Community Development Block Grant Formula
Targeting Assistance to High-Need Communities Could Be Enhanced Gao ID: GAO-05-622T April 26, 2005Congress 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.
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. The use of two formulas rather than one is an important reason communities with similar needs do not receive similar funding. The use of population size as a need indicator significantly reduces the extent to which funding is directed to high-need communities. 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. 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 Congress 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.
GAO-05-622T, Community Development Block Grant Formula: Targeting Assistance to High-Need Communities Could Be Enhanced
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Testimony:
Before the Subcommittee on Federalism and the Census, Committee on
Government Reform, House of Representatives:
United States Government Accountability Office:
GAO:
For Release on Delivery Expected at 10:00 a.m. EDT:
Tuesday, April 26, 2005:
Community 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:
GAO Highlights:
Highlights of GAO-05-622T, a report to House Committee on Government
Reform, Subcommittee on Federalism and the Census.
Why GAO Did This Study:
The subcommittee 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. The subcommittee
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.
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.
* The use of two formulas rather than one is an important reason
communities with similar needs do not receive similar funding.
* The use of population size as a need indicator significantly
reduces the extent to which funding is directed to high-need
communities.
* 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.
* 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.
[Hyperlink, http://www.gao.gov/cgi-bin/getrpt?GAO-05-622T].
To view the full product, including the scope
and methodology, click on the link above.
For more information, contact Paul L. Posner, (202) 512-9573,
posnerp@gao.gov.
[End of Section]
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,[Footnote 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 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.
Grant Formula Design Embodies Several Policy Considerations:
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 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
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.
Two Formulas Are Used to Target Program Funding:
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:
* the number of residents (population),
* the number of residents living in poverty, and:
* the number of overcrowded housing units.
Beginning in fiscal year 1978, Congress added a second three-factor
formula that included the following need indicators:
* the number of residents living in poverty,
* the number of older housing units, and:
* 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.
Declining Budget Resources Underscore the Need for More Efficient
Targeting of Available Funding:
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:
[See PDF for image]
[End of figure]
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.
Given the Program's Broadly Defined Purposes, HUD's Evaluation Criteria
for Grant Targeting Appear Reasonable:
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 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:
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:
* 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.
* 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.
* 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.[Footnote 2]
While HUD Formula Options Improve Needs Targeting, Additional Options
Should Also Be Explored before Deciding on a Particular Reform Strategy:
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 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 low-
income 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 Observations:
In conclusion, the prospect of increasing budgetary stringency at the
federal level appropriately prompts a reexamination of programs that
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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FOOTNOTES
[1] GAO, 21st Century Challenges: Reexamining the Base of the Federal
Government, GAO-05-325SP, February 2005.
[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.
