Access to Capital and Credit in Appalachia and the Impact of the 

Financial Crisis and Recession on Commercial Lending and Finance 

in the Region

Final Report

July 2013 

Prepared for:

Appalachian Regional Commission

1666 Connecticut Avenue NW 

Suite 700 

Washington, DC 20009-1068 

Contract No. CO-16929-11

Prepared by: 

Josh Silver, Vice President of Research & Policy

Archana Pradhan, Senior Research Analyst

National Community Reinvestment Coalition

Washington, DC 20005 

Spencer M. Cowan, Vice President

Woodstock Institute 

Chicago, Illinois 60602 

Acknowledgements:

The Appalachian Regional Commission (ARC) contracted with the National Community Reinvestment 

Coalition (NCRC) to complete this study. NCRC subcontracted with the Woodstock Institute for portions 

of the study dealing with Community Development Financial Institutions (CDFIs), alternative financial 

institutions and publicly supported financing programs, and equity financing. Keith Witt, GIS specialist at

the Appalachian Regional Commission; Ray Daffner, manager of the Entrepreneurship Initiative at 

ARC; and Ray Christman, advisor to the ARC federal co-chairman, provided project management, data 

mapping support, and editorial oversight. The ARC Capital Policy Initiative Advisory Committee 

reviewed and provided comments on the report draft.

TABLE OF CONTENTS

Executive Summary.....................................................................................................................................13

Recommendations..................................................................................................................................14

Major Themes and Summary of Key Findings ........................................................................................17

PREFACE: MAPS OF APPALACHIA................................................................................................................27

CHAPTER 1 SMALL BUSINESS LENDING TRENDS IN APPALACHIA COMPARED TO THE UNITED STATES

..........................................................................................................................................................33

1.1 Summary ...........................................................................................................................................33

1.2 Introduction ......................................................................................................................................34

1.3 Trends within Appalachia..................................................................................................................36

1.3.1 All Small Business Loans.............................................................................................................36

1.3.2 Lending Trends for the Smallest Businesses..............................................................................45

1.3.3 Small Business Lending in Low- and Moderate-Income Tracts..................................................52

1.3.4 Credit Card Lending....................................................................................................................58

1.3.5 Relationship between Lending and Bank Branches...................................................................66

1.3.6 Spatial Patterns of Lending ........................................................................................................67

CHAPTER 2 LOAN DEMAND IN APPALACHIA .........................................................................................69

2.1 Introduction and Summary...............................................................................................................69

2.2 Pepperdine Capital Access Index ......................................................................................................70

2.2.1 Description of Sample Size.........................................................................................................70

2.2.2 Demand for External Financing..................................................................................................77

2.2.3 Accessibility/Success Rate..........................................................................................................85

2.2.4 Expected Future Demand ..........................................................................................................90

2.2.5 Summary of Pepperdine Results................................................................................................94

2.3 Kauffman Firm Survey.......................................................................................................................95

2.3.1 Description of Sample Size.........................................................................................................95

2.3.2 Demand for Financing..............................................................................................................101

2.4 Comparing Pepperdine and Kauffman Surveys..............................................................................105

2.5 Conclusion.......................................................................................................................................105

1 
CHAPTER 3 FINANCIAL INDUSTRY IN APPALACHIA..............................................................................107

3.1 Summary .........................................................................................................................................107

3.2 Distribution of Banks by Asset Size................................................................................................. 108

3.3 Branching Patterns, Loan to Deposit Ratios, and Loans per Branch...............................................117

3.4 Distribution of Credit Unions across Appalachia and the United States........................................128

CHAPTER 4 CRA INVESTING AND COMMUNITY DEVELOPMENT LENDING OF BANKS BASED IN 

APPALACHIA ....................................................................................................................................................... 131

4.1 Summary .........................................................................................................................................131

4.2 Introduction ....................................................................................................................................132

4.3 Large Banks – Investment and Community Development Lending................................................132

4.4 Mid-Size Banks – Investment and Community Development Lending...........................................136

4.5 Total Investment and Community Development Lending Levels...................................................138

4.6 Examples of Small Business Financing ............................................................................................138

4.7 Community Development Financing by County Classification .......................................................139

CHAPTER 5 SMALL BUSINESS ADMINISTRATION (SBA) LENDING........................................................143

5.1 Summary .........................................................................................................................................143

5.2 Small Business Administration (SBA) 7a Lending............................................................................144

5.2.1 SBA 7a Lending to Minority-Owned businesses ......................................................................150

5.3 SBA 504 Lending..............................................................................................................................154

CHAPTER 6 DEVELOPMENT LENDING – TREASURY CDFIs, New Markets Tax Credit CDEs, 

MICROCREDIT LENDERS (SBA) IN APPALACHIA ........................................................................................159

6.1 Summary .........................................................................................................................................159

6.2 Introduction ....................................................................................................................................160

6.3 Distribution of Loans.......................................................................................................................162

6.4 Numbers and Types of CDFIs with Headquarters in Appalachia ....................................................165

6.5 CDFI Lending in Appalachia.............................................................................................................169

6.6 Types of Borrowers.........................................................................................................................172

6.7 Loan Purpose...................................................................................................................................179

6.8 New Markets Tax Credit Investments in Appalachia ......................................................................186

6.9 Microloans ......................................................................................................................................190

CHAPTER 7 REVOLVING LOAN FUNDS ................................................................................................. 193

7.1 Summary .........................................................................................................................................193

2 
7.2 Introduction ....................................................................................................................................193

7.3 ARC-Funded Revolving Loan Funds................................................................................................. 194

7.4 Economic Development Administration Funded Revolving Loan Funds........................................199

CHAPTER 8 EQUITY INVESTORS ...........................................................................................................203

8.1 Summary.........................................................................................................................................203

8.2 Venture Capital Funds and Angel Investors....................................................................................203

8.2.1 Venture Capital Funds..............................................................................................................203

8.2.2 Angel investors.........................................................................................................................213

CHAPTER 9 LITERATURE REVIEW.........................................................................................................215

9.1 Summary .........................................................................................................................................215

9.2 Introduction ....................................................................................................................................215

9.3 Why is Lending to Small Businesses Important? ............................................................................216

9.4 Small Businesses and the Great Recession.....................................................................................217

9.5 Supply and Demand Barriers to Acquiring Loans............................................................................218

9.6 Relationship and Transactional Lending .........................................................................................219

9.7 Bank Branches and Lending ............................................................................................................220

9.8 Impacts of Consolidation ................................................................................................................221

9.9 Discrimination................................................................................................................................. 222

9.10 CDFIs .............................................................................................................................................224

9.11 Venture Funds............................................................................................................................... 225

9.12 Revolving Loan Funds....................................................................................................................226

9.13 Angel Investors..............................................................................................................................227

9.14 Policy Recommendations for Rural Small Business Development ...............................................228

Appendix:..................................................................................................................................................231

Table A: Ratios and Indices, 2007 .........................................................................................................231

Table B: Ratios and Indices, 2010 .........................................................................................................245

3 
List of Figures:

Figure 1-1: Percent of Small Businesses Receiving Loans in Appalachia and US........................................35

Figure 1-2: Ratio of Small Business Loans to Small Businesses by Region..................................................37

Figure 1-3: Ratio of Small Business Loans to Small Businesses by County Type........................................38

Figure 1-4: Ratio of Small Business Loans to Small Businesses by Economic Status..................................38

Figure 1-5: Ratio of Number of Small Business Loans to Small Businesses by State..................................41

Figure 1-6: Ratio of Loans to Small Business with Revenue Less than $1 Million to Small Businesses with 

Revenue Less than $1 Million by Region ....................................................................................................46

Figure 1-7: Ratio of Loans to Small Business with Revenue Less than $1 Million to Small Businesses with 

Revenue Less than $1 Million by County Type ...........................................................................................46

Figure 1-8: Ratio of Loans to Small Business with Revenue Less than $1 Million to Small Businesses with 

Revenue Less than $1 Million by Economic Status.....................................................................................47

Figure 1-9: Ratio of Loans to Small Business with Revenue Less than $1 Million to Small Businesses with 

Revenue Less than $1 Million by State .......................................................................................................50

Figure 1-10: Ratio of Small Business Loans to Total Businesses in LMI Tracts by Region...........................53

Figure 1-11: Ratio of Small Business Loans to Total Businesses in LMI Tracts by County Type .................53

Figure 1-12: Ratio of Small Business Loans to Total Businesses in LMI Tracts by Economic Status...........53

Figure 1-13: Ratio of Small Business Loans to Total Businesses in LMI Tracts by State .............................56

Figure 1-14: Credit Card Market Share by Region ......................................................................................60

Figure 1-15: Credit Card Market Share by County Type .............................................................................60

Figure 1-16: Credit Card Market Share by Economic Status.......................................................................60

Figure 1-17: Credit Card Market Share by State.........................................................................................63

Figure 2-1: Pepperdine Respondents Compared to Appalachian Businesses by Revenue Size .................72

Figure 2-2: Pepperdine Respondents Compared to National Businesses by Revenue Size .......................72

Figure 2-3: Details about the Respondents, by Number of Employees......................................................73

Figure 2-4: Details about the Respondents, by Firm Age ...........................................................................73

Figure 2-5: Details about the Respondents, by Industry ............................................................................74

Figure 2-6: Ratio of Pepperdine Survey Respondents to Total Non-Farm Businesses by Region ..............75

Figure 2-7: Ratio of Pepperdine Survey Respondents to Total Non-Farm Businesses by County Type .....75

Figure 2-8: Ratio of Pepperdine Survey Respondents to Total Non-Farm Businesses by Economic Status

....................................................................................................................................................................75

Figure 2-9: Ratio of Pepperdine Survey Respondents to Total Non-Farm Businesses by Appalachian 

States ..........................................................................................................................................................76

Figure 2-10: Percentage of Respondents Who Attempted to Raise Outside Financing in the Last Three 

Months........................................................................................................................................................79

Figure 2-11: Percentage of Respondents Who Attempted to Raise Outside Financing in the Last Three 

Months, National Revenue Breakouts........................................................................................................79

Figure 2-12: Reasons for Businesses Not Attempting to Raise External Financing (Multiple Selections)..80

Figure 2-13: Reasons for Businesses Not Attempting to Raise External Financing (Multiple Selections), 

National Revenue Breakouts ......................................................................................................................80

4 
Figure 2-14: Percentage Indicating “Yes” to “Is the current business financing environment restricting...”

....................................................................................................................................................................81

Figure 2-15: Percentage Indicating “Yes” to “Is the current business financing environment restricting...,” 

National Revenue Breakouts ......................................................................................................................81

Figure 2-16: Is it Difficult or Easy to Raise New External Financing?..........................................................82

Figure 2-17: Is it Difficult or Easy to Raise New External Financing? National Revenue Breakouts...........82

Figure 2-18: Strength of Demand for Financing .........................................................................................83

Figure 2-19: Strength of Demand for Financing, National Revenue Breakouts..........................................83

Figure 2-20: Demand for Financing by Purpose and Size ...........................................................................84

Figure 2-21: Demand for Financing by Purpose and Size, National Revenue Breakouts ...........................84

Figure 2-22: Financing Success Rates by Type and Business Size for Prior Three Months.........................86

Figure 2-23: Financing Success Rates by Type and Business Size for Prior Three Months, National 

Revenue Breakouts.....................................................................................................................................86

Figure 2-24: Financing Success Rates by Type and Business Size for Prior Three Months, Appalachian 

Revenue Breakouts.....................................................................................................................................87

Figure 2-25: Percentage of Owners Who Transferred Personal Assets to Business over Prior Six Months

....................................................................................................................................................................88

Figure 2-26: Percentage of Owners Who Transferred Personal Assets to Business over Prior Six Months, 

National Revenue Breakouts ......................................................................................................................88

Figure 2-27: Type of Personal Assets Transferred to Business during Prior Six Months (Multiple 

Selections)...................................................................................................................................................89

Figure 2-28: Type of Personal Assets Transferred to Business during Prior Six Months, (Multiple 

Selections), National Revenue Breakouts...................................................................................................89

Figure 2-29: Percentage of Businesses that Are Planning to Raise Financing in the Next Six Months.......91

Figure 2-30: Percentage of Businesses that Are Planning to Raise Financing in the Next Six Months, 

National Revenue Breakouts ......................................................................................................................91

Figure 2-31: Strength of Expected Demand for New External Financing in the Next Six Months (Scale 1-4: 

Slight, Moderate, High, Extremely High) ....................................................................................................92

Figure 2-32: Likely Sources of Financing .....................................................................................................92

Figure 2-33: Number of Employees Planned to Be Hired in the Next Six Months .....................................93

Figure 2-34: Ratio of Kauffmann Survey Respondents to Total Non-Farm Businesses by Region, 2009 ...96

Figure 2-35: Ratio of Kauffmann Survey Respondents to Total Non-Farm Businesses by County Type, 

2009 ............................................................................................................................................................96

Figure 2-36: Ratio of Kauffmann Survey Respondents to Total Non-Farm Businesses by Economic Status, 

2009 ............................................................................................................................................................96

Figure 2-37: Ratio of Kauffman Survey Respondents to Total Non-Farm Businesses by Appalachian

States, 2009.................................................................................................................................................97

Figure 2-38: Percent of African American respondents .............................................................................99

Figure 2-39: Percent of Hispanic respondents............................................................................................99

Figure 2-40: Percent of Asian respondents................................................................................................. 99

Figure 2-41: Percent of female respondents............................................................................................100

Figure 2-42: Median number of employee in Kauffman Survey in Appalachia and US............................100

5 
Figure 2-43: Percent of respondents applying for loans in Appalachia and US........................................102

Figure 2-44: Percent of respondents desiring credit, but not applied because feared rejection in 

Appalachia and US ....................................................................................................................................102

Figure 2-45: Percent of respondents applied for loans and always approved in Appalachia and US ......102

Figure 2-46: Percent of respondents applied for loans and always denied in Appalachia and US ..........103

Figure 3-1: Percentage of Mid-Size Banks by Region*..............................................................................109

Figure 3-2: Percentage of Mid-Size Banks by County Type* ....................................................................110

Figure 3-3: Percentage of Mid-Size Banks by Economic Status*..............................................................110

Figure 3-4: Percentage of Mid-Size Banks by State* ................................................................................113

Figure 3-5: Branches not Headquartered in Appalachia by Region..........................................................119

Figure 3-6: Branches not Headquartered in Appalachia by County Type.................................................119

Figure 3-7: Branches not Headquartered in Appalachia by Economic Status..........................................119

Figure 3-8: Branches not headquartered in Appalachia by State.............................................................120

Figure 3-9: Number of Loans per Branch by Region.................................................................................126

Figure 3-10: Number of Loans per Branch by County Type......................................................................126

Figure 3-11: Number of Loans per Branch by Economic Status ...............................................................126

Figure 4-1: Investments in Small Business by Region* .............................................................................141

Figure 4-2: Investments in Small Business by County Type*....................................................................141

Figure 4-3: Investments in Small Business by Economic Status*..............................................................141

Figure 5-1: Number of SBA 7a Loans per 10,000 Small Businesses by Region .........................................145

Figure 5-2: Number of SBA 7a Loans per 10,000 Small Businesses by County Type ................................145

Figure 5-3: Number of SBA 7a Loans per 10,000 Small Businesses by Economic Status..........................145

Figure 5-4: Number of SBA 7a Loans per 10,000 Small Businesses by State............................................148

Figure 5-5: Number of SBA 504 Loans per 10,000 Small Businesses by Region.......................................155

Figure 5-6: Number of SBA 504 Loans per 10,000 Small Businesses by County Type..............................155

Figure 5-7: Number of SBA 504 Loans per 10,000 Small Businesses by Economic Status .......................155

Figure 5-8: Number of SBA 504 Loans per 10,000 Small Businesses by State..........................................156

Figure 6-1: Percent of Counties Receiving Loans by Region.....................................................................163

Figure 6-2: Percent of Counties Receiving Loans by County Type............................................................163

Figure 6-3: Percent of Counties Receiving Loans by Economic Status .....................................................163

Figure 6-4: Number of CDFIs by Type in Appalachia.................................................................................167

Figure 6-5: Number of CDFIs by Type in the United States ......................................................................167

Figure 6-6: Amount of Loans by Region....................................................................................................170

Figure 6-7: Amount of Loans by County Type...........................................................................................170

Figure 6-8: Amount of Loans by Economic Status....................................................................................170

Figure 6-9: Business Lending as a Percent of Amount Loaned by Region ................................................175

Figure 6-10: Business Lending as a Percent of Amount Loaned by County Type.....................................175

Figure 6-11: Business Lending as a Percent of Amount Loaned by Economic Status...............................175

Figure 6-12: Percent of Amount Loaned by Purpose................................................................................179

Figure 6-13: Percent of Amount Loaned by Purpose by Region...............................................................181

Figure 6-14: Percent of Amount Loaned by Purpose by County Type......................................................182

Figure 6-15: Percent of Amount Loaned by Purpose by Economic Status...............................................183

6 
Figure 6-16: Amount of NMTC Credits by Region.....................................................................................189

Figure 6-17: Amount of NMTC Credits by County Type............................................................................189

Figure 6-18: Amount of NMTC Credits by Economic Status.....................................................................189

Figure 7-1: Amount of Loans from ARC-funded Revolving Loan Funds by Region...................................195

Figure 7-2: Amount of Loans from ARC-funded Revolving Loan Funds by County Type..........................195

Figure 7-3: Amount of Loans from ARC-funded Revolving Loan Funds by Economic Status ...................195

Figure 7-4: EDA RLFs and Current Capital Base by Region, 2011..............................................................200

Figure 7-5: EDA RLFs and Current Capital Base by County Type, 2011.....................................................200

Figure 7-6: EDA RLFs and Current Capital Base by Economic Status, 2011 ..............................................200

Figure 8-1: Number of Venture Fund Investments by Sector in Appalachia, 2007 ..................................205

Figure 8-2: Number of Venture Fund Investments by Sector in Appalachia, 2010 ..................................205

Figure 8-3: Number of Investees by Region..............................................................................................211

Figure 8-4: Number of Investees by County Type.....................................................................................211

Figure 8-5: Number of Investees by Economic Status..............................................................................211

7 
List of Maps: 

Map 0-1: Subregions in Appalachia ............................................................................................................28

Map 0-2: County Types in Appalachia ........................................................................................................29

Map 0-3: ARC’s County Economic Status Designations in Fiscal Year 2007 ...............................................30

Map 0-4: ARC’s County Economic Status Designations in Fiscal Year 2010 ...............................................31

Map 1-1: Small Business Lending Index, 2007............................................................................................39

Map 1-2: Small Business Lending Index, 2010............................................................................................40

Map 1-3: Smallest Business Lending Index, 2007.......................................................................................48

Map 1-4: Smallest Business Lending Index, 2010.......................................................................................49

Map 1-5: Small Business Lending in LMI Tracts Index, 2007* ....................................................................54

Map 1-6: Small Business Lending in LMI Tracts Index, 2010* ....................................................................55

Map 1-7: Credit Card Business Lending Index, 2007 ..................................................................................61

Map 1-8: Credit Card Business Lending Index, 2010 ..................................................................................62

Map 3-1: Percentage of Mid-Size Banks in Appalachia, 2007 ..................................................................111

Map 3-2: Percentage of Mid-Size Banks in Appalachia, 2010 ..................................................................112

Map 3-3: Number of Bank Branches per County in Appalachia, 2007 .....................................................121

Map 3-4: Number of Bank Branches per County in Appalachia, 2010 .....................................................122

Map 3-5: Percent of Bank Branches without Bank Headquarter in Appalachia, 2007.............................123

Map 3-6: Percent of Bank Branches without Bank Headquarter in Appalachia, 2010.............................124

Map 5-1: SBA 7a Lending Index, 2007 ......................................................................................................146

Map 5-2: SBA 7a Lending Index, 2010 ......................................................................................................147

Map 6-1: Certified Community Development Financial Institutions in Appalachia, 2010 .......................166

Map 6-2: Number of CDFI Loans to Businesses by County, 2007.............................................................173

Map 6-3: Number of CDFI Loans to Businesses by County, 2010.............................................................174

Map 6-4: Amount of Loans to Businesses by County, 2007 .....................................................................177

Map 6-5: Amount of Loans to Businesses by County, 2010 .....................................................................178

Map 6-6: Total Number of New Market Tax Credit Investments, by County, 2000-2010........................187

Map 6-7: Total Amount of New Market Tax Credit Authority Invested, by County, 2000-2010..............188

Map 7-1: EDA Revolving Loan Fund Current Capital Base, by County, 2011............................................201

Map 8-1: Amount of Venture Fund Investment by County, 2007............................................................207

Map 8-2: Amount of Venture Fund Investment by County, 2010............................................................208

Map 8-3: Angel Capital Association Members by County, 2011 ..............................................................214

8 
List of Tables:

Table 1-1: Percent Change in Number of Small Businesses Receiving Loans in Appalachia and US ..........35

Table 1-2: Percent of Small Businesses Receiving Loans in Appalachia and US .........................................36

Table 1-3: Ratio of Small Business Loans to Small Businesses in Appalachia .............................................42

Table 1-4: Disparity Ratios of Ratio of Small Business Loans to Small Businesses .....................................43

Table 1-5: Average Small Business Loan Amount in Appalachia* ..............................................................44

Table 1-6: Ratio of Loans to Small Business with Revenue Less than $1 Million to Small Businesses with 

Revenue Less than $1 Million in Appalachia...............................................................................................51

Table 1-7: Disparity Ratios of Ratio of Loans to Small Business with Revenue Less than $1 Million to Small 

Businesses with Revenue Less than $1 Million...........................................................................................52

Table 1-8: Ratio of Small Business Loans to Total Businesses in Low-Moderate Income (LMI) Tracts in 

Appalachia...................................................................................................................................................57

Table 1-9: Disparity Ratios of Ratio of Small Business Loans to Total Businesses in Low-Moderate Income 

(LMI) Tracts.................................................................................................................................................58

Table 1-10: Correlation among Credit Card Lending and Non-Credit Card Lending...................................59

Table 1-11: Credit Card Market Share in Appalachia..................................................................................64

Table 1-12: Credit Card Market Share (in Dollars) in Appalachia ...............................................................65

Table 1-13: Correlation among Branches and All Small Business Lending .................................................66

Table 1-14: Correlation among Branches and Non-Credit Card Lending....................................................66

Table 1-15: Correlation among Branches and Credit Card Lending............................................................66

Table 1-16: Correlation among Deposits and All Small Business Lending ..................................................66

Table 1-17: Spatial Autocorrelation of Lending Variables..........................................................................67

Table 2-1: Number of Respondents for Pepperdine Survey and Total Number of Non-Farm Businesses in 

Appalachia...................................................................................................................................................77

Table 2-2: Number of Respondents for Kauffman Survey and Total Number of Non-Farm Businesses in 

Appalachia...................................................................................................................................................98

Table 2-3: Percent of respondents in Kauffman Survey by race and ethnicity in Appalachia and US......100

Table 2-4: Percent of female respondents in Kauffman Survey in Appalachia and US ............................101

Table 2-5: Median number of employee in Kauffman Survey in Appalachia and US...............................101

Table 2-6: Percent of respondents applying and not applying loan in Appalachia and US ......................103

Table 2-7: Percent of respondents approved and denied for loans.........................................................103

Table 2-8: Percent of loan denied respondents by denied reason in Appalachia and US........................104

Table 2-9: Percent of firms receiving equity investment from various sources in Appalachia and US ....104

Table 2-10: Median equity amount from spouses, parents, and angels in Appalachia and US ...............104

Table 3-1: Percentage of Banks by Asset Size in Appalachia, 2007 ..........................................................114

Table 3-2: Percentage of Banks by Asset Size in Appalachia, 2010 ..........................................................115

Table 3-3: Correlation among Percentage of Midsize Banks and Non-Credit Card Lending ....................116

Table 3-4: Correlation among Percentage of Midsize Banks and Credit Card Lending ............................116

Table 3-5: Correlation among Percentage of Midsize Banks and Credit Card Lending and Non-Credit Card 

Lending (All Small Business Lending) ........................................................................................................116

9 
Table 3-6: Number of Bank Branches Headquartered and not Headquartered in Appalachia by Asset Size, 

2007 ..........................................................................................................................................................118

Table 3-7: Number of Bank Branches Headquartered and not Headquartered in Appalachia by Asset Size, 

2010 ..........................................................................................................................................................118

Table 3-8: Change in Number of Bank Branches Headquartered and not Headquartered in Appalachia by 

Asset Size, 2007 to 2010 ...........................................................................................................................118

Table 3-9: Bank Branches Headquartered and not Headquartered in Appalachia, 2007 to 2010...........125

Table 3-10: Ratio of Small Business Loans to Deposits and Loans per Branches .....................................127

Table 3-11: Percentage of Credit Unions by Asset Size in Appalachia, 2007............................................129

Table 3-12: Percentage of Credit Unions by Asset Size in Appalachia, 2010............................................130

Table 4-1: Descriptive Statistics of Large Banks in Appalachia ................................................................. 134

Table 4-2: Large Banks by Year of CRA Exams..........................................................................................134

Table 4-3: Top Five Banks in Appalachia...................................................................................................135

Table 4-4: Large Banks by CRA Rating.......................................................................................................135

Table 4-5: Comparing Investment and CD Lending to Asset Ratio and Ratings for Investment Test.......135

Table 4-6: Total Dollar Amount by Type of CD Investment and CD Lending ............................................135

Table 4-7: Descriptive Statistics of Mid-Size Banks in Appalachia............................................................137

Table 4-8: Mid-Size Banks by Year of CRA Exams.....................................................................................137

Table 4-9: Mid-Size Banks by CRA Rating..................................................................................................137

Table 4-10: Comparing Investment and CD Lending to Asset Ratio and Ratings for Investment Test..... 137

Table 4-11: Total Dollar Amount by Type of CD Investment and CD Lending..........................................138

Table 4-12: CD Investment and CD Lending by County Type....................................................................142

Table 5-1: Number of SBA 7a Loans per 10,000 Businesses in Appalachia ..............................................149

Table 5-2: Disparity Ratios of SBA 7a Loans per 10,000 Businesses.........................................................150

Table 5-3: SBA 7a Loans to Minority-Owned Small Businesses in Appalachia .........................................152

Table 5-4: SBA 7a Loans to Women-Owned Businesses in Appalachia ....................................................153

Table 5-5: Number of SBA 504 Loans per 10,000 Businesses in Appalachia ............................................157

Table 5-6: SBA 504 Loans to Minority-Owned Businesses in Appalachia.................................................158

Table 6-1: CDFI Lending, by Asset Size, Nationally and in Appalachia, 2007 and 2010............................161

Table 6-2: CDFI Lending by County, 2007 and 2010 .................................................................................164

Table 6-3: Certified Community Development Financial Institutions by locations in Appalachia, 2010..168

Table 6-4: CDFI Lending in Appalachia, 2007 and 2010............................................................................171

Table 6-5: CDFI Lending by Type of Borrower, 2007 and 2010 ................................................................176

Table 6-6: Number and Amount of Loans by Purpose, 2007 and 2010....................................................180

Table 6-7: Number of Loans by Purpose by Type of County, 2007 and 2010...........................................184

Table 6-8: Amount of Loans by Purpose by Type of County, 2007 and 2010...........................................185

Table 6-9: New Markets Tax Credit Projects and Amounts in Appalachia, 2007 and 2010 .....................190

Table 6-10: Approved Microloans per 10,000 Businesses in Appalachia .................................................191

Table 7-1: ARC-funded Revolving Loan Fund Lending ..............................................................................196

Table 7-2: Total Project Investment in Projects Funded by ARC-funded Revolving Loan Funds..............197

Table 7-3: Leverage per Dollar of ARC-funded RLF Loans.........................................................................197

Table 7-4: Percent of Project Investment by Source ................................................................................198

10
Table 7-5: Jobs Created and Retained by Businesses Receiving ARC-funded RLF Loans..........................199

Table 7-6: EDA RLFs in Appalachia, (Number and Current Capital Base), 2011 .......................................202

Table 8-1: Venture Fund Investments in Appalachia, 2007......................................................................209

Table 8-2: Venture Fund Investments in Appalachia, 2010......................................................................209

Table 8-3: Venture Fund Investments by Subregion, Type, and Economic Status...................................212

11
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12
EXECUTIVE SUMMARY

In 2011, the Appalachian Regional Commission (ARC) engaged the National Community Reinvestment 

Coalition (NCRC) to carry out a study of recent trends in the availability of capital and credit for small 

businesses in the Appalachian Region, and to provide a comparison between conditions in 2010 and those 

in 2007, before the recent recession. ARC is an independent federal-state commission serving a 13-state 

region of the United States. Created by Congress in 1965, its mission is to improve economic and social 

conditions in this chronically under-served 

part of the nation, which is home to some 25 

million people.

While Appalachia, as a whole, has 

historically underperformed the rest of the 

nation economically, it has shown growth and 

improvement over the past 50 years. This is 

particularly true of northern and southern 

Appalachia, and within larger metro areas 

such as Pittsburgh, Pennsylvania; Asheville, 

North Carolina; and Huntsville, Alabama. 

Much of Central Appalachia, however, 

remains economically distressed, and 

dependent on a narrower economic base.

Research conducted for ARC by NCRC 

before the 2007–2010 financial crisis

expressed cautious optimism that disparities 

in access to capital and credit between 

Appalachia and the nation as a whole were 

diminishing, and that the Region was becoming more like the nation as a whole in terms of access to 

lending.1

 This study finds that the gap has widened since the recession, particularly in counties designated 

as “economically distressed” by ARC.

The financial crisis affected the entire country, yet often to varying degrees. During the recession lending 

plummeted nationwide: more than 60 percent of small businesses received loans in 2007, while less than 

20 percent received loans in 2010. But in Appalachia, lending decreased to a greater extent, standing at 18 

percent below national levels at the end of the recession; while in the Region’s economically distressed 

counties, lending was 56 percent below national levels. Further, growing disparities were found in lending 

to businesses with revenues of less than $1 million and in counties with limited access to non-credit-card 

bank lending.

National-level independent loan-demand surveys conducted in 2012 by Pepperdine University and the 

Ewing Marion Kauffman Foundation reported that over 60 percent of the respondents—both in the nation 

1 The previous NCRC study for ARC is online at 

http://www.arc.gov/research/researchreportdetails.asp?REPORT_ID=8

Subregions in Appalachia

13

 
as a whole and in the Appalachian Region—indicated that the current business financing environment 

was restrictive; and that three quarters of the businesses reported that raising equity and debt financing 

was difficult. The studies also reported that an increasing percentage of businesses in Appalachia and the 

nation as a whole desired credit but did not apply for it because they feared rejection; and that the 

percentage of Appalachian businesses denied credit was two and a half times higher than the national 

sample. Appalachian businesses also had much lower rates of success in securing equity financing for 

businesses: their national counterparts were up to four times more successful in obtaining capital from 

angel investors, venture funds, or family and friends. Perhaps not surprisingly, owners of the smallest 

businesses in Appalachia were much more likely to transfer their savings and use personal credit cards 

to fund their businesses than were their counterparts in the nation, or than the owners of larger businesses 

in Appalachia. 

This study also looks at trends in the banking industry between 2007 and 2010. Distribution of banks by 

asset size is similar in the nation as a whole and in Appalachia, and despite the financial crisis, the 

number of bank branches in Appalachia increased between 2007 and 2010. Interestingly, the number of 

branches of banks not headquartered in the Region increased sharply, while the number of branches of 

banks headquartered in Appalachia decreased. In a confirmation of previous findings, this study found 

that a greater number of bank branches was statistically correlated to a higher number of loans. It appears 

that banks not headquartered in Appalachia opened a disproportionate number of branches in 

economically advantaged counties in Appalachia during this period, most likely due to more favorable 

economic and demographic opportunities and conditions. Lending per branch remained at a higher level 

in the nation than in Appalachia, with national loan rates per branch over 60 percent greater than loan 

rates for banks in Appalachia. 

This study, in addition to presenting findings on access to capital and credit and bank lending in 

Appalachia, assessed the impact of strategies to address capital gaps in underserved communities, 

including the use of regulatory oversight for reinvestment, such as the Community Reinvestment Act 

(CRA); loan guarantee programs, such as the SBA 7a program; and initiatives that capitalize missionfocused lenders and community-based lenders. Findings indicate that Community Development Financial 

Institutions (CDFIs) appear to effectively target capital to particularly underserved areas; and that bank 

CRA investments in Appalachia increased during the period of study. Conversely, the SBA 7a and New 

Market Tax Credit (NMTC) programs were substantially underused in Appalachia compared with the 

nation as a whole. 

The remainder of this executive summary describes the major findings from each section of the study, and 

presents recommendations for action that could be taken by public agencies and institutions to address the 

capital and credit needs of Appalachia and other underserved regions of the United States. 

RECOMMENDATIONS

While the Appalachian Region, particularly economically distressed counties in the Region, experienced a 

greater downturn in access to capital and credit than did the nation as a whole, the infrastructure for a 

lending rebound remains in place. However, existing programs to address these disparities in business 

lending have achieved uneven success. 

14
Stakeholders such as banks and bank regulators, equity investors and entrepreneurs, state and federal 

policy makers, non-profit lenders, and community leaders must undertake strenuous efforts to reduce 

disparities in access to credit. As financial markets recover from the recession, financing gaps are likely

to remain or widen if measures to aggressively combat growth in unequal access to credit and capital are 

not pursued. 

It is recommended that stakeholders focus on increasing private- and public-sector investment to 

underserved areas in Appalachia in the following ways:

Use the Community Reinvestment Act (CRA) to Better Target Underserved Areas. Banks’ CRA ratings 

depend on the extent to which they lend to and invest in low- and moderate-income and distressed areas. 

Public agencies, nonprofit organizations, and other practitioners should work with banks to increase their 

programs and financing in traditionally underserved areas in Appalachia. Stakeholders should also partner 

with banks to increase their small-business investments, since this study found that CRA small-business 

investments significantly lagged CRA investments for affordable housing. As part of this report, NCRC 

has provided ARC with a detailed database of banks and lending levels, by county. Stakeholders can use 

this database to identify banks to approach for developing partnerships to address credit gaps identified by 

this report. An example of a partnership that can use the detailed data in this report is West Virginia’s 

Alliance for Economic Inclusion, a partnership among the Federal Deposit Insurance Corporation, the 

West Virginia Development Office, financial institutions, and community-based stakeholders. In addition, 

bank CRA loans and investment could be targeted to support nonprofit mission-driven financial 

institutions such as CDFIs, as noted below.

Support Bank Branching in Appalachia. This report finds a statistically significant relationship between 

the number of bank branches and the number of small business loans on a county level in Appalachia, a 

relationship that was identified in a previous NCRC study for ARC.2

 Bank branching in distressed and 

rural counties should be promoted by using the CRA as an incentive for branch development. Banks’ 

ratings are influenced by how many branches they locate in distressed areas and in low- and moderateincome communities. This report found that there was significant branch growth by banks with 

headquarters not in Appalachia, but that much of the branch growth was occurring in relatively 

advantaged Appalachian counties. The use of the CRA, together with subsidy programs (when necessary), 

could perhaps direct some of this branch expansion to disadvantaged counties. For example, New York’s 

Banking Development District Program provides partial property tax exemptions and encourages local 

public deposits for banks opening branches in underserved areas.3

Expand Support for Public Sector Financing Programs and Nonprofit Intermediaries. Public-sector 

programs and nonprofit intermediaries do not have the capacity to lend at the volumes of private-sector 

institutions or to fill credit gaps by themselves. Yet the public programs and nonprofit intermediaries are 

important components of collective efforts to target lending and investments to underserved counties. 

This report generally finds that programs such as the CDFI Fund and the SBA Microloan Program, which 

support organizations whose mission is to serve disadvantaged populations, are more effective at targeting 

2 The previous NCRC study for ARC is online at 

http://www.arc.gov/research/researchreportdetails.asp?REPORT_ID=8

3 See http://www.dfs.ny.gov/banking/bdd.htm for information on New York’s Banking Development District 

Program.

15

 
underserved counties than are programs that provide subsidies or guarantees to predominantly privatesector institutions, such as the SBA 7a or the New Markets Tax Credit programs. Therefore, it is 

recommended that support for nonprofit intermediaries in Appalachia be increased through a range of 

strategies, including building investor consortia or financing intermediaries to aggregate the CRA and 

leverage philanthropic capital; and developing new mechanisms—perhaps with public support—to access 

market-based financing. In addition, the SBA 7a and NMTC programs should devise methods to increase 

the effectiveness of targeting resources to underserved communities. The SBA, for example, could 

increase its outreach and training to banks located in rural counties, and increase and promote the use of 

packagers and servicers to lower transaction costs and increase participation among community banks. 

The NMTC program could provide additional prioritization for tax credit allocations to Community 

Development Entities (New Markets Funds) located in the rural regions they serve, and reduce the 

transaction costs of investments to increase the feasibility of using NMTC credits in the smaller 

transactions typically found in rural communities.

Increase Equity Financing in Underserved Areas. This report finds that equity financing is concentrated 

in high-technology corridors that are home to prominent research universities, and that limited access to 

equity financing may be a factor in higher rates of loan denials in Appalachia. Efforts should be 

undertaken to increase equity investments in the rural and underserved areas of Appalachia, including 

expanding venture capital financing and angel investing, and enhancing access to capital from family and 

friends. Some effective strategies could include fostering the formation of Angel Investment Funds 

composed of local accredited investors; supporting the formation or recapitalization of development 

venture capital funds in Appalachia, that focus on “triple bottom line” investments targeting rural and 

underserved geographies; and reviewing best-practice models for increasing access to investment from 

family and friends.

Increase Technical Assistance to Small Businesses. The Pepperdine University and Kauffman 

Foundation surveys found that Appalachian businesses had less success in acquiring loans than national 

businesses did, and that small Appalachian businesses had greater rates of high-cost credit card lending 

than of non-credit-card lending. In addition, Appalachian businesses had fewer resources from friends and 

family than national businesses did, which most likely contributed to their lower amounts of collateral

and less success in securing financing. Technical assistance to businesses could help address these 

deficiencies by focusing on topics such as business planning, assistance in securing collateral, and advice 

for boosting creditworthiness. The technical assistance could be provided by a range of non-profit and 

for-profit organizations. 

Improved Data Collection on Business Lending. CRA examinations for large banks tend to overlook 

rural areas or areas without bank branches. CRA exams should increase the attention and weight rural 

communities receive when determining bank ratings. In addition, improved data on small business 

lending is imperative. The new Consumer Financial Protection Bureau (CFPB) is required per the DoddFrank Wall Street Reform and Consumer Protection Act of 2010 to improve the publicly available smallbusiness data by including the race and gender of the small-business owner, and indicating the purpose of 

the loan, and whether the loan was approved and rejected. The CFPB has not yet issued a proposed 

regulation for this data, which would have been very helpful for this report’s analysis of loan demand and 

lending to minority- and women-owned small businesses. More information on loan terms and conditions 

also would have greatly informed the credit card lending analysis in this report regarding the 

16
sustainability and affordability of credit-card small-business lending. The data reporting requirement for 

mid-size banks should be reinstituted so future reports can better analyze the contributions of these 

important banks for rural areas. Finally, all public agencies should collect detailed data on community 

development lending and investing, so future research can capture this financing on a county level and 

more precisely distinguish between financing for affordable housing and financing for small businesses.

MAJOR THEMES AND SUMMARY OF KEY FINDINGS

1. Small-business lending declined sharply in Appalachia between 2007 and 2010—from over 

800,000 loans totaling $24 billion to 255,000 loans totaling $13 billion. As a result, the percentage of 

small businesses receiving loans in Appalachia during this period trailed the percentage for the 

nation as a whole. This trend was more pronounced in Central Appalachia and in economically 

distressed counties throughout the Region.

• In 2003, 41 percent of small businesses, both in the nation as a whole and in Appalachia, received 

loans. In both 2007 and 2010, small-business lending in Appalachia was about 18 percent lower 

than lending nationally.

• Small-business lending, both 

nationally and in Appalachia, 

plummeted between 2007 and 

2010. For the nation as a whole, 

the percentage of small 

businesses receiving loans was 

almost 62 percent in 2007, but 

only 19.5 percent in 2010. For 

Appalachia, the statistics were 

50 percent and 16 percent. 

• Lending to businesses with revenues under $1 million contracted to the greatest extent during this 

period, both in Appalachia and in the nation as a whole. In 2010, only 8 percent of these 

businesses received loans, a rate that was less than 50 percent of both national and regional 

lending rates overall to all small businesses. 

• In 2010, the business lending rate in economically distressed counties in Appalachia was just 44 

percent of the national rates, and lending in Central Appalachia was 43 percent lower than 

lending nationally. The percentage of businesses in ARC-designated economic attainment 

counties (i.e., better performing counties) that received loans in 2010 was 1.85 times greater than 

the percentage of businesses in economically distressed counties that received loans. In 2007, the 

percentage was 2.25. 

• Small businesses in Northern Appalachia had the greatest access to credit, while small businesses 

in Central Appalachia had the least access, both before and after the recession. For example, in 

2007 about 55 percent of the small businesses in Northern Appalachia received loans, while just 

33.3 percent of the small businesses in Central Appalachia received loans. In 2010, the rates were 

19.4 percent in Northern Appalachia and 11.1 percent in Central Appalachia. 

Percent of Small Businesses Receiving Loans, 

Appalachia and Nation as a Whole

17
• An examination of lending by state showed that the Appalachian portions of Pennsylvania and 

Maryland had the greatest access to loans, while the Appalachian portions of Kentucky and 

Mississippi had the least access to loans. 

• In 2007 and 2010, higher levels of 

credit card lending generally occurred 

in counties where higher levels of noncredit-card bank lending also occurred, 

indicating that credit card lending was 

not acting as a credit substitute in 

these counties. In some clusters of 

counties with limited access to noncredit-card lending, borrowers used 

credit cards at a higher rate than the 

national average to meet their 

borrowing needs. Maps of credit card 

lending indicate that credit card 

lending concentrations were highest in 

the counties with the least access to 

overall small-business credit, 

including a number of counties in 

Kentucky and Tennessee. 

• A higher number of bank branches 

was statistically correlated to a higher 

number of loans. Previous studies have 

also identified this relationship.

2. This study and national loan demand surveys from Pepperdine University and the Ewing Marion 

Kauffman Foundation suggest that there is significantly higher unmet loan demand in Appalachia 

than in the nation as a whole. 

Appalachian businesses appear to have less success in obtaining loans than businesses nationally. One 

possible explanation is that there are fewer non-bank financing resources available from family and 

friends, angel investors, and venture capital investors in the Region, which may contribute to higher loanrejection rates for Appalachian businesses.

• The smaller the business, the more likely it is to be affected by overall economic conditions and 

to have greater difficulty raising both debt and equity capital. This holds true both in Appalachia 

and in the nation as a whole. Just over 60 percent of respondents to a national survey—both in 

Appalachia and in the nation as a whole—indicated that the current business financing 

environment was restrictive. Three quarters of the businesses in Appalachia and in the nation as a 

whole stated that raising equity and debt financing was difficult.

Small Business Lending Index, 2010

Ratio of Number of Small Business Loans to Small Businesses, Indexed Values (U.S. =1)

18
• From 2007 through 2009, an increasing percentage of businesses in Appalachia and the 

United States desired credit but did not apply because they feared rejection. In the nation as a 

whole, the percentage of businesses not applying for loans due to fear of rejection increased 

from 15.7 percent in 2007 to 21.1 percent in 2009; in Appalachia the figures were 18.1 

percent in 2007 and 23.1 percent in 2009.

• The percentage of firms denied 

credit in 2009 was significantly 

higher in Appalachia (22.9 percent 

then the percentage of firms in the 

nation as a whole (8.7 percent). 

Due to low sample sizes, it is not 

possible to offer a statistically 

significant conclusion as to reasons 

for denial, but it appears that 

insufficient collateral and business 

and personal credit history were 

larger factors in Appalachia than in 

the nation as a whole.

• Survey respondents were less successful in securing business loans from banks than in obtaining 

credit cards or trade credit. For example, about 45 percent of the survey respondents, both in 

Appalachia and in the nation as a whole, who sought business bank loans in 2012 secured them, 

compared with the 62 percent of respondents in the nation and the 58 percent of respondents in 

Appalachia who successfully secured personal credit cards. 

• Appalachian businesses had much lower rates of success in securing equity financing in 2012. 

For example, only 5 percent of the businesses in Appalachia that sought angel capital succeeded 

in acquiring it, compared with 20 percent of businesses in the national sample. None of the 

Appalachian respondents with revenues between $500,000 and $1 million secured angel 

investments, while 12 percent of the national respondents did so. 

• In the second-smallest business revenue 

category ($500,000 to $1 million), 

respondents in Appalachia were 

strikingly less successful than their 

counterparts in the nation as a whole in 

raising debt or equity financing in 2012. 

For example, 35 percent of these 

businesses in the nation as a whole 

secured business loans from banks, 

while only 17 percent of their 

Appalachian counterparts did so. 

Likewise, 54 percent of these businesses 

secured credit-card financing, while just 

Percent of Businesses with Revenues of 

$500,000 to $1 Million Receiving Loans, 2012

Source: Pepperdine Capital Access Index, special tabulations 

provided to NCRC in 2012

Percent of Respondents Denied Loans, 

Appalachia and Nation as a Whole

Source: Kauffman Firm Survey data; special tabulations provided to NCRC in 2012

19
14 percent of their Appalachian counterparts did so. None of the Appalachian respondents in this 

category secured angel investments, while 12 percent of respondents in the nation as a whole 

were successful in securing angel financing. 

• Appalachian businesses experienced much lower rates in obtaining capital from friends and 

family (47%) in 2012, compared with firms across the nation (71%). While this is not surprising 

in a region experiencing greater economic distress than the nation, it may explain one reason for 

the more limited success smaller businesses in Appalachia had in securing loans. 

• Owners of the second-smallest businesses in Appalachia, those with revenues between $500,000 

and $1 million, were much more likely to transfer their savings and use personal credit cards to 

fund their businesses than were their counterparts in the nation as a whole or than owners of 

larger businesses in Appalachia. For example, 81 percent of these business owners in Appalachia 

transferred their personal savings and investments to their small businesses in 2012, compared 

with 68 percent of their counterparts in the nation as a whole. 

3. The banking industry is undergoing significant changes Appalachia as well as in the nation 

as a whole, with significant implications for meeting future loan demand and supporting 

economic growth.

• Access to banks is integral to access to credit, as a higher level of bank branches is correlated 

with a higher number of loans in Appalachia. 

• The distribution of banks by asset size is similar in the nation as a whole and in Appalachia. In 

2007 and in 2010, about two thirds of all banks, both in Appalachia and in the nation as a whole, 

were small, with assets of less than $250 million. The percentage of banks with assets above $1 

billion is also similar in the nation as a whole and in Appalachia. 

• The percentage of mid-size banks—those with assets between $250 million and $1 billion—is 

modestly higher in Appalachia than in the nation as a whole. A statistically significant correlation 

existed between the percentage of mid-size banks and lending levels on a county level. 

• Despite the financial crisis, the number of bank branches in Appalachia increased modestly 

between 2007 and 2010, from 8,580 to 8,677. During that time, the number of branches of banks 

not headquartered in the Region increased by 19.5 percent, while the number of branches of 

banks that were headquartered in Appalachia decreased by 7.2 percent. Banks not headquartered 

in Appalachia opened branches in economically advantaged counties in Appalachia at a 

disproportionate rate during that time period, most likely due to the more favorable economic and 

demographic opportunities and conditions in those counties. Continuation of this trend could pose 

growing credit access issues for small businesses located in more rural or more economically 

disadvantaged portions of the Region. 

• Lending on a per-branch basis remained at a higher level in the nation as a whole than in 

Appalachia: in 2010, the national rate was 41 small business loans per branch, while the rate for 

Appalachian banks was 25 loans per branch. 

20
• The small-business loan-to-deposit ratios in Appalachia declined from 5.4 percent in 2007 to 2.7 

percent in 2010, while the national ratio declined from 4.5 percent to 2 percent during the same 

time period. Within Appalachia, the small-business loan-to-deposit ratio was the lowest in Central 

Appalachia (1.6 percent) in 2010. The gap in the small-business loan-to-deposit ratio between 

distressed counties and attainment counties in 2010 was substantial, reporting ratios of 1.3 

percent and 3.7 percent, respectively.

• The number of credit unions in Appalachia shrank from 2007 to 2010. In addition, Appalachia in 

2007 and 2010 had a lower proportion of the largest credit unions, (those with assets over $100 

million), than did the nation as a whole. Credit unions could represent an untapped resource for 

Appalachian businesses, particularly in Central Appalachia, which had a higher percentage of 

large credit unions other subregions. Credit union lending patterns cannot be fully analyzed, as 

credit unions are not required to publicly report small-business lending.

4. Community Reinvestment Act (CRA) investing is surprisingly strong in Appalachia, compared 

with the nation as a whole, but most of this investing is targeted to affordable housing, not to smallbusiness and community economic development investing. 

• Banks can make community development loans for construction financing for economic 

development purposes (industrial parks, incubators, etc.) as well as equity investments handled by

Small Business Investment Corporations (SBICs).

• Banks in Appalachia undergoing CRA exams are a significant resource for investment and 

community development lending. Large banks (those with assets over $1 billion) headquartered 

in Appalachia have a total of $433 billion in assets and mid-size banks (assets between $250 

million and $1 billion) have combined assets of $68 billion. 

• Despite the financial crisis, the level of community investing and lending over a CRA exam 

time period of approximately three years was greater during this study than in previous 

studies conducted during 2007. In the sample for this study, total community development 

financing was $8.8 billion, compared with $5.4 billion during the previous study. An 

important caveat is that much of the increase was due to the growth in assets of the five 

largest banks headquartered in Appalachia, which have a wide geographical reach including 

several counties and states beyond Appalachia. It is not clear to what degree this financing 

was in Appalachian counties.

• The level of community 

development financing 

(investment and lending) was 

much greater for housing than for 

small businesses from 2007 

through 2011. For example, large 

banks headquartered in 

Appalachia invested $762 million 

in housing compared with the 

Community Development Investment by Large Banks 

Headquartered in Appalachia, 2007 through 2011

21
$150 million they invested in small-business development on their most recent CRA exams. 

While it would not be desirable to decrease the amount targeted for housing development, 

stakeholders can work with banks in Appalachia to increase investments for small businesses.

• Banks with higher overall CRA ratings and high ratings on their investment tests or community 

development tests offered greater amounts of community development financing on a per-asset 

basis. Large banks had their lowest ratings on the investment tests; thus, an opportunity exists to 

work with large banks to improve their investment test ratings and their level of equity 

investments for small businesses.

• Disparities in community development financing mirror disparities identified for general business 

lending in the Region. Counties in Central Appalachia and economically distressed counties 

throughout Appalachia have banks with total assets of $14 billion and $3 billion, respectively, 

while other counties have banks with assets in the tens or hundreds of billions of dollars. Banks 

located in Central Appalachia, rural counties, and economically distressed counties invested 

much less than banks located in metropolitan or economically advantaged counties. 

5. The key Small Business Administration (SBA) loan programs—7a Loan Guarantees and the 

504 Loan program—are used sparingly in Appalachia, particularly in disadvantaged counties. 

• The SBA 7a program guarantees a small volume of loans, when compared with overall business 

lending. Loans receiving SBA 7a guarantees were approximately 1 percent of the loans reported 

by banks covered by CRA for 2007 and 2010, both nationally and in Appalachia. This reflects the 

complexity of these programs for banks to administer and the changing rules and guidelines that 

accompany the programs.

• The use of SBA programs was weaker in Appalachia than nationally, as measured by loans per 

10,000 small businesses. SBA 7a lending levels were 30 percent less in Appalachia than the 

nation in 2010. In 2010, about 15.3 SBA 7a loans were issued per 10,000 small businesses in 

Appalachia, compared with 21.9 loans per 10,000 small businesses in the nation as a whole. 

Number of SBA 7a Loans per 10,000 Small Businesses, by Region

45.7

29.8

43.4

31.7

13.5

21.5

25.9

21.8

15.3

19.0

17.1

11.8

13.8

13.3

0.00 10.00 20.00 30.00 40.00 50.00

United States

Appalachian Region

Northern Appalachia

North Central Appalachia

Central Appalachia

South Central Appalachia

Southern Appalachia

2007 2010

22
• Within Appalachia, the SBA 7a lending rate per 10,000 small businesses was lower in distressed, 

rural, and Central Appalachian counties than in attainment, metropolitan, and Northern 

Appalachian counties. In 2010, lenders made 9.7 SBA 7a loans per 10,000 small businesses in 

distressed counties, while they made 20.2 loans per 10,000 small businesses in attainment 

counties. 

• In 2007, SBA 7a lending was 

provided in proportion to the 

percentage of the minority 

population in Appalachia, but 

by 2010, lending to minorityowned businesses had dropped 

and was no longer in 

proportion to the minority 

population. However, the gap 

between the percent of SBA 

7a lending to minority-owned 

businesses and the percent of 

minorities in the population in 

2007 and in 2010 was greater 

for the nation than for 

Appalachia. SBA 7a lending 

to woman-owned businesses 

in 2007 and in 2010 was not in 

proportion to the percentage of 

woman in the population, and 

this gap was greatest in 

disadvantaged counties such, 

as distressed counties and 

those counties in Central 

Appalachia. 

• Only 325 SBA 504 loans were offered in Appalachia in 2010. SBA 504 lending volumes were the 

lowest in Central Appalachia, rural counties, and distressed counties. In 2007, lenders made one 

SBA 504 loan per 10,000 small businesses in distressed counties, while they made 6.5 loans per 

10,000 small businesses in attainment counties. The disparity in 2010 was similar. 

SBA 7a Lending Index, 2010

Ratio of SBA 7a Loans per 10,000 Small Businesses, Indexed Values (U.S. =1)

23
6. There is growing community development lending capacity in Appalachia through the presence 

of an expanding Community Development Financial Institutions (CDFI) and revolving loan fund 

network, but this network is still undercapitalized for the needs and demands of small businesses. 

The New Markets Tax Credit, a major federal resource for economic development, is used only in a 

very limited way in Appalachia. CDFIs have a primary mission of community or economic 

development, are accountable to low income communities, and are certified by the U.S. Department 

of Treasury.

• There are 71 CDFIs headquartered in the Appalachian Region. However, the vast majority of 

loans CDFIs made in Appalachia in 2007 and 2010 were from institutions that did most of their 

lending outside of Appalachia. 

• CDFI lending in Appalachia increased by 88 percent from 2007 to 2010, from $197 million to 

$371 million. 

• CDFIs lend in the majority of counties in Appalachia and have increased their targeting of 

disadvantaged counties. For example, CDFIs increased the amount they lent in Central 

Appalachia by 52 percent between 2007 and 2010, from $90.6 million to $137.4 million. CDFIs 

increased lending in rural counties by over 50 percent between 2007 and 2010, from $82.3 to 

$124.2 million. 

• CDFIs issued 4,613 loans in 

Appalachia during 2007, and 4,661 

loans in Appalachia during 2010. 

Of the 2007 loans, 1,416 (30.7 

percent) were for small businesses, 

and of the 2010 loans, 2,363 (50.7 

percent) were for small businesses.

• The majority of CDFI loans for 

businesses in Appalachia, in both 

2007 and 2010, were directed to

Central Appalachian counties, 

while the plurality of dollars CDFIs 

lent to businesses went to 

businesses in rural counties.

• The percentage of CDFI lending for 

microenterprise was higher in 

Appalachia than in the nation as a 

whole in 2007 and 2010, but was 

still only 4 percent of the CDFI 

loan dollars in Appalachia. 

Amount of Loans by CDFIs to Businesses, by County, 2010

24
• There are only 12 

Community Development 

Entities (CDEs)

(organizations certified 

through the NMTC 

program) located in 

Appalachia, and they have 

received a total allocation of 

$321 million in credit 

authority, out of $30 billion 

in tax credits allocated 

through 2010—just 1

percent of the credit 

authority allocated. 

• Forty-seven CDEs have 

invested $706 million in 

351 projects located in 62 

Appalachian counties, about 

3.4 percent of the total 

amount of credit authority 

invested nationwide. 

• Within this relatively limited NMTC investment pool, the targeting of disadvantaged counties in 

Appalachia decreased between 2007 and 2010. For example, NMTC funding in Central 

Appalachia declined from 25 percent of investments in Appalachia in 2007, to just 7 percent of 

investments in 2010. A similar decrease occurred in rural counties.

• Another federal program used by 

CDFIs and other development 

lenders is the SBA microloan 

program. In Appalachia, the SBA 

microloan program financed 163 

loans in 2007, and 244 loans in 

2010. 

• The SBA microloan program was 

effective in targeting 

disadvantaged counties. Central 

Appalachia, rural counties, and 

distressed counties received the 

most microloans per small 

business during 2007 and 2010. 

Amount of Loans from ARC-Funded Revolving 

Loan Funds, by Region

New Market Tax Credit Investments, by County, 2000-2010

25
• The 35 ARC-funded RLFs in Appalachia made 87 loans totaling $7.7 million in 2007, and 73 

loans totaling $6.5 million in 2010, leveraging an additional $52.1 million in 2007 and $60.8 

million in 2010 from banks and other sources. 

• The great majority of ARC RLF funding in 2007 and 2010 was in Northern and Southern 

Appalachia, in transitional counties, and in small metropolitan counties and rural counties. 

• There were 69 EDA-funded RLFs in 54 Appalachia counties in 2011, with an aggregate capital 

base of nearly $120 million. Over half of this capital was held by just 8 RLFs.

7. There is a very limited base of venture and other forms of equity capital available to businesses 

and entrepreneurs in Appalachia, which constitutes a major barrier to new business and job 

creation.

Two sources of equity investment for businesses were examined in this study: venture capital funds and 

angel investors. The total amount of investment in Appalachia by venture capital funds declined by about 

27 percent between 2007 and 2010. Because venture capital funds tend to invest in high growth fields, 

such as biotechnology, health care, and information technology, their investments are highly concentrated 

in Northern Appalachian cities, including Pittsburgh, Pennsylvania, and Ithaca, New York; and in large 

metropolitan and ARC-designated transitional or economically competitive counties. Angel investments 

in Appalachia also declined between 2007 and 2010, by about 23 percent, with the majority of 

investments in software, healthcare, and biotechnology sectors. Because angel investors tend to invest 

near where they live, and because most live near urban areas or universities, they appear to be a limited 

source of equity investment for businesses in rural and distressed communities. The key findings with 

respect to venture fund and angel investors are:

• Less than 2 percent of Appalachian venture fund investments were in rural counties and less than 

1 percent were in Central Appalachia or at-risk counties.

• Venture capital investment was concentrated in larger metropolitan areas within Appalachia— 

Allegheny County, Pennsylvania, (which includes the city of Pittsburgh) dominates venture fund 

investment in Appalachia, followed by other knowledge-based or medical centers, including 

Jefferson County, Alabama (which includes city of Birmingham), Gwinnett County, Georgia (a 

suburb of Atlanta), Tompkins County, New York (which includes the city of Ithaca), and 

Clermont County, Ohio (a suburb of Cincinnati)—where greater opportunities exist for 

investment in companies in fields such as health care, information technology, and energy 

technology.

• The pattern of venture fund investment in Appalachia is consistent with the geographic 

distribution of biomedical, information technology, and knowledge-based industries. 

• While less is known about angel investments because of their informal nature, the data suggest 

that their pattern is similar to that of venture capital fund investments: concentrated near large 

metropolitan areas and universities, and leaving distressed and rural counties underserved.

26
PREFACE: MAPS OF APPALACHIA

This preface contains maps of Appalachia that describe the various subregions and county classifications 

used by this report. The maps describe subregions in Appalachia, county types (typology of urban and 

rural counties), and counties by economic status. The report describes trends in lending and investing in 

the subregions and county classifications. The analysis, maps, tables, and figures generated from these 

classifications are based on ARC’s current geographic boundary, which consists of 420 counties. The 

only exception is the economic status classification on tables and figures with 2007 data, which are based 

on the 410 counties in ARC’s boundary in 2007. 

27
Map 0-1: Subregions in Appalachia

28
Map 0-2: County Types in Appalachia

29
Map 0-3: ARC’s County Economic Status Designations in Fiscal Year 2007

30
Map 0-4: ARC’s County Economic Status Designations in Fiscal Year 2010

31
[Page Intentionally Left Blank]

32
CHAPTER 1 SMALL BUSINESS LENDING TRENDS IN APPALACHIA 

COMPARED TO THE UNITED STATES

1.1 SUMMARY

This chapter compares small business non-credit card and credit card lending in the nation to Appalachia 

and within Appalachia from 2007 through 2010. The chapter identifies disparities between Appalachia 

and the nation and within Appalachia. The chapter also examines the relationship between non-credit card 

and credit card lending, and between lending and branching. 

Major findings concerning overall small business lending include: 

• In 2003, 41 percent of the small businesses in both the nation and Appalachia received loans. By 

2007, 61.6 percent and 50.3 percent of the small businesses in the nation and Appalachia, 

respectively, received loans. In 2010, the percentages plummeted to 19.5 percent and 16.2 percent 

for the nation and Appalachia, respectively. In both 2007 and 2010, lending in Appalachia was 

about 18 percent lower than lending nationally. 

• The number of small business loans in Appalachia declined from 808,877 in 2007 to 255,231 in 

2010. The dollar amount of small business lending decreased from $23.9 billion in 2007 to $13.3 

billion in 2010.

• When comparing the nation to Central Appalachia, the subregion in Appalachia receiving the 

least lending, lending in Central Appalachia was 43 percent less than lending nationally by 2010. 

• Considering subregions in Appalachia, small businesses in Northern Appalachia experienced the 

greatest access to credit while small businesses in Central Appalachia had the least access. For 

example, in 2007, about 55 percent of the small businesses in Northern Appalachia received loans 

while just 33.3 percent of the small businesses in Central Appalachia received loans. By 2010, the 

ratios were 19.4 percent and 11.1 percent in Northern and Central Appalachia, respectively.

• When examining lending by state, the Appalachia portions of Pennsylvania and Maryland had the 

greatest access to loans while Kentucky and Mississippi had the least access to loans. In addition, 

counties on the eastern boundaries of Appalachia had significantly greater access to credit than 

those on the western boundaries of Appalachia. 

• The more urbanized a county, the better the access to credit within Appalachia. Likewise small 

businesses in counties with relatively favorable economic conditions had more access to credit 

than businesses in economically distressed counties. The disparities in access to credit narrowed 

slightly but remained significant from 2007 through 2010. For example, the percentage of 

businesses in attainment counties receiving loans was 2.25 times greater and 1.85 times greater 

than the percentage of businesses in economically distressed counties receiving loans in 2007 and 

2010, respectively. 

33
• Lending to the smallest businesses with revenues under $1 million contracted to the greatest 

extent in both Appalachia and the United States. By 2010, only 8 percent of the smallest 

businesses in both Appalachia and the nation received loans. 

• Lending to small businesses in low- and moderate-income census tracts exhibited significant 

disparities by subregion, degree of urbanization, and economic status of counties in 2007 and 

2010. Just as with lending overall, the greatest disparity by subregion was between Northern and 

Central Appalachia. Also, in 2007 and 2010, the percentage of small businesses in low- and 

moderate-income tracts in attainment counties receiving loans was 1.9 and 2.2 times greater, 

respectively, than the percentage of small businesses receiving loans in low- and moderateincome tracts in economically distressed counties.

• Comparing credit card small business lending to non-credit card lending revealed that both types 

of lending tended to increase or decrease together on a county level. Credit card lending is not 

acting as a substitute where non-credit card lending is relatively scarce, but instead higher levels 

of credit card lending generally occur in counties where higher levels of non-credit card lending 

also occur. 

• In some clusters of counties with limited access to non-credit card lending, borrowers utilize 

credit cards to a greater degree than national average to meet their borrowing needs. Index maps 

of credit card lending indicate that lending concentrations are the highest in the counties such as 

those in Kentucky and Tennessee with the least access to overall small business credit. 

Conversely, the credit card lending index is the lowest in Northern Appalachia where overall 

small business credit was most favorable compared to the nation. Overall, however, this study 

observes that credit card and non-credit card lending generally increase or decrease together on a 

county level. 

• On a county level, a higher number of branches is statistically correlated to a higher number of 

loans. Previous studies including NCRC’s 2007 study for ARC have also identified this 

relationship. 

• Spatial autocorrelation analysis reveals that lending patterns described above are non-random. In 

other words, lending is clustered in Northern Appalachia, non-distressed counties, and 

metropolitan counties. 

The Great Recession significantly lowered small business lending levels from 2007 through 2010 in the 

nation and Appalachia. Disparities between the nation and Appalachia and within Appalachia remained 

persistent from 2007 through 2010. The disturbing news is the massive reduction in lending for all 

communities. The possible silver lining in this economic crisis is that stakeholders have an opportunity to 

address disparities in a concerted manner as lending rebounds. The big question for policymakers and 

stakeholders is whether aggressive, concerted, and coordinated action can be taken to ensure that 

widening disparities do not occur as lending levels increase when economic conditions improve.

1.2 INTRODUCTION

This chapter describes trends in small business lending in Appalachia in 2007, a year immediately 

preceding the financial crisis, and 2010, a year following not only the worst impacts of the Great 

34
Recession but also a year experiencing high unemployment levels. Choosing 2007 and 2010 enables the 

analysis to assess lending before and after the financial crisis and to assess the impacts of the financial 

crisis on access to credit in Appalachia. A few comparisons will be made to 2003, a year analyzed by 

NCRC’s previous report for the Appalachian Regional Commission (ARC).4

Lending increased to a larger extent in the nation, as a whole, than Appalachia from 2003 through 2007. 

The decline in lending in the nation and Appalachia was of similar magnitude from 2007 through 2010 

(see Figure 1-1). From 2003 through 2007, lending surged 80.9 percent in the nation and 52.5 percent in 

Appalachia. In 2007 through 2010, lending decreased by about 69 percent in both the nation and 

Appalachia (see Table 1-1). 

Because lending increased by a greater extent in the nation than Appalachia during the 2003 through 2007 

time period, a greater percentage of small businesses in the nation than Appalachia received loans in both 

2007 and 2010. In 2003, 41 percent of the small businesses in both the nation and Appalachia received 

loans. By 2007, 61.6 percent and 50.3 percent of the small businesses in the nation and Appalachia, 

respectively, received loans. In 2010, the percentages plummeted to 19.5 percent and 16.2 percent for the 

nation and Appalachia, respectively (see Table 1-2).

Figure 1-1: Percent of Small Businesses Receiving Loans in Appalachia and US

Table 1-1: Percent Change in Number of Small Businesses Receiving Loans in Appalachia and US

4 The previous NCRC study for ARC can be accessed via 

http://www.arc.gov/research/researchreportdetails.asp?REPORT_ID=8

41.7%

41.4%

61.6%

50.3%

19.5%

16.2%

0% 20% 40% 60% 80%

US

Appalachia

Per capita personal income index 2003 2007 2010

2003 2007 2010 2003-2007 2007-2010 2003-2010

US 7,428,630 13,437,779 4,197,610 80.9% -68.8% -43.5%

Appalachia 530,309 808,877 255,231 52.5% -68.4% -51.9%

Number of Small Businesses Loans % Change

35

 
Table 1-2: Percent of Small Businesses Receiving Loans in Appalachia and US

1.3 TRENDS WITHIN APPALACHIA

1.3.1 ALL SMALL BUSINESS LOANS

Considering subregions in Appalachia, small businesses in Northern Appalachia experienced the greatest 

access to credit while small businesses in Central Appalachia encountered the least access. About 55 

percent of the small businesses in Northern Appalachia received loans while just 33.3 percent of the small 

businesses in Central Appalachia received loans in 2007. By 2010, this disparity remained, with 19.4 

percent and 11.1 percent of the small businesses receiving loans, respectively, in Northern and Central 

Appalachia (see Figure 1-2). 

The more urbanized a geographical area, the better the access to credit within Appalachia. Almost 59 

percent of the small businesses in large metropolitan counties (more than 1 million people) received loans 

while 35.1 percent of the small businesses in rural counties received loans in 2007. This disparity occurs 

in 2010 as well but is narrowed with 17.8 percent and 12.7 percent of the small businesses in large 

metropolitan counties and rural counties, respectively, receiving loans (see Figure 1-3 and Table 1-4). In 

2007, the percentage of small businesses in large metropolitan counties receiving loans was 1.7 times 

greater than the percentage of small businesses in rural counties receiving loans. By 2010, this disparity 

ratio shrank modestly to 1.4 (see Table 1-4).

Not surprisingly, more favorable economic conditions corresponded to increased access to loans on a 

county level. Banks issued loans to just 28.1 percent of the small businesses in economically distressed 

counties and to 63.3 percent of the small businesses in attainment counties in 2007. The disparities 

narrowed but remained in 2010 (see Figure 1-4 and Table 1-4).

Lending by dollar amount reinforces the finding of disparities by county categories. In Appalachia, as a 

whole, the amount of small business lending declined from $23.9 billion in 2007 to $13.3 billion in 2010. 

Central Appalachia received considerably lower dollar amounts compared to other subregions. For 

example, in 2010, Central Appalachia received less than half a billion dollars in lending compared to 

Northern Appalachia’s $5.1 billion (see Table 1-5).

It is important to keep in mind, however, that the disadvantaged counties in Appalachia are most likely to 

have the smallest businesses which typically receive lower dollar amounts of lending. To some extent, 

therefore, the disparities in dollar amounts reflect the stage of development of the small business sector at 

a county level. The disparities by average dollar amount increased from 2007 to 2010. For example, small 

businesses in Central Appalachia and Northern Appalachia received, on average, $21,100 and $27,500, 

respectively in 2007. This disparity increased to $37,600 and $54,700, respectively, in 2010 (see Table 1-

5). 

2003 2007 2010 2003 2007 2010 2003 2007 2010

US 7,428,630 13,437,779 4,197,610 17,828,895 21,808,201 21,530,378 41.7% 61.6% 19.5%

Appalachia 530,309 808,877 255,231 1,280,941 1,607,645 1,577,370 41.4% 50.3% 16.2%

Percent of Businesses 

Number of Small Businesses Loans Number of businesses Receiving Loans

36
Considering lending by state, the Appalachian portions of Maryland and Pennsylvania experienced the 

most access to credit while Kentucky and Mississippi experienced the least access to credit during 2007 

and 2010 (see Figure 1-5). The sharp decline in lending due to the financial crisis compressed disparities 

between the Appalachian and non-Appalachian parts of states. In nine states in 2007, a higher percentage 

of small businesses received loans in the non-Appalachian counties vis-à-vis the Appalachian counties. 

By 2010, this was the case in just six states and the differences in the percentages of businesses receiving 

loans were generally not that large. For example, banks made loans to 16.8 percent and 15.3 percent of 

the small businesses in non-Appalachia and Appalachian Virginia, respectively.

The maps display indexed values. In other words, the small business loan to small business ratio for a 

county is divided by the median ratio for the nation. The counties are then divided into even quintiles for 

the index values. The red shades are for the lower quintiles indicating that counties have less access to 

small business lending than their national counterparts. The blue shades are for the best quintiles showing 

that the counties have more access to credit than their national counterparts. The maps are reproduced for 

the ratios in this chapter.

The maps illustrate that Northern Appalachia is the region with the most access to credit. In particular, 

Pennsylvania has the most “dark blue” counties and the number of Pennsylvania counties in dark blue 

increases from 2007 to 2010. Another striking pattern is that counties on the eastern boundaries of 

Appalachia have more access to credit than their national counterparts whereas counties on the western 

boundaries of Appalachia have less access than their national counterparts. The least access (dark red) 

clusters around the Kentucky, Tennessee, Ohio, and Mississippi portions of Appalachia. The appendix 

has the ratio and index values for each county. 

Figure 1-2: Ratio of Small Business Loans to Small Businesses by Region

61.6%

50.3%

55.5%

46.2%

33.3%

50.9%

50.0%

19.5%

16.2%

19.4%

15.8%

11.1%

15.7%

14.8%

0% 20% 40% 60% 80%

United States

Appalachian Region

Northern Appalachia

North Central Appalachia

Central Appalachia

South Central Appalachia

Southern Appalachia

Per capita personal income index 2007 2010

37
Figure 1-3: Ratio of Small Business Loans to Small Businesses by County Type

Figure 1-4: Ratio of Small Business Loans to Small Businesses by Economic Status

59.1%

53.1%

42.8%

45.4%

35.1%

17.8%

17.1%

14.1%

14.8%

12.7%

0% 20% 40% 60% 80%

Large Metro (1 million + people)

Small Metro (< 1 million people)

Nonmetro, Adjacent to Large Metro

Nonmetro, Adjacent to Small Metro

Rural (Nonmetro, Nonadjacent)

Per capita personal income index 2007 2010

28.1%

36.1%

50.5%

57.9%

63.3%

8.6%

13.5%

16.2%

19.7%

15.9%

0% 20% 40% 60% 80%

Distressed

At-Risk

Transitional

Competitive

Attainment

Per capita personal income index 2007 2010

38
Map 1-1: Small Business Lending Index, 2007

Ratio of Number of Small Business Loans to Small Businesses, Indexed Values (U.S. =1)

Statistics of Indexed Values

Count 420

Minimum 0.191

Maximum 2.133

Mean 0.988

Standard Deviation 0.321

Histogram of Indexed Values

39
Map 1-2: Small Business Lending Index, 2010

Ratio of Number of Small Business Loans to Small Businesses, Indexed Values (U.S. =1)

Statistics of Indexed Values

Count 420

Minimum 0.148

Maximum 2.35

Mean 0.978

Standard Deviation 0.393

Histogram of Indexed Values

40
Figure 1-5: Ratio of Number of Small Business Loans to Small Businesses by State

49.2%

50.3%

47.2%

49.8%

55.9%

47.3%

38.8%

31.6%

41.3%

63.9%

61.5%

64.0%

28.4%

25.0%

29.5%

69.0%

46.4%

70.2%

55.5%

56.8%

55.2%

52.8%

44.8%

54.4%

63.0%

59.1%

66.2%

55.0%

54.2%

55.3%

46.8%

45.9%

47.4%

61.8%

46.8%

63.2%

47.7%

15.5%

15.8%

15.0%

13.8%

14.4%

13.5%

13.8%

10.7%

14.8%

17.5%

18.6%

17.4%

12.2%

10.4%

12.8%

24.6%

17.1%

25.0%

17.5%

17.6%

17.4%

19.0%

14.7%

19.8%

20.4%

20.5%

20.2%

16.9%

16.9%

16.9%

15.3%

14.0%

16.2%

16.6%

15.3%

16.8%

17.1%

0% 20% 40% 60% 80%

Alabama

Appalachian Alabama

Non-Appalachian Alabama

Georgia

Appalachian Georgia

Non-Appalachian Georgia

Kentucky

Appalachian Kentucky

Non-Appalachian Kentucky

Maryland

Appalachian Maryland

Non-Appalachian Maryland

Mississippi

Appalachian Mississippi

Non-Appalachian Mississippi

New York

Appalachian New York

Non-Appalachian New York

North Carolina

Appalachian North Carolina

Non-Appalachian North...

Ohio

Appalachian Ohio

Non-Appalachian Ohio

Pennsylvania

Appalachian Pennsylvania

Non-Appalachian Pennsylvania

South Carolina

Appalachian South Carolina

Non-Appalachian South...

Tennessee

Appalachian Tennessee

Non-Appalachian Tennessee

Virginia

Appalachian Virginia

Non-Appalachian Virginia

West Virginia (entire state)

2007 2010

41
Table 1-3: Ratio of Small Business Loans to Small Businesses in Appalachia

For 2007, 10 of the 420 counties do not have economic status indicated as they were not part of the ARC in 2007. This is the case for all the 

tables and figures in the report.

Number of Small 

Business Loans

Number of 

Small 

Businesses

Ratio of Small 

Business Loans to 

Small Businesses

Number of Small 

Business Loans

Number of Small 

Businesses

Ratio of Small 

Business Loans to 

Small Businesses

United States 13,437,779 21,808,201 61.6% 4,197,610 21,530,378 19.5%

Appalachian Region 808,877 1,607,645 50.3% 255,231 1,577,370 16.2%

Subregions

Northern Appalachia 265,062 477,301 55.5% 93,452 482,014 19.4%

North Central Appalachia 59,519 128,944 46.2% 19,678 124,926 15.8%

Central Appalachia 38,372 115,266 33.3% 12,085 109,122 11.1%

South Central Appalachia 156,224 307,059 50.9% 47,760 304,728 15.7%

Southern Appalachia 289,700 579,075 50.0% 82,256 556,580 14.8%

County Types

Large Metro (1 million + people) 233,652 395,470 59.1% 71,892 404,330 17.8%

Small Metro (< 1 million people) 325,376 613,330 53.1% 103,484 604,234 17.1%

Nonmetro, Adjacent to Large Metro 46,778 109,171 42.8% 14,654 103,911 14.1%

Nonmetro, Adjacent to Small Metro 136,848 301,104 45.4% 43,204 291,784 14.8%

Rural (Nonmetro, Nonadjacent) 66,223 188,570 35.1% 21,997 173,111 12.7%

Economic Status

Distressed 25,190 89,777 28.1% 7,461 86,870 8.6%

At-Risk 52,515 145,503 36.1% 18,619 138,028 13.5%

Transitional 448,203 887,755 50.5% 147,088 905,985 16.2%

Competitive 176,965 305,587 57.9% 57,498 292,327 19.7%

Attainment 85,936 135,857 63.3% 24,565 154,160 15.9%

Alabama 154,821 314,870 49.2% 46,944 302,408 15.5%

Appalachian Alabama 100,348 199,561 50.3% 30,438 192,538 15.8%

Non-Appalachian Alabama 54,473 115,309 47.2% 16,506 109,870 15.0%

Georgia 412,517 828,977 49.8% 109,030 792,556 13.8%

Appalachian Georgia 133,604 239,021 172.3% 34,345 238,124 14.4%

Non-Appalachian Georgia 278,913 589,956 37.1% 74,685 554,432 13.5%

Kentucky 118,063 304,048 38.8% 41,075 297,724 13.8%

Appalachian Kentucky 24,487 77,542 31.6% 7,926 74,004 10.7%

Non-Appalachian Kentucky 93,576 226,506 41.3% 33,149 223,720 14.8%

Maryland 248,007 388,206 63.9% 68,107 389,579 17.5%

Appalachian Maryland 8,846 14,392 61.5% 2,796 14,993 18.6%

Non-Appalachian Maryland 239,161 373,814 64.0% 65,311 374,586 17.4%

Mississippi 82,053 288,779 28.4% 30,367 248,541 12.2%

Appalachian Mississippi 17,516 69,965 25.0% 6,079 58,339 10.4%

Non-Appalachian Mississippi 64,537 218,814 29.5% 24,288 190,202 12.8%

New York 969,471 1,404,794 69.0% 308,303 1,253,951 24.6%

Appalachian New York 31,216 67,312 46.4% 10,676 62,314 17.1%

Non-Appalachian New York 938,255 1,337,482 70.2% 297,627 1,191,637 25.0%

North Carolina 341,140 615,139 55.5% 108,924 624,133 17.5%

Appalachian North Carolina 64,128 112,966 56.8% 19,459 110,530 17.6%

Non-Appalachian North Carolina 277,012 502,173 55.2% 89,465 513,603 17.4%

Ohio 364,863 691,536 52.8% 126,530 667,427 19.0%

Appalachian Ohio 52,594 117,389 44.8% 16,697 113,316 14.7%

Non-Appalachian Ohio 312,269 574,147 54.4% 109,833 554,111 19.8%

Pennsylvania 448,313 711,343 63.0% 155,425 763,099 20.4%

Appalachian Pennsylvania 189,468 320,365 59.1% 68,219 332,120 20.5%

Non-Appalachian Pennsylvania 258,845 390,978 66.2% 87,206 430,979 20.2%

South Carolina 149,892 272,316 55.0% 45,139 266,975 16.9%

Appalachian South Carolina 38,232 70,528 54.2% 11,394 67,579 16.9%

Non-Appalachian South Carolina 111,660 201,788 55.3% 33,745 199,396 16.9%

Tennessee 199,969 427,304 46.8% 64,456 422,466 15.3%

Appalachian Tennessee 83,223 181,133 45.9% 24,897 178,251 14.0%

Non-Appalachian Tennessee 116,746 246,171 47.4% 39,559 244,215 16.2%

Virginia 298,547 483,178 61.8% 91,083 547,124 16.6%

Appalachian Virginia 19,900 42,503 46.8% 6,740 44,052 15.3%

Non-Appalachian Virginia 278,647 440,675 63.2% 84,343 503,072 16.8%

West Virginia (entire state) 45,315 94,968 47.7% 15,565 91,210 17.1%

2007 2010

42
Table 1-4: Disparity Ratios of Ratio of Small Business Loans to Small Businesses

Year Numerator Denominator Disparity Ratio

US vs. Appalachia 2007 61.6% 50.3% 1.22

2010 19.5% 16.2% 1.21

Northern vs. Central 2007 55.5% 33.3% 1.67

2010 19.4% 11.1% 1.75

Large Metro vs. Rural 2007 59.1% 35.1% 1.68

2010 17.8% 12.7% 1.4

Attainment vs. Distressed 2007 63.3% 28.1% 2.25

2010 15.9% 8.6% 1.85

43
Table 1-5: Average Small Business Loan Amount in Appalachia* 

*Loan amounts are in 1,000s

$, Amount of 

Small Business 

Loans

Number of Small 

Business Loans

$, Average 

Loan Amount

$, Amount of 

Small Business 

Loans

Number of Small 

Business Loans

$, Average 

Loan Amount

United States $324,325,913 13,437,779 $24.1 $173,436,472 4,197,610 $41.3

Appalachian Region $23,965,813 808,877 $29.6 $13,342,550 255,231 $52.3

Subregions

Northern Appalachia $7,282,956 265,062 $27.5 $5,110,722 93,452 $54.7

North Central Appalachia $1,606,299 59,519 $27.0 $1,101,456 19,678 $56.0

Central Appalachia $811,477 38,372 $21.1 $454,146 12,085 $37.6

South Central Appalachia $5,050,897 156,224 $32.3 $2,615,336 47,760 $54.8

Southern Appalachia $9,214,184 289,700 $31.8 $4,060,890 82,256 $49.4

County Types

Large Metro (1 million + people) $6,619,126 233,652 $28.3 $3,699,442 71,892 $51.5

Small Metro (< 1 million people) $10,848,636 325,376 $33.3 $6,067,377 103,484 $58.6

Nonmetro, Adjacent to Large Metro $1,152,371 46,778 $24.6 $656,345 14,654 $44.8

Nonmetro, Adjacent to Small Metro $3,638,568 136,848 $26.6 $1,955,243 43,204 $45.3

Rural (Nonmetro, Nonadjacent) $1,707,112 66,223 $25.8 $964,143 21,997 $43.8

Economic Status

Distressed $492,229 25,190 $19.5 $233,528 7,461 $31.3

At-Risk $1,284,550 52,515 $24.5 $814,478 18,619 $43.7

Transitional $13,722,200 448,203 $30.6 $7,728,094 147,088 $52.5

Competitive $5,838,093 176,965 $33.0 $3,445,967 57,498 $59.9

Attainment $2,170,027 85,936 $25.3 $1,120,483 24,565 $45.6

Alabama $5,827,455 154,821 $37.6 $2,633,016 46,944 $56.1

Appalachian Alabama $3,791,119 100,348 $37.8 $1,665,817 30,438 $54.7

Non-Appalachian Alabama $2,036,336 54,473 $37.4 $967,199 16,506 $58.6

Georgia $11,224,500 412,517 $27.2 $4,977,683 109,030 $45.7

Appalachian Georgia $3,630,619 133,604 $27.2 $1,532,318 34,345 $44.6

Non-Appalachian Georgia $7,593,881 278,913 $27.2 $3,445,365 74,685 $46.1

Kentucky $3,185,311 118,063 $27.0 $2,133,357 41,075 $51.9

Appalachian Kentucky $499,233 24,487 $20.4 $280,712 7,926 $35.4

Non-Appalachian Kentucky $2,686,078 93,576 $28.7 $1,852,645 33,149 $55.9

Maryland $6,040,036 248,007 $24.4 $2,841,401 68,107 $41.7

Appalachian Maryland $271,031 8,846 $30.6 $175,767 2,796 $62.9

Non-Appalachian Maryland $5,769,005 239,161 $24.1 $2,665,634 65,311 $40.8

Mississippi $2,529,250 82,053 $30.8 $1,515,669 30,367 $49.9

Appalachian Mississippi $551,393 17,516 $31.5 $272,295 6,079 $44.8

Non-Appalachian Mississippi $1,977,857 64,537 $30.6 $1,243,374 24,288 $51.2

New York $19,914,993 969,471 $20.5 $9,643,636 308,303 $31.3

Appalachian New York $721,522 31,216 $23.1 $383,744 10,676 $35.9

Non-Appalachian New York $19,193,471 938,255 $20.5 $9,259,892 297,627 $31.1

North Carolina $11,537,683 341,140 $33.8 $5,633,301 108,924 $51.7

Appalachian North Carolina $2,045,398 64,128 $31.9 $947,159 19,459 $48.7

Non-Appalachian North Carolina $9,492,285 277,012 $34.3 $4,686,142 89,465 $52.4

Ohio $10,142,341 364,863 $27.8 $7,875,456 126,530 $62.2

Appalachian Ohio $1,160,385 52,594 $22.1 $863,491 16,697 $51.7

Non-Appalachian Ohio $8,981,956 312,269 $28.8 $7,011,965 109,833 $63.8

Pennsylvania $13,184,892 448,313 $29.4 $8,033,802 155,425 $51.7

Appalachian Pennsylvania $5,519,598 189,468 $29.1 $3,915,651 68,219 $57.4

Non-Appalachian Pennsylvania $7,665,294 258,845 $29.6 $4,118,151 87,206 $47.2

South Carolina $4,776,873 149,892 $31.9 $2,294,399 45,139 $50.8

Appalachian South Carolina $1,241,053 38,232 $32.5 $590,460 11,394 $51.8

Non-Appalachian South Carolina $3,535,820 111,660 $31.7 $1,703,939 33,745 $50.5

Tennessee $6,511,859 199,969 $32.6 $3,836,647 64,456 $59.5

Appalachian Tennessee $2,749,951 83,223 $33.0 $1,500,176 24,897 $60.3

Non-Appalachian Tennessee $3,761,908 116,746 $32.2 $2,336,471 39,559 $59.1

Virginia $7,961,982 298,547 $26.7 $4,116,094 91,083 $45.2

Appalachian Virginia $509,108 19,900 $25.6 $309,375 6,740 $45.9

Non-Appalachian Virginia $7,452,874 278,647 $26.7 $3,806,719 84,343 $45.1

West Virginia (entire state) $1,275,403 45,315 $28.1 $905,585 15,565 $58.2

2007 2010

44
1.3.2 LENDING TRENDS FOR THE SMALLEST BUSINESSES

The Community Reinvestment Act (CRA) small business loan data can report separately the lending for 

small businesses with revenues less than $1 million.5

 In addition, the demographic data provided by Dun 

and Bradstreet provides information on the number of small businesses on a county level that have 

revenues less than $1 million. Thus, ratios assessing access to the smallest businesses can be computed by 

dividing the number of loans to small businesses with revenues under $1 million by the number of small 

businesses with revenues under $1 million. 

One noteworthy caveat is that the CRA small business loan data contain records for which the revenue 

size of the small business is unknown. Thus, the data are not precise regarding the number of loans to the 

smallest businesses with revenues less than $1 million.

The disparities between the nation and Appalachia are narrower for the smallest businesses. When 

considering all small businesses, about 61 percent and 50.3 percent of the small businesses in the nation 

and in Appalachia, respectively, received loans during 2007 (see Table 1-3). In contrast, almost the same 

portion of the smallest businesses received loans when comparing Appalachia and the nation in 2007 – 

about 36.5 percent in the nation and 32.8 percent in Appalachia (see Figure 1-6). The sharp contraction in 

lending from 2007 through 2010 resulted in reducing this disparity, even though a much lower percentage 

of the smallest businesses received loans in 2010 (about 8 percent of the smallest businesses in both the 

nation and in Appalachia). 

Just as with small businesses overall, the smallest businesses with revenues under $1 million experienced 

the most access to credit in Northern Appalachia and the least access in Central Appalachia during 2007 

and 2010 (see Figure 1-6). Urbanized areas had moderately more access to credit in 2007. By 2010, 

however, the dramatic decrease in lending almost eliminated disparities resulting in 7 to 8 percent of the 

smallest businesses receiving loans in urban and rural counties (see Figure 1-7 and Table 1-7). For 

counties by economic status, disparities are significant in 2007 and narrowed by 2010 (see Figure 1-8 and 

Table 1-7). 

In 2007, the smallest businesses in Appalachia Georgia had the best access to loans (36.8 percent received 

loans) followed closely by their counterparts in North Carolina, Mississippi, and Pennsylvania (see Figure 

1-9). The smallest businesses in Appalachian Kentucky had the least access to loans (24.5 percent). By 

2010, differences narrowed considerably with 6 to 9 percent of the smallest businesses receiving loans 

with the exception of Pennsylvania where 12 percent received loans. 

The maps of the smallest business loan index (see Maps 1-3 and 1-4) reveal a pattern that is similar to the 

overall small business loan index. Just as with overall small business lending, access is most favorable 

compared to the nation for the Northern Appalachian subregion and for the counties along the eastern 

boundary of Appalachia. Kentucky and Tennessee counties again appear to have the least favorable 

5 While more detailed data for lending to businesses with smaller revenue categories are desirable and will become 

available in future years, the current data allow for a comparison between credit access for the “larger” and 

“smaller” of the small businesses. The Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010 

mandates improvements to the publicly available small business loan data including information on the race and 

gender of the small business owner and more detail on revenue size of the small business.

45

 
access compared to the nation. The other striking trend is that there is a growth in the “darkest blue” 

counties in Pennsylvania and West Virginia in 2010. Although lending to the smallest businesses 

plummeted in 2010, more counties in the Appalachian portions of Pennsylvania and West Virginia 

experienced favorable access compared to the nation in 2010.

Figure 1-6: Ratio of Loans to Small Business with Revenue Less than $1 Million to Small 

Businesses with Revenue Less than $1 Million by Region

Figure 1-7: Ratio of Loans to Small Business with Revenue Less than $1 Million to Small 

Businesses with Revenue Less than $1 Million by County Type

 

36.5%

32.8%

34.4%

30.5%

24.9%

32.6%

33.5%

8.8%

8.4%

10.6%

9.0%

6.3%

8.3%

7.0%

0% 20% 40%

United States

Appalachian Region

Northern Appalachia

North Central Appalachia

Central Appalachia

South Central Appalachia

Southern Appalachia

2007 2010 Per capita personal income index

34.8%

32.7%

30.0%

32.0%

31.3%

8.4%

8.9%

7.0%

8.3%

7.8%

0% 20% 40%

Large Metro (1 million + people)

Small Metro (< 1 million people)

Nonmetro, Adjacent to Large Metro

Nonmetro, Adjacent to Small Metro

Rural (Nonmetro, Nonadjacent)

Per capita personal income index 2007 2010

46
Figure 1-8: Ratio of Loans to Small Business with Revenue Less than $1 Million to Small 

Businesses with Revenue Less than $1 Million by Economic Status

23.5%

30.8%

33.0%

34.1%

37.0%

4.9%

8.2%

8.6%

10.1%

6.2%

0% 20% 40%

Distressed

At-Risk

Transitional

Competitive

Attainment

2007 2010 Per capita personal income index

47
Map 1-3: Smallest Business Lending Index, 2007

Ratio of Loans to Smallest Business to Number of Smallest Businesses, Indexed Values (U.S. =1)

Smallest businesses are defined as businesses with revenue less than $1 million

Statistics of Indexed Values

Count 420

Minimum 0.346

Maximum 2.46

Mean 1.00

Standard Deviation 0.325

Histogram of Indexed Values

48
Map 1-4: Smallest Business Lending Index, 2010

Ratio of Loans to Smallest Business to Number of Smallest Businesses, Indexed Values (U.S. =1)

Smallest businesses are defined as businesses with revenue less than $1 million

Statistics of Indexed Values

Count 420

Minimum 0.07

Maximum 4.16

Mean 1.07

Standard Deviation 0.582

Histogram of Indexed Values

49
Figure 1-9: Ratio of Loans to Small Business with Revenue Less than $1 Million to Small 

Businesses with Revenue Less than $1 Million by State

28.2%

28.7%

27.2%

31.8%

36.7%

29.7%

26.1%

24.5%

26.6%

33.1%

34.9%

33.0%

29.8%

36.6%

28.0%

34.0%

31.1%

34.1%

35.5%

36.5%

35.3%

30.2%

27.8%

30.6%

36.1%

36.4%

35.9%

34.3%

33.8%

34.5%

29.8%

28.9%

30.5%

34.3%

32.5%

34.5%

31.1%

7.6%

7.5%

7.7%

5.9%

6.4%

5.6%

6.8%

6.3%

7.0%

6.9%

9.2%

6.9%

6.8%

6.7%

6.8%

6.7%

8.0%

6.6%

9.5%

10.3%

9.4%

8.6%

7.3%

8.9%

10.0%

11.7%

8.7%

8.7%

8.0%

8.9%

7.5%

6.6%

8.2%

7.1%

9.2%

7.0%

10.4%

0% 20% 40%

Alabama

Appalachian Alabama

Non-Appalachian Alabama

Georgia

Appalachian Georgia

Non-Appalachian Georgia

Kentucky

Appalachian Kentucky

Non-Appalachian Kentucky

Maryland

Appalachian Maryland

Non-Appalachian Maryland

Mississippi

Appalachian Mississippi

Non-Appalachian Mississippi

New York

Appalachian New York

Non-Appalachian New York

North Carolina

Appalachian North Carolina

Non-Appalachian North Carolina

Ohio

Appalachian Ohio

Non-Appalachian Ohio

Pennsylvania

Appalachian Pennsylvania

Non-Appalachian Pennsylvania

South Carolina

Appalachian South Carolina

Non-Appalachian South Carolina

Tennessee

Appalachian Tennessee

Non-Appalachian Tennessee

Virginia

Appalachian Virginia

Non-Appalachian Virginia

West Virginia (entire state)

2007 2010

50
Table 1-6: Ratio of Loans to Small Business with Revenue Less than $1 Million to Small 

Businesses with Revenue Less than $1 Million in Appalachia

Note: Small businesses here are defined as businesses with revenue less than $1 million. 

Number of 

Loans to Small 

Businesses

Number of 

Small 

Businesses 

Ratio of Small 

Business Loans to 

the Number of 

Small Businesses

Number of 

Loans to Small 

Businesses

Number of 

Small 

Businesses 

Ratio of Small 

Business Loans to 

the Number of 

Small Businesses

United States 5,142,053 14,082,734 36.5% 1,478,317 16,795,089 8.8%

Appalachian Region 319,347 973,071 32.8% 102,596 1,218,466 8.4%

Subregions

Northern Appalachia 107,112 311,233 34.4% 38,038 359,519 10.6%

North Central Appalachia 23,437 76,774 30.5% 8,152 90,545 9.0%

Central Appalachia 15,720 63,130 24.9% 5,223 82,854 6.3%

South Central Appalachia 62,905 193,221 32.6% 20,026 240,039 8.3%

Southern Appalachia 110,173 328,713 33.5% 31,157 445,509 7.0%

County Types

Large Metro (1 million + people) 88,497 254,432 34.8% 26,766 317,198 8.4%

Small Metro (< 1 million people) 126,037 385,401 32.7% 41,068 461,194 8.9%

Nonmetro, Adjacent to Large Metro 18,791 62,582 30.0% 5,647 81,227 7.0%

Nonmetro, Adjacent to Small Metro 57,161 178,405 32.0% 18,751 225,813 8.3%

Rural (Nonmetro, Nonadjacent) 28,861 92,251 31.3% 10,364 133,034 7.8%

Economic Status

Distressed 10,183 43,272 23.5% 3,267 66,620 4.9%

At-Risk 22,782 74,086 30.8% 8,690 105,956 8.2%

Transitional 178,893 542,406 33.0% 60,459 698,994 8.6%

Competitive 67,648 198,577 34.1% 22,370 221,525 10.1%

Attainment 32,416 87,692 37.0% 7,810 125,371 6.2%

Alabama 50,408 178,914 28.2% 17,740 234,551 7.6%

Appalachian Alabama 32,847 114,451 28.7% 11,281 151,024 7.5%

Non-Appalachian Alabama 17,561 64,463 27.2% 6,459 83,527 7.7%

Georgia 160,190 504,275 31.8% 37,094 632,155 5.9%

Appalachian Georgia 53,926 146864 36.7% 12,589 195,563 6.4%

Non-Appalachian Georgia 106,264 357,411 29.7% 24,505 436,592 5.6%

Kentucky 44,861 172,029 26.1% 15,867 231,720 6.8%

Appalachian Kentucky 10,329 42,113 24.5% 3,550 56,770 6.3%

Non-Appalachian Kentucky 34,532 129,916 26.6% 12,317 174,950 7.0%

Maryland 88,463 267,506 33.1% 21,254 306,480 6.9%

Appalachian Maryland 3,348 9,585 34.9% 1,014 11,065 9.2%

Non-Appalachian Maryland 85,115 257,921 33.0% 20,240 295,415 6.9%

Mississippi 33,042 110,970 29.8% 13,526 199,011 6.8%

Appalachian Mississippi 8,299 22,696 36.6% 3,135 47,041 6.7%

Non-Appalachian Mississippi 24,743 88,274 28.0% 10,391 151,970 6.8%

New York 319,773 941,768 34.0% 64,059 960,637 6.7%

Appalachian New York 12,302 39,522 31.1% 3,731 46,797 8.0%

Non-Appalachian New York 307,471 902,246 34.1% 60,328 913,840 6.6%

North Carolina 143,591 404,308 35.5% 46,834 491,798 9.5%

Appalachian North Carolina 27,713 75,894 36.5% 9,054 88,119 10.3%

Non-Appalachian North Carolina 115,878 328,414 35.3% 37,780 403,679 9.4%

Ohio 133,031 441,066 30.2% 43,439 504,178 8.6%

Appalachian Ohio 20,051 72,027 27.8% 6,356 87,471 7.3%

Non-Appalachian Ohio 112,980 369,039 30.6% 37,083 416,707 8.9%

Pennsylvania 172,515 477,667 36.1% 57,407 576,195 10.0%

Appalachian Pennsylvania 78,284 214,837 36.4% 28,896 246,758 11.7%

Non-Appalachian Pennsylvania 94,231 262,830 35.9% 28,511 329,437 8.7%

South Carolina 60,073 175,055 34.3% 17,938 206,280 8.7%

Appalachian South Carolina 15,101 44,702 33.8% 4,152 51,881 8.0%

Non-Appalachian South Carolina 44,972 130,353 34.5% 13,786 154,399 8.9%

Tennessee 75,617 253,961 29.8% 24,798 329,096 7.5%

Appalachian Tennessee 31,201 108,112 28.9% 9,272 140,043 6.6%

Non-Appalachian Tennessee 44,416 145,849 30.5% 15,526 189,053 8.2%

Virginia 109,072 317,783 34.3% 30,539 427,157 7.1%

Appalachian Virginia 8,356 25,697 32.5% 3,083 33,379 9.2%

Non-Appalachian Virginia 100,716 292,086 34.5% 27,456 393,778 7.0%

West Virginia (entire state) 17,590 56,571 31.1% 6,483 62,555 10.4%

2007 2010

51
Table 1-7: Disparity Ratios of Ratio of Loans to Small Business with Revenue Less than $1 Million 

to Small Businesses with Revenue Less than $1 Million

1.3.3 SMALL BUSINESS LENDING IN LOW- AND MODERATE-INCOME TRACTS

The contraction in credit for all small businesses during the recession was so sharp that disparities 

between small businesses in low- and moderate-income (LMI) tracts and their counterparts narrowed by 

2010.6

 In 2007, the gap between the percentage of loans received by small businesses overall and in lowand moderate-income tracts was about nine percentage points. By 2010, this gap was reduced to less than 

two percentage points. About 16.2 percent of all small businesses in Appalachia received loans in 2010 

while 13.9 percent of small businesses in low- and moderate-income tracts received loans (see Figure 1-1 

and Figure 1-10).

In 2007 and 2010, small businesses in low- and moderate-income tracts in Northern Appalachia 

experienced the most access to credit while those in Central Appalachia experienced the least access. The 

disparity remained significant in 2010; 17 percent of small businesses in low- and moderate-income tracts 

in Northern Appalachia received loans while 8.3 percent of their counterparts in Central Appalachia 

received loans during 2010 (see Figure 1-10).

Disparities also remained in 2010 when considering lending in low- and moderate-income tracts by 

degree of urbanization and economic status of county (see Figure 1-11 and 1-12). Small businesses in 

low- and moderate-income tracts in rural counties and distressed counties experienced considerably less 

access to loans in both years compared to other categories of counties, even those counties such as at-risk 

counties with similar economic conditions or degree of urbanization. 

In 2007, small businesses in low- and moderate-income tracts in Appalachian Maryland received the most 

loans (55 percent) followed by their counterparts in North Carolina, Pennsylvania, and Georgia. Small 

businesses in Appalachia Mississippi experienced the least access to loans. By 2010, the disparities had 

narrowed somewhat with lending in low- and moderate-income tracts plummeting (see Figure 1-13). The 

index maps for small business lending in LMI tracts display patterns consistent with the index maps for 

small business lending overall and for the smallest business lending. Northern Appalachia is the sub 

region with the most access compared to the nation and counties on the eastern boundary also tend to 

have the most access.

6 LMI tracts are defined per the CRA regulation as census tracts whose median income are less than or equal to 80 

percent of area median income.

Year Numerator Denominator Disparity Ratio

2007 36.51% 32.82% 1.11

2010 8.80% 8.42% 1.05

2007 34.42% 24.90% 1.38

2010 10.58% 6.30% 1.68

2007 34.78% 31.29% 1.11

2010 8.44% 7.79% 1.08

2007 36.97% 23.53% 1.57

2010 6.23% 4.90% 1.27

US vs. Appalachia

Northern vs. Central

Large Metro vs. Rural

Attainment vs. Distressed

52

 
Figure 1-10: Ratio of Small Business Loans to Total Businesses in LMI Tracts by Region

Figure 1-11: Ratio of Small Business Loans to Total Businesses in LMI Tracts by County Type

Figure 1-12: Ratio of Small Business Loans to Total Businesses in LMI Tracts by Economic Status

40.2%

43.9%

36.8%

26.6%

42.3%

41.8%

13.9%

16.9%

12.3%

8.3%

14.6%

13.4%

0% 20% 40% 60%

Appalachian Region

Northern Appalachia

North Central Appalachia

Central Appalachia

South Central Appalachia

Southern Appalachia

Per capita personal income index 2007 2010

45.0%

41.9%

33.2%

34.4%

27.7%

14.6%

15.1%

13.1%

12.0%

9.3%

0% 20% 40% 60%

Large Metro (1 million + people)

Small Metro (< 1 million people)

Nonmetro, Adjacent to Large Metro

Nonmetro, Adjacent to Small Metro

Rural (Nonmetro, Nonadjacent)

Per capita personal income index 2007 2010

24.8%

34.3%

41.2%

47.1%

47.3%

6.6%

12.9%

14.6%

16.3%

14.3%

0% 20% 40% 60%

Distressed

At-Risk

Transitional

Competitive

Attainment

Per capita personal income index 2007 2010

53
Map 1-5: Small Business Lending in LMI Tracts Index, 2007* 

Ratio of Small Business Loans to Small Business in LMI Tracts, Indexed Values (Appalachia = 1)

Statistics of Indexed Values

Count 251

Minimum 0.226

Maximum 2.92

Mean 1.01

Standard Deviation 0.382

Histogram of Indexed Values

*Counties grayed out in the map have no LMI census tracts based on CRA definition.

54
Map 1-6: Small Business Lending in LMI Tracts Index, 2010* 

Ratio of Small Business Loans to Small Business in LMI Tracts, Indexed Values (Appalachia = 1)

Statistics of Indexed Values

Count 251

Minimum 0

Maximum 3.1

Mean 1.026

Standard Deviation 0.502

Histogram of Indexed Values

*Counties grayed out in the map have no LMI census tracts based on CRA definition.

55
Figure 1-13: Ratio of Small Business Loans to Total Businesses in LMI Tracts by State

38.8%

41.8%

34.6%

38.7%

45.3%

37.1%

32.4%

24.7%

36.1%

48.0%

55.1%

47.6%

20.4%

20.6%

20.4%

57.5%

41.4%

58.1%

41.7%

47.3%

40.9%

38.6%

32.9%

39.8%

45.2%

46.2%

44.6%

38.4%

41.4%

37.6%

37.7%

37.9%

37.6%

47.4%

32.4%

48.7%

37.9%

13.5%

14.7%

11.7%

10.9%

12.6%

10.4%

12.0%

7.3%

14.1%

13.3%

19.2%

13.0%

9.3%

8.4%

9.4%

19.8%

17.4%

19.9%

14.2%

16.1%

13.9%

16.0%

11.5%

17.0%

15.2%

17.3%

13.8%

13.0%

13.5%

12.8%

13.6%

12.8%

14.1%

13.7%

12.7%

13.8%

13.8%

0% 20% 40% 60% 80%

Alabama

Appalachian Alabama

Non-Appalachian Alabama

Georgia

Appalachian Georgia

Non-Appalachian Georgia

Kentucky

Appalachian Kentucky

Non-Appalachian Kentucky

Maryland

Appalachian Maryland

Non-Appalachian Maryland

Mississippi

Appalachian Mississippi

Non-Appalachian Mississippi

New York

Appalachian New York

Non-Appalachian New York

North Carolina

Appalachian North Carolina

Non-Appalachian North Carolina

Ohio

Appalachian Ohio

Non-Appalachian Ohio

Pennsylvania

Appalachian Pennsylvania

Non-Appalachian Pennsylvania

South Carolina

Appalachian South Carolina

Non-Appalachian South Carolina

Tennessee

Appalachian Tennessee

Non-Appalachian Tennessee

Virginia

Appalachian Virginia

Non-Appalachian Virginia

West Virginia (entire state)

2007 2010

56
Table 1-8: Ratio of Small Business Loans to Total Businesses in Low-Moderate Income (LMI) 

Tracts in Appalachia

Number of 

Small 

Business 

Loans in LMI 

Tracts

Number of 

Small 

Businesses 

in LMI 

Tracts

Ratio of Small 

Business 

Loans to Small 

Businesses in 

LMI Tracts

Number of 

Small 

Business 

Loans in LMI 

Tracts

Number of 

Small 

Businesses 

in LMI 

Tracts

Ratio of Small 

Business 

Loans to Small 

Businesses in 

LMI Tracts

Appalachian Region 122,030 303,787 40.2% 38,947 280,212 13.9%

Subregions

Northern Appalachia 39,921 90,846 43.9% 14,315 84,887 16.9%

North Central Appalachia 10,435 28,342 36.8% 3,259 26,594 12.3%

Central Appalachia 9,889 37,224 26.6% 2,820 34,038 8.3%

South Central Appalachia 19,259 45,540 42.3% 6,163 42,171 14.6%

Southern Appalachia 42,526 101,835 41.8% 12,390 92,522 13.4%

County Types

Large Metro (1 million + people) 42,116 93,654 45.0% 12,865 88,093 14.6%

Small Metro (< 1 million people) 55,429 132,355 41.9% 18,174 120,656 15.1%

Nonmetro, Adjacent to Large Metro 3,824 11,504 33.2% 1,354 10,310 13.1%

Nonmetro, Adjacent to Small Metro 11,863 34,524 34.4% 3,910 32,611 12.0%

Rural (Nonmetro, Nonadjacent) 8,798 31,750 27.7% 2,644 28,542 9.3%

Economic Status

Distressed 7,908 31,925 24.8% 2,000 30,339 6.6%

At-Risk 10,808 31,518 34.3% 3,746 29,142 12.9%

Transitional 62,629 152,098 41.2% 20,941 143,360 14.6%

Competitive 31,672 67,185 47.1% 9,686 59,410 16.3%

Attainment 6,641 14,040 47.3% 2,574 17,961 14.3%

Alabama

Appalachian Alabama 18,419 44,098 41.8% 5,880 40,027 14.7%

Non-Appalachian Alabama 10,528 30,435 34.6% 3,192 27,188 11.7%

Georgia

Appalachian Georgia 17,448 38545 45.3% 4,518 35,876 12.6%

Non-Appalachian Georgia 55,780 150,447 37.1% 14,017 134,929 10.4%

Kentucky

Appalachian Kentucky 5,288 21,433 24.7% 1,412 19,404 7.3%

Non-Appalachian Kentucky 16,339 45,317 36.1% 5,886 41,644 14.1%

Maryland

Appalachian Maryland 2,195 3,984 55.1% 755 3,938 19.2%

Non-Appalachian Maryland 40,847 85,734 47.6% 10,417 80,321 13.0%

Mississippi

Appalachian Mississippi 1,283 6,216 20.6% 420 5,017 8.4%

Non-Appalachian Mississippi 12,378 60,765 20.4% 4,699 49,954 9.4%

New York

Appalachian New York 4,800 11,604 41.4% 1,723 9,925 17.4%

Non-Appalachian New York 177,381 305,499 58.1% 53,229 267,226 19.9%

North Carolina

Appalachian North Carolina 7,172 15,151 47.3% 2,264 14,047 16.1%

Non-Appalachian North Carolina 41,716 102,118 40.9% 13,210 94,783 13.9%

Ohio

Appalachian Ohio 8,834 26,811 32.9% 2,818 24,474 11.5%

Non-Appalachian Ohio 49,662 124,833 39.8% 18,782 110,554 17.0%

Pennsylvania

Appalachian Pennsylvania 27,758 60,107 46.2% 9,921 57,412 17.3%

Non-Appalachian Pennsylvania 38,389 86,103 44.6% 12,115 87,994 13.8%

South Carolina

Appalachian South Carolina 5,376 12,976 41.4% 1,572 11,602 13.5%

Non-Appalachian South Carolina 18,508 49,173 37.6% 5,737 44,758 12.8%

Tennessee

Appalachian Tennessee 13,574 35,847 37.9% 4,269 33,345 12.8%

Non-Appalachian Tennessee 20,325 54,056 37.6% 6,958 49,270 14.1%

Virginia

Appalachian Virginia 2,131 6,571 32.4% 791 6,234 12.7%

Non-Appalachian Virginia 20,325 54,056 37.6% 10,950 79,481 13.8%

West Virginia (entire state) 7,752 20,444 37.9% 2,604 18,911 13.8%

2007 2010

57
Table 1-9: Disparity Ratios of Ratio of Small Business Loans to Total Businesses in Low-Moderate 

Income (LMI) Tracts

1.3.4 CREDIT CARD LENDING

Small business credit card loans usually have higher interest rates and are of shorter term than non-credit 

card loans. In addition to having less desirable loan terms and conditions, credit card loans are usually 

easier for borrowers to qualify and receive. An initial impression, therefore, would be that credit card 

small business lending would be utilized more in regions with less access to loans overall and in regions 

with fewer creditworthy small businesses. Thus, the expectation would be that banks that specialize in 

credit card small business lending would have a higher market share in distressed counties and rural 

counties in Appalachia.

In both 2007 and 2010, about 55 percent and 62 percent of the small business loans in Appalachia and the 

nation, respectively, were credit card loans. 

Within Appalachia, businesses in large metropolitan counties experienced the greatest access to small 

business loans overall (see Figure 1-3 above) and experienced the most credit card lending in 2007, as 

shown in Figure 1-15. In addition, businesses in large metropolitan counties experienced levels of credit 

card lending (as measured by credit card market share) comparable to the other categories of counties in 

2010, expect for rural counties which had the smallest market share of credit card loans in both years. 

Likewise, distressed counties had a lower market share of credit card loans than attainment counties (see 

Figure 1-16) although attainment counties had the greatest access to overall small business loans (see 

Figure 1-4 above).

Not surprisingly, credit card market share by dollar amount is considerably smaller than market share by 

number of loans. Whereas credit card loans constituted about 54 percent of all small business loans in 

Appalachia during 2010 when considering the number of loans, credit card loans were just 6.6 percent of 

small business loans in Appalachia when considering dollar amounts (see Table 1-12 for credit card 

market share by dollar amounts). This difference in market share arises because the average dollar amount 

is considerably smaller for credit card loans than for small business loans. The disparities in market share 

when examining credit card market share by dollar amount is similar to the disparities in market share 

when examining market share by loan count. For example, market share by dollar amount and loan count 

shows that credit card market share is the highest in distressed and attainment counties.

Although the market share for credit card lending is considerably smaller when considering dollar 

amounts, it is nevertheless important to consider that a substantial portion of the need for small business 

loans is being met by credit card lending. A little more than half the loans when considering number of 

Year Numerator Denominator Disparity Ratio

2007 43.94% 26.57% 1.65

2010 16.86% 8.28% 2.04

2007 44.97% 27.71% 1.62

2010 14.60% 9.26% 1.58

2007 47.30% 24.77% 1.91

2010 14.33% 6.59% 2.17

Northern vs. Central

Large Metro vs. Rural

Attainment vs. Distressed

58
loans in Appalachia are credit card loans. This means that either the small businesses in Appalachia 

cannot qualify for non-credit card loans or find it much easier to obtain credit card loans. And it is not 

clear that the lower average dollar amounts of credit card lending fully meets the credit needs of a broad 

swath of small businesses in Appalachia.

The descriptive analysis suggests that if any correlation occurs, it might be that counties with greater 

access to small business loans overall are also counties with greater market share of credit card loans. In 

these counties, credit card lending is not acting as a substitute where non-credit card lending is relatively 

scarce. Instead, credit card and non-credit card lending tend to increase together. The results of statistical 

correlation tests, shown in Table 1-10, appear to confirm that higher levels of credit card lending 

generally occurs in counties where higher levels of non-credit card lending also occur. 

Table 1-10: Correlation among Credit Card Lending and Non-Credit Card Lending

*Statistically significant at the 0.001 level. Normalized here means loans divided by small businesses, count refers to number of loans and 

amount refers to dollar amount of loans.

The index maps (see Maps 1-7 and 1-8) indicate that credit card lending index is the highest in western 

counties such as those in Kentucky and Tennessee with the least access to overall small business credit. 

Conversely, the credit card lending index is the lowest in Northern Appalachia where overall small 

business credit was most favorable compared to the nation. In some categories of counties with limited 

access to non-credit card lending, borrowers utilize credit cards to a degree greater then national average 

to meet their borrowing needs. However, overall in Appalachia, credit card and non-credit card lending 

increased or decreased together on a county level instead of exhibiting an inverse relationship. 

2007 2010

Normalized (Count) 0.5497* 0.3910*

Not normalized (Count) 0.9546* 0.8944*

Not normalized (Amount) 0.9061* 0.8881*

Correlation Coefficients

59
Figure 1-14: Credit Card Market Share by Region 

Figure 1-15: Credit Card Market Share by County Type

Figure 1-16: Credit Card Market Share by Economic Status

62.8%

54.7%

54.6%

52.9%

52.0%

51.0%

57.7%

62.0%

54.8%

49.9%

57.3%

58.8%

54.6%

59.3%

0% 20% 40% 60% 80%

United States

Appalachian Region

Northern Appalachia

North Central Appalachia

Central Appalachia

South Central Appalachia

Southern Appalachia

2007 2010 Per capita personal income index

60.1%

53.2%

55.0%

52.2%

48.7%

55.8%

54.9%

56.0%

55.1%

49.9%

0% 20% 40% 60% 80%

Large Metro (1 million +

people)

Small Metro (< 1 million

people)

Nonmetro, Adjacent to

Large Metro

Nonmetro, Adjacent to

Small Metro

Rural (Nonmetro,

Nonadjacent)

2007 2010

53.0%

49.4%

52.2%

56.8%

67.0%

60.0%

51.1%

54.6%

50.6%

66.8%

0% 20% 40% 60% 80%

Distressed

At-Risk

Transitional

Competitive

Attainment

2007 2010

60
Map 1-7: Credit Card Business Lending Index, 2007

Ratio of Credit Card Business Loans to Small Business Loans, Indexed Values (U.S. =1)

Statistics of Indexed Values

Count 420

Minimum 0.315

Maximum 1.292

Mean 0.934

Standard Deviation 0.146

Histogram of Indexed Values

61
Map 1-8: Credit Card Business Lending Index, 2010

Ratio of Credit Card Business Loans to Small Business Loans, Indexed Values (U.S. =1)

Statistics of Indexed Values

Count 420

Minimum 0.279

Maximum 1.58

Mean 0.975

Standard Deviation 0.234

Histogram of Indexed Values

62
Figure 1-17: Credit Card Market Share by State

53.0%

52.6%

64.3%

63.7%

55.0%

51.2%

69.9%

55.0%

50.5%

41.6%

73.2%

58.5%

52.4%

49.9%

59.9%

56.3%

60.0%

53.6%

56.5%

57.5%

55.7%

53.9%

63.4%

43.5%

51.4%

52.3%

52.2%

66.2%

67.4%

61.1%

59.1%

70.8%

54.9%

44.7%

41.2%

76.9%

65.4%

54.6%

52.1%

58.2%

58.7%

55.5%

46.1%

59.8%

63.3%

56.4%

57.2%

66.8%

54.0%

55.4%

0% 20% 40% 60% 80% 100%

Alabama

Appalachian Alabama

Georgia

Appalachian Georgia

Kentucky

Appalachian Kentucky

Maryland

Appalachian Maryland

Mississippi

Appalachian Mississippi

New York

Appalachian New York

North Carolina

Appalachian North Carolina

Ohio

Appalachian Ohio

Pennsylvania

Appalachian Pennsylvania

South Carolina

Appalachian South Carolina

Tennessee

Appalachian Tennessee

Virginia

Appalachian Virginia

West Virginia (entire state)

2007 2010

63
Table 1-11: Credit Card Market Share in Appalachia

Number of 

Credit Card 

Business 

Loans

Number of 

Small 

Business 

Loans

Ratio of Credit 

Card Business 

Loans to Small 

Business Loans

Number of 

Credit Card 

Business 

Loans

Number of 

Small 

Business 

Loans

Ratio of Credit 

Card Business 

Loans to Small 

Business Loans

United States 8,440,545 13,437,779 62.8% 2,602,710 4,197,610 62.0%

Appalachian Region 442,857 808,877 54.7% 139,864 255,231 54.8%

Subregions

Northern Appalachia 144,641 265,062 54.6% 46,644 93,452 49.9%

North Central Appalachia 31,478 59,519 52.9% 11,270 19,678 57.3%

Central Appalachia 19,963 38,372 52.0% 7,104 12,085 58.8%

South Central Appalachia 79,628 156,224 51.0% 26,100 47,760 54.6%

Southern Appalachia 167,147 289,700 57.7% 48,746 82,256 59.3%

County Types

Large Metro (1 million + people) 140,520 233,652 60.1% 40,108 71,892 55.8%

Small Metro (< 1 million people) 172,949 325,376 53.2% 56,769 103,484 54.9%

Nonmetro, Adjacent to Large Metro 25,721 46,778 55.0% 8,209 14,654 56.0%

Nonmetro, Adjacent to Small Metro 71,385 136,848 52.2% 23,805 43,204 55.1%

Rural (Nonmetro, Nonadjacent) 32,282 66,223 48.7% 10,973 21,997 49.9%

Economic Status

Distressed 13,354 25,190 53.0% 4,477 7,461 60.0%

At-Risk 25,919 52,515 49.4% 9,521 18,619 51.1%

Transitional 233,843 448,203 52.2% 80,354 147,088 54.6%

Competitive 100,517 176,965 56.8% 29,110 57,498 50.6%

Attainment 57,565 85,936 67.0% 16,402 24,565 66.8%

Alabama 82,094 154,821 53.0% 24,546 46,944 52.3%

Appalachian Alabama 52,819 100,348 52.6% 15,880 30,438 52.2%

Non-Appalachian Alabama 29,275 54,473 53.7% 8,666 16,506 52.5%

Georgia 265,068 412,517 64.3% 72,227 109,030 66.2%

Appalachian Georgia 85,049 133,604 63.7% 23,147 34,345 67.4%

Non-Appalachian Georgia 180,019 278,913 64.5% 49,080 74,685 65.7%

Kentucky 64,901 118,063 55.0% 25,109 41,075 61.1%

Appalachian Kentucky 12,539 24,487 51.2% 4,682 7,926 59.1%

Non-Appalachian Kentucky 52,362 93,576 56.0% 20,427 33,149 61.6%

Maryland 173,251 248,007 69.9% 48,221 68,107 70.8%

Appalachian Maryland 4,868 8,846 55.0% 1,534 2,796 54.9%

Non-Appalachian Maryland 168,383 239,161 70.4% 46,687 65,311 71.5%

Mississippi 41,451 82,053 50.5% 13,572 30,367 44.7%

Appalachian Mississippi 7,288 17,516 41.6% 2,507 6,079 41.2%

Non-Appalachian Mississippi 34,163 64,537 52.9% 11,065 24,288 45.6%

New York 709,385 969,471 73.2% 237,225 308,303 76.9%

Appalachian New York 18,272 31,216 58.5% 6,977 10,676 65.4%

Non-Appalachian New York 691,113 938,255 73.7% 230,248 297,627 77.4%

North Carolina 178,922 341,140 52.4% 59,487 108,924 54.6%

Appalachian North Carolina 31,989 64,128 49.9% 10,142 19,459 52.1%

Non-Appalachian North Carolina 146,933 277,012 53.0% 49,345 89,465 55.2%

Ohio 218,498 364,863 59.9% 73,684 126,530 58.2%

Appalachian Ohio 29,625 52,594 56.3% 9,801 16,697 58.7%

Non-Appalachian Ohio 188,873 312,269 60.5% 63,883 109,833 58.2%

Pennsylvania 268,990 448,313 60.0% 86,302 155,425 55.5%

Appalachian Pennsylvania 101,577 189,468 53.6% 31,483 68,219 46.1%

Non-Appalachian Pennsylvania 167,413 258,845 64.7% 54,819 87,206 62.9%

South Carolina 84,640 149,892 56.5% 27,009 45,139 59.8%

Appalachian South Carolina 21,991 38,232 57.5% 7,212 11,394 63.3%

Non-Appalachian South Carolina 62,649 111,660 56.1% 19,797 33,745 58.7%

Tennessee 111,411 199,969 55.7% 36,334 64,456 56.4%

Appalachian Tennessee 44,891 83,223 53.9% 14,229 24,897 57.2%

Non-Appalachian Tennessee 66,520 116,746 57.0% 22,105 39,559 55.9%

Virginia 189,332 298,547 63.4% 60,873 91,083 66.8%

Appalachian Virginia 8,649 19,900 43.5% 3,642 6,740 54.0%

Non-Appalachian Virginia 180,683 278,647 64.8% 57,231 84,343 67.9%

West Virginia (entire state) 23,300 45,315 51.4% 8,628 15,565 55.4%

2007 2010

64
Table 1-12: Credit Card Market Share (in Dollars) in Appalachia

Credit Card 

Small Business 

Loans (Amount)

Small Business 

Loans 

(Amount) Ratio 

Credit Card 

Small Business 

Loans (Amount)

Small Business 

Loans 

(Amount) Ratio 

United States $66,209,352 $324,325,913 20.4% $17,790,765 $173,436,472 10.3%

Appalachian Region $3,153,370 $23,965,813 13.2% $882,000 $13,342,550 6.6%

Subregions

Northern Appalachia $1,009,829 $7,282,956 13.9% $277,263 $5,110,722 5.4%

North Central Appalachia $224,571 $1,606,299 14.0% $79,525 $1,101,456 7.2%

Central Appalachia $136,295 $811,477 16.8% $43,428 $454,146 9.6%

South Central Appalachia $566,875 $5,050,897 11.2% $170,865 $2,615,336 6.5%

Southern Appalachia $1,215,800 $9,214,184 13.2% $310,919 $4,060,890 7.7%

County Types

Large Metro (1 million + people) $1,030,786 $6,619,126 15.6% $254,774 $3,699,442 6.9%

Small Metro (< 1 million people) $1,232,436 $10,848,636 11.4% $371,807 $6,067,377 6.1%

Nonmetro, Adjacent to Large Metro $175,825 $1,152,371 15.3% $45,576 $656,345 6.9%

Nonmetro, Adjacent to Small Metro $495,863 $3,638,568 13.6% $141,774 $1,955,243 7.3%

Rural (Nonmetro, Nonadjacent) $218,460 $1,707,112 12.8% $68,069 $964,143 7.1%

Economic Status

Distressed $89,830 $492,229 19.4% $26,227 $233,528 11.2%

At-Risk $174,761 $1,284,550 13.9% $57,558 $814,478 7.1%

Transitional $1,625,194 $13,722,200 12.3% $498,585 $7,728,094 6.5%

Competitive $733,299 $5,838,093 12.5% $185,284 $3,445,967 5.4%

Attainment $445,802 $2,170,027 18.0% $114,346 $1,120,483 10.2%

Alabama $560,283 $5,827,455 9.6% $143,249 $2,633,016 5.4%

Appalachian Alabama $365,222 $3,791,119 9.6% $93,549 $1,665,817 5.6%

Non-Appalachian Alabama $195,061 $2,036,336 9.6% $49,700 $967,199 5.1%

Georgia $1,966,896 $11,224,500 17.5% $476,195 $4,977,683 9.6%

Appalachian Georgia $632,333 $3,630,619 17.4% $155,584 $1,532,318 10.2%

Non-Appalachian Georgia $1,334,563 $10,725,267 12.4% $320,611 $3,445,365 6.8%

Kentucky $481,491 $3,185,311 15.1% $174,969 $2,133,357 8.2%

Appalachian Kentucky $87,093 $499,233 17.4% $29,285 $280,712 10.4%

Non-Appalachian Kentucky $394,398 $2,686,078 14.7% $145,684 $1,852,645 7.9%

Maryland $1,352,290 $6,040,036 22.4% $317,715 $2,841,401 11.2%

Appalachian Maryland $34,830 $271,031 12.9% $9,315 $175,767 5.3%

Non-Appalachian Maryland $1,317,460 $5,769,005 22.8% $308,400 $2,665,634 11.6%

Mississippi $267,409 $2,529,250 10.6% $72,663 $1,515,669 4.8%

Appalachian Mississippi $46,518 $551,393 8.4% $14,396 $272,295 5.3%

Non-Appalachian Mississippi $220,891 $1,977,857 11.2% $58,267 $1,243,374 4.7%

New York $5,447,179 $19,914,993 27.4% $1,583,619 $9,643,636 16.4%

Appalachian New York $121,931 $721,522 16.9% $37,739 $383,744 9.8%

Non-Appalachian New York $5,325,248 $19,193,471 27.7% $1,545,880 $9,259,892 16.7%

North Carolina $1,351,977 $11,537,683 11.7% $385,199 $5,633,301 6.8%

Appalachian North Carolina $230,973 $2,045,398 11.3% $71,474 $947,159 7.5%

Non-Appalachian North Carolina $1,121,004 $9,492,285 11.8% $313,725 $4,686,142 6.7%

Ohio $1,654,136 $10,142,341 16.3% $528,291 $7,875,456 6.7%

Appalachian Ohio $213,093 $1,160,385 18.4% $68,729 $863,491 8.0%

Non-Appalachian Ohio $1,441,043 $8,981,956 16.0% $459,562 $7,011,965 6.6%

Pennsylvania $1,914,658 $13,184,892 14.5% $526,992 $8,033,802 6.6%

Appalachian Pennsylvania $712,425 $5,519,598 12.9% $185,671 $3,915,651 4.7%

Non-Appalachian Pennsylvania $1,202,233 $7,665,294 15.7% $341,321 $4,118,151 8.3%

South Carolina $628,783 $4,776,873 13.2% $168,168 $2,294,399 7.3%

Appalachian South Carolina $171,727 $1,241,053 13.8% $47,390 $590,460 8.0%

Non-Appalachian South Carolina $457,056 $3,535,820 12.9% $120,778 $1,703,939 7.1%

Tennessee $811,532 $6,511,859 12.5% $238,302 $3,836,647 6.2%

Appalachian Tennessee $317,373 $2,749,951 11.5% $88,728 $1,500,176 5.9%

Non-Appalachian Tennessee $494,159 $3,761,908 13.1% $149,574 $2,336,471 6.4%

Virginia $1,489,279 $7,961,982 18.7% $426,930 $4,116,094 10.4%

Appalachian Virginia $57,820 $509,108 11.4% $22,004 $309,375 7.1%

Non-Appalachian Virginia $1,431,459 $7,452,874 19.2% $404,926 $3,806,719 10.6%

West Virginia (entire state) $162,032 $1,275,403 12.7% $58,136 $905,585 6.4%

2007 2010

65
1.3.5 RELATIONSHIP BETWEEN LENDING AND BANK BRANCHES

Correlation statistical analysis suggests a relationship among bank branches and the level of small 

business lending at a county level. NCRC’s previous study for ARC in 2007 found that higher levels of 

lending occurred in counties with higher numbers of branches.

7

 This study reconfirms previous findings. 

Tables 1-13 through 1-15 show statistically significant correlation coefficients between the number of 

branches and the number and dollar amount of small business loans for 2007 and 2010. For both credit 

card and non-credit card lending, a higher number of branches is associated with a higher number of loans 

on a county level in Appalachia. The same relationship is found between the dollar amount of deposits 

and the number of small business loans on a county level for 2007 and 2010 (see Table 1-16).

Table 1-13: Correlation among Branches and All Small Business Lending

Table 1-14: Correlation among Branches and Non-Credit Card Lending

Table 1-15: Correlation among Branches and Credit Card Lending

Table 1-16: Correlation among Deposits and All Small Business Lending

*Statistically significant at the 0.001 level. Normalized here means loans divided by small businesses, count refers to number of loans and 

amount refers to dollar amount of loans.

7 The previous NCRC study for ARC can be accessed via 

http://www.arc.gov/research/researchreportdetails.asp?REPORT_ID=8

2007 2010

Normalized (Count) 0.3720* 0.3272*

Not normalized (Count) 0.9348* 0.9735*

Not normalized (Amount) 0.9256* 0.9694*

Correlation Coefficients

2007 2010

Normalized (Count) 0.2688* 0.2358*

Not normalized (Count) 0.9639* 0.9643*

Not normalized (Amount) 0.9210* 0.9672*

Correlation Coefficients

2007 2010

Normalized (Count) 0.3829* 0.336

Not normalized (Count) 0.8978 0.9324

Not normalized (Amount) 0.8789 0.9016

Correlation Coefficients

2007 2010

Normalized (Count) 0.2573* 0.2187*

Not normalized (Count) 0.8610* 0.9095*

Not normalized (Amount) 0.8592* 0.93*

Correlation Coefficients

66

 
1.3.6 SPATIAL PATTERNS OF LENDING

As Table 1-17 shows, spatial autocorrelation analysis reveals that the lending patterns described above are 

non-random and are clustered. In other words, the spatial autocorrelation analysis confirms that lending is 

indeed concentrated in Northern Appalachia, non-distressed counties, and metropolitan counties. 

Clustering in the lending pattern was analyzed using the global spatial autocorrelation statistics for year 

2007 and 2010 in the Appalachia Region. Global spatial statistics are estimated using the Moran's I 

measure.

Table 1-17: Spatial Autocorrelation of Lending Variables

*All values are statistically significant at the 0.001 level. Moran’s I values above 0 represent a clustered pattern.

2007 2010

All small business lending 0.02 0.02

Ratio of small business loans to small business 0.16 0.11

All non-credit card small business lending 0.02 0.02

Ratio of non-credit card small business loans to small business 0.11 0.08

All credit card small business lending 0.02 0.02

Ratio of credit card small business loans to small business 0.16 0.12

SBA lending 0.02 0.02

SBA loans per 10,000 small businesses 0.07 0.01

Lending Type Moran's I

67
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68
CHAPTER 2 LOAN DEMAND IN APPALACHIA

2.1 INTRODUCTION AND SUMMARY

This report has documented a significant decline in small business lending in the nation and in 

Appalachia. The percentage decline in lending from 2007 and 2010 was similar in the nation and 

Appalachia, but Appalachian small businesses experienced less access to credit for the entire 2007 

through 2010 time period. This chapter assesses how much of the decrease in lending in the nation and 

Appalachia was due to demand factors. In other words, were small businesses more or less likely in 

Appalachia than the nation to be deterred from applying because they feared rejection or did small 

businesses in Appalachia desire and apply for lending at similar rates as their national counterparts? The 

chapter’s analysis is based on two surveys conducted by Pepperdine University and the Kauffman 

Foundation specifically for this report. While we cannot offer definitive conclusions, the analysis will 

offer important insights and contribute to programmatic and policy recommendations. 

Summary findings from the Pepperdine survey include:

• The smaller the business, the more they are affected by overall economic conditions and have 

greater difficulty raising both debt and equity capital. This holds true for Appalachia and the 

nation. 

• Survey respondents were less successful in securing business loans from banks than obtaining 

credit cards or trade credit. For example, about 45 percent of the survey respondents in 

Appalachia and the nation who sought business bank loans secured them compared to 62 and 58 

percent of the respondents in the nation and Appalachia, respectively, in the case of personal 

credit cards. Credit cards are a more expensive form of credit than bank loans.

• Much lower rates of success occurred in securing equity financing for businesses; for example, 

only 5 percent of the businesses who sought angel capital in Appalachia succeeded in acquiring it 

compared to 20 percent of firms in the national sample. For businesses with revenue of between 

$500,000 and $1million, none of the Appalachian respondents secured angel investments, while

12 percent of the national respondents did so.

• For the second smallest business revenue category ($500,000 to $1 million), respondents in 

Appalachia were strikingly less successful than their counterparts in the nation in raising debt or 

equity financing. For example, 35 percent and 17 percent of these businesses in the nation and 

Appalachia, respectively, secured business loans from banks. Likewise, 54 percent and 14 percent 

of the businesses in this revenue category in the nation and Appalachia, respectively, secured 

business credit card financing. None of the Appalachian respondents in this category secured 

angel investments; 12 percent of the national respondents did. 

• A large difference in response between the nation and Appalachia was noted in the ability to 

secure financing from friends and family, with Appalachian firms having much lower rates of 

successfully obtaining this capital (47 percent) than do firms across the nation (71 percent). While 

this is not surprising in a region experiencing greater economic distress than the nation, it may 

69
also explain a large reason for differences in the success of smaller businesses in Appalachia and 

the nation in securing loans. 

• In addition, the smallest businesses owners in Appalachia were much more likely to transfer their 

savings and use personal credit cards to fund their businesses than their counterparts in 

Appalachia or the nation. For example, 81 percent and 68 percent of small business owners with 

revenues between $500,000 and $1 million in Appalachia and the nation, respectively, transferred 

their personal savings and investments to their small businesses. 

• The differences in success rates of securing financing by small businesses was greater than the 

differences in demand in Appalachia and the nation. While the Pepperdine survey reported less 

demand for financing by the second smallest revenue category in Appalachia as compared to the 

nation, the overall success rates for all smaller businesses in securing financing was significantly 

less for Appalachian businesses when compared to the nation.

• Just over 60 percent of the respondents in the nation and in Appalachia indicated that the current 

business financing environment was restrictive. Three quarters of the businesses in Appalachia 

and the nation stated that raising equity and debt financing was difficult.

Summary findings from the Kauffman survey includes: 

• An increasing percentage of businesses in Appalachia and the United States desired credit but did 

not apply because they feared rejection. In the United States, the percentage of businesses in this 

category increased from 15.7 percent in 2007 to 21.1 percent in 2009; In Appalachia the figures 

were 18.1 percent in 2007 and 23.1 percent in 2009. 

• The percentage of firms denied credit were significantly higher in Appalachia (22.9 percent in 

2009) compared to the United States (8.7 percent in 2009). Due to low sample sizes, it is not 

possible to offer a statistically significant conclusion as to reasons for denial, but it appears that 

insufficient collateral, business and personal credit history were larger factors in Appalachia than 

the United States.

• The Kauffman survey confirms that outcomes were significantly worse for Appalachian 

businesses when compared to their national peers in terms of higher denial rates and less success 

in securing credit. 

Differences in obtaining loans between Appalachian businesses and their national peers were starker than 

suggested by differences in demand for lending between these groups. A possible explanation for less 

success in securing loans in Appalachia was the relatively fewer resources available from friends and 

family, or from angel investors, for Appalachian businesses. More limited non-debt financing may have 

contributed to less collateral and higher loan rejection rates for Appalachian businesses.

2.2 PEPPERDINE CAPITAL ACCESS INDEX

2.2.1 DESCRIPTION OF SAMPLE SIZE

The Graziadio School of Business and Management at Pepperdine University, with the support of Dun 

and Bradstreet Credibility Corporation, produces a quarterly survey of the demand by small and medium 

70
sized small businesses for credit and capital and the level of accessibility of credit and capital. The survey 

consists of 25 questions and generated 4,686 complete responses for the United States, as a whole, and 

384 responses from Appalachia during the first quarter of 2012. The year 2012 marked the first year in 

which Pepperdine tracked survey responses by ZIP code so this survey provides a current snapshot of 

demand for small business loans in Appalachia as well as the nation. Below, the Kauffman survey results 

covering the years 2007 and 2009 will facilitate a comparison over time between the United States and 

Appalachia. 

Of the 383 survey responses in Appalachia to the Pepperdine survey, the plurality was small businesses 

with annual revenues under $500,000. Forty percent or 154 of the respondents were small businesses with 

revenues under $500,000, 14 percent or 54 small businesses had revenues between $500,000 and $1 

million, 23 percent or 88 small businesses had revenues between $1 and $5 million, and 15 percent or 58 

businesses had revenues between $5 and $100 million. The percentage of the smallest businesses is for 

the national survey is very similar; about 39 percent of the businesses had revenues under $500,000 and 

15 percent of the businesses had revenues between $500,000 and $1 million. In general, compared to 

small business demographics, the Pepperdine survey over-represents the largest of the small businesses, 

yet the survey results nevertheless indicate general difficulties in obtaining credit and do not seem to 

obscure problems experienced by the smallest of the small businesses (see Figures 2-1 and 2-2). 

More than half of the small businesses have less than 5 employees. While the plurality are small in terms 

of revenue and number of employees, most are established with more than three years in business; 13 

percent are three years or younger, 25 percent have operated between three and ten years, and 62 percent 

have been in operations for ten or more years. In terms of industries, 33 percent are in services, 14 percent 

are in manufacturing, 13 percent are in construction, and 11 percent are in retail (see Figures 2-3-2-5).

The Pepperdine survey has a higher percentage of business respondents in relatively advantaged counties 

in Appalachia. For example, 32.9 percent of the respondents were in large metropolitan counties whereas 

27.6 percent of the Appalachian businesses were in these counties. Likewise, the portion of respondents 

was 5.8 percentage points higher than the portion of businesses in attainment counties. In contrast, rural, 

distressed, and at-risk counties had a lower percentage of respondents than the percentage of Appalachian 

businesses. In terms of the Appalachian portion of states, Georgia and New York were over-represented 

by the Pepperdine survey whereas Tennessee and North Carolina were under-represented. It is perhaps 

surprising that the respondents are not even more skewed towards urban and large metropolitan areas and 

it is useful that there is a sizable representation in rural and economically disadvantaged counties. Yet, the 

over-representation in advantaged parts of Appalachia suggests that the results likely understate the 

obstacles small business face (see Table 2-1 and Figures 2-6 through 2-9). 

71
Figure 2-1: Pepperdine Respondents Compared to Appalachian Businesses by Revenue Size

Figure 2-2: Pepperdine Respondents Compared to National Businesses by Revenue Size 

72
Figure 2-3: Details about the Respondents, by Number of Employees

Figure 2-4: Details about the Respondents, by Firm Age

51%

15%

25%

4%

3% 2%

0-5

6-10

11-50

51-100

101-200

More than 200

73
Figure 2-5: Details about the Respondents, by Industry

74
Figure 2-6: Ratio of Pepperdine Survey Respondents to Total Non-Farm Businesses by Region

Figure 2-7: Ratio of Pepperdine Survey Respondents to Total Non-Farm Businesses by County 

Type

Figure 2-8: Ratio of Pepperdine Survey Respondents to Total Non-Farm Businesses by Economic 

Status

30.8%

8.1%

5.5%

15.4%

40.2%

31.0%

8.2%

6.4%

19.1%

35.3%

0% 10% 20% 30% 40% 50%

Northern Appalachia

North Central Appalachia

Central Appalachia

South Central Appalachia

Southern Appalachia

% of Respondents

% of Total Non-Farm Businesses

Per capita personal income index

32.9%

40.5%

5.2%

15.4%

6.0%

27.6%

38.6%

6.2%

17.5%

10.2%

0% 10% 20% 30% 40% 50%

Large Metro (1 million + people)

Small Metro (< 1 million people)

Nonmetro, Adjacent to Large Metro

Nonmetro, Adjacent to Small Metro

Rural (Nonmetro, Nonadjacent)

% of Respondents

% of Total Non-Farm Businesses

Per capita personal income index

1.3%

5.7%

54.0%

25.6%

13.3%

5.0%

8.3%

56.3%

19.7%

10.7%

0% 20% 40% 60%

Distressed

At-Risk

Transitional

Competitive

Attainment

% of Respondents

% of Total Non-Farm Businesses

Per capita personal income index

75
Figure 2-9: Ratio of Pepperdine Survey Respondents to Total Non-Farm Businesses by 

Appalachian States

12.5%

21.9%

3.1%

0.8%

1.8%

6.5%

5.2%

6.3%

20.6%

4.4%

8.9%

2.9%

5.0%

11.8%

15.8%

4.3%

1.0%

3.4%

3.5%

7.1%

6.8%

22.0%

4.2%

11.1%

2.5%

6.2%

0% 10% 20% 30%

Appalachian Alabama

Appalachian Georgia

Appalachian Kentucky

Appalachian Maryland

Appalachian Mississippi

Appalachian New York

Appalachian North Carolina

Appalachian Ohio

Appalachian Pennsylvania

Appalachian South Carolina

Appalachian Tennessee

Appalachian Virginia

West Virginia (entire state)

% of Respondents

% of Total Non-Farm Businesses

76
Table 2-1: Number of Respondents for Pepperdine Survey and Total Number of Non-Farm 

Businesses in Appalachia

2.2.2 DEMAND FOR EXTERNAL FINANCING

Interestingly, the percentage of respondents who attempted to secure outside financing in the nation and 

Appalachia was almost identical at 33 percent and 32 percent, respectively, during the first quarter of 

2012. Within Appalachia, a lower percentage of the smallest businesses sought financing; 28 percent for 

small businesses with revenues under $500,000 and 23 percent for small businesses with revenues 

between $500,000 and $1 million. A considerably lower percentage of Appalachian than national 

businesses with revenues between $500,000 to $1 million sought financing; 23 percent compared to 32 

percent (see Figures 2-10 and 2-11). 

A lower percentage of businesses in Appalachia (13 percent) than in the nation (22 percent) did not seek 

financing because they feared that they would be rejected. Within Appalachia, the smallest businesses 

with revenues under $500,000 were most likely to report fear of rejection (19 percent). While 45 percent 

Number % Number %

Appalachian Region 383 100.0% 2,370,133 100.0% 0.0%

Subregions

Northern Appalachia 118 30.8% 734,046 31.0% -0.2%

North Central Appalachia 31 8.1% 194,406 8.2% -0.1%

Central Appalachia 21 5.5% 151,625 6.4% -0.9%

South Central Appalachia 59 15.4% 453,146 19.1% -3.7%

Southern Appalachia 154 40.2% 836,910 35.3% 4.9%

County Types

Large Metro (1 million + people) 126 32.9% 653,148 27.6% 5.3%

Small Metro (< 1 million people) 155 40.5% 914,640 38.6% 1.9%

Nonmetro, Adjacent to Large Metro 20 5.2% 146,590 6.2% -1.0%

Nonmetro, Adjacent to Small Metro 59 15.4% 413,861 17.5% -2.1%

Rural (Nonmetro, Nonadjacent) 23 6.0% 241,894 10.2% -4.2%

Economic Status

Distressed 5 1.3% 119,009 5.0% -3.7%

At-Risk 22 5.7% 196,926 8.3% -2.6%

Transitional 207 54.0% 1,333,327 56.3% -2.2%

Competitive 98 25.6% 468,080 19.7% 5.8%

Attainment 51 13.3% 252,791 10.7% 2.7%

Appalachian States

Appalachian Alabama 48 12.5% 279,464 11.8% 0.7%

Appalachian Georgia 84 21.9% 375,318 15.8% 6.1%

Appalachian Kentucky 12 3.1% 102,484 4.3% -1.2%

Appalachian Maryland 3 0.8% 24,381 1.0% -0.2%

Appalachian Mississippi 7 1.8% 81,414 3.4% -1.6%

Appalachian New York 25 6.5% 84,118 3.5% 3.0%

Appalachian North Carolina 20 5.2% 169,406 7.1% -1.9%

Appalachian Ohio 24 6.3% 161,336 6.8% -0.5%

Appalachian Pennsylvania 79 20.6% 521,595 22.0% -1.4%

Appalachian South Carolina 17 4.4% 100,714 4.2% 0.2%

Appalachian Tennessee 34 8.9% 263,348 11.1% -2.2%

Appalachian Virginia 11 2.9% 60,127 2.5% 0.3%

West Virginia (entire state) 19 5.0% 146,428 6.2% -1.2%

Respondents, 2012 Total Non-farm Businesses, 2011 Percentage 

point 

Differences

77
of the respondents in the nation stated they did not seek financing because they had sufficient cash flow, 

only 28 percent of respondents in Appalachia selected this reason for not seeking financing. Only 16 

percent of the Appalachian smallest businesses with revenues under $500,000 did not seek financing 

because they thought they had sufficient cash flow to cover expenses (see Figures 2-12 and 2-13). 

 Just over 60 percent of the respondents in the nation and Appalachia indicated that the current business 

financing environment was restrictive. The smallest businesses with revenues under $500,000 were most 

likely to respond that the current environment was restrictive; 76 percent of these Appalachian businesses 

thought so (see Figure 2-14). A smaller percentage (69 percent) of national businesses in this revenue 

category thought that the financing environment was restrictive. Businesses between $500,000 and $1 

million in revenue also thought that the current business financing environment was restrictive, with 64 

percent of businesses nationally reporting this assessment, while 58 percent of Appalachian businesses 

reported these concerns (see Figure 2-15). 

Three quarters of the businesses in Appalachia and the nation stated that raising equity and debt financing 

was difficult. Within Appalachia, the smallest businesses again indicated the most difficulty; 87 percent 

of the respondents with revenues less than $500,000 said raising debt or equity financing was difficult 

(see Figure 2-16). A greater percentage of the smallest business with revenues under $500,000 in 

Appalachia than the nation responded that it was difficult to raise equity and debt financing. For 

businesses with revenues of $500,000 to $1 million, a greater percentage of national businesses reported 

these difficulties (see Figure 2-17). 

On a scale of 1 to 4, the survey respondents in the nation and Appalachia indicated a “moderate” level of 

demand for access to credit for various purposes such as planned growth, working capital fluctuations, or 

refinancing existing loans. The responses did not vary significantly by revenue category of businesses in 

the nation or within Appalachia (see Figures 2-18 and 2-19). Businesses interested in accessing financing 

in Appalachia and the United States were most likely to seek financing for planned growth and working 

capital and least likely to seek financing to endure worsening operating conditions or refinancing existing 

loans. Within Appalachia, the smallest businesses with revenues under $500,000 were most likely to 

desire financing for planned growth or working capital (see Figures 2-20 and 2-21). 

78
Figure 2-10: Percentage of Respondents Who Attempted to Raise Outside Financing in the Last 

Three Months

Figure 2-11: Percentage of Respondents Who Attempted to Raise Outside Financing in the Last 

Three Months, National Revenue Breakouts

79
Figure 2-12: Reasons for Businesses Not Attempting to Raise External Financing (Multiple 

Selections)

Figure 2-13: Reasons for Businesses Not Attempting to Raise External Financing (Multiple 

Selections), National Revenue Breakouts

80
Figure 2-14: Percentage Indicating “Yes” to “Is the current business financing environment 

restricting...”

Figure 2-15: Percentage Indicating “Yes” to “Is the current business financing environment 

restricting...,” National Revenue Breakouts

81
Figure 2-16: Is it Difficult or Easy to Raise New External Financing?

Figure 2-17: Is it Difficult or Easy to Raise New External Financing? National Revenue Breakouts

82
Figure 2-18: Strength of Demand for Financing

(among those indicating demand)

Scale 1-4: slight, moderate, high, extremely high need

Figure 2-19: Strength of Demand for Financing, National Revenue Breakouts

(among those indicating demand) 

Scale 1-4: slight, moderate, high, extremely high need

83
Figure 2-20: Demand for Financing by Purpose and Size 

(% with any need reported)

Figure 2-21: Demand for Financing by Purpose and Size, National Revenue Breakouts

(% with any need reported)

84
2.2.3 ACCESSIBILITY/SUCCESS RATE

In terms of types of financing secured in the prior three months, survey respondents in the nation and 

Appalachia were most successful in securing trade credit, personal credit cards, business credit cards, and 

personal loans. Survey respondents were less successful in securing business loans from banks than 

obtaining credit cards or trade credit. For example, about 45 percent of the survey respondents in 

Appalachia and the nation who sought business bank loans secured them compared to 62 and 58 percent 

of the respondents in the nation and Appalachia, respectively, that secured personal credit cards. Much 

lower rates of success occurred in securing equity financing for businesses; for example, only 5 percent of 

the businesses who sought angel capital in Appalachia succeeded in acquiring it. The largest difference in 

responses from the nation and Appalachia occurred in securing financing from friends and family, with 

much lower rates of securing this capital in Appalachia, which is not surprising in a region experiencing 

higher rates of economic distress than the nation as a whole (see Figure 2-22). 

For the second smallest business revenue category ($500,000 to $1 million), respondents in Appalachia 

were strikingly less successful than their counterparts in the nation in raising debt or equity financing. For 

example, 35 percent and 17 percent of these businesses in the nation and Appalachia, respectively, 

secured business loans from banks. Likewise, 54 percent and 14 percent of the businesses in this revenue 

category in the nation and Appalachia, respectively, secured business credit card financing. However, 

none of the Appalachian respondents in this category secured angel investments, while 12 percent of the 

national respondents did so (see Figures 2-23 and 2-24). 

Another way of gauging the degree of success in securing financing is the extent to which business 

owners feel compelled to transfer their personal assets to their businesses. In the nation and Appalachia, 

about 42 percent and 45 percent of the respondents, respectively, transferred their personal assets to their 

businesses within the last month. Within Appalachia, about 55 percent of the smallest businesses with 

revenues less than $500,000 followed closely by businesses with revenues between $500,000 and $1 

million transferred assets to the businesses. A lower percentage of owners of small businesses in these 

two revenue categories from the national sample transferred assets to their businesses (see Figures 2-25 

and 2-26). 

This higher rate of asset transfers by the smaller businesses is most likely due to less success than their 

larger counterparts to secure the amount of credit needed by their business (see Figure 2-26). In addition, 

the smallest businesses owners in Appalachia were much more likely to transfer their savings and use of 

their personal credit cards to fund their businesses than their counterparts in Appalachia or the nation (see 

Figure 2-27). For example, 81 percent and 68 percent of small business owners with revenues between 

$500,000 and $1 million in Appalachia and the nation, respectively, transferred their personal savings and 

investments to their small businesses. Likewise, 52 percent and 44 percent of the owners of small 

businesses with revenues under $500,000 in Appalachia and the nation, respectively, used their personal 

credit cards for financing their businesses (see Figures 2-27 and 2-28). 

85
Figure 2-22: Financing Success Rates by Type and Business Size for Prior Three Months

Figure 2-23: Financing Success Rates by Type and Business Size for Prior Three Months, National 

Revenue Breakouts

86
Figure 2-24: Financing Success Rates by Type and Business Size for Prior Three Months, 

Appalachian Revenue Breakouts

87
Figure 2-25: Percentage of Owners Who Transferred Personal Assets to Business over Prior Six 

Months

Figure 2-26: Percentage of Owners Who Transferred Personal Assets to Business over Prior Six 

Months, National Revenue Breakouts

88
Figure 2-27: Type of Personal Assets Transferred to Business during Prior Six Months (Multiple 

Selections)

Figure 2-28: Type of Personal Assets Transferred to Business during Prior Six Months, (Multiple 

Selections), National Revenue Breakouts

89
2.2.4 EXPECTED FUTURE DEMAND

During the next six months, the percentage of business respondents in the United States who plan on 

seeking financing was almost the same (31 percent) as the percentage who sought financing in the last 

three months (33 percent). In contrast, the percentages diverge somewhat in Appalachia with 32 percent 

seeking financing during the first quarter of 2012 and 28 percent planning on seeking financing during the 

next six months. The smaller businesses in Appalachia with revenues between $500,000 and $1 million 

were the least likely to plan to seek financing in the next six months (23 percent) and were even less 

likely than businesses with revenues under $500,000 (see Figure 2-29). In addition, the businesses in the 

two smallest revenue categories in Appalachia were significantly less likely to seek financing than their 

counterparts in the nation (see Figures 2-29 and 2-30). 

On a scale of 1 to 4, both businesses in the nation and Appalachia reported a moderate level of interest in 

seeking financing in the next six months (see Figure 2-31). There were not significant differences by 

revenue category of businesses.

Another interesting survey finding is that 68 and 64 percent of the respondents seeking financing in the 

nation and Appalachia, respectively, believed they would secure bank business loans (see Figure 2-32). 

Compared to any other category of financing, the highest percentage of respondents seeking financing 

thought that they would approach banks. This is a stark contrast to success rates for securing financing 

from banks, which was reported to be 45 percent.. Perhaps respondents believe that obtaining credit from 

banks will become easier, which may be based either on perceptions or actual experience from their peers. 

Similarly, a much higher percentage of respondents thought they would approach equity sources of 

financing than those that reported successfully securing equity funding (see Figure 2-32). 

In terms of adding workers, the number of workers projected to be added is modest. About one third of 

the businesses in the nation and Appalachia plans to add no additional employees, and an additional third 

plans to add one to two employees. About one fifth plan to hire three to five employees, and ten percent

of firms to hire 6 to10 employees. A pessimistic interpretation is that two thirds of respondents plan to 

hire between zero and two employees; an optimistic interpretation is that one third of firms plan to hire 

three to ten employees. It must be added that almost half of the Appalachian smallest businesses with 

revenues under $500,000 do not plan to add employees and these businesses constitute about 40 percent 

of the respondents in Appalachia (see Figure 2-33). The bottom line is that a sizable percentage of 

respondents in Appalachia plan to add a modest number of employees, but another sizable percentage will 

not add employees. 

90
Figure 2-29: Percentage of Businesses that Are Planning to Raise Financing in the Next Six 

Months

Figure 2-30: Percentage of Businesses that Are Planning to Raise Financing in the Next Six 

Months, National Revenue Breakouts

91
Figure 2-31: Strength of Expected Demand for New External Financing in the Next Six Months 

(Scale 1-4: Slight, Moderate, High, Extremely High)

Figure 2-32: Likely Sources of Financing

92
Figure 2-33: Number of Employees Planned to Be Hired in the Next Six Months

93
2.2.5 SUMMARY OF PEPPERDINE RESULTS

Overall, total business demand for loans appears to be similar in Appalachia and the nation. However, 

small businesses in the two smallest revenue categories in Appalachia have lower levels of demand for 

loans than do firms in the nation as a whole. While the smallest businesses in Appalachia indicated they 

sought financing at lower rates then businesses in the national as a whole, the smallest businesses in 

Appalachia and the nation reported similar perceptions regarding barriers to financing. In addition, the 

smallest businesses in Appalachia experienced much less success than those in the nation in obtaining 

financing. 

The box below summarizes the findings of the Pepperdine survey, comparing the national and 

Appalachian samples, and focuses on the two smallest revenue categories of businesses:

Summary of Results: Pepperdine Survey

• Percentage of respondents who attempted to raise financing – fewer Appalachian firms

• Firms fearing rejection of loan requests – hard to interpret 

• Is current financing environment restricting – national and Appalachian perspectives 

similar

• Difficult or easy to raise debt/equity financing – national and Appalachian perspectives 

similar

• Success rate of securing debt and equity financing – disadvantage Appalachia, 

particularly businesses in $500,000 to $1 million revenue category. 

• Transferring personal assets to business – disadvantage Appalachia

• Percentage of businesses planning to raise financing – fewer Appalachian firms

• Adding employees in next 6 months – national and Appalachian perspectives similar

94
2.3 KAUFFMAN FIRM SURVEY

2.3.1 DESCRIPTION OF SAMPLE SIZE

Kauffman Firm Survey (KFS) data are a comprehensive longitudinal study of small businesses in the 

United States. The sample for the survey was created by a random sampling approach from Dun & 

Bradstreet’s (D&B) database of new firms established in 2004. From approximately 256,000 such firms, 

Kauffman researchers selected a random sample of 32,469 firms for the baseline survey, conducted 

between July 2005 and July 2006. In order to proportionally represent high-technology industry and 

woman-owned businesses in the sample, KFS utilized a stratified sampling approach. 

KFS researchers completed detailed interviews for 4,928 firms and conducted the first follow-up for those 

firms between June 2006 and January 2007. The subsequent interviews were conducted annually for the 

firms that survived. The survey focuses on the business characteristics, strategy and innovation, 

demographics of the owners, and business finances. The business finance section, the main focus of the 

questionnaire, includes topics on debt financing and equity investments.8

The survey samples firms in 2007, 2008, and 2009. More than 200 small businesses based in Appalachia 

responded in 2007 and 2008 and 178 responded in 2009. Like Pepperdine’s respondents, the Kauffman 

sample tends to be over-represented in advantaged counties but perhaps not to the same extent as 

Pepperdine’s. For the 2009 survey, Northern Appalachia has a higher percentage of respondents, while 

businesses in Central Appalachia are underrepresented. Interestingly, the survey had an overrepresentation in large metropolitan areas and an under-representation in small metropolitan areas. The 

disparity between the portion of respondents and businesses in rural counties is smaller in the Kauffman 

survey than in the Pepperdine survey, but like Pepperdine, attainment counties are over-represented. 

When considering the Appalachian portion of states, Georgia, New York, and Ohio businesses are over 

represented, while West Virginia, North Carolina, and Alabama businesses are underrepresented. 

Overall, Kauffman survey results likely understate the difficulties small businesses in Appalachia 

encounter, but perhaps not to the same extent as do the Pepperdine data (see Table 2-2 and Figures 2-34 

through 2-37). 

The percentage of African American owned firms in the sample was similar for Appalachia and the 

nation, but a lower percentage of Hispanic owned firms was in the Appalachian sample. A slightly higher 

percentage of Appalachian firms responding to the survey were women-owned. The median number of 

employees for respondent firms was small, at 3.5 to 4 employees, in the United States and Appalachian 

samples, respectively (see Tables 2-3 through 2-5 and Figures 2-38 through 2-42). 

8 For more information see “Mulcahy, Timothy M., Robb, Alicia and DesRoches, David, Kauffman Firm Survey: 

Fifth Follow-Up (August 15, 2011). Available at SSRN: 

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1934895

95

 
Figure 2-34: Ratio of Kauffmann Survey Respondents to Total Non-Farm Businesses by Region, 

2009

Figure 2-35: Ratio of Kauffmann Survey Respondents to Total Non-Farm Businesses by County 

Type, 2009

Figure 2-36: Ratio of Kauffmann Survey Respondents to Total Non-Farm Businesses by 

Economic Status, 2009

33.1%

8.1%

4.7%

15.1%

39.0%

29.9%

7.8%

7.0%

19.4%

35.9%

0% 10% 20% 30% 40% 50%

Northern Appalachia

North Central Appalachia

Central Appalachia

South Central Appalachia

Southern Appalachia

% of Respondents

% of Total Non-Farm Businesses

Per capita personal income index

32.0%

33.1%

5.2%

19.8%

9.9%

25.6%

38.3%

6.6%

18.3%

11.2%

0% 10% 20% 30% 40% 50%

Large Metro (1 million + people)

Small Metro (< 1 million people)

Nonmetro, Adjacent to Large Metro

Nonmetro, Adjacent to Small Metro

Rural (Nonmetro, Nonadjacent)

% of Respondents

% of Total Non-Farm Businesses

Per capita personal income index

4.1%

7.6%

58.7%

14.5%

15.1%

5.7%

8.8%

57.2%

18.4%

9.9%

0% 10% 20% 30% 40% 50% 60% 70%

Distressed

At-Risk

Transitional

Competitive

Attainment

% of Respondents

% of Total Non-Farm Businesses

Per capita personal income index

96
Figure 2-37: Ratio of Kauffman Survey Respondents to Total Non-Farm Businesses by 

Appalachian States, 2009

9.3%

22.1%

3.5%

1.2%

2.3%

7.0%

4.1%

10.5%

20.3%

5.2%

10.5%

1.7%

2.3%

12.3%

15.3%

4.8%

0.9%

3.9%

4.0%

7.1%

7.0%

20.5%

4.4%

11.4%

2.7%

5.8%

0% 10% 20% 30%

Appalachian Alabama

Appalachian Georgia

Appalachian Kentucky

Appalachian Maryland

Appalachian Mississippi

Appalachian New York

Appalachian North Carolina

Appalachian Ohio

Appalachian Pennsylvania

Appalachian South Carolina

Appalachian Tennessee

Appalachian Virginia

West Virginia (entire state)

% of Respondents

% of Total Non-Farm Businesses

97
Table 2-2: Number of Respondents for Kauffman Survey and Total Number of Non-Farm 

Businesses in Appalachia

Number % Number % Number % Number % 

Appalachian Region 206 100.0% 198 100.0% 172 100.0% 1,896,006 100.0% 0.0%

Subregions

Northern Appalachia 70 34.0% 67 33.8% 57 33.1% 566,597 29.9% 3.3%

North Central Appalachia 15 7.3% 15 7.6% 14 8.1% 146,998 7.8% 0.4%

Central Appalachia 9 4.4% 9 4.5% 8 4.7% 133,545 7.0% -2.4%

South Central Appalachia 30 14.6% 29 14.6% 26 15.1% 367,703 19.4% -4.3%

Southern Appalachia 82 39.8% 78 39.4% 67 39.0% 681,163 35.9% 3.0%

County Types

Large Metro (1 million + people) 68 33.0% 64 32.3% 55 32.0% 485,930 25.6% 6.3%

Small Metro (< 1 million people) 70 34.0% 66 33.3% 57 33.1% 725,240 38.3% -5.1%

Nonmetro, Adjacent to Large Metro 10 4.9% 10 5.1% 9 5.2% 124,355 6.6% -1.3%

Nonmetro, Adjacent to Small Metro 37 18.0% 37 18.7% 34 19.8% 347,838 18.3% 1.4%

Rural (Nonmetro, Nonadjacent) 21 10.2% 21 10.6% 17 9.9% 212,643 11.2% -1.3%

Economic Status

Distressed 9 4.4% 9 4.5% 7 4.1% 108,061 5.7% -1.6%

At-Risk 16 7.8% 16 8.1% 13 7.6% 166,976 8.8% -1.2%

Transitional 118 57.3% 112 56.6% 101 58.7% 1,084,519 57.2% 1.5%

Competitive 32 15.5% 32 16.2% 25 14.5% 349,302 18.4% -3.9%

Attainment 31 15.0% 29 14.6% 26 15.1% 187,148 9.9% 5.2%

Appalachian States

Appalachian Alabama 21 10.2% 20 10.1% 16 9.3% 233,034 12.3% -3.0%

Appalachian Georgia 44 21.4% 41 20.7% 38 22.1% 290,899 15.3% 6.8%

AppalachianKentucky 7 3.4% 7 3.5% 6 3.5% 90,314 4.8% -1.3%

Appalachian Maryland 2 1.0% 2 1.0% 2 1.2% 16,748 0.9% 0.3%

Appalachian Mississippi 7 3.4% 7 3.5% 4 2.3% 74,511 3.9% -1.6%

Appalachian New York 14 6.8% 14 7.1% 12 7.0% 75,529 4.0% 3.0%

Appalachian North Carolina 9 4.4% 9 4.5% 7 4.1% 134,841 7.1% -3.0%

Appalachian Ohio 20 9.7% 20 10.1% 18 10.5% 132,051 7.0% 3.5%

Appalachian Pennsylvania 45 21.8% 42 21.2% 35 20.3% 389,103 20.5% -0.2%

Appalachian South Carolina 10 4.9% 10 5.1% 9 5.2% 82,719 4.4% 0.9%

Appalachian Tennessee 20 9.7% 19 9.6% 18 10.5% 215,470 11.4% -0.9%

Appalachian Virginia 3 1.5% 3 1.5% 3 1.7% 51,490 2.7% -1.0%

West Virginia (entire state) 4 1.9% 4 2.0% 4 2.3% 109,297 5.8% -3.4%

Percentage 

Point 

Differences 

for 2009

Respondents, 

2007

Respondents, 

2008 Respondents, 2009

Number of Total Nonfarm Businesses, 

2009

98
Figure 2-38: Percent of African American respondents

Figure 2-39: Percent of Hispanic respondents

Figure 2-40: Percent of Asian respondents

7.2%

5.6%

7.8%

9.3%

7.7%

9.6%

0% 2% 4% 6% 8% 10% 12%

US

Appalachia

2007 2008 2009

4.8%

0.9%

5.1%

0.4%

4.9%

0.4%

0% 1% 2% 3% 4% 5% 6%

US

Appalachia

2007 2008 2009

4.3%

1.8%

4.4%

1.9%

4.5%

2.1%

0% 1% 2% 3% 4% 5%

US

Appalachia

2007 2008 2009

99
Figure 2-41: Percent of female respondents

Figure 2-42: Median number of employee in Kauffman Survey in Appalachia and US

Table 2-3: Percent of respondents in Kauffman Survey by race and ethnicity in Appalachia and US

30.4%

35.0%

31.4%

35.4%

30.0%

33.8%

0% 10% 20% 30% 40%

US

Appalachia

2007 2008 2009

3.5

3

3.5

4

3.5

4

0% 100% 200% 300% 400% 500%

US

Appalachia

2007 2008 2009

2007 2008 2009 2007 2008 2009 2007 2008 2009

US 7.2% 7.8% 7.7% 4.8% 5.1% 4.9% 4.3% 4.4% 4.5%

Appalachia 5.6% 9.3% 9.6% 0.9% 0.4% 0.4% 1.8% 1.9% 2.1%

Sample Size

2007 2008 2009

US 2,669 2,585 2,380

Appalachia 201 187 172

African American Hispanic Asian

100
Table 2-4: Percent of female respondents in Kauffman Survey in Appalachia and US

Table 2-5: Median number of employee in Kauffman Survey in Appalachia and US

 

2.3.2 DEMAND FOR FINANCING

A small percentage of firms in the sample set in both the United States and Appalachia applied for loans. 

The percentage of firms applying for credit increased slightly in the United States from 11.8 percent in 

2007 to 12.2 percent in 2009 whereas in Appalachia the trend was reversed. About 11 percent of firms in 

the sample in Appalachia applied for credit in 2007 and 9.8 percent applied in 2009 (see Table 2-6 and 

Figure 2-43). 

An increasing percentage of businesses in Appalachia and the United States desired credit but did not 

apply because they feared rejection. In the United States, the percentage of businesses in this category 

increased from 15.7 percent in 2007 to 21.1 percent in 2009; In Appalachia the figures were 18.1 percent 

in 2007 and 23.1 percent in 2009 (see Table 2-6 and Figure 2-44). 

As shown in Table 2-7 and Figure 2-46, the percentage of firms denied credit was significantly higher in 

Appalachia (22.9 percent in 2009) compared to the United States (8.7 percent in 2009). Due to low 

sample sizes, it is not possible to offer a statistically significant conclusion as to reasons for denial, but it 

appears that insufficient collateral, business and personal credit history were larger factors for loan denial 

in Appalachia than in the United States. 

Just as in the United States, very small percentages of firms in Appalachia received equity investments 

from any sources. As in the United States, the most frequent sources of equity investments were from 

spouses, parents, or other individuals, which was the source of equity investments for between 1 to 2 

percent of the businesses in Appalachia. The median amount of equity investments have low sample sizes 

in Appalachia but it appears that the median amounts of equity investments from spouses and parents in 

Appalachia and the United States are similar (see Tables 2-9 and 2-10).

2007 2008 2009

US 30.4% 31.4% 30.0%

Appalachia 35.0% 35.4% 33.8%

Sample Size

2007 2008 2009

US 2,659 2,571 2,372

Appalachia 200 186 172

2007 2008 2009

US 3.5 3.5 3.5

Appalachia 3 4 4

Sample Size

2007 2008 2009

US 1,255 1,253 1,074

Appalachia 107 88 77

101
Figure 2-43: Percent of respondents applying for loans in Appalachia and US

Figure 2-44: Percent of respondents desiring credit, but not applied because feared rejection in 

Appalachia and US

Figure 2-45: Percent of respondents applied for loans and always approved in Appalachia and US

11.8%

11.4%

12.4%

9.6%

12.2%

9.8%

0% 5% 10% 15%

US

Appalachia

2007 2008 2009

15.7%

18.1%

18.9%

19.6%

21.1%

23.1%

0% 5% 10% 15% 20% 25%

US

Appalachia

2007 2008 2009

67.3%

71.3%

76.0%

66.7%

65.5%

60.8%

0% 20% 40% 60% 80%

US

Appalachia

2007 2008 2009

102
Figure 2-46: Percent of respondents applied for loans and always denied in Appalachia and US

Table 2-6: Percent of respondents applying and not applying loan in Appalachia and US

Table 2-7: Percent of respondents approved and denied for loans

5.8%

12.6%

7.4%

15.5%

8.7%

22.9%

0% 5% 10% 15% 20% 25%

US

Appalachia

2007 2008 2009

2007 2008 2009 2007 2008 2009

US 11.8% 12.4% 12.2% 15.7% 18.9% 21.1%

Appalachia 11.4% 9.6% 9.8% 18.1% 19.6% 23.1%

Sample Size

2007 2008 2009

US 2,669 2,605 2,408

Appalachia 203 186 172

Applied for new loans

Needed credit, but not applied 

because feared rejection

2007 2008 2009 2007 2008 2009 2007 2008 2009

US 67.3% 76.0% 65.5% 26.9% 16.7% 25.7% 5.8% 7.4% 8.7%

Appalachia 71.3% 66.7% 60.8% 16.1% 17.8% 16.3% 12.6% 15.5% 22.9%

Sample Size

2007 2008 2009

US 338 342 310

Appalachia 24 19 18

Always approved

Sometimes approved and 

sometimes denied Always denied

103
Table 2-8: Percent of loan denied respondents by denied reason in Appalachia and US

*Note: Out of the denied loans sample

Table 2-9: Percent of firms receiving equity investment from various sources in Appalachia and 

US

*Average sample size for US

Table 2-10: Median equity amount from spouses, parents, and angels in Appalachia and US

2007 2008 2009 2007 2008 2009

Insufficient collateral 43.1% 34.3% 41.0% 68.2% 100.0% 50.5%

Loan requested was too large 30.9% 26.3% 17.8% 33.2% 69.4% 15.1%

Inadequate documentation 5.0% 15.4% 5.4% 0.0% 37.7% 3.5%

Business credit history 25.6% 33.0% 34.8% 41.8% 28.2% 67.7%

Personal credit history 45.8% 47.7% 44.2% 74.5% 58.8% 78.3%

Not being in business long enough 31.2% 12.5% 13.7% 17.6% 3.5% 0.0%

Others 8.1% 18.9% 3.6% 0.0% 0.0% 0.0%

Sample Size

2007 2008 2009

US 89 108 103

Appalachia 7 4 6

Loan denied reason*

US Appalachia

2007 2008 2009 Average 2007 2008 2009 Average

Spouses of owners 0.72% 1.11% 1.44% 1.08% 0.55% 1.55% 2.02% 1.35%

Parents of owners 2.18% 1.70% 1.43% 1.78% 4.20% 3.42% 1.46% 3.06%

Individuals, not spouses of owners 1.55% 1.10% 1.10% 1.26% 0.24% 1.64% 1.84% 1.22%

Other companies 1.05% 1.13% 0.82% 1.00% 0.13% 1.55% 0.00% 0.55%

Government agencies 0.20% 0.13% 0.13% 0.15% 0.49% 0.11% 0.00% 0.21%

Venture capitalists 0.20% 0.17% 0.35% 0.23% 0.00% 0.00% 0.49% 0.16%

Other sources 0.09% 0.49% 0.33% 0.30% 0.00% 0.00% 0.00% 0.00%

Sample Size

2007 2008 2009

US* 1,870 1,806 1,700

Appalachia 123 115 113

Obtained equity financing from

US Appalachia

2007 2008 2009 2007 2008 2009

Spouses $20,000 $5,000 $10,000 $12,501 $1,000 $5,200 

Parents $20,000 $20,000 $18,000 $20,000 $52,500 $100,000 

Angels $87,500 $300,000 $100,000 $2,050,000 $2,500,000 $440,000 

Sample Size

2007 2008 2009 2007 2008 2009

Spouses 13 16 21 2 1 2

Parents 41 31 21 4 4 2

Angels 38 27 29 2 4 4

Median equity 

amount from

US Appalachia

US Appalachia

104
2.4 COMPARING PEPPERDINE AND KAUFFMAN SURVEYS

The Kauffman survey reports a considerably lower percentage of businesses seeking financing than does 

the Pepperdine survey, but Pepperdine’s data include all types of financing sought, not just loans, and 

Pepperdine is reporting data for 2012 which most likely reflects increases in demand for financing due to 

more favorable economic conditions. Yet, even at higher rates of demand, Pepperdine’s data show only 

about one third of small and medium size businesses seek financing. The approval rates for receiving 

loans are difficult to compare across the two surveys due to differences in the phrasing of survey 

questions. Businesses that did not apply for financing because of a fear of rejection were of similar 

magnitude in the two surveys (between 13 and 20 percent). In both surveys, only a small percentage of 

the respondents used equity financing.

2.5 CONCLUSION

During the last five years, demand for financing was tepid in both the nation and Appalachia. Differences 

in demand for credit do not appear to account for sizable differences in access rates in the nation and 

Appalachia. The Pepperdine survey reported a smaller percentage of businesses with revenues between 

$500,000 and $1 million in Appalachia than the nation seeking financing but differences in other revenue 

categories were not as pronounced. The Kauffman survey reported low levels of demand in the nation and 

Appalachia and also suggested that demand did not vary between Appalachia and the nation. 

Considering the results of both Pepperdine and Kauffman, it is likely that demand for financing is 

somewhat less in Appalachia than the nation, particularly among the smallest businesses. Yet, the 

differences in successfully obtaining financing between businesses in Appalachia and the nation are 

starker than the differences in demand would suggest. The Pepperdine survey reported that the smaller 

businesses in Appalachia were less likely to secure financing and were more likely to transfer their 

personal assets to their businesses than were their counterparts in the nation as a whole.

Small businesses were considerably more successful in acquiring credit card loans than bank loans 

according to the survey research. This raises concerns since credit card loans are more expensive and are 

of shorter term than small business bank loans.

105
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106
CHAPTER 3 FINANCIAL INDUSTRY IN APPALACHIA

3.1 SUMMARY

Access to banks is integral to access to credit. As documented above, a higher level of bank branches in a 

county is correlated with a higher number of loans in Appalachia. This analysis will document the 

distribution of banks by asset size in Appalachia with comparisons made to the nation. The distribution of 

branches is also examined.

• The distribution of banks by asset size is similar in the nation and the Appalachia. In 2007 and 

2010, about two thirds of the banks were small banks with assets of less than $250 million in both 

Appalachia and the United States. The percentage of large banks with assets above $1 billion is 

also similar in the United States and Appalachia.

• The percentage of mid-size banks with assets between $250 million and $1 billion is modestly 

higher in Appalachia than the nation. Since this was the greatest difference between Appalachia 

and the nation, the analysis within Appalachia focused on mid-size banks. The largest percentage 

of mid-size banks was in the advantaged subregions and counties (Northern Appalachia, 

metropolitan counties, and competitive counties) and the lowest percentage was in disadvantaged 

counties (Central Appalachia, rural, and economically distressed counties).

• A statistically significant correlation existed between the percentage of mid-size banks and 

lending levels on a county level. A higher percentage of mid-size banks was associated with 

higher lending levels. NCRC’s previous study for ARC documented a significant role for midsize banks in small business lending and was able to present robust results because mid-size 

banks were required to report small business lending in 2003 (the regulatory agencies deleted the 

mandatory reporting requirement after 2003).9 It is likely that there is a stronger correlation 

between the presence of mid-size banks and lending in advantaged counties than is recorded by 

this study. 

• Despite the financial crisis, the number of bank branches in Appalachia increased from 8,580 in 

2007 to 8,677 in 2010. Interestingly, branches per capita did not reveal striking disparities by 

category of county in Appalachia, yet current trends, if continued, are likely to result in 

disparities. The number of branches in Appalachia of banks not headquartered in Appalachia 

increased by 19.5 percent from 2007 through 2010 while the number of branches of banks 

headquartered in Appalachia decreased by 7.2 percent during the time period. Banks not 

headquartered in Appalachia were disproportionately opening branches in advantaged counties in 

Appalachia, most likely due to more favorable economic and demographic opportunities and 

conditions. 

• Lending on a per branch basis remained at a higher level in the nation than in Appalachia; in 2010 

banks reported 41 small business loans per branch in the nation, while Appalachian banks 

provided only 25 loans per branch.. Within Appalachia, the largest differences occurred in 

9 The previous NCRC study for ARC can be accessed via 

http://www.arc.gov/research/researchreportdetails.asp?REPORT_ID=8

107

 
Southern Appalachia compared to Central Appalachia, large metropolitan counties compared to 

rural counties, and attainment counties compared to distressed counties. 

• Small business loan-to-deposit ratios in Appalachia declined from 5.4 percent in 2007 to 2.7 

percent in 2010, and from 4.5 percent to 2 percent for the nation during the same time period. 

Within the subregions, the small business loan-to-deposit ratio was the lowest in Central 

Appalachia (1.6 percent) in 2010. The gap in the small business loan-to-deposit ratio between

distressed counties and attainment counties was substantial, reporting ratios of 1.3 percent and 3.7 

percent, respectively, in 2010.

• This chapter also considers credit unions lending. Credit union lending patterns cannot be fully 

analyzed since credit unions are not required to publicly report small business lending. The 

number of credit unions in Appalachia shrank from 2007 to 2010, and Appalachia has a lower 

proportion than does the nation of the largest credit unions with assets over $100 million. Credit 

unions could represent an untapped resource for Appalachia, particularly in Central Appalachia, 

which had a higher percentage of credit unions above $100 million than do other subregions. 

If current trends continue, disparities in branching within Appalachia may appear since banks not 

headquartered in Appalachia are increasing their branches disproportionately in advantaged counties in 

Appalachia while banks headquartered in Appalachia are decreasing their branches. In addition, mid-size 

banks tend to be located disproportionately in advantaged parts of Appalachia. Finally, credit unions are 

likely an untapped resource for addressing disparities in small business lending.

3.2 DISTRIBUTION OF BANKS BY ASSET SIZE

The financial crisis thinned the ranks of banks in the nation as a whole, and in Appalachia from between 9 

to 10 percent. The number of banks in the nation declined from 8,544 in 2007 to 7,667 in 2010. Likewise, 

the number of banks in Appalachia decreased from 732 in 2007 to 664 in 2010. 

The distribution of banks by asset size is remarkably similar in the nation and Appalachia, with the great 

majority of banks having assets of less than $250 million, which are considered small banks by regulatory 

agencies. In 2007 and 2010, about two thirds of the banks in both the nation and in Appalachia were small 

banks with assets of less than $250 million. On the other end of the scale, the percentage of large banks 

with assets over $1 billion is similar in Appalachia and the nation (see Tables 3-1 and 3-2).

A moderately higher percentage of mid-size banks with assets of between $250 million and $1 billion in 

assets were located in Appalachia than the nation in 2007 and 2010. For example, in 2010, 28.9 percent 

and 24.7 percent, respectively, of the banks in Appalachia and the nation, were mid-size banks.

When considering subregions, Northern Appalachia had the highest percentage of mid-size banks (45.2 

percent in 2010 and 36.8 percent in 2007) while Central Appalachia (19.1 percent in 2010 and 18.6 

percent in 2007) and North Central Appalachia (16.8 percent in 2010 and 10.7 percent in 2007) had the 

lowest percentage of mid-size banks (see Figure 3-1).

Metropolitan counties had the highest percentage of mid-size banks while rural counties had the lowest 

percentage (see Figure 3-2). When considering the economic status of counties, distressed counties had 

108
the highest percentage of small banks (87 percent) and the lowest percent of mid-size banks (12 percent) 

during both years (see Figure 3-3).

Generally speaking, the descriptive statistical analysis above suggests that counties with the highest 

percentage of mid-size and the lowest percentage of small banks had the highest ratio of loans per small 

businesses. Correlation analysis confirms this relationship. In the tables below, the correlations among the 

percentage of mid-size banks, non-credit card, and credit card small business lenders are positive and 

statistically significant (see Tables 3-3 through 3-5). The correlation coefficients are small, but the 

coefficients likely understate the extent of the correlation among mid-size banks and small business 

lending. Mid-size banks with assets between $250 million and $1 billion are not required to report small 

business lending activity, but a number of them voluntarily report this information. Hence, it is not 

surprising that the correlation coefficients are small, but to find a statistically significant relationship with 

voluntary data reporting suggests that the relationship is actually stronger than indicated. This finding is 

consistent with NCRC’s previous study for ARC that revealed a significant role for mid-size banks in 

small business lending (NCRC had access to complete small business lending activity for mid-size banks 

because these banks were previously required by bank regulators to report the small business loan data in 

earlier years).10

Figure 3-1: Percentage of Mid-Size Banks by Region* 

*Mid-size banks have assets ranging from $250 million to $1 billion.

10 The previous NCRC study for ARC can be accessed via 

http://www.arc.gov/research/researchreportdetails.asp?REPORT_ID=8

22.3%

26.2%

36.8%

10.7%

18.6%

30.6%

27.3%

24.7%

28.9%

45.2%

16.8%

19.1%

35.1%

24.8%

0% 10% 20% 30% 40% 50%

United States

Appalachian Region

Northern Appalachia

North Central

Appalachia

Central Appalachia

South Central

Appalachia

Southern Appalachia

2007 2010

109

 
Figure 3-2: Percentage of Mid-Size Banks by County Type*

Figure 3-3: Percentage of Mid-Size Banks by Economic Status* 

*Mid-size banks have assets ranging from $250 million to $1 billion.

33.3%

31.0%

22.4%

22.1%

19.7%

31.5%

34.5%

24.2%

26.8%

23.1%

0% 10% 20% 30% 40% 50%

Large Metro (1 million + people)

Small Metro (< 1 million people)

Nonmetro, Adjacent to Large Metro

Nonmetro, Adjacent to Small Metro

Rural (Nonmetro, Nonadjacent)

2007 2010

12.5%

19.4%

27.5%

43.0%

28.6%

12.8%

23.4%

31.5%

44.3%

32.0%

0% 10% 20% 30% 40% 50%

Distressed

At-Risk

Transitional

Competitive

Attainment

2007 2010

110
Map 3-1: Percentage of Mid-Size Banks in Appalachia, 2007

111
Map 3-2: Percentage of Mid-Size Banks in Appalachia, 2010

112
Figure 3-4: Percentage of Mid-Size Banks by State* 

*Mid-size banks have assets ranging from $250 million to $1 billion.

19.4%

20.0%

29.3%

32.0%

19.4%

16.7%

35.1%

33.3%

23.7%

22.2%

29.1%

37.5%

32.1%

26.1%

19.2%

14.7%

36.6%

38.1%

37.6%

43.5%

26.6%

29.8%

40.3%

34.6%

14.7%

21.5%

24.1%

24.6%

22.1%

19.7%

17.1%

39.1%

0.0%

28.6%

20.0%

35.8%

53.8%

36.0%

25.0%

21.8%

20.6%

43.3%

47.1%

41.0%

40.9%

33.5%

37.4%

38.3%

25.0%

21.5%

0% 10% 20% 30% 40% 50% 60%

Alabama

Appalachian Alabama

Georgia

Appalachian Georgia

Kentucky

Appalachian Kentucky

Maryland

Appalachian Maryland

Mississippi

Appalachian Mississippi

New York

Appalachian New York

North Carolina

Appalachian North Carolina

Ohio

Appalachian Ohio

Pennsylvania

Appalachian Pennsylvania

South Carolina

Appalachian South Carolina

Tennessee

Appalachian Tennessee

Virginia

Appalachian Virginia

West Virginia (entire state)

2007 2010

113
Table 3-1: Percentage of Banks by Asset Size in Appalachia, 2007

Number Percentage Number Percentage Number Percentage Number Percentage Number Percentage

United States 5,966 69.8% 1,904 22.3% 555 6.5% 119 1.4% 8,544 100%

Appalachian Region 484 66.1% 192 26.2% 49 6.7% 7 1.0% 732 100%

Subregions

Northern Appalachia 83 50.9% 60 36.8% 18 11.0% 2 1.2% 163 100%

North Central Appalachia 85 82.5% 11 10.7% 7 6.8% 0 0.0% 103 100%

Central Appalachia 90 79.6% 21 18.6% 2 1.8% 0 0.0% 113 100%

South Central Appalachia 65 58.6% 34 30.6% 11 9.9% 1 0.9% 111 100%

Southern Appalachia 161 66.5% 66 27.3% 11 4.5% 4 1.7% 242 100%

County Types

Large Metros (pop. 1 million +) 70 56.9% 41 33.3% 8 6.5% 4 3.3% 123 100%

Small Metros (pop. <1 million) 130 60.2% 67 31.0% 17 7.9% 2 0.9% 216 100%

Nonmetro, Adjacent to Large Metros 48 71.6% 15 22.4% 4 6.0% 0 0.0% 67 100%

Nonmetro, Adjacent to Small Metros 143 71.9% 44 22.1% 12 6.0% 0 0.0% 199 100%

Rural (nonmetro, not adj. to a metro) 93 73.2% 25 19.7% 8 6.3% 1 0.8% 127 100%

Economic Status

Distressed 83 86.5% 12 12.5% 1 1.0% 0 0.0% 96 100%

At-Risk 78 75.7% 20 19.4% 5 4.9% 0 0.0% 103 100%

Transitional 255 63.8% 110 27.5% 32 8.0% 3 0.8% 400 100%

Competitive 39 45.3% 37 43.0% 6 7.0% 4 4.7% 86 100%

Attainment 23 65.7% 10 28.6% 2 5.7% 0 0.0% 35 100%

Alabama 120 75.0% 31 19.4% 6 3.8% 3 1.9% 160 100%

Appalachian Alabama 69 72.6% 19 20.0% 5 5.3% 2 2.1% 95 100%

Non-Appalachian Alabama 51 81.0% 12 19.0% 1 1.6% 1 1.6% 63 100%

Georgia 233 66.2% 103 29.3% 15 4.3% 1 0.3% 352 100%

Appalachian Georgia 62 63.9% 31 32.0% 4 4.1% 0 0.0% 97 100%

Non-Appalachian Georgia 171 64.5% 72 27.2% 11 4.2% 1 0.4% 265 100%

Kentucky 158 76.7% 40 19.4% 8 3.9% 0 0.0% 206 100%

Appalachian Kentucky 59 81.9% 12 16.7% 1 1.4% 0 0.0% 72 100%

Non-Appalachian Kentucky 99 73.9% 28 20.9% 7 5.2% 0 0.0% 134 100%

Maryland 56 57.7% 34 35.1% 7 7.2% 0 0.0% 97 100%

Appalachian Maryland 0 0.0% 1 33.3% 2 66.7% 0 0.0% 3 100%

Non-Appalachian Maryland 56 59.6% 33 35.1% 5 5.3% 0 0.0% 94 100%

Mississippi 66 68.0% 23 23.7% 7 7.2% 1 1.0% 97 100%

Appalachian Mississippi 18 66.7% 6 22.2% 2 7.4% 1 3.7% 27 100%

Non-Appalachian Mississippi 48 68.6% 17 24.3% 5 7.1% 0 0.0% 70 100%

New York 93 45.8% 59 29.1% 42 20.7% 9 4.4% 203 100%

Appalachian New York 8 50.0% 6 37.5% 2 12.5% 0 0.0% 16 100%

Non-Appalachian New York 85 45.9% 53 28.6% 40 21.6% 9 4.9% 185 100%

North Carolina 56 50.0% 36 32.1% 15 13.4% 5 4.5% 112 100%

Appalachian North Carolina 10 43.5% 6 26.1% 6 26.1% 1 4.3% 23 100%

Non-Appalachian North Carolina 46 51.7% 30 33.7% 9 10.1% 4 4.5% 89 100%

Ohio 189 71.3% 51 19.2% 16 6.0% 9 3.4% 265 100%

Appalachian Ohio 58 77.3% 11 14.7% 6 8.0% 0 0.0% 75 100%

Non-Appalachian Ohio 131 68.9% 40 21.1% 10 5.3% 9 4.7% 190 100%

Pennsylvania 122 49.6% 90 36.6% 29 11.8% 5 2.0% 246 100%

Appalachian Pennsylvania 58 51.3% 43 38.1% 10 8.8% 2 1.8% 113 100%

Non-Appalachian Pennsylvania 64 53.3% 47 39.2% 19 15.8% 3 2.5% 120 100%

South Carolina 52 55.9% 35 37.6% 5 5.4% 1 1.1% 93 100%

Appalachian South Carolina 12 52.2% 10 43.5% 0 0.0% 1 4.3% 23 100%

Non-Appalachian South Carolina 40 60.6% 25 37.9% 5 7.6% 0 0.0% 66 100%

Tennessee 140 69.0% 54 26.6% 8 3.9% 1 0.5% 203 100%

Appalachian Tennessee 63 67.0% 28 29.8% 3 3.2% 0 0.0% 94 100%

Non-Appalachian Tennessee 77 73.3% 26 24.8% 5 4.8% 1 1.0% 105 100%

Virginia 54 45.4% 48 40.3% 12 10.1% 5 4.2% 119 100%

Appalachian Virginia 14 53.8% 9 34.6% 3 11.5% 0 0.0% 26 100%

Non-Appalachian Virginia 40 41.7% 39 40.6% 9 9.4% 5 5.2% 96 100%

West Virginia (entire state) 53 77.9% 10 14.7% 5 7.4% 0 0.0% 68 100%

Less than $250M $250M to $1B $1B to $10B Greater than $10B Total

114
Table 3-2: Percentage of Banks by Asset Size in Appalachia, 2010

Number Percentage Number Percentage Number Percentage Number Percentage Number Percentage

United States 5,101 66.5% 1,893 24.7% 565 7.4% 108 1.4% 7,667 100%

Appalachian Region 423 63.7% 192 28.9% 44 6.6% 5 0.8% 664 100%

Subregions

Northern Appalachia 62 42.5% 66 45.2% 17 11.6% 1 0.7% 146 100%

North Central Appalachia 73 76.8% 16 16.8% 6 6.3% 0 0.0% 95 100%

Central Appalachia 85 77.3% 21 19.1% 4 3.6% 0 0.0% 110 100%

South Central Appalachia 61 55.0% 39 35.1% 10 9.0% 1 0.9% 111 100%

Southern Appalachia 142 70.3% 50 24.8% 7 3.5% 3 1.5% 202 100%

County Types

Large Metros (pop. 1 million +) 54 58.7% 29 31.5% 6 6.5% 3 3.3% 92 100%

Small Metros (pop. <1 million) 116 57.1% 70 34.5% 16 7.9% 1 0.5% 203 100%

Nonmetro, Adjacent to Large Metros 42 67.7% 15 24.2% 5 8.1% 0 0.0% 62 100%

Nonmetro, Adjacent to Small Metros 128 67.4% 51 26.8% 11 5.8% 0 0.0% 190 100%

Rural (nonmetro, not adj. to a metro) 83 70.9% 27 23.1% 6 5.1% 1 0.9% 117 100%

Economic Status

Distressed 82 87.2% 12 12.8% 0 0.0% 0 0.0% 94 100%

At-Risk 68 72.3% 22 23.4% 4 4.3% 0 0.0% 94 100%

Transitional 233 59.7% 123 31.5% 33 8.5% 1 0.3% 390 100%

Competitive 24 39.3% 27 44.3% 6 9.8% 4 6.6% 61 100%

Attainment 16 64.0% 8 32.0% 1 4.0% 0 0.0% 25 100%

Alabama 108 75.0% 31 21.5% 3 2.1% 2 1.4% 144 100%

Appalachian Alabama 62 71.3% 21 24.1% 2 2.3% 2 2.3% 87 100%

Non-Appalachian Alabama 46 83.6% 10 18.2% 1 1.8% 0 0.0% 55 100%

Georgia 189 70.5% 66 24.6% 11 4.1% 2 0.7% 268 100%

Appalachian Georgia 50 73.5% 15 22.1% 3 4.4% 0 0.0% 68 100%

Non-Appalachian Georgia 139 69.5% 51 25.5% 8 4.0% 2 1.0% 200 100%

Kentucky 148 74.7% 39 19.7% 11 5.6% 0 0.0% 198 100%

Appalachian Kentucky 56 80.0% 12 17.1% 2 2.9% 0 0.0% 70 100%

Non-Appalachian Kentucky 92 71.9% 27 21.1% 9 7.0% 0 0.0% 128 100%

Maryland 47 54.0% 34 39.1% 6 6.9% 0 0.0% 87 100%

Appalachian Maryland 0 0.0% 0 0.0% 1 100.0% 0 0.0% 1 100%

Non-Appalachian Maryland 47 54.7% 34 39.5% 5 5.8% 0 0.0% 86 100%

Mississippi 57 62.6% 26 28.6% 7 7.7% 1 1.1% 91 100%

Appalachian Mississippi 17 68.0% 5 20.0% 2 8.0% 1 4.0% 25 100%

Non-Appalachian Mississippi 40 60.6% 21 31.8% 5 7.6% 0 0.0% 66 100%

New York 74 38.3% 69 35.8% 41 21.2% 9 4.7% 193 100%

Appalachian New York 5 38.5% 7 53.8% 1 7.7% 0 0.0% 13 100%

Non-Appalachian New York 69 38.8% 62 34.8% 40 22.5% 9 5.1% 178 100%

North Carolina 45 45.0% 36 36.0% 15 15.0% 4 4.0% 100 100%

Appalachian North Carolina 13 54.2% 6 25.0% 4 16.7% 1 4.2% 24 100%

Non-Appalachian North Carolina 32 42.1% 30 39.5% 11 14.5% 3 3.9% 76 100%

Ohio 168 70.3% 52 21.8% 12 5.0% 7 2.9% 239 100%

Appalachian Ohio 50 73.5% 14 20.6% 4 5.9% 0 0.0% 68 100%

Non-Appalachian Ohio 118 69.0% 38 22.2% 8 4.7% 7 4.1% 171 100%

Pennsylvania 91 41.9% 94 43.3% 28 12.9% 4 1.8% 217 100%

Appalachian Pennsylvania 41 40.2% 48 47.1% 12 11.8% 1 1.0% 102 100%

Non-Appalachian Pennsylvania 50 49.0% 46 45.1% 16 15.7% 3 2.9% 102 100%

South Carolina 45 54.2% 34 41.0% 4 4.8% 0 0.0% 83 100%

Appalachian South Carolina 13 59.1% 9 40.9% 0 0.0% 0 0.0% 22 100%

Non-Appalachian South Carolina 32 55.2% 25 43.1% 4 6.9% 0 0.0% 58 100%

Tennessee 118 61.8% 64 33.5% 8 4.2% 1 0.5% 191 100%

Appalachian Tennessee 54 59.3% 34 37.4% 3 3.3% 0 0.0% 91 100%

Non-Appalachian Tennessee 64 64.6% 30 30.3% 5 5.1% 1 1.0% 99 100%

Virginia 49 42.6% 44 38.3% 18 15.7% 4 3.5% 115 100%

Appalachian Virginia 16 57.1% 7 25.0% 5 17.9% 0 0.0% 28 100%

Non-Appalachian Virginia 33 37.1% 37 41.6% 13 14.6% 4 4.5% 89 100%

West Virginia (entire state) 46 70.8% 14 21.5% 5 7.7% 0 0.0% 65 100%

Less than $250M $250M to $1B $1B to $10B Greater than $10B Total

115
Table 3-3: Correlation among Percentage of Midsize Banks and Non-Credit Card Lending

Correlation Coefficients

2007 2010

Normalized (Count) 0.1777* 0.09*

Not normalized (Count) 0.1336* 0.0978*

Table 3-4: Correlation among Percentage of Midsize Banks and Credit Card Lending

Correlation Coefficients

2007 2010

Normalized (Count) 0.19* 0.1382*

Not normalized (Count) 0.1147* 0.109*

Table 3-5: Correlation among Percentage of Midsize Banks and Credit Card Lending and NonCredit Card Lending (All Small Business Lending)

Correlation Coefficients

2007 2010

Normalized (Count) 0.2085* 0.1296*

Not normalized (Count) 0.1237* 0.1067*

*Statistically significant at the 0.001 level. Normalized here means loans divided by small businesses, count refers to number of loans and 

amount refers to dollar amount of loans.

116
3.3 BRANCHING PATTERNS, LOAN TO DEPOSIT RATIOS, AND LOANS PER BRANCH

Overall, the distribution of branches across the region is stable and branches per capita are relatively 

uniform with small disparities across subregion or county classification. However, the disparities in loanto-deposit ratios and loans per branch were significant and were manifested in large metropolitan counties 

compared to rural counties and in Southern Appalachia compared to Central Appalachia. 

Tables 3.6 and 3.7 reveal that the numbers of branches in Appalachia increased from 8,580 in 2007 to 

8,677 in 2010. In both years, about 63 percent of the branches in Appalachia were those of large banks 

with assets above $1 billion. Interestingly, the number of branches of banks not headquartered in 

Appalachia increased 19.5 percent from 2007 through 2010 while the branches of banks headquartered in 

Appalachia decreased 7.2 percent during the time period (see Table 3-8). The largest decrease of branches 

of banks headquartered in the region was those of large banks, whose branches decreased 24 percent from 

2007 through 2010. 

As Table 3-9 shows, the numbers of branches across subregions and county categories were relatively 

stable between 2007 and 2010. Northern Appalachia, for example, experienced a slight decline of 15 

branches while Central Appalachia gained 8 branches. Southern Appalachia gained 33 branches but this 

gain was offset by a larger population increase, which actually contributed to a higher number of persons 

per branches than the other subregions (though the higher number of persons per branch for Southern 

Appalachia is probably not indicative of less service or branch access since the differences among persons 

per branch are not great across the subregions).

The percentage of branches of banks without headquarters in Appalachia is lowest in Central Appalachia, 

in rural counties, and in distressed counties. Conversely, the percentage of branches of banks without 

headquarters in Appalachia is highest in Northern Appalachia, large metropolitan counties, and attainment 

counties (see Figures 3-5 through 3-7 and Maps 3-5 through 3-6). The tendency for a higher percentage of 

branches of banks not headquartered in Appalachia to be in the economically advantaged counties 

suggests that banks not headquartered in Appalachia are gravitating to those parts of the region 

experiencing the most favorable economic and population growth. This might be exacerbating unequal 

access over time since the growth in branches is being driven by banks not headquartered in Appalachia, 

but this trend did not noticeably increase unequal access as measured by the number of branches or 

number of persons per branch during 2007 through 2010. 

Table 3-10 demonstrates a dramatic decline in small business loan-to-deposit ratios between 2007 and 

2010 for both the nation and Appalachia. The small business loan-to-deposit ratio declined from 5.4 

percent during 2007 to 2.7 percent during 2010 in Appalachia and from 4.5 percent to 2 percent for the 

nation. 

Within the subregions, the small business loan-to-deposit ratio was highest in Southern Appalachia (7 

percent) and lowest in Central Appalachia (3.1 percent) in 2007. By 2010, the disparities had narrowed 

but Central Appalachia remained the subregion with the lowest loan-to-deposit ratio of 1.6 percent.

Likewise, disparities narrowed when considering the economic status of counties. The small business 

loan-to-deposit ratio in distressed and attainment counties, respectively, was 1.3 percent and 3.7 percent 

in 2010 while the comparable ratios were 2.9 percent and 8.6 percent in 2007. 

117
The number of small business loans per branch was considerably higher in the nation (132 loans per 

branch) than in Appalachia (81 loans per branch) during 2007 (see Table 3-10). Loans per branch 

declined significantly by 2010 for both the nation and Appalachia but significant disparities remained. In 

2010, the number of small business loans per branch was 41.2 in the nation and 25.2 in Appalachia. 

Within Appalachia, the largest differences occur when comparing Southern Appalachia to Central 

Appalachia, large metropolitan counties to rural counties, and distressed counties to attainment counties. 

Table 3-6: Number of Bank Branches Headquartered and not Headquartered in Appalachia by 

Asset Size, 2007

Type of Branches

Less than $250M $250M to $1B $1B to $10B Greater than $10B Total

count % count % count % count % 

Not Headquartered 45 1.68% 132 4.93% 472 17.62% 2,030 75.77% 2,679

Headquartered 1,486 25.18% 1,569 26.59% 1,655 28.05% 1,191 20.18% 5,901

Total 1,531 17.84% 1,701 19.83% 2,127 24.79% 3,221 37.54% 8,580

Table 3-7: Number of Bank Branches Headquartered and not Headquartered in Appalachia by 

Asset Size, 2010

Type of Branches

Less than $250M $250M to $1B $1B to $10B Greater than $10B Total

count % count % count count % count 

Not Headquartered 77 2.41% 144 4.50% 598 18.69% 2,381 74.41% 3,200

Headquartered 1,267 23.13% 1,692 30.89% 1,620 29.58% 898 16.40% 5,477

Total 1,344 15.49% 1,836 21.16% 2,218 25.56% 3,279 37.79% 8,677

Table 3-8: Change in Number of Bank Branches Headquartered and not Headquartered in 

Appalachia by Asset Size, 2007 to 2010

Type of Branches

Less than $250M $250M to $1B $1B to $10B Greater than $10B Total

count % count % count % count % count % 

Not Headquartered 32 71.11% 12 9.09% 126 26.69% 351 17.29% 521 19.45%

Headquartered -219 -14.74% 123 7.84% -35 -2.11% -293 -24.60% -424 -7.19%

Total -187 -12.21% 135 7.94% 91 4.28% 58 1.80% 97 1.13%

 

118
Figure 3-5: Branches not Headquartered in Appalachia by Region

Figure 3-6: Branches not Headquartered in Appalachia by County Type

Figure 3-7: Branches not Headquartered in Appalachia by Economic Status

31%

34%

24%

15%

36%

33%

37%

43%

24%

19%

36%

39%

0% 20% 40% 60%

Appalachian Region

Northern Appalachia

North Central Appalachia

Central Appalachia

South Central Appalachia

Southern Appalachia

2007 2010 Per capita personal income index

38%

35%

30%

25%

17%

48%

41%

36%

27%

20%

0% 20% 40% 60%

Large Metro (1 million + people)

Small Metro (< 1 million people)

Nonmetro, Adjacent to Large Metro

Nonmetro, Adjacent to Small Metro

Rural (Nonmetro, Nonadjacent)

2007 2010 Per capita personal income index

10%

20%

30%

40%

58%

15%

25%

36%

46%

60%

0% 20% 40% 60% 80%

Distressed

At-Risk

Transitional

Competitive

Attainment

2007 2010 Per capita personal income index

119
Figure 3-8: Branches not headquartered in Appalachia by State

22%

38%

19%

45%

14%

56%

53%

38%

30%

59%

23%

28%

13%

28%

50%

24%

76%

18%

52%

53%

48%

39%

57%

25%

28%

14%

0% 20% 40% 60% 80% 100%

Appalachian Alabama

Appalachian Georgia

Appalachian Kentucky

Appalachian Maryland

Appalachian Mississippi

Appalachian New York

Appalachian North Carolina

Appalachian Ohio

Appalachian Pennsylvania

Appalachian South Carolina

Appalachian Tennessee

Appalachian Virginia

West Virginia (entire state)

2007 2010

120
Map 3-3: Number of Bank Branches per County in Appalachia, 2007

121
Map 3-4: Number of Bank Branches per County in Appalachia, 2010

122
Map 3-5: Percent of Bank Branches without Bank Headquarter in Appalachia, 2007

123
Map 3-6: Percent of Bank Branches without Bank Headquarter in Appalachia, 2010

124
Table 3-9: Bank Branches Headquartered and not Headquartered in Appalachia, 2007 to 2010

Number of 

Total 

Branches

Number of 

Branches 

Headquartered 

in Appalachia 

Number of 

Branches not 

Headquartered 

in Appalachia 

% of Branches 

not 

Headquartered 

in Appalachia 

Number 

of 

Persons 

per 

Branch

Number of 

Total 

Branches

Number of 

Branches 

Headquartered 

in Appalachia 

Number of 

Branches not 

Headquartered 

in Appalachia 

% of Branches 

not 

Headquartered 

in Appalachia 

Number 

of 

Persons 

per 

Branch

United States 93,434 - - 3,245 94755 - - 3,299

Appalachian Region 8,580 5,901 2,679 31% 2,886 8,677 5,477 3,200 37% 2,909 

Subregions

Northern Appalachia 3,115 2,068 1,047 34% 2,698 3,100 1,755 1,345 43% 2,705 

North Central Appalachia 818 625 193 24% 2,925 833 629 204 24% 2,909 

Central Appalachia 752 637 115 15% 2,539 760 619 141 19% 2,524 

South Central Appalachia 1,604 1,028 576 36% 2,863 1,659 1,058 601 36% 2,844 

Southern Appalachia 2,291 1,543 748 33% 3,259 2,325 1,416 909 39% 3,354 

County Types

Large Metro (1 million + people) 1,813 1,128 685 38% 3,083 1,798 927 871 48% 3,210 

Small Metro (< 1 million people) 3,316 2,143 1,173 35% 3,009 3,386 2,001 1,385 41% 3,009 

Nonmetro, Adjacent to Large Metro 618 432 186 30% 2,712 624 397 227 36% 2,719 

Nonmetro, Adjacent to Small Metro 1,840 1,373 467 25% 2,712 1,868 1,355 513 27% 2,702 

Rural (Nonmetro, Nonadjacent) 993 825 168 17% 2,546 1,001 797 204 20% 2,538 

Economic Status

Distressed 515 463 52 10% 2,777 531 452 79 15% 2,718 

At-Risk 797 639 158 20% 2,813 831 621 210 25% 2,880 

Transitional 5,037 3,548 1,489 30% 2,845 5,273 3,372 1,901 36% 2,857 

Competitive 1,570 946 624 40% 2,928 1,559 837 722 46% 2,942 

Attainment 423 176 247 58% 3,408 483 195 288 60% 3,640 

Alabama 1,445 3,239 1,528 3,128

Appalachian Alabama 897 699 198 22% 3,340 934 670 264 28% 3,281 

Non-Appalachian Alabama 548 - - - 3,072 594 - - - 2,888

Georgia 2,715 3,402 2,660 3,642

Appalachian Georgia 816 507 309 38% 3,328 787 396 391 50% 3,727 

Non-Appalachian Georgia 1,899 - - - 3,434 1,873 - - 3,606

Kentucky 1,763 2,411 1,784 2,432

Appalachian Kentucky 483 391 92 19% 2,437 489 374 115 24% 2,422 

Non-Appalachian Kentucky 1,280 - - - 2,401 1,295 - - 2,436

Maryland 1,760 3,199 1,755 3,290

Appalachian Maryland 84 46 38 45% 2,950 85 20 65 76% 2,972 

Non-Appalachian Maryland 1,676 - - - 3,212 1,670 - - 3,306

Mississippi 1,074 2,729 1,107 2,680

Appalachian Mississippi 222 191 31 14% 2,816 230 189 41 18% 2,736 

Non-Appalachian Mississippi 852 - - - 2,706 877 - - 2,666

New York 5,136 3,750 5,337 3,631

Appalachian New York 341 150 191 56% 3,133 341 162 179 52% 3,127 

Non-Appalachian New York 4,795 - - - 3,793 4,996 - - 3,665

North Carolina 2,614 3,477 2,709 3,520

Appalachian North Carolina 521 243 278 53% 3,161 536 254 282 53% 3,170 

Non-Appalachian North Carolina 2,093 - - - 3,556 2,173 - - 3,606

Ohio 3,944 2,911 3,893 2,963

Appalachian Ohio 731 451 280 38% 2,793 731 378 353 48% 2,793 

Non-Appalachian Ohio 3,213 - - - 2,938 3,162 - - 3,003

Pennsylvania 4,592 2,739 4,533 2,802

Appalachian Pennsylvania 2,225 1,567 658 30% 2,607 2,206 1,339 867 39% 2,626 

Non-Appalachian Pennsylvania 2,367 - - - 2,862 2,327 - - 2,970

South Carolina 1,318 3,370 1,411 3,278

Appalachian South Carolina 356 146 210 59% 3,170 374 161 213 57% 3,132 

Non-Appalachian South Carolina 962 - - - 3,444 1,037 - - 3,331

Tennessee 2,166 2,839 2,198 2,887

Appalachian Tennessee 983 752 231 23% 2,756 1,014 763 251 25% 2,747 

Non-Appalachian Tennessee 1,183 - - - 2,908 1,184 - - 3,072

Virginia 2,532 3,072 2,621 3,083

Appalachian Virginia 307 222 85 28% 2,496 320 232 88 28% 2,406 

Non-Appalachian Virginia 2,225 - - - 3,152 2,301 - - 3,177

West Virginia (entire state) 614 536 78 13% 2,996 630 539 91 14% 2,941 

2007 2010

125
Figure 3-9: Number of Loans per Branch by Region

Figure 3-10: Number of Loans per Branch by County Type

Figure 3-11: Number of Loans per Branch by Economic Status

132.3

81.0

70.5

60.3

45.4

84.2

112.7

41.2

25.3

25.3

19.6

13.8

24.7

31.5

0 20 40 60 80 100 120 140 160 180 200

United States

Appalachian Region

Northern Appalachia

North Central Appalachia

Central Appalachia

South Central Appalachia

Southern Appalachia

2007 2010

109.5

82.8

65.3

65.7

57.4

34.0

25.9

20.6

20.2

18.8

0 20 40 60 80 100 120 140 160 180 200

Large Metros (pop. 1 million +)

Small Metros (pop. <1 million)

Nonmetro, Adjacent to Large Metros

Nonmetro, Adjacent to Small Metros

Rural (nonmetro, not adj. to a metro)

Per capita personal income index 2007 2010

42.9

58.5

74.9

97.6

190.5

12.5

19.4

23.7

31.2

46.8

0 20 40 60 80 100 120 140 160 180 200

Distressed

At-Risk

Transitional

Competitive

Attainment

Per capita personal income index 2007 2010

126
Table 3-10: Ratio of Small Business Loans to Deposits and Loans per Branches

Ratio of SB Loans 

to Deposits

SB Loans per 

Branches

Ratio of SB Loans 

to Deposits

SB Loans per 

Branches

United States 4.5% 132.3 2.1% 41.2

Appalachian Region 5.4% 81.0 2.7% 25.3

Subregions

Northern Appalachia 4.4% 70.5 2.8% 25.3

North Central Appalachia 4.5% 60.3 2.8% 19.6

Central Appalachia 3.1% 45.4 1.6% 13.8

South Central Appalachia 5.8% 84.2 2.8% 24.7

Southern Appalachia 7.0% 112.7 2.8% 31.5

County Types

Large Metro (1 million + people) 5.1% 109.5 2.6% 34.0

Small Metro (< 1 million people) 6.1% 82.8 3.1% 25.9

Nonmetro, Adjacent to Large Metro 4.3% 65.3 2.3% 20.6

Nonmetro, Adjacent to Small Metro 5.0% 65.7 2.5% 20.2

Rural (Nonmetro, Nonadjacent) 4.4% 57.4 2.3% 18.8

Economic Status

Distressed 2.9% 42.9 1.3% 12.5

At-Risk 4.3% 58.5 2.4% 19.4

Transitional 5.7% 74.9 3.0% 23.7

Competitive 4.9% 97.6 2.4% 31.2

Attainment 8.6% 190.5 3.7% 46.8

Alabama 6.8% 93.1 2.7% 27.0

Appalachian Alabama 6.6% 97.1 2.5% 28.4

Non-Appalachian Alabama 7.2% 86.3 3.2% 24.7

Georgia 5.9% 139.4 2.5% 37.4

Appalachian Georgia 7.8% 150.3 3.5% 39.7

Non-Appalachian Georgia 5.3% 134.8 2.3% 36.4

Kentucky 4.7% 62.0 2.9% 21.2

Appalachian Kentucky 3.2% 46.5 1.7% 14.5

Non-Appalachian Kentucky 5.1% 67.7 3.2% 23.7

Maryland 5.6% 130.0 2.2% 35.8

Appalachian Maryland 6.7% 88.1 3.9% 27.7

Non-Appalachian Maryland 5.5% 132.3 2.1% 36.3

Mississippi 5.6% 65.3 3.1% 23.8

Appalachian Mississippi 6.1% 67.3 2.8% 22.7

Non-Appalachian Mississippi 5.5% 64.8 3.2% 24.0

New York 2.6% 169.5 1.1% 52.7

Appalachian New York 4.4% 76.8 2.1% 26.6

Non-Appalachian New York 2.6% 176.5 1.0% 54.6

North Carolina 5.0% 121.2 2.4% 37.2

Appalachian North Carolina 5.6% 111.8 2.6% 32.7

Non-Appalachian North Carolina 4.9% 123.6 2.3% 38.3

Ohio 4.5% 81.6 3.2% 28.9

Appalachian Ohio 4.0% 63.8 2.9% 20.3

Non-Appalachian Ohio 4.6% 85.6 3.2% 30.8

Pennsylvania 4.6% 84.0 2.5% 29.7

Appalachian Pennsylvania 4.5% 69.9 2.8% 25.8

Non-Appalachian Pennsylvania 4.7% 98.3 2.3% 33.7

South Carolina 6.7% 103.5 2.9% 29.2

Appalachian South Carolina 6.5% 97.8 2.7% 27.8

Non-Appalachian South Carolina 6.7% 105.6 3.0% 29.7

Tennessee 5.5% 82.3 3.0% 26.1

Appalachian Tennessee 5.8% 75.0 2.8% 21.6

Non-Appalachian Tennessee 5.3% 88.5 3.1% 30.0

Virginia 3.4% 106.5 1.5% 31.4

Appalachian Virginia 4.0% 48.7 2.2% 15.7

Non-Appalachian Virginia 3.4% 115.9 1.4% 33.9

West Virginia (entire state) 4.4% 57.7 2.8% 19.3

2007 2010

127
3.4 DISTRIBUTION OF CREDIT UNIONS ACROSS APPALACHIA AND THE UNITED STATES

Unlike banks, credit unions are not examined under CRA and do not publicly report their small business 

lending.11 Scrutinizing the distribution of credit unions across Appalachia, however, can perhaps identify 

a source of financing for small business lending. This analysis will focus on the larger credit unions as 

they are the most likely to offer substantial numbers of small business loans and/or make community 

development investments.

The financial crisis significantly reduced the ranks of credit unions. The number of credit unions in the 

United States shrank from 8,268 in 2007 to 7,491 in 2010 (see Tables 3-11 and 3-12). In Appalachia, the 

number of credit unions contracted from 931 in 2007 to 852 in 2010. 

The Appalachian Region has fewer larger credit unions in the $100 to $500 million asset range and with 

assets of more than $500 million in assets. In 2010, for example, 13.7 percent of the credit unions and 9.9 

percent of the credit unions in the nation and in Appalachia, respectively, had assets of between $100 to 

$500 million. Likewise, 5 percent and 2.1 percent of the credit unions in the country and Appalachia, 

respectively, had assets of more than $500 million. 

Rural counties, which are not adjacent to metropolitan counties, have the lowest numbers and percentages 

of larger credit unions with assets above $100 million. In both 2007 and 2010, these counties did not have 

a single credit union with assets above $500 million and only one credit union with assets between $100 

million and $500 million. In 2010, the percentage of credit unions in rural counties with assets between 

$100 and $500 million was 1.9 percent as opposed to 9.9 percent of all of Appalachia. 

Interestingly, distressed counties had a relatively high percentage of credit unions with assets between 

$100 and $500 million. In distressed counties during 2010, three credit unions (20 percent) had assets 

between $100 and $500 million. The overall number of credit unions in economically distressed counties 

is low. In 2010 there were 15 credit unions located in distressed counties, 72 in at-risk counties, 538 in 

transitional counties, 220 in competitive counties, and 7 in attainment counties. 

Relatively large credit unions could be an untapped resource for Central Appalachia, a subregion that is 

disadvantaged in terms of access to lending and banks. In both years reviewed, a significantly higher 

percentage of Central Appalachian credit unions had assets above $100 million, but the total number of 

credit unions in Central Appalachia was between 3 and 5 times less than the total number of credit unions 

in other subregions. 

11 Another chapter examines lending activity of CDFI credit unions that report lending data to the United States 

Treasury Department. This chapter looks at the distribution of all credit unions that are captured by the databases of 

the National Credit Union Administration, the federal regulator of credit unions.

128

 
Table 3-11: Percentage of Credit Unions by Asset Size in Appalachia, 2007

Number Percentage Number Percentage Number Percentage Number Percentage Number Percentage Number Percentage

United States 1,373 16.61% 2,292 27.72% 2,582 31.23% 769 9.30% 943 11.41% 309 3.74% 8,268

Appalachian Region 171 18.37% 325 34.91% 285 30.61% 63 6.77% 73 7.84% 14 1.50% 931

Subregions

Northern Appalachia 104 20.51% 191 37.67% 152 29.98% 29 5.72% 27 5.33% 4 0.79% 507

North Central Appalachia 16 15.38% 35 33.65% 37 35.58% 7 6.73% 9 8.65% 0 0.00% 104

Central Appalachia 10 30.30% 7 21.21% 9 27.27% 1 3.03% 5 15.15% 1 3.03% 33

South Central Appalachia 12 9.45% 36 28.35% 48 37.80% 12 9.45% 13 10.24% 6 4.72% 127

Southern Appalachia 29 18.13% 56 35.00% 39 24.38% 14 8.75% 19 11.88% 3 1.88% 160

County Types

Large Metro (1 million + people) 57 22.53% 99 39.13% 65 25.69% 14 5.53% 15 5.93% 3 1.19% 253

Small metro (< 1 million people) 60 13.64% 146 33.18% 143 32.50% 41 9.32% 41 9.32% 9 2.05% 440

Nonmetro, Adjacent to Large Metro 15 26.32% 16 28.07% 19 33.33% 3 5.26% 3 5.26% 1 1.75% 57

Nonmetro, Adjacent to Small Metro 21 18.42% 37 32.46% 37 32.46% 5 4.39% 13 11.40% 1 0.88% 114

Rural (Nonmetro, Nonadjacent) 18 29.51% 27 44.26% 15 24.59% 0 0.00% 1 1.64% 0 0.00% 61

Economic Status

Distressed 8 40.00% 6 30.00% 4 20.00% 0 0.00% 2 10.00% 0 0.00% 20

At-Risk 12 17.14% 26 37.14% 26 37.14% 3 4.29% 3 4.29% 0 0.00% 70

Transitional 111 17.56% 221 34.97% 194 30.70% 45 7.12% 54 8.54% 7 1.11% 632

Competitive 34 19.21% 59 33.33% 54 30.51% 10 5.65% 14 7.91% 6 3.39% 177

Attainment 1 33.33% 1 33.33% 0 0.00% 1 33.33% 0 0.00% 0 0.00% 3

Alabama 24 16.22% 47 31.76% 38 25.68% 13 8.78% 21 14.19% 5 3.38% 148

Appalachian Alabama 14 15.56% 28 31.11% 19 21.11% 9 10.00% 17 18.89% 3 3.33% 90

Non-Appalachian Alabama 10 17.24% 19 32.76% 19 32.76% 4 6.90% 4 6.90% 2 3.45% 58

Georgia 33 18.54% 50 28.09% 59 33.15% 19 10.67% 11 6.18% 6 3.37% 178

Appalachian Georgia 9 28.13% 10 31.25% 11 34.38% 1 3.13% 1 3.13% 0 0.00% 32

Non-Appalachian Georgia 24 16.44% 40 27.40% 48 32.88% 18 12.33% 10 6.85% 6 4.11% 146

Kentucky 10 10.53% 37 38.95% 31 32.63% 5 5.26% 9 9.47% 3 3.16% 95

Appalachian Kentucky 3 18.75% 5 31.25% 5 31.25% 0 0.00% 3 18.75% 0 0.00% 16

Non-Appalachian Kentucky 7 8.86% 32 40.51% 26 32.91% 5 6.33% 6 7.59% 3 3.80% 79

Maryland 18 15.65% 28 24.35% 29 25.22% 9 7.83% 23 20.00% 8 6.96% 115

Appalachian Maryland - 0.00% 3 23.08% 4 30.77% 1 7.69% 5 38.46% 0 0.00% 13

Non-Appalachian Maryland 18 17.65% 25 24.51% 25 24.51% 8 7.84% 18 17.65% 8 7.84% 102

Mississippi 26 26.26% 42 42.42% 20 20.20% 6 6.06% 4 4.04% 1 1.01% 99

Appalachian Mississippi 4 25.00% 11 68.75% 1 6.25% 0 0.00% 0 0.00% 0 0.00% 16

Non-Appalachian Mississippi 22 26.51% 31 37.35% 19 22.89% 6 7.23% 4 4.82% 1 1.20% 83

New York 104 21.36% 152 31.21% 145 29.77% 33 6.78% 34 6.98% 19 3.90% 487

Appalachian New York 6 12.24% 14 28.57% 18 36.73% 4 8.16% 5 10.20% 2 4.08% 49

Non-Appalachian New York 98 22.37% 138 31.51% 127 29.00% 29 6.62% 29 6.62% 17 3.88% 438

North Carolina 14 12.07% 30 25.86% 44 37.93% 14 12.07% 8 6.90% 6 5.17% 116

Appalachian North Carolina 1 3.70% 6 22.22% 11 40.74% 4 14.81% 3 11.11% 2 7.41% 27

Non-Appalachian North Carolina 13 14.61% 24 26.97% 33 37.08% 10 11.24% 5 5.62% 4 4.49% 89

Ohio 90 20.88% 122 28.31% 141 32.71% 32 7.42% 43 9.98% 3 0.70% 431

Appalachian Ohio 12 20.00% 21 35.00% 16 26.67% 5 8.33% 5 8.33% 1 1.67% 60

Non-Appalachian Ohio 78 21.02% 101 27.22% 125 33.69% 27 7.28% 38 10.24% 2 0.54% 371

Pennslyvania 165 27.55% 192 32.05% 165 27.55% 34 5.68% 34 5.68% 9 1.50% 599

Appalachian Pennsyvania 82 21.41% 149 38.90% 116 30.29% 20 5.22% 15 3.92% 1 0.26% 383

Non-Appalachian Pennsyvania 83 38.43% 43 19.91% 49 22.69% 14 6.48% 19 8.80% 8 3.70% 216

South Carolina 9 10.59% 25 29.41% 27 31.76% 11 12.94% 9 10.59% 4 4.71% 85

Appalachian Carolina 2 9.09% 7 31.82% 8 36.36% 4 18.18% 1 4.55% 0 0.00% 22

Non-Appalachian Carolina 7 11.11% 18 28.57% 19 30.16% 7 11.11% 8 12.70% 4 6.35% 63

Tennessee 26 13.54% 51 26.56% 72 37.50% 15 7.81% 22 11.46% 6 3.13% 192

Appalachian Tennessee 12 11.54% 27 25.96% 40 38.46% 8 7.69% 12 11.54% 5 4.81% 104

Non-Appalachian Tennessee 14 15.91% 24 27.27% 32 36.36% 7 7.95% 10 11.36% 1 1.14% 88

Virginia 40 19.51% 61 29.76% 46 22.44% 22 10.73% 26 12.68% 10 4.88% 205

Appalachian Virginia 1 14.29% 4 57.14% 1 14.29% 1 14.29% 0 0.00% 0 0.00% 7

Non-Appalachian Virginia 39 19.70% 57 28.79% 45 22.73% 21 10.61% 26 13.13% 10 5.05% 198

West Virginia (entire state) 25 22.32% 40 35.71% 35 31.25% 6 5.36% 6 5.36% 0 0.00% 112

Greater than $500M

Total

Less than $2M $2M to $10M $10M to $50M $50M to $100M $100M to $500M

129
Table 3-12: Percentage of Credit Unions by Asset Size in Appalachia, 2010

Number Percentage Number Percentage Number Percentage Number Percentage Number Percentage Number Percentage

United States 1,013 13.52% 1,825 24.36% 2,444 32.63% 811 10.83% 1021 13.63% 377 5.03% 7,491

Appalachian Region 116 13.62% 261 30.63% 295 34.62% 78 9.15% 84 9.86% 18 2.11% 852

Subregions

Northern Appalachia 74 15.81% 151 32.26% 159 33.97% 45 9.62% 34 7.26% 5 1.07% 468

North Central Appalachia 12 12.50% 29 30.21% 38 39.58% 7 7.29% 10 10.42% 0 0.00% 96

Central Appalachia 8 25.81% 7 22.58% 9 29.03% 1 3.23% 4 12.90% 2 6.45% 31

South Central Appalachia 8 6.84% 28 23.93% 47 40.17% 14 11.97% 14 11.97% 6 5.13% 117

Southern Appalachia 14 10.00% 46 32.86% 42 30.00% 11 7.86% 22 15.71% 5 3.57% 140

County Types

Large Metro (1 million + people) 33 14.67% 80 35.56% 77 34.22% 12 5.33% 19 8.44% 4 1.78% 225

Small metro (< 1 million people) 44 10.50% 116 27.68% 152 36.28% 49 11.69% 46 10.98% 12 2.86% 419

Nonmetro, Adjacent to Large Metro 9 18.00% 14 28.00% 16 32.00% 7 14.00% 3 6.00% 1 2.00% 50

Nonmetro, Adjacent to Small Metro 15 14.42% 31 29.81% 35 33.65% 7 6.73% 15 14.42% 1 0.96% 104

Rural (Nonmetro, Nonadjacent) 15 27.78% 20 37.04% 15 27.78% 3 5.56% 1 1.85% 0 0.00% 54

Economic Status

Distressed 6 40.00% 5 33.33% 1 6.67% 0 0.00% 3 20.00% 0 0.00% 15

At-Risk 11 15.28% 25 34.72% 28 38.89% 4 5.56% 4 5.56% 0 0.00% 72

Transitional 69 12.83% 158 29.37% 188 34.94% 59 10.97% 53 9.85% 11 2.04% 538

Competitive 30 13.64% 70 31.82% 77 35.00% 14 6.36% 23 10.45% 6 2.73% 220

Attainment - 0.00% 3 42.86% 1 14.29% 1 14.29% 1 14.29% 1 14.29% 7

Alabama 10 7.87% 35 27.56% 44 34.65% 9 7.09% 22 17.32% 7 5.51% 127

Appalachian Alabama 6 7.69% 22 28.21% 21 26.92% 7 8.97% 17 21.79% 5 6.41% 78

Non-Appalachian Alabama 4 8.16% 13 26.53% 23 46.94% 2 4.08% 5 10.20% 2 4.08% 49

Georgia 19 12.42% 42 27.45% 53 34.64% 17 11.11% 15 9.80% 7 4.58% 153

Appalachian Georgia 3 11.11% 10 37.04% 11 40.74% 1 3.70% 2 7.41% 0 0.00% 27

Non-Appalachian Georgia 16 12.70% 32 25.40% 42 33.33% 16 12.70% 13 10.32% 7 5.56% 126

Kentucky 8 9.30% 30 34.88% 30 34.88% 6 6.98% 9 10.47% 3 3.49% 86

Appalachian Kentucky 3 20.00% 5 33.33% 5 33.33% 0 0.00% 2 13.33% 0 0.00% 15

Non-Appalachian Kentucky 5 7.04% 25 35.21% 25 35.21% 6 8.45% 7 9.86% 3 4.23% 71

Maryland 10 9.01% 27 24.32% 28 25.23% 12 10.81% 25 22.52% 9 8.11% 111

Appalachian Maryland - 0.00% 2 12.50% 4 25.00% 4 25.00% 6 37.50% 0 0.00% 16

Non-Appalachian Maryland 10 10.53% 25 26.32% 24 25.26% 8 8.42% 19 20.00% 9 9.47% 95

Mississippi 19 20.65% 38 41.30% 19 20.65% 8 8.70% 7 7.61% 1 1.09% 92

Appalachian Mississippi 4 26.67% 9 60.00% 1 6.67% 1 6.67% 0 0.00% 0 0.00% 15

Non-Appalachian Mississippi 15 19.48% 29 37.66% 18 23.38% 7 9.09% 7 9.09% 1 1.30% 77

New York 87 19.73% 108 24.49% 146 33.11% 35 7.94% 40 9.07% 25 5.67% 441

Appalachian New York 5 11.36% 10 22.73% 16 36.36% 5 11.36% 5 11.36% 3 6.82% 44

Non-Appalachian New York 82 20.65% 98 24.69% 130 32.75% 30 7.56% 35 8.82% 22 5.54% 397

North Carolina 6 6.06% 22 22.22% 36 36.36% 16 16.16% 12 12.12% 7 7.07% 99

Appalachian North Carolina - 0.00% 3 13.64% 7 31.82% 6 27.27% 4 18.18% 2 9.09% 22

Non-Appalachian North Carolina 6 7.79% 19 24.68% 29 37.66% 10 12.99% 8 10.39% 5 6.49% 77

Ohio 67 17.31% 97 25.06% 135 34.88% 36 9.30% 47 12.14% 5 1.29% 387

Appalachian Ohio 11 20.37% 15 27.78% 18 33.33% 2 3.70% 7 12.96% 1 1.85% 54

Non-Appalachian Ohio 56 16.82% 82 24.62% 117 35.14% 34 10.21% 40 12.01% 4 1.20% 333

Pennslyvania 117 21.63% 156 28.84% 168 31.05% 46 8.50% 43 7.95% 11 2.03% 541

Appalachian Pennsyvania 52 14.86% 122 34.86% 121 34.57% 34 9.71% 20 5.71% 1 0.29% 350

Non-Appalachian Pennsyvania 65 34.03% 34 17.80% 47 24.61% 12 6.28% 23 12.04% 10 5.24% 191

South Carolina 6 7.79% 17 22.08% 30 38.96% 7 9.09% 11 14.29% 6 7.79% 77

Appalachian Carolina 1 5.00% 5 25.00% 9 45.00% 2 10.00% 3 15.00% 0 0.00% 20

Non-Appalachian Carolina 5 8.77% 12 21.05% 21 36.84% 5 8.77% 8 14.04% 6 10.53% 57

Tennessee 18 9.94% 44 24.31% 77 42.54% 14 7.73% 21 11.60% 7 3.87% 181

Appalachian Tennessee 7 7.07% 23 23.23% 43 43.43% 8 8.08% 12 12.12% 6 6.06% 99

Non-Appalachian Tennessee 11 13.41% 21 25.61% 34 41.46% 6 7.32% 9 10.98% 1 1.22% 82

Virginia 34 17.62% 53 27.46% 41 21.24% 28 14.51% 24 12.44% 13 6.74% 193

Appalachian Virginia 1 16.67% 3 50.00% 1 16.67% 1 16.67% 0 0.00% 0 0.00% 6

Non-Appalachian Virginia 33 17.65% 50 26.74% 40 21.39% 27 14.44% 24 12.83% 13 6.95% 187

West Virginia (entire state) 23 21.70% 32 30.19% 38 35.85% 7 6.60% 6 5.66% 0 0.00% 106

Greater than $500M

Total

Less than $2M $2M to $10M $10M to $50M $50M to $100M $100M to $500M

130
CHAPTER 4 CRA INVESTING AND COMMUNITY DEVELOPMENT 

LENDING OF BANKS BASED IN APPALACHIA

4.1 SUMMARY

A unique database of Community Reinvestment Act (CRA) exams was constructed to calculate the levels 

of equity investment and community development lending of banks headquartered in Appalachia. 

Community development lending can involve construction lending for light industrial parks or small 

business incubators, while equity investments include those in Small Business Investment Corporations 

(SBIC). Major findings from the CRA exam analysis reveals:

• Banks in Appalachia undergoing CRA exams are a significant resource for investment and 

community development lending. Large banks (assets more than $1 billion) headquartered in 

Appalachia have a total of $433 billion in assets and mid-size banks (assets between $250 million 

and $1 billion) have combined assets of $68 billion. 

• Despite the financial crisis, the level of community investing and lending over a CRA exam time 

period of approximately three years was greater during this study than in NCRC’s previous study 

for ARC.12 In the sample for this study, total community development financing was $8.8 billion 

compared to $5.4 billion during the previous study. An important caveat is that much of the 

increase was due to the growth in assets of the five largest banks headquartered in Appalachia, 

which have a wide geographical reach including several counties and states beyond Appalachia. 

Nevertheless, this finding is encouraging in terms of the resources available to Appalachia for 

community development. 

• The level of community development financing (investment and lending) was much greater for 

housing than small business. For example, large banks headquartered in Appalachia invested 

$762 million in housing compared to just $150 million in small business development on their 

most recent CRA exams. While it would not be desirable to decrease the amount targeted for 

housing development, stakeholders can work with banks in Appalachia to increase investments 

for small businesses.

• Banks with higher overall CRA ratings and high ratings on their investment test or community 

development tests offered greater amounts of community development financing on a per asset 

basis. Large banks had their lowest ratings on the investment test; thus, an opportunity exists to 

work with large banks to improve their investment test ratings and their level of equity 

investments for small businesses.

• Disparities in community development financing mirror disparities identified for lending within 

the Region. Central Appalachia and economically distressed counties have banks with total assets 

of $14 billion and $3 billion, respectively, while other counties have banks with assets in the tens 

or hundreds of billions of dollars. Banks located in Central Appalachia, non-metropolitan 

12 The previous NCRC study for ARC can be accessed via 

http://www.arc.gov/research/researchreportdetails.asp?REPORT_ID=8

131

 
counties, and economically distressed counties made equity investments in small business of 

under $1 million while banks in South Central Appalachia, small metropolitan counties, and 

competitive counties made the largest dollar amount of investments of about $150 million in each 

county category. 

Banks located in Appalachia have a surprisingly high level of assets and make significant amounts of 

community development investments and loans. Yet, the familiar disparities by county economic status 

are apparent in community development investing and lending patterns, as they are for small business 

development. Yet, potential exists for reducing these disparities. For example, banks located in small 

metropolitan counties had high levels of equity investment in small business while non-metropolitan 

counties adjacent to small metropolitan counties had low levels of equity investments. Perhaps, some of 

the banks located in small metropolitan counties could finance equity investments in the adjacent nonmetropolitan counties. 

4.2 INTRODUCTION

The Community Reinvestment Act (CRA) of 1977 imposes upon banks and thrifts an affirmative and 

continuing obligation to meet the credit needs of communities in which they are chartered, including lowand moderate-income communities. Three federal financial supervisory agencies (the Federal Reserve 

Board, the Federal Deposit Insurance Corporation, and the Office of the Comptroller of the Currency) 

enforce CRA and conduct periodic CRA exams about once every two or three years for banks with assets 

above $250 million.13 CRA exams assess the level of loans, investments, and services that banks with 

assets above $250 million provide to low- and moderate-income communities. Banks receive CRA 

ratings for their overall performance as well as their performance in each state and multi-state 

metropolitan statistical area (MSA) in which they have branches. Banks and thrifts have strong incentives 

to increase their levels of lending, investing, and services to low- and moderate-income communities. 

Low CRA ratings can result in reputational risk and/or delay bank applications for federal agency 

approval to purchase or merge with other institutions, or open additional bank branches. 

This chapter will assess the impacts CRA has had on increasing certain types of lending and investing for 

small businesses by banks located in Appalachian counties. The chapter will use the most recent CRA 

exams of banks headquartered in Appalachia to document levels of financing for small business 

development. So far this study has been devoted to examining access to lending for individual small 

businesses. In this chapter, the report scrutinizes the level of community development financing. 

Community development lending and investing provides the financing that builds the infrastructure and 

support systems for small businesses. For example, community development financing supports the 

development of small business incubators and Small Business Investment Corporations (SBICs).

4.3 LARGE BANKS – INVESTMENT AND COMMUNITY DEVELOPMENT LENDING

The CRA regulations define large banks as banks with $1 billion or more in assets. These institutions 

undergo a CRA exam that has a lending test, an investment test, and a service test. For our purposes in 

this chapter, the lending test contains information on community development lending that supports 

affordable housing, economic development including the financing of small businesses, and revitalization 

13 Small banks with assets less than $250 million are examined once every four or five years.

132

 
of distressed rural areas and low- and moderate-income census tracts. The investment test likewise 

analyzes the number, innovativeness, and responsiveness of community development investments.

NCRC analyzed CRA exams of large banks located in Appalachia and calculated the dollar amount of 

community development loans and investments that were devoted primarily for housing and small 

business. Community development loans and investments finance schools, childcare centers, and other 

community facilities. The focus in this chapter is tabulating and comparing the dollar amount of small 

business and housing because these two activities were generally easier to classify and compare. 

Therefore, the total amount of community development lending and investing presented in the chapter 

will not equal the sum of housing and community development. 

A limitation is that this analysis does not determine the county in which the community development loan 

or investment is made. The publicly available CRA data do not include information on the county or any 

other geographical location of community development loans and investments. Likewise, CRA exams are 

not a consistent source of this information since the majority of exams contain just the metropolitan area 

or state, not the county of the community development loan and/or investment. 

Rather, this analysis describes the community development financing activities of banks located in 

Appalachia. Not all of this community development financing flows to Appalachia because banks, 

particularly the largest banks, have assessment areas in which they are rated in several states. Yet, this 

analysis provides information on the types of bank resources available for community development in 

Appalachia. Certainly, a number of large banks in this analysis could be encouraged to increase their 

community development financing in geographical areas identified in this report as relatively lacking in 

credit and capital for small business.

In Appalachia, 60 banks have undergone large bank exams in the last few years. Their median asset size 

was $1.5 billion, and their combined asset size was approximately $433 billion (see Table 4-1). 

Interestingly, 21 banks had assets of less than $1 billion. Banks with assets under $1 billion can 

voluntarily opt for the large bank exam; some do so because they voluntarily report the small business 

loan data or have some other reason. The median time period covered by the CRA exam was three years. 

The vast majority (95 percent) had their exams in 2008 and later (see Table 4-2).

Of the large banks, the top five were very large banks with the vast majority of assets (see Table 4-3). 

Regions Bank and Branch Banking and Trust each had more than $100 billion in assets and Compass 

Bank had more than $40 billion in assets. The top five had $329 billion in assets or 76 percent of the total 

assets of large banks located in Appalachia.

Large banks were more successful on their overall ratings and were least successful on their investment 

test ratings (see Table 4-4). Thirteen percent of the large banks in Appalachia received an Outstanding 

rating overall and about 87 percent received a Satisfactory rating overall. On the lending and investment 

tests, 12 and 18 percent, respectively, received Outstanding ratings. However, on the Investment Test 31.7 

percent and 5 percent received a Low Satisfactory and a Needs to Improve rating, respectively. In 

contrast, just 18.3 percent of the large banks received a Low Satisfactory on the Lending Test, which 

includes an analysis of community development lending. 

133
The implication of the ratings distribution is that the large banks have the most improvements to make on 

their investment test. Making more equity investments in small businesses is an eligible investment; 

hence an opportunity exists to leverage more investments for small businesses by large banks in 

Appalachia. 

In fact, the ratings on the component tests are related to community development investment and lending 

levels, suggesting that motivating the banks to improve their ratings on the component tests has the 

potential to increase community development lending and investment in Appalachia. The median ratio of 

community development investment to assets over the median exam time period was 0.46 percent for 

large banks with Outstanding ratings on the Investment Test but only 0.27 percent for large banks with 

Low Satisfactory ratings on the Investment Test. Likewise, the median ratio of community development 

loans to assets was 0.98 percent for large banks with Outstanding ratings on the Lending Test and 0.59 

percent for those with Low Satisfactory ratings on the Lending Test (see Table 4-5). 

The dollar amount of community development financing for housing is significantly larger than for small 

business development, particularly for community development investing (see Table 4-6). Large banks in 

Appalachia made investments of $762 million in housing compared to just $150 million in small business 

development. The dollar amount of investments in housing was five times greater than that for small 

business development. Likewise, large banks in Appalachia made $155 million in community 

development lending for housing compared to $82 million for small business or about twice as much in 

housing.14 While it would not be desirable to decrease the financing for affordable housing, stakeholders 

could narrow the disparity by working with banks to increase community development financing for 

small businesses.

Table 4-1: Descriptive Statistics of Large Banks in Appalachia

Number 60

Median asset size $1,500,000,000 

Total assets of large banks $433,523,204,000 

Median CRA exam cycle 3.0

Note: BNY Mellon, N.A. Bank, which had assets in the large bank range, was not included in this analysis. This bank’s performance was 

evaluated under the Wholesale Bank examination procedures.

Table 4-2: Large Banks by Year of CRA Exams

Year of CRA Exams Total Percent

2002 1 1.7%

2006 1 1.7%

2007 1 1.7%

2008 9 15.0%

2009 14 23.3%

2010 22 36.7%

2011 12 20.0%

14 It was easier to classify investments for either housing or small business than it was for community development 

loans. Hence, although total community development loan levels were higher than investment levels, our findings 

report lower dollar amounts for community development housing and small business loans than investments.

134

 
Table 4-3: Top Five Banks in Appalachia

Bank Name Asset Size State County City

Regions Bank $138,000,000,000 AL Jefferson Birmingham

Branch Banking and Trust Company $126,000,000,000 NC Forsyth Winston Salem

Compass Bank $43,800,000,000 AL Jefferson Birmingham

BancorpSouth Bank $13,232,594,000 MS Lee Tupelo

First National Bank of Pennsylvania $8,800,000,000 PA Mercer Greenville

Total amount $329,832,594,000

Table 4-4: Large Banks by CRA Rating

Ratings

Overall Test Lending Test Investment Test

Count Percent Count Percent Count Percent

Outstanding 8 13.3% 7 11.7% 11 18.3%

High Satisfactory 0 0 42 70.0% 27 45.0%

Satisfactory 52 86.7% 0 0 0 0%

Low Satisfactory 0 0 11 18.3% 19 31.7%

Needs to Improve 0 0 0 0 3 5.0%

Substantial Noncompliance 0 0 0 0 0 0%

Table 4-5: Comparing Investment and CD Lending to Asset Ratio and Ratings for Investment Test

Ratings

CD Investment Median 

Ratio over the CRA

Exam Time Period

CD Lending Median 

Ratio over the CRA 

Exam Time Period

Outstanding 0.46% 0.98%

High Satisfactory 0.28% 0.33%

Satisfactory 0% 0%

Low Satisfactory 0.27% 0.59%

Needs to Improve 0.03% 0.06%

Substantial Noncompliance 0% 0%

All 0.27% 0.54%

Table 4-6: Total Dollar Amount by Type of CD Investment and CD Lending 

CD Investment/CD lending type

CD Investment (Total 

Dollar Amount) 

CD Lending (Total 

Dollar Amount)

For housing $762,090,445 $155,718,530

For small business $150,518,999 $82,210,009

Total $2,384,716,017 $5,420,868,107

Ratio of housing/small business 5.06 1.89

135
4.4 MID-SIZE BANKS – INVESTMENT AND COMMUNITY DEVELOPMENT LENDING

Mid-size banks (called intermediate small banks in the CRA regulations) are institutions with 

approximately $250 million to $1 billion in assets (the asset level is adjusted annually to take inflation 

into account). Their CRA exams differ from large bank exams in that they have a lending test and a 

community development test. The community development test is of most interest in this chapter because 

it considers community development lending and investing. 

With a median asset size of about $460 million, 119 mid-size banks were headquartered in Appalachia 

and about 95 percent of them had their most recent CRA exam in 2008 and later. The total assets of the 

mid-size banks were $68 billion. Interestingly, this aggregate asset level compares favorably to the large 

banks’ asset level of approximately $104 billion in assets after removing the five largest banks. Thus,

mid-size banks should not be under-estimated as a resource for community development financing in 

Appalachia, especially since their assessment areas (geographical areas on CRA exams) are more likely to 

be completely within the boundaries of Appalachia than their large bank counterparts.

As measured by ratings, mid-size banks were more successful on their community development test than 

their lending test (see Table 4-9). Almost 19 percent of the Appalachian mid-size banks received an 

Outstanding rating on their community development test while just 8.4 percent and 7.6 percent received 

Outstanding on their Lending test or on their overall rating, respectively. 

Higher ratings are associated with higher levels of community development lending and investment (see 

Table 4-10). For example, mid-size banks with Outstanding ratings on the community development test 

had a median investment to asset ratio over the exam time period of 0.53 percent compared to 0.13 

percent for banks with Satisfactory ratings. The median dollar amount was $3.5 million in investments for 

a mid-size bank with an Outstanding rating compared to $529,000 in median investments for a mid-size 

bank with a Satisfactory rating. Another interesting finding is that the mid-size banks with Outstanding 

ratings on the community development test have higher investment to asset ratios (0.53 percent compared 

to 0.46 percent) and community development lending to asset ratios (1.67 percent compared to 0.98 

percent) than large banks with Outstanding ratings. 

This is not to imply that mid-size banks performed better than large banks since the ratings are not 

directly comparable due to differences in the component tests. Instead, this suggests that mid-size banks 

were likely performing at a level comparable to large banks and thus should not be over-looked as a 

resource for small business financing in Appalachia. In addition, a preference for Outstanding ratings 

applies to mid-size as well as large banks; it is desirable to work with as many mid-size banks as possible 

to secure Outstanding ratings which correspond to higher levels of community development financing. 

Just as with large banks, the mid-size banks financed affordable housing to a much greater extent than 

small business development (see Table 4-11). Mid-size banks’ investment in affordable housing was 3.5 

times greater than in small business with $81.6 million for affordable housing and $23.2 million for small 

business during the exam time periods. Likewise, mid-size banks’ community development lending was 

1.8 times greater for affordable housing ($261.1 million) than for small business development ($143.9 

million). Again, the point is not to decrease affordable housing in favor of small business but to present 

more opportunities for mid-size banks to increase their financing of small business development.

136
Table 4-7: Descriptive Statistics of Mid-Size Banks in Appalachia

Number 119

Median asset size 

$460,803,000 

Total assets of large banks 

$68,489,771,500 

Median CRA exam cycle 2.83

Table 4-8: Mid-Size Banks by Year of CRA Exams 

Year of CRA Exams Total Percent

2006 1 0.84%

2007 5 4.20%

2008 19 15.97%

2009 32 26.89%

2010 37 31.09%

2011 25 21.01%

Table 4-9: Mid-Size Banks by CRA Rating

Ratings

Overall Test Lending test Community 

Development test

Count Percent Count Percent Count Percent

Outstanding 9 7.6% 10 8.4% 22 18.5%

High Satisfactory 0 0% 3 2.5% 1 0.8%

Satisfactory 108 90.8% 103 86.6% 91 76.5%

Low Satisfactory 0 0% 0 0% 2 1.7%

Needs to Improve 2 1.7% 1 0.8% 1 0.8%

Substantial Noncompliance 0 0% 0 0% 0 0%

Table 4-10: Comparing Investment and CD Lending to Asset Ratio and Ratings for Investment Test

CRA Ratings For 

Community Development 

Test

Median of 

Community 

Investment

CD Investment 

Median Ratio 

over the CRA 

Exam Time 

Period

Median of

Community

Lending

CD Lending 

Median Ratio 

over the CRA 

Exam Time 

Period

Outstanding 3,530,000 0.53% 8,512,850 1.67%

High Satisfactory* 99,000 0.01% 0 0.00%

Satisfactory 529,063 0.13% 3,950,000 0.93%

Low Satisfactory 1,102,500 0.14% 8,950,000 1.20%

Needs to Improve* 5,705 0.00% 0 0%

Substantial Noncompliance 0 0% 0 0%

All 853,284 0.17% 4,300,000 1.03%

*Note: Only one bank with this rating for community development test

137
Table 4-11: Total Dollar Amount by Type of CD Investment and CD Lending

CD Investment/CD Lending Type

CD Investment (Total 

Dollar Amount) 

CD Lending (Total Dollar 

Amount)

For housing $81,675,532 $261,096,724

For small business $23,168,686 $143,946,174

Total $246,613,338 $820,135,971

Ratio of housing/small business 3.53 1.81

4.5 TOTAL INVESTMENT AND COMMUNITY DEVELOPMENT LENDING LEVELS

Despite the financial crisis, the level of community development lending and investing was greater for 

banks in this study than in NCRC’s previous study for ARC.15 In the sample for this study, the total 

community development lending and investing was $8.8 billion for large and mid-size banks based in 

Appalachia during a three-year time period compared to $5.4 billion in the last study.16 In addition, the 

amount of community development lending and investing for small business was $399 million for this 

study compared to $291 million in other previous study. 

An explanation for the difference in community development lending and investment levels documented 

by the two studies is the difference of the top five banks. In the current study, the top five banks had 

assets of $329.8 billion while the top five in the 2007 study had assets of $253.3 billion. This surge in 

assets translated to community development investments and lending of the top five as being $3 billion 

more for the 2012 study than the 2007 study. 

This finding should not lead to conclusions that consolidation in the banking industry has a beneficial 

impact for community development lending and investing. While regions of the country might have 

experienced increases, the amount of community development lending, as indicated by data from the 

Federal Financial Institutions Examination Council, has declined significantly in the nation over the last 

few years.17 Moreover, it must be remembered that the community development lending and investing of 

the top five banks located in Appalachia is not solely for Appalachian counties. The top five banks are 

very large institutions serving several states, meaning that their community development lending and 

investing is most likely spread across a large area. Yet, the top five banks are clearly an important 

resource for Appalachia.

4.6 EXAMPLES OF SMALL BUSINESS FINANCING

Community development financing geared for small businesses included investments in Small Business 

Investment Corporations (SBICs), grants to Community Development Financial Institutions (CDFI), and 

financing for small business-related infrastructure projects such as industrial parks undertaken by local 

15 The previous NCRC study for ARC can be accessed via 

http://www.arc.gov/research/researchreportdetails.asp?REPORT_ID=8. 16 The median time period covered by a CRA exam in this study was approximately three years where it was 2.5 

years in the previous study.

17 The level of community development lending declined nationally from $63.8 billion in 2007 to $40.3 billion in 

2010.

138

 
industrial development agencies. Immediately below are examples of small business financing throughout 

Appalachia discussed on CRA exams. These include: 

• Branch Banking and Trust, a large bank located in Winston Salem, North Carolina which is in a 

small metropolitan county in South Central Appalachia, made a Small Business Investment 

Corporation (SBIC) investment in Capital Partners of $134.1 million. 

• Bartow County Bank of Georgia, located in Southern Appalachia, made a grant to Appalachian 

Community Enterprises, a CDFI that specializes in small business lending of under $35,000. 

• Southern Community Bank and Trust, a bank with $1.8 billion in assets located in Winston 

Salem, North Carolina, had a $4 million investment in Salem Halifax Capital Partners SBIC 

recorded by the previous exam. The most recent CRA exam discussed a $1.7 million investment 

in Solomon Hess SBA loan fund, which is a community development entity. 

• United Bank, a bank with $3.5 billion in assets located in Parkersburg, West Virginia, made a 

$920,000 investment in United Venture Fund, a new markets venture capital company that 

specializes in small business equity investment. The bank also made a $20,000 equity investment 

in the Ohio Valley Community Development Corporation (CDC) that finances small businesses.

• Wesbanco, a bank with $5.4 billion in assets located in Wheeling, West Virginia, made an equity 

investment in the Appalachian Center for Economic Networks and in venture capital firms 

serving Appalachia including Adena Ventures and Mountaineer Capital.

• Carter Bank and Trust, a bank with $2.7 billion in assets located in South Central Appalachia in 

Martinsville, Virginia, made a $4 million loan to the Industrial Development Authority of Henry 

County for land acquisition to develop an industrial park. 

• Hometrust Bank, a bank with assets of $1.5 billion in Haywood County, North Carolina, invested 

$800,000 in a SBIC called Plexus Fund and made $9.8 million in SBA 504 loans. 

• CB&S Bank, a $1.2 billion bank based in Russellville, Alabama made four lines of credit of 

$839,000 available to a nonprofit corporation in a low-income tract in Greenville, Mississippi; the 

nonprofit provides venture capital to small businesses and rental space to start-ups. The bank 

donated $100,000 for the development of the Shoals Center for Business and Economic 

Development in Florence, Alabama; the Center provides site assistance for relocation of 

businesses and research support for businesses.

• The Farmers National Bank of Canfield, Ohio, with assets of $879 million, participates in loan 

funds for small business including a public-private fund, Ohio GROW Now, and the Ohio Capital 

Access Program for working capital loans.

In a number of cases, the CRA exams recorded a paucity of investments by banks and stated that 

investment opportunities are limited in the bank’s assessment area (geographical area on CRA exams). 

Hopefully, stakeholders working with ARC can identify and develop the infrastructure needed to support 

more investment, particularly in counties discussed immediately below in which banks make low 

amounts of small business investments.

4.7 COMMUNITY DEVELOPMENT FINANCING BY COUNTY CLASSIFICATION

In order to assess community development lending and investment resources within Appalachia, Table 4-

12 below shows the dollar amount of bank assets with large bank and mid-size bank CRA exams and 

their financing for small business development. 

139
At $14 billion, Central Appalachia has the lowest dollar amount of assets subject to mid-size and large 

bank CRA exams while Southern Appalachia has the largest dollar amount of assets at $230 billion. 

Large metropolitan counties have the largest dollar amount of assets while non-metropolitan counties 

adjacent to large metropolitan counties have the lowest dollar amount of assets. Economically distressed 

counties have just $3 billion in bank assets while competitive counties have $331 billion in bank assets 

subject to mid-size and large bank CRA exams.

Banks located in South Central Appalachia, small metropolitan counties, and competitive counties offered 

the largest amounts of investments for small businesses at $147.9 million, $158.3 million, and $150.6 

million, respectively during the CRA exam time period. Conversely, banks located in Central Appalachia, 

non-metropolitan counties adjacent to large metropolitan counties, and economically distressed counties 

offered the smallest amounts of investments for small businesses at $419,000, $430,000, and $753,000, 

respectively (see Figures 4-1 through 4-3). 

Banks with the largest amounts of community development lending for small businesses ranging from 

$78 million to $143 million were located in Southern Appalachia, small metropolitan counties, and 

transitional counties. In contrast, banks making the lowest amounts of community development lending 

for small businesses of $2 million to $6 million were in North Central Appalachia, non-metropolitan 

counties adjacent to large metropolitan counties, and economically distressed counties (see Table 4-12). 

The median ratios of investments to assets and community development loans to assets suggest that banks 

across Appalachia are robust in terms of their CRA development financing. The ratios are not consistently 

lower in disadvantaged counties in Appalachia. For example, the ratios in distressed and at-risk counties 

are generally higher than in transitional, competitive, and attainment counties. Moreover, there does not 

appear to be a direct correlation between the ratios and the level of financing for small business. The 

median ratio of investment to assets for large metropolitan counties is higher than that for small 

metropolitan counties but the level of investment for small business in small metropolitan counties is 17 

times higher. Interestingly, total asset levels for the banks in large metropolitan and small metropolitan 

counties are also similar. It appears that the explanation for the vast difference in investment dollars for 

small business in these two county categories is the specialization of the banks; banks in small 

metropolitan counties might be specialists in small business financing while those in large metropolitan 

counties devote the majority of their investment dollars to affordable housing or other community 

development activities.

140
Figure 4-1: Investments in Small Business by Region*

Figure 4-2: Investments in Small Business by County Type*

Figure 4-3: Investments in Small Business by Economic Status*

*Note: The dollar amounts in these graphs reflect investments of banks located in the various county categories. The dollar amounts are not 

necessarily what each county category receives. In other words, the investments are made by the institutions in these county types and not 

necessarily made to recipients in these county types.

$173,687,685

$8,434,258

$5,089,539

$419,000

$147,888,280

$11,856,608

$0 $50,000,000 $100,000,000 $150,000,000 $200,000,000

Appalachian Region

Northern Appalachia

North Central Appalachia

Central Appalachia

South Central Appalachia

Southern Appalachia

$9,258,082

$158,302,779

$430,000

$2,597,557

$3,099,267

$0 $50,000,000 $100,000,000 $150,000,000 $200,000,000

Large Metro (1 million + people)

Small Metro (< 1 million people)

Nonmetro, Adjacent to Large Metro

Nonmetro, Adjacent to Small Metro

Rural (Nonmetro, Nonadjacent)

$753,000

$972,500

$20,888,888

$150,573,522

$499,775

$0 $50,000,000 $100,000,000 $150,000,000 $200,000,000

Distressed

At-Risk

Transitional

Competitive

Attainment

141
Table 4-12: CD Investment and CD Lending by County Type

Number 

of Banks Total Assets

Amount of 

Investment for 

Small Business

Amount of CD 

Lending for 

Small Business

CD 

Investment 

Median 

Ratio over 

the CRA 

Exam Time 

Period

CD Lending 

Median Ratio 

over the CRA 

Exam Time 

Period

Appalachian Region 179 $502,012,975,500 $173,687,685 $226,156,183 0.18% 0.86%

Subregions

Northern Appalachia 65 $81,708,803,000 $8,434,258 $62,821,604 0.14% 0.64%

North Central Appalachia 16 $17,028,048,000 $5,089,539 $1,926,800 0.21% 0.39%

Central Appalachia 18 $14,001,292,000 $419,000 $22,487,483 0.24% 1.11%

South Central Appalachia 37 $158,978,380,000 $147,888,280 $60,742,973 0.29% 0.94%

Southern Appalachia 43 $230,296,452,500 $11,856,608 $78,177,323 0.30% 1.05%

County Types

Large Metro (1 million + people) 26 $203,142,230,000 $9,258,082 $19,003,513 0.37% 0.73%

Small Metro (< 1 million people) 68 $196,915,140,500 $158,302,779 $110,343,728 0.15% 1.00%

Nonmetro, Adjacent to Large Metro 14 $18,610,952,000 $430,000 $6,127,120 0.12% 1.27%

Nonmetro, Adjacent to Small Metro 45 $42,972,537,000 $2,597,557 $35,491,475 0.28% 0.53%

Rural (Nonmetro, Nonadjacent) 26 $40,372,116,000 $3,099,267 $55,190,347 0.27% 0.98%

Economic Status

Distressed 7 $3,141,537,000 $753,000 $2,399,000 0.37% 1.20%

At-Risk 20 $15,302,094,000 $972,500 $32,729,247 0.26% 1.05%

Transitional 120 $148,672,748,500 $20,888,888 $143,149,423 0.17% 0.93%

Competitive 27 $331,555,451,000 $150,573,522 $46,817,513 0.30% 0.61%

Attainment 5 $3,341,145,000 $499,775 $1,061,000 0.23% 0.49%

142
CHAPTER 5 SMALL BUSINESS ADMINISTRATION (SBA) LENDING

5.1 SUMMARY

The analysis of private sector lending identified categories of counties within Appalachia that are 

relatively underserved when considering small business loans and investments. The analysis of public 

sector programs like the SBA 7a guarantee lending program and the SBA 504 loan program are an 

important complement to the private sector lending analysis provided in this study. Studying public sector 

programs informs judgments about whether these public programs have the capacity to address credit 

gaps in underserved geographies and/or whether they are effectively targeting counties relatively 

underserved by the private sector.

• The SBA (Small Business Administration) 7a program guarantees a small volume of loans 

nationally when compared to overall business lending. Loans receiving SBA 7a guarantees were 

approximately 1 percent of the loans reported by banks covered by CRA for 2007 and 2010. 

• SBA lending was not as successful in reaching small businesses in Appalachia as in the nation. In 

2010, about 15.3 SBA 7a loans were issued per 10,000 small businesses in Appalachia as 

compared with 21.9 loans per 10,000 small businesses in the nation. SBA 7a lending levels were 

30 percent less in Appalachia than the nation by 2010. 

• Within Appalachia, SBA 7a lending per 10,000 small businesses was lower in distressed, rural, 

and Central Appalachian counties when compared to attainment, metropolitan, and Northern 

Appalachian counties. In 2010, lenders made 9.7 loans per 10,000 small businesses in distressed 

counties, while they made 20.2 SBA 7a loans per 10,000 small businesses in attainment counties. 

• In 2007, SBA 7a lending was provided in proportion to the portion of the population that was 

minority in Appalachia, but by 2010 lending to minority-owned businesses dropped and was no 

longer in proportion to the population. However, the gap between the percent of 7a lending to 

minority-owned businesses and the percent of minorities in the population was greater for the 

nation than for Appalachia. SBA 7a lending to woman-owned businesses was not in proportion to 

the percentage of women in the population, and this gap was greatest in disadvantaged counties 

such as distressed counties and those in Central Appalachia.

• SBA 504 lending assists small businesses in acquiring land and large equipment. It has small 

lending volumes in Appalachia; only 325 SBA 504 loans were offered in Appalachia in 2010. 

Within Appalachia, SBA 504 lending volumes were the lowest in Central Appalachia, rural 

counties, and distressed counties. In 2007, lenders made one SBA 504 loan per 10,000 small 

businesses in distressed counties while they made 6.5 loans per 10,000 small businesses in 

attainment counties. The disparity in 2010 was similar. 

Private lenders, the SBA, community organizations, and other stakeholders should work together to more 

effectively target SBA lending to disadvantaged parts of Appalachia. Instead of mirroring disparities in 

private sector lending, SBA lending should be counteracting the disparities in a more effective fashion.. In 

particular, greater outreach to woman-owned businesses is needed, while trends in lending to minorityowned businesses should be monitored to ensure that their access to SBA lending does not diminish. 

143
5.2 SMALL BUSINESS ADMINISTRATION (SBA) 7A LENDING 

The Small Business Administration (SBA) provides guarantees insuring lenders against losses on loans 

under its 7a program. 18 These guarantees are intended to assist lenders that target traditionally 

underserved small businesses. According to the SBA website, “The 7(a) Loan Program includes financial 

help for businesses with special requirements. For example, funds are available for loans...to businesses 

that operate in rural areas, and for other very specific purposes.” 

In keeping with this mission, the SBA releases data on whether the small business borrower is minorityowned or women-owned. This analysis will assess to what extent SBA loans effectively target 

disadvantaged communities such as distressed counties or rural counties and whether SBA loans are 

effectively reaching minority and women entrepreneurs. 

In 2007, the number of SBA 7a loans was just a small fraction of the total number of small business loans 

reported per the CRA requirements. Banks reported 13,078,967 and 781,396 CRA small business loans in 

the nation and in Appalachia in 2007, respectively. In contrast, banks reported just 99,606 and 4,796 SBA 

7a loans in the nation and in Appalachia, respectively, during 2007. SBA lending levels were less than 1 

percent of CRA loan levels in Appalachia and the United States in 2007 and 2010. 

SBA lending declined by about half from 2007 through 2010. In Appalachia, the decline in SBA 7a loans 

was from 4,796 loans in 2007 to 2,415 loans in 2010 (see Table 5-1).

Banks are less successful in making SBA 7a loans in Appalachia as compared to the nation. In the nation, 

banks issued 45.7 SBA 7a loans per 10,000 businesses but just 29.8 SBA 7a loans per 10,000 businesses 

were issued in Appalachia during 2007 (see Figure 5-1). About 15.3 SBA 7a loans per 10,000 small 

businesses were issued in Appalachia in 2010, while 21.9 SBA 7a loans per small businesses were issued 

in the nation during 2010 (see Table 5-2 which shows similar disparity ratios in 2007 and 2010 comparing 

Appalachia and the nation). The index maps (see Maps 5-1 and 5-2) for SBA 7a lending reinforce the 

finding that SBA 7a is less successful in Appalachia overall than the nation. Except for the Northern 

Appalachia subregion, SBA 7a lending levels are generally lower than that of the country as shown by the 

predominance of the white and red shaded counties. 

Within Appalachia, SBA 7a lending is not focusing on disadvantaged communities. In distressed 

counties, lenders offered 10.8 SBA 7a loans per 10,000 small businesses while in attainment counties, 

lenders offered 49.2 SBA 7a loans per 10,000 small businesses during 2007 (see Figure 5-3). In 2010, 

lenders made 9.7 loans per 10,000 small businesses in distressed counties and 20.2 SBA 7a loans per 

10,000 small businesses in attainment counties. Similar disparities occur when comparing metropolitan 

and rural counties and Northern Appalachia to Central Appalachia (see Figures 5-1 and 5-2).

18 See SBA website, http://www.sba.gov/category/navigation-structure/loans-grants/small-businessloans/sba-loan-programs/7a-loan-program.

144

 
Figure 5-1: Number of SBA 7a Loans per 10,000 Small Businesses by Region

Figure 5-2: Number of SBA 7a Loans per 10,000 Small Businesses by County Type

Figure 5-3: Number of SBA 7a Loans per 10,000 Small Businesses by Economic Status

45.7

29.8

43.4

31.7

13.5

21.5

25.9

21.8

15.3

19.0

17.1

11.8

13.8

13.3

0.00 10.00 20.00 30.00 40.00 50.00

United States

Appalachian Region

Northern Appalachia

North Central Appalachia

Central Appalachia

South Central Appalachia

Southern Appalachia

2007 2010

45.8

29.1

25.5

22.4

13.2

16.5

16.4

12.9

14.2

11.8

0.00 10.00 20.00 30.00 40.00 50.00

Large Metros (pop. 1 million +)

Small Metros (pop. <1 million)

Nonmetro, Adjacent to Large Metros

Nonmetro, Adjacent to Small Metros

Rural (nonmetro, not adj. to a metro)

Per capita personal income index 2007 2010

10.8

14.8

27.5

39.5

49.2

9.7

10.5

15.5

16.0

20.2

0.00 10.00 20.00 30.00 40.00 50.00 60.00

Distressed

At-Risk

Transitional

Competitive

Attainment

Per capita personal income index 2007 2010

145
Map 5-1: SBA 7a Lending Index, 2007

Ratio of SBA 7a Loans per 10,000 Small Businesses, Indexed Values (U.S. =1)

Statistics of Indexed Values

Count 420

Minimum 0

Maximum 10.03

Mean 0.773

Standard Deviation 0.866

Histogram of Indexed Values

146
Map 5-2: SBA 7a Lending Index, 2010

Ratio of SBA 7a Loans per 10,000 Small Businesses, Indexed Values (U.S. =1)

Statistics of Indexed Values

Count 420

Minimum 0.1

Maximum 9.366

Mean 0.697

Standard Deviation 0.884

Histogram of Indexed Values

147
Figure 5-4: Number of SBA 7a Loans per 10,000 Small Businesses by State

19.5

20.3

18.0

33.1

37.2

31.5

31.4

14.1

37.3

41.5

22.9

42.2

20.5

8.6

24.3

55.4

30.5

56.6

27.7

23.1

28.7

56.4

38.1

60.1

47.8

46.9

48.4

23.8

20.6

24.9

26.0

20.0

30.5

34.3

15.1

36.2

33.0

11.5

11.4

11.7

14.9

16.4

14.2

17.5

13.4

18.9

16.6

22.7

16.4

29.3

9.6

35.3

22.7

32.3

22.2

16.7

17.1

16.6

29.6

20.4

29.1

18.2

15.4

20.4

11.7

10.7

12.1

13.3

11.7

14.4

15.0

10.7

15.4

16.9

0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00

Alabama

Appalachian Alabama

Non-Appalachian Alabama

Georgia

Appalachian Georgia

Non-Appalachian Georgia

Kentucky

Appalachian Kentucky

Non-Appalachian Kentucky

Maryland

Appalachian Maryland

Non-Appalachian Maryland

Mississippi

Appalachian Mississippi

Non-Appalachian Mississippi

New York

Appalachian New York

Non-Appalachian New York

North Carolina

Appalachian North Carolina

Non-Appalachian North Carolina

Ohio

Appalachian Ohio

Non-Appalachian Ohio

Pennsylvania

Appalachian Pennsylvania

Non-Appalachian Pennsylvania

South Carolina

Appalachian South Carolina

Non-Appalachian South Carolina

Tennessee

Appalachian Tennessee

Non-Appalachian Tennessee

Virginia

Appalachian Virginia

Non-Appalachian Virginia

West Virginia (entire state)

2007 2010

148
Table 5-1: Number of SBA 7a Loans per 10,000 Businesses in Appalachia

Number of 

SBA 7a 

Loans

Number of 

Small 

Businesses

Number of SBA 

7a Loans per 

10,000 Small 

Businesses

Number of 

SBA 7a 

Loans

Number of 

Small 

Businesses

Number of SBA 

7a Loans per 

10,000 Small 

Businesses

United States 99,606 21,808,201 45.7 47,000 21,530,378 21.8

Appalachian Region 4,796 1,607,645 29.8 2,415 1,577,370 15.3

Subregions

Northern Appalachia 2,073 477,301 43.4 915 482,014 19.0

North Central Appalachia 409 128,944 31.7 213 124,926 17.1

Central Appalachia 156 115,266 13.5 129 109,122 11.8

South Central Appalachia 660 307,059 21.5 420 304,728 13.8

Southern Appalachia 1,498 579,075 25.9 738 556,580 13.3

County Types

Large Metros (pop. 1 million +) 1,813 395,470 45.8 669 404,330 16.5

Small Metros (pop. <1 million) 1,783 613,330 29.1 993 604,234 16.4

Nonmetro, Adjacent to Large Metros 278 109,171 25.5 134 103,911 12.9

Nonmetro, Adjacent to Small Metros 673 301,104 22.4 415 291,784 14.2

Rural (nonmetro, not adj. to a metro) 249 188,570 13.2 204 173,111 11.8

Economic Status

Distressed 97 89,777 10.8 84 86,870 9.7

At-Risk 215 145,503 14.8 145 138,028 10.5

Transitional 2,440 887,755 27.5 1,406 905,985 15.5

Competitive 1,206 305,587 39.5 468 292,327 16.0

Attainment 668 135,857 49.2 312 154,160 20.2

Alabama 613 314,870 19.5 347 302,408 11.5

Appalachian Alabama 405 199,561 20.3 219 192,538 11.4

Non-Appalachian Alabama 208 115,309 18.0 128 109,870 11.7

Georgia 2,747 828,977 33.1 1,180 792,556 14.9

Appalachian Georgia 888 239021 37.2 391 238,124 16.4

Non-Appalachian Georgia 1,859 589,956 31.5 789 554,432 14.2

Kentucky 954 304,048 31.4 522 297,724 17.5

Appalachian Kentucky 109 77,542 14.1 99 74,004 13.4

Non-Appalachian Kentucky 845 226,506 37.3 423 223,720 18.9

Maryland 1,612 388,206 41.5 647 389,579 16.6

Appalachian Maryland 33 14,392 22.9 34 14,993 22.7

Non-Appalachian Maryland 1,579 373,814 42.2 613 374,586 16.4

Mississippi 591 288,779 20.5 728 248,541 29.3

Appalachian Mississippi 60 69,965 8.6 56 58,339 9.6

Non-Appalachian Mississippi 531 218,814 24.3 672 190,202 35.3

New York 7,781 1,404,794 55.4 2,849 1,253,951 22.7

Appalachian New York 205 67,312 30.5 201 62,314 32.3

Non-Appalachian New York 7,576 1,337,482 56.6 2,648 1,191,637 22.2

North Carolina 1,702 615,139 27.7 1,044 624,133 16.7

Appalachian North Carolina 261 112,966 23.1 189 110,530 17.1

Non-Appalachian North Carolina 1,441 502,173 28.7 855 513,603 16.6

Ohio 3,898 691,536 56.4 1,974 667,427 29.6

Appalachian Ohio 447 117,389 38.1 231 113,316 20.4

Non-Appalachian Ohio 3,451 574,147 60.1 1,611 554,111 29.1

Pennsylvania 3,398 711,343 47.8 1,392 763,099 18.2

Appalachian Pennsylvania 1,504 320,365 46.9 513 332,120 15.4

Non-Appalachian Pennsylvania 1,894 390,978 48.4 879 430,979 20.4

South Carolina 647 272,316 23.8 313 266,975 11.7

Appalachian South Carolina 145 70,528 20.6 72 67,579 10.7

Non-Appalachian South Carolina 502 201,788 24.9 241 199,396 12.1

Tennessee 1,112 427,304 26.0 561 422,466 13.3

Appalachian Tennessee 362 181,133 20.0 209 178,251 11.7

Non-Appalachian Tennessee 750 246,171 30.5 351 244,215 14.4

Virginia 1,659 483,178 34.3 820 547,124 15.0

Appalachian Virginia 64 42,503 15.1 47 44,052 10.7

Non-Appalachian Virginia 1,595 440,675 36.2 773 503,072 15.4

West Virginia (entire state) 313 94,968 33.0 154 91,210 16.9

2007 2010

149
Table 5-2: Disparity Ratios of SBA 7a Loans per 10,000 Businesses

Year Numerator Denominator Disparity Ratio

US vs. Appalachia 2007 45.67 29.83 1.53

2010 21.83 15.31 1.43

Northern vs. Central 2007 43.43 13.53 3.21

2010 18.98 11.82 1.61

Large Metro vs. Rural 2007 45.84 13.20 3.47

2010 16.55 11.78 1.40

Attainment vs. Distressed 2007 49.17 10.80 4.55

2010 20.24 9.67 2.09

5.2.1 SBA 7A LENDING TO MINORITY-OWNED BUSINESSES

In 2010, SBA 7a lending to minority-owned small businesses was highest in the Southern Appalachia 

subregion at 204 loans (see Table 5-3). Lending to minority-owned businesses was also much higher in 

metropolitan counties (263 loans) than all rural counties (65 loans). Lastly, minority-owned small 

businesses received the great majority of their loans (310) in transitional, competitive, and attainment 

counties while only receiving 19 loans in distressed and at-risk counties. The pattern was similar in 2007 

for SBA 7a lending to minority-owned small businesses.

Minority-owned businesses experienced favorable access to SBA 7a lending in Appalachia during 2007. 

Minority-owned small businesses received about 24 percent of the SBA loans while 15 percent of the 

population was minority in Appalachia during 2007. The resulting ratio of 1.57 indicates that the 

percentage of SBA 7a lending to minority-owned firms was greater than the percentage of the population 

that was minority. In contrast, the ratio of 1 for the nation indicates that the percentage of lending to 

minority-owned firms equaled the percentage of the population that was minority. 

By 2010, the ratios dipped below 1 in both the nation and Appalachia, but the ratio was considerably 

lower in the nation, meaning that minority-owned firms in Appalachia experienced more access to SBA 

7a loans. However, in both the nation and Appalachia, the financial crisis had a disproportionately 

unfavorable impact on access to SBA 7a loans for minority-owned small businesses. 

Within Appalachia, the areas with large percentages of minorities (Southern Appalachia, large 

metropolitan counties, and attainment counties) had ratios above 1 in 2007 and ratios of between 0.9 and 

1 in 2010, meaning that lenders were relatively successful in serving minority-owned small businesses in 

areas of Appalachia with large minority populations. Interestingly, lenders were generally more 

successful in serving minority-owned firms in areas with lower percentages of minorities. For examples, 

the ratios were 3.5 in Central Appalachia, 2.1 in rural counties, and 2.4 in distressed counties in 2007. 

SBA 7a lending was less successful in serving women-owned small businesses. The ratios between small 

business lending to women-owned small businesses and the female population was about 0.5 and 0.4 in 

2007 and 2010, respectively, for both Appalachia and the nation. The ratios were higher in advantaged 

counties such as large metropolitan counties and attainment counties (see Table 5-4). While improvement 

150
is needed in all counties, the greatest improvement in access to loans for women-owned businesses is 

needed in the disadvantaged counties.

151
Table 5-3: SBA 7a Loans to Minority-Owned Small Businesses in Appalachia

Overall 

Approved 

Loans

Approved 

Loans to 

Minority 

Percent 

of Loans 

to 

Minority

Percent of 

Minority 

Population

Ratio of % 

of Loans to 

Minority to 

% Minority

Overall 

Approved 

Loans

Approved 

Loans to 

Minority

Percent 

of Loans 

to 

Minority

Percent of 

Minority 

Population

Ratio of % of 

Loans to 

Minority to 

% Minority

United States 99,606 35,186 35.3% 34.7% 1.02 47,000 9,695 20.6% 36.3% 0.57

Appalachian Region 4,796 1,147 23.9% 15.2% 1.57 2,415 328 13.6% 16.4% 0.83

Subregions

Northern Appalachia 2,073 239 11.5% 9.5% 1.21 915 54 5.9% 10.4% 0.57

North Central Appalachia 409 55 13.4% 6.5% 2.08 213 9 4.2% 6.6% 0.64

Central Appalachia 156 24 15.4% 4.4% 3.50 129 10 7.8% 4.6% 1.70

South Central Appalachia 660 175 26.5% 14.1% 1.88 420 51 12.1% 14.4% 0.84

Southern Appalachia 1,498 654 43.7% 29.3% 1.49 738 204 27.6% 30.0% 0.92

County Types

Large Metro (1 million + people) 1,813 590 32.5% 21.8% 1.49 669 153 22.9% 25.2% 0.91

Small metro (< 1 million people) 1,783 357 20.0% 16.1% 1.24 993 110 11.1% 16.8% 0.66

Nonmetro, Adjacent to Large Metro 278 47 16.9% 10.1% 1.67 134 13 9.7% 11.3% 0.86

Nonmetro, Adjacent to Small Metro 673 89 13.2% 9.9% 1.34 415 31 7.5% 9.9% 0.76

Rural (Nonmetro, Nonadjacent) 249 64 25.7% 12.0% 2.14 204 21 10.3% 11.0% 0.93

Economic Status

Distressed 97 33 34.0% 13.9% 2.44 84 9 10.7% 12.5% 0.86

At-Risk 215 48 22.3% 11.7% 1.91 145 10 6.9% 10.0% 0.69

Transitional 2,440 387 15.9% 13.0% 1.22 1,406 132 9.4% 13.7% 0.68

Competitive 1,206 279 23.1% 21.2% 1.09 468 55 11.8% 21.2% 0.55

Attainment 668 369 55.2% 27.3% 2.02 312 122 39.1% 38.7% 1.01

Alabama 613 183 29.9% 32.0% 0.93 347 60 17.3% 33.0% 0.52

Appalachian Alabama 405 113 27.9% 31.3% 0.89 219 34 15.5% 29.0% 0.54

Non-Appalachian Alabama 208 70 33.7% 33.3% 1.01 128 26 20.3% 40.1% 0.51

Georgia 2,747 1,523 55.4% 42.3% 1.31 1,180 430 36.4% 44.1% 0.83

Appalachian Georgia 888 466 52.5% 24.5% 2.15 391 139 35.6% 31.8% 1.12

Non-Appalachian Georgia 1,859 1,057 56.9% 48.3% 1.18 789 291 36.9% 49.5% 0.75

Kentucky 954 155 16.2% 12.8% 1.27 522 49 9.4% 13.7% 0.69

Appalachian Kentucky 109 17 15.6% 4.4% 3.58 99 8 8.1% 4.6% 1.76

Non-Appalachian Kentucky 845 138 16.3% 16.2% 1.01 423 41 9.7% 17.1% 0.57

Maryland 1,612 890 55.2% 43.2% 1.28 647 211 32.6% 45.3% 0.72

Appalachian Maryland 33 6 18.2% 10.7% 1.70 34 0 0.0% 13.6% 0.00

Non-Appalachian Maryland 1,579 884 56.0% 44.7% 1.25 613 211 34.4% 46.8% 0.74

Mississippi 591 217 36.7% 41.2% 0.89 728 99 13.6% 42.0% 0.32

Appalachian Mississippi 60 36 60.0% 41.5% 1.45 56 8 14.3% 35.2% 0.41

Non-Appalachian Mississippi 531 181 34.1% 41.1% 0.83 672 91 13.5% 43.8% 0.31

New York 7,781 3,248 41.7% 40.6% 1.03 2,849 618 21.7% 41.7% 0.52

Appalachian New York 205 33 16.1% 9.1% 1.77 201 9 4.5% 9.9% 0.45

Non-Appalachian New York 7,576 3,215 42.4% 42.5% 1.00 2,648 609 23.0% 43.5% 0.53

North Carolina 1,702 590 34.7% 33.4% 1.04 1,044 202 19.4% 34.7% 0.56

Appalachian North Carolina 261 55 21.1% 18.3% 1.15 189 18 9.5% 18.7% 0.51

Non-Appalachian North Carolina 1,441 535 37.1% 36.7% 1.01 855 184 21.5% 38.2% 0.56

Ohio 3,898 567 14.5% 18.0% 0.81 1,974 184 9.3% 18.9% 0.49

Appalachian Ohio 447 48 10.7% 8.9% 1.21 231 16 6.9% 8.2% 0.85

Non-Appalachian Ohio 3,451 519 15.0% 20.1% 0.75 1,743 168 9.6% 21.2% 0.46

Pennslyvania 3,398 639 18.8% 19.2% 0.98 1,392 171 12.3% 20.5% 0.60

Appalachian Pennsyvania 1,504 168 11.2% 9.2% 1.21 513 34 6.6% 10.5% 0.63

Non-Appalachian Pennsyvania 1,894 471 24.9% 28.6% 0.87 879 137 15.6% 29.0% 0.54

South Carolina 647 234 36.2% 35.4% 1.02 313 74 23.6% 36.0% 0.66

Appalachian Carolina 145 39 26.9% 26.8% 1.00 72 23 31.9% 25.5% 1.25

Non-Appalachian Carolina 502 195 38.8% 38.1% 1.02 241 51 21.2% 39.5% 0.54

Tennessee 1,112 517 46.5% 23.4% 1.99 561 119 21.2% 24.4% 0.87

Appalachian Tennessee 362 106 29.3% 10.7% 2.74 209 30 14.4% 11.2% 1.28

Non-Appalachian Tennessee 750 411 54.8% 33.0% 1.66 352 89 25.3% 34.6% 0.73

Virginia 1,659 742 44.7% 33.6% 1.33 820 199 24.3% 35.2% 0.69

Appalachian Virginia 64 19 29.7% 10.3% 2.88 47 5 10.6% 9.6% 1.11

Non-Appalachian Virginia 1,595 723 45.3% 36.3% 1.25 773 194 25.1% 37.9% 0.66

West Virginia (entire state) 313 41 13.1% 6.7% 1.95 154 4 2.6% 6.8% 0.38

2007 2010

152
Table 5-4: SBA 7a Loans to Women-Owned Businesses in Appalachia

Overall 

Approved 

Loans

Approved 

Loans to 

Female

Percent of 

Loans to 

Female

Percent of 

Female 

Population

Ratio of % 

of Loans to 

Female to 

% of 

Female 

Overall 

Approved 

Loans

Approved 

Loans to 

Female

Percent 

of Loans 

to Female

Percent of 

Female 

Population

Ratio of % 

of Loans to 

Female to 

% Female

United States 99,606 22,053 22.1% 50.9% 0.44 47,000 8,584 18.3% 50.8% 0.36

Appalachian Region 4,796 1,143 23.8% 51.0% 0.47 2,415 476 19.7% 50.9% 0.39

Subregions

Northern Appalachia 2,073 451 21.8% 51.0% 0.43 915 150 16.4% 50.8% 0.32

North Central Appalachia 409 73 17.9% 50.6% 0.35 213 36 16.9% 50.5% 0.33

Central Appalachia 156 30 19.2% 50.6% 0.38 129 16 12.4% 50.4% 0.25

South Central Appalachia 660 166 25.2% 51.2% 0.49 420 96 22.9% 51.2% 0.45

Southern Appalachia 1,498 423 28.2% 51.1% 0.55 738 178 24.1% 51.2% 0.47

County Types

Large Metro (1 million + people) 1,813 482 26.6% 51.4% 0.52 669 164 24.5% 51.3% 0.48

Small metro (< 1 million people) 1,783 434 24.3% 51.1% 0.48 993 191 19.2% 51.1% 0.38

Nonmetro, Adjacent to Large Metro 278 61 21.9% 50.7% 0.43 134 22 16.4% 50.6% 0.32

Nonmetro, Adjacent to Small Metro 673 112 16.6% 50.6% 0.33 415 63 15.2% 50.4% 0.30

Rural (Nonmetro, Nonadjacent) 249 54 21.7% 50.8% 0.43 204 36 17.7% 50.7% 0.35

Economic Status

Distressed 97 22 22.7% 50.6% 0.45 84 12 14.3% 50.4% 0.28

At-Risk 215 47 21.9% 50.8% 0.43 145 22 15.2% 50.7% 0.30

Transitional 2,440 525 21.5% 51.0% 0.42 1,406 243 17.3% 50.8% 0.34

Competitive 1,206 325 27.0% 51.5% 0.52 468 101 21.6% 51.7% 0.42

Attainment 668 195 29.2% 50.5% 0.58 312 98 31.4% 50.7% 0.62

Alabama 613 171 27.9% 51.5% 0.54 347 68 19.6% 51.5% 0.38

Appalachian Alabama 405 120 29.6% 51.4% 0.58 219 40 18.3% 51.4% 0.36

Non-Appalachian Alabama 208 51 24.5% 52.1% 0.47 128 28 21.9% 51.6% 0.42

Georgia 2,747 805 29.3% 51.1% 0.57 1,180 283 24.0% 51.2% 0.47

Appalachian Georgia 888 247 27.8% 50.6% 0.55 391 101 25.8% 50.8% 0.51

Non-Appalachian Georgia 1,859 558 30.0% 51.3% 0.59 789 182 23.1% 51.4% 0.45

Kentucky 954 180 18.9% 50.9% 0.37 522 82 15.7% 50.8% 0.31

Appalachian Kentucky 109 12 11.0% 50.6% 0.22 99 13 13.1% 50.5% 0.26

Non-Appalachian Kentucky 845 168 19.9% 51.0% 0.39 423 69 16.3% 50.9% 0.32

Maryland 1,612 478 29.7% 51.7% 0.57 647 158 24.4% 51.7% 0.47

Appalachian Maryland 33 9 27.3% 49.2% 0.55 34 4 11.8% 49.1% 0.24

Non-Appalachian Maryland 1,579 469 29.7% 51.8% 0.57 613 154 25.1% 51.8% 0.49

Mississippi 591 149 25.2% 51.5% 0.49 728 112 15.4% 51.4% 0.30

Appalachian Mississippi 60 22 36.7% 51.7% 0.71 56 20 35.7% 51.6% 0.69

Non-Appalachian Mississippi 531 127 23.9% 51.5% 0.46 672 92 13.7% 51.4% 0.27

New York 7,781 1,872 24.1% 51.7% 0.47 2,849 505 17.7% 51.6% 0.34

Appalachian New York 205 52 25.4% 50.7% 0.50 201 29 14.4% 50.6% 0.29

Non-Appalachian New York 7,576 1,820 24.0% 51.7% 0.46 2,648 476 18.0% 51.7% 0.35

North Carolina 1,702 493 29.0% 51.2% 0.57 1,044 251 24.0% 51.3% 0.47

Appalachian North Carolina 261 60 23.0% 51.3% 0.45 189 42 22.2% 51.3% 0.43

Non-Appalachian North Carolina 1,441 433 30.1% 51.2% 0.59 855 209 24.4% 51.3% 0.48

Ohio 3,898 799 20.5% 51.3% 0.40 1,974 288 14.6% 51.2% 0.29

Appalachian Ohio 447 79 17.7% 50.8% 0.35 231 28 12.1% 50.6% 0.24

Non-Appalachian Ohio 3,451 720 20.9% 51.4% 0.41 1,743 260 14.9% 51.3% 0.29

Pennslyvania 3,398 663 19.5% 51.4% 0.38 1,392 241 17.3% 51.3% 0.34

Appalachian Pennsyvania 1,504 330 21.9% 51.1% 0.43 513 90 17.5% 50.9% 0.34

Non-Appalachian Pennsyvania 1,894 333 17.6% 51.7% 0.34 879 151 17.2% 51.6% 0.33

South Carolina 647 162 25.0% 51.4% 0.49 313 65 20.8% 51.4% 0.40

Appalachian Carolina 145 34 23.5% 51.3% 0.46 72 17 23.6% 51.4% 0.46

Non-Appalachian Carolina 502 128 25.5% 51.4% 0.50 241 48 19.9% 51.4% 0.39

Tennessee 1,112 344 30.9% 51.3% 0.60 561 138 24.6% 51.3% 0.48

Appalachian Tennessee 362 102 28.2% 51.2% 0.55 209 46 22.0% 51.1% 0.43

Non-Appalachian Tennessee 750 242 32.3% 51.3% 0.63 352 92 26.1% 51.4% 0.51

Virginia 1,659 462 27.9% 50.9% 0.55 820 196 23.9% 50.9% 0.47

Appalachian Virginia 64 14 21.9% 50.4% 0.43 47 11 23.4% 50.3% 0.47

Non-Appalachian Virginia 1,595 448 28.1% 51.0% 0.55 773 185 23.9% 51.0% 0.47

West Virginia (entire state) 313 62 19.8% 50.9% 0.39 154 35 22.7% 50.7% 0.45

2007 2010

153
5.3 SBA 504 LENDING

SBA 504 lending is SBA guaranteed lending that is devoted to assisting small businesses with financing 

for construction, land acquisition, infrastructure improvements, and large equipment. SBA 504 lending is 

an important source of lending for significantly increasing the capacity of small businesses. 

SBA 504 lending is a small program. In 2007, lenders issued 10,669 SBA 504 loans in the nation and 391 

loans in Appalachia. By 2010, SBA 504 lending declined to 7,833 loans in the nation and 325 loans in 

Appalachia. 

In 2007 and 2010, SBA 504 lending was lowest in traditionally underserved areas in Appalachia. Central 

Appalachia received 10 SBA 504 loans in 2007, rural counties received 17 loans, and distressed counties 

had only 9 loans during this period (see Table 5-5 and Figures 5-5 through 5-7). In 2010, the numbers 

were almost identical in underserved areas; Central Appalachia received 9 loans, rural counties received 

16 loans, and distressed counties had only 4 SBA 504 loans (see Table 5-5).

When measured by loans per small business, the SBA 504 program is not as effective in distressed 

counties as it is in non-distressed Appalachian counties. In 2007, lenders made one SBA 504 loan per 

10,000 small businesses in distressed counties and 6.5 loans per 10,000 small businesses in attainment 

counties (see Figure 5-7). The disparity in 2010 was similar. 

Like SBA 7a lending, SBA 504 lending to minority-owned businesses was concentrated in Southern 

Appalachia, metropolitan counties, and transitional, competitive, and attainment counties (see Table 5-6).

154
Figure 5-5: Number of SBA 504 Loans per 10,000 Small Businesses by Region

Figure 5-6: Number of SBA 504 Loans per 10,000 Small Businesses by County Type

Figure 5-7: Number of SBA 504 Loans per 10,000 Small Businesses by Economic Status

4.9

2.4

2.1

1.6

0.9

1.5

3.6

3.6

2.1

1.7

1.0

0.8

1.7

3.0

0.00 1.00 2.00 3.00 4.00 5.00 6.00

United States

Appalachian Region

Northern Appalachia

North Central Appalachia

Central Appalachia

South Central Appalachia

Southern Appalachia

Per capita personal income index 2007 2010

4.0

2.2

1.6

2.1

0.9

2.8

2.2

1.5

1.6

0.9

0.00 1.00 2.00 3.00 4.00 5.00

Large Metros (pop. 1 million +)

Small Metros (pop. <1 million)

Nonmetro, Adjacent to Large Metros

Nonmetro, Adjacent to Small Metros

Rural (nonmetro, not adj. to a metro)

Per capita personal income index 2007 2010

1.0

0.8

2.3

2.7

6.5

0.5

0.8

2.0

2.2

4.0

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00

Distressed

At-Risk

Transitional

Competitive

Attainment

Per capita personal income index 2007 2010

155
Figure 5-8: Number of SBA 504 Loans per 10,000 Small Businesses by State

3.6

3.4

4.1

4.4

5.1

4.1

1.9

0.9

2.3

2.7

2.8

2.6

1.0

0.4

1.2

2.6

1.5

2.7

2.7

2.2

2.8

4.9

3.4

5.2

1.9

1.9

1.9

2.9

2.6

3.1

1.4

1.1

1.6

3.8

1.2

4.1

0.9

3.7

3.6

3.8

3.3

3.4

3.2

1.8

0.8

2.2

2.0

1.3

2.0

0.5

0.7

0.5

2.4

1.8

2.4

1.7

1.0

1.8

3.9

2.0

4.3

1.8

1.5

2.0

1.7

1.8

1.7

1.6

2.4

1.0

2.9

0.7

3.1

1.0

0.00 1.00 2.00 3.00 4.00 5.00 6.00

Alabama

Appalachian Alabama

Non-Appalachian Alabama

Georgia

Appalachian Georgia

Non-Appalachian Georgia

Kentucky

Appalachian Kentucky

Non-Appalachian Kentucky

Maryland

Appalachian Maryland

Non-Appalachian Maryland

Mississippi

Appalachian Mississippi

Non-Appalachian Mississippi

New York

Appalachian New York

Non-Appalachian New York

North Carolina

Appalachian North Carolina

Non-Appalachian North Carolina

Ohio

Appalachian Ohio

Non-Appalachian Ohio

Pennsylvania

Appalachian Pennsylvania

Non-Appalachian Pennsylvania

South Carolina

Appalachian South Carolina

Non-Appalachian South Carolina

Tennessee

Appalachian Tennessee

Non-Appalachian Tennessee

Virginia

Appalachian Virginia

Non-Appalachian Virginia

West Virginia (entire state)

2007 2010

156
Table 5-5: Number of SBA 504 Loans per 10,000 Businesses in Appalachia

Total Number 

of SBA 504 

Loans

Number of 

Small 

Businesses

Number of SBA 

504 Loans per 

10,000 Small 

Businesses

Total 

Number of 

SBA 504 

Loans

Number of 

Small 

Businesses

Number of SBA 

504 Loans per 

10,000 Small 

Businesses

United States 10,669 21,808,201 4.9 7,833 21,530,378 3.6

Appalachian Region 391 1,607,645 2.4 325 1,577,370 2.1

Subregions

Northern Appalachia 102 477,301 2.1 83 482,014 1.7

North Central Appalachia 21 128,944 1.6 12 124,926 1.0

Central Appalachia 10 115,266 0.9 9 107,888 0.8

South Central Appalachia 47 307,059 1.5 53 305,962 1.7

Southern Appalachia 211 579,075 3.6 168 556,580 3.0

County Types

Large Metros (pop. 1 million +) 158 395,470 4.0 112 404,330 2.8

Small Metros (pop. <1 million) 134 613,330 2.2 135 604,234 2.2

Nonmetro, Adjacent to Large Metros 18 109,171 1.6 16 103,911 1.5

Nonmetro, Adjacent to Small Metros 64 301,104 2.1 46 287,892 1.6

Rural (nonmetro, not adj. to a metro) 17 188,570 0.9 16 177,003 0.9

Economic Status

Distressed 9 89,777 1.0 4 86,870 0.5

At-Risk 12 145,503 0.8 11 139,327 0.8

Transitional 200 887,755 2.3 183 904,686 2.0

Competitive 82 305,587 2.7 65 292,327 2.2

Attainment 88 135,857 6.5 62 154,160 4.0

Alabama 114 314,870 3.6 112 302,408 3.7

Appalachian Alabama 67 199,561 3.4 70 192,538 3.6

Non-Appalachian Alabama 47 115,309 4.1 42 109,870 3.8

Georgia 364 828,977 4.4 261 792,556 3.3

Appalachian Georgia 123 239021 5.1 82 238,124 3.4

Non-Appalachian Georgia 241 589,956 4.1 179 554,432 3.2

Kentucky 58 304,048 1.9 55 297,724 1.8

Appalachian Kentucky 7 77,542 0.9 6 74,004 0.8

Non-Appalachian Kentucky 51 226,506 2.3 49 223,720 2.2

Maryland 103 388,206 2.7 78 389,579 2.0

Appalachian Maryland 4 14,392 2.8 2 14,993 1.3

Non-Appalachian Maryland 99 373,814 2.6 76 374,586 2.0

Mississippi 30 288,779 1.0 13 248,541 0.5

Appalachian Mississippi 3 69,965 0.4 4 58,339 0.7

Non-Appalachian Mississippi 27 218,814 1.2 9 190,202 0.5

New York 365 1,404,794 2.6 299 1,253,951 2.4

Appalachian New York 10 67,312 1.5 11 62,314 1.8

Non-Appalachian New York 355 1,337,482 2.7 288 1,191,637 2.4

North Carolina 167 615,139 2.7 106 624,133 1.7

Appalachian North Carolina 25 112,966 2.2 11 110,530 1.0

Non-Appalachian North Carolina 142 502,173 2.8 95 513,603 1.8

Ohio 337 691,536 4.9 263 667,427 3.9

Appalachian Ohio 40 117,389 3.4 23 113,316 2.0

Non-Appalachian Ohio 297 574,147 5.2 240 554,111 4.3

Pennsylvania 136 711,343 1.9 135 763,099 1.8

Appalachian Pennsylvania 60 320,365 1.9 50 332,120 1.5

Non-Appalachian Pennsylvania 76 390,978 1.9 85 430,979 2.0

South Carolina 80 272,316 2.9 46 266,975 1.7

Appalachian South Carolina 18 70,528 2.6 12 67,579 1.8

Non-Appalachian South Carolina 62 201,788 3.1 34 199,396 1.7

Tennessee 59 427,304 1.4 66 422,466 1.6

Appalachian Tennessee 20 181,133 1.1 42 178,251 2.4

Non-Appalachian Tennessee 39 246,171 1.6 24 244,215 1.0

Virginia 186 483,178 3.8 160 547,124 2.9

Appalachian Virginia 5 42,503 1.2 3 44,052 0.7

Non-Appalachian Virginia 181 440,675 4.1 157 503,072 3.1

West Virginia (entire state) 9 94,968 0.9 9 91,210 1.0

2007 2010

157
Table 5-6: SBA 504 Loans to Minority-Owned Businesses in Appalachia

Number of 

Loans

Number of 

Loans to 

Minority 

Number 

of Loans

Number of 

Loans to 

Minority 

United States 10,669 3,035 7,833 1,556

Appalachian Region 391 101 325 44

Subregions

Northern Appalachia 102 13 83 11

North Central Appalachia 21 2 12 0

Central Appalachia 10 2 9 0

South Central Appalachia 47 10 53 9

Southern Appalachia 211 74 168 24

County Types

Large Metro (1 million + people) 158 50 112 17

Small Metro (< 1 million people) 134 32 135 14

Nonmetro, Adjacent to Large Metro 18 4 16 4

Nonmetro, Adjacent to Small Metro 64 10 46 9

Rural (Nonmetro, Nonadjacent) 17 5 16 0

Economic Status

Distressed 9 2 4 0

At-Risk 12 3 11 0

Transitional 200 42 183 23

Competitive 82 19 65 10

Attainment 88 35 62 11

Alabama 114 44 112 16

Appalachian Alabama 67 22 70 10

Non-Appalachian Alabama 47 22 42 6

Georgia 364 157 261 54

Appalachian Georgia 123 47 82 12

Non-Appalachian Georgia 241 110 179 42

Kentucky 58 12 55 10

Appalachian Kentucky 7 1 6 0

Non-Appalachian Kentucky 51 11 49 10

Maryland 103 34 78 18

Appalachian Maryland 4 3 2 1

Non-Appalachian Maryland 99 31 76 17

Mississippi 30 11 13 4

Appalachian Mississippi 3 0 4 0

Non-Appalachian Mississippi 27 11 9 4

New York 365 68 299 59

Appalachian New York 10 2 11 2

Non-Appalachian New York 355 66 288 57

North Carolina 167 55 106 14

Appalachian North Carolina 25 4 11 0

Non-Appalachian North Carolina 142 51 95 14

Ohio 337 49 263 16

Appalachian Ohio 40 4 23 0

Non-Appalachian Ohio 297 45 240 16

Pennsylvania 136 18 135 21

Appalachian Pennsylvania 60 6 50 8

Non-Appalachian Pennsylvania 76 12 85 13

South Carolina 80 17 46 5

Appalachian South Carolina 18 5 12 2

Non-Appalachian South Carolina 62 12 34 3

Tennessee 59 16 66 17

Appalachian Tennessee 20 5 42 9

Non-Appalachian Tennessee 39 11 24 8

Virginia 186 65 160 33

Appalachian Virginia 5 2 3 0

Non-Appalachian Virginia 181 63 157 33

West Virginia (entire state) 9 0 9 0

2007 2010

158
CHAPTER 6 DEVELOPMENT LENDING – TREASURY CDFIS, NEW 

MARKETS TAX CREDIT CDES, MICROCREDIT LENDERS (SBA) IN 

APPALACHIA

6.1 SUMMARY

This chapter examines Community Development Financial Institution (CDFI) lending in Appalachia. The 

chapter looks at the types of CDFIs lending, the number and amount of loans, the types of borrowers, and 

the loan purpose. This chapter also examines New Market Tax Credit (NMTC) investment in Appalachia, 

including the number of investments by region and type of county, as well as SBA microlenders. 

Major findings regarding CDFIs in Appalachia include: 

• There are 71 CDFIs headquartered in the Appalachian Region. However, the vast majority of 

loans that CDFIs made in Appalachia were made by institutions that did most of their lending 

outside of Appalachia. 

• CDFI lending in Appalachia increased by 88 percent from 2007 through 2010 from $197 million 

to $371 million. 

• CDFIs lend in the majority of counties in Appalachia and have increased their targeting of 

disadvantaged counties. For example, CDFIs increased the amount they lent in the Central 

subregion by 52 percent between 2007 and 2010, from $90.6 million to $137.4 million.19 CDFIs 

increased lending in rural counties by over 50 percent, from $82.3 to $124.2 million between 

2007 and 2010. 

• CDFIs issued 4,613 and 4,661 loans in Appalachia during 2007 and 2010, respectively. Of these 

loans, 1,416 (30.7%) and 2,363(50.7%) were for small businesses in 2007 and 2010, respectively.

• Just considering loans to small businesses, the majority of CDFI dollars for businesses in 2007 

and 2010 was directed towards Central Appalachian counties. While less than half, the plurality 

of dollars CDFIs lent to businesses, went to rural counties not adjacent to metropolitan counties 

and to distressed counties. 

• The percentage of CDFI lending for microenterprise is higher in Appalachia than the nation but 

was still only 4 percent of the CDFI loan dollars in Appalachia.

The major findings regarding NMTC investments are:

• There are only 12 CDEs located in Appalachia, and they have received a total allocation of $321 

million in credit authority, out of $30 billion in tax credits allocated through 2010, or 1.1% of the 

credit authority allocated.

• Forty-seven CDEs have invested at least part of their credit authority in 351 projects in 62 

Appalachian counties, an investment of $706.7 million or about 3.4 percent of the total amount of 

credit authority invested. 

19 This discussion focuses on the total dollar amount of loans originated, which is one measure of the impact that 

CDFIs have in Appalachia. That measure shows how much money CDFIs directly added to the economy. Another 

possible measure is the number of loans made, which shows how many borrowers CDFIs served. Both are good 

indicators of impact.

159

 
• Targeting of disadvantaged counties in Appalachia decreased from 2007 to 2010. For example, 

funding in Central Appalachia declined from 25 percent of the credits in Appalachia in 2007 to 7 

percent by 2010. A similar decrease occurred in rural counties. 

CDFI lending in Appalachia increased by 88 percent between 2007 and 2010, with much of that lending 

in disadvantaged counties. CDFIs made loans in nearly two-thirds of rural counties and counties in

Central Appalachia. Businesses received most of the money that CDFIs lent in Appalachia, over 72 

percent of the value of loans in 2007 and over 62 percent in 2010. The majority of CDFI lending in 

Appalachia, however, was by institutions that did most of their lending outside of the region.

The amount of New Markets Tax Credit authority for CDE investments in Appalachia increased by 117 

percent between 2007 and 2010, despite a 10 percent decrease nationally over the same period. NMTC 

investment, however, decreased in rural and distressed counties and in Central Appalachia between those 

years. Most of the increased investment occurred in Northern Appalachia, in large and small metropolitan

counties, and in transitional and competitive counties.

6.2 INTRODUCTION

This section of the report discusses Community Development Financial Institutions (CDFIs) serving 

Appalachia and shows how CDFI lending changed between 2007 and 2010. CDFIs are financial 

institutions which have a primary mission of promoting community or economic development and serve a 

defined population or target area which is underserved by mainstream financial institutions. The 

Department of the Treasury certifies when a financial institution qualifies as a CDFI. CDFIs lend and 

make equity investments, provide support services and technical assistance, and they serve both 

businesses and individuals.

The five basic types of CDFIs are: 1) banks; 2) credit unions; 3) depository institution holding companies; 

4) loan funds; and 5) venture funds. In general, banks and credit unions are more likely to lend to 

individuals, with fewer loans to businesses. Holding companies reflect the activities of their subsidiaries, 

whether they are banks or credit unions. Loan funds work mostly with businesses, but they also make 

some loans to individuals. Venture funds tend to make equity investments and work almost exclusively 

with businesses. 

Based on data from the CDFI Fund’s Community Investment Impact System (CIIS) dataset,20 CDFIs 

reporting transaction level data originated 70,704 loans in the total amount of $4.2 billion in 2007 and 

51,241 loans in the total amount of $5.1 billion in 2010 in the entire nation, as shown in Table 6-1. In 

2007, 41 CDFIs reported loans for projects in Appalachia, and 46 reported loans for projects in 

Appalachia in 2010. The vast majority of loans that CDFIs made in Appalachia, however, were made by 

institutions that did most of their lending outside of Appalachia. For example, CDFIs with assets of more 

than $30 million made 1,980 loans in the total amount of $205 million in Appalachia in 2010. Those same 

CDFIs made 6,857 loans overall, with a total value of $3.1 billion. About 18 percent of the total amount 

of their overall lending was in Appalachia. Likewise, all CDFIs reporting loans in Appalachia made 

20 The CIIS data are from CDFIs that have received funding from the CDFI Fund within the preceding three years. 

CDFIs receiving funding in 2004, for example, would report in 2005-2007. The CIIS data used in this study, 

therefore, will include CDFIs that received funding between 2004 and 2009. Because the funding tends to be 

allocated to relatively more successful CDFIs, the CIIS data should include the CDFIs most active in the region.

160

 
22,794 loans overall but just 4,661 loans in Appalachia during 2010. The impact of CDFI lending in 

Appalachia, therefore, is attributable to CDFIs located both inside and outside the region. 

Small CDFIs, with assets of under $10 million, tend to focus on lending locally, and so CDFIs in that size 

range that lend in Appalachia have a high percentage of their loans in the region. Mid-size CDFIs that 

lend in Appalachia, on the other hand, have a much smaller percentage of their business in Appalachia. 

For example, only 10 percent of the $248 million in loans made by mid-size CDFIs (in the $10-30 million 

size range) that made loans in Appalachia were for projects in Appalachia in 2010. Large CDFIs, with 

more than $30 million in assets, make a significantly higher percentage of their loans in Appalachia, both 

in the number and dollar amount of those loans. 

Table 6-1: CDFI Lending, by Asset Size, Nationally and in Appalachia, 2007 and 2010

2007

Under $1 

Million

$1 Million to < 

$10 Million

$10 Million to 

< $30 Million

$30 Million or 

More

Not Reporting 

Assets Total

Number of CDFIs

All CDFIs 8 50 33 30 5 126

CDFIs lending in Appalachia 0 12 14 12 3 41

Total Number of Loans Closed

By all CDFIs 181 10,334 15,286 20,146 24,757 70,704

By CDFIs making loans in Appalachia 0 3,576 6,460 13,806 23,336 47,178

Number of loans closed in Appalachia 0 314 249 3,969 81 4,613

Number of loans in Appalachia as a percent of 

loans made by CDFIs lending in Appalachia 0.0% 8.8% 3.9% 28.7% 0.3% 9.8%

Total Amount of Loans Closed

(in $ millions)

By all CDFIs $3.1 $212.9 $645.7 $2,314.1 $979.2 $4,155.0

By CDFIs making loans in Appalachia $0.0 $45.1 $312.2 $942.4 $948.7 $2,248.3

Amount of loans closed in Appalachia $0.0 $14.0 $12.9 $166.8 $3.2 $197.0

Amount of loans in Appalachia as a percent of 

loans made by CDFIs lending in Appalachia 0.0% 31.2% 4.1% 17.7% 0.3% 8.8%

2010

$1 to < $1 

Million

$1 Million to < 

$10 Million

$10 Million to 

< $30 Million

$30 Million or 

More

Not Reporting 

Assets Total

Number of CDFIs

All CDFIs 8 58 36 40 31 173

CDFIs lending in Appalachia 1 10 12 13 10 46

Total Number of Loans Closed

By all CDFIs 206 7,396 12,617 19,585 11,437 51,241

By CDFIs making loans in Appalachia 61 984 6,790 6,857 8,102 22,794

Number of loans closed in Appalachia 61 679 331 1,980 1,610 4,661

Number of loans in Appalachia as a percent of 

loans made by CDFIs lending in Appalachia 100.0% 69.0% 4.9% 28.9% 19.9% 20.4%

Total Amount of Loans Closed

(in $ millions)

By all CDFIs $3.1 $293.3 $622.1 $3,111.2 $1,103.8 $5,133.6

By CDFIs making loans in Appalachia $0.3 $39.9 $248.2 $1,140.3 $837.1 $2,265.8

Amount of loans closed in Appalachia $0.3 $23.0 $24.9 $205.3 $117.4 $370.7

Amount of loans in Appalachia as a percent of 

loans made by CDFIs lending in Appalachia 100.0% 57.5% 10.0% 18.0% 14.0% 16.4%

Asset Range

161
6.3 DISTRIBUTION OF LOANS

Between 2007 and 2010, CDFIs increased the percentage of counties they serve in the Central and South 

Central subregions (see Figure 6-1). They also increased the percentage of loans in rural counties (see 

Figure 6-2) and distressed and at-risk counties to which they made loans (see Figure 6-3). This trend is 

encouraging and shows the targeting of counties that are underserved by mainstream lenders.21 For 

example, Central Appalachia, which is a subregion relatively underserved by banks, has the highest 

percentage of counties (82.9 percent) receiving loans by CDFIs in 2010. Likewise, the percentage of rural 

counties receiving loans from CDFIs increased to 63.3 percent, which was higher than the percentage of 

counties in small metropolitan counties receiving loans. CDFIs lend in the majority of counties in 

Appalachia, and in the majority of most classifications of county (see Table 6-2). 

21 Maps of CDFI lending to businesses are included in Section 6.7.

162

 
Figure 6-1: Percent of Counties Receiving Loans by Region

Figure 6-2: Percent of Counties Receiving Loans by County Type

Figure 6-3: Percent of Counties Receiving Loans by Economic Status

47.1%

75.3%

82.9%

49.2%

41.9%

57.9%

49.2%

50.0%

56.8%

65.8%

58.7%

47.0%

53.9%

46.9%

0% 20% 40% 60% 80% 100%

Southern Appalachia

South Central Appalachia

Central Appalachia

North Central Appalachia

Northern Appalachia

Appalachian Region

United States

2007 2010

63.3%

55.2%

37.1%

61.1%

76.5%

46.3%

53.8%

53.1%

60.4%

73.5%

0% 20% 40% 60% 80% 100%

Rural (Nonmetro, Nonadjacent)

Nonmetro, Adjacent to Small Metro

Nonmetro, Adjacent to Large Metro

Small Metro (< 1 million people)

Large Metro (1 million + people)

2007 2010

83.3%

70.8%

55.9%

58.2%

63.4%

100.0%

76.9%

53.8%

50.0%

52.6%

0% 20% 40% 60% 80% 100%

Attainment

Competitive

Transitional

At-Risk

Distressed

2007 2010

163
Table 6-2: CDFI Lending by County, 2007 and 2010

Number 

of 

Counties

Number 

Receiving 

Loans

Percent 

Receiving 

Loans

Number 

of 

Counties

Number 

Receiving 

Loans

Percent 

Receiving 

Loans

United States 3,033 1,423 46.9% 3,143 1,547 49.2%

Appalachian Region 410 221 53.9% 420 243 57.9%

 Subregions

 Northern Appalachia 83 39 47.0% 86 36 41.9%

 North Central Appalachia 63 37 58.7% 63 31 49.2%

 Central Appalachia 79 52 65.8% 82 68 82.9%

 South Central Appalachia 81 46 56.8% 85 64 75.3%

 Southern Appalachia 104 52 50.0% 104 49 47.1%

 County Types

 Large Metro (1 million + people) 34 25 73.5% 34 26 76.5%

 Small Metro (< 1 million people) 106 64 60.4% 108 66 61.1%

 Nonmetro, Adjacent to Large Metro 32 17 53.1% 35 13 37.1%

 Nonmetro, Adjacent to Small Metro 130 70 53.8% 134 74 55.2%

 Rural (Nonmetro, Nonadjacent) 108 50 46.3% 109 69 63.3%

 Economic Status

 Distressed 78 41 52.6% 82 52 63.4%

 At-Risk 78 39 50.0% 79 46 58.2%

 Transitional 221 119 53.8% 229 128 55.9%

 Competitive 26 20 76.9% 24 17 70.8%

 Attainment 7 7 100.0% 6 5 83.3%

Alabama 67 23 34.3% 67 16 23.9%

 Appalachian Alabama 37 12 32.4% 37 10 27.0%

 Non-Appalachian Alabama 30 11 36.7% 30 6 20.0%

Georgia 159 65 40.9% 159 49 30.8%

 Appalachian Georgia 37 31 83.8% 37 29 78.4%

 Non-Appalachian Georgia 122 34 27.9% 122 20 16.4%

Kentucky 120 48 40.0% 120 87 72.5%

 Appalachian Kentucky 51 35 68.6% 54 51 94.4%

 Non-Appalachian Kentucky 69 13 18.8% 66 36 54.5%

Maryland 24 13 54.2% 24 12 50.0%

 Appalachian Maryland 3 1 33.3% 3 0 0.0%

 Non-Appalachian Maryland 21 12 57.1% 21 12 57.1%

Mississippi 82 53 64.6% 82 40 48.8%

 Appalachian Mississippi 24 7 29.2% 24 6 25.0%

 Non-Appalachian Mississippi 58 46 79.3% 58 34 58.6%

New York 62 55 88.7% 62 52 83.9%

 Appalachian New York 14 13 92.9% 14 11 78.6%

 Non-Appalachian New York 48 42 87.5% 48 41 85.4%

North Carolina 100 41 41.0% 100 97 97.0%

 Appalachian North Carolina 29 13 44.8% 29 29 100.0%

 Non-Appalachian North Carolina 71 28 39.4% 71 68 95.8%

Ohio 88 43 48.9% 88 37 42.0%

 Appalachian Ohio 29 12 41.4% 32 7 21.9%

 Non-Appalachian Ohio 59 31 52.5% 56 30 53.6%

Pennsylvania 67 34 50.7% 67 38 56.7%

 Appalachian Pennsylvania 52 20 38.5% 52 23 44.2%

 Non-Appalachian Pennsylvania 15 14 93.3% 15 15 100.0%

South Carolina 46 10 21.7% 46 16 34.8%

 Appalachian South Carolina 6 2 33.3% 6 4 66.7%

 Non-Appalachian South Carolina 40 8 20.0% 40 12 30.0%

Tennessee 95 40 42.1% 95 47 49.5%

 Appalachian Tennessee 50 24 48.0% 52 24 46.2%

 Non-Appalachian Tennessee 45 16 35.6% 43 23 53.5%

Virginia 95 45 47.4% 95 62 65.3%

 Appalachian Virginia 23 16 69.6% 25 19 76.0%

 Non-Appalachian Virginia 72 29 40.3% 70 43 61.4%

West Virginia (entire state) 55 35 63.6% 55 30 54.5%

2007 2010

164
6.4 NUMBERS AND TYPES OF CDFIS WITH HEADQUARTERS IN APPALACHIA

There are 71 CDFIs headquartered in the Appalachian Region. They are located in 43 of the 420 counties 

in Appalachia. Those CDFIs, however, may also have branches in other counties within Appalachia. In 

addition, CDFIs headquartered outside of Appalachia may also have branches in Appalachia or provide 

financial services to people and businesses in Appalachia. Businesses and residents in Appalachia, 

therefore, almost certainly have access to a CDFI beyond the 43 counties with a CDFI headquarters.

As Table 6-3 shows, most of the CDFIs in Appalachia are loan funds which can lend to businesses or 

individuals. Loan fund financing of businesses can range from small loans for microenterprises to larger 

loans for established companies, as well as equity investments. Loan funds can finance real estate 

development, including single- and multi-family developments, as well as commercial properties and 

community facilities. They also lend to individuals for home purchase, improvement, and refinance. The 

next most common type of CDFI in Appalachia is credit unions. Credit unions can offer an array of 

services to businesses and individuals comparable to those offered by banks. One significant difference 

between loan funds and credit unions is that the former focus more on lending and investing in 

businesses, while the latter lend more often to individuals. Venture funds focus almost exclusively on 

businesses, making both loans and equity investments. There is just one venture fund that is a CDFI in 

Appalachia. 

Southern Appalachia was the subregion with the highest percentage (32.4 percent) of CDFIs in the region. 

Northern Appalachia had the second highest percentage of CDFIs with 29.6 percent. In contrast, North 

Central and Central Appalachia had just 7 percent and 14.1 percent of the CDFIs in Appalachia, 

respectively (see Table 6-3). 

While not having the majority of CDFIs, rural, non-metropolitan, and distressed counties still have a 

sizable percentage of CDFIs. Twenty-seven percent of the Appalachian CDFIs were located in rural 

counties and 23 percent were located in non-metropolitan counties adjacent to metropolitan counties. 

When considering economic status of counties, the plurality of CDFIs (47.9 percent) were located in 

transitional counties. The next highest percentage (19.7 percent) was in distressed counties and 

competitive counties. Only 12.7 percent of the CDFIs were located in at-risk counties.

165
Map 6-1: Certified Community Development Financial Institutions in Appalachia, 2010 

166
Figure 6-4: Number of CDFIs by Type in Appalachia

Figure 6-5: Number of CDFIs by Type in the United States

167
Table 6-3: Certified Community Development Financial Institutions by locations in Appalachia, 

2010

Data Source: US Department of Treasury: Certified Community Development Financial Institutions as of 12/31/2010

Number

Percent (total as 

US)

Percent (total as 

Appalachia)

United States 91 203 48 572 25 939 100.0% -

Appalachian Region 7 22 7 34 1 71 7.6% 100.0%

Subregions

Northern Appalachia 0 7 0 14 0 21 2.2% 29.6%

North Central Appalachia 0 1 0 4 0 5 0.5% 7.0%

Central Appalachia 0 1 0 8 1 10 1.1% 14.1%

South Central Appalachia 1 3 1 7 0 12 1.3% 16.9%

Southern Appalachia 6 10 6 1 0 23 2.4% 32.4%

County Types

Large Metros (pop. 1 million +) 0 7 0 6 0 13 1.4% 18.3%

Small Metros (pop. <1 million) 1 8 1 13 0 23 2.4% 32.4%

Nonmetro, Adjacent to Large Metros 1 1 1 1 0 4 0.4% 5.6%

Nonmetro, Adjacent to Small Metros 0 4 0 8 0 12 1.3% 16.9%

Rural (nonmetro, not adj. to a metro) 5 2 5 6 1 19 2.0% 26.8%

Economic Status

Distressed 5 1 5 3 0 14 1.5% 19.7%

At-Risk 0 2 0 6 1 9 1.0% 12.7%

Transitional 2 12 2 18 0 34 3.6% 47.9%

Competitive 0 7 0 7 0 14 1.5% 19.7%

Attainment 0 0 0 0 0 0 0.0% 0.0%

Alabama 3 15 4 0 0 22 2.3% -

Appalachian Alabama 1 9 1 0 0 11 1.2% 15.5%

Non-Appalachian Alabama 2 6 3 0 0 11 1.2% -

Georgia 6 3 2 10 0 21 2.2% -

Appalachian Georgia 0 0 0 1 0 1 0.1% 1.4%

Non-Appalachian Georgia 6 3 2 9 0 20 2.1% -

Kentucky 1 1 1 10 1 14 1.5% -

Appalachian Kentucky 0 1 0 7 1 9 1.0% 12.7%

Non-Appalachian Kentucky 1 0 1 3 0 5 0.5% -

Maryland 2 1 1 12 1 17 1.8% -

Appalachian Maryland 0 1 0 0 0 1 0.1% 1.4%

Non-Appalachian Maryland 2 0 1 12 1 16 1.7% -

Mississippi 12 6 12 6 0 36 3.8% -

Appalachian Mississippi 5 1 5 0 0 11 1.2% 15.5%

Non-Appalachian Mississippi 7 5 7 6 0 25 2.7% -

New York 2 19 0 56 2 79 8.4% -

Appalachian New York 0 4 0 4 8 0.9% 11.3%

Non-Appalachian New York 2 15 0 52 2 71 7.6% -

North Carolina 1 6 1 10 3 21 2.2% -

Appalachian North Carolina 0 0 0 4 0 4 0.4% 5.6%

Non-Appalachian North Carolina 1 6 1 6 3 17 1.8% -

Ohio 0 3 0 21 0 24 2.6% -

Appalachian Ohio 0 0 0 1 0 1 0.1% 1.4%

Non-Appalachian Ohio 0 3 0 20 0 23 2.4% -

Pennsylvania 1 6 0 25 1 33 3.5% -

Appalachian Pennsylvania 0 2 0 10 0 12 1.3% 16.9%

Non-Appalachian Pennsylvania 1 4 0 15 1 21 2.2% -

South Carolina 2 1 1 5 0 9 1.0% -

Appalachian South Carolina 0 0 0 0 0 0 0.0% 0.0%

Non-Appalachian South Carolina 2 1 1 5 0 9 1.0% -

Tennessee 3 3 1 10 0 17 1.8% -

Appalachian Tennessee 0 3 0 3 0 6 0.6% 8.5%

Non-Appalachian Tennessee 3 0 1 7 0 11 1.2% -

Virginia 1 6 1 10 0 18 1.9% -

Appalachian Virginia 1 0 1 1 0 3 0.3% 4.2%

Non-Appalachian Virginia 0 6 0 9 0 15 1.6% -

West Virginia (entire state) 0 1 0 3 0 4 0.4% 5.6%

Grand Total

CDFIs by types

Bank or 

Thrift

Credit 

Union

Depository 

Institution Holding 

Company

Loan 

Fund

Venture 

Capital Fund

168
6.5 CDFI LENDING IN APPALACHIA

CDFIs increased the amount they lent in the Central subregion between 2007 and 2010, from $90.6 

million to $137.4 million,22 an increase of 52 percent (see Figure 6-6). The amount declined as a 

percentage of all CDFI lending in Appalachia, however, because of the enormous increase in lending in 

the South Central subregion, where the dollar amount of loans more than doubled. Central Appalachia, a 

region not served well by banks, still received the largest percentage of CDFI loan dollars in 2010. CDFIs 

increased lending in rural counties by over 50 percent, from $82.3 to $124.2 million between 2007 and 

2010 (see Figure 6-7). That represents a decrease in the percentage of the amount loaned because of the 

large increase in lending in small metropolitan counties and counties adjacent to metropolitan counties, 

which more than doubled. Despite the decreased percentage of overall CDFI lending, rural counties still 

had the second highest percentage of CDFI loan dollars in 2010. Lending increased most in transitional 

and competitive counties, with smaller, but still substantial increases in both distressed and at-risk 

counties (see Figure 6-8). In both 2007 and 2010, only a small percentage of the amount CDFIs lend is for 

projects in attainment counties.

Overall, CDFI lending in Appalachia increased significantly between 2007 and 2010, by 88 percent, from 

$197 million to $371 million, compared with a 24 percent increase in the amount of loans originated 

nationally, as shown in Table 6-4. The percentage of loans in Appalachia, both in number of loans and 

value of loans made, increased as a percentage of lending nationally. CDFIs stepped up their activity in 

Appalachia significantly during the economic downturn, albeit more in terms of dollar amounts than 

number of loans. 

The changes in CDFI lending between 2007 and 2010 reflect the aggregate impact of factors affecting 

CDFIs and mainstream financial institutions. The overall 24 percent increase in the amount of loans, for 

example, may be partially attributable to a reduction in lending by mainstream financial institutions 

following the downturn in the economy. With traditional sources of credit less available, more borrowers 

may have turned to CDFIs for business and personal loans. Shifts in CDFI lending within the region may 

also reflect differences in the decrease in lending by mainstream banks, with some regions or types of 

counties more adversely affected than others. For example, the large increase in CDFI lending in Central 

Appalachia is consistent with a cutback in mainstream lending in more marginal communities, with 

CDFIs then stepping in to fill the void. CDFIs clearly target less advantaged counties, and so they may 

have been well-positioned to service these counties when other sources of credit became less available. 

22 This discussion focuses on the total dollar amount of loans originated, which is one measure of the impact that 

CDFIs have in Appalachia. That measure shows how much money CDFIs directly added to the economy. Another 

possible measure is the number of loans made, which shows how many borrowers CDFIs served. Both are good 

indicators of impact.

169

 
Figure 6-6: Amount of Loans by Region

Figure 6-7: Amount of Loans by County Type

Figure 6-8: Amount of Loans by Economic Status

$20.7

$119.1

$137.4

$7.7

$85.8

$10.1

$17.8

$90.6

$11.0

$67.5

$0 $20 $40 $60 $80 $100 $120 $140 $160

Southern Appalachia

South Central Appalachia

Central Appalachia

North Central Appalachia

Northern Appalachia

Millions of Dollars 2007 2010

$124.2

$48.1

$2.5

$139.1

$56.9

$82.3

$21.7

$3.2

$60.8

$29.1

$0 $20 $40 $60 $80 $100 $120 $140 $160

Rural (Nonmetro, Nonadjacent)

Nonmetro, Adjacent to Small Metro

Nonmetro, Adjacent to Large Metro

Small Metro (< 1 million people)

Large Metro (1 million + people)

Millions of Dollars 2007 2010

$6.0

$88.2

$142.1

$46.5

$87.9

$3.4

$23.3

$79.9

$22.7

$67.6

$0 $20 $40 $60 $80 $100 $120 $140 $160

Attainment

Competitive

Transitional

At-Risk

Distressed

Millions of Dollars 2007 2010

170
Table 6-4: CDFI Lending in Appalachia, 2007 and 2010

Percent

Amount of 

Loans Percent Percent

Amount of 

Loans Percent

Number Appalachia (in $ millions) Appalachia Number Appalachia (in $ millions) Appalachia

United States 70,704 $4,155.0 51,241 $5,133.6

Appalachian Region 4,613 6.5% $197.0 4.7% 4,661 9.1% $370.7 7.2%

 Subregions

 Northern Appalachia 2,845 61.7% $67.5 34.3% 1,296 27.8% $85.8 23.1%

 North Central Appalachia 128 2.8% $11.0 5.6% 98 2.1% $7.7 2.1%

 Central Appalachia 910 19.7% $90.6 46.0% 1,457 31.3% $137.4 37.0%

 South Central Appalachia 401 8.7% $17.8 9.0% 1,560 33.5% $119.1 32.1%

 Southern Appalachia 329 7.1% $10.1 5.1% 250 5.4% $20.7 5.6%

 County Types

 Large Metro (1 million + people) 361 7.8% $29.1 14.7% 508 10.9% $56.9 15.3%

 Small Metro (< 1 million people) 2,848 61.7% $60.8 30.9% 2,114 45.4% $139.1 37.5%

 Nonmetro, Adjacent to Large Metro 56 1.2% $3.2 1.6% 93 2.0% $2.5 0.7%

 Nonmetro, Adjacent to Small Metro 552 12.0% $21.7 11.0% 752 16.1% $48.1 13.0%

 Rural (Nonmetro, Nonadjacent) 796 17.3% $82.3 41.8% 1,194 25.6% $124.2 33.5%

 Economic Status

 Distressed 651 14.1% $67.6 34.3% 955 20.5% $87.9 23.7%

 At-Risk 252 5.5% $22.7 11.5% 441 9.5% $46.5 12.5%

 Transitional 3,359 72.8% $79.9 40.6% 2,304 49.4% $142.1 38.3%

 Competitive 229 5.0% $23.3 11.8% 910 19.5% $88.2 23.8%

 Attainment 122 2.6% $3.4 1.7% 51 1.1% $6.0 1.6%

Alabama 85 $5.6 69 $9.0

 Appalachian Alabama 56 65.9% $3.7 66.1% 58 84.1% $8.2 91.5%

 Non-Appalachian Alabama 29 34.1% $1.9 33.9% 11 15.9% $0.8 8.5%

Georgia 636 $23.1 376 $25.7

 Appalachian Georgia 255 40.1% $5.5 23.7% 173 46.0% $11.1 43.3%

 Non-Appalachian Georgia 381 59.9% $17.7 76.3% 203 54.0% $14.6 56.7%

Kentucky 849 $87.0 2,010 $146.0

 Appalachian Kentucky 824 97.1% $85.4 98.1% 1,276 63.5% $132.1 90.5%

 Non-Appalachian Kentucky 25 2.9% $1.6 1.9% 734 36.5% $13.9 9.5%

Maryland 158 $20.8 199 $86.9

 Appalachian Maryland 2 1.3% $0.0 0.0% 0 0.0% $0.0 0.0%

 Non-Appalachian Maryland 156 98.7% $20.8 100.0% 199 100.0% $86.9 100.0%

Mississippi 542 $26.3 506 $50.6

 Appalachian Mississippi 14 2.6% $0.9 3.3% 14 2.8% $0.9 1.9%

 Non-Appalachian Mississippi 528 97.4% $25.5 96.7% 492 97.2% $49.6 98.1%

New York 10,973 $898.4 6,297 $497.7

 Appalachian New York 2,642 24.1% $39.2 4.4% 892 14.2% $39.6 8.0%

 Non-Appalachian New York 8,331 75.9% $859.2 95.6% 5,405 85.8% $458.1 92.0%

North Carolina 780 $46.6 4,830 $504.7

 Appalachian North Carolina 38 4.9% $1.9 4.2% 1,049 21.7% $84.9 16.8%

 Non-Appalachian North Carolina 742 95.1% $44.6 95.8% 3,781 78.3% $419.7 83.2%

Ohio 258 $112.1 616 $91.1

 Appalachian Ohio 36 14.0% $3.0 2.7% 10 1.6% $2.6 2.8%

 Non-Appalachian Ohio 222 86.0% $109.1 97.3% 606 98.4% $88.6 97.2%

Pennsylvania 956 $179.5 1,114 $227.5

 Appalachian Pennsylvania 193 20.2% $27.0 15.0% 400 35.9% $45.6 20.0%

 Non-Appalachian Pennsylvania 763 79.8% $152.5 85.0% 714 64.1% $182.0 80.0%

South Carolina 26 $2.0 73 $8.8

 Appalachian South Carolina 4 15.4% $0.0 1.9% 5 6.8% $0.4 4.8%

 Non-Appalachian South Carolina 22 84.6% $2.0 98.1% 68 93.2% $8.4 95.2%

Tennessee 2,215 $59.6 2,552 $69.3

 Appalachian Tennessee 149 6.7% $11.1 18.6% 184 7.2% $19.9 28.8%

 Non-Appalachian Tennessee 2,066 93.3% $48.5 81.4% 2,368 92.8% $49.3 71.2%

Virginia 777 $30.4 1,250 $58.8

 Appalachian Virginia 289 37.2% $9.2 30.4% 496 39.7% $18.7 31.8%

 Non-Appalachian Virginia 488 62.8% $21.1 69.6% 754 60.3% $40.1 68.2%

West Virginia (entire state) 111 $10.0 104 $6.6

2007 2010

171
6.6 TYPES OF BORROWERS

CDFIs have historically made more loans to individuals than to businesses but have loaned more money 

to businesses than to individuals. CDFIs made loans to businesses in 169 counties in 2007 and in 204 

counties in 2010 (see Map 6-2 and Map 6-3). In most counties, however, CDFIs made relatively few 

loans to businesses in either of those years. CDFIs made more than 5 loans to businesses in only 45 

counties in 2007 and in only 76 counties in 2010. 

CDFI lending to businesses as a percentage of total amount of loans made declined both nationally and in 

Appalachia between 2007 and 2010, but declined to a greater degree in Appalachia (see Figure 6-9). The 

percentage of the total amount loaned that went to businesses in Appalachia dropped from 72 percent in 

2007 to 62 percent in 2010. The highest percentage of lending to businesses was in the Southern 

subregion, where over 97 percent of the amount loaned went to businesses, followed by the Central 

subregion at over 85 percent business lending. Business lending was over 97 percent of the total amount 

of CDFI lending in large metropolitan counties, and over 90 percent in rural counties (see Figure 6-10). In 

all but transitional counties, business lending was over 85 percent of the total amount loaned by CDFIs in 

2010 (see Figure 6-11).

CDFIs reporting transaction level data originated 70,704 loans in 2007, 77 percent of which were to 

individuals or other CDFIs (see Table 6-5). Loans to CDFIs made up less than one percent of the total 

lending, both in terms of the number of loans and the amount of the loans. Those loans totaled $4.2 

billion, 77 percent of which went to businesses. In Appalachia, the pattern was similar, although loans to 

businesses constituted a higher percentage of loans made and lower percentage of amount loaned in 2007. 

The pattern in 2010 was dramatically different, both nationally and in Appalachia, with respect to the 

number of loans made. Nationally, 46 percent of CDFI loans were to business, and 51 percent of loans in 

Appalachia were to businesses. 

CDFIs were consistent in the types of counties in which they made loans to businesses in both 2007 and 

2010. CDFIs loaned more than $500,000 to businesses in 29 counties in 2007 and in 49 counties in 2010 

(see Map 6-4 and Map 6-5). The majority of total lending to businesses in both years went to projects in 

the Central subregion. While less than half, the plurality of money CDFIs lent to businesses went to rural 

and distressed counties.

172
Map 6-2: Number of CDFI Loans to Businesses by County, 2007

173
Map 6-3: Number of CDFI Loans to Businesses by County, 2010

174
Figure 6-9: Business Lending as a Percent of Amount Loaned by Region

Figure 6-10: Business Lending as a Percent of Amount Loaned by County Type

Figure 6-11: Business Lending as a Percent of Amount Loaned by Economic Status

91.6%

30.5%

85.9%

77.6%

59.9%

62.2%

74.0%

90.7%

46.3%

88.9%

86.5%

51.3%

72.1%

77.0%

0% 20% 40% 60% 80% 100%

Southern Appalachia

South Central Appalachia

Central Appalachia

North Central Appalachia

Northern Appalachia

Appalachian Region

United States

2007 2010

90.1%

47.0%

77.1%

27.8%

97.7%

91.2%

67.0%

95.2%

35.1%

97.0%

0% 20% 40% 60% 80% 100%

Rural (Nonmetro, Nonadjacent)

Nonmetro, Adjacent to Small Metro

Nonmetro, Adjacent to Large Metro

Small Metro (< 1 million people)

Large Metro (1 million + people)

2007 2010

99.7%

54.2%

40.8%

87.2%

89.2%

94.5%

91.2%

45.9%

85.6%

90.9%

0% 20% 40% 60% 80% 100%

Attainment

Competitive

Transitional

At-Risk

Distressed

2007 2010

175
Table 6-5: CDFI Lending by Type of Borrower, 2007 and 2010

Note: Some CDFIs also lend to other CDFIs, although the percentage of loans and amount loaned to CDFIs is small by comparison to loans to 

individuals. The two have been combined to simplify the table and show more clearly the percentage of business lending that CDFIs do.

Businesses Individuals 

and CDFIs Businesses Individuals and CDFIs

Number of Loans

Percent in 

Appalachia

Percent in 

Appalachia

Percent in 

Appalachia

Percent in 

Appalachia

United States 16,121 54,583 23,746 27,495

Appalachian Region 1,416 8.8% 3,197 5.9% 2,363 10.0% 2,298 8.4%

 Subregions

 Northern Appalachia 402 28.4% 2,443 76.4% 672 28.4% 624 27.2%

 North Central Appalachia 93 6.6% 35 1.1% 59 2.5% 39 1.7%

 Central Appalachia 521 36.8% 389 12.2% 870 36.8% 587 25.5%

 South Central Appalachia 154 10.9% 247 7.7% 538 22.8% 1,022 44.5%

 Southern Appalachia 246 17.4% 83 2.6% 224 9.5% 26 1.1%

 County Types

 Large Metro (1 million + people) 306 21.6% 55 1.7% 495 20.9% 13 0.6%

 Small Metro (< 1 million people) 402 28.4% 2,446 76.5% 692 29.3% 1,422 61.9%

 Nonmetro, Adjacent to Large Metro 19 1.3% 37 1.2% 44 1.9% 49 2.1%

 Nonmetro, Adjacent to Small Metro 203 14.3% 349 10.9% 346 14.6% 406 17.7%

 Rural (Nonmetro, Nonadjacent) 486 34.3% 310 9.7% 786 33.3% 408 17.8%

 Economic Status

 Distressed 409 28.9% 242 7.6% 569 24.1% 386 16.8%

 At-Risk 127 9.0% 125 3.9% 286 12.1% 155 6.7%

 Transitional 605 42.7% 2,754 86.1% 994 42.1% 1,310 57.0%

 Competitive 185 13.1% 44 1.4% 464 19.6% 446 19.4%

 Attainment 90 6.4% 32 1.0% 50 2.1% 1 0.0%

Businesses Individuals 

and CDFIs Businesses Individuals 

and CDFIs

Total Amount of Loans (in $ millions)

Percent in 

Appalachia

Percent in 

Appalachia

Percent in 

Appalachia

Percent in 

Appalachia

United States $3,197.6 $957.4 $3,799.5 $1,334.1

Appalachian Region $142.1 4.4% $54.9 5.7% $230.7 6.1% $140.0 10.5%

 Subregions

 Northern Appalachia $34.7 24.4% $32.9 59.9% $51.4 22.3% $34.4 24.6%

 North Central Appalachia $9.6 6.7% $1.5 2.7% $6.0 2.6% $1.7 1.2%

 Central Appalachia $80.5 56.7% $10.0 18.3% $118.0 51.1% $19.4 13.8%

 South Central Appalachia $8.2 5.8% $9.6 17.4% $36.4 15.8% $82.8 59.1%

 Southern Appalachia $9.1 6.4% $0.9 1.7% $19.0 8.2% $1.7 1.2%

 County Types

 Large Metro (1 million + people) $28.2 19.8% $0.9 1.6% $55.6 24.1% $1.3 0.9%

 Small Metro (< 1 million people) $21.3 15.0% $39.5 71.9% $38.6 16.7% $100.4 71.7%

 Nonmetro, Adjacent to Large Metro $3.0 2.1% $0.2 0.3% $2.0 0.8% $0.6 0.4%

 Nonmetro, Adjacent to Small Metro $14.5 10.2% $7.2 13.1% $22.6 9.8% $25.5 18.2%

 Rural (Nonmetro, Nonadjacent) $75.0 52.8% $7.2 13.2% $111.9 48.5% $12.2 8.7%

 Economic Status

 Distressed $61.5 43.3% $6.2 11.2% $78.4 34.0% $9.5 6.8%

 At-Risk $19.5 13.7% $3.3 6.0% $40.6 17.6% $6.0 4.2%

 Transitional $36.7 25.8% $43.2 78.7% $57.9 25.1% $84.2 60.1%

 Competitive $21.2 14.9% $2.1 3.7% $47.8 20.7% $40.4 28.9%

 Attainment $3.2 2.2% $0.2 0.3% $6.0 2.6% $0.0 0.0%

Average Amount of Loan

United States $198,352 $17,540 $160,006 $48,520

Appalachian Region $100,356 $17,168 $97,631 $60,941

2007 2010

176
Map 6-4: Amount of Loans to Businesses by County, 2007

177
Map 6-5: Amount of Loans to Businesses by County, 2010

178
6.7 LOAN PURPOSE

Despite the increased dollar amount of lending for all business purposes, lending in all three business 

purpose categories – business, microenterprise, and commercial or multi-family real estate development – 

declined as a percentage of overall CDFI lending in Appalachia between 2007 and 2010 (see Figure 6-

12). In terms of amount and percentage of lending, the major increase was in lending for home purchase 

or improvement, which increased from $39 million in 2007 to over $128 million in 2010 (see Table 6-6).

Although lending for business purposes declined between 2007 and 2010, the percentage of CDFI lending 

in terms of dollar amounts was considerably higher for business purposes in Appalachia (at 48.4 percent) 

than nationally (at 22.6 percent). The percentage of CDFI lending for microenterprise is higher in 

Appalachia than the nation but is still only about 4 percent of the CDFI loan dollars in Appalachia. 

The total amount of CDFI lending in Appalachia for business purposes – to a business with more than 

five employees or in an amount of more than $35,000 – increased by almost 65 percent between 2007 and 

2010. The total amount of CDFI lending in Appalachia for microenterprise purposes – that is to a 

business with not more than five employees and in an amount not more than $35,000 – increased by 72 

percent. CDFI lending for commercial and multi-family real estate development and rehabilitation 

increased slightly in terms of the total amount loaned between 2007 and 2010.

Figure 6-12: Percent of Amount Loaned by Purpose

43.6%

4.1%

4.0%

48.4%

39.7%

35.1%

2.6%

22.6%

33.0%

7.3%

4.3%

55.3%

44.2%

35.2%

1.7%

18.9%

0% 10% 20% 30% 40% 50% 60%

All Other

Commercial/Multi-Family

Microenterprise

Business

All Other

Commercial/Multi-Family

Microenterprise

Business

Appalachian Region United States

2007 2010

179
Table 6-6: Number and Amount of Loans by Purpose, 2007 and 2010

Business lending accounted for over three-quarters of the total amount that CDFIs loaned in the Central 

subregion in 2007 and 2010 (see Figure 6-13). In the North Central subregion, business lending declined 

by 84 percent, while lending for commercial and multi-family construction and rehabilitation nearly 

tripled. Business lending accounted for over 80 percent of total CDFI lending in rural counties and about 

60 percent of lending in large metropolitan counties (see Figure 6-14). In small metropolitan countis and 

counties adjacent to metropolitan counties, home purchase and improvement loans increased substantially 

between 2007 and 2010. Finally, business lending constituted over 80 percent of the amount that CDFIs 

loaned in distressed counties, and over 72 percent in at-risk counties (see Figure 6-15). 

Over half of the loans that CDFIs made in the Central subregion and in rural, and distressed counties in 

both 2007 and 2010 were either for business or microenterpirse (see Table 6-7). In terms of the amount of 

loans to business and microenterprise, CDFIs clearly focused on increasing business and microenterprise 

lending in the Central subregion and in rural counties between 2007 and 2010 (see Table 6-8). For 

example, the amount of business lending in the Central subregion increased by almost 50 percent and 

microenterprise lending by 73 percent. Distressed counties, however, did not fare as well. While the total 

amount loaned for business increased by almost 24 percent, the increase was less than in at-risk, 

transitional, and competitive counties. 

Number of Loans

2007

Pct. of 

Total

Pct. of 

Total

Pct. of 

Total

Pct. of 

Total

Pct. of 

Total

Pct. of 

Total

Pct. of 

Total

United States 5,774 8.2% 6,639 9.4% 2,401 3.4% 902 1.3% 13,695 19.4% 39,517 55.9% 1,776 2.5%

Appalachian Region 729 15.8% 564 12.2% 41 0.9% 52 1.1% 1,050 22.8% 2,109 45.7% 68 1.5%

2010

United States 7,843 15.3% 11,425 22.3% 2,808 5.5% 995 1.9% 22,448 43.8% 3,008 5.9% 2,714 5.3%

Appalachian Region 1,206 25.9% 945 20.3% 44 0.9% 83 1.8% 2,101 45.1% 93 2.0% 189 4.1%

Amount of Loans

(in $ millions)

2007

United States $785.8 18.9% $71.6 1.7% $1,460.9 35.2% $291.0 7.0% $498.8 12.0% $336.4 8.1% $710.56 17.1%

Appalachian Region $108.9 55.3% $8.5 4.3% $14.4 7.3% $4.6 2.4% $39.0 19.8% $12.2 6.2% $9.33 4.7%

2010

United States $1,159.6 22.6% $135.6 2.6% $1,802.8 35.1% $331.8 6.5% $1,052.7 20.5% $10.0 0.2% $641.11 12.5%

Appalachian Region $179.3 48.4% $14.7 4.0% $15.2 4.1% $10.3 2.8% $128.3 34.6% $0.3 0.1% $22.69 6.1%

Business Microenterprise Other

Commercial or MultiFamily Real Estate 

Construction or Rehab

Real Estate, SingleFamily Construction 

or Rehab

Home Purchase or 

Home Improvement Consumer

180
Figure 6-13: Percent of Amount Loaned by Purpose by Region

32.5%

19.2%

13.7%

34.6%

75.2%

2.2%

3.7%

18.9%

18.8%

0.8%

3.0%

77.5%

60.2%

22.4%

0.2%

17.2%

40.5%

6.7%

3.9%

48.9%

8.6%

31.3%

30.6%

29.5%

67.0%

2.8%

5.2%

25.0%

17.4%

1.4%

2.6%

78.6%

17.5%

5.5%

1.9%

75.2%

51.2%

13.1%

2.9%

32.7%

0% 20% 40% 60% 80% 100%

All Other

Commercial/Multi-Family

Microenterprise

Business

All Other

Commercial/Multi-Family

Microenterprise

Business

All Other

Commercial/Multi-Family

Microenterprise

Business

All Other

Commercial/Multi-Family

Microenterprise

Business

All Other

Commercial/Multi-Family

Microenterprise

Business

Southern Appalachia South Central Appalachia Central Appalachia North Central Appalachia Northern Appalachia

2007 2010

181
Figure 6-14: Percent of Amount Loaned by Purpose by County Type

16.1%

0.4%

3.0%

80.5%

60.3%

4.0%

4.7%

31.0%

25.0%

32.1%

17.8%

25.0%

75.2%

2.9%

2.8%

19.1%

13.0%

13.9%

7.6%

65.5%

14.2%

0.1%

2.7%

83.0%

36.8%

14.5%

6.1%

42.7%

46.7%

27.4%

5.2%

20.7%

70.3%

1.9%

3.2%

24.5%

5.0%

25.6%

10.6%

58.8%

0% 20% 40% 60% 80% 100%

All Other

Commercial/Multi-Family

Microenterprise

Business

All Other

Commercial/Multi-Family

Microenterprise

Business

All Other

Commercial/Multi-Family

Microenterprise

Business

All Other

Commercial/Multi-Family

Microenterprise

Business

All Other

Commercial/Multi-Family

Microenterprise

Business

Rural (Nonmetro, 

Nonadjacent)

Nonmetro, Adjacent to 

Small Metro

Nonmetro, Adjacent to 

Large Metro

Small Metro (< 1 million 

people)

Large Metro (1 million + 

people)

2007 2010

182
Figure 6-15: Percent of Amount Loaned by Purpose by Economic Status

33.4%

51.8%

8.4%

6.3%

46.6%

6.1%

4.4%

42.9%

67.0%

2.8%

4.5%

25.7%

20.4%

3.5%

3.2%

72.8%

15.7%

1.2%

2.8%

80.4%

5.2%

62.9%

23.7%

8.2%

10.8%

24.8%

7.4%

57.0%

60.9%

7.9%

4.6%

26.6%

21.4%

1.1%

2.4%

75.1%

13.1%

0.0%

2.6%

84.3%

0% 20% 40% 60% 80% 100%

All Other

Commercial/Multi-Family

Microenterprise

Business

All Other

Commercial/Multi-Family

Microenterprise

Business

All Other

Commercial/Multi-Family

Microenterprise

Business

All Other

Commercial/Multi-Family

Microenterprise

Business

All Other

Commercial/Multi-Family

Microenterprise

Business

Attainment Competitive Transitional At-Risk Distressed

2007 2010

183
Table 6-7: Number of Loans by Purpose by Type of County, 2007 and 2010

Number of Loans

2007

Pct. of 

Total

Pct. of 

Total

Pct. of 

Total

Pct. of 

Total

Pct. of 

Total

Pct. of 

Total

Pct. of 

Total

Subregions

 Northern Appalachia 289 10.2% 81 2.8% 25 0.9% 1 0.0% 419 14.7% 2,018 70.9% 12 0.4%

 North Central Appalachia 65 50.8% 15 11.7% 7 5.5% 5 3.9% 31 24.2% 3 2.3% 2 1.6%

 Central Appalachia 300 33.0% 159 17.5% 1 0.1% 22 2.4% 381 41.9% 3 0.3% 44 4.8%

 South Central Appalachia 44 11.0% 85 21.2% 2 0.5% 19 4.7% 217 54.1% 25 6.2% 9 2.2%

 Southern Appalachia 31 9.4% 224 68.1% 6 1.8% 5 1.5% 2 0.6% 60 18.2% 1 0.3%

County Types

 Large Metro (1 million + 

people) 106 29.1% 183 50.3% 25 6.9% 3 0.8% 2 0.5% 41 11.3% 4 1.1%

 Small Metro (< 1 million 

people) 251 8.8% 118 4.1% 5 0.2% 18 0.6% 581 20.4% 1,850 65.0% 24 0.8%

 Nonmetro, Adjacent to Large 

Metro 7 12.5% 8 14.3% 2 3.6% 0 0.0% 1 1.8% 36 64.3% 2 3.6%

 Nonmetro, Adjacent to Small 

Metro 95 17.2% 94 17.0% 7 1.3% 7 1.3% 208 37.7% 134 24.3% 7 1.3%

 Rural (Nonmetro, 

Nonadjacent) 270 34.0% 161 20.3% 2 0.3% 24 3.0% 258 32.5% 48 6.0% 31 3.9%

Economic Status

 Distressed 231 35.5% 142 21.8% 0 0.0% 17 2.6% 234 35.9% 4 0.6% 23 3.5%

 At-Risk 83 32.9% 27 10.7% 3 1.2% 5 2.0% 119 47.2% 4 1.6% 11 4.4%

 Transitional 328 9.8% 222 6.6% 19 0.6% 27 0.8% 687 20.5% 2,050 61.0% 26 0.8%

 Competitive 83 36.2% 78 34.1% 17 7.4% 3 1.3% 10 4.4% 30 13.1% 8 3.5%

 Attainment 4 3.3% 95 77.9% 2 1.6% 0 0.0% 0 0.0% 21 17.2% 0 0.0%

2010

Subregions

 Northern Appalachia 490 37.8% 160 12.3% 25 1.9% 0 0.0% 560 43.2% 43 3.3% 18 1.4%

 North Central Appalachia 13 13.3% 3 3.1% 4 4.1% 7 7.1% 33 33.7% 0 0.0% 38 38.8%

 Central Appalachia 490 33.6% 307 21.1% 5 0.3% 37 2.5% 516 35.4% 15 1.0% 87 6.0%

 South Central Appalachia 173 11.1% 303 19.4% 4 0.3% 32 2.1% 976 62.6% 35 2.2% 37 2.4%

 Southern Appalachia 40 16.0% 172 68.8% 6 2.4% 7 2.8% 16 6.4% 0 0.0% 9 3.6%

County Types

 Large Metro (1 million + 

people) 244 48.0% 220 43.3% 20 3.9% 5 1.0% 7 1.4% 0 0.0% 12 2.4%

 Small Metro (< 1 million 

people) 388 18.4% 251 11.9% 9 0.4% 20 0.9% 1,359 64.3% 33 1.6% 54 2.6%

 Nonmetro, Adjacent to Large 

Metro 8 8.6% 33 35.5% 2 2.2% 1 1.1% 1 1.1% 43 46.2% 5 5.4%

 Nonmetro, Adjacent to Small 

Metro 137 18.2% 166 22.1% 6 0.8% 14 1.9% 367 48.8% 5 0.7% 57 7.6%

 Rural (Nonmetro, 

Nonadjacent) 429 35.9% 275 23.0% 7 0.6% 43 3.6% 367 30.7% 12 1.0% 61 5.1%

Economic Status

 Distressed 329 34.5% 189 19.8% 4 0.4% 29 3.0% 355 37.2% 3 0.3% 46 4.8%

 At-Risk 127 28.8% 111 25.2% 8 1.8% 8 1.8% 138 31.3% 8 1.8% 41 9.3%

 Transitional 488 21.2% 421 18.3% 13 0.6% 44 1.9% 1,169 50.7% 82 3.6% 87 3.8%

 Competitive 257 28.2% 182 20.0% 17 1.9% 2 0.2% 439 48.2% 0 0.0% 13 1.4%

 Attainment 5 9.8% 42 82.4% 2 3.9% 0 0.0% 0 0.0% 0 0.0% 2 3.9%

Business Microenterprise Other

Commercial or MultiFamily Real Estate 

Construction or Rehab

Real Estate, SingleFamily Construction 

or Rehab

Home Purchase or 

Home Improvement Consumer

184
Table 6-8: Amount of Loans by Purpose by Type of County, 2007 and 2010

Amount of Loans 

(in $ millions)

2007

Pct. of 

Total

Pct. of 

Total

Pct. of 

Total

Pct. of 

Total

Pct. of 

Total

Pct. of 

Total

Pct. of 

Total

Subregions

 Northern Appalachia $22.1 32.7% $2.0 2.9% $8.9 13.1% $0.1 0.1% $20.3 30.1% $11.3 16.7% $2.9 4.3%

 North Central Appalachia $8.3 75.2% $0.2 1.9% $0.6 5.5% $0.3 2.7% $1.2 10.7% $0.1 0.5% $0.4 3.5%

 Central Appalachia $71.1 78.6% $2.3 2.6% $1.3 1.4% $2.4 2.6% $9.2 10.1% $0.1 0.1% $4.1 4.6%

 South Central Appalachia $4.5 25.0% $0.9 5.2% $0.5 2.8% $1.6 8.8% $8.2 46.4% $0.3 1.5% $1.8 10.3%

 Southern Appalachia $3.0 29.5% $3.1 30.6% $3.2 31.3% $0.3 3.1% $0.1 0.6% $0.4 4.4% $0.1 0.5%

County Types

 Large Metro (1 million + 

people) $16.0 58.8% $2.9 10.6% $7.0 25.6% $0.2 0.8% $0.4 1.6% $0.3 1.1% $0.4 1.6%

 Small Metro (< 1 million 

people) $14.9 24.5% $1.9 3.2% $1.2 1.9% $1.4 2.4% $26.4 43.5% $10.6 17.4% $4.3 7.0%

 Nonmetro, Adjacent to Large 

Metro $0.7 20.7% $0.2 5.2% $0.9 27.4% $0.0 0.0% $0.0 0.7% $0.1 4.1% $1.3 41.9%

 Nonmetro, Adjacent to Small 

Metro $9.3 42.7% $1.3 6.1% $3.1 14.5% $0.5 2.2% $5.7 26.3% $1.0 4.6% $0.8 3.8%

 Rural (Nonmetro, 

Nonadjacent) $68.2 83.0% $2.2 2.7% $0.1 0.1% $2.5 3.1% $6.4 7.8% $0.2 0.2% $2.5 3.0%

Economic Status

 Distressed $57.0 84.3% $1.8 2.6% $0.0 0.0% $1.6 2.3% $5.5 8.1% $0.0 0.0% $1.8 2.7%

 At-Risk $17.1 75.1% $0.6 2.4% $0.2 1.1% $0.8 3.6% $3.0 13.1% $0.0 0.1% $1.0 4.5%

 Transitional $21.3 26.6% $3.7 4.6% $6.3 7.9% $1.9 2.4% $29.6 37.0% $11.7 14.6% $5.6 6.9%

 Competitive $13.3 57.0% $1.7 7.4% $5.8 24.8% $0.3 1.5% $1.0 4.1% $0.3 1.1% $0.9 4.1%

 Attainment $0.3 8.2% $0.8 23.7% $2.1 62.9% $0.0 0.0% $0.0 0.0% $0.2 5.2% $0.0 0.0%

2010

Subregions

 Northern Appalachia $41.9 48.9% $3.4 3.9% $5.7 6.7% $0.0 0.0% $31.1 36.3% $0.0 0.1% $3.6 4.1%

 North Central Appalachia $1.3 17.2% $0.0 0.2% $1.7 22.4% $0.5 6.6% $1.2 15.9% $0.0 0.0% $2.9 37.7%

 Central Appalachia $106.4 77.5% $4.1 3.0% $1.0 0.8% $3.3 2.4% $13.3 9.7% $0.1 0.0% $9.2 6.7%

 South Central Appalachia $22.5 18.9% $4.4 3.7% $2.7 2.2% $3.3 2.8% $81.5 68.4% $0.2 0.1% $4.7 3.9%

 Southern Appalachia $7.2 34.6% $2.8 13.7% $4.0 19.2% $3.2 15.3% $1.2 5.8% $0.0 0.0% $2.4 11.4%

County Types

 Large Metro (1 million + 

people) $37.3 65.5% $4.3 7.6% $7.9 13.9% $3.1 5.4% $0.4 0.7% $0.0 0.0% $3.9 6.9%

 Small Metro (< 1 million 

people) $26.6 19.1% $3.8 2.8% $4.0 2.9% $1.5 1.1% $96.6 69.5% $0.1 0.1% $6.3 4.5%

 Nonmetro, Adjacent to Large 

Metro $0.6 25.0% $0.5 17.8% $0.8 32.1% $0.1 2.0% $0.0 1.3% $0.0 1.9% $0.5 19.8%

 Nonmetro, Adjacent to Small 

Metro $14.9 31.0% $2.3 4.7% $1.9 4.0% $1.3 2.6% $20.1 41.8% $0.0 0.1% $7.6 15.8%

 Rural (Nonmetro, 

Nonadjacent) $99.9 80.5% $3.8 3.0% $0.5 0.4% $4.4 3.5% $11.2 9.0% $0.0 0.0% $4.4 3.5%

Economic Status

 Distressed $70.7 80.4% $2.4 2.8% $1.0 1.2% $2.1 2.4% $8.9 10.1% $0.0 0.0% $2.8 3.2%

 At-Risk $33.9 72.8% $1.5 3.2% $1.6 3.5% $1.1 2.3% $5.3 11.3% $0.0 0.1% $3.1 6.8%

 Transitional $36.6 25.7% $6.4 4.5% $4.0 2.8% $6.9 4.8% $75.6 53.2% $0.2 0.2% $12.5 8.8%

 Competitive $37.9 42.9% $3.9 4.4% $5.4 6.1% $0.3 0.3% $38.6 43.8% $0.0 0.0% $2.2 2.5%

 Attainment $0.4 6.3% $0.5 8.4% $3.1 51.8% $0.0 0.0% $0.0 0.0% $0.0 0.0% $2.0 33.4%

Business Microenterprise Consumer Other

Commercial or MultiFamily Real Estate Real Estate, SingleFamily Construction 

Home Purchase or 

Home Improvement

185
6.8 NEW MARKETS TAX CREDIT INVESTMENTS IN APPALACHIA

Congress established the New Markets Tax Credit program (NMTC Program) in 2000 to create an 

incentive for increased investment in businesses and real estate projects in low-income communities. The 

NMTC Program provides investors a credit against federal income taxes equal to 39 percent of their 

original equity investment in Community Development Entities (CDEs). CDEs are existing domestic 

businesses with: 1) a primary mission to serve or provide investment capital in low-income communities 

or to low-income individuals; and 2) have representatives of the community on the governing or advisory 

board of the organization. The investor receives the federal tax credits over a seven-year period. 

Based on data from the CDFI Fund, CDEs have used credits to finance 3,060 projects with a total 

investment of $20.9 billion since the beginning of the program.23 Forty-seven CDEs have invested at 

least part of their credit authority in 351 projects in 62 Appalachian counties, an investment of $706.7 

million or about 3.4 percent of the total amount of credit authority invested (see Map 6-6 and Map 6-7). 

There are only 12 CDEs located in Appalachia, and they have received a total allocation of $321 million 

in credit authority out of $30 billion in tax credits allocated, or 1.1% of the credit authority allocated, 

which means that most of the NMTC investment in Appalachia is from CDEs outside of the region. Of 

the projects in Appalachia, 56 are in 16 distressed counties.24 The credit authority invested in projects in 

Appalachia constitute only about 20 percent of the total cost of the projects, and so the total cost of 

projects receiving NMTC investment in Appalachia is over $3.5 billion. 

The majority of credit authority invested in projects completed in 2007 and 2010 was in the Northern and 

Southern subregions, 68 percent in 2007 and over 79 percent in 2010 (see Figure 6-16). Investment in 

projects completed in 2010 in the Central subregion decreased by over 40 percent, going from 25 percent 

of credit authority invested in Appalachia in 2007 to 7 percent in 2010. Large and small metropolitan

counties dominated, with 72 percent of the investment in projects completed in both 2007 and 2010 (see 

Figure 6-17). Funding for projects completed in 2010 in rural counties also decreased by over 40 percent, 

dropping from 26 percent of credit authority invested in Appalachia in 2007 to 7 percent in 2010. 

Competitive and transitional counties received 72 percent of the credit authority invested in projects 

completed in 2007 and 87 percent in projects completed in 2010 (see Figure 6-18). Credits invested in 

distressed counties in projects completed in 2010 decreased by 16 percent and dropped from 29 percent of 

credits for project completed in 2007 to 10 percent for projects completed in 2010. 

Overall, CDEs invested a total of $3.9 billion in credit authority in projects completed nationally in 2007, 

including $83 million by 11 CDEs for projects completed in Appalachia (see Table 6-9). CDEs invested a 

total of $3.5 billion in credit authority for projects completed nationally in 2010, including $180 million 

invested by 19 CDEs in projects completed in Appalachia. The amount of NMTC credit authority 

increased by 117 percent for projects completed in 2010, compared with projects completed in 2007, 

despite a 10.5 percent decrease in credit authority invested in completed projects nationally.

23 The CDFI Fund only reports investment in completed projects and reports the data as if all investment had 

occurred in the last year that the project received NMTC investment. The data do not include credit authority that 

may have been invested in projects that have not been completed as of the time of the data report. The data also only 

report the CDE making the largest investment, although other CDEs may have invested as well.

24 The status of counties is based on the status as of 2010. Some of the counties may have changed status between 

the time the investment was made and 2010.

186

 
One possible explanation for the pattern of NMTC funding is that tax credit projects require for-profit 

partners to take advantage of the credits. The recession may have made for-profit investors less willing to 

help fund projects in more distressed communities. Investors also may naturally favor projects in more 

urbanized areas, perhaps because more investors are already operating in those areas and are more 

familiar with the local economic conditions. 

Map 6-6: Total Number of New Market Tax Credit Investments, by County, 2000-2010 

187
Map 6-7: Total Amount of New Market Tax Credit Authority Invested, by County, 2000-2010

188
Figure 6-16: Amount of NMTC Credits by Region

Figure 6-17: Amount of NMTC Credits by County Type

Figure 6-18: Amount of NMTC Credits by Economic Status

$43.5

$13.0

$12.0

$12.1

$99.9

$41.9

$3.0

$20.5

$2.9

$14.6

$0 $20 $40 $60 $80 $100

Southern Appalachia

South Central Appalachia

Central Appalachia

North Central Appalachia

Northern Appalachia

Millions of Dollars 2007 2010

$12.6

$20.4

$0.0

$77.7

$69.8

$21.5

$1.7

$0.0

$6.7

$53.1

$0 $20 $40 $60 $80 $100

Rural (Nonmetro, Nonadjacent)

Nonmetro, adjacent to Small Metro

Nonmetro, Adjacent to Large Metro

Small Metro (< 1 million people)

Large Metro (1 million + people)

Millions of Dollars 2007 2010

$0.0

$78.0

$78.3

$7.0

$17.2

$0.0

$17.6

$41.9

$2.9

$20.5

$0 $20 $40 $60 $80 $100

Attainment

Competitive

Transitional

At-Risk

Distressed

Millions of Dollars 2007 2010

189
Table 6-9: New Markets Tax Credit Projects and Amounts in Appalachia, 2007 and 2010

6.9 MICROLOANS 

According to the SBA, the microloan program provides small, short-term loans to small businesses and 

nonprofit child-care centers. The SBA provides funds to intermediary lenders that are nonprofit 

community-based organizations. These intermediaries make loans to eligible borrowers. The maximum 

loan amount is $50,000, and the average microloan nationally is about $13,000. The loans can be used for 

working capital, purchase of inventory or supplies, purchase of furniture or fixtures, and purchase of 

machinery or equipment.25

In 2007, the average microloan amount was modestly higher in Appalachia ($16,851) than the nation 

($13,021). By 2010, the average loan amounts were approximately aligned; the national average was 

$12,099 and the Appalachian average was $12,902. 

Access to the microloan program improved in Appalachia relative to the nation. In 2007, about one 

microloan was issued per 10,000 small businesses in both Appalachia and the nation. In 2010, less than 

one microloan was issued per 10,000 small businesses nationally while about 1.6 microloans were issued 

per 10,000 businesses in Appalachia. 

The microloan program is effectively targeting disadvantaged counties in Appalachia. In 2007, Central 

Appalachia, rural counties, and distressed counties received the most microloans per small 10,000 

businesses. The trend is the same in 2010 with 6.96, 3.58, and 4.6 microloans per 10,000 businesses in 

Central Appalachia, rural counties, and distressed counties, respectively (see Table 6-10). 

25 See http:/www.sba.gov/content/microloan-program. 

Percent of Percent of Percent of Percent of

Number

Appalachian 

Total

Amount 

(in $ millions)

Appalachian 

Total Number

Appalachian 

Total

Amount 

(in $ millions)

Appalachian 

Total

United States $3,909.0 $3,500.0

Appalachian Region 24 $83.0 2.1% 58 $180.4 5.2%

 Subregions

 Northern Appalachia 4 16.7% $14.6 17.6% 26 44.8% $99.9 55.4%

 North Central Appalachia 5 20.8% $2.9 3.5% 14 24.1% $12.1 6.7%

 Central Appalachia 7 29.2% $20.5 24.7% 1 1.7% $12.0 6.7%

 South Central Appalachia 2 8.3% $3.0 3.7% 8 13.8% $13.0 7.2%

 Southern Appalachia 6 25.0% $41.9 50.5% 9 15.5% $43.5 24.1%

 County Types

 Large Metro (1 million + people) 7 29.2% $53.1 63.9% 19 32.8% $69.8 38.7%

 Small Metro (< 1 million people) 5 20.8% $6.7 8.1% 21 36.2% $77.7 43.0%

 Nonmetro, Adjacent to Large Metro 0 0.0% $0.0 0.0% 0 0.0% $0.0 0.0%

 Nonmetro, adjacent to Small Metro 4 16.7% $1.7 2.1% 12 20.7% $20.4 11.3%

 Rural (Nonmetro, Nonadjacent) 8 33.3% $21.5 25.9% 6 10.3% $12.6 7.0%

 Economic Status

 Distressed 7 29.2% $20.5 24.7% 3 5.2% $17.2 9.5%

 At-Risk 5 20.8% $2.9 3.5% 12 20.7% $7.0 3.9%

 Transitional 6 25.0% $41.9 50.5% 22 37.9% $78.3 43.4%

 Competitive 6 25.0% $17.6 21.3% 21 36.2% $78.0 43.2%

 Attainment 0 0.0% $0.0 0.0% 0 0.0% $0.0 0.0%

2007 2010

190

 
Table 6-10: Approved Microloans per 10,000 Businesses in Appalachia

Overall 

Approved 

Loans

Number of 

Businesses

Number of 

Approved Loans 

per 10,000 

Businesses

Overall 

Approved 

Loans

Number of 

Businesses

Number of 

Approved Loans 

per 10,000 

Businesses

United States 2,524 21,808,201 1.16 3,572 21,530,378 1.66

Appalachian Region 163 1,607,645 1.01 244 1,577,370 1.55

Subregions

Northern Appalachia 73 477,301 1.53 94 482,014 1.95

North Central Appalachia 11 128,944 0.85 13 124,926 1.04

Central Appalachia 49 115,266 4.25 76 109,122 6.96

South Central Appalachia 22 307,059 0.72 37 304,728 1.21

Southern Appalachia 8 579,075 0.14 24 556,580 0.43

County Types

Large Metro (1 million + people) 40 395,470 1.01 48 404,330 1.19

Small Metro (< 1 million people) 49 613,330 0.80 66 604,234 1.09

Nonmetro, Adjacent to Large Metro 10 109,171 0.92 11 103,911 1.06

Nonmetro, Adjacent to Small Metro 25 301,104 0.83 57 291,784 1.95

Rural (Nonmetro, Nonadjacent) 39 188,570 2.07 62 173,111 3.58

Economic Status

Distressed 40 89,777 4.46 40 86,870 4.60

At-Risk 6 145,503 0.41 33 138,028 2.39

Transitional 85 887,755 0.96 122 905,985 1.35

Competitive 23 305,587 0.75 44 292,327 1.51

Attainment 8 135,857 0.59 5 154,160 0.32

Alabama 2 314,870 0.06 7 302,408 0.23

Appalachian Alabama 0 199,561 0.00 0 192,538 0.00

Non-Appalachian Alabama 2 115,309 0.17 7 109,870 0.64

Georgia 43 828,977 0.52 74 792,556 0.93

Appalachian Georgia 8 239021 0.33 24 238,124 1.01

Non-Appalachian Georgia 35 589,956 0.59 50 554,432 0.90

Kentucky 116 304,048 3.82 323 297,724 10.85

Appalachian Kentucky 47 77,542 6.06 59 74,004 7.97

Non-Appalachian Kentucky 69 226,506 3.05 264 223,720 11.80

Maryland 8 388,206 0.21 22 389,579 0.56

Appalachian Maryland 0 14,392 0.00 0 14,993 0.00

Non-Appalachian Maryland 8 373,814 0.21 22 374,586 0.59

Mississippi 0 288,779 0.00 0 248,541 0.00

Appalachian Mississippi 0 69,965 0.00 0 58,339 0.00

Non-Appalachian Mississippi 0 218,814 0.00 0 190,202 0.00

New York 210 1,404,794 1.49 772 1,253,951 6.16

Appalachian New York 17 67,312 2.53 36 62,314 5.78

Non-Appalachian New York 193 1,337,482 1.44 736 1,191,637 6.18

North Carolina 58 615,139 0.94 67 624,133 1.07

Appalachian North Carolina 14 112,966 1.24 29 110,530 2.62

Non-Appalachian North Carolina 44 502,173 0.88 38 513,603 0.74

Ohio 122 691,536 1.76 142 667,427 2.13

Appalachian Ohio 12 117,389 1.02 8 113,316 0.71

Non-Appalachian Ohio 110 574,147 1.92 134 554,111 2.42

Pennsylvania 72 711,343 1.01 106 763,099 1.39

Appalachian Pennsylvania 54 320,365 1.69 57 332,120 1.72

Non-Appalachian Pennsylvania 18 390,978 0.46 49 430,979 1.14

South Carolina 7 272,316 0.26 33 266,975 1.24

Appalachian South Carolina 0 70,528 0.00 0 67,579 0.00

Non-Appalachian South Carolina 7 201,788 0.35 33 199,396 1.65

Tennessee 5 427,304 0.12 5 422,466 0.12

Appalachian Tennessee 1 181,133 0.06 0 178,251 0.00

Non-Appalachian Tennessee 4 246,171 0.16 5 244,215 0.20

Virginia 112 483,178 2.32 129 547,124 2.36

Appalachian Virginia 9 42,503 2.12 25 44,052 5.68

Non-Appalachian Virginia 103 440,675 2.34 104 503,072 2.07

West Virginia (entire state) 1 94,968 0.11 6 91,210 0.66

2007 2010

191
[Page Intentionally Left Blank]

192
CHAPTER 7 REVOLVING LOAN FUNDS

7.1 SUMMARY

This chapter examines lending by ARC-funded Revolving Loan Funds (RLFs). The chapter looks at the 

amount of RLF lending, the total amount invested in the project, and the extent to which RLF lending 

leveraged additional funds for the projects, as well as the source of those leveraged funds. This chapter 

also describes the current capital base of RLFs funded by the Economic Development Administration 

(EDA) as of 2011. 

The key findings with respect to the ARC-funded RLFs are: 

• The thirty-five ARC-funded RLFs in Appalachia made 87 loans in the amount of $7.7 million in 

2007 and 73 loans in the amount of $6.5 million in 2010. 

• The $7.7 million in ARC-funded RLF loans leveraged an additional $52.1 million in 2007 and the 

$6.5 million in ARC-funded RLF loans in 2010 leveraged an additional $60.8 million in 2010. 

ARC-funded RLF lending in Appalachia attracted more in private investment than in publicsector investment.

• The great majority of ARC RLF funding in 2007 and 2010 was in Northern and Southern 

Appalachia, transitional counties, and in small metropolitan counties and rural counties.

• There are 69 EDA-funded RLFs located in 54 Appalachia counties as of 2011, having an 

aggregate capital base of nearly $120 million. Over half of this capital held by just eight RLFs.

Both the ARC and Economic Development Administration (EDA) fund RLFs in Appalachia. ARCfunded RLFs are most active in the Northern and Southern Appalachian subregions and in transitional 

counties. The $7.7 million of ARC-funded RLF loans leveraged $52.1 million of additional investment in 

2007, and $6.5 million of loans leveraged $60.8 million of additional investment in 2010. EDA-funded 

RLFs are highly concentrated, with over half the capital held by just eight funds located mostly in the 

Northern Appalachian subregion and in transitional counties.

7.2 INTRODUCTION

A revolving loan fund (RLF) pools public- and private-sector funds and lends them to businesses. 

Frequently, RLF loans are made to businesses located in disadvantaged communities or in areas not well 

served by mainstream financial institutions. Several federal agencies have provided funding for RLFs. 

The Department of Housing and Urban Development (HUD) and the Economic Development 

Administration (EDA) both started funding RLFs in 1975, and the Department of Agriculture’s 

Intermediary Relending Program began in 1985.26 The Environmental Protection Agency has an RLF for 

brownfield remediation, and the ARC has funded RLFs in Appalachia since 1977.27 In addition, 19 states 

26 We did not receive data that we requested on the Intermediary Relending Program from the Department of 

Agriculture.

27 Public Sector Business Loan Funds: Views and Recommendations from Practitioners. A joint report of the 

National Association of Development Organizations (NADO) Research Foundation and the Development District of 

Appalachia. May 2010.

193

 
operated RLFs as of 2006, either directly through a state agency or by funding RLFs operated by nongovernmental entities.28

RLF loans usually offer more favorable terms than those available from mainstream financial institutions. 

For example, RLF loans generally have below-market interest rates and may be subordinated to other 

financing. The interest the borrower pays on the loans provides the RLFs with operating capital and the 

repayment of principal replenishes the capital that RLFs use to make new loans.

7.3 ARC-FUNDED REVOLVING LOAN FUNDS

The vast majority of ARC-funded RLF lending, over 75 percent of the total, was in the Northern and 

Southern subregions in both 2007 and 2010 (see Figure 7-1). Lending in the Central subregion declined 

by over 80 percent, from six loans totaling $514,900 in 2007 to just one loan of $100,000 in 2010. RLF 

lending in small metropolitan counties and counties adjacent to metropolitan counties increased from 61 

percent of the total in 2007 to 72 percent in 2010, but the total amount of the loans remained virtually 

constant at about $4.7 million. RLF lending in rural counties declined by about 40 percent, from $1.8 

million in 2007 to $1.1 million in 2010 (see Figure 7-2). RLF lending in transitional counties dropped 

slightly, from $4.5 million in 2007 to $4.4 million in 2010. Lending in distressed counties decreased by 

56 percent, from 12 loans totaling $771,500 in 2007 to five loans totaling $341,500 in 2010 (see Figure 7-

3).

Overall, the thirty-five ARC-funded RLFs active in Appalachia since 2006 have made a total of 461 

loans, an average of 2.2 loans per RLF per year. The ARC-funded RLFs made 87 loans in a total amount 

of $7.7 million, an average of $88,370 per loan, in 2007 and 73 loans totaling $6.5 million, an average of 

$89,239 per loan, in 2010 (see Table 7-1). Because RLFs make relatively few loans, any conclusions 

drawn from the data must be considered with some caution. The small number of loans in any given year 

makes the data sensitive to the timing of the loans, with loans made shortly before the beginning or after 

the end of the 2007 to 2010 time period being excluded. For example, a loan closed in the South Central 

subregion in December 2006 would not appear in the data even though the funds might actually have 

been used in 2007. Excluding such a loan would have more of an apparent impact on the data than if there 

were more loans overall.

28 National Conference of State Legislatures Legisbrief, Vol. 14, No. 1, January 2006.

194

 
Figure 7-1: Amount of Loans from ARC-funded Revolving Loan Funds by Region

Figure 7-2: Amount of Loans from ARC-funded Revolving Loan Funds by County Type

Figure 7-3: Amount of Loans from ARC-funded Revolving Loan Funds by Economic Status

$2,501.5

$0.0

$100.0

$748.0

$3,165.0

$2,625.9

$0.0

$514.9

$1,118.9

$3,428.5

$0 $1,000 $2,000 $3,000 $4,000 $5,000

Southern Appalachia

South Central Appalachia

Central Appalachia

North Central Appalachia

Northern Appalachia

Thousands of Dollars 2007 2010

$1,083.3

$2,250.1

$465.0

$2,456.1

$260.0

$1,803.2

$2,226.8

$951.5

$2,470.4

$236.3

$0 $1,000 $2,000 $3,000 $4,000 $5,000

Rural (Nonmetro, Nonadjacent)

Nonmetro, Adjacent to Small Metro

Nonmetro, Adjacent to Large Metro

Small Metro (< 1 million people)

Large Metro (1 million + people)

Thousands of Dollars 2007 2010

$0.0

$615.0

$4,375.9

$1,182.1

$341.5

$0.0

$1,280.0

$4,474.2

$1,162.5

$771.5

$0 $1,000 $2,000 $3,000 $4,000 $5,000

Attainment

Competitive

Transitional

At-Risk

Distressed

Thousands of Dollars 2007 2010

195
Table 7-1: ARC-funded Revolving Loan Fund Lending

RLF loans may either provide stand-alone funding or, more commonly, are made as gap financing in 

conjunction with other private- or public-sector loans and investments in the same enterprise. Because 

they may be subordinated to other financing, the borrower can use the initial RLF loan commitment to 

leverage additional funds. For example, HUD funds some RLFs through its Community Development 

Block Grant (CDBG) program, and those CDBG-backed loans generate $2.69 in additional private-sector 

and $0.77 in public-sector investment. Between 1995 and 1998, EDA-funded RLFs had a median 

leverage of $1.97 for their loans.29

The $7.7 million in ARC-funded RLF loans leveraged an additional $52.1 million to finance $59.8 

million in total project investments in 2007, and the $6.5 million of ARC-funded RLF loans leveraged an 

additional $60.8 million to finance $67.3 million in total project investment in 2010 (see Table 7-2). Any 

direct comparison of leverage ratios among RLFs receiving funding from the various government sources 

must be done with caution because the funding agencies may have different bases for reporting what 

counts as leveraged funds.

29 Walker, Christopher, et al., 2002. Public-Sector Loans to Private-Sector Businesses: An Assessment of HUDSupported Local Economic Development Lending Activities. Washington, DC: The Urban Institute. 

Amount Amount

Number (in $ thousands) Number (in $ thousands)

Appalachian Region 87 $7,688.2 73 $6,514.5

Subregions

 Northern Appalachia 37 42.5% $3,428.5 44.6% 36 49.3% $3,165.0 48.6%

 North Central Appalachia 15 17.2% $1,118.9 14.6% 12 16.4% $748.0 11.5%

 Central Appalachia 6 6.9% $514.9 6.7% 1 1.4% $100.0 1.5%

 South Central Appalachia 0 0.0% $0.0 0.0% 0 0.0% $0.0 0.0%

 Southern Appalachia 29 33.3% $2,625.9 34.2% 24 32.9% $2,501.5 38.4%

County Types

 Large Metro (1 million + people) 3 3.4% $236.3 3.1% 3 4.1% $260.0 4.0%

 Small Metro (< 1 million people) 23 26.4% $2,470.4 32.1% 20 27.4% $2,456.1 37.7%

 Nonmetro, Adjacent to Large Metro 10 11.5% $951.5 12.4% 6 8.2% $465.0 7.1%

 Nonmetro, Adjacent to Small Metro 30 34.5% $2,226.8 29.0% 29 39.7% $2,250.1 34.5%

 Rural (Nonmetro, Nonadjacent) 21 24.1% $1,803.2 23.5% 15 20.5% $1,083.3 16.6%

Economic Status

 Distressed 12 13.8% $771.5 10.0% 5 6.8% $341.5 5.2%

 At-Risk 14 16.1% $1,162.5 15.1% 14 19.2% $1,182.1 18.1%

 Transitional 52 59.8% $4,474.2 58.2% 46 63.0% $4,375.9 67.2%

 Competitive 9 10.3% $1,280.0 16.6% 8 11.0% $615.0 9.4%

 Attainment 0 0.0% $0.0 0.0% 0 0.0% $0.0 0.0%

Percent of 

Total

Percent of 

Total

Percent of 

Total

Percent of 

Total

2007 2010

196

 
Table 7-2: Total Project Investment in Projects Funded by ARC-funded Revolving Loan Funds

ARC-funded RLF lending in Appalachia has attracted more in private investment than in other publicsector investment. Government-funded RLFs invested $1.26 for every dollar from ARC-funded RLFs in 

2007 and $2.26 in 2010, an increase of 79 percent (see Table 7-3). Banks and other lenders invested $4.03 

for every dollar from ARC-funded RLFs in 2007 and $5.16 in 2010, an increase of 28 percent. Overall, 

including the equity invested by the borrowers, ARC-funded RLF lending leveraged $6.82 in other 

investment in 2007 and $9.78 in 2010, an increase of 43 percent. 

Table 7-3: Leverage per Dollar of ARC-funded RLF Loans

The data in Table 7-3 show substantial variation in the extent to which ARC-funded RLF loans were able 

to leverage additional funding among the different categories of counties in Appalachia, but the limited 

number of loans makes those variations less significant than they might be with a larger sample. For 

example, the amount leveraged increased from $11.28 per dollar of ARC-funded RLF loans in the Central 

region in 2007 to $35.99 in 2010. The data for 2007 are based on only six loans made that year in the 

Amount Amount

Number (in $ thousands) Number (in $ thousands)

Appalachian Region 87 $59,773.0 73 $67,298.9

Subregions

 Northern Appalachia 37 42.5% $30,870.7 51.6% 36 49.3% $36,162.0 53.7%

 North Central Appalachia 15 17.2% $6,128.0 10.3% 12 16.4% $4,206.7 6.3%

 Central Appalachia 6 6.9% $6,320.4 10.6% 1 1.4% $3,699.0 5.5%

 South Central Appalachia 0 0.0% $0.0 0.0% 0 0.0% $0.0 0.0%

 Southern Appalachia 29 33.3% $16,453.9 27.5% 24 32.9% $23,231.2 34.5%

County Types

 Large Metro (1 million + people) 3 3.4% $929.3 1.6% 3 4.1% $5,415.0 8.0%

 Small Metro (< 1 million people) 23 26.4% $19,893.6 33.3% 20 27.4% $20,210.1 30.0%

 Nonmetro, Adjacent to Large Metro 10 11.5% $12,058.5 20.2% 6 8.2% $12,381.0 18.4%

 Nonmetro, Adjacent to Small Metro 30 34.5% $15,642.9 26.2% 29 39.7% $18,941.8 28.1%

 Rural (Nonmetro, Nonadjacent) 21 24.1% $11,248.7 18.8% 15 20.5% $10,351.0 15.4%

Economic Status

 Distressed 12 13.8% $7,706.4 12.9% 5 6.8% $4,772.5 7.1%

 At-Risk 14 16.1% $5,635.7 9.4% 14 19.2% $6,538.8 9.7%

 Transitional 52 59.8% $37,517.9 62.8% 46 63.0% $47,587.6 70.7%

 Competitive 9 10.3% $8,913.0 14.9% 8 11.0% $8,400.0 12.5%

 Attainment 0 0.0% $0.0 0.0% 0 0.0% $0.0 0.0%

Percent of 

Total

Percent of 

Total

Percent of 

Total

Percent of 

Total

2007 2010

Government Bank and Borrower Government Bank and Borrower

RLF Loans Other Loans Equity Project Total RLF Loans Other Loans Equity Project Total

Appalachian Region $1.26 $4.03 $1.48 $6.77 $2.26 $5.16 $1.91 $9.33

Subregions

 Northern Appalachia $1.72 $4.59 $1.69 $8.00 $2.69 $5.92 $1.82 $10.43

 North Central Appalachia $1.10 $1.96 $1.42 $4.48 $1.31 $0.59 $2.72 $4.62

 Central Appalachia $2.64 $7.50 $1.14 $11.28 $14.95 $15.49 $5.55 $35.99

 South Central Appalachia $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

 Southern Appalachia $0.47 $3.51 $1.29 $5.27 $1.49 $5.14 $1.65 $8.29

County Types

 Large Metro (1 million + people) $0.20 $2.27 $0.47 $2.93 $0.00 $19.77 $0.06 $19.83

 Small Metro (< 1 million people) $0.88 $4.74 $1.43 $7.05 $2.22 $3.50 $1.50 $7.23

 Nonmetro, Adjacent to Large Metro $1.90 $7.57 $2.20 $11.67 $2.76 $19.92 $2.94 $25.63

 Nonmetro, Adjacent to Small Metro $1.38 $2.72 $1.92 $6.02 $1.58 $3.39 $2.45 $7.42

 Rural (Nonmetro, Nonadjacent) $1.44 $3.06 $0.73 $5.24 $4.08 $2.72 $1.75 $8.56

Economic Status

 Distressed $2.13 $5.51 $1.35 $8.99 $4.66 $5.45 $2.87 $12.98

 At-Risk $1.37 $1.38 $1.10 $3.85 $1.14 $2.53 $0.86 $4.53

 Transitional $1.43 $4.44 $1.52 $7.39 $2.60 $5.14 $2.13 $9.88

 Competitive $0.07 $4.15 $1.74 $5.96 $0.65 $10.15 $1.86 $12.66

 Attainment $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00

2007 2010

197
region, and the data for 2010 are based on a single loan. Similarly, the data for large metropolitan

counties are based on only three loans for each year. With such small samples, the impact of a single loan 

with unusual financial characteristics can skew the average much more than with a larger sample.

Overall, ARC-funded RLFs provided almost 13 percent of project investment in 2007 and just under 10 

percent in 2010 (see Table 7-4). Other government RLFs increased their share of project investment from 

16 percent in 2007 to 22 percent in 2010, while bank and other loans continued to represent almost half of 

the total project investment in both years. Borrower equity remained about 19 percent of the total project 

investment.

Table 7-4: Percent of Project Investment by Source

Loans from ARC-funded RLFs contributed to projects that created 968 jobs and retained an additional 

960 jobs in 2007 and created 700 jobs and retained 1,091 jobs in 2010 (see Table 7-5) according to 

reports from the RLFs.30 The data, however, are based on subjective criteria and are not consistently 

measured or verified. In both 2007 and 2010, RLFs made loans to projects that retained more jobs than 

they created in the Northern and North Central subregions, while in the Central and Southern subregions, 

the loans were to projects that created more jobs than they retained. The loans were to projects that 

retained more jobs than they created in all types of counties except rural counties in 2007. In distressed 

counties, the loans were to projects that created more jobs than they retained in both 2007 and 2010, 

which may reflect the correlation between levels of unemployment and the economic status designation 

for those counties. 

The total project investment per job created increased from $30,994 to $37,566 or 21 percent between 

2007 and 2010, but the investment per job for ARC-funded RLFs decreased by 9 percent, from $3,987 to 

$3,636. The variation among the investment per job in the different categories of counties may reflect the 

30 The jobs data are self-reported and the criteria for reporting a job “created” or “retained” may vary from fund to 

fund. 

Government Bank and Borrower Government Bank and Borrower

RLF Loans Other Loans Equity Project Total RLF Loans Other Loans Equity Project Total

Appalachian Region 12.9% 16.3% 51.9% 19.0% 9.7% 21.9% 49.9% 18.5%

Subregions

 Northern Appalachia 11.1% 19.1% 51.0% 18.8% 8.8% 23.6% 51.8% 15.9%

 North Central Appalachia 18.3% 20.1% 35.7% 25.9% 17.8% 23.3% 10.5% 48.4%

 Central Appalachia 8.1% 21.5% 61.1% 9.3% 2.7% 40.4% 41.9% 15.0%

 South Central Appalachia 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

 Southern Appalachia 16.0% 7.4% 56.0% 20.6% 10.8% 16.1% 55.3% 17.8%

County Types

 Large Metro (1 million + people) 25.4% 5.1% 57.7% 11.8% 4.8% 0.0% 94.9% 0.3%

 Small Metro (< 1 million people) 12.4% 10.9% 58.8% 17.8% 12.2% 27.0% 42.6% 18.3%

 Nonmetro, Adjacent to Large Metro 7.9% 15.0% 59.7% 17.4% 3.8% 10.4% 74.8% 11.0%

 Nonmetro, Adjacent to Small Metro 14.2% 19.7% 38.8% 27.3% 11.9% 18.8% 40.3% 29.1%

 Rural (Nonmetro, Nonadjacent) 16.0% 23.2% 49.0% 11.8% 10.5% 42.7% 28.5% 18.3%

Economic Status

 Distressed 10.0% 21.3% 55.1% 13.5% 7.2% 33.3% 39.0% 20.5%

 At-Risk 20.6% 28.3% 28.4% 22.7% 18.1% 20.5% 45.8% 15.6%

 Transitional 11.9% 17.0% 52.9% 18.1% 9.2% 23.9% 47.3% 19.6%

 Competitive 14.4% 1.0% 59.7% 25.0% 7.3% 4.8% 74.3% 13.6%

 Attainment 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

2007 2010

198

 
effects of the small sample sizes of loans in some categories, but it may also be affected by the kinds of 

facility financed and whether the business is capital- or labor-intensive. For example, a loan to help open 

a small medical clinic in a rural area might cost more per job created because of the need for sophisticated 

and expensive diagnostic equipment, but the benefit to the community in improved access to health care 

could justify the expenditure. Opening a restaurant, on the other hand, would produce more jobs for a 

lower cost and might provide badly needed employment for local residents. 

Table 7-5: Jobs Created and Retained by Businesses Receiving ARC-funded RLF Loans

7.4 ECONOMIC DEVELOPMENT ADMINISTRATION FUNDED REVOLVING LOAN FUNDS

Nearly half of all EDA-funded RLFs are in the Northern Appalachian subregion as of 2011,31 both in 

terms of the number of RLFs and the percentage of the overall current capital base of EDA-funded RLFs 

in Appalachia, while the Central Appalachian subregion has the fewest (see Figure 7-4). More than half of 

those RLFs are in small metropolitan counties and counties adjacent to metropolitan counties, while most 

of the RLF current capital base is controlled by RLFs located in small metropolitan counties and counties 

adjacent to large metropolitan counties (see Figure 7-5). The overwhelming majority of EDA-funded 

RLFs, over 70 percent in terms of numbers and current capital base, are located in transitional counties 

(see Figure 7-6).

Overall, there are 69 EDA-funded RLFs in 54 counties in Appalachia as of 2011 (see Table 7-6). Those 

RLFs have a current capital base of nearly $120 million, with over half of that held by just eight RLFs 

(see Map 7-1). EDA RLFs with the highest total current capital base are located in the following counties: 

Kanawha, WV ($16.7 million), Chautauqua, NY ($12.2 million), Allegheny, PA ($7.8 million), and 

Venango, PA ($6.5 million), and they account for 36 percent of the EDA-funded RLF current capital 

base.

31 We do not have data on EDA-funded RLFs for 2007 or 2010 to perform the same longitudinal analysis as in the 

other sections of this report. The data are for 2011 only, and they do not contain the actual level of investment for 

any individual project or in any geographic area.

Jobs 

Created Jobs Retained

ARC RLF Investment per 

Job

Total Project 

Investment per 

Job

Jobs 

Created Jobs Retained

ARC RLF Investment per 

Job

Total Project 

Investment per 

Job

Appalachian Region 968 960.5 $3,986.64 $30,994.54 700.5 1091 $3,636.32 $37,565.68

Subregions

 Northern Appalachia 246.5 511.5 $4,523.09 $40,726.52 389 917 $2,423.43 $27,689.13

 North Central Appalachia 105 291 $2,825.52 $15,474.63 44.5 63 $6,958.14 $39,132.09

 Central Appalachia 412 2 $1,243.72 $15,266.67 60 0 $1,666.67 $61,650.00

 South Central Appalachia 0 0 $0.00 $0.00 0 0 $0.00 $0.00

 Southern Appalachia 204.5 156 $7,284.11 $45,641.94 207 111 $7,866.25 $73,054.14

County Types

 Large Metro (1 million + people) 14 37 $4,633.82 $18,222.06 13 23 $7,222.22 $150,416.67

 Small Metro (< 1 million people) 221.5 266.5 $5,062.30 $40,765.57 334 423 $3,244.52 $26,697.62

 Nonmetro, Adjacent to Large Metro 56 123 $5,315.64 $67,365.92 38 152 $2,447.37 $65,163.16

 Nonmetro, Adjacent to Small Metro 200.5 376 $3,862.63 $27,134.18 202.5 314 $4,356.47 $36,673.41

 Rural (Nonmetro, Nonadjacent) 476 158 $2,844.16 $17,742.42 113 179 $3,709.76 $35,448.63

Economic Status

 Distressed 451 222 $1,146.36 $11,450.74 72 6 $4,378.21 $61,185.90

 At-Risk 83 105 $6,183.40 $29,977.02 80.5 68 $7,960.38 $44,032.43

 Transitional 319 545 $5,178.53 $43,423.55 515 873 $3,152.63 $34,285.01

 Competitive 115 88.5 $6,289.93 $43,798.53 33 144 $3,474.58 $47,457.63

 Attainment 0 0 $0.00 $0.00 0 0 $0.00 $0.00

2007 2010

199

 
Figure 7-4: EDA RLFs and Current Capital Base by Region, 2011

Figure 7-5: EDA RLFs and Current Capital Base by County Type, 2011

Figure 7-6: EDA RLFs and Current Capital Base by Economic Status, 2011

14.2%

10.7%

6.3%

19.8%

49.0%

14.5%

10.1%

11.6%

15.9%

47.8%

0% 20% 40% 60% 80%

Southern Appalachia

South Central Appalachia

Central Appalachia

North Central Appalachia

Northern Appalachia

% of EDA RLFs % Current Capital Base 

13.7%

17.3%

23.4%

36.5%

9.1%

17.4%

26.1%

11.6%

37.7%

7.2%

0% 20% 40% 60% 80%

Rural (Nonmetro, Nonadjacent)

Nonmetro, Adjacent to Small Metro

Nonmetro, Adjacent to Large Metro 

Small Metro (< 1 million people)

Large Metro (1 million + people)

% of EDA RLFs % Current Capital Base 

0.0%

9.1%

74.4%

12.1%

4.4%

0.0%

7.2%

71.0%

14.5%

7.2%

0% 20% 40% 60% 80%

Attainment

Competitive

Transitional

At-Risk

Distressed

% of EDA RLFs % Current Capital Base 

200
Map 7-1: EDA Revolving Loan Fund Current Capital Base, by County, 2011

201
Table 7-6: EDA RLFs in Appalachia, (Number and Current Capital Base), 2011

Count of EDA RLFs % of EDA RLFs Current Capital Base % Current Capital Base 

United States 566 $858,479,866

Appalachian Region 69 12.2% $119,818,178 14.0%

Subregions

Northern Appalachia 33 47.8% $58,674,815 49.0%

North Central Appalachia 11 15.9% $23,667,308 19.8%

Central Appalachia 8 11.6% $7,599,178 6.3%

South Central Appalachia 7 10.1% $12,822,841 10.7%

Southern Appalachia 10 14.5% $17,054,036 14.2%

County Types

Large Metro (1 million + people) 5 7.2% $10,920,134 9.1%

Small Metro (< 1 million people) 26 37.7% $43,705,756 36.5%

Nonmetro, Adjacent to Large Metro 8 11.6% $28,061,107 23.4%

Nonmetro, Adjacent to Small Metro 18 26.1% $20,672,128 17.3%

Rural (Nonmetro, Nonadjacent) 12 17.4% $16,459,054 13.7%

Economic Status

Distressed 5 7.2% $5,308,442 4.4%

At-Risk 10 14.5% $14,445,422 12.1%

Transitional 49 71.0% $89,144,181 74.4%

Competitive 5 7.2% $10,920,134 9.1%

Attainment 0 0.0% $0 0.0%

Alabama 8 $11,187,133

Appalachian Alabama 4 5.8% $5,237,977 4.4%

Non-Appalachian Alabama 4 - $5,949,157 -

Georgia 12 $29,114,948

Appalachian Georgia 2 2.9% $2,633,783 2.2%

Non-Appalachian Georgia 10 - $26,481,165 -

Kentucky 15 $17,564,833

Appalachian Kentucky 6 8.7% $6,313,560 5.3%

Non-Appalachian Kentucky 9 - $11,251,273 -

Maryland 10 $13,420,026

Appalachian Maryland 3 4.3% $1,991,193 1.7%

Non-Appalachian Maryland 7 - $11,428,833 -

Mississippi 9 $21,831,670

Appalachian Mississippi 4 5.8% $9,182,277 7.7%

Non-Appalachian Mississippi 5 - $12,649,394 -

New York 23 $75,216,799

Appalachian New York 6 8.7% $13,288,273 11.1%

Non-Appalachian New York 17 - $61,928,526 -

North Carolina 8 $6,350,295

Appalachian North Carolina 1 1.4% $1,781,334 1.5%

Non-Appalachian North Carolina 7 - $4,568,960 -

Ohio 19 $26,640,835

Appalachian Ohio 7 10.1% $11,117,548 9.3%

Non-Appalachian Ohio 12 - $15,523,287 -

Pennsylvania 24 $48,804,353

Appalachian Pennsylvania 19 27.5% $33,187,173 27.7%

Non-Appalachian Pennsylvania 5 - $15,617,181 -

South Carolina 11 $15,412,634

Appalachian South Carolina 0 0.0% $0 0.0%

Non-Appalachian South Carolina 11 - $15,412,634 -

Tennessee 9 $15,469,762

Appalachian Tennessee 5 7.2% $9,983,562 8.3%

Non-Appalachian Tennessee 4 - $5,486,200 -

Virginia 12 $28,372,125

Appalachian Virginia 3 4.3% $2,343,563 2.0%

Non-Appalachian Virginia 9 - $26,028,563 -

West Virginia (entire state) 9 13.0% $22,757,936 19.0%

202
CHAPTER 8 EQUITY INVESTORS

8.1 SUMMARY

This chapter examines two sources of equity investment for businesses: venture capital funds and angel 

investors. The examination reveals how venture fund equity investments are concentrated in relatively 

few counties, mostly in the Northern Appalachian subregion and in large and small metropolitan counties. 

The key findings with respect to venture fund and angel investors are: 

• Less than 2 percent of the venture fund investments were in rural counties and less than 1 percent 

in Central Appalachia or at-risk counties.

• Allegheny County, PA (city of Pittsburgh) dominates venture fund investment in Appalachia 

followed by other knowledge-based or medical centers including Jefferson County, AL (city of 

Birmingham), Gwinnett County, GA (a suburb of Atlanta), Tompkins County, NY (city of 

Ithaca), and Clermont County, OH (a suburb of Cincinnati). 

• The pattern of venture fund investment in Appalachia is consistent with the geographic 

distribution of biomedical, computer-related, and knowledge-based industries. 

• While less is known about angel investors because of their informal nature, the data suggest that 

the pattern of investment is similar to venture capital fund investment; concentrated near large 

metropolitan areas and universities, leaving distressed and rural counties underserved. 

The total amount of venture capital fund investment declined by about 27 percent between 2007 and 

2010. Because venture capital funds tend to invest in high growth fields, such as biotechnology, medical 

and health, and computers, the investments are highly concentrated in the Northern Appalachian 

subregion, including Pittsburgh, PA, and Ithaca, NY, and in large metropolitan and transitional or 

competitive counties. Angel investments also declined between 2007 and 2010 by about 23 percent, with 

the majority of investments in software, healthcare, and biotechnology sectors. Because angel investors 

tend to invest near where they live, and because most live near urban areas or universities, angel investors 

appear to be a limited source of equity investment for businesses in rural and distressed communities. 

8.2 VENTURE CAPITAL FUNDS AND ANGEL INVESTORS

Equity investors fall into two distinct categories: investment funds and informal investors. Venture 

capital funds pool assets from multiple investors and invest those funds in businesses run by other people. 

Informal investors are wealthy individuals who invest their own money in businesses. One category of 

informal investors are “friends and family” investors who fund businesses run by relatives, friends, or 

people they know personally. Angel investors are people who invest their own money in businesses run 

by unrelated entrepreneurs. 

8.2.1 VENTURE CAPITAL FUNDS

Typically, a venture fund is created by fund managers who raise money from large investors, such as 

pension funds, foundations, or wealthy individuals. Some venture funds are funded entirely from 

investors, while others, such as those in the Small Business Investment Company (SBIC) program, may 

receive some of the capital they invest from public sector sources. For example, venture capital funds in 

203
the SBIC program are initially funded by private investors, but they can receive additional capital from 

the sale of SBA-guaranteed securities, up to three times the amount of the initial private investment in the 

fund. Some venture capital funds are entirely profit motivated, while others, such as Community 

Development Venture Funds, balance their profit motive with additional social objectives, such as 

investing in distressed communities. 

Venture capital funds are a crucial source of financing for companies moving from the start-up phase, 

with an innovative idea or promising technology, through the development phase to the point at which the 

company can go public or be acquired by an established company that can bring the product or service to 

market. When a fund makes equity investments, the fund managers take seats on the boards of directors in 

most cases. Over the life of the investment, the venture capitalists may also participate in running the 

company and help secure additional investments from other venture funds, if necessary. 

Not surprisingly, venture capital funds tend to invest in fields in which innovative technology plays an 

important role, including biomedical- and computer-related industries. Based on the number of companies 

receiving venture capital fund investments, over 57 percent of venture capital fund investments in 

Appalachia were in companies in the biotechnology, medical/health or computer-related sectors in 2007 

and over 68 percent were in companies in those sectors in 2010 (see Figures 8-1 and 8-2).

204
Figure 8-1: Number of Venture Fund Investments by Sector in Appalachia, 2007

Figure 8-2: Number of Venture Fund Investments by Sector in Appalachia, 2010

12

9

13

19

19

23

21 Biotechnology

Communications and Internet

Computer Hardware and 

Electronics

Computer Software and 

Services

Industrial/Energy

Medical/Health

Other Products

12

10

11

21

10

16

8 Biotechnology

Communications and Internet

Computer Hardware and 

Electronics

Computer Software and 

Services

Industrial/Energy

Medical/Health

Other Products

205
The distribution of venture capital fund investment in Appalachia is consistent with the geographic 

distribution of biomedical/health and electronics/computer-related business incubation. Allegheny 

County, PA (city of Pittsburgh) dominates venture fund investment in Appalachia, as shown in Maps 8-1 

and 8-2 and Tables 8-1 and 8-2. The next highest numbers of investees (companies receiving 

investments) in 2007 are in either large or small metropolitan counties, including Jefferson County, AL 

(city of Birmingham), Gwinnett County, GA (a suburb of Atlanta), and Tompkins County, NY (city of 

Ithaca), home to Cornell University. In 2010, the next highest numbers of investees are in Tompkins 

County, NY, and Clermont County, OH (a suburb of Cincinnati). Outside of those few counties, no other 

counties in Appalachia had more than three companies receiving venture fund investment. Only 44 

counties overall had firms receiving investment in 2007, and only 26 had firms receiving investment in 

2010. 

206
Map 8-1: Amount of Venture Fund Investment by County, 2007

207
Map 8-2: Amount of Venture Fund Investment by County, 2010

208
Table 8-1: Venture Fund Investments in Appalachia, 2007

Table 8-2: Venture Fund Investments in Appalachia, 2010

County State Investees

Venture 

Funds 

Investing

No. of 

Separate 

Investments County State Investees

Venture 

Funds 

Investing

No. of 

Separate 

Investments

Jefferson AL 6 7 7 Greene PA 1 1 1

Limestone AL 1 1 1 Juniata PA 1 1 1

Madison AL 2 2 2 Luzerne PA 1 1 1

Shelby AL 1 1 1 Lycoming PA 1 1 1

Cherokee GA 1 1 1 Mercer PA 1 1 1

Gwinnett GA 8 16 16 Northumberland PA 2 1 2

Allegany MD 1 1 1 Schuylkill PA 1 1 1

Washington MD 1 4 4 Snyder PA 1 1 1

Forsyth NC 1 1 1 Somerset PA 1 1 1

Cortland NY 1 1 1 Union PA 1 1 1

Tompkins NY 5 8 13 Washington PA 2 2 2

Athens OH 1 3 3 Westmoreland PA 2 5 5

Clermont OH 1 3 3 Greenville SC 2 4 4

Mahoning OH 1 1 1 Hamilton TN 2 2 2

Allegheny PA 45 31 77 Knox TN 2 3 3

Blair PA 1 1 1 Botetourt VA 1 2 2

Butler PA 2 5 5 Montgomery VA 3 2 3

Cambria PA 1 1 1 Smyth VA 1 1 1

Centre PA 1 1 1 Washington VA 1 1 1

Clarion PA 1 1 1 Harrison WV 1 1 1

Columbia PA 1 1 1 Monongalia WV 2 3 3

Erie PA 2 1 2 Wood WV 1 1 1

County State Investees

Venture 

Funds 

Investing

No. of 

Separate 

Investments County State Investees

Venture 

Funds 

Investing

No. of 

Separate 

Investments

Jefferson AL 3 4 4 Juniata PA 1 1 1

Madison AL 1 1 1 Lawrence PA 1 1 1

Gwinnett GA 2 2 2 Somerset PA 1 1 1

Forsyth NC 3 7 7 Westmoreland PA 3 5 6

Transylvania NC 1 1 1 Greenville SC 2 4 4

Cortland NY 1 1 1 Hamilton TN 1 1 1

Tompkins NY 7 8 14 Knox TN 2 4 4

Clermont OH 5 6 10 Montgomery VA 1 1 1

Mahoning OH 1 1 1 Tazewell VA 1 1 1

Allegheny PA 42 39 74 Cabell WV 1 1 1

Armstrong PA 1 1 1 Harrison WV 1 1 1

Blair PA 1 3 3 Kanawha WV 1 1 1

Centre PA 3 4 5 Monongalia WV 1 1 1

209
The extreme concentration of companies receiving venture fund investment in so few counties, 55 percent 

in four counties in 2007 and 61 percent in three counties in 2010, results in a high degree of geographic 

concentration with respect to county characteristics, as shown in Figures 8-3 through 8-5. Allegheny 

County, with over 38 percent of investees in 2007 and over 47 percent in 2010, is a competitive, large 

metropolitan county in Northern Appalachia, and the data reflect its impact, as shown in Table 8-3. Over 

85 percent of all investees in 2007 were in Northern or Southern Appalachia, large or small metropolitan 

counties, and in competitive or transitional counties. Less than two percent were in rural counties and less 

than one percent in Central Appalachia or distressed or at-risk counties.

210
Figure 8-3: Number of Investees by Region

Figure 8-4: Number of Investees by County Type

Figure 8-5: Number of Investees by Economic Status

8

8

1

9

62

21

11

0

6

78

0 10 20 30 40 50 60 70 80 90

Southern Appalachia

South Central Appalachia

Central Appalachia

North Central Appalachia

Northern Appalachia

2007 2010

2

4

1

25

56

2

10

2

34

68

0 10 20 30 40 50 60 70 80

Rural (Nonmetro, Nonadjacent)

Nonmetro, Adjacent to Small Metro

Nonmetro, Adjacent to Large Metro

Small Metro (< 1 million people)

Large Metro (1 million + people)

2007 2010

3

58

27

0

0

11

58

45

0

1

0 10 20 30 40 50 60 70

Attainment

Competitive

Transitional

At-Risk

Distressed

2007 2010

211
Table 8-3: Venture Fund Investments by Subregion, Type, and Economic Status

Number Percent Number Percent Number Percent Number Percent

Appalachian Region 116 129 88 101

Subregions

 Northern Appalachia 78 67.2% 74 57.4% 62 70.5% 65 64.4%

 North Central Appalachia 6 5.2% 11 8.5% 9 10.2% 10 9.9%

 Central Appalachia 0 0.0% 0 0.0% 1 1.1% 1 1.0%

 South Central Appalachia 11 9.5% 12 9.3% 8 9.1% 14 13.9%

 Southern Appalachia 21 18.1% 32 24.8% 8 9.1% 11 10.9%

County Types

 Large Metro (1 million + people) 68 58.6% 71 55.0% 56 63.6% 57 56.4%

 Small Metro (< 1 million people) 34 29.3% 43 33.3% 25 28.4% 37 36.6%

 Nonmetro, Adjacent to Large Metro 2 1.7% 2 1.6% 1 1.1% 1 1.0%

 Nonmetro, Adjacent to Small Metro 10 8.6% 11 8.5% 4 4.5% 4 4.0%

 Rural (Nonmetro, Nonadjacent) 2 1.7% 2 1.6% 2 2.3% 2 2.0%

Economic Status

 Distressed 1 0.9% 3 2.3% 0 0.0% 0 0.0%

 At-Risk 0 0.0% 0 0.0% 0 0.0% 0 0.0%

 Transitional 45 39.1% 50 39.1% 27 30.7% 33 32.7%

 Competitive 58 50.4% 55 43.0% 58 65.9% 65 64.4%

 Attainment 11 9.6% 20 15.6% 3 3.4% 3 3.0%

2007 2010

Investees

Venture Funds 

Investing Investees

Venture Funds 

Investing

212
8.2.2 ANGEL INVESTORS 

Angel investors have recently been recognized as an important source of capital for entrepreneurs. 

Because they constitute a newly-recognized class of investor, the definition of an angel investor is still 

being determined. One suggested taxonomy distinguishes between two categories of equity investors, 

venture funds and informal investors.

32 As noted earlier, venture funds pool assets from multiple 

investors such as pension funds, foundations, or wealthy individuals and invest these funds in businesses 

run by other people. The second category of equity investor is the informal investor, a person who invests 

his/her own money. The taxonomy further distinguishes between two types of informal investors. One 

type is the “friends and family” investor who funds businesses owned by relatives, friends, or people 

he/she knows personally. The other type of informal investor is a person who invests in businesses run by 

unrelated entrepreneurs, and these investors are the angel investors. 

Most angel investors are actively involved with the businesses they invest in, providing technical or 

financial guidance. Some, however, are more passive and have little involvement with the businesses after 

they make the initial investment. 

According to the Center for Venture Research, over 57,000 businesses received angel investor funding in 

2007, with a total of $26.0 billion invested. Nearly 60 percent of the investments were in software, 

healthcare, and biotechnology sectors. In 2010, nearly 62,000 businesses received angel investor funding, 

but the total invested was only $20.1 billion. Over 60 percent of the amount invested was in software, 

healthcare, and biotechnology sectors.

More recently, some angel investors have become more formally organized into groups to combine the 

resources of individuals into larger pools for funding and to broaden the expertise available in making 

investments. According to one estimate, between 10,000 and 15,000 individuals belong to angel groups. 

A leading industry association, the Angel Capital Association (ACA), surveyed its members, providing 

one of the most complete sources of data on angel investors.33 The vast majority of angel investor 

respondents to that survey, over 80 percent, invest in start-up or early stage companies, but 35 percent 

also finance expansion of profitable small companies. More than half of the respondents expected to 

invest in five or fewer companies in the next year, and more than half of angel investor respondents had 

average investments of less than $250,000 per company, with only about 9 percent investing an average 

of more than $500,000 per company. 

One key factor in determining the availability of angel investor capital for businesses in Appalachia is the 

geographic limitation that angel investors have on where they will invest. Forty-four percent of the 

respondents to the ACA survey indicated that they would only invest within a four-hour drive of the 

group’s location, while other studies have suggested that the range may be even more limited 

geographically, perhaps as little as a one hour drive, noting that angel investors prefer to invest close to 

32 This taxonomy is from Shane, S., 2008. The Importance of Angel Investing in Financing the Growth of 

Entrepreneurial Ventures: A Working Paper. Prepared for the Small Business Administration, Office of Advocacy. 33 The data are from Shane, S., and A. M. Mixon, 2008, Angel Groups: An Examination of the Angel Capital 

Association Survey, and Angel Investment Groups – Trends and Statistics, May, 2008. This survey was of members 

of the Angel Capital Association for the Angel Capital Education Foundation. Because the survey was of a nonrandom group of angel investors, the results may not reflect the universe of angel investors outside of the ACA 

members.

213

 
where they are located.34 That limitation suggests that businesses in Appalachia will need to find local 

angel investors if they are to access that source of capital. 

Because most angel investors are individuals and most of the investments they make are informal and not 

reported publicly, it is difficult to know where individual angels are located. Locating some of the more 

organized angel groups, however, is possible. Map 8-3 shows the counties in or adjacent to Appalachia 

with a member or members of the ACA located in the county. As the map shows, angel groups are 

primarily located near urban centers and universities and are not likely to serve most counties in 

Appalachia.

Map 8-3: Angel Capital Association Members by County, 2011

34 Morrissette, Stephen G., 2007. A Profile of Angel Investors, The Journal of Private Equity, 10(3): 52-66, and 

DeGennaro, Ramon P., 2010. Angel Investors: Who They Are and What They Do; Can I Be One, Too?, The Journal 

of Wealth Management, 13(2): 55-60.

214

 
CHAPTER 9 LITERATURE REVIEW

9.1 SUMMARY

Small businesses will remain a significant source of employment in the nation and in rural areas like 

Appalachia. Small businesses also appear to have beneficial impacts on employment on a county level in 

Appalachia. Supporting small business development is therefore a sensible policy in Appalachia. A 

significant number of small businesses, perhaps a majority, need access to credit to start and expand. 

However, access to credit contracted significantly during the Great Recession, particularly for small 

businesses in low- and moderate-income areas. A lack of consumer demand may also have particularly 

discouraged small businesses from applying for loans during the Great Recession. Barriers to access also 

include supply factors such as a tightening of underwriting by banks and a cutback in lending to start-ups. 

Smaller banks employ “relationship” lending which is based on knowledge gained by banks through their 

daily interactions with small business customers. Larger banks use “transactional” lending which is 

underwriting based on credit scores and other quantifiable factors. Both small and large banks are 

important for promoting access to credit for small businesses. Small banks’ lending tended to remain 

stable during the Great Recession while large banks contracted their lending. However, a region cannot 

rely on small banks since their lending has not grown significantly during the last several years. The 

studies tend to agree that bank branches promote lending to small businesses as relationships with bank 

customers preserve lending levels even during recessions.

Consolidation presents contradictory influences on small business lending, sometimes decreasing lending 

and other times not decreasing lending, depending on the institutional and market context. Banks in 

concentrated markets appear more likely to discriminate. Alternative financial institutions can be a 

resource particularly in markets with high levels of consolidations or experiencing bank branch closures. 

The literature suggests that CDFIs have increased their lending and assets during the current recession 

and that RLFs target disadvantaged communities. In addition, venture funds associated with Community 

Development Venture Capital could be a resource since they have sizable investments in six states with 

Appalachian counties. 

Policy recommendations reviewed in this chapter include increasing public sector efforts to support new 

and existing businesses rather than focusing on attracting larger businesses; supporting business 

development in Central Appalachia that utilizes the subregion’s natural resources like renewable energy; 

and specifying recommendations for the New York Banking Development District program that provides 

insight for other public sector efforts promoting branching in underserved areas with the aim of also 

increasing small business lending. 

9.2 INTRODUCTION

An extensive literature on small business lending has examined the importance of small businesses to the 

economy, the determinants of lending to small business, and the impacts of consolidation on small 

business lending. This inquiry is important since small businesses represent one of the most vulnerable 

yet critical parts of the national economy and in the Appalachian Region. 

215
9.3 WHY IS LENDING TO SMALL BUSINESSES IMPORTANT? 

Small businesses with less than 500 employees constitute 99 percent of firms and employ half the 

workers in the private sector according to the SBA.35 In rural America, the importance of small 

businesses and self-employment is magnified. According to Goetz, Fleming, and Rupasingha (2012), 

there is now one self-employed worker for every three wage workers in rural areas, which is higher than 

the ratio for urban areas (though the ratio for urban areas is catching up). They also find that higher shares 

of locally-owned firms are associated with higher per capita income growth on a county level, but that 

this relationship is more pronounced in urban than rural areas.36

Mojica, Gebremedhin, and Schaeffer of the West Virginia University (2009) find positive impacts of 

entrepreneurship on employment and population levels in Appalachia.37 Using data from 1995 through 

2005, they assess the change in the number of proprietors and firm births on changes in population and 

employment controlling for demographic characteristics, the education level of the labor force, population 

density, infrastructure, and the level of taxation. They find that population growth is positively impacted 

by the number of proprietors and number of firm births on a county level in Appalachia. They also find 

that an increase in the number of self-employed proprietors and firm births increases employment. The 

authors recommend government policies such as subsidies and tax breaks as a means of supporting an 

increase in entrepreneurship with its benefits on population and employment growth. It would seem that 

public policies supporting increased lending to entrepreneurs would also benefit Appalachia. 

The pursuit of economic development through entrepreneurship also has its skeptics. Some critics view 

self-employment as low paying and as a last resort for unemployed workers (Goetz, Fleming, and 

Rupasingha, 2012). In order to further address the economic rewards of self-employment, Loftstrom 

(2009) conducts descriptive and econometric analysis focusing on low-skilled entrepreneurs defined as 

those with a high-school diploma or less.38 Lofstrom notes the importance of this inquiry as the number of 

self-employed increased from 9.9 million in 1980 to 17.3 million in 2007. Low-skilled entrepreneurs are 

about 40 percent of self-employed workers in the nation.

Overall, Lofstrom finds that low-skilled entrepreneurs earn less than their counterparts that are wage and 

salary employees. However, the top 25 percent of earners among male entrepreneurs earn more than the 

top 25 percent of wage and salary employees. The highest earning women entrepreneurs are not as 

successful. Just the top 10 percent native born entrepreneurs earn more than the top 10 percent native born 

women wage and salary workers. Most entrepreneurs experience an earnings disadvantage. Depending on 

the specific econometric model, native born male entrepreneurs earn 17 to 26 percent less than their wage 

and salary counterparts. The difference is less for immigrant male entrepreneurs. Over time, low-skilled 

male entrepreneurs partially overcome the earnings gap. Women entrepreneurs do not fare as well, 

experiencing earnings that are between 22 and 40 percent less than their wage and salary counterparts, 

and are not able to make up the gap over 15 to 25 years. 

35 See U.S. Small Business Administration, FAQ’s available via http://www.sba.gov/sites/default/files/sbfaq.pdf 36 Goetz, Stephen J., David A. Fleming, and Anil Rupasingha. 2012 Economic Impacts of Self-Employment, 

Journal of Agricultural and Applied Economics, 44,3(August 2012): 315-321. 37 Mojica, Maribel N., Tesfa G. Gebremedhin, and Peter V. Schaeffer. 2009. A County-Level Assessment of 

Entrepreneurship and Economic Growth in Appalachia Using Simultaneous Equations. Morgantown, WV: West 

Virginia University, Division of Resource Management. 38 Loftstrom, Magnus. Does Self-Employment Increase the Economic Well-Being of Low-Skilled Workers? 2009. 

San Francisco, CA: Public Policy Institute of California and IZA.

216

 
Loftstrom does not conclude that his research indicates that low-skilled entrepreneurship should be 

discouraged but that expectations should be realistic. In addition, he states that his research has not 

thoroughly examined wealth accumulation opportunities of low-skilled entrepreneurs or their impacts on 

job creation. Mojica and Goetz find positive impacts of entrepreneurs on job creation. Policies, including 

those increasing access to credit and capital, therefore remain sensible pursuits particularly in regions 

experiencing high unemployment and less access to large-scale employers. Indeed, access to credit is 

necessary for small business growth. Laderman and Reid (2010) report that 60 percent of small businesses 

received traditional bank loans. 

9.4 SMALL BUSINESSES AND THE GREAT RECESSION

Just like the NCRC report for ARC, Laderman and Reid (2010) report that small businesses lending 

declined significantly during the Great Recession from 5.2 million loans or $137 billion during 2007 to 

1.6 million loans or $73 billion in 2009.39 Laderman and Reid find that the decline in lending was greater 

in low- and moderate-income (LMI) neighborhoods than middle- and upper-income (MUI) 

neighborhoods and that the disparity in access increased. In 2007, there was 1 loan for every 13.3 small 

businesses in LMI neighborhoods and 1 loan for every 10.7 small businesses in MUI neighborhoods. By 

2010, there was 1 loan for every 28.4 small businesses in LMI neighborhoods and one loan for every 22.6 

small businesses in MUI neighborhoods. They also find that the growth in small business lending 

paralleled the growth of subprime lending in areas like Las Vegas and Phoenix during 2003 and 2007. 

During the Great Recession, many of the same areas that had experienced a surge in subprime lending 

also experienced a drop in small business lending as foreclosures rose (there was a statistically significant 

relationship between declines in small business lending and increases in foreclosure).

Chow and Dunkelberg (2011) compare the effect of the Great Recession on small businesses relative to 

earlier recessions using data from a survey of members of the National Federation of Independent 

Business (NFIB).40 The authors compare trends over time for responses on questions related to firm sales, 

capital outlays, loan-seeking behavior, and perceptions of the current and future business climate to 

similar points in earlier recessions starting in 1971. They consistently find that small businesses are 

performing substantially worse in terms of sales and job creation during this recession than prior ones. 

The small businesses also have lower expectations and are more pessimistic about future conditions. 

Their analysis indicates that this is not due to a credit-crunch caused by the financial crisis, as the 

respondents’ reported difficulty in accessing lending is not substantially worse during this recession than 

previous ones. Instead, they conclude that it is a lack of consumer demand that is responsible for the poor 

performance of small companies.

39 Laderman, Elizabeth and Carolina Reid. 2010. The Community Reinvestment Act and Small Business Lending in 

Low- and Moderate-Income Neighborhoods during the Financial Crisis, Working Paper 2010-05. San Francisco, 

CA: Federal Reserve Bank of San Francisco.

40 Chow Michael J. and W.C. Dunkelberg, 2011. The Small Business Sector in Recent Recoveries. A paper 

presented at the Federal Reserve Board conference, Washington DC, November 2011, available via 

http://www.federalreserve.gov/newsevents/conferences/chowdunk-20111109.pdf.

217

 
Montoriol-Garriga and Wang (2012) find that access to credit was reduced to a greater extent for small 

firms than larger ones.41 They examine the effect of the Great Recession on access to credit by small 

firms relative to large firms. The authors find that while small firms continued to pay higher interest rates 

than larger firms during the recession, the spread in interest rates declined on loans for small and large 

firms. This effect was largest for loans issued by banks facing severe capital constraints. The authors 

attribute these results to greater credit rationing by banks following the recession, when small firms which 

were especially risky were denied credit, while those who received loans were less risky, leading to a 

lower spread on their loans.

9.5 SUPPLY AND DEMAND BARRIERS TO ACQUIRING LOANS

During 2010, the Federal Reserve System’s Community Affairs Offices hosted 40 meetings involving 

small businesses, lenders, and other stakeholders in several cities across the country to discuss supply and 

demand for credit and capital.42 On the supply side, small businesses and banks alike remarked that 

underwriting standards tightened during the Great Recession, resulting in less lending. For example, 

because asset values dropped, several lenders required additional collateral including more equity that 

small businesses needed for loans. In addition, larger banks tend to rely on credit scores for loans under 

$200,000 but personal and small business credit scores declined during the Great Recession. In some 

cases, credit scores declined simply because banks reduced credit limits on credit cards, which artificially 

increased debt ratios. 

In rural areas, bank failures reduced access to credit. In some cases, small businesses lost the bank they 

relied upon and the bank that assumed the failed bank chose not to continue the relationships or the loans 

with the small businesses. Small businesses reported that reductions occurred in lines of credit needed to 

deal with cash flow difficulties, refinance loans, and small dollar loans in amounts under $200,000. In 

addition, participants at the Federal Reserve meetings noted that loans for start-ups have been reduced 

with some banks only lending to small businesses with five years of operations. 

As a result of less lending from traditional banks, small businesses reported using more credit cards, 

which had interest rates significantly higher than their previous loans, including lines of credit. CDFIs and 

credit unions also noted an increase in demand from small businesses having a harder time securing loans 

from banks. Other small businesses turned to factoring companies and payday lenders.

On the demand side, diminished sales and weakening asset values reduced applications for loans. 

Reduced confidence by small businesses led to an increased demand for technical assistance. Both 

existing businesses and unemployed workers seeking to start businesses sought technical assistance. The 

phenomena of the unemployed seeking help in starting small businesses was particularly noted in a 

meeting occurring in Appalachia, specifically Morgantown, West Virginia.

Participants at the Federal Reserve meetings recommended greater CRA consideration for equity 

investments in small businesses and grants that fund technical assistance. Also, the New Markets Tax 

41 Montoriol-Garriga, Judit and J.Christina Wang. 2012. Rationing of Bank Credit to Small Businessess: Evidence 

from the Great Recession. Working Paper, available via 

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2042584

42 Addressing the Financing Needs of Small Businesses: Summary of Key Themes from the Federal Reserve 

System’s Small Business Meeting Series (2010) available via 

http://www.federalreserve.gov/newsevents/conferences/sbc_small_business_summary.pdf. 

218

 
Credit program could be more supportive of small business financing by encouraging more investments 

in entities that lend to small businesses. Finally, lenders were encouraged to make greater use of “second 

look” programs that employ sound and flexible underwriting. 

9.6 RELATIONSHIP AND TRANSACTIONAL LENDING

In general, researchers agree on the typologies of lending techniques used by large and small banks. They 

distinguish among asset-based lending, credit scoring, and relationship lending (Mitchell and Pearce, 

2005).43 The first two represent “transactional” lending as they are based on the “hard” or objective 

information about a borrower. For underwriting purposes, asset-based lending uses information about 

accounts receivable, inventory, and other forms of collateral. Credit scoring is based on the owner’s 

history of using credit. In contrast, relationship lending is based on “soft” information about the potential 

borrower. In other words, banks rely on the subjective information about a borrower that they received 

out of lasting relationships rather than on financial condition of the borrowers. Another indication of 

relationship lending as reported by Cavalluzzo, Cavalluzzo, and Wolken (2002) is that 84 percent of the 

loans received by small businesses came from lending institutions located in the same city.44 The median 

distance between the firm and the lender was just three miles.

Relationship lending is mainly associated with small banks whereas transactional lending is typically 

employed by large banks. Berger and Udell (2001) say that banks employing relationship lending should 

delegate more authority to loan officers than those that use objective information.45 Small banks are better 

equipped to delegate authority than larger ones; smaller banks have considerably fewer loan officers, 

making it easier for smaller banks to manage, trust, and rely upon the quality of loan officers’ decisions. 

Relationship lending is therefore typically done in lower volumes than transactional lending at large 

banks. Using automated technology such as credit scoring, transactional lending benefits from economies 

of scale. Large transactional lenders serve relatively high number of customers, enabling them to lower 

costs per borrower by spreading fixed costs over a large customer base.

Cole, Goldberg, and White (1999) provide detailed analysis of differences between relationship 

“character” lending and transactional “by-the-number” lending.46 They found that large banks mostly use 

“by-the-number” approach and small banks use “character” information which is based on pre-existing 

relationships between the bank and a borrower. Using the National Survey of Small Business Finances, 

Cole, et al. classify small banks as those with assets under $1 billion and large banks as those with assets 

above $1 billion. Cole’s regression analyses appear to confirm the different lending approaches of small 

and large banks. For example, higher debt-to-asset ratios increase the likelihood that large banks will not 

approve small business loans whereas small banks are not influenced by debt-to-asset ratios. Cole et al. 

hypothesize that small banks possess superior non-financial information about their customers and are 

thus able to make decisions not based purely on the numbers. In the same vein, small banks are more 

43 Mitchell, Karlyn and Douglas.K. Pearce. 2005. Availability of Financing to Small Firms using the Survey of 

Small Business Finance. Working paper submitted to the Small Business Administration, May 2005. 44 Cavaluzzo, K.S., L.C. Cavaluzzo and J.D. Wolken. 2002. Competition, Small Business Financing, and 

Discrimination: Evidence From a New Survey. Journal of Business. January 2002, vol. 75, no.4 45 Berger, A.N., R.J. Rosen and G.F. Udell. 2001. The Effect of Market Size and Structure on Competition: The 

Case of Small Business Lending. Chicago, IL: Federal Reserve Bank of Chicago. October 2001. 46 Cole, R.A., L.G. Goldberg, and L.J. White. 1999. Cookie-Cutter versus Character: the Micro Structure of Small 

Business Lending by Large and Small Banks, in J.L. Blanton, A. Williams, and S.L.W. Rhine, ed.: Business Access 

to Capital and Credit (Federal Reserve System Research Conference, March 8, 1999).

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likely to approve loans to small business customers that have deposits at their banks while large banks are 

indifferent to deposit relationships. It appears that small banks are able to better utilize the experience and 

information gained through the deposit relationship than large banks. 

In a region like Appalachia with considerable numbers of mid-size and small banks under $1 billion in 

assets, small businesses are likely to benefit from relationships with these banks. Chakravarty and 

Yilmazer (2008) find that the probability of being approved for a loan increases with the amount of assets 

of a business and the net worth of the owner while the chances of being denied increases with the number 

of delinquencies and lower credit scores.47 Interestingly, however, holding all of these factors constant, 

the probability of receiving a loan during a recession increases if a small business has a pre-existing 

relationship with the bank and the number of years of that relationship. Thus, even during recessions, 

relationship lending can preserve access to credit. Getting small businesses “banked” is an important step 

to promoting their access to credit.

The SBA reports that small banks held their lending steady during the current recession whereas very 

large banks contracted their lending. At the same time, the SBA cautions upon relying on small banks as a 

“shock absorber” during recessions since small bank lending during the last decade has not grown 

dramatically (SBA FAQ, 2011). While small and mid-size banks are important and are more likely to 

practice relationship banking, stakeholders should work with small businesses to establish relationships 

with branches of banks of all sizes as a means of increasing access to credit and shielding small 

businesses against dramatic decreases in credit.48

9.7 BANK BRANCHES AND LENDING

Bank branches are generally found to boost small business lending. Frame, Srinivasan, and Woosley 

report that the number of bank branches increases the share of small business loans in a bank’s portfolio. 

Immergluck and Smith (2001) report that their research in Chicago reveals that banks with the highest 

percentage of their branches in low- and moderate-income (LMI) census tracts make the highest 

percentage of their loans in these tracts while banks with the lowest percentage of branches in LMI tracts 

make the lowest percentage of their loans in LMI tracts.49

Goetz and Rupasingha (2011) estimate the impacts of education levels, income growth, industry mix, 

population density, and bank branches on the growth of self-employment.50 They conclude that after 

controlling for other variables, branches per capita increased self-employment, particularly for small rural 

counties, for most of the years in their analysis. In particular, branches per capita increased selfemployment for 2000 through 2007 but not in 2008 and 2009. The worst years of the Great Recession 

47Chakravarty, Sugato and Tansel Yilmazer. 2008. A Multistage Model of Loans and the Role of Relationships: A 

Working Paper, available via http://www.cfs.purdue.edu/csr/research/Chakravartyresearch/relationships_final_FM_08%2001%2008.pdf. 48 Small Business Administration, Office of Advocacy. September 2011. Frequently Asked Questions about Small 

Business Finance. http://www.sba.gov/sites/default/files/files/Finance%20FAQ%208-25-

11%20FINAL%20for%20web.pdf. 49 Immergluck, Dan and Geoff Smith. 2001. Bigger, Faster...But Better? How Changes in the Financial Services 

Industry Affect Small Business Lending in Urban Areas. Chicago, Illinois: Woodstock Institute. September 2001 50 Goetz, Stephan J. and Anil Rupasingha. 2011. The Determinants of Rural Self-Employment: Insights from 

County-Level Data. A paper prepared for the Federal Reserve Bank Conference on Small Business and 

Entrepreneurship During an Economic Recovery, Board of Governors of the Federal Reserve System, Washington 

DC, November 9-10, 2011.

220

 
eliminated the positive impact of bank branching on self-employment, most likely because of the deep 

retrenchment in lending. As lending rebounds, however, bank branches are likely to exhibit once again 

their positive impact on self-employment. 

Laderman and Reid (2010) reinforce the importance of branching. Laderman (2008) finds that only 10 

percent of small business lending is from banks with no branches in the local market.51 They state that 

lending declined in neighborhoods experiencing the loss of banks either through bank failure or 

consolidations. Finally, Kobeissi (2009), in a slightly different approach, finds that CRA-related small 

business lending increases small business start-ups on a local level after controlling for employment 

growth, bank deposits, University-sponsored Research and Development (R&D), and other economic 

variables.52

9.8 IMPACTS OF CONSOLIDATION

A definitive answer regarding the impacts of consolidation on small business lending will probably never 

be reached. It is quite likely that the economic, institutional, and regulatory context in which mergers 

occur determine their influence on the level of lending in communities. Reviewing the literature, 

Hancook, Peek, and Wilcox (2005) hint at the possibilities of different outcomes.53 In a study in the late 

1990’s, for example, Berger found that merged banks reduce their small business lending, but that other 

lenders in the community increased their lending in response, often replacing the lost lending of the 

merged banks. Similarly in the late 1990’s, Peek and Rosengren (1998) concluded that the small business 

lending behavior of the merged bank resembled the behavior of the acquiring bank instead of the acquired 

bank.54 So if the acquiring bank had conducted less small business lending, the newly merged bank was 

likely to reduce its level of small business lending. Other studies showed that mergers of smaller banks 

actually increased small business lending while mergers of larger banks had little effect.

While the impacts of bank mergers are likely to be influenced differently by economic and market 

characteristics, Hancock, Peek and Wilcox (2005) identify important institutional characteristics that are 

likely to have more uniform impacts at least for larger banks. Hancock, Peek, and Wilcox distinguish 

between acquisition of banks and merger of bank charters. When a bank holding company (BHC) 

acquires another BHC, the acquiring BHC can either absorb the acquired BHC’s banks completely or let 

the acquired bank(s) continue lending as a separate entity. In other words, the acquiring BHC can let the 

acquired bank(s) remain as a separately chartered institution or it can merge the bank(s) and eliminate the 

charter of the acquired bank(s).

To assess the impacts of acquisitions versus mergers (of bank charters), the study looked at small business 

lending patterns of the 50 largest bank holding companies excluding credit card lenders. The authors 

used annual data for the period of 1997-2002. Among the top 50 BHCs in the country, Hancock et al. 

conclude that larger BHCs (in terms of asset size) tend to reduce their small business lending. In addition, 

51 Laderman, Elizabeth. 2008. The Quantity and Character of Out-of-Market Small Business Lending. Federal 

Reserve Bank of San Francisco Economic Review. 

52 Kobeissi, Nada. 2009. Impact of the Community Reinvestment Act on New Business Start-Ups and Economic 

Growth in Local Markets. Journal of Small Business Management 2009 47(4), pp. 489-513. 53 Hancock, D., J. Peek and J.A. Wilcox. 2005. The Effects of Mergers and Acquisitions on Small Business Lending 

by Large Banks. Submitted to the Small Business Administration. March 2005. 54 Peek, J., and E.S. Rosengren. 1998. Bank consolidation and small business lending: It’s not just bank size that 

matters. Journal of Banking and Finance, 799-819. 1998. 

221

 
the reduction is more pronounced when acquiring BHCs merge banks than when the banks are allowed to 

operate as separately chartered institutions. 

Frame, Srinivasan, and Woosley (2001) reinforce the conclusion of Hancock et al. by finding that the 

share of small business loans in a bank holding company’s portfolio increases with the number of 

subsidiary banks.55 Frame et al. hypothesize that a subsidiary bank structure is associated with 

decentralized decision-making in which relationship lending is employed. 

Erel (2011) reinforces the conclusion that mergers do not necessarily result in reduced lending.56 He 

examines the effect of bank mergers on the mean difference in the ratio of the total (acquirer and target) 

bank small business loans divided by assets prior to the merger with the same ratio following the merger. 

The result of the analysis finds no statistically significant decrease in this ratio for the total sample, and in 

nearly a third of the cases, the change was actually positive. He attributes the lack of an adverse effect of 

bank consolidation on small business lending to changes in credit technology. Specifically, the advent of 

credit-scoring may have increased the amount of “hard” information on borrowers available to lenders 

even at great distances, reducing the need for “soft” information available only when the borrower is in 

close proximity to the lender. Consequently, as “hard” information becomes more useful in assessing the 

credit risk of borrowers, mergers in certain cases will not result in aggregate changes in small business 

lending.

The few studies that examine the impacts of mergers on small business lending in low- and moderateincome tracts generally find that mergers and acquisitions decrease small business lending in these tracts. 

Reviewing the literature, Immergluck and Smith (2001) state that Samolyk and Richardson (2001) find 

that banks involved in mergers have smaller growth rates of lending in low- and moderate-income tracts 

than banks not involved in mergers.57 From 1996 to 1998, the merging banks’ share of small business 

loans in low- and moderate-income tracts was lower than banks not involved in mergers. Interestingly and 

importantly, Immergluck and Smith (2001) report that merging banks tend not to decrease their lending in 

low- and moderate-income census tracts in geographical areas covered by their CRA exams; the decrease 

in lending in low- and moderate-income tracts occurs in areas outside the scope of the CRA exams. 

Regulatory enforcement via CRA exams and the merger application process can mitigate decreases in 

lending as a result of mergers and in some cases may actually increase lending after mergers.

9.9 DISCRIMINATION

The research generally concludes that while discrimination may not be widespread, it occurs in certain 

loan markets and to certain borrowers. Mitchell and Pearce (2005) using data from the 1998 Survey of 

Small Business Finances (SSBF) employ a new approach in that they examine possible discrimination in 

relationship versus transaction lending and by lender type (banks and non-bank finance companies). 

They state that many researchers regard line of credit lending as “quintessential” relationship loans since 

line of credit lending represents a sustained commitment on the part of the bank to make periodic loans to 

55 Frame, W.S., A. Srinivasan and L. Woosley. 2001. The Effects of Credit Scoring on Small Business Lending.

Journal of Money, Credit, and Banking. Vol. 33, No. 3. August 2001. 56 Erel Isil. The Effect of Bank Mergers on Loan Prices: Evidence from the United States. The Review of Financial 

Studies, 24(4). Pages 1068-1101. 2011. 57 Samolyk, K.A. and C.A. Richardson. 2001. The Impact of Bank Consolidation on CRA Business Lending.

Chicago, Illinois: Federal Reserve Bank of Chicago.

222

 
borrowers over a specified time period. Only banks with close relationships with businesses are likely to 

engage in line of credit lending according to observers. Accordingly, line of credit lending is classified in 

regression equations as “relationship” loans while “one-shot” deals or transaction loans are commercial 

mortgages, motor vehicle loans, equipment loans, and capital leases. Mitchell and Pearce construct 

variables that reflect the degree of market concentration (as measured by the Herfindahl-Hirschman(HHI) 

index) and variables that capture characteristics of the small businesses including gender, ethnicity, 

creditworthiness, history of bankruptcy, and asset levels.

Mitchell and Pearce find that African-American and Hispanic business owners are less likely to have 

bank transaction loans than whites after controlling for market and business characteristics, but that there 

is no statistically significant difference in the likelihood of receiving a bank line of credit (relationship 

lending). Also, minorities are more likely to have transaction loans from non-banks. In line with the 

observation that discrimination is not uniform, the authors could not detect discrimination against Asians 

and women. The counter-intuitive finding that minorities are less likely to receive transaction loans 

suggests that smaller banks employing relationship lending may remain an important source of lending 

for minorities. Moreover, the trend for large banks to engage in transaction lending may decrease, not 

increase access to credit for minorities. Cole et al. also found that smaller banks are more likely to make 

loans to African-Americans than large banks with assets above $1 billion.

Cavaluzzo, Cavaluzzo and Wolken (2002) assess the interplay of discrimination and market 

concentration. In the seminal Economics of Discrimination, Gary Becker hypothesized that discrimination 

is more likely in highly concentrated markets lacking significant competition. In less competitive markets, 

firms can get away with discrimination while in more competitive markets the discriminating firms are 

likely to lose out in the competitive race against firms more willing to hire or serve minorities. 

Supplementing data from the 1993 National Survey of Small Business Finances with creditworthiness 

data obtained from Dun and Bradstreet, Cavaluzzo, et al. examined the connection between demographic 

characteristics of small business borrowers, market concentration, and the ability to access credit.

A series of regression analyses revealed that increases in market concentration as measured by HHI 

indices result in African-Americans and women being more likely to experience denials of loan 

applications. Additionally, results show that African-Americans and women are more likely to have 

unmet credit needs (as measured by a fear to apply because of possibilities of discrimination or actual 

rejection) when market concentration increases. Finally, a one percentage increase in concentration as 

measured by an HHI index causes an 11.40 basis point increase in the price of a line of credit for Hispanic 

small businesses.

A legacy of disparate treatment could be the differences in using loans and credit card financing by 

gender and race. The SBA reports that women are more likely than males to start businesses without 

seeking financing. In addition, women-owned businesses are about half as likely as male-owned 

businesses to obtain business loans from banks. This places women-owned firms are at a double 

disadvantage because a business’ relationship with a bank includes general business technical assistance. 

Likewise the SBA reports that Hispanic and African-American owners are more likely to rely upon credit 

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cards than other businesses. This puts them at a disadvantage because a relationship with a bank leads to 

non-credit lending which is less expensive (SBA FAQ, 2011).58

9.10 CDFIS

The diversity of CDFIs complicates efforts to describe what they do and the impacts that they have 

(Benjamin, Rubin, and Zielenbach 2004).59 Most of the early research on CDFIs, therefore, is 

descriptive, ranging from case studies to summaries of financial and investment data. More recently, two 

datasets have allowed for more quantitative and detailed description of the industry. Even so, the 

majority of the literature focuses on institutional characteristics rather than on transaction level analysis of 

lending patterns. 

The CDFI Data Project: Providing Capital, Building Communities, Creating Impact reports are a series 

of annual papers with data on approximately 500 CDFIs, with the most recent edition being FY 2008. 

The reports are based on data from the Common Data Project, with voluntary submissions from 

participating institutions, most of which are CDFIs. In addition to an industry summary, the report 

contains data on the major sub-categories of CDFIs: community development banks; community 

development credit unions; community development loan funds; and microenterprise funds. The data 

include the numbers and sizes of CDFIs, the sectors they finance, and the impact of CDFI investment.60 

Fabiani and Greer (2007)61 provide an overview of the CDFI industry with their analysis of data 

submitted by 223 CDFIs for the Community Investment Impact System (CIIS) in FY 2003, the first year 

that CIIS became operational. In that year, the CDFIs only reported institution level data, and so the 

report is limited to that level of analysis. The study examined the differences in characteristics among the 

different types of CDFIs, and how they vary among the different communities they serve.Bershadker et 

al. (2007)62 use institutional level CIIS data to provide a longitudinal analysis of CDFI performance for 

FY 2005 to FY 2007. They find that approximately 11 percent of CDFIs reported making loans in 

Appalachia in the three years covered in the report. Over 60 percent reported making loans in rural areas, 

while only about 50 percent reported making loans in major urban areas.63

Beginning in FY 2005, the CIIS dataset included transaction level data as well, allowing for finer 

quantitative analysis of lending patterns and performance. For example, Cowan et al. (2008)64 merged 

58 Small Business Administration, Office of Advocacy. September 2011. Frequently Asked Questions about Small 

Business Finance. http://www.sba.gov/sites/default/files/files/Finance%20FAQ%208-25-

11%20FINAL%20for%20web.pdf

59 Benjamin, Lehn, Julia Sass Rubin, and Sean Zielenbach. 2004. Community Development Financial Institutions: 

Current Issues and Future Prospects, Journal of Urban Affairs 26(2):177-195. 60 The most recent report is available online at http://opportunityfinance.net/store/product.asp?pID=177. 61 Fabiani, D., and J. Greer. 2007. Growth, Diversity, Impact: A Snapshot of CDFIs in FY 2003. Washington, DC: 

Community Development Financial Institution Fund, U. S. Department of the Treasury.

62 Bershadker, Andrew, Michael Bzdil, James Greer, and Supapol Siris. 2007. Three year Trend Analysis of 

Community Investment Impact System Institutional Level Report Data, FY 2003-2005. Community Development 

Institutions Fund. December 2007.

63 A comparison of the institution level data on lending in Appalachia with the transaction level data on the location 

of the project shows that more institutions report lending in Appalachia than report loans for projects in Appalachia. 64 Cowan, Spencer M., Danielle Spurlock, Janneke Ratcliffe, and Haiou Zhu. 2008. Community Development 

Financial Institutions and the Segmentation of Underserved Markets. Center for Community Capital Working Paper, 

August 27, 2008.

224

 
the transaction and institution level data to examine whether the composition of the ownership of CDFIs 

affected its lending patterns. They found that minority controlled CDFIs were significantly more likely to 

lend in minority census tracts, which suggests that willingness to lend is affected by the relationship 

between the institution and the community. 

A more recent look at the CDFI industry is Swack, Northrup, and Hangen (2012)65, which looks at data 

from 592 CDFIs, including all CDFI credit unions and banks, and about half of CDFI loan funds. The 

data are from regulatory reports, loan applications submitted to the CDFI Fund, and key informant 

interviews. The study covers the period from 2005 to 2010 and focused on capitalization, liquidity, and 

risk management. The authors found that CDFIs increased both assets and their loan portfolios in 

response to the recession, with both CDFI credit unions and CDFI banks growing faster than their nonCDFI counterparts. The report observes that, “. . . true to their mission, CDFIs appear to be ‘stepping into 

the breach’ to attempt to close gaps faced by constituents who cannot access traditional market capital.”

9.11 VENTURE FUNDS

While a small number of venture funds are also CDFIs, most venture funds are traditional, for-profit 

investors that make equity investments in small businesses. Those investments have long been 

recognized as critical for start-ups and for expansion of existing businesses (Barkley and Markley 

2001).66

The National Venture Capital Association Yearbook (2011)67 provides a comprehensive overview of the 

industry, with comparative data for past years. For example, it documents the negative impact that the 

recent recession has had on venture capital firms, with capital under management down 38 percent from 

the peak in 2005. It also shows the concentration of venture capital, with over 80 percent of investment in 

only five states, up from 71 percent in those same states in 2000 (Barkley and Markley 2001). Two of the 

states, New York and Pennsylvania, have counties in the Appalachian Region. 

Rubin (2010)68 observes that the economic characteristics that attract traditional venture fund 

investments, such as highly concentrated investment opportunities and supporting infrastructure, are not 

found in rural or poor communities.69 As a result, such communities tend to attract little conventional 

venture fund investment. That situation, she suggests, has led to the founding of Community 

Development Venture Capital (CDVC) - venture firms with a combined economic and social mission. 

One study (Rubin 2008)70 found that CDVC funds invested over 80 percent of their capital in 10 states, 

65 Swack, Michael, Jack Northrup, and Eric Hangen, 2012. CDFI Industry Analysis Summary Report. Durham, 

NH: Carsey Institute.

66 Barkley, David L., and Deborah M. Markley. 2001. Nontraditional Sources of Venture Capital for Rural America, 

Rural America 16(1):19-26. 67 Prepared for the National Venture Capital Association by Thomson Reuters, available online at www.nvca.org. 

The yearbook is updated annually and is available online.

68 Rubin, Julia Sass. 2010. Venture Capital in Underserved Communities, Urban Affairs Review, 45(6):821-835. 69 Another paper that describes reasons for the lack of traditional investment in rural areas is Brown-Graham, Anita, 

and William Lambe. 2008. Measures and Methods: Four Tenets for Rural Economic Development in the New 

Economy. Carsey Institute Paper 46.

70 Rubin, Julia Sass. 2008. Community Development Venture Capital in Rural Communities. A paper presented at 

the CDFI Fund Research Conference, Washington, DC, June 2008.

225

 
with over 55 percent of the overall investment in six states with counties in the Appalachian Region.71

Building on an earlier paper (Rubin 2006),72 Rubin also examines the evolution of CDVC firms and notes 

recent developments which have led to the decline in the number of new CVDC firms and the negative 

impact that has had on investment in targeted communities.

9.12 REVOLVING LOAN FUNDS

A revolving loan fund (RLF) combines public- and private-sector capital into a pool that is then 

transferred either as a below-market interest rate loan or grant to state and local government agencies or 

non-profit organizations that then lend the money at below-market rates to local businesses (Pulsipher 

2006).73 They serve businesses that may have difficulty securing credit from commercial lenders, 

primarily start-ups and businesses looking to expand. RLFs can provide gap financing and help those 

businesses leverage the RLF funding for additional private-sector loans.

Mikesell and Wallace (1996)74 also note that RLFs can meet some of the demand for venture capital by 

start-up businesses in rural areas, but they are critical of the model because it is not financially sustainable 

without periodic infusions of new public funds. They suggest making more market rate loans and tighter 

standards for lending to improve the sustainability of RLFs.

Walker et al. (2002)75 examine the performance and impact of loans from RLFs receiving funding from 

the Department of Housing and Urban Development. The study is based on nearly 1,000 loans made with 

Community Development Block Grant or Section 108 funds. They looked at the extent of lending, the 

types of communities where the loans were made, loan performance, and the results in terms of jobs 

created and leveraging of private-sector investment. They found that more than half of the loans are to 

communities with poverty rates above 20 percent and about one quarter are to minority-owned 

businesses. About 25 percent of the loans defaulted, amounting to about 13 percent of the principal 

loaned. 

The National Association of Development Organizations Research Foundation and the Development 

District Association of Appalachia (2010)76 provide a more recent look at RLFs. They mix data on RLFs 

with specific examples, including companies in Appalachia, of how RLF funding has helped businesses 

develop and expand. They also note the need for improvement and modernization of the various federal 

programs that provide funding in order to meet the changing business environment in the current 

economy, as well as recommendations on ways that RLFs can improve their operations.

71 Pennsylvania (16.8%), Tennessee (12.8%), Ohio (9.3%), Kentucky (6.8%), West Virginia (6.3%), and New York 

(4.1%).

72 Rubin, Julia Sass. 2006. Financing Rural Innovation with Community Development Venture Capital: Models, 

Options and Obstacles. Community Development Investment Review 2(4):15-27. 73 Pulsipher, Ian. 2006. Revolving Loan Funds for Small Business Development, National Conference of State 

Legislatures Legisbrief, 14(1). 74 Mikesell, James J., and George B. Wallace. 1996. Are Revolving Loan Funds a Better Way to Finance Rural 

Development? Agriculture Information Bulletin No. 724-05, U. S. Department of Agriculture. 75 Walker, Christopher, Martin D. Abravenal, Patrick Boxall, Roger C. Kormendi, Kenneth Temkin, and Marsha 

Tornovich. 2002. Public-sector Loans to Private-sector Businesses: An Assessment of HUD-supported Local 

Economic Development Lending Activities. Washington, DC: The Urban Institute. 76 National Association of Development Organizations Research Foundation and the Development District 

Association of Appalachia. 2010. Public Sector Business Loan Funds: Views and Recommendations from 

Practitioners.

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9.13 ANGEL INVESTORS

Angel investors are the most informal and private source of capital for growing companies. The Center 

for Venture Research77 is one key source for data on angel investors. The Center estimates that 57,120 

entrepreneurs received $26.0 billion in angel investments in 2007, and that there were 258,200 individual 

angel investors that year. Angel investors felt the impact of the recession, as evidenced by the fact that 

investment declined to $20.1 billion in 2010, but the number of entrepreneurs receiving funding increased 

to 61,900. There were a total of 265,400 active angel investors that year. 

Another source of data on angel investors is the Angel Capital Association (ACA),78 a voluntary 

organization of individual angel investors and angel investor groups, with around 6,000 individual 

members. Shane (2008a)79 notes that groups that are members of ACA are self-selected and probably 

differ from the universe of angel investors in significant ways. For example, all of the members are 

accredited investors, meaning they meet the Security and Exchange Commission’s criteria for angel 

investing. Many angel investors, however, are not accredited. Based on the 2007 ACA survey of its 

members, the angel groups in ACA range in size from 3 to 280 individuals, with an average of 48 

individual members. They prefer to invest at the early or seed stage of business development, and 44 

percent will only make investments within a 4 hour drive. 

Morrissette (2007)80 provides a summary of earlier literature on angel investors. He discusses the 

demographics of the investors, the characteristics of the investments they make, how they find investment 

opportunities, and the motivation for their investments. He also summarizes the differences between 

angel investors and venture capital investors, noting that angels invest approximately 11 times as much as 

venture capitalists.

Using multiple data sources, Shane (2008b)

81 examines the role of angel investing as a source of capital 

for businesses. He reviews existing literature showing that many angel investors only make a single

investment in their lifetime and many spend little time monitoring or working with the company they 

invested in. He notes that angels strongly prefer to invest in corporations rather than individual 

entrepreneurs and estimates that fewer than 16,000 companies sought an angel investment within a three 

year period. He also examines the type of investments that angels make and finds that about 40 percent is 

debt rather than equity. 

DeGennaro (2010)82 examines the role of angel groups, as opposed to individual angel investors. He 

notes that groups offer advantages to both the investors who join and to the entrepreneurs in whom they 

invest. For example, groups can pool capital to make larger investments and have a wider range of 

77 Sohl, Jeffrey. 2011. The Angel Investor market in 2010: A Market on the Rebound. Center for Venture Research, 

and Sohl, Jeffrey. 2008. The Angel Investor Market in 2007: Mixed Signs of Growth. The Center for Venture 

Research data are available online at http://paulcollege.unh.edu/cvr-analysis-reports. 78 www.angelcapitalassociation.org. 79 Shane, Scott. 2008a. Angel Groups: An Examination of the Angel Capital Association Survey. Available at 

SSRN: http://ssrn.com/abstract=1142645. 80 Morrissette, Stephen G., 2007. A Profile of Angel Investors, The Journal of Private Equity, 10(3): 52-66. 81 Shane, Scott. 2008b. The Importance of Angel Investing in Financing the Growth of Entrepreneurial Ventures. 

Small Business Administration, Office of Advocacy. Parts of this report were included in Shane, Scott A., 2009, 

Fools Gold? The Truth Behind Angel Investing in America. New York, NY:Oxford University Press. 82 DeGennaro, Ramon P., 2010. Angel Investors: Who They Are and What They Do; Can I Be One, Too?, The 

Journal of Wealth Management, 13(2): 55-60.

227

 
experience with which to judge potential investments. For the investees, the wider range of experience 

within the angel group also means that it can mentor the entrepreneur more effectively.

9.14 POLICY RECOMMENDATIONS FOR RURAL SMALL BUSINESS DEVELOPMENT

Markley and Dabson (2008) examine the role state policy has had in promoting entrepreneurship in 

Kentucky.83The authors argue that economic development policies in the state have traditionally 

emphasized attracting existing businesses rather than fostering the creation of new companies or the 

growth of existing ones; such strategies have limited effect because it is unlikely that Kentucky or other 

states will be able to outbid low-cost international competitors such as China in attracting large firms. The 

authors make several broad recommendations, including 1) an increase in the resources devoted to small 

business development versus those devoted to attracting investment from large employers; 2) increased 

coordination among state agencies, universities, and local governments in the planning and support of 

small business development programs; 3) accountability programs to track the effectiveness of existing 

small business programs with the aim of improving their implementation and assuring they meet the 

needs of local entrepreneurs; 4) creating financial institutions, including micro-enterprise centers, which 

can provide seed money to local start-ups. 

Economic Transition in Central Appalachia: Ideas for the Appalachian Regional Development Initiative

(2010) makes several recommendations concerning policies that can help Central Appalachia transition 

from a mining-based economy to one based on sustainable ecological practices and the creation of green 

jobs.84 Among the recommendations is that ARC develop metrics that identify regions in the most need of 

government assistance; engage community-based organizations in the formulation and implementation of 

economic development strategies; provide access to capital and education necessary to promote 

investment in energy efficient buildings by homeowners and local governments; provide financing for the 

development of renewable energy (wind, solar, etc.) initiatives which can make use of the natural 

advantages afforded by the region’s environment; promote sustainable forest initiatives, small-scale

agriculture, and land restoration by owners of small-to-medium sized plots, which the report argues would 

promote the development of the region by enhancing its advantages with respect to tourism. 

In addition to reviewing recommendations specific to small business development in Appalachia, it is 

useful to review a report (10 Years In: A Review of the Banking Development District Program) by New 

York State on its Banking Development District (BDD) program subsidizing bank branches in 

underserved areas since expanding bank branches appears to promote lending to small businesses. The 

report was based on surveys of participating banks as well as community organizations.85

One issue for Appalachia is that the great majority of the BDDs are urban; 25 are in New York City and 

four are in Buffalo. The remaining nine are in Albany, Cayuga, Jefferson, Nassau, Oneida, Onondaga, 

Orange, and Rockland counties. Of these counties, none are in Appalachia. The report discusses that the

83 Markley D. and B. Dabson. 2008. Creating a System of Support for Entrepreneurs and Small Businesses in 

Kentucky: Insights and Policy Recommendations. A paper by the Rural Policy Research Institute for the Mountain 

Association for Community Economic Development, August 2008. 84 Mountain Association for Community Economic Development and Kentuckians for the Commonwealth. 2010. 

Economic Transition in Central Appalachia: Ideas for the Appalachian Regional Development Initiative. 2010. 85 New York State Banking Department. 2010. 10 Years In: A Review of the Banking Development District 

Program, available via http://www.dfs.ny.gov/banking/bdd.htm. 

228

 
relatively few branches outside of New York City is due to a lack of awareness of the program in upstate 

New York.

The New York report offers a number of recommendations to facilitate the financing of bank branches 

benefiting from the program. For example, options should be expanded to collateralize loans made by 

participating banks. Also, a two year maturity on the certificate of deposits received by BDD branches is 

too short and undermines branch profitability. The report recommends a ten year maturity. In addition, the 

report argues that there should be no limit to the number of times a bank can apply for renewal to the 

program given the length of time that it might take the subsidized branch to become profitable.

The report also discusses a number of operational issues. For example, banks argue that allowing multiple 

branches within a single district or creating overlapping boundaries for the BDDs would undermine their 

profitability, especially in rural areas where demand is limited. The report, however, favored allowing 

multiple branches in a single district as it would encourage the provision of more services to underserved 

communities. The report recommends that overlapping districts would be approved on a case-by-case 

basis.

Community groups argue that more low-cost banking services could be provided by BDD banks. The 

report agrees, suggesting that the Banking Department should encourage the creation of new services. The 

report also maintains that a requirement of participating banks to provide financial education to customers 

would benefit both the bank and customers by mitigating the possibility of default and encouraging the 

provision and use of financial services, particularly to small businesses. The report suggests that banks 

partner with a specialized third party organization to provide educational services. The issue of small 

business loans is not discussed directly by the report. A BDD program should include goals for small 

business lending and financial education for small business owners as well.

229
[Page Intentionally Left Blank]

230
APPENDIX:

TABLE A: RATIOS AND INDICES, 2007

(Note: If index is greater than 1, corresponding ratios and indices are in blue color; and if index is equal to or less than 1, corresponding ratios 

and indices are in red color.) 

FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2007

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods 

Index

01007 Bibb, AL At-Risk 0.29 0.55 15.40 0.23 0.28 0.69 0.98 0.55 0.76 0.78

01009 Blount, AL Transitional 0.46 0.51 26.62 0.31 0.52 1.09 0.90 0.95 1.01 1.42

01015 Calhoun, AL Transitional 0.47 0.50 36.10 0.26 0.34 1.09 0.88 1.28 0.85 0.93

01017 Chambers, AL Transitional 0.34 0.60 9.88 0.24 0.22 0.80 1.06 0.35 0.78 0.60

01019 Cherokee, AL Transitional 0.46 0.44 11.95 0.33 NA 1.07 0.78 0.42 1.07 NA

01021 Chilton, AL Transitional 0.38 0.58 3.55 0.27 0.37 0.88 1.02 0.13 0.89 1.02

01027 Clay, AL At-Risk 0.28 0.49 9.49 0.22 NA 0.65 0.87 0.34 0.71 NA

01029 Cleburne, AL Transitional 0.40 0.52 0.00 0.38 NA 0.93 0.92 0.00 1.24 NA

01033 Colbert, AL Transitional 0.34 0.52 4.91 0.18 0.29 0.79 0.92 0.17 0.60 0.81

01037 Coosa, AL At-Risk 0.35 0.60 0.00 0.40 0.31 0.82 1.06 0.00 1.31 0.85

01043 Cullman, AL Transitional 0.43 0.53 7.32 0.26 NA 1.01 0.93 0.26 0.86 NA

01049 DeKalb, AL Transitional 0.36 0.51 10.39 0.27 NA 0.84 0.90 0.37 0.89 NA

01051 Elmore, AL Transitional 0.48 0.58 7.06 0.30 NA 1.12 1.03 0.25 0.97 NA

01055 Etowah, AL Transitional 0.46 0.49 9.47 0.23 0.34 1.08 0.85 0.34 0.76 0.92

01057 Fayette, AL At-Risk 0.32 0.43 0.00 0.31 NA 0.76 0.75 0.00 1.00 NA

01059 Franklin, AL Distressed 0.24 0.67 0.00 0.17 NA 0.57 1.17 0.00 0.54 NA

01065 Hale, AL Distressed 0.22 0.60 0.00 0.16 0.19 0.51 1.06 0.00 0.51 0.51

01071 Jackson, AL Transitional 0.27 0.62 9.87 0.22 NA 0.63 1.08 0.35 0.70 NA

01073 Jefferson, AL Transitional 0.57 0.54 32.10 0.25 0.42 1.34 0.96 1.14 0.83 1.16

01075 Lamar, AL At-Risk 0.29 0.41 0.00 0.35 NA 0.68 0.71 0.00 1.14 NA

01077 Lauderdale, AL Transitional 0.45 0.49 10.12 0.27 0.40 1.06 0.86 0.36 0.86 1.11

231
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2007

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods 

Index

01079 Lawrence, AL Transitional 0.25 0.52 0.00 0.21 0.23 0.60 0.92 0.00 0.67 0.62

01083 Limestone, AL Transitional 0.57 0.46 27.85 0.33 0.42 1.33 0.82 0.99 1.08 1.14

01087 Macon, AL Distressed 0.37 0.66 0.00 0.18 0.29 0.87 1.17 0.00 0.60 0.81

01089 Madison, AL Competitive 0.60 0.54 42.09 0.31 0.49 1.39 0.96 1.49 1.01 1.35

01093 Marion, AL At-Risk 0.30 0.62 5.00 0.20 NA 0.71 1.09 0.18 0.66 NA

01095 Marshall, AL Transitional 0.49 0.45 10.89 0.34 NA 1.14 0.79 0.39 1.12 NA

01103 Morgan, AL Transitional 0.48 0.48 12.20 0.29 0.44 1.12 0.85 0.43 0.96 1.22

01107 Pickens, AL Distressed 0.38 0.26 13.66 0.68 0.44 0.88 0.45 0.48 2.21 1.20

01111 Randolph, AL At-Risk 0.32 0.63 6.44 0.27 NA 0.76 1.11 0.23 0.89 NA

01115 St. Clair, AL Transitional 0.54 0.56 13.66 0.34 NA 1.27 0.99 0.48 1.11 NA

01117 Shelby, AL Attainment 0.77 0.57 25.36 0.38 0.37 1.79 1.00 0.90 1.22 1.00

01121 Talladega, AL Transitional 0.36 0.62 7.90 0.23 0.18 0.84 1.09 0.28 0.74 0.49

01123 Tallapoosa, AL Transitional 0.42 0.54 7.02 0.27 0.25 0.97 0.95 0.25 0.88 0.69

01125 Tuscaloosa, AL Transitional 0.58 0.45 10.79 0.40 0.51 1.37 0.80 0.38 1.31 1.39

01127 Walker, AL Transitional 0.46 0.39 2.18 0.32 0.40 1.09 0.68 0.08 1.03 1.11

01133 Winston, AL At-Risk 0.24 0.59 9.71 0.19 NA 0.56 1.03 0.34 0.61 NA

13011 Banks, GA Transitional 0.42 0.53 15.35 0.40 NA 0.98 0.92 0.54 1.31 NA

13013 Barrow, GA Competitive 0.54 0.66 39.88 0.36 0.42 1.27 1.17 1.42 1.19 1.14

13015 Bartow, GA Competitive 0.54 0.59 20.85 0.38 0.47 1.26 1.04 0.74 1.25 1.29

13045 Carroll, GA Transitional 0.49 0.55 26.98 0.41 0.46 1.15 0.97 0.96 1.34 1.27

13047 Catoosa, GA Competitive 0.45 0.60 8.23 0.27 NA 1.05 1.05 0.29 0.87 NA

13055 Chattooga, GA Transitional 0.36 0.49 27.25 0.36 NA 0.85 0.86 0.97 1.18 NA

13057 Cherokee, GA Attainment 0.67 0.69 55.25 0.39 0.54 1.56 1.21 1.96 1.28 1.47

13083 Dade, GA Transitional 0.39 0.59 28.06 0.24 0.37 0.91 1.04 1.00 0.79 1.00

13085 Dawson, GA Attainment 0.56 0.61 23.31 0.39 NA 1.31 1.07 0.83 1.28 NA

13097 Douglas, GA Competitive 0.55 0.67 47.63 0.35 0.47 1.28 1.17 1.69 1.14 1.29

13105 Elbert, GA Transitional 0.36 0.55 11.07 0.24 NA 0.85 0.97 0.39 0.77 NA

13111 Fannin, GA Transitional 0.45 0.52 13.35 0.41 0.52 1.05 0.91 0.47 1.35 1.43

13115 Floyd, GA Transitional 0.51 0.54 20.41 0.38 0.44 1.19 0.96 0.72 1.23 1.20

13117 Forsyth, GA Attainment 0.75 0.69 45.90 0.42 NA 1.76 1.21 1.63 1.38 NA

13119 Franklin, GA Transitional 0.39 0.57 9.76 0.31 NA 0.91 1.01 0.35 1.02 NA

232
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2007

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods 

Index

13123 Gilmer, GA Transitional 0.39 0.54 14.55 0.31 NA 0.91 0.95 0.52 1.01 NA

13129 Gordon, GA Transitional 0.53 0.45 13.10 0.50 NA 1.24 0.78 0.46 1.63 NA

13135 Gwinnett, GA Attainment 0.59 0.69 53.48 0.35 0.46 1.37 1.20 1.90 1.15 1.25

13137 Habersham, GA Transitional 0.48 0.47 23.80 0.41 NA 1.13 0.84 0.84 1.35 NA

13139 Hall, GA Transitional 0.54 0.59 25.27 0.35 0.44 1.26 1.03 0.90 1.13 1.19

13143 Haralson, GA Transitional 0.48 0.53 9.18 0.41 0.46 1.12 0.93 0.33 1.35 1.26

13147 Hart, GA Transitional 0.48 0.60 28.00 0.37 NA 1.13 1.05 0.99 1.22 NA

13149 Heard, GA Transitional 0.33 0.64 0.00 0.24 0.32 0.77 1.13 0.00 0.79 0.88

13157 Jackson, GA Transitional 0.58 0.62 28.85 0.37 NA 1.35 1.10 1.02 1.22 NA

13187 Lumpkin, GA Transitional 0.62 0.55 9.18 0.46 NA 1.44 0.97 0.33 1.50 NA

13195 Madison, GA Transitional 0.40 0.56 17.41 0.30 NA 0.93 0.98 0.62 0.99 NA

13213 Murray, GA Transitional 0.38 0.63 0.00 0.29 NA 0.88 1.11 0.00 0.94 NA

13223 Paulding, GA Attainment 0.61 0.68 48.76 0.38 0.53 1.42 1.20 1.73 1.24 1.45

13227 Pickens, GA Competitive 0.57 0.73 38.70 0.33 0.51 1.33 1.29 1.37 1.07 1.38

13233 Polk, GA Transitional 0.34 0.60 22.13 0.25 NA 0.79 1.06 0.79 0.82 NA

13241 Rabun, GA Transitional 0.43 0.55 16.15 0.36 NA 1.00 0.96 0.57 1.16 NA

13257 Stephens, GA Transitional 0.46 0.53 42.19 0.33 NA 1.08 0.94 1.50 1.09 NA

13281 Towns, GA Transitional 0.48 0.51 0.00 0.40 NA 1.13 0.90 0.00 1.30 NA

13291 Union, GA Transitional 0.63 0.47 0.00 0.61 NA 1.48 0.82 0.00 2.00 NA

13295 Walker, GA Transitional 0.39 0.59 5.42 0.27 0.29 0.91 1.04 0.19 0.88 0.80

13311 White, GA Transitional 0.60 0.47 0.00 0.48 NA 1.40 0.83 0.00 1.57 NA

13313 Whitfield, GA Competitive 0.49 0.56 17.31 0.31 0.41 1.14 0.98 0.61 1.03 1.13

21001 Adair, KY At-Risk 0.26 0.60 0.00 0.17 0.25 0.62 1.06 0.00 0.55 0.67

21011 Bath, KY At-Risk 0.31 0.38 23.56 0.42 NA 0.73 0.67 0.84 1.38 NA

21013 Bell, KY Distressed 0.24 0.66 0.00 0.14 0.21 0.57 1.17 0.00 0.45 0.57

21019 Boyd, KY Transitional 0.33 0.48 34.16 0.19 0.42 0.76 0.85 1.21 0.62 1.15

21025 Breathitt, KY Distressed 0.18 0.65 10.42 0.12 0.17 0.42 1.14 0.37 0.39 0.48

21043 Carter, KY Distressed 0.23 0.57 5.27 0.17 NA 0.54 1.01 0.19 0.57 NA

21045 Casey, KY Distressed 0.21 0.60 0.00 0.18 0.22 0.49 1.06 0.00 0.60 0.59

21049 Clark, KY Transitional 0.45 0.46 31.24 0.34 0.38 1.06 0.80 1.11 1.11 1.04

21051 Clay, KY Distressed 0.21 0.53 28.06 0.15 0.20 0.49 0.93 1.00 0.49 0.54

233
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2007

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods 

Index

21053 Clinton, KY Distressed 0.18 0.65 0.00 0.15 0.13 0.41 1.15 0.00 0.49 0.35

21057 Cumberland, KY Distressed 0.17 0.56 15.29 0.14 NA 0.39 0.99 0.54 0.45 NA

21061 Edmonson, KY At-Risk 0.26 0.59 13.21 0.23 0.20 0.61 1.04 0.47 0.76 0.54

21063 Elliott, KY Distressed 0.18 0.57 0.00 0.14 0.08 0.43 1.00 0.00 0.46 0.23

21065 Estill, KY Distressed 0.31 0.56 0.00 0.17 0.29 0.71 0.99 0.00 0.56 0.78

21069 Fleming, KY At-Risk 0.32 0.45 8.03 0.47 NA 0.75 0.78 0.28 1.55 NA

21071 Floyd, KY Distressed 0.33 0.54 14.47 0.22 0.23 0.78 0.95 0.51 0.70 0.63

21079 Garrard, KY Transitional 0.44 0.42 39.02 0.37 NA 1.04 0.73 1.39 1.22 NA

21087 Green, KY At-Risk 0.27 0.56 21.93 0.21 NA 0.62 0.99 0.78 0.70 NA

21089 Greenup, KY Transitional 0.30 0.50 0.00 0.20 NA 0.69 0.89 0.00 0.64 NA

21095 Harlan, KY Distressed 0.24 0.66 0.00 0.14 0.18 0.57 1.16 0.00 0.46 0.49

21099 Hart, KY At-Risk 0.20 0.57 0.00 0.17 NA 0.46 1.01 0.00 0.55 NA

21109 Jackson, KY Distressed 0.24 0.60 0.00 0.20 0.21 0.56 1.05 0.00 0.64 0.59

21115 Johnson, KY Distressed 0.28 0.51 6.23 0.22 NA 0.66 0.89 0.22 0.71 NA

21119 Knott, KY Distressed 0.25 0.56 23.17 0.15 0.25 0.58 0.98 0.82 0.47 0.69

21121 Knox, KY Distressed 0.29 0.55 33.44 0.21 0.24 0.68 0.98 1.19 0.68 0.66

21125 Laurel, KY At-Risk 0.37 0.53 23.27 0.24 0.57 0.85 0.93 0.83 0.78 1.57

21127 Lawrence, KY Distressed 0.26 0.54 52.69 0.18 0.44 0.61 0.95 1.87 0.58 1.19

21129 Lee, KY Distressed 0.19 0.66 0.00 0.13 0.17 0.45 1.16 0.00 0.43 0.46

21131 Leslie, KY Distressed 0.15 0.52 11.85 0.11 0.14 0.35 0.91 0.42 0.35 0.38

21133 Letcher, KY Distressed 0.31 0.48 6.92 0.23 0.32 0.73 0.85 0.25 0.74 0.87

21135 Lewis, KY Distressed 0.22 0.65 0.00 0.20 0.18 0.51 1.14 0.00 0.65 0.49

21137 Lincoln, KY At-Risk 0.22 0.58 33.65 0.20 NA 0.52 1.01 1.19 0.64 NA

21147 McCreary, KY Distressed 0.18 0.62 0.00 0.16 0.16 0.42 1.08 0.00 0.52 0.43

21151 Madison, KY Transitional 0.52 0.47 24.23 0.41 0.40 1.22 0.82 0.86 1.33 1.09

21153 Magoffin, KY Distressed 0.29 0.60 0.00 0.22 0.24 0.67 1.05 0.00 0.72 0.67

21159 Martin, KY Distressed 0.23 0.48 17.45 0.14 0.18 0.53 0.85 0.62 0.45 0.50

21165 Menifee, KY Distressed 0.39 0.24 0.00 0.74 0.59 0.92 0.42 0.00 2.41 1.63

21169 Metcalfe, KY 0.17 0.54 23.56 0.14 NA 0.39 0.95 0.84 0.47 NA

21171 Monroe, KY Distressed 0.28 0.61 31.88 0.28 0.29 0.65 1.07 1.13 0.90 0.79

21173 Montgomery, KY Transitional 0.58 0.30 16.83 0.57 NA 1.36 0.52 0.60 1.86 NA

234
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2007

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods 

Index

21175 Morgan, KY Distressed 0.22 0.54 0.00 0.20 0.21 0.51 0.95 0.00 0.65 0.57

21181 Nicholas, KY 0.22 0.46 0.00 0.26 0.33 0.51 0.81 0.00 0.84 0.89

21189 Owsley, KY Distressed 0.12 0.60 0.00 0.11 0.11 0.28 1.05 0.00 0.36 0.30

21193 Perry, KY Distressed 0.28 0.54 0.00 0.17 0.24 0.66 0.95 0.00 0.54 0.65

21195 Pike, KY At-Risk 0.39 0.50 11.95 0.28 0.37 0.91 0.88 0.42 0.90 1.00

21197 Powell, KY Distressed 0.47 0.31 0.00 0.42 NA 1.11 0.54 0.00 1.37 NA

21199 Pulaski, KY At-Risk 0.32 0.59 5.98 0.20 0.27 0.74 1.05 0.21 0.67 0.73

21201 Robertson, KY 0.22 0.43 0.00 0.44 NA 0.51 0.76 0.00 1.45 NA

21203 Rockcastle, KY Distressed 0.35 0.49 12.63 0.28 NA 0.81 0.86 0.45 0.93 NA

21205 Rowan, KY At-Risk 0.41 0.44 13.63 0.38 0.37 0.96 0.77 0.48 1.25 1.02

21207 Russell, KY Distressed 0.26 0.64 7.12 0.14 NA 0.60 1.13 0.25 0.45 NA

21231 Wayne, KY Distressed 0.21 0.61 0.00 0.16 0.17 0.48 1.08 0.00 0.52 0.46

21235 Whitley, KY Distressed 0.33 0.51 18.09 0.21 0.43 0.77 0.90 0.64 0.69 1.16

21237 Wolfe, KY Distressed 0.35 0.49 21.51 0.37 0.30 0.81 0.87 0.76 1.22 0.83

24001 Allegany, MD Transitional 0.48 0.56 18.30 0.27 0.53 1.12 0.99 0.65 0.87 1.45

24023 Garrett, MD Transitional 0.65 0.48 38.64 0.39 0.61 1.52 0.85 1.37 1.29 1.67

24043 Washington, MD Competitive 0.67 0.56 20.64 0.38 0.50 1.56 0.99 0.73 1.23 1.38

28003 Alcorn, MS At-Risk 0.23 0.42 2.21 0.33 NA 0.55 0.74 0.08 1.09 NA

28009 Benton, MS Distressed 0.13 0.63 28.29 0.23 NA 0.31 1.12 1.00 0.73 NA

28013 Calhoun, MS At-Risk 0.22 0.35 5.17 0.61 NA 0.53 0.62 0.18 2.00 NA

28017 Chickasaw, MS Distressed 0.29 0.31 0.00 0.55 NA 0.69 0.54 0.00 1.78 NA

28019 Choctaw, MS Distressed 0.14 0.49 19.63 0.30 NA 0.32 0.86 0.70 0.97 NA

28025 Clay, MS Distressed 0.26 0.34 25.72 0.41 0.19 0.61 0.60 0.91 1.33 0.53

28057 Itawamba, MS Transitional 0.23 0.43 4.18 0.33 NA 0.53 0.76 0.15 1.09 NA

28069 Kemper, MS Distressed 0.08 0.48 20.68 0.15 NA 0.19 0.85 0.73 0.50 NA

28081 Lee, MS Transitional 0.34 0.39 7.63 0.36 NA 0.80 0.69 0.27 1.16 NA

28087 Lowndes, MS At-Risk 0.43 0.36 27.02 0.48 0.30 0.99 0.63 0.96 1.55 0.81

28093 Marshall, MS At-Risk 0.21 0.67 6.31 0.23 0.17 0.50 1.18 0.22 0.73 0.45

28095 Monroe, MS At-Risk 0.29 0.32 2.21 0.60 0.31 0.67 0.56 0.08 1.96 0.85

28097 Montgomery, MS Distressed 0.15 0.54 0.00 0.20 NA 0.36 0.96 0.00 0.65 NA

28103 Noxubee, MS Distressed 0.23 0.44 36.73 0.33 0.19 0.55 0.77 1.30 1.06 0.52

235
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2007

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods 

Index

28105 Oktibbeha, MS At-Risk 0.37 0.43 7.80 0.46 NA 0.86 0.76 0.28 1.50 NA

28107 Panola, MS Distressed 0.16 0.60 9.24 0.20 0.08 0.37 1.05 0.33 0.64 0.23

28115 Pontotoc, MS Transitional 0.21 0.44 6.52 0.31 NA 0.48 0.78 0.23 0.99 NA

28117 Prentiss, MS At-Risk 0.20 0.45 6.74 0.28 NA 0.46 0.79 0.24 0.90 NA

28139 Tippah, MS At-Risk 0.12 0.58 7.11 0.15 NA 0.28 1.02 0.25 0.50 NA

28141 Tishomingo, MS At-Risk 0.16 0.44 0.00 0.20 NA 0.37 0.78 0.00 0.66 NA

28145 Union, MS Transitional 0.16 0.51 2.89 0.23 NA 0.36 0.90 0.10 0.75 NA

28155 Webster, MS Distressed 0.27 0.28 7.82 0.76 NA 0.64 0.50 0.28 2.46 NA

28159 Winston, MS Distressed 0.23 0.34 0.00 0.41 NA 0.53 0.60 0.00 1.34 NA

28161 Yalobusha, MS At-Risk 0.18 0.51 5.71 0.31 NA 0.41 0.90 0.20 1.00 NA

36003 Allegany, NY Transitional 0.37 0.63 35.31 0.30 0.50 0.85 1.10 1.25 0.98 1.37

36007 Broome, NY Transitional 0.53 0.55 0.00 0.30 0.46 1.24 0.96 0.00 0.96 1.27

36009 Cattaraugus, NY Transitional 0.40 0.59 27.80 0.32 0.31 0.94 1.03 0.99 1.04 0.85

36013 Chautauqua, NY Transitional 0.45 0.55 40.23 0.30 0.35 1.05 0.96 1.43 0.98 0.96

36015 Chemung, NY Transitional 0.41 0.59 40.75 0.27 0.31 0.97 1.04 1.45 0.88 0.84

36017 Chenango, NY Transitional 0.48 0.51 27.44 0.36 0.38 1.12 0.90 0.97 1.18 1.04

36023 Cortland, NY Transitional 0.42 0.56 36.46 0.29 NA 0.98 0.98 1.29 0.95 NA

36025 Delaware, NY Transitional 0.49 0.62 13.49 0.31 NA 1.14 1.09 0.48 1.02 NA

36077 Otsego, NY Transitional 0.57 0.60 18.00 0.40 1.00 1.33 1.05 0.64 1.30 2.74

36095 Schoharie, NY Transitional 0.48 0.68 13.99 0.34 0.53 1.12 1.20 0.50 1.10 1.45

36097 Schuyler, NY Transitional 0.47 0.59 48.90 0.34 NA 1.10 1.04 1.74 1.11 NA

36101 Steuben, NY Transitional 0.38 0.63 66.25 0.28 0.30 0.89 1.10 2.35 0.90 0.81

36107 Tioga, NY Transitional 0.43 0.59 37.38 0.26 NA 1.02 1.04 1.33 0.86 NA

36109 Tompkins, NY Transitional 0.52 0.64 45.48 0.35 0.48 1.22 1.13 1.61 1.15 1.30

37003 Alexander, NC Transitional 0.60 0.41 54.05 0.45 NA 1.41 0.72 1.92 1.46 NA

37005 Alleghany, NC At-Risk 0.47 0.48 0.00 0.30 NA 1.09 0.85 0.00 0.99 NA

37009 Ashe, NC Transitional 0.58 0.38 10.35 0.47 0.53 1.36 0.68 0.37 1.53 1.46

37011 Avery, NC Transitional 0.68 0.40 7.43 0.50 NA 1.58 0.71 0.26 1.63 NA

37021 Buncombe, NC Transitional 0.63 0.54 24.91 0.39 0.54 1.46 0.95 0.88 1.26 1.49

37023 Burke, NC Transitional 0.48 0.43 23.70 0.32 0.39 1.12 0.76 0.84 1.04 1.08

37027 Caldwell, NC Transitional 0.51 0.45 13.54 0.34 0.39 1.20 0.80 0.48 1.12 1.06

236
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2007

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods 

Index

37039 Cherokee, NC At-Risk 0.58 0.47 12.68 0.48 0.51 1.35 0.83 0.45 1.58 1.41

37043 Clay, NC Transitional 0.54 0.49 0.00 0.41 NA 1.26 0.86 0.00 1.34 NA

37059 Davie, NC Competitive 0.62 0.50 4.29 0.38 0.55 1.46 0.87 0.15 1.24 1.50

37067 Forsyth, NC Competitive 0.56 0.54 31.90 0.32 0.44 1.30 0.94 1.13 1.03 1.20

37075 Graham, NC Distressed 0.59 0.36 0.00 0.60 0.69 1.38 0.63 0.00 1.94 1.88

37087 Haywood, NC Transitional 0.53 0.51 26.65 0.34 0.38 1.25 0.90 0.95 1.09 1.04

37089 Henderson, NC Competitive 0.65 0.54 15.21 0.40 0.48 1.53 0.95 0.54 1.29 1.32

37099 Jackson, NC Transitional 0.57 0.51 0.00 0.38 NA 1.34 0.89 0.00 1.23 NA

37111 McDowell, NC Transitional 0.55 0.54 0.00 0.35 0.41 1.28 0.94 0.00 1.15 1.13

37113 Macon, NC Transitional 0.62 0.51 6.10 0.40 NA 1.46 0.91 0.22 1.30 NA

37115 Madison, NC Transitional 0.55 0.51 34.07 0.45 0.53 1.30 0.89 1.21 1.46 1.46

37121 Mitchell, NC At-Risk 0.47 0.50 17.78 0.36 0.74 1.11 0.89 0.63 1.16 2.03

37149 Polk, NC Competitive 0.62 0.60 6.92 0.36 NA 1.46 1.06 0.25 1.17 NA

37161 Rutherford, NC At-Risk 0.48 0.45 14.57 0.31 0.32 1.12 0.78 0.52 1.02 0.86

37169 Stokes, NC Transitional 0.48 0.47 13.82 0.31 0.52 1.13 0.82 0.49 1.01 1.43

37171 Surry, NC Transitional 0.45 0.47 84.30 0.27 0.29 1.06 0.83 2.99 0.89 0.80

37173 Swain, NC At-Risk 0.53 0.48 21.44 0.36 0.63 1.25 0.85 0.76 1.16 1.73

37175 Transylvania, NC Transitional 0.60 0.51 8.94 0.38 NA 1.41 0.90 0.32 1.23 NA

37189 Watauga, NC Transitional 0.64 0.44 17.79 0.43 NA 1.49 0.78 0.63 1.41 NA

37193 Wilkes, NC Transitional 0.54 0.39 24.72 0.41 NA 1.27 0.68 0.88 1.35 NA

37197 Yadkin, NC Transitional 0.44 0.47 9.79 0.31 NA 1.04 0.82 0.35 1.00 NA

37199 Yancey, NC At-Risk 0.64 0.41 0.00 0.56 NA 1.49 0.71 0.00 1.81 NA

39001 Adams, OH At-Risk 0.29 0.49 23.32 0.21 0.27 0.68 0.87 0.83 0.70 0.73

39007 Ashtabula, OH 0.41 0.60 44.66 0.27 0.29 0.97 1.05 1.59 0.89 0.78

39009 Athens, OH Distressed 0.38 0.66 2.98 0.23 0.31 0.89 1.16 0.11 0.74 0.84

39013 Belmont, OH Transitional 0.46 0.48 22.89 0.26 0.36 1.07 0.84 0.81 0.85 1.00

39015 Brown, OH Transitional 0.39 0.52 57.88 0.32 0.34 0.92 0.92 2.05 1.03 0.94

39019 Carroll, OH Transitional 0.44 0.55 79.22 0.29 0.54 1.04 0.97 2.81 0.94 1.49

39025 Clermont, OH Competitive 0.66 0.60 71.66 0.39 0.55 1.54 1.05 2.54 1.28 1.51

39029 Columbiana, OH Transitional 0.44 0.61 44.32 0.26 0.28 1.03 1.08 1.57 0.85 0.78

39031 Coshocton, OH Transitional 0.37 0.65 34.65 0.20 0.28 0.87 1.15 1.23 0.64 0.77

237
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2007

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods 

Index

39053 Gallia, OH At-Risk 0.33 0.62 27.23 0.24 0.28 0.76 1.08 0.97 0.77 0.77

39059 Guernsey, OH At-Risk 0.39 0.52 62.68 0.30 0.39 0.92 0.92 2.23 0.99 1.07

39067 Harrison, OH Transitional 0.38 0.51 72.84 0.20 0.37 0.90 0.90 2.59 0.66 1.02

39071 Highland, OH Transitional 0.38 0.50 19.00 0.35 0.40 0.89 0.88 0.67 1.13 1.09

39073 Hocking, OH Transitional 0.36 0.51 29.80 0.22 NA 0.84 0.90 1.06 0.73 NA

39075 Holmes, OH Transitional 0.58 0.54 45.36 0.34 NA 1.35 0.96 1.61 1.09 NA

39079 Jackson, OH At-Risk 0.26 0.55 4.64 0.18 0.24 0.60 0.96 0.16 0.60 0.65

39081 Jefferson, OH Transitional 0.43 0.49 40.82 0.23 0.29 1.00 0.86 1.45 0.76 0.79

39087 Lawrence, OH At-Risk 0.40 0.49 21.62 0.28 0.19 0.92 0.87 0.77 0.90 0.52

39099 Mahoning, OH 0.54 0.58 45.89 0.29 0.36 1.26 1.02 1.63 0.94 0.98

39105 Meigs, OH Distressed 0.25 0.57 0.00 0.16 0.24 0.59 1.00 0.00 0.54 0.65

39111 Monroe, OH At-Risk 0.32 0.57 0.00 0.22 0.31 0.74 1.00 0.00 0.73 0.86

39115 Morgan, OH At-Risk 0.28 0.54 20.37 0.20 0.27 0.66 0.95 0.72 0.64 0.74

39119 Muskingum, OH Transitional 0.38 0.55 39.51 0.26 0.30 0.88 0.96 1.40 0.86 0.82

39121 Noble, OH At-Risk 0.34 0.54 0.00 0.24 NA 0.78 0.95 0.00 0.80 NA

39127 Perry, OH Transitional 0.33 0.55 39.13 0.26 0.26 0.78 0.97 1.39 0.85 0.70

39131 Pike, OH Distressed 0.28 0.57 5.83 0.20 0.29 0.66 1.00 0.21 0.67 0.80

39141 Ross, OH Transitional 0.38 0.51 38.17 0.27 0.23 0.89 0.90 1.35 0.88 0.64

39145 Scioto, OH At-Risk 0.37 0.52 0.00 0.24 0.33 0.87 0.92 0.00 0.77 0.92

39155 Trumbull, OH 0.51 0.59 37.78 0.29 0.33 1.19 1.04 1.34 0.96 0.90

39157 Tuscarawas, OH Transitional 0.48 0.54 51.02 0.28 0.33 1.13 0.96 1.81 0.92 0.91

39163 Vinton, OH Distressed 0.24 0.50 0.00 0.22 0.23 0.57 0.87 0.00 0.72 0.64

39167 Washington, OH Transitional 0.46 0.52 21.58 0.28 0.43 1.07 0.91 0.77 0.90 1.19

42003 Allegheny, PA Competitive 0.59 0.57 60.23 0.35 0.46 1.39 1.00 2.14 1.13 1.25

42005 Armstrong, PA Transitional 0.47 0.59 39.76 0.26 0.35 1.09 1.04 1.41 0.86 0.95

42007 Beaver, PA Transitional 0.54 0.59 54.31 0.31 0.41 1.25 1.03 1.93 1.00 1.11

42009 Bedford, PA Transitional 0.56 0.47 58.58 0.40 0.40 1.30 0.82 2.08 1.31 1.09

42013 Blair, PA Transitional 0.57 0.48 51.58 0.34 0.37 1.32 0.85 1.83 1.12 1.01

42015 Bradford, PA Transitional 0.45 0.49 20.72 0.35 NA 1.05 0.86 0.74 1.15 NA

42019 Butler, PA Competitive 0.62 0.56 44.86 0.38 0.44 1.45 0.99 1.59 1.22 1.21

42021 Cambria, PA Transitional 0.52 0.53 30.74 0.32 0.41 1.21 0.93 1.09 1.06 1.11

238
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2007

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods 

Index

42023 Cameron, PA Transitional 0.41 0.47 142.05 0.21 NA 0.95 0.82 5.04 0.70 NA

42025 Carbon, PA Transitional 0.61 0.61 51.61 0.37 0.55 1.42 1.07 1.83 1.20 1.50

42027 Centre, PA Transitional 0.66 0.55 49.40 0.42 0.51 1.54 0.97 1.75 1.37 1.40

42031 Clarion, PA Transitional 0.50 0.49 0.00 0.34 NA 1.17 0.86 0.00 1.12 NA

42033 Clearfield, PA Transitional 0.59 0.42 24.22 0.43 0.50 1.38 0.73 0.86 1.41 1.37

42035 Clinton, PA Transitional 0.50 0.46 34.76 0.31 0.39 1.17 0.81 1.23 1.02 1.07

42037 Columbia, PA Transitional 0.58 0.53 39.14 0.39 0.52 1.36 0.94 1.39 1.28 1.42

42039 Crawford, PA Transitional 0.53 0.48 47.04 0.36 0.34 1.23 0.85 1.67 1.18 0.92

42047 Elk, PA Transitional 0.52 0.48 20.21 0.33 NA 1.22 0.84 0.72 1.06 NA

42049 Erie, PA Transitional 0.58 0.47 66.60 0.39 0.41 1.35 0.82 2.36 1.27 1.13

42051 Fayette, PA At-Risk 0.59 0.47 49.73 0.39 0.54 1.39 0.83 1.77 1.29 1.49

42053 Forest, PA Distressed 0.36 0.48 30.30 0.28 NA 0.84 0.85 1.08 0.91 NA

42057 Fulton, PA Transitional 0.50 0.59 35.84 0.34 NA 1.17 1.04 1.27 1.10 NA

42059 Greene, PA Transitional 0.39 0.54 5.32 0.23 NA 0.91 0.96 0.19 0.75 NA

42061 Huntingdon, PA Transitional 0.48 0.50 60.58 0.32 0.36 1.11 0.88 2.15 1.05 0.97

42063 Indiana, PA Transitional 0.59 0.45 20.25 0.39 0.43 1.37 0.79 0.72 1.28 1.19

42065 Jefferson, PA Transitional 0.55 0.39 22.91 0.38 0.27 1.29 0.69 0.81 1.23 0.73

42067 Juniata, PA Transitional 0.58 0.52 0.00 0.42 NA 1.35 0.91 0.00 1.38 NA

42069 Lackawanna, PA Transitional 0.70 0.53 45.47 0.42 0.51 1.63 0.93 1.61 1.38 1.40

42073 Lawrence, PA Transitional 0.58 0.55 31.15 0.34 0.51 1.35 0.96 1.11 1.09 1.40

42079 Luzerne, PA Transitional 0.65 0.53 56.10 0.40 0.50 1.53 0.92 1.99 1.30 1.36

42081 Lycoming, PA Transitional 0.54 0.48 32.68 0.31 0.40 1.26 0.84 1.16 1.00 1.08

42083 McKean, PA Transitional 0.53 0.44 12.07 0.45 0.43 1.24 0.77 0.43 1.46 1.19

42085 Mercer, PA Transitional 0.49 0.56 47.44 0.29 0.37 1.13 0.99 1.68 0.94 1.00

42087 Mifflin, PA Transitional 0.52 0.52 39.68 0.37 0.40 1.23 0.91 1.41 1.19 1.11

42089 Monroe, PA Transitional 0.80 0.61 58.20 0.44 NA 1.87 1.08 2.07 1.44 NA

42093 Montour, PA Competitive 0.58 0.54 20.94 0.41 NA 1.35 0.95 0.74 1.34 NA

42097 Northumberland, PA Transitional 0.52 0.54 30.18 0.31 0.48 1.22 0.95 1.07 1.00 1.31

42099 Perry, PA Competitive 0.61 0.57 34.53 0.40 0.20 1.43 1.00 1.23 1.29 0.55

42103 Pike, PA Competitive 0.91 0.68 56.14 0.48 0.88 2.13 1.20 1.99 1.56 2.42

42105 Potter, PA Transitional 0.44 0.54 33.84 0.31 0.45 1.03 0.95 1.20 1.01 1.24

239
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2007

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods 

Index

42107 Schuylkill, PA Transitional 0.57 0.55 25.27 0.35 0.37 1.34 0.97 0.90 1.14 1.02

42109 Snyder, PA Transitional 0.62 0.54 45.89 0.40 NA 1.44 0.95 1.63 1.31 NA

42111 Somerset, PA Transitional 0.60 0.46 34.18 0.39 0.48 1.39 0.81 1.21 1.29 1.30

42113 Sullivan, PA Transitional 0.45 0.50 0.00 0.32 NA 1.05 0.88 0.00 1.03 NA

42115 Susquehanna, PA Transitional 0.54 0.59 8.61 0.36 NA 1.25 1.03 0.31 1.18 NA

42117 Tioga, PA Transitional 0.47 0.49 17.12 0.36 NA 1.10 0.87 0.61 1.18 NA

42119 Union, PA Transitional 0.54 0.61 39.56 0.29 NA 1.27 1.07 1.40 0.94 NA

42121 Venango, PA Transitional 0.43 0.53 49.00 0.28 0.32 1.00 0.94 1.74 0.93 0.87

42123 Warren, PA Transitional 0.53 0.37 36.51 0.42 NA 1.23 0.65 1.30 1.37 NA

42125 Washington, PA Transitional 0.59 0.52 51.76 0.35 0.42 1.39 0.91 1.84 1.13 1.15

42127 Wayne, PA Transitional 0.71 0.66 16.37 0.41 NA 1.66 1.17 0.58 1.33 NA

42129 Westmoreland, PA Transitional 0.63 0.52 44.15 0.38 0.43 1.48 0.91 1.57 1.24 1.17

42131 Wyoming, PA Transitional 0.67 0.56 49.19 0.45 NA 1.58 0.98 1.75 1.46 NA

45007 Anderson, SC Transitional 0.50 0.55 9.13 0.32 0.35 1.16 0.97 0.32 1.04 0.95

45021 Cherokee, SC At-Risk 0.43 0.53 10.96 0.29 NA 1.01 0.93 0.39 0.95 NA

45045 Greenville, SC Competitive 0.60 0.59 28.84 0.36 0.42 1.40 1.03 1.02 1.19 1.16

45073 Oconee, SC Transitional 0.52 0.61 11.12 0.32 NA 1.21 1.07 0.39 1.05 NA

45077 Pickens, SC Transitional 0.50 0.59 19.74 0.35 0.55 1.17 1.03 0.70 1.12 1.51

45083 Spartanburg, SC Transitional 0.51 0.56 17.64 0.31 0.39 1.19 0.99 0.63 1.02 1.07

47001 Anderson, TN Transitional 0.43 0.60 16.69 0.25 0.33 1.01 1.05 0.59 0.83 0.90

47007 Bledsoe, TN At-Risk 0.26 0.59 42.37 0.20 NA 0.61 1.04 1.50 0.67 NA

47009 Blount, TN Competitive 0.55 0.52 24.83 0.35 0.45 1.28 0.92 0.88 1.14 1.23

47011 Bradley, TN Transitional 0.50 0.51 17.00 0.35 0.39 1.17 0.90 0.60 1.13 1.08

47013 Campbell, TN At-Risk 0.27 0.65 4.36 0.16 0.21 0.63 1.14 0.15 0.53 0.57

47015 Cannon, TN Transitional 0.31 0.56 0.00 0.23 0.30 0.72 0.98 0.00 0.75 0.83

47019 Carter, TN At-Risk 0.47 0.55 12.25 0.28 0.47 1.09 0.96 0.43 0.91 1.29

47025 Claiborne, TN At-Risk 0.30 0.66 6.36 0.20 0.23 0.70 1.16 0.23 0.64 0.62

47027 Clay, TN At-Risk 0.30 0.52 0.00 0.28 0.27 0.69 0.91 0.00 0.93 0.75

47029 Cocke, TN Distressed 0.38 0.53 5.28 0.28 0.40 0.89 0.93 0.19 0.92 1.09

47031 Coffee, TN Transitional 0.30 0.61 10.66 0.18 0.29 0.71 1.08 0.38 0.58 0.79

47035 Cumberland, TN Transitional 0.49 0.61 15.30 0.26 NA 1.13 1.08 0.54 0.86 NA

240
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2007

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods 

Index

47041 DeKalb, TN Transitional 0.29 0.53 0.00 0.27 NA 0.67 0.94 0.00 0.88 NA

47049 Fentress, TN Distressed 0.28 0.63 7.86 0.17 0.20 0.66 1.11 0.28 0.56 0.54

47051 Franklin, TN Transitional 0.30 0.57 3.68 0.20 NA 0.71 1.00 0.13 0.65 NA

47057 Grainger, TN At-Risk 0.39 0.58 38.28 0.30 NA 0.92 1.03 1.36 0.98 NA

47059 Greene, TN Transitional 0.43 0.53 5.23 0.31 0.40 1.02 0.93 0.19 1.01 1.10

47061 Grundy, TN Distressed 0.35 0.68 0.00 0.23 0.28 0.83 1.19 0.00 0.74 0.76

47063 Hamblen, TN Transitional 0.46 0.55 18.33 0.28 0.36 1.09 0.97 0.65 0.91 0.98

47065 Hamilton, TN Competitive 0.50 0.55 20.35 0.28 0.43 1.17 0.96 0.72 0.90 1.17

47067 Hancock, TN Distressed 0.24 0.68 37.04 0.13 0.19 0.57 1.20 1.31 0.42 0.51

47073 Hawkins, TN Transitional 0.41 0.56 21.07 0.32 0.27 0.96 0.99 0.75 1.04 0.75

47087 Jackson, TN At-Risk 0.44 0.40 0.00 0.44 0.65 1.03 0.70 0.00 1.44 1.79

47089 Jefferson, TN Transitional 0.49 0.49 22.59 0.34 NA 1.15 0.86 0.80 1.11 NA

47091 Johnson, TN Distressed 0.43 0.62 21.25 0.28 0.43 1.02 1.10 0.75 0.91 1.16

47093 Knox, TN Competitive 0.55 0.55 36.95 0.31 0.44 1.29 0.96 1.31 1.00 1.20

47099 Lawrence, TN 0.28 0.62 32.11 0.21 NA 0.65 1.09 1.14 0.70 NA

47101 Lewis, TN 0.30 0.57 0.00 0.18 NA 0.70 1.01 0.00 0.57 NA

47105 Loudon, TN Competitive 0.59 0.56 24.98 0.42 NA 1.39 0.98 0.89 1.38 NA

47107 McMinn, TN Transitional 0.37 0.48 8.56 0.29 0.31 0.88 0.85 0.30 0.95 0.86

47111 Macon, TN Transitional 0.41 0.35 0.00 0.44 0.38 0.95 0.61 0.00 1.44 1.05

47115 Marion, TN Transitional 0.29 0.62 13.09 0.22 0.26 0.69 1.09 0.46 0.72 0.72

47121 Meigs, TN At-Risk 0.36 0.48 15.06 0.27 NA 0.84 0.84 0.53 0.89 NA

47123 Monroe, TN At-Risk 0.40 0.52 11.59 0.29 NA 0.94 0.92 0.41 0.93 NA

47129 Morgan, TN At-Risk 0.35 0.62 22.62 0.21 0.39 0.82 1.09 0.80 0.70 1.07

47133 Overton, TN Transitional 0.36 0.62 0.00 0.21 0.40 0.84 1.10 0.00 0.69 1.10

47137 Pickett, TN Distressed 0.27 0.66 0.00 0.21 NA 0.63 1.16 0.00 0.69 NA

47139 Polk, TN Transitional 0.31 0.54 0.00 0.25 0.31 0.73 0.96 0.00 0.81 0.86

47141 Putnam, TN Transitional 0.46 0.45 7.51 0.26 0.39 1.07 0.79 0.27 0.86 1.07

47143 Rhea, TN Transitional 0.37 0.58 11.26 0.30 NA 0.87 1.02 0.40 0.97 NA

47145 Roane, TN Transitional 0.38 0.61 26.60 0.25 NA 0.89 1.07 0.94 0.82 NA

47151 Scott, TN Distressed 0.24 0.69 7.38 0.14 0.24 0.56 1.22 0.26 0.47 0.66

47153 Sequatchie, TN Transitional 0.33 0.56 24.91 0.23 0.27 0.77 0.98 0.88 0.73 0.75

241
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2007

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods 

Index

47155 Sevier, TN Transitional 0.50 0.52 15.23 0.30 NA 1.16 0.92 0.54 0.99 NA

47159 Smith, TN Transitional 0.29 0.49 15.59 0.26 0.29 0.67 0.86 0.55 0.83 0.80

47163 Sullivan, TN Transitional 0.56 0.47 27.99 0.35 0.47 1.30 0.83 0.99 1.13 1.29

47171 Unicoi, TN Transitional 0.44 0.48 0.00 0.36 NA 1.04 0.84 0.00 1.18 NA

47173 Union, TN At-Risk 0.33 0.54 19.82 0.23 0.32 0.76 0.95 0.70 0.74 0.86

47175 Van Buren, TN Transitional 0.30 0.64 0.00 0.23 NA 0.69 1.13 0.00 0.75 NA

47177 Warren, TN Transitional 0.29 0.55 13.36 0.18 NA 0.67 0.97 0.47 0.57 NA

47179 Washington, TN Transitional 0.58 0.49 19.32 0.36 0.40 1.35 0.85 0.69 1.18 1.11

47185 White, TN At-Risk 0.29 0.62 16.10 0.21 NA 0.68 1.09 0.57 0.68 NA

51005

Alleghany + Clifton 

Forge city + Covington 

city, VA

Transitional 0.41 0.59 0.00 0.26 NA 0.95 1.04 0.00 0.84 NA

51017 Bath, VA Competitive 0.28 0.56 0.00 0.20 NA 0.65 0.99 0.00 0.64 NA

51021 Bland, VA Transitional 0.53 0.30 0.00 0.54 NA 1.24 0.53 0.00 1.76 NA

51023 Botetourt, VA Attainment 0.58 0.53 28.18 0.35 NA 1.36 0.94 1.00 1.14 NA

51027 Buchanan, VA At-Risk 0.36 0.54 9.13 0.21 0.21 0.84 0.96 0.32 0.70 0.58

51035 Carroll + Galax city, 

VA Transitional 0.44 0.53 23.61 0.28 NA 1.04 0.93 0.84 0.90 NA

51045 Craig, VA Transitional 0.47 0.55 282.49 0.25 NA 1.10 0.97 10.03 0.83 NA

51051 Dickenson, VA Distressed 0.49 0.41 0.00 0.44 0.41 1.14 0.72 0.00 1.44 1.12

51063 Floyd, VA Transitional 0.58 0.54 11.40 0.36 NA 1.35 0.96 0.40 1.17 NA

51071 Giles, VA Transitional 0.39 0.49 42.19 0.25 NA 0.92 0.86 1.50 0.83 NA

51077 Grayson, VA Transitional 0.43 0.50 0.00 0.31 NA 1.00 0.89 0.00 1.01 NA

51089 Henry + Martinsville 

city, VA 0.41 0.53 7.23 0.23 0.33 0.95 0.93 0.26 0.75 0.90

51091 Highland, VA Transitional 0.46 0.72 0.00 0.49 NA 1.07 1.26 0.00 1.59 NA

51105 Lee, VA At-Risk 0.27 0.64 0.00 0.23 0.19 0.62 1.13 0.00 0.77 0.52

51121 Montgomery + Radford 

city, VA Transitional 0.52 0.18 30.85 0.33 0.21 1.22 0.32 1.10 1.06 0.57

51141 Patrick, VA 0.48 0.55 151.06 0.32 NA 1.13 0.97 5.36 1.04 NA

51155 Pulaski, VA Transitional 0.47 0.52 0.00 0.29 0.61 1.11 0.92 0.00 0.93 1.67

51163

Rockbridge + Buena 

Vista city + Lexington 

city, VA

Transitional 0.53 0.54 4.52 0.33 NA 1.23 0.94 0.16 1.07 NA

242
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2007

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods 

Index

51167 Russell, VA At-Risk 0.46 0.39 7.20 0.44 0.34 1.07 0.68 0.26 1.44 0.93

51169 Scott, VA Transitional 0.31 0.48 17.84 0.31 0.33 0.74 0.84 0.63 1.02 0.90

51173 Smyth, VA Transitional 0.46 0.34 6.04 0.41 0.37 1.08 0.60 0.21 1.35 1.03

51185 Tazewell, VA Transitional 0.48 0.43 3.56 0.30 0.38 1.13 0.75 0.13 0.97 1.03

51191 Washington + Bristol 

city, VA Transitional 0.54 0.40 0.00 0.43 0.37 1.25 0.70 0.00 1.41 1.00

51195 Wise + Norton city, VA At-Risk 0.44 0.49 3.83 0.32 0.39 1.03 0.86 0.14 1.04 1.07

51197 Wythe, VA Transitional 0.47 0.41 5.41 0.37 NA 1.11 0.72 0.19 1.20 NA

54001 Barbour, WV Distressed 0.32 0.64 28.41 0.26 NA 0.75 1.13 1.01 0.83 NA

54003 Berkeley, WV Transitional 0.64 0.57 31.50 0.40 0.43 1.49 1.01 1.12 1.31 1.18

54005 Boone, WV At-Risk 0.37 0.51 0.00 0.27 0.29 0.86 0.90 0.00 0.87 0.80

54007 Braxton, WV Distressed 0.34 0.56 25.91 0.24 NA 0.80 0.99 0.92 0.79 NA

54009 Brooke, WV Transitional 0.48 0.46 37.04 0.31 NA 1.13 0.81 1.31 1.00 NA

54011 Cabell, WV Transitional 0.47 0.57 57.77 0.26 0.39 1.10 1.00 2.05 0.86 1.07

54013 Calhoun, WV Distressed 0.25 0.69 0.00 0.19 0.15 0.57 1.21 0.00 0.63 0.40

54015 Clay, WV Distressed 0.34 0.57 0.00 0.28 0.27 0.80 1.00 0.00 0.90 0.73

54017 Doddridge, WV At-Risk 0.25 0.49 0.00 0.17 NA 0.59 0.86 0.00 0.55 NA

54019 Fayette, WV Distressed 0.47 0.45 20.21 0.30 0.34 1.10 0.79 0.72 0.97 0.94

54021 Gilmer, WV Distressed 0.34 0.47 0.00 0.32 NA 0.79 0.83 0.00 1.03 NA

54023 Grant, WV Transitional 0.40 0.68 0.00 0.22 NA 0.94 1.19 0.00 0.72 NA

54025 Greenbrier, WV Transitional 0.49 0.49 26.05 0.33 NA 1.14 0.87 0.92 1.07 NA

54027 Hampshire, WV Transitional 0.41 0.64 25.97 0.32 0.25 0.96 1.13 0.92 1.03 0.68

54029 Hancock, WV Transitional 0.46 0.57 52.42 0.28 NA 1.07 1.00 1.86 0.91 NA

54031 Hardy, WV Transitional 0.40 0.67 41.84 0.30 NA 0.94 1.17 1.49 0.98 NA

54033 Harrison, WV Transitional 0.53 0.52 43.03 0.32 0.58 1.25 0.91 1.53 1.05 1.59

54035 Jackson, WV At-Risk 0.41 0.47 33.97 0.32 NA 0.96 0.82 1.21 1.05 NA

54037 Jefferson, WV Competitive 0.72 0.64 29.40 0.41 0.69 1.68 1.13 1.04 1.35 1.89

54039 Kanawha, WV Transitional 0.45 0.50 23.67 0.26 0.37 1.05 0.88 0.84 0.85 1.02

54041 Lewis, WV At-Risk 0.29 0.56 12.86 0.32 NA 0.67 0.99 0.46 1.05 NA

54043 Lincoln, WV Distressed 0.44 0.50 29.41 0.32 0.31 1.04 0.88 1.04 1.04 0.84

54045 Logan, WV Distressed 0.31 0.54 0.00 0.20 0.26 0.72 0.95 0.00 0.64 0.71

54047 McDowell, WV Distressed 0.26 0.54 66.01 0.14 0.22 0.62 0.95 2.34 0.47 0.61

243
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2007

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods 

Index

54049 Marion, WV Transitional 0.46 0.53 60.38 0.27 0.24 1.07 0.92 2.14 0.88 0.66

54051 Marshall, WV Transitional 0.48 0.45 15.09 0.31 0.38 1.13 0.79 0.54 1.00 1.04

54053 Mason, WV Distressed 0.32 0.59 9.81 0.21 NA 0.75 1.04 0.35 0.69 NA

54055 Mercer, WV At-Risk 0.58 0.38 12.07 0.41 0.30 1.35 0.68 0.43 1.34 0.81

54057 Mineral, WV Transitional 0.52 0.53 128.33 0.32 0.27 1.22 0.94 4.56 1.04 0.75

54059 Mingo, WV Distressed 0.34 0.51 15.42 0.21 0.28 0.79 0.89 0.55 0.67 0.77

54061 Monongalia, WV Transitional 0.65 0.45 59.11 0.46 0.54 1.53 0.80 2.10 1.50 1.47

54063 Monroe, WV Transitional 0.49 0.58 32.15 0.41 NA 1.14 1.02 1.14 1.33 NA

54065 Morgan, WV Transitional 0.79 0.58 24.75 0.55 1.07 1.85 1.01 0.88 1.79 2.92

54067 Nicholas, WV At-Risk 0.60 0.36 7.24 0.45 NA 1.40 0.63 0.26 1.46 NA

54069 Ohio, WV Transitional 0.52 0.49 53.96 0.30 0.40 1.22 0.86 1.92 0.97 1.11

54071 Pendleton, WV Transitional 0.34 0.68 0.00 0.24 NA 0.78 1.19 0.00 0.78 NA

54073 Pleasants, WV Transitional 0.29 0.56 25.45 0.25 NA 0.67 0.98 0.90 0.83 NA

54075 Pocahontas, WV At-Risk 0.40 0.50 51.19 0.31 NA 0.94 0.89 1.82 1.02 NA

54077 Preston, WV At-Risk 0.56 0.49 35.59 0.45 0.40 1.31 0.86 1.26 1.48 1.10

54079 Putnam, WV Competitive 0.56 0.49 44.63 0.36 NA 1.31 0.86 1.58 1.17 NA

54081 Raleigh, WV Transitional 0.51 0.48 29.71 0.28 NA 1.18 0.85 1.05 0.93 NA

54083 Randolph, WV At-Risk 0.38 0.64 35.36 0.26 NA 0.89 1.12 1.26 0.86 NA

54085 Ritchie, WV At-Risk 0.33 0.44 0.00 0.29 NA 0.77 0.77 0.00 0.95 NA

54087 Roane, WV Distressed 0.41 0.63 57.97 0.25 NA 0.95 1.10 2.06 0.82 NA

54089 Summers, WV Distressed 0.39 0.58 0.00 0.29 0.30 0.91 1.02 0.00 0.95 0.83

54091 Taylor, WV At-Risk 0.43 0.49 55.05 0.34 NA 1.00 0.86 1.95 1.12 NA

54093 Tucker, WV At-Risk 0.35 0.56 21.69 0.30 NA 0.83 0.98 0.77 0.98 NA

54095 Tyler, WV At-Risk 0.37 0.59 24.94 0.28 NA 0.86 1.03 0.89 0.91 NA

54097 Upshur, WV At-Risk 0.50 0.52 7.64 0.35 NA 1.18 0.91 0.27 1.13 NA

54099 Wayne, WV At-Risk 0.46 0.54 52.22 0.29 0.23 1.08 0.94 1.85 0.96 0.63

54101 Webster, WV Distressed 0.54 0.38 74.81 0.52 0.65 1.27 0.67 2.66 1.70 1.77

54103 Wetzel, WV At-Risk 0.31 0.51 0.00 0.22 NA 0.72 0.90 0.00 0.73 NA

54105 Wirt, WV Distressed 0.35 0.56 40.82 0.28 NA 0.81 0.99 1.45 0.92 NA

54107 Wood, WV Transitional 0.44 0.48 30.92 0.26 0.35 1.03 0.84 1.10 0.86 0.97

54109 Wyoming, WV Distressed 0.27 0.62 21.48 0.16 0.13 0.63 1.10 0.76 0.53 0.37

244
TABLE B: RATIOS AND INDICES, 2010

(Note: If index is greater than 1, corresponding ratios and indices are in blue color; and if index is equal to or less than 1, corresponding ratios 

and indices are in red color)

FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2010

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit 

Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods Index

01007 Bibb, AL At-Risk 0.11 0.55 0.00 0.07 0.11 0.83 0.91 0.00 0.99 0.94

01009 Blount, AL Transitional 0.11 0.56 6.76 0.04 0.12 0.82 0.93 0.36 0.59 1.01

01015 Calhoun, AL Transitional 0.13 0.52 13.35 0.06 0.12 0.92 0.87 0.72 0.88 1.04

01017 Chambers, AL Transitional 0.08 0.72 0.00 0.04 0.05 0.59 1.2 0.00 0.5 0.41

01019 Cherokee, AL Transitional 0.12 0.42 38.24 0.08 NA 0.86 0.7 2.06 1.04 NA

01021 Chilton, AL Transitional 0.1 0.62 3.69 0.05 0.08 0.69 1.04 0.2 0.65 0.68

01027 Clay, AL Transitional 0.07 0.69 0.00 0.03 NA 0.47 1.16 0.00 0.39 NA

01029 Cleburne, AL Transitional 0.1 0.72 12.22 0.03 NA 0.72 1.19 0.66 0.47 NA

01033 Colbert, AL Transitional 0.17 0.43 5.39 0.1 0.19 1.23 0.72 0.29 1.34 1.64

01037 Coosa, AL Transitional 0.11 0.58 32.57 0.06 0.07 0.78 0.96 1.75 0.83 0.64

01043 Cullman, AL Transitional 0.1 0.61 11.65 0.04 NA 0.75 1.01 0.63 0.52 NA

01049 DeKalb, AL Transitional 0.09 0.58 9.03 0.03 NA 0.65 0.96 0.49 0.44 NA

01051 Elmore, AL Competitive 0.13 0.64 9.39 0.06 NA 0.97 1.07 0.51 0.77 NA

01055 Etowah, AL Transitional 0.13 0.54 11.87 0.05 0.09 0.92 0.9 0.64 0.7 0.8

01057 Fayette, AL Transitional 0.09 0.39 0.00 0.06 NA 0.66 0.65 0.00 0.87 NA

01059 Franklin, AL Transitional 0.12 0.47 10.38 0.07 NA 0.89 0.79 0.56 1.01 NA

01065 Hale, AL Distressed 0.06 0.76 0.00 0.03 0.06 0.45 1.27 0.00 0.38 0.51

01071 Jackson, AL Transitional 0.08 0.79 0.00 0.03 NA 0.54 1.31 0.00 0.45 NA

01073 Jefferson, AL Competitive 0.19 0.52 13.37 0.08 0.15 1.37 0.87 0.72 1.14 1.28

01075 Lamar, AL Transitional 0.11 0.48 9.51 0.07 NA 0.78 0.8 0.51 0.92 NA

01077 Lauderdale, AL Transitional 0.19 0.47 10.85 0.1 0.17 1.38 0.78 0.58 1.37 1.48

01079 Lawrence, AL Transitional 0.1 0.39 0.00 0.07 0.11 0.74 0.66 0.00 0.94 0.95

01083 Limestone, AL Transitional 0.18 0.5 4.85 0.09 0.18 1.32 0.83 0.26 1.25 1.56

01087 Macon, AL Distressed 0.1 0.58 0.00 0.07 0.08 0.73 0.97 0.00 1.01 0.66

01089 Madison, AL Attainment 0.17 0.53 20.16 0.08 0.14 1.24 0.88 1.09 1.11 1.24

01093 Marion, AL Transitional 0.1 0.79 5.59 0.03 NA 0.71 1.31 0.3 0.47 NA

01095 Marshall, AL Transitional 0.13 0.48 4.63 0.07 NA 0.91 0.8 0.25 0.9 NA

01103 Morgan, AL Competitive 0.14 0.57 11.53 0.07 0.16 1.02 0.95 0.62 0.95 1.35

01107 Pickens, AL At-Risk 0.22 0.17 7.66 0.21 0.25 1.56 0.28 0.41 2.93 2.15

245
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2010

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit 

Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods Index

01111 Randolph, AL Transitional 0.06 0.72 6.62 0.02 NA 0.41 1.2 0.36 0.31 NA

01115 St. Clair, AL Transitional 0.15 0.64 11.28 0.07 NA 1.06 1.06 0.61 0.98 NA

01117 Shelby, AL Attainment 0.24 0.53 18.2 0.11 0.08 1.7 0.88 0.98 1.49 0.71

01121 Talladega, AL Transitional 0.08 0.71 6.25 0.03 0.08 0.61 1.18 0.34 0.46 0.71

01123 Tallapoosa, AL Transitional 0.13 0.65 11.16 0.05 0.14 0.94 1.09 0.6 0.75 1.18

01125 Tuscaloosa, AL Transitional 0.2 0.44 5.27 0.11 0.19 1.43 0.74 0.28 1.56 1.69

01127 Walker, AL Transitional 0.2 0.29 7.29 0.11 0.18 1.46 0.48 0.39 1.55 1.54

01133 Winston, AL Transitional 0.07 0.62 5.48 0.03 NA 0.52 1.04 0.3 0.42 NA

13011 Banks, GA Transitional 0.12 0.63 8.04 0.08 NA 0.9 1.05 0.43 1.13 NA

13013 Barrow, GA Transitional 0.14 0.7 7 0.06 0.11 0.98 1.17 0.38 0.86 0.93

13015 Bartow, GA Transitional 0.14 0.62 11.85 0.07 0.12 1.02 1.04 0.64 0.98 1.05

13045 Carroll, GA Transitional 0.12 0.71 13.11 0.06 0.11 0.87 1.18 0.71 0.82 0.94

13047 Catoosa, GA Transitional 0.13 0.69 11.57 0.05 NA 0.96 1.14 0.62 0.69 NA

13055 Chattooga, GA At-Risk 0.15 0.49 0.00 0.11 NA 1.09 0.82 0.00 1.55 NA

13057 Cherokee, GA Attainment 0.17 0.72 19.47 0.07 0.17 1.21 1.2 1.05 0.91 1.51

13083 Dade, GA Transitional 0.1 0.75 10.41 0.04 0.07 0.72 1.25 0.56 0.5 0.59

13085 Dawson, GA Competitive 0.17 0.69 0.00 0.08 NA 1.21 1.15 0.00 1.16 NA

13097 Douglas, GA Transitional 0.11 0.74 11.17 0.05 0.09 0.81 1.24 0.6 0.67 0.82

13105 Elbert, GA At-Risk 0.09 0.71 12.42 0.03 NA 0.64 1.18 0.67 0.44 NA

13111 Fannin, GA Transitional 0.15 0.46 11.22 0.13 0.12 1.05 0.77 0.6 1.74 1.05

13115 Floyd, GA Transitional 0.14 0.55 4.68 0.08 0.15 1.03 0.92 0.25 1.1 1.28

13117 Forsyth, GA Attainment 0.22 0.73 24.28 0.07 NA 1.57 1.22 1.31 1.01 NA

13119 Franklin, GA Transitional 0.11 0.57 27.67 0.07 NA 0.79 0.96 1.49 0.91 NA

13123 Gilmer, GA Transitional 0.13 0.59 22.43 0.08 NA 0.92 0.98 1.21 1.18 NA

13129 Gordon, GA Transitional 0.2 0.38 45.77 0.15 NA 1.44 0.63 2.46 2.04 NA

13135 Gwinnett, GA Attainment 0.13 0.72 20.24 0.05 0.14 0.95 1.2 1.09 0.67 1.21

13137 Habersham, GA Transitional 0.13 0.56 3.17 0.08 NA 0.97 0.94 0.17 1.13 NA

13139 Hall, GA Transitional 0.16 0.62 11.53 0.07 0.13 1.14 1.03 0.62 1.04 1.14

13143 Haralson, GA Transitional 0.09 0.74 19.69 0.05 0.09 0.69 1.23 1.06 0.66 0.75

13147 Hart, GA At-Risk 0.15 0.6 17.89 0.09 NA 1.08 1 0.96 1.23 NA

13149 Heard, GA Transitional 0.08 0.7 0.00 0.05 0.07 0.59 1.16 0.00 0.66 0.64

13157 Jackson, GA Transitional 0.15 0.71 7.14 0.07 NA 1.09 1.19 0.38 0.92 NA

13187 Lumpkin, GA Transitional 0.19 0.62 14.25 0.11 NA 1.38 1.03 0.77 1.49 NA

13195 Madison, GA Transitional 0.16 0.56 6.09 0.1 NA 1.14 0.93 0.33 1.4 NA

246
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2010

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit 

Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods Index

13213 Murray, GA Transitional 0.12 0.63 11.2 0.05 NA 0.83 1.05 0.6 0.74 NA

13223 Paulding, GA Competitive 0.13 0.75 7.43 0.06 0.12 0.94 1.25 0.4 0.78 1.07

13227 Pickens, GA Competitive 0.14 0.75 11.16 0.06 0.13 0.99 1.26 0.6 0.86 1.1

13233 Polk, GA Transitional 0.08 0.67 0.00 0.04 NA 0.58 1.12 0.00 0.57 NA

13241 Rabun, GA Transitional 0.18 0.41 12.67 0.15 NA 1.28 0.68 0.68 2.05 NA

13257 Stephens, GA Transitional 0.12 0.62 28.31 0.06 NA 0.87 1.03 1.52 0.78 NA

13281 Towns, GA Transitional 0.18 0.39 7.98 0.16 NA 1.3 0.66 0.43 2.25 NA

13291 Union, GA Transitional 0.18 0.52 13.2 0.13 NA 1.27 0.86 0.71 1.8 NA

13295 Walker, GA Transitional 0.11 0.68 14.78 0.04 0.09 0.76 1.14 0.8 0.6 0.76

13311 White, GA Transitional 0.17 0.61 4.16 0.1 NA 1.23 1.02 0.22 1.37 NA

13313 Whitfield, GA Transitional 0.18 0.57 24.39 0.09 0.17 1.27 0.95 1.31 1.24 1.48

21001 Adair, KY Distressed 0.09 0.74 6.81 0.04 0.05 0.64 1.24 0.37 0.5 0.47

21011 Bath, KY Distressed 0.09 0.77 0.00 0.03 NA 0.65 1.29 0.00 0.44 NA

21013 Bell, KY Distressed 0.07 0.82 22.39 0.03 0.06 0.47 1.36 1.21 0.38 0.55

21019 Boyd, KY Transitional 0.12 0.56 11.1 0.06 0.09 0.86 0.93 0.6 0.84 0.78

21025 Breathitt, KY Distressed 0.05 0.89 0.00 0.02 0.02 0.39 1.49 0.00 0.28 0.21

21043 Carter, KY Distressed 0.07 0.77 5.82 0.03 NA 0.52 1.29 0.31 0.39 NA

21045 Casey, KY Distressed 0.08 0.9 0.00 0.03 0.07 0.55 1.5 0.00 0.42 0.58

21049 Clark, KY Transitional 0.14 0.48 7.93 0.09 0.12 1.04 0.8 0.43 1.21 1.07

21051 Clay, KY Distressed 0.09 0.49 0.00 0.06 0.07 0.62 0.81 0.00 0.8 0.6

21053 Clinton, KY Distressed 0.06 0.76 28.99 0.02 0.04 0.43 1.26 1.56 0.24 0.36

21057 Cumberland, KY Distressed 0.04 0.76 0.00 0.02 NA 0.27 1.27 0.00 0.22 NA

21061 Edmonson, KY At-Risk 0.13 0.41 14.22 0.11 0.15 0.95 0.69 0.77 1.47 1.32

21063 Elliott, KY Distressed 0.04 0.88 0.00 0.03 0.00 0.31 1.46 0.00 0.35 0.00

21065 Estill, KY Distressed 0.08 0.69 0.00 0.03 0.07 0.56 1.15 0.00 0.47 0.61

21069 Fleming, KY At-Risk 0.14 0.6 0.00 0.07 NA 0.99 0.99 0.00 1.04 NA

21071 Floyd, KY Distressed 0.12 0.63 3.9 0.06 0.06 0.85 1.05 0.21 0.87 0.53

21079 Garrard, KY Transitional 0.16 0.44 9.48 0.1 NA 1.18 0.74 0.51 1.4 NA

21087 Green, KY At-Risk 0.13 0.25 11.11 0.12 NA 0.95 0.42 0.6 1.65 NA

21089 Greenup, KY Transitional 0.1 0.66 0.00 0.05 NA 0.75 1.1 0.00 0.68 NA

21095 Harlan, KY Distressed 0.08 0.71 0.00 0.05 0.06 0.55 1.19 0.00 0.64 0.48

21099 Hart, KY Distressed 0.19 0.21 38.02 0.19 NA 1.39 0.35 2.05 2.66 NA

21109 Jackson, KY Distressed 0.05 0.56 14.75 0.02 0.04 0.38 0.93 0.79 0.31 0.33

21115 Johnson, KY Distressed 0.12 0.66 0.00 0.08 NA 0.9 1.1 0.00 1.09 NA

247
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2010

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit 

Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods Index

21119 Knott, KY Distressed 0.07 0.71 0.00 0.05 0.06 0.49 1.19 0.00 0.7 0.54

21121 Knox, KY Distressed 0.1 0.68 12.51 0.04 0.09 0.72 1.13 0.67 0.61 0.76

21125 Laurel, KY At-Risk 0.12 0.66 5.18 0.05 0.08 0.84 1.09 0.28 0.63 0.71

21127 Lawrence, KY Distressed 0.07 0.77 10.93 0.02 0.01 0.51 1.28 0.59 0.33 0.12

21129 Lee, KY Distressed 0.1 0.63 0.00 0.09 0.09 0.75 1.05 0.00 1.31 0.78

21131 Leslie, KY Distressed 0.03 0.8 0.00 0.02 0.02 0.21 1.33 0.00 0.23 0.22

21133 Letcher, KY Distressed 0.12 0.4 38.82 0.1 0.13 0.89 0.66 2.09 1.38 1.11

21135 Lewis, KY Distressed 0.08 0.77 0.00 0.04 0.03 0.59 1.29 0.00 0.62 0.25

21137 Lincoln, KY Distressed 0.06 0.72 13.71 0.03 NA 0.41 1.2 0.74 0.38 NA

21147 McCreary, KY Distressed 0.03 0.83 84.13 0.02 0.02 0.25 1.38 4.53 0.24 0.19

21151 Madison, KY Transitional 0.12 0.69 1.99 0.06 0.07 0.86 1.15 0.11 0.81 0.64

21153 Magoffin, KY Distressed 0.08 0.75 12.71 0.03 0.06 0.56 1.26 0.68 0.43 0.54

21159 Martin, KY Distressed 0.04 0.67 0.00 0.02 0.03 0.32 1.11 0.00 0.27 0.22

21165 Menifee, KY Distressed 0.05 0.76 0.00 0.03 0.07 0.37 1.26 0.00 0.39 0.57

21169 Metcalfe, KY Distressed 0.05 0.65 25.16 0.03 NA 0.39 1.09 1.35 0.4 NA

21171 Monroe, KY Distressed 0.13 0.68 68.97 0.07 0.09 0.93 1.13 3.71 0.99 0.79

21173 Montgomery, KY Transitional 0.24 0.39 51.4 0.19 NA 1.73 0.66 2.77 2.63 NA

21175 Morgan, KY Distressed 0.04 0.86 14.61 0.01 0.03 0.27 1.44 0.79 0.2 0.26

21181 Nicholas, KY At-Risk 0.06 0.56 0.00 0.03 0.09 0.44 0.93 0.00 0.47 0.74

21189 Owsley, KY Distressed 0.02 0.75 0.00 0.03 0.03 0.15 1.25 0.00 0.48 0.3

21193 Perry, KY Distressed 0.09 0.56 4.94 0.05 0.08 0.66 0.93 0.27 0.65 0.7

21195 Pike, KY At-Risk 0.18 0.55 51.75 0.12 0.14 1.28 0.91 2.79 1.71 1.23

21197 Powell, KY Distressed 0.3 0.18 12.8 0.26 NA 2.14 0.3 0.69 3.67 NA

21199 Pulaski, KY At-Risk 0.09 0.71 8.08 0.04 0.05 0.66 1.19 0.44 0.52 0.39

21201 Robertson, KY Distressed 0.09 0.8 0.00 0.06 NA 0.63 1.33 0.00 0.84 NA

21203 Rockcastle, KY Distressed 0.06 0.68 0.00 0.03 NA 0.44 1.14 0.00 0.44 NA

21205 Rowan, KY At-Risk 0.13 0.57 26.79 0.1 0.11 0.97 0.95 1.44 1.37 0.99

21207 Russell, KY Distressed 0.07 0.84 7.41 0.03 NA 0.53 1.4 0.4 0.37 NA

21231 Wayne, KY Distressed 0.07 0.95 8.72 0.02 0.06 0.49 1.58 0.47 0.3 0.48

21235 Whitley, KY Distressed 0.11 0.59 4.89 0.06 0.1 0.76 0.98 0.26 0.81 0.88

21237 Wolfe, KY Distressed 0.06 0.43 30.63 0.06 0.06 0.46 0.71 1.65 0.77 0.48

24001 Allegany, MD Transitional 0.15 0.54 34.04 0.07 0.12 1.05 0.89 1.83 1.01 1.05

24023 Garrett, MD Transitional 0.24 0.52 16.65 0.1 0.22 1.7 0.86 0.9 1.42 1.95

24043 Washington, MD Competitive 0.19 0.56 19.38 0.1 0.17 1.38 0.94 1.04 1.34 1.49

248
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2010

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit 

Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods Index

28003 Alcorn, MS At-Risk 0.1 0.41 0.00 0.05 NA 0.73 0.68 0.00 0.76 NA

28009 Benton, MS Distressed 0.05 0.68 0.00 0.03 NA 0.35 1.13 0.00 0.38 NA

28013 Calhoun, MS At-Risk 0.13 0.26 0.00 0.12 NA 0.92 0.44 0.00 1.65 NA

28017 Chickasaw, MS Distressed 0.16 0.26 9.66 0.1 NA 1.13 0.44 0.52 1.37 NA

28019 Choctaw, MS Distressed 0.06 0.48 0.00 0.04 NA 0.45 0.8 0.00 0.56 NA

28025 Clay, MS Distressed 0.14 0.31 0.00 0.11 0.11 0.98 0.52 0.00 1.49 0.91

28057 Itawamba, MS Transitional 0.09 0.29 9.35 0.06 NA 0.64 0.48 0.5 0.78 NA

28069 Kemper, MS Distressed 0.03 0.58 26.63 0.01 NA 0.23 0.97 1.43 0.15 NA

28081 Lee, MS Transitional 0.15 0.41 18.2 0.1 NA 1.05 0.68 0.98 1.41 NA

28087 Lowndes, MS At-Risk 0.19 0.3 7.68 0.13 0.13 1.36 0.5 0.41 1.84 1.1

28093 Marshall, MS Distressed 0.07 0.78 14.8 0.03 0.06 0.54 1.29 0.8 0.36 0.53

28095 Monroe, MS At-Risk 0.1 0.44 15.75 0.07 0.11 0.74 0.73 0.85 0.98 0.99

28097 Montgomery, MS Distressed 0.07 0.6 0.00 0.03 NA 0.47 1 0.00 0.48 NA

28103 Noxubee, MS Distressed 0.11 0.24 21.44 0.09 0.08 0.77 0.4 1.15 1.24 0.67

28105 Oktibbeha, MS At-Risk 0.15 0.33 14.62 0.11 NA 1.1 0.56 0.79 1.59 NA

28107 Panola, MS Distressed 0.05 0.72 2.83 0.02 0.01 0.38 1.2 0.15 0.3 0.06

28115 Pontotoc, MS Transitional 0.06 0.67 3.94 0.03 NA 0.47 1.12 0.21 0.44 NA

28117 Prentiss, MS At-Risk 0.07 0.39 8.26 0.04 NA 0.52 0.66 0.44 0.59 NA

28139 Tippah, MS At-Risk 0.05 0.61 0.00 0.02 NA 0.33 1.02 0.00 0.25 NA

28141 Tishomingo, MS At-Risk 0.07 0.62 11 0.03 NA 0.49 1.04 0.59 0.37 NA

28145 Union, MS Transitional 0.06 0.69 3.37 0.02 NA 0.41 1.16 0.18 0.29 NA

28155 Webster, MS Distressed 0.15 0.32 0.00 0.14 NA 1.11 0.53 0.00 1.91 NA

28159 Winston, MS Distressed 0.07 0.36 34.26 0.05 NA 0.53 0.61 1.85 0.73 NA

28161 Yalobusha, MS Distressed 0.04 0.58 0.00 0.03 NA 0.31 0.96 0.00 0.37 NA

36003 Allegany, NY At-Risk 0.18 0.62 32.15 0.09 0.13 1.32 1.03 1.73 1.22 1.08

36007 Broome, NY Transitional 0.19 0.66 35.06 0.1 0.18 1.35 1.11 1.89 1.34 1.56

36009 Cattaraugus, NY Transitional 0.2 0.66 0.00 0.08 0.17 1.46 1.1 0.00 1.16 1.43

36013 Chautauqua, NY Transitional 0.2 0.68 25.83 0.08 0.18 1.42 1.14 1.39 1.14 1.54

36015 Chemung, NY Transitional 0.14 0.71 71.43 0.06 0.13 1.02 1.19 3.85 0.81 1.1

36017 Chenango, NY Transitional 0.14 0.53 12.81 0.08 0.13 1 0.89 0.69 1.08 1.1

36023 Cortland, NY Transitional 0.2 0.6 35.95 0.08 NA 1.41 1 1.94 1.07 NA

36025 Delaware, NY Transitional 0.11 0.7 5.54 0.05 NA 0.82 1.17 0.3 0.63 NA

36077 Otsego, NY Transitional 0.15 0.6 8.15 0.07 0.33 1.05 1 0.44 0.97 2.89

36095 Schoharie, NY Transitional 0.14 0.76 25.83 0.06 0.13 1.01 1.27 1.39 0.83 1.16

249
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2010

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit 

Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods Index

36097 Schuyler, NY Transitional 0.14 0.67 86.28 0.07 NA 1.02 1.12 4.65 0.93 NA

36101 Steuben, NY Transitional 0.17 0.58 88.32 0.09 0.23 1.2 0.97 4.76 1.29 1.96

36107 Tioga, NY Transitional 0.12 0.74 23.15 0.05 NA 0.85 1.23 1.25 0.74 NA

36109 Tompkins, NY Transitional 0.2 0.65 16.32 0.1 0.23 1.48 1.09 0.88 1.34 1.96

37003 Alexander, NC Transitional 0.18 0.52 0.00 0.12 NA 1.32 0.87 0.00 1.65 NA

37005 Alleghany, NC Transitional 0.13 0.59 0.00 0.08 NA 0.91 0.99 0.00 1.12 NA

37009 Ashe, NC Transitional 0.17 0.32 15.76 0.13 0.14 1.2 0.53 0.85 1.86 1.24

37011 Avery, NC Transitional 0.25 0.41 21.16 0.19 NA 1.8 0.68 1.14 2.59 NA

37021 Buncombe, NC Transitional 0.18 0.56 21.05 0.1 0.19 1.32 0.93 1.13 1.39 1.62

37023 Burke, NC Transitional 0.15 0.48 11.51 0.09 0.12 1.11 0.79 0.62 1.28 1.02

37027 Caldwell, NC Transitional 0.14 0.53 7.21 0.08 0.11 1.04 0.88 0.39 1.08 0.95

37039 Cherokee, NC At-Risk 0.16 0.53 4.57 0.12 0.16 1.17 0.89 0.25 1.63 1.42

37043 Clay, NC Transitional 0.16 0.65 21.74 0.1 NA 1.13 1.08 1.17 1.4 NA

37059 Davie, NC Competitive 0.2 0.56 20.55 0.1 0.19 1.42 0.93 1.11 1.45 1.61

37067 Forsyth, NC Competitive 0.19 0.54 20.63 0.1 0.15 1.35 0.9 1.11 1.4 1.3

37075 Graham, NC At-Risk 0.33 0.34 0.00 0.3 0.36 2.35 0.56 0.00 4.16 3.1

37087 Haywood, NC Transitional 0.14 0.58 0.00 0.08 0.12 1.03 0.96 0.00 1.07 1.05

37089 Henderson, NC Competitive 0.19 0.55 13.91 0.1 0.19 1.37 0.93 0.75 1.4 1.61

37099 Jackson, NC Transitional 0.17 0.48 11.1 0.11 NA 1.19 0.8 0.6 1.5 NA

37111 McDowell, NC Transitional 0.16 0.64 5 0.06 0.12 1.13 1.06 0.27 0.89 1.02

37113 Macon, NC Transitional 0.16 0.45 3.16 0.1 NA 1.18 0.75 0.17 1.36 NA

37115 Madison, NC Transitional 0.16 0.41 8.5 0.11 0.15 1.15 0.68 0.46 1.54 1.32

37121 Mitchell, NC At-Risk 0.16 0.5 0.00 0.11 0.18 1.15 0.83 0.00 1.49 1.55

37149 Polk, NC Competitive 0.16 0.71 13.61 0.07 NA 1.18 1.18 0.73 0.91 NA

37161 Rutherford, NC At-Risk 0.18 0.48 0.00 0.11 0.15 1.29 0.81 0.00 1.59 1.33

37169 Stokes, NC Transitional 0.18 0.54 4.79 0.11 0.17 1.29 0.91 0.26 1.59 1.49

37171 Surry, NC Transitional 0.16 0.59 80.15 0.08 0.09 1.12 0.99 4.32 1.14 0.8

37173 Swain, NC At-Risk 0.11 0.48 18.96 0.09 0.07 0.82 0.81 1.02 1.28 0.57

37175 Transylvania, NC Transitional 0.17 0.54 12.91 0.09 NA 1.21 0.89 0.7 1.19 NA

37189 Watauga, NC Transitional 0.2 0.42 25.59 0.14 NA 1.45 0.71 1.38 1.87 NA

37193 Wilkes, NC Transitional 0.17 0.42 10.49 0.1 NA 1.2 0.7 0.56 1.43 NA

37197 Yadkin, NC Transitional 0.16 0.46 14.68 0.1 NA 1.14 0.77 0.79 1.38 NA

37199 Yancey, NC At-Risk 0.26 0.34 0.00 0.22 NA 1.9 0.56 0.00 3.01 NA

39001 Adams, OH At-Risk 0.07 0.73 6.16 0.04 0.04 0.5 1.22 0.33 0.5 0.38

250
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2010

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit 

Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods Index

39007 Ashtabula, OH Transitional 0.12 0.68 17.36 0.05 0.1 0.9 1.14 0.93 0.75 0.84

39009 Athens, OH Distressed 0.09 0.77 9.35 0.04 0.05 0.65 1.28 0.5 0.52 0.47

39013 Belmont, OH Transitional 0.14 0.45 29.71 0.08 0.13 1.02 0.74 1.6 1.04 1.14

39015 Brown, OH Transitional 0.12 0.64 8.45 0.07 0.1 0.85 1.07 0.45 0.95 0.85

39019 Carroll, OH Transitional 0.17 0.52 25.48 0.09 0.12 1.25 0.87 1.37 1.26 1.02

39025 Clermont, OH Competitive 0.21 0.63 35.35 0.1 0.18 1.54 1.05 1.9 1.38 1.53

39029 Columbiana, OH Transitional 0.16 0.53 18.02 0.09 0.12 1.17 0.88 0.97 1.26 1.07

39031 Coshocton, OH Transitional 0.1 0.63 0.00 0.04 0.1 0.69 1.05 0.00 0.53 0.86

39053 Gallia, OH At-Risk 0.07 0.77 17.09 0.03 0.06 0.49 1.29 0.92 0.48 0.49

39059 Guernsey, OH At-Risk 0.15 0.62 3.94 0.09 0.15 1.08 1.03 0.21 1.29 1.34

39067 Harrison, OH Transitional 0.12 0.56 0.00 0.09 0.1 0.87 0.93 0.00 1.21 0.9

39071 Highland, OH Transitional 0.09 0.64 23.41 0.05 0.05 0.65 1.07 1.26 0.65 0.43

39073 Hocking, OH Transitional 0.1 0.61 29.89 0.05 NA 0.69 1.01 1.61 0.71 NA

39075 Holmes, OH Transitional 0.2 0.54 31.09 0.09 NA 1.47 0.91 1.67 1.31 NA

39079 Jackson, OH At-Risk 0.06 0.59 0.00 0.02 0.04 0.44 0.98 0.00 0.33 0.32

39081 Jefferson, OH At-Risk 0.17 0.49 14.56 0.09 0.17 1.23 0.81 0.78 1.21 1.49

39087 Lawrence, OH At-Risk 0.13 0.52 0.00 0.08 0.06 0.91 0.86 0.00 1.1 0.55

39099 Mahoning, OH Transitional 0.18 0.55 20.47 0.09 0.15 1.32 0.92 1.1 1.21 1.27

39105 Meigs, OH Distressed 0.07 0.75 0.00 0.04 0.06 0.51 1.25 0.00 0.49 0.51

39111 Monroe, OH Distressed 0.09 0.59 12.8 0.05 0.06 0.65 0.98 0.69 0.69 0.54

39115 Morgan, OH Distressed 0.07 0.78 0.00 0.03 0.07 0.48 1.31 0.00 0.4 0.59

39119 Muskingum, OH Transitional 0.16 0.53 27.27 0.08 0.16 1.15 0.89 1.47 1.15 1.35

39121 Noble, OH At-Risk 0.13 0.71 14.01 0.06 NA 0.94 1.18 0.75 0.88 NA

39127 Perry, OH At-Risk 0.09 0.58 0.00 0.05 0.06 0.67 0.97 0.00 0.65 0.55

39131 Pike, OH Distressed 0.06 0.7 11.99 0.04 0.05 0.46 1.16 0.65 0.49 0.39

39141 Ross, OH Transitional 0.11 0.51 30.05 0.06 0.09 0.76 0.85 1.62 0.78 0.8

39145 Scioto, OH At-Risk 0.13 0.62 22.61 0.06 0.13 0.97 1.04 1.22 0.83 1.11

39155 Trumbull, OH Transitional 0.18 0.6 30.91 0.09 0.17 1.32 1 1.66 1.19 1.48

39157 Tuscarawas, OH Transitional 0.16 0.59 26.68 0.07 0.11 1.14 0.98 1.44 0.99 0.96

39163 Vinton, OH Distressed 0.06 0.63 0.00 0.03 0.06 0.43 1.06 0.00 0.41 0.5

39167 Washington, OH Transitional 0.13 0.62 12.62 0.07 0.13 0.95 1.04 0.68 0.94 1.17

42003 Allegheny, PA Competitive 0.22 0.45 17.71 0.12 0.19 1.58 0.74 0.95 1.71 1.61

42005 Armstrong, PA Transitional 0.16 0.47 12.16 0.09 0.12 1.19 0.79 0.65 1.19 1.06

42007 Beaver, PA Transitional 0.19 0.5 24.1 0.1 0.17 1.4 0.83 1.3 1.37 1.45

251
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2010

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit 

Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods Index

42009 Bedford, PA Transitional 0.16 0.51 6.45 0.1 0.09 1.19 0.86 0.35 1.32 0.8

42013 Blair, PA Transitional 0.2 0.49 12.42 0.1 0.15 1.47 0.81 0.67 1.44 1.28

42015 Bradford, PA Transitional 0.21 0.49 32.04 0.14 NA 1.51 0.82 1.73 1.94 NA

42019 Butler, PA Competitive 0.22 0.51 14.14 0.12 0.18 1.57 0.85 0.76 1.61 1.6

42021 Cambria, PA Transitional 0.17 0.38 9.07 0.1 0.17 1.22 0.63 0.49 1.44 1.43

42023 Cameron, PA Transitional 0.19 0.38 28.65 0.14 NA 1.35 0.64 1.54 1.97 NA

42025 Carbon, PA Transitional 0.15 0.62 20.36 0.07 0.14 1.06 1.03 1.1 1 1.2

42027 Centre, PA Transitional 0.2 0.52 17.23 0.12 0.16 1.41 0.87 0.93 1.62 1.37

42031 Clarion, PA Transitional 0.19 0.34 4.12 0.13 NA 1.37 0.56 0.22 1.77 NA

42033 Clearfield, PA Transitional 0.28 0.31 7.11 0.22 0.24 2.03 0.51 0.38 2.99 2.07

42035 Clinton, PA Transitional 0.15 0.63 5.26 0.08 0.14 1.08 1.04 0.28 1.04 1.21

42037 Columbia, PA Transitional 0.19 0.6 8.36 0.11 0.19 1.34 1 0.45 1.56 1.66

42039 Crawford, PA Transitional 0.24 0.42 14.96 0.15 0.22 1.72 0.7 0.81 2.05 1.93

42047 Elk, PA Competitive 0.24 0.33 0.00 0.15 NA 1.74 0.55 0.00 2.14 NA

42049 Erie, PA Transitional 0.23 0.37 10.22 0.15 0.19 1.68 0.62 0.55 2.07 1.67

42051 Fayette, PA At-Risk 0.22 0.33 8.87 0.16 0.2 1.61 0.55 0.48 2.18 1.71

42053 Forest, PA Distressed 0.15 0.4 0.00 0.1 NA 1.05 0.67 0.00 1.33 NA

42057 Fulton, PA Transitional 0.15 0.44 10.74 0.11 NA 1.09 0.73 0.58 1.54 NA

42059 Greene, PA Transitional 0.15 0.55 15.02 0.07 NA 1.1 0.91 0.81 1.04 NA

42061 Huntingdon, PA Transitional 0.15 0.52 0.00 0.1 0.05 1.1 0.86 0.00 1.35 0.4

42063 Indiana, PA Transitional 0.24 0.34 0.00 0.14 0.22 1.75 0.56 0.00 1.98 1.91

42065 Jefferson, PA Transitional 0.3 0.24 3.81 0.19 0.25 2.17 0.41 0.21 2.69 2.13

42067 Juniata, PA Transitional 0.22 0.47 20 0.16 NA 1.6 0.78 1.08 2.2 NA

42069 Lackawanna, PA Transitional 0.21 0.5 22.41 0.12 0.18 1.52 0.84 1.21 1.71 1.59

42073 Lawrence, PA Transitional 0.19 0.41 11.87 0.09 0.19 1.41 0.69 0.64 1.29 1.65

42079 Luzerne, PA Transitional 0.21 0.49 21.38 0.12 0.14 1.51 0.81 1.15 1.73 1.23

42081 Lycoming, PA Transitional 0.16 0.66 16.16 0.06 0.18 1.15 1.09 0.87 0.89 1.55

42083 McKean, PA Transitional 0.28 0.4 8.19 0.19 0.21 2 0.66 0.44 2.65 1.86

42085 Mercer, PA Transitional 0.17 0.44 24.81 0.09 0.15 1.23 0.73 1.34 1.27 1.31

42087 Mifflin, PA Transitional 0.14 0.64 0.00 0.06 0.08 0.98 1.06 0.00 0.88 0.73

42089 Monroe, PA Transitional 0.2 0.55 12.59 0.09 NA 1.45 0.92 0.68 1.27 NA

42093 Montour, PA Competitive 0.17 0.56 20.04 0.1 NA 1.23 0.93 1.08 1.41 NA

42097 Northumberland, PA Transitional 0.16 0.57 18.46 0.08 0.15 1.18 0.96 0.99 1.04 1.34

42099 Perry, PA Competitive 0.15 0.57 4.64 0.08 0.09 1.06 0.95 0.25 1.09 0.81

252
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2010

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit 

Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods Index

42103 Pike, PA Transitional 0.18 0.77 9.39 0.07 0.14 1.33 1.28 0.51 0.91 1.24

42105 Potter, PA Transitional 0.2 0.4 33.73 0.14 0.19 1.45 0.67 1.82 1.88 1.69

42107 Schuylkill, PA Transitional 0.18 0.6 17.06 0.08 0.13 1.28 1 0.92 1.17 1.14

42109 Snyder, PA Transitional 0.2 0.57 20.24 0.12 NA 1.43 0.94 1.09 1.63 NA

42111 Somerset, PA Transitional 0.23 0.35 10.94 0.15 0.18 1.64 0.59 0.59 2.08 1.56

42113 Sullivan, PA Transitional 0.11 0.65 27.78 0.06 NA 0.8 1.08 1.5 0.83 NA

42115 Susquehanna, PA Transitional 0.19 0.6 18.63 0.1 NA 1.36 1.01 1 1.42 NA

42117 Tioga, PA Transitional 0.2 0.53 24.89 0.12 NA 1.48 0.88 1.34 1.72 NA

42119 Union, PA Transitional 0.17 0.61 16.94 0.07 NA 1.2 1.01 0.91 0.99 NA

42121 Venango, PA Transitional 0.15 0.4 3.31 0.08 0.17 1.1 0.67 0.18 1.13 1.51

42123 Warren, PA Transitional 0.29 0.39 9.2 0.17 NA 2.07 0.66 0.5 2.35 NA

42125 Washington, PA Competitive 0.19 0.47 17.86 0.1 0.16 1.36 0.78 0.96 1.39 1.36

42127 Wayne, PA Transitional 0.14 0.79 14.49 0.06 NA 1 1.32 0.78 0.84 NA

42129 Westmoreland, PA Competitive 0.22 0.4 13.17 0.13 0.17 1.62 0.67 0.71 1.84 1.49

42131 Wyoming, PA Transitional 0.2 0.54 40.43 0.11 NA 1.47 0.9 2.18 1.53 NA

45007 Anderson, SC Transitional 0.14 0.64 9.76 0.06 0.1 1 1.07 0.53 0.88 0.89

45021 Cherokee, SC At-Risk 0.13 0.69 3.86 0.05 NA 0.92 1.16 0.21 0.69 NA

45045 Greenville, SC Transitional 0.2 0.62 14.09 0.1 0.14 1.44 1.03 0.76 1.35 1.25

45073 Oconee, SC Transitional 0.14 0.7 2.35 0.07 NA 1.01 1.16 0.13 0.92 NA

45077 Pickens, SC Transitional 0.13 0.66 6.74 0.07 0.15 0.96 1.09 0.36 0.91 1.31

45083 Spartanburg, SC Transitional 0.16 0.64 9.7 0.07 0.13 1.15 1.06 0.52 1.03 1.16

47001 Anderson, TN Transitional 0.14 0.6 17.54 0.06 0.12 1.01 1 0.94 0.9 1.04

47007 Bledsoe, TN At-Risk 0.06 0.86 0.00 0.02 NA 0.41 1.44 0.00 0.33 NA

47009 Blount, TN Transitional 0.18 0.57 15.14 0.09 0.16 1.31 0.95 0.82 1.28 1.4

47011 Bradley, TN Transitional 0.14 0.56 6.37 0.07 0.14 1.04 0.93 0.34 0.98 1.21

47013 Campbell, TN At-Risk 0.06 0.87 4.67 0.03 0.06 0.44 1.45 0.25 0.35 0.5

47015 Cannon, TN Transitional 0.09 0.57 0.00 0.05 0.09 0.67 0.95 0.00 0.64 0.75

47019 Carter, TN At-Risk 0.11 0.66 8.11 0.05 0.08 0.79 1.1 0.44 0.73 0.69

47025 Claiborne, TN At-Risk 0.05 0.81 6.63 0.03 0.03 0.37 1.35 0.36 0.37 0.29

47027 Clay, TN Distressed 0.1 0.38 0.00 0.08 0.06 0.74 0.64 0.00 1.06 0.56

47029 Cocke, TN Distressed 0.1 0.75 16.83 0.04 0.08 0.69 1.26 0.91 0.59 0.73

47031 Coffee, TN Transitional 0.08 0.84 2.8 0.03 0.08 0.57 1.4 0.15 0.4 0.67

47035 Cumberland, TN Transitional 0.11 0.71 2.88 0.05 NA 0.77 1.19 0.16 0.63 NA

47041 DeKalb, TN Transitional 0.08 0.52 0.00 0.05 NA 0.57 0.86 0.00 0.69 NA

253
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2010

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit 

Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods Index

47049 Fentress, TN Distressed 0.07 0.8 0.00 0.02 0.05 0.51 1.33 0.00 0.32 0.47

47051 Franklin, TN Transitional 0.09 0.81 0.00 0.03 NA 0.64 1.35 0.00 0.47 NA

47057 Grainger, TN At-Risk 0.1 0.72 9.65 0.04 NA 0.7 1.2 0.52 0.52 NA

47059 Greene, TN Transitional 0.11 0.6 2.56 0.05 0.15 0.78 1 0.14 0.63 1.3

47061 Grundy, TN Distressed 0.07 0.82 0.00 0.05 0.07 0.51 1.36 0.00 0.62 0.64

47063 Hamblen, TN Transitional 0.14 0.6 19.1 0.07 0.13 1.01 1 1.03 0.94 1.16

47065 Hamilton, TN Transitional 0.15 0.54 17.45 0.07 0.16 1.11 0.91 0.94 0.93 1.4

47067 Hancock, TN Distressed 0.04 0.89 0.00 0.01 0.03 0.26 1.48 0.00 0.07 0.22

47073 Hawkins, TN Transitional 0.12 0.57 12.82 0.08 0.08 0.89 0.95 0.69 1.08 0.69

47087 Jackson, TN At-Risk 0.17 0.25 32.47 0.16 0.09 1.24 0.41 1.75 2.16 0.82

47089 Jefferson, TN Transitional 0.14 0.6 7.61 0.08 NA 1.04 1 0.41 1.06 NA

47091 Johnson, TN Distressed 0.1 0.73 0.00 0.04 0.1 0.72 1.22 0.00 0.55 0.85

47093 Knox, TN Competitive 0.18 0.54 14.86 0.08 0.16 1.33 0.9 0.8 1.12 1.42

47099 Lawrence, TN Distressed 0.08 0.72 11.4 0.04 NA 0.55 1.2 0.61 0.54 NA

47101 Lewis, TN At-Risk 0.08 0.75 63.61 0.03 NA 0.54 1.24 3.43 0.48 NA

47105 Loudon, TN Competitive 0.17 0.64 6.85 0.08 NA 1.23 1.06 0.37 1.17 NA

47107 McMinn, TN Transitional 0.12 0.62 12.28 0.06 0.14 0.85 1.03 0.66 0.81 1.22

47111 Macon, TN Transitional 0.17 0.32 0.00 0.15 0.15 1.2 0.54 0.00 2.01 1.33

47115 Marion, TN Transitional 0.06 0.71 0.00 0.03 0.05 0.47 1.18 0.00 0.44 0.43

47121 Meigs, TN At-Risk 0.08 0.58 0.00 0.04 NA 0.61 0.97 0.00 0.62 NA

47123 Monroe, TN Transitional 0.1 0.5 0.00 0.06 NA 0.73 0.84 0.00 0.79 NA

47129 Morgan, TN At-Risk 0.09 0.79 11.92 0.02 0.05 0.65 1.31 0.64 0.33 0.42

47133 Overton, TN At-Risk 0.08 0.7 21.98 0.04 0.08 0.57 1.17 1.18 0.52 0.73

47137 Pickett, TN Distressed 0.06 0.77 26.81 0.01 NA 0.43 1.29 1.44 0.19 NA

47139 Polk, TN Transitional 0.11 0.57 76.42 0.07 0.11 0.78 0.94 4.12 1.03 0.98

47141 Putnam, TN Transitional 0.14 0.5 9.93 0.08 0.16 1.02 0.84 0.53 1.08 1.38

47143 Rhea, TN At-Risk 0.11 0.84 0.00 0.04 NA 0.76 1.4 0.00 0.51 NA

47145 Roane, TN Transitional 0.12 0.64 3.44 0.06 NA 0.84 1.06 0.19 0.77 NA

47151 Scott, TN Distressed 0.05 0.85 0.00 0.02 0.05 0.37 1.42 0.00 0.21 0.47

47153 Sequatchie, TN Transitional 0.06 0.73 11.26 0.03 0.05 0.4 1.22 0.61 0.38 0.43

47155 Sevier, TN Transitional 0.14 0.61 2.55 0.07 NA 1 1.02 0.14 0.95 NA

47159 Smith, TN Transitional 0.09 0.36 8.53 0.07 0.08 0.67 0.6 0.46 1.03 0.65

47163 Sullivan, TN Transitional 0.18 0.51 18.28 0.09 0.2 1.31 0.86 0.98 1.19 1.75

47171 Unicoi, TN Transitional 0.16 0.46 0.00 0.12 NA 1.18 0.76 0.00 1.6 NA

254
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2010

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit 

Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods Index

47173 Union, TN At-Risk 0.1 0.61 0.00 0.05 0.09 0.69 1.01 0.00 0.73 0.82

47175 Van Buren, TN At-Risk 0.05 0.63 0.00 0.03 NA 0.39 1.04 0.00 0.46 NA

47177 Warren, TN At-Risk 0.08 0.69 17.74 0.03 NA 0.55 1.14 0.96 0.36 NA

47179 Washington, TN Transitional 0.19 0.46 13.21 0.1 0.13 1.34 0.76 0.71 1.4 1.1

47185 White, TN At-Risk 0.08 0.74 0.00 0.04 NA 0.6 1.24 0.00 0.59 NA

51005 Alleghany + Clifton Forge 

city + Covington city, VA Transitional 0.1 0.65 0.00 0.06 NA 0.75 1.08 0.00 0.85 NA

51017 Bath, VA Competitive 0.14 0.73 173.91 0.09 NA 1.04 1.21 9.37 1.21 NA

51021 Bland, VA Transitional 0.13 0.55 0.00 0.08 NA 0.97 0.92 0.00 1.1 NA

51023 Botetourt, VA Attainment 0.12 0.63 9.22 0.05 NA 0.88 1.04 0.5 0.68 NA

51027 Buchanan, VA At-Risk 0.08 0.61 0.00 0.03 0.05 0.59 1.02 0.00 0.46 0.4

51035 Carroll + Galax city, VA Transitional 0.13 0.61 4.37 0.06 NA 0.92 1.02 0.24 0.88 NA

51045 Craig, VA Transitional 0.16 0.86 0.00 0.08 NA 1.18 1.44 0.00 1.05 NA

51051 Dickenson, VA Distressed 0.22 0.42 15.22 0.14 0.21 1.61 0.7 0.82 1.93 1.78

51063 Floyd, VA Transitional 0.17 0.64 9.7 0.1 NA 1.22 1.06 0.52 1.33 NA

51071 Giles, VA Transitional 0.15 0.64 20.24 0.09 NA 1.08 1.06 1.09 1.22 NA

51077 Grayson, VA Transitional 0.11 0.58 0.00 0.06 NA 0.76 0.97 0.00 0.81 NA

51089 Henry + Martinsville city, 

VA Transitional 0.14 0.47 7.52 0.09 0.17 1.03 0.79 0.41 1.25 1.51

51091 Highland, VA Transitional 0.08 0.82 0.00 0.05 NA 0.6 1.36 0.00 0.72 NA

51105 Lee, VA At-Risk 0.07 0.75 0.00 0.04 0.05 0.48 1.26 0.00 0.52 0.46

51121 Montgomery + Radford 

city, VA Transitional 0.14 0.64 13.64 0.08 0.06 0.99 1.06 0.73 1.08 0.51

51141 Patrick, VA Transitional 0.14 0.55 65.48 0.08 NA 0.98 0.92 3.53 1.15 NA

51155 Pulaski, VA Transitional 0.13 0.64 26.84 0.07 0.15 0.96 1.07 1.45 0.91 1.31

51163 Rockbridge + Buena Vista 

city + Lexington city, VA Transitional 0.16 0.58 12.51 0.1 NA 1.12 0.97 0.67 1.32 NA

51167 Russell, VA At-Risk 0.23 0.54 15.76 0.14 0.15 1.66 0.89 0.85 1.93 1.33

51169 Scott, VA At-Risk 0.14 0.38 21.39 0.11 0.18 1.04 0.63 1.15 1.57 1.56

51173 Smyth, VA Transitional 0.19 0.43 5.82 0.14 0.12 1.39 0.72 0.31 1.98 1.05

51185 Tazewell, VA Transitional 0.16 0.6 3.56 0.07 0.14 1.14 1 0.19 1.01 1.17

51191 Washington + Bristol city, 

VA Transitional 0.22 0.43 1.87 0.15 0.11 1.58 0.72 0.1 2.13 0.95

51195 Wise + Norton city, VA At-Risk 0.16 0.49 4.02 0.1 0.15 1.19 0.81 0.22 1.36 1.29

51197 Wythe, VA Transitional 0.15 0.47 10.28 0.1 NA 1.08 0.79 0.55 1.45 NA

255
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2010

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit 

Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods Index

54001 Barbour, WV At-Risk 0.09 0.74 0.00 0.05 NA 0.65 1.24 0.00 0.76 NA

54003 Berkeley, WV Transitional 0.18 0.67 17.45 0.11 0.15 1.3 1.12 0.94 1.56 1.28

54005 Boone, WV At-Risk 0.11 0.6 27.29 0.06 0.08 0.76 1 1.47 0.81 0.7

54007 Braxton, WV Distressed 0.08 0.74 12.92 0.05 NA 0.61 1.23 0.7 0.73 NA

54009 Brooke, WV Transitional 0.1 0.56 0.00 0.05 NA 0.71 0.93 0.00 0.63 NA

54011 Cabell, WV Transitional 0.17 0.57 26.68 0.1 0.15 1.24 0.95 1.44 1.36 1.28

54013 Calhoun, WV Distressed 0.05 0.83 0.00 0.01 0.04 0.4 1.39 0.00 0.2 0.36

54015 Clay, WV Distressed 0.05 0.76 0.00 0.02 0.03 0.35 1.27 0.00 0.24 0.29

54017 Doddridge, WV At-Risk 0.2 0.69 0.00 0.05 NA 1.44 1.15 0.00 0.66 NA

54019 Fayette, WV At-Risk 0.21 0.58 39.35 0.13 0.18 1.51 0.96 2.12 1.84 1.56

54021 Gilmer, WV At-Risk 0.15 0.42 0.00 0.11 NA 1.1 0.69 0.00 1.55 NA

54023 Grant, WV Transitional 0.2 0.34 33.56 0.13 NA 1.48 0.56 1.81 1.74 NA

54025 Greenbrier, WV At-Risk 0.18 0.54 13.66 0.13 NA 1.29 0.9 0.74 1.75 NA

54027 Hampshire, WV Transitional 0.1 0.8 9.22 0.05 0.06 0.73 1.33 0.5 0.63 0.56

54029 Hancock, WV Transitional 0.1 0.53 12.53 0.05 NA 0.73 0.88 0.67 0.7 NA

54031 Hardy, WV Transitional 0.13 0.47 26.88 0.09 NA 0.96 0.79 1.45 1.24 NA

54033 Harrison, WV Transitional 0.18 0.55 29.1 0.11 0.22 1.3 0.91 1.57 1.49 1.93

54035 Jackson, WV Transitional 0.17 0.46 14.73 0.12 NA 1.2 0.77 0.79 1.65 NA

54037 Jefferson, WV Competitive 0.16 0.7 3.5 0.08 0.14 1.12 1.17 0.19 1.15 1.18

54039 Kanawha, WV Transitional 0.2 0.51 27.28 0.12 0.19 1.44 0.86 1.47 1.6 1.65

54041 Lewis, WV At-Risk 0.09 0.6 0.00 0.07 NA 0.68 1 0.00 0.96 NA

54043 Lincoln, WV Distressed 0.1 0.61 0.00 0.06 0.06 0.69 1.01 0.00 0.86 0.54

54045 Logan, WV At-Risk 0.13 0.69 0.00 0.05 0.1 0.95 1.14 0.00 0.67 0.89

54047 McDowell, WV Distressed 0.06 0.63 0.00 0.03 0.05 0.4 1.05 0.00 0.44 0.44

54049 Marion, WV Transitional 0.15 0.6 25.92 0.09 0.11 1.12 0.99 1.4 1.3 0.96

54051 Marshall, WV Transitional 0.13 0.57 30.79 0.09 0.09 0.92 0.95 1.66 1.3 0.8

54053 Mason, WV At-Risk 0.1 0.7 0.00 0.07 NA 0.72 1.17 0.00 0.95 NA

54055 Mercer, WV At-Risk 0.24 0.4 9.86 0.16 0.09 1.71 0.66 0.53 2.27 0.82

54057 Mineral, WV Transitional 0.16 0.57 0.00 0.1 0.14 1.16 0.96 0.00 1.42 1.19

54059 Mingo, WV Distressed 0.14 0.67 0.00 0.07 0.11 0.99 1.11 0.00 0.97 0.97

54061 Monongalia, WV Transitional 0.25 0.5 17.87 0.17 0.22 1.79 0.84 0.96 2.41 1.9

54063 Monroe, WV At-Risk 0.15 0.6 0.00 0.1 NA 1.08 1.01 0.00 1.34 NA

54065 Morgan, WV Transitional 0.19 0.57 0.00 0.15 0.09 1.41 0.95 0.00 2.02 0.79

54067 Nicholas, WV At-Risk 0.23 0.44 0.00 0.15 NA 1.69 0.73 0.00 2.12 NA

256
FIPS Appalachian County

ARC’s 

Economic 

Status, FY 

2010

Ratios Indices (U.S. = 1) Indices 

(Appalachia=1)

Small 

Business 

Lending 

Ratio

Credit 

Card 

Business 

Lending 

Ratio

SBA 7a 

Lending 

Ratio

Lending to 

Smallest 

Business 

Ratio

Small Business 

Lending in LMI 

Neighborhoods 

Ratio

Small 

Business 

Lending 

Index

Credit Card 

Business 

Lending 

Index

SBA 7a 

Lending 

Index

Lending to 

Smallest 

Business 

Index

Small Business 

Lending in LMI 

Neighborhoods Index

54069 Ohio, WV Transitional 0.18 0.61 55.04 0.08 0.17 1.31 1.01 2.96 1.07 1.47

54071 Pendleton, WV Transitional 0.09 0.71 0.00 0.03 NA 0.63 1.18 0.00 0.45 NA

54073 Pleasants, WV Transitional 0.05 0.76 26.04 0.03 NA 0.4 1.27 1.4 0.38 NA

54075 Pocahontas, WV At-Risk 0.16 0.59 18.32 0.08 NA 1.16 0.98 0.99 1.14 NA

54077 Preston, WV Transitional 0.19 0.62 0.00 0.14 0.17 1.4 1.03 0.00 1.91 1.49

54079 Putnam, WV Competitive 0.26 0.52 22.59 0.16 NA 1.86 0.87 1.22 2.15 NA

54081 Raleigh, WV Transitional 0.23 0.54 7.43 0.13 NA 1.65 0.9 0.4 1.84 NA

54083 Randolph, WV Transitional 0.1 0.7 6.2 0.05 NA 0.73 1.16 0.33 0.75 NA

54085 Ritchie, WV At-Risk 0.11 0.45 54.15 0.12 NA 0.81 0.75 2.92 1.64 NA

54087 Roane, WV Distressed 0.09 0.8 0.00 0.03 NA 0.66 1.33 0.00 0.36 NA

54089 Summers, WV Distressed 0.13 0.48 0.00 0.09 0.08 0.96 0.8 0.00 1.29 0.71

54091 Taylor, WV At-Risk 0.15 0.61 0.00 0.13 NA 1.11 1.02 0.00 1.86 NA

54093 Tucker, WV At-Risk 0.06 0.62 43.67 0.04 NA 0.46 1.03 2.35 0.59 NA

54095 Tyler, WV At-Risk 0.08 0.58 0.00 0.05 NA 0.58 0.97 0.00 0.73 NA

54097 Upshur, WV At-Risk 0.18 0.47 0.00 0.12 NA 1.33 0.79 0.00 1.64 NA

54099 Wayne, WV At-Risk 0.16 0.54 14.27 0.09 0.03 1.17 0.9 0.77 1.3 0.26

54101 Webster, WV Distressed 0.15 0.36 0.00 0.13 0.27 1.09 0.6 0.00 1.79 2.3

54103 Wetzel, WV At-Risk 0.11 0.53 0.00 0.08 NA 0.77 0.88 0.00 1.04 NA

54105 Wirt, WV Distressed 0.06 0.69 0.00 0.05 NA 0.45 1.15 0.00 0.65 NA

54107 Wood, WV Transitional 0.16 0.56 15.18 0.08 0.15 1.15 0.94 0.82 1.12 1.33

54109 Wyoming, WV Distressed 0.07 0.6 12.67 0.06 0.01 0.52 0.99 0.68 0.86 0.11

257