State and Local Governments: Growing Fiscal Challenges Will	 
Emerge during the Next 10 Years (22-JAN-08, GAO-08-317).	 
                                                                 
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.							 
-------------------------Indexing Terms------------------------- 
REPORTNUM:   GAO-08-317 					        
    ACCNO:   A79992						        
  TITLE:     State and Local Governments: Growing Fiscal Challenges   
Will Emerge during the Next 10 Years				 
     DATE:   01/22/2008 
  SUBJECT:   Balanced budgets					 
	     Budget activities					 
	     Budget administration				 
	     Budget amendments					 
	     Budget deficit					 
	     Budgets						 
	     Deficit reduction					 
	     Federal/state relations				 
	     Financial analysis 				 
	     Financial management				 
	     Fiscal policies					 
	     Health care cost control				 
	     Health care costs					 
	     Local governments					 
	     Policy evaluation					 
	     Risk assessment					 
	     Risk management					 
	     State budgets					 
	     State governments					 
	     State/local relations				 
	     Strategic planning 				 
	     Tax expenditures					 

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GAO-08-317 

United States Government Accountability Office: 
GAO: 

Report to Congressional Committees: 

State And Local Governments: 

Growing Fiscal Challenges Will Emerge during the Next 10 Years

Contents: 

Preface: 

Results in Brief: 

Background: 

The State and Local Government Sectorï¿½s Fiscal Difficulties Are Likely 
to Emerge within the Next 10 Years: 

Decline in Fiscal Balances Is Related to Rapidly Growing Health-Related 
Costs: 

Substantial Policy Changes Would Be Needed to Prevent the Fiscal 
Decline in the State and Local Government Sector: 

State and Local Fiscal Challenges Add to the Nationï¿½s Fiscal 
Difficulties: 

Appendixes: 

Appendix I: State and Local Fiscal Model Methodology: 

Projection of Receipts of the State and Local Government Sector: 

Projection of Expenditures of the State and Local Government Sector: 

Measures of Fiscal Balance: 

Appendix II: Projections of Income Earned on Assets and Interest Paid on
Liabilities of the State and Local Sector: 

Budget Outcomes and Financial Assets and Liabilities: 

Projecting Income Receipts on Assets and Interest Payments on Debt: 

Appendix III: Pension and Retiree Health Care Projections: 

Development of Factors for Employment, Retirement, Wages, and Benefits: 

Projections of Necessary Contributions to Pension Funds for State and 
Local Government Sector: 

Projections of Health Care Costs for State and Local Employees and 
Retirees: 

Appendix IV: State and Local Model Equations: 

Pension Equations: 

Receipts: 

Expenditures: 

Interest Rates: 

Balance Measures and Investment: 

Financial Assets and Liabilities: 

Appendix V: Alphabetical List of State and Local Sector Model Variables 
and Definitions: 

Tables: 

Table 1: Base Case Model Assumptions: 

Figures: 

Figure 1: Balance Measures for State and Local Model, as a Percentage 
of GDP: 

Figure 2: State and Local Government Expenditures, as a Percentage of 
GDP: 

Figure 3: State and Local Government Tax Receipts, as a Percentage of 
GDP: 

Figure 4: Alternative Growth Scenarios: Tax Receipts, as a Percentage 
of GDP: 

Figure 5: Alternative Growth Scenarios: State and Local Expenditures, 
as a Percentage of GDP: 

Figure 6: Operating Balance Net of Funds for Capital Expenditures, Base 
Case and Alternative Cases Based on Health Care Costs: 

Figure 7: Federal and State and Local Surpluses and Deficits, as a
Percentage of GDP: 

Figure 8: Receipt Classifications of State and Local Governments: 

Figure 9: Expenditure Classifications of State and Local Governments: 

[End of section] 

Preface: 

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 the 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 and health-related retirement 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. 

Signed by: 

David M. Walker: 
Comptroller General of the Untied States: 

[End of section] 

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.[Footnote 1]

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[Footnote 2]--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.[Footnote 3] 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.

Results in Brief:

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, so 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.[Footnote 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.

Background:

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.[Footnote 5] 
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.

The State and Local Government Sector Should Remain Fiscally Sound for 
the Next Several Years, but Fiscal Difficulties Are Likely to Emerge 
within a Decade:

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.[Footnote 6] 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.[Footnote 7] 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.[Footnote 8] 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.

Table 1: Base Case Model Assumptions:

Type of receipt or expenditure: Receipts, Taxes; 
Assumption: Historical experience net of policy changes and unusual 
capital gains used to determine how taxes will grow over time; 
maintains current tax structure. 

Type of receipt or expenditure: Receipts, Federal grants; 
Assumption: Medicaid grant projections from Congressional Budget 
Office's (CBO) intermediate case[A] for all simulation years, other 
grants from CBO for the next 10 years. After 10 years these grants grow 
with population and economywide inflation. 

Type of receipt or expenditure: Expenditures, Compensation of state and 
local workers; 
Assumption: Assume workforce grows at the same rate as the growth in 
population, and state/local wage growth is same as private-sector wage 
growth (as projected by CBO). 

Type of receipt or expenditure: Expenditures, Pension contributions; 
Assumption: Develop estimate of steady rate of employer contributions 
to fully fund promised pensions (see app. III for more details). 

Type of receipt or expenditure: Expenditures, Health insurance cost for 
employees and retirees; 
Assumption: Estimated pay-as-you-go expenditures on health insurance 
(see app. III for more details). 

Type of receipt or expenditure: Expenditures, Medicaid expenditures of 
state governments; 
Assumption: Rise in state Medicaid expenditures is based on the same 
projections as CBO's intermediate projections for growth in federal 
Medicaid grants to states. 

Type of receipt or expenditure: Expenditures, Nonhealth expenditures; 
Assumption: These expenditures grow with population and economywide 
inflation. 

Source: GAO analysis. 

[A] CBO provides an intermediate case projection for Medicaid costs and 
a faster and slower growing projection. 

[End of table] 

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: Balance Measures for State and Local Model, as a Percentage 
of GDP: 

[See PDF for image] 

This is a line graph with two lines (Operating Balance and Net 
lending). The vertical axis represents Percent of GDP from -6 to +2 and 
the horizontal axis represents fiscal years 1980 through 2050. The 
following data is depicted: 

1980: 
Operating Balance: 0.35873454; 
Net lending: -0.236601541. 

1981: 
Operating Balance: 0.34624089; 
Net lending: -0.169415676. 

1982: 
Operating Balance: 0.363124424; 
Net lending: -0.387096774. 

1983: 
Operating Balance: 0.774990811; 
Net lending: -0.138547233. 

1984: 
Operating Balance: 0.8152395; 
Net lending: 0.223736398. 

1985: 
Operating Balance: 0.810172263; 
Net lending: 0.035542497. 

1986: 
Operating Balance: 0.820186878; 
Net lending: -0.103074303. 

1987: 
Operating Balance: 0.348462918; 
Net lending: -0.346028062. 

1988: 
Operating Balance: 0.415329754; 
Net lending: -0.289980015. 

1989: 
Operating Balance: 0.461047699; 
Net lending: -0.302676683. 
	
1990: 
Operating Balance: 0.12076304; 
Net lending: -0.649652772. 

1991: 
Operating Balance: -0.002324922; 
Net lending: -0.823896329. 

1992: 
Operating Balance: 0.105991132; 
Net lending: -0.675323856. 	
	
1993: 
Operating Balance: 0.17518701; 
Net lending: -0.573797579. 

1994: 
Operating Balance: 0.15154266; 
Net lending: -0.429852097. 

1995: 
Operating Balance: 0.226784; 
Net lending: -0.446084594. 
	
1996: 
Operating Balance: 0.382430375; 
Net lending: -0.292955008. 

1997: 
Operating Balance: 0.540310442; 
Net lending: -0.225184543. 

1998: 
Operating Balance: 0.711478221; 
Net lending: -0.113181662. 

1999: 
Operating Balance: 0.528375987; 
Net lending: -0.240602477. 

2000: 
Operating Balance: 0.51757156; 
Net lending: -0.309666904. 

2001: 
Operating Balance: 0.213052923; 
Net lending: -0.800750395. 

2002: 
Operating Balance: -0.212758845; 
Net lending: -1.20443952. 

2003: 
Operating Balance: -0.034231078; 
Net lending: -1.04098241. 

2004: 
Operating Balance: 0.03822412; 
Net lending: -0.899039488. 

2005: 
Operating Balance: 0.262769152; 
Net lending: -0.762696896. 

2006: 
Operating Balance: 0.21944499; 
Net lending: -0.788126765. 

2007: 
Operating Balance: 0.412715768; 
Net lending: -0.638191188. 

2008: 
Operating Balance: 0.345168264; 
Net lending: -0.630180116. 

2009: 
Operating Balance: 0.333020741; 
Net lending: -0.603351831. 

2010: 
Operating Balance: 0.302656141; 
Net lending: -0.605141837. 

2011: 
Operating Balance: 0.257109542; 
Net lending: -0.630946404. 

2012: 
Operating Balance: 0.206218592; 
Net lending: -0.658720463. 

2013: 
Operating Balance: 0.16406332; 
Net lending: -0.681176041. 

2014: 
Operating Balance: 0.119677687; 
Net lending: -0.707576964. 

2015: 
Operating Balance: 0.076206951; 
Net lending: -0.734940534. 

2016: 
Operating Balance: 0.026942361; 
Net lending: -0.769406462. 

2017: 
Operating Balance: -0.032335263; 
Net lending: -0.814653671. 

2018: 
Operating Balance: -0.109446599; 
Net lending: -0.878965311. 

2019: 
Operating Balance: -0.19227354; 
Net lending: -0.950160744. 

2020: 
Operating Balance: -0.28349272; 
Net lending: -1.030954125. 

2021: 
Operating Balance: -0.386388384; 
Net lending: -1.123450085. 

2022: 
Operating Balance: -0.483216203; 
Net lending: -1.211277239. 

2023: 
Operating Balance: -0.557423995; 
Net lending: -1.275859781. 

2024: 
Operating Balance: -0.667104614; 
Net lending: -1.376919803. 

2025: 
Operating Balance: -0.781500085; 
Net lending: -1.482014441. 

2026: 
Operating Balance: -0.866176187; 
Net lending: -1.558258502. 

2027: 
Operating Balance: -0.971014018; 
Net lending: -1.654401786. 

2028: 
Operating Balance: -1.059403133; 
Net lending: -1.733838263. 

2029: 
Operating Balance: -1.167739726; 
Net lending: -1.833479877. 

2030: 
Operating Balance: -1.259948277; 
Net lending: -1.916759702. 

2031: 
Operating Balance: -1.372065386; 
Net lending: -2.020169149. 

2032: 
Operating Balance: -1.467520327; 
Net lending: -2.107108096. 

2033: 
Operating Balance: -1.596865114. 
Net lending: -2.227702996. 

2034: 
Operating Balance: -1.728257684; 
Net lending: -2.350527637. 

2035: 
Operating Balance: -1.829728717; 
Net lending: -2.443203659. 

2036: 
Operating Balance: -1.964429157. 
Net lending: -2.569286091. 

2037: 
Operating Balance: -2.100986709; 
Net lending: -2.697381312. 

2038: 
Operating Balance: -2.205225568; 
Net lending: -2.7928813. 

2039: 
Operating Balance: -2.325201615; 
Net lending: -2.904115765. 

2040: 
Operating Balance: -2.430489372; 
Net lending: -3.00097328. 

2041: 
Operating Balance: -2.569712106; 
Net lending: -3.131648373. 

2042: 
Operating Balance: -2.709424661; 
Net lending: -3.262966376. 

2043: 
Operating Balance: -2.818508477; 
Net lending: -3.363795891. 

2044: 
Operating Balance: -2.947568546; 
Net lending: -3.484450067. 

2045: 
Operating Balance: -3.058317306; 
Net lending: -3.58693672. 

2046: 
Operating Balance: -3.188512473; 
Net lending: -3.709010629.

2047: 
Operating Balance: -3.300350183; 
Net lending: -3.812861884. 

2048: 
Operating Balance: -3.432143889; 
Net lending: -3.936539309. 

2049: 
Operating Balance: -3.545556545; 
Net lending: -4.041974237. 

2050: 
Operating Balance: -3.686995315; 
Net lending: -4.175627129. 

Sources: Historical data from NIPA and GAO analysis. 

[End of graph]

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 
historically 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 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.[Footnote 9] 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 their own 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.

Decline in Fiscal Balances Is Related to Rapidly Growing Health-Related 
Costs:

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.[Footnote 10] 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.[Footnote 11] 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.[Footnote 12] 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.[Footnote 13] These base-case assumptions are 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: State and Local Government Expenditures, as a Percentage of 
GDP: 

[See PDF for image] 

This figure is a multiple line graph, depicting State and Local 
Government Expenditures, as a Percentage of GDP: 

2000: 
Non-Healthcare Expenditures: 10.72; 	
Healthcare Expenditures: 2.67. 
	
2001: 
Non-Healthcare Expenditures: 11.03; 	
Healthcare Expenditures: 2.96. 
	
2002: 
Non-Healthcare Expenditures: 11.21; 	
Healthcare Expenditures: 3.14. 
	
2003:	
Non-Healthcare Expenditures: 11.12; 	
Healthcare Expenditures: 3.22. 

2004: 
Non-Healthcare Expenditures: 10.84; 	
Healthcare Expenditures: 3.31. 

2005:	
Non-Healthcare Expenditures: 10.8; 	
Healthcare Expenditures: 3.29. 

2006: 
Non-Healthcare Expenditures: 10.7; 	
Healthcare Expenditures: 3.28. 
	
2007:	
Non-Healthcare Expenditures: 10.66; 	
Healthcare Expenditures: 3.25. 

2008:	
Non-Healthcare Expenditures: 10.52; 	
Healthcare Expenditures: 3.34. 

2009:	
Non-Healthcare Expenditures: 10.37; 	
Healthcare Expenditures: 3.43. 

2010:	
Non-Healthcare Expenditures: 10.24; 	
Healthcare Expenditures: 3.52. 

2011:	
Non-Healthcare Expenditures: 10.13; 	
Healthcare Expenditures: 3.61. 

2012:	
Non-Healthcare Expenditures: 10.03; 	
Healthcare Expenditures: 3.71. 

2013: 
Non-Healthcare Expenditures: 9.93; 	
Healthcare Expenditures: 3.82. 

2014:	
Non-Healthcare Expenditures: 9.84; 	
Healthcare Expenditures: 3.92. 

2015:	
Non-Healthcare Expenditures: 9.76; 	
Healthcare Expenditures: 4.04.

2016:
Non-Healthcare Expenditures: 9.68; 	
Healthcare Expenditures: 4.15. 

2017: 
Non-Healthcare Expenditures: 9.61; 	
Healthcare Expenditures: 4.28. 

2018:	
Non-Healthcare Expenditures: 9.55; 	
Healthcare Expenditures: 4.45. 

2019:	
Non-Healthcare Expenditures: 9.51; 	
Healthcare Expenditures: 4.62. 

2020: 
Non-Healthcare Expenditures: 9.47; 	
Healthcare Expenditures: 4.79. 

2021: 
Non-Healthcare Expenditures: 9.44; 	
Healthcare Expenditures: 4.96. 

2022: 
Non-Healthcare Expenditures: 9.41; 	
Healthcare Expenditures: 5.1. 

2023: 
Non-Healthcare Expenditures: 9.39; 	
Healthcare Expenditures: 5.18. 

2024: 
Non-Healthcare Expenditures: 9.36; 	
Healthcare Expenditures: 5.34. 

2025: 
Non-Healthcare Expenditures: 9.34; 	
Healthcare Expenditures: 5.49. 

2026: 
Non-Healthcare Expenditures: 9.31; 
Healthcare Expenditures: 5.58. 

2027: 
Non-Healthcare Expenditures: 9.29; 	
Healthcare Expenditures: 5.7. 

2028:	
Non-Healthcare Expenditures: 9.26; 	
Healthcare Expenditures: 5.78. 

2029:	
Non-Healthcare Expenditures: 9.24; 	
Healthcare Expenditures: 5.91. 

2030: 
Non-Healthcare Expenditures: 9.21; 	
Healthcare Expenditures: 5.99. 

2031: 
Non-Healthcare Expenditures: 9.18; 	
Healthcare Expenditures: 6.12. 

2032:
Non-Healthcare Expenditures: 9.15; 	
Healthcare Expenditures: 6.2. 

2033: 
Non-Healthcare Expenditures: 9.12; 	
Healthcare Expenditures: 6.36. 

2034:	
Non-Healthcare Expenditures: 9.09; 	
Healthcare Expenditures: 6.52. 

2035: 
Non-Healthcare Expenditures: 9.06; 	
Healthcare Expenditures: 6.6. 

2036: 
Non-Healthcare Expenditures: 9.02; 	
Healthcare Expenditures: 6.76. 

2037: 
Non-Healthcare Expenditures: 8.99; 	
Healthcare Expenditures: 6.92. 

2038:	
Non-Healthcare Expenditures: 8.96; 	
Healthcare Expenditures: 7. 

2039: 
Non-Healthcare Expenditures: 8.92; 	
Healthcare Expenditures: 7.12. 

2040:	
Non-Healthcare Expenditures: 8.88; 	
Healthcare Expenditures: 7.2. 

2041: 
Non-Healthcare Expenditures: 8.85; 	
Healthcare Expenditures: 7.36. 

2042:	
Non-Healthcare Expenditures: 8.81; 
Healthcare Expenditures: 7.51. 

2043:	
Non-Healthcare Expenditures: 8.77; 	
Healthcare Expenditures: 7.59. 

2044:	
Non-Healthcare Expenditures: 8.73; 	
Healthcare Expenditures: 7.71. 

2045: 
Non-Healthcare Expenditures: 8.7; 	
Healthcare Expenditures: 7.78. 

2046: 
Non-Healthcare Expenditures: 8.66; 	
Healthcare Expenditures: 7.9. 

2047:	
Non-Healthcare Expenditures: 8.62; 	
Healthcare Expenditures: 7.98. 

2048:
Non-Healthcare Expenditures: 8.58; 	
Healthcare Expenditures: 8.1. 

2049:	
Non-Healthcare Expenditures: 8.54; 
Healthcare Expenditures: 8.17. 

2050: 
Non-Healthcare Expenditures: 8.5; 	
Healthcare Related Expenditures: 8.31. 

Source: Historical data from NIPA and GAO analysis. 

Note: Health care expenditures include health care benefits for 
employees and retirees and medical spending on behalf of other 
individuals, such as Medicaid and State Children's Health Insurance 
Program (SCHIP) (a children's health care program); Nonhealth care 
expenditures include all expenditures with the exception of health care 
and interest payments.

[End of figure] 

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.

Figure 3: State and Local Government Tax Receipts as a Percentage of 
GDP: 

[See PDF for image] 

This figure is a multiple line graph depicting State and Local 
Government Tax Receipts as a Percentage of GDP in four categories: 
sales tax; property tax; personal income tax; and corporate taxes. The 
vertical axis of the graph represents percentage of GDP from 0 to 4. 
The horizontal axis of the graph represents years from 2000 to 2050. 
While specific data points are not available, the following 
approximations can be gleaned from the graph: 

Corporate taxes: remain fairly constant at approximately 0.5%; 
Personal income tax: remains fairly constant at approximately 2.5%; 
Property tax: remains fairly constant at approximately 3.0 to 3.3%; 
Sales tax: shows a steady slight decline from approximately 3.2 to 
2.8%. 

Source: Historical data from NIPA and GAO analysis. 

[End of figure] 

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.[Footnote 14]

Substantial Policy Changes Would Be Needed to Prevent the Fiscal 
Decline in the State and Local Government Sector:

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.

Tax Receipts Would Need to Rise Considerably Faster than Historical 
Experience to Enable the Operating Balance to Remain in Historical 
Range:

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.

Figure 4: Alternative Growth Scenarios: Tax Receipts as Percentage of 
GDP: 

[See PDF for image] 

This figure is a multiple line graph depicting Alternative Growth 
Scenarios: Tax Receipts as Percentage of GDP in three categories: base 
case; taxes at historical growth; and tax increase to balance. The 
vertical axis of the graph represents percentage of GDP from 0 to 12. 
The horizontal axis of the graph represents years from 2000 to 2050. 
While specific data points are not available, the following 
approximations can be gleaned from the graph: 

Base case: remains fairly constant at approximately 9.2%; 
Taxes at historical growth: shows a steady, slight increase from 
approximately 9.2% in 2007 to approximately 10.0% in 2050; 
Tax increase to balance: shows a steady, slight increase from 
approximately 9.2% in 2015 to approximately 11.0% in 2050; 

Source: Historical data from NIPA and GAO analysis. 

Note: Historical data are from 2000-2006. Projections are from 2007- 
2050. 

[End of graph] 

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.

State and Local Expenditures Would Need to Be Cut Substantially to 
Maintain Fiscal Balance:

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: Alternative Growth Scenarios: State and Local Expenditures, 
as Percentage of GDP: 

[See PDF for image] 

This figure is a multiple line graph depicting Alternative Growth 
Scenarios: State and Local Expenditures, as Percentage of GDP in two 
categories: base case and maintain balance. The vertical axis of the 
graph represents percentage of GDP from 0 to 20. The horizontal axis of 
the graph represents years from 2000 to 2050. While specific data 
points are not available, the following approximations can be gleaned 
from the graph: 

Base case: From 2005 to 2015, remains fairly steady at approximately 
14.5%. From 2015 to 2050, shows a steady increase from 14.5% to 
approximately 19.5%; 
Maintain balance: From 2015 to 2050, shows a steady increase from 14.5% 
to approximately 15.5%

Source: Historical data from NIPA and GAO analysis. 

Note: Historical data are from 2000-2006. Projections are from 2007- 
2050. 

[End of figure] 

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 they procure 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 citizen, relative to the 
base case.

Health Care Cost Growth Would Need to Be Held to Low Level to Prevent 
Declines in Fiscal Balances:

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.

Figure 6: Operating Balance Net of Funds for Capital Expenditures, Base 
Case and Alternative Cases Based on Health Care Costs: 

[See PDF for image] 

This figure is a multiple line graph depicting Operating Balance Net of 
Funds for Capital Expenditures, Base Case and Alternative Cases Based 
on Health Care Costs in three categories: base case; optimistic; and 
pessimistic. The vertical axis of the graph represents percentage of 
GDP from -8 to 2. The horizontal axis of the graph represents years 
from 1980 to 2050. While specific data points are not available, the 
following approximations can be gleaned from the graph: 

Base case: between 0 and 1% from 1980 to 2007; steady decline from 
approximately 0% in 2007 to approximately -3% in 2050; 
Optimistic: Steady increase from approximately 0% in 2007 to 
approximately +1% in 2050; 
Pessimistic: Steady and accelerating decrease from approximately 0% in 
2007 to approximately -8% in 2050. 

Source: Historical data from NIPA and GAO analysis. 

Note: Historical data are from 2000-2006. Projections are from 2007- 
2050. Optimistic Scenario Assumptions: State and local employee and 
retiree per capita health benefits grow at the same rate as per capita 
GDP, while other health expenditures grow according to CBO's low cost 
scenario. Pessimistic Scenario Assumptions: State and local employee 
and retiree per capita health benefits grow at the rate consistent with 
per capita GDP plus twice the excess cost factor, while other health 
expenditures grow according to CBO's high cost scenario. 

[End of figure] 

State and Local Fiscal Challenges Add to the Nation's Fiscal 
Difficulties:

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.[Footnote 15]

Figure 7: Federal and State/Local Surpluses and Deficits as a Share of 
GDP: 

[See PDF for image] 

This figure is a multiple line graph depicting Federal and State/Local 
Surpluses and Deficits as a Share of GDP in four categories: baseline 
extended, federal only; baseline extended, federal, state, and local; 
alternative, federal only; and alternative, federal, state, and local. 
The vertical axis of the graph represents percentage of GDP from -30 to 
5. The horizontal axis of the graph represents years from 2000 to 2050. 
While specific data points are not available, the following 
approximations can be gleaned from the graph: 

Baseline extended, federal only: starting in 2007 at approximately -
2.5%. slight gradually increase to 0 by about 2012, then a steady 
decrease from 2012 to approximately -13% by 2050; 
Baseline extended, federal, state, and local: starting in 2007 at 
approximately -2.5%, slight gradually increase to 0 by about 2012, then 
a steady decrease from 2012 to approximately -17% by 2050; 
Alternative, federal only: starting in 2007 at approximately -2.5%, a 
steady decrease from to approximately -25% by 2050; 
Alternative, federal, state, and local starting in 2007 at 
approximately -2.5%, a steady decrease from to approximately -30% by 
2050. 

Sources: Historical data from NIPA, and GAO analysis. 

Note: Historical data are from 2000-2006. Projections are from 2007- 
2050. The state and local balance measure is similar to the federal 
unified budget measure. 

[End of figure] 

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 
[email protected], and Thomas J. McCool, Director, Center for 
Economics, who can be reached at (202) 512-2642 or [email protected] 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. 

[End of section] 

Appendix I: State and Local Fiscal Model Methodology: 

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. 

Projection of Receipts of the State and Local Government Sector: 

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. 

Figure 8: Receipt Classifications of State and Local Governments: 

[See PDF for image] 

1. Taxes: 
* Personal income tax (state personal income tax and local personal 
income tax); 
* Sales tax (general sales tax and selective (excise) sales tax; 
* Corporate income tax; 
* Property tax; 
* Other taxes on production; 
* Estate tax. 

2. Contributions to government insurance. 

3. Income on financial assets owned by state and local governments. 

4. Transfer receipts: 
* Federal Medicaid grants; 
* Non-Medicaid federal grants; 
* Federal investment grants (for long-term investments such as roads, 
bridges, and other infrastructure); 
* Transfers from businesses and persons (fines, tobacco settlements). 

5. Surplus or deficit on government enterprises (e.g., liquor stores, 
public power, public transit, public housing). 

Source: GAO organization of NIPA classifications. 

Note: Unlike the NIPA, we do not distinguish between current and 
noncurrent receipts. 

[End of figure] 

Tax Receipts: 

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.[Footnote 16] 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. 

Personal income tax receipts: 

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.[Footnote 17] 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.[Footnote 18] 

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 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 discernible 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. 

Sales Tax Receipts: 

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. 

General Sales Tax Receipts: 

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. 
[Footnote 19] 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. 

Taxes on Corporate Income: 

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. 

Property Tax Receipts: 

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. 

Other Taxes on Production: 

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. 

Estate and Gift Taxes: 

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. 

Contributions for Government Social Insurance: 

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. 

Income Receipts on Assets: 

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. 

Current Transfer Receipts: 

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. 

Federal Medicaid Grants: 

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.[Footnote 20] Thereafter, we use CBO's projections 
for Medicaid outlays as a share of GDP from the most recent long-term 
budget publication.[Footnote 21] 

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. 

Other Federal Grants to Finance Current Expenditures: 

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. 

Federal Investment Grants: 

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. 

Transfers from Business and Persons: 

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. 

Current Surplus or Deficit of Government Enterprises: 

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. 

Projection of Expenditures of the State and Local Government Sector: 

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. 

Figure 8: Expenditure Classifications of State and Local Governments: 

[See PDF for image] 

1. Consumption expenditures; 
* Compensation of employees; 
- Wages and salaries; 
- Pension fund contributions; 
- Heath care payments; 
- Other employee benefits (e.g., life insurance); 
* Consumption of fixed capital (i.e., depreciation); 
* Miscellaneous consumption expenditures; 
- Purchases of intermediate goods; 
- Offsets related to tuition, hospitals, and certain other services not 
considered enterprises; 
- Own account investments--offsets related to expenditures classified 
as consumption expenditures in the given year but really related to 
longer-term investments; 

2. Transfer payments to citizens; 
* Medicaid and other health payments; 
* Non-Medicaid transfers; 

3. Interest paid on outstanding state and local debt; 

4. Subsidies; 

5. Purchases of fixed assets and purchases of nonproduced assets 
(mostly land). 

Source: GAO organization of NIPA classifications. 

[End of figure] 

Consumption Expenditures: 

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. 

Compensation of State and Local Government Employees: 

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. 

Wages and Salaries: 

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. 

Pension Plan Contributions: 

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. 

Health Insurance Contributions: 

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: 

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. 

Consumption of Fixed Capital (Depreciation): 

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). 

Miscellaneous Consumption Expenditures: 

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 consumptions expenditures. 

Transfer Payments to Persons: 

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. 

Medical Care Transfer Payments: 

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. 

Nonmedical Transfer Payments to Persons: 

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. 

Interest Paid: 

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. 

Subsidies: 

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. 

Gross Investment and Purchases of Nonproduced Assets: 

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. 

Measures of Fiscal Balance: 

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. 

Net Lending or Borrowing: 

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. 

Operating Balance Net of Funds for Capital Expenditures: 

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. 

Net State and Local Government Saving: 

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. 

[End of section] 

Appendix II: Projections of Income Earned on Assets and Interest Paid on 
Liabilities of the State and Local Sector: 

This appendix describes how we develop 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 estimate the 
effective rate earned on the sector's financial assets and the 
effective rate paid on its credit market liabilities in each year and 
apply these rates to the prior year's assets and liabilities, 
respectively, to provide estimates of the sector's asset income and 
interest payments. 

Budget Outcomes and Financial Assets and Liabilities: 

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 identify. 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. 

Method Used When Budget Outcomes Are in Historical Range: 

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.[Footnote 
22] 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. 

Alternative Method for Determining Changes in Financial Assets and 
Liabilities: 

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. 

Projecting Income Receipts on Assets and Interest Payments on Debt: 

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. 

Income Receipts on Financial Assets: 

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. 

Interest Paid on Credit Market Liabilities: 

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. 

[End of section] 

Appendix III: Pension and Retiree Health Care Projections: 

This appendix provides information on the development of simulations of 
future pension and health care expenditures for retirees of state and 
local governments. [Footnote 23] 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. 

Development of Factors for Employment, Retirement, Wages, and Benefits: 

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. 

Steps to Project Future Employment Levels: 

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[Footnote 24]--that is, we assume that the ratio of 
state and local employment to total population remains constant. 
[Footnote 25] 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. 

Steps to Project Future State and Local Government Real Wages: 

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.[Footnote 26] 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. 

Steps to Project Growth in the Number of Pension Beneficiaries: 

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.[Footnote 27] 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. 

Steps to Project Real Benefits Per Beneficiary: 

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[Footnote 28] and total Social Security recipients, which 
excludes disability recipients.[Footnote 29] 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.[Footnote 30] 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 need to identify 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. 

Steps to Project Employee Contributions to Pension Funds: 

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. 

Projections of Necessary Contributions to Pension Funds for State and 
Local Government Sector: 

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[Footnote 31]--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.[Footnote 32] 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.[Footnote 33] 

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. 

Projections of Health Care Costs for State and Local Employees and 
Retirees: 

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.[Footnote 34] 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 develop 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.[Footnote 35] 

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. 

[End of section] 

Appendix IV: State and Local Model Equations: 

This appendix lists the 105 equations that are used to simulate the 
base case for the State and Local Model. 

Notation: 

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. 

Pension Equations: 

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. EGSL = (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. JECISTLCR = JECISTLCR(-1) * ( (JECIWSP / JECIWSP(-1)) / (CPIU / 
CPIU(-1))) * (LYFCST - YEAR>=0) + JECISTLCR(-1) * (JECISTLCR(-1) / 
JECISTLCR(-2)) * (LYFCST - YEAR<0): 

7. GSLCWAGEALLR = (GSLCWAGEALLR(-1) * (JECISTLCR / JECISTLCR(-1)) * 
(EGSLALL / EGSLALL(-1))): 

8. GSLCWAGEALL = GSLCWAGEALL(-1) * (GSLCWAGEALLR / GSLCWAGEALLR(-1)) * 
(CPIU / CPIU(-1)): 

9. GSLCWAGE = (GSLCWAGE(-1)) * (JECISTLC / JECISTLC(-1)) * (EGSL / 
EGSL(-1)): 

10. PVGSLCWAGEALLR = GSLCWAGEALLR / (1 + (RPENREAL / 100))^(YEAR - 
LYACTUAL): 

11. TPVGSLCWAGEALLR = PVGSLCWAGEALLR + TPVGSLCWAGEALLR(-1): 

12. DEATHRATE = DEATHRATE(-1): 

13. WN = (BENEFICIARIES - (1 - DEATHRATE(-1)) * BENEFICIARIES(-1)) / 
BENEFICIARIES: 

14. WC = BENEFICIARIES(-1) / BENEFICIARIES: 

15. WD = (DEATHRATE(-1) * BENEFICIARIES(-1) / BENEFICIARIES): 

16. WJECISTLCR = WN * JECISTLCR + WC * JECISTLCR(-8) - WD * JECISTLCR(- 
21): 

17. (PENBENR / BENEFICIARIES) = (PENBENR(-1) / BENEFICIARIES(-1)) * 
(WJECISTLCR / WJECISTLCR(-1)): 

18. PENBEN = PENBEN(-1) * (PENBENR / PENBENR(-1)) * (CPIU / CPIU(-1)): 

19. PVPENBENR = PENBENR / (1 + (RPENREAL / 100))^(YEAR - LYACTUAL): 

20. TPVPENBENR = PVPENBENR + TPVPENBENR(-1): 

21. EECONPENR / GSLCWAGEALLR = EECONPENR(-1) / GSLCWAGEALLR(-1): 

22. EECONPEN / GSLCWAGEALL = EECONPEN(-1) / GSLCWAGEALL(-1): 

23. PVEECONPENR = EECONPENR / (1 + (RPENREAL / 100))^(YEAR - LYACTUAL): 

24. TPVEECONPENR = PVEECONPENR + TPVEECONPENR(-1): 

25. L1TOTALFALYACT = L1TOTALFALYACT(-1): 

26. TPVGSLCPEN = TPVPENBENR - L1TOTALFALYACT - TPVEECONPENR: 

27. GSLCPEN = (TPVGSLCPEN(2400)/ TPVGSLCWAGEALLR (2400))* GSLCWAGE: 

28. L1TOTALFA = L1TOTALFA(-1) * (1 + (RPENREAL / 100)) * (CPIU / CPIU(- 
1)) + EECONPEN + GSLCPEN - PENBEN: 

Receipts: 

29. GSLRCPTC = TXGSL + TXSIGSL + YGSLA + YGSLTRF + SURGSLE: 

30. TXGSL = TXPGSL + TXIMGSLS + TXIMGSLPROP + TXIMGSLO + TXCORPGSL + 
EXOGTAXSHIFT: 

31. TXPGSLINC = TXPGSTATE + TXPGLOCAL: 

32. TXPGSL = TXPGSLINC + TXPGSLO: 

33. TXPGSTATE_RESID = TXPGSTATE_RESID(-1): 

34. LOG(TXPGSTATE / (JPGDP / 100)) = - 4.663012457 + 1.106786257 * 
LOG(YPTAXABLE / (JPGDP / 100)) + TXPGSTATE_RESID: 

35. TXPGLOCAL = TXPGLOCAL(-1) * YPTAXABLE / YPTAXABLE(-1): 

36. TXPGSLO = TXPGSLO(-1) * (YPTAXABLE / YPTAXABLE(-1)): 

37. CBASER_RESID = CBASER_RESID(-1): 

38. LOG(CBASER) = - 0.356670 + 0.926710 * LOG(YPCOMPWSDR) + 
CBASER_RESID: 

39. CBASE = CBASER * (JPGDP / 100): 

40. TXIMGSLS = TXIMGSLSGEN + TXIMGSLSOTH: 

41. TXIMGSLSGEN = TXIMGSLSGEN(-1) * (CBASE / CBASE(-1)): 

42. TXIMGSLSOTH_RESID = TXIMGSLSOTH_RESID(-1): 

43. LOG(TXIMGSLSOTH / (JPGDP / 100)) = - 2.19455711 + 0.802359052 * 
LOG(YPCOMPWSDR) + TXIMGSLSOTH_RESID: 

44. TXIMGSLPROP = TXIMGSLPROP(-1) * (REST_ALT / REST_ALT(-1)): 

45. RESTR_ALT_RESID = RESTR_ALT_RESID(-1): 

46. LOG(RESTR_ALT) = - 0.3439280383 + 1.125733159 * LOG(GDPR) + 
RESTR_ALT_RESID: 

47. REST_ALT = RESTR_ALT * (JPGDP / 100): 

48. TXIMGSLO = TXIMGSLO(-1) * (GDP / GDP(-1)): 

49. TXCORPGSL = TXCORPGSL(-1) * (ZB / ZB(-1)): 

50. TXSIGSL = TXSIGSL(-1) * (YPCOMPWSD/ YPCOMPWSD (-1)): 

51. YGSLA = (RATEASSETS / 100) * SLG_AFINL(-1): 

52. RATEASSETS = 0.8110814608 * RATEASSETS(-1) + (1 - 0.8110814608) * 
RMTBM3 - 1.47259976 * D86: 

53. YGSLTRF = GFAIDSL + YGSLTRFBUS + YGSLTRFP: 

54. GFAIDSL = GFAIDSLO + (GFAIDSLSSMED - CLAWBACK): 

55. CLAWBACK = CLAWBACKPER * GFAIDSLSSMED: 

56. YGSLTRFBUS = YGSLTRFBUS(-1) * (GDP / GDP(-1)): 

57. YGSLTRFP = YGSLTRFP(-1) * (GDP / GDP(-1)): 

58. SURGSLE = 0: 

Expenditures: 

59. GSLEXPC = GSLC + YPTRFGSL + GSLINTPAY + SUBGSL + EXOGEXPSHIFT: 

60. GSLC = GSLCWSS + GSLCKF + GSLCO: 

61. GSLCWSS = GSLCWAGE + GSLCPEN + GSLCHLTH + GSLCOTHBEN: 

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)): 

Interest Rates: 

79. RMMUNIAAA_RESID = RMMUNIAAA_RESID(-1): 

80. RMMUNIAAA = 0.707151184659468 + 0.761815685970831 * RMTCM10Y + 
RMMUNIAAA_RESID: 

81. GSLINTPAY = (RATEOWED / 100) * SLG_LCRED(-1): 

82. RATEOWED = 0.8765652676 * RATEOWED(-1) + (1 - 0.8765652676) * 
RMMUNIAAA: 

83. NETASSETPAY = GSLINTPAY - YGSLA: 

84. SUBGSL = SUBGSL(-1) * (NP / NP(-1)) * (JPGDP / JPGDP(-1)): 

Balance Measures and Investment: 

85. NETSAVGSL = GSLRCPTC - GSLEXPC: 

86. NETSIGSL = NETSIGSL(-1) * (YPCOMPWSD / YPCOMPWSD (-1)): 

87. GSLRCPT = GSLRCPTC + GSLRCPTKTRF: 

88. GSLRCPTKTRF = IGRANT + ESTATETAX: 

89. IGRANT = IGRANT(-1) * (IGRANTCBO / IGRANTCBO(-1)): 

90. ESTATETAX = ESTATETAX(-1) * (1 + RMTCM10Y / 100): 

91. GSLEXP = GSLEXPC + GSLGI + GSLNETPCHNA - GSLCKFALL: 

92. GSLNETPCHNA = GSLNETPCHNA(-1) * (JPGDP / JPGDP(-1)) * (NP / NP(- 
1)): 

93. NETLENDGSL = GSLRCPT - GSLEXP: 

94. OPBALNETCAP = GSLRCPT - (GSLGI + GSLNETPCHNA - D(DBTGSLLT)) - 
(GSLEXPC - GSLCKF) - SURGSLE - NETSIGSL: 

Financial Assets and Liabilities: 

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. DSLG_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)): 

[End of section] 

Appendix V: Alphabetical List of State and Local Sector Model Variables 
and Definitions: 

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) [hyperlink, 
http://www.census.gov/prod/www/abs/br_month.html] or Census Historical 
Retail Trade Data (SIC-Based) [hyperlink, 
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 healthcare 
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 [hyperlink, 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. 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. 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. 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) [hyperlink, 
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) [hyperlink, 
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 lines 3 plus B.103 lines 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 [hyperlink, 
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 [hyperlink, 
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 [hyperlink, 
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 business, 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. 

[End of section] 

Footnotes: 

[1] We previously provided a summary of this work. See State and Local 
Governments: Persistent Fiscal Challenges Will Likely Emerge within the 
Next Decade, GAO-07-1080SP (Washington, D.C.: July 2007). 

[2] The federal unified budget is a comprehensive budget in which 
receipts and outlays from both federal funds and trust funds (e.g., 
Social Security) are consolidated. 

[3] The state and local fiscal model is not designed for certain types 
of analyses. The simulations are not intended to provide precise 
predictions. Even though we know that these governments regularly make 
changes in tax laws and expenditures, the model essentially holds 
current policy in place and analyzes the fiscal future for the sector 
as if those policies were maintained because it would be highly 
speculative to make any assumptions about future policy adjustments. 

[4] See GAO, The Nation's Long-Term Fiscal Outlook: August 2007 Update, 
GAO-07-1261R (Washington D.C.: August 2007). 

[5] The count of general local governments includes the District of 
Columbia and excludes Indian tribes and outlying areas. 

[6] To develop values for "unusual capital gains," we estimated a 
relationship between the level of capital gains over time and marginal 
tax rates. We viewed any observed gains (or losses), relative to the 
estimated relationship, as "unusual." 

[7] Advances in productivity across types of production processes vary 
considerably. It is commonly believed that more labor-intensive outputs 
suffer from lower rates of productivity growth. This so-called "Baumol 
effect" may indicate that increases in productivity in the provision of 
public services, such as public hospitals and education, tend to be low 
because these services rely so heavily on labor. 

[8] CBO provides estimates of these private wage increases as measured 
by the employment cost index (ECI). The ECI grows about 1 percent per 
annum faster than the Consumer Price Index (CPI). 

[9] Fiscal gap is calculated for the years 2007 to 2080. 

[10] For Medicaid, our cost growth projections align with CBO's most 
recent budget baseline for the first 10 years and are based on CBO's 
December 2005 long-term projections in the later years. 

[11] We developed estimates of cost growth for health insurance based 
on research and discussions with experts. 

[12] In addition to expenses related to health care, the interest 
payments that state and local governments will need to make on their 
outstanding debt are also projected to grow substantially during the 
projection time frame, but this finding is merely an outgrowth of the 
sustained deficits the model predicts across future years. 

[13] In fact, because the school age population is projected to decline 
as a proportion of the total population, we ran alternative simulations 
to examine outcomes with slower employment growth than the growth 
projected using the base case. We adjusted the growth in state and 
local employees to take into account slower growing school age 
populations in future years. This slowed the projected cost increases 
for compensation of state and local workers--a very large expenditure 
classification. As a result, operating balance deficits first appear 5 
years later than in our base case. Although this led to a slower 
decline in fiscal balances, it did not avoid declining balances in the 
long run. 

[14] Because CBO's baseline adjusts discretionary spending, such as non-
Medicaid grants to state and local governments, only for inflation, our 
projections for these grants decline as a share of GDP over the next 10 
years--the time frame of CBO's projections. Beyond that, we grow these 
expenditures at the rate of population growth plus inflation. 

[15] In our simulations that combine the fiscal outcomes for all levels 
of government, the methodology underlying the federal simulations 
differs slightly from our past approach. Our federal budget simulations 
have incorporated the negative effect on economic growth of large 
deficits that divert funds from private investment. In order to combine 
the federal and state and local budget simulations using a consistent 
set of economic assumptions, this feedback from deficits to economic 
growth is removed. With or without feedback, the simulations imply that 
fiscal policy is unsustainable over the long term. 

[16] CBO, The Budget and Economic Outlook: An Update (Washington, D.C.: 
August 2007). 

[17] For a description of the method applied to tax bases rather than 
receipts, see Russell S. Sobel and Randall G. Holcombe, "Measuring the 
Growth and Variability of Tax Bases over the Business Cycle," National 
Tax Journal 49 No. 4 (December, 1996): 535-52. While following this 
method generally, for our work we only use the long-run elasticity 
described in these studies and not the full error-correction model 
which also takes into consideration the short-run cyclical changes. 

[18] Suppose, for example, that the estimated long-run elasticity is 
1.2 and the economy has been growing at a steady 3.0 percent for a 
number of years. The long-run elasticity of 1.2 implies that the trend 
growth in receipts is 3.6 percent (1.2 * 3.0). 

[19] See Richard F. Dye and Therese J. McGuire, "Growth and 
Variablility of State Individual Income and General Sales Taxes," 
National Tax Journal, Vol. 44, No. 1 (1991), and Russell S. Sobel and 
Randall G. Holcombe, "Measuring the Growth and Variability of Tax Bases 
Over the Business Cycle," National Tax Journal, Vol. 49, No. 4 (1996). 

[20] See CBO, Budget and Economic Outlook (August 2007). 

[21] See CBO, Long Term Budget Outlook (December 2005). 

[22] The changes in the sector's financial assets and liabilities are 
in Federal Reserve flow of funds table F.105 and the corresponding 
outstanding levels are in table L.105. 

[23] In 2007 we issued State and Local Government Retiree Benefits: 
Current Status of Benefit Structures, Protections, and Fiscal Outlook 
for Funding Future Costs, GAO-07-1156 (Washington, D.C.: September 
2007), in which we provided estimates of pension and health costs for 
state and local government retirees based on data available at that 
time. Since then, the annual revision of the NIPA as well as the 
Medical Expenditure Panel Survey (MEPS) of the Department of Health and 
Human Services became available. The results presented in this report 
reflect those updates. 

[24] See The 2007 Annual Report of the Board of Trustees of the Federal 
Old-Age and Survivors Insurance and Disability Insurance Trust Funds 
(Washington, D.C.: May 1, 2007), Table A2. 

[25] This assumption implies that if there were no growth in the 
productivity of state and local workers, the output of services per 
person served would remain the same. As such, any increased growth in 
services provided per citizen hinges on the degree to which 
productivity in public sector services advances. 

[26] CPI data are from CBO, The Budget and Economic Outlook: An Update 
(Washington, D.C.: August 2007). These data are available through 2017. 
For later years, we hold the growth rate constant at the rate that CBO 
assumes between 2016 and 2017. 

[27] The Excel Solver function was used to find the weights that 
minimized the sum of the squared residuals between actual and fitted 
beneficiaries. 

[28] See Annual Statistical Supplement to the Social Security Bulletin, 
2005 (Washington, D.C.: February 2006), Table 6.F1 Number of benefits 
terminated, by type, 1940-2004. 

[29] See Annual Statistical Supplement to the Social Security Bulletin, 
2005 (Washington, D.C.: February 2006), Table 5.A4 Number and total 
monthly benefits, by trust fund and type of benefit, December 1940- 
2004, selected years. 

[30] While we use a constant death rate throughout our simulation 
analysis, but actually, the Social Security Administration is 
projecting a decline in death rate during this time frame. 

[31] The starting value of pension assets for state and local 
government pension plans--in 2006--is obtained from the Federal Reserve 
Flow of Funds Accounts. 

[32] Public pension funds hold substantial assets, amounting to $3.0 
trillion at year-end 2006. Because the calculation we make implies that 
all assets are used to pay benefits, the estimated contribution rate 
would be negative over short intervals. But, in fact, some of the 
assets already in the pension funds are related to liabilities that 
will not be paid for many years into the future. As the time horizon 
increases, the present value of liabilities grows relative to assets, 
resulting in an increase in the estimated contribution rate. When the 
projection horizon lengthens sufficiently, however, the contribution 
rate stabilizes. That is, at some point there is virtually no 
difference in contribution rates estimated over successively longer 
projection periods. A 400-year projection horizon is long enough to 
provide an estimated contribution rate invariant to further increases 
in the projection period. The result is an estimate of the contribution 
rate necessary to fund pension payments on a sustainable basis. 

[33] When evaluating state and local government pensions, standard 
practice is to use a discount rate based on the expected rate of return 
on pension fund investments. To develop a measure of the expected 
pension return, we analyzed data from Flow of Funds Accounts table 
L.119 (State and Local Employee Government Retirement Funds). We 
calculated each asset category's annual share of total fund assets and 
assigned a rate of return to each category. The asset groups included 
money-like assets (sum of checkable deposits and currency, time and 
savings deposits, money market mutual funds, and repurchase agreement 
securities (RPs), open market paper, treasury securities, agency-and 
government-sponsored enterprise-(GSE) backed securities, municipal 
securities, corporate and foreign bonds, mortgages, corporate equities, 
mutual fund shares, and other miscellaneous assets). Although data are 
available beginning in 1952 for pension fund assets, yields for all of 
the asset categories are only available starting in 1965. Accordingly, 
for each year from 1965 through 2005 we calculated the weighted average 
nominal return by summing the product of each asset's share and its 
return. Factoring out each year's CPIU increase provides an estimate of 
the real pension fund return. Because there has been a long-term shift 
in pension fund portfolios away from fixed income assets towards 
equities, the average real return over this period is not 
representative of likely future returns. To find an estimated real 
pension yield more representative of the recent composition of 
retirement fund investments, we used the average asset shares during 
the most recent 10-year period as portfolio weights. Multiplying these 
10-year weights by each asset category's average real return over the 
entire period from 1965 through 2005 and summing the products results 
in an estimated real pension return of 5.0 percent. In our base case, 
therefore, we use a real discount rate of 5.0 percent to find the 
present value of future cash flows. 

[34] Implicitly we assume that the medical coverage continues to pay 
about the same percentage of medical costs for employees and retirees 
as it currently does. 

[35] Because our state and local government retiree health care cost 
estimates are based on data that did not incorporate possible savings 
attributable to the Medicare part D drug subsidy that began in 2006, 
the estimates may overstate retiree health slightly. 

[End of section] 

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