Community Development Block Grant Formula: Targeting Assistance  
to High-Need Communities Could Be Enhanced (26-APR-05,		 
GAO-05-622T).							 
                                                                 
Congress asked GAO to comment on the Department of Housing and	 
Urban Development's (HUD) 2005 report on the Community		 
Development Block Grant (CDBG), "CDBG Formula Targeting to	 
Community Development Need." The CDBG program distributes funding
to communities using two separate formulas that take into account
poverty, older housing, community size, and other factors. That  
study evaluates the program's funding formula from two		 
perspectives: (1) to what extent do communities with similar	 
needs receive similar CDBG funding, and (2) to what extent are	 
program funds directed to communities with greater community	 
development needs. The HUD report is particularly salient in	 
light of the administration's 2006 budget request which 	 
criticizes the program for not effectively targeting high-need	 
communities. Congress asked us to provide our views on the HUD	 
study based on our experience and past assistance to various	 
congressional committees on a wide variety of federal formula	 
funding issues. 						 
-------------------------Indexing Terms------------------------- 
REPORTNUM:   GAO-05-622T					        
    ACCNO:   A22738						        
  TITLE:     Community Development Block Grant Formula: Targeting     
Assistance to High-Need Communities Could Be Enhanced		 
     DATE:   04/26/2005 
  SUBJECT:   Block grants					 
	     Community development				 
	     Community development programs			 
	     Federal grants					 
	     Grants to local governments			 
	     Program evaluation 				 
	     Program management 				 
	     2000 Decennial Census				 
	     HUD Community Development Block Grant		 
	     Program						 
                                                                 
	     Mental Health and Substance Abuse Block		 
	     Grant						 
                                                                 

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GAO-05-622T

United States Government Accountability Office

GAO Testimony

Before the Subcommittee on Federalism and the Census, Committee on
Government Reform, House of Representatives

For Release on Delivery

Expected at 10:00 a.m. EDT COMMUNITY

Tuesday, April 26, 2005

DEVELOPMENT BLOCK GRANT FORMULA

        Targeting Assistance to High-Need Communities Could Be Enhanced

Statement of Paul L. Posner, Managing Director Federal Budget Analysis and
Intergovernmental Relations

GAO-05-622T

[IMG]

April 26, 2005

COMMUNITY DEVELOPMENT BLOCK GRANT FORMULA

Targeting Assistance to High-Need Communities Could Be Enhanced

What GAO Found

HUD's report on the CDBG formula provides a thoughtful and sophisticated
analysis of those elements of the formula that impede effective and
equitable targeting of limited federal resources. Central to HUD's
analysis is an index of need that encompasses a wide variety of indicators
related to poverty, housing infrastructure, and population growth and
decline. While we would question some of the factors in their index,
overall we believe it serves as a reasonable basis for evaluating CDBG
targeting.

The study identifies a number of causes that explain the poor performance
of the current formula.

o  	The use of two formulas rather than one is an important reason
communities with similar needs do not receive similar funding.

o  	The use of population size as a need indicator significantly reduces
the extent to which funding is directed to high-need communities.

o  	Changing the poverty measure to one based on the poverty status of
households rather than individuals would avoid large grants to communities
with large student populations.

o  	An increasing number of communities have attained the minimum
population size necessary to be eligible for formula funding and this has
also reduced funding to communities with the highest needs.

In addition to presenting formula options that address a number of these
problems, HUD's study also presents an option that would include per
capita income in the formula. The inclusion of per capita income could be
justified on the grounds that it directs more funding to communities with
weaker economic capacity to meet needs from local resources. However, some
of the effect of this factor is offset by introducing an additional factor
-metropolitan per capita income. The metropolitan per capita income factor
directs more rather than less funding to communities located in
high-income metropolitan areas. This works at cross purposes with the
local per capita income factor.

GAO suggests that the subcommittee consider a needs-based criterion to
determine eligibility and eliminate the grandfathering of eligibility into
the formula before this approach is adopted as a means of improving the
targeting performance of the program.

                 United States Government Accountability Office

Mr. Chairman and Members of the Subcommittee:

I am pleased to be here today to discuss policy considerations associated
with fashioning a grant targeting policy and provide our observations on
the Department of Housing and Urban Development's (HUD) report titled:
"CDBG Formula Targeting to Community Development Need." In our recent
report on 21st Century Challenges,1 we argue for the importance of a
thorough assessment of federal programs and policies across the board due
to long term fiscal challenges the nation currently faces. In that report
we specifically recommend that programs such as the Community Development
Block Grant (CDBG) be judged according to whether they target assistance
to those with the greatest needs and the least capacity to meet them.

The CDBG program is a significant direct federal-to-local grant program.
It supports a wide array of local community development activities that
are primarily to benefit low-and moderate-income persons. Program funding
is allocated to local communities using two statutory formulas that take
into account various indicators of community development need. The HUD
report observes that this formula provides widely different payments to
recipients with similar needs and that funds going to the neediest
communities have decreased over time on a per capita basis. The study then
presents several alternative measures of community need that would
systematically focus support on those communities with the greatest need.
This subcommittee asked us to evaluate the HUD report.

The HUD study takes on even greater significance in light of the
administration's proposal to consolidate 18 federal community and economic
development programs, including CDBG, into a single block grant. The
administration proposal would reduce overall funding by 30 percent. Such a
cut raises issues regarding the need to more sharply focus limited funding
on those communities in greatest need. In this regard the administration's
initiative criticizes the CDBG program as being poorly targeted,
indicating that 38 percent of the funds go to eligible communities and
states with poverty rates below the national average. To improve
targeting, the administration proposal cites both need, specifically
poverty, and economic capacity indicators such as unemployment and job
loss as important indicators of the need for development funding.
Criticisms of

1GAO, 21st Century Challenges: Reexamining the Base of the Federal
Government, GAO-05-325SP, February 2005.

poor targeting raise fundamental questions about the relationship between
formula design choices and federal policy goals.

Over the years we have evaluated and provided technical assistance on a
number of formula grant programs. Consequently, we have a broad
perspective on formula design issues. Today I will draw on our past work
on a variety of grant programs to discuss several key issues that can
contribute to good formula design. I will then provide our observations on
HUD's evaluation of the current formula and the alternative targeting
policies outlined in their report. Finally, I will offer some suggestions
the subcommittee may wish to consider to better account for differences in
local communities' economic capacities to meet local needs with local
resources. We did not independently verify the reliability of the data
used in HUD's report nor did we verify their analysis.

To briefly summarize our observations, I would first note that good
formula design and grant targeting depend on a number of important policy
choices. While the HUD study provides a thoughtful analysis of grant
targeting based on improved measurement of program need, additional issues
merit further consideration, including taking into account not only the
need for community infrastructure improvement but also communities'
economic capacities to address those needs. In addition, the subcommittee
should consider revising eligibility criteria to encompass both needs and
economic capacity.

As agreed with the subcommittee, I will not be commenting on issues
related to the state program that provides funding for non-entitlement
communities. I would be happy to discuss these issues during our question
and answer period if time allows.

Over the years we have reported on a wide variety of grant formula issues.
During the 1970s and 1980s, we issued a number of reports on the funding
formulas used to direct Revenue Sharing funds to local communities based
on both their capacity and willingness to utilize local resources to
address local needs. In anticipation of the 2000 census, we examined the
potential effect of the decennial census population undercount on the
distribution of federal grant funds for 25 large formula grant programs,
including Medicaid. Over the years we have also assisted the Congress in
revising the funding formulas under the Ryan White CARE Act, the Older
Americans Act, Substance Abuse and Mental Health Block grants, and Title I
education grants so that program funding would be more responsive to
changes in program needs. This wide range of experience provides us

Grant Formula Design Embodies Several Policy Considerations

with an in-depth understanding of the issues associated with the equitable
and efficient targeting of federal grant dollars.

Based on our past experience, I would like to offer a number of
observations on the design of grant funding formulas. First, grant
formulas reflect an intergovernmental partnership that structures how
costs are to be shared among the various levels of government. When
federal resources represent a declining share of the cost of meeting
national goals, a greater effort to target high-need communities is
necessary if federal funding is to make a significant contribution to
closing the fiscal gap between high- and low-need communities.

Second, targeting grant funding involves two key decisions: 1) determining
which communities are eligible for assistance and 2) how to distribute
funding among eligible communities. A clear statement of policy goals and
objectives is essential as a guide for establishing grantee eligibility
standards and identifying a manageable number of statistical indicators
that can reliably direct formula funding to communities with the greatest
need. Because the CDBG program has a wide variety of policy goals - the
elimination of slums, historic preservation, and promoting more rational
land use, among others - identifying eligibility standards and a
reasonable set of indicators to represent program need is especially
challenging. For example, the CDBG program's goal of improving the
physical infrastructure of economically distressed communities is
reflected in several of the need indicators used in the program's formula,
such as poverty and older housing. However, there are no indicators for
historic preservation or rational land use.

In addition to program needs, consideration of fiscal equity or fairness
suggests additional targeting factors beyond need indicators. Here there
are two issues: 1) wide differences in communities' ability to meet local
needs with local resources and 2) geographic differences in the cost of
financing local development projects. Regarding local resources, high
income communities generally have stronger tax bases from which to fund
program needs without relying on federal assistance compared to lower
income areas. Accordingly, the allocation of scarce resources might
reflect variations in local funding capacity. In addition, the cost issue
arises for areas faced with a high cost-of-living since they would need to
pay more for the workers who actually deliver services at the local level.

Performance indicators are sometimes considered as a targeting factor
though they present challenges as well. Ideally, performance indicators
would reflect only grantee performance and not program outcomes that

Two Formulas Are Used to Target Program Funding

result from factors local officials have little ability to control. For
example, it makes little sense to reward a state that has substantially
reduced welfare dependence because it enjoyed a particularly strong
economy but did no better than other grantees in terms of efficiently
managing its welfare programs. Accurate performance indicators are
particularly difficult to develop, especially as they pertain to goals
that may take literally decades to realize. As a consequence, they require
an even higher degree of scrutiny than needs-based indicators before being
incorporated into funding formulas.

For this reason a more common approach to promoting accountability is to
require grantees to provide matching funds for projects funded under the
program. Grantees are likely to be more vigilant in screening and funding
individual projects if they must put a significant portion of their own
resources at risk. While often difficult to enforce, at a minimum, such a
requirement forces public discussion of how grant funds are to be
employed.

Before I turn to discussing the HUD study and its findings, I would first
like to provide a brief description of the eligibility standards and
funding formulas now used to target CDBG funding. To obtain entitlement
status, a city must be the principal city of a metropolitan statistical
area, as designated by the Office of Management and Budget (OMB), or have
a population of at least 50,000 residents. An urban county must have a
population of at least 200,000 residents. The formulas used to distribute
funding among eligible communities reflect several broad dimensions of
need. Originally, CDBG funding was distributed to entitlement communities
based on a simple three-factor formula that took into account:

o  the number of residents (population),

o  the number of residents living in poverty, and

o  the number of overcrowded housing units.

Beginning in fiscal year 1978, Congress added a second three-factor
formula that included the following need indicators:

o  the number of residents living in poverty,

o  the number of older housing units, and

Declining Budget Resources Underscore the Need for More Efficient
Targeting of Available Funding

o  slow population growth or decline.

Under this dual formula approach, grantees receive the larger amount
allocated by either the first formula, commonly referred to as formula A,
or the second formula, commonly referred to as formula B. The use of two
formulas, each with three factors, results in allotments exceeding the
funds available for distribution. To avoid this outcome, all grantee
allotments are proportionally reduced to conform to the amount available
for distribution by formula.

Since the advent of the entitlement portion of the program, the number of
participating communities has nearly doubled, increasing from 606 in
fiscal year 1975 to more than 1,100 in fiscal year 2004. This trend can be
expected to continue both because population will continue to grow and
because new standards for designating metropolitan areas, as promulgated
by OMB and utilized by the program, are also likely to increase the number
of eligible communities.

Since 1978 program funding has declined to roughly half its peak of $10.2
billion when measured in purchasing power of today's dollars. When
population growth is factored in, the decline in real per capita spending
has declined by two-thirds, as illustrated in the accompanying figure.

Figure 1: Trends in CDBG Funding Per Capita 1975-2005

Dollars per capita 50

10

0 1975 1980 1985 1990 1995 2000

Fiscal year

Current dollars per capita Constant 2005 dollars per capita Source: GAO
analysis of Census, HUD, and OMB data.

The policy implication of these trends is that with more limited
resources, narrowing the gap between high- and low-need communities can
only be realized by concentrating this more limited funding on high-need
communities. This requires a new look at the program's eligibility
standards and funding formulas.

The HUD study relies on two generally accepted equity or fairness
principles to evaluate the targeting of CDBG funding: 1) equals should be
treated equally and 2) those with greater needs should receive more than
those with lesser needs. The first principle is based on the idea that
communities with similar needs should receive roughly similar per capita
funding amounts. The second standard is based on the idea that to reduce
the gap between high- and low-need communities, additional funding must be
targeted to communities with greater needs. This criterion is especially
pertinent because, as the HUD report observes, Congress designed a formula
intended to allocate CDBG funds according to variations in community
needs. However, determining the extent to which program funding is
disproportionately allocated to communities with the highest needs
involves value judgments that are the responsibility of policymakers
rather than technicians and administrators. The HUD study measures the

Given the Program's Broadly Defined Purposes, HUD's Evaluation Criteria
for Grant Targeting Appear Reasonable

extent to which funding is targeted to high-need communities and leaves it
to policymakers to decide the appropriate degree of needs-based targeting.

Before I address the conclusions reached in the HUD study, I first want to
spend a couple of moments discussing the factors underlying the study's
need criterion, since all conclusions rest upon its validity. One of the
criticisms directed at the CDBG program in the administration's fiscal
year 2006 budget proposal is that there is a "lack of clarity in the
program's purpose," a statement which is supported by the long list of
specific program objectives cited in HUD's report. Given the broad and
diffuse goals established for the program, it is difficult to identify a
few clear and succinct indicators of program need appropriate for this
program. Though HUD's need criterion is not immune from criticism, it is,
in our view, reasonable given the program's diverse objectives. HUD's
criterion is strongly related to poverty and older housing occupied by
low-income households and a number of other variables related to local
poverty conditions such as education, crime, and racial segregation. These
variables represent 80 percent of HUD's overall index of need. This, I
feel, represents a reasonable approach for distinguishing between high-and
low-need communities.

Other indicators included in HUD's need criterion may be more
questionable. For example, overcrowded housing, one of the elements in the
current formula, may be more indicative of a strong local economy that
reflects strong demand pressures in the local housing market rather than
economic decline. In addition, low population densities and strong
population growth, both reflected in HUD's need criterion, may be more
indicative of strong rather than weak economic conditions. However, to the
extent that these indicators may be problematic, they represent a
comparatively small part of the overall need criterion. Consequently, even
if these factors were eliminated from the need index it is unlikely that
they would affect their main conclusions to any significant degree.

Many Features of CDBG Funding Formulas Limit Their Ability to Consistently
Target High-Need Communities

While HUD Formula Options Improve Needs Targeting, Additional Options
Should Also Be Explored before Deciding on a Particular Reform Strategy

The HUD study reaches a number of valid conclusions regarding the
targeting performance of the program's funding formulas. I will just
mention their conclusions to echo the more detailed analysis presented in
the HUD report:

o  	The primary reasons entitlement communities with similar community
development needs receive wide differences in funding are 1) using two
formulas rather than a single formula and 2) the factor that reflects
older housing in formula B results in especially large disparities in
funding among communities with similar needs because units occupied by
higher income residents typically are not in need of rehabilitation at
public expense.

o  	Formula A is most responsible for reducing the extent to which funding
is targeted to high-need communities, because its reliance on general
population precludes greater targeting based on community development
needs.

o  	Changing the poverty measure to one based on the poverty status of
households rather than individuals would avoid awarding large grants to
low-need college towns.2

In our view, the HUD study has clearly identified the major elements that
limit the current formula's ability to efficiently and effectively target
funding to high-need communities, and it puts forward a number of formula
alternatives that would strengthen the program in this regard. Proposals
range from a comparatively modest reform to options that result in a more
substantial redistribution of program funding.

The study describes two formula alternatives to improve grant targeting
among entitlement communities that incorporate new need indicators. The
first option, formula alternative one, introduces revised indicators of
poverty, older housing units and slow population growth and decline, and
places greater emphasis on the poverty indicators. It provides modest
improvements by narrowing wide differences in funding received by
communities with similar needs and it directs a larger portion of funding
to high-need communities. The second option, alternative two, takes a

2 Data on persons in poverty are from the Bureau of the Census which
includes off-campus college students, who often receive support from their
families that is not recorded by Census.

somewhat more aggressive approach by eliminating the use of two formulas
and replacing them with a single formula that includes a range of
indicators related to need. It provides a substantial improvement in the
program's ability to provide comparable funding for communities with
comparable needs.

However, it is important to point out that neither the poverty indicator
used in the current formula nor the alternative HUD proposes takes into
account geographic differences in the cost-of-living. As a consequence,
both the current formula and the two alternatives probably overstate needs
in communities with relatively low cost-of-living and understate them in
communities with a higher cost-of-living.

I would characterize the first two alternatives as making technical
improvements, in that they utilize better indicators of need and eliminate
the primary causes of wide differences in funding for communities with
similar needs. In contrast, a third option, formula alternative three,
introduces two additional factors-community per capita income and the per
capita income of the wider metropolitan area in which the grantee is
located. Community per capita income (PCI) is used to increase funding for
low-income communities and reduce funding for higher income communities.
The metropolitan PCI factor partly offsets the effect of community PCI by
increasing funding for communities in high-income metropolitan areas. The
net effect of both factors is that the two factors, to some extent, work
at cross purposes. For example, if two communities located in different
metropolitan areas had the same PCI, the community located in the
metropolitan area with a lower area-wide income would receive less aid
than the community located in the high-income metropolitan area.

The HUD report suggests using the two per capita income factors because
they provide a means of directing more funding to high-need communities.
However, they really are much more than a technical means of producing
more targeting to high-need communities. And for that reason, I would like
to talk about their introduction into the formula in a little more detail.

While these two factors do direct more funding to high-need communities,
they also widen rather than narrow differences in funding among
communities with similar needs, in effect, increasing the error rate if
measured simply in terms of targeting need. The HUD report does not
provide any discussion that would justify allowing funding differences to
widen under this option. The policy question this raises is: Can these

differences be justified by differences in funding capacity or cost
differences?

Clearly, the introduction of per capita income can be justified on the
grounds that it provides a means of taking into account the underlying
economic strength of communities and their ability to fund local needs
from local resources. I would also observe that doing so is consistent
with the administration's Strengthening America's Community Initiative,
which emphasizes indicators of economic conditions such as job loss and
unemployment. However, introducing economic capacity also raises the
question of to what extent should low income places be targeted? For
example, should a community with half the average income be given a grant
that is twice the average, or possibly even more? The HUD study provides
one answer to this question. The subcommittee may wish to consider
possibilities with either a greater or lesser effect.

The inclusion of the metropolitan PCI introduces more controversial issues
as well. This factor, rather than targeting more funding to lowincome
areas, does the opposite. It actually targets more funding to communities
in higher income metropolitan areas. However, the rationale for doing so
is not discussed in HUD's report. One possible reason for introducing
metropolitan PCI as a factor is that it would take account of geographic
differences in the cost-of-living. However, consensus within the research
community has not yet been achieved regarding the magnitude of these cost
differences. Technical experts are therefore unable to provide guidance
regarding how these cost differences may be offset in a funding formula.
As a consequence, there is no objective basis to determine if HUD's use of
metropolitan per capita income is appropriate.

Concluding	In conclusion, the prospect of increasing budgetary stringency
at the federal level appropriately prompts a reexamination of programs
that

Observations 	respond to challenges faced by communities throughout the
nation. The administration's proposal to restructure assistance for
community development opens up important issues regarding how to focus
such aid on the nation's more hard pressed areas.

For the most part, the HUD study does a very effective job of identifying
the critical decisions regarding grant targeting for congressional
consideration. However, additional formula options are not explored as
part of the process of reaching a decision on how best to target CDBG
funding. If program funding continues to decline in inflation-adjusted

dollars, it may be appropriate to go beyond simply a needs-based targeting
policy and consider alternatives to also take into account the underlying
strength of local economies to meet those needs.

Finally, while the formula is a central instrument in targeting program
funding, the criteria used to establish entitlement status could also play
an important role in directing a larger share of program funding to
communities with the greatest need. Rather than the current program's
reliance on population size as the primary criterion, the subcommittee may
also wish to consider either including a needs-based element in
eligibility standards or establishing a minimum threshold allotment in
order to qualify for entitlement status. Finally, the subcommittee may
wish to reconsider the grandfathering provisions that allow communities
that no longer meet eligibility standards to continue participating in the
entitlement program.

In closing, I would like to emphasize that the targeting issues raised by
the HUD report are important no matter what level of financial support
Congress provides for community development activities. The prospect of
reduced support for such efforts, as proposed by the administration, would
make consideration of these targeting issues particularly salient. I would
also note that GAO's report on 21st Century Challenges calls for a
reexamination of federal policies and programs to respond to a growing
fiscal imbalance. Central to such a reexamination is assessing how to
better target federal assistance to those with the greatest need and the
least capacity to meet those needs.

Mr. Chairman, this concludes my statement. I would be happy to answer any
questions you or other members of the subcommittee may have. For future
comments or questions regarding this testimony, please contact Paul L.
Posner, Managing Director for Federal Budget Analysis and
Intergovernmental Relations, at (202) 512-9573. Individuals making key
contributions to this testimony included Jerry C. Fastrup, Michael
Springer, Robert Dinkelmeyer, and Michelle Sager.

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