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

Updated February 3, 2020 (R45100)
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Tables

Appendixes

Summary

Antipoverty interventions that provide resources to local communities, based on the characteristics of those communities, have been of interest to Congress. One such policy, dubbed the "10-20-30 provision," was implemented in the American Recovery and Reinvestment Act of 2009 (ARRA, P.L. 111-5). Title I, Section 105 of ARRA required the Secretary of Agriculture to allocate at least 10% of funds 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. 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.

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. Therefore, policy interventions at the community level (such as applying the 10-20-30 provision to other programs besides those cited in ARRA), and not only at the individual or family level, could continue to be of interest to Congress.

Poverty rates are computed using data from household surveys. The list of counties identified to be 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, 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 the American Community Survey (ACS) and the Small Area Income and Poverty Estimates program (SAIPE). Therefore, to determine whether an area is "persistently" poor in a time span that ends after the year 2000, it must first be decided whether ACS or SAIPE poverty estimates will be used for the later part of that time span.

When determining the rounding method and data source to be used to compile a list of persistent poverty counties, the following may be relevant to consider:

Introduction

Antipoverty interventions that provide resources to local communities, based on the characteristics of those communities, have been of interest to Congress. One such policy, dubbed the "10-20-30 provision," was implemented in the American Recovery and Reinvestment Act of 2009 (ARRA, P.L. 111-5). Title I, Section 105 of ARRA required the Secretary of Agriculture to allocate at least 10% of funds provided in that act from three rural development program accounts to persistent poverty counties; that is, to counties that have had poverty rates of 20% or more for the past 30 years, as measured by the 1980, 1990, and 2000 decennial censuses.1

One notable characteristic of this provision is that it did not increase spending for the rural development programs addressed in ARRA, but rather targeted existing funds differently. Given Congress's interest both in addressing poverty and being mindful about levels of federal spending, the past four Congresses included 10-20-30 language in multiple appropriations bills, some of which were enacted into law. However, the original language used in ARRA could not be used verbatim, because the data source used by ARRA to define persistent poverty—the decennial census—stopped collecting income information. As a consequence, the appropriations bills varied slightly in their definitions of "persistent poverty counties" as it was applied to various programs and departments, sometimes even within different sections of the same bill if the bill 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 bills included legislation for rural development, public works and economic development, technological innovation, and brownfields site assessment and remediation. Most recently, in the 116th Congress, much of the language used in these previous bills 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

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, nor does it examine the range of programs or policy goals for which the 10-20-30 provision might be an appropriate policy tool.

Motivation for Targeting Funds to Persistent Poverty Counties

Research has suggested that areas for which the poverty rate (the percentage of the population that is below poverty) reaches 20% experience systemic problems that are more acute than in lower-poverty areas. The poverty rate of 20% as a critical point has been discussed in academic literature as relevant for examining social characteristics of high-poverty versus low-poverty areas.3 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.4 The ill 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 or family level, have been and may continue to be of interest to Congress.6

Defining "Persistent Poverty" Counties

Persistent poverty counties are counties that have had poverty rates of 20% or greater for at least 30 years. The county poverty rates for 1999 and previous years are measured using decennial census data, and for more recent years, either the Small Area Income and Poverty Estimates (SAIPE) or the American Community Survey (ACS). The data sources used, and the level of precision of rounding for the poverty rate, affects the list of counties identified as persistent poverty counties, as will be described below.

Computing the Poverty Rate for an Area

Poverty rates are computed by the Census Bureau for the nation, states, and smaller geographic areas such as counties.7 The official definition of poverty in the United States is based on the money income of families and unrelated individuals. Income from each family member (if family members are present) is added together and compared against a dollar amount called a poverty threshold, which represents a level of economic hardship and varies according to the size and characteristics of the family (ranging from one person to nine persons or more). Families (or unrelated individuals) whose income is less than their respective poverty threshold are considered to be in poverty.8

Every person in a family has the same poverty status. Thus, it is possible to compute a poverty rate based on counts of persons (dividing the number of persons below poverty within a county by the county's total population,9 and multiplying by 100 to express as a percentage).

Data Sources Used in Identifying Persistent Poverty Counties

Poverty rates are computed using data from household surveys. Currently, the only data sources that provide poverty estimates for all U.S. counties are the ACS and SAIPE. Before the mid-1990s, the only poverty data available at the county level came from the Decennial Census of Population and Housing, which was only collected once every 10 years, and used to be 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.10 Therefore, to determine whether an area is persistently poor in a time span that ends after 2000, it must first be decided whether ACS or SAIPE poverty estimates will be used for the later part of that time span.

The ACS and the SAIPE program serve different purposes. The ACS was developed to provide continuous measurement of a wide range of topics similar to that formerly provided by the decennial census long form, available down to the local community level. ACS data for all counties are available annually, but are based on responses over the previous five-year time span (e.g., 2013-2017). The SAIPE program was developed specifically for estimating poverty at the county level for school-age children and for the overall population, for use in funding allocations for the Improving America's Schools Act of 1994 (P.L. 103-382). SAIPE data are also available annually, and reflect one calendar year, not five. However, unlike the ACS, SAIPE does not provide estimates for a wide array of topics. For further details about the data sources for county poverty estimates, see the Appendix.

Considerations When Identifying and Targeting Persistent Poverty Counties

Selecting the Data Source: Strengths and Limitations of ACS and SAIPE Poverty Data

Because poverty estimates can be obtained from multiple data sources, the Census Bureau has provided guidance on the most suitable data source to use for various purposes.11

Characteristics of Interest: SAIPE for Poverty Alone; ACS for Other Topics in Addition to Poverty

The Census Bureau recommends using SAIPE poverty estimates when estimates are needed at the county level, especially for counties with small populations, and when additional demographic and economic detail is not needed at that level.12 When additional detail is required, such as for county-level poverty estimates by race and Hispanic origin, detailed age groups (aside from the elementary and secondary school-age population), housing characteristics, or education level, the ACS is the data source recommended by the Census Bureau.

Geographic Area of Interest: SAIPE for Counties and School Districts Only; ACS for Other Small Areas

For counties (and school districts) of small population size, SAIPE data have an advantage over ACS data in that the SAIPE model uses administrative data to help reduce the uncertainty of the estimates. However, ACS estimates are available for a wider array of geographic levels, such as ZIP code tabulation areas, census tracts (subcounty areas of roughly 1,200 to 8,000 people), cities and towns, and greater metropolitan areas.

Reference Period of Estimate: SAIPE for One Year, ACS for a Five-Year Span

While the ACS has greater flexibility in the topics measured and the geographic areas provided, it can only provide estimates in five-year ranges for the smallest geographic areas. Five years of survey responses are needed to obtain a sample large enough to produce meaningful estimates for populations below 65,000 persons. In this sense the SAIPE data, because they are based on a single year, are more current than the data of the ACS. The distinction has to do with the reference period of the data—both data sources release data on an annual basis; the ACS estimates for small areas are based on the prior five years, not the prior year alone.

Other Considerations

Treatment of Special Populations in the Official Poverty Definition

Poverty status is not defined for persons in institutions, such as nursing homes or prisons, nor for persons residing in military barracks. These populations are excluded from totals when computing poverty statistics. Furthermore, the homeless population is not counted explicitly in poverty statistics. The ACS is a household survey, thus homeless individuals who are not in shelters are not counted. SAIPE estimates are partially based on Supplemental Nutrition Assistance Program (SNAP) administrative data and tax data, so the part of the homeless population that either filed tax returns or received SNAP benefits might be reflected in the estimates, but only implicitly.

In the decennial census, ACS, and SAIPE estimates, poverty status also is not defined for persons living in college dormitories.13 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

Given the ways that the special populations above either are or are not reflected in poverty statistics, it may be worthwhile to consider whether counties that have large numbers of people in those populations would receive an equitable allocation of funds. Other economic measures may be of use, depending on the type of program for which funds are being targeted.

"Persistence" Versus Flexibility to Recent Situations

The 10-20-30 provision was developed to identify counties with persistently high poverty rates. Therefore, using that funding approach by itself would not allow flexibility to target counties that have recently experienced economic hardship, such as counties that had a large manufacturing plant close within the past three years. Other interventions besides the 10-20-30 provision may be more appropriate for counties that have had a recent spike in the poverty rate.

Effects of Rounding and Data Source Selection on Lists of Counties

In ARRA, persistent poverty counties were defined as "any county that has had 20 percent or more of its population living in poverty over the past 30 years, as measured by the 1980, 1990, and 2000 decennial censuses."15 Poverty rates published by the Census Bureau are typically reported to one decimal place. The numeral used in the ARRA language was the whole number 20. Thus, for any collection of poverty data, there are two reasonable approaches to compiling a list of persistent poverty counties: using poverty rates of at least 20.0% in all three years, or using poverty rates that round up to the whole number 20% or greater in all three years (i.e., poverty rates of 19.5% or more in all three years). The former approach is more restrictive and results in a shorter list of counties; the latter approach is more inclusive.

Table 1 illustrates the number of counties identified as persistent poverty counties using the 1990 and 2000 decennial censuses, and various ACS and SAIPE datasets for the last data point, under both rounding schemes. The rounding method and data source selection can each have large impacts on the number of counties listed. 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.

Table 1. Number of Counties Identified as Persistently Poor, Using Different Datasets and Rounding Methods

Counties identified as having poverty rates of 20% or more (applying rounding methods as indicated below) in 1989 (from 1990 Census), 1999 (from Census 2000), and latest year from datasets indicated below.

Dataset

Rounded to One Decimal Place (20.0% or Greater)

Rounded to Whole Number (19.5% or Greater)

Difference Between Rounding Methods

ACS, 2007-2011

397

445

48

ACS, 2008-2012

404

456

52

ACS, 2009-2013

402

458

56

ACS, 2010-2014

401

456

55

ACS, 2011-2015

397

453

56

ACS, 2012-2016

392

446

54

ACS, 2013-2017a

386

436

50

ACS, 2014-2018 a

384

430

46
 

  

  

Mean difference: 52.13
 

  

  

  

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
 

  

  

Mean difference: 55.38
 

  

  

  

Differences between datasets released in same year

  

  

  

Difference, SAIPE 2011 minus ACS 2007-2011

36

50

  

Difference, SAIPE 2012 minus ACS 2008-2012

31

35

  

Difference, SAIPE 2013 minus ACS 2009-2013

25

32

  

Difference, SAIPE 2014 minus ACS 2010-2014

26

30

  

Difference, SAIPE 2015 minus ACS 2011-2015

22

23

  

Difference, SAIPE 2016 minus ACS 2012-2016

28

23

  

Difference, SAIPE 2017 minus ACS 2013-2017

25

24

  

Difference, SAIPE 2018 minus ACS 2014-2018

11

13

  

Mean difference:

25.50

28.75

  

Source: Congressional Research Service (CRS) tabulation of data from U.S. Census Bureau, 1990 Census, Census 2000, 2012-2018 Small Area Income and Poverty Estimates, and American Community Survey 5-Year Estimates for 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, and 2014-2018.

Notes: ACS: American Community Survey. SAIPE: Small Area Income and Poverty Estimates. Comparisons between ACS and SAIPE estimates are between datasets released in the same year (both are typically released in December of the year following the reference period). There are 3,143 county-type areas in the United States.

The selection of the data source and rounding method has a large effect on the number of counties identified as being in persistent poverty. The longest list of persistent poverty counties (SAIPE, 19.5% or greater, that is, rounded up to the whole number 20%) minus the shortest list of persistent poverty counties (ACS, 20.0% or greater) yields the maximum difference. Comparing datasets that were released in the same year, the maximum differences in the lists of counties were:

SAIPE 2011, whole number - ACS, 2007-2011, one decimal = 98 counties
SAIPE 2012, whole number - ACS, 2008-2012, one decimal = 87
SAIPE 2013, whole number - ACS, 2009-2013, one decimal = 88
SAIPE 2014, whole number - ACS, 2010-2014, one decimal = 85
SAIPE 2015, whole number - ACS, 2011-2015, one decimal = 79

SAIPE 2016, whole number - ACS, 2012-2016, one decimal = 77

SAIPE 2017, whole number - ACS, 2013-2017, one decimal = 74

SAIPE 2018, whole number - ACS, 2014-2018, one decimal = 59

The lists of persistent poverty counties vary by about 81 counties on average (mean: 80.88), depending on which data source is used for the most recent poverty rate estimate, and which rounding method is applied to identify persistent poverty.

a. These counts include Rio Arriba County, NM, despite an ACS data collection error that occurred in that county in both 2017 and 2018. The Census Bureau detected the error after the five-year data for 2013-2017 had been released, but before the 2014-2018 data had been released. As a result, the 2014-2018 poverty rate for Rio Arriba County was not published, and the 2013-2017 poverty rate (formerly reported as 26.4%) was removed from the Census Bureau website. The 2012-2016 ACS poverty rate for Rio Arriba County was 23.4%, and the 2018 SAIPE poverty rate was 22.0%. Because the ACS poverty rate immediately before the error (2012-2016) and the SAIPE poverty rate were both above 20.0%, Rio Arriba County is included in this table's counts of persistent poverty counties. ACS five-year data are likely to be affected by the error for several subsequent years. For details, see https://www.census.gov/programs-surveys/acs/technical-documentation/errata/125.html.

Example List of Persistent Poverty Counties

The list of persistent poverty counties below (Table 2) is based on data from the 1990 Census, Census 2000, and the 2018 SAIPE estimates, and included counties with poverty rates of 19.5% or greater (that is, counties with poverty rates that were at least 20% with rounding applied to the whole number). These same counties are mapped in Figure 1.

Table 2. List of Persistent Poverty Counties, Based on 1990 Census, Census 2000, and 2018 Small Area Income and Poverty Estimates (SAIPE), Using Poverty Rates of 19.5% or Greater

Count

FIPS Geographic Identification Code

State

County

Congressional District(s) Representing the Countya

Poverty Rate 1989 (1990 Census)

Poverty Rate 1999 (Census 2000)

Poverty Rate 2018, from SAIPE

1

01005

Alabama

Barbour

2

25.2

26.8

30.9

2

01007

Alabama

Bibb

6

21.2

20.6

21.8

3

01011

Alabama

Bullock

2

36.5

33.5

42.5

4

01013

Alabama

Butler

2

31.5

24.6

24.5

5

01023

Alabama

Choctaw

7

30.2

24.5

22.1

6

01025

Alabama

Clarke

1, 7

25.9

22.6

22.8

7

01035

Alabama

Conecuh

2

29.7

26.6

24.4

8

01041

Alabama

Crenshaw

2

24.3

22.1

19.5

9

01047

Alabama

Dallas

7

36.2

31.1

31.4

10

01053

Alabama

Escambia

1

28.1

20.9

23.6

11

01061

Alabama

Geneva

2

19.5

19.6

21.3

12

01063

Alabama

Greene

7

45.6

34.3

30.1

13

01065

Alabama

Hale

7

35.6

26.9

25.6

14

01085

Alabama

Lowndes

7

38.6

31.4

25.1

15

01087

Alabama

Macon

3

34.5

32.8

30.2

16

01091

Alabama

Marengo

7

30.0

25.9

24.0

17

01099

Alabama

Monroe

1

22.7

21.3

21.9

18

01105

Alabama

Perry

7

42.6

35.4

35.3

19

01107

Alabama

Pickens

7

28.9

24.9

23.1

20

01109

Alabama

Pike

2

27.2

23.1

23.6

21

01113

Alabama

Russell

3

20.4

19.9

21.7

22

01119

Alabama

Sumter

7

39.7

38.7

34.7

23

01131

Alabama

Wilcox

7

45.2

39.9

33.4

24

02050

Alaska

Bethel Census Area

at large

30.0

20.6

32.7

25

02070

Alaska

Dillingham Census Area

at large

24.6

21.4

22.0

26

02158

Alaska

Kusilvak Census Areab

at large

31.0

26.2

35.1

27

02290

Alaska

Yukon-Koyukuk Census Area

at large

26.0

23.8

23.7

28

04001

Arizona

Apache

1

47.1

37.8

37.3

29

04009

Arizona

Graham

1

26.7

23.0

20.2

30

04012

Arizona

La Paz

4

28.2

19.6

23.7

31

04017

Arizona

Navajo

1

34.7

29.5

28.5

32

04023

Arizona

Santa Cruz

3

26.4

24.5

24.4

33

05011

Arkansas

Bradley

4

24.9

26.3

21.7

34

05017

Arkansas

Chicot

1

40.4

28.6

31.4

35

05027

Arkansas

Columbia

4

24.4

21.1

22.4

36

05041

Arkansas

Desha

1

34.0

28.9

24.3

37

05057

Arkansas

Hempstead

4

22.7

20.3

20.6

38

05069

Arkansas

Jefferson

1, 4

23.9

20.5

22.2

39

05073

Arkansas

Lafayette

4

34.7

23.2

22.8

40

05077

Arkansas

Lee

1

47.3

29.9

43.0

41

05079

Arkansas

Lincoln

1

26.2

19.5

27.5

42

05093

Arkansas

Mississippi

1

26.2

23.0

25.7

43

05095

Arkansas

Monroe

1

35.9

27.5

26.0

44

05099

Arkansas

Nevada

4

20.3

22.8

21.3

45

05103

Arkansas

Ouachita

4

21.2

19.5

23.3

46

05107

Arkansas

Phillips

1

43.0

32.7

35.4

47

05111

Arkansas

Poinsett

1

25.6

21.2

23.7

48

05123

Arkansas

St. Francis

1

36.6

27.5

35.6

49

05129

Arkansas

Searcy

1, 3

29.9

23.8

24.3

50

05147

Arkansas

Woodruff

1

34.5

27.0

23.7

51

06019

California

Fresno

4, 16, 21, 22

21.4

22.9

21.3

52

06025

California

Imperial

51

23.8

22.6

21.4

53

06047

California

Merced

16

19.9

21.7

21.2

54

06107

California

Tulare

21, 22, 23

22.6

23.9

22.2

55

08011

Colorado

Bent

4

20.4

19.5

33.9

56

08021

Colorado

Conejos

3

33.9

23.0

21.4

57

08023

Colorado

Costilla

3

34.6

26.8

25.2

58

08099

Colorado

Prowers

4

21.0

19.5

21.6

59

08109

Colorado

Saguache

3

30.6

22.6

24.6

60

12001

Florida

Alachua

3

23.5

22.8

19.8

61

12039

Florida

Gadsden

5

28.0

19.9

23.6

62

12047

Florida

Hamilton

5

27.8

26.0

27.6

63

12049

Florida

Hardee

17

22.8

24.6

27.0

64

12079

Florida

Madison

5

25.9

23.1

22.8

65

13003

Georgia

Atkinson

8

26.0

23.0

26.1

66

13005

Georgia

Bacon

1

24.1

23.7

22.8

67

13007

Georgia

Baker

2

24.8

23.4

22.9

68

13017

Georgia

Ben Hill

8

22.0

22.3

26.2

69

13027

Georgia

Brooks

8

25.9

23.4

24.5

70

13031

Georgia

Bulloch

12

27.5

24.5

22.9

71

13033

Georgia

Burke

12

30.3

28.7

22.0

72

13037

Georgia

Calhoun

2

31.8

26.5

37.2

73

13043

Georgia

Candler

12

24.1

26.1

24.6

74

13059

Georgia

Clarke

9, 10

27.0

28.3

27.0

75

13061

Georgia

Clay

2

35.7

31.3

29.8

76

13065

Georgia

Clinch

1

26.4

23.4

25.8

77

13071

Georgia

Colquitt

8

22.8

19.8

23.9

78

13075

Georgia

Cook

8

22.4

20.7

24.5

79

13081

Georgia

Crisp

2

29.0

29.3

27.4

80

13087

Georgia

Decatur

2

23.3

22.7

23.2

81

13093

Georgia

Dooly

2

32.9

22.1

29.1

82

13095

Georgia

Dougherty

2

24.4

24.8

29.5

83

13099

Georgia

Early

2

31.4

25.7

26.5

84

13107

Georgia

Emanuel

12

25.7

27.4

25.1

85

13109

Georgia

Evans

12

25.4

27.0

26.6

86

13131

Georgia

Grady

2

22.3

21.3

20.0

87

13141

Georgia

Hancock

10

30.1

29.4

30.7

88

13163

Georgia

Jefferson

10

31.3

23.0

22.3

89

13165

Georgia

Jenkins

12

27.8

28.4

31.8

90

13167

Georgia

Johnson

10

22.2

22.6

30.0

91

13193

Georgia

Macon

2

29.2

25.8

30.5

92

13197

Georgia

Marion

2

28.2

22.4

22.8

93

13201

Georgia

Miller

2

22.1

21.2

22.4

94

13205

Georgia

Mitchell

2

28.7

26.4

29.3

95

13209

Georgia

Montgomery

12

24.5

19.9

22.1

96

13225

Georgia

Peach

2

24.0

20.2

24.0

97

13239

Georgia

Quitman

2

33.0

21.9

25.5

98

13243

Georgia

Randolph

2

35.9

27.7

30.8

99

13251

Georgia

Screven

12

22.9

20.1

25.9

100

13253

Georgia

Seminole

2

29.1

23.2

25.4

101

13259

Georgia

Stewart

2

31.4

22.2

37.9

102

13261

Georgia

Sumter

2

24.8

21.4

25.7

103

13263

Georgia

Talbot

2

24.9

24.2

24.8

104

13265

Georgia

Taliaferro

10

31.9

23.4

24.6

105

13267

Georgia

Tattnall

12

21.9

23.9

25.6

106

13269

Georgia

Taylor

2

29.5

26.0

22.9

107

13271

Georgia

Telfair

8

27.3

21.2

31.9

108

13273

Georgia

Terrell

2

29.1

28.6

27.8

109

13277

Georgia

Tift

8

22.9

19.9

19.6

110

13279

Georgia

Toombs

12

24.0

23.9

24.9

111

13283

Georgia

Treutlen

12

27.1

26.3

26.3

112

13287

Georgia

Turner

8

31.3

26.7

27.9

113

13289

Georgia

Twiggs

8

26.0

19.7

21.3

114

13299

Georgia

Ware

1

21.1

20.5

23.6

115

13301

Georgia

Warren

10

32.6

27.0

25.7

116

13303

Georgia

Washington

10

21.6

22.9

25.9

117

13309

Georgia

Wheeler

12

30.3

25.3

39.6

118

13315

Georgia

Wilcox

8

28.6

21.0

30.8

119

16065

Idaho

Madison

2

28.6

30.5

23.9

120

17003

Illinois

Alexander

12

32.2

26.1

27.6

121

17059

Illinois

Gallatin

15

21.4

20.7

20.5

122

17077

Illinois

Jackson

12

28.4

25.2

25.7

123

17153

Illinois

Pulaski

12

30.2

24.7

19.7

124

20161

Kansas

Riley

1

21.2

20.6

20.7

125

21001

Kentucky

Adair

1

25.1

24.0

23.8

126

21011

Kentucky

Bath

6

27.3

21.9

20.4

127

21013

Kentucky

Bell

5

36.2

31.1

31.3

128

21025

Kentucky

Breathitt

5

39.5

33.2

32.5

129

21043

Kentucky

Carter

5

26.8

22.3

31.1

130

21045

Kentucky

Casey

1

29.4

25.5

26.0

131

21051

Kentucky

Clay

5

40.2

39.7

38.2

132

21053

Kentucky

Clinton

1

38.1

25.8

25.7

133

21057

Kentucky

Cumberland

1

31.6

23.8

23.3

134

21063

Kentucky

Elliott

5

38.0

25.9

25.2

135

21065

Kentucky

Estill

6

29.0

26.4

25.3

136

21071

Kentucky

Floyd

5

31.2

30.3

34.2

137

21075

Kentucky

Fulton

1

30.3

23.1

30.2

138

21095

Kentucky

Harlan

5

33.1

32.5

33.4

139

21099

Kentucky

Hart

2

27.1

22.4

22.2

140

21109

Kentucky

Jackson

5

38.2

30.2

26.5

141

21115

Kentucky

Johnson

5

28.7

26.6

25.0

142

21119

Kentucky

Knott

5

40.4

31.1

32.1

143

21121

Kentucky

Knox

5

38.9

34.8

31.9

144

21125

Kentucky

Laurel

5

24.8

21.3

20.6

145

21127

Kentucky

Lawrence

5

36.0

30.7

25.5

146

21129

Kentucky

Lee

5

37.4

30.4

34.4

147

21131

Kentucky

Leslie

5

35.6

32.7

30.8

148

21133

Kentucky

Letcher

5

31.8

27.1

31.1

149

21135

Kentucky

Lewis

4

30.7

28.5

25.2

150

21137

Kentucky

Lincoln

5

27.2

21.1

20.6

151

21147

Kentucky

McCreary

5

45.5

32.2

33.7

152

21153

Kentucky

Magoffin

5

42.5

36.6

28.4

153

21159

Kentucky

Martin

5

35.4

37.0

39.1

154

21165

Kentucky

Menifee

6

35.0

29.6

26.4

155

21169

Kentucky

Metcalfe

1

27.9

23.6

23.3

156

21171

Kentucky

Monroe

1

26.9

23.4

23.0

157

21175

Kentucky

Morgan

5

38.8

27.2

27.3

158

21189

Kentucky

Owsley

5

52.1

45.4

39.2

159

21193

Kentucky

Perry

5

32.1

29.1

28.9

160

21195

Kentucky

Pike

5

25.4

23.4

23.8

161

21197

Kentucky

Powell

6

26.2

23.5

22.0

162

21203

Kentucky

Rockcastle

5

30.7

23.1

22.8

163

21205

Kentucky

Rowan

5

28.9

21.3

22.7

164

21207

Kentucky

Russell

1

25.6

24.3

21.6

165

21231

Kentucky

Wayne

5

37.3

29.4

28.1

166

21235

Kentucky

Whitley

5

33.0

26.4

25.7

167

21237

Kentucky

Wolfe

6

44.3

35.9

31.4

168

22001

Louisiana

Acadia Parish

3

30.5

24.5

25.2

169

22003

Louisiana

Allen Parish

4

29.9

19.9

19.6

170

22007

Louisiana

Assumption Parish

2, 6

28.2

21.8

20.7

171

22009

Louisiana

Avoyelles Parish

5

37.1

25.9

27.4

172

22013

Louisiana

Bienville Parish

4

31.2

26.1

26.9

173

22017

Louisiana

Caddo Parish

4

24.0

21.1

22.4

174

22021

Louisiana

Caldwell Parish

5

28.8

21.2

20.8

175

22025

Louisiana

Catahoula Parish

5

36.8

28.1

26.0

176

22027

Louisiana

Claiborne Parish

4

32.0

26.5

32.7

177

22029

Louisiana

Concordia Parish

5

30.6

29.1

27.2

178

22035

Louisiana

East Carroll Parish

5

56.8

40.5

45.7

179

22037

Louisiana

East Feliciana Parish

5, 6

25.0

23.0

19.6

180

22039

Louisiana

Evangeline Parish

4

35.1

32.2

27.3

181

22041

Louisiana

Franklin Parish

5

34.5

28.4

27.3

182

22043

Louisiana

Grant Parish

5

25.5

21.5

20.0

183

22045

Louisiana

Iberia Parish

3

25.8

23.6

24.1

184

22047

Louisiana

Iberville Parish

2, 6

28.0

23.1

23.8

185

22049

Louisiana

Jackson Parish

5

23.9

19.8

21.6

186

22061

Louisiana

Lincoln Parish

5

26.6

26.5

25.8

187

22065

Louisiana

Madison Parish

5

44.6

36.7

41.7

188

22067

Louisiana

Morehouse Parish

5

31.0

26.8

27.4

189

22069

Louisiana

Natchitoches Parish

4

33.9

26.5

32.8

190

22071

Louisiana

Orleans Parish

1, 2

31.6

27.9

23.8

191

22073

Louisiana

Ouachita Parish

5

24.7

20.7

21.3

192

22077

Louisiana

Pointe Coupee Parish

6

30.3

23.1

20.3

193

22081

Louisiana

Red River Parish

4

35.1

29.9

23.1

194

22083

Louisiana

Richland Parish

5

33.2

27.9

26.2

195

22085

Louisiana

Sabine Parish

4

27.1

21.5

23.1

196

22091

Louisiana

St. Helena Parish

5, 6

34.4

26.8

19.6

197

22097

Louisiana

St. Landry Parish

3, 4, 5

36.3

29.3

32.7

198

22101

Louisiana

St. Mary Parish

3

27.0

23.6

21.0

199

22107

Louisiana

Tensas Parish

5

46.3

36.3

31.6

200

22117

Louisiana

Washington Parish

5

31.6

24.7

24.6

201

22119

Louisiana

Webster Parish

4

25.1

20.2

26.0

202

22123

Louisiana

West Carroll Parish

5

27.4

23.4

23.3

203

22125

Louisiana

West Feliciana Parish

5

33.8

19.9

24.4

204

22127

Louisiana

Winn Parish

5

27.5

21.5

22.8

205

26073

Michigan

Isabella

4

24.9

20.4

23.4

206

28001

Mississippi

Adams

3

30.5

25.9

29.4

207

28005

Mississippi

Amite

3

30.9

22.6

22.2

208

28007

Mississippi

Attala

2

30.2

21.8

23.7

209

28009

Mississippi

Benton

1

29.7

23.2

22.1

210

28011

Mississippi

Bolivar

2

42.9

33.3

29.4

211

28017

Mississippi

Chickasaw

1

21.3

20.0

20.3

212

28019

Mississippi

Choctaw

1

25.0

24.7

20.4

213

28021

Mississippi

Claiborne

2

43.6

32.4

36.3

214

28023

Mississippi

Clarke

3, 4

23.4

23.0

21.6

215

28025

Mississippi

Clay

1

25.9

23.5

21.9

216

28027

Mississippi

Coahoma

2

45.5

35.9

35.9

217

28029

Mississippi

Copiah

2

32.0

25.1

26.5

218

28031

Mississippi

Covington

3

31.2

23.5

26.5

219

28035

Mississippi

Forrest

4

27.5

22.5

24.0

220

28037

Mississippi

Franklin

3

33.3

24.1

20.1

221

28041

Mississippi

Greene

4

26.8

19.6

22.6

222

28043

Mississippi

Grenada

2

22.3

20.9

22.3

223

28051

Mississippi

Holmes

2

53.2

41.1

33.2

224

28053

Mississippi

Humphreys

2

45.9

38.2

37.0

225

28055

Mississippi

Issaquena

2

49.3

33.2

40.5

226

28063

Mississippi

Jefferson

2

46.9

36.0

35.5

227

28065

Mississippi

Jefferson Davis

3

33.3

28.2

26.0

228

28067

Mississippi

Jones

4

22.7

19.8

19.9

229

28069

Mississippi

Kemper

3

35.1

26.0

27.5

230

28075

Mississippi

Lauderdale

3

22.8

20.8

25.5

231

28079

Mississippi

Leake

2

29.6

23.3

25.4

232

28083

Mississippi

Leflore

2

38.9

34.8

35.1

233

28087

Mississippi

Lowndes

1

22.1

21.3

23.1

234

28091

Mississippi

Marion

4

29.6

24.8

27.2

235

28093

Mississippi

Marshall

1

30.0

21.9

20.9

236

28097

Mississippi

Montgomery

2

34.0

24.3

22.9

237

28099

Mississippi

Neshoba

3

26.6

21.0

26.9

238

28101

Mississippi

Newton

3

20.9

19.9

21.2

239

28103

Mississippi

Noxubee

3

41.4

32.8

29.0

240

28105

Mississippi

Oktibbeha

1, 3

30.1

28.2

27.3

241

28107

Mississippi

Panola

2

33.8

25.3

21.8

242

28111

Mississippi

Perry

4

29.1

22.0

22.0

243

28113

Mississippi

Pike

3

32.9

25.3

30.6

244

28119

Mississippi

Quitman

2

41.6

33.1

37.6

245

28123

Mississippi

Scott

3

27.4

20.7

25.5

246

28125

Mississippi

Sharkey

2

47.5

38.3

33.6

247

28127

Mississippi

Simpson

3

22.7

21.6

19.8

248

28133

Mississippi

Sunflower

2

41.8

30.0

32.6

249

28135

Mississippi

Tallahatchie

2

41.9

32.2

33.4

250

28143

Mississippi

Tunica

2

56.8

33.1

26.5

251

28147

Mississippi

Walthall

3

35.9

27.8

23.4

252

28151

Mississippi

Washington

2

33.8

29.2

32.6

253

28153

Mississippi

Wayne

4

29.5

25.4

21.4

254

28157

Mississippi

Wilkinson

3

42.2

37.7

30.3

255

28159

Mississippi

Winston

1

26.6

23.7

21.0

256

28163

Mississippi

Yazoo

2

39.2

31.9

37.1

257

29001

Missouri

Adair

6

24.9

23.3

23.9

258

29035

Missouri

Carter

8

27.6

25.2

22.6

259

29069

Missouri

Dunklin

8

29.9

24.5

26.1

260

29133

Missouri

Mississippi

8

29.7

23.7

26.8

261

29149

Missouri

Oregon

8

27.4

22.0

23.8

262

29153

Missouri

Ozark

8

22.1

21.6

22.0

263

29155

Missouri

Pemiscot

8

35.8

30.4

29.1

264

29179

Missouri

Reynolds

8

24.2

20.1

20.3

265

29181

Missouri

Ripley

8

31.5

22.0

23.5

266

29203

Missouri

Shannon

8

24.1

26.9

22.6

267

29215

Missouri

Texas

8

22.9

21.4

24.6

268

29221

Missouri

Washington

8

27.2

20.8

21.7

269

29223

Missouri

Wayne

8

29.0

21.9

23.3

270

29229

Missouri

Wright

8

25.3

21.7

23.9

271

29510

Missouri

St. Louis city

1

24.6

24.6

22.8

272

30003

Montana

Big Horn

at large

35.3

29.2

25.6

273

30005

Montana

Blaine

at large

27.7

28.1

20.8

274

30035

Montana

Glacier

at large

35.7

27.3

27.0

275

30085

Montana

Roosevelt

at large

27.7

32.4

25.4

276

31173

Nebraska

Thurston

1

30.9

25.6

23.9

277

35003

New Mexico

Catron

2

25.6

24.5

23.3

278

35006

New Mexico

Cibola

2

33.6

24.8

28.6

279

35013

New Mexico

Doña Ana

2

26.5

25.4

24.9

280

35019

New Mexico

Guadalupe

2

38.5

21.6

24.3

281

35023

New Mexico

Hidalgo

2

20.7

27.3

25.7

282

35029

New Mexico

Luna

2

31.5

32.9

27.2

283

35031

New Mexico

McKinley

2, 3

43.5

36.1

32.3

284

35033

New Mexico

Mora

3

36.2

25.4

23.5

285

35037

New Mexico

Quay

3

25.1

20.9

24.1

286

35039

New Mexico

Rio Arriba

3

27.5

20.3

22.0

287

35041

New Mexico

Roosevelt

2, 3

26.9

22.7

22.6

288

35045

New Mexico

San Juan

3

28.3

21.5

23.1

289

35047

New Mexico

San Miguel

3

30.2

24.4

28.2

290

35051

New Mexico

Sierra

2

19.6

20.9

25.7

291

35053

New Mexico

Socorro

2

29.9

31.7

29.6

292

35055

New Mexico

Taos

3

27.5

20.9

21.4

293

36005

New York

Bronx

13, 14, 15, 16

28.7

30.7

27.3

294

37015

North Carolina

Bertie

1

25.9

23.5

23.5

295

37017

North Carolina

Bladen

7, 9

21.9

21.0

29.1

296

37047

North Carolina

Columbus

7

24.0

22.7

25.3

297

37065

North Carolina

Edgecombe

1

20.9

19.6

22.9

298

37083

North Carolina

Halifax

1

25.6

23.9

22.0

299

37117

North Carolina

Martin

1

22.3

20.2

20.3

300

37131

North Carolina

Northampton

1

23.6

21.3

21.4

301

37147

North Carolina

Pitt

1, 3

22.1

20.3

23.2

302

37155

North Carolina

Robeson

9

24.1

22.8

24.5

303

37177

North Carolina

Tyrrell

3

25.0

23.3

25.2

304

37181

North Carolina

Vance

1

19.6

20.5

26.3

305

37187

North Carolina

Washington

1

20.4

21.8

21.1

306

38005

North Dakota

Benson

at large

31.7

29.1

30.8

307

38079

North Dakota

Rolette

at large

40.7

31.0

24.7

308

38085

North Dakota

Sioux

at large

47.4

39.2

32.9

309

39009

Ohio

Athens

6, 15

28.7

27.4

30.7

310

40001

Oklahoma

Adair

2

26.7

23.2

24.6

311

40005

Oklahoma

Atoka

2

31.1

19.8

20.8

312

40015

Oklahoma

Caddo

3

27.8

21.7

19.5

313

40021

Oklahoma

Cherokee

2

28.8

22.9

21.0

314

40023

Oklahoma

Choctaw

2

32.7

24.3

23.0

315

40029

Oklahoma

Coal

2

27.4

23.1

22.6

316

40055

Oklahoma

Greer

3

23.4

19.6

26.0

317

40057

Oklahoma

Harmon

3

34.2

29.7

23.9

318

40061

Oklahoma

Haskell

2

27.1

20.5

23.3

319

40063

Oklahoma

Hughes

2

26.9

21.9

24.6

320

40069

Oklahoma

Johnston

2

28.5

22.0

19.5

321

40089

Oklahoma

McCurtain

2

30.2

24.7

21.1

322

40107

Oklahoma

Okfuskee

2

29.4

23.0

26.1

323

40119

Oklahoma

Payne

3

21.7

20.3

22.8

324

40127

Oklahoma

Pushmataha

2

30.2

23.2

19.7

325

40141

Oklahoma

Tillman

4

22.9

21.9

21.1

326

42101

Pennsylvania

Philadelphia

2, 3, 5

20.3

22.9

24.3

327

45005

South Carolina

Allendale

6

35.8

34.5

37.3

328

45009

South Carolina

Bamberg

6

28.2

27.8

26.7

329

45011

South Carolina

Barnwell

2

21.8

20.9

22.4

330

45027

South Carolina

Clarendon

6

29.0

23.1

26.4

331

45029

South Carolina

Colleton

1, 6

23.4

21.1

20.0

332

45031

South Carolina

Darlington

7

19.9

20.3

23.5

333

45033

South Carolina

Dillon

7

28.1

24.2

32.1

334

45039

South Carolina

Fairfield

5

20.6

19.6

23.7

335

45049

South Carolina

Hampton

6

27.7

21.8

25.8

336

45061

South Carolina

Lee

5

29.6

21.8

28.1

337

45067

South Carolina

Marion

7

28.6

23.2

25.5

338

45069

South Carolina

Marlboro

7

26.6

21.7

30.0

339

45075

South Carolina

Orangeburg

2, 6

24.9

21.4

25.9

340

45089

South Carolina

Williamsburg

6

28.7

27.9

26.0

341

46007

South Dakota

Bennett

at large

37.6

39.2

32.5

342

46017

South Dakota

Buffalo

at large

45.1

56.9

45.7

343

46023

South Dakota

Charles Mix

at large

31.4

26.9

20.9

344

46027

South Dakota

Clay

at large

24.6

21.2

19.5

345

46031

South Dakota

Corson

at large

42.5

41.0

33.6

346

46041

South Dakota

Dewey

at large

44.4

33.6

25.8

347

46071

South Dakota

Jackson

at large

38.8

36.5

32.7

348

46085

South Dakota

Lyman

at large

24.7

24.3

21.1

349

46095

South Dakota

Mellette

at large

41.3

35.8

35.2

350

46102

South Dakota

Oglala Lakotac

at large

63.1

52.3

54.0

351

46109

South Dakota

Roberts

at large

26.4

22.1

21.3

352

46121

South Dakota

Todd

at large

50.2

48.3

48.4

353

46123

South Dakota

Tripp

at large

20.6

19.9

19.9

354

46137

South Dakota

Ziebach

at large

51.1

49.9

43.9

355

47013

Tennessee

Campbell

2, 3

26.8

22.8

21.6

356

47025

Tennessee

Claiborne

2

25.7

22.6

23.4

357

47029

Tennessee

Cocke

1

25.3

22.5

22.5

358

47049

Tennessee

Fentress

6

32.3

23.1

20.6

359

47061

Tennessee

Grundy

4

23.9

25.8

21.2

360

47067

Tennessee

Hancock

1

40.0

29.4

29.9

361

47069

Tennessee

Hardeman

7

23.3

19.7

23.5

362

47075

Tennessee

Haywood

8

27.5

19.5

20.5

363

47091

Tennessee

Johnson

1

28.5

22.6

20.7

364

47095

Tennessee

Lake

8

27.5

23.6

36.5

365

47151

Tennessee

Scott

3

27.8

20.2

21.2

366

47173

Tennessee

Union

3

21.3

19.6

19.8

367

48007

Texas

Aransas

27

25.2

19.9

19.9

368

48025

Texas

Bee

34

27.4

24.0

26.7

369

48041

Texas

Brazos

17

26.7

26.9

23.2

370

48047

Texas

Brooks

15

36.8

40.2

31.0

371

48061

Texas

Cameron

34

39.7

33.1

27.9

372

48079

Texas

Cochran

19

28.3

27.0

21.9

373

48107

Texas

Crosby

19

29.5

28.1

23.7

374

48109

Texas

Culberson

23

29.8

25.1

20.3

375

48115

Texas

Dawson

11

30.5

19.7

22.9

376

48127

Texas

Dimmit

23

48.9

33.2

24.6

377

48131

Texas

Duval

15

39.0

27.2

25.5

378

48137

Texas

Edwards

23

41.7

31.6

22.1

379

48141

Texas

El Paso

16, 23

26.8

23.8

20.5

380

48145

Texas

Falls

17

27.5

22.6

21.7

381

48153

Texas

Floyd

13, 19

27.1

21.5

21.0

382

48163

Texas

Frio

23

39.1

29.0

27.5

383

48169

Texas

Garza

19

23.1

22.3

24.6

384

48191

Texas

Hall

13

29.1

26.3

24.1

385

48207

Texas

Haskell

19

20.8

22.8

23.1

386

48215

Texas

Hidalgo

15, 28, 34

41.9

35.9

30.0

387

48225

Texas

Houston

8

25.6

21.0

26.1

388

48247

Texas

Jim Hogg

15

35.3

25.9

25.2

389

48249

Texas

Jim Wells

34

30.3

24.1

21.2

390

48255

Texas

Karnes

15

36.5

21.9

21.8

391

48271

Texas

Kinney

23

28.6

24.0

21.1

392

48273

Texas

Kleberg

34

27.4

26.7

25.0

393

48275

Texas

Knox

13

23.6

22.9

20.4

394

48279

Texas

Lamb

19

27.1

20.9

20.0

395

48283

Texas

La Salle

23, 28

37.0

29.8

29.6

396

48315

Texas

Marion

4

60.6

22.4

21.9

397

48323

Texas

Maverick

23

50.4

34.8

25.9

398

48327

Texas

Menard

11

31.1

25.8

21.6

399

48347

Texas

Nacogdoches

1

25.2

23.3

21.6

400

48371

Texas

Pecos

23

29.6

20.4

19.5

401

48377

Texas

Presidio

23

48.1

36.4

22.4

402

48389

Texas

Reeves

23

28.8

28.9

21.5

403

48405

Texas

San Augustine

1

29.7

21.2

22.2

404

48427

Texas

Starr

28

60.0

50.9

33.2

405

48445

Texas

Terry

19

25.5

23.3

22.4

406

48463

Texas

Uvalde

23

31.1

24.3

22.9

407

48479

Texas

Webb

28

38.2

31.2

25.7

408

48489

Texas

Willacy

34

44.5

33.2

35.0

409

48505

Texas

Zapata

28

41.0

35.8

32.1

410

48507

Texas

Zavala

23

50.4

41.8

32.0

411

49037

Utah

San Juan

3

36.4

31.4

22.6

412

51027

Virginia

Buchanan

9

21.9

23.2

27.6

413

51029

Virginia

Buckingham

5

19.5

20.0

20.2

414

51051

Virginia

Dickenson

9

25.9

21.3

25.2

415

51105

Virginia

Lee

9

28.7

23.9

24.8

416

51121

Virginia

Montgomery

9

22.1

23.2

24.1

417

51195

Virginia

Wise

9

21.6

20.0

25.4

418

51540

Virginia

Charlottesville city

5

23.7

25.9

23.1

419

51660

Virginia

Harrisonburg city

6

21.5

30.1

28.0

420

51720

Virginia

Norton city

9

26.7

22.8

20.8

421

51730

Virginia

Petersburg city

4

20.3

19.6

24.1

422

51750

Virginia

Radford city

9

32.2

31.4

30.4

423

51760

Virginia

Richmond city

4

20.9

21.4

22.3

424

53075

Washington

Whitman

5

24.2

25.6

25.4

425

54001

West Virginia

Barbour

1

28.5

22.6

20.0

426

54005

West Virginia

Boone

3

27.0

22.0

22.5

427

54007

West Virginia

Braxton

2

25.8

22.0

21.6

428

54013

West Virginia

Calhoun

2

32.0

25.1

22.8

429

54015

West Virginia

Clay

2

39.2

27.5

25.1

430

54019

West Virginia

Fayette

3

24.4

21.7

22.5

431

54021

West Virginia

Gilmer

1

33.5

25.9

24.8

432

54043

West Virginia

Lincoln

3

33.8

27.9

23.3

433

54045

West Virginia

Logan

3

27.7

24.1

24.6

434

54047

West Virginia

McDowell

3

37.7

37.7

35.4

435

54055

West Virginia

Mercer

3

20.4

19.7

22.7

436

54059

West Virginia

Mingo

3

30.9

29.7

27.0

437

54087

West Virginia

Roane

2

28.1

22.6

22.1

438

54089

West Virginia

Summers

3

24.5

24.4

26.9

439

54099

West Virginia

Wayne

3

21.8

19.6

20.9

440

54101

West Virginia

Webster

3

34.8

31.8

23.0

441

54109

West Virginia

Wyoming

3

27.9

25.1

24.1

442

55078

Wisconsin

Menominee

8

48.7

28.8

26.5

443

56001

Wyoming

Albany

at large

19.8

21.0

20.4

Source: Congressional Research Service (CRS) tabulation of data from U.S. Census Bureau, 1990 Census, Census 2000, 2018 Small Area Income and Poverty Estimates, and Nation-Based Relationship File for Congressional Districts and Counties (116th Congress).

Notes: FIPS: Federal Information Processing Standard.

a. Numbers are ordinal, referring to the name of the congressional district(s) present in the county. For example, Barbour County, AL, is represented by Alabama's 2nd Congressional District (indicated by the 2). A congressional district may span multiple counties; conversely, a single county may be split among multiple congressional districts. Part of Clarke County, AL, for example, is represented by Alabama's 1st Congressional District (indicated by the 1) and part by the 7th Congressional District (indicated by the 7). Counties labeled "at large" are located in states that have only one member of the House of Representatives for the entire state.

b. Changed name and geographic code effective July 1, 2015, from Wade Hampton Census Area (02270) to Kusilvak Census Area (02158).

c. Changed name and geographic code effective May 1, 2015, from Shannon County (46113) to Oglala Lakota County (46102).

Figure 1. Persistent Poverty Counties Using Two Rounding Methods, Based on
1990 Census, Census 2000, and 2018 Small Area Income and Poverty Estimates

Source: Created by Congressional Research Service (CRS) using data from U.S. Census Bureau, 1990 Census, Census 2000, and 2018 Small Area Income and Poverty Estimates.

Appendix. Details on the Data Sources

Decennial Census of Population and Housing, "Long Form"

Poverty estimates are computed using data from household surveys, which are based on a sample of households. In order to obtain meaningful estimates for any geographic area, the sample has to include enough responses from that area so that selecting a different sample of households from that area would not likely result in a dramatically different estimate. If estimates for smaller geographic areas are desired, a larger sample size is needed. A national-level survey, for instance, could produce reliable estimates for the United States without obtaining any responses from many counties, particularly counties with small populations. In order to produce estimates for all 3,143 county areas in the nation, however, not only are responses needed from every county, but those responses have to be plentiful enough from each county so that the estimates are meaningful (i.e., their margins of error are not unhelpfully wide).

Before the mid-1990s, the only data source with a sample size large enough to provide meaningful estimates at the county level (and for other small geographic areas) was the decennial census. The other household surveys available prior to that time did not have a sample size large enough to produce meaningful estimates for small areas such as counties. Income questions were asked on the census long form, which was sent to one-sixth of all U.S. households; the rest received the census short form, which did not ask about income. While technically still a sample, one-sixth of all households was a large enough sample to provide poverty estimates for every county in the nation, and even for smaller areas such as small towns. The long form was discontinued after Census 2000, and therefore poverty data are no longer available from the decennial census for the 50 states, the District of Columbia, and Puerto Rico.16 Beginning in the mid-1990s, however, two additional data sources were developed to ensure that poverty estimates for small areas such as counties would still be available: the American Community Survey (ACS), and the Small Area Income and Poverty Estimates program (SAIPE).

American Community Survey (ACS)

The ACS replaced the decennial census long form. It was developed to accommodate the needs of local government officials and other stakeholders who needed detailed information on small communities on a more frequent basis than once every 10 years. To that end, the ACS questionnaire was designed to reflect the same topics asked in the census long form.

In order to produce meaningful estimates for small communities, however, the ACS needs to collect a number of responses comparable to what was collected in the decennial census.17 In order to collect that many responses while providing information more currently than once every 10 years, the ACS collects information from respondents continuously, in every month, as opposed to at one time of the year, and responses over time are pooled to provide estimates at varying geographic levels. To obtain estimates for geographic areas of 65,000 or more persons, one year's worth of responses are pooled—these are the ACS one-year estimates. For the smallest geographic levels, which include the complete set of U.S. counties, five years of monthly responses are needed: these are the ACS five-year estimates. Even though data collection is ongoing, the publication of the data takes place only once every year, both for the one-year estimates and the estimates that represent the previous five-year span.

Small Area Income and Poverty Estimates (SAIPE)

The SAIPE program was developed in the 1990s in order to provide state and local government officials with poverty estimates for local areas in between the decennial census years. In the Improving America's Schools Act of 1994 (IASA, P.L. 103-382), which amended the Elementary and Secondary Education Act of 1965 (ESEA), Congress recognized that providing funding for children in disadvantaged communities created a need for poverty data for those communities that were more current than the once-a-decade census. In the IASA, Congress provided for the development and evaluation of the SAIPE program for its use in Title I-A funding allocations.18

SAIPE estimates are model-based, meaning they use a mathematical procedure to compute estimates using both survey data (ACS one-year data) and administrative data (from tax returns and numbers of participants in the Supplemental Nutrition Assistance Program, or SNAP). The modeling procedure produces estimates with less variability than estimates computed from survey data alone, especially for counties with small populations.

Guidance from the U.S. Census Bureau,
"Which Data Source to Use"19

The CPS ASEC20 provides the most timely and accurate national data on income and is the source of official national poverty estimates, hence it is the preferred source for national analysis. Because of its large sample size, the ACS is preferred for subnational data on income and poverty by detailed demographic characteristics. The Census Bureau recommends using the ACS for 1-year estimates of income and poverty at the state level. Users looking for consistent, state-level trends should use CPS ASEC 2-year averages and CPS ASEC 3-year averages for state to state comparisons.

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

The SIPP21 is the only Census Bureau source of longitudinal poverty data. As SIPP collects monthly income over 2.5 to 5 year panels, it is also a source of poverty estimates for time periods more or less than one year, including monthly poverty rates.

Table A-1 below reproduces the Census Bureau's recommendations, summarized for various geographic levels:

Table A-1. U.S. Census Bureau's Guidance on Poverty Data Sources by Geographic Level and Type of Estimate

 

Cross-Sectional Estimates

  

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

CPS ASEC 3-year averages

ACS 1-year estimates

ACS 1-year estimates

  

Substate (areas with populations of 65,000 or more)

ACS 1-year estimates/

SAIPE for counties and school districts

ACS 1-year estimates

ACS 1-year estimates / SAIPE for counties and school districts

None

Substate (areas with populations less than 20,000)a

SAIPE for counties and school districts/

ACS using 5-year period estimates for all other geographic 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 entitiesb

None

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 expansion to the notes. Original table downloaded from http://www.census.gov/topics/income-poverty/poverty/guidance/data-sources.html, January 16, 2020.

Notes:

ACS: American Community Survey.

CPS ASEC: Current Population Survey, Annual Social and Economic Supplement.

SAIPE: Small Area Income and Poverty Estimates.

SIPP: Survey of Income and Program Participation.

a. Author's note: Data for areas with populations of 20,000 to 65,000 persons previously had been produced using ACS three-year estimates, but are now only produced using the ACS five-year estimates. ACS three-year estimates are no longer produced (with 2011-2013 data as the last in the series). For details, see https://www.census.gov/programs-surveys/acs/guidance/estimates.html.

b. Use non-overlapping periods for ACS trend analysis with multiyear estimates. For example, comparing 2006-2010 ACS five-year estimates with 2011-2015 ACS five-year estimates is preferred for identifying change.

Author Contact Information

Joseph Dalaker, Analyst in Social Policy ([email address scrubbed], [phone number scrubbed])

Acknowledgments

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

Footnotes

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.

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 Technology Innovation Act of 1980; Division D, Title I, in reference to the Community Development Financial Institutions (CDFI) Fund Program Account; and 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. The sections varied in the data sources used to define "persistent poverty counties," which means the sections varied in the lists of counties targeted for the funding set-aside. These same programs, with the addition of Transit 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, Title V, Section 533, and CDFI in Division C, Title I), and the Further Consolidation Appropriations Act, 2020 (P.L. 116-94; rural programs in Division B, Title VII, Section 740; CERCLA in Division D, Title II; and Transit Infrastructure Grants in Division H, Title I). Additionally, the following bills referencing 10-20-30 had been introduced but not enacted into public law at the time of this report's release: H.R. 2055 and S. 1066 (An Act Targeting Resources to Communities in Need). Persistent poverty counties were referenced in the following bills, using policy tools other than the 10-20-30 provision: H.R. 3538 and S. 2028 (Rural Jobs Act), H.R. 186 and S. 2100 (Veterans Jobs Opportunity Act), H.R. 4808 and S. 2684 (Housing, Opportunity, Mobility, and Equity Act of 2019), H.R. 5495 (Federal Electronic Equipment Donation Act of 2019), and H.R. 2228 (to offer persistent poverty counties and political subdivisions of such counties the opportunity to have their rural development loans restructured).

3.

For instance, George Galster of Wayne State University conducted a literature review that suggested "that the independent impacts of neighborhood poverty rates in encouraging negative outcomes for individuals like crime, school leaving, and duration of poverty spells appear to be nil unless the neighborhood exceeds about 20 percent poverty." Galster distinguishes the effects of living in a poor neighborhood from the effects of being poor oneself but not necessarily in a poor neighborhood. Cited in George C. Galster, "The Mechanism(s) of Neighborhood Effects: Theory, Evidence, and Policy Implications," presented at the Economic and Social Research Council Seminar, "Neighbourhood Effects: Theory & Evidence," St. Andrews University, Scotland, UK, February 2010.

Additionally, the Census Bureau has published a series of reports examining local areas (census tracts) with poverty rates of 20% or greater. See, for instance, Alemayehu Bishaw, "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.

4.

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."

5.

See, for instance, a 2008 report issued jointly by the Federal Reserve System and the Brookings Institution, "The Enduring Challenge of Concentrated Poverty in America: Case Studies from Communities Across the U.S.," David Erickson et al., eds., 2008. 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 Small Town America, Russell Sage Foundation, 1988.

6.

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 2 for details. Of the public laws passed by the 115th Congress, 10-20-30 language was included in P.L. 115-31 (Consolidated Appropriations Act, 2017), P.L. 115-141 (Consolidated Appropriations Act, 2018), and P.L. 115-334 (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 (The 10-20-30 Act of 2014) was introduced and referred to committee but not passed.
7.

There 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 administrative 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.

8.

For further details about the official definition of poverty, see CRS Report R44780, An Introduction to Poverty Measurement, by Joseph Dalaker.

9.

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 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. 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.

10.

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 island areas; decennial census data are the only small-area poverty data available for them. The 2020 Census questionnaire for these island areas are to cover 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.

11.

This guidance is posted on the Census Bureau's website at https://www.census.gov/topics/income-poverty/poverty/guidance/data-sources.html, and is reproduced in the Appendix.

12.

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.

13.

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.

14.

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.
15.

P.L. 111-5, Section 105.

16.

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 10.
17.

A sample of approximately 18.3 million households received the Census 2000 long form. Scott Boggess and Nikki L. Graf, "Measuring Education: A Comparison of the Decennial Census and the American Community Survey," presented at Joint Statistical Meetings, San Francisco, CA, August 7, 2003. http://census.gov/content/dam/Census/library/working-papers/2003/acs/2003_Boggess_01_doc.pdf.
From 2014 to 2018, 17.7 million housing unit addresses were sampled in the ACS. http://www.census.gov/acs/www/methodology/sample-size-and-data-quality/sample-size/index.php.

18.

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.
19.

Downloaded from http://www.census.gov/topics/income-poverty/poverty/guidance/data-sources.html, November 29, 2016.

20.

Author's note: CPS ASEC: Current Population Survey Annual Social and Economic Supplement.
21.

Author's note: SIPP: Survey of Income and Program Participation; mentioned here only as part of a quotation.