Telecommunications: USDA Should Evaluate the Performance of the Rural Broadband Loan Program
Published: 2014-05-22T00:00:00

Fast Facts:

Highlights:
section_title: Why GAO Did This Study
paragraphs: 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.
section_title: What GAO Found
paragraphs: 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its Rural Broadband Access Loan and Loan Guarantee Program (“loan program”), though over 40 percent of these loans are no longer active.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. Forty-three are no longer active, either because they were cancelled before they were paid out (25 rescinded) or because the provider defaulted by failing to abide by the terms of the loan (18 defaulted).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 Loans that default or are rescinded can represent an inefficient use of RUS resources.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.
section_title: What GAO Recommends
paragraphs: 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.

section_title: Letter
paragraphs: Access to affordable broadband telecommunications 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  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. 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 High- Cost 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) data on  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. 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. 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  ICC, provided a framework for additional changes, and solicited  comments regarding those changes. 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.
subsections: section_title: Background
paragraphs: 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. 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  broadband. There is also evidence that rural low-income households are  less likely to use broadband than metropolitan low-income households.  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. 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. On February 6, 2013, RUS  published in the Federal Register the final rule implementing the loan  program, as reauthorized in 2008. Entities eligible to receive these  loans include corporations, limited liability companies, cooperative or  mutual organizations, Indian tribes, and state or local governments. The  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. 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. 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. 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.” Incumbent service providers are broadband  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. See table 1 below for yearly authorized  total value of loans as well as loans approved.
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. 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  high. 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. 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.
section_title: Since 2003, 100 Loans Have Been Made to Geographically and Demographically Diverse Areas, but Over 40 Percent Are No Longer Active
subsections: section_title: Communities Receiving RUS Loans Are Geographically and Demographically Diverse
paragraphs: 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. 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. 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). 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  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.
RUS broadband borrowers are located in regions with varying  topography, including differing weather and terrain. For example, we  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 per- mile 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.
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  infrastructure needs associated with the oil and natural gas industry,  according to providers with whom we spoke.
section_title: Communities Served by RUS Broadband Borrowers Generally Meet Past and Present Definitions of Rural
paragraphs: 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.
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. In  2008, for instance, the definition of an eligible community was changed to  exclude communities adjacent to urbanized areas. 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. We found that the majority of active  RUS loans were made to providers in communities that satisfy the 2008  and 2004 definitions of rural used by RUS. 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. 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. The majority of the active loans fell into the “large rural town”  and “small town/isolated rural” categories.
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.
section_title: Over 40 Percent of Approved Loans Are No Longer Active Because They Are in Default or Have Been Rescinded
paragraphs: 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. 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. 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  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.”
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, “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.” Since the loan program  was created in 2002, about 18 percent of all loans have defaulted. 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. For loans made to providers that  experience financial challenges, RUS staff must conduct additional follow- up 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,  RUS officials said the agency is undergoing a staff reorganization that will  establish a branch to oversee loan performance.
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. 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. 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.
section_title: RUS Loans Can Help Promote Limited Broadband Deployment and Economic Development, but Performance Goals Do Not Fully Align with Program’s Purpose
subsections: section_title: RUS Loans Can Help Overcome Financial Barriers to Deploying Broadband in Rural Areas
paragraphs: 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. 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 RUS- financed 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  rescinded projects had an average of 5.1 broadband providers.  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.
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.
section_title: RUS Loans Can Create Opportunities for Modest Economic Development and Enhance the Quality of Life in Rural Areas
subsections: section_title: Economic Development
paragraphs: 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.
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. 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.
section_title: Quality of Life
paragraphs: 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  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 telehealth 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 workforce- development 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.
section_title: USDA Annual Performance Goals and Measures Do Not Fully Align with the Loan Program’s Purpose
paragraphs: 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 2010 (that updated the Government Performance and Results Act of  1993) which along with OMB requirements 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. 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.
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. This method does not  measure actual adoption of RUS-financed broadband services. As the  National Broadband Plan states, “adoption is necessary for utilization, but  utilization is necessary to extract value from a  connection.”  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. 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.
section_title: FCC Reforms of USF and ICC Have Created Temporary Uncertainty That May Be Hindering Investment in Broadband
subsections: section_title: About Half of RUS Broadband Borrowers with Active Loans Have Received USF Support, and Most Received ICC Support
paragraphs: 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). 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. 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.
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.
section_title: Reforms May Change Amount of USF and ICC Support for RUS Broadband Borrowers
paragraphs: 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;  capped total per-line support as well as capital and operating  expenses; eliminated or began phasing down certain types of support; and created the Connect America Fund (CAF), which will ultimately  replace the high-cost fund for certain providers.
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.
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-and- keep” framework as the ultimate end state for all telecommunications  traffic exchanged with a local carrier. According to FCC, under this  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.
section_title: Uncertain Levels of Future Support Hinder Broadband Investment, according to Stakeholders
paragraphs: 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.
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. FCC is implementing a  “transitional recovery mechanism” to facilitate providers’ gradual transition  away from ICC revenues reduced as part of the Transformation Order.  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.
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  borrowers to repay their outstanding loans and  complete the construction of wireline broadband systems.” 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  lesser and more sporadic investment in high-cost areas, due to the lower  levels of support funding and increased uncertainty.” 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. 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. 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.
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  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. Moreover, elements of the USF/ICC Transformation  Order have been appealed.
section_title: Conclusions
paragraphs: 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.
section_title: Recommendations
paragraphs: We recommend the Secretary of Agriculture take the following two  actions: 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.
section_title: Agency Comments and Our Evaluation
paragraphs: 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  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  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.
section_title: Appendix I: Scope and Methodology
paragraphs: 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. 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.  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  rescinded projects from the RUS loan information noted above. 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,  employment, and annual payroll). 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. 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  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 site- visit 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 in- person 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. 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  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.
section_title: Appendix II: Economic Development Model Methodology and Results
paragraphs: 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.
subsections: section_title: Scope and Data
paragraphs: 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. 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  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. 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. 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.
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  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.
section_title: Model
subsections: section_title: Structure
paragraphs: 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  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.
section_title: Control Groups
paragraphs: 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,  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.
section_title: Rural Definition
paragraphs: 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. 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  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.
section_title: Development of Control Groups
paragraphs: 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.
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.
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.
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.
section_title: Results
paragraphs: 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  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.
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  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.
section_title: Appendix III: Comments from the Federal Communications Commission
section_title: Appendix IV: GAO Contact and Staff Acknowledgments
subsections: section_title: GAO Contact
section_title: Staff Acknowledgments
paragraphs: 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.