Impacts of Regional Approaches 

to Rural Development

Initial Evidence on the Delta 

Regional Authority

John Pender

Richard Reeder

Economic

Research

Service

Economic

Research

Report

Number 119

June 2011

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Recommended citation format for this publication: 

Pender, John, and Richard Reeder. Impacts of Regional Approaches to

Rural Development: Initial Evidence on the Delta Regional Authority,

ERR-119, U.S. Department of Agriculture, Economic Research

Service, June 2011.

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A Report from the Economic Research Service

Abstract

The Delta Regional Authority (DRA) began funding rural development projects in 

economically distressed counties in the Mississippi River Delta region in 2002. To assess 

the initial economic outcomes of DRA funding, we compared nonmetropolitan DRA 

counties with similar counties elsewhere in the same region as well as in the Southeast. 

Per capita income, net earnings, and transfer payments grew more rapidly in DRA counties than in similar non-DRA counties, and those impacts were stronger in counties in 

which DRA spending was higher. Each additional dollar of DRA spending was associated with an increase of $15 in the growth of annual personal income from 2002 to 2007, 

including an increase of $8 in annual earnings (primarily in the health care and social 

services sector) and an additional $5 in annual transfer (Government) payments (mainly 

due to increased medical transfer payments such as Medicare and Medicaid). Our findings suggest that investments supported by the DRA in improved medical facilities and 

DRA efforts to increase the supply of health professionals may be promoting additional 

health sector earnings and medical transfer payments. 

Keywords: Regional development programs, Delta Regional Authority, economic 

impacts, quasi-experimental methods

Acknowledgments

The authors are grateful to J. Matthew Fannin, Associate Professor, Department of 

Agricultural Economics and Agribusiness, Louisiana State University AgCenter; Tom 

Hertz, Economist, Resource and Rural Economics Division, Economic Research Service; 

Mark Partridge, Professor, Department of Agricultural, Environmental and Development 

John Pender, jpender@ers.usda.gov

Richard Reeder, rreeder@ers.usda.gov

Impacts of Regional Approaches 

to Rural Development

Initial Evidence on the Delta 

Regional Authority

Economic 

Research 

Report 

Number 119

June 2011
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Economics, Ohio State University; and David Sears, Office of Community 

and Economic Development, USDA Rural Development, whose detailed 

reviews helped to significantly improve the manuscript. We are also grateful 

to members of senior management of the Economic Research Service, 

USDA Rural Development staff, and the Delta Regional Authority, who 

also reviewed the manuscript and contributed valuable comments; participants in seminars on the research conducted at the Economic Research 

Service, the Southern Regional Science Association 2010 annual meeting, 

the Agricultural and Applied Economics Association 2010 annual meeting, 

and at Mississippi State University and Louisiana State University; and to 

Priscilla Smith, who edited the manuscript, and to Wynnice Pointer-Napper, 

who designed the layout. 
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Contents

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Regional Approaches to Rural Development . . . . . . . . . . . . . . . . . . . . . . 3

Rationale for Regional Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

History of Federal Regional Development Programs . . . . . . . . . . . . . . . 5

Literature on Impacts of Regional Development Programs . . . . . . . . . . . 8

The Delta Regional Authority . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Initial Economic Outcomes of the DRA . . . . . . . . . . . . . . . . . . . . . . . . . 21

Estimation approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Study Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Outcome and Control Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Differences in Outcomes Between DRA Counties 

and Matched Non-DRA Counties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Differences in Prior Trends in Outcomes . . . . . . . . . . . . . . . . . . . . . . . . 27

Effects of the Level of DRA Spending Per Capita . . . . . . . . . . . . . . . . . 29

Robustness of the Results to Alternative Specifications . . . . . . . . . . . . . . 31

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Appendix A—Matching and Double Difference (DD) Estimation 

of Impacts of DRA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

DD Estimates with Matching Methods . . . . . . . . . . . . . . . . . . . . . . . . . 52

Pre-DRA Differences in Outcome Trends . . . . . . . . . . . . . . . . . . . . . . . 55

Appendix B—Regression Model To Estimate Impacts of DRA . . . . . . 59

Appendix C—Data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

Appendix D—Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
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Summary

What Is the Issue?

Regional development approaches are attracting increasing attention, particularly as vehicles for encouraging rural economic development. At the Federal 

level, the Denali Commission was authorized in 1998 to promote regional 

development in Alaska and the Delta Regional Authority (DRA) was authorized in 2000 to do the same in the Mississippi Delta region. Since then, 

four additional regional development commissions have been authorized, 

and startup funds have been appropriated for two of these. Despite increased 

emphasis on such regional approaches, evidence of their economic impacts 

is limited, especially for newer programs such as the DRA. In this study, we 

demonstrate an approach to investigating the initial economic outcomes of 

such programs, using publicly available data and the best available methods 

to examine the DRA as a test case. We focus on changes in personal income 

per capita (and its components), employment per capita, and population from 

2002 to 2007.

What Were the Study Findings?

The DRA began funding projects in 252 economically distressed counties in 

the Mississippi Delta region in 2002. In its first 7 years, the DRA invested $75 

million in basic public and transportation infrastructure, business development, 

job training, and employment-related education. Growth in annual personal 

income per capita averaged about $600 higher in DRA-recipient counties from 

2002 to 2007 than in economically and demographically similar non-DRA 

counties in the Delta Region and in the Southeast. This greater growth represented an additional increase in per capita income in DRA-recipient counties (relative to similar non-DRA counties) of about 3 percentage points over 

this 5-year period. Comparison of trends in per capita income in the matched 

groups of counties showed that these trends were very similar from 1990 to 

2002, but began to diverge after the DRA began operating in 2002.

The major sources of greater growth in personal income were greater growth 

in per capita net earnings and personal transfer payments, both of which were 

statistically significantly greater in DRA counties. (Transfer payments are 

payments from a Government to an individual, e.g., Medicare, Social Security, 

etc.) These impacts were greater in counties where DRA spending per capita 

was larger, with each $1 of additional DRA spending per capita associated 

with an additional $15 in growth of personal income per capita, including $8 in 

additional net earnings (primarily in the health care and social service sector) 

and $5 in additional transfer payments (mainly medical transfer payments). 

The incremental impacts of DRA spending on personal income, earnings, 

and transfer payments suggest that the DRA may be leveraging additional 

public or private sources of funds. In particular, the DRA’s health programs, 

including funding of medical facilities and its J-1 visa waiver program to 

attract foreign doctors, appear to be contributing to increased health sector 

earnings and medical transfer payments by increasing the availability of 

health care services. The DRA’s health awareness campaigns, such as those 

focusing on diabetes prevention and treatment, may also be increasing the 
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demand for health services. Since other public funds, such as medical transfer 

payments, are apparently being leveraged by the DRA, the income increments associated with DRA spending cannot be seen solely as a return to 

DRA investments.

We did not find statistically significant differences in growth of employment 

per capita between DRA and similar non-DRA counties; this result may 

be due to the difficulty of measuring those impacts given the small size of 

the program and the relatively short timeframe considered. We found some 

evidence of slower population growth in the DRA counties, but this difference was found to be a continuation of differences in trends in population 

growth prior to initiation of the DRA.

(For a list of DRA counties, see: http://www.dra.gov/about/maps.aspx/.)

How Was the Study Conducted?

We used a quasi-experimental matching approach to select non-DRA 

nonmetropolitan counties in the Delta region and elsewhere in the 

Southeastern United States that had similar economic and demographic 

characteristics to DRA-recipient nonmetropolitan counties prior to implementation of the DRA, and compared mean changes in outcomes between 

these groups of counties. We also used multivariate regression analysis on 

the matched groups of counties to identify the effects of the level of DRA 

program spending per capita on the outcomes. 

The findings are robust to alternative methods of selecting the comparison 

groups of counties; use of alternative sets of variables for matching; including 

or dropping groups of counties for which confounding factors were present 

(such as counties heavily affected by Hurricane Katrina or the presence of 

other development or health programs); and use of alternative starting and 

ending years in the comparisons.
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Introduction

Regional economic development approaches are again becoming popular. 

More than 60 years after the Tennessee Valley Authority (TVA) was initiated as part of the New Deal and more than 30 years after the Appalachian 

Regional Commission (ARC) and Economic Development Administration 

(EDA) were established as part of President Johnson’s War on Poverty, 

Congress authorized the Denali Commission in 1998 and the Delta Regional 

Authority (DRA) in 2000 to promote rural development in Alaska and the 

Mississippi Delta region, respectively. Since 2000, four additional regional 

rural development commissions have been authorized--the Northern Great 

Plains Regional Authority (NGPRA), Northern Border, Southeast Crescent 

and Southwest Border Commissions—and limited startup funds have been 

appropriated for two of these.1

Other regional or place-based development policies are also being pursued 

through enterprise zone programs enacted in numerous States since the early 

1980s, the Federal Empowerment Zone and Enterprise Community program 

initiated in 1993, and other Federal and State policies and programs. In addition, there are several recent initiatives among Federal agencies to promote 

regional development approaches, following guidance from the Office of 

Management and Budget promoting “place-based policies” (Orszag et al., 

2009). 2 The 2009 Office of Management and Budget guidance defines 

“place-based” policies as those that “leverage investments by focusing 

resources in targeted places and drawing on the compounding effect of wellcoordinated action.”

The growth of regional development approaches is due in part to positive 

impacts found in evaluations of earlier programs such as the ARC (Isserman 

and Rephann, 1995; Brandow et al., 2000; BizMiner/Brandow Co. Inc. and 

Economic Development Research Group, 2007) and the EDA (Barrows and 

Bromley, 1975; Martin and Graham, 1980; Burchell et al., 1997; Burchell et 

al., 1998; Haughwout, 1999; Arena et al., 2008). There is also substantial and 

rapidly growing literature on the impacts of State and local enterprise zones 

(e.g., Papke, 1994; Boarnet and Bogart, 1996; Dowall, 1996; Bondonio and 

Engberg, 2000; Lambert and Coomes, 2001; O’Keefe, 2004; Bondonio and 

Greenbaum, 2007; Elvery, 2009; Neumark and Kolko, 2010), and several 

recent studies of the Federal Empowerment Zone program (Oakley and Tsao, 

2006, 2007; Busso and Kline, 2008; Hanson, 2009; Krupka and Noonan, 

2009; Busso, Gregory, and Kline, 2010; Hanson and Rohlin, 2010). Small 

or no effects of State and local enterprise zones on employment are found in 

most of these studies, while some recent studies find robust impacts on local 

housing values.3 Some studies of the Federal Empowerment Zones program 

find significant positive impacts on several indicators (e.g., Busso and Kline, 

2008; Krupka and Noonan, 2009; Busso, Gregory, and Kline, 2010; Hanson 

and Rohlin, 2010), although the results vary across different studies and 

methods. To date, however, there have been no published studies assessing the 

outcomes of the DC or the DRA. This study addresses this information need, 

investigating initial outcomes of the DRA on rural development outcomes in 

the Mississippi Delta region.

1The two that have received some 

startup funds include the Northern 

Border Commission and the Southeast 

Crescent Commission. These newly 

authorized commissions are discussed 

further in a later section of the paper.

2Some have argued instead for “people-based” policies, which give money 

to individuals who decide for themselves how and where they wish to invest it (for example, see Glaeser, 2005). 

However, many economists maintain 

that some sort of “place-based” policies are required to overcome local 

market imperfections such as an inadequate supply of public goods in poor 

places (Crane and Manville, 2008) and 

imperfect mobility of labor and capital 

in remote places (Hite, 1997; Partridge 

and Rickman, 2008).

3Several good reviews of earlier literature on these programs are available 

(e.g., Peters and Fisher, 2002, 2004; 

Fisher and Peters, 1997; Wilder and 

Rubin, 1996; Bartik, 1991; Eisinger, 

1988).
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The DRA is a partnership among the Federal Government and 8 Delta States, 

targeting 252 economically distressed counties (for a full list of the counties, see: http://www.dra.gov/about/maps.aspx/). It initiated operations in 

2001 and began funding projects in 2002. Between 2002 and 2009 the DRA 

invested $75 million in projects related to basic public infrastructure, business development, transportation infrastructure, job training, and employment-related education. The program reports that it leveraged an additional 

$354 million in other public investment and $1.5 billion in private investment 

during this period. 

In this study we investigate the initial outcomes (during 2002 to 2007) of the 

DRA’s investments in nonmetropolitan DRA counties. The outcome variables 

used in the assessment include county-level changes in personal income per 

capita and its components, employment per capita, and population. This study 

is of an exploratory nature, seeking to identify whether significant differences in outcomes can be measured for an economic development program 

as small as the DRA after only 6 years of implementation. It is not a formal 

impact evaluation, but rather a test of whether available data and econometric 

methods can discern potential impacts and help to illuminate the possible 

mechanisms of impact. If some initial impacts are evident, this may point to 

useful avenues of further research to better understand these impacts, and to 

rule out alternative explanations. If no significant impacts are evident, it does 

not mean that the program had no impact; rather it may simply mean that the 

impacts that have occurred are not measurable with the data and methods 

available, given the relatively small size of the initial DRA funding levels, or 

that a longer time must elapse for measurable impacts to occur.

The next section provides background on regional approaches to rural 

economic development in general, and the third section discusses the DRA 

in particular. The fourth section briefly discusses the methods and data used 

in the analysis and presents the main results (more detailed discussion of the 

methods, data, and results are provided in appendices A, B, C and D). 
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Regional Approaches to Rural Development

Rationale for Regional Approaches

Rural development researchers and practitioners have long recognized that 

local communities cannot fully control development on their own and that 

collaboration with neighboring communities at a regional level can be invaluable. In recent years, regional approaches to development have grown in 

importance because of the increasingly competitive global economy. The 

ability of rural communities to compete in the global marketplace depends 

on important regional characteristics such as workforce capacity, natural 

and social amenities, and infrastructure (Weiler, 2004). Shaffer, Deller, and 

Marcouiller (2006) maintain that to achieve long-term community development, the rural community in today’s global economy must make use of 

“both internal and external resources to achieve change, drawing on its own 

strengths and capabilities and looking beyond its boundaries for supplemental 

resources.” This requires “collaboration and partnership building within and 

across communities.” 

One of the main reasons why regional approaches may be desirable involves 

spillovers (externalities)—where activities in one place affect people in 

neighboring places. Much of the recent research on spillover impacts 

concerns local infrastructure and facilities, such as roads (Pfaff et al, 2007; 

Voss and Chi, 2006), railroads (Clark, 2006), airports (Espey and Lopez, 

2000), telecommunications (Yilmaz, Haynes, and Dinc, 2002), nuclear power 

plants (Folland and Hough, 2000), and universities (Anselin, Varga, and Acs, 

2000). However, research has also examined some of the spillovers associated with development processes, including the rural impacts of urban sprawl 

(Thomas and Howell, 2003; Heimlich and Anderson, 2001; Carruthers 

and Vias, 2005; Byun, Waldorf, and Esparza, 2005). Spillovers can have 

either positive (e.g., spillovers of knowledge from one locality to another) 

or negative impacts (e.g., pollution and congestion). In either case, regional 

approaches can increase the ability of communities to take such spillovers 

into account in their development programs, taking advantage of positive 

spillovers and minimizing or compensating communities for negative ones.

Rural areas have been found to be affected differently by urban growth-related 

spillovers, depending on distance to an urban area, extent of commuting, and 

other factors. This has led to the conclusion that policies designed to stimulate 

the development of urban and rural areas (policy shocks) are likely to have 

different impacts in different places, or regions (i.e., a one-size-fits-all approach 

is not optimal) (Henry and Barkley, 1997; Renkow, 2003, Partridge et al., 2007; 

Partridge et al., 2008). Hence, regional approaches can improve on uniform 

nationwide approaches to development policies. 

Regional approaches are viewed as particularly important in helping poor 

places compete in the global economy (Scott and Storper, 2003). In addition, 

the increasingly popular “sector strategies” for economic development generally operate at a regional level, requiring collaboration across the jurisdictional boundaries of workforce investment areas, community college districts, 

economic development regions, municipal jurisdictions, and county jurisdictions, among others (Ligot-Gordon et al., 2008). Regional approaches can 
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also help to address noneconomic issues, such as environmental protection, 

transportation access, and the availability of health services (Richgels and 

Sande, 2009). 

Regional collaboration has several additional potential benefits, including that it:

• allows for a larger scale of operation of infrastructure and public services, 

which can take advantage of economies of scale, thereby lowering costs 

to residents and allowing more services to be provided

• allows for a more optimal location of facilities and services–in the places 

where they cost the least and can have the most benefit

• reduces excessive competition among the region’s communities when 

trying to attract businesses

• allows for the formulation of regional development strategies aimed at 

developing one or more economic specializations that could help the 

region compete in the global economy

• relies on concentrated, regionwide local planning and grant-writing 

resources, making it easier to obtain Federal and State economic development assistance 

Despite these advantages, regional efforts do not often come easily in a country 

with a strong tradition of local independence. Local political leaders are 

accountable only to the voters in the locality that elects them, which can lead 

to difficulties in getting local leaders to support regional policies unless they 

clearly benefit their jurisdictions. This can be especially problematic when 

neighboring communities have a long history of competition and conflict, 

resulting in a lack of trust and working relationships needed for collaboration 

(Lackey et al., 2002). In addition, since much regional policy is designed and 

initiated by organizations that lack traditional local sources of funding (such as 

local property and sales taxes), providing local funding both for the planning 

and implementation of regional projects can be challenging.4

Another potential drawback of regional approaches concerns the difficulty of getting support for policies that address the diverse needs of small 

towns or different segments of the population. To get sufficient support for 

regional policies, those supporting such activity must appeal to the majority 

of the region’s voters and to the local power structure, including important 

local leaders of business and government. However, some towns and some 

segments of the population may not approve of these majority-driven policies, especially if their needs and interests differ substantially from that 

of the region as a whole. For example, the interests of the poor and other 

minorities may not always receive significant attention from regional policies, even though such policies may have more potential to address such 

problems than do single-jurisdiction policies. Thus, even where regional 

policies are in place, they may fall short of achieving economically and 

socially optimal outcomes.

Even with these problems, the number of regional partnerships for economic 

development has increased in recent years (Olberding, 2002). However, 

researchers generally view the current structure of Federal, State, and local 

governments and their development programs as having weak capacity to 

4Local matching funding is generally 

required, even with Federal regional 

development programs.
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address regional challenges (John, Brooks, and McDowell, 1998) and doing 

little to foster these kinds of partnerships in rural America (Drabenstott and 

Sheaff, 2002). As a consequence, Federal regional development programs 

have been created to encourage more regional activity, particularly in the 

more distressed regions.

History of Federal Regional 

Development Programs

New Deal and Great Society Programs

Federal regional development programs have a long history, going back to 

the Tennessee Valley Authority (TVA), created in 1933 (Roth et al., 2002). 

The TVA’s nonpower programs were terminated in the late 1990s, leaving 

communities in this region (and others) to rely on programs that date to the 

1960s. The two most notable of these programs are the Appalachian Regional 

Commission (ARC) and the Economic Development Administration (EDA), 

both created in the 1960s.5 Both provide general administrative funding to 

multi-county economic development districts that plan and implement their 

projects, and Federal funds are used to leverage State, local, and private 

investments in their projects. Both programs have emphasized investments in infrastructure, but they have also financed other kinds of projects 

that fit into the strategies devised by the multi-county entities. In addition, 

both programs target their investments to the most economically distressed 

regions. The main differences between these two programs are that the EDA 

is a nationwide program, whereas the ARC is confined to a designated multiState region, and the ARC is a Federal-State partnership, in which governors 

of ARC’s member States have a vote in deciding which projects to support 

(unlike the EDA).

Although other Federal programs also emphasize regional approaches—most 

notably transportation and environmental programs—the ARC and EDA 

are the largest programs aimed at comprehensive economic and community 

development at a regional level. They are also among the Federal programs 

that have received the most significant program evaluations over the last 20 

years (this literature is reviewed in a later subsection). 

New Regional Development Commissions

Two new regional development programs (the Denali Commission and the 

Delta Regional Authority) were created during the latter years of the Clinton 

Administration. The Denali Commission Act was authored by Senator Ted 

Stevens of Alaska and enacted in October 1998 (PL105-277). Its overall goals 

are to lower the cost of living and raise the standard of living in Alaska, with 

a focus on remote communities. Its specific objectives include delivering costeffective services; promoting rural development; and providing additional 

power generation and transmission facilities, advanced telecommunications, 

water and sewer systems and other infrastructure, plus job training and other 

economic development services, especially in distressed areas. Subsequent 

legislation in November 1999 expanded the mission to include health care 

facilities (Denali Commission, 2001). 

5USDA began its own regional 

program at roughly the same time, 

the Resource Conservation and 

Development (RC&D) program 

(Gadsby, 2002). RC&D is similar to 

EDA, being a nationwide program that 

funds projects for multicounty entities. 

However, its objectives are different 

and its funding levels are lower ($51 

million in fiscal year 2009).
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The Delta Regional Authority (DRA) was authorized in December 2000 

by Title V of the FY 2001 Omnibus Appropriations Act (PL106-554); cited 

in the law as the “Delta Regional Authority Act.” This occurred 12 years 

after the seven-State Lower Mississippi Delta Development Commission 

(LMDDC) was established. That Commission was chaired by then-Arkansas 

Governor Bill Clinton and published its Delta Vision report in 1990, which 

advocated the creation of a permanent regional planning and development 

entity in the Delta region. Ten years later, President Clinton’s Delta Initiative, 

led by Transportation Secretary Slater, published the Delta Vision, Delta 

Voices report, which also advocated such an entity (U.S. Department of 

Transportation, 2000). Later that year, with the support of President Clinton 

and key members of Congress, the DRA was enacted. 

In 2002, the Farm Security and Rural Investment Act (PL 107-171) reauthorized the DRA. Although funding was appropriated for the DRA, the funds 

appropriated have been well below the level authorized ($30 million per year), 

declining from an initial appropriation of $20 million in 2001 to $5 million in 

2004, but increasing since to $13 million in 2009 and 2010 (table 1). Outlays by 

the DRA have been somewhat smaller than appropriations.6 Since its inception, 

DRA expenditures have been much smaller than those of the EDA, the ARC, or 

the Denali Commission (see table 1). We discuss the DRA in more detail in a 

later section. The 2002 Farm Act also authorized the creation of a new regional 

6By the end of FY 2008, the DRA 

had a resource balance of about $27 

million, nearly half of which was obligated and the rest unobligated (DRA, 

2008a).

Table 1

Appropriations and outlays for selected regional development 

programs

Fiscal year

Appropriations1 Outlays2

DRA DRA EDA ARC3

Denali 

Commission

Million dollars

1999 - - 355 136 1

2000 - - 356 125 38

2001 20.0 - 356 86 11

2002 10.0 1 355 101 -144

2003 7.9 6 375 74 2

2004 5.0 12 337 68 16

2005 6.0 9 332 65 49

2006 11.9 6 284 63 42

2007 11.9 8 243 67 33

2008 11.7 8 238 69 46

2009 13.0 9 243 62 60

2010 13.0 13 422 65 79

DRA=Delta Regional Authority.

EDA=Economic Development Administration.

ARC=Appalachian Regional Commission.

2010=forecast. 

1Source: Annual U.S. Congress appropriations bills, various years.

2Source: Table 12.3, Historical Tables from the President’s Budget, FY 2011. Available at: http://

www.whitehouse.gov/omb/budget/Historicals/. 

3Excludes Appalachian Highway Program.

4Negative number due to deobligated funds.
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authority, the Northern Great Plains Regional Authority (NGPRA). The 

NGPRA has not been able to spend Federal funds because a Federal co-chair 

was never appointed. 

The 2008 Food, Conservation, and Energy Act (PL 110-246) continued 

authorization of the DRA and NGPRA through 2012 and authorized three 

new regional development commissions: the Northern Border, Southeast 

Crescent, and Southwest Border Commissions (USDA/ERS, 2008). In 2009, 

appropriations legislation provided funding to start the Northern Border 

Regional Commission (NBRC), and a Federal Co-Chair has been appointed 

and recently confirmed in March 2010. The NBRC is therefore set to begin 

operations, while the other three commissions still lack a Federal Co-Chair 

and funding.7

Other Federal Initiatives Related 

to Regional Development 

Beginning in the mid-1990s, USDA administered the Empowerment Zone/

Enterprise Community program that provided assistance to selected highpoverty rural places attempting to overcome barriers to development (Reid, 

1999; U.S. GAO, 2004), and the Rural Economic Area Partnership (REAP) 

initiative that assisted rural places experiencing other problems, such as 

outmigration (USDA, 1994; USDA/RD, 2008; USDA/ERS, 2000, pp. 11-12). 

Both programs encouraged strategic regional planning and collaboration. In 

2005, in response to the disasters associated with Hurricanes Katrina, Rita, 

and Wilma, Congress enacted legislation providing targeted tax relief to 

promote redevelopment in affected localities, called Gulf Opportunity Zones 

(Richardson, 2006).

More recently, the Obama Administration began promoting regional 

approaches to rural development. In August 2009, the White House issued 

a memorandum promoting “place-based” policies, in which each Federal 

agency was asked to initiate three to five place-based programs (Orszag et 

al., 2009). This memorandum noted that regional approaches are particularly important for rural areas. Several new USDA initiatives are associated 

with this new emphasis on rural regional approaches (discussed in the next 

paragraph). Other new Federal Government regional efforts include the 

Sustainable Communities Initiative of the U.S. Department of Housing and 

Urban Development (HUD), the U.S. Department of Transportation (DOT) 

and the U.S. Environmental Protection Agency (EPA), which links grants and 

technical assistance from these agencies for regionally integrated planning; 

the Small Business Administration’s Entrepreneurial Development Initiative 

to help small businesses participating in regional economic clusters; and 

EDA’s regional innovation clusters initiative aimed at building on regions’ 

industrial competitive strengths. 

Meanwhile, USDA began to make use of strategic planning grants and 

efforts to coordinate its programs with other Federal programs in “an 

integrated effort to advance regional development” (Vilsack, 2010). These 

efforts were included in USDA’s fiscal year 2011 budget proposals (USDA/

FSA, 2010). One of these initiatives is the Regional Innovation Initiative 

“to aid in planning and coordination of USDA and other sources of assistance ... (so that) ... communities within regions ... that work together 

7

A small amount of startup funds 

have been appropriated for the 

Southeast Crescent Commission. For 

more information about these other 

commissions that have not yet started 

up, see Reeder (2009).
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can produce more prosperity for all” (USDA/FSA, 2010, p. 57). In addition, the Great Regions Initiative makes use of the existing Rural Business 

Opportunity Grants program to target grants to intermediaries (nonprofits, 

tribes, etc.) that provide assistance to development strategies employing 

regional approaches to rural development objectives. These new regional 

initiatives for rural development have also been promoted in the White 

House’s Council of Economic Advisors (CEA) report, Strengthening the 

Rural Economy (CEA, 2010).

Although it is too early to say whether this new regional policy activity will 

result in a major change in Federal rural development policy, it is certainly 

suggestive of a change in attitude toward a topic that has been largely ignored 

at times in the past. 

Literature on Impacts of Regional 

Development Programs

Several studies have assessed economic impacts of ARC and EDA programs 

using various methods (see box, “Summary of literature on economic 

impacts of Appalachian Regional Commission (ARC) and Economic 

Development Administration (EDA)”). Using a quasi-experimental approach 

comparing ARC counties to similar (matched) non-ARC counties, Isserman 

and Rephann (1995) estimated substantially higher growth in ARC counties 

from 1969 to 1991 in personal income per capita; population; total personal 

income; earnings in total and in several industries; dividends, interest, and 

rent; and in transfer payments. They found similar results when focusing 

on Central Appalachia, the poorest subregion of Appalachia, and estimated 

that the $13 billion spent cumulatively by the ARC between 1965 and 1991 

“meant $8.4 billion more income for Appalachia in 1991.”

Isserman and Rephann (1995, pp. 362-363) indicated that the greater growth 

in ARC counties compared to matched non-ARC counties could not be 

attributed to ARC programs with certainty, acknowledging that a “leap of 

faith” is required in control group research. They considered one alternative 

explanation (not already addressed by their analysis)—differences in racial 

composition—stating that when racial composition was considered in the 

matching, the differences in growth were even stronger. They did not explicitly consider other alternative explanations, such as effects of the 1970s coal 

boom (as suggested by U.S. GAO (1996)), but they did investigate factors 

associated with differences in growth rates between ARC counties and their 

matched twins using multivariate regressions. These results show that coal 

counties grew faster than twin noncoal counties in some respects (in total and 

per capita income), but also that being a coal county does not explain differences in growth of other outcome variables or explain all of the differences in 

income growth. 

An unpublished study by Freshwater et al. (1997) examined both the ARC 

and the TVA, examining the 1980s experience of the counties assisted by 

these two regional development programs. This study used a 3-equation, 

simultaneous equation, model explaining change in manufacturing earnings 

per worker, manufacturing employment, and educational attainment in 2,053 

counties in the Eastern United States, with dummy variables representing 
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Summary of literature on economic impacts of Appalachian Regional Commission (ARC) 

and Economic Development Administration (EDA)

Program Study Outcomes measured Methods Key findings

ARC Isserman and 

Rephann (1995)

Total personal income 

(PI) and per capita 

personal income 

(PCPI); population; 

total earnings and 

earnings by sector; 

dividends, interest and 

rent (DIR) ; and transfer 

payments (TP)

Comparison of changes in 

outcomes from 1969 to 1991 

for ARC counties vs. matched 

non-ARC counties, using 

Mahalanobis metric (MM) 

matching

• ARC counties grew faster in 

PI, PCPI, population, DIR, TP, 

and earnings, with more rapid 

growth especially in services; 

finance, insurance and real estate; 

manufacturing and retail trade. 

Similar results also were found in 

Central Appalachia, the poorest 

subregion.

ARC Brandow (2000) Number of jobs created 

or retained, other 

public and private 

funds leveraged, 

indirect and induced 

economic impacts, 

local tax revenues, 

economic diversification 

and quality of life 

indicators, impact/cost 

ratios (e.g., cost per job 

created/retained)

For 99 projects that closed 

from 1990 to 1997: examined 

self-reported direct number 

of jobs created or retained; 

used input-output model 

(IMPLAN) to predict indirect 

and induced economic 

impacts; fiscal impacts 

estimated based on economic 

impacts; subjective qualitative 

data used to measure 

impacts on quality of life, 

environment, other outcomes

• Each $1 of ARC funding 

associated with $2.61 of other 

public funding and almost $100 of 

private investment.

•$1,222 cost to ARC per direct job 

created; $4,574 total public cost 

per job created (smaller costs per 

total jobs created/retained).

• $20 in additional annual direct 

wage income per $1 of ARC 

funds; $5.40 per $1 of total public 

funds (larger total income impacts 

per $1 spent).

ARC BizMiner/

Brandow Co. Inc. 

and EDR Group 

(2007)

Same as Brandow 

(2000)

Similar to Brandow (2000), 

using data on 78 projects 

closed between 1999 and 

2005

• Each $1 of ARC funding 

associated with $4.87 of other 

public funding and $75 of private 

investment.

• $1,274 average cost to ARC per 

direct job created; $8,102 total 

public cost new job created 

(smaller costs per total job 

created/retained).

• $28 in additional annual direct 

wage income per $1 of ARC 

funds; $4.40 per $1 of total public 

funds (larger total additional 

income per $1 spent).

—continued
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Summary of literature on economic impacts of Appalachian Regional Commission (ARC) 

and Economic Development Administration (EDA)—Continued

Program Study Outcomes measured Methods Key findings

ARC Glaeser and 

Gottlieb (2008)

Population and income 

per capita

Ordinary least squares (OLS) 

regression models explaining 

growth in population and 

income per capita in counties 

in ARC states from 1970 

to 1980 and 1970 to 2000, 

using the initial value of 

each dependent variable as 

the only control variable and 

a dummy variable for ARC 

coverage 

• Population growth was 

significantly faster in ARC 

counties during 1970-80, 

insignificant difference in growth 

from 1970-2000.

• Income per capita growth was 

significantly slower in ARC 

counties during 1970-2000; there 

was an insignificant difference 

from 1970-1980.

• Authors claim results not sensitive 

to inclusion of other control 

variables, but results for other 

specifications not shown.

ARC and 

TVA

Freshwater et al. 

(1997)

Manufacturing 

employment and 

earnings and 

educational attainment

Used 3-equation 

simultaneous equation model 

covering 2,053 counties in 

Eastern U.S. to estimate 

changes associated with 

ARC and TVA during 1980s. 

Dummy variables used to 

identify ARC and TVA 

assisted counties and effects 

of assistance.

• TVA had more positive effects 

than ARC. Rural ARC counties 

had significantly slower growth 

in manufacturing earnings and 

employment. Authors speculated 

that this could be explained, 

in part, by move South of 

manufacturing during this time 

period. 

EDA Barrows and 

Bromley (1975)

Number of jobs 

directly created by the 

EDA project

OLS regressions explaining 

number of jobs created 

(using data from a 1972 

EDA evaluation of projects 

completed in 1967-69) 

on county and project 

characteristics; discriminant 

analysis of factors 

distinguishing projects 

creating no jobs and projects 

creating at least one job 

• Firms using more unskilled 

labor were associated with larger 

employment impacts.

EDA Martin and 

Graham (1980)

Personal income OLS regressions explaining 

changes in growth rate of 

county-level personal income 

(growth rate from first year of 

aid to last year of aid minus 

growth rate prior to aid, and 

growth rate after aid ended 

minus growth rate before aid) 

on variables reflecting the 

amount, nature and timing 

of EDA assistance, and 

non-EDA factors affecting 

income growth rates

• Greater EDA funding relative 

to county income, greater 

percentage of funding for public 

works projects, and earlier 

receipt of funding are associated 

with greater increase in income 

growth during the project period.

• None of the EDA variables had a 

significant impact on changes in 

income growth rates in the postassistance period.

—continued
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Summary of literature on economic impacts of Appalachian Regional Commission (ARC) 

and Economic Development Administration (EDA)—Continued

Program Study Outcomes measured Methods Key findings

EDA Burchell et al. 

(1997)

Number of jobs 

created or retained, 

other public and 

private funds 

leveraged, indirect 

job impacts, local tax 

revenues, economic 

diversification, impact/

cost ratios cost per job 

created/retained

For 203 projects that closed 

in 1990: examined selfreported number of direct 

and indirect permanent 

jobs created or retained 

and other related public or 

private investments; the 

additionality of jobs was 

assessed based on whether 

EDA assistance identified 

as “critical” or “essential”; 

tax impacts estimated 

based on impacts on private 

investment

• $3,058 median cost to EDA per 

direct permanent job created; 

$4,857 total public cost per job 

created.

• Each $1 of EDA funds associated 

with $1 of other public 

investment and $10 of private 

investment.

• Each $1 million in EDA funds 

result in 50 non-project-related 

direct jobs and 64 project-related 

indirect jobs.

• Each $1 of EDA funds adds $10 

to the local tax base.

EDA Burchell et al. 

(1998)

Private investment 

leveraged and number 

of total jobs created 

or retained (direct, 

indirect and induced), 

cost per job created or 

retained

Input-output model 

(IMPLAN) used to predict 

indirect and induced 

employment impacts of 

direct employment impacts 

estimated by Burchell et al. 

(1997) 

Multiple regression 

models used to estimate 

total employment impacts 

controlling for other 

variables, using countylevel annual data from 

1990 to 1994; OLS and 

two-stage least squares 

(2SLS) regressions used, 

with 2SLS used to account 

for endogenous wage levels 

in employment regression 

and employment in wage 

regression

• Median employment multiplier 

(total jobs created/direct jobs 

created) equals 1.50; private 

investment multiplier (total 

private investment induced/direct 

private investment) equals 1.44 

(from input-output model).

• Total employment increase of 

7-10 jobs per $10,000 EDA public 

works investment (point estimate 

of 9 jobs, implying cost of $1,100 

per total jobs created (regression 

analysis).

• Statistically insignificant impact 

of EDA spending on earnings per 

employee (regression analysis).

EDA U.S. General 

Accounting 

Office (1999)

Total county 

employment

GAO repeated the OLS 

regression specification of 

Burchell et al. (1998), as 

well as alternatives including 

prior county employment 

or population as additional 

control variables

• The Burchell et al. (1998) finding 

of significant positive impact 

of EDA spending on county 

employment was not robust to 

including prior employment or 

population in the specification.

—continued
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Summary of literature on economic impacts of Appalachian Regional Commission (ARC) 

and Economic Development Administration (EDA)—Continued

Program Study Outcomes measured Methods Key findings

EDA Haughwout (1999) County private nonfarm 

employment and farm 

employment, average 

compensation per 

employee

Regression model very similar 

to Burchell et al. (1998), 

except includes prior county 

employment (as in GAO, 

1999) but not payroll per 

worker (which were included 

in both Burchell et al. and 

GAO studies) as controls

• Significant positive impact of 

EDA spending on private nonfarm 

employment, robust to a few 

alternative specifications.

• Impact in preferred specification 

implies EDA cost per job created 

of $9,953.

• Insignificant impact of EDA 

spending on compensation per 

employee (as in Burchell et al.) 

and on farm employment.

EDA Glasmeier (2002) Cost per job created Using data provided by EDA 

on cost per job created by 

projects completed in 1990 

(based on the Burchell et 

al., 1997 study) and in 1993 

(based on another study using 

similar methods), examined 

how cost per job varied by 

year of completion, in rural 

vs. urban areas, and by other 

county characteristics

• The estimated average EDA cost 

per job created was higher in rural 

than urban areas in 1990 ($5,938 

vs. $1,988), but lower in 1993 

($6,904 vs. $7,399).

EDA Arena et al. 

(2008)

County employment OLS and 2SLS regressions 

explaining annual county 

employment in 1990-

2005, separately for urban 

(metro) vs. rural (nonmetro) 

counties, using a similar 

specification to Burchell et 

al. (1998), but investigating 

impacts of different types 

of EDA projects, impacts 

by number of years since 

project completion, and 

robustness to some alternative 

specifications of control 

variables; sample selected to 

avoid confounding of EDA 

funding in different years 

(included only counties with 

either no EDA activity or 

where one or more EDA 

projects were completed by 

the fourth year of a 9-year 

period, and with no EDA 

funding in remaining 5 years)

• EDA funding has a significant 

positive impact on employment in 

rural areas across OLS regression 

specifications; similar magnitude 

with 2SLS but statistically 

insignificant.

• Impact of EDA funding in rural 

areas seen within one year of 

project completion and remains 

at a similar level for 5 years after 

project completion.

• Incremental mean EDA cost per 

job created: $2,001-$4,611 across 

specifications.

• EDA cost per job was smallest 

for business incubator projects 

($144-$216 and largest for 

community infrastructure 

projects ($2,920-$6,872).

• Statistically insignificant impacts 

of EDA funding found in urban 

areas.

Acronyms (in order of appearance by rows): ARC = Appalachian Regional Commission EDA = Economic Development Administration 

PI = Total personal income PCPI = Per capita personal income DIR = dividends, interest and rent TP = transfer payments MM = Mahalanobis 

metric matching estimator OLS = ordinary least squares regression TVA = Tennessee Valley Authority IMPLAN = IMpact analysis for PLANning, 

a commercially available input-output modeling software package GAO = U.S. General Accounting Office (renamed the U.S. Government 

Accountability Office in 2004) 2SLS = Two-stage least squares regression.
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ARC- and TVA-assisted counties. The results were mixed but tended to 

show more positive impacts of the TVA than the ARC. In rural counties, 

TVA was associated with significantly faster rates of growth of manufacturing earnings and employment than other eastern counties, while rural 

counties in ARC had significantly slower growth in these two measures of 

manufacturing economic performance. The authors provided some possible 

explanations for the negative effects of the ARC on manufacturing earnings 

and employment, including the observation that manufacturing activity was 

moving southward during the 1980s, away from many ARC counties and 

toward TVA counties, for reasons unrelated to the two programs and unaccounted for by their impact estimation model. Hence their impact estimates 

may have been biased by this.

Studies by Brandow et al. (2000) and BizMiner/Brandow, Inc., and EDR 

Group (2007), using the reported number of jobs directly created by ARC 

projects combined with estimated multiplier impacts using an input-output 

model (IMPLAN), estimated large impacts of the ARC on employment and 

public revenues. For example, Brandow et al. (2000) estimated that each 

$1 of ARC funding leveraged $2.61 of other public funds and almost $100 

of private investment, increased employment at an average cost to ARC of 

$1,222 per direct job created and a cost in total public funds of $4,574 per 

direct job created (the costs per total number of jobs created, considering 

multiplier impacts, were less), and generated $5.40 of additional wage income 

per $1 of total public funds invested. The impacts estimated by BizMiner/

Brandow, Inc. and EDR Group (2007) were of similar magnitude.

Similar positive impacts of EDA’s programs have been found by several 

evaluations. For example, Burchell et al. (1997), using self-reported impacts 

from project officials, estimated for all projects completed in 1990 that each 

$1 of EDA funds leveraged $1 of other public funds and $10 of private investment, that each $1 million of EDA funds produced 327 direct permanent jobs 

plus 50 nonproject-related direct jobs and 64 project-related indirect jobs, 

produced direct permanent jobs at a median cost to EDA of $3,058 per job 

and $4,857 in total public cost per job, and increased local public revenues at 

a rate of $10 per $1 of EDA funds. Using the IMPLAN input-output model, 

Burchell et al. (1998) extended these results to estimate that for each direct 

permanent job created by EDA projects, an additional 0.5 jobs were created 

through indirect and induced labor market multiplier effects8; and that an 

additional $0.44 in private investment was stimulated through multiplier 

effects for each $1.00 in private investments directly resulting from the projects. These estimates imply that the cost per total number of jobs created 

would be less than those cited above, given such multiplier effects. 

Impact estimates based on self-reported figures from grant recipients and 

multipliers estimated by input-output models may be unreliable for several 

reasons. Grant recipients have incentives to overstate the positive impacts of 

their programs so as to help ensure continued funding, and may be unaware 

of (or unlikely to report) negative impacts, such as jobs that were displaced by 

project investments (for example, construction of a new hospital in a community may displace jobs in existing health care facilities). Multiplier estimates 

from input-output models account for indirect and induced impacts, but 

these are subject to several criticisms. They are based on static linear fixed 

coefficient models of production, and typically assume that local factors of 

8Indirect multiplier effects refer to 

impacts resulting from interindustry 

linkages (e.g., employment in local 

industries that supply inputs to the 

firms directly affected by the project, 

employment in other local firms that 

provide supplies to those supplier firms, 

and so on). Induced multiplier effects 

result from the increase in demand due 

to increased income of residents of the 

region, which leads to its own chain of 

direct and indirect demand effects.
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production respond perfectly elastically (at fixed prices) to increased demand; 

hence they ignore increases in costs of labor, land, or other inputs that may 

result from new investments (Kilkenny and Partridge, 2009). They ignore 

external positive and negative impacts of new developments, such as economies of agglomeration resulting from spillovers of knowledge and technology 

among firms, increased pollution, or congestion in use of infrastructure and 

public services (Edmiston, 2004). And they are often based on data estimated from national accounts rather than detailed survey data for the local 

economy (Rickman and Schwer, 1995). Evidence from several econometric 

impact studies suggests that input-output models often overestimate impacts 

of new industrial development on local economies (Edmiston, 2004; Fox and 

Murray, 2004; Kilkenny and Partridge, 2009).

To address concerns about the limitations of self-reported direct impacts and 

multipliers from input-output models, several studies have used econometric 

analysis of county-level data on employment. Burchell et al. (1998) estimated 

impacts of EDA spending on total employment (including direct as well as 

multiplier effects) using econometric regression models with county-level 

data for 1990 to 1994, estimating that county employment increased by nine 

jobs per $10,000 of EDA spending, implying a mean cost per job created of 

$1,100. Haughwout (1999) (one of the co-authors of the Burchell et al. (1998) 

study), estimated a similar econometric model to Burchell et al. (1998) but 

considered several alternative specifications. He found that EDA spending 

had a robust positive impact across the specifications considered, although 

the magnitude of impacts was substantially smaller than in the Burchell 

et al. (1998) study, implying an EDA cost of $9,953 per job created in the 

preferred specification. More recently, Arena et al. (2008) estimated impacts 

of EDA funding using county-level data for 1990 to 2005, considering several 

different regression specifications. They found robust positive impacts of 

EDA funding on employment in rural areas across specifications but insignificant impacts in urban areas; their estimates of employment impacts in rural 

areas imply a mean EDA cost per job created ranging from $2,001 to $4,611. 

They also estimated impacts by type of EDA project and found the smallest 

cost per job created for business incubator projects and the largest cost for 

community infrastructure projects. The findings from these county-level 

regression analyses were broadly consistent with the findings from the inputoutput model studies in terms of the estimated cost per job created. 

Econometric impact studies of regional development programs have also 

not been without critics. For example, the U.S. General Accounting Office 

(GAO) argued that the Isserman and Rephann (1995) study did not establish a causal connection between the ARC’s specific programs and specific 

outcomes, such as a highway construction program leading to growth in 

manufacturing (U.S. GAO, 1996, p. 4). GAO also argued that none of the 

studies of ARC and EDA available in 1996, including the Isserman and 

Rephann study, attempted to rule out alternative causal explanations for 

observed growth differences, such as the rise in coal prices that occurred in 

the 1970s (but see the discussion on page 8 on this point).

Glaeser and Gottlieb (2008) also disputed the conclusions of Isserman and 

Rephann. Their own analysis used ordinary least squares (OLS) regressions, 

taking growth in county population or per capita income from 1970 to 1980 and 

1970 to 2000 as dependent variables, using as the only explanatory variables 
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a dummy variable for whether the county was an ARC county and the initial 

level of either population or income per capita, and focusing on counties in the 

ARC States (excluding counties close to the coast). They found that ARC counties had more rapid population growth than non-ARC counties from 1970 to 

1980, but no significant difference in population growth from 1970 to 2000, and 

that ARC counties had slower growth in per capita income from 1970 to 2000. 

Based on this analysis, Glaeser and Gottlieb conclude that “it is unlikely that 

the effects of a $13-billion program spread over a giant swath of America over 

three decades can be accurately evaluated” (p. 200). Although their research 

points out how such studies are potentially vulnerable to problems related to 

omitted variables, Glaeser and Gottlieb’s analysis and conclusions are subject 

to the same criticism, since they did not attempt to control for the many factors 

that could have affected differential growth rates between ARC and non-ARC 

counties, as did Isserman and Rephann.9

In a review of the Burchell et al. (1998) study of EDA’s impacts, the GAO 

(1999) used the same data as Burchell et al. to replicate their results and 

then showed how inclusion of an additional variable (either the employment 

level or the population level in the county in the 1980s) caused the estimated impact of EDA spending to be small and statistically insignificant. 

Haughwout (1999) addressed this criticism to some extent by including the 

1988 county employment level as an explanatory variable in his specifications, which may explain why he found a smaller impact of EDA spending 

than Burchell et al. However, there were other differences in the specifications used by Haughwout (1999) compared to those of Burchell, et al. (1998) 

and U.S. GAO (1999). Hence, it is not possible to determine exactly why the 

estimates of Haughwout differed from prior studies. 

9Glaeser and Gottlieb (2008, p. 199) 

state that their results are not sensitive 

to including other controls, but provide 

no indication of what other controls they 

considered or evidence of the results.
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The Delta Regional Authority

The Delta Regional Authority, patterned after the ARC, is a Federal-State 

partnership involving eight States (Illinois, Missouri, Kentucky, Tennessee, 

Arkansas, Mississippi, Louisiana, and Alabama). It is led by a Federal 

Co-Chair and the Governors of the participating States, one of whom serves 

as the State Co-Chair (on a rotating basis). Within these States, 252 counties and parishes are currently eligible for the program (fig. 1). Originally 

240 counties were eligible, including the 219 counties in the 7-State LMDDC 

region, plus 1 additional parish in Louisiana and 20 counties in Alabama. 

Four of the Alabama counties were added by the 2002 Farm Security and 

Rural Investment Act. The 2008 Food, Conservation, and Energy Act added 

10 additional parishes in Louisiana and 2 counties in Mississippi.

The population of the DRA region was 9.5 million in 2000. The region is 

the most economically distressed large region of the country, with 250 of the 

DRA counties and parishes having per capita incomes below the national 

average, a poverty rate for the region 55 percent higher than the national 

average, a high school dropout rate almost 20 percent above the national 

Figure 1

Counties eligible for Delta Regional Authority (DRA) development projects

Notes: The DRA operates in Alabama, Mississippi, Louisiana, Arkansas, Tennessee, Missouri, Kentucky, and Illinois. 

For a list of the 252 DRA-eligible counties, see http://www.dra.gov/about/maps.aspx/. 

Source: Delta Regional Authority, 2010.

DRA counties

Non-DRA counties
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average, and infant mortality rates nearly 30 percent above the national 

average (http://www.dra.gov/delta-facts/). An Economic Research Service 

(ERS) analysis of the 219 counties within the original Lower Mississippi 

Delta region found that most were persistent poverty counties (Reeder and 

Calhoun, 2002). Thus this region is classified as distressed and poor, regardless of which measure is used.

The DRA Act requires that at least 75 percent of the DRA funds be used to 

serve the needs of distressed counties. DRA project funding has far exceeded 

this threshold, with 94 percent of project funding approved during 2002 to 

2008 invested in distressed counties (DRA, 2009), because the vast majority 

of eligible DRA counties and parishes are distressed. In 2010, 223 of the 252 

DRA counties and parishes were classified as distressed according to the 

criteria established by the EDA (DRA, 2010).10

The DRA is authorized to provide grants to States and public and nonprofit 

entities for development projects, with the following order of priority: (1) 

basic public infrastructure in distressed or isolated areas of distress; (2) transportation infrastructure facilitating regional economic development; (3) business development, with emphasis on entrepreneurship; and (4) job training 

or employment-related education. At least 50 percent of project grant funds 

are required to be for transportation and basic public infrastructure projects. “Basic public infrastructure” is defined by the DRA as including water 

and wastewater facilities, electric and gas utilities, broadband delivery, and 

solid waste landfills (DRA, 2010). Development of Geographic Information 

Systems (GIS) to support such basic infrastructure is also classified as a 

subpart of basic public infrastructure (Ibid.). “Transportation infrastructure” 

means basic physical structures such as roads, bridges, rail, port facilities and 

airports, but local, State and Federal highway and bridge maintenance projects are not eligible for DRA funding. Often transportation projects involve 

an extension of a road or provision of another type of transportation structure 

to an industrial park or within an industrial park or port facility. From 2002 

to 2008, 76 percent of DRA project funds were invested in these priority projects (DRA, 2009).

The multicounty Local Development Districts (LDDs) operating as lead organizations for this program are those already established by EDA, or if no such 

district exists, some other entity meeting statutory requirements concerning 

representation on the board. The DRA was also given authority to cover up to 

80 percent of the LDD’s administrative expenses.

The DRA held its first meeting in late 2001 and completed its first 5-year plan 

in 2002. That plan articulated four long-term goals of the DRA, including 

increasing income levels, reducing unemployment and underemployment, reducing dependency on Federal support and transfer payments, and 

providing the infrastructure necessary to support economic and domestic 

development (DRA, 2008b). The plan established 5-year targets for several 

outcomes, including income, unemployment, poverty, transfer payments, 

public assistance, education, single parent households, and labor participation.

In its second 5-year plan, published in 2008, the DRA built upon this framework, highlighting five key categories of investment to be funded by its 

competitive grants:

10The DRA and EDA consider a 

county or parish distressed if it has 

(1) an unemployment rate at least 

1 percentage point greater than the 

national average for the most recent 

24-month period; or (2) per capita 

income that is 80 percent or less of the 

national average for the most recent 

period for which data are available; or 

(3) a special need arising from actual 

or threatened severe unemployment or 

economic adjustment problems due to 

various causes.
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• Health as an economic engine

• Information technology

• Transportation

• Workforce education and leadership

• Traditional localized projects supporting basic infrastructure, transportation, workforce training, and business development

The DRA recognized from the outset that its ability to achieve improvements in outcomes would be limited by its modest budget and staff resources. 

The model pursued was to concentrate on developing the assets needed to 

sustain long-term growth in selected critical mass communities11 by coordinating the efforts of multiple organizations and leveraging additional public 

and private investments. One fact that contributes to the potential leverage 

of DRA funds is that the DRA Act allows DRA funds to be used to supplement other Federal program funds above the maximum amounts of Federal 

support authorized by other applicable laws, up to 90 percent Federal support 

of project costs in general and up to 100 percent support of projects providing 

transportation or basic public services in distressed counties or isolated areas 

of distress (PL 106-554, Sections 382D(b) and 382F(b)).12 In poor counties 

where the ability to provide local matching funds can pose a major constraint, 

the ability to use DRA funds beyond normal Federal statutory limits may be 

especially important in enabling projects to be implemented.

The allocation of DRA funds to the member States is determined by a 

formula that considers equity among the States (accounting for 50 percent of 

the score used for the allocation), the total population of the DRA counties/

parishes of each State (10 percent), the distressed population of the DRA 

counties/parishes of each State (20 percent), and the distressed DRA county 

area of each State (20 percent). During the first 8 years of the program 

(2002-09), Louisiana received the largest share of grant funds (20.4 percent), 

followed by Arkansas (15.2 percent), Mississippi (14.2 percent), Missouri 

(11.6 percent), Tennessee (11.1 percent), Alabama (10.4 percent), Kentucky 

(8.6 percent), and Illinois (8.5 percent). 

A joint Federal-State annual process is used by the DRA and its member 

States to select projects for funding (DRA, 2010). Early in the calendar year, 

a call for preapplications is issued and publicized by the LDDs, which also 

provide education and assistance to applicants.13 By March, these preapplications are received by the LDDs and forwarded to the DRA and each State’s 

Governor. The Federal Co-Chairman and his or her staff review the preapplications to determine whether they meet eligibility requirements established by the DRA Act and DRA’s policies and clarifications. For example, 

projects are classified as eligible depending on whether they demonstrate that 

they will be sustainable, provide funding only to eligible entities (excluding 

private/for-profit entities and entities deemed ineligible due to poor prior grant 

history), and provide funds for eligible purposes. The DRA Act specifically 

prohibits use of DRA funds to assist businesses to relocate from one area of 

the Delta region to another, or to supplant existing funding streams. By April, 

the Federal Co-Chairman provides the list of eligible projects to the State 

Governors, who then select projects by June for precertification within the 

11The DRA defines critical mass 

communities as “those communities in 

which the necessary elements exist in 

sufficient quantity to create and sustain 

a vital economy,” including healthy 

people, an expanding population, a 

skilled workforce, multiple cultures, 

new companies, an entrepreneurial 

culture, and a communitywide culture 

of learning (DRA, 2008b).

12A similar but more restrictive 

provision applies to the ARC, which 

under the ARC Act can provide 

Federal funds to distressed counties 

in excess of the maximum portions 

authorized by other laws, but restricts 

the maximum Federal contribution to 

be not more than 80 percent (PL 89-4, 

Section 214(b)).

13The timeline described is that used 

in 2010. Timelines in earlier years may 

have been somewhat different.
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context of their own DRA-approved State plans and priorities. Pre-certified 

entities are then invited to prepare and submit full applications, with the DRA 

staff playing a collaborative and collegial role in helping “these ‘pre-grantees’ 

to best insure their applications are sound and all contingencies are satisfied” (DRA, 2010, p. 84). The DRA staff review and certify the applications, 

which are then forwarded to the State Governors for their certification. The 

process is completed and applicants are notified by early in the fall.

This process is fairly selective. Of the 240 counties and parishes that were 

eligible for DRA projects during 2002 to 2008, only 166 had projects 

approved (DRA, 2009). In 2009, of 261 preapplications submitted, only 88 

were deemed eligible by the Federal Co-Chairman (DRA, 2010). Forty-two 

of the ineligible applicants appealed the decision and 12 of these were 

reversed, resulting in 100 eligible applicants. Of these, 69 projects were ultimately selected by the State Governors and approved. 

The DRA began funding projects through its grant program in 2002. During 

its first 8 years of operation (2002-09), the DRA invested $74.6 million in 

510 projects (DRA, 2010). Due to a relatively large initial appropriation, the 

number and value of projects approved was greater in 2002 than in subsequent years, although the size of the program has increased since the low 

point in 2004 (fig. 2). The DRA reports that this investment helped to attract 

$353.8 million in other public funds ($4.75 in additional public funds per 

$1 invested by DRA) and $1,544.4 million in private investment ($20.71 

in private investment per $1 invested by DRA) (DRA, 2010).14 The most 

common type of investment supported by DRA funds was investment in 

water and/or sewer systems; these accounted for 29 percent of DRA project 

funds invested during 2002 to 2008.15 Following this were investments in 

roads (12 percent), industrial parks (9 percent), education and training (8 

percent), port facilities (8 percent), medical facilities (7 percent), and business 

development (5 percent).

The amount of DRA funding per capita was small; averaging $16.75 per capita 

in the counties/parishes receiving DRA grant funds between 2002 and 2007 

(table 2). The amounts varied significantly across and within States, with 

14We are unable to determine how 

these estimates were collected or verify 

their validity. Regardless of how accurately such numbers are collected and 

reported, the claim that funds of one 

program “leveraged” funds provided 

by other investors is difficult to test, 

since one cannot readily determine 

whether such other investments would 

have occurred at the same level without 

those of the program. An indirect 

test of the leverage of the DRA funds 

is whether significant differences in 

outcomes are associated with DRA 

spending. Given the small size of DRA 

spending per capita, substantial measurable impacts on outcome measures 

are unlikely without the presence of 

leveraged funds of other programs and 

investors. We discuss this issue further 

later in the paper.

15The figures cited in this paragraph 

on allocation of project funds by project type are based on the project names 

and funding amounts listed in the DRA 

Federal Grant Profile, as more detailed 

descriptions of the projects were not 

available. Hence, there could be errors 

in the classification of some of these 

projects by type.

Figure 2

Value of DRA grants approved, 2002-09

Million dollars

Notes: DRA = Delta Regional Authority.

Source: DRA, 2010.

2002 03 04 05 06 07 08 09

0

5

10

15

20

25

30
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the largest mean per capita funding in Illinois ($32.73) and the smallest in 

Mississippi ($13.09). These small amounts of funding per capita suggest that 

identifying economic impacts of DRA funding using statistical methods is 

likely to prove difficult. However, given the amounts of other public and private 

investments that DRA reports to have leveraged, the total per capita investments stimulated that were stimulated by per capita DRA investment could 

have been substantially greater (on the order of 25 times larger according to the 

leverage ratios reported by DRA). Hence, the prospect of measuring impacts of 

the program is not as remote as it may appear, if the program was effective in 

catalyzing other investment funds.

Table 2

Delta Regional Authority grant value per capita in recipient counties, 

2002-07

State Recipient counties Mean grant value Standard deviation 

Number ———— Dollars per capita ————

Alabama 17 19.61 17.92

Arkansas 26 19.59 19.72

Illinois 14 32.73 45.68

Kentucky 11 16.94 19.19

Louisiana 31 13.22 24.41

Mississippi 30 13.09 15.16

Missouri 19 13.45 9.86

Tennessee 18 13.13 14.05

Delta region 166 16.75 22.08

Source: USDA, Economic Research Service, calculated from DRA data (2009) and from U.S. 

Department of Commerce, Bureau of Economic Analysis (for county population), 2002 to 2007.
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Initial Economic Outcomes of the DRA

In this section we assess the initial economic outcomes of the DRA’s grant 

program using a combination of quasi-experimental matching methods 

and multivariate regression analysis. We focus on the period 2002 to 2007 

because the program began to be implemented in 2002 and because the 

data used for the analysis were available up to 2007 at the time the analysis 

was completed.

Estimation approach

The basic problem in assessing the impacts of any program on program 

participants is that we don’t observe the counterfactual situation (i.e., what 

would have happened to program participants in the absence of the program). 

In situations where random assignment of participants is possible, this 

ensures that the mean outcome observed for the nonparticipant group should 

be the same as the counterfactual mean outcome that would have been 

observed for the participants had they not been affected by the program, as 

long as the program does not affect the outcomes of the nonparticipants.16

When such conditions are satisfied, an experimental approach can provide a 

reliable estimate of program impacts. This is why use of randomized experiments is the preferred method for assessing impacts of social programs when 

this is possible (Heckman et al., 1998). This approach has been used to identify the impacts of job training and social welfare programs in the United 

States, for example (Ibid.; Moffitt, 2004).

Unfortunately, randomized evaluations are often not possible. This is 

certainly the case for assessing impacts of programs that are already operating and for which the selection of beneficiaries was not random, such as 

the DRA. Without random assignment, some method of estimating the counterfactual outcomes is necessary. Often evaluators estimate both the factual 

and counterfactual outcomes using a predictive model, such as in the evaluations of ARC by Brandow et al. (2000) and BizMiner/Brandow Co. Inc. and 

EDR Group (2007). The validity of such model-based evaluations hinges on 

the validity of the model assumptions, which are often questionable, and the 

empirical validity of the model predictions is often difficult to gauge. One 

benefit of using empirical ex post evaluation methods is that they can provide 

a test of the predictions of model-based ex ante methods, as demonstrated by 

Burchell et al. (1998) in their use of both approaches to evaluate the impacts 

of EDA programs. 

The most commonly used methods of ex post economic impact evaluation 

of programs include multiple regression analysis, double-difference (DD) 

estimation, and quasi-experimental matching methods (Ravallion, 2008). 

Multiple regression analysis usually specifies a parametric statistical model 

for how the program and other observed confounding factors affect the 

outcomes of interest, estimating the impact of the program conditional upon 

the levels of those other factors. DD estimation computes the impact of the 

program by estimating the mean difference in outcomes for participant and 

nonparticipant groups during or after the program, and subtracting the mean 

difference in outcomes between the two groups before the program. Matching 

methods compare mean outcomes between groups of participants and 

16If, to the contrary, a program has 

“spillover effects” on nonparticipants, 

then the difference between mean 

outcomes for participants and nonparticipants does not fully reflect the mean 

impacts of the program, even if these 

two groups are randomly selected.
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nonparticipants selected to be similar in observed preprogram characteristics 

that are thought to jointly affect program participation and outcomes. Since 

matching tries to mimic the randomized experimental approach in selecting 

“treatment” and “control” groups that are similar, this approach is referred to 

as a quasi-experimental design (Cook and Campbell, 1979).

These approaches have different strengths and weaknesses. Parametric 

regression models provide the most efficient estimator (i.e., the smallest 

degree of uncertainty) if the parametric assumptions are correct; and a large 

variety of methods have been developed to test and correct for violations of 

the basic assumptions. However, these models can give biased results if the 

parametric assumptions are violated or if factors that affect both program 

participation (or other explanatory variables) and outcomes are excluded from 

the model. Matching methods avoid dependence of the results on parametric 

assumptions about how the program and other factors affect outcomes; as 

long as there are sufficient numbers of good matches in the nonparticipant 

group for each observation in the participant group, matching can produce 

valid impact estimates regardless of the true relationship between observed 

variables and outcomes. However, like regression methods, matching 

methods are sensitive to omission of relevant factors that jointly affect participation and outcomes. DD estimation addresses the problem of unobserved 

confounding factors by subtracting out initial mean differences between 

the participant and nonparticipant groups. This approach is effective if the 

confounding factors are fixed over time and have the same additive impact on 

both groups, since the effects of these factors will be subtracted out (whether 

or not such confounding factors are observed). However, DD estimation does 

not address differences that may arise if the two groups had different trends 

in outcome variables even before the program. 

Using combinations of these methods has the potential to address the limitations of individual methods, resulting in more robust conclusions (Ravallion, 

2008). For example, use of matching in combination with DD estimation can 

help to reduce the potential that the two groups were on different trajectories 

prior to the program (since they will be matched in terms of preprogram 

characteristics, including possibly their preprogram outcome trajectories), 

while the DD estimator subtracts out the effects of fixed factors that could 

cause a simple matched comparison to yield biased conclusions. Similarly, 

combining DD estimation with regression reduces the confounding effects 

of fixed or commonly changing factors, and enables controlling for factors 

that may differ between the groups using the regression model. Finally, 

combining matching with regression analysis can help reduce the dependence 

of the regression results on parametric assumptions by assuring that program 

participants are compared to nonparticipants having similar levels of the 

explanatory variables.

For these reasons, we use a combination of these methods in our analysis. 

First, we estimate the impacts of the DRA on changes in outcome variables from 2002 to 2007, comparing DRA-recipient and matched non-DRA 

counties. This combines DD estimation with matching and is similar to the 

approach used by Isserman and Rephann (1995) to evaluate the impacts 

of the ARC, as well as several studies of impacts of enterprise zones and 

Empowerment Zones.17 The difference between our approach to matching 

and that of Isserman and Rephann is that we try several different matching 

17Recent studies by ERS researchers 

have also used this approach, including 

studies assessing the economic impacts 

of the Conservation Reserve Program 

(Sullivan et al., 2004) and access to rural broadband (Stenberg et al., 2009).
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estimators (including variants of the Mahalanobis metric (MM) estimator 

used by Isserman and Rephann and other regional scientists, and variants of propensity score matching (PSM), which is commonly used by 

labor economists) because of strengths and weaknesses of each approach. 

For simplicity of exposition, in our presentation of results we focus on the 

results using PSM with kernel matching (PSM-KM), because this estimator 

provided the best matches in our data.18 Although there are some differences in our results across estimators, our main conclusions are robust to 

the choice of matching estimator.

To test whether our results using matching and DD estimation are biased 

by unobserved confounding factors, we use a test suggested by Imbens 

and Wooldridge (2009): test for significant differences in outcomes using 

preprogram data. If there are significant differences in preprogram trends in 

outcome variables between participants and nonparticipants, it contradicts the 

assumption that the differences in trends observed during the program period 

are due to the program. As we shall see, we find few such differences in prior 

trends across different outcome variables. We also compare preprogram and 

post-program trends in selected outcome variables between the matched 

groups using graphs, helping to identify whether differences observed during 

the program period could be explained by preprogram differences in trends. 

We also combine matching and DD estimation with regression analysis to 

correct for errors caused by imperfect matching and to investigate the impacts 

of the level of DRA program spending. Studies that have used only matching 

approaches, such as Isserman and Rephann (1995), fail to correct their 

estimates for the fact that the matches found are imperfect (i.e., there exist 

differences in the mean values of the covariates between the matched groups, 

even though the matching generally reduces these differences). Regressions 

can be used to correct for the effects of these differences. Furthermore, 

matching approaches fail to account for different levels of program intensity, 

as reflected by differences in the level of program spending per capita. By 

using regression analysis on the matched samples, we are able to address 

both of these limitations. Furthermore, we allow the regression coefficients to 

differ between the participant and nonparticipant groups (called a “switching 

regression”), which allows us to test and account for heterogeneous program 

impacts (i.e., impacts that differ according to different levels of the explanatory variables (Crump et al., 2008)).19 Our statistical tests strongly supported 

the model with heterogeneous impacts. 

Study Population

The population and units of observation for this study include nonmetropolitan DRA-recipient counties and other nonmetropolitan counties in the 

eight DRA States and in three additional States of the Southeastern United 

States—Georgia, South Carolina and North Carolina. We focus on nonmetropolitan counties because we seek to understand the impacts of the program 

on rural development.20 The Southeastern States were included to identify 

possible matched counties to compare to DRA-recipient counties because of 

their similarities to the Delta region (especially the southern Delta region) in 

terms of outcome indicators such as income per capita and poverty, in their 

economic structure, and in their broad historical context. Within the DRA 

region, DRA-eligible counties that did not receive DRA program funding 

18The theoretical strengths and 

weaknesses of different matching 

approaches and the results of using 

different approaches are discussed in 

appendix A.

19The switching regression model is 

defined in detail in appendix B.

20Although “nonmetropolitan” is not 

a perfect proxy for rural (see http://

www.ers.usda.gov/Briefing/Rurality/

WhatIsRural/), we use this classification because we are using countylevel data, limiting our ability to focus 

specifically on rural areas within 

metropolitan or nonmetropolitan counties. An alternative approach could be 

to focus on nonmetro counties that do 

not include any urban areas, but this 

would limit the analysis to 123 counties, including only 36 fully rural DRA 

recipient counties.
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during the time period studied (2002 to 2007) were not included as possible 

comparison counties because of concerns about spillover impacts of DRA 

projects to nearby DRA-eligible but nonrecipient counties, which could 

make such counties a poor choice to represent the counterfactual nonprogram situation. Concerns about selection bias are also greater in comparing 

DRA-recipient to eligible nonrecipient counties, since eligible nonrecipient 

counties may be different from recipients in important but unobserved ways, 

such as in their ability to organize to obtain and manage project funding. We 

also excluded ARC counties as either DRA-treated counties or as possible 

controls, to avoid confounding the impacts of the DRA with the impacts of 

the ARC.21

In total, there are 196 nonmetro counties among the 252 DRA-eligible 

counties. Of these, 133 received DRA funds during 2002 to 2007. Two of 

these counties are also part of the ARC, so were excluded, as were 131 

non-DRA nonmetropolitan ARC counties from the pool of potential control 

counties. The resulting population included 131 DRA-recipient counties 

and 330 non-DRA-eligible counties in the 11 States included in the study. 

The common support requirement22 used in the matching eliminated 28 

of the DRA-recipient counties, leaving 103 DRA-recipient counties in the 

study sample. The DRA-recipient counties that failed to meet the common 

support requirement were mainly counties having significant cotton and rice 

harvested areas, although several other differences exist between these counties and the other DRA-recipient counties.23 The per capita areas harvested of 

cotton and rice were included because of strong trends in commodity prices 

during the study period that could have affected relative changes in farm 

earnings in DRA vs. non-DRA counties, especially for rice. In initial analysis 

of the data, these variables were not included among the covariates and much 

more rapid growth in average farm earnings was found in DRA counties, 

suggesting unrealistically large positive impacts of the DRA on earnings and 

income. Inclusion of cotton and rice area in the matching procedures substantially reduced these differences. Rice production was very limited in the 

study counties outside of the DRA region, so it was not possible to find good 

matches for major rice-producing counties. Hence, our findings cannot be 

interpreted as applying to all DRA counties, but rather are limited to nonmetropolitan DRA counties without significant rice area.

Outcome and Control Variables

The outcome variables investigated include county-level personal income 

per capita and its components (net earnings; dividends, interest, and rent; 

and personal transfer payments); employment per capita; and population. 

We also investigated impacts on earnings and employment per capita by 

major industry classification for the seven largest industries in nonmetropolitan counties of the Delta Region (construction, manufacturing, retail, 

education, health care and social services, farming, and government). More 

than 70 percent of the adult working population in the nonmetropolitan 

DRA-recipient counties was employed in these industries in 2000, with more 

than 5 percent of adults employed in each. In almost all cases, the earnings 

and employment impacts by industry were statistically insignificant, so we 

do not report these in general. We report the impacts on earnings from the 

regression analysis for only one industry—health care and social services—

21In initial analysis of the data, we 

included DRA-eligible nonrecipient 

counties, ARC counties, and counties in 

West Virginia and Virginia as possible 

controls. The results were qualitatively 

similar, with significantly faster growth 

in income and transfer payments per 

capita in DRA-recipient counties than in 

matched nonrecipient counties. (These 

results are available upon request). We 

excluded counties in West Virginia in 

the final analysis because these were 

mostly counties in the ARC. Very few 

counties in Virginia were selected as 

matched controls, so Virginia was 

dropped to simplify the analysis.

22See appendix A for an explanation 

of the common support requirement.

23The mean 2002 harvested cotton 

and rice areas per capita in the 28 

counties that failed the common support requirement were 2.45 acres of 

cotton and 2.57 acres of rice, compared 

to 0.54 acres of cotton and 0.04 acres 

of rice for the other nonmetro DRArecipient counties. These differences 

are highly statistically significant 

(p-level less than 0.01 percent). Other 

statistically significant differences 

between the DRA counties that failed 

the common support requirement and 

other DRA-recipient counties are also 

evident. In general, the dropped DRA 

counties have many characteristics associated with greater poverty.
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because this is the only industry in which statistically significant impacts 

were found.24 We also investigated and report the impacts on different types 

of transfer payments. 

The control variables (“covariates”) included in the analysis include many of 

the same variables used in other studies of impacts of rural interventions on 

rural economic growth (e.g., Isserman and Rephann (1995), Stenberg et al. 

(2009)), including indicators of prior outcomes (personal income per capita in 

2000, the poverty rate in 2000, shares of personal income from asset returns 

and from transfer payments in 2001, population in 2000), economic structure (share of adults in 2000 employed in the seven largest industries), and 

spatial structure (distances to the nearest urban center of different sizes in 

1980 (25,000 or more; 100,000 or more; 250,000 or more; 500,000 or more; 

1 million or more) and population density in 1990). 

Additional covariates not included in Isserman and Rephann (1995) were 

included in the analysis because these were judged to possibly differ between 

DRA-recipient counties and non-DRA counties and to potentially affect 

changes in outcomes. These covariates included indicators of the demographic and educational structure of the population in 2000 (rural share, farm 

household share, African American share, share age 17 or less and share age 

65 or more, share of adults with more than a high school diploma), employment conditions in 1999 (share of men and share of women working full 

time all year), cotton and rice areas harvested per capita in 2002, Federal 

economic development grant funds received per capita during 2000-01, 

and whether the county was in a Gulf Opportunity Zone. Federal economic 

development funding is a potentially important confounding factor, since 

such funds may augment or displace funds provided by the DRA. Failure 

to account for this (and other) confounding factors could have biased the 

conclusions of prior studies of the impacts of particular economic development interventions. The variable for Gulf Opportunity Zone counties was 

included to account for potential impacts of Hurricanes Katrina and Rita, 

and the effects of the Katrina Emergency Tax Relief Act of 2005 (KETRA) 

and the Gulf Opportunity Zone (GO Zone) Act of 2005. Of the twelve counties most affected by flooding resulting from Hurricane Katrina, only one—

Tangipahoa Parish in Louisiana—is a nonmetropolitan county. Excluding this 

parish from the analysis had little impact on the results.

The data sources used for these variables are summarized in appendix C.

24These results are available in 

Pender and Reeder (2010).
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Differences in Outcomes Between DRA 

Counties and Matched Non-DRA Counties

The results of the estimation using the DD estimator with PSM-KM matching 

are reported in the second column of table 3. We find that growth in per 

capita personal income from 2002 to 2007 was greater in the DRA counties than in the matched non-DRA counties, with the difference statistically 

significant at the 90-percent confidence level. The mean income growth 

was about $600 per capita higher in DRA counties than in the matched 

non-DRA counties. The magnitude of this difference was similar using all of 

the variants of the matching estimators—between $500 and $600, and was 

statistically significant in most cases (see appendix A). Looking at the major 

components of personal income, we find a statistically significant difference 

only in growth of personal transfer payments (growth in transfer payments 

Table 3

Mean changes in outcomes, Delta Regional Authority minus matching 

counties (standard errors in parentheses)1

Dependent variable

2002-2007 

(DD estimator)

2000-2002

(preprogram DD)

Personal income per capita 597.0*

(333.5)

19.61

(138.1)

Major components of personal income

Net earnings per capita 240.2

(229.1)

123.8

(141.8)

Dividends, interest and rent per capita 164.6

(127.9)

-33.2

(33.6)

Personal transfer payments per capita 192.1**

(81.5)

-3.3

(41.0)

Employment per capita -0.0028

(0.0053)

0.0036

(0.0043)

Population -447.5

(543.0)

-132.2

(88.9)

Transfer payments by type

Retirement and disability 16.4

(26.8)

-8.4

(15.4)

Medical 120.5**

(59.2)

-2.4

(35.7)

Income maintenance 35.3**

(16.0)

19.9***

(7.1)

Unemployment insurance 6.3

(10.1)

-12.4

(8.4)

Veterans benefits 9.6

(6.4)

-2.4

(2.6)

Federal education and training assistance 2.1

(8.1)

-1.3

(6.2)

*, **, *** Difference statistically significant at 10-percent, 5-percent, and 1-percent levels, 

respectively. DD=Double difference; see page 21, paragraph 4 for explanation.

1Based on 103 DRA-recipient counties and 330 non-DRA counties. Using propensity 

score—kernel matching (PSM-KM) (standard errors in parentheses). 

Source: USDA, Economic Research Service analysis.
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was almost $200 per capita higher in DRA counties than in the matched 

non-DRA counties), although there was a positive but not statistically 

significant difference in growth of net earnings per capita and in dividends, 

interest, and rent per capita. The positive and statistically significant impact 

on transfer payments was robust across most matching estimators, as was the 

positive but statistically insignificant impact on earnings as well as on dividends, interest, and rent (appendix A).25 We find no statistically significant 

difference in growth of employment per capita or in population growth.

Since we found a robust positive association between DRA counties and 

growth in personal transfer payments, we investigated differences for specific 

types of transfer payments to better illuminate the nature of the impacts 

of the DRA. We found significant differences for two types of transfer 

payments—medical transfer payments (primarily Medicare and Medicaid) 

and income maintenance program payments (mainly Supplemental Security 

Income, Temporary Assistance for Needy Families, and the Supplemental 

Nutrition Assistance Program), with $120 per capita higher growth in 

medical transfer payments in DRA counties and $35 per capita higher growth 

in income maintenance program payments. These results were robust across 

most matching estimators (appendix A).

These differences in growth of medical and income maintenance transfer 

payments might be due to differences in demographic changes between 

DRA and non-DRA counties. For example, if the share of the elderly population grew more in DRA counties, this could explain differences in growth 

of Medicare payments. Similarly, differences in growth of different racial 

groups could cause differences in use of income maintenance programs, to 

the extent that participation in these programs differs across racial groups.26

However, we found no statistically significant differences between DRA and 

matched non-DRA counties in terms of changes in the age composition of 

their populations using any estimator, or in the racial composition of their 

populations using the preferred PSM-KM matching estimator.27 Thus the 

evidence does not support these alternative explanations.

Differences in Prior Trends in Outcomes

As noted earlier, if there were differences in prior (and continuing) trends 

in outcomes between the DRA and matched non-DRA counties, this could 

bias the results of our analysis. The third column of table 3 presents our 

analysis of this issue. We find statistically insignificant differences between 

the matched groups in the growth rates from 2000 to 2002 of all but one of 

the outcome variables. For those variables, we therefore have no evidence of a 

difference in prior trends affecting our results. The one outcome variable for 

which we did find a significant difference in prior trends was income maintenance transfer payments per capita. We do not know why such payments were 

growing faster in DRA counties prior to implementation of the DRA, but it is 

unlikely that the DRA was responsible for this difference. Hence, we suspect 

that the difference in growth of these payments from 2002 to 2007 is also not 

due to the DRA.

In addition to testing for differences in prior trends, it is useful to visualize 

these trends. Figure 3 shows the trends in mean personal income per capita 

in the DRA-recipient counties and matched non-DRA counties (using the 

25The greater statistical significance 

for transfer payments is in part because 

the variance in estimated transfer payments is smaller. This may be because 

data on transfer payments are based 

on administrative records and hence 

may be more reliable than estimates 

of earnings or dividends, interest, 

and rent. There also may simply be 

more actual variation in earnings and 

dividends, interest, and rent per capita. 

Statistically insignificant results for 

these outcomes do not prove that there 

was no effect on these outcomes, only 

that such impacts are hard to detect 

given the sample size available and the 

large variance of these outcomes.

26Recall that our matching estimators matched the DRA and non-DRA 

counties according to their composition 

by age and racial groups in 2000, and 

the resulting counties matched well. 

Hence we are looking for differences 

in change of these age and race groups 

during the study period as a possible 

explanation for observed changes in 

transfer payments and other outcomes.

27We did find more rapid growth 

in the African American share of the 

population in DRA counties according 

to two of the less-preferred matching 

estimators (i.e., those that didn’t match 

as well as PSM-KM) (see appendix A). 

If such a difference truly exists, this 

could account for more rapid growth in 

income maintenance payments in DRA 

counties.
28

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PSM-KM estimator) during 1997 to 2007. The figure shows that the trends 

in personal income per capita were very similar up to 2002, and then income 

per capita began to grow more rapidly in the DRA-recipient counties, especially in 2005 to 2007.28 This figure demonstrates that the differential growth 

rates in income per capita in DRA and matched non-DRA counties were not 

the result of a difference in prior trends. 

In contrast to the great similarity between the two groups of counties in their 

prior levels and trends of personal income per capita, there were differences 

between these groups in their prior levels and trends of mean population 

(although these differences were not statistically significant) (fig. 4). Thus 

the negative (though statistically insignificant) value for the difference in 

28A figure comparing mean personal 

income per capita in the two groups 

from 1990 to 2007 shows the same result, with the trends for the two groups 

almost identical from 1990 to 2002. 

We present the figure for the shorter 

time series to better illustrate the divergence that begins after 2002.

Figure 3

Mean annual per capita personal income in DRA-recipient and 

matched nonrecipient counties (using PSM-KM)1

1,000 dollars

1PSM-KM = Propensity score matching with kernel matching. 

DRA = Delta Regional Authority.

Source: USDA, Economic Research Service analysis.

1997 98 99 2000 01 02 03 04 05 06 07

15

17

19

21

23

25

27

DRA recipients

Matched non-DRA

Figure 4

Mean population, DRA-recipient and matched nonrecipient counties

(using PSM-KM)1

Mean population (1,000 of people)

1PSM-KM = Propensity score matching with kernel matching. 

DRA = Delta Regional Authority.

Source: USDA, Economic Research Service analysis.

1997 98 99 2000 01 02 03 04 05 06 07

20

22

24

26

28

30

DRA recipients

Matched non-DRA
29

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population growth reported in table 3 reflects a trend of declining population 

in DRA-recipient counties prior to initiation of the DRA. 

Effects of the Level 

of DRA Spending Per Capita

Table 4 presents the key results of the switching regression analyses 

conducted for each outcome variable.29 The magnitudes of the estimated 

average program effect (the coefficient of the DRA-recipient variable) are 

similar in most cases to those estimated by the matching estimator, though 

some of the results are more statistically significant given the smaller standard errors in the regression results. For example, the estimated average 

program effect on change in personal income per capita is $512, somewhat 

smaller than the estimate in table 3 ($597), but the regression results are more 

statistically significant. The estimated mean impact on net earnings per capita 

is also similar, though somewhat smaller, in table 4 ($223 rather than $240), 

but this effect is statistically significant at the 10-percent level in the regression. Similarly, the estimated average effects on change in transfer payments 

per capita and change in medical transfer payments per capita are somewhat 

smaller than the estimates in table 3, but the results are more statistically 

significant. For several other outcomes, results that had statistically insignificant impacts in the matching analysis are statistically significant and of the 

same sign and order of magnitude in the regression results (e.g., retirement 

and disability payments and veterans’ benefits per capita, and population). As 

discussed in the preceding section, the negative trend of population in DRA= 

recipient counties began prior to initiation of the DRA, so this result appears 

to be the result of a prior difference in trends. For one outcome—income 

maintenance transfer payments—the estimated average effect of the DRA 

was statistically insignificant in the regression but statistically significant in 

the matching result.

Table 4 also reports the estimated marginal effects of DRA program spending 

(the coefficient of the level of DRA spending per capita). We find that several 

outcomes are associated with greater DRA spending. Personal income per 

capita grew significantly more in counties with more DRA spending per capita, 

with each $1 of additional DRA spending per capita associated with $15 of 

additional growth in personal income per capita. This suggests that DRA 

spending is having a strong impact on personal income growth, well beyond the 

simple amount of funds transferred. It seems unlikely that all of this increase 

can be attributed solely to DRA spending (which would imply an unbelievably 

large marginal benefit-cost ratio). This suggests that other public or private 

funds are being leveraged by DRA spending, as claimed by the DRA, although 

we cannot verify the specific magnitude of other funds leveraged. 

The largest component of the estimated increase in personal income per 

capita is in net earnings (almost $8 of additional net earnings growth per $1 

of DRA spending), although this estimate is less statistically significant than 

the estimated impact on transfer payments (about $5 of additional transfer 

payments per $1 of DRA spending).30 This suggests that DRA spending 

(combined with other funds that this spending may be leveraging) is having 

a noticeable marginal impact on earnings growth, and is not only having an 

effect by increasing transfer payments. 

29Table 4 reports only the coefficients of the dummy variable for 

whether the county is a DRA-recipient 

county (the average program effect) 

and the level of DRA spending per capita (the marginal effect of additional 

program spending). The full regression 

results for changes in personal income 

per capita are provided in appendix B, 

table B1. Other regression results are 

available upon request.

30Note that the coefficients of DRA 

spending for the three sources of 

personal income in table 4 (net income; 

dividends, interest, rent; and transfer 

payments) add up to the total marginal 

impact of DRA spending on personal 

income. Thus the estimated increase in 

dividends, interest, and rent is about $2 

per $1 of DRA spending, though this 

coefficient is statistically insignificant.
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Across major industries, we find a significant impact of additional DRA 

spending only for the health care and social services sector, with each $1 of 

additional spending associated with about $8 of additional earnings.31 These 

impacts were apparently masked in the matching analysis, which failed to 

account for differences in the level of DRA spending. 

Although the impacts of the DRA apparently go beyond transfer payments, 

DRA spending is also having a noticeable marginal impact on transfer 

31The results for the health and 

social services sector are based on a 

constrained regression (with equal 

coefficients of the covariates for both 

DRA and non-DRA counties), because 

of a small sample size due to data 

suppression for earnings in this sector. 

Given the small sample size, we have 

less confidence in the results for this 

sector than for others.

Table 4

Average and marginal effects of DRA spending based on switching 

regressions for changes in outcomes, DRA and matching counties, 

2002-07 (standard errors in parentheses)1

Dependent variable DRA recipient

DRA funds

per capita

Personal income per capita ($) 512.1***

(174.5)

15.32**

(6.34)

Major components of personal income

Net earnings per capita ($) 222.8*

(129.7)

7.88*

(4.44)

Net earnings per capita from health care 

and social services ($) (N=52)2

321.4

(210.2)

8.21**

(3.89)

Dividends, interest and rent per capita ($) 122.7

(81.7)

2.32

(3.52)

Personal transfer payments per capita ($) 166.4***

(45.1)

5.12***

(1.34)

Employment per capita -0.0035

(0.0037)

0.00001

(0.00013)

Transfer payments by type

Retirement and disability ($) 31.9**

(15.3)

1.67***

(0.50)

Medical ($) 115.5***

(36.1)

2.49**

(1.02)

Income maintenance ($) 6.9

(7.9)

0.36

(0.280

Unemployment insurance ($) -2.4

(5.4)

0.08

(0.130

Veterans benefits ($) 9.2**

(4.7)

0.34**

(0.13)

Federal education and training assistance 

($) (N=194)

-0.5

(5.0)

0.14

(0.17)

Population -363.4**

(187.2)

1.071

(3.020)

*, **, *** Coefficient statistically significant at 10-percent, 5-percent, and 1-percent levels, 

respectively. DRA=Delta Regional Authority.

1Using PSM-NN without replacement to select sample. PSM-NN= propensity score – nearest 

neighbor matching (see text for explanation of matching approaches). 

2Regressions for earnings in health care and social services assume equal coefficients of covariates in treated and control samples with an intercept shift; full switching regressions were not 

possible due to the small number of observations with unsuppressed data.

N=206, except where noted.

Source: USDA, Economic Research Service analysis.
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payments. The additional transfer payments are mainly medical transfers 

($2.49 per $1 of additional DRA spending) and retirement and disability 

benefits ($1.67 per $1 of additional DRA spending). These findings indicate 

that DRA spending leverages other forms of government spending. In the case 

of medical transfer payments, this may be partly due to DRA investments in 

medical facilities, though this is not the only potential mechanism of impact. 

The DRA seeks to stimulate the supply and demand for medical services 

in other ways besides its investments in medical facilities. The DRA is 

increasing the supply of medical services by bringing foreign doctors to the 

Delta region through its J-1 visa waiver (“Delta Doctors”) program. The 

DRA is one of the few Government agencies that are allowed to recommend 

such waivers to the State Department (as is the ARC) and has assisted in 

the placement of more than 100 physicians in the region through the Delta 

Doctors program (http://www.dra.gov/programs/doctors/). The DRA is also 

stimulating demand for medical services through its Healthy Delta Initiative, 

which includes a campaign to address major health problems in the region, 

especially diabetes (http://www.dra.gov/programs/health-improvement/). 

The positive impacts of DRA spending on medical transfer payments and on 

net earnings per capita are consistent with the fact that the health care sector 

is the only one found to have greater earnings as a result of greater DRA 

spending, which suggests that a major near-term impact of the DRA has been 

to promote health sector earnings and medical transfer payments.

Robustness of the Results 

to Alternative Specifications

In addition to investigating the robustness of our findings to alternative 

matching and regression methods, we also investigated the effects of a large 

number of alternative scenarios, including:

• alternative sets of covariates in the analysis (e.g., with or without controlling for the area of cotton and rice, distance to urban centers, population 

density, GO Zones, or demographic variables)

• including additional covariates to control for possible additional 

confounding factors, including the presence of a Critical Access Hospital 

and the number of Federally Qualified Health Centers, the preprogram 

level of total transfer payments or medical transfer payments per capita, 

and the rate of population growth in the 1990s 

• alternative functional forms for the variables in the analysis (i.e., using 

logarithmic transformations of the continuous variables vs. untransformed variables)

• alternative definitions of the potential population of controls (i.e., 

including counties in the Appalachian region in early specifications, 

including only counties in DRA States in the analysis, using nonrecipient 

DRA-eligible counties as possible controls, dropping from the analysis 

counties that suffered large losses in Hurricane Katrina, dropping counties that are affected by the Delta Health Alliance, and dropping counties 

that are part of a Federal Empowerment Zone, Enterprise Community, or 

Renewal Community)
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• alternative specifications of the base and ending years in the analysis (i.e., 

considering 2001 rather than 2002 as the base year and 2005 or 2006 as 

ending years)

Many of these scenarios are discussed in greater detail, and selected results 

for them are presented in appendix D.32 Our main findings—that growth in 

personal income and transfer payments, especially medical transfer payments, 

was more rapid in DRA-recipient counties than matching counties and that 

greater growth was associated with greater DRA spending—were generally 

robust to these alternative specifications.

32The full results using alternative 

specifications are available from the 

authors upon request. Some of these 

results are discussed in more detail in 

Pender and Reeder (2010).
33

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Conclusions

The results of this analysis suggest that even though the DRA is a relatively 

small program and its impacts could only be investigated during its first 6 

years of implementation, the program is associated with measurable positive 

impacts on some outcomes, including per capita personal income, net earnings, and transfer payments. These impacts were larger in counties where 

DRA spending per capita was greater, with each $1 of additional DRA 

spending per capita associated with an additional $15 in growth of personal 

income per capita, including $8 in additional earnings, primarily in the 

health care and social service sector, and $5 in additional transfer payments, 

mainly due to additional medical transfer payments. The effect of higher 

DRA spending on health sector earnings and medical transfer payments 

is consistent with the fact that spending on medical facilities is one of the 

priority areas of DRA spending. It is also consistent with the DRA’s efforts to 

increase the supply of doctors and promote improved health awareness in the 

Delta Region. 

The fact that we do not find measurable impacts of the DRA so far on other 

outcome indicators, such as on earnings per capita in most industries or on 

employment per capita, does not mean that there have not been any such 

impacts or will not be in the future. Given the relatively small amount spent 

by the DRA in DRA-recipient counties so far and the time required for 

investments in infrastructure to affect economic growth, it is not surprising 

that it is difficult to detect impacts on more fundamental indicators, such as 

jobs and broadbased earnings, after only 6 years of program implementation. 

Furthermore, many of the largest investments made by the DRA have been 

in community facilities such as improved water and sewer systems, which 

improve the quality of life but may have little direct near-term impact on 

employment or income, although they may promote community economic 

development in the longer term by attracting new residents and industries and 

reducing outmigration. 

It is perhaps more surprising to find such large incremental impacts of DRA 

spending on personal income, earnings, and transfer payments. The results 

suggest that these impacts are not simply the direct result of DRA funds 

circulating in the local economies of the Delta Region, since the multipliers 

are far larger than those typically estimated for spending in rural areas. 

Rather, it appears that DRA programs related to improving the supply of 

health facilities and doctors and improving health awareness of Delta residents may be leveraging additional resources through medical programs 

such as Medicare and Medicaid, and that these are contributing to increased 

earnings in the health care and social service sector. This suggests that the 

DRA and associated investments by other agencies are addressing supply 

constraints for health services that limit the ability of rural people in the 

region to use the medical transfer payments that they are entitled to, and it 

highlights the importance of increasing the supply of health services in rural 

areas as well as providing support to payments for health care. 

The finding that each $1 of DRA spending is associated with $15 of additional growth in personal income suggests that the program has been 

successful in leveraging other public and possibly private funds. We see this 
34

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directly with the estimated impact on transfer payments, although this is 

not the kind of leverage that is claimed by the program. The DRA may also 

be leveraging other economic development program funding, as intended, 

although we have not investigated this leverage impact directly in this analysis. This would be a worthwhile topic of future research. To the extent that 

other public funds (including transfer payments) are being leveraged, one 

cannot claim the estimated impact on personal income or earnings as the 

return to DRA investments exclusively, since other public funds were also 

used to achieve this impact. 

The estimated impacts of the DRA on income in the health care sector 

raise the general question of the potential for investments in that sector to 

contribute to economic development in rural areas. Ours is not the first study 

to notice the potential economic impacts of health sector investments in rural 

areas of the United States. There is a growing body of literature on such 

impacts, led by researchers at the National Center for Rural Health Works 

at Oklahoma State University and other research centers (e.g., Doeksen et 

al.,1998; Doeksen and Schott, 2003; St. Clair, Doeksen, and Schott, 2007; St. 

Clair and Doeksen, 2009). However, most of that literature estimates impacts 

of health sector investments using an economic input-output model, without 

being validated by empirical ex post estimates of the impacts of actual investments. The present study is the only one that we are aware of that estimates 

such impacts using quasi-experimental and other econometric methods with 

county-level income data. Although we did not start out specifically hypothesizing impacts of the DRA on income in the health sector, the fact that we 

found evidence of such impacts suggests that further empirical research on 

the impacts of this type of investment in rural areas could prove fruitful.

It would also be useful to investigate impacts of the DRA on other outcomes 

beyond the economic ones considered in this study. Given the impacts on 

medical transfer payments and health sector earnings that we have observed, 

assessing impacts on health outcomes would also be valuable. Furthermore, 

since many of the investments by the DRA focus on improving the quality of 

life and not necessarily economic improvements (in the short term), it would 

be very useful to identify and assess impacts on other outcome indicators more 

directly affected by these investments, such as improvements in drinking water 

quality and sanitation, and health and environmental indicators related to these 

improvements. As many of these impacts may take a longer time to become 

evident, longer term assessments are likely to be needed.

As we have noted, this study is not intended to be a formal or thorough evaluation of all of the impacts of the DRA, but rather is a test of whether any 

initial economic impacts could be detected using publicly available data and 

the best available assessment methods. We have contributed to the literature 

evaluating impacts of regional rural development programs by focusing on an 

important regional program that has not yet been assessed in any published 

reports, by using a variety of statistical methods and specifications to control 

for confounding influences and to check the robustness of our results, by 

controlling for several factors that have not been adequately accounted for 

in prior assessments of economic development programs, and by assessing 

impacts of the level of program spending per capita as well as the presence 

of the program. Our findings do not replicate the findings of several other 

influential studies of impacts of rural development programs, since we do 
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not find significant impacts of the DRA on employment. This is perhaps not 

too surprising given the small size of the program. However, our findings 

suggest that economic impacts can occur through mechanisms other than by 

increasing employment, such as by leveraging increased transfer payments. 

This suggests additional avenues that could be fruitful to pursue in future 

research on the impacts of regional economic development programs.
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Appendix A—Matching and Double Difference 

(DD) Estimation of Impacts of DRA

Quasi-experimental matching approaches seek to mimic random assignment 

by selecting nonparticipants that are similar to participants in terms of selected 

observable characteristics. This matching method can reduce biases caused by 

differences between program participants and nonparticipants (or “treated” vs. 

“controls”) in these observable characteristics (i.e., this addresses the problem 

of “selection on observables”) (Heckman et al., 1998). However, this does not 

assure that differences between the treated and controls in unobserved characteristics are negligible, and to the extent that such unobserved differences 

contribute to differences in outcomes, this could still bias the results of the 

analysis (i.e., the problem of “selection on unobservables”).

We address these problems by combining the use of matching methods 

with difference-in-differences (DD) estimation. The DD estimator estimates the average impact of a program on the participants (or “average 

impact of the treatment on the treated” (ATT)) as the difference between 

the mean outcomes for the treated and control groups after the program is 

implemented, minus the difference in outcomes before the program is implemented; that is, (EYT1 – EYC1) – (EYT0 – EYC0), where EYT1 and EYT0

are the mean outcomes for the treated group in period 1 and 0, respectively 

(where period 1 is during or after program implementation and period 0 

is before implementation), and EYC1 and EYC0 are the mean outcomes for 

the control group. This is equivalent to the change in mean outcome for the 

treatment group minus the change in mean outcome for the control group 

((EYT1 – EYT0) – (EYC1 – EYC0)). This estimator subtracts out the effects of 

any time invariant additive factors that differ between the treated and control 

groups and any common trends affecting both groups. Thus, as long as the 

effects of differences between the two groups are additive and time invariant, 

this method eliminates bias due to selection on unobservables or observables 

(Imbens and Wooldridge, 2009). 

Unfortunately, the assumption that differences between the two groups 

are time invariant may fail to hold in practice. For example, development 

programs may be attracted to locations where incomes are rising more 

rapidly (or more slowly) for reasons other than the program. One way to 

address this potential problem is to use the DD estimator for matched treatment and control groups, in which the variables used for matching are those 

that are expected to differ between the groups and to influence changes in 

outcomes over time (Ravallion, 2008). This approach is similar to the conditional difference-in-differences estimator proposed by Heckman et al. (1998), 

which they found to be a promising method to address selection bias in evaluating a job training program. Smith and Todd (2005) also found that this 

approach substantially reduced the bias in evaluating a job training program 

caused by time invariant sources of cross sectional variation, and that the 

advantages were robust across a range of matching methods and model specifications using different subsamples of the data and different survey instruments. Isserman and Rephann (1995) used this approach to assess the impacts 

of the ARC, combining Mahalanobis metric matching with DD estimation of 

differences in growth rates of income, population and earnings between ARC 

and matched non-ARC counties. Ravallion and Chen (2005) also used this 
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approach, using propensity score matching to reduce observable preproject 

differences between participants and nonparticipants in a development project 

in China, and then DD estimation for the matched sample. 

We use several alternative matching estimators combined with DD estimation. 

Propensity score matching (PSM) has been used in many studies of impacts of 

social programs. PSM matches participants and nonparticipants according to 

the probability of program participation (or “propensity score”, denoted P(X), 

where X includes the observable characteristics used to predict participation). 

Rosenbaum and Rubin (1983) proved under the assumption of “unconfoundedness” that Yo is also independent of treatment status conditional upon P(X), 

provided that 0 < P(X) < 1.33 Under this assumption, matching on the propensity score is sufficient to ensure that the outcomes for the matched nonparticipant group are statistically indistinguishable from the outcomes that the 

participants would have experienced in the absence of the program. 

We use PSM nearest neighbor matching (PSM-NN), with and without 

replacement. Matching with replacement allows control observations to be 

used as the best match for more than one treated observation; hence it tends 

to obtain better matches with less potential bias resulting from imperfect 

matches. However, use of fewer control observations results in larger standard 

errors and in many cases a larger mean squared error, despite less bias (Zhao, 

2004; Smith and Todd, 2005). We also use PSM with kernel matching (PSMKM), which estimates matching observations based on a weighted average of 

observations from the nonparticipant pool, with the weights a declining function of the distance of each observation (in terms of its propensity score) from 

the observation in the treatment group to be matched (Heckman et al., 1998). 

Kernel PSM is able to obtain lower standard errors than NN matching, since 

it uses more information to construct the counterfactual observations, but this 

may be at a cost of increased bias (Caliendo and Kopeinig, 2005). We use the 

Epanechnikov kernel function and a bandwidth of 0.06, which are the default 

options in the Stata procedure used for PSM (Leuven and Sianesi, 2003). 

In general, results of PSM-KM are not very sensitive to the choice of the 

kernel function, as with nonparametric regression approaches (Caliendo and 

Kopeinig, 2005; DiNardo and Tobias, 2001). The choice of the bandwidth 

parameter appears to be more important, but involves a tradeoff between 

bias and variance—i.e., a high bandwidth yields a smoother density function 

estimation and reduced variance, but may be more biased by smoothing out 

underlying features of the actual function (Caliendo and Kopeinig, 2005). 

To avoid observations with very high (P(X) near 1) or low (P(X) near 0) 

propensity scores, which will have poor matches, we impose a condition of 

“common support”, which drops treatment observations whose estimated 

propensity score is higher than the maximum or less than the minimum estimated propensity score of the control group (Leuven and Sianesi, 2003).

We also use the Mahalanobis metric (MM) matching estimator. The MM 

estimator minimizes the distance function dTC = (XT – XC)’ -1 (XT – XC), 

where XT and XC are vectors of matching variables for the treatment and 

potential control observations (considering all possible controls, and not only 

matched ones), and  is the variance-covariance matrix of XC. 

33The assumption of unconfoundedness is the assumption that the 

outcome that would have occurred 

without the treatment (denoted as Yo) 

is independent of treatment status (D), 

conditional upon X. The assumption 

that 0 < P(X) < 1 ensures that there are 

members of the comparison group for 

both treated and untreated units of observation. That is, if P(X) = 0 there are 

no treated observations for this value of 

X, and if P(X) = 1, there are no control 

observations. To estimate the average 

effect of the treatment on the treated 

(ATT), the assumption that P(X) > 0 

is not necessary, since the requirement 

is only to find matches for each treated 

observation (the requirement P(X) < 1 

is necessary in this case). If instead of 

ATT, the average treatment effect on 

the population (ATE) is to be estimated 

(including the potential impact of the 

treatment on controls), then the assumption P(X) > 0 is also necessary.
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There is no theorem comparable to that of Rosenbaum and Rubin (1983) 

providing a theoretical justification for the MM method, and it often is more 

biased (in terms of differences in mean values of XT and XC in matched 

samples) than PSM, especially when a large number of covariates are 

involved (Gu and Rosenbaum, 1993; Zhao, 2004). Intuitively, PSM achieves 

balance by implicitly giving greatest weight to matching on the variables that 

have significant association with the treatment assignment. MM matching 

attempts to achieve balance in all covariates, weighted by the inverse variance matrix of the covariates, and so may overweight variables that have little 

association with the treatment assignment (and hence are of little concern 

regarding bias), especially with a large number of covariates. Nevertheless, 

the MM estimator often has lower standard errors than the PSM estimator 

and in many cases lower mean squared error, despite being more biased 

(Zhao, 2004). 

Another advantage of the MM estimator relative to PSM is that the estimated 

standard errors for MM are asymptotically consistent, provided that the bias 

resulting from imperfect matching on covariates is corrected (Abadie and 

Imbens, 2006).34 To address the bias, we use the MM version of the matching 

estimator developed by Abadie et al. (2004), which corrects the bias in estimating the ATT using a linear least squares regression of the outcome on the 

covariates for the matched control observations.35 Abadie and Imbens (2007) 

showed, using Monte Carlo simulations, that their bias corrected estimator 

substantially reduces bias and mean squared error compared to matching 

without bias adjustment and to linear and quadratic regression models. This 

estimator is available only for nearest neighbor matching with replacement, 

so we implement it for that case only.

For PSM, the estimated standard errors are not valid, both because of imperfect matching and because the estimated standard errors do not account for 

the fact that the propensity scores are estimated in a first stage estimation. 

We address the bias in one version of the PSM model (nearest neighbor with 

replacement) using the bias corrected estimator of Abadie et al. (2004). In 

this case, we use the estimated propensity score from a first stage probit 

model as the single covariate in the covariate matching algorithm.36 This 

reproduces the ATT estimated by the standard PSM model when no bias 

correction is used, although the estimated standard error is different. With 

the bias correction, this estimator corrects for the effects of differences in 

propensity scores (but not in the individual covariates) between the treated 

and matched control observations on the estimated counterfactual outcome. 

We use bootstrapping to estimate the standard errors for all PSM estimators 

used (PSM-NN with replacement, with or without bias correction; PSM-NN 

without replacement, PSM-KM). This is standard practice among researchers 

to account for the fact that the propensity scores are estimated in a first stage 

estimation, but it doesn’t address the error caused by imperfect matches. 

Abadie and Imbens (2008) proved that the use of bootstrapping is not generally valid for matching estimators, and demonstrated the inconsistency of the 

bootstrap estimator for a specific case of nearest neighbor covariate matching 

(for a scalar covariate) with replacement. They argue that bootstrapping 

may be valid with kernel PSM estimation because the number of matches 

increases with sample size, but do not prove this. Despite this problem, we 

34Abadie and Imbens (2006) proved 

the consistency and asymptotic 

normality of a class of bias-corrected 

covariate matching estimators that 

includes the Mahalanobis metric as a 

special case (Ibid., footnote 4, p. 239).

35Formally, Abadie et al. (2004) estimate the counterfactual outcome for each 

treated observation i (Yoi) as: 

Yoi = (1/#m(i)) km(i){Yok + μo(Xi

) - 

μo(Xk)}, where m(i) is the set of matched 

control observations to treated observation i, #m(i) is the number of matched 

observations in this set, Yok is the outcome of matched control observation k 

(within m(i)), and μo(X) is the estimated 

linear regression function of the outcome 

on the covariates within the matched 

control group. The terms μo(Xi

) - μo(Xk) 

correct the estimated counterfactual 

outcome for differences resulting from 

differences in the values of the covariates 

between the treated (Xi

) and matched 

control observations (Xk).

36We use a probit model to estimate 

propensity scores. Other parametric 

probability models, such as a logit 

or linear probability model, are also 

commonly used, as well as nonparametric probability models. Results 

of propensity score estimation with 

a binary treatment are generally not 

highly sensitive to the choice of probability model (Zhao, 2004; Caliendo 

and Kopeinig, 2005).
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use bootstrapping to estimate the standard errors for our PSM models due to 

lack of a suitable alternative.37

As we have seen, no matching method is clearly superior to all others in 

terms of both bias reduction and efficiency. Furthermore, PSM models suffer 

from inconsistent estimation of the standard errors. Although the MM estimator with bias correction has the advantages of being bias corrected and 

using asymptotically valid estimates of the standard errors, it generally has 

to correct for larger biases than PSM estimates, and thus can be greatly 

affected by the linear regression model used to correct for bias. This is an 

important drawback, since one of the advantages of matching methods over 

parametric regression methods is that they seek to avoid dependence on parametric assumptions about the relationships between the covariates and the 

outcome variable. Given these tradeoffs, we investigate the robustness of our 

conclusions to these different matching methods. To investigate how much 

difference is made by the bias correction, we report the results of the MM 

estimator and the PSM-NN estimator (without replacement in both cases) 

both with and without the bias correction. 

Comparisons between the covariates in the unmatched and matched samples 

are shown in tables A-1 and A-2. The mean values of many of the covariates differ between DRA counties and non-DRA counties in the unmatched 

samples. In general, these comparisons indicate that DRA-recipient counties 

were poorer and more dependent upon Federal spending than non-DRA counties in the Delta and Southeast States, with a smaller share of the adult population employed and greater dependence on service occupations. Such initial 

differences may affect differences in outcomes during the study period, and 

therefore need to be controlled for using econometric methods.

Table A-1 indicates that most of these mean differences in characteristics 

are much smaller in the matched samples using the propensity score–nearest 

neighbor matching method (PSM-NN) with replacement. Statistically significant differences remain in the matched samples for only a few variables: the 

share of adults employed in manufacturing (less in DRA counties), cotton 

harvested area per capita (more in DRA counties), and the elderly share of 

the population (less in DRA counties). In all of these cases, the statistical 

significance is weak (between the 5-percent and 10-percent level) and the 

mean differences are relatively small. Across all covariates, the maximum 

absolute standardized bias is reduced from over 100 percent to 27 percent.38

The pseudo R2 of the probit model is much lower in the matched sample, and 

a likelihood ratio (LR) test of overall balance in the matched sample indicates 

that differences in the covariates are statistically insignificant, with a p value 

of 0.103.39 Hence, this matching method performs well to reduce, if not eliminate, all differences between the DRA-recipient counties and the matched 

non-DRA counties in their pre-DRA characteristics.

Table A-2 provides similar comparisons between the matched samples 

using the other matching methods investigated.40 Figure A-1 shows the 

DRA-recipient nonmetro counties (with common support) and the matched 

nonmetro non-DRA counties, using the PSM-NN estimator without replacement. This matching estimator results in larger biases for some variables (with 

a maximum absolute bias of nearly 36 percent) and more statistically significant 

differences (compared to matching with replacement), because the constraint 

37In a recent unpublished working paper, Abadie and Imbens (2009) 

derive the asymptotic standard error 

for the PSM estimator of the average 

treatment effect (considering nearest 

M neighbor matching with replacement), taking into account the fact that 

the propensity scores are estimated. 

Remarkably, they find that the standard 

error is less when the propensity score 

is estimated, indicating that use of 

uncorrected standard errors will lead 

to conservative inferences when rejecting the null hypothesis (i.e., the true 

probability of falsely rejecting the null 

hypothesis will be less than the p-value 

of the test). However, this result is only 

for the population average treatment effect and need not apply to the variance 

of the ATT (op cit., p. 8), which is what 

we are interested in estimating.

38The sample standardized bias 

for covariate X is defined as (m(Xt

)-

m(Xc

))/square root(st

2+ scr2), where 

m(Xt

) and m(Xc) are the sample means 

for the treated and control groups 

(whether matched or unmatched), 

respectively; and st

2 and scr2 are the 

sample variances for the treated group 

and control reservoir (unmatched 

controls), respectively (Rosenbaum and 

Rubin, 1985). The standardized bias 

is divided by this denominator (rather 

than the variance of the difference in 

means, as in a t statistic) so that the 

measure is not affected by sample size 

and is comparable between different 

matching methods.

39The overall balance test is a likelihood ratio test of the joint statistical 

significance of all covariates in a probit 

model for program participation in the 

matched sample. If the samples are 

well matched, the covariates should 

have a statistically insignificant impact 

in this model.

40The comparisons between unmatched samples do not vary across the 

matching methods, so these comparisons are not shown again in table A-2. 

The mean levels of all covariates for 

the DRA counties are the same for all 

matching methods, so these are reported 

only once in table A-2 for comparison 

purposes. The difference between these 

matching methods is in their choice of 

matched non-DRA counties.
49

Impacts of Regional Approaches to Rural Development: Initial Evidence on the Delta Regional Authority / ERR-119

Economic Research Service/USDA

of nonreplacement limits the ability to use the best matching counties more 

than once. With this estimator, there are statistically significant differences 

between the DRA and matching non-DRA counties in terms of the poverty 

rate (greater in DRA counties), the share of adults employed in manufacturing 

(less), whether the county is in a GO Zone (more likely), rice-harvested area 

per capita (greater), the farm share of the population (less), the child share of 

the population (greater), and the share of women working full time all year 

(less). Despite having larger biases and more significant differences for several 

individual covariates, the PSM-NN estimator without replacement has a lower 

overall measure of bias, with a smaller pseudo R2 and smaller LR test statistic 

than the PSM-NN estimator with replacement. Hence it is not clear whether the 

PSM model with or without replacement is preferable. 

The PSM kernel matching (PSM-KM) estimator performs the best, with 

no statistically significant mean differences for any covariates, the smallest 

maximum bias (24 percent), the smallest pseudo R2, and the smallest LR test 

statistic. The Mahalanobis metric (MM) estimator performs the poorest in 

terms of bias, with significant differences remaining between the DRA and 

matched samples for 12 of the covariates, the largest maximum bias (nearly 62 

percent), and the largest pseudo R2 and LR test statistic (indicating statistically 

significant difference overall between the matched samples). 

Figure A-1

Matched DRA-recipient nonmetro counties and non-DRA 

nonmetro counties1

1Using PSM-NN without replacement. PSM-NN without replacement=propensity score

nearest neighbor matching, without replacement.

DRA=Delta Regional Authority. The DRA operates in Louisiana, Alabama, Mississippi, 

Arkansas, Tennessee, Missouri, Kentucky, and Illinois. 

For a list of the 252 DRA-eligible counties, see http://www.dra.gov/about/maps.aspx/.

Source: USDA, Economic Research Service analysis, 2010.

DRA-recipient nonmetro counties

Matched non-DRA nonmetro counties
50

Impacts of Regional Approaches to Rural Development: Initial Evidence on the Delta Regional Authority / ERR-119

Economic Research Service/USDA

Table A-1

Comparison of characteristics of unmatched and matched Delta Regional Authority and comparison 

samples1

Variable Sample

Mean

Percent 

Treated Control bias p>|t|

Personal income per capita, 2000 ($)

Unmatched 18,755 20,703 -70.3 0.000***

Matched 19,147 19,139 0.3 0.981

Population, 2000

Unmatched 23,876 26,265 -12.4 0.273

Matched 24,483 26,102 -8.4 0.559

Poverty rate, 2000 (percent)

Unmatched 19.96 15.63 81.8 0.000***

Matched 19.02 18.20 15.6 0.212

Share of personal income from personal transfer 

payments, 2001

Unmatched 0.2681 0.2277 83.3 0.000***

Matched 0.2616 0.2620 -0.9 0.951

Share of personal income from dividends, interest 

and rent, 2001

Unmatched 0.1659 0.1839 -51.9 0.000***

Matched 0.1702 0.1674 8.2 0.458

Share of adults employed in agriculture, forestry, 

fishing or hunting, 2000

Unmatched 0.0568 0.0540 7.8 0.446

Matched 0.0484 0.0481 0.8 0.943

Share of adults employed in construction, 2000

Unmatched 0.0720 0.0775 -26.7 0.011**

Matched 0.0750 0.0726 11.5 0.408

Share of adults employed in manufacturing, 2000

Unmatched 0.1978 0.2225 -33.0 0.001***

Matched 0.1990 0.2180 -25.4 0.069*

Share of adults employed in retail trade, 2000

Unmatched 0.1141 0.1136 3.3 0.757

Matched 0.1149 0.1152 -1.4 0.925

Share of adults employed in public administration, 

2000

Unmatched 0.0559 0.0527 13.4 0.200

Matched 0.0542 0.0496 19.3 0.120

Share of adults employed in educational services, 

2000

Unmatched 0.0926 0.0835 33.2 0.001***

Matched 0.0927 0.0891 12.9 0.386

Share of adults employed in health care or social 

services, 2000

Unmatched 0.1156 0.1077 31.5 0.002***

Matched 0.1167 0.1165 0.9 0.953

Federal economic development grant funds 

per capita, 2000-01 ($) 

Unmatched 367.21 285.78 17.6 0.097*

Matched 336.88 304.91 6.9 0.538

Gulf Opportunity Zone counties (share of counties)

Unmatched 0.1832 0.0394 46.8 0.000***

Matched 0.2233 0.3010 -25.3 0.207

Cotton-harvested acres per capita, 2002

Unmatched 0.9494 0.3330 43.1 0.000***

Matched 0.5409 0.2451 20.7 0.079*

Rice-harvested acres per capita, 2002

Unmatched 0.5834 0.0017 57.8 0.000***

Matched 0.0430 0.0282 1.5 0.395

Distance to the nearest urban center of 25,000 

or more, 1980 (miles)

Unmatched 37.28 35.03 8.4 0.424

Matched 37.04 38.22 -4.4 0.737

Distance to the nearest urban center of 100,000 

or more, 1980 (miles)

Unmatched 85.43 82.53 5.3 0.611

Matched 85.72 86.25 -1.0 0.943

Distance to the nearest urban center of 250,000 

or more, 1980 (miles)

Unmatched 149.04 139.37 11.1 0.290

Matched 146.89 157.16 -11.8 0.380

Distance to the nearest urban center of 500,000 

or more, 1980 (miles)

Unmatched 236.17 225.97 7.1 0.503

Matched 235.90 244.16 -5.8 0.682

Distance to the nearest urban center of 1,000,000 

or more, 1980 (miles)

Unmatched 377.79 397.67 -9.9 0.358

Matched 371.46 395.03 -11.8 0.398

—continued
51

Impacts of Regional Approaches to Rural Development: Initial Evidence on the Delta Regional Authority / ERR-119

Economic Research Service/USDA

These results are consistent with results of other studies that compare different 

matching methods (Gu and Rosenbaum, 1993; Zhao, 2004), and demonstrate 

that no matching method is clearly superior in terms of both bias reduction and 

efficiency. Hence, as noted earlier, we report the results of several methods and 

investigate the robustness of our conclusions to the method.

DD Estimates With Matching Methods

The results of the estimation using the DD estimator for changes in the 

outcome measures using the different matching methods are reported in 

Table A-3. We find that growth in per capita personal income from 2002 to 

2007 was greater in the DRA counties than in the matched non-DRA counties, with the difference statistically significant (at the 10-percent level or less) 

for four of the six matching estimators. In all cases, the mean difference in 

the growth of annual per capita income from 2002 to 2007 was in the range 

of $500 to $660 per capita, a fairly large difference. This difference was not 

statistically significant using either bias corrected estimator, however. This is 

due mainly to larger standard errors of the bias corrected estimators.

Table A-1

Comparison of characteristics of unmatched and matched Delta Regional Authority and comparison 

samples1—Continued

Variable Sample

Mean

Percent 

Treated Control bias p>|t|

Population density, 1990 (persons/square mile)

Unmatched 40.29 46.06 -17.3 0.103

Matched 42.61 41.19 4.3 0.738

Rural share of population, 2000

Unmatched 0.6785 0.7066 -12.3 0.222

Matched 0.7070 0.7452 -16.7 0.256

Farm share of population, 2000

Unmatched 0.0317 0.0501 -54.1 0.000***

Matched 0.0341 0.0381 -11.7 0.296

Black share of population, 2000

Unmatched 0.2805 0.1868 43.3 0.000***

Matched 0.2590 0.2524 3.1 0.828

Share of population age 17 or less, 2000

Unmatched 0.2583 0.2505 29.3 0.002***

Matched 0.2546 0.2511 13.0 0.345

Share of population age 65 or more, 2000

Unmatched 0.1495 0.1520 -9.2 0.398

Matched 0.1504 0.1577 -27.4 0.053*

Share of adults with more than a high school 

education, 2000

Unmatched 0.3269 0.3490 -30.6 0.004***

Matched 0.3370 0.3301 9.5 0.508

Share of men working full time all year, 2000

Unmatched 0.5732 0.6144 -71.4 0.000***

Matched 0.5764 0.5778 -2.5 0.860

Share of women working full time all year, 2000

Unmatched 0.3937 0.4308 -102.7 0.000***

Matched 0.3915 0.3912 0.8 0.957

Overall balance tests Pseudo R2 LR chi2 p>chi2

Unmatched

Matched

0.429 236.26 0.000***

0.140 40.12 0.103

*, **, *** Difference statistically significant at 10-percent, 5-percent, and 1-percent levels, respectively.

1Using PSM-NN with replacement. PSM-NN=propensity score nearest neighbor matching. DRA=Delta Regional Authority.

Source: USDA, Economic Research Service analysis.
52

Impacts of Regional Approaches to Rural Development: Initial Evidence on the Delta Regional Authority / ERR-119

Economic Research Service/USDA

Table A-2

Comparison of characteristics of matched Delta Regional Authority and comparison samples1

Variable

Mean 

treated

PSM-NN without 

replacement PSM-KM Mahalanobis

Control

 Percent 

bias p>|t| Control

 Percent 

bias p>|t| Control

 Percent 

bias p>|t|

Personal income per capita, 2000 ($) 19,147 19,385 -8.6 0.512 19,205 -2.1 0.876 19,865 -25.9 0.036**

Population, 2000 24,483 23,345 5.9 0.655 26,026 -8.0 0.542 28,348 -20.0 0.069*

Poverty rate, 2000 (percent) 19.02 17.56 27.6 0.035** 18.07 18.0 0.160 16.46 48.3 0.000***

Share of personal income from 

personal transfer payments, 2001 0.2616 0.2536 16.5 0.250 0.2585 6.5 0.650 0.2372 50.3 0.000***

Share of personal income from 

dividends, interest and rent, 2001 0.1702 0.1708 -1.7 0.884 0.1673 8.4 0.473 0.1683 5.5 0.629

Share of adults employed in 

agriculture, forestry, fishing or 

hunting, 2000 0.0484 0.0496 -3.4 0.757 0.0456 7.9 0.462 0.0464 5.6 0.589

Share of adults employed in 

construction, 2000 0.0750 0.0752 -1.1 0.937 0.0763 -6.2 0.657 0.0762 -6.2 0.628

Share of adults employed in 

manufacturing, 2000 0.1990 0.2202 -28.3 0.040** 0.2113 -16.4 0.235 0.2388 -53.1 0.000***

Share of adults employed in retail 

trade, 2000 0.1149 0.1134 9.2 0.523 0.1139 6.3 0.671 0.1145 2.3 0.843

Share of adults employed in public 

administration, 2000 0.0542 0.0518 10.4 0.433 0.0523 8.3 0.523 0.0460 34.4 0.004***

Share of adults employed in 

educational services, 2000 0.0927 0.0901 9.5 0.557 0.0888 14.1 0.329 0.0865 22.2 0.140

Share of adults employed in health 

care or social services, 2000 0.1167 0.1139 11.4 0.414 0.1171 -1.6 0.911 0.1058 43.6 0.001***

Federal economic development grant 

funds per capita, 2000-01 ($) 336.88 298.61 8.3 0.479 281.99 11.9 0.313 287.84 10.6 0.275

Gulf Opportunity Zone counties 0.2233 0.1262 31.6 0.067* 0.2976 -24.2 0.226 0.2039 6.3 0.735

Cotton harvested acres per capita, 

2002 0.5409 0.3899 10.5 0.406 0.3181 15.6 0.197 0.2558 19.9 0.116

Rice harvested acres per capita, 

2002 0.0430 0.0048 3.8 0.007*** 0.0239 1.9 0.257 0.0236 1.9 0.249

Distance to the nearest urban center 

of 25,000 or more, 1980 (miles) 37.04 34.62 9.0 0.502 36.98 0.2 0.986 35.84 4.4 0.730

Distance to the nearest urban center 

of 100,000 or more, 1980 (miles) 85.72 76.31 17.2 0.209 87.50 -3.3 0.814 84.62 2.0 0.878

Distance to the nearest urban center 

of 250,000 or more, 1980 (miles) 146.89 147.20 -0.4 0.980 154.10 -8.3 0.557 136.83 11.6 0.361

Distance to the nearest urban center 

of 500,000 or more, 1980 (miles) 235.90 220.83 10.5 0.447 238.68 -1.9 0.892 210.46 17.8 0.202

Distance to the nearest urban center 

of 1,000,000 or more, 1980 (miles) 371.46 362.64 4.4 0.745 383.39 -6.0 0.666 316.47 27.5 0.020**

Population density, 1990 (persons/

sq. mile) 42.61 38.48 12.4 0.295 41.83 2.4 0.854 43.48 -2.6 0.838

Rural share of population, 2000 0.7070 0.7187 -5.2 0.721 0.7372 -13.2 0.356 0.6974 4.2 0.753

Farm share of population, 2000 0.0341 0.0416 -22.1 0.047** 0.0336 1.5 0.882 0.0399 -17.0 0.067*

Black share of population, 2000 0.2590 0.2118 21.9 0.132 0.2301 13.4 0.338 0.1995 27.5 0.039**

—continued
53

Impacts of Regional Approaches to Rural Development: Initial Evidence on the Delta Regional Authority / ERR-119

Economic Research Service/USDA

Changes in personal income per capita for the DRA-recipient counties (with 

common support) and the matched non-DRA nonmetro counties are shown 

in figures A-2 and A-3 (using PSM-NN without replacement). No strong 

geographical pattern of changes in personal income is evident for either 

group. Comparing the cumulative distribution of changes in per capita 

personal income for DRA-recipient and non-DRA counties indicates that 

the distribution of changes in income per capita of DRA-recipient counties stochastically dominates that of matched non-DRA counties (fig. A-4). 

Thus, it is evident that the mean difference in income growth per capita is 

not driven by outliers in these distributions.

Among the major components of personal income (net earnings; dividends, 

interest, and rent; and transfer payments), transfer payments grew statistically 

significantly more rapidly in the DRA counties, using four of the six estimators. The difference in growth in transfer payments according to the PSM-NN 

estimator with replacement was not significant, in part because the standard 

errors tend to be larger for this estimator, as discussed earlier. For all major 

income components, the predicted sign of the difference was positive (i.e., 

greater growth in DRA counties), although the differences were not statistically significant except for transfer payments. 

Among the different types of transfer payments, the difference between 

DRA counties and matched non-DRA counties was largest and most robust 

for medical transfer payments. The estimated mean differences in growth in 

medical transfer payments were positive and statistically significant for all 

estimators except PSM-NN with replacement. Growth in income mainteTable A-2

Comparison of characteristics of matched Delta Regional Authority and comparison samples1—Continued

Variable

Mean 

treated

PSM-NN without 

replacement PSM-KM Mahalanobis

Control

 Percent 

bias p>|t| Control

 Percent 

bias p>|t| Control

Percent 

bias p>|t|

Share of population age 17 or less, 

2000 0.2546 0.2484 23.5 0.097* 0.2523 8.5 0.547 0.2525 7.7 0.584

Share of population age 65 or more, 

2000 0.1504 0.1549 -16.9 0.210 0.1531 -10.0 0.464 0.1485 7.4 0.524

Share of adults with more than a 

high school education, 2000 0.3370 0.3343 3.7 0.798 0.3383 -1.8 0.897 0.3389 -2.8 0.836

Share of men working full time all 

year, 2000 0.5764 0.5870 -18.3 0.198 0.5827 -10.9 0.444 0.6112 -60.3 0.000***

Share of women working full time 

all year, 2000 0.3915 0.4044 -35.8 0.010*** 0.3922 -2.1 0.886 0.4137 -61.7 0.000***

Pseudo 

R2

LR 

chi2 p>chi2

Pseudo 

R2

LR 

chi2 p>chi2

Pseudo 

R2

LR 

chi2 p>chi2

Overall balance tests – matched 

samples 0.097 27.65 0.589 0.047 13.33 0.996 0.218 62.29 0.000***

*, **, *** Difference statistically significant at 10-percent, 5-percent, and 1-percent levels, respectively.

1Using PSM-NN matching without replacement, PSM-KM and Mahalanobis nearest neighbor matching. PSM-NN=propensity score nearest 

neighbor matching; PSM-KM = propensity score kernel matching (see text for explanations of these matching methods).

Source: USDA, Economic Research Service analysis.
54

Impacts of Regional Approaches to Rural Development: Initial Evidence on the Delta Regional Authority / ERR-119

Economic Research Service/USDA

nance program payments was greater in DRA counties according to most of 

the estimators. 

Population growth was less (or population decline was greater) in DRA counties according to some estimators (PSM-NN without replacement, MM-NN 

with and without bias correction). There was no statistically significant difference between DRA counties and matched non-DRA counties in the change in 

the share of the population that is elderly, according to any of the estimators. 

Hence, the changes in population growth or difference in growth of Medicare 

transfer payments in DRA counties do not appear to be driven by differences 

in growth of the elderly population. We find more growth in the share of 

the population that is African American in DRA counties using two of the 

estimators (PSM-NN without replacement in MM-NN without bias correcTable A-3

Mean changes in outcomes, Delta Regional Authority minus matching counties 2002-07 

(using DD estimator, standard errors in parentheses)

Dependent variable

PSM-NN

with replacement

PSM-NN

without 

replacement

PSM-KM

with 

replacement

MM-NN 

with replacement

Not bias 

corrected

Bias 

corrected

Not bias 

corrected

Not bias 

corrected

Not bias 

corrected

Bias 

corrected

Personal income per capita 604.7*

(331.2)

539.8

(386.4)

660.3***

(248.4)

597.0*

(333.5)

498.9*

(281.5)

619.0

(448.7)

Major components of personal income

Net earnings per capita 278.5

(283.1)

225.5

(316.4)

324.7

(249.9)

240.2

(229.1)

192.9

(210.6)

175.9

(329.0)

Dividends, interest and rent per capita 170.8

(176.9)

213.0

(142.0)

166.0

(108.8)

164.6

(127.9)

76.6

(130.6)

82.5

(173.9)

Personal transfer payments per capita 155.3

(120.7)

101.0

(85.4)

169.5**

(71.1)

192.1**

(81.5)

229.4***

(68.3)

360.5***

(93.7)

Employment per capita -0.0022

(0.0072)

-0.0060

(0.0064)

-0.0024

(0.0052)

-0.0028

(0.0053)

0.0020

(0.0055)

-0.0039

(0.0075)

Transfer payments by type

Retirement and disability 9.3

(33.4)

25.7

(26.3)

20.0

(24.8)

16.4

(26.8)

38.9*

(22.4)

78.4**

(32.2)

Medical 93.0

(77.4)

69.6

(61.9)

111.3**

(45.6)

120.5**

(59.2)

116.5**

(52.3)

258.5***

(71.3)

Income maintenance 37.6*

(20.6)

23.7

(18.1)

25.8*

(13.6)

35.3**

(16.0)

47.0***

(16.2)

18.2

(13.0)

Unemployment insurance 7.5

(14.6)

-34.2

(24.1)

2.7

(7.2)

6.3

(10.1)

14.2

(8.7)

-9.1

(10.9)

Veterans benefits 5.3

(10.7)

7.0

(7.7)

6.1

(5.3)

9.6

(6.4)

13.7**

(6.9)

12.7

(8.4)

Federal education and training assistance 5.4

(11.5)

8.5

(17.0)

1.1

(6.0)

2.1

(8.1)

0.9

(6.6)

-1.9

(7.7)

Population -253.3

(406.1)

-7.6

(431.0)

-449.9**

(213.0)

-447.5

(543.0)

-548.2***

(208.9)

-590.4**

(243.5)

, **, *** Difference statistically significant at 10-percent, 5-percent, and 1-percent levels, respectively. 

DD estimator= difference-in-difference estimator. PSM-NN=propensity score nearest neighbor matching ; PSM-KM = propensity score kernel 

matching ; MM-NN = Mahalanobis metric matching.

Source: USDA, Economic Research Service analysis.
55

Impacts of Regional Approaches to Rural Development: Initial Evidence on the Delta Regional Authority / ERR-119

Economic Research Service/USDA

Figure A-2

Change in annual per capita personal income in DRA-recipient 

nonmetro counties, 2002-07

DRA=Delta Regional Authority. The DRA operates in Louisiana, Alabama, Mississippi,

Arkansas, Tennessee, Missouri, Kentucky, and Illinois. 

For a list of the 252 DRA-eligible counties, see http://www.dra.gov/about/maps.aspx/.

Source: USDA, Economic Research Service analysis, 2010.

717 - 3,853

3,854 - 5,109

5,110 - 6,365

6,366 - 8,161

8,162 - 13,715

Change in per capita personal income

 Dollars

tion). This could be related to the greater decline in population observed in 

DRA counties using those same estimators (i.e., greater decline in the White 

population), and could be related to differences in growth in income maintenance payments per capita, to the extent that African Americans are poorer 

and more likely to use such programs in the region studied. These are not 

necessarily effects of the DRA, however, although these tendencies are more 

apparent in DRA-recipient counties. 

Pre-DRA Differences in Outcome Trends

Table A-4 provides estimates of the differences between DRA-recipient 

counties and matching non-DRA counties in their pre-2002 outcome trends. 

For most outcome variables and most matching estimators, there were not 

statistically significant differences in these pre-2002 outcome trends. Here we 

comment on outcome variables for which there was a significant difference 

using at least one of the matching estimators. 

Pre-2002 growth in per capita personal income, net earnings, transfer 

payments, employment, and medical transfer payments was more rapid in the 

DRA-recipient counties than matched non-DRA counties, according to the 
56

Impacts of Regional Approaches to Rural Development: Initial Evidence on the Delta Regional Authority / ERR-119

Economic Research Service/USDA

Figure A-4

Cumulative density functions of change in personal income 

per capita, 2002-071

Cumulative probability

0

.2

.4

.6

.8

1

0 5,000 10,000 15,000

c.d.f. of Untreated c.d.f. of Treated 

Change in per capita income, 2002−07

 

DRA-recipient counties

Non-DRA counties

1Matched DRA-recipient counties and non-DRA counties, using PSM-NN without

replacement.

PSM-NN without replacement = Propensity score nearest neighbor matching.

DRA = Delta Regional Authority.

Source: USDA, Economic Research Service analysis.

Figure A-3

Change in annual per capita personal income in matching

non-DRA counties, 2002-07

DRA=Delta Regional Authority. The DRA operates in Louisiana, Alabama, Mississippi,

Arkansas, Tennessee, Missouri, Kentucky, and Illinois. 

For a list of the 252 DRA-eligible counties, see http://www.dra.gov/about/maps.aspx/.

Source: USDA, Economic Research Service analysis, 2010.

Change in per capita personal income

 Dollars

1,836 - 2,839

2,840 - 3,829

3,830 - 4,978

4,979 - 6,519

6,520 - 12,002
57

Impacts of Regional Approaches to Rural Development: Initial Evidence on the Delta Regional Authority / ERR-119

Economic Research Service/USDA

uncorrected MM estimator. However, none of these differences was significant using any of the other estimators. Given the large biases noted earlier for 

the MM estimator and the lack of robustness of these results, these results are 

not substantial evidence of a difference in these trends prior to 2002.

Growth in income assistance program payments was significantly greater in 

DRA-recipient counties using all matching estimators. Unemployment insurance payments grew less rapidly in DRA-recipient counties (weakly significant) according to two of the estimators (PSM-NN without replacement and 

MM-uncorrected).

Population grew less rapidly (or declined more rapidly) in DRA-recipient 

counties from 2000 to 2002 according to the same two estimators (PSM-NN 

without replacement and MM—uncorrected).

Table A-4

Mean changes in outcomes, Delta Regional Authority minus matching counties, 2000-02

Dependent variable

PSM-NN

with 

replacement

PSM-NN

without 

replacement

PSM-KM 

with 

replacement

MM-NN 

with 

replacement

Not bias 

corrected

Bias 

corrected

Not bias 

corrected

Not bias 

corrected

Not bias 

corrected

Bias 

corrected

Personal income per capita 2.2

(195.9)

219.3

(270.4)

243.7

(152.8)

87.1

(138.1)

506.0***

(169.6)

-53.6

(244.7)

Major components of personal income

Net earnings per capita 84.5

(171.9)

220.8

(218.9)

182.8

(121.0)

123.8

(141.8)

404.5***

(151.4)

22.6

(204.2)

Dividends, interest and rent per capita -55.6

(56.4)

-34.0

(53.3)

6.8

(35.4)

-33.2

(33.6)

14.9

(47.2)

-40.1

(65.4)

Personal transfer payments per capita -26.6

(56.6)

32.6

(50.5)

54.2

(47.8)

-3.3

(41.0)

86.8**

(36.2)

-35.9

(43.5)

Employment per capita 0.0035

(0.0048)

0.0021

(0.0060)

0.0035

(0.0039)

0.0036

(0.0043)

0.0098**

(0.0044)

-0.0052

(0.0058)

Transfer payments by type

Retirement and disability -17.7

(17.4)

-17.1

(14.7)

7.2

(9.3)

-8.4

(15.4)

-1.5

(12.3)

-1.7

(14.3)

Medical -7.4

(40.6)

11.3

(38.9)

34.6

(36.2)

-2.4

(35.7)

73.7**

(33.2)

-43.6

(35.7)

Income maintenance 16.3*

(9.8)

13.1**

(6.4)

23.9***

(5.3)

19.9***

(7.1)

27.9***

(5.8)

17.6***

(5.3)

Unemployment insurance -17.7

(13.1)

14.0

(18.1)

-14.7*

(7.9)

-12.4

(8.4)

-17.1*

(8.8)

-1.9

(10.6)

Veterans benefits -1.5

(2.7)

-2.6

(2.6)

-1.6

(1.9)

-2.4

(2.6)

-0.4

(2.4)

0.3

(3.5)

Federal education and training assistance -3.7

(9.5)

9.1

(8.6)

-1.4

(4.8)

-1.3

(6.2)

-2.5

(6.3)

-6.8

(6.9)

Population -95.2

(129.5)

-46.6

(141.7)

-205.2***

(74.9)

-132.2

(88.9)

-260.9***

(79.8)

-76.0

(92.0)

*, **, *** Difference statistically significant at 10-percent, 5-percent, and 1-percent levels, respectively.

PSM-NN=propensity score nearest neighbor matching ; PSM-KM=propensity score kernel matching ; MM-NN=Mahalanobis metric matching.

Source: USDA, Economic Research Service analysis.
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These results indicate that there may have been differences between 

DRA-recipient counties and matched non-DRA counties in outcome trends 

prior to implementation of the DRA for some outcome variables. However, 

few of these differences are robust to the choice of estimator, with many 

of these seen only with the uncorrected MM estimator or the PSM-NN 

estimator without replacement, both of which were more biased than other 

estimators. 
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Appendix B—Regression Model To Estimate 

Impacts of DRA

One important drawback of quasi-experimental methods is that they only 

estimate mean differences in outcomes between program participants and 

nonparticipants, as if all participants received the same program funding. 

Presumably, the impacts of a program are likely to be larger for participants 

that received more funding. We investigated this issue using switching regression models for matched DRA and non-DRA counties, which also address the 

bias caused by imperfect matching and allow for heterogeneous impacts of 

DRA spending depending on the levels of other covariates.41 The matching 

counties used in the switching regressions were based on the PSM-NN model 

without replacement.

The switching regression models have the following form: 

(1) ( ) ( ) ∆ α β μ γ μ ε YTi T T Ti p T Ti x Ti = + - + - + P X for program participants (T), and

(2) ( ) ∆ α γ μ ε YCj C C Cj x Cj = + - + X for nonparticipants (C).

Note that equations (1) and (2) do not specify that the changes in outcomes 

(Y) are functions of the changes in covariates (X), as in Wooldridge 

(2002, p. 284), but rather as functions of preprogram values of the covariates 

(X). The reason for this specification is the endogeneity of X; i.e., changes 

in values of covariates, such as changes in population and in the economic 

and demographic structure of the counties studied, could be affected by the 

DRA program, potentially biasing the estimation results. Furthermore, X 

is not observed for all relevant covariates, many of which are observed only 

during decennial census years. Equations (1) and (2) represent a reduced 

form specification in which the X are derived as linear functions of their 

preprogram values X and the effects of the program (i.e., X = f(X, P)), 

and these linear functions substituted into the structural linear model of Y 

(Y=g(X,P)=g(f(X,P),P)=h(X,P)). It is not possible to identify the parameters of the structural model g(X,P) (and in particular the structural model 

impact dg/dp) based on estimation of h(X,P) without restrictive assumptions. 

Nevertheless, the impact of P on Y estimated using h(X,P) (i.e., dh/dP) is 

of interest in its own right, as the impact of the program conditional on initial 

conditions (but not conditional on the contemporaneous values of the covariates). Our specification of equations (1) and (2) is similar to the form specified by Abadie (2005, equation (8)).

The switching regression is implemented using the following pooled regression of all matched observations:

(3)	 Yi = C + (T – C) DRAi + T DRAi (PTi – p) + C (Xi – x) + (T – C)

DRAi (Xi – x) + Ci + DRAi (Ti – Ci) , where DRAi = 1 for DRA-recipient 

counties and = 0 for matched non-DRA counties. The coefficients of the interactions between DRAi

 and Xi – μx (which equal T – C) can be interpreted as 

measuring the effect of variations in Xi on the impact of the DRA (i.e., program 

effect heterogeneity). This form of the pooled switching regression model is 

41The switching regression model 

was also used to test for the significance of such heterogeneous impacts, 

using a Chow test for differences in 

coefficients of the covariates in the regressions for DRA vs. non-DRA counties (Crump et al., 2008). In almost all 

cases, this test strongly rejected the 

null hypothesis of homogeneous impacts; so the heterogeneous switching 

regression model was used.
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similar to that given in Wooldridge (2002), p. 613 (equation 18.16); except that 

we include the demeaned variable for the level of program spending.

YTi is the change in per capita outcome Y from before to during the 

program for program participant i (i.e., YT1i – YT0i, using the notation for 

periods used earlier); YCj is the change in per capita outcome Y from 

before to during the program for program nonparticipant j; PTi is the level of 

program investment per capita during the program period for program participant i; μp is the mean level of program investment per capita in the population of treated units; XTi is a vector of preprogram characteristics of program 

participant i that influence YTi; XCj is a vector of preprogram characteristics 

of program nonparticipant j that influence YCj; μx is the mean of X in the 

matched populations; T, C, T, T, and C are parameters to be estimated; 

and Ti and Cj are error terms with E(Ti)=0 and E(Cj)=0. Although linear 

functional form restrictions are imposed in this model (unlike the simple 

DD-matching estimator model, which imposes no restrictions on the relationship between Y and X), these regression functions allow for heterogeneous 

impacts of the covariates X on outcomes (i.e., T and C are not necessarily 

equal). Subtracting the means of P and X ensures that the difference between 

the intercept terms in regressions (1) and (2) (T – C) estimates the average 

treatment effect of the program (Wooldridge, 2002, p. 613).42 In estimating 

these regressions, the population means of P and X are replaced by the 

sample means.43

Partial results of the switching regressions for various outcome variables 

(showing only the estimated values of T-C and T) are reported in 

table 4. The full results of the switching regression model for changes in 

personal income per capita are reported in table B-1. These results show 

that the DRA had a positive average effect on income growth and that 

greater DRA spending was associated with larger impacts on income 

growth. They also show that some of the covariates associated with industrial structure – including the shares of employment in manufacturing, 

retail trade and public administration – were associated with heterogeneous impacts of the DRA. In particular, the higher the share of employment in each of these sectors, the smaller the impact of the DRA. This 

suggests that DRA actions and investments were less beneficial to these 

sectors than to other industrial sectors. 

42The population average treatment 

effect (ATE) is not the same as what 

is estimated by the matching - DD 

models, which were used to estimate 

the average effect of the treatment on 

the treated (ATT). However, since 

the switching regression models were 

run for matched samples, the ATE 

and ATT are likely to be similar. 

Estimation of the ATT using switching 

regression models requires additional 

calculations (Wooldridge, 2002).

43Formally, the use of sample means 

rather than population means affects 

the standard errors of the estimates, 

although this typically has a minor 

effect on the estimated standard errors 

(Wooldridge, 2002, p. 613). We do not 

correct our standard errors for this additional source of error.
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Table B-1

Full switching regression results for change in personal income per capita 

Variable

Non-DRA counties DRA counties

Coefficient

Standard 

error Coefficient

Standard 

error

Mean change () 4,373.8*** 124.3 4,885.9*** 122.5

DRA spending per capita ($) NA NA 15.324** 6.337

Per capita income in 2000 ($) 0.378*** 0.118 0.121 0.108

Population in 2000 0.004 0.010 0.021 0.019

Poverty rate in 2000 ( percent) -70.34 88.61 110.90 89.00

Share of income from transfer payments in 2001 5,537.5 7,773.0 -10,530.2 6,922.9

Share of income from dividends, int, rent in 2001 -11,437.3 7,137.9 1,241.9 7,733.2

Share of adults employed in agriculture, forestry, 

fishing, or hunting in 2000 9,530.5 11,246.1 -5,161.5 8,840.7

Share of adults employed in construction in 2000 6,116.0 7,881.3 -4,880.4 9,227.5

Share of adults employed in manufacturing in 2000 4,727.0 3,813.1 -10,386.8*** 3,700.5

Share of adults employed in retail trade in 2000 12,519.5 9,740.5 -34,266.0*** 11,579.6

Share of adults employed in public admin in 2000 14,537.8 9,595.0 -19,031.2** 9,080.7

Share of adults employed in education in 2000 2356.1 7,674.4 -3,364.6 7,833.6

Share of adults employed in health & social services in 2000 -10,654.4 7,838.2 -10,372.8 7,718.2

Federal economic development grants per capita in 2000 ($) -0.074 0.312 0.379 0.388

Gulf Opportunity Zone counties -744.8 506.0 497.2 432.7

Cotton harvested area per capita in 2002 -533.8*** 173.5 235.2* 137.9

Rice harvested area per capita in 2002 -2175.5 2,524.4 139.8 1,132.5

Distance to nearest city of 25,000 or more (miles) -15.474*** 5.628 -6.306 5.586

Distance to nearest city of 100,000 or more (miles) 5.640* 2.985 -0.936 3.091

Distance to nearest city of 250,000 or more (miles) -0.153 1.765 2.091 1.951

Distance to nearest city of 500,000 or more (miles) 0.019 1.334 0.976 1.309

Distance to nearest city of 1,000,000 or more (miles) 0.727 0.770 -0.938 0.931

Population density in 1990 (persons/square mile) -6.823 10.589 -0.134 7.408

Rural share of population in 2000 1,753.8* 968.5 -791.9 956.3

Farm share of population in 2000 1,972.8 6,366.7 -13,806.4 8,952.9

Black share of population in 2000 1,508.7 1,283.2 -1,766.2 1637.1

Share of population age 17 or less in 2000 12,354.3 9441.5 11,152.0 8,820.0

Share of population age 65 or more in 2000 -13,076.5 11,511.5 11,713.6 13,405.8

Share of adults with more than high school in 2000 16,313.8*** 3,607.4 -3,611.2 3,703.0

Share of men working full time all year in 2000 -1,333.0 4,467.4 -937.6 5,113.2

Share of women working full time all year in 2000 -15,026.7*** 5,587.6 -3,355.4 5,983.3

*, **, *** Coefficient statistically significant at 10-percent, 5-percent, and 1-percent levels, respectively.

Source: USDA, Economic Research Service analysis.
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Appendix C—Data sources

The data on personal income and employment and their components and on 

population by county were taken from the Regional Economic Information 

System (REIS) of the U.S. Department of Commerce, Bureau of Economic 

Analysis (BEA) (http://www.bea.gov/regional/reis/). The estimates of 

personal income and employment are based on administrative records, 

censuses, and surveys, and are designed to be consistent with State and 

national levels of personal income reported the National Income and Product 

Accounts.44 For total personal income and employment and major components of personal income and employment, the data are available by county 

from 1969 to 2007. For earnings and employment by industry, the data are 

only available from 2001 to 2007. 

The data on poverty rate and demographic and education characteristics of 

counties in 2000 and employment conditions in 1999 were taken from the 

2000 Census of Population (http://www.census.gov/main/www/cen2000.

html/). The data on areas of cotton and rice harvested in 2002 were taken 

from the 2002 Census of Agriculture (http://www.agcensus.usda.gov/

Publications/2002/). 

The data on distances to urban centers of different sizes and population 

density were provided by Peter Stenberg, and were based on geographic 

information systems analysis conducted by researchers of the Economic 

Research Service as part of a study of broadband Internet in rural areas 

(Stenberg et al., 2009).

The data on economic development grant spending in 2000 and 2001 were 

taken from the Consolidated Federal Funds Report (CFFR) (http://www.

census.gov/govs/cffr/). Classification of specific Federal programs as rural 

economic development programs used the classification developed by the 

U.S. Government Accountability Office (GAO) in a report on Federal rural 

economic development programs (U.S. GAO, 2006). This GAO report notes 

several problems with the CFFR data, but this is the only comprehensive 

source available for these programs.

The data on DRA spending by county were taken from the DRA’s Federal 

Grant Program Profile (DRA, 2009), which lists all DRA projects funded 

from 2002 to 2008 by year, project name, location and approved funding 

amount. Since the approved amounts of funding may not be spent in the 

same year that approval occurred, the amount of funds actually spent in 

each county during 2002 to 2007 may be less than amounts approved during 

this period. Despite this, these data were judged to be more reliable than the 

amounts reported as DRA outlays in the CFFR.

The list of GO Zone counties is taken from the Gulf Opportunity Zone Act 

of 2005.

44See http://www.bea.gov/regional/

pdf/lapi2007/lapi2007.pdf/ for details 

on the methodology used to produce 

the local area personal income and 

employment estimates.
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Appendix D—Sensitivity Analysis

Scenarios

We investigated the sensitivity of our matching and regression results to 

a series of alternative scenarios considering hypotheses about factors that 

may have confounded our results. The factors investigated included the 

following:45

• Losses from Hurricane Katrina. Counties that experienced large losses 

from Hurricane Katrina may have suffered unusually large declines 

in population and income. On the other hand, insurance payments and 

Government programs intended to promote recovery after Hurricane 

Katrina may have led to higher income per capita in some affected 

counties, at least temporarily. As noted in the text, we sought to control 

for these concerns by including whether a county was in the Gulf 

Opportunity Zone (GO Zone) as one of the covariates in the matching 

estimation. However, this may not adequately reflect differences in 

damages and payments resulting from Hurricane Katrina. To address this, 

we used estimates of asset loss ratios (the ratio of losses to asset values) 

from the Federal Emergency Management Agency’s (FEMA) HAZUS 

model (http://www.fema.gov/plan/prevent/hazus/). In two scenarios, we 

dropped counties that suffered loss ratios greater than 1 percent or greater 

than 0.5 percent.

• Drop counties from States other than the DRA States. It may be that 

counties in States outside of the DRA States (GA, NC, and SC) are 

different from DRA counties in important unmeasured ways that could 

influence the differences in outcomes. These other States are different 

from the DRA States in some ways in their history and culture, 

although there are many commonalities as part of the Southern United 

States. Furthermore, the use of the double-difference estimator seeks 

to subtract out the effects of such fixed or slowly changing factors, and 

dropping these States inhibits the ability to find counties that are good 

matches in terms of the observed covariates. Nevertheless, we investigate the robustness of our results to dropping counties in these other 

States from the analysis. 

• Keep DRA counties that have not received DRA grants as possible 

matches. Some of these nearby counties may be the best matches for 

DRA-recipient counties in terms of the observed covariates. These counties were dropped from the baseline analysis because of the concern 

that unobserved factors likely account for why these counties did not 

receive DRA grant funding (e.g., they may have less effective leadership), and these unobserved factors could be associated with differences 

in outcomes (i.e., the problem of “selection on unobservables” is likely to 

be greater when comparing eligible recipients and nonrecipients). These 

counties also may be more likely to be affected by spillover effects (either 

positive or negative) of the DRA program investments and activities. 

Nevertheless, we investigate the robustness of our findings to including 

these counties as possible controls.

45We are grateful to two reviewers 

of this paper—Mark Partridge and 

Matt Fannin—for suggesting several of 

these sensitivity analyses. We are also 

grateful to Beau Beaulieu and other 

seminar participants at Mississippi 

State University for suggesting investigating the effects of the Delta Health 

Alliance on the results.
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• Drop counties that are in the Delta Health Alliance. The Delta Health 

Alliance (DHA) is a nonprofit organization established in 2004 to improve 

the health of people in 18 counties in the Yazoo-Mississippi Delta region of 

the State of Mississippi (http://www.deltahealthalliance.com/). Although 

it was established more recently and focuses on a fairly small subset of the 

DRA counties, the activities of this organization could have influenced 

outcomes in the DRA region compared to non-DRA counties, especially 

those related to health sector services. Thus, we investigate how dropping 

these counties from our analysis affects the results. 

• Drop counties that are part of a Federal Empowerment Zone (EZ), 

Enterprise Community (EC), or Renewal Community (RC). Several 

rural EZs, ECs and RCs have been established in rural parts of the 

Mississippi Delta region. The impacts of these programs therefore could 

be confounded with the impacts of the DRA in our analysis. Hence, 

we investigate how dropping counties that are part of an EZ, EC or RC 

affects our results.

• Include as covariates whether the county or a neighboring county had a 

Critical Access Hospital in 2002 and the change in this status between 

2002 and 2007. The Critical Access Hospital (CAH) program was 

launched in 1999 to assist rural hospitals, many of which closed in the 

1980s and 1990s due to rising costs and payment restrictions imposed by 

Medicare’s prospective payment system, which paid less to rural hospitals than urban ones (McNamara, 2009). The program allowed small 

rural hospitals (25 beds or less) in remote rural areas to become CAHs, 

which allowed them to receive cost-based reimbursement from Medicare. 

Rapid expansion of the program occurred from 1999 through 2005, and 

various studies have shown that cost-based reimbursement significantly 

improved the financial situation of rural hospitals, in many cases enabling 

increased investments by the CAHs in staff, training and equipment 

(Ibid.). If DRA-recipient counties or their neighbor counties experienced 

more growth in CAH facilities than matched non-DRA counties during 

our study period, the impacts of the CAH program could be confounded 

with the impacts of the DRA, especially impacts on medical transfer 

payments and earnings in the health sector. To address this concern, we 

include as covariates in one scenario variables representing (1) the presence of a CAH in the county in 2002, (2) the presence of a CAH in a 

neighboring county in 2002, (3) change in the presence of a CAH in the 

county between 2002 and 2007, and (4) change in the presence of a CAH 

in a neighboring county between 2002 and 2007. The data on CAHs were 

taken from the CAH Flex Monitoring Team’s website: http://www.flexmonitoring.org/cahlistRA.cgi/.

• Include as covariates the number of Federally Qualified Health Centers 

in the county in 2002 and the change in number from 2002 to 2007.

Federally Qualified Health Centers (FQHCs) are located in medically 

underserved areas and provide patients access to care regardless of their 

insurance status or ability to pay. Like CAHs, FQHCs receive cost-based 

reimbursement from Medicare. As with CAHs, if the number of FQHCs 

grew more rapidly in DRA-recipient counties than in matched non-DRA 

counties, this may have affected our results. Hence, we include the 

number of FQHCs in each county in 2002 and the change the number 

from 2002 to 2007 as covariates in one scenario (the same scenario in 
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which we include indicators for CAHs). The data on FQHCs was taken 

from the Area Resource File maintained by the U.S. Department of 

Health and Human Services (http://arf.hrsa.gov/purchase.htm/).

• Include medical transfer payments per capita or total transfer payments 

per capita in 2002 as a covariate. Given the robust finding that medical 

transfer payments and total transfer payments per capita grew more 

rapidly in DRA-recipient counties than in matched non-DRA counties, we 

include the initial levels of these variables in two separate scenarios.

• Include lagged population growth as a covariate. In one scenario, we 

include the ratio of county population in 2000 to county population in 

1990 as a covariate to account for possible persistent effects of differences 

in population growth rates. Given evidence that there are differences in 

prior trends in population levels and growth in the matched samples in 

our baseline analysis (see table 3 and figure 4), this scenario helps to 

address possible biases caused by these differences.

• Use different starting and ending years in the analysis. To see whether 

our results may be an artifact of the particular starting or ending year, we 

investigate impacts using alternative starting and ending years. 

Results

The results of the sensitivity analysis for estimated impacts on per capita 

personal income and transfer payments are shown in table D-1.46 The 

results are quite robust to almost all of the scenarios, especially the regression results. The matching results are not statistically significant in some 

scenarios, although the magnitude of the estimated average treatment effects 

are in a similar range ($350 to $710 for personal income per capita, $160 

to $240 per for transfer payments per capita) for all but one scenario—the 

scenario in which counties outside of the DRA States are dropped. For that 

scenario, the estimated average treatment effect is much smaller and statistically insignificant for both personal income and transfer payments per capita, 

using both the matching estimator and the regression estimator. However, the 

estimated marginal impacts of DRA spending are still statistically significant 

and fairly similar in magnitude ($12.35 increased growth in personal income 

and $5.51 increased growth in transfer payments per $1 spent by DRA) to the 

baseline analysis for this scenario. 

One possible explanation for why the estimated average treatment effect is 

smaller and not statistically significant for this scenario is that this results 

from poorer matching results when dropping counties in GA, NC, and SC 

from the analysis. For example, the pseudo-R2 of the probit model distinguishing the two matched groups was 0.047 in the baseline analysis (see 

table A-2) but increases to 0.079 in this scenario, indicating poorer matching. 

A second possible explanation is that the effects of the DRA spilled over 

to non-DRA counties within the DRA States, so that the impacts of the 

DRA cannot be measured by comparing counties within the same States. 

Such spillovers could result from the ability of non-DRA counties to access 

the benefits of investments made in DRA counties, or from displacement 

effects whereby DRA States shift development funds to non-DRA counties 

in response to the availability of additional funds for DRA counties. A third 

possibility is that the DRA truly had no mean impact. However, it is difficult 

46Results for other outcome variables 

are available upon request.
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to understand how the DRA could have a positive marginal impact of funds 

spent, as we still find in this scenario, but a zero mean impact. Furthermore, 

this would not explain why a positive mean impact is found for the DRA in so 

many alternative scenarios, which rule out many possible alternative explanations for the findings in the baseline analysis.

Although these results are not fully conclusive, the preponderance of the 

evidence supports the conclusion that the DRA has had a positive impact on 

personal income and transfer payments per capita. The positive marginal 

impacts of DRA spending are particularly robust. 

Table D-1

Selected results under alternative matching scenarios

Scenario

Total

 no. of 

obs.

No. of 

matched 

DRA 

counties

Change in per capita 

personal income

Change in per capita 

transfer payments

PSM-KM

Switching regression

PSM-KM

Switching regression

T-C T T-C T

Baseline 461 103 597.0*

(333.5)

512.1***

(174.5)

15.32***

(6.34)

192.1**

(81.5)

166.4***

(45.1)

5.12***

(1.34)

Drop counties with loss ratios > 1 percent 

from Hurricane Katrina

457 100 541.8*

(312.3)

519.6***

(176.8)

15.13**

(6.48)

195.5**

(98.3)

148.4***

(45.9)

5.04***

(1.37)

Drop counties with loss ratios > 0.5 percent 

from Hurricane Katrina

452 98 429.6

(332.5)

446.2**

(176.7)

16.21**

(6.67)

162.3*

(84.7)

147.8***

(48.1)

4.52***

(1.39)

Drop counties outside of DRA States (i.e., 

in GA, NC, SC)

319 76 30.8

(369.6)

-124.1

(179.3)

12.35*

(7.03)

51.9

(126.3)

34.6

(45.2)

5.51***

(1.44)

Keep DRA counties that are nonrecipients 

as possible matches

516 121 354.2*

(211.7)

271.5*

(159.9)

12.34**

(5.97)

160.3**

(76.0)

186.4***

(42.1)

5.29***

(1.43)

Drop counties in the Delta Health Alliance 450 99 587.3**

(272.4)

459.4***

(173.7)

14.69**

(6.38)

203.6***

(77.5)

168.4***

(45.8)

4.74***

(1.34)

Drop counties that are part of a Federal 

Empowerment Zone, Enterprise Community 

or Renewal Community

415 78 983.9***

(307.7)

682.6***

(183.2)

15.62

(9.86)

222.0**

(93.1)

189.6***

(47.0)

5.85***

(1.90)

Include as covariates: i) presence of a Critical 

Access Hospital (CAH) in 2002 in county or 

neighboring counties; ii) change in presence 

of CAH in county or neighboring counties, 

2002-07; iii) number of Federally Qualified 

Health Centers (FQHC) in county in 2002; 

and iv) change in number of FQHCs in 

county, 2002-07

461 100 575.0

(473.1)

454.3**

(177.7)

16.88**

(6.80)

211.4**

(91.5)

177.1***

(46.7)

5.48***

(1.41)

Include lagged medical transfer payments 

as a covariate

461 103 539.1*

(319.1)

450.8***

(171.3)

14.89**

(6.26)

196.2**

(84.1)

156.0***

(44.5)

5.12***

(1.36)

Include lagged total transfer payments as 

a covariate

461 100 604.0*

(361.0)

529.5***

(173.5)

14.76**

(6.45)

238.8***

(73.8)

197.2***

(45.9)

4.76***

(1.28)

Include lagged population ratio (2000/1990)

as a covariate

461 103 372.4

(374.6)

382.2**

(167.7)

14.87**

(6.23)

172.2**

(82.2)

158.4***

(44.0)

5.01***

(1.29)

Investigate changes from 2002-06 instead 

of from 2002-07

461 103 487.9**

(240.2)

458.0***

(147.7)

11.58**

(5.38)

158.1***

(55.0)

139.6***

(34.8)

3.66***

(1.03)

Investigate changes from 2001-07 instead 

of from 2002-07

461 103 710.6**

(282.6)

618.3***

(175.6)

13.62**

(6.20)

157.7*

(86.6)

126.5***

(46.1)

5.39***

(1.38)

DRA=Delta Regional Authority ; PSM-KM=propensity score kernel matching.

Source: USDA, Economic Research Service analysis.