The 10-20-30 Plan and Persistent Poverty Counties

Anti-poverty 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 plan,” 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 plan 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 plan, 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. 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 decennial census was the only source of county poverty estimates. 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.

Lists of persistent poverty counties may differ by roughly 80 to 100 counties in a particular year, depending on the data source selected to compile the list and the rounding method used for the poverty rate estimates. When determining the method to be used to compile a list of persistent poverty counties, the following may be relevant to consider:

Characteristics of interest: SAIPE is suited for poverty or median income alone; ACS for other topics in addition to poverty and income.

Geographic areas of interest: SAIPE is recommended for counties and school districts only; ACS produces estimates for other small geographic areas as well.

Reference period of estimate: SAIPE for one year; ACS for a five-year span.

Rounding method for poverty rates: rounding to 20.0% (one decimal place) yields a shorter list than rounding to 20% (whole number).

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.

The 10-20-30 Plan and Persistent Poverty Counties

February 8, 2018 (R45100)
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Contents

Summary

Anti-poverty 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 plan," 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 plan 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 plan, 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. 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 decennial census was the only source of county poverty estimates. 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.

Lists of persistent poverty counties may differ by roughly 80 to 100 counties in a particular year, depending on the data source selected to compile the list and the rounding method used for the poverty rate estimates. When determining the method to be used to compile a list of persistent poverty counties, the following may be relevant to consider:

  • Characteristics of interest: SAIPE is suited for poverty or median income alone; ACS for other topics in addition to poverty and income.
  • Geographic areas of interest: SAIPE is recommended for counties and school districts only; ACS produces estimates for other small geographic areas as well.
  • Reference period of estimate: SAIPE for one year; ACS for a five-year span.
  • Rounding method for poverty rates: rounding to 20.0% (one decimal place) yields a shorter list than rounding to 20% (whole number).
  • 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.


The 10-20-30 Plan and Persistent Poverty Counties

Introduction

Anti-poverty 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 plan," 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.1

One notable characteristic of this plan 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, several bills2 introduced in the 115th Congress seek to apply similar 10-20-30 language to various programs and in different executive departments, though the bills vary slightly in their definitions of "persistent poverty counties." These bills include legislation for rural development, public works and economic development, technological innovation, and environmental response and compensation. Much of the language used in these bills was included in P.L. 115-31 (Consolidated Appropriations Act, 2017).3

This report explains why targeting funds to persistent poverty counties might be of interest, 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 plan'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 plan 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.4 For instance, property values in high-poverty areas do not yield as high a return on investment as in low-poverty areas, and that low return provides a financial disincentive for property owners to spend money on maintaining and improving property.5 The ill effects of high poverty rates have been documented both for urban and rural areas.6 Therefore, policy interventions at the community level, and not only at the individual or family level, could be of interest to Congress.

Defining "Persistent Poverty" Counties

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., 2012-2016). 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 Elementary and Secondary Education Act. 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 (sub-county 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 plan 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 fallen on hard times, such as counties that had a large manufacturing plant close within the past three years. Other interventions besides the 10-20-30 plan 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 have a large impact 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 80 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

 

 

 

Mean difference: 53.50

 

 

 

 

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

 

 

 

Mean difference: 57.67

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

 

Mean difference

28.00

32.17

 

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

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


The lists of persistent poverty counties varied by about 86 counties on average (mean: 85.67), 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 2016 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 2016 Small Area Income and Poverty Estimates (SAIPE), Using Poverty Rates of 19.5% or Greater

Count

FIPS Geographic Identification Code

State

County

Poverty Rate 1989 (1990 Census)

Poverty Rate 1999 (Census 2000)

Poverty Rate 2016, from SAIPE

Congressional District(s) Representing the Countya

1

01005

Alabama

Barbour

25.2

26.8

29.9

2

2

01007

Alabama

Bibb

21.2

20.6

20.1

6

3

01011

Alabama

Bullock

36.5

33.5

32.6

2

4

01013

Alabama

Butler

31.5

24.6

24.8

2

5

01023

Alabama

Choctaw

30.2

24.5

22.7

7

6

01025

Alabama

Clarke

25.9

22.6

29.0

1,7

7

01035

Alabama

Conecuh

29.7

26.6

28.1

2

8

01041

Alabama

Crenshaw

24.3

22.1

20.5

2

9

01047

Alabama

Dallas

36.2

31.1

35.4

7

10

01053

Alabama

Escambia

28.1

20.9

23.3

1

11

01061

Alabama

Geneva

19.5

19.6

20.9

2

12

01063

Alabama

Greene

45.6

34.3

34.0

7

13

01065

Alabama

Hale

35.6

26.9

23.7

7

14

01085

Alabama

Lowndes

38.6

31.4

31.7

7

15

01087

Alabama

Macon

34.5

32.8

30.0

3

16

01091

Alabama

Marengo

30.0

25.9

25.8

7

17

01099

Alabama

Monroe

22.7

21.3

25.7

1

18

01105

Alabama

Perry

42.6

35.4

35.0

7

19

01107

Alabama

Pickens

28.9

24.9

25.8

7

20

01109

Alabama

Pike

27.2

23.1

25.1

2

21

01119

Alabama

Sumter

39.7

38.7

32.4

7

22

01131

Alabama

Wilcox

45.2

39.9

31.9

7

23

02050

Alaska

Bethel Census Area

30.0

20.6

25.5

at large

24

02070

Alaska

Dillingham Census Area

24.6

21.4

19.6

at large

25

02158

Alaska

Kusilvak Census Areab

31.0

26.2

37.8

at large

26

02290

Alaska

Yukon-Koyukuk Census Area

26.0

23.8

23.0

at large

27

04001

Arizona

Apache

47.1

37.8

33.2

1

28

04009

Arizona

Graham

26.7

23.0

22.9

1

29

04012

Arizona

La Paz

28.2

19.6

24.8

4

30

04017

Arizona

Navajo

34.7

29.5

28.2

1

31

04023

Arizona

Santa Cruz

26.4

24.5

20.9

3

32

05011

Arkansas

Bradley

24.9

26.3

23.5

4

33

05017

Arkansas

Chicot

40.4

28.6

30.1

1

34

05027

Arkansas

Columbia

24.4

21.1

24.2

4

35

05035

Arkansas

Crittenden

27.1

25.3

25.5

1

36

05041

Arkansas

Desha

34.0

28.9

26.5

1

37

05057

Arkansas

Hempstead

22.7

20.3

20.7

4

38

05069

Arkansas

Jefferson

23.9

20.5

23.3

1,4

39

05073

Arkansas

Lafayette

34.7

23.2

27.4

4

40

05077

Arkansas

Lee

47.3

29.9

35.9

1

41

05079

Arkansas

Lincoln

26.2

19.5

27.6

1

42

05093

Arkansas

Mississippi

26.2

23.0

24.6

1

43

05095

Arkansas

Monroe

35.9

27.5

27.0

1

44

05099

Arkansas

Nevada

20.3

22.8

20.4

4

45

05101

Arkansas

Newton

29.6

20.4

19.9

3,4

46

05103

Arkansas

Ouachita

21.2

19.5

24.7

4

47

05107

Arkansas

Phillips

43.0

32.7

32.3

1

48

05111

Arkansas

Poinsett

25.6

21.2

22.7

1

49

05123

Arkansas

St. Francis

36.6

27.5

31.6

1

50

05129

Arkansas

Searcy

29.9

23.8

23.1

1,3

51

05147

Arkansas

Woodruff

34.5

27.0

26.3

1

52

06019

California

Fresno

21.4

22.9

25.5

4,16,21,22

53

06025

California

Imperial

23.8

22.6

23.6

51

54

06047

California

Merced

19.9

21.7

20.3

16

55

06107

California

Tulare

22.6

23.9

24.7

21,22,23

56

08003

Colorado

Alamosa

24.8

21.3

24.8

3

57

08011

Colorado

Bent

20.4

19.5

34.1

4

58

08021

Colorado

Conejos

33.9

23.0

22.7

3

59

08023

Colorado

Costilla

34.6

26.8

30.4

3

60

08099

Colorado

Prowers

21.0

19.5

20.9

4

61

08109

Colorado

Saguache

30.6

22.6

27.6

3

62

12001

Florida

Alachua

23.5

22.8

22.3

3

63

12039

Florida

Gadsden

28.0

19.9

20.6

5

64

12047

Florida

Hamilton

27.8

26.0

28.9

5

65

12049

Florida

Hardee

22.8

24.6

23.8

17

66

12079

Florida

Madison

25.9

23.1

31.9

5

67

12107

Florida

Putnam

20.0

20.9

21.5

3

68

13003

Georgia

Atkinson

26.0

23.0

26.4

8

69

13005

Georgia

Bacon

24.1

23.7

22.9

1

70

13007

Georgia

Baker

24.8

23.4

27.8

2

71

13017

Georgia

Ben Hill

22.0

22.3

26.4

8

72

13027

Georgia

Brooks

25.9

23.4

24.9

8

73

13031

Georgia

Bulloch

27.5

24.5

24.0

12

74

13033

Georgia

Burke

30.3

28.7

26.7

12

75

13037

Georgia

Calhoun

31.8

26.5

33.0

2

76

13043

Georgia

Candler

24.1

26.1

25.0

12

77

13059

Georgia

Clarke

27.0

28.3

27.9

9,10

78

13061

Georgia

Clay

35.7

31.3

35.2

2

79

13065

Georgia

Clinch

26.4

23.4

26.0

1

80

13071

Georgia

Colquitt

22.8

19.8

25.0

8

81

13075

Georgia

Cook

22.4

20.7

25.1

8

82

13081

Georgia

Crisp

29.0

29.3

30.3

2

83

13087

Georgia

Decatur

23.3

22.7

29.9

2

84

13093

Georgia

Dooly

32.9

22.1

28.5

2

85

13095

Georgia

Dougherty

24.4

24.8

30.5

2

86

13099

Georgia

Early

31.4

25.7

31.4

2

87

13107

Georgia

Emanuel

25.7

27.4

27.4

12

88

13109

Georgia

Evans

25.4

27.0

24.4

12

89

13131

Georgia

Grady

22.3

21.3

21.0

2

90

13141

Georgia

Hancock

30.1

29.4

33.5

10

91

13163

Georgia

Jefferson

31.3

23.0

25.2

10

92

13165

Georgia

Jenkins

27.8

28.4

34.2

12

93

13167

Georgia

Johnson

22.2

22.6

29.4

10

94

13193

Georgia

Macon

29.2

25.8

32.1

2

95

13197

Georgia

Marion

28.2

22.4

23.4

2

96

13201

Georgia

Miller

22.1

21.2

23.1

2

97

13205

Georgia

Mitchell

28.7

26.4

29.9

2

98

13209

Georgia

Montgomery

24.5

19.9

22.7

12

99

13225

Georgia

Peach

24.0

20.2

21.4

2

100

13239

Georgia

Quitman

33.0

21.9

27.1

2

101

13243

Georgia

Randolph

35.9

27.7

30.5

2

102

13251

Georgia

Screven

22.9

20.1

27.6

12

103

13253

Georgia

Seminole

29.1

23.2

23.4

2

104

13259

Georgia

Stewart

31.4

22.2

39.2

2

105

13261

Georgia

Sumter

24.8

21.4

28.9

2

106

13263

Georgia

Talbot

24.9

24.2

23.5

2

107

13265

Georgia

Taliaferro

31.9

23.4

28.8

10

108

13267

Georgia

Tattnall

21.9

23.9

29.8

12

109

13269

Georgia

Taylor

29.5

26.0

24.7

2

110

13271

Georgia

Telfair

27.3

21.2

30.8

8

111

13273

Georgia

Terrell

29.1

28.6

31.1

2

112

13277

Georgia

Tift

22.9

19.9

22.1

8

113

13279

Georgia

Toombs

24.0

23.9

23.4

12

114

13283

Georgia

Treutlen

27.1

26.3

27.1

12

115

13287

Georgia

Turner

31.3

26.7

30.4

8

116

13289

Georgia

Twiggs

26.0

19.7

23.5

8

117

13299

Georgia

Ware

21.1

20.5

25.5

1

118

13301

Georgia

Warren

32.6

27.0

28.2

10

119

13303

Georgia

Washington

21.6

22.9

26.9

10

120

13309

Georgia

Wheeler

30.3

25.3

39.1

12

121

13315

Georgia

Wilcox

28.6

21.0

31.0

8

122

16065

Idaho

Madison

28.6

30.5

24.1

2

123

17003

Illinois

Alexander

32.2

26.1

29.0

12

124

17059

Illinois

Gallatin

21.4

20.7

20.5

15

125

17077

Illinois

Jackson

28.4

25.2

23.4

12

126

17153

Illinois

Pulaski

30.2

24.7

21.1

12

127

21001

Kentucky

Adair

25.1

24.0

26.1

1

128

21011

Kentucky

Bath

27.3

21.9

24.9

6

129

21013

Kentucky

Bell

36.2

31.1

38.7

5

130

21025

Kentucky

Breathitt

39.5

33.2

34.3

5

131

21043

Kentucky

Carter

26.8

22.3

22.6

5

132

21045

Kentucky

Casey

29.4

25.5

27.8

1

133

21051

Kentucky

Clay

40.2

39.7

42.1

5

134

21053

Kentucky

Clinton

38.1

25.8

25.3

1

135

21057

Kentucky

Cumberland

31.6

23.8

25.3

1

136

21063

Kentucky

Elliott

38.0

25.9

30.7

5

137

21065

Kentucky

Estill

29.0

26.4

27.3

6

138

21071

Kentucky

Floyd

31.2

30.3

30.4

5

139

21075

Kentucky

Fulton

30.3

23.1

30.0

1

140

21095

Kentucky

Harlan

33.1

32.5

37.1

5

141

21099

Kentucky

Hart

27.1

22.4

21.8

2

142

21109

Kentucky

Jackson

38.2

30.2

31.9

5

143

21115

Kentucky

Johnson

28.7

26.6

25.9

5

144

21119

Kentucky

Knott

40.4

31.1

38.2

5

145

21121

Kentucky

Knox

38.9

34.8

39.2

5

146

21125

Kentucky

Laurel

24.8

21.3

23.2

5

147

21127

Kentucky

Lawrence

36.0

30.7

27.1

5

148

21129

Kentucky

Lee

37.4

30.4

39.0

5

149

21131

Kentucky

Leslie

35.6

32.7

31.8

5

150

21133

Kentucky

Letcher

31.8

27.1

31.6

5

151

21135

Kentucky

Lewis

30.7

28.5

26.8

4

152

21137

Kentucky

Lincoln

27.2

21.1

22.9

5

153

21147

Kentucky

McCreary

45.5

32.2

39.3

5

154

21153

Kentucky

Magoffin

42.5

36.6

31.9

5

155

21159

Kentucky

Martin

35.4

37.0

39.3

5

156

21165

Kentucky

Menifee

35.0

29.6

24.1

6

157

21169

Kentucky

Metcalfe

27.9

23.6

22.7

1

158

21171

Kentucky

Monroe

26.9

23.4

23.2

1

159

21175

Kentucky

Morgan

38.8

27.2

27.2

5

160

21177

Kentucky

Muhlenberg

20.7

19.7

19.6

1

161

21189

Kentucky

Owsley

52.1

45.4

45.2

5

162

21193

Kentucky

Perry

32.1

29.1

30.9

5

163

21195

Kentucky

Pike

25.4

23.4

31.4

5

164

21197

Kentucky

Powell

26.2

23.5

26.5

6

165

21201

Kentucky

Robertson

24.8

22.2

24.6

6

166

21203

Kentucky

Rockcastle

30.7

23.1

24.2

5

167

21205

Kentucky

Rowan

28.9

21.3

25.4

5

168

21207

Kentucky

Russell

25.6

24.3

25.0

1

169

21231

Kentucky

Wayne

37.3

29.4

26.9

5

170

21235

Kentucky

Whitley

33.0

26.4

29.3

5

171

21237

Kentucky

Wolfe

44.3

35.9

32.2

6

172

22001

Louisiana

Acadia Parish

30.5

24.5

23.1

3

173

22003

Louisiana

Allen Parish

29.9

19.9

21.4

4

174

22007

Louisiana

Assumption Parish

28.2

21.8

21.2

2,6

175

22009

Louisiana

Avoyelles Parish

37.1

25.9

25.9

5

176

22013

Louisiana

Bienville Parish

31.2

26.1

23.5

4

177

22017

Louisiana

Caddo Parish

24.0

21.1

26.4

4

178

22021

Louisiana

Caldwell Parish

28.8

21.2

22.6

5

179

22025

Louisiana

Catahoula Parish

36.8

28.1

27.5

5

180

22027

Louisiana

Claiborne Parish

32.0

26.5

34.9

4

181

22029

Louisiana

Concordia Parish

30.6

29.1

28.5

5

182

22031

Louisiana

De Soto Parish

29.8

25.1

21.1

4

183

22035

Louisiana

East Carroll Parish

56.8

40.5

43.5

5

184

22037

Louisiana

East Feliciana Parish

25.0

23.0

20.0

5,6

185

22039

Louisiana

Evangeline Parish

35.1

32.2

29.4

4

186

22041

Louisiana

Franklin Parish

34.5

28.4

29.2

5

187

22043

Louisiana

Grant Parish

25.5

21.5

21.5

5

188

22045

Louisiana

Iberia Parish

25.8

23.6

23.4

3

189

22047

Louisiana

Iberville Parish

28.0

23.1

22.8

2,6

190

22049

Louisiana

Jackson Parish

23.9

19.8

21.2

5

191

22061

Louisiana

Lincoln Parish

26.6

26.5

30.2

5

192

22065

Louisiana

Madison Parish

44.6

36.7

41.3

5

193

22067

Louisiana

Morehouse Parish

31.0

26.8

28.7

5

194

22069

Louisiana

Natchitoches Parish

33.9

26.5

31.9

4

195

22071

Louisiana

Orleans Parish

31.6

27.9

24.1

1,2

196

22073

Louisiana

Ouachita Parish

24.7

20.7

24.5

5

197

22077

Louisiana

Pointe Coupee Parish

30.3

23.1

19.7

6

198

22079

Louisiana

Rapides Parish

22.6

20.5

19.9

5

199

22081

Louisiana

Red River Parish

35.1

29.9

26.3

4

200

22083

Louisiana

Richland Parish

33.2

27.9

27.2

5

201

22091

Louisiana

St. Helena Parish

34.4

26.8

24.7

5,6

202

22097

Louisiana

St. Landry Parish

36.3

29.3

26.6

3,4,5

203

22101

Louisiana

St. Mary Parish

27.0

23.6

22.2

3

204

22105

Louisiana

Tangipahoa Parish

31.5

22.7

21.5

1,5

205

22107

Louisiana

Tensas Parish

46.3

36.3

31.6

5

206

22113

Louisiana

Vermilion Parish

26.5

22.1

19.6

3

207

22117

Louisiana

Washington Parish

31.6

24.7

27.8

5

208

22119

Louisiana

Webster Parish

25.1

20.2

24.1

4

209

22123

Louisiana

West Carroll Parish

27.4

23.4

23.8

5

210

22125

Louisiana

West Feliciana Parish

33.8

19.9

23.7

5

211

22127

Louisiana

Winn Parish

27.5

21.5

24.3

5

212

24510

Maryland

Baltimore city

21.9

22.9

21.8

2,3,7

213

26073

Michigan

Isabella

24.9

20.4

23.4

4

214

28001

Mississippi

Adams

30.5

25.9

31.4

3

215

28005

Mississippi

Amite

30.9

22.6

24.0

3

216

28007

Mississippi

Attala

30.2

21.8

23.8

2

217

28009

Mississippi

Benton

29.7

23.2

25.3

1

218

28011

Mississippi

Bolivar

42.9

33.3

35.3

2

219

28017

Mississippi

Chickasaw

21.3

20.0

22.2

1

220

28019

Mississippi

Choctaw

25.0

24.7

23.1

1

221

28021

Mississippi

Claiborne

43.6

32.4

38.2

2

222

28023

Mississippi

Clarke

23.4

23.0

20.9

3,4

223

28025

Mississippi

Clay

25.9

23.5

23.9

1

224

28027

Mississippi

Coahoma

45.5

35.9

41.2

2

225

28029

Mississippi

Copiah

32.0

25.1

27.1

2

226

28031

Mississippi

Covington

31.2

23.5

23.5

3

227

28035

Mississippi

Forrest

27.5

22.5

25.4

4

228

28037

Mississippi

Franklin

33.3

24.1

20.2

3

229

28041

Mississippi

Greene

26.8

19.6

24.1

4

230

28043

Mississippi

Grenada

22.3

20.9

22.5

2

231

28049

Mississippi

Hinds

21.2

19.9

20.8

2,3

232

28051

Mississippi

Holmes

53.2

41.1

42.5

2

233

28053

Mississippi

Humphreys

45.9

38.2

38.9

2

234

28055

Mississippi

Issaquena

49.3

33.2

40.5

2

235

28061

Mississippi

Jasper

30.7

22.7

22.7

3

236

28063

Mississippi

Jefferson

46.9

36.0

33.7

2

237

28065

Mississippi

Jefferson Davis

33.3

28.2

26.9

3

238

28067

Mississippi

Jones

22.7

19.8

20.5

4

239

28069

Mississippi

Kemper

35.1

26.0

28.3

3

240

28071

Mississippi

Lafayette

25.1

21.3

20.7

1

241

28075

Mississippi

Lauderdale

22.8

20.8

23.7

3

242

28077

Mississippi

Lawrence

27.9

19.6

19.7

3

243

28079

Mississippi

Leake

29.6

23.3

24.9

2

244

28083

Mississippi

Leflore

38.9

34.8

35.6

2

245

28087

Mississippi

Lowndes

22.1

21.3

21.2

1

246

28091

Mississippi

Marion

29.6

24.8

27.0

4

247

28093

Mississippi

Marshall

30.0

21.9

23.0

1

248

28097

Mississippi

Montgomery

34.0

24.3

24.2

2

249

28099

Mississippi

Neshoba

26.6

21.0

22.5

3

250

28101

Mississippi

Newton

20.9

19.9

21.7

3

251

28103

Mississippi

Noxubee

41.4

32.8

31.6

3

252

28105

Mississippi

Oktibbeha

30.1

28.2

28.3

1,3

253

28107

Mississippi

Panola

33.8

25.3

22.7

2

254

28111

Mississippi

Perry

29.1

22.0

21.4

4

255

28113

Mississippi

Pike

32.9

25.3

30.1

3

256

28119

Mississippi

Quitman

41.6

33.1

34.3

2

257

28123

Mississippi

Scott

27.4

20.7

22.6

3

258

28125

Mississippi

Sharkey

47.5

38.3

35.0

2

259

28127

Mississippi

Simpson

22.7

21.6

23.1

3

260

28133

Mississippi

Sunflower

41.8

30.0

35.1

2

261

28135

Mississippi

Tallahatchie

41.9

32.2

37.2

2

262

28143

Mississippi

Tunica

56.8

33.1

31.3

2

263

28147

Mississippi

Walthall

35.9

27.8

24.1

3

264

28151

Mississippi

Washington

33.8

29.2

34.2

2

265

28153

Mississippi

Wayne

29.5

25.4

25.3

4

266

28157

Mississippi

Wilkinson

42.2

37.7

35.1

3

267

28159

Mississippi

Winston

26.6

23.7

23.0

1

268

28161

Mississippi

Yalobusha

26.4

21.8

22.9

2

269

28163

Mississippi

Yazoo

39.2

31.9

34.8

2

270

29001

Missouri

Adair

24.9

23.3

23.8

6

271

29035

Missouri

Carter

27.6

25.2

21.2

8

272

29069

Missouri

Dunklin

29.9

24.5

27.2

8

273

29119

Missouri

McDonald

20.6

20.7

21.4

7

274

29133

Missouri

Mississippi

29.7

23.7

28.4

8

275

29143

Missouri

New Madrid

26.9

22.1

25.0

8

276

29149

Missouri

Oregon

27.4

22.0

24.9

8

277

29153

Missouri

Ozark

22.1

21.6

25.3

8

278

29155

Missouri

Pemiscot

35.8

30.4

30.9

8

279

29179

Missouri

Reynolds

24.2

20.1

22.1

8

280

29181

Missouri

Ripley

31.5

22.0

27.7

8

281

29185

Missouri

St. Clair

22.4

19.6

20.7

4

282

29203

Missouri

Shannon

24.1

26.9

26.4

8

283

29215

Missouri

Texas

22.9

21.4

29.9

8

284

29221

Missouri

Washington

27.2

20.8

22.0

8

285

29223

Missouri

Wayne

29.0

21.9

26.0

8

286

29229

Missouri

Wright

25.3

21.7

24.2

8

287

29510

Missouri

St. Louis city

24.6

24.6

24.3

1

288

30003

Montana

Big Horn

35.3

29.2

25.5

at large

289

30005

Montana

Blaine

27.7

28.1

24.3

at large

290

30035

Montana

Glacier

35.7

27.3

28.3

at large

291

30085

Montana

Roosevelt

27.7

32.4

23.9

at large

292

31173

Nebraska

Thurston

30.9

25.6

25.3

1

293

35003

New Mexico

Catron

25.6

24.5

23.2

2

294

35005

New Mexico

Chaves

22.4

21.3

22.0

2

295

35006

New Mexico

Cibola

33.6

24.8

26.9

2

296

35013

New Mexico

Doña Ana

26.5

25.4

25.6

2

297

35019

New Mexico

Guadalupe

38.5

21.6

25.1

2

298

35023

New Mexico

Hidalgo

20.7

27.3

26.7

2

299

35029

New Mexico

Luna

31.5

32.9

27.6

2

300

35031

New Mexico

McKinley

43.5

36.1

34.4

2,3

301

35033

New Mexico

Mora

36.2

25.4

25.2

3

302

35037

New Mexico

Quay

25.1

20.9

24.6

3

303

35039

New Mexico

Rio Arriba

27.5

20.3

22.5

3

304

35041

New Mexico

Roosevelt

26.9

22.7

22.3

2,3

305

35047

New Mexico

San Miguel

30.2

24.4

25.7

3

306

35051

New Mexico

Sierra

19.6

20.9

27.0

2

307

35053

New Mexico

Socorro

29.9

31.7

25.4

2

308

35055

New Mexico

Taos

27.5

20.9

22.4

3

309

36005

New York

Bronx

28.7

30.7

28.6

13,14,15,16

310

36047

New York

Kings

22.7

25.1

20.6

7,8,9,10,11,12

311

37015

North Carolina

Bertie

25.9

23.5

24.4

1

312

37017

North Carolina

Bladen

21.9

21.0

26.4

7,9

313

37047

North Carolina

Columbus

24.0

22.7

24.6

7

314

37065

North Carolina

Edgecombe

20.9

19.6

23.9

1

315

37075

North Carolina

Graham

24.9

19.5

19.9

11

316

37083

North Carolina

Halifax

25.6

23.9

27.0

1

317

37117

North Carolina

Martin

22.3

20.2

22.5

1

318

37131

North Carolina

Northampton

23.6

21.3

22.4

1

319

37147

North Carolina

Pitt

22.1

20.3

21.5

1,3

320

37155

North Carolina

Robeson

24.1

22.8

27.8

9

321

37177

North Carolina

Tyrrell

25.0

23.3

27.3

3

322

37181

North Carolina

Vance

19.6

20.5

24.2

1

323

37187

North Carolina

Washington

20.4

21.8

26.1

1

324

38005

North Dakota

Benson

31.7

29.1

29.4

at large

325

38079

North Dakota

Rolette

40.7

31.0

26.7

at large

326

38085

North Dakota

Sioux

47.4

39.2

35.3

at large

327

39009

Ohio

Athens

28.7

27.4

28.8

6,15

328

39105

Ohio

Meigs

26.0

19.8

21.1

6

329

39163

Ohio

Vinton

23.6

20.0

20.8

15

330

40001

Oklahoma

Adair

26.7

23.2

29.0

2

331

40005

Oklahoma

Atoka

31.1

19.8

19.9

2

332

40015

Oklahoma

Caddo

27.8

21.7

21.3

3

333

40021

Oklahoma

Cherokee

28.8

22.9

23.2

2

334

40023

Oklahoma

Choctaw

32.7

24.3

26.8

2

335

40029

Oklahoma

Coal

27.4

23.1

21.9

2

336

40055

Oklahoma

Greer

23.4

19.6

26.7

3

337

40057

Oklahoma

Harmon

34.2

29.7

26.1

3

338

40061

Oklahoma

Haskell

27.1

20.5

21.1

2

339

40063

Oklahoma

Hughes

26.9

21.9

24.7

2

340

40069

Oklahoma

Johnston

28.5

22.0

21.9

2

341

40077

Oklahoma

Latimer

23.3

22.7

21.0

2

342

40089

Oklahoma

McCurtain

30.2

24.7

25.7

2

343

40107

Oklahoma

Okfuskee

29.4

23.0

25.1

2

344

40119

Oklahoma

Payne

21.7

20.3

25.1

3

345

40127

Oklahoma

Pushmataha

30.2

23.2

22.0

2

346

40133

Oklahoma

Seminole

24.0

20.8

22.6

5

347

40135

Oklahoma

Sequoyah

24.7

19.8

19.6

2

348

40141

Oklahoma

Tillman

22.9

21.9

23.6

4

349

42101

Pennsylvania

Philadelphia

20.3

22.9

25.3

1,2,13

350

45005

South Carolina

Allendale

35.8

34.5

38.2

6

351

45009

South Carolina

Bamberg

28.2

27.8

28.4

6

352

45011

South Carolina

Barnwell

21.8

20.9

22.6

2

353

45027

South Carolina

Clarendon

29.0

23.1

24.9

6

354

45029

South Carolina

Colleton

23.4

21.1

23.4

1,6

355

45031

South Carolina

Darlington

19.9

20.3

21.1

7

356

45033

South Carolina

Dillon

28.1

24.2

25.6

7

357

45039

South Carolina

Fairfield

20.6

19.6

21.2

5

358

45049

South Carolina

Hampton

27.7

21.8

22.3

6

359

45053

South Carolina

Jasper

25.3

20.7

20.7

6

360

45061

South Carolina

Lee

29.6

21.8

27.7

5

361

45067

South Carolina

Marion

28.6

23.2

25.2

7

362

45069

South Carolina

Marlboro

26.6

21.7

28.1

7

363

45075

South Carolina

Orangeburg

24.9

21.4

22.7

2,6

364

45089

South Carolina

Williamsburg

28.7

27.9

29.8

6

365

46007

South Dakota

Bennett

37.6

39.2

33.8

at large

366

46017

South Dakota

Buffalo

45.1

56.9

39.5

at large

367

46023

South Dakota

Charles Mix

31.4

26.9

21.7

at large

368

46027

South Dakota

Clay

24.6

21.2

20.0

at large

369

46031

South Dakota

Corson

42.5

41.0

39.1

at large

370

46041

South Dakota

Dewey

44.4

33.6

27.5

at large

371

46071

South Dakota

Jackson

38.8

36.5

30.7

at large

372

46085

South Dakota

Lyman

24.7

24.3

22.3

at large

373

46095

South Dakota

Mellette

41.3

35.8

33.0

at large

374

46102

South Dakota

Oglala Lakotac

63.1

52.3

40.7

at large

375

46121

South Dakota

Todd

50.2

48.3

48.6

at large

376

46137

South Dakota

Ziebach

51.1

49.9

43.7

at large

377

47013

Tennessee

Campbell

26.8

22.8

24.1

2,3

378

47025

Tennessee

Claiborne

25.7

22.6

25.4

2

379

47029

Tennessee

Cocke

25.3

22.5

24.2

1

380

47049

Tennessee

Fentress

32.3

23.1

21.4

6

381

47061

Tennessee

Grundy

23.9

25.8

22.6

4

382

47067

Tennessee

Hancock

40.0

29.4

30.9

1

383

47069

Tennessee

Hardeman

23.3

19.7

25.2

7

384

47075

Tennessee

Haywood

27.5

19.5

20.0

8

385

47091

Tennessee

Johnson

28.5

22.6

25.4

1

386

47095

Tennessee

Lake

27.5

23.6

42.7

8

387

47151

Tennessee

Scott

27.8

20.2

22.0

3

388

47173

Tennessee

Union

21.3

19.6

22.2

3

389

48025

Texas

Bee

27.4

24.0

27.0

34

390

48041

Texas

Brazos

26.7

26.9

24.9

17

391

48047

Texas

Brooks

36.8

40.2

32.2

15

392

48061

Texas

Cameron

39.7

33.1

29.1

34

393

48079

Texas

Cochran

28.3

27.0

23.5

19

394

48083

Texas

Coleman

24.9

19.9

20.1

11

395

48107

Texas

Crosby

29.5

28.1

24.1

19

396

48109

Texas

Culberson

29.8

25.1

23.6

23

397

48115

Texas

Dawson

30.5

19.7

21.4

11

398

48127

Texas

Dimmit

48.9

33.2

27.6

23

399

48131

Texas

Duval

39.0

27.2

26.3

15

400

48137

Texas

Edwards

41.7

31.6

23.3

23

401

48141

Texas

El Paso

26.8

23.8

22.7

16,23

402

48145

Texas

Falls

27.5

22.6

25.6

17

403

48153

Texas

Floyd

27.1

21.5

22.8

13,19

404

48163

Texas

Frio

39.1

29.0

25.8

23

405

48169

Texas

Garza

23.1

22.3

30.0

19

406

48191

Texas

Hall

29.1

26.3

25.8

13

407

48207

Texas

Haskell

20.8

22.8

24.5

19

408

48215

Texas

Hidalgo

41.9

35.9

31.2

15,28,34

409

48225

Texas

Houston

25.6

21.0

22.1

8

410

48229

Texas

Hudspeth

38.9

35.8

21.4

23

411

48247

Texas

Jim Hogg

35.3

25.9

27.9

15

412

48249

Texas

Jim Wells

30.3

24.1

23.2

34

413

48255

Texas

Karnes

36.5

21.9

21.8

15

414

48271

Texas

Kinney

28.6

24.0

20.0

23

415

48273

Texas

Kleberg

27.4

26.7

22.9

34

416

48275

Texas

Knox

23.6

22.9

21.1

13

417

48279

Texas

Lamb

27.1

20.9

21.5

19

418

48283

Texas

La Salle

37.0

29.8

26.2

23,28

419

48315

Texas

Marion

60.6

22.4

22.6

4

420

48323

Texas

Maverick

50.4

34.8

24.3

23

421

48327

Texas

Menard

31.1

25.8

21.4

11

422

48347

Texas

Nacogdoches

25.2

23.3

25.4

1

423

48377

Texas

Presidio

48.1

36.4

24.1

23

424

48389

Texas

Reeves

28.8

28.9

25.0

23

425

48405

Texas

San Augustine

29.7

21.2

23.8

1

426

48427

Texas

Starr

60.0

50.9

39.9

28

427

48445

Texas

Terry

25.5

23.3

21.8

19

428

48463

Texas

Uvalde

31.1

24.3

25.3

23

429

48465

Texas

Val Verde

36.4

26.1

20.7

23

430

48479

Texas

Webb

38.2

31.2

31.8

28

431

48489

Texas

Willacy

44.5

33.2

38.3

34

432

48505

Texas

Zapata

41.0

35.8

29.1

28

433

48507

Texas

Zavala

50.4

41.8

34.4

23

434

49037

Utah

San Juan

36.4

31.4

31.0

3

435

51027

Virginia

Buchanan

21.9

23.2

25.1

9

436

51051

Virginia

Dickenson

25.9

21.3

25.6

9

437

51105

Virginia

Lee

28.7

23.9

29.9

9

438

51121

Virginia

Montgomery

22.1

23.2

20.3

9

439

51131

Virginia

Northampton

26.6

20.5

20.3

2

440

51195

Virginia

Wise

21.6

20.0

23.5

9

441

51540

Virginia

Charlottesville city

23.7

25.9

22.8

5

442

51660

Virginia

Harrisonburg city

21.5

30.1

28.4

6

443

51720

Virginia

Norton city

26.7

22.8

23.0

9

444

51730

Virginia

Petersburg city

20.3

19.6

25.2

4

445

51750

Virginia

Radford city

32.2

31.4

27.2

9

446

51760

Virginia

Richmond city

20.9

21.4

26.2

4

447

53047

Washington

Okanogan

21.5

21.3

19.7

4

448

53075

Washington

Whitman

24.2

25.6

25.9

5

449

54001

West Virginia

Barbour

28.5

22.6

22.4

1

450

54005

West Virginia

Boone

27.0

22.0

24.2

3

451

54007

West Virginia

Braxton

25.8

22.0

21.3

2

452

54013

West Virginia

Calhoun

32.0

25.1

21.8

2

453

54015

West Virginia

Clay

39.2

27.5

29.0

2

454

54017

West Virginia

Doddridge

23.0

19.8

19.7

1

455

54019

West Virginia

Fayette

24.4

21.7

19.7

3

456

54021

West Virginia

Gilmer

33.5

25.9

27.1

1

457

54043

West Virginia

Lincoln

33.8

27.9

24.2

3

458

54045

West Virginia

Logan

27.7

24.1

24.4

3

459

54047

West Virginia

McDowell

37.7

37.7

36.3

3

460

54055

West Virginia

Mercer

20.4

19.7

20.2

3

461

54059

West Virginia

Mingo

30.9

29.7

28.2

3

462

54087

West Virginia

Roane

28.1

22.6

22.1

2

463

54089

West Virginia

Summers

24.5

24.4

24.7

3

464

54097

West Virginia

Upshur

21.2

20.0

20.2

2

465

54099

West Virginia

Wayne

21.8

19.6

21.5

3

466

54101

West Virginia

Webster

34.8

31.8

30.0

3

467

54103

West Virginia

Wetzel

20.5

19.8

19.5

1

468

54109

West Virginia

Wyoming

27.9

25.1

23.9

3

469

55078

Wisconsin

Menominee

48.7

28.8

27.2

8

Source: Congressional Research Service (CRS) tabulation of data from U.S. Census Bureau, 1990 Census, Census 2000, 2016 Small Area Income and Poverty Estimates, and Nation-Based Relationship File for Congressional Districts and Counties (115th 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 2016 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 2016 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. 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 http://www.census.gov/programs-surveys/acs/guidance/estimates.html.

Author Contact Information

[author name scrubbed], Analyst in Social Policy ([email address scrubbed], [phone number scrubbed])

Acknowledgments

The author is grateful for the assistance of Emma Sifre, Research Assistant, in updating the text and tables, 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.

These are H.R. 244 (Consolidated Appropriations Act, 2017, which became P.L. 115-31), H.R. 3267 (Commerce, Justice, Science, and Related Agencies Appropriations Act, 2018), H.R. 3268 (Agricultural, Rural Development, Food and Drug Administration, and Related Agencies Appropriations Act, 2018), H.R. 3280 (Financial Services and General Government Appropriations Act, 2018), (Interior and Environment, Agriculture and Rural Development, Commerce, Justice, Science, Financial Services and General Government, Homeland Security, Labor, Health and Human Services, Education, State and Foreign Operations, Transportation, Housing and Urban Development, Defense, Military Construction and Veterans Affairs, Legislative Branch, and Energy and Water Development Appropriations Act, 2018).

3.

The act included 10-20-30 language in numerous sections: Section 750, 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 E, Title I, in reference to the Community Development Financial Institutions Fund Program Account; and Division G, Title II, in reference to the Comprehensive Environmental Response, Compensation, and Liability Act of 1980 and its role in providing state and tribal assistance grants. The sections varied in the data sources used to define "persistent poverty counties," which means the sections varied in the lists of counties targeted. This report discusses how data source selection can affect the list of counties identified as persistently poor.

In the 114th Congress, 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), none of which were enacted into law. However, both P.L. 115-31 and the bills cited in footnote 2 above 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.

4.

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.

5.

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."

6.

See, for instance, a 2008 report issued jointly by the Federal Reserve System and the Brookings Institution, "The Enduring Challenge of Concentrated Poverty in 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.

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 [author name scrubbed].

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.

See http://www.census.gov/topics/income-poverty/poverty/guidance/data-sources.html.

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.