The Political Economy of the U.S. Department of Agriculture Rural Business-Cooperative Service

Abstract

The U.S. Department of Agriculture Rural Business-Cooperative Service (RBS) was established with the goal of fostering rural development through grants and low-interest loans. However, rural revitalization is only effective if funds reach their intended recipients. With congressional oversight of the U.S. Department of Agriculture and geographic concentration of RBS benefits, the RBS may deviate from its stated objectives to satisfy congressional preferences. Using RBS grant and loan data from 2006 to 2014, the author performs a test of the congressional dominance model. The empirical results show that congressional membership on the relevant House and Senate subcommittees is associated with greater RBS funding. Congressional influence is a plausible mechanism through which development funding fails to reach those in the areas of greatest need.

Keywords

congressional dominance public choice rural development U.S. Department of Agriculture

Big Sandy, Montana is a salt-of-the-earth agricultural community, and also happens to be the home town of Senator Tester. Senator Tester and I visited several Rural Development projects . . . I saw firsthand how our Business and Industry Guaranteed Loan Program was used to purchase and restore the Grand Union Hotel in Fort Benton. This loan helped restore the Hotel, built in 1882, strengthening the tax base and bringing jobs to Fort Benton. Rural Development support has an enormous impact in this tiny rural town.

—U.S. Department of Agriculture [USDA] Rural Development (2015b)

Throughout its history, the United States has transitioned from an agricultural to a manufacturing and now to a service economy. As rural agricultural areas experienced decline, government rural development programs began to emerge (Cowan, 2016). In the wake of the Great Depression, the Federal Emergency Relief Administration began aiding rural families, while the War on Poverty in the 1960s emphasized rural poverty. One recent emphasis of government policies targeting rural revitalization is entrepreneurship.

Previous research explores the effectiveness of government funding across several programs encouraging business activity, including the Appalachian Regional Commission and U.S. Department of Agriculture (USDA) Rural Development (Goetz, Partridge, Deller, & Fleming, 2010). For example, Johnson (2009) found that a loan of $1,000 per capita from the USDA Business and Industry Guaranteed Loan Program comes with a 3% to 6% increase in county employment-per-capita growth over the 2 years following the loan. However, development is only effective if funds reach their intended recipients. While Hall (2010) found that federal economic development grants were targeted at rural areas, they did not reach the areas of greatest need. By using data from a USDA business program, this study explores one way through which development funding may fail to reach those in the areas of greatest need.

As with the program evaluated in Johnson (2009), the program considered in this study is part of USDA Rural Development. In fiscal year 2015, USDA Rural Development invested $29.75 billion in nearly 171,000 projects (USDA Rural Development, 2016a). The Rural Business-Cooperative Service (RBS) is a key part of Rural Development that focuses on supporting business development in rural areas through grants and low-interest loans. In fiscal year 2015, the RBS made grants and loans of $1.5 billion to over 12,500 rural businesses (USDA Rural Development, 2016a). Fifer Orchards in Delmar, Delaware is representative of the types of businesses helped by the RBS. In 2015, the fourth-generation family farm received a $200,000 grant to increase its production of asparagus, strawberries, and tomatoes for sale in local markets (USDA Rural Development, 2016a). Given congressional oversight of the USDA, RBS funds may fail to reach the areas of greatest need as political influences divert funds in accordance with the congressional dominance model.

Theoretically constructed and first empirically tested by Weingast and Moran (1983), the congressional dominance model argues that bureaus are dominated by the preferences of congressional committees. Weingast and Moran highlight three aspects of the congressional–bureaucratic incentive system that produce congressional dominance. First, bureaus compete for their share of the congressional budget, with congressional committees favoring bureaus most beneficial to the committee members. Second, congressional oversight provides a series of possible punishments for bureaus not acting in the best interest of Congress. Furthermore, Congress controls bureaucratic appointment.

While Weingast and Moran (1983) empirically supported the congressional dominance model by finding congressional influence over the Federal Trade Commission, subsequent research has provided evidence for the model across a variety of bureaus. Garret and Sobel (2003) demonstrated that half of all Federal Emergency Management Agency (FEMA) disaster payments are politically motivated. Garrett, Marsh, and Marshall (2006) calculated that $4 billion to $11 billion of USDA direct disaster relief during the 1990s was attributable to political factors. Young and Sobel (2013) found that membership on congressional committees dictated the dispersion of Great Recession recovery spending more than Keynesian countercyclical factors. Ryan (2014) showed that, during a period of H1N1 vaccine shortages, states with Democratic representatives on the House Oversight Committee received an additional 60,000 doses per week from the Centers for Disease Control and Prevention. Hall, Ross, and Yencha (2015) found that districts with representation on the House Transportation and Appropriations committees received larger subsidies from the Essential Air Service program.

This study contributes to the literature exploring the distribution of federal economic development grants to rural areas. While prior research found that areas with the weakest economies do not receive more federal economic development funding (Hall, 2010), little is known about possible explanations for why development funds do not reach targeted areas. With RBS grant and loan data from 2006 to 2014, I explore whether congressional dominance is consistent with the distribution of development funds. Is RBS funding associated with variables in line with the stated intent of the programs, or is RBS funding associated with congressional dominance variables? This study also contributes to the growing congressional dominance literature. Although a large portion of previous work focuses on crisis situations where bureaus use highly discretionary spending, RBS funding is determined by a competitive application process. Since institutions affect the extent of political influence on bureaus (Beaulier, Hall, & Lynch, 2011; Sobel, Coyne, & Leeson, 2007), it is important to explore under what institutions evidence supporting congressional dominance is either strengthened or weakened. With a competitive application process, there may be no evidence supporting congressional dominance of the RBS.

The next section of the study provides background information on the RBS, while highlighting the programs under consideration. The empirical approach is then described, followed by an explanation of the data collected. The empirical results and conclusions complete the study.

RBS Background

The RBS is one of three rural development agencies created out of the Agricultural Reorganization Act of 1994 (Cowan, 2016). The primary objectives of the RBS are to create and retain employment in rural areas. While there are several programs within the RBS, this study focuses on only three: Rural Cooperative Development Grants (RCDG), Rural Economic Development Grants (REDG), and Rural Economic Development Loans (REDL). These programs are selected because they do not use formulas in allocated funds. Other larger programs, such as the Business and Industry Guaranteed and Direct Loans, use formulas to allocate funds across states, which limits the potential influence of congressional oversight.¹ While the combined average yearly spending of the RCDG, REDG, and REDL of roughly $55 million is relatively small compared with total RBS spending, funds distributed to rural communities can have a significant local economic impact. In congressional testimonies and press releases, the USDA routinely promotes estimates for the number of jobs its projects help to create. For example, in a July 2016 press release, a $780,000 loan to an agricultural parts company for business expansion and the purchase of machinery was projected to create 51 jobs (USDA Rural Development, 2016e). Fifty jobs in a rural community can have a significant local economic impact.

Although the RCDG, REDG, and REDL programs are all part of the RBS, they are different programs with different objectives. The USDA website provides information for each program. The RCDG program awards grants of up to $200,000 to nonprofit corporations and institutions of higher education to assisting local businesses with business plans, leadership training, and feasibility studies (USDA Rural Development, 2016c). One example of a RCDG recipient is the Virginia Foundation for Agriculture, Innovation and Rural Sustainability. The cooperative has used its $200,000 technical assistance grant to help meat-processing cooperatives connect with local consumers (USDA Rural Development, 2015a). The REDG and REDL programs provide grants of up to $300,000 and loans of up to $1 million to local utility organizations (USDA Rural Development, 2016d). The utility organizations serve as intermediaries that disperse funds to help start or expand local businesses. Loans are provided at a 0% interest rate, while grants require a 20% match from the intermediary. One example of an REDL recipient is the Slope Electric Cooperative operating in southwestern North Dakota. Funds made available through the cooperative are being used by the West River Veterinary Clinic to construct a new 12,000 square-foot facility in the small town of Hettinger, North Dakota (USDA Rural Development, 2016a).

While the RBS has programs operating in all 50 states, the 201 RCDG, 118 REDG, and 164 REDL awards over the 2006-2014 period were distributed across 40 states. Table 1 displays the states receiving the most funding and those receiving no funding.

Table 1.

Total RCDG, REDG, and REDL Obligations by State, 2006 to 2014.

Top 10 states		Bottom 10 states
State	Obligations	State	Obligations
Iowa	65,499,570	Connecticut	0
North Carolina	42,667,072	Florida	0
Kansas	42,581,436	Maryland	0
Tennessee	38,979,086	Maine	0
Illinois	31,705,259	New Hampshire	0
Minnesota	30,589,428	Nevada	0
Mississippi	28,981,988	New York	0
South Carolina	26,769,370	Rhode Island	0
North Dakota	26,181,318	Vermont	0
Missouri	20,679,000	West Virginia	0

Note. RCDG = Rural Cooperative Development Grants; REDG = Rural Economic Development Grants; REDL = Rural Economic Development Loans.

There is not only considerable funding variation across states but also across time. Figure 1 displays total funding among the three programs for each year between 2006 and 2014. Each year, representatives from the RBS must issue statements before both House and Senate appropriations subcommittees. The statements generally highlight the effectiveness of RBS programs in fostering rural development while requesting additional funding because of an ever-growing need. As evidenced by Figure 1, additional funding requests are not always approved. Despite requests for more funds to meet an ever-growing need, funding across the three programs does not always increase year to year.²

Figure 1.

Total RCDG, REDG, and REDL Obligations by Year, 2006 to 2014.

Despite different program objectives, the RCDG, REDG, and REDL programs have the same type of institutional structures governing the distribution of funds. All three programs rely on national competitive grants. The specific rules of the game governing the evaluation and selection of funding recipients is outlined in the Electronic Code of Federal Regulations. RCDG applications are evaluated by a panel of USDA employees using a point system weighted by the relative importance of 11 different criteria (RCDG, 2004).³ The REDG and REDL programs use a similar point system with 11 different criteria (REDL and Grant Program, 2007).⁴

Although the programs rely on competitive applications for awarding funds, bureaucratic discretion still exists. With the RCDG, many of the criteria allow for subjective evaluation. For example, with evaluating commitment, the “Agency will evaluate the applicant’s commitment to providing technical assistance and other services to underserved and economically distressed areas in rural areas of the United States” (RCDG, 2004). Additionally, although applications are evaluated using the 11 criteria, administration within the RBS determine funding levels. Qualified reviewers are appointed and offer funding-level recommendations based on the 11 criteria. However, after receiving the recommendations, RBS administration makes the final funding decisions.

REDG and REDL program funding are also subject to bureaucratic discretion. In addition to the 11 criteria, RBS administrators can also award “discretionary points.” For example, administrators can award additional points to projects deemed to be in areas experiencing long-term economic deterioration. Additionally, the Electronic Code of Federal Regulations provides the possibility of exceptions:

the RBS Administrator may, on a case-by-case basis, make exceptions to any requirement or provision . . . when such an exception is in the best interests of the Federal Government and is otherwise not in conflict with applicable law. (REDL and Grant Program, 2007)

While the competitive application system still allows for bureaucratic discretion, the extent of discretion is less than those in prior studies testing the congressional dominance model. Previous programs studied do not require a competitive application process to receive funding. Thus, studying RCDG, REDG, and REDL funding explores an institutional environment where congressional influence may be less pronounced. With less bureaucratic discretion, congressional dominance may be more difficult to implement.

As a government agency, the RBS interacts with Congress in a few important ways. Agriculture subcommittees in both the House and Senate with oversight of the RBS play an integral part in the creation of bills that influence RBS operations. Additionally, they are influential in RBS programmatic operations. Leadership within the RBS routinely testifies before Congress concerning the effectiveness of their programs. For example, after the passage of the 2014 Farm Bill, the Deputy Under Secretary for Rural Development Doug O’Brien issued a statement concerning the effectiveness of new programs before the Senate Subcommittee on Jobs, Rural Economic Growth, and Energy Innovation (USDA Rural Development, 2014). While the House and Senate agriculture subcommittees exert influence over program operations, the appropriations subcommittees likely exert greater influence, as they determine the level of available funding. Each year, representatives from the RBS issue statements before both the House and Senate agriculture appropriations subcommittees.⁵ The statements typically outline the funding for significant RBS programs while highlighting the effectiveness of the programs despite a growing need.

Empirical Approach

I estimate the relationship between congressional committee assignments and the amount of RCDG, REDG, and REDL funding received at the state level by estimating an equation of the following form:

f u n d s_{i t} = α s u b c o m m i t t e e s_{i t} + β c o n t r o l s_{i t} + μ_{t} + ε_{i t}

where $f u n d s_{i t}$ is the total amount of RCDG, REDG, and REDL funding obligations allocated to state $i$ in year $t$ , $c o n t r o l s_{i t}$ accounts for state-level characteristics in line with the intent of the programs, $μ_{t}$ mt are year fixed effects that control for aggregate year-specific effects, and $ε_{i t}$ is an error term.⁶ The vector $s u b c o m m i t t e e s_{i t}$ is composed of the number of representatives on relevant subcommittees for state $i$ in year $t$ .

There are four types of subcommittees under consideration: House agriculture, House appropriations, Senate agriculture, and Senate appropriations.⁷ Table 2 lists the relevant subcommittees over the 2006-2014 period. While the types of subcommittees under consideration remain constant over the period, the names of the relevant subcommittees change. Average membership remains relatively constant for the four types of subcommittees, except for the House agriculture subcommittee, between 2006-2007 and 2012-2013.

Table 2.

RBS Oversight Committees and Average Membership.

	Years	Average members
House Agriculture Committee, Subcommittee on Conservation, Credit, Rural Development, and Research	2006	17
House Agriculture Committee, Subcommittee on Specialty Crops, Rural Development, and Foreign Agriculture	2007-2008	11
House Agriculture Committee, Subcommittee on Rural Development, Biotechnology, Specialty Crops, and Foreign Agriculture	2009-2010	10
House Agriculture Committee, Subcommittee on Rural Development, Research, Biotechnology, and Foreign Agriculture	2011-2012	12
House Agriculture Committee, Subcommittee on Livestock, Rural Development, and Credit	2013-2014	24
House Appropriations Committee, Subcommittee on Agriculture, Rural Development, Food and Drug Administration, and Related Agencies	2006-2014	12
Senate Agriculture, Nutrition, and Forestry Committee, Subcommittee on Forestry, Conservation, and Rural Revitalization	2006	11
Senate Agriculture, Nutrition, and Forestry Committee, Subcommittee on Rural Revitalization, Conservation, Forestry, and Credit	2007-2010	11
Senate Agriculture, Nutrition, and Forestry Committee, Subcommittee on Jobs, Rural Economic Growth, and Energy Innovation	2011-2014	11
Senate Appropriations Committee, Subcommittee on Agriculture, Rural Development, and Related Agencies	2006	15
Senate Appropriations Committee, Subcommittee on Agriculture, Rural Development, Food and Drug Administration, and Related Agencies	2007-2014	14

Note. RBS = Rural Business-Cooperative Service.

Agriculture subcommittees in both the House and Senate with oversight of the RBS are important to include because they play an integral part in the creation of bills that influence RBS operations. Additionally, they are influential in RBS programmatic operations.

In addition to the congressional dominance variables, I also include state-level variables in line with the intent of RBS programs. RBS programs are designed to foster rural development, especially in economically depressed areas (USDA RBS, 2014). Therefore, control variables that capture both the rural population and economic well-being should be included. Three control variables are used: state’s percentage of national rural population, state’s percentage of national rural population with incomes below the poverty level, and state’s percentage of national nonmetropolitan unemployment. These control variables are selected because they are the variables used in the formula to allocate funding to states for several other RBS programs, including the Business and Industry Guaranteed and Direct Loans (USDA Rural Development, 2016b). To the extent that the objectives of the RCDG, REDG, and REDL align with other RBS programs, using these controls is appropriate. Inclusion of these controls reveals whether the political variables or the variables in line with the intent of the programs are more strongly related to how funding is truly allocated.

Data

Data for the level of RCDG, REDG, and REDL funding are obtained for all 50 states over the 2006-2014 period. Table 3 contains descriptive statistics for each state for each year over the period. On average, each of the 50 states received $1.1 million per year with an approximately 25% chance of having a representative on a particular subcommittee.

Table 3.

Descriptive Statistics.

Variable	M	SD	Minimum	Maximum
Total Spending	1,110,070	2,065,324	0	18,047,500
Rural Population Share	2.000	1.491	0.161	6.467
Rural Poverty Share	2.000	1.734	0.030	8.134
Rural Unemployment Share	1.996	1.663	0.113	7.209
House Agriculture Subcommittee	0.287	0.619	0	4
House Appropriations Subcommittee	0.238	0.498	0	3
Senate Agriculture Subcommittee	0.220	0.461	0	2
Senate Appropriations Subcommittee	0.284	0.452	0	1

House subcommittee data were retrieved from https://history.house.gov, which is a government website that maintains a thorough record of the House of Representatives. Senate subcommittee data were retrieved from https://www.senate.gov, which is a government website containing both current and historical records of the Senate. For both the House and Senate, data were collected from the 109th Congress through the 113th Congress. Each subcommittee variable reflects the total number of representatives for each state serving on the relevant subcommittee.

RCDG, REDG, and REDL funding data were retrieved from https://www.usaspending.gov, which is a government website created in 2007 in response to the Federal Funding Accountability and Transparency Act of 2006. Data sets were downloaded containing information on all RBS programs and RCDG, REDG, and REDL information was extracted through searching by Catalog of Federal Domestic Assistance (CFDA) numbers.⁸ Funding obligations were aggregated to the state level and cross-referencing for each year since 2009 was done with the USDA’s Rural Development yearly progress reports. The data contain funding for each fiscal year; however, since the amount of funding for a fiscal year is determined during the previous year, each fiscal year funding amount is analyzed along with variables from the prior year. For example, subcommittee representation in 2009 is used to explain fiscal year 2010 funding.

To explore whether factors that are supposed to be related to the allocation of funding actually are, I include three variables, in line with the stated intent of the programs. Rural population data were obtained from the U.S. Census Bureau 2000 and 2010 Census of Population. The 2000 Census is used for 2006-2009, while the 2010 Census is used for 2010-2014. Rural poverty and unemployment data were obtained for each year from the American Community Survey 1-year estimates.

Results

The results are consistent with the congressional dominance model, both for the House and the Senate and for agriculture and appropriations subcommittees. Table 4 reports the results of the four models employed.⁹

Table 4.

Estimated Effects on the Amount of Funding Received.

Variable	Model 1	Model 2	Model 3	Model 4
House Agriculture Subcommittee	523,635** (253,010)	356,938 (275,380)	607,272* (311,182)	383,846 (278,337)
House Appropriations Subcommittee	292,085 (218,100)	156,077 (284,897)	217,265 (357,391)	73,861 (447,785)
Senate Agriculture Subcommittee	478,658** (209,519)	527,217** (242,579)	616,381* (369,187)	644,485* (346,193)
Senate Appropriations Subcommittee	1,075,107*** (245,440)	1,129,406*** (214,474)	1,120,623** (440,661)	1,144,159*** (440,288)
Rural Population Share		−131,282 (401,060)		−75,928 (484,221)
Rural Poverty Share		174,435 (196,646)		246,699 (305,004)
Rural Unemployment Share		97,354 (309,070)		4,051 (501,037)
R ²	.146	.159	.162	.182
Observations	360	360	450	450
Year FE	YES	YES	YES	YES

Note. FE = fixed effects. Robust standard errors are in parentheses. Constant included but not reported.

p < .1. **p < .05. ***p < .01.

Models 1 and 2 include only states receiving some level of RCDG, REDG, or REDL funding over the period, while Models 3 and 4 include all 50 states. The similar findings between the models including all 50 states, and those only including states receiving funding suggest that the results are not sensitive to the inclusion of states with subcommittee representation but no funding. Models 1 and 3 contain only congressional subcommittees, while Models 2 and 4 also include variables in line with the intent of the programs. In each model, all four subcommittee variables have the expected positive sign, although only the Senate subcommittee variables are statistically significant in each model.

The Senate appropriations and House and Senate agriculture subcommittee variables tend to be statistically significant, but what is their economic significance? The average yearly level of funding received by each state is roughly $1.1 million. Having a representative on the Senate appropriations subcommittee is associated with a roughly $1.1 million increase in funding. Thus, for a state receiving the mean level of funding each year, an additional representative on the Senate appropriations subcommittee is associated with a doubling of funding. With the House and Senate agriculture subcommittees, the equivalent figure is a 50% increase. Smaller coefficients with the agriculture subcommittees is consistent with Garrett et al. (2006). They found that membership on appropriations subcommittees leads to more disaster relief funding compared with membership on oversight subcommittees. Direct control over funding decisions is likely more influential than oversight over program operations. Overall, having representation on these subcommittees is associated with substantial increases in funding.

Since no previous research has explored the development impacts of the REDL, REDG, and RCDG, it is difficult to determine the real-world impacts of an additional million dollars in funding. However, USDA Rural Development does issue press releases of estimates of jobs created and saved for some projects. For example, in a July 2016 press release, a $780,000 loan to an agricultural parts company for business expansion and the purchase of machinery was projected to create 51 jobs (USDA Rural Development, 2016e). The $780,000 figure is roughly equivalent to the amount of additional funding associated with having an additional representative on one of the relevant subcommittees. Fifty additional jobs would have a negligible impact on the state unemployment rate; however, 50 jobs in a rural community can have a significant local economic impact. In addition to local economic significance, 50 jobs saved or created can also have political significance. Saving jobs with RBS funding could be used to garner support from rural residents across an entire state.

The findings for the variables in line with the intent of the programs indicate that these factors are not related to the allocation of funding. Neither rural poverty share, rural unemployment share, nor rural population share achieves statistical significance in any of the models and including the variables does not substantially increase the explanatory power of the model. The three RBS programs under consideration may deviate from their stated policy objectives. Based on the results, congressional dominance is one possible mechanism through which federal development funds do not reach the areas of greatest need.

Conclusions and Future Research

I examine the relationship between having a congressional representative on House and Senate subcommittees with oversight of the RBS and the funding of three programs within the RBS. The results are consistent with the congressional dominance model. Both economically and statistically significant associations exist between congressional representation and combined RCDG, REDG, and REDL state-level funding. Since both agriculture and appropriations subcommittees are significant, the results are consistent with congressional influence over both funding and program operations. The results indicate that factors that are supposed to determine the allocation of funding do not. Congressional dominance is a plausible mechanism helping to explain why federal rural development funds do not always reach the areas of greatest need. This finding is important for the discussion over revitalizing federal economic development programs (Drabenstott, 2008; Markusen & Glasmeier, 2008) as changes designed to limit political influence may be especially beneficial.

Continuing to explore the congressional dominance model across different rural development programs with different institutions governing the allocation of funding is one of many avenues for additional research. Additional programs within the RBS could be analyzed, such as the Business and Industry Guaranteed and Direct Loans program, which has funding of roughly $1 billion each year. While the program does use a formula to distribute funds to states, congressional influence could shape how the formula is created. In addition to studying political influence in other USDA Rural Development programs, additional research could examine the economic impact of the programs. Subsequent research could follow Gabe and Kraybill (2002) by examining whether actual jobs created coincide with the level of job creation advertised in press releases. Alternatively, subsequent research could follow Kandilov and Renkow (2010), who used a difference-in-differences approach to estimate the effect of the USDA’s Broadband Loan Program.

One important reason to continue researching both the political influence on rural development programs and their economic impact is to make more informed public policy recommendations. Findings of political influence on RBS programs would undermine the legitimacy of the RBS. The results in this study are consistent with congressional influence; however, additional research with clear causal identification would be required to strengthen the case for political influence over RBS programs. Additionally, research on the development impacts of the RBS would help evaluate its efficiency. Additional research is needed to test for the extent of political influence across a wider range of rural development programs, to evaluate RBS development impacts, and to make more informed public policy recommendations.

Footnotes

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

Notes

ORCID iD

Josh Matti

Author Biography

Josh Matti is an assistant professor of economics at Georgia Gwinnett College. His research at the intersection of public economics and local development explores the spatial distribution of consumer services and the effects of government consolidations on development.

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