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

Updated March 27, 2019 (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 70 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:

Poverty status is not defined for all persons: foster children (unrelated individuals under age 15), institutionalized persons, and residents of college dormitories are excluded; the homeless are not targeted by household surveys; and areas with large numbers of students living off-campus may have high poverty rates.

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).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 American Community Survey (ACS) and the Small Area Income and Poverty Estimates program (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 no longer collects income information, and as a result cannot be used to compute poverty estimates. 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.10

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.11 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.12 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.13

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."14 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 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 70 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, 2013-2017

386

436

50
 

  

  

Mean difference: 53.00
 

  

  

  

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
 

  

  

Mean difference: 56.43
 

  

  

  

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

  

Mean difference

27.57

31.00

  

Source: Congressional Research Service (CRS) tabulation of data from U.S. Census Bureau, 1990 Census, Census 2000, 2012-2017 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, and 2013-2017.

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 the following:


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

The lists of persistent poverty counties vary by about 84 counties on average (mean: 84.00), depending on which data source is used for the last data point in the 30-year span, and which rounding method is applied to identify persistent poverty.

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 2017 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 2017 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 2017, from SAIPE

1

01005

Alabama

Barbour

2

25.2

26.8

33.4

2

01007

Alabama

Bibb

6

21.2

20.6

20.2

3

01011

Alabama

Bullock

2

36.5

33.5

34.4

4

01013

Alabama

Butler

2

31.5

24.6

21.3

5

01023

Alabama

Choctaw

7

30.2

24.5

23.7

6

01025

Alabama

Clarke

1,7

25.9

22.6

23.3

7

01035

Alabama

Conecuh

2

29.7

26.6

23.7

8

01041

Alabama

Crenshaw

2

24.3

22.1

19.9

9

01047

Alabama

Dallas

7

36.2

31.1

27.9

10

01053

Alabama

Escambia

1

28.1

20.9

23.3

11

01061

Alabama

Geneva

2

19.5

19.6

21.7

12

01063

Alabama

Greene

7

45.6

34.3

33.2

13

01065

Alabama

Hale

7

35.6

26.9

25.1

14

01085

Alabama

Lowndes

7

38.6

31.4

25.9

15

01087

Alabama

Macon

3

34.5

32.8

30.6

16

01091

Alabama

Marengo

7

30.0

25.9

22.8

17

01099

Alabama

Monroe

1

22.7

21.3

23.8

18

01105

Alabama

Perry

7

42.6

35.4

37.2

19

01107

Alabama

Pickens

7

28.9

24.9

22.3

20

01109

Alabama

Pike

2

27.2

23.1

27.7

21

01113

Alabama

Russell

3

20.4

19.9

23.2

22

01119

Alabama

Sumter

7

39.7

38.7

35.9

23

01131

Alabama

Wilcox

7

45.2

39.9

32.0

24

02050

Alaska

Bethel Census Area

at large

30.0

20.6

28.7

25

02070

Alaska

Dillingham Census Area

at large

24.6

21.4

20.8

26

02158

Alaska

Kusilvak Census Areab

at large

31.0

26.2

37.5

27

02290

Alaska

Yukon-Koyukuk Census Area

at large

26.0

23.8

23.2

28

04001

Arizona

Apache

1

47.1

37.8

33.1

29

04009

Arizona

Graham

1

26.7

23.0

20.9

30

04012

Arizona

La Paz

4

28.2

19.6

20.9

31

04017

Arizona

Navajo

1

34.7

29.5

26.4

32

04023

Arizona

Santa Cruz

3

26.4

24.5

23.6

33

05011

Arkansas

Bradley

4

24.9

26.3

20.9

34

05017

Arkansas

Chicot

1

40.4

28.6

30.1

35

05027

Arkansas

Columbia

4

24.4

21.1

25.5

36

05035

Arkansas

Crittenden

1

27.1

25.3

21.1

37

05041

Arkansas

Desha

1

34.0

28.9

29.0

38

05057

Arkansas

Hempstead

4

22.7

20.3

24.2

39

05069

Arkansas

Jefferson

1,4

23.9

20.5

23.5

40

05073

Arkansas

Lafayette

4

34.7

23.2

24.1

41

05077

Arkansas

Lee

1

47.3

29.9

37.3

42

05079

Arkansas

Lincoln

1

26.2

19.5

23.4

43

05093

Arkansas

Mississippi

1

26.2

23.0

24.3

44

05095

Arkansas

Monroe

1

35.9

27.5

27.3

45

05099

Arkansas

Nevada

4

20.3

22.8

19.5

46

05101

Arkansas

Newton

3,4

29.6

20.4

19.8

47

05103

Arkansas

Ouachita

4

21.2

19.5

19.9

48

05107

Arkansas

Phillips

1

43.0

32.7

39.8

49

05111

Arkansas

Poinsett

1

25.6

21.2

21.0

50

05123

Arkansas

St. Francis

1

36.6

27.5

33.7

51

05129

Arkansas

Searcy

1,3

29.9

23.8

21.6

52

05147

Arkansas

Woodruff

1

34.5

27.0

26.8

53

06019

California

Fresno

4,16,21,22

21.4

22.9

21.1

54

06025

California

Imperial

51

23.8

22.6

20.7

55

06047

California

Merced

16

19.9

21.7

23.0

56

06107

California

Tulare

21,22,23

22.6

23.9

24.0

57

08003

Colorado

Alamosa

3

24.8

21.3

21.3

58

08011

Colorado

Bent

4

20.4

19.5

32.0

59

08021

Colorado

Conejos

3

33.9

23.0

21.2

60

08023

Colorado

Costilla

3

34.6

26.8

27.3

61

08109

Colorado

Saguache

3

30.6

22.6

24.4

62

12001

Florida

Alachua

3

23.5

22.8

21.2

63

12039

Florida

Gadsden

5

28.0

19.9

23.1

64

12047

Florida

Hamilton

5

27.8

26.0

24.0

65

12049

Florida

Hardee

17

22.8

24.6

23.3

66

12079

Florida

Madison

5

25.9

23.1

28.3

67

12107

Florida

Putnam

3

20.0

20.9

26.3

68

13003

Georgia

Atkinson

8

26.0

23.0

24.2

69

13005

Georgia

Bacon

1

24.1

23.7

28.2

70

13007

Georgia

Baker

2

24.8

23.4

24.6

71

13017

Georgia

Ben Hill

8

22.0

22.3

24.5

72

13027

Georgia

Brooks

8

25.9

23.4

23.7

73

13031

Georgia

Bulloch

12

27.5

24.5

24.6

74

13033

Georgia

Burke

12

30.3

28.7

23.1

75

13037

Georgia

Calhoun

2

31.8

26.5

35.1

76

13043

Georgia

Candler

12

24.1

26.1

24.7

77

13059

Georgia

Clarke

9,10

27.0

28.3

26.6

78

13061

Georgia

Clay

2

35.7

31.3

33.1

79

13065

Georgia

Clinch

1

26.4

23.4

27.6

80

13071

Georgia

Colquitt

8

22.8

19.8

25.6

81

13075

Georgia

Cook

8

22.4

20.7

21.3

82

13081

Georgia

Crisp

2

29.0

29.3

29.7

83

13087

Georgia

Decatur

2

23.3

22.7

21.9

84

13093

Georgia

Dooly

2

32.9

22.1

27.6

85

13095

Georgia

Dougherty

2

24.4

24.8

28.2

86

13099

Georgia

Early

2

31.4

25.7

26.7

87

13107

Georgia

Emanuel

12

25.7

27.4

27.6

88

13109

Georgia

Evans

12

25.4

27.0

28.0

89

13131

Georgia

Grady

2

22.3

21.3

20.3

90

13141

Georgia

Hancock

10

30.1

29.4

30.3

91

13163

Georgia

Jefferson

10

31.3

23.0

24.0

92

13165

Georgia

Jenkins

12

27.8

28.4

32.8

93

13167

Georgia

Johnson

10

22.2

22.6

29.0

94

13193

Georgia

Macon

2

29.2

25.8

29.6

95

13197

Georgia

Marion

2

28.2

22.4

23.9

96

13201

Georgia

Miller

2

22.1

21.2

23.8

97

13205

Georgia

Mitchell

2

28.7

26.4

27.5

98

13209

Georgia

Montgomery

12

24.5

19.9

20.5

99

13225

Georgia

Peach

2

24.0

20.2

19.6

100

13239

Georgia

Quitman

2

33.0

21.9

26.1

101

13243

Georgia

Randolph

2

35.9

27.7

33.6

102

13251

Georgia

Screven

12

22.9

20.1

20.5

103

13253

Georgia

Seminole

2

29.1

23.2

29.2

104

13259

Georgia

Stewart

2

31.4

22.2

36.2

105

13261

Georgia

Sumter

2

24.8

21.4

25.5

106

13263

Georgia

Talbot

2

24.9

24.2

22.2

107

13265

Georgia

Taliaferro

10

31.9

23.4

26.5

108

13267

Georgia

Tattnall

12

21.9

23.9

27.3

109

13269

Georgia

Taylor

2

29.5

26.0

23.7

110

13271

Georgia

Telfair

8

27.3

21.2

34.6

111

13273

Georgia

Terrell

2

29.1

28.6

33.0

112

13277

Georgia

Tift

8

22.9

19.9

21.7

113

13279

Georgia

Toombs

12

24.0

23.9

22.3

114

13283

Georgia

Treutlen

12

27.1

26.3

27.4

115

13287

Georgia

Turner

8

31.3

26.7

27.6

116

13289

Georgia

Twiggs

8

26.0

19.7

22.2

117

13299

Georgia

Ware

1

21.1

20.5

22.0

118

13301

Georgia

Warren

10

32.6

27.0

27.9

119

13303

Georgia

Washington

10

21.6

22.9

26.7

120

13309

Georgia

Wheeler

12

30.3

25.3

37.4

121

13315

Georgia

Wilcox

8

28.6

21.0

30.7

122

17003

Illinois

Alexander

12

32.2

26.1

30.3

123

17077

Illinois

Jackson

12

28.4

25.2

29.2

124

17153

Illinois

Pulaski

12

30.2

24.7

22.9

125

20161

Kansas

Riley

1

21.2

20.6

20.4

126

21001

Kentucky

Adair

1

25.1

24.0

22.5

127

21011

Kentucky

Bath

6

27.3

21.9

23.0

128

21013

Kentucky

Bell

5

36.2

31.1

36.7

129

21025

Kentucky

Breathitt

5

39.5

33.2

36.2

130

21043

Kentucky

Carter

5

26.8

22.3

28.2

131

21045

Kentucky

Casey

1

29.4

25.5

25.1

132

21051

Kentucky

Clay

5

40.2

39.7

41.7

133

21053

Kentucky

Clinton

1

38.1

25.8

26.4

134

21057

Kentucky

Cumberland

1

31.6

23.8

22.8

135

21063

Kentucky

Elliott

5

38.0

25.9

29.7

136

21065

Kentucky

Estill

6

29.0

26.4

24.8

137

21071

Kentucky

Floyd

5

31.2

30.3

32.2

138

21075

Kentucky

Fulton

1

30.3

23.1

29.0

139

21095

Kentucky

Harlan

5

33.1

32.5

41.5

140

21099

Kentucky

Hart

2

27.1

22.4

20.0

141

21109

Kentucky

Jackson

5

38.2

30.2

31.4

142

21115

Kentucky

Johnson

5

28.7

26.6

23.3

143

21119

Kentucky

Knott

5

40.4

31.1

34.6

144

21121

Kentucky

Knox

5

38.9

34.8

32.2

145

21125

Kentucky

Laurel

5

24.8

21.3

24.3

146

21127

Kentucky

Lawrence

5

36.0

30.7

32.8

147

21129

Kentucky

Lee

5

37.4

30.4

33.7

148

21131

Kentucky

Leslie

5

35.6

32.7

31.0

149

21133

Kentucky

Letcher

5

31.8

27.1

30.8

150

21135

Kentucky

Lewis

4

30.7

28.5

25.6

151

21137

Kentucky

Lincoln

5

27.2

21.1

21.0

152

21147

Kentucky

McCreary

5

45.5

32.2

34.4

153

21153

Kentucky

Magoffin

5

42.5

36.6

32.1

154

21159

Kentucky

Martin

5

35.4

37.0

35.8

155

21165

Kentucky

Menifee

6

35.0

29.6

25.4

156

21169

Kentucky

Metcalfe

1

27.9

23.6

23.4

157

21171

Kentucky

Monroe

1

26.9

23.4

24.3

158

21175

Kentucky

Morgan

5

38.8

27.2

30.1

159

21189

Kentucky

Owsley

5

52.1

45.4

36.8

160

21193

Kentucky

Perry

5

32.1

29.1

25.9

161

21195

Kentucky

Pike

5

25.4

23.4

28.8

162

21197

Kentucky

Powell

6

26.2

23.5

24.6

163

21201

Kentucky

Robertson

6

24.8

22.2

21.1

164

21203

Kentucky

Rockcastle

5

30.7

23.1

21.3

165

21205

Kentucky

Rowan

5

28.9

21.3

23.7

166

21207

Kentucky

Russell

1

25.6

24.3

23.3

167

21231

Kentucky

Wayne

5

37.3

29.4

24.9

168

21235

Kentucky

Whitley

5

33.0

26.4

26.5

169

21237

Kentucky

Wolfe

6

44.3

35.9

29.9

170

22001

Louisiana

Acadia Parish

3

30.5

24.5

23.1

171

22003

Louisiana

Allen Parish

4

29.9

19.9

20.8

172

22009

Louisiana

Avoyelles Parish

5

37.1

25.9

24.6

173

22013

Louisiana

Bienville Parish

4

31.2

26.1

25.4

174

22017

Louisiana

Caddo Parish

4

24.0

21.1

25.8

175

22021

Louisiana

Caldwell Parish

5

28.8

21.2

23.4

176

22025

Louisiana

Catahoula Parish

5

36.8

28.1

27.8

177

22027

Louisiana

Claiborne Parish

4

32.0

26.5

39.5

178

22029

Louisiana

Concordia Parish

5

30.6

29.1

27.7

179

22031

Louisiana

De Soto Parish

4

29.8

25.1

22.8

180

22035

Louisiana

East Carroll Parish

5

56.8

40.5

46.7

181

22037

Louisiana

East Feliciana Parish

5,6

25.0

23.0

20.6

182

22039

Louisiana

Evangeline Parish

4

35.1

32.2

23.6

183

22041

Louisiana

Franklin Parish

5

34.5

28.4

27.9

184

22043

Louisiana

Grant Parish

5

25.5

21.5

21.0

185

22045

Louisiana

Iberia Parish

3

25.8

23.6

23.8

186

22047

Louisiana

Iberville Parish

2,6

28.0

23.1

22.9

187

22049

Louisiana

Jackson Parish

5

23.9

19.8

24.6

188

22061

Louisiana

Lincoln Parish

5

26.6

26.5

27.9

189

22065

Louisiana

Madison Parish

5

44.6

36.7

38.9

190

22067

Louisiana

Morehouse Parish

5

31.0

26.8

28.0

191

22069

Louisiana

Natchitoches Parish

4

33.9

26.5

30.3

192

22071

Louisiana

Orleans Parish

1,2

31.6

27.9

26.1

193

22073

Louisiana

Ouachita Parish

5

24.7

20.7

24.7

194

22079

Louisiana

Rapides Parish

5

22.6

20.5

19.9

195

22081

Louisiana

Red River Parish

4

35.1

29.9

23.3

196

22083

Louisiana

Richland Parish

5

33.2

27.9

27.8

197

22085

Louisiana

Sabine Parish

4

27.1

21.5

22.2

198

22091

Louisiana

St. Helena Parish

5,6

34.4

26.8

23.2

199

22097

Louisiana

St. Landry Parish

3,4,5

36.3

29.3

25.6

200

22101

Louisiana

St. Mary Parish

3

27.0

23.6

20.5

201

22105

Louisiana

Tangipahoa Parish

1,5

31.5

22.7

20.9

202

22107

Louisiana

Tensas Parish

5

46.3

36.3

34.9

203

22117

Louisiana

Washington Parish

5

31.6

24.7

23.1

204

22119

Louisiana

Webster Parish

4

25.1

20.2

20.3

205

22123

Louisiana

West Carroll Parish

5

27.4

23.4

21.2

206

22125

Louisiana

West Feliciana Parish

5

33.8

19.9

21.0

207

22127

Louisiana

Winn Parish

5

27.5

21.5

23.7

208

24510

Maryland

Baltimore city

2,3,7

21.9

22.9

22.1

209

26073

Michigan

Isabella

4

24.9

20.4

23.8

210

28001

Mississippi

Adams

3

30.5

25.9

32.5

211

28005

Mississippi

Amite

3

30.9

22.6

23.3

212

28007

Mississippi

Attala

2

30.2

21.8

24.1

213

28009

Mississippi

Benton

1

29.7

23.2

21.8

214

28011

Mississippi

Bolivar

2

42.9

33.3

32.4

215

28019

Mississippi

Choctaw

1

25.0

24.7

22.1

216

28021

Mississippi

Claiborne

2

43.6

32.4

42.6

217

28023

Mississippi

Clarke

3,4

23.4

23.0

20.8

218

28025

Mississippi

Clay

1

25.9

23.5

25.4

219

28027

Mississippi

Coahoma

2

45.5

35.9

34.8

220

28029

Mississippi

Copiah

2

32.0

25.1

22.5

221

28031

Mississippi

Covington

3

31.2

23.5

23.0

222

28035

Mississippi

Forrest

4

27.5

22.5

22.7

223

28037

Mississippi

Franklin

3

33.3

24.1

20.6

224

28041

Mississippi

Greene

4

26.8

19.6

21.7

225

28043

Mississippi

Grenada

2

22.3

20.9

20.4

226

28049

Mississippi

Hinds

2,3

21.2

19.9

20.2

227

28051

Mississippi

Holmes

2

53.2

41.1

40.8

228

28053

Mississippi

Humphreys

2

45.9

38.2

38.4

229

28055

Mississippi

Issaquena

2

49.3

33.2

38.1

230

28061

Mississippi

Jasper

3

30.7

22.7

23.8

231

28063

Mississippi

Jefferson

2

46.9

36.0

34.1

232

28065

Mississippi

Jefferson Davis

3

33.3

28.2

28.7

233

28067

Mississippi

Jones

4

22.7

19.8

21.3

234

28069

Mississippi

Kemper

3

35.1

26.0

29.8

235

28071

Mississippi

Lafayette

1

25.1

21.3

21.1

236

28075

Mississippi

Lauderdale

3

22.8

20.8

20.5

237

28077

Mississippi

Lawrence

3

27.9

19.6

19.7

238

28079

Mississippi

Leake

2

29.6

23.3

21.7

239

28083

Mississippi

Leflore

2

38.9

34.8

42.2

240

28087

Mississippi

Lowndes

1

22.1

21.3

22.2

241

28091

Mississippi

Marion

4

29.6

24.8

24.7

242

28093

Mississippi

Marshall

1

30.0

21.9

22.8

243

28097

Mississippi

Montgomery

2

34.0

24.3

23.2

244

28099

Mississippi

Neshoba

3

26.6

21.0

22.4

245

28101

Mississippi

Newton

3

20.9

19.9

20.4

246

28103

Mississippi

Noxubee

3

41.4

32.8

34.5

247

28105

Mississippi

Oktibbeha

1,3

30.1

28.2

25.5

248

28107

Mississippi

Panola

2

33.8

25.3

22.7

249

28111

Mississippi

Perry

4

29.1

22.0

20.5

250

28113

Mississippi

Pike

3

32.9

25.3

28.0

251

28119

Mississippi

Quitman

2

41.6

33.1

40.9

252

28123

Mississippi

Scott

3

27.4

20.7

21.9

253

28125

Mississippi

Sharkey

2

47.5

38.3

35.5

254

28127

Mississippi

Simpson

3

22.7

21.6

23.0

255

28133

Mississippi

Sunflower

2

41.8

30.0

36.2

256

28135

Mississippi

Tallahatchie

2

41.9

32.2

35.2

257

28143

Mississippi

Tunica

2

56.8

33.1

29.1

258

28147

Mississippi

Walthall

3

35.9

27.8

26.1

259

28151

Mississippi

Washington

2

33.8

29.2

32.7

260

28153

Mississippi

Wayne

4

29.5

25.4

23.0

261

28157

Mississippi

Wilkinson

3

42.2

37.7

33.1

262

28159

Mississippi

Winston

1

26.6

23.7

22.5

263

28161

Mississippi

Yalobusha

2

26.4

21.8

21.3

264

28163

Mississippi

Yazoo

2

39.2

31.9

33.9

265

29001

Missouri

Adair

6

24.9

23.3

25.6

266

29035

Missouri

Carter

8

27.6

25.2

20.9

267

29069

Missouri

Dunklin

8

29.9

24.5

24.6

268

29133

Missouri

Mississippi

8

29.7

23.7

28.5

269

29143

Missouri

New Madrid

8

26.9

22.1

25.4

270

29149

Missouri

Oregon

8

27.4

22.0

24.3

271

29153

Missouri

Ozark

8

22.1

21.6

21.5

272

29155

Missouri

Pemiscot

8

35.8

30.4

26.4

273

29179

Missouri

Reynolds

8

24.2

20.1

20.4

274

29181

Missouri

Ripley

8

31.5

22.0

23.9

275

29185

Missouri

St. Clair

4

22.4

19.6

20.4

276

29203

Missouri

Shannon

8

24.1

26.9

35.9

277

29215

Missouri

Texas

8

22.9

21.4

21.2

278

29221

Missouri

Washington

8

27.2

20.8

22.8

279

29223

Missouri

Wayne

8

29.0

21.9

22.9

280

29229

Missouri

Wright

8

25.3

21.7

24.3

281

29510

Missouri

St. Louis city

1

24.6

24.6

21.4

282

30003

Montana

Big Horn

at large

35.3

29.2

26.8

283

30005

Montana

Blaine

at large

27.7

28.1

26.2

284

30035

Montana

Glacier

at large

35.7

27.3

26.2

285

30085

Montana

Roosevelt

at large

27.7

32.4

26.1

286

31173

Nebraska

Thurston

1

30.9

25.6

23.3

287

35003

New Mexico

Catron

2

25.6

24.5

21.2

288

35006

New Mexico

Cibola

2

33.6

24.8

30.1

289

35013

New Mexico

Doña Ana

2

26.5

25.4

26.3

290

35019

New Mexico

Guadalupe

2

38.5

21.6

22.6

291

35023

New Mexico

Hidalgo

2

20.7

27.3

24.8

292

35029

New Mexico

Luna

2

31.5

32.9

28.3

293

35031

New Mexico

McKinley

2,3

43.5

36.1

37.8

294

35033

New Mexico

Mora

3

36.2

25.4

22.6

295

35037

New Mexico

Quay

3

25.1

20.9

23.9

296

35039

New Mexico

Rio Arriba

3

27.5

20.3

28.9

297

35041

New Mexico

Roosevelt

2,3

26.9

22.7

25.7

298

35045

New Mexico

San Juan

3

28.3

21.5

23.1

299

35047

New Mexico

San Miguel

3

30.2

24.4

29.2

300

35051

New Mexico

Sierra

2

19.6

20.9

26.6

301

35053

New Mexico

Socorro

2

29.9

31.7

28.3

302

35055

New Mexico

Taos

3

27.5

20.9

22.8

303

36005

New York

Bronx

13,14,15,16

28.7

30.7

27.9

304

36047

New York

Kings

7,8,9,10,11,12

22.7

25.1

19.8

305

37013

North Carolina

Beaufort

3

19.5

19.5

22.0

306

37015

North Carolina

Bertie

1

25.9

23.5

27.2

307

37017

North Carolina

Bladen

7,9

21.9

21.0

20.7

308

37047

North Carolina

Columbus

7

24.0

22.7

23.1

309

37065

North Carolina

Edgecombe

1

20.9

19.6

25.5

310

37083

North Carolina

Halifax

1

25.6

23.9

28.1

311

37117

North Carolina

Martin

1

22.3

20.2

20.5

312

37131

North Carolina

Northampton

1

23.6

21.3

24.3

313

37147

North Carolina

Pitt

1,3

22.1

20.3

21.7

314

37155

North Carolina

Robeson

9

24.1

22.8

29.0

315

37177

North Carolina

Tyrrell

3

25.0

23.3

24.4

316

37181

North Carolina

Vance

1

19.6

20.5

23.4

317

37187

North Carolina

Washington

1

20.4

21.8

24.8

318

38005

North Dakota

Benson

at large

31.7

29.1

28.4

319

38079

North Dakota

Rolette

at large

40.7

31.0

27.1

320

38085

North Dakota

Sioux

at large

47.4

39.2

35.9

321

39009

Ohio

Athens

6,15

28.7

27.4

28.8

322

39105

Ohio

Meigs

6

26.0

19.8

19.9

323

39163

Ohio

Vinton

15

23.6

20.0

19.8

324

40001

Oklahoma

Adair

2

26.7

23.2

27.2

325

40015

Oklahoma

Caddo

3

27.8

21.7

21.1

326

40021

Oklahoma

Cherokee

2

28.8

22.9

21.2

327

40023

Oklahoma

Choctaw

2

32.7

24.3

26.3

328

40029

Oklahoma

Coal

2

27.4

23.1

22.1

329

40055

Oklahoma

Greer

3

23.4

19.6

25.9

330

40057

Oklahoma

Harmon

3

34.2

29.7

24.4

331

40061

Oklahoma

Haskell

2

27.1

20.5

20.4

332

40063

Oklahoma

Hughes

2

26.9

21.9

23.6

333

40089

Oklahoma

McCurtain

2

30.2

24.7

26.0

334

40107

Oklahoma

Okfuskee

2

29.4

23.0

24.2

335

40119

Oklahoma

Payne

3

21.7

20.3

23.8

336

40127

Oklahoma

Pushmataha

2

30.2

23.2

20.0

337

40133

Oklahoma

Seminole

5

24.0

20.8

22.1

338

40135

Oklahoma

Sequoyah

2

24.7

19.8

20.5

339

40141

Oklahoma

Tillman

4

22.9

21.9

23.0

340

42101

Pennsylvania

Philadelphia

2,3,5

20.3

22.9

25.3

341

45005

South Carolina

Allendale

6

35.8

34.5

36.7

342

45009

South Carolina

Bamberg

6

28.2

27.8

26.5

343

45011

South Carolina

Barnwell

2

21.8

20.9

27.7

344

45027

South Carolina

Clarendon

6

29.0

23.1

23.2

345

45029

South Carolina

Colleton

1,6

23.4

21.1

22.4

346

45031

South Carolina

Darlington

7

19.9

20.3

21.9

347

45033

South Carolina

Dillon

7

28.1

24.2

29.8

348

45049

South Carolina

Hampton

6

27.7

21.8

25.1

349

45053

South Carolina

Jasper

6

25.3

20.7

19.9

350

45061

South Carolina

Lee

5

29.6

21.8

25.8

351

45067

South Carolina

Marion

7

28.6

23.2

27.5

352

45069

South Carolina

Marlboro

7

26.6

21.7

28.0

353

45075

South Carolina

Orangeburg

2,6

24.9

21.4

24.4

354

45089

South Carolina

Williamsburg

6

28.7

27.9

26.8

355

46007

South Dakota

Bennett

at large

37.6

39.2

34.9

356

46017

South Dakota

Buffalo

at large

45.1

56.9

43.3

357

46023

South Dakota

Charles Mix

at large

31.4

26.9

24.9

358

46027

South Dakota

Clay

at large

24.6

21.2

20.2

359

46031

South Dakota

Corson

at large

42.5

41.0

42.4

360

46041

South Dakota

Dewey

at large

44.4

33.6

34.9

361

46071

South Dakota

Jackson

at large

38.8

36.5

36.2

362

46085

South Dakota

Lyman

at large

24.7

24.3

22.2

363

46095

South Dakota

Mellette

at large

41.3

35.8

38.0

364

46102

South Dakota

Oglala Lakotac

at large

63.1

52.3

41.5

365

46121

South Dakota

Todd

at large

50.2

48.3

50.4

366

46137

South Dakota

Ziebach

at large

51.1

49.9

56.7

367

47025

Tennessee

Claiborne

2

25.7

22.6

22.5

368

47029

Tennessee

Cocke

1

25.3

22.5

23.0

369

47049

Tennessee

Fentress

6

32.3

23.1

23.4

370

47061

Tennessee

Grundy

4

23.9

25.8

23.0

371

47067

Tennessee

Hancock

1

40.0

29.4

28.4

372

47069

Tennessee

Hardeman

7

23.3

19.7

23.0

373

47075

Tennessee

Haywood

8

27.5

19.5

20.6

374

47091

Tennessee

Johnson

1

28.5

22.6

24.3

375

47095

Tennessee

Lake

8

27.5

23.6

39.9

376

47151

Tennessee

Scott

3

27.8

20.2

22.8

377

48025

Texas

Bee

34

27.4

24.0

26.6

378

48041

Texas

Brazos

17

26.7

26.9

23.9

379

48047

Texas

Brooks

15

36.8

40.2

35.0

380

48061

Texas

Cameron

34

39.7

33.1

27.7

381

48063

Texas

Camp

4

22.5

20.9

20.3

382

48079

Texas

Cochran

19

28.3

27.0

24.8

383

48083

Texas

Coleman

11

24.9

19.9

25.2

384

48107

Texas

Crosby

19

29.5

28.1

22.3

385

48109

Texas

Culberson

23

29.8

25.1

22.1

386

48115

Texas

Dawson

11

30.5

19.7

21.7

387

48127

Texas

Dimmit

23

48.9

33.2

31.2

388

48131

Texas

Duval

15

39.0

27.2

28.6

389

48137

Texas

Edwards

23

41.7

31.6

22.6

390

48141

Texas

El Paso

16,23

26.8

23.8

21.0

391

48145

Texas

Falls

17

27.5

22.6

27.6

392

48153

Texas

Floyd

13,19

27.1

21.5

20.2

393

48163

Texas

Frio

23

39.1

29.0

27.9

394

48169

Texas

Garza

19

23.1

22.3

25.3

395

48191

Texas

Hall

13

29.1

26.3

24.0

396

48207

Texas

Haskell

19

20.8

22.8

20.6

397

48215

Texas

Hidalgo

15,28,34

41.9

35.9

29.5

398

48225

Texas

Houston

8

25.6

21.0

22.3

399

48247

Texas

Jim Hogg

15

35.3

25.9

27.3

400

48249

Texas

Jim Wells

34

30.3

24.1

23.7

401

48255

Texas

Karnes

15

36.5

21.9

22.1

402

48271

Texas

Kinney

23

28.6

24.0

20.1

403

48273

Texas

Kleberg

34

27.4

26.7

25.5

404

48275

Texas

Knox

13

23.6

22.9

20.6

405

48279

Texas

Lamb

19

27.1

20.9

20.0

406

48283

Texas

La Salle

23,28

37.0

29.8

29.2

407

48315

Texas

Marion

4

60.6

22.4

22.8

408

48323

Texas

Maverick

23

50.4

34.8

27.0

409

48327

Texas

Menard

11

31.1

25.8

20.6

410

48347

Texas

Nacogdoches

1

25.2

23.3

23.9

411

48371

Texas

Pecos

23

29.6

20.4

20.6

412

48377

Texas

Presidio

23

48.1

36.4

23.4

413

48389

Texas

Reeves

23

28.8

28.9

25.1

414

48405

Texas

San Augustine

1

29.7

21.2

21.6

415

48427

Texas

Starr

28

60.0

50.9

32.0

416

48445

Texas

Terry

19

25.5

23.3

21.5

417

48463

Texas

Uvalde

23

31.1

24.3

20.5

418

48465

Texas

Val Verde

23

36.4

26.1

21.4

419

48479

Texas

Webb

28

38.2

31.2

27.3

420

48489

Texas

Willacy

34

44.5

33.2

35.0

421

48505

Texas

Zapata

28

41.0

35.8

30.0

422

48507

Texas

Zavala

23

50.4

41.8

31.6

423

49037

Utah

San Juan

3

36.4

31.4

25.9

424

51027

Virginia

Buchanan

9

21.9

23.2

27.9

425

51051

Virginia

Dickenson

9

25.9

21.3

25.0

426

51105

Virginia

Lee

9

28.7

23.9

28.2

427

51121

Virginia

Montgomery

9

22.1

23.2

23.0

428

51195

Virginia

Wise

9

21.6

20.0

23.3

429

51540

Virginia

Charlottesville city

5

23.7

25.9

20.3

430

51620

Virginia

Franklin city

3

20.6

19.8

20.8

431

51660

Virginia

Harrisonburg city

6

21.5

30.1

23.3

432

51720

Virginia

Norton city

9

26.7

22.8

22.3

433

51730

Virginia

Petersburg city

4

20.3

19.6

21.8

434

51750

Virginia

Radford city

9

32.2

31.4

28.6

435

51760

Virginia

Richmond city

4

20.9

21.4

24.0

436

53047

Washington

Okanogan

4

21.5

21.3

20.4

437

53075

Washington

Whitman

5

24.2

25.6

21.4

438

54001

West Virginia

Barbour

1

28.5

22.6

21.2

439

54005

West Virginia

Boone

3

27.0

22.0

21.4

440

54007

West Virginia

Braxton

2

25.8

22.0

22.5

441

54013

West Virginia

Calhoun

2

32.0

25.1

24.8

442

54015

West Virginia

Clay

2

39.2

27.5

27.3

443

54017

West Virginia

Doddridge

1

23.0

19.8

20.1

444

54019

West Virginia

Fayette

3

24.4

21.7

22.3

445

54021

West Virginia

Gilmer

1

33.5

25.9

26.6

446

54043

West Virginia

Lincoln

3

33.8

27.9

25.7

447

54045

West Virginia

Logan

3

27.7

24.1

29.2

448

54047

West Virginia

McDowell

3

37.7

37.7

31.7

449

54053

West Virginia

Mason

3

22.1

19.9

20.2

450

54055

West Virginia

Mercer

3

20.4

19.7

21.0

451

54059

West Virginia

Mingo

3

30.9

29.7

31.0

452

54087

West Virginia

Roane

2

28.1

22.6

21.2

453

54089

West Virginia

Summers

3

24.5

24.4

28.8

454

54097

West Virginia

Upshur

2

21.2

20.0

22.7

455

54099

West Virginia

Wayne

3

21.8

19.6

20.4

456

54101

West Virginia

Webster

3

34.8

31.8

28.1

457

54103

West Virginia

Wetzel

1

20.5

19.8

20.6

458

54109

West Virginia

Wyoming

3

27.9

25.1

25.7

459

55078

Wisconsin

Menominee

8

48.7

28.8

27.6

460

56001

Wyoming

Albany

at large

19.8

21.0

19.5

Source: Congressional Research Service (CRS) tabulation of data from U.S. Census Bureau, 1990 Census, Census 2000, 2017 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, Alabama, is represented by Alabama's 2nd Congressional District (indicated by the 2). A congressional district may span multiple counties; conversely, a single county may be split among multiple congressional districts. Part of Clarke County, Alabama, 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 2017 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 2017 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. 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.15 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.16

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"17

The CPS ASEC18 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 before 2006 should use CPS ASEC 2-year averages.

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 SIPP19 is the only Census Bureau source of longitudinal poverty data. It provides national estimates and since the 2004 Panel, provides reliable state-level estimates for select states. As SIPP collects monthly income over 3 or 4 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

  

ACS 1-year estimates

  

ACS 1-year estimates/

CPS ASEC 2-year averagesa

  

SIPP for select statesb

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)d

  

SAIPE for counties and school districts/

ACS using 5-year period estimates for all other geographic entities/

Census 2000

  

ACS  5-year estimates/

Census 2000

  

SAIPE for counties  and school districts/

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

  

None

State-to-Nation comparison

  

CPS ASEC

  

CPS ASEC

  

CPS ASEC

  

SIPP for select statesb

Source: Congressional Research Service (CRS) formatted reproduction of table by U.S. Census Bureau, with an expansion to the notes. Original table available at http://www.census.gov/topics/income-poverty/poverty/guidance/data-sources.html.

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. Use CPS ASEC two-year averages when examining state trends that include years prior to 2000.

b. Reliable estimates are available for select states, generally the most populous 20 states, beginning in the 2004 Panel.

c. 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.

d. 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.

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, Senior Research Librarian, for assistance with legislative research, and Calvin DeSouza, GIS Analyst, 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 Fund Program Account; and Division E, Title II, in reference to the Comprehensive Environmental Response, Compensation, and Liability Act 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.

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) 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 actually 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.

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.

11.

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.

12.

Details on the poverty universe in the ACS are available at https://www2.census.gov/programs-surveys/acs/tech_docs/subject_definitions/2016_ACSSubjectDefinitions.pdf#page=108 and for the SAIPE estimates at https://www.census.gov/programs-surveys/saipe/guidance/model-input-data/denominators/poverty.html.

13.

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.
14.

P.L. 111-5, Section 105.

15.

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 2012 to 2016, 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.

16.

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.
17.

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

18.

Author's note: CPS ASEC: Current Population Survey Annual Social and Economic Supplement.
19.

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