State and Local Governments' Fiscal Outlook: 2018 Update

Fiscal sustainability presents a national challenge shared by all levels of government. Since 2007, GAO has published simulations of long-term fiscal trends in the state and local government sector, which have consistently shown that the sector faces long-term fiscal pressures. While most states have requirements related to balancing their budgets, deficits can arise because the planned annual revenues are not generated at the expected rate, demand for services exceeds planned expenditures, or both, resulting in a near-term operating deficit.

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 components of state and local expenditures likely to contribute to the sector's fiscal pressures. In addition, this report identifies considerations related to federal policy and other factors that could contribute to uncertainties in the state and local government sector's long-term fiscal outlook.

GAO's model uses the Bureau of Economic Analysis's National Income and Product Accounts as the primary data source and presents the results in the aggregate for the state and local sector as a whole. The model shows the level of receipts and expenditures for the sector until 2067, based on current and historical spending and revenue patterns. In addition, the model assumes that the current set of policies in place across state and local government remains constant to show a simulated long-term outlook.

GAO's simulations suggest that the state and local government sector will likely face an increasing difference between revenues and expenditures during the next 50 years as reflected by the operating balance--a measure of the sector's ability to cover its current expenditures out of its current receipts. While both expenditures and revenues are projected to increase as a percentage of gross domestic product (GDP), a difference between the two is projected to persist because expenditures are expected to grow faster than revenues throughout the simulation period.

GAO's simulations also suggest that growth in the sector's overall spending is largely driven by health care expenditures--in particular, Medicaid spending and spending on health benefits for state and local government employees and retirees. These expenditures are projected to grow as a share of GDP during the simulation period. GAO's simulations also suggest that revenues from personal income taxes and federal grants to states and localities will increase during the simulation period. However, revenues will grow more slowly than expenditures such that the sector faces a declining fiscal outlook.

GAO also identified federal policy changes that could affect the state and local government sector's fiscal outlook. For example, the effects of the recently-enacted Tax Cuts and Jobs Act will likely depend on how states incorporate the Act into their state income tax rules. In addition, other factors, such as economic growth and rates of return on pension assets, could shift future fiscal outcomes for the sector.

Fiscal sustainability presents a national challenge shared by all levels of  government. The federal government and state and local governments  share responsibility in fulfilling important national goals and providing  essential services to citizens. State and local governments provide  primary and secondary education, health care, libraries, police and fire  protection services, social programs, roads and other infrastructure,  public colleges and universities, and more. These subnational  governments rely on the federal government for significant portions of  their revenue. Given the nature of the partnership among levels of  government in providing services, understanding potential future fiscal  conditions of the state and local government sector is important for  federal policymaking.

To provide Congress and the public with this broader context, we  developed a fiscal model of the state and local government sector, which  we first reported on in 2007 and have regularly updated since. This  report updates our state and local fiscal model to simulate the fiscal  outlook for the state and local government sector. This includes  identifying the components of state and local expenditures that are likely  to contribute to the sector’s fiscal pressures. This report also identifies  considerations related to federal policy and other factors that could  contribute to uncertainties in the state and local government sector’s  fiscal outlook.

To develop simulations of the fiscal outlook for the state and local  government sector, we use the Department of Commerce’s Bureau of  Economic Analysis’s (BEA) National Income and Product Accounts  (NIPA) as the primary data source and project the level of receipts and  expenditures for the sector until 2067, based on current and historical  spending and revenue patterns. Our model assumes that historical  relationships between taxes and their tax bases persist, that the federal  government continues to provide services to people at a level consistent  with current policies, and that other current policies and relationships  affecting revenues do not change. Our model also assumes that the state  and local government sector continues to provide levels of services to  each resident consistent with current government policies. The model  presents results in the aggregate for the state and local government  sector as a whole, consistent with the nature of the NIPA source data.  Because of this, the fiscal outlook for individual states and localities  cannot be identified.

As part of our simulations, we also assess potential fiscal outcomes for  the sector using an indicator of fiscal balance called the operating  balance. The state and local government sector’s operating balance is a  measure of its ability to cover expenditures in a given year using  revenues collected in the same year. Because subnational governments  are generally required to balance or nearly balance their operating  budgets, the design of our operating balance measure reflects this  characteristic of subnational governments’ operating budgets. For  additional information on the model’s key assumptions, see appendix I.

To identify considerations related to federal policy that could contribute to  uncertainties in the sector’s fiscal outlook, we reviewed recent legislation,  relevant legal precedents, economic literature, our prior reports, and  reports from selected think tanks that analyze aspects of fiscal federalism.  These think tanks include the Nelson A. Rockefeller Institute of  Government, the Pew Charitable Trusts, the Tax Foundation Center for  Federal Tax Policy, and the Urban Institute and Brookings Institution Tax  Policy Center. We also conducted semistructured interviews on federal  and intergovernmental policy issues and related data with associations  representing state government officials, including the National Association  of State Budget Officers, the National Conference of State Legislatures,  the National Association of State Auditors, Comptrollers and Treasurers;  specialists from credit rating agencies and their research divisions, such  as Moody’s Analytics and Standard & Poor’s Global Ratings; and senior officials from the Federal Reserve Bank of Chicago, BEA, and the  Congressional Budget Office (CBO). We selected these organizations  and related reports based on a literature review and our prior work on  state and local government fiscal issues. These interviews primarily  focused on tax- and health-related federal policies because of their  relevance to our simulation results. Specifically, our simulations have  continually suggested that trends in tax revenues and health care  spending have contributed to large and recurring fiscal challenges for the  sector. The policy considerations we identified are not exhaustive. That is,  we did not fully analyze every federal tax- and health-related policy that  could create uncertainties for the state and local government sectors’  fiscal outlook.

Further, to identify other factors that could contribute to uncertainties in  the sector’s fiscal outlook, we developed simulations using alternative  assumptions of the growth of key model variables to describe how the  operating balance measure would respond to changes in the growth  projections of key variables over the simulation period. For additional  information on our methodology for developing these simulations, see  appendix II.

We conducted our work for this model update from February 2018 to  December 2018 in accordance with all sections of our Quality Assurance  Framework that are relevant to our objectives. The framework requires  that we plan and perform the engagement to obtain sufficient and  appropriate evidence to meet our stated objectives, and to discuss any  limitations in our work. We believe that the information and data obtained,  and the analysis conducted, provide a reasonable basis for any findings  and conclusions in this product.

State and Local Governments Will Need to Make Policy Changes to Maintain Long-Term Fiscal Balance

Our simulations suggest that the sector will likely continue to face a  difference between revenue and spending during the next 50 years. This  long-term outlook is measured by the operating balance—a measure of  the sector’s ability to cover its current expenditures out of current  receipts. While both expenditures and revenues are projected to  increase as a percentage of gross domestic product (GDP) during the  simulation period, a difference between the two is projected to persist  because expenditures are generally expected to grow at a faster rate than  revenues. (see figure 1). Absent any policy changes by state and local  governments, revenues are likely to be insufficient to maintain the  sector’s capacity to provide services at levels consistent with current  policies during the next 50 years. Our simulations suggest that state and  local governments will need to make policy changes to avoid fiscal  imbalances before then and assure that revenues are at least equal to  expenditures.

We simulated the state and local government sector’s operating balance  (the difference between the sector’s operating revenues and operating  expenditures) in order to understand the sector’s long-term fiscal outlook.  The sector’s operating expenditures were 15.1 percent of GDP in 2017.  As shown in figure 2, these state and local government sector operating  expenditures are comprised of employee compensation, social benefit  payments, interest payments, capital outlays, and other expenditures.  The sector’s operating revenues were 13.8 percent of GDP in 2017. As  shown in figure 3, these state and local government sector operating  revenues are comprised of taxes, transfer receipts, and other types of  revenues.

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 actions—such as revenue increases  or expenditure reductions—that must be taken today and maintained for  each year going forward to achieve fiscal balance during the simulation  period. While we measured the gap as the amount of reductions in  expenditures needed to prevent negative operating balances, increases  in revenues, reductions in expenditures, or a combination of the two of  sufficient magnitude would allow the sector to close the fiscal gap.

Our simulations suggest that the fiscal gap is about 14.7 percent of total  expenditures or about 2.4 percent of GDP. That is, assuming no change  in projected total revenues, eliminating the difference between the  sector’s expenditures and revenues during the 50-year simulation period  would likely require action to be taken today, and maintained for each  year equivalent to a 14.7 percent reduction in the sector’s total  expenditures (see figure 4). Alternatively, assuming no change in  projected total expenditures, closing the fiscal gap by increasing revenue  would also likely require actions of similar magnitude. More likely,  eliminating the difference between expenditures and revenues would  involve some combination of spending reductions and revenue  increases.

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

Medicaid and Employee Health Benefits Are Key Drivers of Long-Term Spending

Our simulations suggest that growth in the sector’s overall spending is  largely driven by health care expenditures. As shown in figure 5, these  expenditures are projected to increase from about 4.1 percent of GDP in  2018 to 6.3 percent of GDP in 2067. Two types of health care  expenditures—Medicaid spending and spending on health benefits for  state and local government employees and retirees—will likely constitute  a growing expenditure for state and local governments during the  simulation period. Medicaid expenditures are expected to rise, on  average, by 1 percentage point more than GDP each year.

According to CBO, growth in Medicaid spending reflects growth in both  the number of people receiving Medicaid benefits and the cost of  Medicaid benefits each person receives. Specifically, CBO reported that  between 2019 and 2028, Medicaid spending is projected to grow at an  average rate of 5.5 percent per year—nearly 5 percentage points of this  growth is due to an increase in per capita costs and about 1 percentage  point of this growth is due to an increase in enrollment. Data from CBO  and the Centers for Medicare & Medicaid Services (CMS) also suggest  that growth in Medicaid spending per capita is generally expected to  outpace GDP growth in the future—referred to as excess cost growth.  Our estimates of Medicaid excess cost growth using CMS data suggest  that Medicaid spending per capita will grow, on average, about 0.5  percent faster than GDP per capita for the period from 2018 through  2067.

Our simulations also suggest that health benefits for state and local  government employees and retirees—a type of employee compensation  spending—are likely to rise, on average, by 0.9 percentage points more  than GDP each year. Growth in these health benefits also reflects growth  in the projected number of employees and retirees and growth in the  projected amount of health benefits for each employee and retiree.  Growth in spending by states and local governments on health care per  capita, which includes spending on employee and retiree health benefits,  is generally expected to outpace GDP per capita. Data from CMS suggest  that national health expenditures per capita are likely to grow on average  about 0.8 percent faster than GDP per capita each year during the  simulation period from 2018 through 2067. If employee and retiree health  benefits follow trends in overall national health spending, they will likely  make up an increasingly large share of total employee compensation  going forward (see figure 6).

While state and local government contributions to employee pension  plans—another type of employee compensation spending—will likely  decline as a percentage of GDP, as shown in figure 6, our simulations  nonetheless suggest that state and local governments may need to take  steps to manage their pension obligations in the future. From 1998  through 2007, state and local governments’ pension contributions  amounted to about 8 percent of wages and salaries on average. In  addition, for the period from 2008 through 2017, pension contributions  amounted to about 12.3 percent of wages and salaries on average. Our  simulations suggest that those pension contributions will need to be about  12.9 percent of wages and salaries for state and local governments to  meet their long-term pension obligations. This is the case even though  pension asset values have increased in recent years, from about $2.4  trillion in 2008 to about $4.2 trillion in 2017 (adjusted for inflation and  measured in 2012 dollars). This suggests that state and local  governments may need to take additional steps to manage their pension  obligations by reducing benefits or increasing employees’ contributions.

Along with pension contributions, other types of state and local  government expenditures are projected to grow more slowly than GDP.  For example, in 2017, wages and salaries of state and local government  employees constituted a large expenditure for the sector. However, these  expenditures are projected to decline as a percentage of GDP during the  simulation period. Our simulations also suggest that state and local  governments’ capital outlays—which include spending on infrastructure,  such as buildings, highways and streets, sewer systems, and water  systems, as well as equipment and land— will grow more slowly than  GDP if state and local governments continue to provide current levels of  capital per resident.

Growth in Medicaid Grants and Personal Income Taxes Drive Revenues

Our simulations suggest that federal grants overall will increase as a  share of GDP, while Medicaid grants will likely grow more quickly than  other types of federal grants (see figure 7). Thus, Medicaid grants will  likely make up an increasing share of revenues in the future. Since  Medicaid is a matching formula grant program, the projected increase in  federal Medicaid grants, therefore, reflects expected increased Medicaid  expenditures that will be shared by state governments. Our simulations  also suggest that 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 likely 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 8.8  percent of GDP in 2018 to 9.4 percent of GDP by the end of the  simulation period. This increase is driven largely by the growth in  personal income taxes, as shown in figure 8. Specifically, our simulations  suggest that personal income tax revenues will increase as a share of  GDP by about 1 percentage point during the simulation period. Sales  taxes and property taxes, on the other hand, are projected to remain  relatively constant as a share of GDP during the simulation period  through 2067.

Policy Changes and Other Considerations Could Affect the State and Local Government Sector’s Fiscal Outlook

While our long-term simulations do not account for pending or future  federal policy changes that will result in changes to expenditures and  revenues, an understanding of several recent federal policy changes  related to taxes and health care are important to note because they  present sources of uncertainty for the state and local government sector’s  long-term fiscal outlook. In addition, as is the case in any model that is  reliant on historical data to simulate a long-term outlook, other  considerations, such as economic growth and rates of return on pension  assets, could shift future fiscal outcomes. These policy changes and  uncertainties are discussed below and may help federal policy makers  and state and local governments consider how these changes could  affect the long-term outlook.

Tax-and Health-Related Policies Could Affect the Sector’s Long-Term Fiscal Outlook

Recently enacted legislation, such as Public Law 115-97, commonly  referred to by the President and administrative documents as the Tax  Cuts and Jobs Act (TCJA), could affect the sector’s revenues over the  long-term. Enacted in December 2017, TCJA included significant  changes to corporate and individual tax law, with implications for state  and local government tax collections. In particular, for individual  taxpayers, for tax years 2018 through 2025, tax rates were lowered for  nearly all income levels, some deductions from taxable income were  changed (personal exemptions were eliminated, while the standard  deduction was increased), and certain credits, such as the child tax credit,  were expanded.

The effect of TCJA on the long-term state and local fiscal outlook is still  evolving, and will likely depend on how states incorporate the law’s  changes into their state income tax rules. That is, because some states  link their state income taxes to federal income tax rules, states must  decide whether to let the changes from TCJA flow through to their state  income tax systems, or establish new state income tax rules. For  example, some states have adopted the federal definition of taxable  income as a starting point for state tax calculations, while other states use  the federal definition of adjusted gross income as a starting point. The  choices states make to continue to link to these definitions could have  long-term implications for their state tax revenues. In addition, under  TCJA, the amount of the federal itemized deductions allowed for all state  and local income, sales, and property taxes (commonly referred to as the  state and local tax (SALT) deduction) is now capped at $10,000 for tax  years 2018 to 2025. The magnitude or net effect of these changes is  uncertain in that states are still working to understand the impact of the  tax laws on their revenues. It remains to be seen whether and how states  will see changes in their revenues in the future.

Moreover, a recent U.S. Supreme Court decision involving state sales  taxes could have implications for states’ ability to collect revenue.  Specifically, the court’s ruling in June 2018 in South Dakota v. Wayfair,  Inc. held that states could require out-of-state sellers to collect and remit  sales taxes on purchases made from those out-of-state sellers, even if  the seller does not have a substantial physical presence in the taxing  state. Prior to this ruling, a seller that did not have a substantial physical  presence in a state could not be required to collect and remit a sales tax  on goods sold into the state. Instead, a purchaser may have been  required to pay a use tax (i.e., a tax levied on the consumer for the  privilege of use, ownership, or possession of taxable goods and services)  in the same amount to his or her state government. In 2017, we  reported that states could realize between an estimated $8.5 billion and  $13.4 billion in additional state sales tax revenue across all states if all  sellers were required to collect taxes on all remote sales at current  rates. The extent to which states realize changes in sales tax revenue  will likely depend on how they revise their state laws and enforcement  efforts in response to this June 2018 ruling.

Enacted health care legislation could also affect the long-term fiscal  position of state and local governments. As we have reported in prior  work, the effect of the Patient Protection and Affordable Care Act  (PPACA) on the long-term state and local fiscal outlook could depend on  how states implement PPACA, and on future rates of health care cost  growth. For example, consider the states that have opted, under  PPACA, to expand Medicaid program coverage to millions of lower  income adults. While the federal government is expected to cover a large  share of the costs of the Medicaid expansion, these states are ultimately  expected to bear some of the costs. Specifically, the federal government  reimbursed 100 percent of the costs of the expanded population  beginning in 2014. This reimbursement rate will decline from the 2018  reimbursement rate of 94 percent to 90 percent by 2020. As such, the  reduced federal reimbursement rate may affect those states that  expanded their Medicaid populations in recent years.

As discussed earlier in this report, our simulations suggest that Medicaid  spending will make up an increasing share of the state and local  government sector’s operating expenditures in the future. A weakening of  the economy could add to the fiscal pressures states face in funding  these Medicaid obligations. As our prior work has shown, past recessions  in 2001 and 2007 hampered states’ ability to fund increased Medicaid  enrollment and maintain their existing services. Specifically, Medicaid  enrollment increased during these recessions, in part due to increased  unemployment, which led more individuals to become eligible for the  program.

We have also reported on the use of Medicaid demonstrations, which  allow states to test new approaches to coverage to improve quality and  access, or generate savings or efficiencies. Specifically, CMS may  waive certain Medicaid requirements and approve new types of  expenditures that would not otherwise be eligible for federal Medicaid  matching funds. For example, under demonstrations, states have  extended coverage to certain populations, provided services not  otherwise eligible for Medicaid, and made payments to providers to  incentivize delivery system improvements. We previously reported that,  as of November 2016, nearly three-quarters of states have CMS- approved demonstrations. In fiscal year 2015, federal spending under  demonstrations represented a third of all Medicaid spending nationwide.  We also reported that in 10 states, federal spending on demonstrations  represented 75 percent or more of all federal spending on Medicaid.  Joint financing of Medicaid is a fixture of this federal-state partnership.  Demonstration waivers hold the potential for changing state Medicaid  spending. However, as we have reported, these demonstrations are  required, under HHS policy, to achieve budget neutrality and not raise  costs for the federal government.

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

In addition to federal tax- and health-related policy changes, a number of  other factors could affect the state and local government sector’s long- term fiscal outlook. Specifically, we developed simulations using  alternative assumptions of the growth of key model variables—which  include economic growth, health care excess cost growth, and the rate of  return on pension assets. We determined that changes in the growth  projections of these key variables could affect the operating balance of  state and local governments, thereby shifting future fiscal outcomes for  the sector.

Future trends in GDP growth could affect the state and local government  sector’s fiscal outlook. Data from CBO and the Board of Trustees of the  Federal Old-Age and Survivors Insurance and Federal Disability  Insurance Trust Funds (OASDI Trustees) project real GDP to grow by 1.9  percent per year on average from 2018 through 2028, and by 2.1 percent  per year on average after 2028, respectively. Using these projections,  our simulations suggest that maintaining current policies would cause the  sector’s expenditures to exceed its revenues and that the difference  between revenues and expenditures would become increasingly negative  during the next several decades. However, simulations we developed  using the OASDI Trustees’ alternative projections of real GDP growth  suggest that the difference between revenues and expenditures would  expand before narrowing toward the end of the simulation period if real  GDP were to grow at a faster rate—2.8 percent per year on average—as  shown in figure 9. Our simulations also show that if GDP were to grow at  a slower rate—1.5 percent per year on average—the difference between  revenues and expenditures would expand. This would result in an  increasingly negative operating balance during the simulation period.

As discussed earlier in this report, excess cost growth in health care is  another key determinant of the sector’s fiscal balance. Data from CBO  project Medicaid spending per capita to grow about 1.5 percent faster  than GDP per capita on average for the period from 2019 through 2028.  Data from CMS project Medicaid spending per capita to grow about 0.6  percent faster on average for the period from 2029 through 2067. Data  from CMS also project national health expenditures per capita to grow  about 0.8 percent faster than GDP per capita for the period from 2018  through 2067. Using these projections, our simulations suggest that  maintaining current policies will cause the sector’s expenditures to  exceed its revenues, and that the difference between revenues and  expenditures will become increasingly negative during the next several  decades. However, simulations developed using alternative projections of  excess cost growth in Medicaid and national health expenditures suggest  that the difference between revenues and expenditures may be reduced  but not eliminated within the simulation period if excess cost growth in  health care is zero. In the scenario where excess cost growth rises  faster—0.7 percent on average for Medicaid for the period from 2029  through 2067 and 1 percent for national health expenditures for the period  from 2018 through 2067—our simulations show that the difference  between revenues and expenditures will persist for the remainder of the  simulation period (see figure 10).

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 will need to make pension contributions equivalent  to about 12.9 percent of their wages and salaries to meet their long-term  pension obligations. However, this estimate is sensitive to the rate of  return on state and local governments’ pension assets. Simulations we  developed using a higher rate of return—7.5 percent—suggest that  pension contributions needed to meet pension obligations would be about  3 percent of state and local government employees’ wages and salaries.  In addition, under this scenario, our simulations suggest that the  difference between revenues and expenditures will be reduced, but not  eliminated within the simulation period. Alternatively, we estimated that if  the rate of return on pension assets is relatively low—at 2.5 percent— required pension contributions would need to be about 23 percent of state  and local government employees’ wages and salaries during the  simulation period. Under this scenario, our simulations show that the  sector’s negative operating balance will continue to grow larger  throughout the simulation period. It follows therefore, that high rates of  return on pension assets are associated with an improved outlook for  state and local governments, and vice versa (see figure 11).

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 to this report are  listed in appendix IV.

Appendix I: State and Local Government Fiscal Model Simulation Methodology

To simulate measures of fiscal balance for the state and local government  sector for the long term, we used aggregate data on the state and local  government sector and national data on other variables from the following  sources:

Agency for Healthcare Research and Quality;

Board of Governors of the Federal Reserve System;

Board of Trustees of the Federal Old-Age and Survivors Insurance  and Federal Disability Insurance Trust Funds (OASDI Trustees);

Centers for Medicare & Medicaid Services (CMS);

Our approach generally follows the approach used in GAO-08-317 and in  subsequent updates of that report. Specifically, we developed a model  that projects the levels of receipts and expenditures of the state and local  government sector (henceforth, the sector) in future years based on  current and historical spending and revenue patterns. We use table 3.3 of  the National Income and Product Accounts (NIPA)—State and Local  Government Current Receipts and Expenditures—prepared by BEA at  the U.S. Department of Commerce as an organizing framework for  developing our model of the sector’s revenues and expenditures (see  table 1). In this table, current revenues are grouped in five main  categories.

Current tax receipts. These receipts are tax payments made by  persons or businesses to state and local governments. They include  income taxes, general sales taxes, property taxes, and excise taxes.  Current taxes also include fees for motor vehicle licenses, drivers’  licenses, and business licenses.

Social insurance contributions. These contributions finance the  provision of certain social benefits to qualified persons, and include  contributions from employers and employees for temporary disability  insurance, worker’s compensation insurance, and other programs.

Income receipts from government assets. These receipts include  interest, dividends, and rental income, such as royalties paid on  drilling on the outer continental shelf. Also, state and local  governments earn interest and dividend income on financial assets.

Current transfer receipts. Transfer receipts are receipts for which state  and local governments provide nothing of value in return. Current  transfer receipts include federal grants, fines, fees, donations, and  tobacco settlements. Also included are net insurance settlements,  certain penalty taxes, court fees, and other miscellaneous transfers.

Current surplus of government enterprises. This surplus is a profit- type measure for state and local government enterprises, such as  water, sewer, gas, and electricity providers; toll providers; liquor  stores; air and water terminals; public transit; and state lotteries.  Some types of enterprises, such as state lotteries, consistently earn  surpluses which are used to fund general government activities. In  contrast, many enterprises run deficits, which, in turn, reduce receipts.

State and local governments also receive income from the sale of goods  and services, such as school tuition. In the NIPAs, this income is treated  as an offset against expenditures, not revenue. This income comes from  voluntary purchases that might have been made from a private sector  provider of such services.

In addition to current receipts, state and local governments receive capital  transfer receipts. These receipts include estate and gift taxes, and federal  government investment grants for capital such as highways, transit, air  transportation, and water treatment plants.

State and local government current expenditures are grouped into four  main categories.

Consumption expenditures. Generally, spending for which some value  is provided in return. State and local government consumption  spending is the sum of inputs used to provide goods and services,  including compensation of general government employees,  consumption of general government fixed capital (depreciation), and  intermediate goods and services purchased, less sales to other  sectors and own-account investment.

Current transfer payments. Payments for which nothing of value is  provided in return. For state and local governments, current transfer  payments consist primarily of social benefits, which are payments to  persons to provide for needs that arise from circumstances such as  sickness, unemployment, retirement, and poverty. There are two  kinds of social benefits—benefits from social insurance funds, such as  temporary disability insurance and workers’ compensation, and other  social benefits, such as medical benefits from Medicaid and the state  Children’s Health Insurance Program (CHIP), family assistance from  Temporary Assistance to Needy Families, education assistance, and  other public assistance programs. While NIPA table 3.3 also includes  other current transfer payments to the rest of the world as part of  current transfer payments, these amounts are generally equal to zero.

Interest payments. These include actual and imputed interest and  represent the cost of borrowing by state and local governments to  finance their capital and operational costs.

Subsidies. State and local government subsidies are largely payments  to railroads.

State and local government spending also includes gross investment,  capital transfer payments, and net purchases of nonproduced assets.  Gross investment is spending on capital goods like structures, equipment,  and intellectual property—items that are called fixed assets or capital  because of their repeated or continuous use in providing government  services for more than 1 year. Structures include residential and  commercial buildings, highways and streets, sewer systems, and water  systems. State and local government capital transfer payments include  disaster-related insurance benefits paid to the U.S. territories and the  Commonwealths of Puerto Rico and Northern Mariana Islands. Net  purchases of nonproduced assets are composed of net purchases of land  less oil bonuses (payments to states for the long-term rights to extract  oil).

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 revenues. The operating balance is defined as total receipts  minus (1) capital outlays not financed by medium- and long-term debt  issuance, (2) total current expenditures less depreciation, (3) current  surplus of state and local government enterprises, and (4) net social  insurance fund balance.

Alternative indicators of fiscal balance include net saving and net lending  or borrowing.

Net saving is the difference between current receipts and current  expenditures. Since current expenditures exclude capital investment  but include a depreciation measure, net saving can be thought of as a  measure of the extent to which governments are covering their current  operations from current receipts.

Net lending or borrowing is the difference between total receipts and  total expenditures, and is analogous to the federal unified surplus or  deficit. Total receipts differ from current receipts because they include  capital transfer receipts. Total expenditures differ from current  expenditures because they include capital investment, capital transfer  payments, and net purchases of nonproduced assets. However, they  exclude fixed capital consumption. The former three categories are  cash expenditures, while the latter is a noncash charge. Net lending  or net borrowing represents the governments’ cash surplus or  borrowing requirement. This measure is normally negative because  governments borrow to finance their capital investment (and  sometimes to finance current operations as well).

The following equations describe how we simulated state and local  government receipts and expenditures, as well as the intermediate  variables used in those simulations. For this update, we started with  historical data for 2017, or the most recent year available, and then  simulated each variable for each year from 2018 through 2092 (the  simulation period).

National Demographic, Macroeconomic, and Health Care Variables

To simulate state and local government receipts and expenditures, we  use simulations of various national-level demographic, macroeconomic,  and health care variables derived from projections produced by CBO,  CMS, and the OASDI Trustees, and otherwise derived using our own  assumptions (see table 2). This approach is similar to the approach we  have used in prior model updates.

State and Local Government Defined Benefit Pension Contribution Rate

To simulate state and local government spending on defined benefit  pensions, we first estimate the contribution rate (as a fraction of state and  local government general government wages and salaries) that state and  local governments would need to make each year going forward to  ensure that their pension systems are fully funded on an ongoing basis.  Our goal is to estimate the financial commitments to employees that have  been and are likely to continue to be made by the state and local sector to  better understand the full fiscal outlook for the sector. As such, our  analysis projects the liabilities that the sector is likely to continue to incur  in the future based on simulations of future numbers of retirees receiving  pension benefits and their benefit amounts; future numbers of employees,  their wages and salaries, and their pension contributions; and assets in  state and local government defined benefit pension funds. Although we  are only interested in applying contribution rates over the simulation time  frame, we actually have to derive the contribution rate for a longer time  frame in order to find the steady-state level of necessary contributions.  This longer time frame is required because the estimated contribution rate  increases as the projection horizon increases and eventually converges  to a steady state. If the projection period is of insufficient length, the  steady-state level of contribution is not attained, and the necessary  contribution rate is understated. We simulated variables used to estimate  the pension contribution rate using the approach summarized in table 3.

This approach is similar to the approach we have used in prior model  updates.

Future growth in the number of state and local government retirees— many of whom will be entitled to pension and health care benefits—is  largely driven by the size of the workforce in earlier years. We simulated  the number of state and local government retirees by assuming that the  growth rate in the number of retirees is a weighted average of the growth  rates in lagged general government and government enterprise  employment. We estimated the weights using a regression of the percent  change in the number of retirees on the percent change in employment 1,  6, 11, 16, 21, 26, 31, 36, and 41 years in the past. The coefficients on the  past percentage changes in employment were constrained to be non- negative and to sum to 1. For this regression, we removed cyclical swings  in employment using the Hodrick-Prescott filter.

Similarly, future changes in the real amount of pension benefits will be a  function of past changes in real wages and salaries. As indicated in table  3, we used a weighted average of past values of the state and local  government employment cost index to simulate the employment cost  index for state and local government retirees. We chose the weights to  reflect changes in the share and average real benefit level of three  subsets of the retiree population over time: (1) new retirees entering the  beneficiary pool, (2) deceased retirees leaving the pool, and (3)  continuing retirees from the previous year.

We simulated the weight for new retirees in a year as the number of  retirees less the number of continuing retirees divided by the number  of retirees.

We simulated the weight for deceased retirees as the mortality rate  multiplied by last year’s retirees divided by this year’s retirees.

We simulated the weight for continuing retirees as last year’s retirees  divided by this year’s retirees.

Finally, we simulated the employment cost index for state and local  government retirees as the sum of the weight on new retirees multiplied  by the state and local government employment cost index and the weight  on continuing retirees multiplied by the state and local government  employment cost index 8 years prior, less the weight on deceased  retirees multiplied by the state and local government employment cost  index 21 years prior.

As discussed above, we started with historical data for 2017, or the most  recent year available, simulated all of the variables in table 3 over the  long run, and then used the consumer price index (CPI) and the real  return on pension assets to calculate the total present value of wages and  salaries for state and local government general government and  government enterprise employees, the total present value of real pension  benefits paid to state and local government retirees, and the total present  value of state and local government employees’ pension contributions.  Then, we calculated the total present value of state and local  governments’ pension liabilities as the total present value of real pension  benefits paid to state and local government retirees less the total present  value of state and local government employees’ pension contributions,  and the value of assets in state and local government defined benefit  pension funds in 2017. Finally, we estimated state and local governments’  pension contribution rate as the ratio of the total present value of their  pension liabilities to the total present value of wages and salaries for state  and local government employees.

Interest Rates on State and Local Government Financial Assets and Liabilities

Table 4 summarizes the approach we used to simulate interest rates on  state and local government financial assets and liabilities. This approach  is similar to the approach we have used in prior model updates.

Table 5 summarizes our approach to simulating state and local  government receipts. This approach is similar to the approach we have  used in prior model updates.

These variables track state and local government receipts in table 1  above as follows:

State and local government personal income tax revenue is the sum  of state personal income tax revenue and local personal income tax  revenue;

State and local government personal tax revenue is the sum of  personal income tax revenue and other personal tax revenue;

State and local government revenue from taxes on production and  imports is the sum of general sales tax revenue, excise tax revenue,  property tax revenue, and revenue from other taxes on production and  imports;

State and local government current tax revenue is the sum of  personal tax revenue, revenue from taxes on production and imports,  and corporate income tax revenue;

State and local government current transfer receipts are equal to  federal Medicaid grants minus Medicare Part D payments to the  federal government, plus other federal grants (excluding investment  grants), transfer receipts from businesses, and transfer receipts from  persons;

State and local government current receipts are the sum of current tax  revenue, current transfer receipts, income on assets, social insurance  contributions, and government enterprise surplus;

State and local government capital transfer receipts are the sum of  federal investment grants and estate and gift tax revenue; and

State and local government total receipts are the sum of current  receipts and capital transfer receipts.

Our general approach to simulating state and local government  expenditures is to assume that state and local governments maintain the  current level of public goods and services provision per capita (see table  6). Thus, we generally assume that expenditures keep up with U.S.  population growth and some measure of inflation, where the relevant rate  of inflation varies depending on the specific type of expenditure. However,  we use alternative approaches—described below—to simulate  depreciation, interest payments, and social benefits for health care. This  approach is similar to the approach we have used in prior model updates.

These variables correspond to state and local government expenditures  in table 1 as follows:

Employee compensation is the sum of wages and salaries, pension  contributions, health benefits for current employees, health benefits  for retirees, and other compensation, for state and local government  general government employees.

Consumption expenditures are the sum of employee compensation,  general government fixed capital consumption, and other general  government consumption expenditures.

Social benefit payments are the sum of Medicaid benefits, non- Medicaid health benefits, and non-health social benefits.

Current expenditures are the sum of consumption expenditures, social  benefit payments, interest payments, and subsidy payments.

Total expenditures are the sum of current expenditures, gross  investment, capital transfer payments, and purchases of nonproduced  assets, minus general government and government enterprise fixed  capital consumption.

State and Local Government Financial Assets and Liabilities

Table 7 summarizes our approach for simulating state and local  government financial assets and liabilities. This approach is similar to the  approach we have used in prior model updates.

Our method for simulating the sectors’ short-term debt outstanding  leverages the fact that for any entity, there is a direct relationship between  budget outcomes and changes in financial position. Specifically, if  expenditures exceed receipts, the gap needs to be financed by some  combination of changes in financial assets and changes in financial  liabilities. If governments spend more than they take in, they must pay for  it by issuing debt, cashing in assets, or some combination of the two.  Conversely, if receipts exceed expenditures and the sector is a net  lender, its net financial investment (the net change in financial assets  minus the net change in financial liabilities) must equal the budget  surplus. The relationship between budget outcomes and the sector’s  financial position is shown in the following accounting identity:  total receipts – total expenditures = change in financial assets –  change in financial liabilities.

The sector’s financial liabilities include short-, medium-, and long-term  debt; trade payables; and loans from the federal government, so the  accounting identity can be rewritten as follows:  total receipts – total expenditures = change in financial assets –  change in medium- and long-term debt – change in trade payables –  change in federal government loans – change in short term debt.

For a given difference between total receipts and total expenditures,  various combinations of changes in financial assets and changes in  financial liabilities can satisfy this identity. However, we assumed that  financial assets grow at the same rate as U.S. GDP, that medium- and  long-term debt outstanding is determined using the historical relationship  described in table 7, that federal government loans to state and local  governments are determined using the historical relationship described in  table 7, and that trade payables grow at the same rate as other state and  local government consumption spending. If the first four terms on the right  hand side of the identity are already determined, then only the fifth term— the change in short-term debt—is free to satisfy this identity.

State and Local Government Fiscal Balance

As discussed above, our indicators of fiscal balance are operating  balance, net saving, and net lending or borrowing. This approach is  similar to the approach we have used in prior model updates. Recall that  we defined operating balance as follows:  operating balance = total receipts – (gross investment + capital  transfer payments + net purchases of nonproduced assets – medium-  and long-term debt issuance) – (current expenditures – consumption  of general government fixed assets) – current surplus of state and  local government enterprises – net social insurance fund balance.

By substituting for total receipts and current expenditures using the  relationships described above and rearranging terms, we can also  calculate operating balance using a formula that more easily identifies its  revenue components—the items in the first set of parentheses—and  expenditure components—the items in the second set of parentheses:  operating balance = (current tax revenues + estate and gift tax  revenues + social insurance fund contributions + income receipts from  assets + current transfers + federal investment grants + medium- and  long-term debt issuance) – (compensation of general government  employees + social benefit payments + interest payments + gross  investment + capital transfer payments + net purchases of  nonproduced assets + other general government consumption  expenditures + subsidy payments + net social insurance fund  balance).

Some of our simulations are based on estimated historical relationships  between pairs of variables:

Elasticity of real personal consumption expenditures less food and  services with respect to real wages and salaries;

Elasticity of the real U.S. market value of real estate with respect to

Relationship between effective interest rates on financial assets and

Relationship between state and local government bond yields and 10- year Treasury rates;

Relationship between effective interest rates on long-term state and  local government debt and federal government loans and state and  local government bond yields;

Elasticity of real state personal income tax revenue with respect to

Elasticity of real state and local government excise tax revenue with  respect to real wages and salaries;

Relationship between long-term debt issuance as a fraction of gross  investment and nonproduced asset purchases in excess of federal  investment grants and the change in state and local government bond  yields; and

Relationship between real federal government lending to state and  local governments and real U.S. GDP.

To estimate each of these historical relationships, we used the following  approach: first, we assessed the order of integration of both variables  using unit root tests of the levels and the first differences, where a  variable is integrated of order 0 (I(0) or stationary) if we rejected the null  hypothesis of a unit root in the levels at standard significance levels, and  is integrated of order 1 (I(1) or first-order nonstationary) if we could not  reject the null hypothesis of a unit root in the levels but we could do so for  the first differences. For relationships between variables that were both  stationary, we estimated an autoregressive distributed lag model,  where y is the dependent variable, x is the independent variable, and ε is  an independent, identically distributed error term. The long-run impact on  y of a one unit change in x is given by ∑ .

We initially chose the number of lags based on the Bayesian Information  Criteria and then added additional lags of the dependent variable, if  needed, until the residuals were consistent with a white noise process at  standard significance levels. For relationships between variables that  were both first-order nonstationary, we used the same approach but also  used the Pesaran, Shin, and Smith bounds test for the existence of a  cointegrating (long-run equilibrium) relationship. We concluded that the  variables were cointegrated if we rejected the null hypothesis of no  relationship at standard significance levels. Tables 8 and 9 summarize  the estimated regression models as well as the results of the unit root,  white noise, and cointegration tests.

Indicators of Fiscal Balance for the State and Local Government Sector

We simulated the model for the 75-year period from 2018 through 2092,  and we used the results to calculate the operating balance for the state  and local government sector as a percentage of U.S. GDP. Our results  suggest that if the sector maintains current policy and continues to  provide current per capita levels of public goods and services, then its  operating balance will decline from about -1 percent of U.S. GDP to about  -3 percent of U.S. GDP over the next 50 years.

To shed light on how maintaining the operating balance at or above zero  would affect the state and local government sector, we used the model to  simulate the level of total expenditures that would keep the operating  balance greater than or equal to zero. We then calculated the difference  between the present value of total expenditures simulated assuming the  sector maintains balance, and the present value of total expenditures  simulated assuming the sector maintains current policies, both as a  percentage of the present value of total expenditures assuming the sector  maintains current policies, and as a percentage of the present value of  U.S. GDP. We calculated all of the present values for the 50-year period  from 2018 through 2067, and we used a discount rate equal to the  average of the 3-month Treasury rate and the 10-year Treasury rate for  each year. Our results suggest that the difference between the present  value of total expenditures that maintain balance and the present value of  total expenditures that maintain current policies is about -14.7 percent of  the present value of total expenditures that maintain current policies, or  about -2.4 percent of the present value of U.S. GDP. That is, our  simulations suggest that maintaining balance would require the sector to  spend about 14.7 percent less than it would spend each year to maintain  current policies. We note that a similar exercise based on simulating total  revenues required to maintain the operating balance at or above zero  would generate a similar result.

Our approach has a number of limitations and the results should be  interpreted with caution:

The state and local government 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 to tax laws and expenditures, the model  essentially holds current policy 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.

Fiscal outcomes, as related to the state and local government sector’s  financial position and solvency, may not reflect all aspects of the  sector’s fiscal 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.

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, so  their fiscal outlooks will likely differ from that for the sector.  Nevertheless, it is informative to assess the overall fiscal outlook of  the sector because doing so reveals the outlook for the average state  or local government. In addition, aggregate data on the sector are  available on a more timely basis than data for individual state and  local governments. This allows 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: State and Local Government Fiscal Model Alternative Simulations

Our baseline approach to simulating the fiscal outlook for the state and  local government sector is described in appendix I. As part of our  simulation approach, we used five variables with values for the simulation  period—the period from 2018 through 2092—that are projected outside  the model and that do not rely on maintaining historical relationships: U.S.  population, real U.S. gross domestic product (GDP) growth, national  health care excess cost growth, Medicaid excess cost growth, and the  real rate of return on pension assets.

U.S. population. For our baseline simulations, we used the Board of  Trustees of the Federal Old-Age and Survivors Insurance and Federal  Disability Insurance Trust Funds’ (OASDI Trustees) intermediate  population projections.

Real U.S. GDP. For our baseline simulations, we projected real U.S.

GDP to grow at the same rate as Congressional Budget Office (CBO)  projections for the period from 2018 through 2028 and to grow at the  same rate as the OASDI Trustees’ intermediate projections of real  U.S. GDP growth for the period from 2029 through 2092.

National health expenditures excess cost growth. For our baseline  simulations, we used Centers for Medicare & Medicaid Services’  (CMS) baseline projection of national health expenditures excess cost  growth.

Medicaid excess cost growth. For our baseline simulations, for the  period from 2029 through 2092, we used Medicaid excess cost growth  derived from CMS’s baseline projections.

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.

To assess the sensitivity of our results to changes in these baseline  projections, we selected two alternative projections of each of these  variables, one associated with a faster growth rate or rate of return and  one associated with a slower growth rate or rate of return.

U.S. population. For our alternative simulations, we used the OASDI  Trustees’ high cost and low cost population projections.

Real U.S. GDP. For our alternative simulations, we used the OASDI  Trustees’ high cost and low cost projections of real U.S. GDP growth.

National health expenditures excess cost growth. For our alternative  simulations, we used CMS’s alternative projection of national health  expenditures excess cost growth. As another alternative, we  simulated the model assuming excess cost growth for national health  expenditures is zero.

Medicaid excess cost growth. For our alternative simulations, for the  period from 2029 through 2092, we used Medicaid excess cost growth  derived from CMS’s alternative projections for the period from 2029  through 2092. As another alternative, we simulated the model  assuming Medicaid excess cost growth is zero for the period from  2029 through 2092.

Real rate of return on state and local government pension assets. For  our sensitivity analysis, we used real rates of return of 2.5 percent and  7.5 percent.

Table 10 shows the average annual growth rate or rate of return  associated with the baseline and alternative projections of each variable  for the simulation period.

For our simulations based on alternative assumptions about U.S.  population growth and real U.S. GDP growth, as well as simulations  based on alternative assumptions about real pension asset returns, we  simulated the model changing one variable at a time and leaving the  others fixed at their baseline values. For example, for one simulation we  used the slower assumption for real U.S. GDP growth and the baseline  assumptions for all other variables. For our simulations based on  alternative assumptions about excess cost growth for national health  expenditures and for Medicaid, we changed both variables in the same  direction and left the others fixed at their baseline values. For example,  for one simulation we used zero excess cost growth for both national  health expenditures and for Medicaid, and made the baseline assumption  for the other variables. 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.

State and Local Governments’ Fiscal Outlook: December 2016 Update,  GAO-17-213SP. Washington, D.C.: Dec. 8, 2016.

State and Local Governments’ Fiscal Outlook: December 2015 Update,  GAO-16-260SP. Washington, D.C.: Dec. 16, 2015.

State and Local Governments’ Fiscal Outlook: December 2014 Update,  GAO-15-224SP. Washington, D.C.: Dec. 17, 2014.

State and Local Governments’ Fiscal Outlook: April 2013 Update,  GAO-13-546SP. Washington, D.C.: Apr. 29, 2013.

State and Local Governments’ Fiscal Outlook: April 2012 Update,  GAO-12-523SP. Washington, D.C.: Apr. 5, 2012.

State and Local Government Pension Plans: Economic Downturn Spurs  Efforts to Address Costs and Sustainability, GAO-12-322. Washington,  D.C.: Mar. 2, 2012.

State and Local Governments’ Fiscal Outlook: April 2011 Update,  GAO-11-495SP. Washington, D.C.: Apr. 6, 2011.

State and Local Governments: Knowledge of Past Recessions Can  Inform Future Federal Fiscal Assistance, GAO-11-401. Washington, D.C.:  Mar. 31, 2011.

State and Local Governments: Fiscal Pressures Could Have Implications  for Future Delivery of Intergovernmental Programs, GAO-10-899.  Washington, D.C.: July 30, 2010.

State and Local Governments’ Fiscal Outlook: March 2010 Update,  GAO-10-358. Washington, D.C.: Mar. 2, 2010.

Update of State and Local Government Fiscal Pressures, GAO-09-320R.  Washington, D.C.: Jan. 26, 2009.

State and Local Fiscal Challenges: Rising Health Care Costs Drive Long- term and Immediate Pressures, GAO-09-210T. Washington, D.C.: Nov.  19, 2008.

State and Local Governments: Growing Fiscal Challenges Will Emerge  during the Next 10 Years, GAO-08-317. Washington, D.C.: Jan. 22, 2008.

Our Nation’s Long-Term Fiscal Challenge: State and Local Governments  Will Likely Face Persistent Fiscal Challenges in the Next Decade,  GAO-07-1113CG. Washington, D.C.: July 18, 2007.

State and Local Governments: Persistent Fiscal Challenges Will Likely  Emerge within the Next Decade, GAO-07-1080SP. Washington, D.C.:  July 18, 2007.

Appendix IV: GAO Contacts and Staff Acknowledgments

In addition to the contacts listed above, Brenda Rabinowitz and Courtney  LaFountain (Assistant Directors), David Aja, Brett Caloia, Ann  Czapiewski, Joe Silvestri, Stewart Small, Andrew J. Stephens, Frank  Todisco, Walter Vance, and Chris Woika made significant contributions to  this report.

What's the prognosis for the fiscal health of state and local governments across the nation?

Our annual outlook suggests the sector will have an increasingly tough time covering their bills over the next 50 years. Our model shows both revenue and spending will increase; however, spending will rise faster. Revenues may be insufficient to sustain the amount of government service currently provided.

Our model also suggests health care costs will largely drive the spending increases—in particular, Medicaid spending and spending on health benefits for state and local government employees and retirees.