Input:
	Background

		Direct Loan Program and Repayment Plans

The Direct Loan program provides financial assistance to students and  their parents to help pay for postsecondary education. Under the Direct  Loan program, Education issues several types of student loans (see  sidebar).
Current William D. Ford Federal Direct  Loan Types  Subsidized Stafford Loans: Available to  undergraduate students with financial need  (generally the difference between their cost of  attendance and a measure of their ability to  pay, known as expected family contribution).  Borrowers are not responsible for paying  interest on these loans while in school and  during certain periods of deferment, an option  that allows eligible borrowers to temporarily  postpone loan payments.   Unsubsidized Stafford Loans: Available to  undergraduate and graduate school students  irrespective of financial need. Borrowers must  pay all interest on these loans.  PLUS Loans: Available to graduate student  borrowers and parents of dependent  undergraduates. Borrowers must pay all  interest on these loans.  Consolidation Loans: Available to eligible  borrowers wanting to combine multiple federal  student loans (including those listed above)  into one loan. Repayment periods are  extended up to a maximum of 30 years,  thereby lowering monthly payments.   Interest rates for these loans are tied to the  Department of the Treasury’s 10-year note  rate and can vary by loan type. In addition,  there are limits on the annual and aggregate  amounts that can be borrowed for certain loan  types.
After a prospective borrower applies for and is awarded a Direct Loan,  Education disburses it through the borrower’s school. Once the loan is  disbursed, it is assigned to one of nine loan servicers under contract with  Education. These loan servicers are responsible for such activities as  communicating with borrowers about the status of their loans, providing  information about and enrolling borrowers in repayment plans, and  processing payments. Once borrowers leave school, they are responsible  for making payments directly to their assigned loan servicer.
A variety of repayment plans are available to eligible Direct Loan  borrowers, including Standard, Graduated, Extended, and several IDR  plans. Borrowers are automatically enrolled in the Standard plan if they  do not choose another option, and generally make fixed monthly  payments over a period of 10 years. IDR plans can ease repayment  burden by setting monthly loan payments based on a borrower’s income  and family size and extending the repayment period up to 20 or 25 years,  depending on the plan. Unlike Standard, Graduated, and Extended  repayment plans, IDR plans offer loan forgiveness at the end of the  repayment period and monthly payments may be as low as $0 for some  borrowers.
There are a variety of IDR plans, and these plans have differences in  eligibility requirements, how monthly payment amounts are calculated,  and repayment periods before potential loan forgiveness (see table 1).

		Application Process for Income-Driven Repayment Plans

To participate in an IDR plan, borrowers must submit an application to  their loan servicer that, among other things, includes information about  their income, marital status, and family size (see table 2).
According to Education, Education’s loan servicers review the information  borrowers submit on their IDR applications to determine if borrowers are  eligible for IDR plans. If the servicer determines that a borrower is eligible,  it enrolls the borrower in an IDR plan and calculates the borrower’s  monthly payment amount. To continue making monthly payment  amounts based on income and family size, IDR borrowers must annually  submit the IDR application form certifying their income and family size,  which servicers then use to update monthly payment amounts. If a  borrower’s income changes significantly prior to the borrower’s annual  recertification date, the borrower can use the same application form to  request a recalculation of the monthly payment amount (see fig. 1).  However, borrowers are not required to report any such changes before  their annual recertification date.
If IDR borrowers do not have any discretionary income, their scheduled  monthly payment amount is zero dollars (meaning they will not have to  make a monthly loan payment until their discretionary income is high  enough to warrant one). Scheduled monthly payments of zero dollars  count as qualifying payments towards eventual loan forgiveness at the  end of the 20- to 25-year repayment period. Borrowers who make  monthly payments on IDR plans that are much lower than they would be  under the Standard 10-year repayment plan for a long period of time may  end up paying less than their original loan amount because their  remaining loan balances may be forgiven. However, some borrowers on  IDR plans will fully repay their loans before qualifying for forgiveness.  Extending the repayment period may also result in some borrowers  paying more interest over the life of the loan than they would under the  10-year Standard repayment plan.

		Standards and Guidance for Managing Risk of Fraud and Errors in Federal Programs

Fraud in federal programs occurs when individuals or entities intentionally  misrepresent themselves in order to benefit from the programs. Fraud  poses a significant threat to the integrity of federal programs and erodes  public trust in government. Federal programs are at risk for fraud when  individuals have both the opportunity and incentive to commit fraud.  Although the occurrence of one or more cases of fraud indicates there is  a fraud risk, a fraud risk can exist even if fraud has not yet been identified  or occurred.
Proactive fraud risk management is meant to facilitate a program’s  mission and strategic goals by ensuring that taxpayer dollars and  government services serve their intended purposes. In July 2015, GAO  issued the Fraud Risk Framework, which provides a comprehensive set  of components and leading practices that serve as a guide for agency  managers to use when developing efforts to combat fraud in a strategic,  risk-based way. The Framework recommends that to effectively manage  fraud risks, managers should design and implement specific control  activities to prevent and detect potential fraud, such as data analytics.  After issuance of the Fraud Risk Framework, the Fraud Reduction and  Data Analytics Act of 2015 was enacted to improve federal agency  controls and procedures to assess and mitigate fraud risks, and to  improve agencies’ development and use of data analytics for the purpose  of identifying, preventing, and responding to fraud. The act requires  agencies to establish financial and administrative controls that incorporate  the Fraud Risk Framework’s leading practices. We previously reported  that Education identified itself as subject to the act.
Error also poses a risk to the integrity of federal programs. According to  federal internal control standards, to maintain an effective internal control  system, managers should use quality information to achieve agency  objectives. This includes obtaining information from reliable sources that  is reasonably free from errors and communicating it externally to achieve  objectives.

	Indicators of Potential Fraud or Error in Income and Family Size Information Pose Risks to IDR Plans

		Over 95,000 IDR Plans Were Held by Borrowers Reporting No Income, but Data Suggests They May Have Had Enough Wages to Make Student Loan Payments

Our analysis of Education’s IDR plan data and HHS’s NDNH wage data  for borrowers who reported zero income found that about 95,100  approved IDR plans (11 percent of all IDR plans we analyzed) were held  by borrowers who may have had sufficient wages to warrant a monthly  student loan payment. These plans were held by about 76,200 unique  borrowers who owed nearly $4 billion in outstanding Direct Loans as of  September 2017. According to our analysis, 34 percent of these plans  were held by borrowers who had estimated annual wages of $45,000 or  more, including some with estimated annual wages of $100,000 or more  (see fig. 2).
Our results from matching the Education and HHS data indicate the  possibility that some borrowers misrepresented or erroneously reported  their income, highlighting the risk of potential fraud and errors in IDR  plans. Borrowers may have a financial incentive to commit fraud to  reduce their monthly payment amount and, by extension, possibly  increase the amount of loan debt forgiven at the end of their repayment  periods. However, we cannot determine whether fraud occurred through  data matching alone. Where appropriate, we are referring these results to  Education for further investigation. Among the 76,200 borrowers in our  data matching results, it is possible that some accurately reported zero  income even though they had wages reported in NDNH in the same  quarter in which their IDR application was approved. For example, a  borrower may have earned wages at the start or end of a quarter, but was  not earning wages at the time of submitting the IDR application.  Conversely, our analysis cannot identify borrowers who may have earned  additional taxable income that is not part of NDNH data, but should be  included on IDR applications, such as income for individuals who are self- employed or receiving alimony. Regarding the potential for error,  officials from Education and all four loan servicers we spoke with stated  that it is possible that borrowers could incorrectly report that they had no  taxable income. Officials from Education said, for example, that borrowers  may misunderstand the question about taxable income on the IDR  application, and one loan servicer, echoing this perspective, stated that  some borrowers may mistakenly think that some of their income is  nontaxable when it is in fact taxable.
To examine how borrowers’ failure to report their income could affect the  amount repaid to Education over the course of a year, we used  Education’s online repayment estimator to illustrate how much  hypothetical borrowers with different annual adjusted gross incomes  would expect to pay under each IDR plan (see fig. 3). If a borrower at  one of these income levels instead reported zero income on the IDR  application, Education could lose thousands of dollars per borrower each  year in student loan payments. Such a situation could also potentially  increase the ultimate cost to the federal government and taxpayers for  loan forgiveness because scheduled monthly payments of zero dollars  count toward the borrower’s 20- or 25-year repayment period.

		Education May Miss Indicators of Potential Fraud or Error in Borrowers’ Family Sizes

To examine the extent to which Education’s IDR plan data on family size  may indicate potential fraud or error, we analyzed the family sizes for  about 5 million IDR plans approved between January 1, 2016 and  September 30, 2017. Of these plans, over 2.1 million (43 percent) were  approved with a family size of one, meaning only the borrower was  included (see fig. 4). In addition, over 2.6 million plans (52 percent) were  approved with family sizes of two to five.
At the high end of the spectrum, about 40,900 of the plans we analyzed  (about 1 percent) were approved with family sizes of nine or more (see  fig. 5). We consider IDR plans with family sizes of nine or more atypical  or outliers because they comprise the top 1 percent of all family sizes in  Education’s data. Of these plans, almost 1,200 had family sizes of 16 or  more, including two plans held by different borrowers that were approved  with a family size of 93. In total, the 40,900 plans approved with family  sizes of nine or more corresponded to about 35,200 unique borrowers  who owed almost $2.1 billion in outstanding Direct Loan debt as of  September 2017.
While IDR plans with family sizes of nine or more were atypical in our  data and could indicate fraud or error, IDR plans with smaller or more  typical family sizes could also pose problems. Borrowers may have a  financial incentive to commit fraud because larger family sizes reported  on the IDR application can reduce borrowers’ discretionary income and,  by extension, their monthly payment amounts.
Regarding the potential for error, officials from Education and all four loan  servicers we spoke with said borrowers or loan servicers may  inadvertently make mistakes related to family size. For example, officials  from Education and one loan servicer said borrowers sometimes report  inaccurate family sizes if they are confused about who to count as a  member of their family. Officials from this loan servicer told us that a  borrower initially applied for an IDR plan claiming a family size of five— himself and four other family members who were not his spouse or  children. They said that during a subsequent phone call with loan servicer  staff about the borrower’s loan, the borrower volunteered that the other  members of his family did not live with him, meaning that for IDR  purposes, he had a family size of one. It is unclear whether this  borrower may have misrepresented his family size to receive a lower  monthly payment or did not understand the definition and reported it in  error. In regards to loan servicer error, Education officials said that  servicers may make mistakes when entering family sizes from paper  applications into their computer systems or when determining the total  family size because borrowers provide information on family members in  up to three places on the application.
To examine the effect of family size on monthly payment amounts in IDR  plans, we used Education’s online repayment estimator to illustrate how  much hypothetical borrowers with the same income but different family  sizes would be expected to pay each month under certain IDR plans. We  found that a hypothetical borrower with a family size of one and an  adjusted gross income of $40,000 who enrolls in one of three IDR plans  that base monthly payment amounts on 10 percent of discretionary  income would have a monthly payment amount of $182 (see fig. 6). If  this borrower instead reported a family size of two people, the monthly  payment amount would decrease by $54, to $128. For each additional  person, the monthly payment would decrease by $54. At a family size of  five people, the borrower would have no monthly payment.

	Weaknesses in Education’s Procedures to Verify Income-Driven Repayment Plan Information Reduce Its Ability to Detect Potential Fraud or Error, but Approaches Exist to Address Risks

		Education Does Not Verify Borrower Reports of Zero Income and Has Limited Protocols for Verifying Borrower Family Size

Education does not have procedures to verify borrower reports of zero  income nor, for the most part, procedures to verify borrower reports of  family size; although there are approaches it could use to do so.  Because income and family size are the basis for calculating borrowers’  monthly payment amounts for IDR plans, it is important that this  information is accurate on IDR applications.
While Education instructs loan servicers to review tax returns and other  documentation of taxable income that borrowers are required to provide,  as previously discussed, borrowers are not required to provide  documentation to support self-attestations of zero income or their family  size on IDR applications. Officials from Education and all four loan  servicers we spoke with said that servicers are generally instructed to  take these self-attestations at face value. However, Education has limited,  voluntary procedures for reviewing family size information submitted by  borrowers.
In 2016, Education implemented a voluntary procedure for loan  servicers to contact borrowers who report changes in family size of  four or more from one year to the next in order to verify the accuracy  of the most recently reported family size. Education officials told us  that servicers are not contractually required to follow this procedure.  In addition, this procedure is not applicable to student loan borrowers  when they initially apply for IDR plans.
In October 2018, Education officials told us they began to follow up  with loan servicers about family sizes of 20 or more in IDR program  data to ensure these data match the family size information in the loan  servicer systems from which they originated. Officials said that this  process is to ensure that family size data were accurately transferred  from servicers to Education. Borrowers are not contacted for  verification of the information itself.
Officials from Education and three of the four loan servicers we spoke  with acknowledged that IDR plans are at risk for fraud or error because  verification is generally not performed on borrower reports of zero income  and borrower reports of family size. Officials from Education and two of  the loan servicers also said that certain program requirements discourage  borrowers from providing false information. For example, borrowers are  required to sign the IDR form to certify that all provided information is  true, complete, and correct, and the form warns borrowers that false  statements or misrepresentations are subject to penalties including fines,  imprisonment, or both. However, the extent to which this requirement  may serve as a deterrent is unknown because Education has not  assessed the risk of fraudulent reports on IDR applications. Moreover,  Education officials told us that they were not aware of any IDR borrowers  being investigated or facing penalties for providing false information on  the IDR application. Officials from one loan servicer also said that  borrowers may be deterred from falsely claiming zero income or  misrepresenting their family size because they assume that servicers,  acting on behalf of the government, can check the information on IDR  applications. However, it is also possible that borrowers would assume  that this self-reported information would not be routinely verified because  the only documentation requirements discussed on the application relate  to verifying taxable income.
Education officials also said that the risk of borrowers providing  inaccurate information on IDR applications must be balanced against the  impact of adding verification procedures. They said additional procedures  could make the already complex IDR application process more  burdensome for borrowers to navigate and result in longer application  processing times. While it is important to make IDR plans accessible to  borrowers who could benefit from them, it is also important that Education  design internal control activities to achieve program objectives and  respond to risks, including addressing the risk of fraud and error in  borrower self-reported information.
GAO’s Fraud Risk Framework describes the importance of developing  procedures for preventing, detecting, and responding to the risk of fraud  in government programs.  The risk of fraud exists when there is  opportunity and incentive to commit it. The lack of verification of borrower  reports of zero income and limited verification of borrower reports of  family size on IDR applications creates the opportunity for borrowers to  commit fraud. Because lower income and larger family sizes can reduce  borrowers’ monthly payment amounts and, by extension, possibly  increase the amount of loan debt forgiven at the end of their repayment  periods, there is also an incentive for some borrowers to commit fraud. In  regard to error, federal internal control standards state that agencies  should obtain information from reliable sources that are reasonably free  from error. Education officials and all four loan servicers told us that  borrower-reports of family size or zero income can be susceptible to error  if, for example, borrowers misunderstand the definitions of these items on  IDR applications.
Addressing the risk of fraud and error would also help to minimize the  costs associated with IDR plans that are passed on to the government  and taxpayers. As more borrowers enter IDR plans, the costs of these  plans—including loan forgiveness—increase for the government and  taxpayers. Using data underlying the President’s fiscal year 2017 budget  request, GAO previously reported that Education estimated Direct Loans  repaid with IDR plans would cost the federal government about $74 billon  over their repayment periods. In its fiscal year 2015-2019 strategic plan  for Federal Student Aid, Education acknowledged that as IDR plans  continue to grow in popularity, the cost of loan forgiveness could be a  major issue for the federal government. Education can minimize the  costs associated with IDR plans by ensuring payment amounts are based  on accurate income and family size information.

		Approaches Exist That Could Help Education Identify and Address Potential Fraud or Error in IDR Plans

Education has not fully leveraged available approaches to help detect and  prevent fraud or error in IDR plans. Federal internal control standards call  for agency management officials to identify, analyze, and respond to risks  related to achieving program objectives, such as the risk of using  potentially fraudulent or erroneous information about borrowers to  calculate monthly payment amounts for student loans. Approaches,  such as using data analytic practices and follow-up procedures, can help  identify and address these risks. Two data analytic practices that can help  identify such risks with respect to IDR plans are (1) anomaly detection to  identify atypical or unusual information about borrowers and (2) data  matching with outside data sources to verify information that borrowers  provide. These practices, which can be used on their own or together,  can help prevent fraud from occurring and detect potential fraud or error  that may have occurred. Because data analytics alone may not be  sufficient to determine whether fraud or error has occurred, follow-up  procedures can then be used in the investigation and verification to make  such determinations.
A leading practice in data analytics in GAO’s Fraud Risk Framework is  conducting data mining to identify suspicious activity or transactions,  including anomalies, outliers, and other red flags in the data. Similar to  our family size analysis, borrower-reported family sizes above a certain  threshold on IDR applications could be flagged in loan servicers’ and  Education’s data systems for further verification. Anomaly detection is  used to a limited extent to identify errors in family size on IDR plans by  one loan servicer and by Education. According to officials at Education  and all four loan servicers we spoke with, anomaly detection is not used  to systematically identify potentially fraudulent reports of family size.
Anomaly detection can also identify deviations from expected patterns in  data over time. Because IDR borrowers are required to fill out  applications annually, it would be possible to develop automated queries  to look for unusual patterns in borrower-reported income and family size  from one year to the next. Officials from Education and servicers  described several patterns across applications that could indicate  potential fraud, specifically large swings in income from one year to the  next, reporting zero income for multiple years, and having a large family  size, but relatively low income.
Another leading practice for data analytics in GAO’s Fraud Risk  Framework is conducting data matching to verify key information,  including self-reported data and information necessary to determine  eligibility. The results of our analysis illustrate the usefulness of this  technique to identify potential inconsistencies in the income information  on IDR plans. Education does not have authority to access wage data  from HHS’s NDNH or income data from the Internal Revenue Service  (IRS) for the purpose of verifying IDR borrowers’ income information  through data matching. However, private data sources are also available  for data matching. We reported in 2016 on the benefits of government  agencies using private data to address the risk of fraud. Moreover,  some state agencies (such as those administering the Supplemental  Nutrition Assistance Programs) use a private, commercial verification  service known as The Work Number® to help determine eligibility for  government assistance. We reported in 2016 that 45 states used  income information from The Work Number to help determine eligibility for  food assistance benefits under the Supplemental Nutrition Assistance  Program.
Education may also be able to draw on follow-up procedures it has in  place for verifying information submitted by students and their families  when applying for federal student aid using the Free Application for  Federal Student Aid (FAFSA). Education uses a process called  “verification” to help identify and correct erroneous or missing information  on the application to aid the department’s efforts to reduce improper  payments of federal student aid. Each award year, a portion of FAFSA  applications are selected for verification, and schools are required to work  with the selected applicants to obtain documentation and confirm the  accuracy of information provided on these applications. When selecting  FAFSAs for verification, Education aims to select those applications with  the highest statistical probability of error and the greatest impact of such  error on award amounts. FAFSA applicants who are selected to verify their income for the 2018-2019 or 2019-2020 award years may provide a  signed copy of their prior years’ tax returns. FAFSA applicants may also  obtain documentation from the IRS through the IRS Data Retrieval Tool,  an IRS tax return transcript, or an IRS Verification of Non-filing Letter.  FAFSA applicants selected to verify their household size must provide a  signed statement that provides the name, age, and relationship to the  student of each person in the household.
For IDR plans, Education could implement follow-up procedures for IDR  applications it identifies as at risk for fraud or error and seek additional  documentation from borrowers. For example, to verify reports of no  income, borrowers could be asked to provide an IRS Verification of Non- filing Letter, documentation that the borrower recently lost a job, or  documentation that shows income the borrower receives is nontaxable,  such as public assistance benefits. To verify family size, as is the case  with FAFSA verification, borrowers could be asked to provide a signed  statement with the names, ages, and relationship to the borrower of each  family member. Another option might be to request that borrowers  provide documentation showing that family members (other than the  borrower’s spouse and children) receive mail at the borrower’s address  as well as documentation of the financial support provided by the  borrower. Such follow-up procedures would be consistent with federal  internal control standards advising managers to design control activities  to achieve program objectives and respond to risks.

	Conclusions

While Income-Driven Repayment plans can help borrowers with limited  incomes afford their monthly student loan payments, these plans can also  result in high costs to the federal government and taxpayers. To minimize  these costs, it is important that Education accurately determine monthly  payment amounts under its IDR plans. Because these determinations are  based on income and family size information that borrowers self-report,  there is risk for potential fraud or error. Our data matching analysis  showed, for example, that tens of thousands of borrowers who were not  making monthly loan payments because they reported zero income on  IDR applications may have had enough income to do so. Where  appropriate, we are referring these borrowers to Education for further  investigation. In addition, an increase in family size can cause a  borrower’s payments to decrease, creating a potential incentive for fraud,  and our analysis found atypically large family sizes that are generally not  verified by Education. The results of our analyses highlight the risk for  fraud or error, as well as weaknesses in Education’s procedures. In turn,  the weaknesses we identified raise questions about the strength of  Education’s institutional oversight of a major program involving hundreds  of billions of dollars. The fact that, cumulatively, the borrowers and their  plans we reviewed owed over $6 billion in loans helps illustrate the risk of  potential financial loss for the government from fraud or error absent  comprehensive oversight.
It is important for Education to take steps to obtain data to verify borrower  reports of zero income and to implement other data analytic practices and  follow-up procedures for verifying borrower-reported information. Such  actions would help ensure that (1) IDR payment amounts are based on  information that accurately represents a borrower’s situation and is free  from fraud and error; and (2) the federal government’s fiscal exposure to  IDR loans is safeguarded from the risk of loss. Implementing data analytic  practices and follow-up procedures to review and verify borrower reports  of zero income could help deter borrowers from inaccurately reporting  zero income and detect those who have done so, either fraudulently or in  error. Similarly, implementing practices and procedures to review and  verify reported family sizes could further stem potential fraud or error.  Without such changes, IDR plans will remain vulnerable to fraud and  error, potentially raising program costs for the federal government and  taxpayers.

	Recommendations for Executive Action

We are making the following three recommendations to Education’s  Federal Student Aid office:  The Chief Operating Officer of Federal Student Aid should obtain data in  order to verify income information for borrowers reporting zero income on  IDR applications. For example, Education could pursue access to federal  data sources or obtain access to an appropriate private data source.  (Recommendation 1)
The Chief Operating Officer of Federal Student Aid should implement  data analytic practices, such as data matching, and follow-up procedures  to review and verify that borrowers reporting zero income on IDR  applications do not have sources of taxable income at the time of their  application. (Recommendation 2)
The Chief Operating Officer of Federal Student Aid should implement  data analytic practices, such as data mining, and follow-up procedures to  review and verify family size entries in IDR borrower applications. For  example, Education could review and verify all borrower reports of family  size or a subset identified as being most susceptible to fraud or error.  (Recommendation 3)

	Agency Comments and Our Evaluation

We provided a draft of this report to the Departments of Education  (Education) and Health and Human Services (HHS) for review and  comment. HHS provided technical comments, which we incorporated as  appropriate. We also provided relevant report sections to the Social  Security Administration and the four loan servicers included in our review  for technical comments. Loan servicers provided technical comments,  which we addressed as appropriate.
Education generally agreed with our recommendations, stating that it  plans to implement significant additional verification policies to ensure  that borrowers who participate in IDR plans do not misrepresent their  income or family size to the department.
While Education agreed with our recommendation to obtain data in order  to verify income for borrowers reporting zero income, it suggested that  GAO may wish to convert this recommendation to a Matter for  Congressional Consideration to provide Education with access to IRS  data. In its response, Education stated that the President’s fiscal year  2020 budget request includes a proposal that Congress pass legislation  allowing the IRS to disclose tax return information directly to the  department for the purpose of administering certain federal student  financial aid programs. According to Education, such legislation, if  enacted, would allow borrowers to more easily certify their income on an  annual basis to maintain enrollment in IDR plans, and allow the  department to use the information to mitigate improper payments to  borrowers as a result of misreported income data. Education also stated  that in the meantime, it would explore whether commercially available  data are sufficient in terms of scope, reliability, and cost effectiveness.  Given that there are existing actions Education can take to implement our  recommendation, we believe our recommendation is appropriate.  Moreover, we believe that Education is best positioned to determine  whether the proposal, if enacted, would address our recommendation, or  if it would need to be expanded or modified in order to do so.
Regarding our second recommendation, Education stated that it would  develop data analytic practices to verify borrower reports of zero income  contingent upon the enactment of legislation providing the department  with access to federal income data. However, implementing our  recommendation does not necessarily require Education to wait for such  legislation. Our draft report describes data analytic practices, such as  anomaly detection, which Education could implement using its own data  to identify deviations from expected patterns in data over time. Education  also stated that it plans to develop additional follow-up procedures to  verify borrower reports of zero income, such as requiring borrowers to  substantiate reports of zero income with appropriate documentation. In  addition, Education described plans to formalize procedures to make  referrals to Education’s Office of Inspector General or the Department of  Justice for suspected cases of IDR fraud.  We encourage Education to  combine its follow-up procedures with data analytic practices to satisfy  the recommendation.
Education agreed with our third recommendation to implement data  analytic practices and follow-up procedures to verify family size, noting  that this information could be subject to misrepresentation or erroneous  reporting by borrowers. Education stated that it would review various data  points that can be used to select IDR applications and certifications for  additional review prior to approval, such as providing more scrutiny when  borrowers report unusual increases in family size from one year to the  next. Education also stated that it plans to formalize additional procedures  to require certain borrowers to substantiate their family size. For example,  Education will consider requiring IDR applicants to provide statements  listing each household member and how they are related to the borrower.
As agreed with your offices, unless you publicly announce the contents of  this report earlier, we plan no further distribution until 30 days from the  report date. At that time, we will send copies to relevant congressional  committees, the Secretary of Education, the Chief Operating Officer of  Federal Student Aid, and other relevant parties. In addition, the report will  be available at no charge on the GAO website at http://www.gao.gov.
If you or your staff have any questions about this report, please contact  us at (617) 788-0534 or emreyarrasm@gao.gov or (202) 512-6722 or  bagdoyans@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 that made key contributions to the report are listed in appendix  III.

Appendix I: Objectives, Scope, and Methodology

This report examines (1) whether there are indicators of potential fraud or  error in income and family size information provided by borrowers seeking  to repay their loans with Income-Driven Repayment (IDR) plans and (2)  the extent to which the Department of Education (Education) verifies this  information.
To address these questions, we reviewed relevant IDR policies and  procedures from Education and its four largest loan servicers—Navient,  Nelnet, Great Lakes Educational Loan Services, Inc., and the  Pennsylvania Higher Education Assistance Agency. We selected these  loan servicers because, at the time of our analysis, together they serviced  96 percent of the outstanding balance of loans being repaid with IDR  plans as of September 2017. We also interviewed Education officials  from Federal Student Aid, the office responsible for developing policies  and procedures for administering IDR plans and overseeing how loan  servicers carry them out, as well as the officials from the selected loan  servicers. Additionally, we reviewed relevant federal laws and regulations  and Education’s procedures for verifying information on the Free  Application for Federal Student Aid. We assessed Education’s  procedures against federal standards for internal control for developing  sufficient control activities, risk assessment, and information and  communication. We also assessed Education’s procedures against the  leading practices for data analytics activities in GAO’s Framework for  Managing Fraud Risks in Federal Programs.
To determine whether there were indicators of potential fraud or error in  borrowers’ income and family size information on IDR plans, we obtained  data from Education’s Enterprise Data Warehouse and Analytics (EDWA)  database on borrowers with William D. Ford Federal Direct Loans (Direct  Loans) and IDR plans approved between January 1, 2016 and  September 30, 2017, the most recent data available at the time of our analysis. EDWA is a centralized data warehouse that contains  administrative data reported by loan servicers on IDR borrowers and their  loans. Some borrowers had multiple approved IDR plans in the data we  analyzed. We also obtained national quarterly wage data from the U.S.  Department of Health and Human Services’ (HHS) National Directory of  New Hires (NDNH) for the same time period. NDNH is a national  repository of information reported by employers, states, and federal  agencies. The NDNH is maintained and used by HHS for the federal child  support enforcement program, which assists states in locating parents  and enforcing child support orders. In addition to information on newly  hired employees, NDNH contains (1) data on quarterly wages for existing  employees, collected and reported by state workforce agencies and  federal agencies; and (2) data on all individuals who apply for or received  unemployment compensation, as maintained and reported by state  workforce agencies.
For our analysis of borrower-reported incomes, we matched  approximately 656,600 Education borrowers to NDNH quarterly wage  data to determine if any borrowers who reported zero income on their IDR  applications had wages reported in the same quarter in which their IDR  plans were approved. We took additional steps to further review and  refine these matches and provide reasonable assurance that the NDNH  wage data were associated with the correct borrower by comparing (1)  the borrower’s state of residence as reported in the Education data to the  state agency submitting the NDNH wage data and (2) the borrower’s  name as reported in the Education data to the employee name reported  in the NDNH data. For the refined matches, we then estimated whether  the borrowers may have had sufficient annual wages based on wages  reported in NDNH to potentially warrant monthly student loan payments  greater than zero dollars on their associated IDR plan. Specifically, we  aggregated all NDNH wages reported for the borrower in the quarter in  which their IDR plan was approved to determine a total quarterly wage  amount. We then multiplied the total quarterly wage amount by four—the  number of quarters in a calendar year—to generate an estimate of annual  wages for the borrower. Our approach was based on the methodology  Education instructs loan servicers to use to calculate annual wages when  borrowers provide an alternative to a tax return to document their income  on IDR applications. This methodology may understate or overstate  income given that borrowers may not have earned the same amount in  each of the four quarters. Our estimates of annual wages are based on  the wages reported in NDNH for each borrower and do not take into  account any pre-tax deductions that may apply when determining IDR  payments. Our estimates of annual wages also do not include borrowers’  spousal income or any other taxable income for the borrower that is not  included in the NDNH quarterly wage data—such as unemployment  compensation received or unearned income such as alimony. We did not  independently verify the wages reported in NDNH or the actual total  annual income earned by borrowers identified in our match, as this was  outside the scope of our review.
Using the estimated annual wage, we then determined whether a  borrower would have had a monthly payment greater than zero by using  Education’s IDR plan repayment calculations for each IDR plan. To  calculate the monthly payment, we used (1) the estimated annual wage  from our NDNH data analysis; (2) the family size reported on the  borrower’s approved IDR plan; (3) the borrower’s approved IDR plan  type; and (4) the relevant percentage of the HHS poverty guideline  amount for the borrower’s family size, state of residence, IDR plan  approval year, and IDR plan type. For borrowers on Income-Based  Repayment, New Income-Based Repayment, Pay As You Earn, and  Revised Pay As You Earn plans, we rounded all calculated monthly  payments that were less than $5 down to zero, in accordance with  Education’s repayment calculations. We then identified which borrowers  had calculated payments that were greater than zero. We did not  determine the actual repayment amount borrowers may have had, as this  was outside the scope of our review. Finally, for borrowers for whom we  had calculated a payment greater than zero, we determined the total  outstanding Direct Loan balance (principal and accrued interest) as of  September 2017, based on EDWA data.
For our analysis of borrower-reported family sizes, we analyzed the  overall distribution of family sizes reported on approximately 5 million  approved IDR plans. We reviewed the percentile distribution for family  size on all IDR plans in our analysis and identified those in the top 1  percent of the data—in this case, IDR plans that had a reported family  size of nine or more. We defined these IDR plans as having atypical  family sizes for the Education data. We did not independently verify the  family size reported on the IDR plans. For the borrowers with family sizes  of nine or more, we determined the total outstanding Direct Loan balance  (principal and accrued interest) as of September 2017.
To examine the effects of borrowers inaccurately reporting income and  family size on loan payment amounts, we analyzed the estimated monthly  loan payment amounts for various hypothetical repayment scenarios from  Education’s online repayment estimator as of January 2019, which used  the 2018 HHS poverty guidelines. To examine the effect of various family  sizes on loan payment amounts, we assumed a hypothetical borrower  lived in the continental United States; had an adjusted gross income of  $40,000; an outstanding Direct Loan balance of $30,000 (close to the  average outstanding Direct Loan balance of $33,600 as of September  2018); and an interest rate of 5.1 percent (the Direct Loan 2018-2019  interest rate for an undergraduate borrower). To examine the effect of  various incomes on monthly payment amounts, we assumed hypothetical  borrowers had adjusted gross incomes based on estimated annual wages  common in our data matching analysis ($30,000, $45,000, and $60,000),  a family size of one (meaning just the borrower), and lived in the  continental United States. For this analysis, we also assumed  hypothetical borrowers had an interest rate of 5.1 percent and an  outstanding Direct Loan balance of $50,000, which we selected to be high  enough to qualify these hypothetical borrowers for all IDR plans at each  of the selected income levels.
To assess the reliability of the EDWA data, we reviewed documents  related to the database and Education loan data generally; interviewed  knowledgeable Education officials; performed electronic testing to  determine the validity of specific data elements that we used to perform  our work; compared the data we received to published Education data on  the number of IDR borrowers and amount of their outstanding loans; and  compared borrowers’ personal information to the Social Security  Administration’s Enumeration Verification System to identify borrowers  whose information may not have been accurate. As part of our reliability  assessment of the EDWA data, we selected a nongeneralizable sample  of 16 borrowers and their IDR plan and loan information from the EDWA  data to compare against four selected loan servicers’ records.  Specifically, we stratified borrowers into two groups based on common  and potentially outlying incomes and family sizes in the EDWA data. We  then randomly selected two borrowers from each stratum for each of the  four selected loan servicers (a total of four borrowers per loan servicer).  We reviewed all IDR plan data in our scope for each selected borrower,  including the plan type, family size, income, and total monthly payment.  We did not review original documents, such as the IDR applications or  documentation of income. We discussed the results of our review with  knowledgeable Education and loan servicer officials to gain additional  understanding of each selected borrower’s IDR plan information as well  as any differences between EDWA and loan servicer data.
We originally obtained EDWA data on approximately 6.5 million IDR plans  approved between January 1, 2016 and September 30, 2017 that were  held by almost 4.8 million Direct Loan borrowers. Based on data reliability  issues we identified during our review, we had to limit the scope of our  analysis to a subset of EDWA data that we determined were sufficiently  reliable for our purposes. Education officials disclosed issues that  impacted the IDR plan data reported to Education by one of its loan  servicers. Specifically, Education and the loan servicer had identified  instances where the loan servicer’s internal data were changed for valid  reasons but the changes were not reported to Education correctly. As a  result, we excluded data reported by this servicer from all analyses in our  report. We also identified issues with monthly payment amounts for some  borrowers in the EDWA data. Accordingly, we limited our borrower- reported income analysis to borrowers who reported zero income and had  a scheduled monthly payment of zero dollars. Ultimately, we analyzed  about 878,500 IDR plans held by about 656,600 borrowers for our income  analysis and approximately 5 million IDR plans held by 3.5 million  borrowers for our family size analysis. Consequently, our overall income  and family size analyses results may be understated and are not  generalizable to all IDR plans and borrowers.
Consistent with our report scope, our analyses of borrower-reported  income focused on identifying indications of potential fraud or error;  however, our analyses do not show that fraud or error occurred. It is not  possible to determine whether fraud or error occurred through data  matching alone. As previously discussed, our estimates of annual wages  are based on the NDNH quarterly wage data, and do not take into  account any deductions that may be applicable for determining adjusted  gross income, which is used to determine IDR plan payment amounts. As  a result, our estimates could overstate borrowers’ incomes for IDR plan  purposes. Additionally, wages are reported in NDNH quarterly, so we are  not able to determine when in a quarter a borrower earned wages. For  example, a borrower may have earned wages at the start or end of a  quarter, but was not earning wages at the time of submitting the IDR  application. Because borrowers are only required to certify their income  annually, such a scenario would not constitute fraud or error even though  it would result in a match in our analysis. In addition, our use of  Education’s methodology to annualize wages based on quarterly wages  may understate or overstate income if a borrower did not earn wages at  the same level over the entire year. We are also not able to identify  additional taxable income that is not reported to NDNH but should have  been included on borrowers’ IDR applications, which could understate  borrowers’ incomes. Consequently, our analysis may overstate or  understate the number of borrowers who reported no income on their IDR  application yet may have had sufficient wages to warrant a monthly  student loan payment.
To assess the reliability of the NDNH data, we reviewed documents  related to the database, interviewed knowledgeable HHS officials, and  performed electronic testing to determine the validity of specific data  elements in the NDNH data that we used to perform our work. On the  basis of our own reliability assessment results, we determined that the  NDNH data were sufficiently reliable for the purposes of this report.
We conducted this performance audit from June 2017 to June 2019, in  accordance with generally accepted government auditing standards.  Those standards require that we plan and perform the audit to obtain  sufficient, appropriate evidence to provide a reasonable basis for our  findings and conclusions based on our audit objectives. We believe that  the evidence obtained provides a reasonable basis for our findings and  conclusions based on our audit objectives.

Appendix II: Comments from the U.S. Department of Education

Appendix III: GAO Contacts and Staff Acknowledgments

	GAO Contacts

	Staff Acknowledgments

In addition to the contacts named above, Debra Prescott and Philip Reiff  (Assistant Directors), Nancy Cosentino and Mariana Calderón (Analysts- in-Charge), Sarah Cornetto, Jeffrey G. Miller, and Rachel Stoiko made  key contributions to this report. Additional assistance was provided by  Susan Aschoff, David Ballard, Deborah Bland, Benjamin Bolitzer, Melinda  Cordero, Vijay D’Souza, Kevin Daly, Angie Jacobs, Candace Silva-Martin,  Sheila R. McCoy, Maria McMullen, Kevin Metcalfe, John Mingus, Drew  Nelson, Mimi Nguyen, Matt Valenta, and Ariel Vega.


Input:
	State and Local Governments Will Need to Make Policy Changes to Achieve Fiscal Balance

Our simulations suggest that the sector will likely continue to face a  difference between revenues and expenditures during the next 50 years,  as measured by its operating balance. We simulated the state and local  government sector’s operating balance—a measure of the sector’s ability  to cover its current expenditures out of current revenues—to understand  the sector’s long-term fiscal outlook based on historical revenue patterns  and other assumptions. Because a great majority of states and many  local governments are required to balance or nearly balance their  operating budgets, the operating balance illustrates the magnitude of  fiscal pressures they face. Expenditures and revenues are both  simulated to increase as a percentage of gross domestic product (GDP)   during the simulation period. However, expenditures are generally  expected to grow at a faster rate than revenues, resulting in a declining  operating balance (see figure 1).
One way of measuring the long-term fiscal challenges faced by the state  and local government sector is through an indicator known as the “fiscal  gap.” The fiscal gap is an estimate of annual changes in expenditures  and in revenues our simulations suggest would be needed to maintain the  operating balance equal to zero during the 50-year simulation period. The  sector could close the fiscal gap through an increase in revenues, a  reduction in expenditures, or a combination of the two of sufficient  magnitude. Our simulations suggest that the fiscal gap is about 3.6  percent of GDP over the next 50 years.
The sector will need to take actions in annually reducing its expenditures  or raising revenues, to achieve fiscal balance. Assuming no change in  simulated expenditures, the sector would need to take actions equivalent  to increasing its total revenues by 4.2 percent each year to achieve fiscal  balance. Alternatively, assuming no change in its simulated revenues, the  sector would need to take actions equivalent to decreasing its noninterest  expenditures by an amount equal to 3.2 percent of its total expenditures  each year. Total expenditure reductions required by the sector are 20.7  percent each year, which includes interest payments on debt that are  simulated to be 17.4 percent of annual spending. To eliminate the fiscal  gap, the sector would most likely take actions that include a combination  of expenditure reductions and revenue increases.

	Health Care Cost Growth and Other Factors Contribute to the State and Local Sector’s Fiscal Imbalance

		States’ Spending on Medicaid is a Key Driver of Long-Term Expenditures

Our simulations suggest that growth in the sector’s overall expenditures is  largely driven by health care expenditures. Medicaid will likely constitute a  growing expenditure for state and local governments. In 2018, Medicaid  spending was 2.9 percent of GDP compared to 0.85 percent of GDP for  other kinds of health care spending such as non-Medicaid social benefit  payments and employee health benefit contributions. At the end of our  simulations, Medicaid is simulated to be 4.6 percent of GDP and the other  kinds of health care spending are 1.3 percent of GDP. After 2029,  Medicaid spending in our simulations is derived from Centers for  Medicare & Medicaid Services’ (CMS) projections. On average, Medicaid  expenditures are expected to rise by 1 percentage point more than GDP  each year over the simulation period. Breaking this down, Medicaid  expenditures per capita are expected to increase, on average, about 0.6  percent faster than GDP per capita—referred to as excess cost growth.
Excess Cost Growth   The extent to which health care costs per  capita outpace gross domestic product (GDP)  growth per person.
As shown in figure 4, health care expenditures are simulated to increase  from about 3.94 percent of GDP in 2019 to 5.9 percent of GDP in 2068. In  comparison, nonhealth, noninterest expenditures, which include all other  operational expenditures other than debt interest payments, will decrease  as a share of GDP by 2.74 percentage points over the simulation period.  Per capita, national health expenditures, which make up part of the health  care expenditures in the figure below, are expected to grow on average  0.8 percent faster than GDP each year during the simulation period,  according to CMS.

		Employee Compensation Decreases as a Share of the Sector’s Expenditures during the Simulation Period

Employee compensation is the largest expenditure for the state and local  government sector. It declines from 6.8 percent of GDP in 2018 to 6.1  percent of GDP in 2068. All spending components, including employee  compensation, are simulated to increase in actual dollar amounts during  our simulation period. Of the spending components included in employee  compensation, only health benefits for employees and retirees increase  as a share of employee compensation. In contrast, wages and salaries,  pension contributions, and other forms of compensation decrease as a  share of employee compensation (see figure 5). These percentages  reflect a simulated decrease in state and local government employees’  compensation as a share of GDP.
Our simulations suggest that spending on health benefits for state and  local government employees and retirees is likely to rise, on average, by  0.9 percentage points more than GDP each year. Similar to the growth in  Medicaid spending, growth in spending for these health benefits is due to  an increase in the simulated number of employees and retirees enrolled  as well as an increase in the simulated amount of health benefits for each  employee and retiree. According to our simulations, if employee and  retiree health benefits follow trends in overall national health spending,  they will likely make up an increasingly larger share of total employee  compensation going forward.
Our simulations suggest that annual contributions to state and local  government employee pension plans will need to remain at their historical  10-year average of 12.9 percent of wages and salaries for state and local  governments to meet their long-term pension obligations. Prior to the  last decade, from 1999 to 2008 the state and local government sector  averaged about an 8 percent contribution rate, which was lower than what  our current simulations show is necessary for meeting pension  obligations. State and local government contributions to employee  pension plans are simulated to decline as a share of GDP, as are wages  and salaries of state and local government employees.

		Growth in Federal Medicaid Grants Drives Revenues

Our simulations suggest that federal grants will increase slightly as a  share of GDP. The largest grant receipts are for Medicaid which will likely  grow more quickly than other types of federal grants making up an  increasing share of revenues in the future (see figure 6). The increase in  Medicaid expenditures simulated during this period will likely put  increasing pressure on both federal and state governments. As a  matching formula grant program, the simulated increase in federal  Medicaid grants implies an expected increase in Medicaid expenditures  that will be shared by state governments. Federal investment grants (i.e.,  grants intended to finance capital infrastructure investments) and other  federal grants unrelated to Medicaid (i.e., grants intended to finance  education, social services, housing, and community investment) are  simulated to decline as a share of GDP.
Further, our simulations suggest that if historical relationships between  state and local governments’ tax revenues and tax bases persist, total tax  revenues for the state and local government sector will increase from 8.7  percent of GDP in 2019 to 9.1 percent of GDP by the end of the  simulation period. As shown in figure 7, the different components of total  tax revenues are simulated to remain fairly consistent or slightly increase.  The simulations suggest that personal income tax revenues will increase  as a share of GDP by about 0.5 percentage points during the simulation  period. Sales tax is expected to decrease by approximately 0.2  percentage points and property taxes are simulated to slightly increase as  a share of GDP through 2068 from 2.73 percent to 2.86 percent.

		Economic Growth and Other Factors Could Affect the Sector’s Fiscal Outlook

Sensitivity Analysis   An analysis using alternative assumptions of  one variable to determine the uncertainty, or  sensitivity, of another variable.
Several factors, or key model variables, could affect the state and local  government sector’s long-term fiscal outlook, including economic growth,  health care excess cost growth, and the rate of return on pension assets.  To see how the outlook changes in response to them, we developed  sensitivity analyses—simulations that use alternative assumptions about  their growth. For each of these key variables we use a baseline  assumption, a higher-than-baseline assumption, and a lower-than- baseline assumption. We determined that these alternative assumptions  highlighted the operating balance’s sensitivity to changes, shifting the  future fiscal outcomes for the sector.

			Economic Growth

Future trends in GDP growth could affect the state and local government  sector’s fiscal outlook. In our simulations, GDP growth is based on the  most recent data from the Congressional Budget Office (CBO) and the  Board of Trustees of the Federal Old-Age and Survivors Insurance and  Federal Disability Insurance Trust Funds (OASDI Trustees) which project  real GDP (adjusted for inflation) to grow by 1.9 percent per year on  average from 2018 through 2029, and by 2 percent per year on average  after 2029. Using these projections, our simulations suggest that  maintaining current policies would cause the sector’s operating balance to  become increasingly negative. Using the OASDI Trustees’ alternative  assumptions of real GDP growth at a faster rate—2.7 percent—suggests  that the operating balance, while remaining negative, would have an  improved outlook compared to the baseline.
While growth in revenue and health care spending is largely tied to GDP  in our simulations, spending for other components is tied to inflation and  population growth and grows more slowly than GDP. As such, increases  in GDP growth improve the sector’s outlook. Our simulations, using the  OASDI Trustees’ alternative assumptions also show that if GDP were to  grow at a slower rate—1.4 percent—the difference between revenues  and expenditures would expand, resulting in an increasingly negative  operating balance (see figure 8).
Excess cost growth in health care is another key determinant of the  sector’s fiscal balance. In our simulations Medicaid spending per capita  grows about 1.8 percent faster than GDP per capita on average for the  period from 2020 through 2029. Medicaid spending per capita grows  about 0.6 percent faster on average from the period from 2030 through  2068. Other health expenditures per capita grow about 0.8 percent faster  than GDP per capita for the period from 2019 through 2068. Using these  projections, our simulations suggest that maintaining current policies will  cause the sector’s expenditures to exceed its revenues and this  difference will become increasingly negative during the next several  decades.
The simulations developed assuming zero excess cost growth in  Medicaid and national health expenditures suggest that spending would  be lower as a share of GDP. The difference between revenues and  expenditures would be significantly less negative than the baseline  simulations around the middle of the simulation period before stabilizing,  but remain negative over the simulation period. In the scenario using the  alternative projections from CMS where excess cost growth rises faster— 0.6 percent on average for Medicaid for the period from 2030 through  2068 and 0.9 percent for national health expenditures for the period  between 2019 through 2068—our simulations show that the difference  between revenues and expenditures would persist for the remainder of  the simulation period (see figure 9).
The rate of return on pension assets could also affect the state and local  government sector’s fiscal outlook. Based on an inflation-adjusted rate of  return on pension assets of 5 percent, our simulations suggest that state  and local governments would need to make pension contributions  equivalent to about 13 percent of employees’ wages and salaries to meet  their long-term pension obligations. The simulations we developed using  a higher rate of return—7.5 percent—suggest that pension contributions  would be about 3 percent of state and local government employees’  wages and salaries to meet pension obligations. Under this scenario,  spending would need to be a lower share of GDP and the sector’s outlook  would improve. The difference between revenues and expenditures would  briefly narrow early on before becoming increasingly negative through the  remainder of the simulation period.
Alternatively, we estimated that if the rate of return on pension assets is  relatively low—2.5 percent—required pension contributions would need to  be about 24 percent of state and local government employees’ wages  and salaries. Under this scenario our simulations show that spending  would be a higher share of GDP and the sector’s outlook would worsen  as the sector’s negative operating balance would continue to grow larger  (see figure 10).
This report was prepared under the direction of Michelle A. Sager,  Director, Strategic Issues, who can be reached at 202-512-6806 or  sagerm@gao.gov, and Oliver M. Richard, Director, Center for Economics,  who can be reached at 202-512-8424 or richardo@gao.gov if there are  any questions. GAO staff who made key contributions are listed in  appendix II.

Appendix I: Objectives, Scope, and Methodology

	Data

This update of the state and local government fiscal model used  aggregate data on the state and local government sector and national  data on other variables from the following sources: the Agency for  Healthcare Research and Quality, Bloomberg, the Board of Governors of  the Federal Reserve System, the Board of Trustees of the Federal Old- Age, Survivors, and Disability Insurance Program (OASDI Trustees),  BEA, the Bureau of Labor Statistics, the Census Bureau, the Centers for  Medicare & Medicaid Services (CMS), the Congressional Budget Office  (CBO), and the Social Security Administration. These data sources are  generally the same data sources we used for our prior update. We used  annual observations on historical data through 2018 where available.

	Objectives and Methodology

This report updates GAO’s state and local fiscal model to simulate the  fiscal outlook for the state and local government sector. This includes  identifying the factors that are likely to contribute to the state and local  sector’s fiscal imbalance. The level of receipts and expenditures for the  state and local government sector as a whole in future years is based on  current and historical spending and revenue patterns. We used Table 3.3  of the National Income and Product Accounts (NIPA)—State and Local  Government Current Receipts and Expenditures—prepared by BEA as an  organizing framework for developing our model, and we simulated state  and local government receipts and expenditures using methods similar to  those we have used in prior updates. Our simulations of real U.S. gross  domestic product (GDP) were consistent with the growth path developed  by CBO for the period from 2019 through 2029 and by the OASDI  Trustees for the period thereafter. Our simulations of U.S. population was  consistent with the growth path developed by the OASDI Trustees, and  our simulations of excess cost growth for national health expenditures  and for Medicaid were consistent with CMS projections, all for the entire  simulation period. Our simulations of other variables, such as the GDP  price index, personal income, and 3-month U.S. Department of the  Treasury (Treasury) rates, were consistent with the growth paths for  these variables developed by CBO for as much of the simulation period  as possible. Otherwise, we developed our own assumptions about the  likely future growth path of the variables in our model. In general, we  assumed that current policies remain in place and that all levels of  government continue to provide services at current per capita levels. A  detailed description of the model is in appendix I of GAO, State and Local  Governments’ Fiscal Outlook: 2018 Update, GAO-19-208SP  (Washington, D.C.: December 2018). We describe below where we  updated equations or added equations to the model. Otherwise our  approach is the same as the approach we used in that update.

		State and Local Government Sector Receipts

We simulated the future growth paths of the following types of state and  local government revenues: current tax receipts, contributions to  government social insurance, income on financial assets, current transfer  receipts, the surplus from government enterprises, and capital transfer  receipts. We also simulated the future growth path of state and local  government long-term debt issuance. We updated some of the equations  we used to simulate tax receipts(see table 1). We also added equations  to simulate current transfers from the rest of the world to state and local  governments, disaster-related insurance benefits to state and local  governments, and other capital transfers to state and local governments,  which we had not included in prior updates. The equations we used to  simulate the other types of receipts are the same as the equations we  used in GAO-19-208SP.
We simulated the future growth paths of the following types of state and  local government expenditures: consumption expenditures, current  transfer payments, interest paid on outstanding state and local  government debt, subsidies, capital outlays, and consumption of fixed  assets (depreciation). We also simulated the future growth path of the  state and local government sector’s net social insurance fund balance.  We updated some of the equations we used to simulate the interest paid  on outstanding state and local government debt (see table 1 above). We  also added equations to simulate current transfer payments to the rest of  the world, which we had not included in prior updates. Otherwise, the  approach we used to simulate expenditures is the same as the approach  we used in GAO-19-208SP.

		State and Local Government Sector Fiscal Balance

Our main indicator of the sector’s fiscal balance is its operating balance  net of funds for capital expenditures (henceforth, operating balance),  which is a measure of the sector’s ability to cover its current expenditures  out of current receipts. Operating balance is defined as total receipts  minus (1) capital outlays not financed by long-term debt issuance, (2)  current expenditures less depreciation, (3) current surplus of state and  local government enterprises, and (4) net social insurance fund balance.
We also estimated the annual changes in spending and in receipts that  our simulations suggest would be needed to maintain the operating  balance equal to zero during the 50-year simulation period, which we  refer to as the “fiscal gap.” As discussed above, our baseline simulations  assume that current policies remain in place and that all levels of  government continue to provide services at current per capita levels. We  then simulated the change in total expenditures needed to maintain the  operating balance equal to zero. To estimate the annual change in  spending needed to maintain balance we calculated the present value of  that change as a percentage of the present value of baseline total  expenditures and as a percentage of the present value of U.S. GDP, all  for a 50-year period. We also calculated the interest and non-interest  expenditure components of the change in total expenditures needed to  maintain balance. We used a similar approach to estimate the annual  change in total receipts needed to maintain balance.

		Sensitivity Analysis

We assessed the sensitivity of our baseline results to alternative  projections of real U.S. GDP growth, health care excess cost growth, and  the real rate of return on state and local government pension fund assets.  Following the same approach we used in GAO-19-208SP, for each of  these variables, we selected an alternative projection associated with  faster growth or rate of return and one associated with slower growth or  rate of return.
Real U.S. GDP. For our baseline simulations, we used CBO  projections of real GDP for the period from 2019 through 2029 and the  OASDI Trustees’ intermediate projections of real U.S. GDP growth for  the years thereafter. For our sensitivity analysis, we used the OASDI  Trustees’ high-cost and low-cost projections.
Health care excess cost growth. For our baseline simulations, we  used CMS’s baseline projection of national health expenditures  excess cost growth and we estimated Medicaid excess cost growth  based on CMS’s baseline projections. For our sensitivity analysis, we  used CMS’s alternative projection of national health expenditures  excess cost growth and we estimated Medicaid excess cost growth  based on CMS’s alternative projections. As another alternative, we  simulated the model assuming both zero excess cost growth for  national health expenditures and Medicaid.  Our simulations used  CBO’s projection of federal spending on Medicaid, CHIP, and  exchange subsidies as a fraction of GDP to simulate certain variables  related to state and local government spending on Medicaid and other  health spending.  This projection incorporates excess cost growth for  the period from 2019 through 2029 but assumes zero excess cost  growth starting in 2030, so we could only vary Medicaid excess cost  growth in the alternative simulations for 2030 and later.
Real rate of return on state and local government pension assets. For  our baseline simulations, we assumed a 5 percent real rate of return  on state and local government pension assets. For our sensitivity  analysis, we used 2.5 percent and 7.5 percent.
Table 2 shows the growth rates or rates of return associated with the  baseline and alternative projections of each variable for the simulation  period.
We simulated the model changing either real U.S. GDP growth, health  care excess cost growth, or the real rate of return on pension assets,  leaving the other variables fixed at their baseline values. Thus, our  sensitivity analysis is in the spirit of a partial equilibrium comparative  statics analysis that sheds light on how each of the individual variables  may affect the state and local government sector’s fiscal outlook.  However, these variables are likely to be correlated, so future changes in  one would likely be associated with changes in others.

	Caveats and limitations

Our approach has a number of limitations and the results should be  interpreted with caution. First, the state and local fiscal model is not  designed for certain types of analyses. The simulations are not intended  to provide precise predictions. Even though we know that these  governments regularly make changes in tax laws and expenditures, the  model essentially holds current policies in place and analyzes the fiscal  future for the sector as if those policies were maintained because it would  be highly speculative to make any assumptions about future policy  adjustments.
In addition, fiscal outcomes related to the sector’s financial position and  solvency may not reflect all aspects of the sector’s “health.” Other  indicators include economic indicators that go beyond the sector’s  financial position to include economic growth, income, or distributional  equity, as well as indicators of the quality of services provided by the  sector, including education, health care, infrastructure, and other public  goods and services.
Finally, our unit of analysis is the state and local government sector as a  whole, so our results provide an assessment of the sector’s fiscal outlook.  However, individual state and local governments likely exhibit significant  heterogeneity in their expenditure and revenue patterns and their fiscal  outlooks will likely differ from the sector as a whole. Nevertheless, it is  informative to assess the overall fiscal outlook because doing so reveals  the outlook for state and local governments as a sector. In addition,  aggregate data on the sector is available on a more timely basis than  data for individual state and local governments, allowing for a better  assessment of the sector’s current fiscal outlook. Our results for the  sector also provide a baseline from which to view the experiences of  individual state and local governments. Finally, assessing the fiscal  outlook of the sector as a whole can help mitigate the tendency to  extrapolate from the most visible, but potentially not representative,  experiences of individual states or localities.

Appendix II: GAO Contacts and Staff Acknowledgments

	GAO Contacts

	Acknowledgments

In addition to the contact named above, Peter Del Toro, Courtney  LaFountain, Melissa Wolf (Assistant Directors), Silvia Symber (Analyst-in- Charge), Shelby Clark, Amalia Konstas, Dylan Stagner, Frank Todisco,  Walter Vance, and Alicia White made significant contributions to this  report.

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