TELECOMMUNICATIONS

USDA Should Evaluate 

the Performance of the 

Rural Broadband Loan 

Program

Report to Congressional Requesters

May 2014

GAO-14-471

United States Government Accountability Office
United States Government Accountability Office

Highlights of GAO-14-471, a report to 

congressional requesters 

May 2014

TELECOMMUNICATIONS

USDA Should Evaluate the Performance of the Rural 

Broadband Loan Program

Why GAO Did This Study

Access to affordable broadband 

telecommunications is increasingly 

viewed as vital to economic growth and 

improved quality of life. Broadband is 

particularly critical in rural areas, where 

advanced communications can reduce 

the isolation of remote communities 

and individuals. To extend access to 

broadband and therefore increase 

economic opportunity in rural America, 

RUS finances the construction of 

broadband through a loan program.

GAO was asked to assess issues 

related to the loan program. This report 

addresses the (1) geographic 

distribution and financial performance 

of loans and (2) relationship between 

loans and broadband deployment and 

economic development, and how 

USDA evaluates progress towards 

these outcomes.

To address these research questions, 

GAO interviewed broadband providers 

and stakeholders selected for their

varying experiences, including those 

that have and have not received RUS 

loans. GAO also analyzed RUS and

U.S. Census Bureau data from 2003—

2013 as well as the most recent

National Broadband Map data. 

What GAO Recommends

GAO recommends that USDA (1) 

evaluate loans made by RUS through 

the loan program to identify 

characteristics of loans that may be at 

risk of rescission or default and (2) 

align the goals in its APR to the loan 

program’s purpose, to the extent 

feasible. In commenting on a draft of 

this report, USDA said it will strive to 

fully implement the report’s 

recommendations. 

What GAO Found

The U.S. Department of Agriculture (USDA) Rural Utilities Service (RUS) has 

approved 100 loans to geographically and demographically diverse areas 

through its Rural Broadband Access Loan and Loan Guarantee Program (“loan 

program”), though over 40 percent of these loans are no longer active. The 

geographic distribution of RUS loans is widespread, with broadband providers in 

43 states having received one or more loans through the loan program from 2003

through 2013. About $2 billion in loans have been made to providers in areas 

with diverse demographics and economies, including areas with low population 

densities and income as well as areas in relative proximity to large cities with 

robust local economies. Of the 100 RUS loans approved through the loan 

program, 48 are currently being repaid, and 9 have been fully paid back. Fortythree are no longer active, either because they were cancelled before they were 

paid out (25 rescinded) or because the provider defaulted by failing to abide by 

the terms of the loan (18 defaulted). Approving a loan requires significant 

resources. Loans that default or are rescinded can represent an inefficient use of 

RUS resources. Despite these issues, RUS has not gathered information or 

performed analysis to better understand what might lead a project to default or 

otherwise make a project a poor candidate for receiving a loan. Federal 

guidance, though, emphasizes the importance of assessing the risk associated 

with loan programs. 

RUS loans can help promote limited broadband deployment and economic 

development, but USDA’s performance goals do not fully align with the loan 

program’s purpose. According to GAO analysis of National Broadband Map 

deployment data as of June 2013, areas with RUS loans generally have the 

same number of broadband providers as areas without a loan. However, the 

RUS loan program can enhance the quality and reach of broadband networks in 

rural areas, according to stakeholders. Further, according to GAO analysis of 

RUS loans and U.S. Census Bureau data from 2003 through 2011—the years for 

which RUS and relevant Census data are available—areas affected by at least 

one approved RUS loan were associated with modestly higher levels of 

employment and payroll (1 to 4 percent) after the year of loan approval and in all 

subsequent years, as compared to areas that did not receive RUS loans. As 

stated in the program regulations, the purpose of the RUS loan program is to 

increase broadband deployment (that is, the number of broadband subscribers 

with access to new or improved broadband service) and economic opportunity in 

rural America through the provision of broadband services. USDA’s Annual 

Performance Report (APR) provides information on the achievements of USDA’s 

programs each fiscal year. The goals in USDA’s report, though, do not fully align 

with the purpose of the loan program. For instance, USDA’s APR does not have 

any goals or measures to determine the loan program’s progress towards 

economic development outcomes. As our past work has shown, an attribute of a 

successful performance goal is whether it aligns with division and agency-wide

goals. Agency performance goals that do not link to program goals can lead to 

incentives and behaviors that do not support the achievement of division or 

agency-wide goals. Performance goals aligned with the program’s purpose may 

help USDA and Congress better monitor the outcomes of the loan program.

View GAO-14-471. For more information, 

contact Mark Goldstein at (202) 512-2834 or 

GoldsteinM@gao.gov. 
Page i GAO-14-471 Telecommunications

Letter 1 

Background 4

Since 2003, 100 Loans Have Been Made to Geographically and 

Demographically Diverse Areas, but Over 40 Percent Are No 

Longer Active 10

RUS Loans Can Help Promote Limited Broadband Deployment 

and Economic Development, but Performance Goals Do Not 

Fully Align with Program’s Purpose 18

FCC Reforms of USF and ICC Have Created Temporary 

Uncertainty That May Be Hindering Investment in Broadband 25

Conclusions 31

Recommendations 31

Agency Comments and Our Evaluation 32

Appendix I Scope and Methodology 35

Appendix II Economic Development Model Methodology and Results 40

Appendix III Comments from the Federal Communications Commission 52

Appendix IV GAO Contact and Staff Acknowledgments 55

Tables

Table 1: Rural Utilities Service (RUS) Rural Broadband Access 

Loan and Loan Guarantee Program, Value of Loans, 

Fiscal Years 2003-2013 (Dollar Figures in Millions) 7

Table 2: Number of Counties with Rural Utilities Service (RUS) 

Loans Using Different Definitions of Rural 45

Table 3: Model Results for Counties 90 Percent Rural with Urban 

Clusters 49

Table 4: Model Results for Counties 90 Percent Rural 50

Contents
Page ii GAO-14-471 Telecommunications

Figures

Figure 1: U.S. Counties and Territories with One or More 

Approved RUS Broadband Loans, 2002—2013 11

Figure 2: Status of Broadband Loan-Program Applications, 

2002—2013 16

Abbreviations

APR Annual Performance Report

CAF Connect America Fund 

FCC Federal Communications Commission 

ICC Intercarrier Compensation 

NBM National Broadband Map 

OMB Office of Management and Budget 

RUCA Rural-Urban Commuting Area 

RUS Rural Utilities Service 

USDA U.S. Department of Agriculture

USF Universal Service Fund 

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Page 1 GAO-14-471 Telecommunications

441 G St. N.W.

Washington, DC 20548

May 22, 2014

The Honorable Debbie Stabenow

Chairwoman

The Honorable Thad Cochran

Ranking Member

Committee on Agriculture, Nutrition and Forestry

United States Senate

The Honorable Pat Roberts

United States Senate

Access to affordable broadband telecommunications1 is increasingly 

viewed as vital to long-term economic growth and improved quality of life, 

just as electricity, telephone, and interstate highway systems filled similar 

roles in previous generations. The ability to share large amounts of 

information at ever-greater speeds increases productivity, facilitates 

commerce, and drives innovation. Furthermore, broadband can improve 

citizens’ quality of life. For example, broadband technology makes it 

possible for a patient to visit a local clinic and receive medical attention 

from specialists hundreds of miles away, for a student to access 

information not available from the local library, and for a firefighter to 

download blueprints of a burning building and intervene appropriately. 

Broadband is particularly critical in rural areas, where advanced 

communications can reduce the isolation of remote communities and 

individuals.

To extend access to broadband and therefore increase economic 

opportunity in rural America, the U.S. Department of Agriculture’s (USDA) 

Rural Utilities Service (RUS) finances the construction of broadband 

infrastructure through RUS’s Rural Broadband Access Loan and Loan 

Guarantee Program (“loan program”). The repayment status and 

geographic distribution of loans approved through this program have not 

been widely reported to date. The relationship, if any, between these 

loans and desired outcomes such as broadband deployment and 

 

1

The term broadband commonly refers to high speed Internet access. GAO, 

Telecommunications: Broadband Deployment Is Extensive throughout the United States, 

but It Is Difficult to Assess the Extent of Deployment Gaps in Rural Areas, GAO-06-426

(Washington, D.C.: May 5, 2006).
 

Page 2 GAO-14-471 Telecommunications

economic development is also not well understood. Some stakeholders 

have also expressed concern about the impact of recent Federal 

Communications Commission (FCC) reforms to the Universal Service 

Fund (USF) and Intercarrier Compensation (ICC) systems on the RUS 

broadband loan program.2 The USF in particular has functioned as an 

ongoing subsidy for telecommunications providers, and according to 

some providers, reductions in USF support could jeopardize the ability of 

those providers to pay back or take out RUS loans.

You asked us to review the distribution and status of loans as well as the 

effects of these loans. This report addresses: (1) the geographic 

distribution and financial performance of loans since 2002; (2) the 

relationship, if any, between loans and broadband deployment and 

economic development in rural areas, and how USDA evaluates progress 

toward these outcomes; and (3) the impact of reforms to the USF HighCost program and ICC on the RUS broadband loan program.

To examine the geographic distribution and financial performance of 

loans since 2002, we gathered and analyzed RUS loan data. Specifically, 

we collected information on the recipient, approval date, amount, 

repayment status, as well as the proposed technology and communities 

to be served by the project for each loan approved by RUS since the 

program’s authorization in 2002. We also collected information on the 

proposed recipient, amount, communities to be served, and technology 

type for each rejected loan. Using this information, we analyzed the 

geographic distribution, including whether loans met various definitions of 

rural, and the repayment status of the loans.

To assess the relationship, if any, between RUS loans and broadband 

deployment and economic development in rural areas, we conducted 

statistical analyses and interviews. To assess broadband deployment, we 

used the most recent National Broadband Map’s (NBM)3 data on 

 

2

Throughout this report, we refer to entities that have been approved for loans from the 

RUS loan program as “RUS broadband borrowers.” In using this term, we do not refer to 

borrowers from other RUS loan programs, such as the Telecommunications Infrastructure 

Loan Program.

3

The NBM is an online database that allows users to access broadband availability at the 

neighborhood level. The NBM was created by the National Telecommunications and 

Information Administration in collaboration with FCC, and in partnership with 50 states, 

five territories, and the District of Columbia.
 

Page 3 GAO-14-471 Telecommunications

broadband availability to compare the number of providers in counties 

with approved RUS loan projects to counties with rejected RUS loan 

projects. NBM data currently available represent broadband availability as 

of June 2013. We also compared the number of broadband providers in 

select communities before and after the approval of a RUS loan; we 

selected these communities as part of our site visits, discussed below. 

For this analysis, we gathered information on broadband providers 

present in these communities before the RUS loan from the relevant loan 

files at USDA, and used NBM information to identify providers after the 

RUS loan approval.4 To examine economic development in rural areas, 

we developed a regression model to assess the relationship between 

counties with approved loan projects and specific economic outcomes 

(i.e., number of business establishments, employment, and annual 

payroll). We assessed the reliability of RUS data by interviewing RUS 

officials about their databases and data collection practices. We also 

assessed the reliability of NBM data by reviewing how the map 

developers collect data and conduct quality assurance checks, as well as 

through interviews with stakeholders. Based on this information we 

determined that these data were sufficiently reliable for our reporting 

purposes. We also conducted site visits and phone interviews with 

stakeholders in seven states, which we selected to include communities 

with providers that have and have not received different kinds of RUS 

broadband loans.5 As part of our site visits we interviewed, when 

possible, broadband providers, as well as state and local stakeholders 

such as Chambers of Commerce. We also examined prior academic 

studies of the RUS loan program as well as other research on the general 

impact of broadband availability and adoption on communities. We 

identified these studies through a literature search as well as interviews 

with stakeholders.

Finally, to determine the impact of reforms to the USF and ICC on the 

RUS broadband loan program, we reviewed relevant documentation, 

analyzed USF data, and conducted interviews. We reviewed FCC’s 2011 

USF/ICC Transformation Order that adopted changes to the USF and 

 

4

Few loans have been approved since 2008, so we determined that sufficient time had 

passed for most projects financed with RUS loans to have been substantially completed.

5

Although we obtained, in our view, information from a diverse mix of RUS borrowers, the 

findings from our site visits cannot be generalized to all borrowers because the sites were 

selected as part of a nonprobability sample.
 

Page 4 GAO-14-471 Telecommunications

ICC, provided a framework for additional changes, and solicited 

comments regarding those changes.6 We also reviewed public comments 

on the rulemakings proposed as part of this Transformation Order, as well 

as studies and reports that assess the impact of the reforms. To calculate 

the extent of USF support for RUS broadband borrowers, we examined 

data—for the years 2003 through 2013—reported in FCC’s Universal 

Service Monitoring Report. We assessed the reliability of these data by 

reviewing relevant data collection and verification documents. Based on 

this information, we determined that the data provided to us were 

sufficiently reliable for our reporting purposes. Additionally, we conducted 

interviews with officials from RUS, FCC, a private bank (CoBank), and 

NTCA – The Rural Broadband Association. We also discussed these 

reforms with nine broadband providers that have and have not received 

USF support, as described above. Further details about our scope and 

methodology can be found in appendix I.

We conducted this performance audit from June 2013 to May 2014 in 

accordance with generally accepted government auditing standards. 

Those standards require that we plan and perform the audit to obtain 

sufficient, appropriate evidence to provide a reasonable basis for our 

findings and conclusions based on our audit objectives. We believe that 

the evidence obtained provides a reasonable basis for our findings and 

conclusions based on our audit objectives.

The provision of broadband Internet infrastructure and services in the 

United States is generally privately financed. Rural areas, though, can 

have conditions that increase the cost of broadband deployment, such as 

remote areas with challenging terrain like mountains, which increase 

construction costs, or conditions that make it difficult to recoup 

deployment costs, such as relatively low population densities and 

incomes.7 These conditions make it less likely that a service provider will 

build out or maintain a network. Low population density can mean fewer 

potential subscribers, and lower-income populations are less likely to use 

 

6

In the Matter of Connect America Fund, 26 FCC Rcd. 17663, 27 FCC Rcd. 4040 (2011)

(report and order and Further Notice of Proposed Rulemaking). In this report we refer to 

this order as the USF/ICC Transformation Order. 

7

According to FCC, the costs for new fiber broadband construction can range from 

$11,000 to $24,000 per mile for aerial construction and to $25,000 to $165,000 per mile 

for buried construction.

Background
 

Page 5 GAO-14-471 Telecommunications

broadband. There is also evidence that rural low-income households are 

less likely to use broadband than metropolitan low-income households.8

As a result of these factors, rural areas tend to lag behind urban and 

suburban areas in broadband deployment.

However, because of broadband’s economic and social benefits, several 

public programs aim to encourage greater investment in rural areas. RUS 

administers several such programs intended to accelerate the 

deployment of broadband services. One program is the Rural Broadband 

Access Loan and Loan Guarantee Program, which authorizes RUS to 

provide treasury rate loans, 4 percent loans, and loan guarantees.9 The 

loan program was authorized by the Farm Security and Rural Investment 

Act of 2002 (2002 reauthorization act) and RUS approved the first loans 

under the program in fiscal year 2003.10 On February 6, 2013, RUS 

published in the Federal Register the final rule implementing the loan 

program,11 as reauthorized in 2008.12 Entities eligible to receive these 

loans include corporations, limited liability companies, cooperative or 

mutual organizations, Indian tribes, and state or local governments.13 The 

 

8

See James Prieger, “The Broadband Digital Divide and the Economic Benefits of Mobile 

Broadband for Rural Areas” (2012). Pepperdine University, School of Public Policy 

Working Papers. Paper 41.

9

In fiscal year 2001, RUS was directed by statute to administer a pilot broadband program 

dedicated in part to finance rural broadband infrastructure deployment (see Agriculture, 

Rural Development, Food and Drug Administration, and Related Agencies—

Appropriations Act, 2001, Pub. L. No. 106-387, title III, 114 Stat. 1549, 1549A-22 (2000)). 

10Pub. L. No. 107-171, § 6103, 116 Stat. 134, (2002), codified at 7 U.S.C. § 950bb. In 

addition to the Rural Broadband Access Loan and Loan Guarantee Program discussed 

throughout this report, RUS also has a rural telephone loan program (dating back to 1949, 

now called the Telecommunications Infrastructure Loan Program) that has historically 

supported infrastructure for telephone voice service. Additionally, the Distance Learning 

and Telemedicine Grant Program supports broadband-based applications. RUS also 

administered the Broadband Initiatives Program authorized as part of the American 

Recovery and Reinvestment Act of 2009, Pub. L. No. 111-5, 123 Stat. 115, 118-119 

(Recovery Act). 

11Rural Broadband Access Loans and Loan Guarantees, 78 Fed. Reg. 8353, 8360 (Feb. 

6, 2013); codified at 7 C.F.R. part 1738.

12The final rule substantially adopts the interim rule published on March 14, 2011. The 

2008 act was the Food, Conservation, and Energy Act of 2008, Pub. L. No. 110-246, § 

6102, 122 Stat 1651, 1965 (2008). 

1378 Fed. Reg. 8353, 8360 (Feb. 6, 2013). Throughout this report we refer to entities that 

provide broadband service, including those eligible for RUS loans, as “providers.” 
 

Page 6 GAO-14-471 Telecommunications

program is “technology neutral” in that RUS can finance any type of 

broadband service as long as the applicant plans to deliver broadband to 

every customer in the proposed service area at certain speeds.14 The 

loan program was reauthorized by the 2014 reauthorization act, which 

modified some eligibility requirements and included a new requirement for 

any entity receiving a loan to submit a semiannual report, for the 3 years 

after completion of the project, which includes data on broadband 

adoption rates.15 According to RUS officials we spoke with, until RUS 

publishes regulations implementing the changes in the 2014 

reauthorization act, it will not accept new loan applications.16 To 

administer the loan program, RUS has approximately 22 program staff as 

well as 25 field staff assisting part-time.

According to RUS rules for the loan program, the service area of a project 

eligible for RUS broadband financing must be entirely within a rural area, 

defined for this loan program as any area not contained in an 

incorporated city or town with a population in excess of 20,000 

inhabitants, or an urbanized area contiguous and adjacent to a city or 

town that has a population greater than 50,000 inhabitants. In addition to 

being located within a rural area, a service area must meet the following 

conditions:

• At least 25 percent of the households are underserved, meaning they 

are offered broadband service by no more than one “incumbent 

service provider.”17 Incumbent service providers are broadband 

 

14These speeds are referred to as the “broadband lending speed,” and are a minimum 

bandwidth requirement for all loans. The 2014 reauthorization act establishes “the 

minimum acceptable level of broadband service” as at least 4 megabits per second 

downstream and 1 megabits per second upstream. At least once every 2 years, the 

Secretary of Agriculture is required to review and may adjust this speed definition and may 

consider establishing different minimum speeds for fixed and mobile (wireless) broadband. 

Agricultural Act of 2014, Pub. L. No. 113-79, § 6104, 128 Stat 649 (2014) to be codified as 

amending 7 U.S.C. § 950bb(a)(2). 

15Id. 

16RUS does not have any pending applications. 

17This requirement was modified by the 2014 reauthorization act to one where not less 

than 15 percent of the households in the proposed service territory are unserved or have 

service levels below the minimum acceptable level of broadband service. Id. Unserved 

and underserved areas tend to have conditions that increase the cost of constructing and 

maintaining broadband networks, and have been defined differently for other federal 

programs.
 

Page 7 GAO-14-471 Telecommunications

providers that RUS identifies as directly providing broadband service 

to at least 5 percent of the households within a service area.

• No part of the service area has three or more “incumbent service 

providers.”

• No part of the funded service area overlaps the service area of current 

RUS borrowers and grantees.

• No part of the funded service area is included in a pending application 

before RUS seeking funding to provide broadband service.

RUS has a total authorized value of all the loans it can approve each 

fiscal year, based on lending authority approved in annual appropriations 

and the estimated long-term cost of extending credit over the life of the 

loans approved that fiscal year.18 See table 1 below for yearly authorized 

total value of loans as well as loans approved.

Table 1: Rural Utilities Service (RUS) Rural Broadband Access Loan and Loan Guarantee Program, Value of Loans, Fiscal 

Years 2003-2013 (Dollar Figures in Millions)

Fiscal year Total authorized value of loans Loans approved a Total value of loans made b

2003 $80 2 $56.26

2004 $602 33 $574.56

2005 $550 13 $111.42

2006 $500 15 $329.21

2007 $500 16 $250.96

2008 $300 13 $421.35 c

2009 $400 4 $6.65

2010 $400 0 $0

2011 $400 1 $19.75

2012 $212 1 $68.9

2013 $42 2 $151.77 d

 

18Budgeting for the cost of credit programs is governed by the Federal Credit Reform Act 

of 1990 (Pub. L. No. 101-508, title V, 104 Stat. 1388, 1388-610, codified as amended at 2 

U.S.C. § 661-661f), which requires federal agencies to receive and obligate budget 

authority to cover the estimated long-term cost to the government (which includes 

defaults, delinquencies, and interest subsidies) of providing credit assistance, calculated 

on a net present value basis, and excluding administrative costs. Beginning in fiscal year 

2005, the loan program has received funds that can be carried over into a subsequent 

fiscal year. In fiscal years 2005 through 2007, the loan program received funds that were 

available for 2 fiscal years, and since fiscal year 2008, the funds have been no-year 

money, and are available until expended or rescinded. 
 

Page 8 GAO-14-471 Telecommunications

Source: GAO summary of RUS data and appropriations acts.

a

Some approved loans are no longer active, either because the borrower has defaulted on its loan 

payments or the loan was never paid out. 

b

These loan award totals include loans that were later reduced or rescinded. According to RUS 

officials, RUS did not utilize the entire total authorized amount of loans each year because in some 

years it lacked enough quality applications. 

c

Starting in 2005 Congress allowed RUS to carry over unobligated funds into the following fiscal 

year—initially for one year, and later, indefinitely. According to RUS, the value of loans approved in 

fiscal year 2008 includes loan amounts for which the subsidy cost was appropriated in fiscal year 

2007 as well as in fiscal year 2008. 

d

The fiscal year 2013 award total is higher than the total authorized value of loans that year because 

a loan was approved and rescinded during this fiscal year, a situation that allowed RUS to award 

another loan in the same fiscal year. 

RUS loans can have a variety of repayment outcomes. Loans are paid 

out as eligible project costs are incurred, on an agreed-upon repayment 

schedule. For the purposes of this report, “active” loans are those that are 

being paid back by the borrower as scheduled. “Repaid” loans include 

those that have already been fully paid back. When a loan is never paid 

out, it is referred to as a “rescission.” Loans can also be reduced from the 

approved amount, for instance when a borrower does not need the 

remainder of a loan upon completion of a project. Borrowers can also fail 

to abide by the terms of the loans, such as missing payment deadlines, 

which may result in a “default.”

Whereas RUS broadband loans are used as up-front capital to invest in 

broadband infrastructure, the USF has functioned as an ongoing subsidy 

for telecommunications providers that offer telephone and other 

communications services such as broadband access. The USF includes 

four programs that subsidize these providers.19 Within the USF program, 

the largest amount of annual expenditures involve the High-Cost 

Program, which subsidizes telecommunications providers that serve rural, 

remote, and other areas where the costs of offering telephone service are 

 

19Federal policy has long called for making affordable residential telephone service 

available to the greatest possible number of Americans—a policy known as “universal 

service.” The Communications Act of 1934 established the nation’s telecommunications 

policy, including making communications services available “so far as possible, to all the 

people of the United States.” The USF programs are primarily funded through mandatory 

payments from companies providing telecommunications services—payments usually 

passed along to consumers as a line item fee on their telephone bill.
 

Page 9 GAO-14-471 Telecommunications

high.20 These subsidies allow providers to charge lower rates than 

otherwise would be feasible. This support has been a key revenue 

resource for some telecommunications providers that also offer 

broadband services, including RUS broadband borrowers.

FCC has adopted program rules that change USF support. These 

changes are outlined in an order released in November 2011, an order 

that FCC said “comprehensively reforms and modernizes” the universal 

service system to ensure that affordable voice and broadband service are 

available throughout the nation.21 This USF/ICC Transformation Order 

requires that recipients of high-cost support offer voice and broadband as 

a condition for receiving USF support. FCC also reformed the ICC 

system. ICC is a system of payments between providers for the 

origination, transportation, and termination of telecommunications traffic. 

ICC payments have traditionally been governed by a complex but related 

system of federal and state rules than those governing universal service.

 

20The High-Cost Program consists of several components, each with different eligibility 

criteria and different methods to determine the level of support. We refer to all 

components when referring to the High-Cost Program throughout this report. The other 

three USF programs subsidize telecommunication services for low-income consumers 

(Low Income Program), and telecommunication and broadband services for schools and 

libraries (E-Rate Program) and for rural health care providers (Rural Health Care 

Program). We have previously reported on FCC’s efforts to reform the USF High-Cost 

Program, which will ultimately be replaced by the Connect America Fund. See GAO, 

Telecommunications: FCC Has Reformed the High-Cost Program, but Oversight and 

Management Could be Improved, GAO-12-738 (Washington, D.C.: July 25, 2012).

21USF/ICC Transformation Order, ¶ 1, 26 FCC Rcd., 17667. 
 

Page 10 GAO-14-471 Telecommunications

RUS has approved 100 loans, out of 249 applications, through the 

Broadband Loan Program since 2003. The total dollar amount of the 

loans awarded to date is about $2 billion.22 The agency has approved few 

loans since 2008, including none in fiscal year 2010. RUS officials stated 

that after the passage of the Recovery Act, RUS focused on 

administering the Broadband Initiatives Program.23 Moreover, a lag 

occurred between the passage of the 2008 reauthorization act and 

completion of the regulations to carry out the law, in part because of RUS 

prioritized distribution of Recovery Act funding. 

The geographic distribution of RUS loans is widespread, though with 

more loans going to providers serving the Great Plains, Midwestern and 

Southern states than to the east and west coasts. Providers in 43 states

received one or more loans since 2002 (see fig. 1).24 The states where 

providers received the most loans include Iowa, Kansas, Minnesota, 

Oklahoma, and Texas. RUS officials stated that the loan program is 

intended to be geographically neutral, and they do not target particular 

 

22This is the total amount approved by RUS, not necessarily the actual amount paid out by 

RUS, since some loans are rescinded or reduced or defaulted before all funds were paid 

out. 

23This program was intended to finance broadband infrastructure projects, overlapping the 

agency’s other broadband loan programs. For fiscal year 2010, RUS gave priority to the 

Recovery Act program. 

24One loan was also awarded to a U.S. territory, the U.S. Virgin Islands. Additionally, not 

all loans supported projects located in a single state; 20 loans were awarded to 

communities in multiple states.

Since 2003, 100 

Loans Have Been 

Made to 

Geographically and 

Demographically 

Diverse Areas, but 

Over 40 Percent Are 

No Longer Active

Communities Receiving 

RUS Loans Are 

Geographically and 

Demographically Diverse 
 

Page 11 GAO-14-471 Telecommunications

regions of the country. As discussed further below, not all approved loans 

are still active. We did not observe significant geographic differences 

between loans with different repayment statuses. 

Figure 1: U.S. Counties and Territories with One or More Approved RUS Broadband Loans, 2002—2013 

Note: For the purposes of this graphic, if a county had one or more communities served by an RUS 

loan, the entire county is deemed served. The highlighted areas therefore do not represent the exact 

service areas of all projects supported by the RUS broadband loan program. 

RUS broadband borrowers are located in regions with varying 

topography, including differing weather and terrain.25 For example, we 

 

25We selected our site visits based on a variety of factors, including the size and status of 

the loan and geographic diversity. See appendix I for more information.
 

Page 12 GAO-14-471 Telecommunications

spoke to borrowers in dissimilar regions, including North Dakota (plains 

with some hills); New Mexico (high desert); and Vermont (rocky hills and 

mountains). Some aspects of these rural areas can result in challenges 

for broadband deployment, with some stakeholders stating that the 

unique characteristics of their region affected the amount of time and 

resources they needed to install broadband. Officials at a broadband 

provider we spoke with in New Mexico, for instance, said that the 

extremely hard, rocky ground in the service area results in very high permile costs for installing fiber infrastructure in the ground. Another provider 

in North Dakota told us that the long winters limit the time available for 

installing fiber to about 4 months a year. 

Loans have been made to providers in areas with a variety of 

demographics and economies: 

• On average, counties where RUS loans were approved have a higher 

poverty rate, 15.6 percent, than the national average, 14.9 percent. In 

these loan counties, the rate ranges from 4.2 percent to 41.1 percent, 

based on U.S. Census Bureau (Census) data. 

• Overall, counties with approved RUS loans have a higher percentage 

of adults over age 65, 15.7 percent, than the national average, 13.2 

percent. The range of this age group in loan counties is also large, 

from 4.1 to 44.5 percent. 

• The per capita income of loan recipient communities is also diverse. 

For example, our site visit communities included Portales, New 

Mexico, with per capita income of $15,881, and Dickinson, North 

Dakota, with per capita income of $28,253.26 

• Some areas where we conducted interviews, such as Eagle Butte, 

South Dakota, were highly rural and remote. These areas had low 

population densities and also tended to be lower-income, overall.

• Other site visit locations, including the rural communities of Hudson 

and Catskill, New York, were located in relative proximity to large 

cities, and had some of the characteristics of more urban areas, such 

as robust “creative economies.” For example, Hudson, New York, is 

located within a 2 hour drive from both Albany and New York City, and 

has recently attracted a number of businesses such as art galleries. 

• Two site visit communities, Dickinson, North Dakota, and Hobbs, New 

Mexico, have experienced recent surges in population and 

 

26All figures are in 2012 dollars. 
 

Page 13 GAO-14-471 Telecommunications

infrastructure needs associated with the oil and natural gas industry, 

according to providers with whom we spoke. 

The definition of what constitutes a “rural” community can be difficult 

when determining how to target rural communities for broadband 

assistance. A narrower definition may mean that deserving communities 

are excluded. A broader definition, though, may mean that communities 

not traditionally considered “rural” or “underserved” may be eligible for 

financial assistance, which could then limit funds available to the most 

rural areas. The USDA’s Office of Inspector General has reported that 

rural requirements are important to keep the focus of loans on rural areas 

that are unlikely to receive broadband service through the private 

market.27

When analyzed in the aggregate, the majority of active RUS loans since 

the program’s inception satisfy RUS and other commonly accepted 

definitions of rural. The RUS definition of eligible rural areas for the loan 

program has been changed twice to better target loans to rural areas.28 In 

2008, for instance, the definition of an eligible community was changed to 

exclude communities adjacent to urbanized areas.29 This change followed 

a 2005 finding by the USDA’s Office of Inspector General that some loans 

had been made to areas that were not truly rural, such as suburban 

communities bordering large cities.30 We found that the majority of active 

RUS loans were made to providers in communities that satisfy the 2008 

 

27U.S. Department of Agriculture, Office of Inspector General, Southwest Region, Audit 

Report: Rural Utilities Service Broadband Loan and Loan Guarantee Program, 09601-8-

Te (Washington, D.C.: Mar. 31, 2009). 

28The 2002 definition required that communities not exceed 20,000 inhabitants and not be 

within a metropolitan statistical area. Pub. L. No. 107-171, § 601, 116 Stat. 416. In 2004, 

the definition was loosened to allow some communities within MSAs. Consolidated 

Appropriations Act of 2004, Pub. L. No. 108-199, § 772, 118 Stat. 3, 40. 

29Food, Conservation, and Energy Act of 2008, P.L. No. 110-246, § 6110, 122 Stat. 1657, 

1960. As we previously stated, eligible areas now include communities of 20,000 or fewer 

inhabitants that are not within urbanized areas next to a town of greater than 50,000 

population. In order to be considered rural by RUS, all communities in the proposed 

service area of a loan must meet the definition. 

30U.S. Department of Agriculture, Office of Inspector General, Southwest Region, Audit 

Report: Rural Utilities Service Broadband Grant and Loan Programs, 09601-4-Te 

(Washington, D.C.: Sept. 30, 2005). 

Communities Served by 

RUS Broadband 

Borrowers Generally Meet 

Past and Present 

Definitions of Rural
 

Page 14 GAO-14-471 Telecommunications

and 2004 definitions of rural used by RUS.31 Additionally, the majority of 

active loan communities meet alternative definitions of rural. Specifically, 

our analysis assessed whether loans could be considered rural using the 

standard Census definition as well as USDA’s Rural-Urban Commuting 

Area Codes (RUCA). Census classifies a community as rural if it falls 

outside of an urban area, and most active loans adhered to this 

population density-based definition, whether 2000 or 2010 Census data 

were used.32 The USDA Economic Research Service’s 2013 RUCA is a 

more in-depth set of data that incorporate Census tract commuting 

patterns and other measures of “rurality” in addition to population 

density.33 The majority of the active loans fell into the “large rural town” 

and “small town/isolated rural” categories.34

With only five loans approved since 2008, we were not able to assess 

whether the overall rurality of loans has changed as a result of the 2008 

reauthorization act. Additionally, stakeholders generally did not object to 

the current definition of rural used by RUS. Some site-visit interviewees 

suggested that additional factors besides population, such as 

socioeconomic characteristics, be considered as part of the eligibility 

requirements. 

 

31Specifically, 68 percent of active loans meet the 2008 and 79 percent, the 2004 

definitions of rural, based on our analysis using 2010 Census data. Only about half, 47 

percent, of active loans satisfy the most restrictive criteria—the 2002 definition—used by 

RUS for the loan program. We did not analyze whether specific loans approved by RUS 

were not eligible under the relevant definition of rural at the time. 

32Specifically, 93 percent of active loans fell outside of a 2010 Census urbanized area, 

and 95 percent, outside of a 2000 Census urbanized area. 

33RUCA has 10 tiers along the spectrum of rurality, each of which is further broken down 

into secondary codes. We used the 4-tiered data consolidation recommended for analysis 

by the Washington State Department of Health. See Washington State Department of 

Health, Guidelines for Using Rural-Urban Classification Systems for Public Health 

Assessment, revised February 2009.

34Specifically, 60 percent of these loans fell into rural categories using the 2013 RUCA 

codes. We performed this analysis using both 2006 and 2013 RUCA data, and found the 

loan communities met rurality criteria more often when using the 2013 data, which are 

based on the 2010 Census.
 

Page 15 GAO-14-471 Telecommunications

Of the 100 RUS loans approved since 2002, 57 are active or have been 

repaid (see fig. 2). The other 43 loans are no longer active, either 

because they have been rescinded or are in default. These inactive loans 

represent 43 percent of the total number of loans awarded and 54 percent 

of the total loan dollars awarded to date. 

• The majority of RUS loans approved by RUS to date are active or 

have been repaid. These loans include 48 that are currently repaying 

outstanding obligations as scheduled, as well as 9 that have been 

fully paid back to the agency. Both of these categories include some 

loans that have been reduced, meaning the provider has elected to 

not accept the full loan amount or completed its project ahead of 

schedule and did not require the remaining funds.

• Twenty-five of the 100 loans approved to date have been rescinded, 

meaning that they were cancelled before any portion of the loan was 

paid out to the approved provider. RUS officials stated there are 

varying circumstances where a loan might be rescinded, including 

situations where the provider cannot meet equity requirements or the 

provider experiences significant financial problems before the 

principal has been loaned. Although providers sometimes voluntarily 

request a full rescission, that situation is less common.

• Eighteen loans approved to date are in default, a situation defined by 

RUS officials as when a borrower is unable to meet its payments over 

time and may require intervention by RUS and possibly the 

Department of Justice to recover the funds that have been 

distributed.35 Officials said loans generally default because the 

provider cannot produce the necessary revenue to support the 

broadband network and debt payments, often due to not attracting 

enough subscribers. When a provider misses a payment, RUS takes 

interim steps before classifying the loan as in default.36 A defaulted 

loan may result in the cancellation of any unpaid portion of the loan. 

• Throughout the time period of the loan program, RUS has rejected 

149—60 percent—of the 249 applications received because providers 

did not meet the financial requirements for loans or proposed service 

 

35We did not perform an in depth analysis of the characteristics of a loan more likely to 

default because this would require us to evaluate details of companies’ finances, which 

was outside the scope of this study. However, of the 18 defaulted loans, the majority was 

for wireless projects, and many involved companies that have filed for bankruptcy. Overall, 

loans that defaulted did not involve significantly larger loan amounts than non-defaults. 

36Specifically, RUS begins by notifying the provider of a missed payment to provide it an 

opportunity to catch up on payment or develop a repayment plan. 

Over 40 Percent of 

Approved Loans Are No 

Longer Active Because 

They Are in Default or 

Have Been Rescinded 
 

Page 16 GAO-14-471 Telecommunications

areas did not meet the definition of rural, among other reasons, 

according to RUS officials. The primary reason for rejections is 

insufficient credit support to justify the project’s “business case.” 

Figure 2: Status of Broadband Loan-Program Applications, 2002—2013 

 

a

Providers for these projects have paid back the entirety of their loans to RUS. We classify these as 

active for the purposes of this report. 

b

These projects have been unable to make payments as scheduled and are officially classified as in 

default by RUS. 

c

These loans have been effectively cancelled without any portion ever being advanced to the 

provider. 

d

These loans are in the process of being repaid. 

The USDA‘s Office of Inspector General noted in its 2005 review of the 

loan program that out of 28 approved pilot program loans, 6 were in 

default. Further, the Office of Inspector General said, “[H]ad the progress 

of these projects been timely and thoroughly monitored, RUS might have 

been able to avoid advancing loan funds to companies that were failing. 

Because these loans were not carefully serviced, these funds are not 

available to support future broadband loans.”37 Since the loan program 

 

37U.S. Department of Agriculture, Office of Inspector General, Southwest Region, Audit 

Report: Rural Utilities Service Broadband Grant and Loan Programs, 09601-4-Te 

(Washington, D.C.: Sept. 30, 2005). According to RUS officials, RUS disagreed with this 

finding at the time of the Office of Inspector General report and still disagrees.
 

Page 17 GAO-14-471 Telecommunications

was created in 2002, about 18 percent of all loans have defaulted.38 In 

contrast, a private, but government-sponsored, lender that finances 

broadband projects we spoke with stated that only about 1 to 3 percent of 

its clients will fail to implement their projects. However, officials with that 

private lender noted that their investment strategy differs significantly from 

RUS, with RUS’s program requirements allowing more risk in their loan 

portfolio. As RUS officials told us, private lenders can decline to award 

any loan, whereas RUS must implement the program in accordance with 

its authorizing statutes.

Loans that are approved by RUS require significant resources to review 

and monitor. According to RUS officials, these administrative costs are 

not just one-time expenses associated with the application review, but are 

incurred throughout the life of the loan.39 For loans made to providers that 

experience financial challenges, RUS staff must conduct additional followup work, including work associated with any decisions to rescind a loan. 

Therefore, failure of many defaulted and fully rescinded projects to result 

in broadband service can represent an inefficient use of RUS resources.

RUS acknowledged that dedicating resources to servicing loans in default 

takes away resources that could be used for evaluating new applications, 

but it did not agree that this is an inefficient use of resources. Additionally, 

the rescission of funding for projects in a year when RUS obligates the 

entirety of its total authorized value of loans that year could prevent more 

viable applicants from receiving loans. 

Despite these issues, RUS has not gathered information or performed 

analysis to better understand what might lead a project to default or 

otherwise make a project a poor candidate for receiving the loan. 

According to RUS officials, a lack of staff resources has prevented them 

from studying the reasons for failed projects. Other USDA staff, though, 

such as researchers in the Economic Research Service who are 

responsible for informing and enhancing public and private decision 

making, may have the expertise to examine these issues. Additionally, 

 

38The total dollar value of these 18 defaulted loans is $488.2 million, but this represents 

the total approved loan award. In some cases, RUS did not pay out all of the loan before 

default. Additionally, RUS may have recovered all of the funding it paid out for these 

defaulted loans. 

39Specifically, each approved loan requires, among other things, monitoring of 

construction, review of quarterly financial reports submitted by providers, and security 

arrangements.
 

Page 18 GAO-14-471 Telecommunications

RUS officials said the agency is undergoing a staff reorganization that will 

establish a branch to oversee loan performance.40

Federal guidance emphasizes the importance of assessing the risk 

associated with loan programs. Office of Management and Budget (OMB) 

guidance states that agencies should annually “take steps to evaluate 

and analyze existing asset portfolios” to identify ways to improve credit 

management and recoveries.41 Also, as we have previously found, best 

practices for lenders emphasize an understanding of the risk posed by 

government loans, both on the level of individual loans and the overall 

portfolio.42 To address credit risk, best-practice lenders focus on 

controlling the quality of individual loans approved. These efforts include 

routinely reviewing loan performance. By not identifying the common 

characteristics of defaulted and fully rescinded loans, if any, RUS may 

continue to expend resources on loans that ultimately do not succeed in 

providing funds to broadband providers for new or improved services.

 

40Currently all program staff work both on awarding and servicing loans.

41OMB, Policies for Federal Credit Programs and Non-Tax Receivables, OMB Circular No. 

A-129, § IV.C.(c). (January 2013). 

42GAO, Small Business Administration: New Service for Lender Oversight Reflects Some 

Best Practices, but Strategy for Use Lags Behind, GAO-04-610 (Washington, D.C.: Jun. 8, 

2004).

RUS Loans Can Help 

Promote Limited 

Broadband 

Deployment and 

Economic 

Development, but 

Performance Goals 

Do Not Fully Align 

with Program’s 

Purpose
 

Page 19 GAO-14-471 Telecommunications

Broadband projects in rural areas are generally more expensive, on a 

cost-per-subscriber basis, than projects in metropolitan areas, creating a 

financial barrier to deployment, according to our review of the relevant 

literature. While the numbers of new broadband subscribers continue to 

grow overall, studies and data suggest that the broadband deployment 

rate in metropolitan areas is outpacing the deployment rate in rural 

areas.43 The comparatively lower population density and income of rural 

areas are primary reasons why broadband is less deployed there than in 

suburban and urban areas. As a result, developing a business case to 

build broadband infrastructure that guarantees a return on investment can 

be difficult for rural providers. Aside from RUS loans, there are few 

alternatives for financing rural broadband projects because banks 

typically do not make loans for projects that lack a strong business case.

According to several stakeholders we spoke with, RUS loans have helped 

providers overcome financial barriers to broadband deployment, allowing 

providers to upgrade existing service to faster technologies and expand 

deployment to new subscribers in rural areas. In these areas, RUS loans 

can be an essential source of financing. Officials we spoke with in four 

rural communities said that broadband services would likely not exist in 

very rural areas without federal support. For example, representatives of 

providers in Kansas and Vermont told us their RUS loan was essential to 

construction of broadband infrastructure in their rural service areas. 

According to our analysis of affected areas, the RUS loan program has 

had a mixed record of increasing overall broadband deployment. In order 

to further assess the impact of loans on broadband deployment, we 

compared the number of broadband providers in counties that have RUSfinanced broadband projects to the number of providers in counties that 

applied but were rejected for a RUS loan as well as counties that had fully 

rescinded loans, using the latest data from the NBM. Our goal with this 

analysis was to compare counties that were generally similar but for the 

presence of a RUS loan. Overall, counties with RUS-financed projects 

generally do not have more broadband providers than similar counties. 

Specifically, as of June 2013, counties with RUS projects had an average

of 4.9 broadband providers, whereas counties with rejected and 

 

43For a summary of these issues and federal broadband programs, see Congressional 

Research Service. Broadband Internet Access and the Digital Divide: Federal Assistance 

Programs. (July 17, 2013).

RUS Loans Can Help 

Overcome Financial 

Barriers to Deploying 

Broadband in Rural Areas
 

Page 20 GAO-14-471 Telecommunications

rescinded projects had an average of 5.1 broadband providers.44 While 

this analysis suggests that RUS loans have little to no overall impact on 

the number of broadband providers, the county-level data could mask 

local impacts of loans. Indeed, information we collected during our site 

visits indicate that RUS loans can lead to enhanced broadband 

deployment in specific rural areas within counties. For example, rural 

areas around East Corinth, Vermont, had no broadband access until the 

local provider was awarded a RUS loan. Catskill, New York, only had one 

broadband provider until a RUS loan enabled another provider to build 

into the community. A RUS loan also helped a provider upgrade service 

in rural areas around Eagle Butte, South Dakota, from dial-up access to 

digital subscriber line service. In a February 2014 report, we also found 

that providers have used federal programs to expand their existing 

networks by laying new fiber optic lines or using other technologies to 

make broadband available in areas that were previously unserved or 

underserved.45

Some rural areas do not have enough potential subscribers to justify the 

upgrade or expansion of broadband services even with a RUS loan. One 

provider noted that even with a federal grant—which would not require 

repayment—extending service to some rural geographies or upgrading 

services to state-of-the-art technology could be prohibitively expensive 

over the long-term since the ongoing costs of providing service can be 

higher than the revenue generated by the rural subscribers. 

 

44This analysis specifically looked at wireline broadband providers offering advertised 

download speeds of at least 3 megabits per second. The analysis was conducted at the 

county level because some individual communities do not have complete data in the NBM. 

Counties contain multiple communities, each of which may have a unique broadband 

provider, in addition to larger providers that may be present throughout the county. This 

can lead to an overall large number of reported providers within the county.

45GAO, Telecommunications: Federal Broadband Deployment Programs and Small 

Business, GAO-14-203 (Washington, D.C.: Feb. 7, 2014).

RUS Loans Can Create 

Opportunities for Modest 

Economic Development 

and Enhance the Quality 

of Life in Rural Areas
 

Page 21 GAO-14-471 Telecommunications

Several stakeholders told us that broadband services financed with RUS 

loans help local businesses be more competitive, fostering economic 

development. Having the ability to communicate and conduct business 

online reduces the effect of physical distances that are otherwise a 

barrier. According to state officials in South Dakota, broadband access 

gives rural businesses the opportunity to participate in the wider national 

economy and potentially the global economy. They noted that with 

broadband, ranchers have access to a larger market because they can 

participate in online auctions and show cattle to potential buyers via 

streaming video. Rural development officials in Vermont also told us that 

broadband enables online financial transactions for rural residents to 

conduct both bookkeeping and shopping, provides a forum for community 

outreach, and facilitates increased tourism, among other things. 

According to one official we spoke with in Vermont, some tourists prefer 

to have broadband access while vacationing.

Businesses and entrepreneurs may choose to locate in a community in 

part based on access to broadband. According to two business site 

selection experts–consultants who specialize in helping businesses 

identify new business locations–we spoke with, reliable broadband 

networks are now critical for rural businesses. The lack of broadband 

connectivity in a rural area could significantly hinder the ability of that area 

to attract and retain businesses. These perspectives are consistent with 

broader academic research, which has found that access to reliable and 

affordable broadband is viewed as particularly important for the economic 

development of rural areas because it enables individuals and businesses 

to participate fully in the online economy regardless of geographical 

location. 

Since a variety of factors can influence local economic development, we 

developed a model to assess the impact of the RUS loan program on 

economic development. We used a regression model since this technique 

can help control for a variety of extraneous factors, such as growth of the 

national economy in general. Specifically, our model compared 

employment, payroll, and business establishments of counties affected by 

RUS loans to similarly rural counties that were not affected by loans, 

using RUS and Census data from 2003 through 2011.46 

 

46This type of statistical method only suggests correlations between variables and not 

causation.

Economic Development 
 

Page 22 GAO-14-471 Telecommunications

According to our analysis of RUS loans and economic development data, 

counties affected by at least one approved RUS loan were associated 

with modestly higher levels of employment and payroll after the year of 

loan approval and in all subsequent years, as compared to counties that 

did not receive RUS loans.47 We found that RUS loans are associated 

with a one to four percent higher level of employment and payroll in 

affected counties. As noted above, stakeholders told us that broadband 

access can help make businesses more efficient, which can lead to job 

creation and increased payroll. However, we found no relationship 

between RUS loans and the number of total business establishments in a 

community. We ran the model using several specifications, most of which 

involved alternative comparison groups of unaffected counties, and our 

results were consistent.

Several factors could contribute to the findings from our model. The lack 

of effect on the number of establishments, and the modest impact on

employment and payroll, may be the result of the county being too coarse 

a level of analysis for evidence of impact to emerge, meaning the 

county’s scale overwhelms in size the service area of broadband 

providers receiving loans. Additionally, the effect of the loan program may 

not be strong enough to affect the number of business establishments at 

the county level. While e-commerce is greatly facilitated by broadband 

access, it can hinder rural businesses by making it cheaper and easier for 

local residents to shop on-line rather than at local establishments. 

Nevertheless, our results are broadly consistent with information we 

gathered in site-visit interviews about how enhanced broadband access 

enabled by the RUS loan program can have a modest, positive impact on 

the local economy. As an official at the Chamber of Commerce in 

Columbia, New York, noted, lack of broadband access outside of major 

towns is hindering growth, and investment in broadband infrastructure 

would accelerate economic activity in Columbia County.

Beyond economic development, broadband services financed with RUS 

loans can enhance the quality of life in rural communities. For example, 

broadband can bring educational opportunities to rural communities 

through online-learning technologies. Two South Dakota college officials 

we spoke with said broadband services have enabled their students to 

 

47Our geographic unit of analysis for this work was the county because counties–more 

fully than zip codes, though not completely–encompass the geographical extent of local 

labor markets. For more information on our methodology for this model, see appendix II.

Quality of Life
 

Page 23 GAO-14-471 Telecommunications

access on-line journals through the local university. Video 

teleconferencing enables students at several schools to attend the 

classes of a single instructor. Earning course credits online can also be 

less expensive than taking classes in person, helping students save 

money and expanding access to education to lower-income students. 

Telemedicine and telehealth48 have been hailed as vital to health care in 

rural communities, by expanding the medical services available. 

According to a provider we spoke with, RUS-financed broadband services 

in rural South Dakota have facilitated the remote use of some Veterans 

Affairs services. A provider in Kansas said hospitals in rural areas also 

rely on telemedicine technology to access information and personnel at 

larger medical institutions.

Broadband can also create opportunities to search for jobs and work from 

home. Access that allows individuals to work from home (“telework”) can 

enhance businesses and organizations’ continuity of operations, provide 

new job opportunities to retirees and the disabled, among others, and can 

benefit the environment. According to officials in the Catskills area of New 

York, weekend residents would spend more time in their second homes 

there if they could telework, which could bring greater revenues to local 

merchants and restaurants. Officials with a rural South Dakota broadband 

provider noted that more people are working from home, as evidenced by 

the amount of bandwidth being used. Broadband can also facilitate online 

job searching. South Dakota state officials also cited a state workforcedevelopment program, Dakota Roots, which works to “re-stake” former 

South Dakota residents who would like to return to South Dakota by 

connecting them with employment opportunities posted at their website.

 

48The terms telemedicine and telehealth are often used interchangeably and generally 

refer to technologies that allow rural patients to receive, through remote access, medical 

diagnoses or patient care, often from specialists who are located in urban areas or 

university hospitals. For more information, see GAO, Telecommunications: FCC’s 

Performance Management Weaknesses Could Jeopardize Proposed Reforms of the Rural 

Health Care Program, GAO-11-27 (Washington, D.C.: Nov. 17, 2010).
 

Page 24 GAO-14-471 Telecommunications

USDA’s Annual Performance Report (APR) provides information on the 

achievements of USDA’s programs each fiscal year. The APR is 

produced in part to satisfy requirements in the GPRA Modernization Act 

of 201049 (that updated the Government Performance and Results Act of 

199350) which along with OMB requirements51 aims to ensure agencies 

use performance information in decision making and hold them 

accountable for achieving results and improving government 

performance. As our past work has shown, an attribute of a successful 

performance measure is whether it aligns with division and agency-wide 

goals.52 We have also found that congressional staff wants a clear 

depiction at the program level of the linkages between program 

resources, strategies, and the objectives they aim to achieve.53

The goals in USDA’s APR do not fully align to the purpose of the RUS 

broadband loan program. The purposes of the loan program are to 

improve broadband deployment in rural areas—that is, increase the 

number of broadband subscribers with access to new or improved 

broadband service—and ultimately increase economic opportunity in rural 

America. USDA’s fiscal year 2013 APR includes a strategic objective to 

“enhance rural prosperity,” under which is an annual performance goal 

related to broadband adoption and the broadband loan program: “number 

of borrowers/subscribers receiving new or improved telecommunications 

services.” This performance goal is assessed using data on the number 

of subscribers to be served for each loan, derived from applicants’ 

estimates in their approved loan applications.54 This method does not 

measure actual adoption of RUS-financed broadband services. As the 

 

49Pub. L. No. 111-352, 124 Stat. 3866 (2011).

50Pub. L. No. 103-62, 107 Stat. 285 (1993).

51OMB, Preparation, Submission, and Execution of the Budget, Circular No. A-11 (2013).

52GAO, Tax Administration: IRS Needs to Further Refine Its Tax Filing Season 

Performance Measures, GAO-03-143 (Washington, D.C.: Nov. 22, 2002). In this report, 

we developed nine attributes of successful performance goals and measures based on 

key legislation and other factors. See the report for additional details.

53GAO, Managing For Results: Views on Ensuring the Usefulness of Agency Performance 

Information to Congress, GAO/GGD-00-35 (Washington, D.C.: Jan. 26, 2000).

54USDA reports that “all applications undergo an extensive review to determine eligibility. 

Additionally, all approved applications must show feasibility from a financial and technical 

standpoint. Applicants also are required to perform market surveys of their proposed 

service areas. Therefore, the data are reliable.” 

USDA Annual 

Performance Goals and 

Measures Do Not Fully 

Align with the Loan 

Program’s Purpose
 

Page 25 GAO-14-471 Telecommunications

National Broadband Plan states, “adoption is necessary for utilization, but 

utilization is necessary to extract value from a [broadband] connection.”55

Furthermore, USDA’s APR does not have any goals or measures to 

determine the loan program’s progress towards economic development 

outcomes.

As our past work has shown, agency performance goals that do not link 

to program goals can lead to incentives and behaviors that do not support 

the achievement of division or agency-wide goals.56 As agencies develop 

annual performance goals as envisioned by the GPRA Modernization Act, 

they can serve as a bridge that links long-term goals to agencies’ daily 

operations. For example, an annual goal that is linked to a program and 

also to a long-term goal can be used both to hold agencies and program 

offices accountable for achieving those goals and to assess the 

reasonableness and appropriateness of those goals for the agency as a 

whole. Ensuring the linkage between the loan program’s purpose and the 

annual performance goals may be especially important since, as we 

discuss above, the RUS loan program has had mixed results with respect 

to improving broadband deployment and economic development. 

Performance goals that better evaluate progress toward the loan 

program’s goals may help USDA and Congress better monitor the 

outcomes of the loan program.

 

55FCC, Connecting America: The National Broadband Plan (Mar. 16, 2010), at p. 150. 

56GAO-03-143. 

FCC Reforms of USF 

and ICC Have 

Created Temporary 

Uncertainty That May 

Be Hindering 

Investment in 

Broadband
 

Page 26 GAO-14-471 Telecommunications

About half of active RUS broadband borrowers have received USF 

support, which serves as an ongoing subsidy for telecommunications 

providers. Indeed, some investments by RUS borrowers have been made 

with the assumption on their part that at least some revenue to repay the 

loans would come from the USF. Fifty-one percent of RUS broadband 

borrowers with active loans—that is, loans that have not defaulted or 

been rescinded in full—received support from the USF High-Cost 

Program since 2003 (25 out of 49 borrowers; some of these borrowers 

received multiple loans). About 35 percent of all RUS broadband 

borrowers—that is, all providers with an approved loan regardless of loan 

status—have received USF support (30 out of 86 borrowers; some of 

these borrowers received multiple loans).57 In addition to the RUS 

broadband borrowers that received direct USF support, 10 borrowers that 

do not receive direct support have a parent company that has received 

support.

The level of USF support for RUS broadband borrowers, overall, grew 

from 2003 to 2012. The average amount of USF support for all 30 RUS 

broadband borrowers that have received support was $2,067,328, per 

year, from 2003 to 2013.58 During this time period, the year with the 

highest average amount of USF support for all RUS broadband borrowers 

was 2012, while the lowest was 2003. The support for active RUS 

broadband borrowers specifically has ranged from $8,162,834 in 2012 for 

Paul Bunyan Rural Telephone Cooperative in Minnesota, to $39 in 2007 

for SS Telecom, Inc. in South Dakota.59

According to a RUS official, most broadband borrowers make and receive 

ICC payments—as part of the system of payments between providers for 

telecommunications traffic—since most borrowers offer both broadband 

and traditional telephone service. However, data on the total amount of 

ICC paid and received by individual providers are not available.

 

57Throughout this section “USF support” refers to funding from the High-Cost Program. All 

30 borrowers noted here received support from the High-Cost Program in at least one 

year since 2003.

58The average amount of USF support for RUS borrowers with active loans, per year 

(2003-2013), was $1,245,444.

59Some USF recipients did not receive any support in some years and later received 

support.

About Half of RUS 

Broadband Borrowers with 

Active Loans Have 

Received USF Support, 

and Most Received ICC 

Support
 

Page 27 GAO-14-471 Telecommunications

Reforms begun under the 2011 USF/ICC Transformation Order may 

change the amount of USF support for the majority of RUS broadband 

borrowers that receive support. In particular, with the Transformation 

Order, FCC took a number of actions:

• froze certain USF support for certain providers at 2011 levels;60

• capped total per-line support as well as capital and operating 

expenses;61

• eliminated or began phasing down certain types of support;62 and

• created the Connect America Fund (CAF), which will ultimately 

replace the high-cost fund for certain providers.63

However, many of the changes to the USF High-Cost Program that will 

affect RUS broadband borrowers have not yet been implemented. In the 

Transformation Order, FCC proposed changes to its methods for 

distributing funds to address some of the recognized program 

inefficiencies, but many of the details and mechanics of the transition 

from legacy high-cost support to the CAF have not yet been determined. 

In the meantime, RUS broadband borrowers—which are generally 

smaller providers operating solely in rural areas—will continue to receive 

support, with some modifications, from current support mechanisms 

pending full transition to the CAF or a similar mechanism tailored to small 

rural providers. Details of how future support will be determined are 

pending the completion of an extensive Further Notice of Proposed 

Rulemaking issued with the Transformation Order.64

In the USF/ICC Transformation Order, FCC also reformed the ICC 

system. The current system involves payments—governed by various 

state and federal rules—between providers for telecommunications traffic. 

In the Transformation Order, FCC adopted a uniform national “bill-andkeep” framework as the ultimate end state for all telecommunications 

traffic exchanged with a local carrier.65 According to FCC, under this 

 

60USF/ICC Transformation Order, ¶¶ 128-133. 26 FCC Rcd., 17712-17715.

61Id., ¶¶ 158-168, at pp. 17725-17729.

62Id., ¶¶ 25-27, 507, 516-525, at pp. 17674, 17829, 17832-17834.

63Id., ¶ 20, at p. 17673.

64Id., part XVII, at p. 18045.

65Id., ¶ 35, at p. 17676.

Reforms May Change 

Amount of USF and ICC 

Support for RUS 

Broadband Borrowers
 

Page 28 GAO-14-471 Telecommunications

approach, carriers look first to their subscribers to cover the costs of the 

network, then to explicit universal service support where necessary. 

Because this is a default methodology, carriers remain free to negotiate 

alternative arrangements that include carrier payments. As a result of the 

USF/ICC Transformation Order, all terminating access rates (generally 

governing charges for calls that begin and end in different local calling 

areas) and reciprocal compensation rates (generally for calls within the 

same area) were capped as of December 29, 2011. Over time (9 years 

for small, rural providers), certain terminating access rates and all

reciprocal compensation rates will gradually be phased out. FCC has 

sought further comment on the proper transition and recovery mechanism 

for the remaining ICC rates.

With the elimination of some USF support and other changes 

implemented to date, a majority of RUS broadband borrowers have seen 

reductions in the amount of USF revenue they receive. Specifically, 18 of 

the 30 RUS borrowers directly receiving high-cost support in 2011 

received less support in 2013 than in 2011. These 18 borrowers lost an 

average of 31 percent of their USF support over those 2 years, though 

not all of that loss can be directly attributed to USF reforms.66

Some RUS broadband borrowers may have also seen net reductions in 

ICC support. According to an industry association we spoke with, small, 

rural providers like most RUS borrowers are generally net recipients of 

ICC, paying out less than they receive. The incremental decreases in ICC 

rates implemented to date may have therefore resulted in net revenue 

reductions for these small, rural providers.67 FCC is implementing a 

“transitional recovery mechanism” to facilitate providers’ gradual transition 

away from ICC revenues reduced as part of the Transformation Order.68

This mechanism allows providers to recover a portion of lost ICC 

revenues from increases in end-user (e.g., consumer) rates and, where 

appropriate, universal service support through ICC CAF.

 

66These reductions in High Cost Program support could be the result of many factors, 

including USF reforms as well as decreases in the number of telephone subscribers, since 

support is tied to the number of lines being served.

67FCC recognized in the USF/ICC Transformation Order that ICC reform “was not 100 

percent revenue-neutral relative to” prior revenues. Id., ¶ 848, at p. 17956.

68Id., ¶ 36, at p.17677.

Uncertain Levels of Future 

Support Hinder Broadband 

Investment, according to 

Stakeholders
 

Page 29 GAO-14-471 Telecommunications

Despite concerns, reductions in USF support have not limited the ability 

of broadband borrowers to pay back RUS loans, to date. According to 

RUS officials, the agency evaluates loan applicants’ ability to pay back a 

loan based on their overall financial situation, including any support they

expect to receive from the USF and ICC. RUS does not consider whether 

the parent company of the applicant receives such support. Some RUS 

loans were approved, and investments made by borrowers who assumed 

that at least some revenue to repay the loans would come from the USF. 

As the RUS Administrator noted in a 2012 letter to FCC, “changes to the 

federal USF and ICC can have a direct impact on the ability of existing 

RUS farm bill [broadband] borrowers to repay their outstanding loans and 

complete the construction of wireline broadband systems.”69 Additionally, 

according to RUS officials as well as a RUS broadband borrower we 

spoke with, reduced support for parent companies can have an indirect 

impact on borrowers, such as a decrease in the parent company’s 

investment in the subsidiary. Nevertheless, FCC maintained in the 

Transformation Order that USF reforms “will in general not materially 

impact the ability of these carriers to service their existing debt.” To date, 

no RUS broadband borrower that received USF support has defaulted on 

its loan. In contrast, as noted above, 18 other RUS loan program loans 

have defaulted.

Uncertainty regarding future support has led to some RUS broadband 

borrowers limiting broadband infrastructure investment. As noted above, 

some important details of the reforms to USF high-cost support have not 

been determined. Some broadband providers that we spoke with, 

including those that have and have not received a RUS loan, noted that 

they have postponed infrastructure investments pending these USF 

reforms. One provider in rural New Mexico we spoke with said that it is 

hesitant to build additional broadband infrastructure, especially high-cost 

fiber to the home, because it fears the consequences of USF reforms. 

Another provider in South Dakota maintained that it is not borrowing any 

more money for broadband build out because it does not know how much 

USF revenue to expect in the future. According to a study by a law firm of 

USF reforms, “since telecom services require high upfront capital 

investments that are recovered over a number of years, there will be 

 

69The Rural Telephone Finance Cooperative also noted in a letter to FCC that “reductions 

in USF support and/or net operating revenue without adequate transitions and a robust 

Connect America Fund could make it difficult for telcos to maintain key financial ratios and 

could lead to a greater likelihood of loan covenant breaches and payment defaults.” 
 

Page 30 GAO-14-471 Telecommunications

lesser and more sporadic investment in high-cost areas, due to the lower 

levels of support funding and increased uncertainty.”70 According to 

officials we spoke with at CoBank, a government-sponsored bank that 

supports agriculture and the rural economy, the “bottom line” impact of 

USF reforms is that small, rural providers will likely have reduced access 

to debt capital, restricting their ability to upgrade or expand broadband 

networks. With broadband providers hesitant to invest in infrastructure 

projects, demand has decreased for the RUS loan program. For instance, 

RUS received 29 applications for loans in fiscal years 2011—2013, 

compared to 130 in the first 3 full years of the program.71 Nevertheless, 

FCC recently reported that since adoption of the Transformation Order 

the number of Census blocks with broadband service of at least 3 

megabits per second download speed has increased.72 In addition, on 

April 23, 2014, the FCC issued a news release stating that it had adopted 

an order that will eliminate a rule that may have unintentionally 

encouraged providers to limit their investment in broadband-capable 

networks.

73

Much of the uncertainty regarding future USF support will be resolved 

with the full implementation of the USF/ICC Transformation Order, but the 

complexity of the reforms may result in a long implementation period. The 

rulemaking process being conducted to determine the mechanisms for 

future USF support requires FCC to provide the public with notice of its 

proposed and final rules, and with an opportunity to comment as the rules 

are developed. FCC officials told us that the rulemakings associated with 

the Transformation Order have been numerous and complex, and that the 

current Chairman has remained open to modifying proposed rules based 

on stakeholder feedback. As we have previously found, the complexity 

 

70Michael J. Balhoff and Bradley P. Williams, State USF White Paper: New Rural 

Investment Challenges, Balhoff & Williams, LLC (June 2013).

71Some of this decline in demand is also attributable to the existence of the Recovery Act 

broadband program, as well as the fact that new regulations for the broadband loan 

program took effect in March 2011 (Rural Broadband Access Loans and Loan 

Guarantees, 76 Fed. Reg. 13770 (March 14, 2011) (interim rule)), after which RUS 

required all pending applications be resubmitted. However, USDA reported in its fiscal 

year 2012 performance report that “the level of uncertainty caused by the new USF and 

ICC revisions directly impacted the level of demand for the infrastructure loan program.” 

72Universal Service Implementation Progress Report, WC Docket No. 10-90 (Mar. 18, 

2014).

732014 WL 1653217 (April 23, 2014) (News Release), p. 4.
 

Page 31 GAO-14-471 Telecommunications

and number of rulemakings within a docket and the priority FCC places 

on a rulemaking contribute to the length of time dockets and rulemakings 

remaining open.74 Moreover, elements of the USF/ICC Transformation 

Order have been appealed.75

Broadband is now recognized as a necessity for economic and social life 

in America, and several public programs aim to encourage greater 

investment in rural areas. USDA’s RUS is charged with administering one 

of these programs, which provides low-cost loans to rural communities, 

but the program has experienced mixed results. For instance, over 40 

percent of approved loans are no longer active, with many having not 

resulted in new or improved broadband services. Considerable resources 

are invested by RUS to administer these loans and their failures therefore 

may represent an inefficient use of RUS resources. Studying the 

characteristics of rescinded and defaulted loans could enable USDA to 

better recognize loan applications that may not result in successful 

projects and therefore better target its limited resources.

Even RUS loans that do not default or are rescinded may not significantly 

increase broadband deployment or economic development in rural areas. 

Based on our analysis, we found that RUS loans help promote modest 

broadband deployment and economic development in affected rural 

areas. USDA evaluates progress toward these and other outcomes in its 

APR, though the performance goals it uses do not fully align with the 

purpose of the RUS broadband loan program. Given the modest impact 

of the loan program, aligning its performance goals with the program’s 

purpose could help RUS better evaluate the loan program’s performance 

and provide Congress with more information on the outcomes of the 

program so it can better hold USDA accountable for achieving results and 

improving government performance.

We recommend the Secretary of Agriculture take the following two 

actions:

 

74GAO, Telecommunications: FCC Should Take Steps to Ensure Equal Access to 

Rulemaking Information, GAO-07-1046 (Washington, D.C.: Sept. 6, 2007). The rules 

examined as part of this prior report’s case studies took between 1.0 and 4.5 years to 

complete.

75In re: FCC 11-161, Docket No. 11-9900 (10th Cir.).

Conclusions

Recommendations
 

Page 32 GAO-14-471 Telecommunications

• evaluate loans made by RUS through the broadband loan program to 

identify characteristics of loans that may be at risk of rescission or 

default; and

• align performance goals under the “enhance rural prosperity” strategic 

objective in the APR to the broadband loan program’s purpose, to the 

extent feasible.

We provided a draft of this report to FCC for review and comment. FCC 

provided comments in a letter from the Chief, Wireline Competition 

Bureau. (See app. III.) In its letter, FCC neither agreed nor disagreed with 

the report’s findings. FCC provided comments on the presentation of 

certain facts related to USF programs. FCC said that although our report 

finds that uncertainty about the amount of high-cost support RUS 

borrowers would receive under FCC’s reformed universal service 

program has negatively affected investment in broadband, FCC recently 

adopted an Order eliminating a rule that may have unintentionally 

encouraged carriers to limit their investment in broadband networks. We 

added text to our report about this Order. Additionally, FCC noted the 

adoption of a mechanism to mitigate the impact of reduced ICC on 

providers. We moved our text discussing this mechanism from a footnote 

to the body of the report and also noted that FCC’s USF/ICC 

Transformation Order was never intended to be revenue-neutral for 

providers.

We also provided a draft of this report to USDA for review and comment. 

In an email received on May 9, 2014, a Management Analyst with USDA 

on behalf of the Assistant Administrator, Telecommunications Program, 

stated that RUS generally agreed with the facts presented in the report 

and will strive to fully implement our recommendations. However, RUS 

commented on the presentation of six facts in the report.

First, RUS said that it did not agree with our presentation of a map of 

areas served by RUS broadband loans (fig. 1). RUS’s comments 

indicated that the map may overstate the extent of loan service areas. 

However, the intent of this map is not to illustrate the extent of loan 

service areas but rather to illustrate the geographic distribution of RUS 

loans; the title and legend for the map clearly note that it reflects counties 

and territories with one or more approved RUS loans and we include a 

note explaining our methodology below the map. Thus, we continue to 

believe that its presentation is appropriate for the purpose intended.

Second, RUS noted that the areas around Catskill and Hudson, New 

York, which we describe as being relatively close to large metropolitan 

Agency Comments

and Our Evaluation
 

Page 33 GAO-14-471 Telecommunications

areas, qualified for financing according to the statutory requirements of 

the program; RUS also said that because an area is close to an urban 

area does not mean it is an urban area. We did not assess whether the 

service areas of loan projects complied with statutory requirements, 

though based on our site visits, we agree that the areas around Catskill 

and Hudson are rural, and revised the report to clarify this point.

Third, RUS noted that it disagreed with the 2005 USDA Office of 

Inspector General finding about the pilot loan program that our report 

discusses; RUS said that it did monitor projects, and that the purpose of a 

pilot program is to try different approaches, which inherently involves 

taking on additional risk to determine the best approach. While we found 

the Office of Inspector General’s findings sufficiently reliable for our 

purposes, we added text to our report indicating that RUS disagreed with 

the Inspector General’s findings and continues to do so.

Fourth, RUS said that our comparison of the RUS loan default rate to a 

private lender may give the wrong impression, since RUS takes on more 

risk than a private lender and thus may have more defaults. We 

acknowledge that RUS takes on more risks than private lenders and may 

therefore have a higher default rate. However, even considering the 

additional requirements and risk inherent in the RUS loan program, its 

default rate is six times higher than the private lender with whom we 

spoke and we believe this warrants further examination by USDA. As we 

recommend above, we believe USDA should evaluate loans made by 

RUS through the broadband loan program to identify characteristics of 

loans that may be at risk of rescission or default.

Fifth, RUS agreed that dedicating resources to servicing loans in default 

takes away resources that could be used for evaluating new applications, 

but RUS does not consider this an inefficient use of resources as stated 

in our report; rather, RUS said that additional resources would allow it to 

be more effective in making funds available to rural areas for broadband 

service. We added text to the report further explaining RUS’s view on this 

issue; however, we continue to believe, as we recommend, that a better 

understanding of the characteristics of loans that may be at risk of 

rescission or default would help RUS use its resources more effectively.

Finally, RUS noted two concerns with our analysis of the number of 

broadband providers in counties with and without RUS loans. First, RUS 

said it is unfair to compare the number of providers in a county in 2013 

when loans to that county may have been approved as far back as 2003. 

Second, RUS said that our analysis misrepresents the impact of RUS 

loans on broadband deployment because it assumes the entire county is 
 

Page 34 GAO-14-471 Telecommunications

served when only a small portion of a county may have actually received 

funds for broadband. One of the goals of the RUS loan program is to 

promote broadband service in communities where it would not otherwise 

exist. Accordingly, we assessed the extent to which the number of 

broadband providers in communities that received loans differed from 

communities that did not receive loans. The timing of the loan—whether it 

was made in 2003 or 2011, for instance—is not relevant to this analysis, 

since we were focused on the current state of broadband service. 

Comparing the current state of broadband service in areas that did and 

did not receive loans helps demonstrate the extent to which the loan 

program may be increasing the number of broadband providers. The fact 

that our analysis also uses counties, rather than individual communities, 

is also not a significant limitation because the data should show the 

presence of additional broadband service as a result of the loans even if 

the loans only affect portions of those counties. Therefore, we believe that 

this analysis is appropriate as one source of evidence about the impact of 

the RUS loan program on broadband deployment.

As we agreed with your office, unless you publicly announce the contents 

of this report earlier, we plan no further distribution of it until 30 days from 

the date of this letter. At that time, we will send copies of this report to the 

appropriate congressional committees, Chairman of the FCC, and 

Secretary of the Department of Agriculture. The report also is available at 

no charge on the GAO Web site at http://www.gao.gov.

If you or your staff have any questions regarding this report, please 

contact me at (202) 512-2834 or goldsteinm@gao.gov. Contact points for 

our Offices of Congressional Relations and Public Affairs may be found 

on the last page of this report. GAO staff who made key contributions to 

this report are listed in appendix IV. 

Mark Goldstein

Director, Physical Infrastructure Issues
Appendix I: Scope and Methodology

Page 35 GAO-14-471 Telecommunications

To examine the geographic distribution and financial performance of 

loans, we gathered and analyzed Rural Utilities Service (RUS) loan data 

from 2003—2013. Specifically, we collected information on the recipient, 

approval date, amount, repayment status, as well as the proposed 

technology and communities to be served by the project for each loan 

approved by RUS as part of the Rural Broadband Access Loan and Loan 

Guarantee Program (“loan program”), including loans that have been 

rescinded. We also collected information on the proposed recipient, 

amount, communities to be served, and technology type for each rejected 

loan. Using this information, we assessed the distribution of active, 

rescinded, and rejected loans by state and region. We also analyzed the 

extent to which the loans met various definitions of rural, including those 

adopted by RUS for the loan program in 2002, 2004, and 2008, as well as 

the U.S. Census Bureau (Census) definition (areas not within urbanized 

areas) and the U.S. Department of Agriculture’s (USDA) Rural-Urban 

Commuting Area (RUCA) Census tract-based codes. We selected these 

definitions of “rural” based on discussions with stakeholders and studies 

of defining rural.1 We determined whether loans met these definitions of 

rural by examining the extent to which the proposed communities to be 

served by the loan project together satisfied by the definition of rural.2

Finally, we used the RUS information to summarize the repayment status 

of loans, including the number and type of active, defaulted, and 

rescinded loans.

To assess the relationship, if any, between RUS loans and broadband 

deployment in rural areas, we conducted statistical analysis using 

National Broadband Map data on broadband availability, comparing 

counties with approved RUS loan projects to counties with rejected and 

fully rescinded RUS loan projects. Specifically, we identified counties with 

approved RUS loan projects as well as those with rejected and fully 

 

1

Specifically, Hart et al, “Rural Definitions for Health Policy and Research,” American 

Journal of Public Health (95), July 2005; and GAO, Rural Housing: Changing the 

Definition of Rural Could Improve Eligibility Determinations, GAO-05-110 (Washington, 

D.C.: Dec. 3, 2004).

2

Portions of the service area for some loan projects were not in communities but in 

unincorporated areas, about which we only had information about the county that area 

was in. With no way to map these areas, we excluded them from our review. 

Appendix I: Scope and Methodology
Appendix I: Scope and Methodology

Page 36 GAO-14-471 Telecommunications

rescinded projects from the RUS loan information noted above.3 For 

counties that had both approved and rejected loan projects, we treated it 

as an approved loan county, since it has been “treated” by the RUS loan 

program through its approved loan. Rejected and rescinded loan project 

counties therefore included only those counties with rejected and/or fully 

rescinded loans. We identified the current number of broadband providers 

in those counties using National Broadband Map data downloaded from 

the website created and maintained by the National Telecommunications 

and Information Administration, in collaboration with the Federal 

Communications Commission (FCC). We used the most recent data 

available, which were current as of June 2013. In addition to the analysis 

comparing areas with approved, rejected, and fully rescinded loans, we 

also examined the broadband providers in select communities before and 

after the approval of a RUS loan. We selected these communities as part 

of our site visits, discussed below. We gathered the information on 

broadband providers present in these communities before the RUS loan 

from the relevant RUS loan files at USDA. We again used the National 

Broadband Map information to identify the providers present in these 

communities after the RUS loan approval. Few loans have been 

approved since 2008, so we determined that sufficient time had passed 

for most projects financed with RUS loans to have been substantially 

completed.

To assess the relationship, if any, between RUS loans and economic 

development in rural areas, we conducted statistical analysis of RUS and 

Census data. We developed a regression model using a panel dataset to 

assess the relationship between counties with approved loan projects and 

specific economic outcomes (number of business establishments, 

 

3

The service areas of RUS borrowers must meet RUS’s definition of rural, but can take 

any shape and must not necessarily conform to a political boundary (e.g., Census tract, 

zip code, county). As a result, “treated” areas (that is, areas part of an approved, fully 

rescinded, or rejected loan project) do not perfectly overlap areas for which economic 

outcome data are available (e.g., zip codes, counties). For our purposes throughout this 

report, we consider an area as “treated” (i.e., having received a loan) or “rejected” (i.e., 

having a fully rescinded loan or rejected loan application) if any part of it is included in the 

list of communities affected by the loan.
Appendix I: Scope and Methodology

Page 37 GAO-14-471 Telecommunications

employment, and annual payroll).4 For more information on this model, 

see appendix II.

We assessed the reliability of RUS data by interviewing RUS officials 

about their databases and data collection practices. We also assessed 

the reliability of National Broadband Map data by reviewing its data 

collection procedures and methods, including how the map developers 

collect data and conduct quality assurance checks, as well as through 

interviews with stakeholders.5 We also assessed the reliability of Census 

data by reviewing its data collection procedures and methods. Based on 

this information we determined that the data provided to us were 

sufficiently reliable for our reporting purposes. 

We also examined prior academic studies and government reports. As 

background, we reviewed relevant USDA Office of Inspector General 

reports. Additionally, to inform our quantitative analysis and provide 

additional information on the relationship between broadband and 

economic development, we examined prior academic studies of the RUS 

loan program as well as government and academic research on the 

general impact of broadband availability and adoption on communities. 

We identified these studies through a literature search of the ArticleFirst, 

Engineering Information Compendex, Inside Conferences, NTIS, PAIS 

International, PapersFirst, ProQuest, SciSearch, Social SciSearch, and 

WorldCat databases, as well as interviews with stakeholders. At least two 

GAO analysts reviewed the studies and reports we cite in this report for 

methodological adequacy.

For additional information on the relationships, if any, between RUS loans 

and broadband deployment and economic development, we conducted 

site visits to 16 communities in rural areas. These areas were selected to 

include providers who did and did not receive RUS loans as well as 

 

4

For our purposes throughout this report, we used county as the geographical region of 

quantitative analysis because economic development data are not available for some 

small individual communities. Additionally, counties more fully (although still incompletely) 

than zip codes or Census tracts encompass the geographical extent of local markets.

5

National Broadband Map data has been found to misrepresent broadband availability in 

certain areas, either through providers overstating service areas or overall limited data for 

certain states. However, we could not identify a reason why these data limitations would 

systematically impact communities or counties associated with RUS loans so we 

determined our results would likely not be biased.
Appendix I: Scope and Methodology

Page 38 GAO-14-471 Telecommunications

stakeholders in surrounding communities and state capitals. We also 

identified communities and RUS broadband loans with varying 

experiences and perspectives. The specific criteria used to identify sitevisit locations included the status of the loan (if relevant), the size of the 

loan, location, technology of loan project (if relevant), and date of loan 

approval (if relevant). Based on these criteria, we conducted physical 

visits to North and South Dakota as well as New York and Vermont. 

These site visits included loan service areas (consisting of one or more 

communities) in a variety of regions, with active, rescinded, and rejected 

loans. They also included four loan service areas with loans above the 

median loan size and five loan service areas below median loan size 

($8,249,250). They also included a loan service area with one of the four 

loans approved after 2008, as well as loan projects utilizing three different 

technology types. Although using these criteria allowed us, in our view, to 

obtain information from a diverse mix of RUS broadband borrowers, the 

findings from our site visits cannot be generalized to all borrowers 

because they were selected as part of a nonprobability sample.

As part of our site visits, we interviewed, when possible, local broadband 

providers, including any that have applied for and received RUS loans. 

We also interviewed staff and members of local Chambers of Commerce, 

state and local government officials involved in broadband policy, and 

staff at state and local advocacy and economic development 

organizations. For additional information, we also interviewed broadband 

providers in Kansas, Louisiana, and New Mexico separate from our inperson site visits. In total, we spoke with broadband providers that have 

been approved for 10 of the 100 loans to date.

To determine the impact of reforms to the Universal Service Fund (USF) 

High-Cost program and Intercarrier Compensation (ICC) on the RUS 

broadband loan program, we reviewed FCC’s 2011 USF/ICC 

Transformation Order that proposed changes to the USF and ICC 

mechanisms for determining support.6 We also reviewed stakeholder 

submissions to the rulemakings proposed as part of this Transformation 

Order, as well as studies and reports that assess the impact of the 

reforms. To calculate the extent of USF support for RUS broadband 

borrowers, we examined data—for the years 2003 through 2013—

reported in FCC’s Universal Service Monitoring Report. We assessed the 

 

6

In the Matter of Connect America Fund, 26 FCC Rcd. 17663, 27 FCC Rcd. 4040 (2011). 
Appendix I: Scope and Methodology

Page 39 GAO-14-471 Telecommunications

reliability of this data by reviewing relevant data collection and verification 

documents. Based on this information, we determined that the data 

provided to us were sufficiently reliable for our reporting purposes.

To better understand the impact of these reforms on RUS broadband 

borrowers, we also conducted interviews with officials from RUS, FCC, 

CoBank, and NTCA–The Rural Broadband Association. We also 

discussed these reforms with broadband providers that have and have 

not received USF support, as described above.

We conducted this performance audit from June 2013 to May 2014 in 

accordance with generally accepted government auditing standards. 

Those standards require that we plan and perform the audit to obtain 

sufficient, appropriate evidence to provide a reasonable basis for our 

findings and conclusions based on our audit objectives. We believe that 

the evidence obtained provides a reasonable basis for our findings and 

conclusions based on our audit objectives.
Appendix II: Economic Development Model 

Methodology and Results

Page 40 GAO-14-471 Telecommunications

This appendix discusses the methodology used to develop a model 

analyzing the relationship between the RUS broadband loan program and 

economic activity in rural areas. In this appendix, we provide information 

on the scope, data, model, and results of our analysis.

The time frame for our analysis was 2003 through 2011 because those 

are the years for which RUS and relevant Census data are available. The 

RUS broadband loan program was authorized in 2002 to finance the 

construction of broadband infrastructure in rural areas, following an earlier 

pilot program.1 The first loans, though, were approved in 2003, so the 

relevant information associated with RUS loans begins in 2003. 

Broadband infrastructure projects funded by the RUS broadband loan 

program vary in geographic size. Projects must be designed to serve rural 

communities, although the geographic footprints of the proposed rural 

broadband projects do not consistently correspond to geographic units for 

which relevant data are collected. Some projects span more than one 

county, while others are focused on a specific area within a single county. 

We chose to focus our analysis at the county level, because counties, 

more fully (although still incompletely) than zip codes or Census tracts, 

encompass the geographical extent of local markets. Though the most 

recent loan was approved in 2013, the relevant annual county-level 

Census data on economic activity are only available through 2011. 

To conduct our analysis, we acquired data from RUS on all applications 

for RUS loans. Some of these applications were approved while others 

were rejected by RUS. The data we received from RUS included the list 

of communities (i.e., cities, towns, and Census Designated Places) to be 

served by each loan project. For those projects that were approved, we 

also received data on the approval date and the loan’s current repayment 

status. By matching the list of communities to Census data, we identified 

the counties associated with each project. This information allowed us to 

categorize each county as part of the service area of an approved project, 

a rejected project, a project for which loan funds had been rescinded by 

RUS, or not part of any project service area. Some counties included the 

service areas of more than one approved project; others included the 

service areas of, for instance, both approved and rejected projects. For 

 

1

Throughout this appendix and report we refer to only loans made through the RUS Rural 

Broadband Access Loan and Loan Guarantee Program.

Appendix II: Economic Development Model 

Methodology and Results

Scope and Data
Appendix II: Economic Development Model 

Methodology and Results

Page 41 GAO-14-471 Telecommunications

the purposes of this analysis, we classified every county associated with 

an approved project that has not been rescinded as being in active status, 

regardless of whether that county included the service area of a 

rescinded or rejected project. Counties associated with loans that have 

been rescinded were classified as being in rescinded status. Counties 

associated with rejected loans were classified as rejected. 

We also acquired data from the Census. Specifically, we used County 

Business Pattern data on county employment, annual payroll, and 

number of business establishments from 2003 through 2011. Economic 

development has no universal definition or measure, but can be assessed 

using a variety of proxy measures. We identified our three measures of 

economic activity because they have been used in the past to assess the 

effect of a federal program on economic development. Specifically, in a 

2006 report assessing the impact of Empowerment Zones on economic 

development, we used Census data on unemployment rates and the 

number of business establishments.2 Additionally, in their analysis of the 

RUS loan program, Kandilov and Renkow used Census data on the 

number of business establishments, employment, and annual payroll as 

measures of economic development.3 Further, other studies have 

examined the impact of broadband availability and adoption on economic 

outcomes, using variables such as unemployment, household income, 

and number of firms or establishments.4

 

Because the RUS program targets communities in rural areas, we wanted 

to restrict our analysis to rural counties and to examine whether our 

results were sensitive to the characterization of rural used to select 

counties. We used two county-level data sources to define rural areas at 

 

2

GAO, Empowerment Zone and Enterprise Community Program: Improvements Occurred 

in Communities, but the Effect of the Program Is Unclear, GAO-06-727 (Washington, D.C.: 

Sept. 22, 2006).

3

Ivan T. Kandilov and Mitch Renkow, Infrastructure Investment and Rural Economic 

Development: An Evaluation of USDA’s Broadband Loan Program, Growth and Change: 

Vol. 41 (2010).

4

See Jayakar, Krishna and Eun-A Park, Broadband and Unemployment: Analysis of 

Cross-sectional Data for U.S. Counties, paper presented at the Telecommunications 

Policy Research Conference, 2013. Whitacre, et al., Broadband’s Contribution to 

Economic Health in Rural Areas: A Causal Analysis and an Assessment of the ‘Connected 

Nation’ Program, paper presented at the Telecommunications Policy Research 

Conference, 2013.
Appendix II: Economic Development Model 

Methodology and Results

Page 42 GAO-14-471 Telecommunications

the county level. We used Census information on county rural and urban 

population characteristics as of 2010. As an alternative, we also used 

USDA data that placed each county into one of nine categories on a 

rural–urban continuum. 

Additionally, as part of the proximity-scoring process, to be discussed in 

more detail below, we used additional County Business Patterns data for 

an earlier time period and 2000 Census information on demographic 

characteristics of counties. Finally, we used the Gross Domestic Product 

price index to express payroll dollars in terms of calendar year 2013 

values.

Many factors affect changes in economic activity. In any particular place, 

economic activity can be influenced by natural resources in that area and 

the current fortunes of whatever industries have located there historically, 

among other factors. In any particular time, regional or national 

economies may be growing rapidly or may be in recession. In addition, 

the degree to which any area is connected to the broader economy has 

implications for economic performance. In this regard, the quality of 

access to transportation networks has long been identified as among the 

important factors explaining economic development. More recently, 

access to broadband infrastructure has been thought of in this context, 

and particularly in the case of rural communities, real and perceived lack 

of broadband access has been thought to hinder economic development 

and has provided the motivation for the RUS broadband loan program, 

among others.

Given that broadband infrastructure is one of many factors that could be 

associated with economic activity, it may be difficult in a modeling context 

to assemble all of the factors that may be necessary to distinguish one 

county from another in terms of its resources, industrial structure, and 

labor force characteristics, among other things. One estimation technique 

that can be useful in this kind of situation is to take advantage of the 

panel characteristics of this data. A panel data set is one in which there 

are observations for a given set of cross-section units, in this case 

county-level measures of economic activity, over several time periods, in 

this case years. A fixed-effects model of panel data can have two sets of 

dummy variables, one for each cross-section unit and one set for each

time period. In our model, the former account for those observed and 

Model

Structure
Appendix II: Economic Development Model 

Methodology and Results

Page 43 GAO-14-471 Telecommunications

unobserved effects that are constant over time but affect economic 

activity in a particular county, and the latter for effects that are constant 

across counties but vary over time, such as the condition of the national 

economy. The variable in which we are interested is defined as a zero (0) 

or one (1) RUS broadband loan indicator that varies over time and 

counties. Although the value of the loan indicator never takes a value of 1 

in those counties without an approved project, the timing of approvals in 

those counties with approved projects varies over the time period. 

Our model can be thought of as a treatment model in which some 

counties receive a treatment (the RUS broadband loan). Our goal was to 

estimate whether there is an association between this treatment and 

measures of economic activity, such as employment, payroll, or the 

number of establishments. In a treatment evaluation, however, it is 

important to have an appropriate comparison or control group of 

untreated subjects, in this case, counties that were not in the service area 

of a RUS broadband loan. The estimate of the treatment could be in part 

a reflection of the particular characteristics of those that pursued 

treatment, in this case those counties that were in the service areas 

approved for a RUS broadband loan. If those counties were growing 

faster, or were richer, among other possibilities, then those counties might 

be expected to continue to grow quickly over the analysis period. If these 

counties are compared to an average county, it might appear that the 

broadband loan is associated with the observed pattern of increased 

economic activity. Alternatively, if the treated counties are compared to 

other counties that are more similar, the estimated association with the 

broadband loan might be more modest or nonexistent. 

A focus of our estimation approach was to develop a number of different 

control groups of rural counties without approved broadband loans. We 

identified three broad types of control groups. First, some control groups 

are defined using characteristics of the RUS program itself, such as those 

counties that were in the service areas of projects that were rejected by 

RUS. These were projects that, we assume, their developers believed 

would be successful. In this way, the counties in their service areas can 

be thought of as similar to the set of approved counties in unobservable 

ways. Second, control groups were defined on the basis of similarity to 

key observable characteristics of the set of approved counties. We use a 

proximity-scoring algorithm to match approved counties to other counties 

based on particular concepts of similarity. For example, to develop one 

control group, we looked for counties that were similar to approved 

counties in terms of economic growth in the period leading up to 2003, 

Control Groups
Appendix II: Economic Development Model 

Methodology and Results

Page 44 GAO-14-471 Telecommunications

the beginning of our analysis period. Third, we defined one control group 

on the basis of geographic adjacency to the set of counties in approved 

service areas. This control group may capture less readily observable 

characteristics of local economic activity in and around the service areas 

of approved loans.

Within this estimation framework, a primary issue we considered was 

which counties to include in our analysis. Although we determined that 

counties were the best unit of analysis, in some cases they can still be too 

coarse a level of analysis for any evidence of the RUS loan program’s 

impact to emerge. Accordingly, we used Census data on the rural and 

urban population characteristics of counties, as well as observations from 

our site visits (described above in app. I) to identify a suitable universe of 

counties. One consideration was that the dependent variables of interest 

in our model (i.e., the economic outcomes) likely scale with county 

population; that is, the level of employment and payroll, and the number 

of business establishments will likely increase with a county’s population. 

In some counties that have large rural populations in an absolute sense 

but also include significant urban centers, the economic activity of the 

urban areas can overwhelm the activity in the rural portions of the county; 

these counties may not be well suited for inclusion in a county-level 

analysis. Our solution to this issue was to restrict the set of analyzed 

counties to those meeting a rural threshold, so that any changes in 

economic activity associated with broadband infrastructure had a better 

chance of being captured with county-level data. 

We considered three definitions, and present some program implications 

of these alternative definitions in table 2. The initial threshold we chose 

was 90 percent of county population considered rural according to the 

Census, excluding all counties that are less rural.5 However, table 2 

shows that this threshold had the effect of excluding many counties, 

including the vast majority of counties with an approved loan. Specifically, 

there are 289 counties in our data set with an approved loan, but only 52 

 

5

The Census Bureau’s urban-rural classification is a delineation of geographical areas, 

identifying both individual urban areas and the rural areas of the nation. Census’s urban 

areas represent densely developed territory, and encompass residential, commercial, and 

other non-residential urban land uses. Urban areas are delineated after each decennial 

census by applying specified criteria to decennial census and other data. Census 

identifies two types of urban areas: Urbanized Areas of 50,000 or more people; and Urban 

Clusters of at least 2,500 and less than 50,000 people.

Rural Definition
Appendix II: Economic Development Model 

Methodology and Results

Page 45 GAO-14-471 Telecommunications

of them were rural counties defined in this way. Using only a county’s 

rural population has the consequence of treating many counties located in 

what would typically be thought of as rural areas as not rural because 

they contain small cities. Based on our site visits we determined that 

while counties with large Urbanized Area populations were generally too 

urban to be included in the model, counties with just the smaller Urban 

Clusters were appropriate. Our preferred threshold for counties was 90 

percent of county population considered rural, defined as the sum of rural 

population and urban cluster population as determined by Census. Table 

2 shows that this definition captures almost 90 percent of counties with 

approved loans. 

As an alternative to constructing our own categorization using Census 

data, we also used the rural–urban continuum codes developed by 

USDA’s Economic Research Service to select rural counties. Specifically, 

as an alternative, we considered a county to be rural according to these 

codes if the county was not in a metropolitan area and the urban 

population of a county was less than 20,000 people. This definition 

provided a selection of rural counties that fit somewhere between our 

other two in terms of number of included counties and approximately the 

same share of counties with approvals as the rural and urban 

combination definition. We present results for the two Census-based 

definitions. Results using the USDA definitions were more similar to the 

rural and urban cluster combination. 

Table 2: Number of Counties with Rural Utilities Service (RUS) Loans Using Different Definitions of Rural

Set of Counties

Number of 

counties

Counties with active 

RUS loans

Counties with rejected 

RUS loans

Counties with rescinded 

RUS loans

All 3,038 289 862 593

90 percent Rural (Census) 671 52 160 41

90 percent Rural plus Urban 

Cluster (Census) 2,218 253 606 366

Economic Research Service 

codes 1,576 179 439 231

Source: GAO analysis of RUS loan data and U.S. Census Bureau information.

Note: This analysis reflects counties associated with loans made through the RUS Rural Broadband 

Access Loan and Loan Guarantee Program. Using RUS data on the proposed service areas of all 

RUS loan applications through 2011, we categorized each county as part of the service area of an 

active project, a rejected project, a project for which loan funds had been rescinded by RUS, or not 

part of any project service area. 
Appendix II: Economic Development Model 

Methodology and Results

Page 46 GAO-14-471 Telecommunications

Rejected and Rescinded Loans

We developed three types of control groups. First, we developed groups 

from the program itself, including groups of rejected loan counties and 

rescinded loan counties. Rejected counties can be thought of as similar to 

approved counties in terms of intent by broadband providers to build rural 

broadband infrastructure. Rescinded counties can also be thought of in 

these terms, but these counties were in service areas that were actually 

approved for broadband loans. 

Matching Counties

Second, we developed control groups based on observable 

characteristics of counties rather than attributes of the RUS loan program. 

Specifically, for each approved county we sought to find a non-approved 

county that is similar. Since there are many dimensions along which 

counties may be similar to one another, the use of proximity scoring is 

one way to identify control counties based on how similar they are to 

approved counties based upon a scoring procedure. In our case, we use 

logistic regressions on county-level factors that we think could explain 

whether a county had an approved loan. The proximity score for each 

county is derived from the logistic regression and reflects the probability 

that a county has an approved loan based on the set of explanatory 

variables. We develop two different sets of explanatory variables that we 

use to calculate proximity scores. The explanatory values are used as 

independent variables in the proximity scoring logistic regression in which 

the dependent variable is loan approval. Using the estimated coefficients 

and the values of the explanatory variables, a proximity score is 

calculated for each county. We then used a matching algorithm 

developed by researchers at the Mayo Clinic to select from among the 

non-approved counties a match for each approved county based on the 

proximity scores.6

 

 

6

Specifically, we used the Statistical Analysis System macro ‘gmatch’, developed by Eric 

Bergstralh and Jon Kosanke of the Biomedical Statistics and Informatics Division of the 

Mayo Clinic, available via http://www.mayo.edu/research/departmentsdivisions/department-health-sciences-research/division-biomedical-statisticsinformatics/software/locally-written-sas-macros. 

Development of Control 

Groups
Appendix II: Economic Development Model 

Methodology and Results

Page 47 GAO-14-471 Telecommunications

We developed different control groups based on two sets of explanatory 

variables. The first set consisted of annual growth rates in county-level 

measures of economic activity over the pre-analysis period of 1994—

2002. Specifically, for each county we estimated the annual growth rates 

in employment, payroll, payroll per employee, and the number of 

establishments, using County Business Patterns data. We interpreted the 

matches based on this scoring process to represent a control group that 

is similar to the set of approved counties based on economic growth 

profiles in the time period leading up to the start of the broadband loan 

program in 2003. The second set of explanatory variables consisted of 

demographic variables from the 2000 Census, again from a time 

preceding the broadband loan program. Specifically, for each county we 

examined data on county population, population density, per capita 

income, the share of county population aged 60 and above, the share of 

county population that has less than a high school education, and the 

share of county population that has a college degree or more. We 

interpreted the matches based on this scoring process to represent a 

control group that is similar to the set of approved counties based on 

demographic characteristics that may be related to the costs of providing 

broadband (population density) as well as characteristics related to the 

demand for broadband (income, age, and education profiles). 

Additionally, we developed a control group based on proximity scores 

using both of these sets of variables in the same scoring regression.

Adjacent Counties

Third, another alternative control group was comprised of the set of rural 

counties without approved loans that are geographically adjacent to the 

set of rural counties with approved loans. This set of counties was 

identified by using the Census county adjacency file for 2010. These 

adjacent counties are rural, not in the set of approved counties, and 

adjacent to at least one approved county. These counties included a mix 

of counties with and without applications.

As discussed above, we used a simple fixed-effects model to estimate the 

relationship between RUS broadband loans and various annual measures 

of economic activity at the county level. Since our data consists of 9 

annual observations for multiple counties, we have a panel dataset. Our 

dependent variables are county time series on employment, payroll, and 

the number of establishments, in log form. We regressed the dependent 

variables on year and county fixed effects and an indicator variable of 

whether a county had an approved broadband loan. The loan indicator 

Results
Appendix II: Economic Development Model 

Methodology and Results

Page 48 GAO-14-471 Telecommunications

variable equals 0, except for counties with an approved broadband loan, 

when, beginning one year after the loan award an all subsequent years, it 

equals 1.

Our analysis using this model and our preferred definition of rural 

suggests that RUS broadband loans are associated with a 1 to 4 percent 

higher level of employment and payroll in affected counties in the year 

following the loan approval and all subsequent years (see table 3). The 

estimated effects on payroll were at the higher end of this range. We 

found no consistent relationship between RUS loans and the number of 

new business establishments in a community. Our results were much 

weaker when we restricted the model only to counties that were 

considered rural using the 90 percent rural population threshold (see 

table 4), but results were roughly comparable between the samples 

defined using rural and urban clusters definition and the Economic 

Research Service continuum codes. Generally speaking, for a given 

definition of rural counties, our results were consistent across the range of 

control groups we developed. 

As noted above, these model results are broadly consistent with what 

stakeholders told us, including that broadband access enabled by the 

RUS loan program can help make businesses more efficient, which can 

lead to job creation and increased payroll. The lack of effect on the 

number of establishments, and the modest impact on employment and 

payroll, may be the result of the county being too coarse a level of 

analysis for evidence of impact to emerge. Additionally, the effect of the 

loan program may not be strong enough to affect the number of business 

establishments at the county level. For instance, while e-commerce is 

greatly facilitated by broadband access, it could hinder rural businesses 

by making it cheaper and easier for local residents to shop on-line rather 

than at local establishments.

Table 3 presents results using the rural and urban cluster definition of 

rural counties and provides results for the full range of control groups, and 

table 4 presents results using the rural definition. All of the dependent 

variables were expressed in log form and the parameter estimate on the 

loan variable can be interpreted as the percentage increase in the level of 

economic activity when the loan is in effect. 
Appendix II: Economic Development Model 

Methodology and Results

Page 49 GAO-14-471 Telecommunications

Table 3: Model Results for Counties 90 Percent Rural with Urban Clusters

Dependent variables (measured in logs)

Control group Employment Payroll

Number of business 

establishments

Counties with rejected loans

 Parameter estimate

 [p-value]

 Number of observations

0.016

[ .052]

7,731

0.038

[.001 ]

7,731

0.001

[.865 ]

7,731

Counties with fully rescinded loans

 Parameter estimate

 [p-value]

 Number of observations

0.019

[ .014 ]

5,571

0.037

[.001 ]

5,571

-0.004

[ .365 ]

5,571

Counties matched based on 

county growth factors

 Parameter estimate

 [p-value]

 Number of observations

0.017

[.032]

4,536

0.034

[ .001 ]

4,536

0.005

[ .248 ]

4,536

Counties matched based on 

county demographic factors

 Parameter estimate

 [p-value]

 Number of observations

0.015

[ .074 ]

4,554

0.032

[ .007 ]

4,554

0.001

[.891 ]

4,554

Counties matched based on 

county growth and demographic 

factors

 Parameter estimate

 [p-value]

 Number of observations

0.019

[.018 ]

4,554

0.030

[ .007 ]

4,554

0.007

[ .116 ]

4,554

Counties adjacent to counties with 

approved loan

 Parameter estimate

 [p-value]

 Number of observations

0.017

[.031 ]

6,084

0.029

[.007 ]

6,084

0.007

[ .080 ]

6,084

Source: GAO analysis based on RUS and Census data.

Notes: This table does not include parameter estimates for the county and year fixed effects. Since 

the dependent variables are in log form, the parameter estimates represent percentage changes. 

Additionally, p-values are calculated using robust standard errors that control for heteroskedasticity 

and within county serial correlation. 
Appendix II: Economic Development Model 

Methodology and Results

Page 50 GAO-14-471 Telecommunications

Table 4: Model Results for Counties 90 Percent Rural 

Dependent variables (measured in logs)

Control group Employment Payroll

Number of business 

establishments

Counties with rejected loans

 Parameter estimate

 [p-value]

 Number of observations

0.019

[ .294 ]

1,908

0.043

[.054 ]

1,908

-0.004

[.694 ]

1,908

Counties with fully rescinded loans

 Parameter estimate

 [p-value]

 Number of observations

-0.013

[ .566 ]

837

-0.011

[.723]

837

-0.025

[ .057]

837

Counties matched based on 

county growth factors

 Parameter estimate

 [p-value]

 Number of observations

0.012

[.536 ]

918

0.016

[ .486]

918

-0.004

[ .740]

918

Counties matched based on 

county demographic factors

 Parameter estimate

 [p-value]

 Number of observations

0.037

[ .065]

936

0.042

[ .163 ]

936

-0.003

[.757]

936

Counties matched based on 

county growth and demographic 

factors

 Parameter estimate

 [p-value]

 Number of observations

0.010

[.554]

918

-0.008

[.720 ]

918

0.006

[ .591]

918

Counties adjacent to counties with 

approved loan

 Parameter estimate

 [p-value]

 Number of observations

0.021

[.199 ]

954

0.014

[.526]

954

0.008

[ .435]

954

Source: GAO analysis based on RUS and Census data.

Notes: This table does not include parameter estimates for the county and year fixed effects. Since 

the dependent variables are in log form, the parameter estimates represent percentage changes. 

Additionally, p-values are calculated using robust standard errors that control for heteroskedasticity 

and within county serial correlation. 

Though our findings were consistent across various model specifications, 

our findings require caveats. First, there are no standard metrics for 

measuring the economic impact broadband access and adoption can 
Appendix II: Economic Development Model 

Methodology and Results

Page 51 GAO-14-471 Telecommunications

have on a community, so our choices in data points may not accurately 

capture the true value of broadband to rural communities. Second, since 

RUS broadband loan-project service areas do not conform to county 

boundaries, our decision to treat all counties containing affected 

communities as “treated” may mask some very local affects of the 

broadband loans, or may overstate the extent of some effect. Additionally, 

regression analysis ascertains relationships, not causality, and models by 

definition contain a range of uncertainty.
Appendix III: Comments from the Federal 

Communications Commission 

Page 52 GAO-14-471 Telecommunications

Appendix III: Comments from the Federal 

Communications Commission 
Appendix III: Comments from the Federal 

Communications Commission 

Page 53 GAO-14-471 Telecommunications
Appendix III: Comments from the Federal 

Communications Commission 

Page 54 GAO-14-471 Telecommunications
Appendix IV: GAO Contact and 

Staff Acknowledgments

Page 55 GAO-14-471 Telecommunications

Mark L. Goldstein, (202) 512-2834 or goldsteinm@gao.gov

In addition to the contact named above, Michael Clements (Assistant 

Director); Melissa Bodeau; Stephen Brown; Kyle Browning; Lorraine 

Ettaro; Georgeann Higgins; Bert Japikse; Tara Jayant; and John Mingus, 

Jr. made key contributions to this report.

Appendix IV: GAO Contact and Staff 

Acknowledgments

GAO Contact

Staff 

Acknowledgments

(543327)
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