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

February 8, 2018 – February 27, 2023 R45100

The 10-20-30 Provision: Defining Persistent
February 27, 2023
Poverty Counties
Joseph Dalaker
Research has suggested that areas 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 poverty, or economic hardship as measured by comparing income
against a dollar amount that represents a low level of need. Recent congresses have enacted

antipoverty policy 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 10-
20-30 provision, was first implemented in the American Recovery and Reinvestment Act of 2009 (ARRA, P.L. 111-5). Title
I, Section 105 of ARRA required the Secretary of 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 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
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.
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.
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
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
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).
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
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
antipoverty intervention is intended.
When defining persistent poverty counties in order to target funds for programs or services, the following factors may be
relevant:
 Characteristics of interest: SAIPE is suited for analysis focused solely on poverty or median income; ACS
for poverty and income and other topics (e.g., housing characteristics, disability, education level,
occupation, veteran status).
 Geographic areas of interest: SAIPE is recommended for counties and school districts only; ACS may be
used to produce estimates for other small geographic areas as well (such as cities, towns, and census tracts).
 Reference period of estimate: Both data sources produce annual estimates. However, the SAIPE estimate is
based on one prior year of data while ACS estimates draw on data from the past five years.
 Rounding method for poverty rates: Rounding to 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 of counties with persistent poverty
than rounding to a whole number (e.g., including a county with a poverty rate of 19.9% because it rounds
up to 20%).
 Special populations:
 Poverty status is not defined for all persons. This includes unrelated household members under age 15
(e.g., children in foster care), institutionalized persons, and residents of college dormitories.
 Persons without housing are not included in household surveys.
 Areas with large numbers of college students living off-campus may have higher poverty rates than
might be expected, because poverty is measured using cash income and does not include student loans.
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The 10-20-30 Provision: Defining Persistent Poverty Counties

Introduction
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
the 10-20-30 provision, 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
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
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
development programs addressed in ARRA, but rather targeted existing funds differently. Given
Congress’s interest both in addressing poverty (economic hardship as measured by comparing
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
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
source used by ARRA to define persistent poverty—stopped collecting income information. As a
consequence, the appropriations bills varied slightly in their definitions of persistent poverty
counties as applied to various programs and departments. This variation occurred even within
different sections of the same bill if the bill included language relating to different programs. In
turn, because the definitions of persistent poverty 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
development, public works and economic development, technological innovation, and
brownfields site assessment and remediation.
More recently, up through the end of the 117th Congress, much of the language used in these
previous bills was included in P.L. 117-328 (the Consolidated Appropriations Act, 2023)4 and P.L.
117-103 (the Consolidated Appropriations Act, 2022). References to persistent poverty counties,
with provisions other than a 10% set-aside, also appeared in P.L. 117-169 (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
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
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, An Introduction to
Poverty Measurement, by Joseph Dalaker.
3 Additionally, in the 112th Congress, the 10-20-30 provision was proposed as an amendment to H.R. 1 that was not
adopted.
4 In the 117th Congress, the Consolidated Appropriations Act, 2023 (P.L. 117-328) included 10-20-30 language in
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 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
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.
Further, Division L, Title I of the act refers to persistent poverty counties, though without specifying a figure of 10% to
be set aside. That portion of the act set aside $20 million for National Infrastructure Investment grants for “projects in
historically disadvantaged communities or areas of persistent poverty,” and $20 million for Transit Infrastructure
Grants for areas of persistent poverty; both of these programs include persistent poverty counties in their definitions. It
also enabled the Secretary of Transportation to prioritize persistent poverty counties to receive technical assistance
under the Thriving Communities Initiative.
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The 10-20-30 Provision: Defining Persistent Poverty Counties

Act). Additionally, 74 other bills introduced in the 117th Congress that were not enacted also
referred to persistent poverty counties, with or without requiring a 10% set-aside specifically.
This report discusses how data source selection and the rounding of poverty estimates can affect
the list of counties identified as persistently poor. After briefly explaining why targeting funds to
persistent poverty counties might be of interest, this report explores how persistent poverty is
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
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
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 poverty rate (the percentage of the population
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
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
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
poverty rates have been documented both for urban and rural areas.7 Depending on the years in

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,
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
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,
“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
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,
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.
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
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., Revisiting Rental Housing: Policies, Programs, and Priorities (Washington,
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
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
net depreciation will occur.”
7 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 Enduring Challenge of Concentrated Poverty in America: Case Studies from Communities Across the
U.S.,” David Erickson 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
undertaken for decades; for example, educational attainment and health disability were discussed in a rural context by
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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
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
Defining Persistent Poverty Counties
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 have traditionally been measured
using decennial 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 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 poverty rate, affects the list of counties identified as persistent poverty
counties, as will be described below.
Computing the Poverty Rate for an Area
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
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 poverty
threshold, 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

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
sections (see footnote 4 for 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
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),
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-
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 (Agriculture Improvement Act of 2018), as well as multiple introduced
bills that were not enacted. In the 114th Congress, no bills containing 10-20-30 language were enacted into public law;
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
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
Government Appropriations Act, 2017). The 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 113th
Congress, H.R. 5571 (The 10-20-30 Act of 2014) was introduced and referred to committee.
9 The decennial census does not collect income information in the 50 states, the District of Columbia, and Puerto Rico.
It asks for income information in American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and
the U.S. Virgin Islands (areas for which neither ACS nor SAIPE data are available).
10 There are two definitions of poverty for official use 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 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 the 10-20-30 plan, is a statistical use of poverty data; thus, the statistical definition of poverty (used by the
Census Bureau) applies.
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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
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
percentage.
Data Sources Used in Identifying Persistent Poverty Counties
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-
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
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,
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
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
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
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
counties are available annually, but are based on responses over the previous five-year time span
(e.g., 2017-2021). 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

11 For further details about the official definition of poverty, see CRS Report R44780, An Introduction to Poverty
Measurement, by Joseph Dalaker.
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 related to
anyone else in their residence by birth, marriage, or adoption and who are not asked income questions in household
surveys; persons living in 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 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.
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
territories; decennial census data are the only small-area poverty data available for them. The 2020 Census
questionnaire for these territories 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-
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-
documentation/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.
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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
provided guidance on the most suitable data source to use for various purposes.15
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 county level, especially for counties with small populations, and when additional
demographic and economic detail is not needed at that level.16 When additional detail is required,
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
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
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
ZIP code tabulation areas, census tracts (subcounty areas of roughly 1,200 to 8,000 people), cities
and towns, and greater metropolitan areas.17
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
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
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

15 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 the Appendix.
16 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.
17 Some legislation, including Division L, Title I of P.L. 117-103 (see footnote 3), define areas of persistent poverty to
include census tracts with poverty rates “not less than 20 percent” along with persistent poverty counties and “any
territory or possession of the United States” per 49 U.S.C. §6702(a)(1).
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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
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
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
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
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
living in college dormitories.18 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)
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
the unemployment rate.19
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
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.
Persistence Versus Flexibility to Recent Situations
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
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
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
more of its population living in poverty over the past 30 years, as measured by the 1980, 1990,
and 2000 decennial censuses.”20 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

18 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
https://www.census.gov/programs-surveys/saipe/guidance/model-input-data/denominators/poverty.html.
19 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
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).
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.
20 P.L. 111-5, Section 105.
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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 round up 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
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
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
impacts on the number of counties listed. In most years, using SAIPE for the latest year resulted
in more counties being identified as persistently poor than were identified by using the ACS; the
exceptions were 2019 and 2020. 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 list. Taking
both the data source and the rounding method together (Table 2), the list of persistent poverty
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
below) in 1989 (from 1990 Census), 1999 (from Census 2000), and latest year from
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
397
445
48
ACS, 2008-2012
404
456
52
ACS, 2009-2013
402
458
56
ACS, 2010-2014
401
456
55
ACS, 2011-2015
397
453
56
ACS, 2012-2016
392
446
54
ACS, 2013-2017 a
386
436
50
ACS, 2014-2018 a
384
430
46
ACS, 2015-2019
375
418
43
ACS, 2016-2020
355
397
42
ACS, 2017-2021
344
387
43

Mean difference: 49.5

SAIPE, 2011
433
495
62
SAIPE, 2012
435
491
56
SAIPE, 2013
427
490
63
SAIPE, 2014
427
486
59
SAIPE, 2015
419
476
57
SAIPE, 2016
420
469
49
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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
361
407
46
SAIPE, 2020
306
354
48
SAIPE, 2021
362
414
56

Mean difference: 53.9

Differences between datasets released
in same year

Difference, SAIPE 2011 minus ACS 2007-2011
36
50

Difference, SAIPE 2012 minus ACS 2008-2012
31
35

Difference, SAIPE 2013 minus ACS 2009-2013
25
32

Difference, SAIPE 2014 minus ACS 2010-2014
26
30

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

Difference, SAIPE 2017 minus ACS 2013-2017
25
24

Difference, SAIPE 2018 minus ACS 2014-2018
11
13

Difference, ACS 2015-2019 minus SAIPE 2019
14
11

Difference, ACS 2016-2020 minus SAIPE 2020
49
43

Difference, SAIPE 2021 minus ACS 2017-2021
18
27

Mean difference:
25.9
28.3
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. Comparisons
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.
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.

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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.
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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
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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)
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
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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)
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
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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
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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
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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, Census 2000, 2021 Small Area
Income and Poverty Estimates, and 118th Congress Block Equivalency File (downloaded January 11, 2023).
Notes: FIPS: Federal Information Processing Standard.
a. Numbers are ordinal, referring to the name of the congressional district(s) present in the county. For example, Barbour County,
Alabama, is represented by Alabama’s 2nd Congressional District (indicated by the 2). A congressional district may span multiple
counties; conversely, a single county may be split among multiple congressional districts. Part of Escambia County, Alabama, for
example, is represented by Alabama’s 1st Congressional District (indicated by the 1) and part by the 2nd Congressional District
(indicated by the 2). Counties labeled “at large” are located in states that have one member of the House of Representatives for the
entire state.
b. Changed name and geographic code effective July 1, 2015, from Wade Hampton Census Area (02270) to Kusilvak Census Area (02158).
c. Changed name and geographic code effective May 1, 2015, from Shannon County (46113) to Oglala Lakota County (46102).
Congressional Research Service

22

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

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

CRS-23

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
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
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,
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
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.,
their margins of error are not unhelpfully wide).
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
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
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,
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
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.22 Beginning in the
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
(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
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
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
collect a number of responses comparable to what was collected in the decennial census.23 In
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
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,

22 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 footnote 13.
23 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
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.
From 2017 to 2021, 17.0 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.
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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
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
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.
Small Area Income and Poverty Estimates (SAIPE)
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
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
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
development and evaluation of the SAIPE program for its use in Title I-A funding allocations.24
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
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
data alone, especially for counties with small populations.
Guidance from the U.S. Census Bureau,
“Which Data Source to Use for Poverty”25
The CPS ASEC[26] 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
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
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
CPS ASEC 3-year averages for state to state comparisons.
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
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
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
as poverty among Hispanics or median earnings, should use the ACS, where and when
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.

24 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.
25 Downloaded from http://www.census.gov/topics/income-poverty/poverty/guidance/data-sources.html, January 25,
2023.
26 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.
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link to page 30 link to page 30 The 10-20-30 Provision: Defining Persistent Poverty Counties

Table A-1 below reproduces the Census Bureau’s recommendations, summarized for various
geographic levels.
Table A-1. U.S. Census Bureau’s Guidance on Poverty Data Sources by Geographic
Level and Type of Estimate

Cross-Sectional Estimates

Income/Poverty
Detailed
Year-to-Year
Longitudinal
Geographic Level
Rate
Characteristics
Change
Estimates
CPS ASEC/
United States
CPS ASEC
ACS 1-year
CPS ASEC
SIPP
estimates for
detailed race groups
ACS 1-year
estimates
States
ACS 1-year
ACS 1-year

CPS ASEC 3-year
estimates
estimates
averages
ACS 1-year
ACS 1-year
Substate (areas with estimates/
ACS 1-year
estimates / SAIPE
populations of
estimates
for counties and
None
65,000 or more)
SAIPE for counties
and school districts
school districts
SAIPE for counties
and school districts/
SAIPE for
ACS 5-year
counties and
Substate (areas with ACS using 5-year
estimates/
school districts/
populations less
period estimates for
ACS using 5-year
None
than 20,000)
all other geographic
a
Decennial Census
entities/
period estimates for
2000 and prior
all other geographic
Decennial Census
entitiesb
2000 and prior
State-to-Nation
CPS ASEC
CPS ASEC
CPS ASEC

comparison
Source: 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/
guidance/data-sources.html, January 25, 2023.
Notes:
ACS: American Community Survey.
CPS ASEC: Current Population Survey, Annual Social and Economic Supplement.
SAIPE: Small Area Income and Poverty Estimates.
SIPP: Survey of Income and Program Participation.
a. Author’s note: 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-
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.
b. Use non-overlapping periods for ACS trend analysis with multiyear estimates. For example, comparing
2006-2010 ACS five-year estimates with 2011-2015 ACS five-year estimates is preferred for identifying
change.
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The 10-20-30 Provision: Defining Persistent Poverty Counties

Author Information

Joseph Dalaker

Analyst in Social Policy

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

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

Disclaimer
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
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
connection with CRS’s institutional role. CRS Reports, as a work of the United States Government, are not
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