Federal-Aid Highways
Trends, Effect on State Spending, and Options for Future Program Design Gao ID: GAO-04-802 August 31, 2004In 2004, both houses of Congress approved separate legislation to reauthorize the federal-aid highway program to help meet the Nation's surface transportation needs, enhance mobility, and promote economic growth. Both bills also recognized that the Nation faces significant transportation challenges in the future, and each established a National Commission to assess future revenue sources for the Highway Trust Fund and to consider the roles of the various levels of government and the private sector in meeting future surface transportation financing needs. This report (1) updates information on trends in federal, state, and local capital investment in highways; (2) assesses the influence that federal-aid highway grants have had on state and local highway spending; (3) discusses the implications of these trends for the federal-aid highway program; and (4) discusses options for the federal-aid highway program.
The Nation's investment in its highway system has doubled in the last 20 years, as state and local investment outstripped federal investment--both in terms of the amount of and growth in spending. In 2002, states and localities contributed 54 percent of the Nation's capital investment in highways, while federal funds accounted for 46 percent. However, as state and local governments faced fiscal pressures and an economic downturn, their investment from 1998 through 2002 decreased by 4 percent in real terms, while the federal investment increased by 40 percent in real terms. Evidence suggests that increased federal highway grants influence states and localities to substitute federal funds for funds they otherwise would have spent on highways. Our model, which expanded on other recent models, estimated that states used roughly half of the increases in federal highway grants since 1982 to substitute for state and local highway funding, and that the rate of substitution increased during the 1990s. Therefore, while state and local highway spending increased over time, it did not increase as much as it would have had states not withdrawn some of their own highway funds. These results are consistent with our earlier work and with other evidence. For example, the federal-aid highway program creates the opportunity for substitution because states typically spend substantially more than the amount required to meet federal matching requirements--usually 20 percent. Thus, states can reduce their own highway spending and still obtain increased federal funds. These trends imply that substitution may be limiting the effectiveness of strategies Congress has put into place to meet the federal-aid highway program's goals. For example, one strategy has been to significantly increase the federal investment and ensure that funds collected for highways are used for that purpose. However, federal increases have not translated into commensurate increases in the nation's overall investment in highways, in part because while Congress can dedicate federal funds for highways, it cannot prevent state highway funds from being used for other purposes. GAO identified several options for the future design and structure of the federal-aid highway program that could be considered in light of these issues. For example, increasing the required state match, rewarding states that increase their spending, or requiring states to maintain levels of investment over time could all help reduce substitution. On the other hand, the ability of states to meet a variety of needs and fiscal pressures might be better accomplished by providing states with funds through a more flexible federal program--this could also reduce administrative expenses associated with the federal-aid highway program. While some of these options are mutually exclusive, others could be enacted in concert with each other. The commission separately approved by both houses of Congress in 2004 may be an appropriate vehicle to examine these options.
RecommendationsOur recommendations from this work are listed below with a Contact for more information. Status will change from "In process" to "Open," "Closed - implemented," or "Closed - not implemented" based on our follow up work.
Director: Team: Phone:GAO-04-802, Federal-Aid Highways: Trends, Effect on State Spending, and Options for Future Program Design
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Report to the Ranking Minority Member Subcommittee on Transportation
and Infrastructure, Committee on Environment and Public Works, U.S.
Senate:
August 2004:
FEDERAL-AID HIGHWAYS:
Trends, Effect on State Spending, and Options for Future Program
Design:
GAO-04-802:
GAO Highlights:
Highlights of GAO-04-802, a report to the Ranking Minority Member,
Subcommittee on Transportation and Infrastructure, Committee on
Environment and Public Works, U.S. Senate
Why GAO Did This Study:
In 2004, both houses of Congress approved separate legislation to
reauthorize the federal-aid highway program to help meet the Nation‘s
surface transportation needs, enhance mobility, and promote economic
growth. Both bills also recognized that the Nation faces significant
transportation challenges in the future, and each established a
National Commission to assess future revenue sources for the Highway
Trust Fund and to consider the roles of the various levels of
government and the private sector in meeting future surface
transportation financing needs.
This report (1) updates information on trends in federal, state, and
local capital investment in highways; (2) assesses the influence that
federal-aid highway grants have had on state and local highway
spending; (3) discusses the implications of these trends for the
federal-aid highway program; and (4) discusses options for the federal-
aid highway program.
What GAO Found:
The Nation‘s investment in its highway system has doubled in the last
20 years, as state and local investment outstripped federal investment”
both in terms of the amount of and growth in spending. In 2002, states
and localities contributed 54 percent of the Nation‘s capital
investment in highways, while federal funds accounted for 46 percent.
However, as state and local governments faced fiscal pressures and an
economic downturn, their investment from 1998 through 2002 decreased by
4 percent in real terms, while the federal investment increased by 40
percent in real terms.
Evidence suggests that increased federal highway grants influence
states and localities to substitute federal funds for funds they
otherwise would have spent on highways. Our model, which expanded on
other recent models, estimated that states used roughly half of the
increases in federal highway grants since 1982 to substitute for state
and local highway funding, and that the rate of substitution increased
during the 1990s. Therefore, while state and local highway spending
increased over time, it did not increase as much as it would have had
states not withdrawn some of their own highway funds. These results are
consistent with our earlier work and with other evidence. For example,
the federal-aid highway program creates the opportunity for
substitution because states typically spend substantially more than the
amount required to meet federal matching requirements”usually 20
percent. Thus, states can reduce their own highway spending and still
obtain increased federal funds.
These trends imply that substitution may be limiting the effectiveness
of strategies Congress has put into place to meet the federal-aid
highway program‘s goals. For example, one strategy has been to
significantly increase the federal investment and ensure that funds
collected for highways are used for that purpose. However, federal
increases have not translated into commensurate increases in the
nation‘s overall investment in highways, in part because while Congress
can dedicate federal funds for highways, it cannot prevent state
highway funds from being used for other purposes.
GAO identified several options for the future design and structure of
the federal-aid highway program that could be considered in light of
these issues. For example, increasing the required state match,
rewarding states that increase their spending, or requiring states to
maintain levels of investment over time could all help reduce
substitution. On the other hand, the ability of states to meet a
variety of needs and fiscal pressures might be better accomplished by
providing states with funds through a more flexible federal program”
this could also reduce administrative expenses associated with the
federal-aid highway program. While some of these options are mutually
exclusive, others could be enacted in concert with each other. The
commission separately approved by both houses of Congress in 2004 may
be an appropriate vehicle to examine these options.
What GAO Recommends:
Congress may wish to consider expanding the mandate of the proposed
National Commission to consider options to redesign the federal-aid
highway program in light of these issues.
DOT officials commented on a draft of this report and said that the
report raised important issues that merit further study.
www.gao.gov/cgi-bin/getrpt?GAO-04-802.
To view the full product, including the scope and methodology, click on
the link above. For more information, contact JayEtta Hecker at (202)
512-2834 or heckerj@gao.gov.
[End of section]
Contents:
Letter:
Results in Brief:
Background:
States and Localities Invest More in Highways Than the Federal
Government; However, Recent Federal Investment Has Outpaced State and
Local Investment:
Evidence Suggests Federal Highway Grants Have Increasingly Been Used to
Substitute for Rather Than Supplement Spending from States' Own
Resources:
Substitution May Be Limiting the Effectiveness of Strategies to
Accomplish the Federal-Aid Highway Program's Overall Goals:
We Identified Several Options for the Design and Structure of the
Federal-Aid Highway Program:
Conclusions:
Matter for Congressional Consideration:
Agency Comments and Our Evaluation:
Appendixes:
Appendix I: Objectives, Scope, and Methodology:
Appendix II: Description of Grant Substitution Model, Statistical
Methods, and Results:
Summary of Previous Studies:
Description of GAO's Statistical Model:
Statistical Results:
Appendix III: State Characteristics Associated with States' Level of
Effort to Fund Highways from State Resources:
Appendix IV: Program Options Designed to Reduce Substitution:
Appendix V: GAO Contacts and Staff Acknowledgments:
GAO Contacts:
Staff Acknowledgments:
Tables:
Table 1: Federal-Aid Highway Program Grant Programs and Formulas:
Table 2: Range of Estimates of Highway Substitution Rates by Time
Period Based on a 95 Percent Confidence Level:
Table 3: Options to Reduce Substitution:
Table 4: Summary of Fiscal Substitution Studies:
Table 5: Highway Grant Substitution Rates Reported in Fiscal
Substitution Studies:
Table 6: Variables Considered in the Second Stage State Highway
Expenditure Equation:
Table 7: Variables Used to Explain the Distribution of Federal Highway
Grants:
Table 8: Descriptive Statistics:
Table 9: Instrumental Variables Estimator of Federal Grants per Capita:
Table 10: Instrumental Variables Estimates of State Highway Spending
Model, Without Correcting for Autocorrelation:
Table 11: Instrumental Variables Estimates of State Highway Spending
Model, Correcting for Autocorrelation:
Table 12: Summary Results of the Statistical Testing of the Variable
Coefficients:
Table 13: State Highway Spending Model with Statistically Insignificant
Variables Removed:
Table 14: State Highway Spending Model with Substitution Rates by Time
Period:
Table 15: Statistical Tests for the Endogeneity of Federal Grants and
State Highway Spending:
Table 16: Stepwise Regression Analysis of the Fixed Effects:
Figures:
Figure 1: Illustrative Effects of $1 Increase in Federal Highway Grant:
Figure 2: Federal and State and Local Highway Capital Expenditures,
1982 through 2002 (2001 dollars):
Figure 3: Amount of Yearly Capital Expenditures, 1998 through 2002
(2001 dollars):
Figure 4: Federal and State and Local Highway Capital Investment, 1991
through 2002 (2001 dollars):
Figure 5: Rates of Fiscal Substitution into Nonhighway Uses by Time
Period:
Figure 6: Summary of Federal Grant Substitution Rates Reported in
Various Studies Using Data from Various Time Periods:
Figure 7: State and Local Highway Spending for Capital Projects as a
Percent of Total (Federal Plus State and Local) Capital Spending (1997
through 2000):
Figure 8: Federal Obligations by System in Fiscal Year 2001:
Figure 9: DOT Performance Measures for Condition and System
Performance:
Abbreviations:
DOT: Department of Transportation:
BEA: Bureau of Economic Analysis:
FHWA: Federal Highway Administration:
GPRA: Government Performance and Results Act:
ISTEA: Intermodal Surface Transportation Efficiency Act:
MOE: Maintenance of Effort:
NHTSA: National Highway Traffic Safety Administration:
TEA-21: Transportation Equity Act for the 21ST Century:
Letter August 31, 2004:
The Honorable Harry Reid:
Ranking Minority Member:
Subcommittee on Transportation and Infrastructure:
Committee on Environment and Public Works:
United States Senate:
Dear Senator Reid:
In 2004, both houses of Congress approved separate legislation to
reauthorize the federal-aid highway program to help meet the Nation's
surface transportation needs, enhance mobility, and promote economic
growth. Each bill also recognized that the Nation faces significant
transportation challenges in the future. Many transportation experts
have noted that eventually, the introduction of more fuel-efficient
vehicles and clean fuels may undermine the sustainability of financing
the Nation's surface transportation program through motor fuel taxes.
As such, both bills established a National Commission to assess future
revenue sources for the Highway Trust Fund and to consider the roles of
the various levels of government and the private sector in meeting
future surface transportation financing needs. In the longer term,
broader fiscal challenges face the Nation, including federal and state
budget deficits and the fiscal crisis looming as the baby boomer
generation retires, causing mandatory commitments to Social Security
and Medicare to consume a greater share of the Nation's resources,
squeezing funding available for all domestic discretionary programs.
These challenges require the Nation to think critically about existing
government programs and commitments.
In light of these issues, you asked us to provide information on past
trends in the federal, state, and local capital investment in highways,
and on how federal-aid highway program grants influence the level of
state and local highway spending. We responded to the first part of
your request in June 2003.[Footnote 1] This report (1) updates
information on trends in federal, state, and local capital investment
in highways; (2) assesses the influence that federal-aid highway grants
have had on state and local highway spending; (3) discusses the
implications of these trends for the federal-aid highway program; and
(4) discusses options for the structure and design of the federal-aid
highway program that could be considered in light of these issues. In
addition, this report identifies characteristics associated with
differences among states' levels of effort for highway investment (see
app. III).
To respond to your request, we reviewed data from the Department of
Transportation's (DOT) Federal Highway Administration (FHWA), the
Bureau of the Census, and other sources for the period from 1982
through 2002. We determined that the data were sufficiently reliable
for the purposes of our analyses. We also reviewed and synthesized the
research literature on the influence that federal highway grants have
had on the level of state and local highway spending. Our literature
review revealed a number of studies that used statistical models
developed by the studies' authors to estimate the influence of federal
funding on state spending choices. These models examined different time
periods, employed different statistical methods, and considered
different social, demographic, economic, and political factors that may
affect state highway spending decisions. None of the models used in the
studies we reviewed included the most recent data now available on
highway funding, and none examined whether the effect of federal grants
on state spending changed over the time period covered in the study.
Therefore, based on the models used in the earlier studies, we
developed a statistical model of state highway spending outcomes to
estimate the fiscal effects of federal highway funding on state highway
spending choices. This model included the most recent data available
and examined whether the effect of federal grants on state spending
changed over the time period covered by our data. The purpose of the
statistical model was to isolate the effect that federal grants have on
state highway spending by controlling for other factors that also
affect state spending decisions. The model therefore takes into account
changing state economic conditions, the size and intensity of highway
usage, and other factors that may be associated with states'
willingness to support highway spending. This statistical model was
reviewed by experts in DOT and peer reviewed by three authors of the
earlier studies on the fiscal effects of federal highway grants. These
experts and authors generally agreed with our methods, and we made
revisions based on their comments as appropriate. A more detailed
description of the literature and the statistical model is contained in
appendix II. We conducted our work from August 2003 through July 2004
in accordance with generally accepted government auditing standards.
Results in Brief:
The Nation's capital investment in its highway system has doubled in
the last 20 years, and during that time period as a whole, state and
local investment in highways outstripped federal investment in
highways--both in terms of the amount of and growth in spending.
Between 1982 and 2002, state and local capital investment in highways
increased 150 percent, from $14.1 billion to $35.7 billion in real
terms, whereas the federal investment increased 98 percent, from $15.5
billion to $30.7 billion in real terms.[Footnote 2] For every year
after 1986, states and localities invested more in the Nation's
highways than did the federal government. Most recently, in 2002,
states and localities contributed 54 percent of the Nation's capital
investment in highways, spending $35.7 billion, while the federal
government contributed 46 percent, or $30.7 billion. However, since the
early 1990s, state and local investment in highways has increased at a
slower rate than federal investment in highways. From 1991, when the
Intermodal Surface Transportation Efficiency Act (ISTEA) was enacted,
through 2002, state and local investment increased 23 percent, from
$29.0 to $35.7 billion in real terms. During that same time period,
federal investment increased 47 percent, from $20.9 to $30.7 billion in
real terms. In the period following the enactment of the Transportation
Equity Act for the 21ST Century (TEA-21), from 1998 through 2002,
during which state and local governments faced fiscal pressures and an
economic downturn, the trend intensified, with state and local
investment decreasing by 4 percent--from $37.0 to $35.7 billion in real
terms--and federal investment increasing by 40 percent--from $21.9 to
$30.7 billion in real terms.
The preponderance of evidence suggests that federal-aid highway grants
have influenced state and local governments to substitute federal funds
for state and local funds that otherwise would have been spent on
highways. Therefore, according to our model--which refined and expanded
on other recent models and controlled for the effects of other factors-
-and according to other studies, when federal highway grants increased,
total highway spending did not increase as much as it would have had
states not withdrawn some of their own highway-related funds.
Specifically, our model examined how federal highway spending affected
state spending, and it estimated that state and local governments have
used roughly half of the increases in federal highway grants since 1982
to substitute for funding they would otherwise have spent from their
own resources. In addition, our model estimated that the rate of grant
substitution increased significantly over the past two decades, rising
from about 18 cents on the dollar during the early 1980s to roughly 60
cents on the dollar during the 1990s, when ISTEA and TEA-21 were in
effect.[Footnote 3] Three previous studies of this issue all found that
substitution occurred, although their estimates of levels of
substitution varied, probably due to differences in time periods
studied, definitions of substitution, and statistical methods employed.
There are a number of reasons why substitution may occur. Our earlier
work found that in general, the federal grant system as a whole does
not encourage states to use federal dollars as a supplement rather than
a substitute for their own spending.[Footnote 4] Specifically, the
structure of the federal-aid highway program creates an opportunity for
substitution because states typically spend substantially more in state
and local funds than is required to meet current federal matching
requirements. As a consequence, when federal funding increases, states
are able to reduce their own highway spending and yet obtain the
increased federal funds. If states substitute some of the increase in
federal funds for their own funds, then total highway spending may
increase, but not by as much as it would have had substitution not
occurred.
These trends imply that substitution may be limiting the effectiveness
of the strategies Congress has put into place to meet the federal-aid
highway program's overall goals. Congress and DOT have at various times
enumerated goals for the federal-aid highway program, including, among
other things, enhancing safety, promoting economic growth, enhancing
mobility, supporting interstate and international commerce, and meeting
national security needs. To meet these goals, Congress has put in place
strategies that include significantly increasing the federal investment
in the highway system--particularly since 1991--and ensuring that funds
collected by the federal government for highways are used for that
purpose. However, due to probable substitution, the sizable increases
in dedicated federal funding that Congress has provided for highways
since 1991 have not translated into commensurate increases in the
Nation's overall investment in its highway system. In part this is
because, while Congress can dedicate federal funds for highways, it
cannot prevent state highway funds from being used for other purposes.
Furthermore, Congress has sought to meet the goals of the program
through a strategy of emphasizing states' priorities and decision-
making. Specifically, Congress has incorporated return-to-origin
features into the highway program and returned to each state more of
the fuel and other taxes collected in that state, and has given states
wide latitude in deciding how to use and administer federal grants to
best meet their transportation needs. However, substitution may be
limiting the effectiveness of this strategy. Although the federal-aid
highway program has a considerable regulatory component that requires
states to follow and enact certain laws as a condition of receiving
federal funds, from a funding standpoint, the program's return-to-
origin features and flexibility, combined with substitution and the use
of state and local highway funds for other purposes, means that the
federal-aid highway program is to some extent functioning as a cash
transfer, general purpose grant program. This raises broader questions
about the effectiveness of the federal investment in highways in
accomplishing the program's goals and outcomes. While under the
Government Performance and Results Accountability Act (GPRA), DOT has
established performance measures and outcomes for the federal-aid
highway program to enhance mobility and economic growth, the program's
current structure does not link funding with performance or the
accomplishment of these goals and outcomes.
We identified several options for the design and structure of the
federal-aid highway program that could be considered in light of these
issues. These options include program designs that have been used for
other federal programs and which could reduce substitution. For
example, increasing the required state match on federal highway
projects, rewarding states that increase their highway spending effort,
or requiring states to maintain levels of highway investment over time
to receive federal funds could all reduce substitution. On the other
hand, the ability of states to meet a variety of needs and fiscal
pressures might be better accomplished by providing states with funds
through a more flexible federal program. Adopting such an option could
be seen as recognizing substitution as an appropriate response on the
part of states to increasing fiscal challenges and competing demands.
It could also reduce the level of administrative involvement needed and
thereby reduce administrative expenses associated with the federal-aid
highway program. Finally, policy makers may wish to consider the design
of the federal-aid highway program in the broader context of aligning
the program with program-related goals, possibly taking into account
performance measures and results. While some of these options are
mutually exclusive, others could be enacted in concert with each other.
For instance, requiring states to maintain levels of highway investment
over time or other options to limit substitution could be combined with
an effort to align funding with the accomplishment of performance
measures. Similarly, aligning funding with the accomplishment of
performance measures could also be carried out in conjunction with
creating a more flexible federal program.
The proposed National Commission to assess future revenue sources to
support the Highway Trust Fund may be an appropriate vehicle through
which to examine these options. This commission is to consider how the
program is financed and the roles of the federal and state governments
and other stakeholders in financing it; the appropriate program
structure and mechanisms for delivering that funding are important
components of making these decisions. Therefore, in light of the issues
raised in this report and the fiscal challenges the Nation faces in the
21STCentury, Congress may wish to consider expanding the mandate of the
National Commission to assess possible changes to the federal-aid
highway program to maximize the effectiveness of federal funding and
promote national goals and strategies. Consideration could be given to
the program's design, structure, and funding formulas; the roles of the
various levels of government; and the inclusion of greater performance
and outcome oriented features.
We provided a draft of this report to DOT for review and obtained
comments from departmental officials, including FHWA's Director of
Legislation and Strategic Planning. These officials said that our
analysis raised interesting and important issues regarding state
funding flexibility and the federal-aid highway program that merit
further study. We agree with DOT's characterization of the importance
of the issues raised in this report, and we continue to believe that
Congress has the opportunity to maximize the effectiveness of federal
funding and promote national goals and strategies by expanding the
proposed mandate of the National Commission. DOT also provided some
technical comments, which we incorporated where appropriate.
Background:
Federal funding for highways is provided to the states mostly through a
series of formula grant programs collectively known as the federal-aid
highway program.[Footnote 5] Periodically Congress enacts multiyear
legislation that authorizes the Nation's surface transportation
programs, including highway, transit, highway safety, and motor carrier
programs. This legislation authorizes the federal-aid highway program
and the individual grant programs that comprise it, and it sets overall
funding for it and other surface transportation programs. In 1991, for
example, Congress enacted ISTEA, which authorized $121 billion for
highways for the 6-year period from fiscal years 1992 through 1997, and
in 1998 Congress enacted TEA-21, which authorized $171 billion for the
federal-aid highway program from fiscal years 1998 through 2003. In
2004, the House and Senate each approved separate legislation to
reauthorize the federal-aid highway program, the House authorizing
$226.3 billion and the Senate authorizing $256.4 billion for fiscal
years 2004 through 2009. These authorizations provide multiyear
"contract authority" that gives the states notice several years in
advance of the size of the federal-aid program and the approximate
amount of federal funding they may expect to receive.
Funding for the federal-aid highway program is provided through the
Highway Trust Fund. Established by the Highway Revenue Act of 1956, the
Highway Trust Fund is a dedicated source of revenues generated by
highway user fees such as taxes on motor fuels, tires, and trucks. TEA-
21 established two additional mechanisms to support the dedication of
highway user fees to highways. First, the act established guaranteed
funding for certain highway, transit, and highway safety programs,
including the federal-aid highway program, by protecting them with
"firewalls" from competing for funding with other domestic
discretionary programs through the congressional budget process.
Second, the act provided that the highway program funding
authorizations would be adjusted to reflect changes in estimates of
Highway Trust Fund revenue, ensuring that funding available for the
federal-aid highway program reflected the revenue taken in by the
Highway Trust Fund. Both the Senate and the House have each approved
separate legislation to extend the collection of fuel taxes to the
Highway Trust Fund, the Senate through 2009 and the House through 2011.
Amid concerns that the introduction of more fuel-efficient vehicles and
clean fuels may undermine the sustainability of financing the Highway
Trust Fund through fuel taxes in the future, both houses also included
provisions to create a National Commission to examine future revenue
sources to support the Highway Trust Fund and to consider, among other
things, the roles of the various levels of government and the private
sector in meeting future surface transportation financing needs.
Once Congress authorizes funding, FHWA makes federal funding available
to the states annually at the start of each fiscal year through
apportionments based on formulas specified in law for each of the
several formula grant programs that make up the federal-aid highway
program. Ninety-two percent of the funds apportioned to the states in
fiscal year 2003 were apportioned by formula. The remaining highway
program funds were distributed through allocations to states with
qualifying projects. The highway programs with apportionments based on
formulas are shown in table 1.
Table 1: Federal-Aid Highway Program Grant Programs and Formulas:
Program: Interstate Maintenance Program;
Purpose: Resurfacing, restoring, rehabilitating, and reconstructing
most routes on the Interstate Highway System;
FY 2003 funding (in billions)[A]: $4.2;
Grant formula: Interstate System lane miles (33 1/3%); Vehicle miles
traveled on the Interstate System (33 1/3%); Annual contributions to
the Highway Account of the Highway Trust Fund attributable to
commercial vehicles (33 1/3%);
Minimum apportionment: ½ percent of Interstate Maintenance and National
Highway System apportionments combined.
Program: National Highway System Program;
Purpose: Improvements to rural and urban routes that are part of the
National Highway System (including the Interstate System) and
designated connections to major intermodal terminals;
FY 2003 funding (in billions)[A]: $5.1;
Grant formula: Lane miles on principal arterial routes, excluding the
Interstate System (25%); Vehicle miles traveled on principal arterial
routes, excluding the Interstate System (35%); Diesel fuel used on
highways (30%); Total lane miles on principal arterial highways divided
by the State's total population (10%);
Minimum apportionment: ½ percent of Interstate Maintenance and National
Highway System apportionments combined.
Program: Surface Transportation Program;
Purpose: Projects on any federal-aid highway, bridge projects on any
public road, transit capital projects, intracity and intercity bus
terminals and facilities, and other uses;
FY 2003 funding (in billions)[A]: $5.9;
Grant formula: Total lane miles of federal-aid highways (25%); Total
vehicle miles traveled on federal-aid highways (40%); Estimated tax
payments attributable to highway users paid into the Highway Account of
the Highway Trust Fund (35%);
Minimum apportionment: ½ percent.
Program: Highway Bridge Replacement and Rehabilitation Program;
Purpose: Replacing or rehabilitating deficient highway bridges and
seismic retrofits for bridges on public roads;
FY 2003 funding (in billions)[A]: $3.6;
Grant formula: Relative share of total cost to repair or replace
deficient highway bridges (100%);
Minimum apportionment: ¼ percent (10 percent maximum).
Program: Congestion Mitigation and Air Quality Improvement Program;
Purpose: Projects which reduce transportation related emissions in air
quality nonattainment and maintenance areas for ozone, carbon monoxide,
and particulate matter;
FY 2003 funding (in billions)[A]: $1.4;
Grant formula: Weighted population in non-attainment and maintenance
areas (100%);
Minimum apportionment: ½ percent.
Program: Minimum Guarantee Program;
Purpose: Funding to states based on equity considerations including
specific shares of overall program funds and minimum return on
contributions to the highway account of the Highway Trust Fund. A
portion of the funds are distributed among core highway programs while
remaining funds are eligible under the same rules as the Surface
Transportation Program;
FY 2003 funding (in billions)[A]: $6.4;
Grant formula: 90.5 percent of the percentage share of contributions to
the Highway Account of the Highway Trust Fund from motor fuel and other
taxes collected in that state based on latest available data;
Minimum apportionment: N/A.
Other[B];
FY 2003 funding (in billions)[A]: $0.5.
Source: FHWA.
[A] Reflects amounts apportioned by formula before the distribution of
Minimum Guarantee Program funding among the core programs.
[B] Includes funds for the Appalachian Development Highway System and
Recreational Trails Programs.
[End of table]
As we reported in 1995, the federal funding formula derives from a
complicated set of calculations and is a complex process in which the
underlying data and factors are ultimately not meaningful because they
are overridden by other provisions that yield a predetermined
outcome.[Footnote 6] One reason is the presence of "equity provisions"
that ensure that states receive set amounts based on historic funding
levels and other considerations. These equity provisions were
strengthened after our 1995 report. For example, as table 1 shows, TEA-
21's Minimum Guarantee Program ensures that each state's share of
apportionments from nearly all federal-aid highway funds is not less
than 90.5 percent of that state's percentage share of contributions to
the Highway Account of the Highway Trust Fund.[Footnote 7] Funds from
this program accounted for nearly a quarter of all highway funding in
fiscal year 2003. Under separate legislation approved by both the House
and the Senate, each state's share of apportionments could rise to 95
percent by 2009.[Footnote 8] Furthermore, as table 1 shows, states
receive minimum apportionments regardless of the formula for several
grant programs.
States have broad flexibility to transfer funds between the various
grant programs. For example, states may transfer up to 50 percent of
their Interstate Maintenance and National Highway System Program funds
to other programs, including the Surface Transportation Program, which,
as table 1 shows, has broad eligibility rules. In addition, ISTEA and
TEA-21 provided the states broad authority to transfer federal-aid
highway funds to transit projects and vice versa. Between fiscal years
1992 and 2002, 47 states and the District of Columbia transferred about
$8.8 billion from federal-aid highway funds to transit programs to fund
rail line improvements, motor vehicle purchases, new or improved
passenger facilities, and other projects. During that same time, about
$40 million was transferred from FTA to FHWA for highway projects.
Once FHWA apportions funds to the states, funds are available to be
obligated by the states for construction, reconstruction, and
improvement of highways and bridges on eligible federal-aid highway
routes and for other purposes authorized in law. About 1 million of the
Nation's 4 million miles of roads are eligible for federal aid;
however, these roads accounted for 85 percent of the vehicle miles
traveled on the Nation's roadways in 2001. The roads that are generally
ineligible are functionally classified as local roads or minor
collectors. Around 161,000 miles of federally eligible roadways are on
the National Highway System, of which around 47,000 belong to the
Interstate Highway System. With few exceptions, federal funds for
highways must be matched by funds from other sources--usually state and
local governments. The matching requirement on most projects is 80
percent federal and 20 percent state or local funding. In addition to
matching federal funds, states and localities spend funds to finance
highway capital projects and to maintain existing roadways.
The federal-aid highway program is administered by FHWA, whose
responsibilities include reviewing periodic transportation improvement
plans prepared by state and local governments, approving projects for
federal aid, apportioning grant funding to the states, providing
technical support, and overseeing federally funded projects. In fiscal
year 2004, FHWA received $334 million to provide these services, with
an authorized staff level of 2,931 positions. FHWA personnel are
located in Washington, D.C., and in 52 field offices located in each
state, the District of Columbia, and Puerto Rico, as well as a regional
"resource center" with four offices across the country that provide
specialized technical assistance to the field offices and the states.
The federal-aid highway program has a considerable regulatory
component. As a condition of receiving federal aid, states agree to
apply and enforce certain federal laws on federally aided projects,
such as the environmental assessment provisions in the National
Environmental Policy Act, the Americans With Disabilities Act, the
nondiscrimination protections found in the Civil Rights Act of 1964,
and others. In addition, states are required to establish goals and to
award a set percentage of contracts (the national goal is 10 percent)
on federally aided projects to small businesses owned and controlled by
socially and economically disadvantaged individuals, including
minority and women-owned businesses. Furthermore, in accepting federal-
aid highway funds, states must enact certain laws to improve highway
safety or face penalties in the form of either withholdings or
transfers in their federal grants.[Footnote 9]In addition to these
penalties, states may apply for and receive highway safety incentive
grants through programs administered outside the federal-aid highway
program by the National Highway Traffic Safety Administration (NHTSA).
For example, states in which the use of seat belts exceeds the national
average or improves over time are eligible for incentive grants based
on NHTSA's calculation of the annual savings to the federal government
in medical costs that resulted from the increased use.
In general, there are three possible ways that federal grant funding
can influence state spending for a program, as illustrated in figure 1.
First, increased federal funding may stimulate, or leverage, additional
spending from state resources. For example, a state may have to
increase its own spending in order to meet federal matching
requirements and obtain federal funds, thus increasing the overall
level of spending by more than the amount of the federal
grant.[Footnote 10] As the federal-aid highway program in most cases
requires that states must contribute 20 percent of the total cost of a
project in order to receive federal matching funds of 80 percent of the
total cost, the suggestion is that every $1.00 increase in federal
funds would go towards a total spending increase of $1.25 ($1.00 is 80
percent of $1.25), $0.25 of which would be funded with state and local
government funds ($0.25 is 20 percent of $1.25). The result of a
stimulative effect of federal grant funding is illustrated in the first
panel of figure 1, in which an additional $1.00 of federal aid
increases spending from state resources by 25 cents, increasing the
overall level of highway spending by $1.25. Alternatively, increased
federal funding may supplement state spending by adding to what states
would otherwise have spent, increasing the overall level of spending by
the amount of the federal grant, as illustrated in the second panel of
figure 1. To the extent that states maintain their own spending when
they receive additional federal funding, either because federal policy
requires that they do so or because they do so voluntarily, then the
additional federal aid supplements state spending. Finally, states may
use increased federal funding to substitute for, or replace, what they
would otherwise have spent from state resources, so that the overall
level of spending increases by less than the amount of the federal
grant. This substitution of federal funds for state funds is
illustrated in the third panel of figure 1, in which an additional
$1.00 in federal funding results in only a 50 cent increase to total
spending because in response to the influx of federal funds, the state
withdraws 50 cents of its own spending on the program and uses these
funds for other purposes.[Footnote 11]
Figure 1: Illustrative Effects of $1 Increase in Federal Highway Grant:
[See PDF for image]
[End of figure]
States and Localities Invest More in Highways Than the Federal
Government; However, Recent Federal Investment Has Outpaced State and
Local Investment:
The Nation's capital investment in its highway system has doubled in
the last 20 years, and during that time period as a whole, state and
local investment in highways outstripped federal investment in
highways--both in terms of the amount of and growth in spending.
Between 1982 and 2002, state and local capital investment in highways
increased 150 percent, from $14.1 billion to $35.7 billion in real
terms, whereas the federal investment increased 98 percent, from $15.5
billion to $30.7 billion in real terms.[Footnote 12] For every year
after 1986, states and localities invested more in the Nation's
highways than did the federal government. (See fig. 2.) Most recently,
in 2002, states and localities contributed 54 percent of the Nation's
capital investment in highways, spending $35.7 billion, while the
federal government contributed 46 percent or $30.7 billion in real
terms.
Figure 2: Federal and State and Local Highway Capital Expenditures,
1982 through 2002 (2001 dollars)
[See PDF for image]
[End of figure]
In addition to the billions of dollars states and localities invest in
capital highway projects to expand highway capacity or rehabilitate
existing highways, states and localities spend additional funds
maintaining and policing their roadways. For example, in 2001, states
and localities spent about 27 percent of their total capital and
maintenance funding on maintenance activities, including fixing
potholes, sealing cracks in bridge decks, and fixing highway lighting.
Although states and localities still spend more on highway capital
investment than the federal government, recently, state and local
highway investment has increased at a slower pace than federal highway
investment. In addition, state and local investment has decreased in
real terms three times since 1996: between 1996 and 1997, between 1999
and 2000, and between 2001 and 2002. Last year, we reported that since
TEA-21 was passed, from 1998 through 2001, federal investment increased
faster than state and local investment.[Footnote 13] In real terms,
federal investment increased 29 percent, while state and local
investment increased 2 percent.[Footnote 14] This trend of federal
investment increasing more quickly than state and local investment
continued in 2002. From 2001 through 2002, federal investment increased
8.5 percent, while state and local investment decreased 5 percent in
real terms. Thus, from 1998 through 2002, federal investment increased
40 percent, while state and local investment decreased by 4 percent.
Figure 3 shows the annual federal and state and local capital
expenditures on highways during these years.
Figure 3: Amount of Yearly Capital Expenditures, 1998 through 2002
(2001 dollars)
[See PDF for image]
[End of figure]
The general trend of federal investment in highways increasing at a
faster pace than state and local investment in highways holds over a
longer period of time as well, including the period following the
passage of ISTEA in 1991. Although there was some variation on a year-
by-year basis, from 1991, when ISTEA was enacted, through 2002, state
and local investment increased 23 percent, from $29.0 to $35.7 billion
in real terms. During that same time period, federal investment
increased 47 percent, from $20.9 to $30.7 billion in real terms, as
shown in figure 4.
Figure 4: Federal and State and Local Highway Capital Investment, 1991
through 2002 (2001 dollars)
[See PDF for image]
[End of figure]
Although the reasons for this change in spending patterns by level of
government are unclear, tough economic times, with a majority of states
needing to reduce spending to avoid budget deficits, along with large
increases in federal funds for highways may have influenced these
spending patterns. For example, a recent survey of states by the
National Conference of State Legislatures found that even after the
economy began growing after the March 2001 national recession, 36
states still have budget shortfalls with a cumulative gap of about
$25.7 billion.[Footnote 15]
Evidence Suggests Federal Highway Grants Have Increasingly Been Used to
Substitute for Rather Than Supplement Spending from States' Own
Resources:
The preponderance of evidence suggests that increases in federal-aid
highway grants influence state and local governments to substitute
federal funds for funding they would have otherwise spent on highway
projects from their own resources.[Footnote 16] We built on earlier
studies to develop a model that analyzed data from 1982 through 2000 to
examine whether and to what extent states have substituted increases in
federal highway funds for state highway funds. Our preferred model
analyzes data from 1983 through 2000 because of the statistical
techniques we used.[Footnote 17] Our analysis suggests that significant
substitution has occurred and that the rate of grant substitution
increased significantly over the past two decades, rising from 18
percent in the early 1980s to about 60 percent during the 1990s--the
periods that ISTEA and TEA-21 were in effect. Three previous studies of
this issue also found that substitution existed, although their
estimates of levels of substitution varied.[Footnote 18] The structure
of the federal grant system as a whole may encourage substitution.
Specifically, the structure of the federal-aid highway program creates
an opportunity for substitution because states typically spend
substantially more in state and local funds than is required to meet
current federal matching requirements. As a consequence, when federal
funding increases, states are able to reduce their own highway spending
and yet obtain the increased federal funds. If states substitute some
of the increase in federal funds for their own funds, then total
highway spending may increase, but not by as much as it would have had
substitution not occurred.
Our Statistical Model Suggests Federal Highway Funds Have Increasingly
Been Substituted for State Funds That Were Shifted to Nonhighway Uses:
Our statistical model, which we developed from previous models,
estimates that states have used a significant portion of increases in
federal highway funding to substitute for state and local funding for
highways, and that the rate of substitution increased during the 1990s.
According to our preferred model, for the entire period from 1983
through 2000, state governments used roughly half of the increases in
federal highway grants to substitute for funding they would have
otherwise spent from their own resources on highways.[Footnote 19] When
our model examined four separate time periods from 1983 through 2000
that corresponded to the four authorization periods for the federal-aid
highway program, the results suggest that the rate of grant
substitution increased in the 1990s, during the periods in which ISTEA
and TEA-21 were in effect, in comparison to the early 1980s.[Footnote
20] Specifically, our model suggests that states substituted
approximately 18 cents (not statistically significant) of every dollar
increase in federal aid from 1983 to 1986 for funds they would have
spent on highways from their own resources. Our model suggests that the
substitution rate rose to approximately 36 cents of every dollar
increase in federal aid for the period from 1987 to 1991, and that the
substitution rates then rose again to approximately 60 cents for every
dollar increase in federal aid for the two periods examined in the
1990s: 1992 through 1997 and 1998 through 2000. (See fig. 5.)
Figure 5: Rates of Fiscal Substitution into Nonhighway Uses by Time
Period:
[See PDF for image]
[End of figure]
The rates of grant substitution for the time periods reported in figure
5 are derived from our statistical model of state spending choices and
are subject to some uncertainty. While these estimates represent our
most likely estimates of the rate at which states substituted federal
funds for state and local funds, the actual substitution may be larger
or smaller than these estimates. The uncertainty surrounding our
estimates can be expressed in terms of a level of confidence that a
given range of values encompasses the actual substitution rate. The
range of values surrounding each of our estimates is shown in table 2
at a 95 percent level of confidence. The size of each interval provides
a sense of the uncertainty associated with our estimates. The intervals
associated with the two time periods during the 1980s contain possible
values of zero, meaning that we cannot be 95 percent confident that
substitution occurred during these periods. In contrast, the range of
estimates for both time periods in the 1990s does not encompass zero;
therefore, they are statistically different from zero, which means that
our results imply at least a 95 percent level of confidence that
substitution occurred. Our most likely estimates for the two periods we
looked at in the 1990s are in both cases just under 60 percent, and we
can be 95 percent confident that the actual substitution rate was
between 21 percent and 97 percent.
Table 2: Range of Estimates of Highway Substitution Rates by Time
Period Based on a 95 Percent Confidence Level[A]:
Time period: 1983-1986; Point estimate (percent) 18;
Low estimate (percent) -21%;
High estimate (percent) 57%.
Time period: 1987-1991;
Point estimate (percent) 36%;
Low estimate (percent) -2%;
High estimate (percent) 74%.
Time period: 1992-1997;
Point estimate (percent) 59%;
Low estimate (percent) 22%;
High estimate (percent) 97%.
Time period: 1998-2000;
Point estimate (percent) 58%;
Low estimate (percent) 21%;
High estimate (percent) 95%.
Source: GAO:
[A] Positive values represent grant substitution and negative values
indicate grant stimulation.
[End of table]
These results are roughly consistent with previous studies that, when
taken together, also seem to suggest increasing substitution rates over
time. We made four primary enhancements to the models used in previous
studies in developing our model. First, we used more recent data on
highway expenditures than were available for previous studies. Second,
we used a conservative definition of substitution. Our model defined
substitution as occurring only when, in response to increased federal
highway funds, state and local funds were moved out of highway-related
projects altogether. We did not consider it substitution if in response
to increased federal highway funds, state and local funds were moved
from highway projects that were eligible for federal aid to highway
projects that were not eligible for federal aid. Third, our model is
structured to examine substitution rates over time, rather than being
limited to one estimate covering all the years included in our study.
Finally, compared to previous studies, we employed a more comprehensive
collection of factors related to state spending decisions.
Combined, we believe these enhancements increase the ability of our
model to provide a conservative and more reliable estimate of the
extent to which states substitute federal highway aid for spending that
would otherwise have come from state and local resources. However, all
estimates that are based on statistical models, particularly of complex
processes such as the determination of states' budget choices, are
subject to uncertainty. This uncertainty can derive from both choices
about what factors to include in a model and the inherent impreciseness
in estimating relationships between one factor--in this case federal
highway grants--and another, state and local highway spending. While we
have attempted to take many factors affecting state spending decisions
into account, there may be other factors that are not subject to
precise measurement, such as the influence of citizen and interest
groups on states' funding decisions, that could not be included in our
analysis. As a result of the uncertainty in both the data and the
statistical formulation of our model, the precision of our estimate, or
any other estimate, is limited and our estimate should be considered
one point in a range within which the actual extent of substitution
falls, and one piece of a body of evidence on the existence of
substitution. (See app. II for additional details on our statistical
model.)
In commenting on a draft of this report, DOT officials said that to the
extent substitution occurred and increased during the 1990s, it was
likely due to a number of factors, including changes in states'
revenues and priorities. While our analysis specifically took changing
economic conditions into account when assessing state spending choices,
determining specific causes is beyond the scope of our statistical
model. For example, states faced rising demands for health care and
education during the 1980s and early 1990s that they may have funded,
in part, by reducing their own levels of highway funding effort when
federal highway funding increased. Accordingly, our model establishes
an association between substitution and increases in federal highway
grants; it does not identify the specific causes responsible for these
rising rates.
Earlier Studies Found That Federal Grants Reduced States' Highway
Spending, Although Substitution Estimates Varied:
Three other studies, including two published in the past 3 years, have
reported that states substituted additional federal highway spending
for state spending. These studies reported a wide range of estimates
for the percentage of federal funds that has been used as a substitute
for state and local funds, from zero to nearly 100 percent. The wide
range of estimates is the result of different time periods examined,
different definitions of substitution, and differences in the
statistical methods employed.[Footnote 21]
A study by Brian Knight, which, of the three studies, included the most
recent data, found that from 1983 through 1997, roughly 90 percent of
increased federal aid was substituted for state highway
spending.[Footnote 22] Knight used a different definition of
substitution than we used in our study. Knight defined substitution as
occurring when, in response to increased federal highway funds, state
funds were moved out of highway-related projects. He did not take into
account local spending on highways, which might possibly have mitigated
the reduction in state funds.
Another study, by Shama Gamkhar,[Footnote 23] analyzed data from 1976
through 1990 using two different measures of federal grants. Gamkhar
reported an average substitution rate of 63 percent when measuring
federal grants through grant expenditures (the same measure of federal
grants used by the other studies, including our model) and an average
substitution rate of 22 percent when measuring federal grants through
grant obligations.[Footnote 24] Gamkhar defined substitution the same
way our model did, as when, in response to increased federal highway
funds, state and local funds were moved out of highway-related projects
altogether.
A study by Harry G. Meyers examined data from 1976 through 1982, and
modeled substitution based on two different definitions of
substitution.[Footnote 25] Using a definition of substitution similar
to the definition employed in our model, the study found no evidence of
substitution during this period. Meyers also modeled the substitution
rate based on a different definition of substitution, defining
substitution as occurring when state funds were moved out of federal-
aid highway projects, even if those funds were used for highway
projects that were ineligible for federal aid. Using this definition of
substitution, the study found a substitution rate of 63 percent. The
findings of these studies and GAO's results are summarized in figure 6.
In this figure, we placed next to our finding the findings of the three
models that used the same measure of federal grants and the same or a
similar definition of substitution that we did, organizing these
chronologically.[Footnote 26]
Figure 6: Summary of Federal Grant Substitution Rates Reported in
Various Studies Using Data from Various Time Periods:
[See PDF for image]
[End of figure]
Alternative approaches employed to measure grant substitution:
(a) Substitution defined as the reduction in state and local government
spending on all highway-related projects; federal grants measured as
grant expenditures.
(b) Substitution defined as the increase in state and local government
nonhighway spending; federal grants measured as grant expenditures.
(c) Substitution defined as the reduction in state (but not local)
government spending on all highway-related projects; federal grants
measured as grant expenditures.
(d) Substitution defined as the reduction in state and local government
spending on all highway-related projects; federal grants measured as
grant obligations.
(e) Substitution defined as the reduction in state and local government
spending on federal-aid eligible highway projects; federal grants
measured as grant expenditures.
As can be seen from this figure, generally, those studies with the same
or similar definitions of substitution as our model also suggest that
substitution rates may have increased over time. Specifically, Meyers
reported no evidence of substitution into nonhighway spending from 1976
through 1982; Gamkhar, based on data through 1990, reported higher
rates of substitution, and Knight, based on data through 1997 reported
even higher rates of substitution, although using a somewhat different
definition of substitution. Our model also found evidence of such a
trend.
Structure of the Federal Grant System in General May Encourage
Substitution:
In 1996, we reported that the federal grant system as a whole does not
encourage states to use federal dollars to supplement their own
spending but rather results in states using federal grants to
substitute for their own spending.[Footnote 27] In summarizing research
over the past 30 years for a wide variety of federal grant programs, we
reported that each additional dollar of federal grant funding
substitutes for between 11 and 74 cents of funding states otherwise
would have spent. On balance, we found that for every dollar of
additional federal aid, states have withdrawn about 60 cents of their
own funding.
Our 1996 study found that federal grant programs produced a variety of
fiscal effects, in part depending on the grant program's structure. For
example, grants are considered "open-ended" when there is no limit on
federal matching, and "closed-ended" when total federal matching funds
are capped. The influence of federal matching is essentially the same
for both types of grants until a state obtains the maximum federal
contribution for a closed-ended grant. After this point, closed-ended
grants no longer provide additional matching funds in response to
additional state spending. This lack of additional federal matching
funds reduces the incentive for states to increase their own spending
on aided activities. As a result, we found that open-ended grant
programs, for example, Foster Care, Adoption Assistance and Medicaid,
generally stimulated additional spending from state resources because
the more states spent of their own resources, the more federal
resources they would obtain.[Footnote 28] In contrast, closed-ended
matching grant programs, such as the federal-aid highway program, which
place a limit on the total amount of federal funds that states can
receive through meeting matching requirements, as well as programs that
do not require states to contribute matching funds to receive federal
funds, were associated with higher rates of grant substitution and
stimulated less additional spending on the aided activity.
Structure of Federal-Aid Highway Program Creates an Opportunity for
Substitution:
The federal-aid highway program is particularly susceptible to
substitution because in general the current matching requirement for
states is not high enough to require states to maintain or increase
their spending in order to receive increases in federal funds. In most
cases, the federal-aid highway program requires that the federal
contribution be no more than 80 percent of the total cost of the
project, while the state's matching contribution be at least 20
percent. If the federal highway program worked to stimulate state
spending, this might suggest that every $1.00 increase in federal funds
would result in a total spending increase of $1.25 ($1.00 is 80 percent
of $1.25), $0.25 of which would be funded with state and local
government funds ($0.25 is 20 percent of $1.25). However, because in
most cases state funding already exceeds the required state matching
contribution, often by large amounts, states are not required to
increase or even maintain their level of funding for projects in order
to receive increases in federal funds.
Several studies have demonstrated that state highway spending
substantially exceeds federal matching requirements. The earliest study
we reviewed found that, during the 1960s, 38 percent of aggregate state
capital spending for noninterstate federal-aid highways was in excess
of federal matching requirements.[Footnote 29] This study found that
for the large majority of states, state spending on federal-aid highway
system projects exceeded federal matching requirements by more than 10
percent. Another study found that in 1982, state spending on federal-
aid highway system projects exceeded the required federal match by more
than 19 percent.[Footnote 30] Other studies that have analyzed the
fiscal effects of federal highway aid have also reported that state
spending typically exceeds federal matching requirements.[Footnote 31]
In general, states continue to spend more than their required match on
federal-aid highway projects. In 2000, the most recent year for which
data are available for federal-aid highways, states accounted for
approximately 49 percent of all federal-aid-eligible highway capital
spending, which is over twice the required 20 percent match on most
federal-aid highway projects.[Footnote 32] Figure 7 shows the variation
among states in their highway capital spending as a percent of total
(federal plus state and local) highway capital spending during the
period from 1997 through 2000. Although these data include spending on
nonfederal-aid-eligible highways and therefore can not be used to
determine precisely to what extent states are exceeding federal
matching requirements, they show that in the majority of states, state
and local spending counts for over half of total capital highway
spending.
Figure 7: State and Local Highway Spending for Capital Projects as a
Percent of Total (Federal Plus State and Local) Capital Spending (1997
through 2000)
[See PDF for image]
[End of figure]
Substitution May Be Limiting the Effectiveness of Strategies to
Accomplish the Federal-Aid Highway Program's Overall Goals:
The trends in funding and probable substitution described in this
report imply that substitution may be limiting the effectiveness of
strategies Congress has put into place to help the federal-aid highway
program accomplish its overall goals. Congress and DOT have at various
times enumerated goals for the federal-aid highway program, and, to
meet these goals, Congress has put in place a number of strategies,
including increasing its investment in highways and giving states wide
latitude in deciding how to use and administer federal grants to best
meet their transportation needs. However, because of substitution, the
sizable increases Congress provided in federal funding for highways
have not translated into commensurate increases in the Nation's overall
spending in its highway system. In part, this is because, while
Congress can dedicate federal funds to highways, it cannot prevent
state highway funds from being used for other purposes. Congress has
also sought to meet the goals of the program through a strategy of
emphasizing states' priorities and decision-making. However,
substitution may be limiting the effectiveness of this strategy.
Although the federal-aid highway program has a considerable regulatory
component, from a funding standpoint, the program is to some extent
functioning as a cash transfer, general purpose grant program. This
raises broader questions about the effectiveness of the federal
investment in highways in accomplishing the program's goals and
outcomes, for although DOT has created performance measures and
outcomes under GPRA, currently there is no link between the achievement
of these measures and outcomes and federal funding provided to the
states.
Congress and DOT Have Set Out Goals for the Federal-Aid Highway
Program:
Congress and DOT have at various times enumerated goals for the
federal-aid highway program to, among other things, enhance safe and
reliable travel, promote economic growth, enhance mobility, support
interstate and international commerce, and meet national security
needs. According to DOT's 2003-08 Strategic Plan, the department's
mission is enumerated in 49 U.S.C. 101, which states that "the national
objectives of general welfare, economic growth and stability, and the
security of the United States require the development of transportation
policies and programs that contribute to providing fast, safe,
efficient, and convenient transportation—". In establishing the
Interstate Highway System, Congress, in the Federal-Aid Highway Act of
1956, stated that the Interstate system was to serve principal
metropolitan areas and industrial centers, support the national
defense, and connect with routes of continental importance in Canada
and Mexico. Current law defines the primary focus of the federal-aid
highway program as completion and expansion of the National Highway
System, of which the Interstate is a part, to provide interconnected
routes that serve, among other things, major population centers,
international border crossings, commercial ports, airports, and major
travel destinations.
Congress continued to set out these goals in reauthorization
legislation that the Senate and House each passed in 2004. For example,
the legislation approved by the Senate states that:
"—among the foremost needs that the surface transportation system must
meet to provide for a strong and vigorous national economy are safe,
efficient, and reliable (i) national and interregional personal
mobility (including personal mobility in rural and urban areas) and
reduced congestion; (ii) flow of interstate and international commerce
and freight transportation; and (iii) travel movements essential for
national security."
To meet the program's goals, Congress has set out a number of
strategies, including increasing investment in highways and providing
states flexibility to best meet their transportation needs.
Furthermore, under Congress' direction, DOT has established strategic
goals and performance measures and outcomes for the federal-aid highway
program to enhance mobility and economic growth. Among these goals are
to reduce the growth of congestion on the Nation's highways and improve
the condition of the National Highway System.
One Strategy to Meet Goals Has Been to Increase Investment and Ensure
Federal Highway Funds Go to Highway Program:
Since the Federal-Aid Highway Act was enacted in 1956, every time
Congress has reauthorized the highway program it has expanded either
the size or scope, or both, of the federal-aid highway
program.[Footnote 33] Since 1991, Congress has provided significant
increases in federal spending on highways. ISTEA's authorization of
$121 billion for highways for the 6-year period from fiscal years 1992
through 1997 was a 73 percent increase over the $70 billion authorized
in the prior 6-year bill, and TEA-21's authorization of $171 billion
for the federal-aid highway program from fiscal years 1998 through 2003
represented an increase of 41 percent over ISTEA's authorization level.
In 2004, the House and Senate each approved separate legislation to
reauthorize the federal-aid highway program, increases of 32 percent
and 50 percent over TEA-21, respectively.[Footnote 34] Despite these
increases, numerous congressional transportation leaders stated that
these increases were not enough, and that further spending was required
to meet the country's needs.
Congress has also included features in the design of the federal-aid
highway program to attempt to ensure that funds collected by the
federal government for highways are used for that purpose. Prior to
1956, federal fuel and motor vehicle taxes were directed to the General
Fund of the U.S. Treasury, and there was no relationship between the
receipts from these taxes and federal funding for highways. Amid
concerns that federal taxes on motor fuel were being used for
nontransportation purposes, Congress established the Highway Trust Fund
in 1956 and specifically provided that revenues from most highway user
taxes would be used to finance the greatly expanded highway program
enacted by the Federal-Aid Highway Act of 1956. Despite having a
dedicated source of funding, highways competed for federal funding with
other forms of domestic discretionary spending through the
appropriations process over the years. As a result, Congress often
appropriated less money than was authorized, even though sufficient
funds were being collected in the Highway Trust Fund to support the
authorized levels. So Congress took further action in TEA-21,
establishing guaranteed spending levels for highway programs that
protected highway programs from having to compete for funding through
the congressional budget and appropriations process. It also
established "Revenue Aligned Budget Authority," directly linking
highway revenues collected into the Highway Trust Fund with the
apportionments provided annually to the states for their highway
programs.
Despite congressional efforts to increase the federal investment in the
highway system and to ensure that funds collected by the federal
government for highways are used for that purpose, due to probable
substitution, the sizable increases in dedicated federal funding that
Congress has provided for highways have not translated into
commensurate increases in the Nation's overall investment in its
highway system. Moreover, the effectiveness of Congress' strategy to
dedicate federal funds to highways is limited because Congress has no
similar ability to prevent state and local highway funds, where most of
the investment occurs, from being used for other purposes. Therefore,
while Congress can ensure that certain federal moneys are dedicated to
highways and given to the states for that purpose, it cannot ensure
that state and local highway funds are not used for other purposes.
When substitution occurs, some dedicated federal highway funds replace
state highway funds, and those state highway funds are then used for
other purposes.
Another Strategy Has Been to Emphasize Importance of States' Priorities
and Decision-making:
Congress has also sought to meet the goals of the program by
emphasizing the importance of states' priorities and decision-making
regarding how to meet their most pressing transportation needs. One way
it has done so is by incorporating return-to-origin features into the
program--returning to the states more of the money collected in fuel
taxes. TEA-21's Minimum Guarantee provisions ensure that each state
receives back from most highway programs 90.5 percent of the total
estimated percentage share of contributions to the Highway Account of
the Highway Trust Fund from motor fuel and other taxes collected in
that state. Under separate legislation passed by both the House and
Senate in 2004, this amount could rise to 95 percent by 2009.[Footnote
35]
In addition, Congress has given the states broad flexibility in the use
of its federal aid grant funds by providing states significant
discretion to use these funds flexibly across highway, bridge, transit,
and other transportation projects. States have, if they choose, broad
flexibility in the use of slightly more than half of their federal-aid
highway funds. For example, the Surface Transportation grant program
has broad eligibility rules, and states can use those funds for
highways, bridges, transit capital projects, bus terminals, and many
other uses. States may use some of their Minimum Guarantee Program
grant funds under the same rules;[Footnote 36] in fiscal year 2003, the
funds apportioned under these two programs accounted for one third of
all federal aid highway funds apportioned nationwide. For eight states
that receive higher levels of Minimum Guarantee grant funds, these two
programs account for more than 40 percent of their funding, and in one
of these eight states, for just over 50 percent. While other federal-
aid highway grant funds have more limited uses, states have the
authority to transfer funds from these limited programs to more
flexible programs and uses. For example, states may transfer up to 50
percent of their National Highway System and Interstate Maintenance
program funds to the Surface Transportation Program or certain other
grant programs, and, in the case of the National Highway System
program, 100 percent under certain conditions.
Furthermore, states have broad flexibility in deciding which projects
to pick and how to implement them. The projects for which states use
federal funding must be for construction, reconstruction, and
improvement on eligible federal-aid highway routes. Nevertheless,
federal law (23 U.S.C. §145) provides that the authorization or
appropriation of federal funds "shall in no way infringe on the
sovereign rights of the States to determine which projects shall be
federally financed." Moreover, FHWA's role in overseeing the design and
construction of most projects is limited. Specifically, only high cost
construction or reconstruction projects on the Interstate Highway
System are always subject to "full" oversight in which FHWA prescribes
design and construction standards, approves design plans and estimates,
approves contract awards, inspects construction progress, and renders
final acceptance when projects are completed. For projects that are not
located on the National Highway System, states are required to assume
oversight responsibility for the design and construction of projects
unless a state determines that it is not appropriate for it to do
so.[Footnote 37] As figure 8 shows, in 2002, about $1 out of every $5
obligated for federal-aid projects occurred on the Interstate system,
while projects off the National Highway System accounted for about 57
percent, nearly 3 times as much.
Figure 8: Federal Obligations by System in Fiscal Year 2001:
[See PDF for image]
[End of figure]
Substitution may be limiting the effectiveness of Congress' strategy of
emphasizing the role of states' priorities and decision-making
regarding how to meet their most pressing transportation needs. The
program does have a substantial regulatory component that requires
states to enact and follow certain laws as a condition of receiving
federal funds; for example, states are required to enact drunk-driving
laws, such as .08 blood alcohol laws, and to contract with
disadvantaged business enterprises. However, from a funding standpoint,
the federal-aid highway program's return-to-origin features and
flexibility, combined with substitution and the use of state and local
highway funds for other purposes, means that the program is, to some
extent, functioning as a cash transfer, general purpose grant program.
This raises broader questions about the effectiveness of the federal
investment in highways in accomplishing the program's goals and
outcomes.
Broader Questions Exist about the Program's Goals and Outcomes:
Our findings on substitution lead to broader questions about whether
the federal-aid highway program is effective in meeting its goals. As
required by the Government Performance and Results Act (GPRA), DOT has
articulated goals for the department's programs, including the federal-
aid highway program, to achieve by establishing measurable performance
goals, measures, and outcomes. One of the purposes of GPRA is to
provide decisionmakers a means of allocating resources to achieve
desired results. Linking resources and results will become even more
important than it is today in the years ahead, as the Nation faces a
fiscal crisis in which mandatory commitments to Social Security and
Medicare will consume a greater share of the Nation's resources,
squeezing the funding available for discretionary programs, potentially
including highways. These challenges require the Nation to think
critically about all existing government programs and commitments.
Among its performance goals, DOT has articulated goals for mobility and
economic growth, including to improve the condition of the
transportation system, reduce travel times, and increase access to and
reliability of the transportation system. Two major performance
measures related to the federal-aid highway program are to (1) improve
the percentage of travel on the National Highway System meeting
pavement performance standards for acceptable ride and (2) slow the
growth of congestion--in particular, to limit the annual growth of
urban area travel time under congested conditions to one-fifth of 1
percent below the growth that has been projected. These goals are shown
in figure 9.
Figure 9: DOT Performance Measures for Condition and System
Performance:
[See PDF for image]
[End of figure]
Although DOT has articulated performance measures, the federal-aid
highway program does not have the mechanisms to link funding levels
with the accomplishment of specific performance-related goals and
outcomes. In contrast, NHTSA has some incentive grant programs that
link funding to particular outcomes, such as increasing the use of seat
belts within states. As we have reported, although a variety of tools
are available to measure the costs and benefits of transportation
projects, they often do not drive investment decisions, and many
political and other factors influence project selections.[Footnote 38]
For example, the law in one state requires that most highway funds,
including federal funds, be distributed equally across all the state's
congressional districts. Consequently, there is currently no way to
measure how funding provided to the states is being used to accomplish
particular performance-related results such as reducing congestion or
improving conditions.
We Identified Several Options for the Design and Structure of the
Federal-Aid Highway Program:
We identified several options for the design and structure of the
federal-aid highway program that could be considered in light of the
issues raised by our findings. On the one hand, there are options that
have been used in other federal programs that could limit substitution.
Another option to consider may be to simplify the program towards a
more flexible approach. Another option would be to consider whether a
different program structure and different financing mechanisms could be
used to target funding and more closely align resources with desired
results.
Reduce Substitution:
To increase the extent to which federal-aid highway program funds are
used to supplement state highway funds rather than substitute for them,
several options exist to re-design the program to limit substitution.
These include:
* Revising federal matching requirements to increase the percentage of
projects' costs that must be paid for with state and local funds.
* Instituting the use of funding formulas that reward states that
increase state and local highway funding by increasing their federal
funding, while reducing the federal funding of those states that do
not.
* Adding a requirement that states maintain their own level of highway
spending effort over time in order to receive additional federal funds.
All three options are designed to reduce or eliminate substitution. The
first two options are designed to stimulate additional state spending
on highways, while the third option is designed so that increased
federal funding will supplement state spending rather than replace it.
These objectives may not be perfectly achieved because models of
substitution, like any models, produce estimates that are subject to
uncertainty. As such, there is no way to objectively determine with
certainty what states would have spent in the absence of increased
federal funding. Table 3 summarizes the options, along with possible
approaches that could be taken in implementing them.
Table 3: Options to Reduce Substitution:
Option: Revise matching requirements;
Approaches: Revise state match to a higher percentage than current 20
percent of total funding for project; Keep state match at 20 percent
but count only state spending in excess of base time period for match.
Option: Link federal funding to states' highway funding effort;
Approaches: Provide federal funds to states proportionally, based on
their effort compared to average effort of all states; ; Provide
federal funds to states proportionally, based on each state's own
effort relative to an initial base time period.
Option: Institute a maintenance of effort provision;
Approaches: Require states to maintain existing levels of state
spending in order to receive federal funds.
Source: GAO.
[End of table]
Each of these options and approaches would be likely to have somewhat
different effects and would require careful consideration of various
factors. Some possible effects are summarized below; see appendix IV
for additional discussion of these options.
* The likely effect of revising the matching requirement would depend
on the magnitude of the change. For example, if the requirement was
changed so that states generally had to provide 60 percent of the total
funding for eligible projects, states currently spending less than 60
percent of total highway funds for eligible projects would have an
incentive to increase their spending in order to obtain the maximum
federal match, while those spending more than 60 percent would not have
an incentive to increase their spending. A few states with a low state/
federal spending ratio might have to more than double their current
spending in order to receive additional federal funds. Setting the
required match at 40 percent would give fewer states an incentive to
increase their spending and would generally require less of an increase
in spending from those states with low state/federal spending ratios.
An advantage of continuing to set the state match at 20 percent but
counting only state spending in excess of what each state spent during
a base time period towards the match is that it would stimulate state
spending in all states to a similar degree.
* Using funding formulas that link federal funds to states' highway
funding effort could also be achieved through various
approaches.[Footnote 39] For example, providing federal funds to states
proportionally based on their effort in comparison to the average
effort of all states would put states in competition with each other,
rewarding states whose funding effort is already high and penalizing
states whose funding effort is currently low. On the other hand,
providing federal funds to states proportionally based on each state's
own effort during an initial base time period would put each state in
competition with the funding effort it made in the base period,
rewarding states whose spending grew more quickly in comparison to
their spending during the base period and penalizing states whose
spending stayed the same or dropped when compared to their spending
during the base period.[Footnote 40] Such provisions could be designed
so they could be suspended in a recession or severe economic downturn
in order to prevent states from having to make disproportionate
reductions in other state services to maintain highway funding.
* Instituting a maintenance of effort provision would require each
state to continue to spend what it spent in a defined base period, plus
inflation, in order to obtain increased federal funds. Therefore, it
would not stimulate state spending, but it would attempt to ensure that
states used federal funds to supplement rather than replace state and
local funds. In previous work, we concluded that, to be effective,
maintenance of effort provisions need to define a minimum level of
state spending effort that can be objectively quantified and updated to
keep pace with inflation in program costs so that the maintenance of
effort provision ensures a continued level of activity when measured in
inflation adjusted dollars.[Footnote 41] This could be achieved by
defining a state's base spending level as the amount spent per year
during a recent historical period and then adjusting that base spending
level for inflation.[Footnote 42]
Increase Flexibility in States' Use of Funds and Reduce Administrative
Expenses:
Another potential option would be to build on trends giving states
greater flexibilities and discretion with their federal-aid highway
program funds. In contrast to changes in program designs that would
limit substitution, adopting such an option could be seen as
recognizing substitution as an appropriate response on the part of
states to increasing fiscal challenges and competing demands. Adopting
such an option could also be seen as recognizing that the ability of
states to meet a variety of needs and fiscal pressures might be better
accomplished by providing states with federal funding for highways
through a more flexible federal program.
Such an option would also recognize the changing nature of FHWA's role
and the federal-aid highway program. Currently, FHWA reviews and
approves transportation plans and environmental reviews, and--on some
projects--designs, plans, specifications, estimates, and contract
awards. FHWA also has duties related to the program's considerable
regulatory component. To carry out these responsibilities, FHWA has
among the largest field office structure in DOT, and a larger field
structure than many other federal agencies. FHWA has personnel in over
50 field offices, including one office in each state, and has had a
field office in each state since 1944. However, the federal-aid highway
program has changed considerably in 60 years. In 2004, the program's
return-to-origin features and flexibility, combined with substitution
and the use of state and local highway funds for other purposes, means
that from a funding standpoint, the federal-aid highway program is, to
some extent, functioning as a cash transfer, general purpose grant
program. Devolving funding responsibilities to the states in a manner
consistent with that function would build on the flexibilities already
present and obviate much of the need for FHWA's extensive field
organization, allowing it to be greatly reduced in scope. This could
produce budgetary savings of some portion of FHWA's $334 million annual
budget.
Adopting such an option would involve weighing numerous factors,
including FHWA's role and value. But devolving funding responsibilities
to the states would not require abandoning the program's regulatory
component. Some federal laws and requirements in place originated
outside the transportation program and would doubtless remain in force,
such as civil rights compliance. Others that are currently part of the
transportation program could also remain in effect. Depending on
priorities, these could continue to be overseen by FHWA directly or a
process could be established through which states certify their
compliance with the requirements, as is done in other programs. In this
manner, it would be possible to enforce these laws and requirements
without an extensive field structure, as other federal agencies and
programs do.
Devolving authority to the states could also take the form of devolving
not only the federal programs, but the revenue sources that support it.
Considerable federal effort goes into collecting and accounting for
motor fuel taxes and other highway user fees. One argument for
maintaining a federal fuel tax is that this tax may be a useful public
policy to prevent tax competition between states to avoid the
disinvestment in the highway system that could potentially result. Such
a "turnback" provision was considered in the form of an amendment to
TEA-21 in the House of Representatives in 1998, but it did not pass.
Devolving federal responsibilities to the states is not dissimilar to
the Surface Transportation System Performance Pilot Program that was
proposed in the administration's reauthorization proposal, but which
was not included in either the House or Senate version of the bill. Up
to five states could have participated in the program, which would have
allowed a state to assume some or all of FHWA's authorities and
responsibilities under most federal law or regulations.[Footnote
43]Once approved to participate, a state would have had to identify
annually what goals it wanted to achieve with its federal funds and
what performance measures it would use to gauge success. A state would
also have had to agree to a maintenance of effort requirement that it
maintain its total combined state and federal highway program
expenditures at the level of at least the average level of the three
previous years. A state's participation in the pilot program would have
been terminated if that state did not achieve the agreed performance
for two consecutive years.
Link Federal-Aid Highway Funding with Program Goals:
Another option could be to consider whether a different program
structure and different financing mechanisms could be used to target
funding and more closely align resources with desired results.
Restructuring the program in this way could take several forms. For
example, the program could be reoriented to function more like a
competitive discretionary grant program, in which program sponsors
justify projects seeking federal aid based on an assessment of their
potential benefits. This is not dissimilar to the program used by DOT
to fund large transit capital projects.[Footnote 44] The program could
also be revised to include the use of incentive grant programs similar
to those that NHTSA has to link funding to particular outcomes, such as
increasing the use of seat belts within states.
Adopting such an option would require asking the following questions:
* What policy goals have been established by Congress for the
performance of the federal-aid highway program, what outcomes and
results have been articulated in DOT's strategic plans to fulfill those
goals, and are they the right goals and outcomes?
* What is the appropriate role of each level of government? Would the
roles need to be redefined in order to align federal spending more
closely with a greater performance and outcome orientation? In
particular, what refocusing of federal involvement (e.g., interstate
commerce, homeland security, national defense) would need to occur?
* How could the design of the federal-aid highway program's grants and
funding mechanisms best support accomplishment of agreed-upon
performance goals and outcomes? What funding incentives are needed to
introduce a greater performance and outcome orientation?
* What type of departmental administrative structure for the federal-
aid highway program would best ensure that the performance goals
established by Congress and articulated in DOT's strategic plans and
outcomes are measured and accomplished?
* Can a greater performance and outcome orientation to the federal-aid
highway program be reconciled with congressional and state legislative
policies and preferences toward providing at least some transportation
funding in the form of specific project earmarks?
Conclusions:
Addressing the issues raised in this report would require weighing
competing and sometimes conflicting options and strategies. If, for
example, reducing the level of grant substitution is an important
concern, then design changes in the current program, including adopting
features that have been used in other federal programs, may be
warranted. If, on the other hand, preserving states' flexibility,
including their ability to meet a variety of needs and fiscal pressures
is a higher priority, then design changes in the direction of a
different, more flexible program may be warranted. While some options
are mutually exclusive, others could be enacted in concert. For
instance, an option to limit substitution could be combined with
efforts to align resources with desired results, and returning program
authorities and resources to the states could be accompanied by adding
performance measures.
Beyond these options, our work raises broader and more fundamental
issues given the challenges the Nation faces in the 21ST Century. The
fact that both the federal and state governments face budget deficits
totaling hundreds of billions of dollars and a growing fiscal crisis
requires policymakers to think critically about existing government
programs and commitments and make tough choices in setting priorities
and linking resources to results to ensure that every federal dollar is
wisely and effectively spent. The opportunity to better align the
federal-aid highway program with performance goals and outcomes comes
at a time when both houses of Congress have already approved separate
legislation to create a National Commission to examine future revenue
sources to support the Highway Trust Fund and to consider the roles of
the various levels of government and the private sector in meeting
future surface transportation financing needs. The proposed commission
is to consider how the program is financed and the roles of the federal
and state governments and other stakeholders in financing it; the
appropriate program structure and mechanisms for delivering that
funding are important components of making these decisions. Thus, this
commission may be an appropriate vehicle through which to examine these
options for the future structure and design of the federal-aid highway
program.
Matter for Congressional Consideration:
In light of the issues raised in this report and the fiscal challenges
the Nation faces in the 21ST Century, Congress may wish to consider
expanding the proposed mandate of the National Commission to assess
possible changes to the federal-aid highway program to maximize the
effectiveness of federal funding and promote national goals and
strategies. Consideration could be given to the program's design,
structure, and funding formulas; the roles of the various levels of
government; and the inclusion of greater performance and outcome-
oriented features.
Agency Comments and Our Evaluation:
We provided DOT a draft of this report for review and obtained comments
from departmental officials, including FHWA's Director of Legislation
and Strategic Planning. These officials said that our analysis raised
interesting and important issues regarding state funding flexibility
and the federal-aid highway program that merit further study. DOT
officials also stated that while they recognize that federal-aid
highway grants can influence state and local governments to substitute
federal funds for state and local funds that otherwise might have been
spent on highways, they believe that this substitution is likely due to
numerous factors. Specifically, the officials said that to the extent
substitution occurred and increased during the 1990s, it was also
likely due to changes in states' revenues and priorities. DOT officials
also emphasized that regardless of changes in the availability of state
funds for highway programs, the overall federal share of capital
spending on highways declined during the period we studied, from over
55 percent in the early 1980s to around 45 percent today. DOT officials
also emphasized that there is no evidence that the substitution
discussed in our report resulted in the diversion of federal-aid
highway funds apportioned to the states. They further stated that
substitution may reflect appropriate resource allocations by states and
that preserving states' flexibility has been a priority of the federal-
aid highway program and is a goal of DOT's reauthorization proposal.
Finally, regarding options for changes in the design of the federal-aid
highway program, officials emphasized that FHWA adds considerable value
to the federal-aid highway program by providing program oversight and
sharing its expertise with states to ensure states uniformly address
key areas of national concern including safety and environmental
protection.
We agree with DOT's characterization of the importance of the issues
raised in this report, including the effect that federal-aid highway
grants have on state spending decisions and states' funding
flexibility. We also agree with DOT officials that many factors
influence state budgetary decisions, including changing state budget
priorities and the availability of state revenues. It was for this
reason that we used a statistical model that specifically took changing
economic conditions and revenues into account in order to better
isolate the effect of federal grants on state spending choices. We
believe that our model has reasonably distinguished between the effects
of changing economic conditions and revenues and the effect of federal
grants, and, consistent with earlier models and studies, we found the
relationship between federal grants and state spending, indicating
substitution, to be statistically significant, particularly during the
1990s. However, determining specific causes of substitution is beyond
the scope of our statistical model. For example, while states faced
rising demands for health care and education during the 1980s and 1990s
that could have resulted in states reducing their highway spending when
federal highway funding increased, our model does not identify the
specific causes responsible for rising substitution rates. Although DOT
officials said that the overall federal share of capital spending on
highways declined during the period we studied, these relative shares
do not affect our findings on substitution since substitution can occur
when the federal share of funding is either rising or falling; if
substitution occurs when state funding is rising it simply means that
state spending increased less than the increase that might have
occurred had there been no substitution. While DOT officials stated
that there is no evidence that substitution resulted in the diversion
of federal-aid highway funds, there are important differences between
diversion and substitution. In the context in which DOT officials
raised it, diversion is the transfer of federal funds for purposes
other than those authorized by law, while substitution, as we have
reported it, is the transfer of state funds that would have otherwise
been spent on highways. States can both use federal funds for the
purposes authorized by law and at the same time substitute federal
funds for state funds. Thus, while we agree that there is no evidence
that substitution resulted in the diversion of federal-aid highway
funds, we do not believe our report suggests the existence of such
evidence.
Finally, we agree with DOT officials that states' flexibility and
FHWA's role are important factors in the federal-aid highway program;
however, we believe that options for changing the design, structure,
and funding mechanisms of the federal-aid highway program should be
considered in light of substitution and the issues raised in this
report, and that a variety of factors, including but not limited to
these two, should be weighed when considering such changes. While the
department took no position on the matter for congressional
consideration to expand the mandate of the proposed National
Commission, officials did state that they believe these issues merit
further study. We continue to believe that Congress has the opportunity
to maximize the effectiveness of federal funding and promote national
goals and strategies by expanding the proposed mandate of the National
Commission to consider these issues.
We are sending copies of this report to the Honorable Norman Mineta,
Secretary of Transportation. We will also make copies available to
others upon request. In addition, the report will be available at no
charge on the GAO Web site at [Hyperlink, http://www.gao.gov].
If you have any questions about this report, please contact me at
[Hyperlink, heckerj@gao.gov], or (202) 512-2834, or contact Jerry
Fastrup at [Hyperlink, fastrupj@gao.gov] or (202) 512-7211, or Steve
Cohen at [Hyperlink, cohens@gao.gov] or (202) 512-4864. GAO contacts
and acknowledgments are listed in appendix V.
Sincerely yours,
Signed by:
JayEtta Z. Hecker:
Director, Physical Infrastructure:
[End of section]
Appendixes:
Appendix I: Objectives, Scope, and Methodology:
In light of the increasing federal-aid highway program funding and
concerns over future federal revenues for highways, you asked us to
provide information on past trends in the federal, state, and local
capital investment in highways, and how federal-aid highway program
grants influence the level of state and local highway spending. We
responded to the first part of your request in June 2003. This report
(1) updates information on trends in federal, state, and local capital
investment in highways; (2) assesses the influence that federal-aid
highway grants have had on state and local highway spending; (3)
discusses the implications of these issues on the federal-aid highway
program; and (4) discusses options for the federal-aid highway program
that could be considered in light of these issues. In addition, this
report identifies characteristics associated with differences among
states' levels of effort for highways (see app. III).
To update information on federal, state, and local capital investment
in highways, we obtained 2002 (the most recent year available)
expenditure data from the Federal Highway Administration. We converted
these expenditure data to 2001-year dollars to coincide with the data
in our previous report,[Footnote 45] which presented data from 1982
through 2001.
To assess the influence that federal-aid highway grants have had on all
state and local highway spending, we reviewed and synthesized the
research literature on this issue. Our literature review revealed a
number of studies that used statistical models to estimate the
influence of federal funding on state spending. These models examined
different time periods, employed different statistical methods, and
considered different potential social, demographic, economic, and
political factors that may affect state highway spending decisions.
None of the models used in the studies we reviewed included the most
recent data now available on highway funding, and none examined whether
the effect of federal grants on state spending changed during the time
period covered in the study. Therefore, based on the models used in the
earlier studies, we developed our own statistical model of state
highway capital and maintenance outcomes to estimate the fiscal effects
of federal highway funding on state highway spending. The purpose of
our statistical model was to isolate the effect of federal grants on
highway spending in states by controlling for other factors that affect
state spending decisions. Our model therefore considered a wide range
of potential factors such as economic conditions and the size of a
state's highway system, that may affect state spending choices. In
addition, our model included the most recent data available and
examined whether the effect of federal grants on state spending changed
during the time period.[Footnote 46] A more detailed description of the
literature and our statistical model is contained in appendix II.
Finally, our model was reviewed by experts in the Department of
Transportation (DOT) and peer reviewed by three authors of the earlier
studies on the fiscal effects of federal highway grants. These experts
and authors generally agreed with our methods, and we made revisions
based on their comments as appropriate.
To address the implications of the effect of federal highway grants on
state and local highway spending and options raised by these
implications, we reviewed pertinent legislation and congressional
actions affecting the federal-aid highway program, including goals,
funding trends, program features, and financing mechanisms. We reviewed
the Government Performance and Results Act and DOT's strategic and
performance plans and reports for 2003 and 2004. We then evaluated how
our model results and other analysis on the existence of substitution
affect the design and performance of the federal-aid highway program.
Finally, to identify state characteristics associated with their effort
to fund highways from state resources, we defined a state's level of
effort broadly to include both a state's and its local governments'
spending for highway maintenance and capital construction relative to
the personal income of state residents.[Footnote 47] We determined a
multivariate analysis is required so that other factors, in addition to
the state characteristic under consideration, can be taken into account
and held constant. (See app. III for results.)
To perform this multivariate analysis, we utilized the same statistical
model of state highway spending used to analyze the fiscal effect of
federal highway grants (see app. II). The variables expected to affect
state highway spending fall into four broad categories: (1) fiscal
capacity, (2) the cost of transportation services to the representative
voter/consumer (tax price), (3) federal grants, and (4) indicators of
state preferences for highway spending. The specific variables we
considered are listed in table 6.
We conducted our work from August 2003 through July 2004 in accordance
with generally accepted government auditing standards.
[End of section]
Appendix II: Description of Grant Substitution Model, Statistical
Methods, and Results:
This appendix presents a thorough description of the statistical
analysis that we conducted to estimate the extent to which states
substitute federal highway grants for funds that would have been spent
on highways from their own resources. The first section summarizes the
literature on this topic because we built upon models from previous
studies in developing our model. The next section describes the model
that we developed. The final section describes the statistical tests
that we used and presents the results of those tests.
Summary of Previous Studies:
We reviewed a number of studies on substitution and relied most heavily
on the models used in three of them in developing our statistical
model.[Footnote 48] The three studies are similar in that each draws
upon economic models that explain states' highway spending in terms of
the demand for mobility that flows from the construction and
maintenance of a highway network. Within the context of these models,
the potential for grant substitution arises in the response of state
highway spending to changes in federal grant funding. However, these
models differ in key details, such as the statistical methods used to
estimate the extent of substitution, the definition of state highway
expenditures, and the control variables used in the model. They also
differ in their estimation of substitution rates.
Conceptual Framework:
The models in each of the key studies are built upon the premise that
the political process responds to the preferences of voters/consumers
for highway transportation services. As a result, the models
characterize the demand for and supply of highway spending as depending
on four types of factors:
1. Fiscal capacity (FC), which is the ability of states to fund
services using within-state resources;
2. The tax price (TP) faced by the typical voter/consumer of highway
services, which can be thought of as the cost of an additional unit of
mobility;
3. Intergovernmental grant funding (G), including both grants intended
for highways and grants for other public services; and:
4. Differences in voter/consumer preferences (P) for highway
transportation services.
This relationship can be summarized in the following relationship:
State Highway Expenditure = f(FC,TP, G, P)
In these models, greater tax paying capacity is expected to result in a
higher demand for mobility that in turn increases the demand for a
larger highway network. Similarly, more grant funding (both for
highways as well as for other public services) increases the resources
available to states and is expected to increase total highway spending.
Differences in political culture are also expected to result in
different preferences for transportation services relative to other
public services, such as health and education. Finally, if the typical
voter/consumer faces a higher unit cost of transportation services,
also called the tax price of highway services, the demand for
transportation services is likely to be lower.
The tax price of highway services is, in turn, dependent upon several
factors:
1. A higher cost of inputs (labor, building materials, supplies, etc.)
used to build and maintain highways results in more expensive
transportation services. A higher unit cost of mobility is expected to
reduce the demand for transportation services, but will increase
highway spending as long as the demand for transportation services is
price inelastic.
2. Economies and/or diseconomies of scale may also affect the unit cost
of mobility. A required minimum facility size may result in more lane
miles per resident in smaller states, which may result in a higher unit
cost for the typical voter/consumer. Similarly, very low lane miles per
resident may be associated with more intensive usage, which may also
result in a higher unit cost as well. Thus, unit cost may be U-shaped.
3. A greater number of voter/consumers with whom the cost of highway
services may be shared is expected to reduce unit cost to the typical
state voter, increasing the demand for transportation services. This
will result in higher total highway spending and lower spending per
voter so long as demand is price inelastic.
4. More highway users may lead to greater deterioration in the quality
of highways and greater congestion, raising the unit cost of
transportation services to the typical voter/consumer, reducing the
demand for highway services. The effect on spending is expected to be
positive if demand is price inelastic. In addition to cost
considerations, more users could also be thought of as reflecting a
stronger preference for highway services relative to other goods and
services.
5. Matching grants on the marginal dollar of highway spending reduce
the unit cost of services to the typical voter/consumer. To the extent
that matching requirements apply to additional state spending the
typical voter/consumer pays a smaller share of additional spending,
lowering the cost of additional spending to the typical voter/consumer
and raising the demand for highway services.[Footnote 49]
In table 4, we summarize three studies that are representative of the
variety of models that have been considered in the literature and upon
which we base our analysis.
Table 4: Summary of Fiscal Substitution Studies:
Study: Knight;
Time period: 1983-1997;
State highway expenditures: State (but not local) government spending
for highway-related projects (capital and maintenance, real per
capita);
Fiscal capacity: Personal Income per capita;
Tax price variables:
1. Population;
2. Drivers per capita;
3. Registered vehicles per capita;
Grant variables: Highway grant expenditures per capita[A];
Preferences:
1. Governor Democrat;
2. Percent Democrats in State House;
3. Percent Democrats in State Senate;
Other variables: State fixed effects.
Study: Gamkhar;
Time period: 1976-1990;
State highway expenditures: State & local government spending for
highway-related projects (capital and maintenance, real per capita);
Fiscal capacity: Personal income per capita;
Tax price variables:
1. Effective nonhighway match rate;
2. Registered Vehicles per capita;
3. Vehicle miles traveled per capita;
4. Percent light vehicles;
5. Percent metro population;
6. Population density;
Grant variables:
1. Highway grant expenditures per capita[A];
2. Highway grant obligations per capita;
3. Other fed grants per capita;
Other variables:
1. State fixed effects;
2. Time fixed effects;
3. Percent unemployed;
4. Debt as a percent of income.
Study: Meyers;
Time period: 1976-1982;
State highway expenditures: State capital spending on federal-aid
highways (net of interstate highways, real per capita);
Fiscal capacity: Personal income per capita;
Tax price variables:
1. Effective highway match rate[A];
2. Effective nonhighway match rate;
3. Registered Vehicles per capita;
4. Vehicle miles traveled per capita;
5. Lane miles per capita;
Grant variables:
1. Highway grant expenditures per capita;
2. Other federal grants per capita.
Source: GAO analysis:
[A] Treated as an endogenous variable.
[End of table]
Statistical Methods:
The three studies employ a variety of statistical methods in estimating
the substitution effect of federal highway grants. All use simultaneous
equations estimators, but they treat different variables as endogenous.
Knight and Gamkhar treat federal grant expenditures and state own-
source highway expenditures as jointly determined and therefore use an
instrumental variable estimator for their per capita federal grant
variable to remove the endogenous component associated with this
variable.[Footnote 50] In contrast, Meyers does not treat per capita
federal highway grants as an endogenous variable and may have a biased
estimate of the substitution rate. He does, however, treat the
effective matching rate associated with highway grants (i.e., the ratio
of highway grants to total highway spending) as endogenous and uses an
instrumental variable procedure to correct for potential bias in that
variable.[Footnote 51]
Both Gamkhar and Meyers find autocorrelation in their error terms and,
therefore, make an adjustment for autocorrelation. The Knight study
does not correct for autocorrelation. Finally, both Knight and Gamkhar
use a fixed effect estimating procedure to control for unique
circumstances across states that are not captured by the other control
variables included in their models. Neither study reports the
significance of fixed effects in their model. In addition, Gamkhar also
includes time dummy variables to capture systematic effects over time
that the other control variables do not capture. Knight does not
include a time adjustment in his model. Meyers includes neither a fixed
effects nor time adjustment.
Differing Definitions of State Highway Expenditures:
Each of the three studies define state highway spending differently,
which has important implications regarding how grant substitution is
measured and influences the interpretation of the studies'
results.[Footnote 52] The earliest study, by Meyers, includes state
capital spending only for projects eligible under the federal-aid
highway program, excluding spending for interstate highways. Measuring
the dependent variable in this way means the highway grant coefficient
measures only the response of state capital spending on federal-aid
highway projects to changes in federal funding. As a consequence,
Meyers counts increased state or local spending for maintenance on
federal-aid highway projects, or increased state or local capital and
maintenance on nonfederal-aid highway spending, as grant substitution
in the same way as increased spending for other state services such as
education and health or increased state taxpayer relief would be
counted as substitution.
In contrast, Knight defines state highway spending more broadly to
include all highway spending by state governments, whether for federal-
aid highways or for other state highway projects. However, Knight does
not include local spending on highways in his definition of state
highway spending. As a consequence, increased state maintenance
spending on federal-aid highway projects or spending on state
government highway projects that are not part of the federal-aid system
is not considered grant substitution in his study, even though such
spending is not eligible for federal assistance. However, increased
highway spending by local governments is considered to be grant
substitution in the same way that increased state or local spending for
other state services and increased tax relief are considered
substitution. Finally, the Gamkhar study defines highway spending to
include both capital and maintenance spending by both state and local
governments. This study, therefore, counts only increased state or
local spending for nonhighway purposes, including increased tax relief,
as representing grant substitution.
The Estimated Effect of Federal Highway Grants on State Highway
Spending:
All three studies use federal grants expenditures to measure federal
grants received by states. This variable is statistically significant
in all studies. In addition to grant expenditures, Gamkhar also
considers grant obligations as an alternative measure. Since obligated
funds are available for expenditure for several years, she included
this variable with lagged values.[Footnote 53]
The reported estimates of substitution rates associated with federal
highway grants vary across the three studies. These differences are, in
part, due to differences in the time periods studied, the definitions
of state highway spending, and the statistical methods employed. Among
the highlights of the studies were the following:
* Knight's study reports a grant substitution rate of over 90 percent
for the period from 1983 to 1997. Knight defines substitution as the
reduction in state (but not local) government spending on all highway-
related projects.
* Gamkhar reports a substitution rate of 63 percent for the period 1976
through 1990. Gamkhar defines substitution as the reduction in state
and local government spending on all highway-related projects; Gamkhar
measured federal grants using grant expenditures. When grants were
measured using obligations rather than actual grant expenditures, a
lower substitution rate of 22 percent is reported.[Footnote 54]
* Meyers also reports a 63 percent substitution rate for the period
1976 through 1982. Meyers defines the substitution rate as the
reduction in state and local government spending on federal-aid
eligible highway projects net of spending on the Interstate Highway
Systems; federal grants are measured using grant expenditures. However,
when he defined substitution as the increase in state and local
government nonhighway spending, he reports no substitution.
Table 5 summarizes the definitions used and findings of these three
studies.
Table 5: Highway Grant Substitution Rates Reported in Fiscal
Substitution Studies:
Study: Knight;
Definitions of state highway expenditures: State (but not local)
government spending for highway-related projects (capital and
maintenance, real per capita dollars);
Substitution rate: 91 percent.
Study: Gamkhar;
Definitions of state highway expenditures: State and local government
spending for highway-related projects (capital and maintenance real per
capita dollars);
Substitution rate: 63 percent (grant expenditures); 22 percent (grant
obligations).
Study: Meyers;
Definitions of state highway expenditures: State capital spending on
federal-aid highways (net of interstate highways, real per capita);
Substitution rate: 63 percent;
Definitions of state highway expenditures: State and local government
nonhighway spending, (real per capita)[A];
Substitution rate: 0 percent.
Source: GAO analysis.
[A] Meyers's formal test for substitution into nonhighway spending is
to test whether federal highway grants are systematically related to
state nonhighway spending. He finds no statistical evidence of such a
relationship.
[End of table]
Controls for Other Factors Associated with State Spending Choices:
To isolate the effect of federal highway grants on state highway
spending, these studies include additional variables in their models to
control for other factors also related to state spending. Some of the
control variables are similar across the studies, but others differ.
Fiscal Capacity:
All three studies use per capita personal income to represent states'
funding capacity, and in each study the variable is found to be
statistically significant.
Tax Price:
All three studies include a wide variety of variables that are intended
to capture various components of the tax price faced by the typical
voter/consumer.
Input prices:
All three studies measure financial variables in real dollars by
adjusting for price level differences over time but otherwise do not
explicitly include an input cost adjustment as a tax price proxy,
except to the extent that the fixed effects procedure employed by
Knight and Gamkhar capture these differences.[Footnote 55]
Highway System Size:
Only Meyers uses an indicator of highway system size: lane miles on
federal-aid highways. While this variable has the expected positive
sign it is statistically insignificant. However, a quadratic term to
capture a possible U-shaped functional form was not used.
Highway usage:
All studies use the number of registered vehicles as a measure of
highway usage. In addition, Knight uses the number of drivers, whereas
Meyers includes vehicle miles traveled. Gamkhar includes several
additional proxies for highway use that are not included in the other
studies: the percentage of light motor vehicles, population density,
and percentage of population living in metropolitan areas. However,
none of these factors was statistically significant. In general, only
one of the use variables is statistically significant in each study and
no one measure is statistically significant across studies. In several
instances the coefficient has a negative sign, although a positive
relationship between highway usage and state spending would be
expected.
Highway matching rates:
Although highway grants require state matching, Knight and Gamkhar do
not include highway matching rates as part of their models because they
found that states' highway spending exceeds the amounts required for
their federal grant allotments and, therefore, have only an income
effect but no price effect. Meyers, in contrast, does include the
effective matching rate (highway grants as a percentage of highway
expenditures) and reports a price elasticity of one.
Nonhighway matching rates:
Other grants may also have a price effect because programs such as
Medicaid, Foster Care, and Adoption Assistance are all open-ended
matching grants. Including the effective matching rate associated with
other grant spending (i.e., other grants as a percent of nonhighway
spending) captures the potential price effect of other grants.[Footnote
56] The sign on the effective matching rate is expected to be negative
because higher demand for other state services would reduce the demand
for highway spending. These variables are statistically significant in
both studies. The Knight study does not consider the tax price effect
of nonhighway grant funding.
Cost sharing:
Only Knight, by including population in his model, includes a factor
that could be interpreted as reflecting the cost-reducing effect of
having more taxpayers sharing the cost of highway services. Neither
Gamkhar nor Meyers includes such a factor.
Other Grant Funding:
In addition to the tax price effect of nonhighway grant funding, the
studies may also have income effects. Both Meyers and Gamkhar include
other nonhighway grants per capita in their models to capture the
income effect of these grants.[Footnote 57] The income effect is
expected to have a negative effect on own-source spending as some of
these grants may be substituted into highway spending and supplant
funding from state resources. The Knight study does not consider either
price or income effects associated with nonhighway grant funding.
Political Culture/Preferences:
Only Knight includes variables that are intended to reflect differences
in state preferences for highway spending that may be associated with
the political party of the state governor and the partisan
representation in the state legislature. He finds the party of the
state governor to be statistically significant at the 10 percent level,
while the other political variables are not statistically significant.
Description of GAO's Statistical Model:
Consistent with previous studies, we model state spending choices as
being conditioned on states' fiscal capacities, the tax price faced by
state voters, federal grant funding for highways and for other state
services, and preferences of state voters for highway spending. Because
both theory and the results of previous studies suggest that federal
grants and state spending decisions are jointly determined, we use an
instrumental variables (IV) approach to estimate the fiscal effect of
federal grants.
To capture other factors that may be systematically associated with
differences in state spending choices, we estimate the model using a
fixed effects estimating procedure. The fixed effects procedure is
intended to capture factors such as topographical differences and
weather conditions across states that do not change over time and to
capture other unmeasured factors with large cross-state variation that
exhibit relatively little change over time.[Footnote 58] In addition,
we include a time trend to capture trend changes in state spending that
may not be captured by the other variables included in our model.
The specific variables considered for our model are listed in table
6.[Footnote 59]
Table 6: Variables Considered in the Second Stage State Highway
Expenditure Equation:
Dependent variable (per capita):
Dependent variable (per capita): Variable name: Real state and local
government spending for highway capital and maintenance.
Fiscal capacity (per capita):
Fiscal capacity (per capita): Variable name: Real personal income.
Fiscal capacity (per capita): Variable name: Real income squared.
Tax price: Variable name: Vehicle miles traveled per capita.
Tax price: Variable name: Drivers per capita.
Tax price: Variable name: Registered motor vehicles per capita.
Tax price: Variable name: Effective match rate of nonhighway grants.
Tax price: Variable name: Population.
Tax price: Variable name: Lane miles per capita.
Tax price: Variable name: Squared lane miles per capita.
Federal grants (per capita): Variable name: Real highway grants[A].
Federal grants (per capita): Variable name: Real other federal grants.
State preferences[B]: Variable name: Governor democratic.
State preferences[B]: Variable name: Percentage of state House
represented by Democratic party.
State preferences[B]: Variable name: Percentage of state Senate
represented by Democratic party.
Other variables: Variable name: Utah Olympics (=1 for 1997-2000).
Other variables: Variable name: Time trend.
Other variables: Variable name: Time trend squared.
Other variables: Variable name: Inverse time trend.
Other variables: Variable name: State fixed effects.
Source: GAO analysis.
[A] Predicted values of federal highway grants.
[B] With the exception of some independents, office holders are either
Democratic or Republican. Therefore, the choice of using the percentage
Democrats or Republicans is arbitrary and has no effect on the
statistical results except to change the sign of the regression
coefficient.
[End of table]
Testing the Stability of the Substitution Rate:
With each time period, various rules and regulations change that may
affect the ability of states to substitute federal grants for state
spending. Given the range of estimates over different time periods
reported in past research, we also want to test whether the rate of
grant substitution, if found, systematically differ across the time
periods included in our data. To see if the substitution rate differs
over time, we introduce dummy variables for each of the time periods
covered in our study into our model.[Footnote 60] We then multiply
these dummy variables by the grants variables and included these
interaction variables in the model. If statistically significant, these
variables would provide evidence that substitution has varied from one
time period to another.
Definition of State Highway Spending:
The estimated effect of federal highway grants on state highway
spending is measured by the regression coefficient associated with
federal highway grants. As a consequence, the interpretation of that
coefficient is directly affected by how the dependent variable, state
expenditures, is defined. If we defined state highway spending narrowly
as only capital expenditures on federal-aid highway projects, the
federal grants coefficient in our model would be interpreted as the
response of state capital spending to changes in federal highway aid.
This approach, taken by Meyers,[Footnote 61] represents a definition of
state spending that is consistent with the requirements of the federal-
aid highway program, which restricts federal grants to authorized uses,
such as capital investment on eligible federal-aid highway routes.
Under this approach, grant funds that are used for purposes that are
not eligible for federal aid, would represent grant substitution in the
same way that increased spending for health and education and for state
tax relief would represent grant substitution. Some policymakers may
not view this as substitution, perhaps arguing that state
transportation officials are better positioned to determine the best
use of available funding for highway-related projects. Our analysis
uses this broader definition of state highway spending. Thus, our
measure of grant substitution considers only state grant funds that are
effectively used for nonhighway purposes as substitution.[Footnote 62]
We adopt this approach for two reasons. First, we want to be
conservative in our definition of grant substitution. A broader
definition of state highway spending that includes state and local
spending on highway projects not eligible for federal funding would
yield a lower estimate for the substitution rate because some types of
adjustments would not be treated as grant substitution. Second, an
estimate of grant substitution that is based only on state (but not
local) government spending would be affected by cross-state differences
in the extent to which highway spending is centralized at the state
level. Since there are large differences across states in the extent to
which highway spending is centralized, we include local as well as
state government spending so that our measure of highway spending would
be comparable across states.
Definition of Federal Highway Grants and Specification of the Federal
Grants Equation:
Because federal highway grants are provided on a reimbursement basis,
we obtained from FHWA federal highway grant expenditures that are
contemporaneous with states' reported own-source highway spending. As
with state spending, we express federal grants in real per capita
dollars, using the BEA chain-price index for state and local government
streets and roads. Because federal grant expenditures, by definition,
represent formula grant allotments from current and prior years, any
lagged response in state spending to federal highway grant funds is
already included in our grants variable. We therefore do not include
lagged values of federal highway grants in our model.
Knight provides an economic argument explaining that state highway
spending and federal grant funding are jointly determined because
elected officials reflect the preferences of state voter/consumers both
in state legislatures and in Congress. His study tests for and finds
confirming evidence for his theoretical argument.[Footnote 63] Based on
these findings, we also employ an IV estimator that provides a
consistent estimate of the federal grant coefficient to measure the
fiscal effect of federal grants. Using this approach, we estimate a
first stage instrumental variable equation that models federal highway
funding in terms of exogenous variables that are expected to influence
the distribution of federal grants. The instrumental variables include
the exogenous variables from the state expenditure equation (e.g.,
fiscal capacity, the individual components of tax price, and
preferences) and variables that are highly correlated with federal
grants but uncorrelated with state highway spending (e.g., variables
included in federal grant formulas and those that may affect the
distribution of discretionary grants). Predicted values of federal
grants, derived from the instrumental variables (highway grants)
equation, are then used in lieu of actual grant values to correct for
the bias in ordinary least squares (OLS) estimates of the federal
grants coefficient in the state expenditure equation.
The excluded exogenous variables we consider include state
contributions to the highway trust fund and variables that are intended
to reflect the influence of state representatives on the distribution
of federal highway grants: tenure in Congress, state representation on
transportation committees, and state representation in the majority
party. The exogenous variables we consider are summarized in table 7.
Table 7: Variables Used to Explain the Distribution of Federal Highway
Grants:
Instrumental Variables Equation: Federal Highway Grants:
Exogenous variables from spending equation:
Real personal income per capita.
Real personal income per capita squared.
Real nonhighway federal grants per capita.
Effective nonhighway matching rate.
Lane miles per capita - 1-yr. lag.
Lane miles per capita squared - 1 yr. lag.
Vehicle miles traveled per capita.
Drivers per capita.
Registered vehicles per capita.
Population.
Governor democratic (1=Dem. 0 = other).
Percent Democrats in state house.
Percent Democrats in state senate.
Utah Olympics (=1 for 1997 - 2000).
Time trend.
Time trend squared.
Inverse time trend.
State fixed effects dummy variables.
Excluded exogenous variables:
Percentage of state representatives in majority party (in year grants
were authorized).
Percentage of state representatives on House transportation
authorization committee.
Average tenure of state representatives in House (in year grants were
authorized).
Percentage of state senators in majority party (in the year grants
authorized).
Percentage of state representatives on Senate transportation
authorization committee.
Average tenure of state Senators (in year grants were authorized).
Real federal highway trust fund receipts per capita.
Source: GAO analysis.
[End of table]
Consistent with the state highway spending equation, we include real
per capita income, real nonhighway grant funding, registered vehicles,
licensed drivers, and vehicle miles traveled--including 1-and 2-year
lagged values for each of these variables--and use a fixed effects
estimating procedure. Fixed effects are intended to capture factors
that have substantial variation across states with little variation
over time. Examples would be factors such as state land area--a factor
that has been part of highway funding formulas and that does not change
over time--and constraints that are applied to funding formulas, such
as the ½-of 1 percent minimum state grant that is included in highway
funding formulas (see table 1).
State Funding Capacity:
Consistent with previous studies, we use real per capita personal
income to measure states' taxing capacities. Unlike previous studies,
we also include the squared value of per capita income to capture the
possibility that demand for highways does not increase in proportion to
increases in income, perhaps signifying that as basic transportation
needs are met, increases in income are increasingly allocated to other
uses such as health and education. Personal income is published by the
BEA in the Department of Commerce. We include 1-and 2-year lagged
values of real per capita income in the model to allow for lagged
responses to changes in income and also to reflect cyclical changes
affecting the level of state revenues.
Tax Price:
The tax price faced by state voters/consumers is reflected in a number
of variables included in the model. Highway usage is reflected by
vehicle miles traveled on state highways, and by registered vehicles
and licensed drivers in the state, as reported by FHWA. We include 1-
and 2-year lagged values in each of these variables to allow for lagged
responses in spending to changes in highway usage.
Consistent with prior studies, we do not include the matching rate on
highway grants because states spend more than the required federal
match, and therefore, states pay 100 percent of the cost of funding
additional highway projects, and because highway matching rates vary
little both over time and across states. However, we do include the
effective match rate on other grant funding to capture the price effect
of other grant funding. Medicaid, Foster Care, and Adoption Assistance,
for example, are open-ended matching programs with price effects that
may encourage states to spend less on highways in order to provide
matching funds for these and possibly other matching programs. We
include 1-and 2-year lagged values to capture these effects. Using data
from the Census Bureau, we measure the effective matching rate for
nonhighway spending by deducting states' federal highway grants from
their total federal grants and expressing the net amount (nonhighway
grants) as a proportion of each state's nonhighway spending, also
calculated by deducting highway spending from total spending.
Although previous studies do not include the size of the highway
network to be maintained, we expect the per capita cost of maintaining
an existing highway network to be higher in states with more miles of
road per capita. Therefore, we include this variable in our model along
with its squared value to test for evidence of per capita costs varying
with the scale of the road network--that is, economies or diseconomies
of scale. We obtained data on total lane miles of state highways from
FHWA.
Other Federal Grants:
In addition to federal highway grants, states receive federal grants
for a variety of other purposes, including health, education, and
welfare. While it is possible that state highway funds may be
substituted into spending for other state services, it is also possible
that some state funds that would have otherwise been used for other
purposes may be redirected into highways. For this reason, we also
include other federal grant funding in our model to capture the income
effect of these grants and their potential substitution into highway
spending. While some of this aid is provided on a reimbursement basis
(Medicaid, for example) other grants can remain eligible for
expenditure in subsequent years. For this reason, we include 1-and 2-
year lagged values of other federal grants to capture these potential
effects. Other federal grants are also expressed in real per capita
dollars.
State Preferences:
The political culture of states may affect both the overall level of
spending on public services, as well as spending priorities for
different types of services, such as highways, versus education and
health care. Differences in political culture and spending priorities
may be relatively stable over time, in which case the fixed effects
adjustment may adequately control for cross-state differences in these
spending preferences. Nonetheless, in addition to including fixed
effects, we have also included variables that may be associated with
differences in political culture. For this purpose, we have included
dummy variables that are equal to one if the state governor is
Democratic and zero otherwise, and the percentage of the state Senate
and state House that is represented by the Democratic Party. With the
exception of some independents, office holders are either Democratic or
Republican. Therefore, the choice of using the percentage Democrats or
Republicans is arbitrary and has no effect on the statistical results
except to change the sign of the regression coefficient. We obtained
these data from the Elections section of the Census Bureau's
Statistical Abstract.
Other Variables:
To capture trend changes in state spending that cannot be captured by
the other variables included in our model, while allowing for a
possible curvilinear trend, we have also included time, time squared,
and the inverse of time. Finally, we include a dummy variable for the
state of Utah that was equal to 1 during the years 1997 through 2000
and zero otherwise to account for the unusually large increase in
highway spending in that state just prior to the 2002 Winter Olympics.
The means and standard deviations for the variables included in our
statistical model are shown in table 8.
Table 8: Descriptive Statistics:
Variables: Real State Highway Spending Per Capita;
Units: Dollars per person;
Mean: $203;
Standard deviation: $80.
Variables: Real Federal Highway Grants Per Capita;
Units: Dollars per person;
Mean: $94;
Standard deviation: $59.
Variables: Real Non-Hwy Federal Grants Per Capita;
Units: Dollars per person;
Mean: $671;
Standard deviation: $235.
Variables: Federal Nonhighway Grants percent of Nonhighway
Expenditures;
Units: Ratio;
Mean: 16%;
Standard deviation: 4%.
Variables: Road Miles Per Person;
Units: Lane Miles per 1,000 population;
Mean: 53;
Standard deviation: 53.
Variables: Registered Vehicles per Person;
Units: Registered vehicles per 1,000 population;
Mean: 786;
Standard deviation: 117.
Variables: Vehicle Miles Traveled per Person;
Units: 1,000 miles per person;
Mean: 9;
Standard deviation: 2.
Variables: Licensed Drivers per Person;
Units: Licensed drivers per 1,000 population;
Mean: 679;
Standard deviation: 50.
Variables: Real Per Capita Income;
Units: Dollars per person;
Mean: $21,272;
Standard deviation: $4,223.
Variables: Percent Democratic in State House;
Units: Percent;
Mean: 57%;
Standard deviation: 17%.
Variables: Percent Democratic in State Senate;
Units: Percent;
Mean: 58%;
Standard deviation: 18%.
Variables: Governor Democrat, (1=Democrat; 0=otherwise);
Units: Not applicable;
Mean: 52%;
Standard deviation: 50%.
Source: GAO analysis.
[End of table]
Statistical Methods:
Because we use time series and cross-section data to estimate the
model, we expect autocorrelation to bias the estimates of the standard
errors associated with variables in our model. To reduce the problem of
heteroscedasticity, we normalize variables by expressing them on a per
capita basis (except for those already expressed in ratio or percentage
terms). We conducted statistical tests to determine if our data are
affected by autocorrelation and found statistical evidence of its
presence. Therefore, we estimate all our models using a correction for
autocorrelation. As noted above, we use a fixed effects procedure that
allows for a separate constant term associated with each state to
represent differences in state funding that are unique to each state
and independent of the other variables included in the model.
Additional Analysis of Fixed Effects:
The fixed effects coefficients of our model represent state differences
in highway spending, after controlling for the other explanatory
variables in our model. They are intended to capture the effect of
variables that have comparatively little variation over time but are
systematically associated with differences in spending across states.
To identify those state characteristics that are systematically related
to the fixed effects associated with state highway expenditures, we
perform an additional stepwise regression analysis that regresses the
following explanatory variables on our estimated fixed effects, using
the following:
* Heating degree days,
* State land area,
* Lane miles per capita,
* Population,
* Vehicles per capita,
* Drivers per capita,
* Federal land area,
* Percentage of Democrats in state House,
* Percentage of Democrats in state Senate,
* Governor Democratic,
* Federal nonhighway grants per person, and:
* Ratio of federal nonhighway grants per person to state nonhighway
spending:
We use the mean value of 21 observations from 1980 to 2000 per state to
represent each variable in explaining our estimated fixed effects.
Statistical Results:
We report the statistical results explaining federal highway grants in
terms of exogenous instrumental variables in table 9. Based on the R2
statistics, the fixed effects adjustment accounts for 85 percent of the
variation in federal grants funding and the additional exogenous
variables added to the model increases the R2 by 5 percent to 90
percent. Variables that are statistically significant at the 5 percent
level appear in bold in the table. In addition to the fixed effects
coefficients, per capita income, highway lane miles of roads, and state
contributions to the Highway Trust Fund are strongly associated with
the distribution of federal highway funding.
Table 9: Instrumental Variables Estimator of Federal Grants per Capita:
Model: Constant term only;
R^2: 0.000.
Model: State fixed effects only;
R^2: 0.854.
Model: X - variables only;
R^2: 0.739.
Model: X and group effects;
R^2: 0.895.
Variables: Personal income, real per capita;
Coefficients: 0.015;
Standard error: 0.007;
Probability value: 0.035.
Variables: Personal income, real per capita, (t-1);
Coefficients: 0.008;
Standard error: 0.009;
Probability value: 0.375.
Variables: Personal income, real per capita, (t-2);
Coefficients: - 0.021;
Standard error: 0.006;
Probability value: 0.000.
Variables: Personal income squared, real per capita;
Coefficients: - 0.3121E-06;
Standard error: 0.1500E-06;
Probability value: 0.037.
Variables: Personal income squared, real per capita, (t-1);
Coefficients: -0.1150E-06;
Standard error: 0.2005E-06;
Probability value: 0.566.
Variables: Personal income squared, real per capita, (t-2);
Coefficients: 0.4831E-06;
Standard error: 0.1288E-06;
Probability value: 0.000.
Variables: Nonhighway federal grant, real per capita;
Coefficients: 0.035;
Standard error: 0.027;
Probability value: 0.187.
Variables: Nonhighway federal grant, real per capita, (t-1);
Coefficients: 0.133;
Standard error: 0.032;
Probability value: 0.000.
Variables: Nonhighway federal grant, real per capita, (t-2);
Coefficients: -0.051;
Standard error: 0.026;
Probability value: 0.048.
Variables: Effective nonhighway match rate[A];
Coefficients: -201.622;
Standard error: 129.481;
Probability value: 0.119.
Variables: Effective nonhighway match rate[A] (t-1);
Coefficients: - 388.050;
Standard error: 148.409;
Probability value: 0.009.
Variables: Effective nonhighway match rate[A] (t-2);
Coefficients: 139.497;
Standard error: 118.758;
Probability value: 0.240.
Variables: Vehicle miles traveled per capita;
Coefficients: -7.331;
Standard error: 3.204;
Probability value: 0.022.
Variables: Vehicle miles traveled per capita, (t-1);
Coefficients: - 0.852;
Standard error: 3.721;
Probability value: 0.819.
Variables: Vehicle miles traveled per capita, (t-2);
Coefficients: 2.688;
Standard error: 2.637;
Probability value: 0.308.
Variables: Registered vehicles per capita;
Coefficients: 0.048;
Standard error: 0.024;
Probability value: 0.041.
Variables: Registered vehicles per capita, (t-1);
Coefficients: -0.013;
Standard error: 0.028;
Probability value: 0.643.
Variables: Registered vehicles per capita,(t-2);
Coefficients: -0.007;
Standard error: 0.024;
Probability value: 0.785.
Variables: Drivers per capita;
Coefficients: -0.022;
Standard error: 0.034;
Probability value: 0.514.
Variables: Drivers per capita,(t-1);
Coefficients: -0.018;
Standard error: 0.038;
Probability value: 0.634.
Variables: Drivers per capita, (t-2);
Coefficients: 0.041;
Standard error: 0.033;
Probability value: 0.221.
Variables: Population, in 1,000;
Coefficients: 0.002;
Standard error: 0.002;
Probability value: 0.138.
Variables: Road miles per capita, (t-1);
Coefficients: 1.231;
Standard error: 0.313;
Probability value: 0.000.
Variables: Road miles squared per capita, (t-1);
Coefficients: -0.004;
Standard error: 0.001;
Probability value: 0.002.
Variables: Governor Democrat, (1=Democrat; 0=otherwise);
Coefficients: -3.836;
Standard error: 1.737;
Probability value: 0.027.
Variables: Percent Democratic in State House;
Coefficients: 2.758;
Standard error: 14.498;
Probability value: 0.849.
Variables: Percent Democratic in State Senate;
Coefficients: 32.801;
Standard error: 11.695;
Probability value: 0.005.
Variables: Utah Olympics = 1 for Utah in 1997-2000, 0 otherwise;
Coefficients: -12.757;
Standard error: 12.055;
Probability value: 0.290.
Variables: Time trend;
Coefficients: -3.123;
Standard error: 2.627;
Probability value: 0.234.
Variables: Inverse time trend;
Coefficients: 66.101;
Standard error: 33.632;
Probability value: 0.049.
Variables: Time trend squared;
Coefficients: -0.009;
Standard error: 0.089;
Probability value: 0.923.
Variables: Highway trust fund per capita;
Coefficients: 0.294;
Standard error: 0.088;
Probability value: 0.001.
Variables: Highway trust fund per capita, (t-1);
Coefficients: 0.330;
Standard error: 0.089;
Probability value: 0.000.
Variables: Highway trust fund per capita, (t-2);
Coefficients: 0.225;
Standard error: 0.088;
Probability value: 0.010.
Variables: Percent of the State's House delegation on the authorizing
committee;
Coefficients: 8.233;
Standard error: 5.761;
Probability value: 0.153.
Variables: Percent of the State's Senate delegation on the authorizing
committee;
Coefficients: -14.760;
Standard error: 4.515;
Probability value: 0.001.
Variables: Percent of the State's Senate delegation in the majority
party;
Coefficients: 1.649;
Standard error: 2.223;
Probability value: 0.458.
Variables: Percent of the State's House delegation in the majority
party;
Coefficients: 5.940;
Standard error: 3.863;
Probability value: 0.124.
Variables: Tenure for each State's U.S. House Delegation;
Coefficients: 0.148;
Standard error: 0.283;
Probability value: 0.601.
Variables: Tenure for each State's U.S. Senate Delegation;
Coefficients: 0.063;
Standard error: 0.202;
Probability value: 0.756.
Source: GAO analysis.
[A] Ratio of federal nonhighway grants to state and local nonhighway
expenditures.
[End of table]
Displacement Effect of Federal Highway Grants:
We report the results for the second stage expenditure equation without
a correction for autocorrelation in table 10. Again, regression results
for variables that are statistically significant at the 5-percent level
appear in bold in the table. The model explains 78 percent of the
variation in state own-source highway spending and fixed effects alone
account for 69 percent of the variation. The estimated substitution
rate associated with federal grants is 84 percent[Footnote 64] and is
statistically significant. That is, other things being equal, a dollar
increase in federal highway grants is associated with an 84-cent
reduction in highway spending from state own-source revenues.
Alternatively, the coefficient also implies that states replace 84
cents of each dollar decline in federal funding.[Footnote 65] These
results are similar to the findings reported by Knight, who reported a
substitution rate of 91 percent, higher than the substitution rates
reported by Gamkhar.
Table 10: Instrumental Variables Estimates of State Highway Spending
Model, Without Correcting for Autocorrelation:
Model statistics: Constant term only;
R^2: 0.000.
Model statistics: State fixed effects only;
R :0.685.
Model statistics: X - variables only;
R^2: 0.445.
Model statistics: X and group effects;
R^2: 0.783.
Variables: Predicted FHWA payments/per capita;
Coefficients: -0.8412;
Standard error: 0.233;
Probability: 0.000.
Variables: Personal income, real per capita, (t);
Coefficients: 0.0160;
Standard error: 0.013;
Probability: 0.221.
Variables: Personal income, real per capita, (t-1);
Coefficients: 0.0196;
Standard error: 0.017;
Probability: 0.249.
Variables: Personal income, real per capita, (t-2);
Coefficients: 0.0110;
Standard error: 0.011;
Probability: 0.322.
Variables: Personal income squared, real per capita, (t);
Coefficients: -0.6259E-07;
Standard error: 2.860E-07;
Probability: 0.827.
Variables: Personal income squared, real per capita, (t-1);
Coefficients: -0.0416E-07;
Standard error: 3.857E-07;
Probability: 0.280.
Variables: Personal income squared, real per capita, (t-2);
Coefficients: -0.9145E-07;
Standard error: 2.572E-07;
Probability: 0.722.
Variables: Nonhighway federal grants, real per capita, (t);
Coefficients: 0.0719;
Standard error: 0.051;
Probability: 0.159.
Variables: Nonhighway federal grants, real per capita, (t-1);
Coefficients: 0.0789;
Standard error: 0.070;
Probability: 0.261.
Variables: Nonhighway federal grants, real per capita, (t-2);
Coefficients: -0.0483;
Standard error: 0.051;
Probability: 0.341.
Variables: Effective nonhighway grant match rate[A], (t);
Coefficients: -509.8810;
Standard error: 250.807;
Probability: 0.042.
Variables: Effective nonhighway grant match rate[A], (t-1);
Coefficients: -394.9280;
Standard error: 300.760;
Probability: 0.189.
Variables: Effective nonhighway grant match rate[A], (t-2);
Coefficients: -102.9480;
Standard error: 230.545;
Probability: 0.655.
Variables: Vehicle miles traveled per capita, (t);
Coefficients: - 6.4858;
Standard error: 6.106;
Probability: 0.288.
Variables: Vehicle miles traveled per capita, (t-1);
Coefficients: 7.8859;
Standard error: 7.135;
Probability: 0.269.
Variables: Vehicle miles traveled per capita,(t-2);
Coefficients: 2.0446;
Standard error: 5.113;
Probability: 0.689.
Variables: Registered vehicles per capita, (t);
Coefficients: 0.0444;
Standard error: 0.046;
Probability: 0.338.
Variables: Registered vehicles per capita, (t-1);
Coefficients: 0.0210;
Standard error: 0.054;
Probability: 0.695.
Variables: Registered vehicles per capita, (t-2);
Coefficients: - 0.0683;
Standard error: 0.046;
Probability: 0.135.
Variables: Drivers per capita, (t);
Coefficients: -0.1290;
Standard error: 0.065;
Probability: 0.046.
Variables: Drivers per capita, (t-1);
Coefficients: -0.0498;
Standard error: 0.072;
Probability: 0.491.
Variables: Drivers per capita, (t-2);
Coefficients: 0.0777;
Standard error: 0.064;
Probability: 0.227.
Variables: Governor Democrat, (1=Democrat; 0=otherwise);
Coefficients: 1.2802;
Standard error: 3.400;
Probability: 0.707.
Variables: % Democratic in State House;
Coefficients: -4.5781;
Standard error: 27.136;
Probability: 0.866.
Variables: % Democratic in State Senate;
Coefficients: 46.8598;
Standard error: 23.539;
Probability: 0.047.
Variables: Utah Olympics = 1 for 1997-2000 in Utah, 0 otherwise;
Coefficients: 154.8240;
Standard error: 22.803;
Probability: 0.000.
Variables: Time trend;
Coefficients: -32.6570;
Standard error: 4.883;
Probability: 0.000.
Variables: Inverse time trend;
Coefficients: -95.4868;
Standard error: 54.237;
Probability: 0.078.
Variables: Time trend squared;
Coefficients: 1.0301;
Standard error: 0.153;
Probability: 0.000.
Variables: Population, in 1,000;
Coefficients: 0.0033;
Standard error: 0.003;
Probability: 0.287.
Variables: Road miles per capita, (t-1);
Coefficients: 1.1212;
Standard error: 0.677;
Probability: 0.098.
Variables: Road Miles squared per capita, (t-1);
Coefficients: -0.0013;
Standard error: 0.003;
Probability: 0.630.
Variables: Autocorrelation coefficient;
Coefficients: 0.5298.
Source: GAO analysis.
[A] Ratio of federal nonhighway grants to state and local nonhighway
expenditures.
[End of table]
However, the model also indicates the presence of autocorrelation
(r=0.53, shown in the last row of table 10). As a consequence the
standard error for the grants coefficient is biased downward, which
raises the prospect that the grants coefficient may not be
statistically significant. We therefore re-estimated the model
adjusting for autocorrelation using two methods: Cochrane-Orcutt and
Newey-West.[Footnote 66] The results are reported in table 11.[Footnote
67] The Cochrane-Orcutt procedure produces a feasible generalized-least
squares estimate of the grants coefficient and its standard error. With
this procedure, the point estimate of the substitution rate drops from
84 to 39 percent and is statistically insignificant (shown in the
second column of tables 10 and 11). The Newey-West correction for
autocorrelation does not involve re-estimating the grants coefficient,
so the estimated substitution rate remains at 84 percent. The
coefficient continues to be statistically significant after correcting
for the bias in its standard error.
Table 11: Instrumental Variables Estimates of State Highway Spending
Model, Correcting for Autocorrelation:
Model statistics: Constant term only;
R^2: 0.000.
Model statistics: State fixed effects only;
R^2: 0.481.
Model statistics: X - variables only;
R^2: 0.233.
Model statistics: X and group effects[A];
R^2: 0.558.
Variables: FHWA payments/per capita;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: -0.3859;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.222;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: -0.841;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.002.
Variables: Personal income, real per capita, (t);
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: 0.0028;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.824;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: 0.016;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.226.
Variables: Personal income, real per capita,, (t-1);
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: 0.0177;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.157;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: 0.020;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.211.
Variables: Personal income, real per capita, (t-2);
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: 0.0058;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.567;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: 0.011;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.309.
Variables: Personal income squared, real per capita, (t);
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: 1.083E-07;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.683;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: -0.0626E-07;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.827.
Variables: Personal income squared, real per capita, (t-1);
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: -3.273E-07;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.231;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: -4.160E-07;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.237.
Variables: Personal income squared, real per capita, (t-2);
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: -0.6321E-07;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.785;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: -0.9140E-07;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.716.
Variables: Nonhighway federal grants, real per capita, (t);
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: 0.1065;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.012;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: 0.072;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.140.
Variables: Nonhighway federal grants, real per capita, (t-1);
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: 0.0312;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.595;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: 0.079;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.214.
Variables: Nonhighway federal grants, real per capita, (t-2);
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: -0.0715;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.118;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: -0.048;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.330.
Variables: Effective nonhighway grant match rate[B], (t);
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: -624.9567;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.004;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: -509.881;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.034.
Variables: Effective nonhighway grant match rate[B], (t-1);
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: -311.9012;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.176;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: -394.928;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.124.
Variables: Effective nonhighway grant match rate[B], (t-2);
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: 119.4099;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.553;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: -102.948;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.646.
Variables: Vehicle miles traveled per capita, (t);
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: 2.1109;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.689;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: -6.486;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.268.
Variables: Vehicle miles traveled per capita, (t-1);
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: 3.6408;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.461;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: 7.886;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.159.
Variables: Vehicle miles traveled per capita,(t-2);
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: - 0.2714;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.953;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: 2.045;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.657.
Variables: Registered vehicles per capita, (t);
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: 0.0180;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.640;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: 0.044;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.319.
Variables: Registered vehicles per capita, (t-1);
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: 0.0336;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.348;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: 0.021;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.633.
Variables: Registered vehicles per capita, (t-2);
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: - 0.0342;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.353;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: -0.068;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.118.
Variables: Drivers per capita, (t);
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: -0.0826;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.107;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: -0.129;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.036.
Variables: Drivers per capita, (t-1);
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: -0.0555;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.277;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: -0.050;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.407.
Variables: Drivers per capita, (t-2);
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: 0.0381;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.470;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: 0.078;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.206.
Variables: Governor Democrat, (1=Democrat; 0=otherwise);
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: 3.4241;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.387;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: 1.280;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.735.
Variables: % Democratic in State House;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: -9.4411;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.753;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: -4.578;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.878.
Variables: % Democratic in State Senate;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: -13.8934;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.613;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: 46.860;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.073.
Variables: Utah Olympics, = 1 for 1997-2000 in Utah, 0 otherwise;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: 142.8233;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.000;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: 154.824;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.000.
Variables: Time trend;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: -17.7499;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.199;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: -32.657;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.000.
Variables: Inverse time trend;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: 110.4277;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.895;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: -95.487;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.234.
Variables: Time trend squared;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: 0.5692;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.089;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: 1.030;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.000.
Variables: Population, in 1,000;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: 0.0018;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.741;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: 0.003;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.361.
Variables: Road miles per capita, (t-1);
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: -0.3525;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.711;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: 1.121;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.147.
Variables: Road Miles squared, per capita, (t-1);
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: 0.0034;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Probability: 0.364;
Autocorrelation corrected model estimates: Newey-West estimates:
Coefficients: -0.001;
Autocorrelation corrected model estimates: Newey-West estimates:
Probability: 0.674.
Variables: Autocorrelation coefficient;
Autocorrelation corrected model estimates: Cochrane-Orcutt estimates:
Coefficients: 0.5297.
Source: GAO analysis.
[A] The R2 with the correction for autocorrelation is not comparable to
the R2 without the correction because the dependent variable is
different between the two models due to the autocorrelation adjustment.
[B] Ratio of federal non-highway grants to state and local nonhighway
expenditures.
[End of table]
The Effect of Removing Statistically Insignificant Variables:
The full model includes over 30 variables when all the lags are
included and many of these variables are statistically insignificant.
To simplify the model, we performed F-tests for the statistical
significance of variables and removed variables with a statistical
significance level below 10 percent. We tested variables that were
included with 1-and 2-year lags as a group and removed them as a group
if found insignificant. We summarize the results of these tests in
table 12. The primary result is that neither the highway usage
variables nor the variables intended to capture state differences in
preferences are statistically significant. The only variables that are
systematically associated with differences in state highway spending
are the variables reflecting financial resources that could be used to
fund highways.
Table 12: Summary Results of the Statistical Testing of the Variable
Coefficients:
Variables tested: Personal income, all;
Statistical results F Statistic: 3.6169;
Probability: 0.0000.
Variables tested: Linear;
Statistical results F Statistic: 2.5524;
Probability: 0.0545.
Variables tested: Squared;
Statistical results F Statistic: 1.1269;
Probability: 0.3373.
Variables tested: Nonhighway grants, all;
Statistical results F Statistic: 3.5888;
Probability: 0.0016.
Variables tested: Amounts;
Statistical results F Statistic: 2.3880;
Probability: 0.0677.
Variables tested: Ratio;
Statistical results F Statistic: 3.5081;
Probability: 0.0150.
Variables tested: Use variables[A];
Statistical results F Statistic: 0.8952;
Probability: 0.5447.
Variables tested: Political preference;
Statistical results F Statistic: 0.3476;
Probability: 0.7909.
Variables tested: Time trend;
Statistical results F Statistic: 4.3000;
Probability: 0.0050.
Variables tested: Lane miles;
Statistical results F Statistic: 1.3333;
Probability: 0.2642.
Source: GAO analysis.
[A] Vehicle miles traveled per capita, registered vehicles per capita,
and licensed drivers per capita.
[End of table]
The result of removing statistically insignificant variables is shown
in table 13. With the Cochrane-Orcutt method for autocorrelation
correction, the grant substitution coefficient is 0.50 and with the
Newey-West correction the coefficient is 0.58; both estimates are
statistically significant at the 1 percent significance level. Thus,
the difference in estimated substitution rates under the two methods
narrowed with the simplified model. To be conservative in our findings
regarding grant substitution, we are using the lower estimate of 0.50,
based on the Cochrane-Orcutt method, as our preferred estimate. The 95
percent confidence interval ranges from 12 to 88 percent, which
includes Gamkhar's estimate of 63 percent but not Knight's higher
estimate of 91 percent. Because the Cochrane-Orcutt method does not
include the first observation for each state, these estimates are based
on observations from 1983 through 2000.
Analysis of Remaining Explanatory Variables:
The full model includes per capita income squared to test for nonlinear
effects of income on state spending. However, the squared term is
statistically insignificant. We conclude that state spending is
proportional to income, which implies that both high-and low-income
states respond to changes in income in roughly the same proportion,
once other factors affecting state spending choices are taken into
account. The lag structure on per capita income indicates that the
largest increase occurs in the first year, but prior year changes in
income also affect state expenditures (see table 13).
The effect of nonhighway grants enters into the model in two ways: the
absolute size of other grant funding, measured in per capita terms,
representing the income effect of other-grant funding; and the ratio of
the nonhighway grants to state nonhighway spending, representing the
tax price effect of other-grant funding. The net income effect of other
grants is small but positive. The coefficients on the nonhighway grant
variables sum to a small positive effect with a statistically
significant positive effect in the current year and a statistically
significant negative effect in year 2. This result is contrary to
expectations in that the net effect would be expected to result in some
of the funding from other federal grants to be used as a substitute for
states' own highway spending.
In contrast, the tax price effect of other grants is strongly negative
indicating that matching requirements associated with other federal
programs, such as Medicaid, result in states spending less of their own
resources on highways. For every dollar spent by a state, the federal
government reimburses the state for a percentage of the cost, reducing
the tax price of these services to the state. The lower price for other
public services raises the demand for those services and reduces the
demand for highways, suggesting that highways and other public services
are substitute goods.
We enter the time trend variable into the model in linear, quadratic,
and inverse form to provide a flexible functional form. The inverse
term was statistically insignificant and we dropped it from the model.
The coefficients on the linear and quadratic term indicate a negative
trend for most of the years in state highway spending when other
factors affecting state spending are taken into account.
Varying Substitution Rates Over Time:
As we noted in our summary of previous studies, Meyers reports no
evidence of substitution into nonhighway spending during the 1976 to
1982 time period. Gamkhar, based on data from 1976 through 1990,
reports higher rates of substitution, and Knight's study, based on data
from 1983 through 1997, reports even higher rates of substitution. We
therefore tested for evidence of increasing substitution rates using
the Cochran-Orcutt method, which, as discussed earlier, uses the
estimation period 1983 to 2000. The results are shown in table 14.
Table 13: State Highway Spending Model with Statistically Insignificant
Variables Removed:
Model statistics: Constant term only;
R[2]: 0.000.
Model statistics: State fixed effects only;
R[2]: 0.426.
Model statistics: X - variables only;
R[2]: 0.078.
Model statistics: X and group effects;
R[2]: 0.479.
Variables: FHWA highway grants, real per capita;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Coefficients: -0.501;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Standard errors: 0.194;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Probability values: 0.010;
Autocorrelation Adjustment Method: Cochrane-Orcut:
95 Percent confidence interval: -0.881;
Autocorrelation Adjustment Method: Cochrane-Orcut:
95 Percent confidence interval: -0.121;
Autocorrelation Adjustment Method: Newey-West:
Coefficients: -0.580;
Autocorrelation Adjustment Method: Newey-West:
Standard errors: 0.174;
Autocorrelation Adjustment Method: Newey-West:
Probability: values: 0.001.
Variables: Personal income, real per capita, (t);
Autocorrelation Adjustment Method: Cochrane-Orcut:
Coefficients: 0.006;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Standard errors: 0.002;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Probability values: 0.005;
Autocorrelation Adjustment Method: Cochrane-Orcut:
95 Percent confidence interval: 0.002;
Autocorrelation Adjustment Method: Cochrane-Orcut:
95 Percent confidence interval: 0.010;
Autocorrelation Adjustment Method: Newey-West:
Coefficients: 0.009;
Autocorrelation Adjustment Method: Newey-West:
Standard errors: 0.003;
Autocorrelation Adjustment Method: Newey-West:
Probability: values: 0.000.
Variables: Personal income, real per capita, (t-1);
Autocorrelation Adjustment Method: Cochrane-Orcut:
Coefficients: 0.005;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Standard errors: 0.002;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Probability values: 0.033;
Autocorrelation Adjustment Method: Cochrane-Orcut:
95 Percent confidence interval: 0.001;
Autocorrelation Adjustment Method: Cochrane-Orcut:
95 Percent confidence interval: 0.009;
Autocorrelation Adjustment Method: Newey-West:
Coefficients: 0.006;
Autocorrelation Adjustment Method: Newey-West:
Standard errors: 0.003;
Autocorrelation Adjustment Method: Newey-West:
Probability: values: 0.041.
Variables: Personal income, real per capita, (t-2);
Autocorrelation Adjustment Method: Cochrane-Orcut:
Coefficients: 0.002;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Standard errors: 0.002;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Probability values: 0.280;
Autocorrelation Adjustment Method: Cochrane-Orcut:
95 Percent confidence interval: -0.002;
Autocorrelation Adjustment Method: Cochrane-Orcut:
95 Percent confidence interval: 0.006;
Autocorrelation Adjustment Method: Newey-West:
Coefficients: 0.001;
Autocorrelation Adjustment Method: Newey-West:
Standard errors: 0.002;
Autocorrelation Adjustment Method: Newey-West:
Probability: values: 0.652.
Variables: Nonhighway federal grants, real per capita, (t);
Autocorrelation Adjustment Method: Cochrane-Orcut:
Coefficients: 0.115;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Standard errors: 0.040;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Probability values: 0.004;
Autocorrelation Adjustment Method: Cochrane- Orcut:
95 Percent confidence interval: 0.037;
Autocorrelation Adjustment Method: Cochrane-Orcut:
95 Percent confidence interval: 0.193;
Autocorrelation Adjustment Method: Newey-West:
Coefficients: 0.064;
Autocorrelation Adjustment Method: Newey-West:
Standard errors: 0.046;
Autocorrelation Adjustment Method: Newey-West:
Probability: values: 0.168.
Variables: Nonhighway federal grants, real per capita, (t-1);
Autocorrelation Adjustment Method: Cochrane-Orcut:
Coefficients: 0.044;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Standard errors: 0.049;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Probability values: 0.370;
Autocorrelation Adjustment Method: Cochrane- Orcut:
95 Percent confidence interval: -0.052;
Autocorrelation Adjustment Method: Cochrane-Orcut:
95 Percent confidence interval: 0.140;
Autocorrelation Adjustment Method: Newey-West:
Coefficients: 0.037;
Autocorrelation Adjustment Method: Newey-West:
Standard errors: 0.058;
Autocorrelation Adjustment Method: Newey-West:
Probability: values: 0.522.
Variables: Nonhighway federal grants, real per capita, (t-2);
Autocorrelation Adjustment Method: Cochrane-Orcut:
Coefficients: - 0.081;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Standard errors: 0.041;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Probability values: 0.045;
Autocorrelation Adjustment Method: Cochrane- Orcut:
95 Percent confidence interval: -0.161;
Autocorrelation Adjustment Method: Cochrane-Orcut:
95 Percent confidence interval: - 0.001;
Autocorrelation Adjustment Method: Newey-West:
Coefficients: -0.036;
Autocorrelation Adjustment Method: Newey-West:
Standard errors: 0.047;
Autocorrelation Adjustment Method: Newey-West:
Probability: values: 0.445.
Variables: Effective nonhighway grant match rate[A], (t);
Autocorrelation Adjustment Method: Cochrane-Orcut:
Coefficients: - 630.218;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Standard errors: 204.465;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Probability values: 0.002;
Autocorrelation Adjustment Method: Cochrane- Orcut:
95 Percent confidence interval: -1030.969;
Autocorrelation Adjustment Method: Cochrane-Orcut:
95 Percent confidence interval: - 229.467;
Autocorrelation Adjustment Method: Newey-West:
Coefficients: -475.541;
Autocorrelation Adjustment Method: Newey-West:
Standard errors: 231.228;
Autocorrelation Adjustment Method: Newey-West:
Probability: values: 0.040.
Variables: Effective nonhighway grant match rate, (t-1);
Autocorrelation Adjustment Method: Cochrane-Orcut:
Coefficients: - 310.799;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Standard errors: 208.831;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Probability values: 0.137;
Autocorrelation Adjustment Method: Cochrane- Orcut:
95 Percent confidence interval: -720.108;
Autocorrelation Adjustment Method: Cochrane-Orcut:
95 Percent confidence interval: 98.510;
Autocorrelation Adjustment Method: Newey-West:
Coefficients: -238.395;
Autocorrelation Adjustment Method: Newey-West:
Standard errors: 242.269;
Autocorrelation Adjustment Method: Newey-West:
Probability: values: 0.325.
Variables: Effective nonhighway grant match rate, (t-2);
Autocorrelation Adjustment Method: Cochrane-Orcut:
Coefficients: 214.391;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Standard errors: 186.720;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Probability values: 0.251;
Autocorrelation Adjustment Method: Cochrane- Orcut:
95 Percent confidence interval: -151.580;
Autocorrelation Adjustment Method: Cochrane-Orcut:
95 Percent confidence interval: 580.362;
Autocorrelation Adjustment Method: Newey-West:
Coefficients: -172.916;
Autocorrelation Adjustment Method: Newey-West:
Standard errors: 212.623;
Autocorrelation Adjustment Method: Newey-West:
Probability: values: 0.416.
Variables: Utah Olympics, = 1 for 1997-2000, 0 otherwise;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Coefficients: 140.309;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Standard errors: 27.778;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Probability values: 0.000;
Autocorrelation Adjustment Method: Cochrane- Orcut:
95 Percent confidence interval: 85.864;
Autocorrelation Adjustment Method: Cochrane-Orcut:
95 Percent confidence interval: 194.754;
Autocorrelation Adjustment Method: Newey-West:
Coefficients: 165.540;
Autocorrelation Adjustment Method: Newey-West:
Standard errors: 25.423;
Autocorrelation Adjustment Method: Newey-West:
Probability: values: 0.000.
Variables: Time trend;
Autocorrelation Adjustment Method: Cochrane- Orcut:
Coefficients: -17.804;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Standard errors: 5.107;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Probability values: 0.000;
Autocorrelation Adjustment Method: Cochrane-Orcut:
95 Percent confidence interval: - 27.814;
Autocorrelation Adjustment Method: Cochrane-Orcut:
95 Percent confidence interval: -7.794;
Autocorrelation Adjustment Method: Newey-West:
Coefficients: -15.166;
Autocorrelation Adjustment Method: Newey-West:
Standard errors: 3.150;
Autocorrelation Adjustment Method: Newey-West:
Probability: values: 0.000.
Variables: Time trend squared;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Coefficients: 0.570;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Standard errors: 0.162;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Probability values: 0.000;
Autocorrelation Adjustment Method: Cochrane-Orcut:
95 Percent confidence interval: 0.252;
Autocorrelation Adjustment Method: Cochrane-Orcut:
95 Percent confidence interval: 0.888;
Autocorrelation Adjustment Method: Newey-West:
Coefficients: 0.461;
Autocorrelation Adjustment Method: Newey-West:
Standard errors: 0.098;
Autocorrelation Adjustment Method: Newey-West:
Probability: values: 0.000.
Variables: Autocorrelation coefficient;
Autocorrelation Adjustment Method: Cochrane-Orcut:
Coefficients: 0.584.
Source: GAO analysis.
[A] Ratio of federal nonhighway grants to state and local nonhighway
expenditures:
[End of table]
To test whether the substitution rate has increased over the period of
our sample data, we divided our sample into four time estimation
periods, corresponding with the authorization periods for the federal-
aid highway program.[Footnote 68]
* 1983 to 1986,
* 1987 to 1990,
* 1991 to 1997, and:
* 1998 to 2000.
Allowing the substitution rate to vary over time improves the
explanatory power of the model, increasing the R2 of our preferred
model from 48 percent to 57 percent. The first period from 1983 to 1986
shows a substitution rate of 18 percent that is not significantly
different from zero. The estimated substitution rate increases to 36
percent in the 1987 to 1990 period and is significant at the 10 percent
level. The substitution rate rises to just under 60 percent during the
two periods of the 1990s and is statistically significant at the 1
percent level. As shown in Table 14, these results are roughly
consistent with previous studies that, when taken together, seem to
suggest increasing matching rates over time.
Table 14: State Highway Spending Model with Substitution Rates by Time
Period:
Model: Constant term only;
R[2]: 0.000.
Model: State fixed effects only;
R[2]: 0.494.
Model: X - variables only;
R[2]: 0.145.
Model: X and group effects;
R[2]: 0.565.
Variables: FHWA Grant, real per capita for 1983-1986;
Coefficients: - 0.178;
Standard errors: 0.198;
Probability values: 0.370;
95 Percent confidence interval: -0.566;
95 Percent confidence interval: 0.211.
Variables: FHWA Grant, real per capita for 1987-1990;
Coefficients: - 0.360;
Standard errors: 0.195;
Probability values: 0.065;
95 Percent confidence interval: -0.742;
95 Percent confidence interval: 0.022.
Variables: FHWA Grant, real per capita for 1991-1997;
Coefficients: - 0.592;
Standard errors: 0.190;
Probability values: 0.002;
95 Percent confidence interval: -0.965;
95 Percent confidence interval: -0.22.
Variables: FHWA Grant, real per capita for 1998-2000;
Coefficients: - 0.581;
Standard errors: 0.188;
Probability values: 0.002;
95 Percent confidence interval: -0.95;
95 Percent confidence interval: -0.213.
Variables: Personal income, real per capita, (t);
Coefficients: 0.007;
Standard errors: 0.002;
Probability values: 0.004;
95 Percent confidence interval: 0.002;
95 Percent confidence interval: 0.011.
Variables: Personal income, real per capita, (t-1);
Coefficients: 0.000;
Standard errors: 0.002;
Probability values: 0.857;
95 Percent confidence interval: -0.004;
95 Percent confidence interval: 0.005.
Variables: Personal income, real per capita, (t-2);
Coefficients: 0.004;
Standard errors: 0.002;
Probability values: 0.067;
95 Percent confidence interval: 0;
95 Percent confidence interval: 0.007.
Variables: Non-Hwy federal grants, real per capita, (t);
Coefficients: 0.147;
Standard errors: 0.041;
Probability values: 0.000;
95 Percent confidence interval: 0.068;
95 Percent confidence interval: 0.227.
Variables: Non-Hwy federal grants real per capita, (t-1);
Coefficients: 0.046;
Standard errors: 0.049;
Probability values: 0.346;
95 Percent confidence interval: -0.05;
95 Percent confidence interval: 0.142.
Variables: Non-Hwy federal grants real per capita, (t-2);
Coefficients: -0.084;
Standard errors: 0.041;
Probability values: 0.039;
95 Percent confidence interval: -0.163;
95 Percent confidence interval: -0.004.
Variables: Effective federal nonhighway-grant match rate[A], (t);
Coefficients: -756.328;
Standard errors: 204.332;
Probability values: 0.000;
95 Percent confidence interval: -1156.819;
95 Percent confidence interval: -355.837.
Variables: Effective federal nonhighway-grant match rate[A], (t-1);
Coefficients: -243.191;
Standard errors: 208.038;
Probability values: 0.242;
95 Percent confidence interval: -650.945;
95 Percent confidence interval: 164.563.
Variables: Effective federal nonhighway-grant match rate[A], (t-2);
Coefficients: 241.691;
Standard errors: 188.098;
Probability values: 0.199;
95 Percent confidence interval: -126.981;
95 Percent confidence interval: 610.363.
Variables: Utah Olympics, = 1 for 1997-2000, 0 otherwise;
Coefficients: 144.022;
Standard errors: 26.282;
Probability values: 0.000;
95 Percent confidence interval: 92.51;
95 Percent confidence interval: 195.534.
Variables: Time Trend;
Coefficients: -5.423;
Standard errors: 5.193;
Probability values: 0.296;
95 Percent confidence interval: -15.602;
95 Percent confidence interval: 4.756.
Variables: Time Trend Squared;
Coefficients: 0.187;
Standard errors: 0.168;
Probability values: 0.265;
95 Percent confidence interval: - 0.142;
95 Percent confidence interval: 0.516.
Variables: Autocorrelation coefficient;
Coefficients: 0.51599.
Source: GAO analysis.
[A] Ratio of federal nonhighway grants to state and local nonhighway
expenditures.
[End of table]
When the substitution rate is allowed to vary over time, the time trend
coefficients become statistically insignificant. This lack of
significance suggests that there is no negative time trend in state
spending once the increasing substitution rate associated with
different time periods is taken into account.
We use an IV estimator because we assume federal grants and state
spending are jointly determined. To test the reliability and validity
of the IV estimator we ran three additional statistical tests: (1) a
weak instruments test, (2) a test for exogeneity of excluded exogenous
instruments, and (3) a test for endogeneity of federal grants.
The weak instruments test is intended to verify that the excluded
exogenous instrumental variables included in the grants equation are
correlated with federal grants. If they are not, the IV estimator
provides no advantage to a simple (and more efficient) OLS estimator.
To test the significance of the excluded exogenous variables, we
calculated the partial R2 associated with the excluded exogenous
instruments and found the instruments to be statistically significant
at the 1 percent level.
To test for the exogeneity of excluded exogenous instruments, we
conducted a Hausman over-identifying restrictions test.[Footnote 69]
This test compares the estimated federal grant coefficients for each
time period using the full set of excluded exogenous variables with
coefficients derived from using a subset of instruments composed of
predetermined variables that can safely be assumed to be exogenous. A
finding that the set of grant coefficients from the two models are not
statistically different from one another lends support for the
hypothesis that the full set of excluded exogenous instruments are
independent of the error term in the second stage expenditure equation.
For this test, we used a subset of excluded exogenous variables.
Differences between the grant coefficients for each time period using
all instruments, and the coefficients using the subset of exogenous
instruments, were not statistically significant and are quantitatively
very similar to one another. Thus, we found no evidence that our
excluded exogenous instruments were correlated with the error term of
the expenditure equation.
Finally, we conducted a Hausman test for the endogeneity of the federal
grant variable.[Footnote 70] This test consists of comparing the IV
estimate of the grant coefficient for each time period with the
corresponding grant coefficient based on the OLS estimate. If the
differences were not statistically significant there would be little
justification for using the IV estimator. This test yielded
statistically significant differences between the two sets of
estimates, lending support for the assumption that federal grants and
state spending are jointly determined. The results of each of the three
tests are summarized in table 15.
Table 15: Statistical Tests for the Endogeneity of Federal Grants and
State Highway Spending:
Weak instrument test:
Partial R[2]: 0.10;
Probability value: 0.000.
Hausman over-identifying restrictions test: '83-'86;
Grant coefficient: All instruments: -0.178;
Grant coefficient: Subset of instruments: -0.187;
0.531.
Hausman over-identifying restrictions test: '87-'90;
Grant coefficient: All instruments: -0.360;
Grant coefficient: Subset of instruments: -0.366.
Hausman over-identifying restrictions test: '91-'97;
Grant coefficient: All instruments: -0.592;
Grant coefficient: Subset of instruments: -0.590.
Hausman over-identifying restrictions test: '98-'00;
Grant coefficient: All instruments: -0.581;
Grant coefficient: Subset of instruments: .581.
Hausman endogeneity test: '83-'86;
Grant coefficient: 2SLS: -0.178;
Grant coefficient: OLS: -0.047;
Probability value: .0001.
Hausman endogeneity test: '87-'90;
Grant coefficient: 2SLS: -0.360;
Grant coefficient: OLS: -0.160.
Hausman endogeneity test: '91-'97;
Grant coefficient: 2SLS: -0.592;
Grant coefficient: OLS: -0.340.
Hausman endogeneity test: '98-'00;
Grant coefficient: 2SLS: -0.582;
Grant coefficient: OLS: -0.343.
Source: GAO analysis.
[End of table]
Additional Tests for Varying Substitution Rates:
We also estimated alternative models that allow the substitution rate
to vary according to state size (measured by population), per capita
income, and state per capita spending on mass transit to test for a
varying substitution rate related to these factors. The results of
these models were negative. Overall we found no evidence that
substitution rates systematically differ by either population size or
the level of mass transit spending. We did obtain higher estimates of
substitution rates in states with higher per capita income (56-66
percent in high income states compared to just over 30 percent in lower
income states), but these differences were not statistically different
from the average substitution rate of 50 percent found for the period
from 1983 to 2000.
State Characteristics Associated with Fixed Effects:
In the models reported above, state fixed effects account for most of
the variation in state highway spending. Based on our preferred model
(the model in table 12 using the Cochrane-Orcutt autocorrelation
correction method), differences in state spending associated with these
fixed effects can be as much as $400 per capita. However, these fixed
effects are difficult to interpret since they represent all factors
that are systematically related to cross-sectional differences in state
spending not included in the model (e.g., geography, weather, and other
variables that have substantial cross-sectional variation).
To determine if the differences in state spending measured by the fixed
effects of our model are systematically associated with particular
state characteristics, we performed a step-wise regression using the
fixed effects from our preferred model as the dependent variable. Of
the 12 variables we considered, 3 are statistically significant: per
capita highway lane miles, per capita income, and heating degree days
(see table 16). In the first step, lane miles account for 51 percent of
the cross-state variation in our fixed effects, the second step
equation added per capita income, and increases the explained variation
to 68 percent. The third step equation adds heating degree-days,
raising the variation explained to 77 percent. The remaining variables
are statistically insignificant and provide little additional
explanatory power.
Table 16: Stepwise Regression Analysis of the Fixed Effects:
Step: 1;
Variables: Average lane miles per capita;
Coefficient: 0.58322;
Probability value: 0.002;
R[2]: 51%.
Step: 2;
Variables: Average real income per capita;
Coefficient: -0.01778;
Probability value: 0.000;
R[2]: 68%.
Step: 3;
Variables: Average heating degree days;
Coefficient: 0.01324;
Probability value: 0.004;
R[2]: 77%.
Source: GAO analysis.
[End of table]
The positive coefficient on lane miles per capita may reflect a higher
per capita cost of maintaining a larger highway network. The negative
coefficient on per capita income suggests that, other things being
equal, states with high average real incomes per capita spend less for
highways than states with low average real incomes per capita. Finally,
the positive coefficient on heating-degree days indicates that states
in colder climates spend more on highways than states in warmer
climates, all other things remaining equal.
[End of section]
Appendix III: State Characteristics Associated with States' Level of
Effort to Fund Highways from State Resources:
Based on our model of state highway spending, we found a number of
factors that are systematically related to state highway spending and,
in turn, a state's level of effort to fund highway from state
resources.[Footnote 71] Perhaps most importantly, more federal highway
aid is associated with less state effort to fund highways from state
resources once other factors related to state spending are taken into
account. Our conservative estimate of grant substitution suggests that
about half the increase in federal highway grants is used to reduce
states' level of highway spending effort.
Increases in federal grant funding for nonhighway purposes, such as
health, education, and welfare, are also associated with reduced effort
on the part of states to fund highways. Based on our model of state
highway spending, we found that states with a higher percentage of
their nonhighway spending funded by federal grants reduced their effort
to fund highways, presumably, to provide matching funds for programs
like Medicaid, which is an open-ended matching program.
In addition to federal grants, we found two cost factors that are
systematically related to states' levels of highway spending effort,
other things being equal. States with large highway networks, as
measured by the number of highway lane miles, systematically spend more
per capita. Presumably, a larger road network is more expensive to
maintain and states must therefore devote a larger share of their
funding capacity to maintaining their highway network. In addition, we
found that colder than average temperatures, as measured by heating
degree days, are associated with higher state spending, suggesting that
colder weather creates more wear and tear on the highways and hence the
need for states to make a greater spending effort to maintain their
highway network, other things being equal.
Finally, we found that high per capita income states make less effort
than states with lower incomes. This result is, perhaps, not surprising
since the same effective tax rate, (level of effort), generates more
revenues in high-income states than in states with lower incomes. Thus,
the same level of highway spending can be funded with less effort in
high-income states and low-income states compensate by undertaking a
greater effort to fund highways from state resources.[Footnote 72]
[End of section]
Appendix IV: Program Options Designed to Reduce Substitution:
One program option that could be designed to reduce substitution would
be to modify the matching requirement to leverage additional state
highway spending. While the use of matching requirements as an economic
tool is designed to leverage additional spending, the federal-aid
highway program's current matching requirements, which typically call
for 20 percent state funding and 80 percent federal funding of eligible
projects, permit substitution because most states' highway funding is
already higher than 20 percent of their total highway funds. The
matching requirement, therefore, does not provide states with an
incentive to increase or even maintain their level of funding in order
to receive additional federal funds. Instead, states are free to
substitute federal funds for funds they would have spent from their own
resources and to use their own funds in other ways.
For the matching requirement to leverage additional state spending, the
states' matching portion would have to be set high enough so that
states would not receive additional federal funds without spending
beyond what they would have otherwise spent without additional federal
assistance. This objective cannot be perfectly achieved because models
of substitution, like any models, produce estimates that are subject to
uncertainty, and there is no way to objectively determine with
certainty what states would have spent in the absence of increased
federal funding. However, the likelihood that increased federal funding
will leverage additional state highway spending can be achieved in
several ways.
Increase State Matching Requirements:
The most direct approach would be to change the current 80 percent
federal/20 percent state match ratio to a matching ratio closer to the
45 percent federal/55 percent state division of funding in fiscal year
2002. This would likely mean that some states (those whose spending is
less than 60 percent of combined federal and state spending) would be
required to increase their highway spending in order to qualify for any
increased federal funding, while other states whose spending is already
over 60 percent of combined federal and state spending would not have
to increase or maintain their spending in order to receive increased
federal funds.
Increasing the required state match from 20 percent to 60 percent might
require a few states, whose state highway funding levels are currently
a comparatively small proportion of their total highway spending, to
more than double their current level of highway spending to avoid
losing federal funds. If increases of this magnitude were deemed too
extreme, a more moderate increase in the state match could be
established. For example, raising the state matching share to 40
percent instead of 60 percent would require smaller funding increases
in states whose state and local spending is currently a smaller
proportion of the total highway spending, but it would also reduce the
number of states that would be required to increase their level of
funding in response to increased federal funding.
Another drawback of simply increasing state matching requirements is
that even substantial increases in the requirements (raising the
required state match from 20 percent to 60 percent) would not be likely
to leverage additional state spending in all states. An alternative
that would increase the likelihood of leveraging additional state
spending in all states would be to continue with the 80 percent
federal/20 percent state matching ratio but stipulate that only state
spending in excess of what the state had spent for highways in an
appropriate base time period be counted against its federal matching
requirement. This approach has the advantage of maintaining the current
20 percent state matching rate, yet provides a leveraging incentive in
all states rather than in only those states with below average
spending. However, it might have the effect of making it easier for
those states that were not spending much in the base time period to
increase their spending and receive increased federal funds than it
would be for those states whose spending was already high.
Modify Funding Formulas to Reward State Highway Funding Effort:
Another approach that would reduce substitution by creating an
incentive for states to increase their own highway spending would be to
directly link the level of federal highway aid to each state's level of
highway funding effort. This link could be achieved by setting aside a
fixed percentage of formula grant funding to be distributed in
accordance with states' highway funding efforts. As stated in the text,
to avoid penalizing low income states, each state's highway funding
effort could be defined as the state's highway spending compared to
some measure of the state's taxing capacity. There are a variety of
indicators that could serve as a measure of states' funding capacity.
The most comprehensive that is available annually is Total Taxable
Resources (TTR), which is produced annually by the Department of the
Treasury and used to distribute substance and mental health block
grants. Less comprehensive measures would include Gross State Product
(GSP) and Personal Income (PI), both published annually by the
Department of Commerce.
This approach could be implemented in a variety of ways. One approach
would be to compare each state's funding effort to the average effort
of all states. If, for example, $100 per capita were set aside and
distributed in this way, states whose highway spending efforts were
above the average spending effort would receive funding proportionally
above the $100 per capita average and states whose effort was below the
average spending effort would receive funding proportionally below the
$100 per capita average.
Initially, those states with an above-average highway funding effort
would be rewarded with higher per capita funding, and those states with
a below-average highway funding effort would be penalized with lower
per capita funding. In following years, each state's highway spending
effort would continue to be compared to the average state highway
spending effort, so that states whose funding effort rose relative to
the national average would automatically be rewarded with higher per
capita funding, while states whose effort fell relative to the national
average would automatically be penalized. Distributing the set aside in
this fashion would, in effect, put all states in competition with one
another, automatically rewarding states whose effort rose compared to
the national average and penalizing states whose effort fell compared
to the national average.
The approach just described would reward those states whose funding
effort is currently high and penalize those whose effort is currently
low. However, this approach could be modified to avoid rewarding or
penalizing states based on their current level of effort. Instead, the
linking of federal funds to state effort could be based only on future
changes in each state's level of highway funding effort. In this
approach, each state's highway funding effort would be compared to its
own effort during an initial time period, such as the year (or an
appropriate average of years) prior to initiation of the set aside. For
example, all states could be awarded the same per capita grant amount
in the first year of the set-aside program. Then, in future years, each
state's funding effort would be compared to its own funding effort in
the first year of the set aside program and adjusted accordingly. Each
state whose funding effort increased compared to the initial base year
would receive an increase in federal funding proportionate to the
increase in its own spending. Such an approach would, in effect, put:
each state in competition with the effort it made in the base
period.[Footnote 73] If both approaches to rewarding state highway
funding effort were deemed desirable, a combination of the two
approaches could be employed. The strength of the incentive would
depend on the amount of total formula funding distributed through the
set aside program; the greater the amount of funding distributed in
this manner, the larger the financial consequences to states of
changing their level of highway funding effort.
Introduce a State Maintenance of Effort Requirement:
If, instead of seeking to stimulate additional state spending on
highways, the goal of federal policy makers is for federal grants to
supplement state spending on highways, then instituting a maintenance-
of-effort (MOE) provision may be a more appropriate approach. MOE
provisions require states to maintain existing levels of state spending
on an aided program as a condition of receiving federal funds. As a
tool, MOE requirements are designed not to stimulate additional state
spending but to guard against grant substitution so that increased
federal spending will supplement rather than replace states' own
spending.
As with matching requirements, this objective cannot be perfectly
achieved because models of substitution, like any models, produce
estimates that are subject to uncertainty and there is no way to
objectively determine with certainty what states would have spent in
the absence of increased federal funding. However, the likelihood that
increased federal funding will not be used as a substitute for state
spending can be strengthened if MOE requirements are designed
appropriately. In previous work, we concluded that, to be effective,
MOE provisions should define a minimum level of state spending effort
that can be objectively quantified based on reasonably current
expenditures on the aided activity.[Footnote 74] Adjusting the MOE
requirement for inflation in program costs would ensure the minimum
spending level is maintained when measured in inflation adjusted
dollars. This could be achieved by defining a state's base spending
level as the amount spent per year during a recent historical period
and then adjusting that base spending level for inflation.
One drawback of an MOE provision is that basing it on historical
spending period could result in a base spending period for the MOE
provision that represents an unusually high spending level for some
states, effectively locking them into continued high spending in future
years. This could be ameliorated however by establishing waivers for
states that are able to demonstrate that spending in the base period
chosen is unusually high, to allow a more "typical" spending level for
purposes of the MOE provision.
Developing an indicator of state highway spending effort to link
federal funding to state spending or establishing a state's base
spending level to design an MOE requirement would require careful
consideration. Among other issues, in defining these indicators,
consideration would have to be given to whether to measure:
* Capital expenditures for highways or capital plus maintenance
spending;
* Expenditures on all state roads or for federal-aid roads only;
* State government expenditures only, or spending by state and local
governments;
* Total expenditures, or expenditures normalized on a per capita, per
lane mile, or other basis.
In addition, an indicator of state funding effort or a state's base
funding level for an MOE provision should, to the extent possible, be
established by measuring spending levels that are typical rather than
unusually high or low. Highway capital expenditures in a state can
increase or decrease dramatically from year to year and may be
unusually high or low for variety of reasons (e.g., Utah's unusually
high spending during preparations for the 2002 Winter Olympics, or a
state particularly hard hit by recession that drops spending below its
usual effort). To some extent, such factors can be taken into account
by defining a state's funding effort or base level of spending for an
MOE provision using multi-year averages so that such unique
circumstances are averaged out.
[End of section]
Appendix V: GAO Contacts and Staff Acknowledgments:
GAO Contacts:
JayEtta Z. Hecker (202) 512-2834 Steve Cohen (202) 512-4864 Jerry
Fastrup (202) 512-7211:
Staff Acknowledgments:
In addition to those named above, Jay Cherlow, Catherine Colwell,
Gregory Dybalski, Edda Emmanuelli-Perez, Scott Farrow, Donald Kittler,
Alex Lawrence, Sara Ann Moessbauer, Robert Parker, Paul Posner, Teresa
Renner, Stacey Thompson, and Alwynne Wilbur made key contributions to
this report.
(544074)
FOOTNOTES
[1] GAO, Trends in Federal and State Capital Investment in
Highways, GAO-03-744R (Washington, D.C.: June 18, 2003).
[2] Dollar amounts in this report are adjusted to 2001 dollars,
matching adjustments made in the earlier related report GAO-03-744R.
[3] While these estimates represent our most likely estimates of the
substitution that occurred, they are only estimates. The uncertainty
surrounding our estimates can be expressed in terms of a level of
confidence that a given range of values encompasses the actual
substitution rate. This is discussed later in this report. For example,
the estimate of an 18 percent substitution rate for the early 1980s is
not statistically different from a finding of no substitution.
Regarding our estimate of 60 cents on the dollar during the 1990s, the
actual substitution rate, with a 95 percent level of confidence, may be
as high as 96 percent or as low as 21 percent.
[4] GAO, Federal Grants: Design Improvements Could Help Federal
Resources Go Further (GAO/AIMD-97-7), December 18, 1996.
[5] The federal-aid highway program also includes discretionary grants
and research and development programs. While grants are provided to
states, localities may also sponsor federal-aid projects and can
receive some federal funds, primarily through their state.
[6] GAO, Highway Funding: Alternatives for Distributing Federal Funds
(GAO/RCED-96-6) Nov. 28, 1995.
[7] According to FHWA, although never legally described and named as
such, the portion of the Highway Trust Fund that is not specifically
credited by law to the Mass Transit Account of the Highway Trust Fund
has come to be called the "Highway Account" and receives all Trust Fund
receipts not specifically designated for the Mass Transit Account.
[8] The Senate approved the 95 percent amount while the House bill
contained a "reopener" provision that would delay fiscal year 2006
funding for most federal-aid highway programs from October 2005 until
August 2006 if Congress has not enacted legislation by September 30,
2005, raising each states' guaranteed rates of return to 95 percent,
effective in fiscal year 2009.
[9] Under TEA-21, states are subject to withholdings or transfers in
their federal grants if they fail to enact laws that (1) prohibit open
alcoholic beverage containers in the passenger area of a motor vehicle,
(2) establish minimum penalties for repeat drunk-driving offenders, and
(3) establish laws making it illegal for people to drive with the
specified level of alcohol in their blood of .08 blood alcohol
concentration--the level at which a person's blood contains 2/25TH of 1
percent alcohol.
[10] With matching requirements, states must contribute their own funds
in order to receive federal matching funds. Economic theory suggests
that grants requiring matching, by lowering the effective price of
aided programs relative to other state spending priorities, encourage
states to spend more of their own funds. Matching grants typically
contain either a single rate (e.g., 50 percent) or a range of rates
(e.g., 50 percent to 80 percent) at which the federal government will
match state spending on an aided program.
[11] Although the fiscal effect of grants has been described in the
text only in terms of an increase in federal grant funding, stimulation
and substitution may also occur when federal funding is declining. If
in response to a decline in federal aid, for example, states increase
spending from state resources to compensate for the loss in federal
funding, this too represents grant substitution, the substitution of
state funds for federal funding.
[12] To determine trends in real terms, we adjusted the data to 2001-
year dollars to coincide with the data in our related report, GAO-03-
744R, which presented data from 1982 through 2001. We converted these
data using the Bureau of Economic Analysis' (BEA) Price Indexes for
Gross Government Fixed Investment--Highways and Streets.
[13] The percent change from 1998 through 2001 is computed by comparing
the investment in these 2 years. The calculation does not describe the
variations in the intervening years.
[14] As we reported, federal investment did not follow this pattern
from 1997 to 1998, despite the large increase in funding authorized by
TEA-21. When comparing the change in funding from 1997 through 2001,
federal investment increased 23 percent while state and local
investment increased 16 percent. This lower level of increase in
federal expenditures was likely due to the midyear passage of TEA-21 in
June 1998 and the amount of time it takes states to spend capital
project funds.
[15] National Conference of State Legislatures, State Budget Update:
February 2003.
[16] Alternatively, our results suggest that during periods of
declining federal aid, states may replace some of the decline in
federal funding with additional funding from state resources.
[17] See appendix II for a description of the various statistical
models we considered and the rationale for our selection of a preferred
model.
[18] Shama Gamkhar, "The Role of Federal Budget and Trust Fund
Institutions in Measuring the Effect of Federal Highway Grants on State
and Local Government Highway Expenditure," Public Budgeting and
Finance, Spring 2003; Brian Knight, "Endogenous Federal Grants and
Crowd-out of State Government Spending: Theory and Evidence from the
Federal Highway Aid Program," The American Economic Review, Vol. 92 No.
1, March 2002, pp. 71-92; and Harry Meyers, "Displacement Effects of
Federal Highway Grants," National Tax Journal, Vol. XL, No. 2, June
1987, pp. 221-235.
[19] Our model and all the studies we examined used grant expenditures
recorded by states as the measure of federal grants. Grant expenditures
are recorded when the federal government reimburses states for eligible
project expenses. One study, described later in the report, also used
an alternative measure.
[20] Because grant allotments remain available for expenditures for up
to 4 years, some of the grant expenditures for a given time period
includes grant obligations from prior periods.
[21] Issues related to the differing statistical methods employed in
previous studies are discussed in appendix II.
[22] Knight, op. cit.
[23] Gamkhar, op. cit.
[24] The grant distribution process first allots federal funding to
states. States then obligate these funds for eligible highway projects,
and, finally, the federal government reimburses states at the time
obligated balances are actually spent. Thus, obligations are the second
step in the federal grant making process, and grant expenditures are
the final step of the process.
[25] Meyers, op. cit.
[26] Gamkhar and Meyers's findings on the second and third bars of the
figure used the same measure of federal grants and similar definitions
of substitution. Knight's study used the same measure of federal grants
that we did and a definition of substitution that was closer to our
definition than Meyers's second analysis, and so we placed his finding
as the fourth bar on the figure. Gamkhar and Meyers's alternative ways
of modeling (shown in the fifth and sixth bars of the figure) used
considerably different measures of federal grants (Gamkhar) and
substitution (Meyers) than we did in our model.
[27] GAO/AIMD-97-7.
[28] The median estimate, from the studies reviewed, was that each
additional dollar of federal matching aid leverages an additional $0.38
in state spending.
[29] Edward Miller, "The Economics of Matching Grants: The ABC Highway
Program," National Tax Journal, Vol. XXVII, No. 2 pp. 221-229, June
1974.
[30] Meyers, op. cit., considered his estimate of the over match by
states conservative due to data limitations.
[31] Gamkhar, op. cit., and Knight, op. cit.
[32] States and localities invest in capital projects on both their
federal-aid-eligible highways and roads where federal aid is not
eligible to be used, such as roads functionally classified as local.
The amount of funding spent on only federal-aid-eligible roads is
periodically estimated by FHWA. This estimate is used for the national
number. However, this information is not available for state-by-state
analysis. Thus, figure 7 includes state and local spending on roads
that are not eligible for federal aid, overstating the amount of state
"match" on federal-aid eligible roads.
[33] See CRS report 98-221: ISTEA Reauthorization: Highway and Transit
Legislative Proposals in the 105TH Congress, 2ND Session.
[34] Increases are shown in nominal dollars.
[35] Specifically, the Senate bill provides that each state would
achieve a 95 percent return on payments to the highway account of the
Highway Trust Fund by 2009. While the House bill does not contain this
provision, it would delay fiscal year 2006 funding for most federal-aid
highway programs from October 2005 until August 2006, if Congress has
not enacted legislation by September 30, 2005, raising each state's
guaranteed rate of return to 95 percent, effective in fiscal year 2009.
[36] Specifically, those funds that are not distributed to the core
highway programs. See table 1.
[37] FHWA approves state transportation plans, environmental
assessments, and property acquisition for all federally financed
highway projects. On projects that are not located on the Interstate
system but are part of the National Highway System, states may assume
responsibility for overseeing the design and construction of projects
unless either the state or FHWA determines that this responsibility is
not appropriate. While FHWA and each state enter into an agreement
documenting the types of projects for which the state will assume these
oversight responsibilities, FHWA does not maintain information
centrally on how many states have opted for federal versus state
oversight in cases where discretion is permitted.
[38] GAO, Surface Transportation: Many Factors Affect Investment
Decisions, GAO-04-744 (Washington, D.C.: June 30, 2004).
[39] Defining a state's highway spending effort would need to avoid
unfairly penalizing low-income states, which may not have the resources
to compete with the highway spending of wealthier states. Therefore,
each state's highway funding effort could be defined as the state's
highway spending compared to some measure of the state's taxing
capacity. The most comprehensive measure of states' taxing capacity
that is available annually is Total Taxable Resources (TTR), which is
produced annually by the Department of the Treasury.
[40] Defining a state's spending during a base time period should, to
the extent possible, be established by measuring spending levels that
are typical rather than unusually high or low.
[41] GAO, Proposed Changes in Federal Matching and Maintenance of
Effort Requirements for State and Local Governments, GAO/GGD-81-7
(Washington, D.C.: Dec. 23, 1980); and Block Grants: Issues in
Designing Accountability Provisions, GAO/AIMD-95-226 (Washington,
D.C.: Sept. 1, 1995).
[42] One drawback of a maintenance of effort provision is that basing
it on a historical spending period could result in a base spending
period that represents an unusually high spending level for some
states, effectively locking them into continued high spending in future
years. This could be ameliorated however by establishing waivers for
states that are able to demonstrate that spending in the base period
chosen is unusually high, to allow a more "typical" spending level for
purposes of the maintenance of effort provision.
[43] Under the proposed pilot program, the federal government would not
have devolved its responsibilities (1) to review states and local
governments' transportation plans, (2) to oversee "major" projects
costing over $1 billion, (3) under Title VI of the Civil Rights Act of
1964, or (4) under any laws relating to federally recognized tribes. In
addition, the proposal specified that nothing in it would be
interpreted to relieve any project from the requirements of the
National Environmental Policy Act, nor would it preclude DOT from
issuing rulemaking actions as needed.
[44] Under the Federal Transit Administration's New Starts Program,
local transit agencies compete for project funds based on specific
financial and project justification criteria. FTA assesses the
technical merits of a major transit project proposal and its finance
plan and then notifies Congress that it intends to commit, subject to
appropriations, New Starts funding to certain projects through full
funding grant agreements. The agreement establishes the terms and
conditions for federal participation in the project, including the
maximum amount of federal funds--which by law must be no more than 80
percent of the estimated net cost of the project, but in practice is
often less than that percentage.
[45] GAO, Trends in Federal and State Highway Investment (GAO-03-744R).
[46] We adjusted for the changing cost of highway services over time by
deflating expenditures using the chain-price deflator for state and
local government streets and highways published by the Bureau of
Economic Analysis.
[47] Our previous report defined level of effort more narrowly as
highway capital spending as a percentage of gross state product. We
have adopted a broader definition for the current analysis that is
consistent with the scope of this report's focus on highway spending
broadly defined to include capital as well as maintenance spending.
Also consistent with this report, we have used the personal income of
state residents rather than gross state product to reflect states'
fiscal capacities.
[48] The three studies that we relied on most heavily were: Harry G.
Meyers, op. cit; Brian Knight, op. cit; and Shama Gamkhar, op. cit.
Other studies that we reviewed include: Shama Gamkhar, "Is the Response
of State and Local Highway Spending Symmetric to Increases and
Decreases in Federal Highway Grants?" Public Finance Review, Vol. 28
No. 1, January 2000 pp. 3-25: Janet G. Stotsky, "State Fiscal Responses
to Federal Government Grants," Growth and Change, Summer 1991, pp 17-
31; Roger D. Congleton and Randall W. Bennett, "On the Political
Economy of State Highway Expenditures: Some Evidence of the Relative
Performance of Alternative Choice Models," Public Choice, 84 (1995),
pp. 1-24; Rajeev K. Goel and Michael A. Nelson, "Use or Abuse of
Highway Tax Revenues?: An Economic Analysis of Highway Spending,"
unpublished draft, April 2001; Edward Miller, "The Economics of
Matching Grants: The ABC Highway Program," National Tax Journal, XXVII,
no. 2, pp. 221-229; Herman B. Leonard, By Choice or By Chance: Tracking
the Values in Massachusetts' Public Spending (Pioneer Institute for
Public Policy Research, 1992).
[49] Closed-ended matching programs that limit the availability of
federal matching funds at the margin of spending do not lower the cost
of additional highway spending and hence the tax price faced by the
typical voter/consumer.
[50] If state expenditures and federal grants were jointly determined,
ordinary least squares (OLS) methods would provide biased estimates of
the substitution rate. Gamkhar does, however, use OLS methods in a
model in which federal grants are measured using grant obligations,
arguing that obligations are known prior to states' expenditure
decisions and therefore treated obligations data as exogenous.
[51] Neither Knight nor Gamkhar includes a price effect associated with
federal highway grant funding arguing that such an effect is not
present because states spend substantially more than is required to
satisfy the matching requirements associated with federal funding.
[52] Gamkhar uses the implicit price deflator for government purchases,
and Meyers uses the implicit price deflator for state and local
government purchases to adjust for changes in the purchasing power of a
dollar over time. Knight does not identify the deflator he uses. None
of the studies adjusts for cross-state differences in input costs. All
three studies express state highway spending and federal grants on a
per capita basis.
[53] Gamkhar treats grant obligation data as a predetermined variable
and therefore uses OLS methods to estimate the grant coefficient.
However, if state spending and federal grants are jointly determined
this may yield biased estimates of the substitution rate.
[54] The estimate based on grant obligations data may be downwardly
biased if, as argued by Knight, federal grants and state expenditures
are jointly determined (see Knight op. cit, p. 77).
[55] However, to the extent that cross-state differences in input costs
is relatively stable over time, Knight and Gamkhar may have accounted
for these differences through the fixed effects estimating procedure.
[56] The effective matching rate is measured by expressing other grant
funding as a percentage of total nonhighway spending.
[57] As noted above, both studies also include these grants as a share
of total nonhighway spending to capture any price effect these grants
may have.
[58] Both Knight and Gamkhar adopt this strategy.
[59] Consistent with previous studies, we expressed state highway
spending on a per capita basis and adjusted for the changing cost of
highway services over time by deflating expenditures using the chain-
price deflator for state and local government streets and highways
published by the Bureau of Economic Analysis as part of the National
Income Accounts.
[60] Our grant expenditures variable reflects, in part, obligations
made in prior years. As a consequence, the substitution rate for a
particular time period will, in part, be conditioned on grant
obligations made in prior time periods.
[61] Meyers, op. cit.
[62] Knight, op. cit, takes an intermediate approach by using only
state government capital and maintenance spending, excluding highway
spending by local governments.
[63] Knight, op. cit.
[64] The substitution rate is the coefficient of the federal grants
variable after removing the negative sign.
[65] In a related paper Gamkhar tests whether the substitution rate is
symmetrical during periods of rising and falling federal aid and found
no statistically significant difference; see Shama Gamkhar, op. cit.
[66] We used two software packages to estimate the model: Limdep
version 7 and Stata version 8. Limdep uses the Cochrane-Orcutt method
to correct for autocorrelation, which results in a revised generalized-
least-squares estimate of the model coefficients and their standard
errors, while Stata uses the Newey-West method, which only corrects the
estimates of the standard errors. Each method yields a different but
equally valid estimate of the substitution rate. Because the Cochrane-
Orcutt method does not include the first observation for each state,
these estimates are based on observations from 1983 through 2000. In
contrast, the Newey-West method includes the first observation.
[67] We also adjusted for heteroscedasticity using White's methods. The
effect of this adjustment was minor and therefore we did not report it.
[68] As stated earlier, grant expenditures represent, in part,
obligation authority provided in prior years.
[69] Jeffrey M. Woolridge, Econometric analysis of Cross-section and
Panel Data, The MIT Press, Cambridge, Massachusetts, 2002.
[70] William H. Greene, Econometric Analysis, 5TH Edition, Prentice
Hall, New Jersey, 2003, pp. 80-81.
[71] Since level of effort is defined as state spending relative to
funding capacity, factors that are shown to be directly related to
state spending are also directly related to a state's level of effort.
In an earlier report, GAO, Trends in Federal and State Capital
Investment in Highways, (GAO-03-744R, June 18, 2003), we reported
considerable variation among states in the level of highway funding
effort that persisted over the entire period of our study from 1982
through 2000. In addition, we reported wide movement in the states'
relative levels of effort over time. That is, some states making a low
level of effort in the 1980s ranked above average in the 1990s and vice
versa.
[72] The relationship between income and level of effort is complex. We
found that low-income states make a greater level of effort to fund
highway from state resources compared to higher income states. At the
same time, there was no evidence of a difference in the spending
response of high-and low-income states to a change in income, that is,
the squared term from the substitution model was not statistically
significant. In our model of state highway spending, we found that the
fixed effect coefficients were negatively related to state per capita
income, indicating that high-income states make less effort than states
with lower income. In addition to the fixed effects, we also included
per capita income and per capita income squared in our model to test
whether the spending response to changes in income differed between
high-and low-income states. As reported in appendix II we found no
evidence of a differential response.
[73] To work in the way described here, funding for the set-aside
program, as described in the text, would have to be adjusted annually
to reflect the overall change in states' funding effort. If states'
overall level of state effort remained unchanged from year-to-year,
funding for the set-aside program would not have to change in order to
ensure that states whose effort increased were rewarded and states
whose effort declined were penalized. If, however, the overall average
state effort increased, funding for the set-aside program would have to
increase proportionally to ensure that all states with increased effort
were rewarded and those with declining effort were penalized.
[74] GAO, Proposed Changes in Federal Matching and Maintenance of
Effort Requirements for State and Local Governments, GAO/GGD-81-7,
December 23, 1980.
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