State and Local Governments: Growing Fiscal Challenges Will Emerge during the Next 10 Years
Published: 2008-01-22T00:00:00

Fast Facts:

Highlights:
section_title: Why GAO Did This Study
paragraphs: State and local governments provide an array of services to their residents, such as primary and secondary education, libraries, police and fire services, social programs, roads and other infrastructure, public colleges and universities, and more. These subnational governments may face fiscal stress similar to the federal government. Given the nature of the partnership among levels of government in providing services to Americans and the economic interrelationships among levels of government, 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 sector. This report describes this model and provides (1) simulations of the state and local government sector's long-term fiscal outlook, (2) an analysis of the underlying causes of potential fiscal difficulties for the sector, (3) a discussion of the extent to which the long-term simulations are sensitive to alternative assumptions, and (4) an examination of how the state and local government sector could add to future federal fiscal challenges. The potential fiscal outcomes of the state and local government sector are projected through two fiscal balance measures: net lending or borrowing and what we call the operating balance. Net lending or borrowing--which is roughly analogous to the federal unified surplus or deficit--is a measure of the balance of all receipts and expenditures during a given time frame. Historically, total expenditures have usually exceeded total receipts, and the sector issues debt to cover part of the costs of its capital projects. As such, net lending or borrowing typically measures the need for the sector to borrow funds or draw down assets to cover its expenditures. The operating balance net of funds for capital expenditures--referred to in this report as the "operating balance"--is a measure of the ability of the sector to cover its current expenditures out of current receipts, that is, the balance of expenditures and receipts related to activities taking place in a given year. Most states have some sort of requirement to balance operating budgets. Projects with longer time frames are typically budgeted separately from the operating budgets and financed by a combination of current receipts, federal grants, and the issuance of debt. Because some current receipts may be used to fund part of longer-term investments, we developed a measure of the operating balance that makes adjustments for the extent to which current receipts are unavailable to fund current expenditures because they have been spent on longer-term projects, such as investments in buildings and roads.
section_title: What GAO Found
paragraphs: Our model shows that in less than a decade the state and local government sector will begin to face growing fiscal challenges. Both fiscal balance measures (1) net lending or borrowing and (2) the operating balance--are likely to remain within their historical ranges in the next few years, but both begin to decline thereafter and fall below their historical ranges within a decade. That is, absent policy changes, state and local governments will face an increasing gap between receipts and expenditures in the coming years. Since most state and local governments actually face requirements that their operating budgets be balanced or nearly balanced in most years, the declining fiscal conditions our simulations suggest are really just a foreshadowing of the extent to which these governments will need to make substantial policy changes to avoid these potential growing fiscal imbalances. As is true for the federal sector, the growth in health-related expenditures is the primary driver of the fiscal challenges facing the state and local government sector. In particular, two types of state and local expenditures will likely rise quickly. The first is Medicaid expenditures, and the second is expenditures by these governments for health insurance for state and local employees and retirees. Conversely, other types of expenditures of state and local governments in the aggregate--such as wages and salaries of state and local workers, nonhealth transfer payments (e.g., family assistance), and investments in capital goods--are assumed to grow slower than gross domestic product (GDP). Moreover, under the current policy scenario of the base case, most revenue categories grow at approximately the same rate as GDP. Therefore, the projected rise in health-related expenditures is the root of the fiscal difficulties these simulations suggest will occur. Although health care expenditures clearly appear to be a looming problem for the state and local government sector, the extent of fiscal difficulties faced by any given state or local government will vary with its individual expenditure and tax profile. We also used the model to examine how the fiscal balance measures would be affected over the long-term under assumptions that differed from those of our base case. In particular, we analyzed scenarios that differ across three factors: (1) the rate of growth in tax receipts, (2) the rate of growth in expenditures, and (3) the rate of growth in medical care expenditures. Some of the alternative scenarios were designed to examine the extent to which a change in base-case assumptions for any of these factors would enable the state and local government sector to maintain fiscal balances in their historical ranges. We found that it would be difficult to address the expected future fiscal deficits solely through tax increases or solely through expenditure cuts. Since 1992, we have produced long-term simulations of what might happen to federal deficits and debt under various policy scenarios. Our most recent long-term federal simulations show ever larger deficits resulting in a very large and growing federal debt burden over time.4 In that work, we found that federal fiscal difficulties stem primarily from an expected explosion of health-related expenditures. Our findings thus show that the state and local sector will provide an additional drag on an already declining federal government fiscal outlook and that the critical problem of escalating costs of health care is an economywide problem that will need to be addressed by all levels of government.

section_title: Letter
paragraphs: In speeches and presentations over the past several years, I have called  attention to our large and growing federal fiscal challenge and the risks it  poses to our nation’s future.  For over a decade GAO has run long-term  simulations showing that absent a change in policy, the combined effects of  demographic changes and growing health care costs drive ever-increasing  federal deficits and debt levels. GAO’s most recent federal simulations  show an anticipated persistent gap between expected revenues and  expected spending resulting in a very large and growing federal debt  burden over time.  The primary drivers of the spending are large federal  entitlement programs—Medicare, Medicaid, and Social Security.  Spending  on health care programs (Medicare and Medicaid) in particular represents  the fastest growing and most immediate problem.  I have repeatedly  warned that the current fiscal path of the federal government is “imprudent  and unsustainable.”
Fiscal sustainability presents a national challenge shared by all levels of  government.  The federal government and state and local governments  share in the responsibility of fulfilling important national goals, and these  subnational governments rely on the federal government for a significant  portion of their revenues.  As happens at the federal level, these  subnational governments may also face serious fiscal stress in the future.   To provide Congress and the public with a broader perspective on our  nation’s fiscal outlook, GAO has developed a fiscal model of the state and  local sector.  This unique model enables GAO to simulate fiscal outcomes  for the entire state and local government sector for several decades into the  future.
The findings of these new simulations indicate that the state and local  government sector faces fiscal challenges that in many ways mirror those  of the federal government.  In particular, GAO has found that in the absence  of policy changes, large and recurring fiscal challenges for the state and  local sector will begin to emerge within a decade. For example, the analysis  suggests that state and local governments will need to somewhat increase  their pension contributions to fully fund pension costs for their employees.   Additionally, there is increasing concern that investment in the nation’s  infrastructure is not sufficient to maintain its condition and to  accommodate the increased demand of a growing economy.  Moreover, as  in the federal sector, the growth in health-related costs serves as the  primary driver of the fiscal challenges facing the state and local  government sector.  In particular, two types of state and local expenditures  will likely rise quickly because of escalating medical costs.  The first is  Medicaid expenditures, and the second is the cost of health insurance for  state and local employees and retirees.  At the same time, most revenue  growth is expected to remain roughly flat as a percentage of Gross  Domestic Product.  As such, the projected rise in health-related costs is the  root of the fiscal difficulties these simulations suggest will occur.
Addressing the nation’s long-term fiscal imbalances constitutes a major  challenge for all levels of government.  There are no “quick fixes,” and all  levels of government need to work in tandem to address the complex and  interrelated reforms that need to be made.  Continuing on this  unsustainable path will gradually erode, and ultimately damage, our  economy, our standard of living, and potentially our domestic tranquility  and national security.  This is a challenge that needs to be addressed with a  greater sense of urgency by policymakers since time is currently working  against us.
This report was prepared under the direction of Stanley J. Czerwinski,  Director in our Strategic Issues team, and Thomas J. McCool, Director of  our Center for Economics.
David M. Walker  Comptroller General   of the Untied States State and local governments provide an array of services to their residents,  such as primary and secondary education, libraries, police and fire  services, social programs, roads and other infrastructure, public colleges  and universities, and more.  These subnational governments may face fiscal  stress similar to the federal government.  Given the nature of the  partnership among levels of government in providing services to Americans  and the economic interrelationships among levels of government,  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 sector.  This report describes this model and  provides (1) simulations of the state and local government sector’s long- term fiscal outlook, (2) an analysis of the underlying causes of potential  fiscal difficulties for the sector, (3) a discussion of the extent to which the  long-term simulations are sensitive to alternative assumptions, and (4) an  examination of how the state and local government sector could add to  future federal fiscal challenges.
To develop these long-term simulations, we developed a state and local  model that projects the level of receipts and expenditures of the sector in  future years based on current and historical spending and revenue  patterns.  Key categories of receipts for state and local governments  include several types of state and local taxes (e.g., personal income, sales,  property, and corporate), income on assets owned by the sector (e.g.,  financial assets), and grants from the federal government.  Categories of  expenditures include wages and salaries, health insurance, and pension  costs of state and local employees (e.g., teachers and police); payments of  social benefits (e.g., Medicaid, unemployment); depreciation expense on  state and local capital stock; interest payments on state and local financial  debt; and other expenditures of the sector.
The potential fiscal outcomes of the state and local government sector are  projected through two fiscal balance measures:  net lending or borrowing  and what we call the operating balance.  Net lending or borrowing—which  is roughly analogous to the federal unified surplus or deficit—is a measure  of the balance of all receipts and expenditures during a given time frame.   Historically, total expenditures have usually exceeded total receipts, and  the sector issues debt to cover part of the costs of its capital projects.  As  such, net lending or borrowing typically measures the need for the sector  to borrow funds or draw down assets to cover its expenditures.  The  operating balance net of funds for capital expenditures—referred to in this  report as the “operating balance”—is a measure of the ability of the sector  to cover its current expenditures out of current receipts, that is, the  balance of expenditures and receipts related to activities taking place in a  given year.  Most states have some sort of requirement to balance operating  budgets.  Projects with longer time frames are typically budgeted  separately from the operating budgets and financed by a combination of  current receipts, federal grants, and the issuance of debt.  Because some  current receipts may be used to fund part of longer-term investments, we  developed a measure of the operating balance that makes adjustments for  the extent to which current receipts are unavailable to fund current  expenditures because they have been spent on longer-term projects, such  as investments in buildings and roads.
We developed a “base case” simulation in which we assume that the current  set of policies in place across federal, state, and local governments remains  constant.  In other words, we assume that the tax structure is not changed  in the future and that current policies regarding the provision of  government services remain the same.  The primary data source for the  model is the National Income and Product Accounts (NIPA) developed by  the Bureau of Economic Analysis, U.S. Department of Commerce.  The  state and local model examines the aggregate fiscal outcomes for the  sector and does not examine the condition of any individual state or local  government.  The time frame for the simulations extends until 2050,  paralleling our federal fiscal model.  The two models are designed such that  they can be combined to examine the entire U.S. government sector.   Appendix I contains our overall methodology, which provides a detailed  discussion of the assumptions underlying the projections, the  measurement of these fiscal balances, and the sources of data.  Appendix II  provides a discussion of estimates of future income on assets owned by the  sector and interest paid on the sector’s debt.  Appendix III provides a  discussion of the development of the model’s treatment of pension and  retiree and employee health care costs.  Appendix IV provides all of the  model equations, and appendix V provides variable definitions.
subsections: section_title: Background
paragraphs: The state and local government sector consists of 50 state governments and  87,525 local governments.  These local governments include 3,034 county  governments, 19,429 municipal governments, 16,504 townships, 13,506  school districts, and 35,052 special districts.  State and local governments  provide vital services to citizens such as law enforcement, public  education, and sewage treatment.  Local governments derive their  authority from the states, and the powers and responsibilities granted to  local governments vary considerably.   For example, while states generally  provide authority to local governments to tax real property, local  governments vary in their authority to levy other types of taxes, such as  personal income or sales taxes.
State and local governments collect receipts and receive federal funds to  provide services to their constituents.  In 2006, state and local governments  received $1.9 trillion in total receipts.  Taxes, such as property taxes, sales  and excise taxes, personal income tax, and corporate income taxes, make  up a large component of these receipts—fully $1.2 trillion.  In addition, the  federal government provided over $400 billion to state and local  governments in the form of various grants (including Medicaid), loans, and  loan guarantees.  These federal funds accounted for approximately 22  percent of state and local government total receipts.  State and local  governments also obtain revenues from several other sources, such as  income receipts on financial assets; certain receipts from businesses and  individuals (such as vehicle and licensing fees); and, in some years, from  surpluses on government-run enterprises that provide services such as  energy, liquor, lotteries, and public transit.
State and local governments fund a broad range of services such as public  safety, housing, education, and public transportation programs.  In 2006,  state and local governments spent $691 billion on education—the largest  expenditure category for the sector.  These governments also spent $263  billion on projects such as highways, public transit, agriculture, and natural  resources, and $242 billion on public safety services such as police and fire  departments as well as prisons.  State and local governments also provide a  broad range of other services, such as income security for the poor and  disabled, health-related services, housing and community development,  recreation services such as parks, and utilities such as water, sewage, and  energy.
Budget processes vary considerably across the 50 states.  According to the  National Association of State Budget Officers (NASBO), about half of  states enact budgets annually, while most others enact biennial budgets,  and a few undertake a mix of annual and biennial budgeting.  Most states  budget separately for current operating costs and capital expenditures.   The capital budget is used for states’ capital projects, and states frequently  issue debt to help fund these investments.  Most states have some form of  balanced budget requirement for general funds—the fund that covers  current operating costs—but the nature of these balanced budget  requirements varies considerably.  For example, some states require  governors to submit a balanced budget, while others mandate that  legislatures pass a balanced budget.  Some direct governors to sign a  balanced budget, and some require governors to execute a balanced  budget.  Many of the balanced budget provisions allow states to run small,  short-term deficits.
section_title: The State and Local Government Sector’s Fiscal Difficulties Are Likely to Emerge within the Next 10 Years
paragraphs: Our base case model rests on certain key assumptions.  In particular, in the  base case we assume that current federal, state, and local policies remain  constant.  On the receipt side, this translates into an assumption that the  current tax structures of state and local governments are maintained in  future years and that tax receipt growth reflects past experience, except  that we remove the effect of past policy changes and the effects of unusual  capital gains.  On the expenditure side, we make assumptions that would  generally be consistent with the maintenance of current policies in the  provision of services to citizens.  Since compensation of state and local  employees is a large cost component of providing services to citizens, our  assumptions about the growth in the number of state and local employees  over time, as well as the growth in their wages, are significant components  through which we implement the assumptions of the maintenance of  current policy.  Since 1980, the level of employment in the state and local  sector has grown significantly faster than the U.S. population, but for our  simulation we maintain state and local government employment as a steady  share of the population over time.  This would be consistent with the  maintenance of current policy if there were no productivity gains in the  sector, or a modest increase in real services, to the extent that state and  local workers experience gains in productivity.  Also, we assume that  employees of state and local governments receive pay increases each year  equal to those of private sector workers—an assumption that is generally  consistent with historical experience.  Finally, we assume that the total  cost of many goods procured by the sector to provide services will rise with  increases in the population being served and the rate of inflation in the  economy.  Table 1 summarizes the assumptions of the base case model.
We calculated two measures of fiscal balance for the state and local  government sector for each year until 2050.  The measures are:  Net lending or borrowing—the balance of all receipts and  expenditures during a given time frame.  This indicates the need for the  sector to borrow funds or draw down assets to cover its expenditures.   This measure is roughly analogous to the federal unified surplus or  deficit.
Operating balance net of funds for capital expenditures—or  simply the “operating balance”—a measure of the ability of the  sector to cover its current expenditures out of current receipts.  In  developing this measure we subtract funds used to finance longer-term  projects—such as investments in buildings and roads—from receipts  since these funds would not be available to cover current expenses.   (See app. I for more detail on the measurement of this balance).
Figure 1 shows values of the two balance measures—net lending or  borrowing and operating balance—as a percentage of GDP under our base- case assumptions.  Historical data from 1980 to 2006 are shown along with  our model simulations beginning in 2007 and running through 2050.  The  figure shows that the two measures generally track one another.  It also  shows that, historically, net lending or borrowing has typically been  negative, but rarely by more than 1 percent of GDP.  This indicates that the  sector generally issues debt—primarily to fund capital expenditures—but  has done so at a reasonably stable pace.  Additionally, the operating  balance measure has historically been positive most of the time, ranging  from about zero to about 1 percent of GDP.  Thus, the sector usually has  been able to cover its current expenses with incoming receipts.  But the  simulation suggests that while projected balances for both net lending or  borrowing and the operating balance remain in their historical ranges for  the next several years, the balances will soon begin to decline and will fall  below their historical ranges within a decade.  That is, the model suggests  that the state and local government sector will face increasing fiscal stress  in just a few years.  Our simulations also indicate that by the mid-2020s the  balance measures will both be well below their historical ranges, and will  continue to fall throughout the remainder of the simulation time frame.   These projected deficits—worsening throughout the projection time frame  under an unchanged policy scenario—indicate that, because most state and  local governments cannot actually run such deficits for any length of time,  these governments will need to make tough choices on spending and tax  policy to meet their budget requirements and to promote favorable bond  ratings.
Another way of measuring the long-term challenges faced by the state and  local sector is through a measure known as the “fiscal gap.”  With deficits  rising rapidly as shown in figure 1, the outstanding debt of the state and  local sector will experience unprecedented growth.  The fiscal gap is an  estimate of the action needed today and maintained for each and every year  to achieve fiscal balance over a certain period.  We measured the gap as the  amount of spending reduction or tax increase needed to maintain debt as a  share of GDP at or below today’s ratio.  For the state and local sector, we  calculated that to close the fiscal gap would require action today equal to a  15.2 percent tax increase or a 12.9 percent reduction in spending financed  by non-grant revenues.  The fiscal gap can also be expressed as a share of  the economy or in present value dollars.  We calculated that in 2007 dollars  the fiscal gap amounts to $10.6 trillion, which represents 1.4 percent of the  discounted value of GDP over the same time frame.
section_title: Decline in Fiscal Balances Is Related to Rapidly Growing Health-Related Costs
paragraphs: Based on our review of the evidence, we estimate that expenditure growth  for health care will be significant and these expenditures will constitute a  rapidly growing burden for state and local governments.  Two types of state  and local expenditures in particular will likely rise quickly due to escalating  medical costs.  First, under CBO’s intermediate projections, federal  Medicaid grants to states per recipient will rise by 1 percent more than  GDP per capita in the coming years.  Since Medicaid is a federal and state  program with federal Medicaid grants based on a matching formula, these  estimates indicate that expenditures for Medicaid by state governments  will rise quickly as well.  Second, we estimated future expenditures for  health insurance for state and local employees and retirees.  Specifically,  we assume that the excess cost factor—the growth in these health care  costs per capita above GDP per capita—will average 1.4 percentage points  per year through 2035 and then begin to decline, reaching 0.6 percent by  2050.  This results in a rapidly growing burden from these health-related  activities in state and local budgets.  In contrast, our implementation of  assumptions about current policies indicated that, in aggregate, other  expenditure categories grow less than GDP in our base-case simulations.   For example, even though the wages and salaries of primary and secondary  education employees are a large expenditure of the state and local sector,  under our base-case assumptions these costs are not expected to grow any  faster than the rate of growth of the general economy and will not  represent an increasing burden on governments relative to their revenues.   These base-case assumptions differ from historical experience in which  real spending on primary and secondary education per pupil has risen in  the past few decades.  If such a trend were to continue, spending on  education could place a growing burden on state and local governments in  future years.  Figure 2 shows the projected expenditures of the sector as a  percentage of GDP.
On the receipt side, state and local governments impose a variety of taxes.   Our model projections suggest that most of these tax receipts will show  modest growth in the future—and some are projected to experience a  modest decline—relative to GDP.  Figure 3 shows the expected path of  several tax revenue sources.  We found that personal state income taxes  will show a small rise relative to GDP in coming years.  This likely reflects  that some state governments have a small degree of progressivity in their  income tax structures.  Sales taxes of the sector are expected to experience  a slight decline as a percentage of GDP in the coming years.  Property  taxes—which are mostly levied by local governments—should rise slightly  as a share of GDP in the future.  These differential tax growth projections  indicate that any given jurisdiction’s tax revenue prospects may be uniquely  tied to the composition of taxes it imposes.
The only source of revenue we expect to grow rapidly is federal grants to  state governments for Medicaid.  However, since Medicaid is a matching  formula grant program, the projected escalation in federal Medicaid grants  simply reflects expected increased Medicaid expenditures that will be  shared by state governments.  That is, we assume that current policy  remains in place and the shares of Medicaid expenditures borne by the  federal government and the states remain unchanged.  Federal grants  unrelated to Medicaid are projected, based on CBO analysis, to decline  somewhat relative to GDP in the coming years.
section_title: Substantial Policy Changes Would Be Needed to Prevent the Fiscal Decline in the State and Local Government Sector
paragraphs: We developed several scenarios with alternative assumptions to better  understand the sensitivity of our results.  For these analyses, we focused on  the operating balance measure because this is a proxy for the operating  budgets that most state and local governments have requirements to  generally keep in balance.  The assumptions varied in these alternative  scenarios include (1) the growth of tax receipts, (2) the growth in state and  local expenditures, and (3) the rate of growth in health care costs.
subsections: section_title: Tax Receipts Would Need to Rise Considerably Faster than Historical Experience to Enable the Operating Balance to Remain in Historical Range
paragraphs: In the base-case model, we assume that current policy, such as tax rates  and structures, will remain unchanged.  We also modeled alternative  scenarios with different assumptions about the growth rate of tax receipts.   In the first alternative, we use the historical growth of tax revenues for the  sector since 1980.  The second alternative is a “maintain balance” scenario  in which we assume that taxes are raised to whatever level would be  required to maintain a nonnegative operating balance in every year of the  simulation.  Figure 4 shows the tax growth path for the base case and two  alternative scenarios.
Under the base case, we found that aggregate tax revenues for the entire  state and local sector will likely remain about a constant percentage of  GDP.  In the historical growth scenario, receipts would rise somewhat in  the future relative to GDP.  For the “maintain balance” scenario, tax  receipts need to rise considerably faster than under either of the other  cases to fulfill the requirements of the scenario.  In fact, by 2050, state and  local taxes as a percentage of GDP would have to rise by about 17 percent  above the base case to avoid fiscal deficits.  In other words, it would take a  substantial increase in taxes—a considerably faster increase than that  experienced historically—to maintain a nonnegative operating balance  solely through increased taxes.
section_title: State and Local Expenditures Would Need to Be Cut Substantially to Maintain Fiscal Balance
paragraphs: Our base-case model assumes that current policies are maintained,  primarily by holding the number of employees in the sector constant as a  percentage of population, assuming state and local workers receive pay  increases equal to those of private-sector employees, and assuming the  total cost of many goods procured by the sector to provide services rises  with increases in the population being served and the rate of inflation in the  economy.  We also developed an alternative scenario that calculates how  much the sector would have to limit expenditures in the aggregate in order  to avoid a negative operating balance.  Figure 5 shows these two  expenditure paths.
Figure 5 shows that under the base case, expenditures rise considerably  over the simulation time frame.  In contrast, maintaining balance solely  through spending restraint would require holding expenditure growth to a  much lower rate than the base case.  Since a large percentage of  expenditures of the sector are related to compensation of employees, this  would likely mean that the workforce would not be able to grow as fast as  we allow it to under the base case.  That is, the ratio of employees to the  population would need to decline.  These state and local governments  would also likely need to reduce their purchases of other goods and  services procured to provide government services relative to what would  have occurred under the base case.  Since the base case was designed to  reflect current policies, the results of the maintain balance scenario imply  that there would need to be substantial cuts in expenditures and therefore  services to citizens, relative to the base case.
section_title: Health Care Cost Growth Would Need to Be Held to Low Level to Prevent Declines in Fiscal Balances
paragraphs: For the base-case model, we assumed that Medicaid expenditures grow  according to CBO’s intermediate projections—1 percentage point more  than the growth in GDP per capita—and that employee and retiree health  insurance expenditures grow over the next 30 years by an average of 1.4  percentage points more than GDP per capita and slowing to 0.6 percentage  points by 2050.  Given the importance of health care expenditures as a  driver for the long-term fiscal outlook, we also model the impact of  different health care expenditure growth assumptions.  For a more  optimistic scenario, we lowered Medicaid expenditure growth to CBO’s  lower spending path assumption.  Under this path, Medicaid expenditure  growth would equal the growth in GDP per capita.  We also assumed no  “excess cost growth” for the rate of increase in expenditures on employee  and retiree health insurance, meaning that we hold the growth in these  expenditures to the rate of growth in GDP per capita.  For a more  pessimistic scenario, we used CBO’s high spending path assumption for  Medicaid, under which costs rise at GDP plus 2.5 percent per capita, and  we doubled the per capita rate of growth above GDP for health insurance  expenditures.
Figure 6 shows the projected operating balance under all three health care  cost-growth scenarios.  The differences among the outcomes of these  scenarios highlight the importance of health care to the long-term fiscal  balance of the sector.  With more rapidly rising health care expenditures,  the operating balance falls off considerably more quickly than in the base  case.  Conversely, holding the growth in health care costs per capita to the  overall per capita economic growth enables the sector to avert deficits  during the projection time frame.  Neither historical experience nor expert  opinion, however, suggests that the cost growth of health care will likely be  held to the level embodied in this optimistic scenario in the near future.
section_title: State and Local Fiscal Challenges Add to the Nation’s Fiscal Difficulties
paragraphs: Since 1992, we have produced long-term simulations of what might happen  to federal deficits and debt under various policy scenarios.  Our most  recent long-term federal simulations show ever-larger deficits resulting in a  very large and growing federal debt burden over time.  Just as in the state  and local government sector, the federal fiscal difficulties stem primarily  from an expected explosion of health-related expenditures.  As we have  noted elsewhere, the expected continued rise in health care costs poses a  fiscal challenge not just to government budgets, but to American business  and society as a whole.  The fundamental fiscal problems of the federal  government and these subnational governments are similar and are linked.   Figure 7 shows two simulations for the federal fiscal path under alternative  assumptions, and overlays the simulated fiscal imbalance of the state and  local government sector.
For the federal fiscal simulation denoted “baseline extended,” we follow  CBO baseline projections for the next 10 years:  tax provisions that are  scheduled to expire are assumed to do so and discretionary spending is  assumed to grow with inflation.  After the first 10 years, we use the Social  Security and Medicare Trustees’ 75-year intermediate (“best”) estimates for  those programs and CBO’s midrange Medicaid estimates.  All other  expenditures and receipts are held constant as a share of GDP after the  first 10 years.  Under the alternative federal simulation, we assume that  during the first 10 years of the simulation, expiring tax provisions are  extended and that discretionary spending grows with GDP—a faster pace  than inflation.  After the 10-year time frame, we assume that action is taken  to return and keep revenue at its historical share of GDP plus an additional  amount attributable to deferred taxes (i.e., taxes on withdrawals from  retirement accounts).  This alternative also incorporates somewhat higher  Medicare estimates reflecting a more realistic scenario for physician  payments.  The overlay of the base case state and local simulation shows  that the state and local fiscal situation imposes further burden on the  nation’s economy in the next several decades.
We did our work from September 2007 through December 2007 in  accordance with generally accepted government auditing standards.  We  provided a draft of this report to the Bureau of Economic Analysis of the  Department of Commerce for technical review.
This report was prepared under the direction of Stanley J. Czerwinski,  Director, Strategic Issues, who can be reached at (202) 512-6806 or  czerwinskis@gao.gov, and Thomas J. McCool, Director, Center for  Economics, who can be reached at (202) 512-2642 or mccoolt@gao.gov if  there are any questions.  Amy Abramowitz, Carol Henn, Richard  Krashevski, James McTigue, Michelle Sager, Michael Springer, Jeremy  Schwartz, and Melissa Wolf made key contributions to this publication.
section_title: State and Local Fiscal Model Methodology
paragraphs: This appendix describes our simulations of state and local fiscal  conditions.  As an organizing framework and basic data source, the state  and local government model relies on the National Income and Product  Accounts (NIPA), prepared by the U.S. Department of Commerce.  Table  3.3, State and Local Government Current Receipts and Expenditures, of the  NIPA provides data on receipts and expenditures of all state and local  governments in aggregate.  We also use tables underlying table 3.3 to obtain  more detailed information for some of the expenditure classifications.  We  project the growth in each category of receipts and expenditures using the  Congressional Budget Office’s (CBO) economic assumptions whenever  possible.  In several cases we were not able to obtain existing projections  and needed to develop our own assumptions about the likely future growth  path of certain receipts or expenditures.  We also developed detailed  models to project items such as necessary pension fund contributions, the  costs of health insurance for employees and retirees, and several tax  receipt categories.  Our base-case model assumes current policies remain  in place.  Throughout this appendix we describe how that basic assumption  is realized.
Once all receipts and expenditures of the sector are simulated forward  through 2050, we develop summary indicators of the state and local  government sector’s fiscal status.  Because the model covers the state and  local government sector in the aggregate, the fiscal outcome of individual  states and localities cannot be captured.  Also, the model does not identify  whether it is the state or the local government sector that faces greater  fiscal challenges.  The remainder of this appendix describes (1) how each  of the receipt categories is projected; (2) how each of the expenditure  categories is projected (with the exception of required pension  contributions and the costs of health care, which are discussed more fully  in app. III); and (3) how we develop measures of fiscal balance.
subsections: section_title: Projection of Receipts of the State and Local Government Sector
paragraphs: The model provides projections for each type of receipt of state and local  governments.  The Bureau of Economic Analysis of the Department of  Commerce assembles the NIPA based on data from the quinquennial  Census of Governments, annual surveys of Government Finances, and  other sources. In the NIPA, receipts are divided into five major categories:  tax receipts, contributions for government social insurance, income  receipts on assets, transfer receipts, and the current surplus of government  enterprises.  Figure 8 shows these categories as well as the breakdown of  receipts within each of these classifications.
subsections: section_title: Tax Receipts
paragraphs: As noted above, our base-case simulation is based on current policy and  does not project any possible policy changes that would affect receipts  during the simulation period.  In the case of taxes, this means that we  simulate the receipts that would be collected if tax rates and structures  were to remain unchanged.  Accordingly, several tax receipt categories  grow at the same rate as their underlying tax bases.  For several tax  categories, however, it is more appropriate to project tax receipts  themselves instead of their tax bases.  Our tax receipt projections are  based on a set of economic assumptions, many of which come from CBO.   However, most of CBO’s projections extend only 10 years into the future.   In order to project beyond 10 years, therefore, we used GDP values from  GAO’s long-term federal budget simulations in conjunction with  extrapolations of CBO’s economic assumptions.
subsections: section_title: Personal income tax receipts
paragraphs: Some states have progressive rate structures resulting in receipts growing  faster than incomes.  Because of this, for state income tax receipts, our  modeling reflects this progressivity as we project receipts themselves  rather than assume that receipts grow at the same rate as the tax base.  As  such, we simulate future state personal income tax receipts by estimating  the long-run responsiveness, or elasticity, of receipts to taxable personal  income.  The long-run elasticity estimate depicts the extent to which tax  receipts grow in response to income growth but does not capture their  short-run reaction to changes in income over the business cycle.
We adjusted the historical state income tax receipt data to remove the  effects of both policy changes as well as unusual capital gains that  influenced past receipts. It is necessary to purge past data of policy  changes because these can substantially influence the estimated elasticity,  and including those effects would not maintain the “policy neutral”  paradigm of our base case.  Similarly, unusual swings in capital gains  represent past events that may have had a significant impact on receipts  but are not expected to recur in any predictable way.  To purge the effect of  policy changes from the receipts data, we used data from The Fiscal  Survey of the States:  December 2006, National Association of State Budget  Officers (NASBO).  These data provide estimates of the aggregate effect of  tax changes each year.  To remove the effect of atypical capital gains  realizations on receipts, we estimated a relationship between the share of  federal income tax receipts from capital gains receipts and the highest  marginal tax rate on capital gains.  Any deviation between the actual share  of capital gains receipts and the share implied by this relationship was  removed from state personal income taxes.  Using the adjusted receipts  data, we estimated that a 1 percent increase in real taxable personal  income generates an approximate 1.1 percent increase in real personal  income tax receipts.  The somewhat greater growth in receipts than in  income reflects the progressivity exhibited in some state income tax rate  structures.
To project state personal income taxes in future years using this  relationship, future values of taxable personal income are required.  Ten- year taxable income projections come from CBO.  Thereafter, taxable  income is held constant as a share of GDP at CBO’s projected tenth-year  level.  The estimated long-run elasticity of approximately 1.1 therefore  implies that state personal income taxes increase slightly as a share of the  economy over the projection period.
See appendix IV equation 34 for more information on the state  income tax analysis.
In contrast to state personal income taxes, local personal income taxes as  well as other personal taxes have displayed no discernable trend as a share  of taxable personal income over the last 2 decades.  In the base-case  projections, therefore, we simply let these personal taxes grow at the same  rate as taxable personal income.  This implies that the local income and  other personal tax rates remain unchanged over the simulation period.   Because we allow these personal tax receipts to grow with taxable income  and taxable income grows with GDP, local income and other personal taxes  remain constant as a share of the economy in our long-term projections.
See appendix IV equations 35 and 36 for more information on the  local income tax and other personal tax analysis.
section_title: Sales Tax Receipts
paragraphs: The model divides sales tax receipts into two categories, general and  selective (or excise) sales taxes.  General sales taxes are levied as a  percentage of the price of the items purchased.  In contrast, selective sales  taxes—which are levied on such goods as liquor, gasoline, and tobacco— are often exacted in terms of dollars and/or cents per item purchased, and  the amount of the tax may be adjusted only intermittently.  Accordingly, the  model uses different relationships for the two types of sales taxes.
In the absence of policy changes, general sales tax receipts should grow at  the same rate as the consumption categories subject to the tax—that is, to  the sales tax base.  To evaluate the outlook for state and local government  general sales tax receipts, we estimated the long-term responsiveness of  our measure of the sales tax base to aggregate wage and salary income.   Given projections of aggregate income, this elasticity provides a future  path for the sales tax base.  Receipts are then assumed to grow at the same  rate as the tax base, implying that the average sales tax rate remains  constant over the simulation period.
The first step in this analysis is to develop a broad consumption measure as  a proxy for the tax base using previous work as a guide.   The proxy used  here is total consumption expenditures excluding food and services,  because the two categories are often not part of the sales tax base.  In  addition, because the sales tax base has been negatively affected by  increases in mail order and Internet purchases, we also used Census data  to remove an estimate of remote sales from the tax base.  We then  estimated the long-run elasticity of this sales tax base with respect to  aggregate wages and salaries using historical data.  We found that the  estimated elasticity is 0.93, suggesting that over the long run, a 1.0 percent  increase in real wages and salaries results in a 0.93 percent increase in the  real sales tax base.  In the projections, sales tax receipts grow at the same  rate as the sales tax base.  The sluggish growth in sales tax receipts relative  to the economy reflects the shift in consumer spending toward services and  remote sales, both of which are excluded from our proxy for the sales tax  base.
To accommodate the possible success of efforts to include remote sales in  the tax base, we also estimated the long-run income elasticity of a sales tax  base measure that includes remote sales.  The resulting estimate of 0.97  implies that, even if remote sales were to be taxed, general sales tax  receipts would still be unlikely to fully keep pace with overall economic  growth.
See appendix IV equations 37, 38, 39, and 41 for more information  on the general sales tax analysis Selective Sales (Excise) Tax Receipts In addition to the general sales tax, state and local governments impose a  variety of selective sales (or excise) taxes on gasoline, alcoholic beverages,  tobacco, public utilities, insurance receipts, and other items.  Selective  sales taxes generally take the form of a given amount of tax for each unit  purchased, (i.e., a unit tax).  Most states, for example, levy a tax on gasoline  which takes the form of a certain number of cents per gallon.  Because the  amount of the unit tax is adjusted only periodically, selective sales tax  receipts tend to grow less rapidly than the value of the sales on which they  are levied and less than incomes.  Accordingly, we estimated the  responsiveness of selective sales tax receipts to income rather than the  responsiveness of the tax base to income.  Our estimates indicated that the  long-run elasticity, based on historic data, was 0.80.  This implies that these  receipts tend to grow much less than income and the general economy.
See appendix IV equation 43 for more information on the selective  sales (excise) tax analysis.
section_title: Taxes on Corporate Income
paragraphs: In our simulations, corporate tax receipts grow at the same rate as CBO’s  projections of corporate profits.  After an initial adjustment period, CBO  assumes profits will keep pace with overall economic growth.  In our long- run simulations, therefore, corporate income taxes remain constant as a  share of the economy.
See appendix IV equation 49 for more information on the  corporate income tax analysis.
section_title: Property Tax Receipts
paragraphs: Property tax receipts are assumed to grow with our projections of the  property tax base.  In turn, property tax base projections are based on our  estimate of the relationship between real GDP and the real market value of  real estate owned by both the household sector and the nonfarm,  nonfinancial business sector.  Data for the market value of real estate are  obtained from the sectors’ balance sheets in the Federal Reserve Board’s  flow of funds accounts.  We estimated that the long-run responsiveness of  property values to GDP is 1.13, which implies that the property tax base  tends to grow somewhat more than GDP.
See appendix IV equations 44, 45, 46, and 47 for more information  on the property tax analysis.
section_title: Other Taxes on Production
paragraphs: States and localities also collect a variety of other taxes, fees, and  assessments that are classified in the NIPA as “other taxes on production.”   These include such items as motor vehicle license fees, severance taxes,  and special assessments.  In our projections, we assume these receipts  grow at the same rate as GDP.
See appendix IV equation 48 for more information on the analysis  of other taxes on production.
section_title: Estate and Gift Taxes
paragraphs: In the NIPA, estate and gift taxes are considered capital transfer receipts  rather than current tax receipts.  This distinction has some relevance for  how fiscal balance measures are calculated, as discussed later in this  appendix.  In the projections, we assume that taxes on estates and gifts  grow at a rate equal to the yield on 10-year Treasury securities.
See appendix IV equation 90 for more information on the estate  and gift tax analysis.
section_title: Contributions for Government Social Insurance
paragraphs: Contributions for government social insurance are a small component of  state and local governments’ total current receipts related to payments for  items such as disability insurance and unemployment coverage.  The model  assumes these tax receipts grow at the same rate as total wage and salary  disbursements in the economy.
See appendix IV equation 50 for more information on the analysis  of contributions for government social insurance.
section_title: Income Receipts on Assets
paragraphs: Income receipts on assets include interest receipts, dividends, and rents  and royalties earned on the financial holdings of state and local  governments.  Projections of income receipts on assets require future  values for both the effective rate earned on financial assets and total  financial assets owned by the sector.  These calculations are discussed in  appendix II.
section_title: Current Transfer Receipts
paragraphs: Current transfer receipts include several major categories of state and local  government nontax receipts.  These include three types of federal grants- in-aid:  federal Medicaid grants, other federal grants for current  expenditures, and federal investment grants.  The sector also receives  transfers from business and persons, such as fines and tobacco settlement  payments.
subsections: section_title: Federal Medicaid Grants
paragraphs: To project federal Medicaid grants in our base case, the model uses CBO’s  intermediate growth Medicaid outlay projections.  For the first 10 years  these are available in the most recent edition of CBO’s Budget and  Economic Outlook.  Thereafter, we use CBO’s projections for Medicaid  outlays as a share of GDP from the most recent long-term budget  publication.
Following the NIPA treatment, we subtract the “clawback” from Medicaid  grants.  Because of the passage of Medicare Part D, states pay the federal  government a portion of the costs of prescription drugs previously covered  under the Medicaid program.  Thus, the Medicare Part D program will save  states some of the payments they would have made towards prescription  drugs under Medicaid to enrollees.  The returned portion that states are  required to remit is known as the clawback.  CBO has estimated the  clawback for the first 10 years.  Thereafter, we extrapolate the clawback as  a constant percentage of total federal Medicaid grants in the tenth year of  CBO’s estimates.
See appendix IV equations 54 and 55 for more information on the  analysis of federal Medicaid grants.
section_title: Other Federal Grants to Finance Current Expenditures
paragraphs: In addition to Medicaid grants, the federal government provides grants  intended to finance other current expenditures of state and local  governments.  Examples include grants for education, welfare and social  services, and housing and community services.  For the first 10 years, we  project other federal grants for current spending by subtracting CBO’s  Medicaid grant projections from CBO’s projection of total grants for  current expenditures.  After the first 10 years, we assume that other current  grants grow with inflation plus population growth, keeping the real grant  level per person stable, which is our implementation of a current policy  scenario.
section_title: Federal Investment Grants
paragraphs: Because federal investment grants finance investment, rather than current  expenditures, the NIPA classifies investment grants as a form of capital  transfer.  While capital transfers are not part of the state and local  government sector’s current receipts, they are included in the sector’s total  receipts.  For the first 10 years of the projections, we assume that federal  investment grants grow at the same rate as CBO’s projections for federal  capital transfers.  After the first 10 years, we assume that investment grants  grow with inflation plus population growth, which again reflects our  implementation of a current policy scenario.
section_title: Transfers from Business and Persons
paragraphs: Transfers from business include state and local fines and other nontaxes,  such as tobacco settlements.  Similarly, personal transfer payments to  government include donations, fees, and fines.  Both types of private  transfers are assumed to grow with GDP in the projections.
See appendix IV equations 56 and 57 for more information on the  analysis of transfers from businesses and persons.
section_title: Current Surplus or Deficit of Government Enterprises
paragraphs: The current surplus or deficit of government enterprises is the difference  between receipts and costs for a variety of businesslike operations of state  and local governments.  These enterprises provide such services as water,  sewerage, gas, electricity, toll facilities, liquor stores, air and water  terminals, housing and urban renewal, public transit, and a residual  category covering such items as lotteries.  As we examined the trends in  the various enterprises we found that some types of enterprises tend to  have surpluses (e.g., lotteries, liquor stores), some tend to have deficits  (e.g., public transit, public housing), and some tend to run roughly at a  break-even level (e.g., electricity, water).  The overall balance for the entire  state and local enterprise sector was sometimes positive and sometimes  negative.  We determined that no trend could be established and we  therefore assumed that across all state and local governments and across  all the types of enterprises, the budgets are balanced.  We therefore set the  balance for the enterprise sector equal to zero.
See appendix IV equation 58 for more information on the analysis  of the current surplus or deficit of government enterprises.
section_title: Projection of Expenditures of the State and Local Government Sector
paragraphs: In the NIPA, expenditures are divided into five categories, some much  larger than others.  Figure 9 shows the five types.  Consumption  expenditures, the largest category, includes such items as the  compensation of state and local government employees.  Transfer  payments include Medicaid payments.  Smaller classifications are:  interest  paid on the outstanding debt of these governments, subsidies, and  expenditures for investments in fixed capital and nonproduced assets.
subsections: section_title: Consumption Expenditures
paragraphs: Consumption expenditures include an array of expenses related to direct  spending to finance current operations.  The largest component of these  expenses is compensation of state and local government workers.  In  addition, the implicit cost of depleted capital—or depreciation—is a  consumption expenditure.  Other consumption expenditures include  intermediate goods and services used to provide current services.  Certain  offsets are also made against consumption expenditures in the form of  receipts for services the sector provides and the costs of investment goods  the sector itself produces.
subsections: section_title: Compensation of State and Local Government Employees
paragraphs: The model projects several categories of employee compensation.  These  include (1) wages and salaries, (2) pension plan contributions, (3)  employee and retiree health care costs, and (4) other employee benefits.
In our base-case projections, which hold policy constant, we let  employment grow with the population projections in the 2007 Social  Security Trustees’ report and allowed wages and salaries per employee to  grow with CBO’s projected increase in the employment cost index for  private sector wages and salaries.  These assumptions permit the sector to  keep the number of employees per citizen constant while paying salaries  that keep pace with those in the private sector.  These assumptions reflect  our judgment about how to implement a current policy scenario for these  factors.
See appendix IV equation 9 for more information on the  projections of wages and salaries.
The model includes a set of relationships that solve for the contribution  that state and local sector employers must make in order to fund pension  plans on an ongoing basis.  These relationships are explained in appendix  III.
We developed estimates of the aggregate cost to state and local  governments for the health insurance of both active and retired employees.   These costs are projected on a pay-as-you-go basis and their derivation is  described in appendix III.
Other employee benefits include life insurance and workers compensation  contributions.  These expenditures are assumed to grow with employment  in the sector plus increases in the employee cost index for wages and  salaries.
See appendix IV equation 68 for more information on the  projections of other employee benefits.
section_title: Consumption of Fixed Capital (Depreciation)
paragraphs: Consumption of fixed capital, or depreciation, is another component of  NIPA state and local government consumption.  We calculated depreciation  in a given year as a constant percentage of the prior year’s capital stock.
Thus, the projections of investment, depreciation, and the capital stock are  all interrelated.  From a starting point for the level of capital stock in 2006,  we increase the level of capital going forward each year by an estimate of  gross investment.  We assume that gross investment grows with population  growth and inflation—our implementation of a current policy scenario.  We  then subtract a portion of the previous year’s capital stock to reflect  depreciation.  The depreciation rate used in the projections is 2.8 percent,  which is based on values for real consumption of fixed capital relative to  real capital stock reported in recent years of NIPA data.
See appendix IV equations 70 through 75 for more information on  the projections of the consumption of fixed capital (depreciation).
section_title: Miscellaneous Consumption Expenditures
paragraphs: The final component of consumption expenditures is a miscellaneous  category that is equal to other consumptions expenditures minus two  items:  own-account investment and sales to other services.  1. Other consumption expenditures include such intermediate  purchases as rent, gasoline, utilities, and supplies. 2. Own-account investment is the compensation of employees and the  expenditures related to the sector’s own production of investment  goods, such as software and other capital assets.  Because own-account  investment expenditures represent the acquisition of long-term assets  and are included in purchases of fixed assets, they must be subtracted  from consumption expenditures to avoid double counting these  expenses.   3. Sales to other sectors include tuition and related educational  charges, health and hospital charges, and other sales of goods and  services sold by the state and local sector that are not considered  enterprise sales.  Since these revenues are derived from the provision  of services funded by consumption expenditures, they are netted  against the costs of providing those services.
Despite the three separable components contained within this final  classification of consumption expenditures, the model does not include  explicit relationships explaining each of these components.  Instead, we  assume that other consumption will grow with inflation plus population  growth.
See appendix IV equation 69 for more information on the  projections of miscellaneous consumption expenditures.
section_title: Transfer Payments to Persons
paragraphs: The model divides state and local government transfer payments to  persons into two categories, medical care payments and other transfers to  persons.  Medical care transfers include both Medicaid and other medical  care payments, the latter of which consist of general medical assistance  and the state children’s health insurance program (SCHIP).  The other  transfer category includes a broad array of payments to individuals such as  workers’ compensation, temporary disability, and family assistance.
subsections: section_title: Medical Care Transfer Payments
paragraphs: Because the Medicaid program provides matching grants to state  governments, Medicaid grants and medical care transfer payments  generally have been closely related. The close relationship between  Medicaid grants and transfer payments supports modeling medical care  transfers as a constant multiple of CBO’s projection for Medicaid grants.  In  recent years, the ratio of the sector’s medical transfer payments to  Medicaid grants was 1.726, which implies that the federal government  ultimately paid about 58 percent of total state and local medical care  payments (including Medicaid, general medical assistance, and SCHIP  payments) while states financed 42 percent of the total with their own  funds.  This relationship is applied to CBO’s Medicaid grant projections— which are available to 2050—for our projections of state and local medical  care payments.
See appendix IV equation 77 for more information on the  projections of medical care transfer payments.
section_title: Nonmedical Transfer Payments to Persons
paragraphs: Nonmedical transfer payments include a broad array of transfers such as  temporary disability insurance, workers’ compensation, family assistance,  education assistance, foster care, adoption assistance, and expenditures  for food under the supplemental program for Women, Infants, and  Children.  In our base-case projections of these payments, real spending  per capita is kept constant, reflecting our current policy scenario.   Equivalently, payments grow with inflation and population growth.
See appendix IV equation 78 for more information on the  projections of nonmedical transfer payments to persons.
section_title: Interest Paid
paragraphs: State and local governments pay interest on their outstanding debt.   Interest payment projections require estimates of future effective interest  rates on debt and the amount of debt outstanding.  This discussion requires  more detail and is explained in appendix II.
section_title: Subsidies
paragraphs: Subsidies are a very small remaining category of current expenditures  consisting mainly of payments to railroads.  California’s payments to  electricity suppliers from 2001 through 2003 were also classified as  subsidies.  In the simulations, subsidies are assumed to grow with inflation  and population.
See appendix IV equation 84 for more information on the  projections of subsidies.
section_title: Gross Investment and Purchases of Nonproduced Assets
paragraphs: Because they are capital outlays, gross investment and purchases of  nonproduced assets are considered to be investment expenditures.  As  such they are not considered a current expenditure.  Investment  expenditures cover the acquisition of all longer-lived assets including  structures, equipment, and software, and nonproduced assets consist of  the net acquisition of land less oil bonuses.  We grow both of these factors  at the combined rate of inflation and population growth.
See appendix IV equations 70, 71, and 92 for more information on  the projections of gross investment and the purchase of  nonproduced assets.
section_title: Measures of Fiscal Balance
paragraphs: We use two measures of fiscal balance in our report:  net lending or  borrowing and the operating balance.  In addition, a third measure—net  saving—is not directly discussed but is a conceptually important measure.
subsections: section_title: Net Lending or Borrowing
paragraphs: The first projected balance measure is NIPA’s net lending or borrowing,  which is the difference between total receipts and total expenditures and is  analogous to the federal unified surplus or deficit.  Thus, this balance is  measured as the sum of all receipts discussed in this appendix minus all  expenditures, with one exception.  While the measure of total expenditures  used to calculate net lending or borrowing includes both current  expenditures and capital expenditures, it excludes the consumption of  fixed capital (depreciation) because the latter is not a cash outlay.  The  value of net lending or borrowing must be financed by some combination  of changes in financial assets and liabilities.
See appendix IV equation 93 for more information on  measurement of net lending or borrowing.
section_title: Operating Balance Net of Funds for Capital Expenditures
paragraphs: The second balance measure we use is a GAO-developed measure that we  call the operating balance excluding funds for capital expenditures.  This  measure is designed to be roughly akin to the operating budgets of  subnational governments—budgets which these governments are generally  required to balance or nearly balance.  We develop a measure of receipts  not available to finance current spending as the difference between  investment spending and the change in medium- and long-term debt.   Subtracting this amount from total receipts leaves the estimated receipts  that are available to finance current expenditures.  The expenditure  component of the balance measure excludes both investment spending and  depreciation.
Our operating balance measure includes two further adjustments to NIPA- based totals.  First, we exclude the current surplus/deficit of government  enterprises from receipts because state and local government operating  budgets exclude government enterprises.  This adjustment has no effect on  our base case simulations because we assume the balance is equal to zero,  but its incorporation accommodates potential alternative assumptions  about the current balance of government enterprises.  We also exclude a  category of funds that we call the net balance of social insurance funds.  As  noted earlier, state and local employees as well as employers make  contributions to social insurance funds to pay for such items as temporary  disability and workers’ compensation insurance.  Although not explicitly  mentioned earlier, payments from these funds are embedded in transfer  payments that governments pay to workers when they are disabled or  otherwise entitled to payments from these insurance funds.  In our  simulations, the balance is assumed to grow with total wage and salary  disbursements.  While governments hold balances in these funds, the funds  are not available for operating expenses.
See appendix IV equation 94 for more information on the  measurement of the operating balance net of funds for capital  expenditures.
section_title: Net State and Local Government Saving
paragraphs: The model also solves for net state and local government saving, which is a  key balance measure in the NIPA that has important macroeconomic  implications.  Net state and local government saving is simply the sum of all  current receipts (that is, all receipts discussed earlier except investment  grants and estate taxes) minus the sum of all current expenditures, where  current expenditures include the consumption of fixed capital  (depreciation) but exclude investment spending.
See appendix IV equation 85 for more information on the  measurement of net saving.
section_title: Projections of Income Earned on Assets and Interest Paid on Liabilities of the State and Local Sector
paragraphs: This appendix describes how we developed estimates of financial earnings  and interest paid on outstanding debt for the state and local government  sector.  This analysis starts with a method for translating state and local  government budget surpluses or deficits—as measured by the difference  between its total receipts and expenditures and labeled net lending or  borrowing—into changes in the sector’s financial assets and financial  liabilities.  We also describe how we estimated the effective rate earned on  the sector’s financial assets and the effective rate paid on its credit market  liabilities in each year and applied these rates to the prior year’s assets and  liabilities, respectively, to provide estimates of the sector’s asset income  and interest payments.
subsections: section_title: Budget Outcomes and Financial Assets and Liabilities
paragraphs: For any entity, there is a direct relationship between budget outcomes and  changes in financial position.  In particular, if expenditures exceed  receipts, the gap needs to be financed by some combination of changes in  financial assets and changes in financial liabilities—that is, 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 For a given difference between total receipts and total expenditures, that  is, the value of net lending or borrowing, various combinations of changes  in financial assets and changes in financial liabilities can satisfy this  identity.  To leverage this relationship for our projections we use two  methods.  The first applies when net lending or borrowing is in its historical  range.  The second is necessary for a good portion of our simulations  because the ever-growing deficits that we find are inconsistent with  historical experience, and relying on the first method would produce  unrealistic results.
subsections: section_title: Method Used When Budget Outcomes Are in Historical Range
paragraphs: Traditionally, total expenditures have usually exceeded total receipts, and  the sector has been a net borrower.  But the gap has rarely been large, so  the borrowing requirements have usually been modest.  If our estimate of  net lending or borrowing falls into a range similar to historical experience,  we invoke the accounting identity above by estimating the growth in four  components of financial liabilities of the sector as provided in the Federal  Reserve’s Flow of Funds Accounts.  These components include three types  of credit market instruments: short-term municipal securities, medium- and  long-term municipal securities, and U.S. government loans, as well as  “trade payables,” which are related to the acquisition of goods and services  for conducting operations, and are not credit market liabilities.  The  growth in the values of these four types of liabilities, along with the  estimate of net lending or borrowing, then determines the change in assets  necessary to satisfy the identity. When the size of the balances is consistent  with historical experience, the model projects each of these financial  liabilities as follows:    Short-term debt.  The model includes an econometric equation linking  short-term debt to net saving.  The equation also includes several  dummy variables controlling for periods of unusual changes in short- term debt, and autoregressive and moving average error terms to  control for serial correlation of the residuals and improve the equation’s  fit.  The equation indicates that short-term debt issuance is inversely  related to the sector’s net saving, which implies that past deficits were  financed in part by short-term borrowing.
Medium- and long-term debt.  Changes in medium- and long-term  municipal debt are mostly linked to capital expenditures (including  land) and their financing.  Some combination of tax receipts, federal  investment grants, and debt can be used to finance state and local  government investment.  Accordingly, a relationship was estimated in  which the change in the municipal bond rate explains how much debt is  used to finance the gap between investment spending and federal  investment grants.  The equation also includes dummy variables  covering periods when tax considerations and other unusual factors had  an important role in the amount of debt issued.  These dummy variables  control for unusually large long-term debt issuances in 1978 and 1985  and unusually large decreases in outstanding long-term debt in 1994 and  1995.  The projections assume that similar events will not occur over the  simulation period.  The relationship also includes an autoregressive  term to control for the serial correlation of the error term.
Borrowing from the U.S. Treasury.  The state and local government  sector also borrows modest amounts from the U.S. Treasury.  Our  estimates imply that real growth in borrowing from the Treasury is  negatively affected by real GDP growth.  During periods of relatively  strong growth, the sector borrowed less from the Treasury and during  periods of slow or negative growth, the sector borrowed more.  The  estimated equation also includes dummy variables to control for  unusual borrowing increases in 1984 and 1985 and an unusual decrease  in borrowing in 1988, along with autoregressive and moving average  error terms.
Trade payables.  Trade payables help finance the goods and services  the sector acquires in the conduct of its operations.  Accordingly, our  base-case simulations let trade payables grow at the same rate as other  consumption expenditures, which, in turn, grow with inflation plus  population growth.
As noted above, the historical tendency has been for the state and local  government sector to run small deficits and an occasional surplus as  measured by net lending or borrowing.  The method we have described  documents how we use the accounting identity to grow financial assets and  liabilities in similar circumstances.  If the sector runs large deficits,  however, as we find within a few years of our simulation, this methodology  generates unrealistic financial outcomes.  In particular, if the method were  used throughout our simulation analysis, ever-increasing deficits would  lead to declining values of financial assets—because under this method,  assets are the residual variable that balances the accounting identity.  In  later years assets would decline so substantially that they would become  negative.  Since this makes no economic sense because governments  require funds to meet current expenses, we developed an alternative  method that is triggered when key relationships become out of balance in  our simulation.
section_title: Alternative Method for Determining Changes in Financial Assets and Liabilities
paragraphs: Our methodology “switch” is triggered when receipts fall so substantially  short of expenditures—i.e., the sector is a substantial net borrower—that  assets grow less than gross domestic product (GDP).  If this occurs, in the  next period the model changes how short-term debt is projected.  Rather  than being independently projected, short-term debt then becomes the  residual variable that satisfies the accounting identity.  In this alternative  case, assets grow with GDP.
section_title: Projecting Income Receipts on Assets and Interest Payments on Debt
paragraphs: Income receipts on assets are part of the sector’s receipts while its interest  payments are part of its expenditures.  We have described how the model  determines the change in assets and liabilities in each year.  These earnings  or payments are calculated by setting an appropriate rate and applying that  rate to these asset or liability values, respectively.  In this section we  describe how we determine the effective rates earned and paid and how we  use those rates and the values of assets and liabilities to project asset  income and interest payments of the state and local sector.
subsections: section_title: Income Receipts on Financial Assets
paragraphs: Income receipts on assets are reported as a category of receipts in the  National Income and Product Accounts (NIPA).  We divide the income  receipts on assets by the value of financial assets at the end of the previous  year to calculate historical values for the effective rate earned on assets in  each past year.  The evolution of these past effective rates reflects the  turnover of old assets and the acquisition of new financial assets by state  and local governments.  This process can be captured by setting the  effective rate earned on assets in a given year equal to a weighted average  of the prior period’s effective rate and the given year’s prevailing market  rate on the types of assets that the sector purchases.
Using a simple regression model we developed weights of 0.81 for the prior  year’s effective rate earned and 0.19 for the given year’s yield on 3-month  Treasury securities, projections of which are available from the  Congressional Budget Office (CBO).  As stated, these weights reflect the  gradual turnover and replacement of assets with newer issues.  The  product of the effective rate earned and the prior period’s financial assets  equals the income earned on assets.
section_title: Interest Paid on Credit Market Liabilities
paragraphs: A similar method is used to derive interest paid on outstanding debt of the  sector.  First, we divide the sector’s interest paid by the value of credit  market liabilities outstanding at the end of the previous year to calculate  historical values for the effective rate paid on liabilities in each past year.   To develop weights for the simulations, we then model the effective rate of  interest paid as a weighted average of the effective return in the previous  period and the Aaa municipal bond rate for the given year.  Based on our  analysis, we set the effective rate paid equal to 0.88 times the prior year’s  effective rate paid plus 0.12 times the given year’s projected Aaa municipal  bond yield.  These weights reflect the gradual turnover and replacement of  municipal securities with newer issues.
We generated our own projections of the municipal bond yield based on a  relationship we estimated between the Moody’s Aaa municipal bond yield  and the 10-year Treasury yield.  We then use the estimated relationship and  CBO’s projections of the 10-year Treasury yield to calculate future values of  the municipal bond yield.  The sector’s interest payments are equal to the  product of the effective interest rate paid and the sector’s prior year  liabilities excluding trade payables.  In the model, therefore, explicit  interest payments only apply to the sector’s credit market liabilities.
section_title: Pension and Retiree Health Care Projections
paragraphs: This appendix provides information on the development of simulations of  future pension and health care expenditures for retirees of state and local  governments.  In particular, we provide information on (1) the  development of several key demographic and economic factors such as  future employment, retirement, and wages for the state and local  workforce that are necessary for the simulations of future pension and  retiree health care costs; (2) how we project the necessary contribution  rate to pension funds of state and local governments; and (3) how we  project the future yearly pay-as-you-go expenditures of employee and  retiree health insurance.
subsections: section_title: Development of Factors for Employment, Retirement, Wages, and Benefits
paragraphs: Key underlying information for the pension and health care expenditure  simulations relate to future levels of employment, retirees, and wages.  In  particular, to estimate the expenditures for the post-retirement promises  the sector has and will continue to make as well as expenditures for health  care for active employees, we need to project the number of employees and  retirees in each future year, as well as the dollar value of pension benefits  that will be earned and the extent to which those benefits will be funded  through employee contributions to pension funds.  The cost of health care  and the estimate of employees and retirees receiving health care benefits  are discussed later in this appendix.  We project the following key factors  for each year during the simulation time frame:  (1) the number of state and  local government employees, (2) state and local government real wages, (3)  the number of pension beneficiaries, (4) average real benefits per  beneficiary, and (5) yearly employee contributions to state and local  government pension plans.
subsections: section_title: Steps to Project Future Employment Levels
paragraphs: To project the level of employment in each future year, we assume that  state and local employment grows at the same rate as total population  under the intermediate assumptions of the Board of Trustees of the Old  Age, Survivors, and Disability Insurance (OASDI), commonly referred to as  Social Security—that is, we assume that the ratio of state and local  employment to total population remains constant.  The Trustees assume  that population growth gradually declines from 0.8 percent during the next  decade to a steady rate of 0.3 percent per year beginning in 2044.   Accordingly, state and local government employment growth displays the  same pattern in our projections.
section_title: Steps to Project Future State and Local Government Real Wages
paragraphs: The pension benefits that employees become entitled to are a function of  the wages they earned during their working years.  We assume that the real  employment cost index for the state and local sector will grow at a rate  equal to the difference between the Congressional Budget Office (CBO)  assumptions for the growth in the employment cost index (ECI) for private  sector wages and salaries and inflation as measured by the consumer price  index for all urban consumers.  CBO’s assumptions for growth in the ECI  and the Consumer Price Index for All Urban Consumers (CPI-U) are 3.3  percent and 2.2 percent per year, respectively, implying a real wage growth  of 1.1 percent per year during the simulation time frame.
Aggregate real wages are assumed to grow at the combined rate of growth  in the real employment cost index we have just described, and the level of  employment.  As noted previously, the Trustees project that population  growth slows from 0.8 percent in the upcoming decade to a steady rate of  0.3 percent after 2044.  Because population growth drives employment in  our projections, this slowdown implies that aggregate real wage growth  slows from 1.9 percent per year to a steady long-run rate of 1.4 percent.
section_title: Steps to Project Growth in the Number of Pension Beneficiaries
paragraphs: 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.  While actuaries use  detailed information and assumptions regarding the age, earnings, service  records, and mortality rates applicable to the entities they evaluate,  information in such detail is not available for the state and local  government sector as a whole.  This lack of detailed data necessitated the  development of a method of projecting aggregate state and local  beneficiary growth that is much simpler than the methods that actuaries  employ.
The method we developed reflects the logic that each year’s growth in the  number of beneficiaries is linked to past growth in the number of  employees.  Total state and local government employment from 1929  through 2005 was obtained from the national income and product accounts  (NIPA) tables 6.4a, b, c, and d.  The Census Bureau provided data on the  number of state and local pension beneficiaries from 1992 through 2005  during which continuous observations were available.  Cyclical swings in  the employment series were removed using a Hodrick-Prescott filter.  Then,  both the employment and beneficiary series were logged and first- differenced, transforming the data from levels to proportionate changes.   We developed a routine that searched across 45 years of lagged  employment growth to select a set of weights for the years in which past  employment growth best explained a given year’s growth in beneficiaries.   The routine included the restrictions that the weights must be nonnegative  and sum to one.
The method produced a relationship that reflected the contribution of a  particular past year’s employment change in explaining a given year’s  change in retirees.  In particular, the estimated relationship suggests that  beneficiary growth in a given year is largely determined by employment  growth 21, 22, 23, and 34 years prior to the given period.  This pattern  appears consistent with the categories of workers that the sector employs.   Many fire and police positions, for example, offer faster pension accrual or  early retirement due to the physical demands and risks of the work, while  many other state and local workers have longer careers.
section_title: Steps to Project Real Benefits Per Beneficiary
paragraphs: While, in the long run, the average real benefit level should grow at the  same rate as real wages—that is, at 1.1 percent per year—in the first  decades of the projection the average real benefit will be affected by real  wage changes that occurred before the projection period.  Accordingly, we  developed a relationship that reflects how the average real benefit level will  change over time according to changes in the number and average real  benefit level of three subsets of the retiree population:  (1) new retirees  entering the beneficiary pool, (2) new decedents leaving the pool, and (3)  the majority of the previous year’s retirees who continue to receive benefits  during the given period.  Each group’s real benefit is linked to the real wage  level in the average year of retirement for that group.  Thus, to determine  the average real benefit overall in any future year, we need weights and real  wage indexes for the three groups that can be used to develop a rolling  average real wage of the recipient pool in each future year.
Earlier we described how we project the percentage change in the total  number of beneficiaries between two successive years, but this difference  is actually comprised of two elements:  the percentage change in new  retirees minus the percentage change in decedents.  Therefore, to  determine the weight for new retirees, we also need an estimate of the  number of new decedents in each year.  In order to estimate a “death rate,”  we utilize Social Security Administration data on terminated benefits and  total Social Security recipients, which excludes disability recipients.  Our  death rate for the forecast period is set equal to the number of terminated  Social Security recipients divided by the total number of Social Security  recipients in 2003—3.67 percent.  This analysis then enables a derivation  of weights for each of the three groups as follows:    weight for new retirees:  the number of beneficiaries this year, less the  number of beneficiaries last year who are still alive, divided by the  number of beneficiaries this year;  weight for continuing recipients is equal to last year’s beneficiaries  divided by this year’s beneficiaries; and   weight for the deceased is the death rate multiplied by last year’s  beneficiaries divided by this year’s beneficiaries.
Next, we identied the real employment cost index that determines the real  benefit level for each of these three groups.  We do so by estimating the  average retirement year applicable to each of the three groups.  First, we  assume the average retirement age is 60.  We developed this estimate based  on an analysis of the March Supplement to the Current Population Survey  (CPS) for 2005-2006, which indicated that the average state and local  government retiree had retired at 60 years of age.  We also analyzed  detailed data on the age distribution of Social Security recipients provided  by the Office of the Actuary of the Social Security Administration.  These  data showed that the average age for new decedents is about 81 during the  initial years of OASDI’s simulations, and we thus used a 21-year lag—81  minus 60—to estimate the real wage applicable to this group.  For the  newly retired group, we use the given year’s employment cost index.  For  the remaining retirees—those already retired and remaining in the group— we use information from CPS for 2005 which indicated that the average age  of a retired state or local retiree was 68.  Therefore, we apply an 8-year lag  to the real employment cost index to determine real benefits of this group.   We then use this information to create a weighted average employment  cost index for the retiree pool in any given year.
The ratio of the given year’s weighted average real wage index to the  previous year’s weighted average real wage index should equal the ratio of  the current to the previous year’s average real benefit levels.  Thus, a given  year’s average real benefit level grows at the same rate as the rolling index  of real wages.  The relationship has the desired property of capturing the  effect of historical real wage growth in the initial decades of the projection  before converging to a long-run average annual growth rate of 1.1 percent,  which is consistent with our assumption for real wage growth.  To calculate  aggregate real pension benefit payments, the average real benefit is  multiplied by the number of beneficiaries projected.
section_title: Steps to Project Employee Contributions to Pension Funds
paragraphs: Employee contributions represent an important funding source for state  and local government pension plans.  In 2006, for example, NIPA data  indicate that employees contributed 4.5 percent of their wages and salaries  to their retirement funds.  To estimate future employee contributions, we  simply assume that the 2006 contribution level is held constant as a share  of aggregate wages.
section_title: Projections of Necessary Contributions to Pension Funds for State and Local Government Sector
paragraphs: The purpose of the pension simulations is to estimate the steady  contribution rate 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.
In the previous section we discussed how we calculate a variety of critical  demographic and economic factors that are necessary for this analysis.   The necessary contribution rate can now be derived according to  straightforward logic:  the benefits that are promised to employees  (including liabilities already made and promises that will be made in the  future) must be paid from three sources:  (1) existing pension fund assets  at our starting point in 2006, (2) contributions that employees will continue  to make to those funds in the future, and (3) contributions that employers  will make to those funds in the future.  Mathematically we start with the  present value of future pension benefits.  We then subtract two things:  the  value of pension fund financial assets in 2006—which was approximately  $2.979 trillion—and the present value of employee contributions.  The  present value of the remaining liability is the value that the governments  must fund.  We then divide that present value by the present value of future  wages.  This yields the steady level of employer contribution, relative to  wages, that would need to be made in every year between 2006 and 2050 to  fully fund promised pension benefits.
Although we are only interested in developing necessary contribution rates  over the simulation time frame—that is, until 2050—we actually have to  derive the contribution rate for a longer time frame in order to find the  steady 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 level of contribution is not  attained and the necessary contribution rate is understated. As such, all of  the flows in the calculation extend 400 years into the future.  We use a real  rate of return on pension assets of 5.0 percent to discount future flows  when deriving present values.
Applying this analysis, we found that in aggregate, state and local  government contributions to pension funds would need to increase by less  than half a percent to fund, on an ongoing basis, the pension liabilities they  have and will continue to incur.  In particular, the 2006 pension  contributions for the sector amounted to 9 percent of wages, and our base- case estimate is that the level would need to be 9.3 percent each year to  fully fund pensions.
To examine the sensitivity of our model results we altered our assumptions  regarding the expected real yield, and found that the model results are  highly sensitive to this rate.  For our primary simulations, we based the  expected real yield on actual returns on various investment instruments  over the last 40 years as well as the disposition of the portfolio of assets  held by the sector over the last 10 years.  This generated a real yield of 5  percent.  But some pension experts have expressed concern that returns on  equities in the future may not be quite as high as those in the past.  In fact,  some analysts believe that an analysis of this type should only consider  “riskless returns.”  Under such an approach we would assume that all  pension funds are invested in very safe financial instruments such as  government bonds.  We estimated the necessary steady level of employer  contributions holding all elements in the model stable except the real  expected yield.  In particular, we analyzed a 4 percent real yield and a 3  percent real yield—the latter of which is a reasonable proxy for a riskless  rate of return.  We found that if returns were only 4 percent, the necessary  contribution rate would rise to 13.4 percent, and if we used a risk-free  return of roughly 3 percent, the necessary contribution rates would need to  be much higher—nearly 18.1 percent of wages.  On the other hand, if real  returns were higher than our base-case level—perhaps 6 percent—the  necessary contribution rate would only be only 4.4 percent, much lower  than the current contribution rate.
section_title: Projections of Health Care Costs for State and Local Employees and Retirees
paragraphs: Most state and local governments pay for employee and retiree health  insurance on a pay-as-you-go basis—that is, these benefits are generally not  prefunded.  We made projections of the pay-as-you-go expenditures for  health care for the sector, as a percentage of wages, in each year until  2050.  To estimate expenditures for employee and retiree health insurance  in future years, we made many of the same assumptions as for the pension  analysis.  In particular, we use the same method to develop projections of  employment in the sector, the number of retirees, and the level of wages.   An additional assumption for the health care analysis is that in future years,  the same percentage of employees and retirees of state and local  governments will be enrolled in health insurance through their previous  employer as we observe were enrolled in 2004—the most recent year for  which data were available.  For retirees, we developed this measure from  two data sources.  The Census Bureau’s State and Local Government  Employee-Retirement System survey provided data on the total number of  state and local retirees, and the Health and Human Services Department’s  Medical Expenditure Panel Survey (MEPS) provided data on state and local  government retirees who are covered by employer-provided health  insurance.  Based on these data sources we found that the share of retirees  with health insurance is 44 percent, and we hold this constant through the  simulations.  From the latter data source we also obtained the most recent  year state and local government spending on health care for retirees.  For  active employees we also used MEPS data on employees covered by health  insurance and compared that to BEA data on the total employment in the  sector.  This provided us with a finding that 71 percent of active employees  are receiving health benefits.  Again, we hold this value constant during the  simulation time frame.
One of the most central assumptions we must make to estimate the pay-as- you-go health care expenditures for employees and retirees in future years  is the cost growth of health care itself.  The cost of health care has been  increasing faster than gross domestic product (GDP) for many years.  As  such, we developed assumptions about how much faster health care costs  would grow, relative to the economy, in future years.  The extent to which  the per-person cost of health care is expected to grow beyond GDP per  capita is called the “excess cost factor.”  We developed these estimates  based on our own research and discussions with experts.  In particular, we  assume that the excess cost factor averages 1.4 percentage points per year  through 2035, and then begins to decline, reaching 0.6 percentage points by  2050.
Using these assumptions we developed projections for the expenditures on  health care for employees and retirees each year through 2050.  We found  that the projected expenditures for retiree health insurance, while not a  large component of state budgets, will more than double as a percentage of  wages over the next several decades.  In 2006, these expenditures  amounted to approximately 2.1 percent of wages, and by 2050 we project  that they will grow to nearly 5.1 percent of wages—a 150 percent increase.   As with the projections of necessary pension contributions, our estimates  of these expenditures are highly sensitive to certain of our assumptions.  In  particular, the assumptions regarding health care cost growth are critical.   For example, if health costs were to only rise at the rate of GDP per capita,  expenditures for retiree health care would only grow, as a percentage of  wages, from 2.1 percent today to 3.0 percent by 2050.  Conversely, if health costs were to grow by twice the rate we assume in the base case, these  costs, as a percentage of wages, would constitute 8.7 percent by 2050.
Active employees’ expenditures on health care amounted to 12.8 percent of  wages in 2006 and by the end of the simulations in 2050 are expected to be  22.2 percent of wages.  In the case of the optimistic scenario—with lower  escalation in the cost of health care—we found that expenditures on  employee health care will only rise slightly to 13 percent of wages by 2050.   However, under the pessimistic scenario characterized by more rapidly  growing health costs, expenditures on health care for active employees rise  to 37.7 percent of wages in 2050.
section_title: State and Local Model Equations
paragraphs: This appendix lists the 105 equations that are used to simulate the base  case for the State and Local Model.
Variable(-X): Represents the variable lagged X periods. (Expression>=0): Is an indicator term that is one when the expression  evaluates to greater than zero and is zero otherwise.
AR(X): Indicates an auto-regressive of order X is included in the  econometric specification.
MA(X): A moving average term of order X is included in the specification.
YEAR: The current year being forecasted.
subsections: section_title: Pension Equations
paragraphs: 1.
EGSLALL  = ((NP  / NP(-1)) )  * EGSLALL(-1)  * (LYFCST  - YEAR>=0)   + EGSLALL(-1)  * (EGSLALL(-1)  / EGSLALL(-2))  * (LYFCST  -  YEAR<0) 2. EGSLALL_HP  = (EGSLALL  / EGSLALL(-1))  * EGSLALL_HP(-1) 3. GSL  = (EGSLALL  / EGSLALL(-1))  * EGSL(-1) 4. DLOG(BENEFICIARIES)  = 0.5594068  * DLOG(EGSLALL_HP(-34))  +  0.0003020  * DLOG(EGSLALL_HP(-25))  + 0.0002169  *  DLOG(EGSLALL_HP(-24))  + 0.0225695  * DLOG(EGSLALL_HP(-23))  +  0.1913009  * DLOG(EGSLALL_HP(-22))  + 0.2262039  *  DLOG(EGSLALL_HP(-21))
5.
JECISTLC  = JECISTLC(-1)  * (JECIWSP  / JECIWSP(-1))
6.
section_title: Receipts
section_title: Expenditures
paragraphs: 59. GSLEXPC  = GSLC  + YPTRFGSL  + GSLINTPAY  + SUBGSL  +  60. GSLC  = GSLCWSS  + GSLCKF  + GSLCO 61. GSLCWSS  = GSLCWAGE  + GSLCPEN  + GSLCHLTH  +  62. GSLCHLTH  = RETGSLCHLTH  + EEGSLCHLTH 63. (EEGSLCHLTH  / EGSLHLTH )  = (EEGSLCHLTH (-1)  / EGSLHLTH (- 1))  * (HLTHNHEEXCGR)  * ( (GDP  / NP)  / (GDP(-1)  / NP(-1))) 64. (RETGSLCHLTH  / RETHLTH )  = (RETGSLCHLTH (-1)  / RETHLTH (- 1))  * (HLTHNHEEXCGR)  * ( (GDP  / NP)  / (GDP(-1)  / NP(-1))) 65. RETHLTH  = RETHLTHPERBEN  * BENEFICIARIES  * (EGSL  /  EGSLALL) 66. RETHLTHPERBEN  = RETHLTHPERBEN(-1) 67. EGSLHLTH  = EGSL  * (EGSLHLTH(-1)  / EGSL(-1)) 68. GSLCOTHBEN  = GSLCOTHBEN(-1)  * (JECISTLC  / JECISTLC(-1))  *  (EGSL  / EGSL(-1)) 69. GSLCO  = GSLCO(-1)  * (NP  / NP(-1))  * (JPGDP  / JPGDP(-1)) 70. GSLGI  = GSLGI(-1)  * (NP  / NP(-1))  * (JPGDP  / JPGDP(-1)) 71. GSLGIR  = GSLGI  / (JPGDP  / 100) 72. KGSLR  = KGSLR(-1)  + GSLGIR  - GSLCKFALLR 73. GSLCKFALLR  = 0.027508  * KGSLR(-1) 74. GSLCKFALL  = GSLCKFALLR  * (JPGDP  / 100) 75. GSLCKF  = GSLCKF(-1)  * (GSLCKFALL  / GSLCKFALL(-1)) 76. YPTRFGSL  = YPTRFGSLPAM  + YPTRFGSLPAO 77. YPTRFGSLPAM  = 1.726  * GFAIDSLSSMED 78. YPTRFGSLPAO  = YPTRFGSLPAO(-1)  * (NP  / NP (-1)) * (JPGDP /  JPGDP (-1))
section_title: Interest Rates
paragraphs: 79. RMMUNIAAA_RESID  = RMMUNIAAA_RESID(-1) 80. RMMUNIAAA  = 0.707151184659468  + 0.761815685970831  *  81. GSLINTPAY  = (RATEOWED  / 100)  * SLG_LCRED(-1) 82. RATEOWED  = 0.8765652676  * RATEOWED(-1)  + (1  - 0.8765652676)  *  83. NETASSETPAY  = GSLINTPAY  - YGSLA 84. SUBGSL  = SUBGSL(-1)  * (NP  / NP(-1))  * (JPGDP  / JPGDP(-1))
section_title: Balance Measures and Investment
section_title: Financial Assets and Liabilities
paragraphs: 95. D(DBTGSLLT)  / (GSLGI  + GSLNETPCHNA  - IGRANT)  =  0.478671765326665  - 0.0678320738849175  * D(RMMUNIAAA)  +  0.469267348080956  * D78  + 1.3549185597115  * D85  -  0.571578002864546  * D94  - 0.549417581324232  * D95  + [AR(1)  =  0.720101110280723] 96. D(DBTGSLST)  / GDP  = (0.000435862040461702  - 0.237982866875603  *  D(NETSAVGSL)  / GDP  - 0.00116135948551944  * D75  -  0.00305076556061719  * D76  - 0.00187119472727474  * D77  -  0.00199332729108819  * D87  + [AR(1)  = 0.419998514150027  , AR(3)  =  0.377010382422796  , MA(1)  =  - 0.378513568750189  , MA(2)  =  0.320241719235162  , MA(3)  =  - 0.93530313308922  , BACKCAST  =  1964])  * (1  - SLG_AFINLSWITCH(-1))  + ((D(SLG_AFINL)  -  D(DBTGSLLT)  - D(TRADEPAYABLES)  - D(DBTGSLUS)  -  NETLENDGSL)  / (GDP))  * ( SLG_AFINLSWITCH(-1)) 97. DDBTGSLSTGDP_GRECON  = (0.000435862040461702  -  0.237982866875603  * D(NETSAVGSL)  / GDP  - 0.00116135948551944  *  D75  - 0.00305076556061719  * D76  - 0.00187119472727474  * D77  -  0.00199332729108819  * D87  + [AR(1)  = 0.419998514150027  , AR(3)  =  0.377010382422796  , MA(1)  =  - 0.378513568750189  , MA(2)  =  0.320241719235162  , MA(3)  =  - 0.93530313308922  , BACKCAST  =  1964]) 98. DBTGSLTE  = DBTGSLLT  + DBTGSLST 99. DLOG(DBTGSLUS  / (JPGDP  / 100))  = 0.026989088699108  -  1.46693539308972  * DLOG(GDPR)  + 0.671368273891861  * D84  +  0.347532134271165  * D85  - 1.11842691662586  * D88  + [AR(2)  =  -  0.304187166509849  , MA(1)  =  - 0.961550584145944  , BACKCAST  =  1970] 100. TRADEPAYABLES  = TRADEPAYABLES(-1)  * (GSLCO  / GSLCO(-1)) 101. SLG_LCRED  = DBTGSLLT  + DBTGSLST  + DBTGSLUS 102. SLG_LFINL  = SLG_LCRED  + TRADEPAYABLES 103. SLG_AFINLSWITCH  = ((SLG_AFINL/ SLG_AFINL(-1))  - (GDP/GDP(- 1))<=0)  * (YEAR>(LYACTUAL  + 1))  * (DSLG_AFINL_GRALT  /  SLG_AFINL(-1)  - (GDP/GDP(-1)-1)<=0)  104.  SLG_AFINL_GRALT  = GDP  * DDBTGSLSTGDP_GRECON  +  D(DBTGSLLT)  + D(TRADEPAYABLES)  + D(DBTGSLUS)  +  NETLENDGSL 105.  D(SLG_AFINL)  = (NETLENDGSL  + D(SLG_LFINL))  * (1  -  SLG_AFINLSWITCH(-1))  + (SLG_AFINL(-1)  * ((GDP  / GDP(-1))  - 1))   * ( SLG_AFINLSWITCH(-1))
section_title: Alphabetical List of State and Local Sector Model Variables and Definitions
paragraphs: This appendix describes the variables in the state and local model as well  as their sources.
BENEFICIARIES = Total retired state and local government beneficiaries  receiving periodic benefit payments, thousands; Census Bureau  Government Retirement System. Values prior to 1981 are imputed using a  constant growth rate between available data.
CBASE = Personal consumption less food, services, electronic and mail- order sales, billions of dollars; U.S. Commerce Department, Bureau of  Economic Analysis NIPA table 2.3.5 lines 1 - 7 - 13 less Census Current  Business Reports, Annual Revision of Monthly Retail and Food Services:  Sales and Inventories—January 1992 Through February 2006 Table 2  NAICS code 4541 (for 1992 and later years)  http://www.census.gov/prod/www/abs/br_month.html or Census Historical  Retail Trade Data (SIC-Based)  http://www.census.gov/mrts/www/mrtshist.html SIC code 5961 (through  1991).
CBASER = Real personal consumption less food, services, electronic and  mail-order sales, billions of 2000 dollars; calculated by GAO.
CBASER_RESID = Residual from the sales tax base equation, calculated by  GAO.
CELECMAIL = Electronic and mail order sales, billions of dollars; Census  Bureau NAICS 4541 for 1992 – 2006, SIC Code 5961 for 1978–1991 and  estimated with an exponential function for 1960–1977.
CLAWBACK = Payments from states to the federal government related to  the savings incurred as part of Medicare Part D, billions of dollars; CBO,  The Budget and Economic Outlook (Washington, D.C.: January 2007) Box  3-2.
CLAWBACKPER = Payments from states to the federal government related  to the savings incurred as part of Medicare Part D, as a percentage of  Medicaid Grants from the Federal Government; calculated by GAO.
CPIU=Consumer price index all urban, index 1982 - 1984 = 100; CBO, The  Budget and Economic Outlook: An Update (Washington, D.C.: August  2007), Table C-1.
Di = Dummy variable; 1 in year i, 0 in other years.
DBTGSLLT = Medium and long-term municipal securities outstanding,  billions of dollars; Board of Governors of the Federal Reserve System, Flow  of Funds Table L.105 line 22.
DBTGSLST = Short-term municipal securities outstanding, billions of  dollars; Board of Governors of the Federal Reserve System, Flow of Funds  Table L.105 line 21.
DBTGSLTE = Municipal securities outstanding, billions of dollars; Board of  Governors of the Federal Reserve System, Flow of Funds Table L.105 line  20.
DBTGSLUS = U.S. Government loans to state and local governments  outstanding, billions of dollars; Board of Governors of the Federal Reserve  System, Flow of Funds Table L.105 line 23.
DDBTGSLSTGDP_GRECON = Projected growth in short-term municipal  securities outstanding based on econometric specification, percentage of  GDP; calculated by GAO.
DEATHRATE = Percentage of OASDI beneficiaries that have been  terminated; Calculated by the GAO as terminated OASDI beneficiaries from  SSA table 6.F1 and total OASDI beneficiaries from table 5.A4.  Missing  values imputed with a constant growth rate.
DEPRATE = Consumption of fixed capital, as a percentage of the prior  period’s net capital stock of state and local governments; calculated by the  GAO as GSLCKFALLR/KGSLR(-1).
DSLG_AFINL_GRALT = Alternate projection of the change in total  financial assets of state and local governments based on econometric  projection of short-term municipal securities outstanding, billions of  dollars; calculated by GAO.
EECONPEN = Aggregate pension contributions by state and local  employees, billions of dollars; U.S. Commerce Department, Bureau of  Economic Analysis NIPA Table 6.11A, 6.11B and 6.11C Line 50 and  6.11D  Line 52.
EECONPENR = Aggregate pension contributions by state and local  employees, billions of dollars deflated by the consumer price index;  calculated by GAO.
EEGSLCHLTH = State and local government health care contributions for  active employees, billions of dollars; U.S. Department of Health and Human  Services, Agency for Healthcare Research and Quality, Medical  Expenditure Panel Survey.
ESTATETAX = State and local government estate and gift taxes paid by  persons, billions of dollars; U.S. Department of Commerce, Bureau of  Economic Analysis, NIPA table 5.10 line 9.
EGSL = State and local general government employees, thousands; U.S.  Department of Commerce, Bureau of Economic Analysis Table 6.4A, 6.4B,  and 6.4C. Full-Time and Part-Time Employees by Industry line 83 and 6.4D  Full-Time and Part-Time Employees by Industry line 93.
EGSLALL = State and local government employees, thousands; U.S.  Department of Commerce, Bureau of Economic Analysis Table 6.4A, 6.4B  and 6.4C Full-Time and Part-Time Employees by Industry line 82 and 6.4D  Full-Time and Part-Time Employees by Industry line 92.
EGSLALL_HP = Hodrick-Prescott filtered series of EGSLALL, thousands;  calculated by GAO.
EGSLHLTH = State and local government employees receiving health care  benefits, thousands; U.S. Department of Health and Human Services,  Agency for Healthcare Research and Quality, Medical Expenditure Panel  Survey.
EXOGEXPSHIFT = Exogenous change in expenditures for state and local  governments.  Variable is zero in baseline scenario and non-zero in  sensitivities involving alternative expenditures, billions of dollars;  calculated by GAO.
EXOGTAXSHIFT = Exogenous change in tax revenue for state and local  governments.  Variable is zero in baseline scenario and non-zero in  sensitivities involving alternative tax revenues, billions of dollars;  calculated by GAO.
GDP = Gross domestic product, billions of dollars; U.S. Department of  Commerce, Bureau of Economic Analysis, NIPA Table 1.1.5 line 1;  exogenous, projections from CBO, The Budget and Economic Outlook: An  Update (Washington, D.C.: August 2007), Table C-1.
GDPR = Gross domestic product, billions of chained 2000 dollars; U.S.  Department of Commerce, Bureau of Economic Analysis, NIPA Table 1.1.6  line 1; exogenous, projections from CBO, The Budget and Economic  Outlook: An Update (Washington, D.C.: August 2007), Table C-1.
GFAIDSL = Federal grants-in-aid to state and local governments, billions of  dollars; U.S. Department of Commerce, Bureau of Economic Analysis,  NIPA Table 3.3 line 17; exogenous, projections from CBO, The Treatment of  Federal Receipts and Expenditures in the National Income and Product  Accounts (Washington, D.C.: August 2007) Table 2; interpolated from fiscal  to calendar year by GAO.
GFAIDSLO = Federal non-Medicaid grants to state and local governments,  billions of dollars; calculated by GAO using U.S. Department of Commerce  data as the difference between GFAIDSL and GFAIDSLSSMED; exogenous  projection values calculated in the same way.
GFAIDSLSSMED = Federal Medicaid grants, billions of dollars; U.S.  Department of Commerce, Bureau of Economic Analysis, NIPA Table 3.21U  line 12; exogenous, projections from CBO, The Budget and Economic  Outlook: An Update (Washington, D.C.: August 2007) Table 1-4 and CBO,  The Long-Term Budget Outlook (Washington, D.C.: December 2005), p. 31  and http://www.cbo.gov/ftpdocs/69xx/doc6982/Data.xls.
GSLC = Total consumption expenditures of state and local governments,  billions of dollars; U.S. Department of Commerce, Bureau of Economic  Analysis, NIPA Table 3.3 line 22.
GSLCHLTH = State and local government health benefit contributions,  billions of dollars; U.S. Department of Health and Human Services, Agency  for Healthcare Research and Quality, Medical Expenditure Panel Survey.
GSLCKF = Consumption of general government fixed capital, billions of  dollars; U.S. Department of Commerce, Bureau of Economic Analysis,  NIPA table 3.10.5 line 51.
GSLCKFALL = Consumption of fixed capital, billions of dollars; U.S.  Department of Commerce, Bureau of Economic Analysis, NIPA table 3.3  line 38.
GSLCKFALLR= Consumption of fixed capital, billions of 2000 dollars; GAO  calculation for years through 2006 = 2000 nominal value from U.S.  Department of Commerce, Bureau of Economic Analysis, NIPA Fixed Asset  Table 7.3B line 46 times the relevant year’s quantity index/100 from NIPA  Fixed Asset  Table 7.4B line 46.
GSLCKFALL = Government consumption of fixed capital, billions of  dollars; U.S. Department of Commerce, Bureau of Economic Analysis,  NIPA table 3.3 line 38.
GSLCO = State and local consumption excluding employee compensation  and capital consumption, billions of dollars; U.S. Department of  Commerce, Bureau of Economic Analysis, calculated by GAO as GSLCO =  GSLC-GSLCWSS-GSLCKF.
GSLCOTHBEN = Other general state and local government employee  compensation, billions of dollars; calculated by GAO from U.S. Department  of Commerce, Bureau of Economic Analysis, total compensation less the  sum of wages and salary accruals, pension contributions and health  benefits (GSLCOTHBEN= GSLCWSS-GSLCWAGE-GSLCPEN-GSLCHLTH).
GSLCPEN = State and local government contribution for general  government employees, billions of dollars; U.S. Department of Commerce,  Bureau of Economic Analysis, calculated by GAO based on NIPA tables 7.8  and 6.3A, 6.3B, 6.3C and 6.3D.
GSLCPENALL = State and local government contribution for general and  enterprise employees, billions of dollars; U.S. Department of Commerce,  Bureau of Economic Analysis, NIPA table 7.8 Line 10.
GSLCWSS = Total compensation for state and local government employees,  billions of dollars; U.S. Department of Commerce, Bureau of Economic  Analysis, NIPA table 3.10.5 line 50.
GSLCWAGE = State and local wages for general government employees,  billions of dollars; U.S. Department of Commerce, Bureau of Economics  Analysis, NIPA table 6.3A, 6.3B, 6.3C Line 83 and 6.3D Line 93.
GSLCWAGEALL  = Total state and local wages for general government and  government enterprise employees, billions of dollars; U.S. Department of  Commerce, Bureau of Economics Analysis, NIPA table 6.3A, 6.3B, 6.3C Line  82 and 6.3D Line 92.
GSLCWAGEALLR = Total state and local wages for general and enterprise  government employees deflated by the consumer price index, billions of   2006 dollars; calculated by GAO.
GSLEXP = Total expenditures of state and local governments, billions of  dollars; U.S. Department of Commerce, Bureau of Economic Analysis,  NIPA Table 3.3 line 33.
GSLEXPC = Total current expenditures of state and local governments,  billions of dollars; U.S. Department of Commerce, Bureau of Economic  Analysis, NIPA Table 3.3 line 21.
GSLGI = Gross investment of state and local governments, billions of  dollars; U.S. Department of Commerce, Bureau of Economic Analysis,  NIPA table 3.3 line 35.
GSLGIR  = Gross investment of state and local governments, billions of  chained 2000 dollars; U.S. Department of Commerce, Bureau of Economic  Analysis, NIPA table 3.9.6 line 23.
GSLINTPAY = Interest paid by state and local governments, billions of  dollars; U.S. Department of Commerce, Bureau of Economic Analysis,  NIPA Table 3.3 line 24 GSLNETPCHNA = Net purchases of non-produced assets by state and local  governments, billions of dollars; U.S. Department of Commerce, Bureau of  Economic Analysis, NIPA Table 3.3 line 37.
GSLRCPT = Total receipts of state and local governments, billions of  dollars; U.S. Department of Commerce, Bureau of Economic Analysis,  NIPA Table 3.3 line 30 GSLRCPTC = Total current receipts of state and local governments, billions  of dollars; U.S. Department of Commerce, Bureau of Economic Analysis,  NIPA Table 3.3 line 1.
GSLRCPTKTRF= Capital transfers received (net), state and local  governments, billions of dollars; U.S. Department of Commerce, Bureau of  Economic Analysis, NIPA Table 3.3 line 32.
HIMEDRATIO = Federal Medicaid spending as a percentage of GDP, high  cost scenario, percentage; CBO, The Long-Term Budget Outlook  (Washington, D.C.: December 2005)  http://www.cbo.gov/ftpdocs/69xx/doc6982/Data.xls scenario 1.
HLTHNHEEXCGR =  Multiplier reflecting the difference between growth in  National Health Expenditures Spending per capita and growth in GDP per  capita; GAO Analysis.
IGRANT = Federal investment grants to state and local governments,  billions of dollars; U.S. Department of Commerce, Bureau of Economic  Analysis, NIPA Table 5.10U line 9.
IGRANTCBO = Federal capital transfers; exogenous projections, billions of  dollars; CBO, The Treatment of Federal Receipts and Expenditures in the  National Income and Product Accounts (Washington, D.C.: August 2007)  Table 1; interpolated from fiscal to calendar year by GAO.
JECIWSP = Employment cost index – private wages and salaries,  2005Q4=100.0; BLS Employment Cost Index Historical Index Table 6;  exogenous, projections from CBO, The Budget and Economic Outlook: An  Update (Washington, D.C.: August 2007), Table C-1.
JECISTLC=Employment cost index for state and local workers;  2005Q4=100.0, index; BLS Employment Cost Index Historical Index Table 6  (ftp://ftp.bls.gov/pub/suppl/eci.echistry.txt).
JECISTLCR=Employment cost index for state and local workers, deflated  by the CPIU, index; calculated by GAO.
JPGDP = Chained price index - gross domestic product, index 2000=100;  U.S. Department of Commerce, Bureau of Economic Analysis, NIPA table  1.1.4 line 1; exogenous, projections from The Budget and Economic  Outlook: An Update (Washington, D.C.: August 2007), Table C-1.
KGSLR = Net capital stock of state and local governments, billions of 2000  dollars; U.S. Department of Commerce, Bureau of Economic Analysis,  Fixed Asset Summary Table 9.1 line 21.
L1TOTALFA = Total state and local government employee retirement fund  assets, billions of dollars; Board of Governors of the Federal Reserve  System, Flow of Funds Table L.119 line 1.
L1TOTALFALYACT = Total state and local government employee  retirement fund assets for the last year that actuals are available, billions of  dollars; Board of Governors of the Federal Reserve System, Flow of Funds  Table L.119 line 1.
LOWMEDRATIO = Federal Medicaid spending as a percentage of GDP low  cost scenario, percentage; CBO, The Long-Term Budget Outlook  (Washington, D.C.: December 2005)  http://www.cbo.gov/ftpdocs/69xx/doc6982/Data.xls scenario 3.
LYACTUAL = last year actual data are available, 2006 LYFCST = last year of the forecast period, 2080.
NETASSETPAY = Interest payments less receipts on assets, billions of  dollars; calculated by GAO as GSLINTPAY-YGSLA.
NETLENDGSL = Net lending or net borrowing (-) of state and local  governments, billions of dollars; U.S. Department of Commerce, Bureau of  Economic Analysis, NIPA Table 3.3 line 39.
NETSAVGSL = State and local government net saving, billions of dollars;  U.S. Department of Commerce, Bureau of Economic Analysis, NIPA Table  3.3 line 27.
NETSIGSL = Net social insurance fund balance, billions of dollars; U.S.  Department of Commerce, Bureau of Economic Analysis, NIPA Table 3.3  Line 28.
NP = Total population, thousands; exogenous projections 2007 OASDI  Trustees Report, Table V.A2.-Social Security Area Population.
OPBALNETCAP= GAO’s measure of the operating balance, excludes  receipts used to acquire capital as well as capital-related expenditures; the  balance also excludes the surplus/deficit of government enterprises and the  net balance of social insurance funds, billions of dollars.
PENBEN= Aggregate pension payments made to state and local pension  beneficiaries, billions of dollars; U.S. Department of Commerce, Bureau of  Economic Analysis, NIPA table 6.11A, 6.11B, and 6.11C line 41 and 6.11D  line 43.
PENBENR= Aggregate pension payments made to state and local pension  beneficiaries deflated by the consumer price index, billions of 2000 dollars,  calculated by GAO.
PVEECONPENR = Present value of EECONPENR using RPENREAL for  each year; calculated by GAO.
PVGSLCWAGEALLR = Present value of GSLCWAGEALLR using  RPENREAL for each year; calculated by GAO.
PVPENBENR = Present value of PENBENR using RPENREAL for each  year; calculated by GAO.
RATEASSETS = Effective rate received on state and local government  financial assets, interest rate; historical values calculated by GAO as  RATEASSETS = 100*YGSLA / SLG_AFINL(-1).
RATEOWED = Effective rate paid on state and local government credit  market instruments outstanding, interest rate; historical values calculated  by GAO as RATEOWED = 100*GSLINTPAY / SLG_LCRED(-1).
REST_ALT = Market value of real estate and other property outstanding  excluding business equipment at the end of the period, billions of dollars;  Board of Governors of the Federal Reserve System, Flow of Funds Table  B.100 line 4 plus B.102 line 3 plus B.103 line 3.
RESTR_ALT = Real market value of real estate and other property  outstanding excluding business equipment at the end of the period, billions  of chained 2000 dollars, calculated by GAO.
RESTR_ALT_RESID = Residual from the real estate tax base equation,  calculated by GAO.
RETGSLCHLTH = State and local government health care contributions for  retired employees, billions of dollars; U.S. Department of Health and  Human Services, Agency for Healthcare Research and Quality, Medical  Expenditure Panel Survey.
RETHLTH = State and local government retirees receiving healthcare  benefits, thousands; U.S. Department of Health and Human Services,  Agency for Healthcare Research and Quality, Medical Expenditure Panel  Survey.
RETHLTHPERBEN = State and local government health care retired  enrollees as a percentage of beneficiaries; calculated by GAO.
RMMUNIAAA = Rate on Aaa-rated municipal bonds, percent per annum;  Board of Governors of the Federal Reserve System, Statistical Release  H.15: Selected Interest Rates  http://www.federalreserve.gov/releases/h15/data.htm.
RMMUNIAAA_RESID = Residual from the municipal rate equation,  calculated by GAO.
RMTBM3= Yield on 3 month treasury bill, percent per annum; Board of  Governors of the Federal Reserve System, Statistical Release H.15:  Selected Interest Rates  http://www.federalreserve.gov/releases/h15/data.htm.  Exogenous,  projections from CBO, The Budget and Economic Outlook: An Update  (Washington, D.C.: August 2007), Table C-1.
RMTCM10Y= Yield on 10-year Treasury notes, percent per annum; Board of  Governors of the Federal Reserve System, Statistical Release H.15:  Selected Interest Rates  http://www.federalreserve.gov/releases/h15/data.htm; exogenous,  projections from CBO, The Budget and Economic Outlook: An Update  (Washington, D.C.: August 2007), Table C-1.
RPENREAL = Real return on pension assets, interest rate; calculated by  GAO as the sum of the product of the average real return from 1965 through  2005 (from Federal Reserve Board H.15 and other sources) on each  retirement fund asset category (Flow of Funds table L.119) and each asset  category’s average share of assets over the last ten years = 5.0%.
SLG_AFINL = Total financial assets of state and local governments, billions  of dollars; Board of Governors of the Federal Reserve System, Flow of  Funds Table L.105 line 1.
SLG_AFINLSWITCH = One if assets grow slower than GDP when debt  grows with econometric estimations, zero otherwise, dummy variable;  calculated by GAO.
SLG_LCRED = Credit market instrument liabilities of state and local  governments, billions of dollars; Board of Governors of the Federal  Reserve System, Flow of Funds Table L.105 line 19.
SLG_LFINL = Total liabilities of state and local governments, billions of  dollars; Board of Governors of the Federal Reserve System, Flow of Funds  Table L.105 line 18.
SUBGSL = State and local government subsidy payments, billions of  dollars; U.S. Department of Commerce, Bureau of Economic Analysis,  NIPA Table 3.3 line 25.
SURGSLE = Current surplus of state and local government enterprises,  billions of dollars; U.S. Department of Commerce, Bureau of Economic  Analysis, NIPA Table 3.3 line 20.
TPVEECONPENR = Running sum of PVEECONPENR, billions of  discounted dollars; calculated by GAO.
TPVGSLCPEN = Unfunded pension liability through a given year, billions of  discounted dollars; calculated by GAO.
TPVGSLCWAGEALLR = Running sum of PVGSLCWAGEALLR, billions of  discounted dollars; calculated by GAO.
TPVPENBENR = Running sum of PVPENBENR, billions of discounted  dollars; calculated by GAO.
TRADEPAYABLES = Trade payables of state and local governments  outstanding, billions of dollars; Board of Governors of the Federal Reserve  System, Flow of Funds Table L.105 line 24.
TXCORPGSL = Taxes on corporate income, state and local governments,  billions of dollars; U.S. Department of Commerce, Bureau of Economic  Analysis, NIPA Table 3.3 line 10.
TXGSL = Current tax receipts, state and local governments, billions of  dollars; U.S. Department of Commerce, Bureau of Economic Analysis,  NIPA Table 3.3 line 2.
TXIMGSL = Taxes on production and imports, state and local governments,  billions of dollars; U.S. Department of Commerce, Bureau of Economic  Analysis, NIPA Table 3.3, line 6.
TXIMGSLO = Other taxes on production and imports, state and local  governments, billions of dollars; U.S. Department of Commerce, Bureau of  Economic Analysis, NIPA Table 3.3, line 9.
TXIMGSLPROP = Property tax receipts, state and local governments,  billions of dollars; U.S. Department of Commerce, Bureau of Economic  Analysis, NIPA Table 3.3, line 8.
TXIMGSLS = Sales tax receipts, state and local governments, billions of  dollars; U.S. Department of Commerce, Bureau of Economic Analysis,  NIPA Table 3.3, line 7.
TXIMGSLSGEN = General sales tax receipts, state and local governments,  billions of dollars; U.S. Department of Commerce, Bureau of Economic  Analysis, NIPA Table 3.5, lines 16 and 24.
TXIMGSLSOTH = Other sales tax receipts, state and local governments,  billions of dollars; U.S. Department of Commerce, Bureau of Economic  Analysis, NIPA Table 3.5, line 14 less 16 and 24.
TXIMGSLSOTH_RESID = Residual from the other sales tax receipts  equation, calculated by GAO.
TXPGLOCAL = Local personal income tax receipts, billions of dollars; U.S.  Department of Commerce, Bureau of Economic Analysis, NIPA Table 3.21  Line 4.
TXPGSL = Personal tax receipts, state and local governments, billions of  dollars; U.S. Department of Commerce, Bureau of Economic Analysis,  NIPA Table 3.3 line 3.
TXPGSLINC = Personal income tax receipts, state and local governments,  billions of dollars; U.S. Department of Commerce, Bureau of Economic  Analysis, NIPA Table 3.3 line 4.
TXPGSLO = Other personal tax receipts, state and local governments,  billions of dollars; U.S. Department of Commerce, Bureau of Economic  Analysis, NIPA Table 3.3 line 5.
TXPGSTATE = State personal income tax receipts, billions of dollars; U.S.  Department of Commerce, Bureau of Economic Analysis, NIPA Table 3.20  Line 4.
TXPGSTATE_RESID = Residual from the state personal income tax  receipts equation; calculated by GAO.
TXSIGSL = Contributions for government social insurance, state and local  governments, billions of dollars; U.S. Department of Commerce, Bureau of  Economic Analysis, NIPA Table 3.3 line 11.
WALDGSL = Wage accruals less disbursements, state and local  government, billions of dollars; U.S. Department of Commerce, Bureau of  Economic Analysis, NIPA Table 3.3 line 26.
WC = Weight of current beneficiaries.  Last year beneficiaries as a share of  current year beneficiaries, percentage; calculated by GAO.
WD = Weight of deceased beneficiaries.  Number of deceased beneficiaries  as a share of current year beneficiaries, percentage; calculated by GAO.
WJECISTLCR = Weighted real state and local employment cost index.   Serves as a proxy for the growth in the average pension benefit, index;  calculated by GAO.
WN = Weight of new beneficiaries.  New beneficiaries as a share of total  beneficiaries, percentage; calculated by GAO.
YGSLA = Income receipts on assets, state and local governments, billions  of dollars; U.S. Department of Commerce, Bureau of Economic Analysis,  NIPA Table 3.3 line 12.
YGSLTRF = Current transfer receipts, state and local governments, billions  of dollars; U.S. Department of Commerce, Bureau of Economic Analysis,  NIPA Table 3.3 line 16.
YGSLTRFBUS = Current transfer receipts from businesses, state and local  governments, billions of dollars; U.S. Department of Commerce, Bureau of  Economic Analysis, NIPA Table 3.3 line 18.
YGSLTRFP = Current transfer receipts from persons, state and local  governments, billions of dollars; U.S. Department of Commerce, Bureau of  Economic Analysis, NIPA Table 3.3 line 19.
YPCOMPWSD = Wage and salary disbursements, billions of dollars, U.S.  Department of Commerce, Bureau of Economic Analysis, NIPA Table 2.1  line 3; exogenous, projections from CBO, The Budget and Economic  Outlook: An Update (Washington, D.C.: August 2007), Table C-1.
YPCOMPWSDR = Real wage and salary disbursements, billions of chained  2000 dollars; calculated by GAO.
YPTAXABLE = Taxable personal income, billions of dollars.  Calculated by  GAO as wage and salary disbursements + dividends + interest +  proprietors’ income + rental income from U.S. Department of Commerce,  Bureau of Economic Analysis, NIPA Table 2.1 lines 9, 12, 14 and 15;  exogenous projections from CBO Budget and Economic Outlook.
YPTRFGSL = State and local social benefit payments to individuals, billions  of dollars; U.S. Department of Commerce, Bureau of Economic Analysis,  NIPA Table 3.3 line 23.
YPTRFGSLPAM = State and local medical spending on behalf of  individuals, billions of dollars; U.S. Department of Commerce, Bureau of  Economic Analysis, NIPA Table 3.12 line 32.
YPTRFGSLPAO = State and local non-medical social benefit payments to  individuals, billions of dollars; calculated by GAO as YPTRFGSL –  YPTRFGSLPAM.
ZB = Before-tax corporate profits excluding IVA, billions of dollars; U.S.  Department of Commerce, Bureau of Economic Analysis, NIPA Table 1.12  line 44; exogenous projections from CBO, The Budget and Economic  Outlook: An Update (Washington, D.C.: August 2007), Table C-1.