response, Compensation, and Account; and Division G, Title II, in reference to the Comprehensive Environmental Response, Compensation, and
Liability Act (CERCLA) of 1980 and its role in authorizing funding for brownfields site assessment and remediation. Liability Act (CERCLA) of 1980 and its role in authorizing funding for 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 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 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 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 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 also enabled the Secretary of Transportation to prioritize persistent poverty counties to receive technical assistance
under the Thriving Communities Initiative. under the Thriving Communities Initiative.
Congressional Research Service Congressional Research Service

1 1

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

~~enacted as of the cover date of this report~~Act). Additionally, 74 other bills introduced in the 117th Congress that were not enacted also referred to persistent poverty counties, with or also referred to persistent poverty counties, with or
without requiring a 10% set-aside specifically. without requiring a 10% set-aside specifically.
This report discusses how data source selection This report discusses how data source selection, and the rounding of poverty estimates and the rounding of poverty estimates, can affect 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.5 For instance, property values in high-poverty areas do not yield as high a return on areas.5 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.6 The ill effects of high property owners to spend money on maintaining and improving property.6 The ill effects of high
poverty rates have been documented both for urban and rural areas.7 Depending on the years in poverty rates have been documented both for urban and rural areas.7 Depending on the years in
~~which poverty is measured and the data sources used, between 360 and 500 counties have been~~

5 For instance, George Galster of Wayne State University conducted a literature review that suggested “that the 5 For instance, George Galster of Wayne State University conducted a literature review that suggested “that the
independent impacts of neighborhood poverty rates in encouraging negative outcomes for individuals like crime, independent impacts of neighborhood poverty rates in encouraging negative outcomes for individuals like crime,
school leaving, and duration of poverty spells appear to be nil unless the neighborhood exceeds about 20 percent school leaving, and duration of poverty spells appear to be nil unless the neighborhood exceeds about 20 percent
poverty.” Galster distinguishes the effects of living in a poor neighborhood from the effects of being poor oneself but poverty.” Galster distinguishes the effects of living in a poor neighborhood from the effects of being poor oneself but
not necessarily in a poor neighborhood. Cited in George C. Galster, “The Mechanism(s) of Neighborhood Effects: not necessarily in a poor neighborhood. Cited in George C. Galster, “The Mechanism(s) of Neighborhood Effects:
Theory, Evidence, and Policy Implications,” presented at the Economic and Social Research Council Seminar, Theory, Evidence, and Policy Implications,” presented at the Economic and Social Research Council Seminar,
“Neighbourhood Effects: Theory & Evidence,” St. Andrews University, Scotland, UK, February 2010. “Neighbourhood Effects: Theory & Evidence,” St. Andrews University, Scotland, UK, February 2010.
Additionally, the Census Bureau has published a series of reports examining local areas (census tracts) with poverty Additionally, the Census Bureau has published a series of reports examining local areas (census tracts) with poverty
rates of 20% or greater. See, for instance, Alemayehu Bishaw, Craig Benson, Emily Shrider, and Brian Glassman, rates of 20% or greater. See, for instance, Alemayehu Bishaw, Craig Benson, Emily Shrider, and Brian Glassman,
“Changes in Poverty Rates and Poverty Areas Over Time: 2005 to 2019,” American Community Survey Brief 20-08, “Changes in Poverty Rates and Poverty Areas Over Time: 2005 to 2019,” American Community Survey Brief 20-08,
December 2020; Alemayehu Bishaw, “Changes in Areas With Concentrated Poverty: 2000 to 2010,” U.S. Census December 2020; Alemayehu Bishaw, “Changes in Areas With Concentrated Poverty: 2000 to 2010,” U.S. Census
Bureau, American Community Survey Reports ACS-27, June 2014; and Leatha Lamison-White, “Poverty Areas,” U.S. Bureau, American Community Survey Reports ACS-27, June 2014; and Leatha Lamison-White, “Poverty Areas,” U.S.
Census Bureau Statistical Brief, June 1995. Census Bureau Statistical Brief, June 1995.
6 The effects of poverty rates on property values are explored by George C. Galster, Jackie M. Cutsinger, and Ron 6 The effects of poverty rates on property values are explored by George C. Galster, Jackie M. Cutsinger, and Ron
Malega in “The Costs of Concentrated Poverty: Neighborhood Property Markets and the Dynamics of Decline,” pp. 93-Malega in “The 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, Programs, and Priorities (Washington, (Washington,
DC: Brookings Institution Press, 2008). They indicate that “the relationship between changes in a neighborhood’s DC: 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 non-linear. Substantial poverty rate and maintenance choices by local residential property owners will be lumpy and non-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.... Past some percentage of poverty, however, the owner will switch to an undermaintenance mode whereby building.... Past some percentage of poverty, however, the owner will switch to an undermaintenance mode whereby
net depreciation will occur.” net depreciation will occur.”
7 7 ~~See, for instance,~~For instance, see Rohit Acharya and Brett Morris, “Reducing Poverty Without Community Displacement: Indicators of Inclusive Prosperity in U.S. Neighborhoods,” Brookings Institution, September 2022, pp. 9-14, at https://www.brookings.edu/research/reducing-poverty-without-community-displacement-indicators-of-inclusive-prosperity-in-u-s-neighborhoods/ and a 2008 report issued jointly by the Federal Reserve System and the Brookings Institution, “The a 2008 report issued jointly by the Federal Reserve System and the Brookings Institution, “The
Enduring Challenge of Concentrated Poverty in America: Case Studies from Communities Across the U.S.,” David Enduring Challenge of Concentrated Poverty in America: Case Studies from Communities Across the U.S.,” David
Erickson et al., eds., 2008Erickson et al., eds., 2008, at https://www.brookings.edu/research/the-enduring-challenge-of-concentrated-poverty-in-america/. Additional research into concentrated poverty in both rural and urban areas has been . Additional research into concentrated poverty in both rural and urban areas has been
undertaken for decades; for example, educational attainment and health disability were discussed in a rural context by undertaken for decades; for example, educational attainment and health disability were discussed in a rural context by
~~Calvin Beale in “Income and Poverty,” chapter 11 in Glenn V. Fuguitt, David L. Brown, and Calvin L. Beale, eds.,~~
Rural and Small Town America, Russell Sage Foundation, 1988.
Congressional Research Service Congressional Research Service

2 2

link to page 5 link to page 5 The 10-20-30 Provision: Defining Persistent Poverty Counties

which poverty is measured and the data sources used, between 300 and 500 counties have been identified as persistent poverty counties, out of a total of 3,143 counties or county-equivalent 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 or family level, have been and may continue to be of interest to Congress.8 individual or family level, have been and may continue to be of interest to Congress.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 30 years. The county poverty rates for 1999 and previous years ~~are~~have traditionally been measured using decennial measured using decennial
census data. For more recent years, either the Small Area Income and Poverty Estimates (SAIPE) census data. For more recent years, either the Small 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.9 The data sources used, and the level of precision of rounding for the from the decennial census.9 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 will be poverty rate, affects the list of counties identified as persistent poverty counties, as 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 smaller geographic Poverty rates are computed by the Census Bureau for the nation, states, and smaller geographic
areas such as counties.10 The official definition of poverty in the United States is based on the areas such as counties.10 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 called a members are present) is added together and compared against a dollar amount called 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~~
~~to be in poverty (sometimes also described as below poverty).11~~

~~8 In the 117th Congress, P.L. 117-103 (Consolidated Appropriations Act, 2022~~ Calvin Beale in “Income and Poverty,” chapter 11 in Glenn V. Fuguitt, David L. Brown, and Calvin L. Beale, eds., Rural and Small Town America, Russell Sage Foundation, 1988. 8 In the 117th Congress, P.L. 117-328 (Consolidated Appropriations Act, 2023) used 10-20-30 provisions in multiple ) used 10-20-30 provisions in multiple
sections (see footnosections (see footnote 4 for details), or details), as did P.L. 117-103 (Consolidated Appropriations Act, 2022). Both P.L. 117-169 (Inflation Reduction Act of 2022) and P.L. 117-58 (Infrastructure Investment and Jobs Act) referred to persistent 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 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 Act, 2020), 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 10-20-30 provision; multiple other bills with and P.L. 116-94 (Further Consolidated Appropriations Act, 2020) used the 10-20-30 provision; 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-the provision were introduced but not enacted into public law. Of the public laws passed by the 115th Congress, 10-20-
30 language was included in P.L. 115-31 (Consolidated Appropriations Act, 2017), P.L. 115-141 (Consolidated 30 language was included in P.L. 115-31 (Consolidated Appropriations Act, 2017), P.L. 115-141 (Consolidated
Appropriations Act, 2018), and P.L. 115-334 (Appropriations Act, 2018), and P.L. 115-334 (~~Agricultural~~Agriculture Improvement Act of 2018), as well as multiple Improvement Act of 2018), as well as multiple ~~bills~~
~~introduced but~~introduced bills that were not enacted. In the 114th Congress, no bills containing 10-20-30 language were enacted into public law not enacted. In the 114th Congress, no bills containing 10-20-30 language were enacted into public law,
~~but~~ ; 10-20-30 language was included in H.R. 1360 (America’s FOCUS Act of 2015), H.R. 5393 (Commerce, Justice, 10-20-30 language was included in H.R. 1360 (America’s FOCUS Act of 2015), H.R. 5393 (Commerce, Justice,
Science, and Related Agencies Appropriations Act, 2017), H.R. 5054 (Agriculture, Rural Development, Food and Drug Science, and Related Agencies Appropriations Act, 2017), H.R. 5054 (Agriculture, Rural Development, Food and Drug
Administration, and Related Agencies Appropriations Act, 2017), H.R. 5538 (Department of the Interior, Environment, Administration, and Related Agencies Appropriations Act, 2017), H.R. 5538 (Department of the Interior, Environment,
and Related Agencies Appropriations Act, 2017), and S. 3067 and H.R. 5485 (Financial Services and General and Related Agencies Appropriations Act, 2017), and S. 3067 and H.R. 5485 (Financial Services and General
Government Appropriations Act, 2017). Government Appropriations Act, 2017). ~~However, the~~The Consolidated Appropriations Acts for 2017, 2018, and 2019 Consolidated Appropriations Acts for 2017, 2018, and 2019
used language analogous to the bills introduced in the 114th Congress, with some modification. Additionally, in the used language analogous to the bills introduced in the 114th Congress, with some modification. Additionally, in the
113th Congress, H.R. 5571 (The 10-20-30 Act of 2014) was introduced and referred to committee113th Congress, H.R. 5571 (The 10-20-30 Act of 2014) was introduced and referred to committee ~~but not passed~~. .
9 The decennial census does not collect income information in the 50 states, the District of Columbia, and Puerto Rico 9 The decennial census does not collect income information in the 50 states, the District of Columbia, and Puerto Rico,
~~but still~~. It asks for income information in American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, asks for income information in American Samoa, the Commonwealth of the Northern Mariana Islands, Guam,
and the U.S. Virgin Islandsand the U.S. Virgin Islands~~. Neither ACS nor SAIPE poverty estimates are currently available for these island areas~~ (areas for which neither ACS nor SAIPE data are available). .
10 There are two definitions of poverty 10 There are two definitions of poverty ~~used~~for official use in the United States: one for statistical purposes, which is used by the in the United States: one for statistical purposes, which is used by the
Census Bureau and described in Statistical Policy Directive 14 by the Office of Management and Budget; and the other Census Bureau and described in Statistical Policy Directive 14 by the Office of Management and Budget; and the other
for program administration purposes, which is used by the Department of Health and Human Services and is referred to for program administration purposes, which is used by the Department of Health and Human Services and is referred to
in the Omnibus Budget Reconciliation Act of 1981. Measuring the poverty rates of counties, which are in turn used in in the Omnibus Budget Reconciliation Act of 1981. Measuring the poverty rates of counties, which are in turn used 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) applies.~~
11 For further details about the official definition of poverty, see CRS Report R44780, An Introduction to Poverty
Bureau) applies. Congressional Research Service Congressional Research Service

3 3

~~link to page 29~~ The 10-20-30 Provision: Defining Persistent Poverty Counties

unrelated individuals) whose income is less than their respective poverty threshold are considered to be in poverty (sometimes also described as below poverty).11 Every person in a family has the same poverty status. Thus, it is possible to compute a poverty 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 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,12 and multiplying by 100 to express the rate as a within a county by the county’s total population,12 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 all U.S. counties are the ACS and SAIPE. Before the mid-that provide poverty estimates for all U.S. counties are the ACS and SAIPE. Before the mid-
1990s, the only poverty data available at the county level came from the Decennial Census of 1990s, the only poverty data available 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.13 Therefore, to determine whether an area is persistently poor in a time span poverty estimates.13 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 will be that ends after 2000, it must first be decided whether ACS or SAIPE poverty estimates will be
used for the later part of that time span.used for the later part of that time span.14
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 all decennial census long form, available down to the local community level. ACS data for all
counties are available annually, but are based on responses over the previous five-year time span counties are available annually, but are based on responses over the previous five-year time span
(e.g., (e.g., ~~2016-2020~~2017-2021). The SAIPE program was developed specifically for estimating poverty at the ). The SAIPE program was developed specifically for estimating poverty at the
county level for school-age children and for the overall population, for use in funding allocations county level for school-age children and for the overall population, for use in funding allocations
~~for the Improving America’s Schools Act of 1994 (P.L. 103-382). SAIPE data are also available~~
~~annually, 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~~
~~poverty estimates, see the Appendix.~~

11 For further details about the official definition of poverty, see CRS Report R44780, An Introduction to Poverty Measurement, by Joseph Dalaker. , by Joseph Dalaker.
12 Poverty rates are computed using adjusted population totals because there are some individuals whose poverty status 12 Poverty rates are computed using adjusted population totals because there are some individuals whose poverty status
is not determined. These include unrelated individuals under age 15, such as foster children, who are not is not determined. These include unrelated individuals under age 15, such as foster children, who are not ~~asked income~~
~~questions and who are not~~ related to anyone else in their residence by birth, marriage, or adoptionrelated to anyone else in their residence by birth, marriage, or adoption and who are not asked income questions in household surveys; persons living in ; persons living in
military barracks; and persons in institutions such as nursing homes or prisons. Some surveys (such as those described military barracks; and persons in institutions such as nursing homes or prisons. Some surveys (such as those described
in this report) do not compute poverty status for persons living in college dormitories. These persons are excluded from in this report) do not compute poverty status for persons living in college dormitories. These persons are excluded 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.
13 The decennial census still collects income information in American Samoa, the Commonwealth of the Northern 13 The decennial census still collects income information in American Samoa, the Commonwealth of the Northern
Mariana Islands, Guam, and the U.S. Virgin Islands. Neither the ACS nor the SAIPE program is conducted for these Mariana Islands, Guam, and the U.S. Virgin Islands. Neither the ACS nor the SAIPE program is conducted for these
~~island areas~~territories; decennial census data are the only small-area poverty data available for them. The 2020 Census ; decennial census data are the only small-area poverty data available for them. The 2020 Census
questionnaire for these questionnaire for these ~~island areas~~territories covered the same topics as the ACS; see the Island Areas Censuses Operation covered the same topics as the ACS; see the Island Areas Censuses 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, ACS estimates are still produced, but SAIPE management/planning-docs/IAC-detailed-op-plan.html. For Puerto Rico, ACS estimates are still produced, but SAIPE
estimates stopped being produced after 2003. For details see https://www.census.gov/programs-surveys/saipe/technical-estimates stopped being produced after 2003. For details see https://www.census.gov/programs-surveys/saipe/technical-
documentation/methodology/puerto-rico.htmldocumentation/methodology/puerto-rico.html. 14 Eventually, a 30-year span of persistent poverty is to be able to be measured using data collected after Census 2000 exclusively. Congress has opted to use 1993 SAIPE data instead of 1990 Census data when defining persistent poverty counties for the public works grants referenced in Section 533 of P.L. 117-328 (Consolidated Appropriations Act, 2023). In the 117th Congress, H.R. 6531 as passed by the House, and S. 3552 as reported to the Senate (Targeting Resources to Communities in Need Act of 2022), both would have defined persistent poverty counties using SAIPE data only, requiring a poverty rate of not less than 20% in the latest year available, and in at least 25 of the past 30 years. Congressional Research Service 4 link to page 28 link to page 28.
~~Congressional Research Service~~

4

~~link to page 29~~ link to page 5 link to page 5 The 10-20-30 Provision: Defining Persistent Poverty Counties

for the Improving America’s Schools Act of 1994 (P.L. 103-382). SAIPE data are also available annually, 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 poverty estimates, see the Appendix. 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.1415
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, especially for counties with small populations, and when additional the county level, especially for counties with small populations, and when additional
demographic and economic detail is not needed at that level.demographic and economic detail is not needed at that level.1516 When additional detail is required, When additional 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 small population size, SAIPE data have an advantage over For counties (and school districts) of small 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 available for a wider array of geographic levels, such as estimates. However, ACS estimates are available 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.1617
Reference Period of Estimate: SAIPE for One Year, ACS for a Five-Year Span
While the ACS has greater flexibility in the topics measured and the geographic areas provided, it While the ACS has greater flexibility in the topics measured and the geographic areas provided, it
can only provide estimates in five-year ranges for the smallest geographic areas. Five years of can only provide estimates in five-year ranges for the smallest 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~~
~~estimates for small areas are based on the prior five years, not the prior year alone.~~

1415 This guidance is posted on the Census Bureau’s website at https://www.census.gov/topics/income-poverty/poverty/ This guidance is posted on the Census Bureau’s website at https://www.census.gov/topics/income-poverty/poverty/
guidance/data-sources.html, and is reproduced in thguidance/data-sources.html, and is reproduced in the Appendix.
1516 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 ages 5 to 17 living in families, and for median household income. related children ages 5 to 17 living in families, and for median household income.
1617 Some Some ~~bills~~legislation, including Division L, Title I of P.L. 117-103 (see footno, including Division L, Title I of P.L. 117-103 (see footnote 3) , define define areas of persistent poverty to include to include
census tracts with poverty rates “not less than 20 percent” along with persistent poverty counties and “any territory or 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). possession of the United States” per 49 U.S.C. §6702(a)(1).
Congressional Research Service Congressional Research Service

5 5

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

reference period of the data—both data sources release data on an annual basis; the ACS estimates for small areas are based on the prior five years, not the prior year alone. 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
partially based on Supplemental Nutrition Assistance Program (SNAP) administrative data and partially 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 received SNAP tax data, so the part of the homeless population that either filed tax returns or received 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
living in college dormitories.living in college dormitories.1718 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.1819
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 allocation of funds. Other economic measures may those populations would receive an equitable allocation 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 allow flexibility to target counties that Therefore, using that funding approach by itself would not allow flexibility 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 living in poverty over the past 30 years, as measured by the 1980, 1990, more of its population living in poverty over the past 30 years, as measured by the 1980, 1990,
and 2000 decennial censuses.”and 2000 decennial censuses.”1920 Poverty rates published by the Census Bureau are typically Poverty rates published by the Census Bureau are typically
reported to one decimal place. The numeral used in the ARRA language was the whole number reported to one decimal place. The numeral used in the ARRA language was the whole number
~~20. Thus, for any collection 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 all three years, or using~~

17 18 Details on the poverty universe in the ACS are available at https://www2.census.gov/programs-surveys/acs/ Details on the poverty universe in the ACS are available at https://www2.census.gov/programs-surveys/acs/
tech_docs/subject_definitions/2020_ACSSubjectDefinitions.pdf#page=112 and for the SAIPE estimates at tech_docs/subject_definitions/2020_ACSSubjectDefinitions.pdf#page=112 and for the SAIPE estimates at
https://www.census.gov/programs-surveys/saipe/guidance/model-input-data/denominators/poverty.html. https://www.census.gov/programs-surveys/saipe/guidance/model-input-data/denominators/poverty.html.
1819 For some counties, the percentage-point difference could be large when off-campus students are excluded. Using For some counties, the percentage-point difference could be large when off-campus students are excluded. Using
ACS data for 2009-2011, Whitman County, WA, experienced the largest poverty rate difference among all counties ACS data for 2009-2011, Whitman County, WA, experienced the largest poverty rate difference among all counties
when off-campus students were excluded—its poverty rate fell by 16.5 percentage points. For the United States as a when off-campus students were excluded—its 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 off-campus students were excluded (based on the same dataset). whole, the poverty rate fell from 15.2% to 14.5% when off-campus students were excluded (based on the same dataset).
For details, see Alemayehu Bishaw, “Examining the Effect of Off-Campus College Students on Poverty Rates,” For details, see Alemayehu Bishaw, “Examining the Effect of Off-Campus College Students on Poverty Rates,”
Working Paper SEHSD 2013-17, U.S. Census Bureau, May 1, 2013. Working Paper SEHSD 2013-17, U.S. Census Bureau, May 1, 2013.
1920 P.L. 111-5, Section 105. P.L. 111-5, Section 105.
Congressional Research Service Congressional Research Service

6 6

link to page 11 link to page 11 link to page 13 The 10-20-30 Provision: Defining Persistent Poverty Counties

20. Thus, for any collection 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 all three years, or using poverty rates that poverty rates that round up to the whole number 20% or greater in all three years (i.e., poverty to the whole number 20% or greater in all three years (i.e., poverty
rates of 19.5% or more in all three years). The former approach is more restrictive and results in a rates of 19.5% or more in all 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 illustrates the number of counties identified as persistent poverty counties using the 1990 illustrates 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. ~~From 2011 to 2018~~In most years, using SAIPE for the latest year , using SAIPE for the latest year
resulted in more counties being identified as persistently poor than were identified by using the resulted in more counties being identified as persistently poor than were identified by using the
~~ACS; since then, the reverse has been true~~ACS; the exceptions were 2019 and 2020. Compared to using 20.0% as the cutoff (rounded to . Compared to using 20.0% as the cutoff (rounded to
one decimal place), rounding up to 20% from 19.5% adds approximately 40 to 60 counties to the one decimal place), rounding up to 20% from 19.5% adds approximately 40 to 60 counties to the
list. Taking both the data source and the rounding method togetherlist. Taking both the data source and the rounding method together (Table 2), the list of persistent poverty , the list of persistent poverty
counties could vary by roughly 60 to 100 counties in a given year depending on the method used. counties could vary by roughly 60 to 100 counties in a 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 One
Rounded to
Difference
Decimal Place
Whole
Between
(20.0% or
Number (19.5%
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, 2013-2017 a ACS, 2013-2017 a
386 386
436 436
50 50
ACS, 2014-2018 a ACS, 2014-2018 a
384 384
430 430
46 46
ACS, 2015-2019 ACS, 2015-2019
375 375
418 418
43 43
ACS, 2016-2020 ACS, 2016-2020
355 355
397 397
42 42 ACS, 2017-2021 344 387 43

Mean difference: Mean difference: ~~50.2~~49.5

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~~
~~411~~
~~460~~
49
~~SAIPE, 2018~~
~~395~~
~~443~~
48
Congressional Research Service Congressional Research Service

7 7

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

Rounded to One
Rounded to
Difference
Decimal Place
Whole
Between
(20.0% or
Number (19.5%
Rounding
Dataset
Greater)
or Greater)
Methods SAIPE, 2017 411 460 49 SAIPE, 2018 395 443 48
SAIPE, 2019 SAIPE, 2019
361 361
407 407
46 46
SAIPE, 2020 SAIPE, 2020
306 306
354 354
48 48 SAIPE, 2021 362 414 56

Mean difference: 53. Mean difference: 53.79

Differences between datasets released
in same year

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

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

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

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

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

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

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

Difference, ACS 2015-2019 minus SAIPE 2019 Difference, ACS 2015-2019 minus SAIPE 2019
14 14
11 11

Difference, ACS 2016-2020 minus SAIPE 2020 Difference, ACS 2016-2020 minus SAIPE 2020
49 49
43 43 Difference, SAIPE 2021 minus ACS 2017-2021 18 27

Mean difference: Mean difference:
~~26.7~~25.9
28. 28.43
Source: Congressional Research Service (CRS) tabulation of data from U.S. Census Bureau, 1990 Census, Congressional Research Service (CRS) tabulation of data from U.S. Census Bureau, 1990 Census,
Census 2000, 2012-Census 2000, 2012-~~2020~~2021 Small Area Income and Poverty Estimates, and American Community Survey Small Area Income and Poverty Estimates, and American Community Survey 5Five-Year -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,
2015-2019, 2015-2019, ~~and~~ 2016-20202016-2020, and 2017-2021. .
Notes: ACS: American Community Survey. SAIPE: Small Area Income and Poverty Estimates. Comparisons ACS: American Community Survey. SAIPE: Small Area Income and Poverty Estimates. Comparisons
between ACS and SAIPE estimates are between datasets released in the same year (both are typical y released in between ACS and SAIPE estimates are between datasets released in the same year (both are typical y released in
December of the year fol owing the reference period). There are 3,143 county-type areas in the United States.December of the year fol owing the reference period). There are 3,143 county-type areas in the United States.
~~The selection 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, that is,~~
~~rounded up to the whole number 20%) minus the shortest list of persistent poverty counties (ACS, 20.0% or~~
~~greater) yields the maximum difference. Comparing datasets that were released in the same year, the maximum~~
~~differences in the lists of counties were:~~
~~SAIPE 2011, whole number - ACS, 2007-2011, one decimal = 98 counties~~
~~SAIPE 2012, whole number - ACS, 2008-2012, one decimal = 87~~
~~SAIPE 2013, whole number - ACS, 2009-2013, one decimal = 88~~
~~SAIPE 2014, whole number - ACS, 2010-2014, one decimal = 85~~
~~SAIPE 2015, whole number - ACS, 2011-2015, one decimal = 79~~
~~SAIPE 2016, whole number - ACS, 2012-2016, one decimal = 77~~
~~SAIPE 2017, whole number - ACS, 2013-2017, one decimal = 74~~
~~SAIPE 2018, whole number - ACS, 2014-2018, one decimal = 59~~
~~ACS, 2015-2019, whole number - SAIPE 2019, one decimal = 57~~
~~ACS, 2016-2020, whole number - SAIPE 2020, one decimal = 91~~

~~The lists of persistent poverty counties vary by about 80 counties on average (mean: 79.5), depending on which~~
~~data source is used for the most recent poverty rate estimate, and which rounding method is applied to identify~~
~~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 Arriba County, New Mexico, despite an ACS data collection error that occurred~~
~~in that county in both 2017 and 2018. The Census Bureau detected the error after the five-year data for~~
~~2013-2017 had been released, but before the 2014-2018 data had been released. As a result, the 2014-2018~~
~~poverty rate for Rio Arriba County was not published, and the 2013-2017 poverty rate (formerly reported~~
~~as 26.4%) was removed from the Census Bureau website. 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~~
~~before the error (2012-2016) and the SAIPE poverty rate were both above 20.0%, Rio Arriba County is~~
~~included in this table’s counts of persistent poverty counties. For details see https://www.census.gov/~~
~~programs-surveys/acs/technical-documentation/errata/125.html.~~
~~Example List of Persistent Poverty Counties~~
~~The list of persistent poverty counties below (Table 2) is based on data from the 1990 Census,~~
~~Census 2000, and the 2020 SAIPE estimates, and includes the 354 counties with poverty rates of~~
~~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 in Figure 1.~~
~~This list of 354 counties (out of a total of 3,143 nationwide) is similar but not identical to a list~~
~~that would be compiled if ACS data were used with 1990 and 2000 Census data to determine~~
~~counties with persistent poverty.~~
~~Table 2. List of Persistent Poverty Counties, Based on 1990 Census, Census 2000, and~~
~~2020 Small Area Income and Poverty Estimates (SAIPE), Using Poverty Rates of~~
~~19.5% or Greater~~
~~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)~~
1
~~01005~~
~~Alabama~~
~~Barbour~~
2
~~25.2~~
~~26.8~~
~~25.5~~
2
~~01011~~
~~Alabama~~
~~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~~
~~Conecuh~~
2
~~29.7~~
~~26.6~~
~~22.9~~
7
~~01047~~
~~Alabama~~
~~Dallas~~
7
~~36.2~~
~~31.1~~
~~26.7~~
8
~~01053~~
~~Alabama~~
~~Escambia~~
1
~~28.1~~
~~20.9~~
~~20.4~~
9
~~01061~~
~~Alabama~~
~~Geneva~~
2
~~19.5~~
~~19.6~~
~~21.0~~
10
~~01063~~
~~Alabama~~
~~Greene~~
7
~~45.6~~
~~34.3~~
~~27.9~~
11
~~01065~~
~~Alabama~~
~~Hale~~
7
~~35.6~~
~~26.9~~
~~21.9~~
12
~~01085~~
~~Alabama~~
~~Lowndes~~
7
~~38.6~~
~~31.4~~
~~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)~~
~~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~~ a. These counts include Rio Arriba County, New Mexico, despite an ACS data collection error that occurred in that county in both 2017 and 2018. The Census Bureau detected the error after the five-year data for 2013-2017 had been released, but before the 2014-2018 data had been released. As a result, the 2014-2018 poverty rate for Rio Arriba County was not published, and the 2013-2017 poverty rate (formerly reported as 26.4%) was removed from the Census Bureau website. 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 before the error (2012-2016) and the SAIPE poverty rate were both above 20.0%, Rio Arriba County is included in this table’s counts of persistent poverty counties. For details see https://www.census.gov/programs-surveys/acs/technical-documentation/errata/125.html. Congressional Research Service 8 link to page 14 link to page 27 The 10-20-30 Provision: Defining Persistent Poverty Counties Table 2. Maximum Differences in the Number of Persistent Poverty Counties by Data Source and Rounding Method 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 datasets indicated below. Data Source and Year, Rounding Method, and Number of Counties Maximum Difference Most Counties Fewest Counties (Number of Counties) SAIPE 2011, whole number 495 ACS, 2007-2011, one decimal 397 98 SAIPE 2012, whole number 491 ACS, 2008-2012, one decimal 404 87 SAIPE 2013, whole number 490 ACS, 2009-2013, one decimal 402 88 SAIPE 2014, whole number 486 ACS, 2010-2014, one decimal 401 85 SAIPE 2015, whole number 476 ACS, 2011-2015, one decimal 397 79 SAIPE 2016, whole number 469 ACS, 2012-2016, one decimal 392 77 SAIPE 2017, whole number 460 ACS, 2013-2017, one decimal 386 74 SAIPE 2018, whole number 443 ACS, 2014-2018, one decimal 384 59 ACS, 2015-2019, whole number 418 SAIPE 2019, one decimal 361 57 ACS, 2016-2020, whole number 397 SAIPE 2020, one decimal 306 91 SAIPE 2021, whole number 414 ACS, 2017-2021, one decimal 344 70 Mean difference: 78.6 Source: Congressional Research Service (CRS) tabulation of data from U.S. Census Bureau, 1990 Census, Census 2000, 2012-2021 Small Area Income and Poverty Estimates, and American Community Survey Five-Year Estimates for 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2016-2020, and 2017-2021. Notes: ACS: American Community Survey. SAIPE: Small Area Income and Poverty Estimates. The selection 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 minus the shortest list of persistent poverty counties yields the maximum difference. For example, in 2021 the longest list used SAIPE poverty rates of 19.5% or greater, that is, rounded up to the whole number 20%, while the shortest list used the 2017-2021 ACS Five-Year Estimates, using poverty rates 20.0% or greater. The lists of persistent poverty counties vary by about 79 counties on average (mean: 78.6), depending on which data source is used for the most recent poverty rate estimate, and which rounding method is applied to identify persistent poverty. Comparisons between ACS and SAIPE estimates are between datasets released in the same year (both are typically released in December of the year fol owing the reference period). There are 3,143 county-type areas in the United States. Example List of Persistent Poverty Counties The list of persistent poverty counties below (Table 3)21 is based on data from the 1990 Census, Census 2000, and the 2021 SAIPE estimates, and includes the 414 counties with poverty rates of 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 in Figure 1. 21 This example list reflects the definition used in Section 21202 of the Infrastructure Investment and Jobs Act of 2022 (P.L. 117-58), which amended 49 U.S.C. §6702, regarding local and regional project assistance on multimodal infrastructure investments. Congressional Research Service 9 The 10-20-30 Provision: Defining Persistent Poverty Counties This list of 414 counties (out of a total of 3,143 nationwide) is similar but not identical to a list that would be compiled if ACS data were used with 1990 and 2000 Census data to determine counties with persistent poverty. Table 3. List of Persistent Poverty Counties, Based on 1990 Census, Census 2000, and 2021 Small Area Income and Poverty Estimates (SAIPE), Using Poverty Rates of 19.5% or Greater Poverty Poverty Rate, Rate, FIPS Congressional 1989 1999 Geographic District(s) (from (from Poverty Rate, Identification Representing 1990 Census 2021 (from Count Code State County the Countya Census) 2000) SAIPE) 1 01005 Alabama Barbour 2 25.2 26.8 23.0 2 01007 Alabama Bibb 6 21.2 20.6 20.6 3 01011 Alabama Bul ock 2 36.5 33.5 32.1 4 01013 Alabama Butler 2 31.5 24.6 22.7 5 01023 Alabama Choctaw 7 30.2 24.5 23.3 6 01035 Alabama Conecuh 2 29.7 26.6 22.4 7 01047 Alabama Dallas 7 36.2 31.1 29.5 8 01053 Alabama Escambia 1, 2 28.1 20.9 23.8 9 01061 Alabama Geneva 2 19.5 19.6 21.1 10 01063 Alabama Greene 7 45.6 34.3 33.2 11 01065 Alabama Hale 7 35.6 26.9 22.1 12 01085 Alabama Lowndes 7 38.6 31.4 28.3 13 01087 Alabama Macon 3 34.5 32.8 27.5 14 01091 Alabama Marengo 7 30.0 25.9 24.6 15 01099 Alabama Monroe 1 22.7 21.3 22.3 16 01105 Alabama Perry 7 42.6 35.4 33.7 17 01107 Alabama Pickens 7 28.9 24.9 21.1 18 01109 Alabama Pike 2 27.2 23.1 23.9 19 01113 Alabama Russell 3 20.4 19.9 21.7 20 01119 Alabama Sumter 7 39.7 38.7 35.1 21 01131 Alabama Wilcox 7 45.2 39.9 32.4 22 02050 Alaska Bethel Census Area at large 30.0 20.6 24.6 23 02070 Alaska Dil ingham Census Area at large 24.6 21.4 21.8 24 02158 Alaska Kusilvak Census Area at large 31.0 26.2 30.4 25 02290 Alaska Yukon-Koyukuk Census Area at large 26.0 23.8 23.7 26 04001 Arizona Apache 2 47.1 37.8 28.4 27 04009 Arizona Graham 2, 6 26.7 23.0 20.4 Congressional Research Service 10 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, FIPS Congressional 1989 1999 Geographic District(s) (from (from Poverty Rate, Identification Representing 1990 Census 2021 (from Count Code State County the Countya Census) 2000) SAIPE) 28 04012 Arizona La Paz 9 28.2 19.6 20.3 29 04017 Arizona Navajo 2 34.7 29.5 24.8 30 04023 Arizona Santa Cruz 7 26.4 24.5 20.4 31 05011 Arkansas Bradley 4 24.9 26.3 19.6 32 05017 Arkansas Chicot 1 40.4 28.6 27.9 33 05027 Arkansas Columbia 4 24.4 21.1 23.2 34 05035 Arkansas Crittenden 1 27.1 25.3 23.0 35 05041 Arkansas Desha 1 34.0 28.9 27.6 36 05057 Arkansas Hempstead 4 22.7 20.3 21.2 37 05069 Arkansas Jefferson 4 23.9 20.5 20.3 38 05073 Arkansas Lafayette 4 34.7 23.2 23.7 39 05077 Arkansas Lee 1 47.3 29.9 34.9 40 05079 Arkansas Lincoln 1 26.2 19.5 22.6 41 05093 Arkansas Mississippi 1 26.2 23.0 23.9 42 05095 Arkansas Monroe 1 35.9 27.5 25.3 43 05099 Arkansas Nevada 4 20.3 22.8 21.2 44 05103 Arkansas Ouachita 4 21.2 19.5 20.2 45 05107 Arkansas Phil ips 1 43.0 32.7 35.7 46 05111 Arkansas Poinsett 1 25.6 21.2 22.2 47 05123 Arkansas St. Francis 1 36.6 27.5 32.2 48 05129 Arkansas Searcy 1 29.9 23.8 20.9 49 05147 Arkansas Woodruff 1 34.5 27.0 25.2 50 06047 California Merced 13 19.9 21.7 21.9 51 08011 Colorado Bent 4 20.4 19.5 30.0 52 08023 Colorado Costil a 3 34.6 26.8 23.1 53 08109 Colorado Saguache 3 30.6 22.6 21.6 54 12039 Florida Gadsden 2 28.0 19.9 25.8 55 12047 Florida Hamilton 3 27.8 26.0 25.7 56 12049 Florida Hardee 18 22.8 24.6 21.9 57 12079 Florida Madison 2 25.9 23.1 21.8 58 12107 Florida Putnam 6 20.0 20.9 26.3 59 13003 Georgia Atkinson 8 26.0 23.0 23.8 60 13005 Georgia Bacon 1 24.1 23.7 23.4 Congressional Research Service 11 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, FIPS Congressional 1989 1999 Geographic District(s) (from (from Poverty Rate, Identification Representing 1990 Census 2021 (from Count Code State County the Countya Census) 2000) SAIPE) 61 13007 Georgia Baker 2 24.8 23.4 25.9 62 13017 Georgia Ben Hil 8 22.0 22.3 22.1 63 13027 Georgia Brooks 8 25.9 23.4 23.5 64 13031 Georgia Bul och 12 27.5 24.5 21.3 65 13033 Georgia Burke 12 30.3 28.7 20.7 66 13037 Georgia Calhoun 2 31.8 26.5 34.0 67 13043 Georgia Candler 12 24.1 26.1 22.0 68 13059 Georgia Clarke 10 27.0 28.3 21.5 69 13061 Georgia Clay 2 35.7 31.3 24.3 70 13065 Georgia Clinch 8 26.4 23.4 23.9 71 13071 Georgia Colquitt 8 22.8 19.8 22.4 72 13075 Georgia Cook 8 22.4 20.7 20.1 73 13081 Georgia Crisp 8 29.0 29.3 24.2 74 13087 Georgia Decatur 2 23.3 22.7 23.8 75 13093 Georgia Dooly 2 32.9 22.1 22.5 76 13095 Georgia Dougherty 2 24.4 24.8 26.1 77 13099 Georgia Early 2 31.4 25.7 26.1 78 13107 Georgia Emanuel 12 25.7 27.4 24.1 79 13109 Georgia Evans 12 25.4 27.0 23.9 80 13131 Georgia Grady 2 22.3 21.3 22.4 81 13141 Georgia Hancock 10 30.1 29.4 30.5 82 13163 Georgia Jefferson 12 31.3 23.0 20.3 83 13165 Georgia Jenkins 12 27.8 28.4 27.4 84 13167 Georgia Johnson 12 22.2 22.6 26.7 85 13193 Georgia Macon 2 29.2 25.8 30.3 86 13197 Georgia Marion 2 28.2 22.4 21.3 87 13201 Georgia Mil er 2 22.1 21.2 24.2 88 13205 Georgia Mitchell 2 28.7 26.4 24.7 89 13209 Georgia Montgomery 12 24.5 19.9 20.8 90 13225 Georgia Peach 2 24.0 20.2 19.5 91 13239 Georgia Quitman 2 33.0 21.9 24.5 92 13243 Georgia Randolph 2 35.9 27.7 28.1 93 13251 Georgia Screven 12 22.9 20.1 20.3 Congressional Research Service 12 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, FIPS Congressional 1989 1999 Geographic District(s) (from (from Poverty Rate, Identification Representing 1990 Census 2021 (from Count Code State County the Countya Census) 2000) SAIPE) 94 13253 Georgia Seminole 2 29.1 23.2 23.3 95 13259 Georgia Stewart 2 31.4 22.2 31.4 96 13261 Georgia Sumter 2 24.8 21.4 27.9 97 13263 Georgia Talbot 2 24.9 24.2 23.3 98 13265 Georgia Taliaferro 10 31.9 23.4 23.0 99 13267 Georgia Tattnall 12 21.9 23.9 28.0 100 13269 Georgia Taylor 2 29.5 26.0 22.7 101 13271 Georgia Telfair 8 27.3 21.2 30.2 102 13273 Georgia Terrell 2 29.1 28.6 26.6 103 13277 Georgia Tift 8 22.9 19.9 21.4 104 13279 Georgia Toombs 12 24.0 23.9 22.9 105 13283 Georgia Treutlen 12 27.1 26.3 21.8 106 13287 Georgia Turner 8 31.3 26.7 26.2 107 13289 Georgia Twiggs 8 26.0 19.7 21.1 108 13299 Georgia Ware 1 21.1 20.5 24.3 109 13301 Georgia Warren 12 32.6 27.0 25.6 110 13303 Georgia Washington 12 21.6 22.9 22.6 111 13309 Georgia Wheeler 12 30.3 25.3 33.2 112 13315 Georgia Wilcox 8 28.6 21.0 27.6 113 17003 Il inois Alexander 12 32.2 26.1 21.8 114 17077 Il inois Jackson 12 28.4 25.2 22.3 115 17153 Il inois Pulaski 12 30.2 24.7 22.3 116 21001 Kentucky Adair 1 25.1 24.0 24.2 117 21011 Kentucky Bath 5, 6 27.3 21.9 20.6 118 21013 Kentucky Bell 5 36.2 31.1 32.1 119 21025 Kentucky Breathitt 5 39.5 33.2 31.0 120 21043 Kentucky Carter 4, 5 26.8 22.3 25.6 121 21045 Kentucky Casey 1 29.4 25.5 20.7 122 21051 Kentucky Clay 5 40.2 39.7 35.9 123 21053 Kentucky Clinton 1 38.1 25.8 22.6 124 21057 Kentucky Cumberland 1 31.6 23.8 22.5 125 21063 Kentucky El iott 5 38.0 25.9 26.6 126 21065 Kentucky Estil 6 29.0 26.4 22.6 Congressional Research Service 13 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, FIPS Congressional 1989 1999 Geographic District(s) (from (from Poverty Rate, Identification Representing 1990 Census 2021 (from Count Code State County the Countya Census) 2000) SAIPE) 127 21071 Kentucky Floyd 5 31.2 30.3 28.0 128 21075 Kentucky Fulton 1 30.3 23.1 23.4 129 21095 Kentucky Harlan 5 33.1 32.5 33.2 130 21099 Kentucky Hart 2 27.1 22.4 20.4 131 21109 Kentucky Jackson 5 38.2 30.2 25.2 132 21115 Kentucky Johnson 5 28.7 26.6 24.2 133 21119 Kentucky Knott 5 40.4 31.1 32.0 134 21121 Kentucky Knox 5 38.9 34.8 35.1 135 21125 Kentucky Laurel 5 24.8 21.3 19.5 136 21127 Kentucky Lawrence 5 36.0 30.7 24.8 137 21129 Kentucky Lee 5 37.4 30.4 33.5 138 21131 Kentucky Leslie 5 35.6 32.7 29.8 139 21133 Kentucky Letcher 5 31.8 27.1 29.1 140 21135 Kentucky Lewis 4 30.7 28.5 22.6 141 21147 Kentucky McCreary 5 45.5 32.2 33.5 142 21153 Kentucky Magoffin 5 42.5 36.6 33.2 143 21159 Kentucky Martin 5 35.4 37.0 40.5 144 21165 Kentucky Menifee 5 35.0 29.6 27.8 145 21171 Kentucky Monroe 1 26.9 23.4 22.0 146 21175 Kentucky Morgan 5 38.8 27.2 26.3 147 21177 Kentucky Muhlenberg 2 20.7 19.7 20.9 148 21189 Kentucky Owsley 5 52.1 45.4 35.6 149 21193 Kentucky Perry 5 32.1 29.1 29.9 150 21195 Kentucky Pike 5 25.4 23.4 30.1 151 21197 Kentucky Powell 6 26.2 23.5 22.8 152 21203 Kentucky Rockcastle 5 30.7 23.1 21.3 153 21205 Kentucky Rowan 5 28.9 21.3 20.9 154 21207 Kentucky Russell 1 25.6 24.3 27.0 155 21231 Kentucky Wayne 5 37.3 29.4 23.9 156 21235 Kentucky Whitley 5 33.0 26.4 27.6 157 21237 Kentucky Wolfe 5 44.3 35.9 29.0 158 22001 Louisiana Acadia Parish 3 30.5 24.5 20.6 159 22003 Louisiana Allen Parish 4 29.9 19.9 20.5 Congressional Research Service 14 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, FIPS Congressional 1989 1999 Geographic District(s) (from (from Poverty Rate, Identification Representing 1990 Census 2021 (from Count Code State County the Countya Census) 2000) SAIPE) 160 22009 Louisiana Avoyelles Parish 5 37.1 25.9 30.5 161 22013 Louisiana Bienvil e Parish 4 31.2 26.1 26.0 162 22017 Louisiana Caddo Parish 4 24.0 21.1 23.6 163 22021 Louisiana Caldwell Parish 5 28.8 21.2 22.7 164 22025 Louisiana Catahoula Parish 5 36.8 28.1 26.7 165 22027 Louisiana Claiborne Parish 4 32.0 26.5 29.8 166 22029 Louisiana Concordia Parish 5 30.6 29.1 33.7 167 22031 Louisiana De Soto Parish 4 29.8 25.1 19.6 168 22035 Louisiana East Carrol Parish 5 56.8 40.5 39.9 169 22037 Louisiana East Feliciana Parish 5 25.0 23.0 20.1 170 22039 Louisiana Evangeline Parish 4 35.1 32.2 26.3 171 22041 Louisiana Franklin Parish 5 34.5 28.4 24.0 172 22043 Louisiana Grant Parish 4, 5 25.5 21.5 20.6 173 22045 Louisiana Iberia Parish 3 25.8 23.6 24.0 174 22047 Louisiana Ibervil e Parish 2, 6 28.0 23.1 22.5 175 22049 Louisiana Jackson Parish 5 23.9 19.8 20.4 176 22061 Louisiana Lincoln Parish 5 26.6 26.5 25.4 177 22065 Louisiana Madison Parish 5 44.6 36.7 35.3 178 22067 Louisiana Morehouse Parish 5 31.0 26.8 30.9 179 22069 Louisiana Natchitoches Parish 4 33.9 26.5 22.3 180 22071 Louisiana Orleans Parish 1, 2 31.6 27.9 25.2 181 22073 Louisiana Ouachita Parish 5 24.7 20.7 25.5 182 22079 Louisiana Rapides Parish 5 22.6 20.5 19.9 183 22081 Louisiana Red River Parish 4 35.1 29.9 23.6 184 22083 Louisiana Richland Parish 5 33.2 27.9 25.0 185 22085 Louisiana Sabine Parish 4 27.1 21.5 19.7 186 22091 Louisiana St. Helena Parish 5 34.4 26.8 25.3 187 22097 Louisiana St. Landry Parish 4 36.3 29.3 26.3 188 22101 Louisiana St. Mary Parish 3, 6 27.0 23.6 22.9 189 22107 Louisiana Tensas Parish 5 46.3 36.3 32.1 190 22113 Louisiana Vermilion Parish 3 26.5 22.1 20.2 191 22117 Louisiana Washington Parish 5 31.6 24.7 24.5 192 22119 Louisiana Webster Parish 4 25.1 20.2 24.0 Congressional Research Service 15 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, FIPS Congressional 1989 1999 Geographic District(s) (from (from Poverty Rate, Identification Representing 1990 Census 2021 (from Count Code State County the Countya Census) 2000) SAIPE) 193 22123 Louisiana West Carrol Parish 5 27.4 23.4 22.4 194 22125 Louisiana West Feliciana Parish 5 33.8 19.9 22.2 195 22127 Louisiana Winn Parish 5 27.5 21.5 26.8 196 24510 Maryland Baltimore city 2, 7 21.9 22.9 22.9 197 28001 Mississippi Adams 2 30.5 25.9 25.8 198 28005 Mississippi Amite 2 30.9 22.6 22.0 199 28007 Mississippi Attala 2 30.2 21.8 22.3 200 28009 Mississippi Benton 1 29.7 23.2 19.7 201 28011 Mississippi Bolivar 2 42.9 33.3 34.2 202 28017 Mississippi Chickasaw 1 21.3 20.0 19.9 203 28019 Mississippi Choctaw 1 25.0 24.7 20.9 204 28021 Mississippi Claiborne 2 43.6 32.4 33.3 205 28025 Mississippi Clay 1 25.9 23.5 22.7 206 28027 Mississippi Coahoma 2 45.5 35.9 37.9 207 28029 Mississippi Copiah 2 32.0 25.1 20.9 208 28031 Mississippi Covington 3 31.2 23.5 19.9 209 28035 Mississippi Forrest 4 27.5 22.5 20.1 210 28037 Mississippi Franklin 2 33.3 24.1 22.1 211 28041 Mississippi Greene 4 26.8 19.6 21.2 212 28043 Mississippi Grenada 2 22.3 20.9 22.1 213 28049 Mississippi Hinds 2, 3 21.2 19.9 25.2 214 28051 Mississippi Holmes 2 53.2 41.1 37.8 215 28053 Mississippi Humphreys 2 45.9 38.2 33.3 216 28055 Mississippi Issaquena 2 49.3 33.2 43.9 217 28063 Mississippi Jefferson 2 46.9 36.0 31.7 218 28065 Mississippi Jefferson Davis 3 33.3 28.2 25.2 219 28069 Mississippi Kemper 3 35.1 26.0 26.1 220 28075 Mississippi Lauderdale 3 22.8 20.8 22.8 221 28079 Mississippi Leake 2 29.6 23.3 21.1 222 28083 Mississippi Leflore 2 38.9 34.8 35.6 223 28091 Mississippi Marion 3 29.6 24.8 22.9 224 28093 Mississippi Marshall 1 30.0 21.9 19.9 225 28097 Mississippi Montgomery 2 34.0 24.3 21.2 Congressional Research Service 16 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, FIPS Congressional 1989 1999 Geographic District(s) (from (from Poverty Rate, Identification Representing 1990 Census 2021 (from Count Code State County the Countya Census) 2000) SAIPE) 226 28099 Mississippi Neshoba 3 26.6 21.0 21.2 227 28101 Mississippi Newton 3 20.9 19.9 20.2 228 28103 Mississippi Noxubee 3 41.4 32.8 26.8 229 28105 Mississippi Oktibbeha 1, 3 30.1 28.2 22.2 230 28107 Mississippi Panola 2 33.8 25.3 22.6 231 28111 Mississippi Perry 4 29.1 22.0 20.5 232 28113 Mississippi Pike 3 32.9 25.3 27.6 233 28119 Mississippi Quitman 2 41.6 33.1 33.8 234 28125 Mississippi Sharkey 2 47.5 38.3 35.1 235 28127 Mississippi Simpson 3 22.7 21.6 21.3 236 28133 Mississippi Sunflower 2 41.8 30.0 38.7 237 28135 Mississippi Tallahatchie 2 41.9 32.2 33.7 238 28143 Mississippi Tunica 2 56.8 33.1 26.6 239 28147 Mississippi Walthall 3 35.9 27.8 23.7 240 28151 Mississippi Washington 2 33.8 29.2 28.1 241 28153 Mississippi Wayne 4 29.5 25.4 22.5 242 28157 Mississippi Wilkinson 2 42.2 37.7 32.4 243 28159 Mississippi Winston 3 26.6 23.7 22.2 244 28161 Mississippi Yalobusha 2 26.4 21.8 22.3 245 28163 Mississippi Yazoo 2 39.2 31.9 31.3 246 29001 Missouri Adair 6 24.9 23.3 23.9 247 29035 Missouri Carter 8 27.6 25.2 20.4 248 29069 Missouri Dunklin 8 29.9 24.5 22.3 249 29133 Missouri Mississippi 8 29.7 23.7 23.2 250 29143 Missouri New Madrid 8 26.9 22.1 22.1 251 29149 Missouri Oregon 8 27.4 22.0 21.2 252 29153 Missouri Ozark 8 22.1 21.6 20.4 253 29155 Missouri Pemiscot 8 35.8 30.4 23.7 254 29181 Missouri Ripley 8 31.5 22.0 23.4 255 29203 Missouri Shannon 8 24.1 26.9 21.5 256 29215 Missouri Texas 8 22.9 21.4 22.0 257 29223 Missouri Wayne 8 29.0 21.9 20.0 258 29229 Missouri Wright 8 25.3 21.7 19.8 Congressional Research Service 17 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, FIPS Congressional 1989 1999 Geographic District(s) (from (from Poverty Rate, Identification Representing 1990 Census 2021 (from Count Code State County the Countya Census) 2000) SAIPE) 259 29510 Missouri St. Louis city 1 24.6 24.6 21.5 260 30003 Montana Big Horn at large 35.3 29.2 25.7 261 30035 Montana Glacier at large 35.7 27.3 24.6 262 30085 Montana Roosevelt at large 27.7 32.4 25.3 263 35003 New Mexico Catron 2 25.6 24.5 19.8 264 35005 New Mexico Chaves 1, 2, 3 22.4 21.3 27.6 265 35006 New Mexico Cibola 2 33.6 24.8 26.4 266 35019 New Mexico Guadalupe 1 38.5 21.6 25.8 267 35023 New Mexico Hidalgo 2 20.7 27.3 24.1 268 35029 New Mexico Luna 2 31.5 32.9 27.6 269 35031 New Mexico McKinley 2, 3 43.5 36.1 30.3 270 35033 New Mexico Mora 3 36.2 25.4 21.9 271 35037 New Mexico Quay 3 25.1 20.9 22.3 272 35039 New Mexico Rio Arriba 3 27.5 20.3 20.4 273 35041 New Mexico Roosevelt 3 26.9 22.7 21.1 274 35045 New Mexico San Juan 3 28.3 21.5 24.3 275 35047 New Mexico San Miguel 3 30.2 24.4 21.4 276 35051 New Mexico Sierra 2 19.6 20.9 26.7 277 35053 New Mexico Socorro 2 29.9 31.7 22.2 278 36005 New York Bronx 13, 14, 15, 16 28.7 30.7 26.4 279 37013 North Carolina Beaufort 3 19.5 19.5 20.1 280 37015 North Carolina Bertie 1 25.9 23.5 20.7 281 37017 North Carolina Bladen 7 21.9 21.0 21.6 282 37047 North Carolina Columbus 7 24.0 22.7 23.4 283 37065 North Carolina Edgecombe 1 20.9 19.6 22.4 284 37083 North Carolina Halifax 1 25.6 23.9 27.3 285 37117 North Carolina Martin 1 22.3 20.2 20.1 286 37131 North Carolina Northampton 1 23.6 21.3 23.6 287 37147 North Carolina Pitt 1, 3 22.1 20.3 21.6 288 37155 North Carolina Robeson 7 24.1 22.8 27.9 289 37177 North Carolina Tyrrell 1 25.0 23.3 21.1 290 37181 North Carolina Vance 1 19.6 20.5 20.8 291 37187 North Carolina Washington 1 20.4 21.8 20.7 Congressional Research Service 18 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, FIPS Congressional 1989 1999 Geographic District(s) (from (from Poverty Rate, Identification Representing 1990 Census 2021 (from Count Code State County the Countya Census) 2000) SAIPE) 292 38005 North Dakota Benson at large 31.7 29.1 26.3 293 38079 North Dakota Rolette at large 40.7 31.0 27.2 294 38085 North Dakota Sioux at large 47.4 39.2 42.8 295 39009 Ohio Athens 12 28.7 27.4 20.9 296 39105 Ohio Meigs 2 26.0 19.8 21.1 297 40001 Oklahoma Adair 2 26.7 23.2 20.8 298 40015 Oklahoma Caddo 3 27.8 21.7 26.1 299 40021 Oklahoma Cherokee 2 28.8 22.9 21.8 300 40055 Oklahoma Greer 3 23.4 19.6 23.0 301 40057 Oklahoma Harmon 3 34.2 29.7 27.8 302 40061 Oklahoma Haskel 2 27.1 20.5 21.5 303 40063 Oklahoma Hughes 2 26.9 21.9 21.4 304 40069 Oklahoma Johnston 2 28.5 22.0 20.9 305 40077 Oklahoma Latimer 2 23.3 22.7 20.0 306 40089 Oklahoma McCurtain 2 30.2 24.7 21.5 307 40107 Oklahoma Okfuskee 2 29.4 23.0 26.8 308 40119 Oklahoma Payne 3 21.7 20.3 20.7 309 40133 Oklahoma Seminole 5 24.0 20.8 20.1 310 40135 Oklahoma Sequoyah 2 24.7 19.8 19.6 311 40141 Oklahoma Til man 4 22.9 21.9 20.9 312 42101 Pennsylvania Philadelphia 2, 3, 5 20.3 22.9 22.3 313 45005 South Carolina Allendale 6 35.8 34.5 35.4 314 45009 South Carolina Bamberg 6 28.2 27.8 25.9 315 45011 South Carolina Barnwell 2 21.8 20.9 23.0 316 45027 South Carolina Clarendon 6 29.0 23.1 20.8 317 45029 South Carolina Col eton 1, 6 23.4 21.1 20.9 318 45031 South Carolina Darlington 7 19.9 20.3 20.9 319 45033 South Carolina Dil on 7 28.1 24.2 26.1 320 45039 South Carolina Fairfield 5 20.6 19.6 20.6 321 45049 South Carolina Hampton 6 27.7 21.8 23.5 322 45061 South Carolina Lee 5 29.6 21.8 25.9 323 45067 South Carolina Marion 7 28.6 23.2 29.2 324 45069 South Carolina Marlboro 7 26.6 21.7 24.1 Congressional Research Service 19 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, FIPS Congressional 1989 1999 Geographic District(s) (from (from Poverty Rate, Identification Representing 1990 Census 2021 (from Count Code State County the Countya Census) 2000) SAIPE) 325 45075 South Carolina Orangeburg 2, 6 24.9 21.4 26.5 326 45089 South Carolina Wil iamsburg 6 28.7 27.9 21.2 327 46007 South Dakota Bennett at large 37.6 39.2 31.0 328 46017 South Dakota Buffalo at large 45.1 56.9 35.9 329 46023 South Dakota Charles Mix at large 31.4 26.9 22.6 330 46031 South Dakota Corson at large 42.5 41.0 41.9 331 46041 South Dakota Dewey at large 44.4 33.6 23.7 332 46071 South Dakota Jackson at large 38.8 36.5 28.1 333 46085 South Dakota Lyman at large 24.7 24.3 20.7 334 46095 South Dakota Mellette at large 41.3 35.8 30.0 335 46102 South Dakota Oglala Lakota at large 63.1 52.3 43.5 336 46121 South Dakota Todd at large 50.2 48.3 39.8 337 46123 South Dakota Tripp at large 20.6 19.9 20.5 338 46137 South Dakota Ziebach at large 51.1 49.9 38.1 339 47013 Tennessee Campbell 2, 3 26.8 22.8 19.9 340 47029 Tennessee Cocke 1 25.3 22.5 21.0 341 47049 Tennessee Fentress 6 32.3 23.1 19.9 342 47061 Tennessee Grundy 4 23.9 25.8 20.5 343 47067 Tennessee Hancock 1 40.0 29.4 27.6 344 47069 Tennessee Hardeman 8 23.3 19.7 20.9 345 47075 Tennessee Haywood 8 27.5 19.5 21.3 346 47091 Tennessee Johnson 1 28.5 22.6 23.7 347 47095 Tennessee Lake 8 27.5 23.6 33.4 348 47151 Tennessee Scott 3, 6 27.8 20.2 22.4 349 48025 Texas Bee 27 27.4 24.0 27.1 350 48041 Texas Brazos 10 26.7 26.9 22.6 351 48047 Texas Brooks 15 36.8 40.2 28.6 352 48061 Texas Cameron 34 39.7 33.1 24.6 353 48079 Texas Cochran 19 28.3 27.0 21.3 354 48107 Texas Crosby 19 29.5 28.1 23.2 355 48109 Texas Culberson 23 29.8 25.1 19.6 356 48115 Texas Dawson 19 30.5 19.7 21.3 357 48127 Texas Dimmit 23 48.9 33.2 25.7 Congressional Research Service 20 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, FIPS Congressional 1989 1999 Geographic District(s) (from (from Poverty Rate, Identification Representing 1990 Census 2021 (from Count Code State County the Countya Census) 2000) SAIPE) 358 48131 Texas Duval 28 39.0 27.2 30.0 359 48137 Texas Edwards 23 41.7 31.6 20.1 360 48141 Texas El Paso 16, 23 26.8 23.8 20.1 361 48145 Texas Falls 17 27.5 22.6 20.3 362 48163 Texas Frio 23 39.1 29.0 26.7 363 48169 Texas Garza 19 23.1 22.3 24.8 364 48191 Texas Hall 13 29.1 26.3 24.1 365 48207 Texas Haskel 19 20.8 22.8 20.6 366 48215 Texas Hidalgo 15, 34 41.9 35.9 28.8 367 48225 Texas Houston 17 25.6 21.0 19.8 368 48229 Texas Hudspeth 23 38.9 35.8 24.0 369 48247 Texas Jim Hogg 28 35.3 25.9 23.2 370 48249 Texas Jim Wells 15 30.3 24.1 21.4 371 48255 Texas Karnes 15 36.5 21.9 22.1 372 48273 Texas Kleberg 34 27.4 26.7 24.6 373 48283 Texas La Sal e 23 37.0 29.8 28.6 374 48315 Texas Marion 1 60.6 22.4 23.0 375 48323 Texas Maverick 23 50.4 34.8 20.5 376 48347 Texas Nacogdoches 17 25.2 23.3 20.8 377 48371 Texas Pecos 23 29.6 20.4 22.6 378 48377 Texas Presidio 23 48.1 36.4 21.3 379 48389 Texas Reeves 23 28.8 28.9 22.2 380 48405 Texas San Augustine 1 29.7 21.2 22.7 381 48427 Texas Starr 28 60.0 50.9 31.6 382 48445 Texas Terry 19 25.5 23.3 22.3 383 48463 Texas Uvalde 23 31.1 24.3 20.5 384 48479 Texas Webb 28 38.2 31.2 22.6 385 48489 Texas Wil acy 34 44.5 33.2 34.3 386 48505 Texas Zapata 28 41.0 35.8 28.9 387 48507 Texas Zavala 23 50.4 41.8 25.4 388 49037 Utah San Juan 3 36.4 31.4 26.8 389 51027 Virginia Buchanan 9 21.9 23.2 23.9 390 51051 Virginia Dickenson 9 25.9 21.3 22.0 Congressional Research Service 21 The 10-20-30 Provision: Defining Persistent Poverty Counties Poverty Poverty Rate, Rate, FIPS Congressional 1989 1999 Geographic District(s) (from (from Poverty Rate, Identification Representing 1990 Census 2021 (from Count Code State County the Countya Census) 2000) SAIPE) 391 51105 Virginia Lee 9 28.7 23.9 25.1 392 51121 Virginia Montgomery 9 22.1 23.2 23.3 393 51540 Virginia Charlottesvil e city 5 23.7 25.9 21.6 394 51620 Virginia Franklin city 2 20.6 19.8 19.6 395 51660 Virginia Harrisonburg city 6 21.5 30.1 25.0 396 51720 Virginia Norton city 9 26.7 22.8 20.4 397 51730 Virginia Petersburg city 4 20.3 19.6 22.8 398 51750 Virginia Radford city 9 32.2 31.4 25.6 399 51760 Virginia Richmond city 4 20.9 21.4 21.1 400 54001 West Virginia Barbour 2 28.5 22.6 19.7 401 54005 West Virginia Boone 1 27.0 22.0 24.7 402 54007 West Virginia Braxton 1 25.8 22.0 21.3 403 54013 West Virginia Calhoun 1 32.0 25.1 23.8 404 54015 West Virginia Clay 1 39.2 27.5 22.3 405 54019 West Virginia Fayette 1 24.4 21.7 19.9 406 54021 West Virginia Gilmer 1 33.5 25.9 25.3 407 54043 West Virginia Lincoln 1 33.8 27.9 20.3 408 54045 West Virginia Logan 1 27.7 24.1 23.5 409 54047 West Virginia McDowell 1 37.7 37.7 31.7 410 54059 West Virginia Mingo 1 30.9 29.7 31.1 411 54089 West Virginia Summers 1 24.5 24.4 24.8 412 54101 West Virginia Webster 1 34.8 31.8 25.2 413 54109 West Virginia Wyoming 1 27.9 25.1 25.0 414 55078 Wisconsin Menominee 8 48.7 28.8 24.2
Source: Congressional Research Service (CRS) tabulation of data from U.S. Census Bureau, 1990 Census, Congressional Research Service (CRS) tabulation of data from U.S. Census Bureau, 1990 Census,
Census 2000, Census 2000, ~~2020~~2021 Small Area Income and Poverty Estimates, and 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~~118th Congress Block Equivalency File (downloaded January 11, 2023). ).
Notes: FIPS: Federal Information Processing Standard. FIPS: Federal Information Processing Standard.
a. Numbers are ordinal, referring to the name of the congressional district(s) present in the county. For a. Numbers are ordinal, referring to the name of the congressional district(s) present in the county. For
example, Barbour County, example, Barbour County, Alabama, is represented by Alabama’s 2nd Congressional District (indicated by 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 the 2). A congressional district may span multiple counties; conversely, a single county may be split among
multiple congressional districts. Part of multiple congressional districts. Part of ~~Clarke~~Escambia County, Alabama, for example, is represented by Alabama’s County, Alabama, for example, is represented by Alabama’s
1st Congressional District (indicated by the 1) and part by the 1st Congressional District (indicated by the 1) and part by the ~~7th~~2nd Congressional District (indicated by the Congressional District (indicated by the
72). Counties labeled “at large” are located in states that have one member of the House of Representatives ). Counties labeled “at large” are located in states that have one member of the House of Representatives
for the entire state. for the entire state.
b. Changed name and geographic code effective July 1, 2015, from Wade Hampton Census Area (02270) to b. Changed name and geographic code effective July 1, 2015, from Wade Hampton Census Area (02270) to
Kusilvak Census Area (02158). Kusilvak 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).

Congressional Research Service Congressional Research Service

2322

Figure 1. Persistent Poverty Counties Using Two Rounding Methods, Based on
1990 Census, Census 2000, and ~~2020~~2021 Small Area Income and Poverty Estimates

Source: Created by Created by the Congressional Research Service (CRS) using data from U.S. Census Bureau, 1990 Census, Census 2000, and Congressional Research Service (CRS) using data from U.S. Census Bureau, 1990 Census, Census 2000, and ~~2020~~2021 Small Area Income and Poverty Small Area Income and Poverty
Estimates. Estimates.

CRS- CRS-2423

link to page 8 link to page 8 The 10-20-30 Provision: Defining Persistent Poverty Counties

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 likely result in a dramatically different estimate. If estimates for smaller that area would not likely result in a dramatically different estimate. If estimates for smaller
geographic areas are desired, a larger sample size is needed. A national-level survey, for instance, geographic areas are desired, a larger sample size is needed. A national-level 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 small populations. In order to produce estimates for all 3,143 counties, particularly counties with small populations. In order to produce estimates for all 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 small geographic areas) was the decennial meaningful estimates at the county level (and for other small 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 small areas such as counties. Income questions were enough to produce meaningful estimates for small areas such as counties. Income questions were
asked on the census long form, which was sent to one-sixth of all U.S. households; the rest asked on the census long form, which was sent to one-sixth of all U.S. households; the rest
received the census short form, which did not ask about income. While technically still a sample, received the census short form, which did not ask about income. While technically still a sample,
one-sixth of all households was a large enough sample to provide poverty estimates for every one-sixth of all households was a large enough sample to provide poverty estimates for every
county in the nation, and even for smaller areas such as small towns. The long form was county in the nation, and even for smaller areas such as small 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.2022 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 small areas such as counties would still be available: the American Community Survey for small areas such as counties would still be available: the American Community Survey
(ACS), and the Small Area Income and Poverty Estimates program (SAIPE). (ACS), and the Small 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 small local government officials and other stakeholders who needed detailed information on 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 small communities, however, the ACS needs to In order to produce meaningful estimates for small 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.2123 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,

2022 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, and the U.S. Virgin Islands. SAIPE and ACS estimates are not. See footnothe Northern Mariana Islands, Guam, and the U.S. Virgin Islands. SAIPE and ACS estimates are not. See footnote 13.
2123 A sample of approximately 18.3 million households received the Census 2000 long form. Scott Boggess and Nikki L. A sample of approximately 18.3 million households received the Census 2000 long form. Scott Boggess and Nikki L.
Graf, “Measuring Education: A Comparison of the Decennial Census and the American Community Survey,” presented Graf, “Measuring Education: A Comparison of the Decennial Census and the American Community Survey,” presented
at Joint Statistical Meetings, San Francisco, CA, August 7, 2003. http://census.gov/content/dam/Census/library/at Joint Statistical Meetings, San Francisco, CA, August 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 From ~~2014 to 2018, 17.7~~2017 to 2021, 17.0 million housing unit addresses were sampled in the ACS. http://www.census.gov/acs/www/ million housing unit addresses were sampled in the ACS. 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.
Congressional Research Service Congressional Research Service

2524

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

one year’s worth of responses are pooled—these are the ACS one-year estimates. For the smallest one year’s worth of responses are pooled—these are the ACS one-year estimates. For the 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 allocations.development and evaluation of the SAIPE program for its use in Title I-A funding allocations.2224
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, especially for counties with small populations. data alone, especially for counties with small populations.
Guidance from the U.S. Census Bureau,
“Which Data Source to Use~~”23~~ for Poverty”25
The CPS The CPS ~~ASEC24~~ASEC[26] 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. Because of its large sample size, the ACS is preferred for subnational data on analysis. Because of its large sample size, the ACS is preferred for subnational data 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
as poverty among Hispanics or median earnings, should use the ACS, where and when as poverty among Hispanics or median earnings, should use the ACS, where and when
available. available.

The SIPP[27] 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 periods more or less than one year, including monthly poverty rates.
2224 Details about the origins of the SAIPE project are available on the Census Bureau’s website at Details about the origins of the SAIPE project are available on the Census Bureau’s website at
https://www.census.gov/programs-surveys/saipe/about/origins.html.https://www.census.gov/programs-surveys/saipe/about/origins.html.
2325 Downloaded from http://www.census.gov/topics/income-poverty/poverty/guidance/data-sources.html, Downloaded from http://www.census.gov/topics/income-poverty/poverty/guidance/data-sources.html, ~~March 24,~~
~~2022.~~
24 Author’s note:January 25, 2023. 26 CPS ASEC: Current Population Survey Annual Social and Economic Supplement. CPS ASEC: Current Population Survey Annual Social and Economic Supplement.
27 SIPP: Survey of Income and Program Participation; mentioned here only as part of the quotation. Congressional Research Service Congressional Research Service

2625

link to page link to page 3130 link to page link to page 3130 The 10-20-30 Provision: Defining Persistent Poverty Counties

~~The SIPP25 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~~
~~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 levelsgeographic 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
Longitudinal
Geographic Level
Rate
Characteristics
Change
Estimates
CPS ASEC/ CPS ASEC/
United States United States
CPS ASEC CPS ASEC
ACS 1-year ACS 1-year
CPS ASEC CPS ASEC
SIPP SIPP
estimates for estimates for
detailed race groups detailed race groups
ACS 1-year ACS 1-year
estimates estimates
States ACS 1-year ACS 1-year
ACS 1-year ACS 1-year
~~States~~
CPS ASEC 3-year CPS ASEC 3-year
estimates estimates
estimates estimates

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

comparison Source: Congressional Research Service (CRS) formatted reproduction of table by U.S. Census Bureau, with an Congressional Research Service (CRS) formatted reproduction of table by U.S. Census Bureau, with an
expansion to the notes. Original table downloaded from http://www.census.gov/topics/income-poverty/poverty/expansion to the notes. Original table downloaded from http://www.census.gov/topics/income-poverty/poverty/
guidance/data-sources.html, guidance/data-sources.html, ~~March 24, 2022~~January 25, 2023. .
Notes:
ACS: American Community Survey. ACS: American Community Survey.
CPS ASEC: Current Population Survey, Annual Social and Economic Supplement. CPS ASEC: Current Population Survey, Annual Social and Economic Supplement.
SAIPE: Small Area Income and Poverty Estimates. SAIPE: Small 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, but are now only produced using the ACS five-year estimates. ACS three- using ACS three-year estimates, but are now only produced using the ACS five-year estimates. ACS three-
year estimates are no longer produced (with 2011-2013 data as the last in the series). For details, see year estimates are no longer produced (with 2011-2013 data as the last in the series). For details, see
https://www.census.gov/programs-surveys/acs/guidance/estimates.html. https://www.census.gov/programs-surveys/acs/guidance/estimates.html.

~~25 Author’s note: SIPP: Survey of Income and Program Participation; mentioned here only as part of a quotation.~~
~~Congressional Research Service~~

27

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

b. Use non-overlapping periods for ACS trend analysis with multiyear estimates. For example, comparing 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 2006-2010 ACS five-year estimates with 2011-2015 ACS five-year estimates is preferred for identifying
change. change.
Congressional Research Service 26 The 10-20-30 Provision: Defining Persistent Poverty Counties
Author Information

Joseph Dalaker Joseph Dalaker

Analyst in Social Policy Analyst in Social Policy

Acknowledgments
The author is grateful for the assistance of Sarah The author is grateful for the assistance of Sarah ~~Caldwell, CRS Senior~~K. Braun, CRS Research Librarian, Research Librarian, ~~for assistance~~
and Sarah Caldwell, CRS Senior Research Librarian, with legislative researchwith legislative research,; and Calvin DeSouza, CRS GIS Analyst, in creating the county map. and Calvin DeSouza, CRS GIS Analyst, in creating the county map.
Congressional Research Service 27 The 10-20-30 Provision: Defining Persistent Poverty Counties
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 not be relied upon for purposes other under the direction of Congress. Information in a CRS Report should not 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
its entirety without permission from CRS. However, as a CRS Report may include copyrighted images or its entirety without permission from CRS. However, as a CRS Report may include copyrighted images or
material from a third party, you may need to obtain the permission of the copyright holder if you wish to material from a third party, you may need to obtain the permission of the copyright holder if you wish to
copy or otherwise use copyrighted material. copy or otherwise use copyrighted material.

Congressional Research Service Congressional Research Service
R45100 R45100 · VERSION 1315 · UPDATED
28 28