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. 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. 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. 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 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. 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. 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. 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. This is a work of the U.S. government and is not subject to copyright protection in the United States. It may be reproduced and distributed in its entirety without further permission from GAO. 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Summary: 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. 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.