Federal Grants: Design Improvements Could Help Federal Resources Go
Further (Letter Report, 12/18/96, GAO/AIMD-97-7).

Pursuant to a congressional request, GAO examined the federal
grant-in-aid system from the perspective of fiscal impact, focusing on
the extent to which the federal grant system succeeds in: (1)
encouraging states to use federal dollars to supplement rather than
replace their own spending on nationally important activities; and (2)
targeting grant funding to states with relatively greater programmatic
needs and fewer fiscal resources.

GAO found that: (1) for the most part, the federal grant system does not
encourage states to use federal dollars as a supplement rather than a
replacement for their own spending on nationally important activities,
nor is every grant intended to do so; (2) GAO's review and analysis of
economists' most recent estimates of substitution suggests that every
additional federal grant dollar results in less than a dollar of total
additional spending on the aided activity; (3) about 60 cents of every
federal grant dollar substitutes for state funds that states otherwise
would have spent; (4) part of the fiscal impact of these transfers is to
free up a portion of state funds for other state programs or tax relief;
(5) GAO's analysis indicated that federal aid is not targeted to offset
these fiscal imbalances; (6) a majority of the 87 largest grant programs
did not include features, such as state maintenance-of-effort and
matching requirements, that can encourage states to use grant dollars as
a supplement rather than a replacement for their own spending; (7) most
grant formulas do not allocate funds using a combination of the three
factors that GAO reported can improve grant targeting, including
programmatic needs, fiscal capacity, and service costs; (8) if reducing
substitution is a desired goal, Congress could add or strengthen
matching and maintenance-of-effort provisons for grant programs; (9) if
targeting fiscal relief to states with greater fiscal stress is a
desired goal, grant formulas could be changed to include a combination
of factors that allocate a larger share of federal aid to those states
with relatively greater program needs and fewer resources; and (10) in
redesigning grants, Congress would need to consider how best to balance
any increase in federal grant restrictions needed to reduce substitution
against the decreases in state budgetary flexibility and discretion that
might result.

--------------------------- Indexing Terms -----------------------------

 REPORTNUM:  AIMD-97-7
     TITLE:  Federal Grants: Design Improvements Could Help Federal 
             Resources Go Further
      DATE:  12/18/96
   SUBJECT:  Economic analysis
             Block grants
             Grants-in-aid
             Intergovernmental fiscal relations
             Grant administration
             Grants to states
             State-administered programs
IDENTIFIER:  Community Development Block Grant
             Maternal and Child Health Block Grant
             Medicaid Program
             General Revenue Sharing Program
             
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Cover
================================================================ COVER


Report to the Chairman, Committee on the Budget, House of
Representatives

December 1996

FEDERAL GRANTS - DESIGN
IMPROVEMENTS COULD HELP FEDERAL
RESOURCES GO FURTHER

GAO/AIMD-97-7

Federal Grants

(935166)


Abbreviations
=============================================================== ABBREV

  ACIR - Advisory Commission on Intergovernmental Relations
  CDBG - Community Development Block Grant
  CFDA - Catalog of Federal Domestic Assistance
  FMAP - federal medical assistance percentage
  GSA - General Services Administration
  GSP - gross state product
  MCH - Maternal and Child Health
  MOE - maintenance-of-effort
  OLS - ordinary least squares
  PCI - per capita personal income
  TTR - total taxable resources

Letter
=============================================================== LETTER


B-272987

December 18, 1996

The Honorable John R.  Kasich
Chairman, Committee on the Budget
House of Representatives

Dear Mr.  Chairman: 

This report responds to your request that we examine the federal
grant-in-aid system from the perspective of fiscal impact. 
Grants-in-aid are payments from the federal government to state and
local governments to help them finance activities in areas such as
public assistance, highway construction, and education.\1 In addition
to these well known areas, grants also finance many lesser known
areas, such as public libraries, sport fish restoration, and boating
safety.  In fiscal year 1995, the federal government allocated $225
billion for more than 600 grant programs--about 15 percent of total
federal spending and 23 percent of total state spending.  However,
the largest 87 programs accounted for 95 percent of total grant
funds. 

Federal grants have been established to achieve a variety of goals,
and it is for the Congress to decide among the various objectives for
grants and the manner in which the federal government allocates the
aid.  In this report, you asked us to focus on the extent to which
the grant system succeeds in two objectives frequently cited by
public finance experts:  (1) encouraging states to use federal
dollars to supplement rather than replace their own spending on
nationally important activities and (2) targeting grant funding to
states with relatively greater programmatic needs and fewer fiscal
resources. 

The first issue concerns the extent to which federal grant dollars
replace state dollars, often referred to as substitution or
supplantation.  Public finance experts suggest that one objective of
federal grants is to increase spending beyond what states would have
spent anyway for the aided services.  However, to the degree states
use federal funds to free up their own resources for other state
priorities, specific-purpose federal funds are, in effect, converted
to general fiscal relief.  The second issue concerns the extent to
which federal dollars are distributed to balance differences among
the states in three areas--program needs, ability to fund grant
activities without federal assistance (state fiscal capacity), and
service costs. 

This examination of the fiscal impact of grants adds to a larger body
of GAO work on design issues in certain federal subsidy programs.  It
expands on our case studies of grant targeting for specific programs
by analyzing all formula grants and helps to answer whether grants,
like some loan programs and tax expenditures we have examined,
experience efficiency losses as funds flow through a network of third
parties, who either have their own spending priorities or would have
undertaken the subsidized activity even without federal assistance.\2


--------------------
\1 Hereafter, we use state to mean state and local governments and/or
their agencies. 

\2 See Deficit Reduction:  Opportunities to Address Long-Standing
Government Performance Issues (GAO/T-OCG-95-6, September 13, 1995);
Budget Issues:  Selected GAO Work on Federal Financial Support of
Business (GAO/AIMD/GGD-96-87, March 7, 1996); and Addressing the
Deficit:  Updating the Budgetary Implications of Selected GAO Work
(GAO/OCG-96-5, June 28, 1996). 


   RESULTS IN BRIEF
------------------------------------------------------------ Letter :1

For the most part, the federal grant system does not encourage states
to use federal dollars as a supplement rather than a replacement for
their own spending on nationally important activities, nor is every
grant intended to do so.  Grants are unlikely to supplement
completely a state's own spending.  Thus, some substitution is to be
expected in any grant.  Our review and analysis of economists' most
recent estimates of substitution suggests that every additional
federal grant dollar results in less than a dollar of total
additional spending on the aided activity.  The estimates of
substitution clustered around 60 cents of every federal dollar.  This
means that about 60 cents of every federal grant dollar substitutes
for state funds that states otherwise would have spent.  Excluding
extreme high and low values, substitution estimates ranged from 11 to
74 cents.  Therefore, part of the fiscal impact of these transfers is
to free up a portion of state funds for other state programs or tax
relief. 

With the responsibilities of states increasing in the federal system,
some observers may view this substitution as a legitimate means of
providing states fiscal relief and budgetary flexibility.  The
Congress has various criteria available to address how such relief
should be allocated among the states.  Applying the goals of fiscal
targeting articulated by public finance experts, we examined the
extent to which the fiscal relief provided by grants is allocated to
states with relatively greater programmatic needs and fewer fiscal
resources.  Our analysis indicated that federal aid is not targeted
to offset these fiscal imbalances.  Consequently, lower income states
face greater fiscal strain in financing federally aided services than
higher income states with lower measurable needs.  In addition, our
prior case studies of specific grants programs--including the areas
of transportation, employment, education, and health--showed that
funding in these individual programs was not allocated to states in a
targeted manner.  Our analysis also suggested that the practice of
placing constraints in grant formulas to assure all states a minimum
amount of funding has contributed to this lack of targeting. 

These fiscal substitution and targeting results reflect the way in
which most of the 633 federal grants we examined are designed.  In
particular, a majority of the 87 largest grant programs did not
include features, such as state maintenance-of-effort and matching
requirements, that can encourage states to use federal funds as a
supplement rather than a replacement for their own spending.  Also,
we found that most grant formulas do not allocate funds using a
combination of the three factors that we have reported can improve
grant targeting--programmatic needs, fiscal capacity, and service
costs. 

A number of strategies for increasing the fiscal impact of grants are
available to the Congress, depending on the value the Congress places
on this goal relative to other grant goals and objectives.  Grant
redesign is one strategy.  If reducing substitution is a desired
goal, the Congress could add or strengthen matching and
maintenance-of-effort provisions for grant programs.  If targeting
fiscal relief to states with greater fiscal stress is a desired goal,
grant formulas could be changed to include a combination of factors
that allocate a larger share of federal aid to those states with
relatively greater program needs and fewer resources.  In redesigning
grants, however, the Congress would need to consider how best to
balance any increase in federal grant restrictions needed to reduce
substitution against the decreases in state budgetary flexibility and
discretion that might result.  And, if states do not share the
federal government's programmatic objectives, high levels of
substitution may occur even after design changes. 

Alternatively, the Congress could decide that redesign is not its
preferred approach and that particular programs no longer represent
the best use of scarce federal resources.  This strategy would free
up budgetary resources that could be used to reduce the deficit or
invest in more promising programs.  Like the first strategy, however,
grant spending cuts also involve tradeoffs.  Depending on the size
and area of the reductions, states would incur varying degrees of
budgetary stress and might face the prospect of increased state
taxes, cuts in state programs, or some combination of both. 


   BACKGROUND
------------------------------------------------------------ Letter :2

Intergovernmental grants are a significant part of both federal and
state budgets.  From the first annual cash grant under the Hatch Act
of 1887, the number of grant programs rose to more than 600 in 1995
with outlays of $225 billion, or about 15 percent of total federal
spending.  Most federal grant programs are small and serve narrow
purposes, while a few large programs--such as Medicaid and the
Highway Planning and Construction Program--dominate the grant-in-aid
system.  Of the 633 grants we reviewed, 87 programs--or 14
percent--accounted for 95 percent of total grant funding. 

In 1995, federal grants accounted for about 23 percent of total state
spending.  Here too there is variation in the federal share of state
spending across categories used by the Census Bureau.  Grants
accounted for about 60 percent of public welfare and 64 percent of
housing and community development spending.  The federal share was
much smaller in other categories, about 8 percent overall. 

In theory, grants are to serve purposes beyond returning resources to
taxpayers in the form of state services.  Grants also can serve as a
tool to encourage states to spend federal funds for nationally
important activities for which they otherwise would have spent less. 
The amount of additional spending is affected by the degree to which
federal grant funds actually supplement state funds.  Public finance
literature uses the term substitution to characterize situations in
which states use federal grant dollars to reduce their own spending
for the aided program either initially or over time.  To illustrate
how substitution works, if states use federal funds to replace state
spending on a dollar-for-dollar basis, then federally aided state
services would remain at pre-grant levels--in which case the fiscal
impact of the additional federal dollar on the intended program is
zero.  In practice, substitution effects are not this extreme; total
state spending rises upon receiving federal grant funds--but by less
than the full amount of the grant because states reduce their own
spending for the area.  In effect, substitution allows a portion of
federal grant funds to be spent on other state priorities. 

Figure 1 illustrates how substitution would work for a hypothetical
state spending $5 on an activity and receiving $1 in federal grant
funds for that activity.  As previously noted, federal grant dollars
are rarely used dollar-for-dollar to supplement state spending on
aided activities.  However, we show this case in the top part of the
figure as a contrast to the expenditure level that might result when
substitution occurs.  We show substitution at 60 cents to correspond
to the approximate midpoint of the range of estimates we reviewed. 
The figure indicates that with substitution, although the federal
grant dollar is spent on the aided program, the state can reduce its
own spending by about 60 cents so that total spending increases by 40
cents.  The state can then reallocate the 60 cents that has been
freed up.  In this regard, the figure shows the two options cited by
economists--spending on other state priorities or tax relief.  For
example, states could spend their freed up funds for other public
goods they value, such as education, transportation, or corrections. 
Or, states could reduce or maintain existing tax rates or slow the
rate of increase. 

   Figure 1:  Illustrative Impact
   of $1 in Federal Grants on
   State Spending on Aided
   Activities, With and Without
   Substitution

   (See figure in printed
   edition.)

As noted, there are a variety of approaches available to distribute
grants among the states.  Public finance experts suggest that grants
can be targeted to states with relatively greater programmatic needs
and fewer fiscal resources.  In program areas where states share
financing responsibilities with the federal government, service
levels depend to an important extent on state fiscal capacities.  For
example, after adjusting for differences in state service costs, the
fiscal burden on Mississippi of providing a given level of public
services is greater than the burden on Connecticut because
Mississippi has about three-fourths the tax base of Connecticut. 
Where the federal government has sought a minimum or more comparable
level of a service for all potential beneficiaries-- regardless of
where they live--grants can help reduce disparities between the
capacities of wealthier and poorer communities to provide that
service.  Our past studies of individual grant programs have led us
to conclude that grants can be designed to reduce differences between
states' fiscal resources and programmatic needs by designing formulas
that allocate funds according to measures of states' program needs,
fiscal capacities, and service costs.\3 In those studies, we also
commented on the importance of using data that accurately capture
differences in these factors across states. 


--------------------
\3 The related GAO products listing at the end of this report
contains references to this work. 


      OTHER ROLES GRANTS HAVE
      PLAYED
---------------------------------------------------------- Letter :2.1

The objectives examined in this report are those most often put
forward by public finance experts:  (1) encouraging states to spend
more for public goods that appear underfunded from a national
perspective, and (2) offsetting the differences between states'
programmatic needs in federally aided functions and their fiscal
resources.  Grants have played other roles in intergovernmental
relations as well.  For example, grants have been used to provide
states with (1) funding to offset the costs of meeting federal
regulatory standards or administering federal regulatory programs,
(2) counter-cyclical assistance in times of economic downturns, (3)
general purpose fiscal assistance (e.g., general revenue sharing),
and (4) performance incentives to improve or enhance existing
programs.  Appendix I contains a more detailed discussion of the
various roles grants have played. 


   SCOPE AND METHODOLOGY
------------------------------------------------------------ Letter :3

To examine substitution in the grant system, we synthesized the body
of econometric literature which statistically isolated the fiscal
impact of federal grant funds and estimated their impact on total
spending.  We used this approach because conventional auditing
methods were not sufficient to answer the questions about
substitution.  Such methods do not control for state spending that
would have occurred without a federal grant and cannot sort out the
effects of other factors, such as population and state income growth,
that also influence state spending.  Thus, an audit of a federal
grant program might demonstrate that all federal funds were spent on
the authorized activities.  However, because the audit could not
observe the level of state spending that would have occurred without
the grant, it could not detect substitution in the form of reductions
from that unobserved level. 

To examine targeting in the grant system, we developed a statistical
model to determine the extent to which federal aid in the aggregate
is allocated to offset differences between state programmatic needs
and fiscal resources (see appendix IV for a description of this
model).  We did not analyze targeting for individual grant programs
because there is less consensus on--and few readily available and
suitable proxies for--measures of individual grant program needs and
costs.  For this reason, our prior work on federal grant targeting
has proceeded using a program-by-program approach, with each case
study requiring substantial work to identify and validate suitable
proxies for state programmatic needs and costs.  For this report, our
model used state population as the primary measure of state
programmatic need.  The model also controlled for a variety of other
state need indicators, such as measures of poverty, housing age,
highway mileage, and service costs.  Controlling for programmatic
needs and costs enabled us to isolate more accurately the statistical
effect of state fiscal capacities on federal grant allocations. 

In addition to this statistical analysis, our examination of
substitution and targeting included (1) a comprehensive review of
over 120 journal articles, reports, and econometric studies on
substitution, targeting, and grant design factors related to both,
(2) a synthesis of 50 econometric studies of federal grants,
culminating in the development of point and range estimates of fiscal
impact overall as well as for different time periods and grant
designs, (3) a review of 23 GAO reports on options to achieve greater
targeting in specific formula grant programs, and (4) an analysis of
grants for design features associated with substitution and
targeting.  Our analysis of the design features associated with
fiscal substitution was for both all 633 grants and separately for
the largest 87 programs representing 95 percent of grant funds.  Our
analysis of the design features associated with targeting was for the
149 formula grants that represented 85 percent of grant funds.  We
excluded project grants, which are awarded on a discretionary basis,
because of the difficulty of generalizing about targeting based on
individual grant decisions. 

Because grant implementation issues were outside the scope of our
analysis, this report cannot be used to draw conclusions about how
well a jurisdiction uses grant funds or who benefits.  In addition to
design features, two important determinants of a grant's fiscal
impact are states' priorities, which may differ from the federal
government's, and program management, which may differ across states. 
To illustrate, a grant for computer education programs might feature
few of the design features to limit substitution, but shared goals
and objectives could result in states using the grant funds they
receive to increase substantially total spending on computer
education.  Or, a grant for health services to low-income children
could lack equity factors that target funds to states with higher
concentrations of such children.  Notwithstanding the lack of
targeting, however, a state could still spend more of each federal
assistance dollar it receives to serve its low-income children than
another state receiving more grant funds.  Just as the presence of
suitable design features does not guarantee that funds will be
allocated efficiently or equitably, so the absence of such features
alone does not prove that they are not. 

We asked well-known public finance experts as well as experts on
state and local government to review a draft of this report and
incorporated their suggestions where appropriate.  Appendix II
contains a more detailed description of our scope and methodology. 


   GRANT DESIGN INFLUENCES FISCAL
   SUBSTITUTION
------------------------------------------------------------ Letter :4

The economic literature we reviewed suggested that three types of
grant design features affect the likelihood that states will use
federal funds to supplement, rather than replace, their own spending. 
These features work by (1) restricting the use of funds to specified
purposes, (2) requiring recipients to contribute their own funds to
obtain grant funds, and (3) not restricting federal matching of state
funds. 

The first type of feature concerns the extent to which grant purposes
are restricted.  Categorical grants, which fund narrow-purpose
activities, such as nutrition for the elderly, are the most
restricted.  Block grants, which fund broader categories of
activities, such as community development, are less restricted. 
General purpose grants, such as revenue sharing, require only that
the funds be spent for government purposes.  Generally speaking,
experts agree that conditions attached to aid can encourage states to
use federal funds as a supplement if the conditions are binding. 
Conditions are more likely to be binding if states are not already
spending their own funds for that purpose.  For example, a state with
no computer education program in its schools would be more likely to
spend a federal computer education grant on its intended purpose than
a state that had already invested its own funds in such a program. 
If the state that had already invested funds in computer education
was satisfied with pre-grant spending levels, it would be more likely
to substitute the federal grant funds for its own and shift state
funds to other priorities. 

The second type of feature concerns requirements that states
contribute their own funds in order to receive federal matching
funds.  Economic theory suggests that grants requiring matching
result in less substitution than those that do not because, by
lowering the effective price of aided programs relative to other
state spending priorities, they encourage states to invest 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.  Experts agree that federal matching rates should
correspond to the share of benefits that accrue to non-state
residents.  Public finance economists have argued that federal shares
of less than 50 percent are appropriate, recognizing that in-state
residents generally receive the predominant share of the benefits
from most federally aided programs, such as education or
transportation. 

Another feature, maintenance-of-effort, requires states to maintain
existing levels of state spending on an aided program as a condition
of receiving federal funds.  By requiring states to maintain a given
level of spending from their own funds in addition to the federal
grant funds they receive, maintenance-of-effort can prevent
substitution in those programs where there is no federal matching
requirement or where state spending exceeds the minimum required
state match. 

As we have noted elsewhere, designing effective maintenance-of-effort
provisions can be difficult because it requires balancing federal
interests against states' desire for flexibility in planning and
implementing grant programs.\4 Experts suggest that
maintenance-of-effort provisions should keep pace with both inflation
and program growth so that state spending efforts are truly
maintained over time.  But maintenance-of-effort requirements can
penalize states that take the initiative to start programs without
federal aid by locking them into prior spending levels when federal
grant funds become available.  In contrast, states without prior
spending programs are implicitly rewarded for their lack of
initiative because they would be required to maintain a lower base of
spending in exchange for the federal grant.  As a result, the
prospect of such requirements could defer state program innovation
until federal funds become available. 

The third type of feature concerns the extent to which federal
funding for a program is limited.  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 match
additional state spending on aided activities and lose their price
incentive.  Therefore, a state spending beyond the amount needed to
obtain maximum federal funding is doing so without any price
inducement.  From this economists have concluded that the state would
likely have spent some its own funds without the federal matching
incentive, and federal funds have substituted for some of the state's
own resources. 

Although we discuss the influence of grant design in terms of
isolated features, in practice they work in combination.  For
example, when total federal spending for a grant is capped,
maintenance-of-effort provisions that track inflation and program
growth can increase the likelihood that federal dollars will
supplement rather than replace state spending even after the cap is
reached.  Similarly, a grant which allows a wide range of uses within
a broadly defined federal objective may still contain matching and/or
maintenance-of-effort features to reduce the likelihood of
substitution.  Appendix III contains a more detailed discussion of
the grant design features that influence state spending. 

Apart from design features, other factors, such as the amount of
state spending relative to federal spending and state programmatic
preferences, can also influence the impact of a federal grant on
total spending.  For example, a non-matching categorical grant
without maintenance-of-effort is more likely to supplement state
spending in areas where state governments have invested few, if any,
of their own funds.  Conversely, when a state is already spending an
amount from its own resources that exceeds the amount of federal aid
for a program, categorical restrictions are less likely to be
effective because the state could spend all the grant funds on the
intended program, but reduce spending from its own funds by the same
amount.  In this case, the categorical grant has the same effect on
total program spending as an unrestricted grant, and the state will
use the resources released in accordance with its own spending
priorities, which will not necessarily be the same as the federal
government's. 


--------------------
\4 Block Grants:  Issues in Designing Accountability Provisions
(GAO/AIMD-95-226, September 1, 1995). 


   STUDIES SHOW GRANTS REPLACE A
   PORTION OF STATE FUNDS
------------------------------------------------------------ Letter :5

There is a substantial body of econometric research on the impact of
federal grants on state spending spanning the period from the late
1950s to recent years.  Our review and synthesis of this body of work
found that beginning around 1978, a consensus view emerged that each
additional federal grant dollar contributed to increased total
spending on aided functions, but, because of substitution, total
spending increased by less than a dollar.\5 Of the studies we
reviewed, three-fourths of the estimates from studies published since
1978 indicated some substitution, i.e., that $1 of federal grant aid
did not increase total spending in a state by $1.  Estimates from
these studies suggest that a median of nearly 60 cents of every
federal dollar is used to replace state and local funds that
otherwise would have been spent on the aided activity.  That is, for
every dollar of additional federal aid, states have withdrawn about
60 cents of their own spending.  Omitting extreme high and low
estimates, the middle 50 percent (mid-range) of these estimates was
between 11 and 74 cents.  Table 1 contains a summary of the grant
impact estimates we reviewed. 



                                     Table 1
                     
                     Summary of Econometric Estimates of the
                        Impact of an Additional Dollar of
                       Federal Grants on Total Spending for
                                 Aided Activities

                              Mid-range of estimated
                                     impacts
                            --------------------------
                    Median                              (Substitutio
                 estimated                                     n) or
              impact of $1                                  increase
                in federal                                implied by
                 grants on          25th          75th        median
Subset of            total    percentile    percentile     estimated   Number of
estimates\a     spending\b      estimate      estimate      impact\c   estimates
------------  ------------  ------------  ------------  ------------  ----------
1978-                $0.42         $0.26         $0.89       $(0.58)          37
 present\d
State
 matching of
 federal
 funds\e
Non-                  0.42          0.25          0.50        (0.58)          13
 matching
 programs
Matching              0.85          0.33          1.14        (0.15)          24
 programs
Limits on
 federal
 share
Closed-               0.54          0.37          1.04        (0.46)           8
 ended,
 matching
 programs
Open-ended,          $1.38         $0.71         $1.74         $0.38          15
 matching
 programs
--------------------------------------------------------------------------------
\a Only two estimates were characterized as pertaining to
unrestricted grants and only one as pertaining to grants with
maintenance-of-effort.  Therefore, we did not include those results
as a separate subset. 

\b Calculated as the median, or middle, estimate of the range of
estimates we examined.  "Total spending" refers to a state's spending
from all revenue sources, including federal grants.  Thus, a $0.42
impact means that for every $1 in federal grants, total spending from
all sources goes up 42 cents. 

\c Calculated by subtracting the $1 in federal grants from the median
estimate of total state spending.  Numbers in parentheses are
negative and imply substitution.  Positive numbers imply states spend
more from their own funds in response to federal grants. 

\d The year a study containing an estimate was published.  The
periods of state and local spending examined in the studies ranged
from 1942 to 1990, but centered on the 1960s to the 1970s. 

\e Because a limited number of studies characterized grants as
open-ended, matching, etc., we analyzed estimates by design feature
over the entire time period reviewed.  This approach was appropriate
for identifying relative differences in impact across design
features.  However, it would be inappropriate to draw firm
conclusions about the point estimates for grants with the designs
identified. 

Source:  Studies listed in bibliography. 

As shown in table 1, the econometric studies we reviewed support the
view that certain grant design features promote relatively more total
spending on aided activities.  Matching programs generally involved
less substitution than non-matching programs.  Our synthesis suggests
that 85 cents of every additional matching dollar represented new
spending, implying that states have withdrawn 15 cents of their own
resources.  For non-matching programs, 42 cents of every additional
federal dollar resulted in new spending, implying that states have
withdrawn 58 cents of their own resources.  Open-ended programs were
associated with the smallest amount of substitution and may even have
stimulated additional state spending over and above the amount of
federal aid they received.  Every additional federal dollar for
open-ended matching programs resulted in $1.38 of new spending,
suggesting that states have contributed 38 cents of their own
resources to such programs.  Given that the estimates for open-ended
programs ranged from 71 cents (substitutive) to $1.74 (stimulative),
caution should be used in drawing the conclusion that such programs
generally have stimulated additional state spending.  However, as the
first column in the table shows, the expenditure impacts of
open-ended programs generally exceeded those of closed-ended
programs, which resulted in a median of 54 cents of new spending
(ranging from 37 cents to $1.04). 

The studies we reviewed examined the impact of grants on total
spending as well as on categories of spending for service areas, such
as education, health, highways, and welfare.  Our analysis did not
provide support for any differences in the expenditure impact of
grants across those service areas.  Nevertheless, studies from the
1980s and 1990s in the areas of education, highways, and sewage
suggested that states have withdrawn some of their own funds in
response to federal grants.  For example: 

  -- Education:  Craig and Inman's (1982) study of the impact of an
     additional dollar of federal education grants to state and local
     governments on total education spending from federal, state, and
     local sources found substitution effects ranging from more than
     a dollar for unrestricted federal grants (total spending
     decreased $1.06 for each $1.00 in grants) and non-matching,
     categorical grants (total spending decreased $1.30),\6 to 14
     cents for grants with maintenance-of-effort provisions (total
     spending increased 86 cents).  The study found matching
     categorical grants actually had fiscal impacts on total
     education spending that were larger than $1.00 (total education
     spending increased $1.05). 

  -- Highways:  Meyers (1987) and Stotsky (1991) studied the impact
     of an additional dollar of closed-ended matching grant funds for
     highway construction on state spending and found substitution
     rates of 63 and 95 cents, respectively.  That is, for every $1
     in federal aid, states used between 63 and 95 cents to fund
     other priorities.  Meyers also tested whether the 63 cents of
     federal funds that was not spent on highway construction was
     used for tax relief.  He rejected that hypothesis, finding
     instead that states most likely used the funds for other
     non-aided transportation priorities, such as maintenance. 

  -- Sewage systems:  Jondrow and Levy's (1984) study of the impact
     of an additional dollar of Environmental Protection Agency
     sewage system construction grants on local spending on sewage
     systems found that local governments substituted 67 cents for
     their own spending on sewage treatment plants and sewer lines. 
     The authors also estimated the impact of federal grants on sewer
     lines alone and found complete substitution.  The authors
     concluded that this occurred because, unlike treatment plants
     which generate benefits for surrounding localities, sewer lines
     have purely local benefits and would be fully funded even
     without a federal grant.  Therefore, the federal grants simply
     displaced, rather than supplemented, local spending. 

Because most of the research we reviewed studied periods when
resources and spending were increasing, caution should be used in
drawing conclusions about how states would respond to reductions in
federal grant spending.  Evidence of substitution does not
necessarily mean that states would replace cuts in federal grant
programs with funds from their own sources.  However, states may be
more likely to replace cuts in federal funds used to fund ongoing
state operations and priorities.  From a federal perspective, this
state replacement might be viewed as a positive event.  But from a
state perspective, because federal funds have been woven into the
structure of state budgets, replacing cuts in federal funds would
require cutting funds for other state programs, raising taxes, or
both. 

Few have studied state responses to federal aid reductions, and those
that have provide a mixed picture.  Using a case study approach,
Nathan (1987) found that state governments replaced funding for some
federal programs cut during the 1980s, particularly those that were
not highly redistributive, had active constituencies, or were
primarily managed by state rather than federal agencies.  Our prior
work on the effect of reductions in federal grants during the 1980s
generally supported Nathan's conclusion that states replaced some of
the federal cuts.\7 We reported that states used three strategies to
mitigate federal funding reductions that occurred in most block grant
programs during the early 1980s.  These involved states (1) taking
advantage of available funds from the categorical programs that
preceded the block grants, (2) transferring funds among block grants,
and (3) increasing the use of state funds.  However, in a more recent
econometric study of local responses to federal cutbacks during the
1980s, Stine (1994) found that local governments did not raise local
revenues to replace permanent losses in federal aid. 


--------------------
\5 We relied on post-1978 studies because two definitive critical
reviews of the literature by Barro (1978) and Gramlich (1977)
provided a baseline of information and a standard of analysis for all
subsequent research.  The post-1978 studies featured methodological
improvements, notably longer time periods of study and controls for
factors that simultaneously influence state spending and federal
grant levels.  Many of the earlier studies critiqued by Barro and
Gramlich concluded that grants stimulated additional state spending. 
These conclusions were generally discounted, however, because the
studies generally used flawed models, relied on a single year of
data, and/or lacked a basis in economic theory.  Thus, while studies
both before and after 1978 focused on the question of the fiscal
impact of federal grants, the principal difference was that the
post-1978 studies generally used more advanced techniques to arrive
at their conclusions. 

\6 The authors hypothesize that total reductions in education
spending may have occurred as states reacted to the increases in
federal funding of local education by reducing their own education
aid to local governments by more than the increase in federal
funding. 

\7 See Block Grants Brought Funding Changes and Adjustments to
Program Priorities (GAO/HRD-85-33, February 11, 1986). 


   GRANTS LACK FEATURES TO
   DISCOURAGE SUBSTITUTION
------------------------------------------------------------ Letter :6

Currently, the grant system is comprised of 633 conditional grants,
of which 617 are narrow-purpose categorical grants and 16 are
broader-purpose block grants.  The federal government has not
provided any unconditional grants since the General Revenue Sharing
program, which ended in 1986.\8 Table 2 summarizes the design
features of all 633 grants.  However, because 95 percent of the funds
are associated with the 87 largest grant programs, we also summarized
the design features of the 87 largest grants in table 3. 



                                Table 2
                
                 Grants with Design Features Associated
                  with Substitution, Fiscal Year 1994

                                     Grants with     Fiscal year 1994
                                     each feature      obligations
                                    --------------  ------------------
                                            Percen  Dollars in  Percen
Grant design feature                Number       t    billions       t
----------------------------------  ------  ------  ----------  ------
Broad-purpose (block)                   16     2.5       $52.9    24.7
Categorical                            617    97.5       161.2    75.3
Nonmatching                            318    50.2        67.5    31.5
Matching, federal share exceeds 50     252    39.8       144.0    67.2
 percent
No maintenance-of-effort               599    94.6        94.9    44.3
Closed-ended                           617    97.5       109.0    50.9
======================================================================
Total                                  633   100.0      $214.1   100.0
----------------------------------------------------------------------
Source:  ACIR, Characteristics of Federal Grant-in-Aid Programs to
State and Local Governments, Grants Funded FY 1995, June 1995,
Executive Office of the President, Office of Management and Budget,
and U.S.  General Services Administration, Update to the 1995 CFDA,
December 1995. 



                                Table 3
                
                 Grants with Design Features Associated
                with Substitution, Largest 87 Programs,
                            Fiscal Year 1994

                                     Grants with     Fiscal year 1994
                                     each feature      obligations
                                    --------------  ------------------
                                            Percen  Dollars in  Percen
Grant design feature                Number       t    billions       t
----------------------------------  ------  ------  ----------  ------
Broad-purpose (block)                   15    17.2       $52.9    26.0
Categorical                             72    82.8       150.4    74.0
Nonmatching                             46    52.9        62.0    30.5
Matching, federal share exceeds 50      38    43.7       139.7    68.7
 percent
No Maintenance-of-effort                71    81.6        84.8    41.7
Closed-ended                            77    88.5        98.4    48.4
======================================================================
Total                                   87   100.0      $203.3   100.0
----------------------------------------------------------------------
Source:  ACIR, Characteristics of Federal Grant-in-Aid Programs to
State and Local Governments, Grants Funded FY 1995, June 1995,
Executive Office of the President, Office of Management and Budget,
and U.S.  General Services Administration, Update to the 1995 CFDA,
December 1995. 

Of the 87 largest grant programs, 15 were block (26 percent of funds)
and 72 were narrow-purpose, categorical (74 percent of funds).  To
some extent, then, the federal grant system is designed around narrow
federal purposes, suggesting fewer opportunities for substitution. 
However, if states are already spending more of their own funds than
the federal government provides for these block and categorical
programs, the purposes for which the federal aid is to be spent are
less likely to be binding, and the potential for substitution is
higher. 

With regard to other design features we reviewed, few federal grants
contain the combination of design features that would encourage
states to maintain their spending levels and reduce the extent of
substitution.  About half the 87 largest grants, representing 30
percent of the funds for those programs, did not require state
matching.  Of the grants containing matching provisions, almost all
had federal shares in excess of 50 percent.  This stands in contrast
to expert views that federal shares should generally be less than 50
percent to correspond with the benefits non-state residents
receive.\9 In sum, 97 percent of the largest grants--corresponding to
99 percent of total grant funds--had federal shares between 50 and
100 percent. 

Furthermore, 89 percent of the largest grant programs--representing
48 percent of the funds for those programs ($98.4 billion)--were
closed-ended.  Excluding the largest open-ended program--Medicaid--
from this total, 85 percent of the remaining grant funds were for
closed-ended programs.  Closed-ended programs may result in
substitution when state spending exceeds the amount necessary to
obtain federal matching funds.  At this level of spending, unless
strong maintenance-of-effort provisions are attached, the federal
match loses its price incentive, and can become--in effect--general
purpose income to states.  According to a number of studies we
reviewed, state spending for most closed-ended grant programs was
well beyond the amount needed to obtain the maximum level of federal
funds.\10 Because this additional state spending has occurred without
the incentive provided by federal matching rates exceeding 50
percent, the studies concluded that such generous federal matching
rates may be unnecessary to induce existing levels of state spending
in those areas. 

Finally, 16 of the largest 87 programs--representing 58 percent of
the funds for those programs--had maintenance-of-effort provisions
that would encourage states to maintain a defined contribution to
those programs.  A well-designed maintenance-of-effort provision can
deter substitution in a grant program, particularly in those programs
with no matching requirement or where state spending already exceeds
the amount needed to meet federal matching requirements.  To
determine if federal maintenance-of-effort provisions were designed
to keep pace with program growth, we looked at the top eight
closed-ended programs with maintenance-of-effort provisions.  We
found that none of the maintenance-of-effort provisions sampled were
designed to keep pace with inflation or case-load growth.  For
example, the maintenance-of-effort requirement for the Special
Programs for the Aging grant stipulates that states need only spend
an amount equal to the average of the 3 previous fiscal years in
order to avoid reduced federal funding.  States could maintain
spending at this historical average and still substitute. 
Substitution could occur if states use new or increased federal funds
to finance case-load growth or inflation they otherwise would have
had to finance.  Tables 4 and 5 summarize by budget function the
design features of all 633 grants and the largest 87 grants,
respectively. 



                                                                       Table 4
                                                       
                                                        Grants With Design Features Associated
                                                        with Substitution, by Budget Function,
                                                                   Fiscal Year 1994

                                                                                  Grant design features (percent of function
                           Grant design features (percent of function total)                        total)
                        --------------------------------------------------------  ------------------------------------------
                                                             Matching, federal
                                                              share exceeds 50     No Maintenance-of-
                          Broad-purpose      Non-matching         percent                effort             Closed-ended               Total
                        ------------------  --------------  --------------------  --------------------  --------------------  -----------------------
                                                                                                                                                Funds
                                                                                                                                  Grants  (dollars in
Budget function           Grants     Funds  Grants   Funds     Grants      Funds     Grants      Funds     Grants      Funds    (number)    billions)
----------------------  --------  --------  ------  ------  ---------  ---------  ---------  ---------  ---------  ---------  ----------  -----------
National defense             0.0       0.0    12.5     2.5       87.5       97.5      100.0      100.0      100.0      100.0           8        $0.30
General science              0.0       0.0    33.3    42.0       33.3        3.2      100.0      100.0      100.0      100.0           3         0.01
Energy                       0.0       0.0    50.0    64.6       33.3       32.0      100.0      100.0      100.0      100.0          12         0.19
Natural resources            0.0       0.0    34.9    10.7       55.4       88.1       94.0       88.4      100.0      100.0          83         3.23
Agriculture                  0.0       0.0    46.7    24.6       26.7       19.1      100.0      100.0      100.0      100.0          15         0.97
Commerce & housing           0.0       0.0    25.0    21.4       50.0       42.3      100.0      100.0      100.0      100.0           8         0.08
 credit
Transportation               5.4      84.8    16.2     0.0       73.0       99.6       97.3      100.0      100.0      100.0          37        25.15
Community & regional         5.0      54.5    35.0    76.1       65.0       23.9       97.5       97.7      100.0      100.0          40         7.77
 development
Education, training,         1.9      19.6    58.9    64.9       33.3       34.0       92.3       64.5       99.0       91.8         207        35.64
 employment, & social
 services
Health                       4.5       2.3    69.6     6.8       18.8       92.8       96.4        8.5       99.1        9.0         112        95.80
Income security              6.8      49.2    52.3    65.4       31.8       31.9       84.1       50.4       79.5       69.0          44        36.94
Veterans benefits            0.0       0.0     0.0     0.0      100.0      100.0      100.0      100.0       40.0       43.4           5         0.27
Administration of            0.0       0.0    79.2    33.7       16.7       66.3      100.0      100.0      100.0      100.0          24         0.66
 justice
General government           0.0       0.0     0.0     0.0        0.0        0.0      100.0      100.0      100.0      100.0           1         0.00
Multiple (999)               0.0       0.0    29.4    97.4       58.8        2.3      100.0      100.0       97.1       51.4          34         6.99
=====================================================================================================================================================
Total                        2.5      24.7    50.2    31.5       39.8       67.2       94.6       44.3       97.5       50.9         633      $214.09
-----------------------------------------------------------------------------------------------------------------------------------------------------
Source:  ACIR, Characteristics of Federal Grant-in-Aid Programs to
State and Local Governments, Grants Funded FY 1995, published June
1995; Executive Office of the President, Office of Management and
Budget and U.S.  General Services Administration, Update to the 1995
CFDA, December 1995. 



                                                                       Table 5
                                                       
                                                        Grants With Design Features Associated
                                                       with Substitution, Largest 87 Programs,
                                                         by Budget Function, Fiscal Year 1994

                                                                                  Grant design features (percent of function
                           Grant design features (percent of function total)                        total)
                        --------------------------------------------------------  ------------------------------------------
                                                             Matching, federal
                                                              share exceeds 50     No Maintenance-of-
                          Broad-purpose      Non-matching         percent                effort             Closed-ended               Total
                        ------------------  --------------  --------------------  --------------------  --------------------  -----------------------
                                                                                                                                                Funds
                                                                                                                                  Grants  (dollars in
Budget function           Grants     Funds  Grants   Funds     Grants      Funds     Grants      Funds     Grants      Funds    (number)    billions)
----------------------  --------  --------  ------  ------  ---------  ---------  ---------  ---------  ---------  ---------  ----------  -----------
National defense             0.0       0.0     0.0     0.0      100.0      100.0      100.0      100.0      100.0      100.0           1        $0.26
Natural resources            0.0       0.0     0.0     0.0      100.0      100.0       75.0       90.1      100.0      100.0           4         1.85
Agriculture                  0.0       0.0     0.0     0.0       50.0       27.9      100.0      100.0      100.0      100.0           2         0.59
Transportation              50.0      86.8     0.0     0.0      100.0      100.0      100.0      100.0      100.0      100.0           4        24.55
Community & regional        25.0      57.7    50.0    77.4       50.0       22.6       87.5       97.5      100.0      100.0           8         7.34
 development
Education, training,        14.8      21.9    63.0    65.9       37.0       34.1       74.1       61.3       92.6       90.9          27        31.96
 employment, & social
 services
Health                      21.1       2.3    68.4     5.4       26.3       94.4       84.2        6.4       94.7        6.9          19        93.60
Income security             17.6      50.2    47.1    65.2       47.1       32.2       76.5       49.7       64.7       68.5          17        36.17
Administration of            0.0       0.0     0.0     0.0      100.0      100.0      100.0      100.0      100.0      100.0           1         0.36
 justice
Multiple (999)               0.0       0.0   100.0   100.0        0.0        0.0      100.0      100.0       75.0       48.5           4         6.60
=====================================================================================================================================================
Total                       17.2      26.0    52.9    30.5       43.7       68.7       81.6       41.7       88.5       48.4          87      $203.28
-----------------------------------------------------------------------------------------------------------------------------------------------------
Source:  ACIR, Characteristics of Federal Grant-in-Aid Programs to
State and Local Governments, Grants Funded FY 1995, published June
1995; Executive Office of the President, Office of Management and
Budget and U.S.  General Services Administration, Update to the 1995
CFDA, December 1995. 


--------------------
\8 Several programs continue to provide general purpose fiscal
assistance to selected jurisdictions, such as payments in lieu of
taxes or sharing of receipts generated by the sale or lease of
natural resources on federal lands.  According to OMB, these funds
account for about 1 percent of federal grants-in-aid.  The ACIR
publication we relied on excluded such shared revenue programs from
its list of grants. 

\9 Gramlich, Edward M., "Federalism and Federal Deficit Reduction,"
National Tax Journal, Vol.  40, No.  3, September 1987, pp.  299-313. 
Oates, Wallace E., "Federalism and Government Finance," John M. 
Quigley and Eugene Smolensky, ed., Modern Public Finance, Harvard
University Press, Cambridge, MA, 1994, pp.  126-161. 

\10 Miller (1974), Bezdek and Jones (1988), Huckins and Carnevale
(1988), Gramlich (1990), and Oates (1994). 


   GRANT ALLOCATIONS NOT TARGETED
   TO FISCALLY STRESSED STATES
------------------------------------------------------------ Letter :7

Given large and chronic federal budget deficits, some might argue
that high rates of fiscal substitution are inappropriate because the
federal government should not be collecting taxes on behalf of states
only to return the funds in the form of unrestricted aid.  Others
might argue that this substitution serves the purpose of providing
budgetary relief to the states.  They might also prefer that the
fiscal relief be allocated to more fiscally stressed states.  These
are policy questions that only the Congress can decide.  If
policymakers seek to target aid to fiscally stressed states, the
question arises as to whether such aid is allocated to those states
with relatively greater programmatic needs and fewer fiscal
resources. 

We examined whether existing federal grant allocations can be
justified on the grounds that they provide budgetary relief to
fiscally stressed states.\11 We found that, controlling for
differences in programmatic needs, grant allocations to states were
not significantly higher for states with relatively fewer fiscal
resources.  Specifically, the variable we used to measure fiscal
capacity--total taxable resources--was not a statistically
significant factor in targeting funds to lower-capacity states,
controlling for differences in state (1) program needs, such as
poverty, population under age 18, and highway miles, and (2) service
costs.\12 In effect, this means that the current grant system does
not help lower-capacity states provide levels of aided services
comparable to higher-capacity states. 

To illustrate the lack of a relationship between fiscal capacity and
grant allocations, we ranked the states according to an index of
their per capita federal grants, adjusted for costs, and calculated
averages for five groups of 10 states each (quintiles).  For example,
a state with an average per capita grant would have an index value of
1.0.  We found that state quintiles that ranked the lowest (0.85) and
the highest (1.85) according to their grant allocations had similar
average fiscal capacities. 

We were unable to estimate accurately the effect of the individual
need variables in our model on grant targeting.  While three of the
need variables were statistically significant, the results should not
be used to draw conclusions about their relative importance. 
Reliability questions arose because--in contrast to fiscal
capacity--there was no single or aggregate measure that accurately
represented the program goals and objectives of all the grants we
analyzed.  Used in combination, however, the need variables provided
a valid control to isolate the effect of needs from fiscal capacity
on grant allocations. 

Even so, our prior work on a wide range of individual grant programs
suggests that need factors, in addition to costs and fiscal capacity
factors, have not played an important role in allocating funds.\13
For example: 

  -- The Community Development Block Grant program (CDBG) is intended
     principally to serve low and moderate-income communities and
     those with relatively greater community development needs.  The
     CDBG formula uses poverty, age-of-housing, and community
     population growth rate statistical factors to allocate funds to
     meet those needs.  However, while Greenwich, Connecticut, and
     Camden, New Jersey, are comparable with respect to the age of
     their housing stock, Greenwich was allocated CDBG funds of $0.69
     per person in poverty in 1995--over five times more than
     Camden's $0.13.  Greenwich, with per capita income of $46,070,
     could more easily afford to fund its own community development
     needs than Camden, with per capita income of $7,276--about half
     the national average.\14

  -- Funding shares for the four largest highway grant programs are
     determined by a complex, 13-step set of calculations, which
     provides funds for highway construction or maintenance needs,
     but subsequently adjusts the total funds designated for all four
     programs so that states receive their historical share of total
     funds.  While individual calculations are made for three of the
     four separate programs, the funding for these programs is
     interdependent since a state's total share of funding for all
     four programs is fixed.  This results in some states receiving
     more funds than would be provided if only need factors had been
     used.\15

  -- The Older Americans Act grant formula distributes funds
     according to the number of people over 60 years of age, but does
     not take into account the fact that states with higher
     concentrations of elderly poor, minorities, and individuals over
     85 years of age have higher disability rates.\16

  -- The Ryan White Comprehensive AIDS Resources Emergency Act of
     1990 double counts the number of cases residing in eligible
     metropolitan areas.  Although recent legislative changes have
     reduced the double-counting, the needs indicators still favor
     more urbanized states.  As a result, the oldest eligible
     metropolitan areas receive more generous funding, and newly
     emerging areas with more recent growth in AIDS cases receive
     less funding.\17

  -- The Maternal and Child Health Block Grant directed more aid to
     states with lower concentrations of low-birthweight babies than
     to those with higher concentrations.  Similarly, more aid was
     directed to some states with lower health care costs than to
     those with higher costs.\18


--------------------
\11 Appendix IV contains a more detailed discussion of the model we
used and our results. 

\12 For this analysis, we included only closed-ended formula grants,
which use formula factors to allocate aid.  While 75 percent of
grants rely on agency or legislative decisions--rather than
formulas--to allocate funds for individual projects, the remaining
formula grants comprise 85 percent of total grant funding.  We
excluded open-ended programs because the public finance literature
notes that federal and state spending for such programs is designed
to interact positively so that the more a state spends, the more the
federal government spends.  As a consequence, wealthier states can
afford to spend more to leverage a larger share of total federal
spending in programs such as Medicaid.  Thus, for this analysis,
including open-ended grant programs would have biased the estimated
impact of the fiscal capacity variable.  However, we previously
testified that the Medicaid formula fails to target aid to states
with the lowest fiscal capacities.  See Medicaid Formula:  Fairness
Could Be Improved (GAO/T-HRD-91-5, December 7, 1990). 

\13 See Related GAO Products for a complete list of GAO work on this
issue. 

\14 See Deficit Reduction:  Opportunities to Address Long-Standing
Government Performance Issues (GAO/T-OCG-95-6, September 13, 1995). 

\15 See Highway Funding:  Alternatives for Distributing Federal Funds
(GAO/RCED-96-6, November 28, 1995). 

\16 See Older Americans Act:  Funding Formula Could Better Reflect
State Needs (GAO/HEHS-94-41, May 12, 1994). 

\17 See Ryan White Care Act of 1990:  Opportunities to Enhance
Funding Equity (GAO/HEHS-96-26, November 13, 1995). 

\18 See Maternal and Child Health:  Block Grant Funds Should Be
Distributed More Equitably (GAO/HRD-92-5, April 2, 1992). 


   COMBINATION OF TARGETING
   FACTORS NOT GENERALLY USED
------------------------------------------------------------ Letter :8

Most of the formula grants we reviewed did not use a combination of
the three grant formula factors we have reported can improve
targeting of federal aid.  Nearly 95 percent of the 149 grant
formulas we reviewed, representing 99 percent of formula grant funds,
used a measure of need.  However, only 15 percent of grant formulas,
representing 61 percent of funds (7 percent excluding cash welfare
and Medicaid), used both need and fiscal capacity factors.  Finally,
only 2 percent, representing less than 2 percent of funds, used a
combination of need, fiscal capacity, and cost factors.  As we noted
earlier, where the federal government seeks a minimum or more
comparable level of services for all potential
beneficiaries--regardless of where they live--the inclusion of a
fiscal capacity factor helps to reduce the disparities between the
abilities of wealthier and poorer communities to provide such service
levels.  Cost factors help ensure that states facing higher service
costs are compensated for these differences, which contributes to
comparability in aided service levels. 

The lack of targeting factors was not concentrated in any one budget
function we reviewed.  However, grants that have historically
comprised the social safety net were more likely to include data
elements that reflect fiscal capacity as well as need.  About 24
percent of grants, representing 75 percent of funds (8 percent
excluding cash welfare and Medicaid), in the education, income
security, and health functions used need and fiscal capacity factors. 
Only 3 percent of grants in those functions (less than 2 percent of
funds) also used a cost factor.  In comparison, grants for other
budget functions were less likely to use a combination of targeting
factors.  Notably, no grants in the natural resources,
transportation, administration of justice, agriculture, community and
regional development, veterans, or energy budget functions used
fiscal capacity or cost factors in their formulas.  Table 6
summarizes how the three targeting factors were combined in the 149
formula grants we reviewed, both in total and by budget function. 



                                     Table 6
                     
                       Formula Grants with a Combination of
                      Targeting Factors, by Budget Function,
                                       1994

                                                      Percent of grants with
               Grants with targeting                combinations of targeting
                       factor                                factors
              ------------------------            ------------------------------
                                                              Need &       Three
Budget                  Fiscal             Total              fiscal     factors
function        Need  capacity    Cost    grants    Need    capacity    combined
------------  ------  --------  ------  --------  ------  ----------  ----------
Education,        65        13      12        65   100.0        20.0         0.0
 training,
 employment,
 & social
 services
Income            20         5       2        21    95.2        23.8         4.8
 security
Health             9         5       2        11    81.8        45.5        18.2
Natural           13         0       0        14    92.9         0.0         0.0
 resources
Transportati      11         0       0        11   100.0         0.0         0.0
 on
Administrati       6         0       0         6   100.0         0.0         0.0
 on of
 justice
Agriculture        5         0       0         5   100.0         0.0         0.0
Community &        4         0       0         5    80.0         0.0         0.0
 regional
 development
Veterans           3         0       0         3   100.0         0.0         0.0
 affairs
Energy             1         0       0         1   100.0         0.0         0.0
General            4         0       2         7    57.1         0.0         0.0
 government
Total grants     141        23      18       149    94.6        15.4         2.0
Safety net        94        23      16        97    96.9        23.7         3.1
 functions\a
--------------------------------------------------------------------------------
\a Includes education, training, employment, and social services;
income security; and health. 

Source:  Executive Office of the President, Office of Management and
Budget, and U.S.  General Services Administration, Update to the 1995
CFDA, December 1995. 

The fact that a combination of the three targeting factors did not
appear in most grant formulas, and fiscal capacity did not play a
significant role in explaining the variation in grant funding to
states, raises the logical question as to what factors did influence
grant allocations.  In this regard, the most significant as well as
reliable explanatory variable in the grant targeting model was one
that indicated whether or not a state was very small.\19 This
variable was a proxy for states that benefit most from formula hold
harmless provisions and guaranteed funding floors, which have the
effect of providing a minimum grant to each state regardless of its
size.\20 The results indicated that a very small state with average
needs and fiscal capacity would receive per capita grant funds 20
percent higher than a larger state with the same needs and fiscal
capacity. 

Finally, despite our finding that many grant formulas contained need
factors and some contained fiscal capacity and/or cost factors, the
measures used to allocate funds were often poor proxies for the three
factors.  For example, 28 of the 149 grant formulas we reviewed used
a state's share of the U.S.  population as a proxy for need. 
Generally, population is a poor proxy for program needs because when
population is used funds are allocated to states in proportion to the
number of people in the state, which is not necessarily the same as
the number of people who actually need a particular program's
services.  Also, per capita personal income is a frequently used but
poor proxy for fiscal capacity because it does not comprehensively
measure state income.  Specifically, it fails to capture income
produced, but not received, in a state.  Appendix V provides a more
detailed discussion of the targeting problems that result when poor
proxies of need, fiscal capacity, or cost are used. 


--------------------
\19 Pertains to states with populations less than 0.25 percent of the
nation's population. 

\20 Many formula grant programs contain provisions that provide a
minimum of funds to every state or hold states harmless from changes
to formulas.  Such programs first distribute grant funds to satisfy
the minimum or hold harmless provisions.  Only those funds remaining
after the initial distribution are allocated based on formulas. 
Thus, smaller states will tend to have higher per capita grant
allocations than larger states. 


   OBSERVATIONS
------------------------------------------------------------ Letter :9

Our analysis suggests that most grants are designed neither to reduce
substitution nor to target funding to states with relatively greater
programmatic needs and fewer fiscal resources.  This is an indication
that the federal government may be getting less fiscal impact than it
could from the dollars it spends.  Our literature synthesis implied
that each additional federal grant dollar results in about 40 cents
of added spending on the aided activity.  This means that the fiscal
impact of the remaining 60 cents is to free up state funds that
otherwise would have been spent on that activity for other state
programs or tax relief. 

Grants are not the only type of federal subsidy tool in which design
issues have undermined fiscal impact.  Our prior work has shown that
programs implemented through subsidies, such as loans and tax
expenditures as well as grants, sometimes fall short of expectations
because federal funds are transmitted through a network of third
parties who have their own spending priorities or who would have
undertaken subsidized activities anyway.\21

Given the complex and evolving relationship between the federal and
state governments and their shared responsibilities for most domestic
programs, it is understandable that observers will have different
views of substitution.  Some might see the substitution we identified
as reasonable, given differences in state and federal priorities and
a desire to provide states with managerial flexibility.  As
economists have shown, some substitution is to be expected whenever a
grant is received--whether the funds go to an individual, an
organization, or a state government.  From the perspective of a
recipient, the funds are simply additional income, to be used
according to the recipient's own preferences, within the limitations
imposed by the grant.  This is why a grant's design together with the
degree of state commitment to federal priorities determine the
ultimate fiscal impact of federal grant dollars.  Also, in our
federal system the balance of domestic responsibilities may be
shifting toward the states.  Thus, providing states with a measure of
fiscal relief, albeit indirectly, could be considered a legitimate
role for the federal grant system. 

Others might argue that if the provision of fiscal relief is to be
the primary goal of the federal grant system, then this relief should
be allocated in a manner that allows for adequate oversight and
control by the Congress.  If fiscal relief is accepted as a policy
goal, there are a variety of alternatives available to the Congress
to allocate this relief.  The alternative we examined would target
the relief to states with greater programmatic needs and fewer fiscal
resources.  Our analysis showed that existing grant formulas do not
allocate federal aid to states in a targeted manner.  This may have
occurred because grant formulas or eligibility rules were constructed
too broadly, grant floors and ceilings allocated funds too widely, or
the circumstances that created a need for the program may have
changed. 

Notwithstanding the importance policymakers may place on providing
states with fiscal relief, the question remains as to whether the
federal government can afford this approach and still accomplish
objectives of national importance in an era of increasingly scarce
federal resources.  The issues we have raised concerning grants are
part of a larger problem of how to improve government performance
concurrent with downsizing.  A focus on cost-effectiveness will be
especially important as agencies implement the Government Performance
and Results Act of 1993, thus turning the federal government's focus
to outcome-based measures of grant performance.  As a consequence, it
will be increasingly important to design grant programs so that the
federal dollars needed to produce desired outcomes reach their
intended targets. 

Moreover, substitution raises questions about the federal role in the
federal system.  In many cases, the federal government created grant
programs because of the view that states were not funding certain
services to a degree consistent with national, rather than purely
local, policy objectives.  However, the difference in priorities that
provides the rationale for such grants also makes it more likely that
states will attempt to use grant dollars to replace their own funds,
thus converting specific-purpose aid to general fiscal relief.  While
the federal government may still wish to pursue national objectives
in these areas, it should be recognized that, because of
substitution, such objectives may be costly to achieve. 

The potential for substitution may increase when the federal
government chooses to finance areas in which state spending is
already significant.  Historically, initial federal involvement in
funding state spending in an area may have occurred when little or no
state funds were being committed, thus prompting states to commit
resources for the first time.  But as states' commitment to funding
those areas has grown over time, or the federal government has chosen
to enter an area where state spending has traditionally been large,
the potential for substitution may have grown as well. 

There are many factors that must be reconciled in considering the
budgetary implications of grant design.  Taking one path, the
Congress could consider redesigning grants to reduce substitution and
increase targeting.  For example, to reduce substitution and increase
the likelihood that federal grant funds lead to greater total
spending on aided programs, greater use of state matching, with
reduced federal shares, and maintenance-of-effort provisions that
track inflation and program growth can be considered.  However, as
previously noted, policymakers would need to consider the potential
losses in state spending flexibility that could occur as a result of
adding spending restrictions.  Also, if formula grants were
redesigned to include a combination of targeting factors, a larger
share of federal aid could be allocated to those states and
communities with relatively greater programmatic needs and fewer
fiscal resources.  We recently reported that greater targeting of
grant formulas offers a strategy to bring down federal outlays by
concentrating reductions on jurisdictions with relatively fewer needs
and greater fiscal capacity to absorb cuts.\22

Taking a different path, the Congress could use information about the
relative performance of grant programs to consider which programs may
have outlived their usefulness.  The Congress may decide that the
benefits of particular programs are not being achieved in a
cost-effective manner due to substitution and a lack of targeting. 
Accordingly, the Congress may decide that such programs no longer
represent the best use of scarce federal resources.  Targeted
reductions based on the relative performance of federal programs can
help promote a government whose responsibilities are better matched
to the resources available.  Such reductions could be used either to
cut the deficit or invest in other federal programs that the Congress
judges to be more cost-effective.  However, because the evidence on
whether states would replace reductions in federal grant funds is
inconclusive, and because replacing federal funds would mean
reductions to other state programs or increases in state taxes, the
Congress would need to consider the costs and benefits of individual
programs carefully in selecting which programs to reduce or
eliminate. 


--------------------
\21 For a summary of GAO reports on federal subsidies to businesses,
see Budget Issues:  Selected GAO Work on Federal Financial Support of
Business (GAO/AIMD/GGD-96-87, March 7, 1996). 

\22 Addressing the Deficit:  Updating the Budgetary Implications of
Selected GAO Work (GAO/OCG-96-5, June 28, 1996), p.  207. 


---------------------------------------------------------- Letter :9.1

As arranged with the Committee, we are sending copies of this report
to the Director of the Office of Management and Budget, cognizant
congressional committees, and other interested parties.  We will also
make copies available to others upon request. 

The major contributors to this report are listed in appendix VI.  If
you have any questions, please call me at (202) 512-9573. 

Sincerely yours,

Paul L.  Posner
Director, Budget Issues


THE ROLE OF GRANTS IN THE FEDERAL
SYSTEM
=========================================================== Appendix I

Federal grants have historically served as vehicles through which the
federal government attempted to achieve a variety of national goals
by providing funding to other levels of government to carry out
specific federal policies.  In particular, economists have cited the
role federal grants play in encouraging state and local governments
to provide more of the public goods and services deemed beneficial
from a national--rather than a purely state--perspective.\1

SPENDING ON PUBLIC GOODS

From the perspective of economic theory, federal grants can play an
important role in stimulating spending in areas where public benefits
or costs cross jurisdictional lines.  The problems addressed by the
grant system in these types of situations are termed positive and
negative externalities, respectively.  When a jurisdiction does not
receive--that is, consume--all the benefit from a public good it
produces because some of the benefit accrues to non-residents, the
jurisdiction has little incentive to produce the good in sufficient
supply to meet society's total demand.  According to this logic,
taxpayers from a sparsely populated state would likely be unwilling
to spend their scarce tax dollars to construct and maintain highways
in their state large enough to support private and commercial traffic
from other states.  If other states followed the same thinking, the
highway system would be inadequate from a national standpoint because
state taxpayers do not share the benefits that accrue to
non-residents traveling through their states.  Because individual
states are unlikely to supply the quantity and quality of interstate
highways demanded by interstate travelers, federal grants to states
for the construction and maintenance of highways can be used to
induce the states to fulfill this need. 

REDUCING DISPARITIES IN STATE
CAPACITIES TO PROVIDE MINIMUM
SERVICES

Economists also argue that federal grants can play a role in
distributing income to communities with higher social service needs
and smaller tax bases.  Some states have higher concentrations of
poor people or other service populations and smaller tax bases with
which to pay for their own service needs.  Accordingly, significant
disparities can arise either in the level of services states provide
or in the tax burdens states incur to provide a given level of
services.  Some experts suggest that such fiscal disparities across
states argue for a federal role in helping states with greater needs. 

Federal grants can satisfy this objective by allocating aid to states
through formulas that provide relatively greater funding to states
with higher needs and lower fiscal capacities, such as occurs with
Medicaid.  Or, according to the logic of the General Revenue Sharing
program,\2 they can provide broad funding designed primarily to
reduce disparities in fiscal capacities across communities. 

FUNDING MERITORIOUS GOODS

Another goal for federal grants is supporting state spending on goods
that are deemed meritorious from a national perspective and should
therefore be available to all.  Unlike redistributive grants, grants
for merit goods tend to be for specific categories of goods, such as
the arts, gifted and talented educational programs, or assisted
housing. 

OTHER ROLES FEDERAL GRANTS PLAY

Federal grants have played a variety of roles beyond those most
frequently cited by economists.  Increasingly, grants have become a
vehicle for implementing the federal government's regulatory agenda
at the state and local level.  By attaching conditions to aid, the
federal government has sought to achieve a variety of goals, such as
reduced discrimination, increased highway safety, reduced energy
consumption, and reduced pollution.  Economists have also argued that
federal grants, such as unemployment insurance, can play a role in
stabilizing economic swings that occur at the state and local levels
during recessions, when demand for public services rise as revenues
decline.  The public administration perspective has shifted in recent
years to include a more business-like approach to intergovernmental
aid.  For example, some have argued that grant awards should be
provided in a competitive manner based in part on whether a recipient
achieves performance goals.  Finally, states have been increasingly
vocal about the need for federal grants with fewer restrictions on
how funds are to be spent so that the state can address the unique
needs of its citizens and provide quality and cost-effective
services. 


--------------------
\1 In this appendix, we use state to mean state and local governments
and/or their agencies. 

\2 The General Revenue Sharing program, which ended in 1986, used the
federal government's tax collecting capabilities to redistribute
national income to communities with relatively lower fiscal
capacities. 


SCOPE AND METHODOLOGY
========================================================== Appendix II

This report examines the extent to which the federal grant system
succeeds in two fiscal objectives often cited by public finance
experts.  First, do grants succeed in encouraging states to use
federal dollars to supplement rather than replace their own spending
on nationally important activities?  The use of federal grant dollars
to replace a state's own spending is frequently referred to as
substitution.  Second, do grants succeed in reducing differences--or
mismatches--between states' fiscal resources and programmatic
needs?\1

This appendix details the scope and methodology we used to answer
these questions. 

SUBSTITUTION ANALYSIS

To address substitution, we (1) synthesized the published economic
and political science literature regarding the influence of federal
grants on state spending, (2) identified dimensions of grant design
that influence the extent of substitution, and (3) evaluated the
quantitative estimates of the fiscal impact of federal grant spending
reported in the literature.  To identify the universe of grant
programs and catalog their design features and other characteristics
necessary for our analysis, we used information from the Catalog of
Federal Domestic Assistance, reports by the Advisory Commission on
Intergovernmental Relations, the United States Code Annotated, and
the United States Code of Federal Regulations.  The 633 grants we
identified represented the total of grants available to state
governments in fiscal year 1994. 


--------------------
\1 In this appendix, we use state to mean state and local governments
and/or their agencies. 


      THE INFLUENCE OF GRANT
      DESIGN ON SUBSTITUTION
------------------------------------------------------ Appendix II:0.1

One part of our analysis focused on the theory underlying the
influence of grants on state spending decisions.  We began with five
summary reviews of the literature,\2 and, because the last of these
was published in 1985, we also searched computerized indexes for more
recent studies.\3 From this body of work, we identified three
dimensions of grant design that influence the impact of federal
grants on state spending: 

  -- whether a grant was unrestricted or restricted to a specific
     purpose,

  -- whether or not a state contribution was required--either in the
     form of matching federal payments or maintaining the level of
     fiscal effort that existed prior to the grant, or

  -- whether or not there were ceilings on the total the federal
     government would pay out on matching grants.\4

We also identified articles that provided information on grant impact
for different service areas, such as education, health and hospitals,
highways, social services, and welfare.  We collected this
information to determine whether grants for different service areas
had different impacts, apart from the impacts associated with
different grant designs. 

Next, we identified articles containing quantitative estimates of the
impact of federal grants on state spending and assembled the
information in a database.\5 Each observation in the database was an
estimate from a study, some studies providing multiple estimates. 
For each observation, we recorded key information from the study
(e.g., author, date, sample type, model used, grant impact estimates,
statistical significance of the estimates, potential biases, and
estimated price or income elasticities).  When studies provided
information about the grant design features or functional categories
of spending, we also recorded that information, including (1) grant
form (categorical, block, unrestricted, or all), (2) matching or
non-matching, (3) open-ended or closed-ended, (4) the
presence/absence of maintenance-of-effort (MOE) provisions, and (5)
grant service area (all, welfare, highway, education,
health/hospital, or social services). 

Using this database, we compared the reported estimates of grant
impact for (1) studies completed during different time periods, (2)
studies using different sample types, (3) grants with different
designs, and (4) grants for different service areas. 

First we calculated the mean, the median, and the 25th and 75th
percentile observations (the mid-range) of all the estimates in our
database.\6 Then we extracted subsets of the database that contained
the grant design features we were assessing.  For example, to
summarize the estimated expenditure impact of grants characterized as
"matching," we extracted all records for which the "matching" field
contained a "yes" and calculated the same descriptive statistics.  We
compared the results for matching grants to non-matching grants,
open-ended to closed-ended, etc.\7 Table II.1 summarizes the results
for the different time periods, grant design features, and sample
types we analyzed. 



                                    Table II.1
                     
                     Summary of Econometric Estimates of the
                        Impact of an Additional Dollar of
                       Federal Grants on Total Spending for
                                 Aided Activities

                              Mid-range of estimates
                            --------------------------
              Impact of $1                              (Substitutio
                in federal                                     n) or
                 grants on                                  increase
                     total          25th          75th    implied by   Number of
                spending\a    percentile    percentile    estimate\b   estimates
------------  ------------  ------------  ------------  ------------  ----------
All                  $1.05         $0.45         $1.58         $0.05         109
 estimates
By time
 period\c
Pre-1968              1.41          1.16          1.94          0.41          25
1968-1977             1.17          0.69          1.62          0.17          47
1978-on               0.42          0.26          0.89        (0.58)          37
By design
 feature\d
Open-ended            1.38          0.71          1.74          0.38          15
Closed-               0.54          0.37          1.04        (0.46)           8
 ended
Matching              0.85          0.33          1.14        (0.15)          24
Non-                  0.42          0.25          0.50        (0.58)          13
 matching
By sample
 type
Cross-                1.41          1.04          1.81          0.41          57
 section\e
Time                  0.33          0.27          0.44        (0.67)          15
 series\f
Pooled\g             $0.73         $0.37         $1.07       $(0.27)          37
--------------------------------------------------------------------------------
\a Calculated as the median (middle) estimate of all the estimates we
examined. 

\b Calculated by subtracting $1.00 in federal grants from the median
grant impact estimate. 

\c The year a study containing an estimate was published.  The
periods of state and local spending examined in the studies ranged
from 1942 to 1990, but centered on the 1960s to the 1970s. 

\d Across all time periods.  Only two estimates were characterized as
pertaining to unrestricted grants and only one as pertaining to
grants with maintenance-of-effort.  Therefore, we did not include
those results as separate subsets. 

\e One year of data across all states. 

\f Aggregate state data across multiple time periods. 

\g Data across all states for more than one time period. 

Source:  Studies listed in bibliography. 

Similar to the aggregate results in the table, estimates of federal
grant impact by service area were generally higher in earlier periods
of study and lower in more recent years.  Because our analysis did
not provide support for any differences in the expenditure impact of
grants across different service areas, or apart from the other
features we examined, we did not report those results. 


--------------------
\2 Advisory Commission on Intergovernmental Relations (1977), Barro
(1978), Gramlich (1977), and U.S.  Department of the Treasury (1978
and 1985). 

\3 The following computer indexes were searched for the period 1980
to the present:  Journal of Economic Literature, Congressional
Research Service, Library of Congress, Business Periodicals Ondisc,
and National Technical Information Service. 

\4 As stated in the literature, a grant recipient's preferences for
spending on the aided-good versus other goods would influence their
responsiveness to those grant design features.  Appendix III contains
a more detailed explanation of how grant design features combined
with a recipient jurisdiction's preferences can influence spending. 

\5 We excluded state grant impact estimates from our review because
of concern that they were not comparable to federal grant impact
estimates. 

\6 The database contained some very low and very high observations
that tended to provide a skewed picture of the results.  Calculating
the mid-range eliminated the highest and lowest 25 percent of
observations and provided a better measure of the central tendency of
the data. 

\7 The estimates varied greatly within each category and, like all
preceding surveys of the grant literature, we did not test whether
the differences in impact estimates between grant categories were
statistically significant.  Nonetheless, the medians and mid-ranges
of the category estimates had the relative magnitudes suggested by
theory. 


      ANALYSIS OF GRANTS FOR
      FEATURES ASSOCIATED WITH
      SUBSTITUTION
------------------------------------------------------ Appendix II:0.2

To assess whether grants contained the design features associated
with substitution, we developed a second database of the 633 grants
available to states in fiscal year 1995.  We obtained the data from
an 1995 Advisory Commission on Intergovernmental Relations (ACIR)
study of the federal grant system, entitled Characteristics of
Federal Grant-in-Aid Programs.\8 This study provided summary
information on the matching rates and the open-ended versus
closed-ended status of individual grant programs.\9 ACIR also
provided us with additional unpublished support schedules identifying
grants that contained MOE provisions.  ACIR's data did not include
spending information for each grant.  Therefore, we obtained fiscal
year 1994 estimated obligations for each grant from the electronic
version of the 1994 CFDA database.\10

We sorted and tallied all 633 grants as well as the largest 87
grants, representing 95 percent of grant funds, and their obligations
according to whether they were (1) matching, (2) closed-ended, and
(3) had MOE provisions.  For matching grants, we also tallied those
with federal shares greater than 50 percent.  We compared these
counts and sums to the total for the database or for the largest 87
grants. 

MOE provisions are more effective when they are designed to maintain
state fiscal effort at a level that keeps pace with inflation and
program population growth.  To determine whether MOE provisions in
grants are designed this way, we searched the CFDA database for
grants that contained MOE provisions.  Of the 28 programs we found,
we examined only closed-ended programs because the matching rates
that drive state contributions for open-ended programs would override
the influence of an MOE provision.  We ranked the closed-ended
programs by their funding and selected for review the eight largest,
constituting 92 percent of the funding for those programs.\11

To ensure that MOE provisions for the eight grants we reviewed were
up-to-date, we cross-referenced the public laws and their amendments
to the relevant United States Code Annotated and/or the United States
Code of Federal Regulations.  We then analyzed the MOE provisions to
determine what they entailed and whether they accounted for inflation
or program population growth. 

TARGETING ANALYSIS

To address targeting, we reviewed an extensive body of GAO case
studies of formula grant programs and conducted our own aggregate
analysis.  For one part of the aggregate analysis we used a
multivariate regression model to quantify the extent of targeting in
the overall grant system.  This model and its results are presented
in appendix IV. 

For the other part of the aggregate analysis, we created a database
of the 149 formula grants compiled from the 1994 CFDA.  This database
included information on whether a grant contained any of the three
grant design features GAO has reported can target grants to
jurisdictions with relatively greater disparities between fiscal
resources and programmatic needs.  These are fiscal capacity, cost
differentials, and indicators of program needs.\12 To clarify certain
CFDA data or obtain missing information, we interviewed agency
officials and searched relevant portions of the U.S.  code.  We
sorted and tallied the database according to the three targeting
factors for all the grants and within 12 budget functions, and we
calculated the share of formula grant programs containing the
individual factors and the factors in combination. 

This part of our analysis was limited to the universe of 149 formula
grants, representing 85 percent of federal grant funds to states in
fiscal year 1994.  Project grants--comprising most other federal
grant spending--also could be examined from a targeting perspective. 
However, that analysis would have required us to determine whether
agency funding decisions reflected differences in competing grant
applicants' fiscal capacities, program needs, and service costs. 
Moreover, funding decisions for project grants apply only to
individual project applications, thereby limiting our ability to
generalize from such decisions.  In contrast, formula grants allocate
funds according to a prescribed formula and are of a continuing
nature.  Therefore, our analysis of formula grant targeting could be
limited to a relatively straightforward analysis of grant allocation
formulas for the three targeting features we identified. 

Because your question concerned grants that funded programs, we
excluded grants that exclusively funded administrative and/or
planning activities.  Further, we eliminated grants paid to states in
lieu of real estate taxes owed on federal property located in a
grantee's jurisdiction because targeting factors are not relevant
criteria for allocating such grant funds. 

We performed this review in accordance with generally accepted
government auditing standards.  We conducted our review from June
1995 through June 1996. 


--------------------
\8 The source of the ACIR data was the 1994 Catalog of Federal
Domestic Assistance (CFDA).  The CFDA is a governmentwide compendium
of federal programs, projects, services, and activities that provide
assistance or benefits to the American public that is compiled by the
General Services Administration (GSA).  Agency program managers
provide GSA with information for the catalog.  The catalog is
available in both hard copy and in an electronic database format. 

\9 ACIR described a grant's matching rate in terms of the federal
share of spending.  ACIR also characterized some grants as having
multiple matching rates.  Therefore, in our database we recorded
federal matching shares using the following categories:  at least 50
percent (i.e., federal matching shares of between 50 percent and 100
percent), at least 75 percent, or 100 percent. 

\10 Neither actual 1994 obligations nor more recent estimates were
available to us in electronic format at the time of our review. 
Because we sought to determine order of magnitude differences in
obligations among categories of grants, rather than precise budgetary
information about any individual grant, we chose to rely on CFDA's
estimated obligations. 

\11 The eight programs we reviewed were:  Chapter 1 Grants to Local
Educational Agencies; Special Education Grants to States;
Rehabilitation Services:  Vocational Rehabilitation Grants to States;
Vocational Education:  Basic Grants to States; Senior Community
Service Employment; Special Programs for Aging (Title III, Part B
Grants); Community Mental Health Services Block Grant; and Adult
Education:  State Administered Basic Grant Programs. 

\12 See appendix V for a detailed discussion of the three targeting
design features. 


GRANT DESIGN FEATURES INTENDED TO
INCREASE SPENDING IN NATIONALLY
IMPORTANT AREAS
========================================================= Appendix III

In this report we discussed three grant design features that are
related to substitution.  This appendix discusses these features from
an economic theory perspective.  First, we provide an overview of the
grant spending impacts that are predicted from the framework of the
general consumer demand model.  Thereafter, we review how the
individual features work in theory either to stimulate state spending
or increase substitution.\1

ECONOMIC THEORY PREDICTS IMPACT OF
GRANT DESIGN FEATURES

Over the past 30 years, economists have adapted general consumer
demand theory to model how a government's expenditure patterns are
likely to change in response to a grant.  In that theory consumers
are assumed to maximize their individual welfare subject to their
preferences for the goods and services available to them, the prices
they must pay for the goods, and the resources they have to spend. 
Thus, for grants, the model depicts a government which may "purchase"
(1) goods aided by a grant, (2) all other public or private goods,\2
(3) or some combination.  The quantity of goods the government can
purchase is constrained by a budget consisting of its own revenues
plus additional revenue from federal grants.  The model demonstrates
how the government would purchase as much of the aided and non-aided
goods it could afford, within its budget constraint in accordance
with the taxpayers' collective preferences.  How much more of an
aided good a government purchases using its additional grant income
depends on two factors:  (1) taxpayers' preferences for the aided
good relative to other goods the government could purchase with the
additional resources and (2) the incentives to purchase aided rather
than non-aided goods that are built into the grant. 

According to economic theory, there are three types of incentives
that can be used to encourage grant recipients to increase total
spending on aided goods.  As shown in figure III.1, the incentives
work by restricting the use of funds to specified purposes, requiring
recipients to contribute their own funds to obtain grant funds,
and/or providing unrestricted federal matching of state funds. 

   Figure III.1:  Grant Design
   Features Along Three Dimensions

   (See figure in printed
   edition.)

The theory also states that the effectiveness of these incentives
also depends on the budget priorities of state taxpayers.  For
example, if a community does not share federal priorities for
spending on pollution control, the federal government may have to
build into the grant more restrictions or incentives than if federal
and community priorities were better aligned. 


--------------------
\1 In this appendix, we use state to mean state and local governments
and/or their agencies. 

\2 A government can purchase private goods by lowering taxes and
thereby providing residents more disposable income. 


      GRANT DESIGN FEATURES THAT
      RESTRICT USE OF FUNDS
----------------------------------------------------- Appendix III:0.1

Among the various types of federal grants, unrestricted grants do not
stipulate what grant funds must be spent on and therefore provide the
most discretion to recipient governments.  Unrestricted grants--also
known as unconditional or general-purpose grants--are pure income
transfers from the federal government to recipients that do not
stipulate what grant funds must be spent on or require any
contributions from recipients' own funds.  Such grants provide the
most discretion to recipient governments.  The General Revenue
Sharing program of the 1970s and 1980s is an example of an
unrestricted grant.  The program provided funds that could be used
for virtually any governmental purpose. 

In theory, unrestricted grants are intended to help overcome
geographical inequalities in fiscal well-being, rather than stimulate
public spending for specific purposes.  To achieve this objective, an
unrestricted grant would provide more funds to jurisdictions with
relatively low tax bases and high needs for public services and fewer
funds to more fiscally sound jurisdictions. 

In contrast, conditional grants limit recipient discretion through
restrictions designed around program goals, some of which are broader
than others.  Both categorical grants and block grants are considered
conditional.  However, while categorical grants feature
narrowly-prescribed objectives, block grants authorize funds to be
used for a wide range of activities within broadly-defined functional
areas.\3

Economic theory holds that conditional grants encourage more total
spending on grant activities than unrestricted grants, and that
unrestricted aid is more likely to be used for tax relief.  To
understand why this is so, consider the different spending responses
of recipients to a gift certificate from a sporting goods store
compared to an equivalent amount of cash.  A gift certificate that
exceeds the amount recipients normally would spend on sporting goods
will tend to boost their total spending on sporting goods.  With
cash, they are likely to spend each additional dollar of income
according to their preferences for all goods.  Spending on sporting
goods could be a small share of each additional dollar, such as 5
cents. 

In reality, communities receive federal grant dollars, not gift
certificates, and these dollars are fungible with other community
resources.  For this reason, economists have concluded that grant
recipients rarely are wholly constrained by the legal conditions
attached to a grant.  Rather, there will likely be an element of
substitution in every grant as recipients find ways to replace their
own funds with federal funds, freeing up local resources for other
purposes.  Overall, economic theory recognizes that $1 in conditional
grants will not necessarily result in an additional dollar of state
spending on the grant activity. 

Substitution also occurs when a community may have planned to spend
more of its own resources on a particular purpose, even without a
grant.  In such cases, a conditional grant simply increases the
budget available to the community and becomes, in effect, added
income similar to the income provided through an unconditional grant. 
In this situation a community can substitute some or all of its
conditional grant funds for other purposes, including tax relief.  To
extend the gift certificate analogy, the holder may have been
planning to buy sports equipment before receiving the certificate. 
Because the gift certificate can replace the cash the holder was
planning to spend on sporting goods, the holder has, in effect,
received a grant of additional income that can be used for purposes
unrelated to sports.  A sports enthusiast may add the certificate to
what she was planning to spend on sporting goods; someone else with
less enthusiasm for sports may use the gift certificate to replace
all of his planned spending. 


--------------------
\3 Block Grants:  Issues in Designing Accountability Provisions
(GAO/AIMD-95-226, September 1, 1995). 


      GRANT DESIGN FEATURES THAT
      REQUIRE STATE CONTRIBUTIONS
----------------------------------------------------- Appendix III:0.2

Some federal grants include matching provisions that require states
to share the cost of providing the aided service with the federal
government.  For example, a matching grant may require states to
spend 50 cents from their own revenue sources for each dollar of
federal funds provided.  Thus, 50 cents in state spending on a
matching program yields $1.50 in program funds.  Non-matching grants,
in contrast, provide funds to recipients without any requirement for
state cost-sharing. 

According to economists, matching grants encourage more state
spending on aided goods that non-matching grants, other factors being
equal.  Both matching and non-matching grants provide additional
income to recipient governments.  Because grant funds are partially
fungible, this income, like any other type of income, permits
recipients to consume more of both aided as well as non-aided
activities according to their preferences.  However, matching grants,
in addition to providing additional income, also lower the "price" to
the recipient government of the aided good relative to the other
goods it could purchase with the funds.  For example, with federal
matching of 75 percent of total spending, a state could spend 25
cents on an aided good and obtain 75 cents in federal funds, for a
total maximum increase in spending of $1.  Without matching, another
dollar of spending on an aided good still costs a dollar.  Therefore,
the same federal subsidy of 75 cents yields a maximum of only 75
cents of total additional spending. 

How effective a matching grant will be in increasing a recipient's
spending depends on the recipient's preferences for aided versus
non-aided activities (including tax relief).  If a recipient wants
more of an aided activity, such as a computer education program, the
price effect may produce a strong spending response.  For activities
the recipient desires less, the price effect may be less.  In the
extreme, if the recipient does not want more of an aided activity,
the price effect will be negligible. 

The use of maintenance-of-effort provisions can help make up for the
lack of a price effect in non-matching grants by requiring states to
continue a designated spending level from their own sources in order
to receive the federal assistance.  Because states must maintain a
prescribed level of spending, their ability to substitute federal
funds for their own is limited.  Over time, however, increases in the
population served by the program, inflation, and other determinants
may cause federal spending for the program to rise.  Therefore, to
retain its effectiveness as an incentive for states to contribute
their own funds, a maintenance-of-effort provision should contain
adjustment mechanisms so that required state contributions keep pace
with such trends. 


      GRANT DESIGN FEATURES THAT
      LIMIT FEDERAL CONTRIBUTIONS
----------------------------------------------------- Appendix III:0.3

For most federal matching grants, the federal share of total spending
is limited to a fixed amount or ceiling.  Such grants are considered
"closed-ended." Thus, any state spending beyond the amount needed to
obtain the maximum of federal funds occurs without any incentive in
the form of a price reduction resulting from the federal match. 

Closed-ended grants may also contain maintenance-of-effort
provisions, which require state or local governments to maintain a
prescribed level of expenditures from their own sources on the aided
function.  In theory, maintenance-of-effort provisions have an impact
similar to a matching requirement since the recipient must continue
to spend from its own resources on the aided function at a required
level to receive additional federal aid. 

For a few federal matching grants, the federal share of program
spending is unlimited--or "open-ended." Open-ended grants consist
primarily of a few large entitlement programs, such as Medicaid and
Foster Care.  The federal government has limited control over the
amount of spending on open-ended grant programs, mainly through
variations in the strictness of the grant eligibility requirements. 

According to economists, a closed-ended matching grant will be as
stimulative as an open-ended matching grant as long as state spending
on the aided activity remains below the level needed to obtain the
maximum federal contribution.  In this case, a closed-ended grant has
the same stimulative income and price effects as described for a
matching grant.  However, the fiscal impact of a closed-ended grant
will be different when state spending on the grant activity is above
the federal grant ceiling.  In this situation, the price reduction
created by federal matching is eliminated for the additional spending
beyond the limit of the federal contribution.  Therefore, the grant
has only an income effect, and grant funds simply add to the total
resources of the community with an effect equivalent to an
unconditional grant.  The community can substitute part or all of the
grant funds for its own spending and has full discretion over the use
of the freed-up resources.  As previously described, effective
maintenance-of-effort provisions, which track inflation and program
growth, can make up for the loss of the price incentive for a
closed-ended, matching grants when spending is beyond the federal
limit. 


STATISTICAL ANALYSIS OF FEDERAL
GRANT TARGETING
========================================================== Appendix IV

As part of our targeting analysis, we sought to determine if current
federal grant formulas allocate funds in a manner that targets states
with greater mismatches between programmatic needs and fiscal
resources.  To do this, we developed a grant targeting model, we
modified the model to reflect the influence of funding floors and
hold harmless formula provisions, and we tested the model using a
statistical technique known as multiple regression.\1 The regression
analysis enabled us to estimate the influence of state fiscal
capacity, apart from the influence of the other independent
variables, on per capita federal grant allocations to the 50 states. 
We found that, after controlling for indicators of program needs,
such as poverty, population under age 18, and highway miles, and for
service cost differentials, fiscal capacity did not play a
statistically significant role in allocating aid to states.  In fact,
the most significant variable in the model was a proxy for the
presence of funding floors and hold harmless provisions in grant
formulas. 

The remainder of this appendix discusses, in technical detail (1) the
theory that provided the basis for our analysis and the specification
of a grant allocation model suitable for estimation using multiple
regression, (2) the data we used to estimate the grant targeting
model, and (3) the results of our analysis. 

MODEL SPECIFICATION

In theory,\2 targeted grants should correct for differences in the
fiscal conditions of state governments so that taxpayers in less
wealthy states can provide comparable services at comparable tax
rates to wealthier states.  Under the theory of grant targeting, a
state's fiscal condition can be described in terms of expenditure
needs compared to revenues.  Technically, this is defined as the gap
between the revenues that can be raised from local sources with an
average tax burden on local residents (i.e., fiscal capacity) and the
expenditures required to finance an average level of public services
(i.e., needs). 

States with positive gaps are regarded as being in better fiscal
condition to provide services than those with negative gaps.  States
with average fiscal capacities and average service needs are in the
middle.  In a theoretical redistribution scheme, states with positive
gaps would transfer resources to those with negative gaps through an
unconditional grant or transfer of funds. 

In practice, grants are allocated from a general fund at the federal
level and distributed to eligible states for particular purposes
according to a formula.  The design of a grant targeting formula will
depend on the type and degree of equity desired.  There are two types
of equity policymakers can consider--beneficiary equity and taxpayer
equity.  To achieve beneficiary equity, grant funds would need to be
allocated in proportion to each state's potential program needs and
adjusted for differences in service costs.  Achieving taxpayer equity
requires considering fiscal capacity in addition to the needs and
cost factors used to achieve beneficiary equity. 

Beneficiary and taxpayer equity cannot be achieved simultaneously. 
Maximizing beneficiary equity provides equal federal funding per
beneficiary, resulting in unequal taxpayer burdens across states. 
Maximizing taxpayer equity equalizes state taxpayer burdens,
resulting in unequal federal funding per beneficiary.  Another equity
goal falls between achieving either full taxpayer or full beneficiary
equity, whereby differences in state taxpayer burdens are reduced but
not totally eliminated by allowing some differences in funding per
beneficiary across states.  In prior work we referred to this goal as
"balanced equity." The model in figure IV.1--which we refer to as the
grant targeting model--incorporates the need, cost, and fiscal
capacity factors, consistent with achieving balanced equity. 

   Figure IV.1:  Grant Targeting
   Model

   (See figure in printed
   edition.)

where

G = per capita grant allocation
0 = constant
Needj = program need indicators, such as poverty rates, population
of school age children, unemployment rates, etc.
1,2 = coefficients representing the relative influence of
each need
indicator and the fiscal capacity indicator on the grant allocation
FC = per capita fiscal capacity
C = cost of public services subsidized by federal grants

According to the grant targeting model, the dependent variable is per
capita grant allocations to states, adjusted for costs (G/C).  The
independent variables are a variety of state program need indicators
(Needj) and state per capita fiscal capacity, also adjusted for costs
(FC/C).  The hypothesis implied by the model is that the dependent
variable, G/C, would be a positive function of need; i.e., states
with greater needs should receive larger per capita grants.  In
contrast, the model implies that the dependent variable would be a
negative function of fiscal capacity; i.e., states with greater
resources to provide program services on their own would receive
smaller per capita grants. 

Our objective for estimating the grant targeting model was to
determine the extent to which the fiscal capacity variable explained
the variation in the allocation of federal funds to states,
controlling for a variety of plausible indicators of state program
needs and cost differentials.  Therefore, we tested the hypothesis
that the fiscal capacity variable would have the predicted negative
sign and be statistically significant.  We included the need
indicators primarily as control variables that would enable us to
more accurately assess the impact and significance of the fiscal
capacity variable. 

Our ability to accurately estimate the impact of the model's need
factors on aggregate grant allocations was limited.  In contrast to
the fiscal capacity variable, there is no single or aggregate measure
that accurately represents the program goals and objectives of all
the grants in the system.  Therefore, it was difficult to determine
the effects an individual needs indicator, such as the school age
population, had on the allocation of aggregate grant funds.  Because
each grant program uses a unique set of factors to allocate funds, a
particular need indicator used to distribute funds for one program
may play no role in other programs.  Consequently, in estimating the
influence of a variety of need indicators on aggregate grants
allocations, the effects of the need indicators may, to a certain
extent, cancel one another out.  Thus, the statistical significance
or insignificance of a particular need indicator in this analysis
does not provide an adequate basis for drawing conclusions about its
relative importance in the allocation of federal grants.  However,
used in combination, the need variables provided a valid control to
isolate the effect of needs from that of fiscal capacity on aggregate
grant allocations. 

The grant targeting model describes the allocation of grant funds as
a function solely of state needs and fiscal capacities, adjusted for
costs.  However, many grants contain funding floors and hold-harmless
provisions that guarantee each state a minimum grant allocation,
regardless of their needs and fiscal capacities.  This has the effect
of providing smaller states greater per capita grant allocations than
larger states.  Therefore, in specifying the model, we created two
dummy variables representing very small states (those with
populations less than .25 percent of the total United States
population), and small states (those with populations between .25
percent and .5 percent) to serve as proxies for the influence of
funding floors and hold-harmless provisions on grant allocations.\3

When two variables have a joint effect over and above the effects of
each factor separately it is considered "interaction." Given the
presence of funding floors in most federal grant formulas, we thought
it likely that one or both of the dummy variables would be
statistically significant.  Therefore, to test whether the effect of
fiscal capacity was significantly different for the smaller states,
we included interaction terms to provide separate fiscal capacity
coefficients for very small, small, and all other states. 

We also deflated the two fiscal variables, per capita grant
allocation and fiscal capacity, by an input-cost index to control for
the different costs states face in providing program services.\4
Finally, all variables were constructed as indexes, having weighted
average values of 1.0.  Measuring all variables as indexes allowed
the regression coefficients in the statistical model to be
interpreted as elasticities (i.e., the percent change in the
dependent variable--per capita grant allocation--in response to a 1
percent increase in an independent variable from its mean value). 
This facilitated the interpretation and reporting of results and
minimized problems of multicollinearity among the independent
variables.  Figure IV.2 shows our specification of the grant
targeting model. 

   Figure IV.2:  Specification of
   the Grant Targeting Model

   (See figure in printed
   edition.)

where

g/cg = per capita grant index adjusted for differences in costs
0 = constant
i=1..6 = coefficients representing the influence of each
independent
variable on the grant allocation
Needj = per capita need indicators expressed as indexes relative to
the
national average
y/cy = per capita fiscal capacity index adjusted for differences in
costs
D1 = dummy variable representing states with populations less than
.25 percent of the United States population
D2 = dummy variable representing states with populations between
.25 percent and .50 percent of the United States population
D1y/cy = interaction term representing the joint effect of fiscal
capacity
and D1
D2y/cy = interaction term representing the joint effect of fiscal
capacity
and D2

DATA

We used data for the 50 states for 1994 for per capita grants, U.S. 
population, population under age 18, population over age 60, wages,
unemployment, lane miles, vehicle miles, and housing.  For minority
and urban populations we used 1990 data.  Finally, we used average
1992-1994 data for fiscal capacity and the population in poverty. 
Table IV.1 defines the variables used to estimate the model. 



                               Table IV.1
                
                        Definitions of Variables

Variable
(Index)         Definitions
--------------  ------------------------------------------------------
Grant           A state's per capita grant allocation divided by (1)
                the U.S. average per capita grant allocation and (2)
                the rental/wage cost deflator (c), which adjusts for
                state differences in the costs of providing services.

Fiscal          A state's average per capita total taxable resource
capacity (TTR)  base divided by the U.S. average per capita resource
                base, all divided by the rental/wage cost deflator
                (c).

Poverty         The share of a state's average population living under
                the poverty line divided by the share of the U.S.
                population living under the poverty line.

Unemployment    The share of a state's population that is unemployed
                divided by the share of the U.S. population that is
                unemployed.

Minority        The share of a state's population classified as
                minority divided by the share of the U.S. population
                classified as minority.

Urban           The share of a state's population living in urban
                areas divided by the share of the U.S. population
                living in urban areas.

Population      The share of a state's population under the age of 18
under 18        (a proxy for school age children) divided by the share
                of the U.S. population under the age of 18.

Population      The share of a state's population over the age of 60
over 60         (a proxy for the senior citizen population) divided by
                the share of the U.S. population over the age of 60.

Vehicle miles   The per capita number of interstate vehicle-miles
                travelled in each state relative to the per capita
                number of vehicle-miles travelled in the U.S.

Lane miles      The per capita interstate lane-miles in a state
                divided by the per capita interstate lane-miles in the
                U.S.

Housing         The per capita share of a state's housing stock built
                before 1939 divided by the per capita share of the
                U.S. housing stock built before 1939.

Dummy -very     Takes the value 1 for states with populations less
small states    than .25 percent of the U.S. population and 0 for all
(D1)            other states.

Dummy -small    Takes the value 1 for states with populations between
states (D2)     .25 percent and .50 percent of the U.S. population and
                0 for all other states.

Interaction D1  The product of D1 and the TTR index.

Interaction D2  The product of D2 and the TTR index.
----------------------------------------------------------------------
Because the variables are expressed relative to other states, each
state's index should be compared to 1.00, the national average. 
Table IV.2 displays the data on each variable.  For example, Rhode
Island has a per capita, cost-adjusted, fiscal capacity index (TTR)
of 0.95, very close to the national average.  However, Rhode Island
has a per capita, cost-adjusted grant allocation index of 1.24, which
is 24 percent above the national average.  In contrast, Florida, with
a TTR index that is also close to average (0.93), has a grant
allocation index of 0.76, which is 24 percent below average.  Table
IV.3 is a correlation matrix of the data. 



                                                                      Table IV.2
                                                       
                                                                  Data on Variables

                                                                                                    Populati  Populati                      Interacti
                                            Lane   Vehicle            Unemployme                    on under   on over            Interact         on
State        Grant       TTR   Housing     miles     miles   Poverty          nt  Minority   Urban        18        60   D1   D2    ion D1         D2
--------  --------  --------  --------  --------  --------  --------  ----------  --------  ------  --------  --------  ---  ---  --------  ---------
AL            1.08      0.92      0.51      1.17      1.08      1.16        0.94      1.35    0.81      0.98      1.04  0.0  0.0      0.00       0.00
                                                                                                                          0    0
AK            2.92      1.34      0.16      4.95      0.99      0.64        1.29      1.18    0.85      1.21      0.42  1.0  0.0      1.34       0.00
                                                                                                                          0    0
AZ            0.94      0.88      0.18      1.52      1.03      1.05        1.01      0.92    1.10      1.07      1.04  0.0  0.0      0.00       0.00
                                                                                                                          0    0
AR            1.16      0.90      0.53      1.19      1.04      1.18        0.86      0.89    0.71      1.00      1.15  0.0  0.0      0.00       0.00
                                                                                                                          0    0
CA            0.86      0.95      0.53      0.58      1.02      1.20        1.38      1.57    1.22      1.06      0.84  0.0  0.0      0.00       0.00
                                                                                                                          0    0
CO            0.87      1.02      0.73      1.41      1.07      0.66        0.75      0.56    1.04      1.02      0.82  0.0  0.0      0.00       0.00
                                                                                                                          0    0
CT            0.95      1.15      1.43      0.73      1.22      0.65        0.96      0.69    1.11      0.92      1.10  0.0  0.0      0.00       0.00
                                                                                                                          0    0
DE            1.18      1.23      0.81      0.45      0.71      0.59        0.88      0.99    0.96      0.95      1.00  0.0  1.0      0.00       1.23
                                                                                                                          0    0
FL            0.76      0.93      0.22      0.62      0.82      1.10        1.05      0.84    1.10      0.89      1.40  0.0  0.0      0.00       0.00
                                                                                                                          0    0
GA            0.91      0.99      0.42      1.14      1.49      1.00        0.86      1.42    0.81      1.03      0.81  0.0  0.0      0.00       0.00
                                                                                                                          0    0
HI            1.36      0.99      0.31      0.31      0.62      0.62        0.97      3.36    1.17      0.99      0.96  0.0  1.0      0.00       0.99
                                                                                                                          0    0
ID            1.26      0.93      0.80      2.79      1.07      0.90        0.95      0.26    0.71      1.15      0.92  0.0  1.0      0.00       0.93
                                                                                                                          0    0
IL            0.96      1.07      1.44      0.99      0.99      0.95        0.94      1.13    1.15      1.00      0.99  0.0  0.0      0.00       0.00
                                                                                                                          0    0
IN            0.90      1.00      1.31      1.09      1.19      0.86        0.86      0.49    0.87      0.98      1.01  0.0  0.0      0.00       0.00
                                                                                                                          0    0
IA            1.09      1.04      1.96      1.48      0.92      0.73        0.67      0.17    0.83      0.99      1.19  0.0  0.0      0.00       0.00
                                                                                                                          0    0
KS            1.01      1.07      1.39      1.84      0.97      0.87        0.89      0.51    0.93      1.03      1.07  0.0  0.0      0.00       0.00
                                                                                                                          0    0
KY            1.19      0.96      0.87      1.12      1.20      1.30        0.84      0.41    0.70      0.97      1.02  0.0  0.0      0.00       0.00
                                                                                                                          0    0
LA            1.21      0.97      0.58      1.10      1.06      1.72        1.18      1.71    0.93      1.10      0.92  0.0  0.0      0.00       0.00
                                                                                                                          0    0
ME            1.10      0.94      2.29      1.55      0.87      0.87        1.18      0.09    0.62      0.94      1.09  0.0  1.0      0.00       0.94
                                                                                                                          0    0
MD            0.81      1.02      0.81      0.70      1.20      0.72        0.89      1.48    1.08      0.97      0.89  0.0  0.0      0.00       0.00
                                                                                                                          0    0
MA            1.34      1.07      2.21      0.67      1.07      0.68        1.03      0.54    1.17      0.90      1.08  0.0  0.0      0.00       0.00
                                                                                                                          0    0
MI            1.02      0.98      1.17      0.80      0.94      0.97        0.96      0.87    0.96      1.02      0.98  0.0  0.0      0.00       0.00
                                                                                                                          0    0
MN            0.93      1.07      1.37      1.11      1.00      0.79        0.74      0.28    0.93      1.04      0.97  0.0  0.0      0.00       0.00
                                                                                                                          0    0
MS            1.45      0.85      0.45      1.32      0.87      1.55        1.01      1.89    0.63      1.08      0.98  0.0  0.0      0.00       0.00
                                                                                                                          0    0
MO            1.05      1.03      1.18      1.28      1.38      1.04        0.81      0.64    0.93      1.00      1.10  0.0  0.0      0.00       0.00
                                                                                                                          0    0
MT            1.98      0.94      1.28      7.16      1.19      0.90        0.84      0.36    0.68      1.06      1.05  0.0  1.0      0.00       0.94
                                                                                                                          0    0
NE            1.15      1.11      1.73      1.55      0.86      0.67        0.50      0.32    0.90      1.04      1.10  0.0  0.0      0.00       0.00
                                                                                                                          0    0
NV            0.85      1.09      0.14      2.04      1.11      0.79        1.07      0.70    1.01      0.99      0.92  0.0  0.0      0.00       0.00
                                                                                                                          0    0
NH            0.95      1.01      1.67      1.12      0.93      0.59        0.83      0.10    0.69      0.98      0.93  0.0  1.0      0.00       1.01
                                                                                                                          0    0
NJ            0.88      1.06      1.33      0.43      0.66      0.69        1.12      1.08    1.22      0.93      1.07  0.0  0.0      0.00       0.00
                                                                                                                          0    0
NM            1.44      0.90      0.43      3.19      1.59      1.35        0.95      1.19    0.93      1.15      0.88  0.0  0.0      0.00       0.00
                                                                                                                          0    0
NY            1.09      1.06      1.97      0.49      0.53      1.11        1.06      1.36    1.16      0.95      1.04  0.0  0.0      0.00       0.00
                                                                                                                          0    0
NC            0.89      1.05      0.55      0.77      0.87      0.97        0.73      1.23    0.66      0.95      0.99  0.0  0.0      0.00       0.00
                                                                                                                          0    0
ND            2.00      1.02      1.48      4.61      0.95      0.74        0.66      0.28    0.74      1.03      1.14  1.0  0.0      1.02       0.00
                                                                                                                          0    0
OH            0.96      1.01      1.41      0.83      1.13      0.90        0.90      0.64    1.01      0.98      1.05  0.0  0.0      0.00       0.00
                                                                                                                          0    0
OK            1.07      0.92      0.74      1.53      1.14      1.26        0.90      0.92    0.91      1.03      1.07  0.0  0.0      0.00       0.00
                                                                                                                          0    0
OR            1.13      0.96      0.90      1.28      1.11      0.79        0.94      0.36    0.90      0.97      1.06  0.0  0.0      0.00       0.00
                                                                                                                          0    0
PA            1.04      1.02      1.99      0.71      0.66      0.85        0.98      0.60    0.95      0.92      1.23  0.0  0.0      0.00       0.00
                                                                                                                          0    0
RI            1.24      0.95      1.96      0.49      0.86      0.74        1.18      0.45    1.21      0.92      1.18  0.0  1.0      0.00       0.95
                                                                                                                          0    0
SC            1.07      0.92      0.46      1.19      1.18      1.16        1.02      1.58    0.72      0.99      0.95  0.0  0.0      0.00       0.00
                                                                                                                          0    0
SD            1.88      1.06      1.71      4.84      1.23      0.99        0.54      0.44    0.67      1.11      1.13  0.0  1.0      0.00       1.06
                                                                                                                          0    0
TN            0.95      1.00      0.55      1.15      1.33      1.15        0.80      0.86    0.80      0.96      1.01  0.0  0.0      0.00       0.00
                                                                                                                          0    0
TX            0.98      0.98      0.38      1.03      1.03      1.24        1.07      1.22    1.03      1.10      0.82  0.0  0.0      0.00       0.00
                                                                                                                          0    0
UT            1.03      0.86      0.59      2.72      1.58      0.63        0.62      0.30    1.09      1.35      0.70  0.0  0.0      0.00       0.00
                                                                                                                          0    0
VT            1.51      0.96      2.37      2.84      1.11      0.64        0.84      0.08    0.43      0.96      0.96  1.0  0.0      0.96       0.00
                                                                                                                          0    0
VA            0.81      1.03      0.58      1.01      1.33      0.67        0.83      1.14    0.91      0.94      0.88  0.0  0.0      0.00       0.00
                                                                                                                          0    0
WA            1.10      1.05      0.83      0.90      1.14      0.77        1.06      0.55    0.97      1.01      0.91  0.0  0.0      0.00       0.00
                                                                                                                          0    0
WV            1.52      0.87      1.41      1.58      1.16      1.41        1.25      0.20    0.50      0.90      1.21  0.0  0.0      0.00       0.00
                                                                                                                          0    0
WI            0.99      1.02      1.60      0.70      0.72      0.73        0.85      0.40    0.88      1.01      1.05  0.0  0.0      0.00       0.00
                                                                                                                          0    0
WY            2.46      1.21      0.92      9.87      2.12      0.75        0.89      0.30    0.86      1.10      0.89  1.0  0.0      1.21       0.00
                                                                                                                          0    0
-----------------------------------------------------------------------------------------------------------------------------------------------------


                                                                      Table IV.3
                                                       
                                                              Correlation Matrix of Data

                                                  Populati  Populati                                                                        Interacti
                                  Lane            on under   on over  Povert  Unemployme           Vehicle                        Interact         on
Variable     Grant   Housing     miles  Minority        18        60       y          nt   Urban     miles       TTR    D1    D2    ion D1         D2
--------  --------  --------  --------  --------  --------  --------  ------  ----------  ------  --------  --------  ----  ----  --------  ---------
Grant       1.0000
Housing     0.0430    1.0000
Lane        0.8070   -0.0090    1.0000
 miles
Minority   -0.0930   -0.5590   -0.2980    1.0000
Populati    0.3960   -0.3680    0.4890    0.0250    1.0000
 on
 under
 18
Populati   -0.2700    0.4470   -0.2250   -0.2620   -0.6330    1.0000
 on over
 60
Poverty    -0.0600   -0.3400   -0.1010    0.3030    0.0960    0.1300  1.0000
Unemploy    0.0510   -0.1740   -0.1910    0.3210   -0.1600   -0.1380  0.3150      1.0000
 ment
Urban      -0.3430   -0.1290   -0.3390    0.3330   -0.0250   -0.0620       -      0.2500  1.0000
                                                                      0.2230
Vehicle     0.2510   -0.2640    0.5630   -0.2060    0.4040   -0.3280  0.0990     -0.2030       -    1.0000
 miles                                                                                    0.1750
TTR         0.3580    0.1270    0.2390   -0.0700   -0.0430   -0.2780       -     -0.0600  0.2020   -0.0370    1.0000
                                                                      0.5370
D1          0.7360    0.0870    0.6450   -0.1780    0.2230   -0.2970       -     -0.0080       -    0.2430    0.3990  1.00
                                                                      0.2610              0.2810                        00
D2          0.1930    0.2140    0.1530   -0.0450   -0.0040    0.0970       -     -0.0140       -   -0.2120    0.0010     -  1.00
                                                                      0.2500              0.1490                      0.12    00
                                                                                                                        90
Interact    0.7770    0.0360    0.6680   -0.1520    0.2640   -0.3530       -      0.0350       -    0.2590    0.4590  0.99     -    1.0000
 ion D1                                                               0.2590              0.2490                        00  0.12
                                                                                                                              70
Interact    0.1890    0.2010    0.1380   -0.0340   -0.0130    0.0950       -     -0.0320       -   -0.2210    0.0420     -  0.99   -0.1270     1.0000
 ion D2                                                               0.2590              0.1400                      0.12    50
                                                                                                                        80
-----------------------------------------------------------------------------------------------------------------------------------------------------
Multicollinearity among the possible regressors did not appear to be
a serious problem.\5

In addition, variance inflation factors that measure the degree of
association between each independent variable and all the other
independent variables in the model suggested that collinearity was
not a problem in our sample.\6

ESTIMATION RESULTS

We first estimated the model using ordinary least squares (OLS).  The
results of this regression are shown in table IV.4. 



                               Table IV.4
                
                      Regression Results of Models

                                                       Model
                                              ------------------------
Index                                                OLS   Weighted LS
--------------------------------------------  ----------  ------------
TTR                                               -0.401        -0.218
                                                (-0.699)      (-0.537)
Housing                                          0.170\a       0.195\a
                                                 (2.880)       (4.274)
Lane miles                                       0.108\a       0.108\a
                                                 (3.449)       (3.663)
Minority                                         0.146\b       0.180\a
                                                 (2.290)       (4.079)
Population over 60                                 0.252         0.186
                                                 (0.713)       (0.626)
Poverty                                            0.119         0.051
                                                 (0.674)       (0.363)
Population under 18                                0.611         0.919
                                                 (0.950)       (1.594)
Unemployment                                       0.108         0.165
                                                 (0.458)       (0.837)
Urban                                             -0.293      -0.475\c
                                                (-1.375)      (-2.665)
Vehicle miles                                     -0.122        -0.149
                                                (-0.876)      (-1.211)
D1                                              -3.306\a      -3.575\a
                                                (-3.285)      (-4.238)
D2                                                -1.055        -0.755
                                                (-1.273)      (-1.116)
Interaction D1                                   3.603\a       3.778\a
                                                 (3.768)       (5.493)
Interaction D2                                     1.180         0.907
                                                 (1.473)       (1.398)
Constant                                           0.336         0.058
                                                 (0.242)       (0.048)
Adjusted R\2                                       0.862         0.979
----------------------------------------------------------------------
Note:  t-statistics are shown in parenthesis. 

\a significant at the 99 percent confidence level

\b significant at the 90 percent confidence level

\c significant at the 95 percent confidence level

The model explained 86 percent of the variation in per capita grant
allocations.  Although the sign of the fiscal capacity variable, TTR,
was negative as hypothesized, the variable was not statistically
significant.  According to this result, controlling for costs and a
variety of need indicators, the fiscal capacity variable had no
impact on per capita grant allocations to the larger states, which
received 94.2 percent of the grant allocations we analyzed.  Also,
the dummy variable representing very small states was significant at
the 99 percent confidence level; the dummy variable representing
small states was not significant.  Furthermore, the interaction
variable for very small states was positive and significant at the 99
percent confidence level.  These results suggest that a very small
state with average needs and fiscal capacity would receive 30 percent
higher grant funds per capita than a larger state with the same needs
and fiscal capacity.  They also suggest that per capita grant
allocations were a positive function of fiscal capacity for the
states that benefitted most from hold harmless provisions in
formulas. 

The coefficients for lane-miles, age of housing, and minority
population were positive and statistically significant, suggesting
that relatively more per capita grant funds were allocated to states
with greater lane-mileage, older housing stock, and higher minority
populations, weighted for their different population shares.  The
coefficients for the other six need indicators in our model were not
statistically significant.  As noted previously, because we used
program-specific need indicators to explain the variation in
aggregate grant allocations, caution must be used in drawing
conclusions about the significance or insignificance of any
particular need indicator. 

We tested whether the variance of the error terms of our estimated
equation was homoscedastic or constant by using a basic version of
the White test.\7 The results suggested that the age of housing
variable was significantly associated with the error term and that we
should reject the hypothesis that the variance of the error terms was
constant.  In technical terms, this is known as heteroscedasticity. 
This indicated that, while the OLS estimated coefficients were
unbiased, the standard errors could be biased, making tests of the
statistical significance of the coefficients imprecise.  To correct
for this potential bias, we re-ran the equation using independent
variables that were weighted by the age of housing variable.\8

The results of the weighted model are also shown in table IV.4.  The
weighted version of the model explained almost 98 percent of the
variation in per capita grant allocations.  In this version, the
fiscal capacity indicator continued to be statistically
insignificant, have a negative coefficient, and the dummy and
interaction variables had essentially the same order of magnitude and
significance as in the unweighted model.\9 However, in this version,
the per capita grant funds a very small state with average needs and
average fiscal capacity would receive were only 20 percent higher
than the funds a larger state with the same average needs and fiscal
capacity would receive. 

From all of these results, we concluded that a state's fiscal
capacity was not an important factor in targeting most closed-ended
grant funds to lower-capacity states.  Moreover, we concluded that
for very small states, per capita grant allocations were a positive
function of fiscal capacity. 


--------------------
\1 For this analysis, we included only formula grants, which use
formula factors to allocate aid.  We also excluded open-ended
programs because the public finance literature notes that federal and
state spending for such programs is designed to interact positively
so that the more a state spends, the more the federal government
spends.  As a consequence, wealthier states can afford to spend more
to leverage a larger share of total federal spending in programs such
as Medicaid.  Thus, including open-ended grant programs would have
biased the estimated impact of the fiscal capacity variable. 

\2 This theory is found in public finance literature.  See, for
example, Katherine L.  Bradbury, Helen F.  Ladd, Mark Perrault,
Andrew Reschovsky, and John Yinger, "State Aid to Offset Fiscal
Disparities Across Communities," National Tax Journal Vol.  37, No. 
2, (June 1984), pp.  151-170; Robert M.  Stein and Keith E.  Hamm, "A
Comparative Analysis of the Targeting Capacity of State and Federal
Intergovernmental Aid Allocations:  1977, 1982," Social Science
Quarterly Vol.  68, No.  3 (Sept.  1987), pp.  447-465.  For
applications to federal grant programs, see Maternal and Child
Health:  Block Grant Funds Should be Distributed More Equitably
(GAO/HRD-92-5, April 2, 1992) and Older American Act:  Funding
Formula Could Better Reflect State Needs (GAO/HEHS-94-41, May 12,
1994). 

\3 Grant allocations to these states represented 5.8 percent of the
total for the United States. 

\4 We used a general wage-rental input-cost index. 

\5 All the correlation coefficients for the independent variables
were less than 0.7.  A correlation coefficient of 0.8 or higher
indicates a degree multicollinearity that could make the measure of
the statistical significance of an independent variable unreliable. 
The correlation coefficients between the dummy and interaction
variables did exceed the 0.8 threshold--0.990 for very small states
and 0.995 for small states.  However, this was not a concern because
high correlations are a standard result for an interaction variable
that is a multiplicative function of a dummy variable. 

\6 The variance inflation factors for the need and fiscal capacity
variables were well below the threshold value of 10.0, which would
indicate a potentially harmful degree of multicollinearity.  The
variance inflation factors for the dummy and interaction variables
did exceed the threshold.  However, as noted previously, this was not
a concern. 

\7 This test calls for (1) regressing the square of the residuals
from the OLS estimate on each of the independent variables, (2)
performing a chi-square test on the results, and (3) examining each
independent variable for statistical significance, indicating that
the non-constant error is associated with that variable. 

\8 We used the SPSS weighted least squares command, which divided the
observations for each independent variable by each housing
observation raised to a variety of powers ranging from -2.0 to 2.0,
in 0.5 increments.  The program then selected the power that
maximized the log-likelihood function.  In this case, the program
selected the 1.5 power.  For a more detailed discussion of the
weighted least squares technique, see Damodar N.  Gujarati, Basic
Econometrics, 3rd ed.  (New York:  McGraw Hill, 1995), pp.  362-366
and 381-383. 

\9 The coefficient for school age population became borderline
significant.  Urban density also became significant, but had a
negative sign, indicating greater urban density was not associated
with higher grant allocations. 


CHALLENGES IN MEASURING TARGETING
FACTORS
=========================================================== Appendix V

In our analysis of grant targeting, we discussed how many of the
formula grants we reviewed used poor proxies to measure state program
needs,\1 fiscal capacities, and cost differentials.  In this
appendix, we define in greater detail the three targeting factors and
discuss how numerous formula grants contained measures that were poor
proxies for those factors. 

FORMULA FACTORS TARGET NEEDS,
FISCAL CAPACITY, AND COSTS

Experts in public finance generally agree that targeted grants are
designed to allocate funds according to three formula factors: 

  -- Workload:  A proxy for the share of a state's population needing
     services relative to the national average.  For example, the
     ratio of each state's low-income children to its population
     relative to the U.S.  ratio would be a possible workload factor
     to distribute funds from the Maternal and Child Health Services
     Block Grant. 

  -- Fiscal Capacity:  A proxy for a state's ability to generate
     revenues from its own economic resources within the limits of
     its taxing authority.  We have suggested the use of a U.S. 
     Department of the Treasury-developed proxy, total taxable
     resources (TTR), because it captures all potential sources of
     taxes. 

  -- Cost Differential:  A proxy for the relative costs of providing
     program services in a state, such as the formula used to
     determine the cost of producing housing in the HOME Investment
     Partnerships Program. 

Formula grants--which comprise the vast majority of federal grant
funds to states--are allocated to beneficiaries according to a
mathematical statement that contains statistical measures, such as
state population or per capita income.  The effectiveness with which
a formula grant targets funds depends on both the presence of the
factors cited above and the quality of the statistical information
used to measure the factors. 

MANY FORMULAS USE POOR PROXIES TO
MEASURE TARGETING FACTORS

A formula could contain measures of workload, fiscal capacity, and
costs that would, in theory, target funds in the most equitable way. 
However, if a proxy used to measure a factor was inadequate, the
distribution could still be inequitable.  Numerous GAO reports on
formula grant programs have found that formula factors used to
allocate funds were often poor proxies for measuring communities'
needs, fiscal capacity, or costs of providing services.\2


--------------------
\1 In this appendix, we use state to mean state and local governments
and/or their agencies. 

\2 References to GAO reports on targeting issues can be found on the
Related GAO Products list at the end of this report. 


      WORKLOAD FACTORS ARE NOT
      ALWAYS GOOD PROXIES FOR
      PROGRAM NEEDS
------------------------------------------------------- Appendix V:0.1

Several of our reports have shown that formula workload factors were
not appropriate proxies for the program recipients' needs.  For
example, in 1995 we reported that applying the formula factors
specified in the Ryan White Comprehensive AIDS Resources Emergency
Act of 1990 results in double counting the number of cases living in
eligible metropolitan areas.  Although, recent legislative changes
have reduced the double-counting,\3 the needs indicators still favor
more urbanized states.  As a result, the oldest eligible metropolitan
areas receive more generous funding, and newly emerging areas with
more recent growth in AIDS cases receive less funding. 

The Maternal and Child Health (MCH) program was created in 1981 when
10 categorical program grants were consolidated into one block grant. 
Federal funding was allocated in the same proportions originally
established under these 10 programs.  In 1992 we reported that this
method of distributing funding did not compensate states for their
varying concentrations of children at risk.  To distribute program
funds in a more targeted manner, we recommended that the MCH formula
use a state's concentration of at-risk children as a proxy for
programmatic needs.  Nevertheless, the MCH formula still distributes
funds according to its 1981 allocations. 


--------------------
\3 In May 1996, the Congress enacted and the President signed the
Ryan White Care Act Amendments of 1996, PL 104-146, partially
correcting the formula that produced the inequities. 


      POPULATION A POOR PROXY FOR
      WORKLOAD
------------------------------------------------------- Appendix V:0.2

Sixteen of the 149 grant formulas we reviewed used state population
shares as a basis for allocating grant funds.  Such population data
is a poor proxy for workload because it allocates funds to states in
proportion to the number of people in the state, not in proportion to
the number of people who may actually need the program services.  For
example, the formula for the Child Care for Families At-Risk of
Welfare Dependency program allocates funds to states based on a
state's share of the child population of the United States rather
than its share of the child population at-risk.  This means that
states that have a higher population of at-risk children relative to
other states would not receive a higher share of program funds, thus
reducing the amount of funds the state can spend on each child and
creating disparities in the provision of child care services for
at-risk children. 

Furthermore, when combined with workload factors in a grant formula,
a population factor may dilute a workload factor's allocational
effects.  For example, the Environmental Protection Agency's
Hazardous Waste Management State Program Support program uses three
workload factors in its allocation formula:  (1) the number of
hazardous waste management facilities in a state, (2) the amount of
waste produced, and (3) state population.  Although the formula
allocates funds largely based on the two workload factors, the use of
a population factor could reduce the allocation of funds to states
with greater needs in favor of states with higher populations. 


      PER CAPITA INCOME A POOR
      PROXY FOR FISCAL CAPACITY
------------------------------------------------------- Appendix V:0.3

Per capita personal income (PCI) is the fiscal capacity measure most
commonly used in federal grant formulas.  As defined and compiled by
the Department of Commerce, PCI is intended to measure the income
received by state residents including wages and salaries, rents,
dividends, interest earnings, and income from nonresident corporate
business.  It also includes an adjustment for the rental value of
owner-occupied housing on the ground that such ownership is analogous
to the interest income earned from alternative financial investments. 

Nevertheless, PCI is a relatively poor choice for measuring fiscal
capacity primarily because it does not comprehensively measure
income.  In particular, PCI fails to capture income that is produced
in a state, but not realized (such as corporate retained earnings and
unrealized capital gains).  Furthermore, PCI ignores tax exporting. 
The income of nonresidents received from activities within a state is
considered relevant to a state's fiscal capacity because taxation of
such income (for example, through retail sales, other excise taxes,
or corporate income taxes) reduces the burdens on resident taxpayers. 
On both grounds, PCI is a relatively poor indicator of fiscal
capacity. 

We previously reported that total taxable resources (TTR) is a better
measure of fiscal capacity than PCI because it is a more
comprehensive indicator of economic income and addresses tax
exporting.\4 TTR, developed by the U.S.  Department of the Treasury,
is an average of PCI and per capita gross state product (GSP).  GSP
measures all income produced within a state, whether received by
residents, nonresidents, or retained by business corporations.  By
averaging GSP with PCI, the TTR measure covers more types of income
than PCI alone, including income received by nonresidents.  Thus, the
use of a TTR-based measure of fiscal capacity would improve the
targeting of program funds to states with lower fiscal capacities. 

The choice of fiscal capacity measure is particularly important for
open-ended grant programs, such as Foster Care IV-E Program and
Medicaid, which account for about 40 percent of all grant funds to
state and local governments.  For open-ended programs, the federal
government's share of the total program costs varies according to a
state's fiscal capacity.  Currently, such reimbursement is made on
the basis of a PCI-based measure called the federal medical
assistance percentage (FMAP), which ranges from 50 percent for
wealthier states to 80 percent for poorer states.  In 1990 testimony
on how fairness in the Medicaid formula could be improved, we stated
that the differences in TTR and PCI were substantial.\5 As a
consequence, the federal share of Medicaid was too low in states
where fiscal capacity was overstated by using PCI. 


--------------------
\4 Maternal and Child Health:  Block Grant Funds Should Be
Distributed More Equitably (GAO/HRD-92-5, April 2, 1992). 

\5 Medicaid Formula:  Fairness Could Be Improved (GAO/T-HRD-91-5,
December 7, 1990). 


      STATE SPENDING A POOR PROXY
      FOR COSTS
------------------------------------------------------- Appendix V:0.4

Only 12 percent of the formula grants we reviewed contained a factor
designed to target more funds to states with higher costs associated
with providing services.  However, we found that for most of those
grants state expenditure data were used to allocate funds instead of
a measurement of actual program cost differentials.  We have reported
that service costs can differ substantially from state to state, and
federal grants that do not contain a cost factor purchase fewer
services in the states with higher costs.  We have also reported that
using state expenditure data as a proxy for costs can introduce
perverse incentives to an allocation formula. 

For example, in 1994 we found that the existing funding formula used
to allocate funds to states under title III of the Older Americans
Act of 1965 did not take into account the sometimes substantial
differences in service costs from state to state.\6 Because scant
data existed on the actual costs of providing title III services, we
recommended modifying the formula to incorporate a broad-based cost
index we developed that we believed provided a reasonable proxy for
title III service costs.  We noted that a broad-based index was
preferable to an index constructed from program expenditures because
using a state's program expenditures could have the perverse effect
of rewarding the states that inefficiently administered the program. 
In their comments on our report, the Administration on Aging voiced
its concern about using GAO's broad-based cost index because judgment
had been used to construct it.  In response, we commented that we
believed our methodology for developing the index was reasonable and
conservative and that a similar cost measure was currently included
in two other federal grant formulas. 

Likewise, in our report on remedial education programs\7 we cited
several problems in the use of per-pupil expenditures, the cost
factor used to allocate federal education grant funds.\8 A state's
cost may have been higher because it (1) had a greater fiscal
capacity, (2) chose to procure more expensive educational
instruction, or (3) gave education a relatively higher funding
priority.  The formula did not differentiate between the reasons for
differences in average state spending.  Instead, it allocated fewer
funds to the states that either could not or did not spend as much on
education. 


--------------------
\6 Older Americans Act:  Funding Formula Could Better Reflect State
Needs (GAO/HEHS-94-41, May 12, 1994). 

\7 The Chapter 1 program, authorized by Chapter 1 of Title I of the
Elementary and Secondary Education Act of 1965, provides the largest
share of federal assistance to elementary and secondary school
students.  These funds are used primarily to hire remedial education
instructors. 

\8 Remedial Education:  Modifying Chapter 1 Formula Would Target More
Funds to Those Most in Need (GAO/HRD-92-16, July 28, 1992). 


MAJOR CONTRIBUTORS TO THIS REPORT
========================================================== Appendix VI

ACCOUNTING AND INFORMATION
MANAGEMENT DIVISION, WASHINGTON,
D.C. 

Margaret T.  Wrightson, Assistant Director, (202) 512-3516
Elizabeth H.  Curda, Evaluator-in-Charge
Bill J.  Keller, Evaluator
Amy Lee, Intern

OFFICE OF THE CHIEF ECONOMIST

Richard S.  Krashevski, Senior Economist

HEALTH, EDUCATION, AND HUMAN
SERVICES DIVISION, WASHINGTON,
D.C. 

Jerry C.  Fastrup, Assistant Director


BIBLIOGRAPHY
=========================================================== Appendix 0

REFERENCES FOR GRANT IMPACT
ESTIMATES

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Bahl, Roy W., and Robert J.  Saunders.  "Determinants of Changes in
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Bowman, John H.  "Tax Exportability, Intergovernmental Aid, and
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Brazer, Harvey E.  City Expenditures in the United States, Occasional
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Campbell, Alan K., and Seymour Sacks.  Metropolitan America, Fiscal
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Craig, Steven G., and Robert P.  Inman.  "Federal Aid and Public
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Ehrenberg, Ronald G.  "The Demand for State and Local Government
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Feldstein, Martin.  "The Effect of a Differential Add-on Grant: 
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Gramlich, Edward M.  "State and Local Governments and Their Budget
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Gramlich, Edward M.  "State and Local Budgets the Day After It
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143-155. 





RELATED GAO PRODUCTS
============================================================ Chapter 1

Addressing the Deficit:  Updating the Budgetary Implications of
Selected GAO Work (GAO/OCG-96-5, June 28, 1996). 

Highway Funding:  Alternatives for Distributing Federal Funds
(GAO/RCED-96-6, November 28, 1995). 

Ryan White Care Act of 1990:  Opportunities to Enhance Funding Equity
(GAO/HEHS-96-26, November 13, 1995). 

Department of Labor:  Senior Community Service Employment Program
Delivery Could Be Improved Through Legislative and Administrative
Action (GAO/HEHS-96-4, November 2, 1995). 

Rural Development:  USDA's Approach to Funding Water and Sewer
Projects (GAO/RCED-95-258, September 22, 1995). 

Deficit Reduction:  Opportunities to Address Long-Standing Government
Performance Issues (GAO/T-OCG-95-6, September 13, 1995). 

Block Grants:  Issues in Designing Accountability Provisions
(GAO/AIMD-95-226, September 1, 1995). 

Addressing the Deficit:  Budgetary Implications of Selected GAO Work
for Fiscal Year 1996 (GAO/OCG-95-2, March 15, 1995). 

Block Grants:  Characteristics, Experience, and Lessons Learned
(GAO/HEHS-95-74, February 9, 1995). 

Older Americans Act:  Funding Formula Could Better Reflect State
Needs (GAO-HEHS-94-41, May 12, 1994). 

Medicaid:  Alternatives for Improving the Distribution of Funds to
States (GAO/HRD-93-112FS, August 20, 1993). 

Remedial Education:  Modifying Chapter 1 Formula Would Target More
Funds to Those Most in Need (GAO/HRD-92-16, July 28, 1992). 

Maternal and Child Health:  Block Grant Funds Should Be Distributed
More Equitably (GAO/HRD-92-5, April 2, 1992). 

Mental Health Grants:  Funding Not Distributed According to State
Needs (GAO/T-HRD-91-32, May 16, 1991). 

Medicaid Formula:  Fairness Could Be Improved (GAO/T-HRD-91-5,
December 7, 1990). 

Drug Treatment:  Targeting Aid to States Using Urban Population as
Indicator of Drug Use (GAO/HRD-91-17, November 27, 1990). 

Substance Abuse Funding:  High Urban Weight Not Justified by
Urban-Rural Differences in Need (GAO/T-HRD-91-38, June 25, 1991). 

Substance Abuse and Mental Health:  Hold-harmless Provisions Prevent
More Equitable Distribution of Federal Assistance Among States
(GAO/T-HRD-90-3, October 30, 1989). 

Block Grants:  Proposed Formulas for Substance Abuse, Mental Health
Provide More Equity (GAO/HRD-87-109BR, July 16, 1987). 

Substance Abuse:  Description of Proposed State Allotment Grant
Formulas (GAO/HRD-86-140FS, September 10, 1986). 

Local Governments:  Targeting General Fiscal Assistance Reduces
Fiscal Disparities (GAO/HRD-86-13, July 24, 1986). 

Highway Funding:  Federal Distribution Formulas Should Be Changed
(GAO/RCED-86-114, March 31, 1986). 

Changing Medicaid Formula Can Improve Distribution of Funds To States
(GAO-GGD-83-27, March 9, 1983). 

Proposed Changes in Federal Matching and Maintenance of Effort
Requirements for State and Local Governments (GAO/GGD-81-7, December
23, 1980). 


*** End of document. ***