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," was24, 2021 Poverty Counties Joseph Dalaker Research has suggested that areas for which the poverty rate (the percentage of the Analyst in Social Policy population that is below poverty, or economic hardship as measured by comparing income against a dollar amount that represents a low level of need) reaches 20% experience more acute systemic problems than in lower-poverty areas. 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 allocateal ocate at least 10% of funds from three rural development program accounts to persistent poverty counties; that is, to counties—counties that have hadmaintained 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 wil 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 to beas 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. Before the mid-1990s, In the past, the decennial census was the only source of county poverty estimates. However, currently, the only data sources that provide poverty estimates for all U.S. counties are 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 al U.S. counties: the American Community Survey the American Community Survey (ACS) and the Small Smal 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"persistently poor in a time span that ends after the year 2000, it must first be decidedpolicymakers and researchers must first decide whether ACS or SAIPE poverty estimates will wil be used for the later part of that time span.

• Characteristics of interest: SAIPE is suited for poverty or median income alone; ACS for other topics in addition to poverty and income.
• 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 produces may be used to produce estimates for other small smal geographic areas as wel (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.  geographic areas as well.
• Reference period of estimate: SAIPE for one year; ACS for a five-year span.
• Rounding method for poverty rates: roundingRounding to 20.0% (one decimal place) yields a shorter list of counties with persistent poverty than rounding to 20% (whole number).
•  Special populations: Poverty status is not defined for all persons: foster children (unrelated individuals under age 15al persons. This includes unrelated individuals under age 15 (e.g., children in foster care), institutionalized persons, and residents of college dormitories are excluded; the homeless are not explicitly targeted by household surveys; and areas with Congressional Research Service large numbers of students living off-campus may have highhigher poverty rates.

than might be expected, because poverty is measured using cash income and does not include student loans. Congressional Research Service link to page 6 link to page 7 link to page 8 link to page 8 link to page 9 link to page 10 link to page 10 link to page 10 link to page 10 link to page 10 link to page 10 link to page 10 link to page 10 link to page 11 link to page 11 link to page 11 link to page 11 link to page 14 link to page 27 link to page 27 link to page 12 link to page 12 link to page 14 link to page 14 link to page 14 link to page 30 link to page 30 link to page 28 The 10-20-30 Provision: Defining Persistent Poverty Counties Contents Introduction ................................................................................................................... 1 Motivation for Targeting Funds to Persistent Poverty Counties............................................... 2 Defining Persistent Poverty Counties ................................................................................. 3 Computing the Poverty Rate for an Area ....................................................................... 3 Data Sources Used in Identifying Persistent Poverty Counties .......................................... 4 Considerations When Identifying and Targeting Persistent Poverty Counties ............................ 5 Selecting the Data Source: Strengths and Limitations of ACS and SAIPE Poverty Data ...................................................................................................................... 5 Characteristics of Interest: SAIPE for Poverty Alone; ACS for Other Topics in Addition to Poverty............................................................................................ 5 Geographic Area of Interest: SAIPE for Counties and School Districts Only; ACS for Other Smal Areas......................................................................................... 5 Reference Period of Estimate: SAIPE for One Year, ACS for a Five-Year Span .............. 5 Other Considerations.................................................................................................. 6 Treatment of Special Populations in the Official Poverty Definition ............................. 6 Persistence Versus Flexibility to Recent Situations .................................................... 6 Effects of Rounding and Data Source Selection on Lists of Counties ............................ 6 Example List of Persistent Poverty Counties ....................................................................... 9 Figures Figure 1. Persistent Poverty Counties Using Two Rounding Methods, Based on 1990 Census, Census 2000, and 2019 Smal Area Income and Poverty Estimates ......................... 22 Tables Table 1. Number of Counties Identified as Persistently Poor, Using Different Datasets and Rounding Methods ....................................................................................................... 7 Table 2. List of Persistent Poverty Counties, Based on 1990 Census, Census 2000, and 2019 Smal Area Income and Poverty Estimates (SAIPE), Using Poverty Rates of 19.5% or Greater .......................................................................................................... 9 Table A-1. U.S. Census Bureau’s Guidance on Poverty Data Sources by Geographic Level and Type of Estimate.......................................................................................... 25 Appendixes Appendix. Details on the Data Sources ............................................................................. 23 Congressional Research Service link to page 31 The 10-20-30 Provision: Defining Persistent Poverty Counties Contacts Author Information ....................................................................................................... 26 Congressional Research Service 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,"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 allocateal ocate 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

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'Congress’s interest both in addressing povertypoverty (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 past four Congresses included 10-20-30 language in multiple appropriations billsbil s, some of which were enacted into law. However, the original language used in ARRA ARRA could not be re-used verbatim, because the decennial census—the data source used by ARRA ARRA to define persistent poverty—the decennial census—stopped collecting income information. As a consequence, the appropriations billsbil s varied slightly in their definitions of "persistent poverty counties" as it was applied to various programs and departments, sometimes. This variation occurred even within different sections of the same bill bil if the bill bil included language on 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 billsbil s included legislation for rural development, public works and economic development, technological innovation, and brownfields site assessment and remediation. Most recently, in the 116^th116th Congress, much of the language used in these previous billsbil s was included in P.L. 116-6 (Consolidated Appropriations Act, 2019), P.L. 116-93 (Consolidated Appropriations Act, 2020), and P.L. 116-94 (Further Consolidated Appropriations Act, 2020).²

3 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 Measurem ent, by Joseph Dalaker. 3 In the 116th Congress, the Consolidated Appropriations Act, 2019 (P.L. 116-6) included 10-20-30 language in numerous sections: Section 752, in reference to loans and grants for rural housing, business and economic development, and utilities; Section 539, 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 T echnology Innovation Act of 1980; Division D, T itle I, in reference to the Community Development Financial Institutions (CDFI) Fund Program Account; and Division E, T itle 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. T hese same programs, with the addition of T ransit Infrastructure Grants, were included in two appropriations acts for FY2020: the Consolidated Appropriations Act, 2020 ( P.L. 116-93; public works grants in Division B, T itle V, Section 533, and CDFI in Division C, T itle I), and the Further Consolidation Appropriations Act, 2020 ( P.L. 116-94; rural programs in Division B, T itle VII, Section 740; CERCLA in Division D, T itle II; and T ransit Infrastructure Grants in Division H, T itle I). Additionally, more than a dozen bills referencing 10 -20-30 or persistent poverty counties had been introduced in the 116th Congress but not enacted. T hese bills covered a wide range of topics, such as rural jobs, restructuring of rural development loans, hospitals in rural areas, veterans’ job opportunities, internet accessibility, the donation of federal electronic equipment to schools, programs to prevent or eliminate discrimination in h ousing, programs to support victims of trafficking, programs to ameliorate opioid abuse and various other Department of Congressional Research Service 1 The 10-20-30 Provision: Defining Persistent Poverty Counties 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

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.³ areas.4 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.⁴5 The ill il effects of high poverty rates have been documented both for urban and rural areas.⁵ Therefore, policy interventions at the community level, and not only at the individual 6 Depending on the years in which poverty is measured and the data sources used, between 360 and 500 counties have been identified as persistent poverty counties, out of a total of 3,143 counties or county-equivalent Justice programs, and a number of regional authorities and commissions. 4 For instance, George Galster of Wayne State University conduct ed 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 a bout 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, “T he Mechanism(s) of Neighborhood Effects: T heory, Evidence, and Policy Implications,” presented at the Economic and Social Research Council Seminar, “Neighbourhood Effects: T heory & 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 T ime: 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 Statist ical Brief, June 1995. 5 T he effects of poverty rates on property values are explored by George C. Galster, Jackie M. Cutsinger, and Ron Malega in “T he 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, Program s, 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.” 6 See, for instance, a 2008 report issued jointly by the Federal Reserve System and the Brookings Institution, “The Enduring Challenge of Concentrated Poverty in Am erica: Case Studies from Communities Across the U.S.,” David Erickson et al., eds., 2008. 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 Calvin Beale in “Income and Poverty,” chapter 11 in Glenn V. Fuguitt, David L. Brown, and Calvin L. Beale, eds., Rural and Sm all Town Am erica, Russell Sage Foundation, 1988. Congressional Research Service 2 link to page 6 The 10-20-30 Provision: Defining Persistent Poverty Counties 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.7 Defining Persistent Povertyor family level, have been and may continue to be of interest to Congress.⁶

Defining "Persistent Poverty" Counties

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 are measured using decennial census data, and for. For more recent years, either the Small Smal 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.8 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 wil be described below.

Computing the Poverty Rate for an Area

Poverty rates are computed by the Census Bureau for the nation, states, and smallersmal er geographic areas such as counties.⁷9 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 calledcal ed 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 unrelated individuals) whose income is less than their respective poverty threshold are considered to be in poverty.⁸

(sometimes also described as below poverty).10 Every person in a family has the same poverty status. Thus, it is possible to compute a poverty rate based on counts of persons. This is done by (dividing the number of persons below poverty within a county by the county'dividing the number of persons below poverty 7 In 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; see footnote 3 for details. Of the public laws passed by the 115 th 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 (Agricultural Improvement Act of 2018). Multiple other bills were introduced but not enacted into public law. In the 114th Congress, no bills containing 10-20-30 language were enacted into public law, but 10-20-30 language was included in H.R. 1360 (America’s FOCUS Act of 2015), H.R. 5393 (Commerce, Justice, 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). However, 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 (T he 10-20-30 Act of 2014) was introduced and referred to committee but not passed. 8 T he decennial census does not collect income information in the 50 states, the District of Columbia, and Puerto Rico, but still asks for income information in American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands. Neit her ACS nor SAIPE poverty estimates are currently available for these island areas. 9 T here are two definitions of poverty used 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. 10 For further details about the official definition of poverty, see CRS Report R44780, An Introduction to Poverty Measurem ent, by Joseph Dalaker. Congressional Research Service 3 link to page 28 The 10-20-30 Provision: Defining Persistent Poverty Counties within a county by the county’s total population,11s total population,⁹ and multiplying by 100 to express as a percentage).

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 al 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 was onlyis collected once every 10 years, and used to be. In the past, these data were the 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.¹⁰12 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 wil be used for the later part of that time span.

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 al counties are available annually annual y, but are based on responses over the previous five-year time span (e.g., 2013-20172015-2019). The SAIPE program was developed specificallyspecifical y for estimating poverty at the county level for school-age children and for the overall overal population, for use in funding allocations al ocations for the Improving America'’s Schools Act of 1994 (P.L. 103-382). SAIPE data are also available annually annual y, 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.

the Appendix. 11 Poverty rates are computed using adjusted population totals because there are some individuals whose poverty status is not determined. T hese include unrelated individuals under age 15, such as foster children, who are not asked income questions and who are not related to anyone else in their residence by birth, marriage, or adoption; 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. T hese 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. 12 T he 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 island areas; decennial census data are the only small-area poverty data available for them. T he 2020 Census questionnaire for these island areas 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. Congressional Research Service 4 link to page 28 The 10-20-30 Provision: Defining Persistent Poverty Counties 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.¹¹

13 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, especiallyespecial y for counties with small smal populations, and when additional demographic and economic detail is not needed at that level.¹²14 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 smal 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.

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 smallestsmal est 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 reference period of the data—both data sources release data on an annual basis; the ACS estimates for small smal areas are based on the prior five years, not the prior year alone.

13 T his 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 Appe ndix. 14 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. Congressional Research Service 5 The 10-20-30 Provision: Defining Persistent Poverty Counties Other Considerations Other Considerations

Treatment of Special Populations in the Official Poverty Definition

Poverty 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 partiallypartial y 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 receivedrec eived 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 living in college dormitories.¹³15 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.¹⁴

16 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 allocational ocation of funds. Other economic measures may be of use, depending on the type of program for which funds are being targeted.

"Persistence" 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 allowal ow 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."¹⁵”17 Poverty rates published by the Census Bureau are typically typical y reported to one decimal place. The numeral used in the ARRA language was the whole number 20. Thus, for any collectioncol ection 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 al three years, or using 15 Details on the poverty universe in the ACS are available at https://www2.census.gov/programs-surveys/acs/tech_docs/subject_definitions/2018_ACSSubjectDefinitions.pdf?#page=107 and for the SAIPE estimates at https://www.census.gov/programs-surveys/saipe/guidance/model-input -data/denominators/poverty.html. 16 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. 17 P.L. 111-5, Section 105. Congressional Research Service 6 link to page 12 link to page 13 link to page 13 The 10-20-30 Provision: Defining Persistent Poverty Counties three years, or using poverty rates that round up to the whole number 20% or greater in all al three years (i.e., poverty rates of 19.5% or more in all al three years). The former approach is more restrictive and results in a shorter list of counties; the latter approach is more inclusive.

Table 1 illustratesil ustrates 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. Approximately 25 to 30 more counties appear in SAIPE-based lists compared to ACS-based lists using the same rounding method. Compared to using 20.0% as the cutoff (rounded to one decimal place), rounding up to 20% from 19.5% adds approximately 50 to 60 counties to the list. Taking both the data source and the rounding method together, the list of persistent poverty counties could vary by roughly 60 to 100 counties in a given year depending on the method used.

125.html. Example List of Persistent Poverty Counties

The list of persistent poverty counties below (Table 2) is based on data from the 1990 Census, Census 2000, and the 20182019 SAIPE estimates, and includedincludes the 407 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 inin Figure 1.