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 
What GAO Found

United States Government Accountability Office

Why GAO Did This Study

Highlights

Accountability Integrity Reliability

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. 

Highlights of GAO-05-622T, a report to 

House Committee on Government 

Reform, Subcommittee on Federalism and 

the Census 

April 26, 2005 

COMMUNITY DEVELOPMENT BLOCK 

GRANT FORMULA 

Targeting Assistance to High-Need 

Communities Could Be Enhanced 

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. 

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. 
Page 1 GAO-05-622T 

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 

 1

GAO, 21st Century Challenges: Reexamining the Base of the Federal Government, 

GAO-05-325SP, February 2005. 
Page 2 GAO-05-622T 

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 
Page 3 GAO-05-622T 

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 
Page 4 GAO-05-622T 

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 

Two Formulas Are 

Used to Target 

Program Funding 
Page 5 GAO-05-622T 

• 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. 

Declining Budget 

Resources 

Underscore the Need 

for More Efficient 

Targeting of Available 

Funding 
Page 6 GAO-05-622T 

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

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 

0

10

20

30

40

50

1975 1980 1985 1990 1995 2000 2005

Fiscal year

Dollars per capita

Current dollars per capita

Constant 2005 dollars per capita

Source: GAO analysis of Census, HUD, and OMB data.
Page 7 GAO-05-622T 

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. 
Page 8 GAO-05-622T 

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.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. 

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 
Page 9 GAO-05-622T 

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 
Page 10 GAO-05-622T 

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. 

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 

Concluding 

Observations 
Page 11 GAO-05-622T 

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. 

(450403) 
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. However, because this work may contain copyrighted images or 

other material, permission from the copyright holder may be necessary if you wish to 

reproduce this material separately. 
The Government Accountability Office, the audit, evaluation and 

investigative arm of Congress, exists to support Congress in meeting its 

constitutional responsibilities and to help improve the performance and 

accountability of the federal government for the American people. GAO 

examines the use of public funds; evaluates federal programs and policies; 

and provides analyses, recommendations, and other assistance to help 

Congress make informed oversight, policy, and funding decisions. GAO’s 

commitment to good government is reflected in its core values of 

accountability, integrity, and reliability. 

The fastest and easiest way to obtain copies of GAO documents at no cost 

is through GAO’s Web site (www.gao.gov). Each weekday, GAO posts 

newly released reports, testimony, and correspondence on its Web site. To 

have GAO e-mail you a list of newly posted products every afternoon, go 

to www.gao.gov and select “Subscribe to Updates.” 

The first copy of each printed report is free. Additional copies are $2 each. 

A check or money order should be made out to the Superintendent of 

Documents. GAO also accepts VISA and Mastercard. Orders for 100 or 

more copies mailed to a single address are discounted 25 percent. Orders 

should be sent to: 

U.S. Government Accountability Office 

441 G Street NW, Room LM 

Washington, D.C. 20548 

To order by Phone: Voice: (202) 512-6000 

TDD: (202) 512-2537 

Fax: (202) 512-6061 

Contact: 

Web site: www.gao.gov/fraudnet/fraudnet.htm 

E-mail: fraudnet@gao.gov

Automated answering system: (800) 424-5454 or (202) 512-7470 

Gloria Jarmon, Managing Director, JarmonG@gao.gov (202) 512-4400 

U.S. Government Accountability Office, 441 G Street NW, Room 7125 

Washington, D.C. 20548 

Paul Anderson, Managing Director, AndersonP1@gao.gov (202) 512-4800 

U.S. Government Accountability Office, 441 G Street NW, Room 7149 

Washington, D.C. 20548 

GAO’s Mission 

Obtaining Copies of 

GAO Reports and 

Testimony 

Order by Mail or Phone 

To Report Fraud, 

Waste, and Abuse in 

Federal Programs 

Congressional 

Relations 

Public Affairs 

PRINTED ON RECYCLED PAPER