The 10-20-30 Provision: Defining Persistent Poverty Counties
Changes from February 27, 2023 to March 10, 2025
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.
Contents
- Introduction
- Motivation for Targeting Funds to Persistent Poverty Counties
- Defining Persistent Poverty Counties
- Computing the Poverty Rate for an Area
- Data Sources Used in Identifying Persistent Poverty Counties
- Considerations When Identifying and Targeting Persistent Poverty Counties
- Selecting the Data Source: Strengths and Limitations of ACS and SAIPE Poverty Data
- Characteristics of Interest: SAIPE for Poverty Alone; ACS for Other Topics in Addition to Poverty
- Geographic Area of Interest: SAIPE for Counties and School Districts Only; ACS for Other Small Areas
- Reference Period of Estimate: SAIPE for One Year, ACS for a Five-Year Span
- Other Considerations
- Treatment of Special Populations in the Official Poverty Definition
- Persistence Versus Flexibility to Recent Situations
- Effects of Rounding and Data Source Selection on Lists of Counties
- Example List of Persistent Poverty Counties
Tables
- Table 1. Number of Counties Identified as Persistently Poor, Using Different Datasets and Rounding Methods
- Table 2. Maximum Differences in the Number of Persistent Poverty Counties by Data Source and Rounding Method
- Table 3. List of Persistent Poverty Counties, Based on 1993 Small Area Income and Poverty Estimates (SAIPE), Census 2000, and 2023 SAIPE, Using Poverty Rates of 19.5% or Greater
- Table A-1. U.S. Census Bureau's Guidance on Poverty Data Sources by Geographic Level and Type of Estimate
Appendixes
Summary
Research has suggested that areas with a poverty rate 20% or greater experience more acuteagainst a dollar amount that represents a low level of need. Recent congresses have enacted against a dollar amount that represents a low level of need. Recent congresses have enacted
characteristics of those communities, rather than solely on those of individuals or families. One such policy, dubbed 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 , 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 I, Section 105 of ARRA required the Secretary of Agriculture to allocate at least 10% of funds from three rural development
program accounts to program accounts to persistent poverty counties—counties that maintained poverty rates of 20% or more for the past 30 —counties that maintained poverty rates of 20% or more for the past 30
years, as measured by the 1980, 1990, and 2000 decennial censuses.years, as measured by the 1980, 1990, and 2000 decennial censuses.
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.
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 countrythe only source of county poverty estimates across the entire country (there are 3,144 counties or county-equivalent areas, nationwide). After 2000, however, the decennial census is no longer . After 2000, however, the decennial census is no longer
used to collect income data. used to collect income data.
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.
relevant:relevant:
occupation, veteran status).occupation, veteran status).
than rounding to a whole number (e.g., including a county with a poverty rate of 19.9% because it rounds than rounding to a whole number (e.g., including a county with a poverty rate of 19.9% because it rounds
up to 20%).up to 20%).
Introduction
Antipoverty interventions that provide resources to local communities, based on the Antipoverty interventions that provide resources to local communities, based on thecharacteristics 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.the past 30 years, as measured by the 1980, 1990, and 2000 decennial censuses.
development programs addressed in ARRA, but rather targeted existing funds differently. Given development programs addressed in ARRA, but rather targeted existing funds differently. Given
income against a dollar amount that represents a low level of need)income against a dollar amount that represents a low level of need)
levels of federal spending, the levels of federal spending, the
multiple appropriations bills, some of which were enacted into law.multiple appropriations bills, some of which were enacted into law.
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 applied to various programs and departments. This variation occurred even within 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 different sections of the same bill if the bill included language relating to 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.
previous bills was included in P.L. previous bills was included in P.L.
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 provisionnot compare the 10-20-30 provision
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.lower-poverty areas.5 The poverty rate of 20% as a critical point has been discussed in academic The poverty rate of 20% as a critical point has been discussed in academic
literature as relevant for examining social characteristics of high-poverty versus low-poverty literature as relevant for examining social characteristics of high-poverty versus low-poverty
areas.areas.
investment as in low-poverty areas, and that low return provides a financial disincentive for investment as in low-poverty areas, and that low return provides a financial disincentive for
property owners to spend money on maintaining and improving property.property owners to spend money on maintaining and improving property.
poverty rates have been documented both for urban and rural areas.poverty rates have been documented both for urban and rural areas.
identified as persistent poverty counties, out of a total of identified as persistent poverty counties, out of a total of 3,144
areas nationwide.areas nationwide.9 Therefore, policy interventions at the community level, and not only at the 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.individual or family level, have been and may continue to be of interest to Congress.
10 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 have traditionally been measured 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 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 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 Bureau data sources were first implemented in the mid-1990s and both provide poverty estimates
no longer available from the decennial census.no longer available from the decennial census.
of rounding for the poverty rate, affects the list of counties identified as persistent poverty of rounding for the poverty rate, affects the list of counties identified as persistent poverty
counties, as will be described below.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 Poverty rates are computed by the Census Bureau for the nation, states, and smaller geographic
areas such as counties.areas such as counties.
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
to be in poverty (sometimes also described as to be in poverty (sometimes also described as
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 countywithin a county by the county
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.poverty estimates.
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.
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.,
annually, and reflect one calendar year, not five. However, unlike the ACS, SAIPE does not 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 provide estimates for a wide array of topics. For further details about the data sources for county
poverty estimates, see poverty estimates, see
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.
17 Characteristics of Interest: SAIPE for Poverty Alone; ACS for Other Topics in
Addition to Poverty
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.
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
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.
19 Reference Period of Estimate: SAIPE for One Year, ACS for a Five-Year Span
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
estimates for small areas are based on the prior five years, not the prior year alone.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
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.
living in college dormitories.living in college dormitories.
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.
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
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
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.
list of persistent poverty countieslist of persistent poverty counties
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.
23 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. In most years, using SAIPE for the latest year resulted 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 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 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 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 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,
Dataset
Rounded to One Decimal Place (20.0% or Greater)
Rounded to Whole Number (19.5% or Greater)
Difference Between Rounding Methods
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
386
436
50
384
430
46
ACS, 2015-2019
375
418
43
355
397
42
ACS, 2017-2021
344
387
43
ACS, 2018-2022
348
386
38
ACS, 2019-2023
326
361
35
Mean difference: 47.5
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
SAIPE, 2017
411
460
49
SAIPE, 2018
395
443
48
SAIPE, 2019
361
407
46
SAIPE, 2020
306
354
48
SAIPE, 2021
362
414
52
SAIPE, 2022
360
417
57
SAIPE, 2023
340
393
53
Mean difference: 53.8
in same year
36
50
31
35
25
32
26
30
22
23
Difference, SAIPE 2016 minus ACS 2012-2016
28
23
25
24
Difference, SAIPE 2018 minus ACS 2014-2018
11
13
14
11
Difference, ACS 2016-2020 minus SAIPE 2020
49
43
Difference, SAIPE 2021 minus ACS 2017-2021
18
27
Difference, SAIPE 2022 minus ACS 2018-2022
12
31
Difference, SAIPE 2023 minus ACS 2019-2023
14
32
Mean difference:
23.9
28.8
Census 2000, 2012-Census 2000, 2012-
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, 2016-2020, 2015-2019, 2016-2020,
2017-2021, 2018-2022, and 2019-2023. 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 between ACS and SAIPE estimates are between datasets released in the same year (both are
December of the year December of the year
2013-2017 had been released, but before the 2014-2018 data had been released. As a result, the 2014-2018 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 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 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 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 before the error (2012-2016) and the SAIPE poverty rate were both above 20.0%, Rio Arriba County is
included in this tableincluded in this table
programs-surveys/acs/technical-documentation/errata/programs-surveys/acs/technical-documentation/errata/125.html. c. These data were used to define persistent poverty in Division B, Title I of the Further Consolidated Appropriations Act, 2024 (P.L. 118-47), in reference to the Community Development Financial Institutions Fund in the Department of the Treasury.
Table 2. Maximum Differences in the Number of Persistent Poverty Counties
Data Source and Year, Rounding Method,
Maximum Difference
(Number of Counties) Most 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
SAIPE 2022, whole number
417
ACS, 2018-2022, one decimal
348
69
SAIPE 2023, whole number
393
ACS, 2019-2023, one decimal
326
67
Mean difference:
77.0
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-
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, 2016-2020, 2015-2019, 2016-2020,
2017-2021, 2018-2022, and 2019-2023. Notes: ACS: American Community Survey. SAIPE: Small Area Income and Poverty Estimates. The selection of 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 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 persistent poverty. The longest list of persistent poverty counties minus the shortest list of persistent poverty
counties yields the maximum difference. For example, in counties yields the maximum difference. For example, in
or greater, that is, rounded up to the whole number 20%, while the shortest list used the or greater, that is, rounded up to the whole number 20%, while the shortest list used the
Year Estimates, using poverty rates 20.0% or greater. The lists of persistent poverty counties vary by Year Estimates, using poverty rates 20.0% or greater. The lists of persistent poverty counties vary by
estimate, and which rounding method is applied to identify persistent poverty. Comparisons between ACS and 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 SAIPE estimates are between datasets released in the same year (both are typically released in December of the
The list of persistent poverty counties belowThe list of persistent poverty counties below (Table 3)
1993 SAIPE, Census 2000, and the 2021 SAIPE estimates, and includes the Census 2000, and the 2021 SAIPE estimates, and includes the
19.5% or greater (that is, counties with poverty rates that were at least 20% with rounding applied 19.5% or greater (that is, counties with poverty rates that were at least 20% with rounding applied
to the whole number). These same counties are mapped to the whole number). These same counties are mapped
that would be compiled if ACS data were used with 1990 and 2000 Census data to determine that would be compiled if ACS data were used with 1990 and 2000 Census data to determine
counties with persistent poverty.counties with persistent poverty.
Table 3. List of Persistent Poverty Counties, Based on
Count
FIPS Geographic Identification Code
State
County
Poverty Rate, 1993 (from SAIPE)
Poverty Rate, 1999 (from Census 2000)
Poverty Rate,
2023 (from SAIPE)
1
01005
Alabama
Barbour
2
25.0
26.8
25.5
2
01011
Alabama
Bullock
2
33.0
33.5
33.6
3
01013
Alabama
Butler
2
27.1
24.6
23.6
4
01023
Alabama
Choctaw
7
25.0
24.5
24.8
5
01035
Alabama
Conecuh
2
27.4
26.6
26.5
6
01041
Alabama
Crenshaw
2
22.8
22.1
19.5
7
01047
Alabama
Dallas
7
34.2
31.1
31.4
8
01053
Alabama
Escambia
1
24.4
20.9
21.3
9
01063
Alabama
Greene
7
38.8
34.3
31.0
10
01065
Alabama
Hale
7
31.4
26.9
23.0
11
01085
Alabama
Lowndes
7
36.3
31.4
29.4
12
01087
Alabama
Macon
2
35.3
32.8
28.8
13
01091
Alabama
Marengo
7
28.4
25.9
23.5
14
01105
Alabama
Perry
7
42.4
35.4
33.8
15
01107
Alabama
Pickens
7
25.7
24.9
21.5
16
01109
Alabama
Pike
2
25.6
23.1
23.8
17
01119
Alabama
Sumter
7
35.2
38.7
33.5
18
01131
Alabama
Wilcox
7
41.3
39.9
32.7
19
02050
Alaska
Bethel Census Area
at large
33.2
20.6
29.3
20
02070
Alaska
Dillingham Census Area
at large
20.5
21.4
23.4
21
02158
Alaska
at large
41.4
26.2
30.8
22
02290
Alaska
Yukon-Koyukuk Census Area
at large
29.6
23.8
21.9
23
04001
Arizona
Apache
2
40.8
37.8
29.6
24
04017
Arizona
Navajo
2
31.2
29.5
24.7
25
04023
Arizona
Santa Cruz
7
27.4
24.5
20.1
26
05011
Arkansas
Bradley
4
23.8
26.3
23.2
27
05017
Arkansas
Chicot
1
38.8
28.6
29.7
28
05027
Arkansas
Columbia
4
23.6
21.1
23.3
29
05035
Arkansas
Crittenden
1
28.0
25.3
20.6
30
05041
Arkansas
Desha
1
30.6
28.9
25.0
31
05069
Arkansas
Jefferson
4
27.6
20.5
24.3
32
05073
Arkansas
Lafayette
4
30.0
23.2
22.8
33
05077
Arkansas
Lee
1
45.4
29.9
38.7
34
05079
Arkansas
Lincoln
1
29.0
19.5
26.2
35
05093
Arkansas
Mississippi
1
26.2
23.0
24.9
36
05095
Arkansas
Monroe
1
33.0
27.5
26.3
37
05099
Arkansas
Nevada
4
19.9
22.8
23.2
38
05107
Arkansas
Phillips
1
42.7
32.7
34.2
39
05123
Arkansas
St. Francis
1
35.7
27.5
34.3
40
05129
Arkansas
Searcy
1
26.8
23.8
20.2
41
05147
Arkansas
Woodruff
1
31.8
27.0
24.4
42
08003
Colorado
Alamosa
3
24.0
21.3
21.7
43
08011
Colorado
Bent
4
20.0
19.5
28.6
44
08023
Colorado
Costilla
3
33.5
26.8
22.6
45
08099
Colorado
Prowers
4
21.3
19.5
20.1
46
08109
Colorado
Saguache
3
30.5
22.6
20.6
47
12013
Florida
Calhoun
2
22.3
20.0
22.9
48
12039
Florida
Gadsden
2
29.2
19.9
21.5
49
12047
Florida
Hamilton
3
24.3
26.0
21.9
50
12049
Florida
Hardee
18
27.0
24.6
20.5
51
12051
Florida
Hendry
18
22.9
24.1
22.1
52
12077
Florida
Liberty
2
19.8
19.9
20.8
53
12079
Florida
Madison
2
23.8
23.1
19.8
54
12107
Florida
Putnam
6
24.3
20.9
21.1
55
13003
Georgia
Atkinson
8
24.2
23.0
22.4
56
13005
Georgia
Bacon
1
24.2
23.7
22.9
57
13007
Georgia
Baker
2
26.8
23.4
26.2
58
13017
Georgia
Ben Hill
8
23.7
22.3
24.4
59
13027
Georgia
Brooks
8
29.8
23.4
22.8
60
13031
Georgia
Bulloch
12
22.4
24.5
23.7
61
13033
Georgia
Burke
12
29.2
28.7
21.2
62
13037
Georgia
Calhoun
2
29.2
26.5
35.5
63
13043
Georgia
Candler
12
25.5
26.1
21.5
64
13049
Georgia
Charlton
1
21.3
20.9
26.2
65
13059
Georgia
Clarke
10
22.3
28.3
24.1
66
13061
Georgia
Clay
2
35.4
31.3
26.4
67
13065
Georgia
Clinch
8
25.0
23.4
23.3
68
13071
Georgia
Colquitt
8
25.8
19.8
23.4
69
13075
Georgia
Cook
8
22.5
20.7
19.9
70
13081
Georgia
Crisp
8
30.4
29.3
26.0
71
13087
Georgia
Decatur
2
26.9
22.7
22.3
72
13093
Georgia
Dooly
2
29.0
22.1
22.5
73
13095
Georgia
Dougherty
2
27.6
24.8
26.4
74
13099
Georgia
Early
2
32.0
25.7
25.5
75
13101
Georgia
Echols
8
22.9
28.7
21.6
76
13107
Georgia
Emanuel
12
28.4
27.4
26.1
77
13109
Georgia
Evans
12
25.6
27.0
23.7
78
13131
Georgia
Grady
2
24.9
21.3
19.7
79
13141
Georgia
Hancock
10
28.8
29.4
30.3
80
13163
Georgia
Jefferson
12
27.7
23.0
22.5
81
13165
Georgia
Jenkins
12
25.2
28.4
28.9
82
13167
Georgia
Johnson
12
24.5
22.6
26.2
83
13193
Georgia
Macon
2
30.2
25.8
31.6
84
13197
Georgia
Marion
2
24.1
22.4
24.2
85
13201
Georgia
Miller
2
24.0
21.2
21.1
86
13205
Georgia
Mitchell
2
30.7
26.4
23.8
87
13209
Georgia
Montgomery
12
23.1
19.9
20.7
88
13239
Georgia
Quitman
2
28.0
21.9
23.7
89
13243
Georgia
Randolph
2
34.9
27.7
26.7
90
13245
Georgia
Richmond
12
21.9
19.6
22.2
91
13251
Georgia
Screven
12
22.3
20.1
22.5
92
13253
Georgia
Seminole
2
27.6
23.2
22.3
93
13259
Georgia
Stewart
2
29.8
22.2
32.5
94
13261
Georgia
Sumter
2
26.0
21.4
26.3
95
13263
Georgia
Talbot
2
22.3
24.2
27.3
96
13265
Georgia
Taliaferro
10
27.6
23.4
24.5
97
13267
Georgia
Tattnall
12
26.2
23.9
25.7
98
13269
Georgia
Taylor
2
25.6
26.0
26.6
99
13271
Georgia
Telfair
8
26.3
21.2
30.1
100
13273
Georgia
Terrell
2
30.9
28.6
28.1
101
13279
Georgia
Toombs
12
25.0
23.9
22.8
102
13283
Georgia
Treutlen
12
27.0
26.3
24.0
103
13287
Georgia
Turner
8
29.8
26.7
23.9
104
13289
Georgia
Twiggs
8
22.5
19.7
21.3
105
13299
Georgia
Ware
1
22.6
20.5
19.9
106
13301
Georgia
Warren
12
27.1
27.0
24.2
107
13303
Georgia
Washington
12
23.4
22.9
21.6
108
13309
Georgia
Wheeler
12
26.2
25.3
36.3
109
13315
Georgia
Wilcox
8
27.4
21.0
28.4
110
17003
Illinois
Alexander
12
30.1
26.1
25.8
111
17077
Illinois
Jackson
12
21.3
25.2
20.7
112
17153
Illinois
Pulaski
12
25.5
24.7
22.4
113
21001
Kentucky
Adair
1
24.2
24.0
22.1
114
21013
Kentucky
Bell
5
34.8
31.1
28.9
115
21025
Kentucky
Breathitt
5
40.3
33.2
30.3
116
21045
Kentucky
Casey
1
27.3
25.5
21.1
117
21051
Kentucky
Clay
5
40.3
39.7
37.2
118
21053
Kentucky
Clinton
1
35.2
25.8
23.6
119
21057
Kentucky
Cumberland
1
30.5
23.8
23.1
120
21063
Kentucky
Elliott
5
34.4
25.9
25.8
121
21065
Kentucky
Estill
6
29.5
26.4
22.7
122
21071
Kentucky
Floyd
5
32.4
30.3
26.5
123
21075
Kentucky
Fulton
1
29.2
23.1
25.9
124
21095
Kentucky
Harlan
5
33.6
32.5
29.7
125
21109
Kentucky
Jackson
5
36.1
30.2
23.9
126
21115
Kentucky
Johnson
5
29.2
26.6
25.0
127
21119
Kentucky
Knott
5
35.5
31.1
26.1
128
21121
Kentucky
Knox
5
37.9
34.8
35.0
129
21125
Kentucky
Laurel
5
25.3
21.3
21.8
130
21127
Kentucky
Lawrence
5
32.8
30.7
20.6
131
21129
Kentucky
Lee
5
39.3
30.4
31.1
132
21131
Kentucky
Leslie
5
34.1
32.7
26.7
133
21133
Kentucky
Letcher
5
31.8
27.1
23.8
134
21135
Kentucky
Lewis
4
29.0
28.5
22.1
135
21147
Kentucky
McCreary
5
43.8
32.2
35.9
136
21153
Kentucky
Magoffin
5
39.1
36.6
29.2
137
21159
Kentucky
Martin
5
33.0
37.0
48.1
138
21165
Kentucky
Menifee
5
31.6
29.6
25.1
139
21169
Kentucky
Metcalfe
1
25.3
23.6
24.2
140
21171
Kentucky
Monroe
1
24.3
23.4
23.7
141
21175
Kentucky
Morgan
5
37.4
27.2
24.7
142
21177
Kentucky
Muhlenberg
2
22.5
19.7
20.2
143
21189
Kentucky
Owsley
5
46.4
45.4
33.1
144
21193
Kentucky
Perry
5
32.5
29.1
29.7
145
21195
Kentucky
Pike
5
26.0
23.4
23.4
146
21197
Kentucky
Powell
6
28.3
23.5
22.1
147
21201
Kentucky
Robertson
4
21.8
22.2
19.6
148
21203
Kentucky
Rockcastle
5
29.7
23.1
21.8
149
21205
Kentucky
Rowan
5
27.3
21.3
24.0
150
21207
Kentucky
Russell
1
24.1
24.3
22.3
151
21231
Kentucky
Wayne
5
34.3
29.4
25.0
152
21235
Kentucky
Whitley
5
30.6
26.4
26.9
153
21237
Kentucky
Wolfe
5
40.0
35.9
28.6
154
22001
Louisiana
Acadia Parish
3
27.6
24.5
25.0
155
22003
Louisiana
Allen Parish
4
30.5
19.9
20.1
156
22009
Louisiana
Avoyelles Parish
5, 6
34.1
25.9
27.0
157
22013
Louisiana
Bienville Parish
4
27.3
26.1
25.3
158
22017
Louisiana
Caddo Parish
4, 6
25.3
21.1
22.8
159
22021
Louisiana
Caldwell Parish
5
24.3
21.2
20.3
160
22025
Louisiana
Catahoula Parish
5
30.7
28.1
30.0
161
22027
Louisiana
Claiborne Parish
4
29.4
26.5
29.2
162
22029
Louisiana
Concordia Parish
5
29.3
29.1
25.2
163
22035
Louisiana
East Carroll Parish
5
52.0
40.5
46.5
164
22037
Louisiana
East Feliciana Parish
5
25.6
23.0
19.9
165
22039
Louisiana
Evangeline Parish
4
31.1
32.2
22.2
166
22041
Louisiana
Franklin Parish
5
33.2
28.4
23.9
167
22043
Louisiana
Grant Parish
4
23.5
21.5
20.4
168
22045
Louisiana
Iberia Parish
3
23.9
23.6
22.1
169
22047
Louisiana
Iberville Parish
2
27.6
23.1
20.2
170
22061
Louisiana
Lincoln Parish
4
24.4
26.5
28.4
171
22065
Louisiana
Madison Parish
5
39.8
36.7
34.1
172
22067
Louisiana
Morehouse Parish
5
31.5
26.8
31.3
173
22069
Louisiana
Natchitoches Parish
6
31.0
26.5
24.3
174
22071
Louisiana
Orleans Parish
1, 2
37.9
27.9
23.1
175
22073
Louisiana
Ouachita Parish
4, 5
25.1
20.7
21.4
176
22077
Louisiana
Pointe Coupee Parish
6
26.1
23.1
20.1
177
22079
Louisiana
Rapides Parish
4, 6
24.1
20.5
19.9
178
22081
Louisiana
Red River Parish
4
29.3
29.9
24.5
179
22083
Louisiana
Richland Parish
5
32.3
27.9
25.1
180
22091
Louisiana
St. Helena Parish
5
30.1
26.8
22.8
181
22097
Louisiana
St. Landry Parish
6
32.6
29.3
23.2
182
22101
Louisiana
St. Mary Parish
3
26.6
23.6
21.4
183
22107
Louisiana
Tensas Parish
5
40.1
36.3
30.8
184
22117
Louisiana
Washington Parish
5
31.0
24.7
23.3
185
22119
Louisiana
Webster Parish
4
22.7
20.2
20.3
186
22123
Louisiana
West Carroll Parish
5
27.3
23.4
19.6
187
22125
Louisiana
West Feliciana Parish
5
28.7
19.9
22.3
188
22127
Louisiana
Winn Parish
4
26.6
21.5
24.2
189
24039
Maryland
Somerset
1
22.3
20.1
22.9
190
24510
Maryland
Baltimore city
2, 7
25.7
22.9
20.2
191
28001
Mississippi
Adams
2
29.2
25.9
25.2
192
28005
Mississippi
Amite
2
27.0
22.6
22.8
193
28009
Mississippi
Benton
1
28.1
23.2
20.0
194
28011
Mississippi
Bolivar
2
40.1
33.3
38.7
195
28017
Mississippi
Chickasaw
1
20.9
20.0
19.6
196
28021
Mississippi
Claiborne
2
40.4
32.4
32.7
197
28025
Mississippi
Clay
1
26.2
23.5
20.7
198
28027
Mississippi
Coahoma
2
42.2
35.9
30.8
199
28029
Mississippi
Copiah
2
31.2
25.1
21.4
200
28041
Mississippi
Greene
4
26.6
19.6
22.1
201
28043
Mississippi
Grenada
2
23.3
20.9
20.9
202
28049
Mississippi
Hinds
2, 3
26.1
19.9
21.0
203
28051
Mississippi
Holmes
2
50.0
41.1
35.6
204
28053
Mississippi
Humphreys
2
41.9
38.2
32.8
205
28055
Mississippi
Issaquena
2
40.0
33.2
49.6
206
28061
Mississippi
Jasper
3
26.2
22.7
20.1
207
28063
Mississippi
Jefferson
2
39.3
36.0
30.2
208
28065
Mississippi
Jefferson Davis
3
34.8
28.2
25.0
209
28069
Mississippi
Kemper
3
29.8
26.0
25.9
210
28075
Mississippi
Lauderdale
3
23.6
20.8
23.6
211
28079
Mississippi
Leake
2
27.5
23.3
20.6
212
28083
Mississippi
Leflore
2
37.6
34.8
28.8
213
28087
Mississippi
Lowndes
1
21.7
21.3
19.9
214
28091
Mississippi
Marion
3
31.8
24.8
21.5
215
28093
Mississippi
Marshall
1
28.3
21.9
21.1
216
28097
Mississippi
Montgomery
2
28.0
24.3
21.6
217
28099
Mississippi
Neshoba
3
24.6
21.0
20.5
218
28103
Mississippi
Noxubee
3
36.9
32.8
28.9
219
28105
Mississippi
Oktibbeha
1, 3
26.1
28.2
25.5
220
28107
Mississippi
Panola
2
29.6
25.3
26.2
221
28111
Mississippi
Perry
4
26.3
22.0
19.6
222
28113
Mississippi
Pike
3
30.8
25.3
23.6
223
28119
Mississippi
Quitman
2
40.2
33.1
32.1
224
28123
Mississippi
Scott
3
24.1
20.7
21.1
225
28125
Mississippi
Sharkey
2
44.3
38.3
34.5
226
28127
Mississippi
Simpson
3
23.0
21.6
20.1
227
28133
Mississippi
Sunflower
2
45.9
30.0
32.5
228
28135
Mississippi
Tallahatchie
2
38.9
32.2
31.2
229
28143
Mississippi
Tunica
2
43.4
33.1
27.6
230
28147
Mississippi
Walthall
3
37.4
27.8
20.6
231
28151
Mississippi
Washington
2
35.8
29.2
35.5
232
28153
Mississippi
Wayne
4
29.2
25.4
21.0
233
28157
Mississippi
Wilkinson
2
36.5
37.7
32.2
234
28159
Mississippi
Winston
3
26.9
23.7
27.4
235
28161
Mississippi
Yalobusha
2
26.1
21.8
20.7
236
28163
Mississippi
Yazoo
2
38.2
31.9
30.9
237
29069
Missouri
Dunklin
8
28.2
24.5
23.0
238
29133
Missouri
Mississippi
8
30.4
23.7
20.5
239
29143
Missouri
New Madrid
8
25.9
22.1
19.9
240
29153
Missouri
Ozark
8
23.0
21.6
20.2
241
29155
Missouri
Pemiscot
8
34.7
30.4
27.4
242
29179
Missouri
Reynolds
8
23.9
20.1
19.8
243
29181
Missouri
Ripley
8
30.4
22.0
20.5
244
29203
Missouri
Shannon
8
27.5
26.9
22.9
245
29215
Missouri
Texas
8
22.4
21.4
20.3
246
29221
Missouri
Washington
3
28.1
20.8
19.7
247
29223
Missouri
Wayne
8
27.5
21.9
22.4
248
29510
Missouri
St. Louis city
1
32.5
24.6
20.1
249
30003
Montana
Big Horn
2
30.2
29.2
21.7
250
30005
Montana
Blaine
2
22.2
28.1
20.5
251
30035
Montana
Glacier
1
31.4
27.3
28.0
252
30085
Montana
Roosevelt
2
26.9
32.4
24.3
253
31173
Nebraska
Thurston
3
23.9
25.6
19.6
254
35005
New Mexico
Chaves
1, 2, 3
24.9
21.3
20.1
255
35006
New Mexico
Cibola
2
28.1
24.8
23.7
256
35013
New Mexico
Doña Ana
2
30.0
25.4
19.8
257
35019
New Mexico
Guadalupe
1
31.0
21.6
24.9
258
35023
New Mexico
Hidalgo
2
23.4
27.3
24.0
259
35029
New Mexico
Luna
2
34.3
32.9
26.4
260
35031
New Mexico
McKinley
2, 3
38.7
36.1
34.3
261
35033
New Mexico
Mora
3
30.7
25.4
20.9
262
35037
New Mexico
Quay
3
27.7
20.9
22.8
263
35045
New Mexico
San Juan
3
22.3
21.5
19.9
264
35047
New Mexico
San Miguel
3
30.5
24.4
24.7
265
35051
New Mexico
Sierra
2
23.1
20.9
23.5
266
35053
New Mexico
Socorro
2
31.2
31.7
25.2
267
36005
New York
Bronx
13, 14, 15, 16
33.3
30.7
27.7
268
37015
North Carolina
Bertie
1
25.3
23.5
24.3
269
37047
North Carolina
Columbus
7
23.7
22.7
20.1
270
37065
North Carolina
Edgecombe
1
23.1
19.6
22.6
271
37083
North Carolina
Halifax
1
26.4
23.9
25.5
272
37131
North Carolina
Northampton
1
24.5
21.3
20.7
273
37155
North Carolina
Robeson
7, 8
24.5
22.8
27.7
274
37165
North Carolina
Scotland
8
20.3
20.6
28.6
275
37177
North Carolina
Tyrrell
1
26.1
23.3
21.4
276
37181
North Carolina
Vance
1
20.5
20.5
23.2
277
37187
North Carolina
Washington
1
21.0
21.8
22.6
278
38005
North Dakota
Benson
at large
29.3
29.1
22.7
279
38079
North Dakota
Rolette
at large
33.8
31.0
23.5
280
38085
North Dakota
Sioux
at large
37.0
39.2
34.9
281
39009
Ohio
Athens
12
23.4
27.4
25.3
282
39105
Ohio
Meigs
2
23.2
19.8
20.8
283
40001
Oklahoma
Adair
2
25.0
23.2
23.1
284
40005
Oklahoma
Atoka
2
28.3
19.8
20.0
285
40015
Oklahoma
Caddo
3
26.6
21.7
21.1
286
40023
Oklahoma
Choctaw
2
33.3
24.3
23.5
287
40029
Oklahoma
Coal
2
25.9
23.1
21.3
288
40055
Oklahoma
Greer
3
26.2
19.6
25.7
289
40057
Oklahoma
Harmon
3
33.9
29.7
25.1
290
40063
Oklahoma
Hughes
2
26.4
21.9
24.2
291
40069
Oklahoma
Johnston
2
26.7
22.0
19.9
292
40077
Oklahoma
Latimer
2
24.9
22.7
23.1
293
40089
Oklahoma
McCurtain
2
31.4
24.7
22.2
294
40107
Oklahoma
Okfuskee
2
29.4
23.0
25.0
295
40127
Oklahoma
Pushmataha
2
30.2
23.2
23.6
296
40135
Oklahoma
Sequoyah
2
23.6
19.8
22.3
297
40141
Oklahoma
Tillman
4
25.6
21.9
19.7
298
42101
Pennsylvania
Philadelphia
2, 3, 5
26.5
22.9
20.3
299
45005
South Carolina
Allendale
6
34.3
34.5
32.6
300
45009
South Carolina
Bamberg
6
27.9
27.8
27.7
301
45011
South Carolina
Barnwell
2
21.9
20.9
27.2
302
45027
South Carolina
Clarendon
6
29.8
23.1
20.0
303
45029
South Carolina
Colleton
1, 6
24.1
21.1
23.0
304
45031
South Carolina
Darlington
7
21.8
20.3
22.3
305
45033
South Carolina
Dillon
7
28.4
24.2
24.4
306
45039
South Carolina
Fairfield
5
22.2
19.6
20.7
307
45049
South Carolina
Hampton
6
24.4
21.8
24.2
308
45061
South Carolina
Lee
5
31.4
21.8
24.5
309
45067
South Carolina
Marion
7
26.3
23.2
25.4
310
45069
South Carolina
Marlboro
7
24.1
21.7
27.2
311
45075
South Carolina
Orangeburg
2, 6
25.6
21.4
21.7
312
45089
South Carolina
Williamsburg
6
28.0
27.9
24.8
313
46007
South Dakota
Bennett
at large
33.4
39.2
27.7
314
46017
South Dakota
Buffalo
at large
28.9
56.9
33.1
315
46023
South Dakota
Charles Mix
at large
23.1
26.9
21.4
316
46031
South Dakota
Corson
at large
34.5
41.0
33.7
317
46041
South Dakota
Dewey
at large
32.0
33.6
26.2
318
46071
South Dakota
Jackson
at large
31.0
36.5
29.8
319
46095
South Dakota
Mellette
at large
33.4
35.8
26.0
320
46102
South Dakota
at large
49.9
52.3
37.1
321
46121
South Dakota
Todd
at large
44.5
48.3
35.6
322
46137
South Dakota
Ziebach
at large
41.7
49.9
46.2
323
47013
Tennessee
Campbell
2, 3
28.0
22.8
20.6
324
47029
Tennessee
Cocke
1
25.2
22.5
20.4
325
47061
Tennessee
Grundy
4
27.7
25.8
22.8
326
47067
Tennessee
Hancock
1
33.9
29.4
26.7
327
47069
Tennessee
Hardeman
8
24.1
19.7
21.5
328
47075
Tennessee
Haywood
8
27.6
19.5
21.0
329
47091
Tennessee
Johnson
1
24.4
22.6
20.9
330
47095
Tennessee
Lake
8
33.2
23.6
34.0
331
47151
Tennessee
Scott
3, 6
30.5
20.2
21.0
332
48025
Texas
Bee
27
28.2
24.0
24.9
333
48041
Texas
Brazos
10
19.9
26.9
23.7
334
48047
Texas
Brooks
15
38.2
40.2
29.7
335
48061
Texas
Cameron
34
38.5
33.1
23.5
336
48079
Texas
Cochran
19
28.6
27.0
22.0
337
48107
Texas
Crosby
19
29.2
28.1
21.7
338
48109
Texas
Culberson
23
31.3
25.1
20.5
339
48115
Texas
Dawson
19
28.1
19.7
19.7
340
48127
Texas
Dimmit
23
40.3
33.2
27.3
341
48131
Texas
Duval
28
34.3
27.2
29.1
342
48137
Texas
Edwards
23
29.1
31.6
19.7
343
48145
Texas
Falls
17
28.0
22.6
20.1
344
48153
Texas
Floyd
19
27.4
21.5
20.3
345
48163
Texas
Frio
23
35.0
29.0
25.6
346
48191
Texas
Hall
13
27.7
26.3
22.1
347
48215
Texas
Hidalgo
15, 34
41.1
35.9
26.9
348
48225
Texas
Houston
17
25.2
21.0
21.8
349
48229
Texas
Hudspeth
23
28.4
35.8
32.0
350
48247
Texas
Jim Hogg
28
30.8
25.9
24.6
351
48249
Texas
Jim Wells
15
29.5
24.1
21.2
352
48255
Texas
Karnes
15
28.6
21.9
23.6
353
48271
Texas
Kinney
23
26.5
24.0
21.0
354
48273
Texas
Kleberg
34
26.0
26.7
22.1
355
48275
Texas
Knox
13
22.8
22.9
20.5
356
48283
Texas
La Salle
23
35.2
29.8
27.1
357
48315
Texas
Marion
1
27.1
22.4
21.7
358
48323
Texas
Maverick
23
44.8
34.8
22.8
359
48327
Texas
Menard
11
27.0
25.8
20.0
360
48347
Texas
Nacogdoches
17
21.8
23.3
19.6
361
48353
Texas
Nolan
19
21.7
21.7
20.4
362
48371
Texas
Pecos
23
27.0
20.4
21.2
363
48377
Texas
Presidio
23
37.6
36.4
22.9
364
48405
Texas
San Augustine
1
22.8
21.2
20.3
365
48427
Texas
Starr
28
49.9
50.9
28.8
366
48463
Texas
Uvalde
23
32.7
24.3
21.0
367
48465
Texas
Val Verde
23
33.2
26.1
20.2
368
48479
Texas
Webb
28
36.1
31.2
22.5
369
48489
Texas
Willacy
34
41.0
33.2
27.8
370
48505
Texas
Zapata
28
34.8
35.8
30.4
371
48507
Texas
Zavala
23
44.5
41.8
28.9
372
51027
Virginia
Buchanan
9
22.7
23.2
22.8
373
51105
Virginia
Lee
9
30.4
23.9
25.0
374
51540
Virginia
Charlottesville city
5
22.7
25.9
19.6
375
51590
Virginia
Danville city
5
20.1
20.0
23.4
376
51620
Virginia
Franklin city
2
21.7
19.8
19.8
377
51720
Virginia
Norton city
9
23.7
22.8
20.6
378
51730
Virginia
Petersburg city
4
24.3
19.6
21.2
379
54001
West Virginia
Barbour
2
28.2
22.6
20.0
380
54005
West Virginia
Boone
1
25.9
22.0
20.8
381
54007
West Virginia
Braxton
1
28.2
22.0
19.7
382
54013
West Virginia
Calhoun
1
30.9
25.1
21.3
383
54015
West Virginia
Clay
1
35.8
27.5
23.5
384
54021
West Virginia
Gilmer
1
32.3
25.9
26.4
385
54043
West Virginia
Lincoln
1
32.8
27.9
21.7
386
54047
West Virginia
McDowell
1
38.8
37.7
36.2
387
54055
West Virginia
Mercer
1
23.9
19.7
19.7
388
54059
West Virginia
Mingo
1
30.5
29.7
28.8
389
54087
West Virginia
Roane
1
27.9
22.6
19.6
390
54089
West Virginia
Summers
1
29.6
24.4
22.6
391
54101
West Virginia
Webster
1
36.4
31.8
26.3
392
54109
West Virginia
Wyoming
1
28.3
25.1
21.5
393
55078
Wisconsin
Menominee
8
31.0
28.8
27.4
Source: Congressional Research Service (CRS) tabulation of data from U.S. Census Bureau, 1993 and 2023 Small Area Income and Poverty Estimates, Census 2000, and 119th Congress Block Equivalency File (downloaded February 19, 2025).
Notes: FIPS: Federal Information Processing Standard.
a.counties; conversely, a single county may be split among multiple congressional districts. Part of counties; conversely, a single county may be split among multiple congressional districts. Part of
example, is represented by example, is represented by
(indicated by the 2). Counties labeled (indicated by the 2). Counties labeled
entire state.entire state.
Figure 1. Persistent Poverty Counties Using Two Rounding Methods, Based on
and Census 2000 Source: Created by the Congressional Research Service (CRS) using data from U.S. Census Bureau, Created by the Congressional Research Service (CRS) using data from U.S. Census Bureau,
Poverty Estimates, and Census 2000. 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. of households.
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. counties, particularly counties with small populations.
144 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).
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.
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.
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.
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
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 ongoing, the publication of the data takes place
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 AmericaImproving America
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.
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,
the source of official national poverty estimates, hence it is the preferred source for national the source of official national poverty estimates, hence it is the preferred source for national
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.
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 available. The SIPP[30] 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 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 Bureaubelow reproduces the Census Bureau
geographic levels.geographic levels.
Cross-Sectional Estimates
Geographic Level
Income/Poverty Rate
Detailed Characteristics
Year-to-Year Change
Longitudinal Estimates
United States
CPS ASEC
CPS ASEC/
ACS 1-year estimates for detailed race groups
CPS ASEC
SIPP
States
ACS 1-year estimates
averages
ACS 1-year estimates
Substate (areas with populations of 65,000 or more)
ACS 1-year estimates/
SAIPE for counties SAIPE for countiesand school districtsand school districts
ACS 1-year estimates
and school districtsand school districts
None
SAIPE for counties and school districts/
entities/
Decennial Census 2000 and prior
ACS 5-year estimates/
Decennial Census 2000 and prior
SAIPE for counties and school districts/
ACS using 5-year period estimates for all other geographic entitiesbNone
State-to-Nation comparison
CPS ASEC
CPS ASEC
CPS ASEC
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, January 25, 2023.guidance/data-sources.html, 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.
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.htmlhttps://www.census.gov/programs-surveys/acs/guidance/estimates.html
change.
Sarah K. Braun, CRS Research Librarian, assisted with legislative research, and Calvin DeSouza, CRS GIS Analyst, created the county map.
Footnotes
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.
For a more thorough discussion of how poverty is defined and measured, see CRS Report R44780, An Introduction to Poverty Measurement, by Joseph Dalaker.
Additionally, in the 112th Congress, the 10-20-30 provision was proposed as an amendment to H.R. 1 that was not adopted.
In the 118th Congress, the Consolidated Appropriations Act, 2024 (P.L. 118-42) 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 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; and Division F, Title I, for National Infrastructure Investments, though in that case a figure of 5% rather than 10% was to be set aside, among other provisions. In the Further Consolidated Appropriations Act, 2024 (P.L. 118-47), Division B Title I applied the 10-20-30 provision to the Community Development Financial Institutions (CDFI) Fund Program Account.
For example, the following research articles discuss the linkages between persistent poverty and cancer, depression, and academic achievement and school quality. For a discussion of liver cancer, see Matthew Ledenko and Tushar Patel, "Association of county level poverty with mortality from primary liver cancers," Cancer Medicine, vol. 13 no. 15, August 2024, https://doi.org/10.1002/cam4.7463; for a discussion of breast cancer, see Robert B. Hines et al., "Health insurance and neighborhood poverty as mediators of racial disparities in advanced disease stage at diagnosis and nonreceipt of surgery for women with breast cancer," Cancer Medicine, vol. 12 no. 14, July 2023, https://doi.org/10.1002/cam4.6127; for diagnosis, surgery, and survival rates for small-cell lung, breast, and colorectal cancer, see Marianna V. Papageorge et al., "The Persistence of Poverty and its Impact on Cancer Diagnosis, Treatment and Survival," Annals of Surgery, vol. 277 no. 6, June 2023, https://journals.lww.com/annalsofsurgery/abstract/2023/06000/the_persistence_of_poverty_and_its_impact_on.20.aspx. For a meta-analysis of depression and persistent poverty, see Bethany M. Wood et al., "The Price of Growing Up in a Low-Income Neighborhood: A Scoping Review of Associated Depressive Symptoms and Other Mood Disorders among Children and Adolescents," International Journal of Environmental Research and Public Health, vol. 20 no. 19, October 2023, https://doi.org/10.3390/ijerph20196884. For an analysis of persistent poverty's effects on children's academic achievement as distinct from school quality's effects on their achievement, see Geoffrey T. Wodtke et al., "Are Neighborhood Effects Explained by Differences in School Quality?" American Journal of Sociology, vol. 128 no. 5, October 2023, https://www.journals.uchicago.edu/doi/10.1086/724279.
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, Craig Benson, Alemayehu Bishaw, and Brian Glassman, "Persistent Poverty in Counties and Census Tracts," U.S. Census Bureau, American Community Survey Report ACS-51, May 2023, at https://www.census.gov/library/publications/2023/acs/acs-51.html; 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.
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."
The state of Connecticut reorganized its counties in 2022, going from 8 to 9 (bringing the total U.S. count from 3,143 to 3,144), with all Connecticut counties undergoing boundary changes. While this represents a break in the data series, none of Connecticut's counties are persistent poverty counties. Since the Census Bureau began measuring poverty, the highest estimated poverty rates for Connecticut counties included Windham County's poverty rate of 13.3% in 1959 (from the 1960 census) and the 13.3% estimated for the Greater Bridgeport Planning Region in 2022 (from the American Community Survey, using Connecticut's new county designations for the first time)—well below the required 20% over 30 years.
Two public laws enacted by the 118th Congress used the 10-20-30 provision (see footnote 4 for details). In the 117th Congress, P.L. 117-328 (the Consolidated Appropriations Act, 2023) used 10-20-30 provisions in multiple sections, as did P.L. 117-103 (the Consolidated Appropriations Act, 2022). Both P.L. 117-169 (the Inflation Reduction Act of 2022) and P.L. 117-58 (the Infrastructure Investment and Jobs Act) referred to persistent poverty counties without specifically using a figure of 10% for a set-aside, and in that same Congress 74 bills that were introduced but not enacted referred to persistent poverty counties, with or without a 10% set-aside. Of the public laws passed by the 116th Congress, P.L. 116-6 (the Consolidated Appropriations Act, 2019), P.L. 116-93 (the Consolidated Appropriations Act, 2020), and P.L. 116-94 (the 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 (the Consolidated Appropriations Act, 2017), P.L. 115-141 (the Consolidated Appropriations Act, 2018), and P.L. 115-334 (the 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 (the America's FOCUS Act of 2015), H.R. 5393 (the Commerce, Justice, Science, and Related Agencies Appropriations Act, 2017), H.R. 5054 (the Agriculture, Rural Development, Food and Drug Administration, and Related Agencies Appropriations Act, 2017), H.R. 5538 (the Department of the Interior, Environment, and Related Agencies Appropriations Act, 2017), and S. 3067/H.R. 5485 (the 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.
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).
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.
For further details about the official definition of poverty, see CRS Report R44780, An Introduction to Poverty Measurement, by Joseph Dalaker.
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.
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. For estimates and a discussion of persistent poverty in the U.S. Island Areas and Puerto Rico, see Craig Benson and Alemayehu Bishaw, "Persistent Poverty in Puerto Rico and the U.S. Island Areas," U.S. Census Bureau, American Community Survey Report ACS-57, August 7, 2024, at https://www.census.gov/library/publications/2024/acs/acs-57.html.
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.
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.
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).
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.
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.
P.L. 111-5, Section 105.
Rounding is not the only mathematical procedure that could affect the list of counties. The U.S. Economic Development Administration also considered whether the margin of error of the estimated poverty rate includes 20%, as did a 2021 study by the Government Accountability Office. For a discussion, see Craig Benson, Alemayehu Bishaw, and Brian Glassman, "Persistent Poverty in Counties and Census Tracts," U.S. Census Bureau, American Community Survey Report ACS-51, May 2023, https://www.census.gov/library/publications/2023/acs/acs-51.html.
This example list reflects the definition used in Section 533 of the Consolidated Appropriations Act, 2024 (P.L. 118-42), which applied the 10-20-30 provision to Public Works grants authorized by the Public Works and Economic Development Act of 1965 and grants authorized by Section 27 of the Stevenson-Wydler Technology and Innovation Act of 1980; this same definition was used in Division E, Title II, for the State and Tribal Assistance Grants used to carry out Section 104(k) of the Comprehensive Environmental Response, Compensation, and Liability Act of 1980.
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 15. For estimates and a discussion of persistent poverty in the U.S. Island Areas and Puerto Rico, see Craig Benson and Alemayehu Bishaw, "Persistent Poverty in Puerto Rico and the U.S. Island Areas," U.S. Census Bureau, American Community Survey Report ACS-57, August 7, 2024, at https://www.census.gov/library/publications/2024/acs/acs-57.html.
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.
Downloaded from http://www.census.gov/topics/income-poverty/poverty/guidance/data-sources.html, January 25, 2023.
CPS ASEC: Current Population Survey Annual Social and Economic Supplement.
SIPP: Survey of Income and Program Participation; mentioned here only as part of the quotation.