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entitled 'Empowerment Zone and Enterprise Community Program: 
Improvements Occurred in Communities, but the Effect of the Program is 
Unclear' which was released on September 25, 2006. 

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Report to Congressional Committees: 

September 2006: 

Empowerment Zone And Enterprise Community Program: 

Improvements Occurred in Communities, but the Effect of the Program Is 
Unclear: 

GAO-06-727: 

GAO Highlights: 

Highlights of GAO-06-727, a report to congressional committees 

Why GAO Did This Study: 

The EZ/EC program is one of the most recent large-scale federal efforts 
intended to revitalize impoverished urban and rural communities. There 
have been three rounds of EZs and two rounds of ECs, all of which are 
scheduled to end no later than December 2009. 

The Community Renewal Tax Relief Act of 2000 mandated that GAO audit 
and report in 2004, 2007, and 2010 on the EZ/EC program and its effect 
on poverty, unemployment, and economic growth. This report, which 
focuses on the first round of the program starting in 1994, discusses 
program implementation; 
program oversight; 
data available on the use of program tax benefits; 
and the program’s effect on poverty, unemployment, and economic growth. 
In conducting this work, GAO made site visits to all Round I EZs, 
conducted an e-mail survey of 60 Round I ECs, and used several 
statistical methods to analyze program effects. 

What GAO Found: 

Round I Empowerment Zones (EZ) and Enterprise Communities (EC) 
implemented a variety of activities using $1 billion in federal grant 
funding from the Department of Health and Human Services (HHS), and as 
of March 2006, the designated communities had expended all but 15 
percent of this funding. Most of the activities that the grant 
recipients put in place were community development projects, such as 
projects supporting education and housing. Other activities included 
economic opportunity initiatives such as job training and loan 
programs. Although all EZs and ECs also reported using the program 
grants to leverage funds from other sources, reliable data on the 
extent of leveraging were not available. 

According to federal standards, agencies should oversee the use of 
public resources and ensure that ongoing monitoring occurs. However, 
none of the federal agencies that were responsible for program 
oversight—including HHS and the departments of Housing and Urban 
Development (HUD) and Agriculture (USDA)—collected data on the amount 
of program grant funds used to implement specific program activities. 
This lack of data limited both federal oversight and GAO’s ability to 
assess the effect of the program. Moreover, because HHS did not provide 
the states and designated communities with clear guidance on how to 
monitor the program grant funds, the extent of monitoring varied across 
the sites. 

In addition, detailed Internal Revenue Service (IRS) data on the use of 
EZ/EC program tax benefits were not available. Previously, GAO cited 
similar challenges in assessing the use of tax benefits in other 
federal programs and stated that information on tax expenditures should 
be collected to ensure that these expenditures are achieving their 
intended purpose. Although GAO recommended in 2004 that HUD, USDA, and 
IRS work together to identify the data needed to assess the EZ/EC tax 
benefits and the cost effectiveness of collecting the information, the 
three agencies did not reach agreement on an approach. 

Without adequate data on the use of program grant funds or tax 
benefits, neither the responsible federal agencies nor GAO could 
determine whether the EZ/EC funds had been spent effectively or that 
the tax benefits had in fact been used as intended. Using the data that 
were available, GAO attempted to analyze changes in several 
indicators—poverty and unemployment rates and two measures of economic 
growth. Although improvements in poverty, unemployment, and economic 
growth had occurred in the EZs and ECs, our econometric analysis of the 
eight urban EZs could not tie these changes definitively to the EZ 
designation. 

What GAO Recommends: 

While not making recommendations, GAO makes observations that should be 
considered if these or similar programs are authorized in the future. 
HHS, HUD, and USDA provided comments. In particular, HUD disagreed with 
the observation that there was a lack of data to perform program 
oversight. 

[Hyperlink, http://www.gao.gov/cgi-bin/getrpt?GAO-06-727. 

To view the full product, including the scope and methodology, click on 
the link above. To view the survey results, click on the following 
link: [Hyperlink, http://www.gao.gov/cgi-bin/getrpt?GAO-06-734SP. For 
more information, contact William B. Shear at (202) 512-8678 or 
ShearW@gao.gov. 

[End of Section] 

Contents: 

Letter: 

Results in Brief: 

Background: 

Round I EZs and ECs Have Used Their Grant Funds to Implement a Wide 
Range of Program Activities: 

Oversight Was Hindered by Limited Program Data and Variation in 
Monitoring: 

Lack of Detailed Tax Data Made It Difficult to Assess the Use of 
Program Tax Benefits: 

In Aggregate, EZs and ECs Showed Some Improvements, but Our Analysis 
Did Not Definitively Link These Changes to the Program: 

Observations: 

Agency Comments and Our Evaluation: 

Appendixes: 

Appendix I: Objectives, Scope, and Methodology: 

Methodology for Site Visits: 

Methodology for Survey of EC Officials: 

Methodology for Qualitative Analysis of Site Visit and EC Survey Data: 

Methodology for Review of Program Oversight: 

Methodology for Survey of EZ Businesses: 

Methodology for Assessing the Effect of the Program on Poverty, 
Unemployment, and Economic Growth: 

Appendix II: Methodology for and Results of Our Econometric Models: 

Description of Our Models: 

Results of Our Models for Poverty: 

Results of Our Models for Unemployment: 

Results of Our Models for Economic Growth: 

Other Variables Tested for Use in Our Econometric Models: 

Appendix III: List of Communities Designated in Round I of the EZ/EC 
Program: 

Appendix IV: Description of the Empowerment Zones and Enterprise 
Communities We Visited: 

Atlanta Empowerment Zone: 

Baltimore Empowerment Zone: 

Chicago Empowerment Zone: 

Detroit Empowerment Zone: 

New York Empowerment Zone: 

Philadelphia-Camden Empowerment Zone: 

Cleveland Empowerment Zone: 

Los Angeles Empowerment Zone: 

Kentucky Highlands Empowerment Zone: 

Mid-Delta Mississippi Empowerment Zone: 

Rio Grande Valley, Texas Empowerment Zone: 

Providence, Rhode Island Enterprise Community: 

Fayette-Haywood, Tennessee Enterprise Community: 

Appendix V: Comments from the Department of Health and Human Services: 

Appendix VI: Comments from the Department of Housing and Urban 
Development: 

Appendix VII: Comments from the U.S. Department of Agriculture: 

Appendix VIII: GAO Contact and Staff Acknowledgments: 

Tables: 

Table 1: Round I EZ/EC Program Criteria and Benefits: 

Table 2: National Poverty, Unemployment, Economic Growth Data for 1990 
to 2004: 

Table 3: Total EZ/EC Grant Funding Remaining as of March 31, 2006: 

Table 4: Number of Stakeholders Interviewed for EZ and EC Site Visits, 
by Type: 

Table 5: Coding of Data Reliability of HUD and USDA Performance 
Systems: 

Table 6: Confidence Intervals for Average Household Income and Average 
Housing Value in Constant 2004 Dollars: 

Table 7: Factors Selected for Choosing Comparison Tracts: 

Table 8: Estimates of the Association between the EZ Program and the 
Change in Poverty Rate, 1990-2000: 

Table 9: Estimates of the Association between the EZ Program and the 
Change in Unemployment Rate, 1990-2000: 

Table 10: Estimates of the Association between the EZ Program and 
Economic Growth, Measured by the Change in the Number of Businesses, 
from 1995-1999: 

Table 11: Estimates of the Association between the EZ Program and 
Economic Growth, Measured by the Change in the Number of Jobs, 1995- 
1999: 

Table 12: Alternative Variables Considered in Our Analyses: 

Table 13: Changes in Selected Census Variables Observed in the Atlanta 
EZ and Its Comparison Area: 

Table 14: Changes in Selected Economic Growth Variables Observed in the 
Atlanta EZ and Its Comparison Area: 

Table 15: Changes in Selected Census Variables Observed in the 
Baltimore EZ and Its Comparison Area: 

Table 16: Changes in Selected Economic Growth Variables Observed in the 
Baltimore EZ and Its Comparison Area: 

Table 17: Changes in Selected Census Variables Observed in the Chicago 
EZ and Its Comparison Area: 

Table 18: Changes in Selected Economic Growth Variables Observed in the 
Chicago EZ and Its Comparison Area: 

Table 19: Changes in Selected Census Variables Observed in the Detroit 
EZ and Its Comparison Area: 

Table 20: Changes in Selected Economic Growth Variables Observed in the 
Detroit EZ and Its Comparison Area: 

Table 21: Changes in Selected Census Variables Observed in the New York 
EZ, the Bronx and Upper Manhattan (UM) Portions, and the EZ Comparison 
Area (Comp.): 

Table 22: Changes in Selected Economic Growth Variables Observed in the 
New York EZ, the Bronx and Upper Manhattan (UM) Portions, and the EZ 
Comparison Area (Comp.): 

Table 23: Changes in Selected Census Variables Observed in the 
Philadelphia-Camden EZ, the Camden (Cam.) and Philadelphia (Phila.) 
Portions, and the EZ Comparison Area (Comp.): 

Table 24: Changes in Selected Economic Growth Variables Observed in the 
Philadelphia-Camden EZ, the Camden (Cam.) and Philadelphia (Phila.) 
Portions, and the EZ Comparison Area (Comp.): 

Table 25: Changes in Selected Census Variables Observed in the 
Cleveland EZ and Its Comparison Area: 

Table 26: Changes in Selected Economic Growth Variables Observed in the 
Cleveland EZ and Its Comparison Area: 

Table 27: Changes in Selected Census Variables Observed in the Los 
Angeles EZ and Its Comparison Area: 

Table 28: Changes in Selected Economic Growth Variables Observed in the 
Los Angeles EZ and Its Comparison Area: 

Table 29: Changes in Selected Census Variables Observed in the Kentucky 
Highlands EZ: 

Table 30: Changes in Selected Economic Growth Variables Observed in the 
Kentucky Highlands EZ: 

Table 31: Changes in Selected Census Variables Observed in the Mid-
Delta EZ: 

Table 32: Changes in Selected Economic Growth Variables Observed in the 
Mid-Delta EZ: 

Table 33: Changes in Selected Census Variables Observed in the Rio 
Grande Valley EZ: 

Table 34: Changes in Selected Economic Growth Variables Observed in the 
Rio Grande Valley EZ: 

Table 35: Changes in Selected Census Variables Observed in the 
Providence EC: 

Table 36: Changes in Selected Economic Growth Variables Observed in the 
Providence EC: 

Table 37: Changes in Selected Census Variables Observed in the Fayette- 
Haywood EC: 

Table 38: Changes in Selected Economic Growth Variables Observed in the 
Fayette-Haywood EC: 

Figures: 

Figure 1: Oversight Responsibilities in Round I of the EZ/EC Program: 

Figure 2: Remaining Grant Funds by EZ as of March 31, 2006: 

Figure 3: Distribution of EZ and EC Activities by Key Program 
Principle: 

Figure 4: Types of Activities Implemented by Urban and Rural EZs and 
ECs, by Percent of Total Activities: 

Figure 5: Local Government Involvement in Decision Making in the Urban 
EZs:  

Figure 6: Changes in Poverty, Unemployment, and Two Measures of 
Economic Growth Observed in Round I EZs: 

Figure 7: Number and Percentage of EZs and ECs Experiencing a Decrease 
in Poverty from 1990 to 2000: 

Figure 8: Comparison of Decreases in Poverty in Urban and Rural 
Designated Areas and Comparison Areas from 1990 to 2000: 

Figure 9: Number and Percentage of EZs and ECs that Experienced a 
Decrease in Unemployment from 1990 to 2000: 

Figure 10: Comparison of Decreases in Unemployment in Urban and Rural 
Designated Areas and Comparison Areas from 1990 to 2000: 

Figure 11: Number and Percentage of EZs and ECs That Experienced an 
Increase in One or Both Measures of Economic Growth between 1995 and 
2004: 

Figure 12: Comparison of Changes in the Number of Businesses and the 
Number of Jobs in Urban and Rural Designated Areas and Comparison Areas 
between 1995 and 2004: 

Figure 13: Map of the Atlanta EZ and Its Comparison Area: 

Figure 14: Activities Implemented by the Atlanta EZ: 

Figure 15: Map of the Baltimore EZ and Its Comparison Area: 

Figure 16: Activities Implemented by the Baltimore EZ: 

Figure 17: Map of the Chicago EZ and Its Comparison Area: 

Figure 18: Activities Implemented by the Chicago EZ: 

Figure 19: Map of the Detroit EZ and Its Comparison Area: 

Figure 20: Activities Implemented by the Detroit EZ: 

Figure 21: Map of the New York EZ and Its Comparison Area: 

Figure 22: Activities Implemented by the Upper Manhattan portion of the 
New York EZ: 

Figure 23: Activities Implemented by the Bronx portion of the New York 
EZ: 

Figure 24: Map of the Philadelphia-Camden EZ and Its Comparison Area: 

Figure 25: Activities Implemented by the Philadelphia Portion of the 
Philadelphia-Camden EZ: 

Figure 26: Activities Implemented by the Camden Portion of the 
Philadelphia-Camden EZ: 

Figure 27: Map of the Cleveland EZ and Its Comparison Area: 

Figure 28: Activity Implemented by the Cleveland EZ: 

Figure 29: Map of the Los Angeles EZ and Its Comparison Area: 

Figure 30: Activity Implemented by the Los Angeles EZ: 

Figure 31: Map of the Kentucky Highlands EZ: 

Figure 32: Activities Implemented by the Kentucky Highlands EZ: 

Figure 33: Map of the Mid-Delta EZ: 

Figure 34: Activities Implemented by the Mid-Delta EZ: 

Figure 35: Map of the Rio Grande Valley EZ: 

Figure 36: Activities Implemented by the Rio Grande Valley EZ: 

Figure 37: Map of the Providence EC: 

Figure 38: Activities Implemented by the Providence EC: 

Figure 39: Map of the Fayette-Haywood EC: 

Figure 40: Activities Implemented by the Fayette-Haywood EC: 

Abbreviations: 

EC: Enterprise Community: 

EZ: Empowerment Zone: 

HHS: Department of Health and Human Services: 

HUD: Department of Housing and Urban Development: 

IRS: Internal Revenue Service: 

USDA: U.S. Department of Agriculture: 

September 22, 2006: 

The Honorable Charles E. Grassley: 
Chairman: 
The Honorable Max Baucus: 
Ranking Minority Member: 
Committee on Finance: 
United States Senate: 

The Honorable William M. Thomas: 
Chairman: 
The Honorable Charles B. Rangel: 
Ranking Minority Member: 
Committee on Ways and Means: 
House of Representatives: 

The Empowerment Zone and Enterprise Community (EZ/EC) program is one of 
the most recent in a series of large-scale federal efforts intended to 
address one of the nation's most persistent challenges--the 
revitalization of impoverished urban and rural communities. When it was 
enacted in 1993, the EZ/EC program provided grants to public and 
private entities for social services and community redevelopment and 
tax benefits to local businesses to attract or retain jobs and 
businesses in distressed communities. The program differs from earlier 
initiatives with similar goals in that it emphasizes the role of local 
communities in identifying solutions and the use of public-private 
partnerships to attract the investment necessary for sustainable 
economic and community development. To date, Congress has authorized 
three rounds of EZs and two rounds of ECs. Communities designated under 
Round I of the program shared a total of $1 billion in federal grant 
funding and also received tax and other benefits. The EZs received the 
bulk of this funding--$720 million in total--as well as more extensive 
tax benefits than the ECs. Communities designated in the two subsequent 
rounds of the program received a smaller amount of federal funding and 
more tax benefits. All three rounds of the EZ/EC program are scheduled 
to end no later than December 31, 2009. 

The Community Renewal Tax Relief Act of 2000 mandated that we audit and 
report in 2004, 2007, and 2010 on the EZ/EC program and a later 
initiative, the Renewal Community program, and their effect on poverty, 
unemployment, and economic growth.[Footnote 1] This report, the second 
of the mandated series, focuses on the first round of communities 
designated as EZs and ECs in 1994. It (1) describes how the designated 
communities implemented Round I of the EZ/EC program; (2) evaluates the 
extent of federal, state, and local oversight of the program; (3) 
examines the extent to which data are available to assess the use of 
program tax benefits; and (4) analyzes the effects that the Round I EZs 
and ECs had on poverty, unemployment, and economic growth in their 
communities. 

To address our first three objectives, we made site visits to all 11 
Round I EZs and 2 of the 95 ECs--1 urban and 1 rural--to interview 
stakeholders and review documentation.[Footnote 2] To gather 
information from the ECs, we administered an e-mail survey to officials 
from the 60 Round I ECs that were still in operation as of June 2005 
and did not receive a subsequent designation.[Footnote 3] We chose to 
exclude the 34 ECs that received subsequent designations, because we 
did not want their responses to be influenced by those programs. 
Because the states distributed the federal funding to the communities, 
we conducted telephone interviews with state officials in the 13 states 
containing the EZs and ECs that we visited. In addition, we interviewed 
officials from the federal agencies with primary responsibility for the 
program--the Department of Health and Human Services (HHS), the 
Department of Housing and Urban Development (HUD), the Internal Revenue 
Service (IRS), and the U.S. Department of Agriculture (USDA). We also 
analyzed fiscal and program data from the agencies and assessed the 
reliability of these data.[Footnote 4] To address our fourth objective, 
that is, the effect of the program on poverty, unemployment, and 
economic growth, we used several methods. First, we calculated the 
changes in the poverty and unemployment rates from 1990 to 2000 and 
measures of economic growth from 1995 to 2004 in the designated EZs and 
ECs and in comparison areas selected for their similarities to the 
designated communities.[Footnote 5] Then, we used econometric models to 
assess the effects of the program. Finally, we used testimonial 
information gathered during our site visits and our survey results to 
help put these changes in context. 

We conducted our work between November 2004 and July 2006 in accordance 
with generally accepted government auditing standards. Appendix I lists 
the communities we visited. Appendixes I and II provide details on our 
methodology, and appendix III shows a list of communities designated in 
Round I of the EZ/EC program. Appendix IV provides details on each of 
the sites we visited. 

Results in Brief: 

Round I EZs and ECs used most of the $1 billion in program grant funds 
to implement a wide range of activities designed to help revitalize the 
designated communities. As of March 31, 2006, 20 percent of the $720 
million that EZs received and 2 percent of the $280 million that ECs 
received remained unspent, and some designees had received extensions 
of the original 10-year grant period that was set to expire in 2004. In 
general, EZs and ECs undertook more community development activities in 
areas such as education, housing, and infrastructure than they did 
economic opportunity activities such as job training and assistance to 
businesses. Although stakeholders from all EZs and ECs reported using 
the program grants to leverage funds from other sources and some said 
that they had required subgrantees to obtain other funds as a condition 
of receiving EZ/EC funds, reliable data on the extent of leveraging 
were not available. EZ and EC designees also reported other 
accomplishments and challenges and utilized a variety of governance 
structures to implement these activities. 

Data were not collected on program benefits for specific activities, 
limiting the ability of federal agencies to oversee the program, and 
the monitoring performed at the state and local levels varied. 
According to our Standards for Internal Control in the Federal 
Government, federal agencies should oversee the use of public resources 
and ensure that ongoing monitoring occurs.[Footnote 6] However, the 
three agencies responsible for overseeing the program--HHS, HUD, and 
USDA--did not collect data on how program funds were used. For 
instance, HHS data show that EZs and ECs have used most of the EZ/EC 
grant funds but do not show the specific activities or types of 
activities for which the funds were used. And, although the performance 
reporting systems maintained by HUD and USDA do contain some 
information on activities that were carried out, they do not contain 
information on how much of the EZ/EC funds actually were used for 
specific activities or types of activities.[Footnote 7] Further, HHS 
did not provide the states, EZs, and ECs with clear guidance on how to 
monitor the program grant funds, so the types and extent of monitoring 
performed by state and local participants varied. To some degree, the 
lack of reporting requirements may be an outcome of the program's 
design, which was intended to give communities flexibility in using 
program funds and relied on multiple agencies for oversight. But the 
result has been that little information is available on the amount of 
funds spent on specific activities, hindering the agencies' efforts to 
oversee the program. 

Similarly, only limited data are available on the use of EZ/EC tax 
benefits, which were estimated to be much more substantial than the 
amount of program grant funds. We have stated that information on tax 
expenditures should be collected to ensure that these expenditures are 
achieving their intended purpose.[Footnote 8] In 2004, we reported that 
IRS collected data on some but not all of the program tax benefits and 
that the data could not be linked to the individual 
communities.[Footnote 9] We also recommended that HUD, USDA, and IRS 
work together to identify the data needed to measure the use of EZ/EC 
tax benefits and the cost-effectiveness of collecting the information, 
but the three agencies did not reach agreement on a cost-effective 
approach.[Footnote 10] During our work for this report, officials from 
some EZs and ECs told us that some local businesses were using the tax 
benefits. However, these testimonial data were neither sufficient to 
allow us to determine the actual amount of tax benefits used by EZs and 
ECs nationwide nor to assess the extent to which the program tax 
benefits contributed to the achievement of program goals. 

Although improvements in poverty, unemployment, and economic growth had 
occurred in the EZs and ECs, our econometric analysis of the eight 
urban EZs could not tie these changes definitively to the EZ 
designation.[Footnote 11] As mentioned in our previous report, 
measuring the effect of initiatives such as the EZ/EC program is 
difficult for a number of reasons, such as data limitations and the 
difficulty of determining what would have happened in the absence of 
the program.[Footnote 12] Given these limitations, the effects of the 
EZ/EC program remain unclear. In some cases, communities did see 
decreases in poverty and unemployment and increases in economic growth. 
However, when we used econometric analyses to separate the effect of 
the program from other nonprogram factors we found that the comparison 
tracts we selected showed changes that were similar to those in the 
urban EZs. Further, EZ stakeholders and EC survey respondents said that 
program-related factors had influenced changes in their communities but 
noted that other unrelated factors had also had an effect. For example, 
stakeholders who observed a decrease in poverty in their communities 
believed that this change had resulted in part from EZ/EC activities, 
but they also noted that the population in their communities had 
changed, with original EZ/EC residents moving out of the area and 
individuals with higher incomes moving in. Ultimately, the evaluation 
techniques we developed were limited by the absence of data on the use 
of program grants and tax benefits. 

While all three rounds of the EZ/EC program are scheduled to end no 
later than December 31, 2009, we observe two limitations that should be 
considered if these or similar programs are authorized in the future. 
These include (1) oversight limitations that occurred because data were 
not collected on how program grant funds were used for specific 
activities and (2) the limited ability to evaluate the effect of the 
program due to the lack of data on the use of program grant funds, the 
extent of leveraging, and the extent to which program tax benefits were 
used. Given the magnitude of federal grant funds and tax benefits 
provided for the program, more should be done to better understand the 
extent to which these federal expenditures are having the desired 
effect. 

We provided a draft of this report to HHS, HUD, IRS, and USDA. We 
received comments from HHS, HUD, and USDA. HHS commented that a 
statement made in our report--that the agency did not provide guidance 
detailing the steps state and local authorities should take to monitor 
the program--unfairly represented the relationship between HHS and the 
other federal agencies that administered the EZ/EC program. However, we 
note in our report that the program's design may have led to a lack of 
clarity in oversight since no single federal agency had sole oversight 
responsibility. Nonetheless, we believe that, in accordance with 
federal standards, each of the federal agencies that administered the 
program bore at least some responsibility for ensuring that public 
resources were being used effectively and that program goals were being 
met. HUD disagreed with our observation that there was a lack of data 
on the use of program grant funds, the amount of funds leveraged, and 
the use of tax benefits. However, although we found evidence that 
activities were carried out with program funds, information contained 
in HUD's performance reporting system on the amounts of funds used and 
the amounts leveraged was not reliable. Both HUD and USDA provided 
suggestions for future evaluations of similar programs. The agencies' 
comments are discussed later in the report and are reproduced in 
appendixes V through VII. HHS, HUD and USDA also provided technical 
comments that we incorporated into the report where appropriate. 

Background: 

The concept behind the EZ/EC program originated in Great Britain in 
1978 with the inception of the Enterprise Zone program. The main 
objective of the Enterprise Zone program was to foster an attractive 
business environment in specific areas where economic growth was 
lacking. In the United States, some states began to administer similar 
state Enterprise Zones in the 1980s. In 1993, the federal government 
established the federal EZ/EC program to help reduce unemployment and 
revitalize economically distressed areas. The authorizing legislation 
established the eligibility requirements and the package of grants and 
tax benefits for the EZ/EC program (table 1). Multiagency teams from 
HHS, HUD, USDA, and other federal agencies reviewed the applications in 
Round I, and HUD and USDA issued designations based on the 
effectiveness of communities' strategic plans, assurances that the 
plans would be implemented, and geographic diversity.[Footnote 13] In 
Round I, HUD designated a total of 8 urban EZs and 65 urban ECs, and 
USDA designated 3 rural EZs and 30 rural ECs.[Footnote 14] 

Table 1: Round I EZ/EC Program Criteria and Benefits: 

Eligibility criteria; 
To be considered for the program, communities were required to select 
census tracts that; 
* had above-average poverty according to 1990 Census data;; 
* had unemployment rates of at least the national average according to 
1990 Census data;; 
* met certain 1990 population and area criteria; 
and; 
* exhibited other conditions of distress, such as high crime, 
deteriorating infrastructure, or population decline. 

In addition, they were required to submit a strategic plan that 
addressed the four key principles of the program: 
* economic opportunity,; 
* sustainable community development,; 
* community-based partnerships, and; 
* strategic vision for change. 

EZ program benefits; 
Round I EZs received Title XX Social Services Block Grants (EZ/EC 
grants); 
* Six urban EZs each received $100 million.[A]; 
* Three rural EZs each received $40 million. 

Businesses located in EZs initially received three tax benefits: 
* a tax credit for wages paid to employees who both live and work in an 
EZ,; 
* an increased expensing deduction for depreciable property, and; 
* tax- exempt bonds that could be used to issue loans to qualified 
businesses for financing certain property; 
By 2002, businesses in EZs also became eligible for two additional tax 
benefits related to the treatment of gains on the sale of EZ assets and 
stock. 

EC program benefits; 
95 Round I ECs each received $2.95 million in EZ/ EC grants; 
Businesses located in ECs were eligible for one program tax benefit, 
the tax-exempt bond financing. 

Source: GAO. 

[A] This does not include two additional urban communities--Cleveland 
and Los Angeles--that initially received Supplemental EZ designations 
and received full Round I EZ status in 1998, because they did not 
receive EZ/EC grant funds. 

[End of table] 

HHS provided Round I EZs and ECs with a total of $1 billion in EZ/EC 
grant funds. EZs and ECs were allowed to use the EZ/EC grants for a 
broader range of activities than was generally allowed with those types 
of HHS funds. For instance, EZs and ECs could use funding for 
"traditional" activities, such as skills training programs for 
disadvantaged youth or drug and alcohol treatment programs, as well as 
for additional activities, such as the purchase of land or facilities 
related to an eligible program or the capitalization of a revolving 
loan fund. EZs and ECs were also permitted to use grant funds to cover 
some administrative costs and to change their goals and activities over 
time, with approval from HUD or USDA. In addition, HUD and USDA 
expected EZs and ECs to use the EZ/EC grant to leverage additional 
investment. 

Businesses operating in EZs and ECs were eligible for a substantial 
amount of program tax benefits. In 1993, the Joint Committee on 
Taxation estimated that the tax benefits available to businesses in 
Round I communities would result in a $2.5 billion reduction in tax 
revenues between 1994 and 1998. In 2000, the committee estimated that 
the combination of EZ/EC program tax benefits and the Renewal Community 
tax benefits would reduce tax revenues by a total of $10.9 billion 
between 2001 and 2010.[Footnote 15] The tax benefits for ECs expired in 
2004, and the tax benefits for all EZs and Renewal Communities are 
currently set to expire at the end of 2009. 

Four federal agencies are responsible for administering the program in 
Round I. Oversight responsibilities for Round I were divided among 
three agencies, with HHS providing fiscal oversight and HUD and USDA 
providing program oversight (fig. 1). HHS issued grants to the states, 
which served as pass-through entities--that is, they distributed funds 
to individual EZs and ECs. According to their regulations, HUD and USDA 
are required to evaluate the progress each EZ and EC made on its 
strategic plan based on information gathered on site visits and on 
information reported to them by the designated communities. In 
addition, IRS is responsible for administering the program tax 
benefits. 

Figure 1: Oversight Responsibilities in Round I of the EZ/EC Program: 

[See PDF for image] 

Source: GAO analysis. 

[End of figure] 

In assessing the extent of EZ/EC program improvements, it is useful to 
understand the overall national trends in poverty, unemployment, and 
economic growth. National trends in these indicators have varied since 
Round I of the program was established. As shown in table 2, the 
national poverty and unemployment rates showed improvements (i.e., 
declines) in 2000 compared with 1990, but both were somewhat higher in 
2004. In 1990, Round I EZs and ECs had poverty and unemployment rates 
that exceeded these national averages, as was required for program 
eligibility. 

Table 2: National Poverty, Unemployment, Economic Growth Data for 1990 
to 2004: 

Indicator: Poverty; 
1990: 13.5%; 
1995: 13.8%; 
2000: 11.3%; 
2003: 12.5%; 
2004: 12.7%. 

Indicator: Unemployment; 
1990: 5.6%; 
1995: 5.6%; 
2000: 4.0%; 
2003: 6.0%; 
2004: 5.5%. 

Indicator: Number of businesses; 
1990: 6.1 million; 
1995: 6.6 million; 
2000: 7.1 million; 
2003: 7.3 million; 
2004: [A]. 

Indicator: Number of jobs; 
1990: 93.4 million; 
1995: 100.3 million; 
2000: 114.1 million; 
2003: 113.4 million; 
2004: [A]. 

Sources: Census Bureau and Bureau of Labor Statistics. 

[A] Data were not yet available for 2004. 

[End of table] 

In terms of economic growth, the table shows that the number of 
businesses increased gradually between 1990 and 2003, and the number of 
jobs increased from 1990 to 2000 but fell slightly between 2000 and 
2003. 

Round I EZs and ECs Have Used Their Grant Funds to Implement a Wide 
Range of Program Activities: 

EZs and ECs used most of the program grant funds to implement a wide 
range of activities to carry out their respective revitalization 
strategies. In total, as of March 31, 2006, EZs and ECs had used all 
but 15 percent of the available grants. EZs and ECs implemented a 
variety of activities, but, in general, focused more on community 
development than economic opportunity. In addition, all designated 
communities reported leveraging additional resources, though a lack of 
reliable data prevented us from determining how much. Several designees 
also noted other accomplishments, such as increasing local coordination 
and capacity. The governance structures that Round I EZs and ECs 
established to implement these activities varied and included 
organizations to manage the day-to-day operations of the EZs, boards, 
and advisory committees. 

Most EZ/EC Grant Funds Have Been Expended, but Many EZs and Some ECs 
Received Grant Extensions: 

As of March 31, 2006, Round I EZs and ECs had spent all but 15 percent 
of the program grant funds they received. HHS data show that 20 percent 
of the program grant funds provided to EZs and 2 percent of the funds 
provided to ECs were unspent (table 3). In addition, HUD data show that 
the Cleveland and Los Angeles EZs, which originally received 
Supplemental EZ designations, had used significant portions of the 
Economic Development Initiative grants and Section 108 Loan Guarantees 
that came with their designations.[Footnote 16] Specifically, each of 
them had spent slightly more than 70 percent of their grants; 
Cleveland had used 72 percent of its loan guarantees, but Los Angeles 
had used less--about 33 percent. 

Table 3: Total EZ/EC Grant Funding Remaining as of March 31, 2006: 

EZs; 
Total funding: $720 million; 
Amount remaining: $146.6 million; 
Percent remaining: 20%. 

ECs; 
Total funding: $280 million; 
Amount remaining: $4.5 million; 
Percent remaining: 2%. 

Total; 
Total funding: $1 billion; 
Amount remaining: $151 million; 
Percent remaining: 15%. 

Source: GAO analysis of HHS data. 

[End of table] 

Most of the remaining $151 million in EZ/EC grants consists of the 
funds of four urban EZs: Atlanta, New York, Philadelphia-Camden, and 
Chicago, with Atlanta and New York accounting for the majority of the 
unspent funds (fig. 2). When the Atlanta EZ received a Renewal 
Community designation from HUD in 2002, the EZ designation was 
terminated, but HHS allowed the city of Atlanta to continue spending 
its remaining EZ grant funds through December 2009. The city of Atlanta 
elected to administer its remaining EZ grants in conjunction with its 
Renewal Community initiative, and prepared a strategic plan to address 
administration of both the remaining HHS funds and the HUD-designated 
Renewal Community. The Atlanta Renewal Community officials told us that 
they did not use the EZ funds for about 4 years after receiving the 
designation because of the time required for start-up but added that 
they planned to begin utilizing the funds soon. The New York EZ 
received matching funds from both the state and city governments, for a 
total of $300 million. New York EZ officials stated that they used 
equal parts of funding from these three sources for each activity, 
potentially explaining why they have drawn down funds at a slower rate 
than other EZs. 

Figure 2: Remaining Grant Funds by EZ as of March 31, 2006: 

[See PDF for image] 

Source: GAO analysis of HHS data. 

Note: Two urban EZs--Philadelphia-Camden and New York--implemented the 
program through two separate entities that split the $100 million 
grant. These separate entities are represented above for Philadelphia- 
Camden, but separate data for the New York EZ were not available from 
HHS. The Cleveland and Los Angeles EZs did not receive EZ grant funds. 

[End of figure] 

Although the grant period for Round I EZs and ECs was originally 
scheduled to end December 21, 2004, several EZs and some ECs received 
extensions from HHS to continue drawing down their remaining funds. The 
recipients had to demonstrate a legitimate need to complete project 
activities outlined in their strategic plans. Eight of the 11 EZs (6 
urban, 2 rural) and 17 of the 95 ECs (11 urban and 6 rural) received 
extensions of their grants until December 31, 2009. In addition, 1 
urban EZ and 9 ECs (6 urban and 3 rural) received extensions for a 
shorter time frame, such as 2005, 2006, or 2007. 

EZs and ECs Implemented a Wide Variety of Activities, Most Related to 
Community Development: 

The designated communities were encouraged to implement both community 
and economic development activities as part of their revitalization 
strategies. The EZ/EC program was designed to be tailored to address 
local needs, and the type of grant funds most EZs and ECs received from 
HHS allowed them to implement a wide range of activities. Overall, both 
EZs and ECs used the program grants to implement a larger number of 
community development activities--such as education, health care, and 
infrastructure--than economic opportunity activities--such as workforce 
development and providing assistance to businesses (fig. 3).[Footnote 
17] 

Figure 3: Distribution of EZ and EC Activities by Key Program 
Principle: 

[See PDF for image] 

Source: GAO analysis of HUD and USDA data. 

Note: This figure shows the percent of the total number of activities 
implemented, not the funds devoted to those activity types. The 
Cleveland and Los Angeles EZs are not included in this graphic because 
they did not receive EZ grant funds. The numbers do not always add up 
to 100 due to rounding. 

[End of figure] 

The activities most often implemented by urban EZs and ECs were 
workforce development, human services, education, and assistance to 
businesses, which accounted for more than 50 percent of the activities 
in urban EZs and 60 percent of the activities in urban ECs (fig. 4). 
For example, the Baltimore EZ implemented a customized training program 
that provided EZ residents with individualized training and a stipend 
during the training period. In the Bronx portion of the New York EZ, 
stakeholders explained that they had funded an organization that 
trained women to become child care providers, a program that not only 
provided job skills and employment opportunities but also improved the 
availability of child care in the area. In addition, the Atlanta EZ and 
the Camden portion of the Philadelphia-Camden EZ implemented 
educational programs for EZ youth, such as after-school or summer 
programs. Also, stakeholders from the Upper Manhattan portion of the 
New York EZ mentioned contributing financial assistance to the business 
development of the Harlem USA project, a 275,000-square-foot retail 
development located in the EZ. Moreover, stakeholders from the 
Providence EC said they provided grants to a nonprofit that offered job 
training to youth and business development programs, such as "business 
incubators" that offered office space and technical assistance to new 
small businesses. 

Figure 4: Types of Activities Implemented by Urban and Rural EZs and 
ECs, by Percent of Total Activities: 

[See PDF for image] 

Source: GAO analysis of HUD and USDA data. 

Note: This figure shows the percent of the total number of activities 
implemented, not the funds devoted to those activity types. The data 
reporting systems for urban and rural designees used slightly different 
categories of activities. The Cleveland and Los Angeles EZs are not 
included in this graphic because they did not receive EZ grant funds. 

[End of figure] 

Rural EZs and ECs implemented many of the same types of activities as 
urban designees, such as business development and job training, but 
often included activities related to health care and public 
infrastructure. For example, stakeholders from the Kentucky Highlands 
and Mid-Delta Mississippi EZs said that they had attracted businesses 
to the areas using EZ loans, grants, or tax benefits, and stakeholders 
from the Rio Grande Valley EZ reported funding job training for EZ 
residents. In addition, stakeholders from Kentucky Highlands said the 
EZ purchased ambulances for an area that previously did not have those 
services. All three rural EZs reported using the EZ/EC grant to improve 
the water or sewerage infrastructure in their EZs, which some said was 
needed to foster additional economic development. Finally, stakeholders 
from the Fayette-Haywood EC reported having implemented several 
activities related to health care, such as recruiting doctors and 
providing funding to reopen a clinic that had been closed for several 
years. For more information on the types of activities implemented by 
the individual communities we visited, see appendix IV. 

EZs and ECs Used Program Grants to Leverage Additional Funds, but 
Reliable Data on the Extent of Leveraging Are Not Available: 

HUD and USDA also expected designees to use their grants to leverage 
additional investment. Stakeholders from all EZs and ECs we visited and 
all EC survey respondents reported having used their EZ/EC grants to 
leverage other resources, including both monetary and in-kind 
donations. EZs and ECs developed different policies that may have 
affected the extent to which they leveraged funds. For example, the Mid-
Delta EZ required that direct grant recipients obtain at least 65 
percent of their funding from other sources. Some other communities, 
such as the Atlanta EZ, did not have similar requirements for 
subgrantees, although in some cases subgrantees did leverage funds on 
their own initiative. EC survey respondents reported using the EZ/EC 
grants to leverage additional resources for capital improvements, 
social services, and funding for businesses, among other things. Some 
EC survey respondents also mentioned that the designation had helped 
them to leverage funds to implement additional programs or to expand EC 
programs. 

All EZs and ECs that provided us with a definition of leveraging said 
that they included all non-EZ/EC grant funds that were used in EZ/EC- 
funded programs. But only two of the four EZs that used the program tax-
exempt bond included the amount of the bonds in their total leveraged 
funds. In addition, some EZs reported as leveraged funds other 
investments made in the EZ area, aside from those directly funded with 
the EZ/EC grant funds, although other designated communities did not. 
For example, the Baltimore EZ included all business investments made 
subsequent to infrastructure improvements the EZ made to an industrial 
park. 

USDA encouraged rural EZs and ECs to report all investment in the EZ as 
leveraged funds, not only those projects that received EZ/EC funds. For 
example, at USDA's instruction, the Fayette-Haywood EC included funding 
from other USDA programs operating in the EC, even when EC funds were 
not involved. However, not all rural sites used this broad definition 
of leveraging. Similarly, at one HUD official's instruction, the 
Cleveland EZ included as leveraged funds other investments made within 
the EZ, such as city Community Development Block Grant funds invested 
in the area.[Footnote 18] However, there was no written guidance 
telling the Cleveland EZ to include other investments, and it no longer 
includes these other investments as leveraged funds in performance 
reports.[Footnote 19] 

Although communities reported using the EZ/EC grants to leverage 
additional resources, we could not verify the actual amounts. HUD's and 
USDA's performance reporting systems include information on the amount 
of funds leveraged for each activity, but for the sample of activities 
we reviewed, either supporting documentation showed an amount 
conflicting with the reported amount or documentation could not be 
found.[Footnote 20] In addition, the definition of "leveraged" varied 
across sites, as the federal agencies did not provide EZs and ECs with 
a consistent definition of what leveraged funds should include. As a 
result, designated communities included different types of funds in the 
amounts they reported as leveraged. 

Designees Reported Other Accomplishments: 

In addition to the activities that were implemented, EZ and EC 
stakeholders with whom we spoke mentioned other accomplishments that 
were not as easy to quantify and report in the performance systems. For 
example, one of the aims of the EZ/EC program was to increase 
collaboration among local governments, nonprofits, community members, 
and the business community. Stakeholders from several sites we visited 
commented on how the designation facilitated increased collaboration 
among different groups of people and organizations. For instance, 
several stakeholders from the Rio Grande Valley EZ noted the value of 
having different communities and people work together, something that 
had not happened prior to the EZ/EC program. Several EC survey 
respondents also mentioned the importance of collaboration and 
partnerships in carrying out the EC program. Stakeholders from some 
sites we visited mentioned that the EZ/EC program had helped to empower 
local residents by giving them a better understanding of how government 
worked. In addition, stakeholders from some EZs said that the EZ/EC 
program had helped to build the capacity of local organizations. In 
Cleveland, local stakeholders said that the funding provided by the EZ 
had helped increase the organizational capacity of four local community 
development corporations and that participation in the governance of 
the EZ helped to foster communication between the groups. 

Designees Reported Implementation Challenges: 

EZ stakeholders also mentioned some issues that had made implementing 
the EZ/EC program more challenging. Stakeholders from some EZs noted 
that an initial lack of experience or expertise on the part of EZ 
officials had made it difficult to implement the program. In addition, 
stakeholders from the Camden portion of the Philadelphia-Camden EZ and 
the Rio Grande Valley EZ said that local subgrantee organizations 
generally had a low level of organizational capacity, which sometimes 
made it difficult to choose qualified applicants to implement EZ 
programs. Stakeholders from several sites also said that it was 
difficult to manage the expectations of both the EZ community and of 
residents and businesses that were not located in the zones and were 
not eligible for EZ/EC program benefits, especially when the 
individuals and businesses were located just across the street from the 
designated area. 

EZs and ECs Established a Variety of Governance Structures and 
Encouraged Community Participation: 

In addition to choosing the activities that their EZs or ECs 
implemented, designated communities were permitted to determine the 
structure they would use to govern and operate the program. Generally, 
these structures included an EZ/EC management entity--either a 
nonprofit organization or an entity that was part of the local 
government. Two urban EZs--New York and Philadelphia-Camden--became two 
separate entities that were managed by different types of organizations 
that split the $100 million EZ grant. In the Philadelphia-Camden EZ, 
for example, the Philadelphia portion was run by the city of 
Philadelphia and the Camden portion by a nonprofit organization. All 
designees had at least one board, and, in some cases, EZs included 
community advisory groups or separate "subzone" boards, which 
represented specific areas of the EZ in their governance structures. 

All three rural EZ boards made decisions about EZ activities without 
the direct involvement of local government entities. However, the 
extent of government involvement in urban EZ boards varied, regardless 
of whether the EZ was managed by a nonprofit or local government 
organization (fig. 5). For example, in two EZs, Cleveland and Chicago, 
local government had extensive control of the program, but in other 
EZs, such as Detroit, the board of the nonprofit organization that 
managed the EZ shared partial decision-making authority with the mayor 
and city council. Other EZs were operated with minimal local government 
involvement, with the boards determining which activities to implement, 
allocating resources, and deciding which entities would implement the 
programs. Appendix IV provides more details on the governance 
structures of the EZs we visited. 

Figure 5: Local Government Involvement in Decision Making in the Urban 
EZs: 

[See PDF for image] 

Source: GAO analysis of EZ documentation and interview data. 

[A] The Los Angeles EZ was operated by a for profit organization--the 
Los Angeles Community Development Bank--until 2002 when it filed for 
bankruptcy. Since then, a Los Angeles city department has continued its 
operations; however, the mayor and city council are not directly 
involved. 

[End of figure] 

Another program expectation was to encourage community participation 
within the designated communities. Regardless of the type of governance 
structure they used, EZs and ECs involved community participants in the 
planning and carrying out of program activities. According to 
stakeholders from all the EZs and the ECs we visited, residents were 
involved in meetings such as "visioning sessions" and town hall 
gatherings during the strategic planning process. Community groups, 
such as local colleges and universities, development corporations, and 
businesses, were also involved prior to designation. In addition, 56 
out of 58 ECs responding to our survey reported that EC residents 
attended listening sessions, generated ideas for activities, or helped 
to establish priorities. Respondents also indicated that a variety of 
other groups participated in the strategic planning process for the 
ECs, including local government officials and representatives from 
community-based organizations. 

After designation, stakeholders from the EZs and ECs we visited said 
that residents often served on boards, and some stakeholders noted they 
relied on the boards to capture a wide range of viewpoints. Most EZs 
and ECs we visited also included as participants business 
representatives, officials from nonprofits, and clergy, among others. 
Some EZs and ECs also included residents from specific neighborhoods 
within the designated area or individuals with special expertise, such 
as in the areas of health care and housing. 

Oversight Was Hindered by Limited Program Data and Variation in 
Monitoring: 

According to our federal standards, federal agencies should oversee the 
use of public resources and ensure that ongoing monitoring 
occurs.[Footnote 21] However, HHS, HUD, and USDA did not collect data 
on how program funds were spent. In addition, HHS did not provide the 
states, EZs, and ECs with clear guidance on how to monitor the program 
grant funds, and the types and extent of monitoring performed by state 
and local participants varied. The lack of reporting requirements may 
be related to the program's design, which was intended to give 
communities flexibility in using program funds and relied on multiple 
agencies for oversight. However, these limitations have hindered the 
agencies' efforts to determine whether the public resources are being 
used effectively and program goals are met. 

Federal Agencies Are Required to Oversee the Use of Public Funds and 
Provide Ongoing Monitoring: 

According to federal standards established in the Standards for 
Internal Control in the Federal Government, program managers need both 
program and fiscal data to determine whether public resources are being 
used effectively and program goals are being met.[Footnote 22] In the 
case of the EZ/EC program, fiscal data would include not only the 
aggregate amount of program grant funding designated communities spent, 
but also data on the amount of funds spent on specific types of 
activities. Program data would include descriptions of the activities 
implemented and program outputs, such as the number of individuals 
trained in a job training program. The standards also state that 
federal agencies should ensure that ongoing monitoring occurs in the 
course of normal operations. For instance, the federal agencies should 
provide guidelines on what monitoring should occur, including whether 
on-site reviews or reporting are required. For the EZ/EC program, HHS 
regulations require states, EZs, and ECs to maintain fiscal control of 
program funds and accounting procedures sufficient to enable them to 
prepare reports and ensure the funds were not used in violation of the 
applicable statute. 

The Federal Agencies' Oversight Efforts Had Shortcomings in Data 
Collection: 

None of the federal agencies collected data showing how program funds 
had been spent. As we have noted, the EZ/EC grants were special Social 
Services Block Grants that gave recipients expanded flexibility in 
using the funds. The regulations for most grants of this type require 
states to report on, among other things, the amount of funding spent on 
each type of activity. However, because HHS did not require this level 
of reporting for the EZ/EC program, the agency's data show how much of 
each grant was used but not how much was spent on specific activities 
or types of activities. Further, HHS's data sometimes do not show how 
much of the grant a specific EC used, since states could aggregate 
drawdowns for multiple communities. For example, there are five urban 
ECs in Texas, but the data reported to HHS show only the aggregate 
amount of funds these ECs used, not the amount used by each. 

Similarly, although HUD's and USDA's reporting systems contained some 
information on the amount of EZ/EC grants budgeted for specific 
activities, the systems did not account for the amounts actually spent 
on those activities. Moreover, we found that the data on the amount of 
EZ/EC grant funding were often not reliable, as some EZs and ECs 
reported budgeted amounts and others reported actual amounts spent. 
Further, in our assessments of the reliability of these data, we found 
documentation showing that the designated communities had undertaken 
certain activities with program funding, but we were often unable to 
find documentation of the actual amounts allocated or 
expended.[Footnote 23] 

Program Monitoring by State and Local Participants Varied: 

Although HHS regulations require states, EZs, and ECs to maintain 
fiscal control of program grant funds, the agency also did not provide 
guidance detailing the steps state and local authorities should take to 
monitor the program. In the absence of clear guidance, the type and 
level of monitoring conducted at the state and local levels varied. For 
example, some state and EZ/EC officials applied guidelines from other 
programs, such as the Community Development Block Grant program, or 
developed their own policies. Officials from almost all states we 
interviewed said they reviewed audits of the EZs and ECs and were 
required to submit aggregate data to HHS, and most had performed site 
visits at least once during the program. State officials also said they 
reviewed requests to draw down grant funds, approving expenditures if 
the requests met the goals outlined in the strategic plans. However, 
most states did not maintain records showing the types of activities 
designated communities undertook. Some states said that they had taken 
corrective actions, such as withholding payments when designated 
communities had not properly reported how funds were used. However, 
only a few states also completed program monitoring activities, such as 
reviewing whether a project took place or benefited EZ or EC residents, 
in conjunction with their fiscal reviews. Most of the EZs and ECs we 
visited conducted on-site monitoring of subgrantees and reviewed their 
financial and performance data, and some communities required annual 
audits of their subgrantees. For example, the Rio Grande Valley EZ 
assigned a program staff member to monitor each subgrantee activity and 
required annual audits. In contrast, the Fayette-Haywood EC did not 
perform any site visits and relied on other funding organizations to 
monitor subgrantees. 

Some instances of misuse of program funds did occur during the EZ/EC 
program. For example, officials at the Mid-Delta EZ reported two cases 
of embezzlement by EZ personnel.According to an EZ official, in one 
case that was discovered through an independent audit, an individual 
was prosecuted for embezzling $28,000 in 1996 (only $1,800 was 
recouped). The second case of embezzlement of $31,000 by two EZ staff, 
discovered when the staff turned themselves in, is currently under 
joint State of Mississippi and FBI investigation as part of a larger 
investigation of misuse of EZ funds starting as early as 1996. In 
addition, three audits by the state of Georgia found that almost all 
the administrative funds designated for the Atlanta EZ ($4 million) had 
been used in the first 3 ˝ years of the program, including 
approximately $44,000 used for questionable costs related to personnel 
and travel expenditures. To address this issue, the Atlanta EZ repaid 
some of the costs in question, provided additional documentation, and 
instituted better recordkeeping procedures. The city of Atlanta also 
initiated a restructuring of the EZ and fired the majority of EZ staff. 

Limitations in EZ/EC Oversight May Have Resulted from the Program 
Design: 

As discussed earlier, the EZ/EC program was designed to give the 
designated communities increased flexibility in deciding how to use 
program funds and used states as pass-through entities for providing 
funds. Part of the philosophy behind the program was to relieve states 
and localities of the burden of excessive reporting requirements. 
Furthermore, no single federal agency had sole responsibility for 
oversight of Round I of the EZ/EC program, although federal standards 
require that agencies provide adequate oversight over public resources. 
In the beginning, the agencies made some efforts to share information, 
but these efforts were not maintained. For example, HUD officials said 
that they had received fiscal data from HHS and reconciled that 
information with their program data on the activities implemented in 
the early years of the program.[Footnote 24] According to HUD, the 
agency made additional attempts to obtain data from HHS but only 
recently received a report. An HHS official said the agency no longer 
regularly shared detailed data with HUD and USDA, which the official 
said was likely due to a lack of program staff. 

These limitations do not necessarily apply to Rounds II and III of the 
EZ/EC program. For example, both fiscal and program oversight of the 
urban and rural EZs and ECs were provided directly through HUD and USDA 
in Round II because the program funding came directly through HUD and 
USDA appropriations. Officials from both agencies explained that 
information on the activity for which funds were used was linked to 
each drawdown of program funds. In addition, a HUD official said they 
had issued improved monitoring guidance in Round II, since designees 
receive funds directly from HUD. However, a USDA official said that 
they provided similar monitoring guidance to designees in Rounds I, II, 
and III. Because this report focuses on Round I of the program, we did 
not determine the effectiveness of the oversight of future rounds of 
the program. 

Lack of Detailed Tax Data Made It Difficult to Assess the Use of 
Program Tax Benefits: 

A lack of detailed tax data limited our ability to assess the extent to 
which businesses in the EZs and ECs used program tax benefits. We have 
previously reported that information on tax expenditures should be 
collected to ensure that these expenditures are achieving their 
intended purpose.[Footnote 25] IRS collects data on the use of some of 
the program tax benefits, but not all of them, and none of the data can 
be linked to the individual communities where the benefits were 
claimed. We also recommended that HUD, USDA, and IRS work together to 
identify the data needed to measure the use of EZ/EC tax benefits and 
the cost-effectiveness of collecting the information, but the three 
agencies did not reach agreement on a cost-effective approach.[Footnote 
26] Officials from some EZs and ECs reported that some of the tax 
benefits were being used, but this information was not sufficient to 
allow us to determine the actual extent of usage. 

IRS Data on the Use of Program Tax Benefits Are Limited: 

Previously, we have noted that information on tax expenditures should 
be collected in order to evaluate their effectiveness as a means of 
accomplishing federal objectives and to ensure that they are achieving 
their intended purpose.[Footnote 27] Inadequate or missing data can 
impede such studies, especially given the difficulties in quantifying 
the benefits of tax expenditures. Nevertheless, we have stated that the 
nation's current and projected fiscal imbalance serves to reinforce the 
importance of engaging in such evaluations. 

However, as described in our 2004 report, the IRS collects limited data 
on the EZ/EC tax benefits. It does not collect data on benefits used in 
individual designated sites and for some benefits it does not have any 
data.[Footnote 28] For example, the IRS collects some information on EZ 
businesses' use of tax credits for employing EZ residents. However, the 
data cannot be separated to show how much was claimed in individual 
EZs. In addition, IRS does not have data on the use of the increased 
expensing deduction for depreciable property, because taxpayers do not 
report this benefit as a separate line item on their returns. The lack 
of data on the use of program tax benefits is consistent with findings 
of other reports we prepared citing data challenges in other similar 
community and economic development programs, such as the Liberty Zone 
program.[Footnote 29] 

Our 2004 report recommended that HUD, IRS, and USDA collaborate to 
identify a cost-effective means of collecting the data needed to assess 
the use of the tax benefits.[Footnote 30] In response, HUD, IRS, and 
USDA identified two methods for collecting the information--through a 
national survey or by modifying the tax forms. However, the three 
agencies did not reach agreement on a cost-effective method for 
collecting additional data. Given the lack of information at the 
federal level, we, some EZs, and other researchers have tried to assess 
the use of EZ/EC tax benefits by surveying businesses.[Footnote 31] 
However, these surveys have had low response rates and a high number of 
undeliverable surveys, suggesting that the results might not be 
representative. Reasons associated with the low response rates were 
cited in previous reports, including the difficulty of locating someone 
at the businesses who knew whether the tax benefit had been claimed and 
issues associated with multiple business locations.[Footnote 32] In 
addition, some EZ officials said that businesses were not willing to 
share their tax information. Further, a high rate of small business 
closures was determined to be a contributing factor to the high number 
of undeliverable surveys. We initiated a survey of businesses as a part 
of the audit work for this engagement, but discontinued the survey due 
to a low response rate.[Footnote 33] 

In the absence of other data, we relied on testimonial information to 
assess how often the EZ tax benefits were used and who used them. 
Although stakeholders from all EZs told us that they did not have any 
data on the extent to which EZ businesses had used program tax 
benefits, they provided us with some information that was consistent 
with the findings of past studies.[Footnote 34] For example, during our 
site visits, EZ stakeholders told us that they believed large 
businesses, which tend to use tax professionals who know and understand 
the benefits, were more likely to use the tax benefits than small 
businesses. They also noted that small businesses were less likely to 
make enough in profits to take advantage of the tax benefits.[Footnote 
35] The stakeholders stated further that the credit for employing EZ 
residents was the most frequently used of the three original tax 
benefits. A few EZ officials commented that retail businesses were more 
likely to use the employment credit and manufacturing businesses were 
more likely to use the increased expensing deduction. 

Stakeholders from only 4 of the 11 EZs and 2 of the 58 ECs that 
responded to our EC survey told us that the tax-exempt bond benefit had 
been used in their communities. EZ stakeholders and EC survey 
respondents cited a variety of reasons that the tax-exempt bond 
financing had not been more widely used. For instance, some said that 
the bonds were not used because of the availability of the Industrial 
Development Revenue Bond, which EZ stakeholders explained had fewer 
restrictions and could be issued for larger amounts.[Footnote 36] In 
addition, some EZ stakeholders and one EC survey respondent said that 
it was difficult to find a large pool of qualified EZ residents to 
satisfy the employment requirement for the bond, which required that at 
least 35 percent of the workforce be EZ residents. Some EZ stakeholders 
also told us that the legal fees for an EZ bond were higher than for 
other types of bonds because the restrictions made the EZ bond more 
complex. For this reason, stakeholders explained, the cost of issuing 
the EZ bond was high relative to the bond cap, particularly early in 
the program.[Footnote 37] Finally, some EC survey respondents noted 
other reasons for not using the bond, such as the complicated nature of 
the bond or a lack of interested businesses or viable projects. 

IRS Officials Reported that They Have Data Sufficient to Enforce the 
Tax Code, but This Information Is Insufficient for Assessing the Extent 
of Usage: 

IRS officials said that the limited data the agency collected did not 
affect its ability to enforce compliance with the tax code. They told 
us that IRS's role is to administer tax laws and said that collecting 
more comprehensive data on the use of program tax benefits would not 
help the agency to achieve this objective. Further, they said that they 
allocate their resources based on the potential effect of abuse on 
federal revenue and noted that these tax benefits are not considered 
high risk, since the amount claimed is small compared with revenues 
collected from other tax provisions or the amount of potential losses 
from abusive tax schemes. Furthermore, both IRS officials and our 
previous reports have suggested that IRS generally does not collect 
information on the frequency of use or types of businesses claiming tax 
benefits unless legislatively mandated to do so.[Footnote 38] 

Although the total program tax benefits were estimated to be much 
larger than the federal grant funding--over $2.5 billion compared with 
the $1 billion in EZ/EC grants--we do not, as we have noted, know the 
actual amount of tax benefits claimed by Round I EZs and ECs nationwide 
or the amounts used in individual communities.[Footnote 39] As a 
result, we could not assess differences in the rates of usage among the 
designated communities. Although we understand IRS's concerns, the lack 
of data is likely to become increasingly problematic in light of the 
fact that future rounds of the EZ/EC program and the Renewal Community 
program rely heavily on tax benefits to achieve revitalization goals. 
It may also be a concern with the Gulf Opportunity Zone Act, which 
provides tax benefits in counties and parishes affected by the 2005 
Gulf Coast hurricanes.[Footnote 40] 

In Aggregate, EZs and ECs Showed Some Improvements, but Our Analysis 
Did Not Definitively Link These Changes to the Program: 

Although EZs and ECs showed some improvements in poverty, unemployment, 
and economic growth, we did not find a definitive connection between 
these changes and the EZ/EC program. As mentioned in our previous 
report, measuring the effect of initiatives such as the EZ/EC program 
is difficult for a number of reasons, such as data limitations and the 
difficulty of determining what would have happened in the absence of 
the program.[Footnote 41] In some cases, communities saw decreases in 
poverty and unemployment and increases in economic growth. But, we 
could not conclusively determine whether these changes were a response 
to the EZ/EC program or to other economic conditions. EZ stakeholders 
and EC survey respondents said that program-related factors had 
influenced changes in their communities but that other unrelated 
factors also had an effect. Although the overall effects of the EZ/EC 
program remain unclear, having data on the use of program grants and 
tax benefits would have allowed for a richer assessment of the program. 

A Number of Challenges Affected Our Efforts to Measure the Effects of 
the EZ/EC Program: 

We attempted to assess the effects of the program on four indicators: 
poverty, unemployment, and two measures of economic growth--the number 
of businesses and the number of jobs.[Footnote 42] Although we used 
several quantitative and qualitative methods, including an econometric 
analysis to try to isolate the EZ/EC program's effect, we could not 
differentiate between the effects of the program and other factors. 
Among the challenges we encountered were the following: 

* A lack of adequate data on the use of program benefits. As mentioned 
earlier, data on the use of EZ/EC grant funds and tax benefits were 
very limited. 

* Limited demographic data. We used poverty and unemployment data from 
the 1990 and 2000 censuses, but these dates do not correspond well to 
the program dates, as communities were designated in 1994 and in some 
cases are still operating. 

* Demonstrating what would have happened in the absence of the program. 
For example, we attempted to identify comparison areas that did not 
receive EZ or EC designations and that reflected similar community 
characteristics of EZs and ECs.[Footnote 43] However, the designated 
communities sometimes had the highest poverty levels in the area, 
making it difficult to find exact matches among nearby census tracts. 

* Accounting for the spillover effects of the program to other areas, 
the effects of similar public and private programs, and the effects of 
regional and local economic trends. 

* Accounting for bias in the choice of program areas. For example, if 
program officials tended to pick census tracts that were already 
experiencing gentrification prior to 1994, we may be overstating the 
effect of the EZ designation.[Footnote 44] Conversely, if officials 
tended to choose census tracts that were experiencing economic declines 
prior to 1994, such as areas affected by the loss of major employers, 
we may be understating the program's impact. 

Several program-specific factors also limited our ability to assess the 
effects of the program. First, the program was designed to be tailored 
to the local sites, and each community was given broad latitude to 
determine its own needs and the program activities it thought would 
address those needs. Thus, each designee may or may not have selected 
program activities that directly related to the three factors--poverty, 
unemployment, and economic growth--mandated for our evaluation. Second, 
the time frame of actual program implementation may have varied among 
the designees. For instance, some EZ stakeholders mentioned that their 
programs took 2 or 3 years to get started, while others were able to 
begin drawing down funds in the first year. Third, the nature of the 
EZ/EC program, which focuses on changes in geographic areas rather than 
on individuals, makes it difficult to determine how the program 
affected residents who lived in an EZ/EC in 1994 but later moved. 
Stakeholders from most of the EZs and ECs we visited said that 
residents were moving out of the designated areas, often after finding 
a job. If true, this phenomenon may have masked some of the program's 
effects on poverty and unemployment, since these individuals would not 
be captured in the 2000 data. 

In Some Cases, EZs and ECs Showed Improvements in Poverty, 
Unemployment, and Economic Growth: 

Some EZs and ECs saw improvements in poverty, unemployment, and 
economic growth. Four of the 11 EZs--Cleveland, Detroit, Philadelphia- 
Camden, and Kentucky Highlands--showed improvements in both poverty and 
unemployment between 1990 and 2000 and at least one measure of economic 
growth between 1995 and 2004 (fig. 6). Some ECs also experienced 
similar improvements. For example, 25 out of 95 ECs saw positive 
changes in poverty and unemployment and at least one measure of 
economic growth.[Footnote 45] None of the EZs and ECs experienced 
negative changes in all three indicators, but many experienced negative 
changes in at least one. For instance, the Atlanta EZ experienced 
negative changes in unemployment and both measures of economic growth. 
However, the extent of these changes varied, particularly in our two 
measures of economic growth. For those EZs that saw improvements in the 
number of jobs, the increases ranged from a low of 2.6 percent in the 
Philadelphia-Camden EZ to a high of 67.8 percent in the Kentucky 
Highlands EZ. Of those EZs that saw decreases in the number of 
businesses, the amount varied from 2.7 percent in the Detroit EZ to 
20.8 percent in the Atlanta EZ. 

Figure 6: Changes in Poverty, Unemployment, and Two Measures of 
Economic Growth Observed in Round I EZs: 

[See PDF for image] 

Source: GAO analysis of Census and Claritas data. 

Note: The changes in poverty and unemployment rates are based on the 
difference between 1990 and 2000 Census data, and the changes in the 
number of businesses and jobs are based on the difference between 1995 
and 2004 data from a private data vendor, Claritas. All poverty and 
unemployment estimates had 95 percent confidence intervals of plus or 
minus 5 percentage points or less. For the change in the number of 
businesses and jobs, we did not consider a change of plus or minus one 
percent or less as being significant. 

[End of figure] 

Most EZs and ECs Saw Some Decrease in the Poverty Rate, but These 
Changes Could Not Be Tied Definitively to the EZ/EC Program: 

In most of the 11 EZs and 95 ECs, both urban and rural, poverty rates 
fell between 1990 and 2000 (fig. 7).[Footnote 46] Most communities 
experienced statistically significant decreases in the poverty rate 
that ranged from 2.6 to 14.6 percent. Specifically, our analysis showed 
the following: 

* Almost all urban EZs experienced significant decreases ranging from a 
low of 4.1 percentage points in the New York EZ to 10.9 percentage 
points in the Detroit EZ. 

* All three rural EZs showed significant decreases--7.3 percentage 
points in the Rio Grande Valley EZ, 10.1 percentage points in the 
Kentucky Highlands EZ, and 10.7 percentage points the Mid-Delta EZ. 

* 44 out of the 65 urban ECs also saw significant decreases in poverty, 
with declines ranging from 2.6 percentage points in the Boston, 
Massachusetts EC to 14.6 percentage points in the Minneapolis, 
Minnesota EC. 

* Most rural ECs saw significant decreases, ranging from 3.4 percentage 
points in the Imperial County, California EC to 12.2 percentage points 
in the Eastern Arkansas EC. 

Figure 7: Number and Percentage of EZs and ECs Experiencing a Decrease 
in Poverty from 1990 to 2000: 

[See PDF for image] 

Source: GAO analysis of Census data. 

Note: All poverty estimates had 95 percent confidence intervals of plus 
or minus 5 percentage points or less. 

[End of figure] 

We also compared changes in poverty in designated areas and comparison 
areas and across urban and rural communities for both EZs and ECs. Our 
analysis showed the following: 

* When combining urban and rural areas, the poverty rate in the 
designated areas fell more than in the comparison areas--5.4 percentage 
points overall, compared with 3.9 percentage points in the comparison 
areas (fig. 8). 

* Rural designees experienced a larger significant decrease in poverty 
than urban designees--7.2 and 5 percentage points, respectively. 

* Urban and rural EZs experienced greater decreases in poverty than 
both their comparison areas and the ECs. 

Figure 8: Comparison of Decreases in Poverty in Urban and Rural 
Designated Areas and Comparison Areas from 1990 to 2000: 

[See PDF for image] 

Source: GAO analysis of Census data. 

Note: There are 1,557 census tracts in the designated areas and 1,504 
in the comparison areas. All poverty estimates had 95 percent 
confidence intervals of plus or minus 5 percentage points or less. 

[End of figure] 

Because we could not separate the program's effects from other factors 
in these analyses, we developed an econometric model for the eight 
urban EZs and their comparison areas that considered a variety of 
factors related to the poverty rate.[Footnote 47] Among the nonprogram 
factors we considered were high school dropouts, the presence of 
households headed by females, and vacant housing units as reported in 
the 1990 Census. Our models indicated that the poverty rate in the 
comparison areas fell slightly more than in the EZs themselves (app. 
II). This result did not demonstrate that the declines in poverty in 
the EZs were directly associated with the EZ program. 

Finally, we conducted interviews of EZ stakeholders and surveyed EC 
officials to determine their views of the effects of the EZ/EC program 
on their communities. Their responses were consistent with the 
inconclusive results of our other analyses: in general, they believed 
that both the EZ/EC program and additional factors had affected the 
prevalence of poverty in their communities.[Footnote 48] Some EZ and EC 
stakeholders said that the EZ/EC designation and program activities had 
addressed poverty by bringing in jobs and helping to stabilize the 
area. For instance, stakeholders from several EZs, including the 
Chicago, Mid-Delta, and Kentucky Highlands EZs, mentioned the role of 
the EZ in job creation. In addition, stakeholders from other EZs, such 
as Detroit and Rio Grande Valley, mentioned the role of EZ programs 
that were related to housing. EC survey respondents commented that the 
EC designation gave them the opportunity to focus on initiatives that 
could improve poverty in the area, such as job creation, infrastructure 
and physical improvements, and housing. 

However, EZ and EC stakeholders also mentioned external factors that 
may have affected the changes in poverty, such as changes in the local 
population when original residents moved away and gentrification. In 
addition, stakeholders from three EZs mentioned the positive effects of 
changes to welfare policy during the EZ/EC program.[Footnote 49] In ECs 
where our data showed that the poverty rate fell, some EC survey 
respondents also mentioned an increase in the availability of social 
services as a contributing factor. At EZs where stakeholders had mixed 
opinions on the changes in poverty, some cited a loss of industry or 
shifts in the national economy. Of the three EC survey respondents in 
areas where poverty either remained the same or increased, respondents 
mentioned the decrease in the number of jobs, increase in housing and 
utility costs, and the out-migration of residents with middle or high 
incomes. 

Decreases in the Unemployment Rate in Some Communities Also Could Not 
Be Definitively Tied to the EZ/EC Program: 

As we did for the poverty rate, we analyzed changes in the unemployment 
rate in EZs and ECs, using the same quantitative and qualitative 
methods. We found an overall decline in unemployment across 
communities; but, once again we could not tie the decrease definitively 
to the program's presence. Further, fewer than half of the individual 
EZs and ECs experienced a decrease in unemployment (fig. 9), with 
declines ranging from 1.5 to 11.7 percentage points, and a number saw 
significant increases--up to 6.5 percentage points.[Footnote 50] Many 
communities did not experience a significant change. Specifically, our 
analysis showed the following: 

* Four of the eight urban EZs saw unemployment fall, with rates 
declining from 2.9 percentage points in the Philadelphia-Camden EZ to 
10 percentage points in the Cleveland EZ. Two of the EZs saw 
unemployment rise--2 percentage points in New York and 6 percentage 
points in Atlanta--and two did not see a statistically significant 
change. 

* Changes in the unemployment rates of the rural EZs were also mixed. 
For example, unemployment in the Kentucky Highlands EZ fell 2 
percentage points, but it rose 3.1 percentage points in the Mid-Delta 
EZ and did not change significantly in the Rio Grande Valley EZ. 

* Twenty-seven, or fewer than half, of the 65 urban ECs saw significant 
decreases from 1.5 percentage points (San Diego, California) to 8.7 
percentage points (Flint, Michigan). Eleven saw a significant increase 
of between 2.1 percentage points (Rochester, New York) and 6.5 
percentage points (Charlotte, North Carolina), while 27 did not 
experience a significant change. 

* Almost half of the rural ECs saw significant decreases, with declines 
ranging from 2.7 percentage points (Fayette-Haywood, Tennessee) to 11.7 
percentage points (Lake County, Michigan). The unemployment rate 
remained about the same in 12 rural ECs, but 4 showed increases of 
between 2.8 and 3.5 percentage points (Williamsburg-Lake City, South 
Carolina and Central Savannah River Area, Georgia, respectively). 

Figure 9: Number and Percentage of EZs and ECs that Experienced a 
Decrease in Unemployment from 1990 to 2000: 

[See PDF for image] 

Source: GAO analysis of Census data. 

Note: All unemployment estimates had 95 percent confidence intervals of 
plus or minus 5 percentage points or less. 

[End of figure] 

Our analysis also looked at changes in unemployment across urban and 
rural communities and compared changes in designated areas and 
comparison areas for both EZs and ECs. The analysis showed the 
following results: 

* The designated areas saw a statistically significant decrease in 
unemployment of 1.4 percentage points, compared with a decrease of just 
under 1 percentage point in the comparison areas (fig. 10). 

* In general, rural designees saw unemployment fall more than urban 
designees, although these differences were not as marked as those we 
identified in our analysis of the changes in poverty. 

* Urban EZs and ECs saw a greater decrease in unemployment than their 
comparison areas, where the rates did not show a statistically 
significant change. 

* Unemployment in rural EZs and their comparison areas remained about 
the same, while rural ECs and their comparison areas both experienced a 
significant decrease of about 2 percentage points. 

Figure 10: Comparison of Decreases in Unemployment in Urban and Rural 
Designated Areas and Comparison Areas from 1990 to 2000: 

[See PDF for image] 

Source: GAO analysis of Census data. 

Note: Areas for which there was no statistically significant change are 
not shown. There are 1,557 census tracts in the designated areas and 
1,504 in the comparison areas. All unemployment estimates had 95 
percent confidence intervals of plus or minus 5 percentage points or 
less. 

[End of figure] 

Although our analyses of changes again showed that EZs experienced a 
larger decrease in unemployment than the comparison areas, these 
analyses did not separate the effect of the program from other factors. 
We again used an econometric model for the eight urban EZs that 
considered other factors, such as average household income and the 
presence of individuals with a high school diploma as reported in the 
1990 Census. This analysis showed that the EZs experienced a decrease 
that was slightly greater than in the comparison areas, but the 
difference was not statistically significant (app. II). 

We also looked at the observations of EZ stakeholders that we 
interviewed and the responses to our EC survey. Once again, these 
observations generally saw both program and external factors as 
affecting the changes in unemployment.[Footnote 51] Some EZ 
stakeholders cited EZ programs--such as providing financial assistance 
to EZ businesses, fostering job creation, and offering job training--as 
helping to reduce unemployment. For example, the Upper Manhattan and 
Bronx portions of the New York EZ and the Chicago EZ required 
subgrantees and borrowers to create a certain number of jobs based on 
the size of the EZ grant or loan received. Similarly, EC survey 
respondents also mentioned the EC's involvement in creating jobs, 
attracting new businesses, and offering loans and technical assistance 
to businesses, along with a variety of social service programs designed 
to support employment. 

EZ stakeholders and EC survey respondents also noted additional factors 
that may have been associated with changes in unemployment. For 
example, some EZs cited the availability of social services not 
sponsored by the EZ as factors that influenced unemployment--for 
instance, daycare, transportation, and adult education or job placement 
programs. Some EZ stakeholders also suggested that changes in the 
national economy and in welfare policy had helped to reduce 
unemployment. Many survey respondents in ECs where unemployment fell 
reported that the decreases could be attributed to activities that may 
or may not have been part of the EC program, including adult 
educational services, higher skill levels among area residents, and 
social services such as childcare, programs for the homeless, and 
substance abuse treatment. Stakeholders from EZs where unemployment did 
not change or rose explained that EZ residents faced barriers to 
employment such as a lack of education or job skills, drug dependency, 
and criminal histories. 

Our Measures Showed that Some Economic Growth Occurred, but Results 
from Our Econometric Model Were Not Conclusive: 

A number of indicators can be used to measure economic growth, 
including data on the change in the number of local businesses, sales 
volumes, or home values. Our poverty and unemployment analyses used 
specific variables available in Census data, but to measure economic 
growth, we chose two measures--the number of businesses and the number 
of jobs.[Footnote 52] Overall, our analysis showed that most EZs and 
ECs experienced an increase in at least one measure of economic growth 
between 1995 and 2004 (fig. 11). Specifically: 

* Two of the eight urban EZs experienced significant increases in the 
number of both businesses and jobs, and three more experienced 
significant increases in one measure. The increases in businesses 
ranged from 4.2 percent in the Philadelphia-Camden EZ to 23.6 percent 
in the New York EZ. The increases in jobs ranged from 2.6 percent in 
the Philadelphia-Camden EZ to 30.5 percent in the Detroit EZ. However, 
some urban EZs experienced decreases in the number of businesses or 
jobs, some of which were large. Five experienced decreases in the 
number of businesses, ranging from 2.7 percent in the Detroit EZ to 
20.8 percent in the Atlanta EZ, and four experienced decreases in the 
number of jobs, from 5.2 percent in the Los Angeles EZ to 22.3 percent 
in the Atlanta EZ. 

* All three rural EZs experienced increases in both businesses and 
jobs, with businesses increasing between 15.6 percent in the Mid-Delta 
EZ and 33 percent in the Kentucky Highlands EZ, and jobs rising between 
5 and 67.8 percent in the same two EZs, respectively. 

* Fourteen of the 64 urban ECs experienced an increase in both economic 
growth measures, and an additional 24 saw an increase in one of the 
measures.[Footnote 53] However, 26 urban ECs saw a decrease in both 
measures. 

* Like rural EZs, the majority of the rural ECs experienced an increase 
in both measures of economic growth. 

Figure 11: Number and Percentage of EZs and ECs That Experienced an 
Increase in One or Both Measures of Economic Growth between 1995 and 
2004: 

[See PDF for image] 

Source: GAO analysis of Claritas data. 

Note: We excluded establishments that were not eligible for program tax 
benefits, such as nonprofit and governmental organizations, from our 
analysis of the change in the number of businesses. However, we 
included jobs at those businesses in our analysis of the change in the 
number of jobs. 

[A] Data were not available for the Miami/Dade County, Florida EC. 

[End of figure] 

Like the analyses of poverty and unemployment, our analysis of the 
changes in economic growth compared urban and rural designees, 
designated and comparison areas, and EZs and ECs (fig. 12). 

* In aggregate, both designated and comparison areas saw little change 
in the number of businesses, and both experienced an increase in the 
number of jobs of about 7 percent. 

* Overall, urban designees saw a decrease in the number of businesses, 
while rural designees saw a substantial increase. Both urban and rural 
designees saw an increase in the number of jobs, but the aggregate 
increase in rural areas was much greater (23.6 percent) than in urban 
areas (5.7 percent). Urban and rural comparison areas generally 
experienced changes similar to the designated areas. 

* Urban EZs experienced a decrease in the number of businesses, while 
the number in comparison areas remained about the same. But urban EZs 
saw an increase in the number of jobs, while their comparison areas saw 
a decrease. 

* Rural EZs fared better than their comparison areas in both measures 
of economic growth. 

Figure 12: Comparison of Changes in the Number of Businesses and the 
Number of Jobs in Urban and Rural Designated Areas and Comparison Areas 
between 1995 and 2004: 

[See PDF for image] 

Source: GAO analysis of Claritas data. 

Note: There are 1,557 census tracts in the designated areas and 1,504 
in the comparison areas. We excluded establishments that were not 
eligible for program tax benefits, such as nonprofit and governmental 
organizations, from our analysis of the change in the number of 
businesses. However, we included jobs at those businesses in our 
analysis of the change in the number of jobs. These analyses do not 
include data for the Miami/Dade County, Florida EC. 

[End of figure] 

As explained earlier, our descriptive analyses could not isolate the 
effects of the EZ/EC program from other factors affecting the 
designated and comparison areas. We conducted an econometric analysis 
that incorporated other factors, such as the percentage of vacant 
housing units and population density as reported in the 1990 Census. 
However, the results of our models explained little of the relative 
changes in the number of businesses or jobs in the urban EZs with 
respect to their comparison areas (app. II). Because our proxy 
measures--the number of businesses and jobs--were not the only 
indicators representative of economic growth, we tested our models 
using different measures, such as the number of home mortgage 
originations, but found similar results. As a result, we could not 
determine with a reasonable degree of confidence the role that the EZs 
might have played in the changes in economic growth that we observed. 

We also reviewed the perceptions of EZ stakeholders interviewed and 
respondents to our survey of ECs on economic growth in their 
communities.[Footnote 54] These observations cited several aspects of 
the program that contributed to economic growth, including loan 
programs and other benefits that aided small businesses, infrastructure 
improvements, and tax benefits, especially when the tax benefits were 
combined with other federal, state, and local benefits. Additionally, 
several stakeholders mentioned that their EZ or EC had acted as a 
catalyst for other local development. EZ stakeholders also noted 
several external factors that affected the change in economic growth, 
such as the increase of jobs in businesses located within the EZ or EC, 
the role of other state and local initiatives in attracting businesses, 
and trends in the national economy. In ECs where our data showed an 
increase in the number of businesses or jobs, some survey respondents 
reported that the result was due to an increase in technical assistance 
for area businesses, such as entrepreneurial training programs, and 
others reported that financial assistance to businesses contributed to 
the growth, both of which may or may not have been EC programs. EZ 
stakeholders also mentioned challenges facing their communities, 
including the lack of infrastructure and residents with incomes that 
were not high enough to support local businesses. In ECs where our data 
showed a decrease in the number of businesses or jobs, survey 
respondents pointed to a decrease in the number of area businesses and 
downsizing of existing businesses as contributing factors. 

Additional Program Data Could Facilitate Evaluations of the Effects of 
the EZ/EC and Similar Programs: 

Our efforts to analyze the effects of Round I designation on poverty, 
unemployment, and economic growth were limited by the absence of data 
on the use of program grant funds, the amount of funds leveraged, and 
the use of tax benefits. Without these data, we could not account for 
the amount of funds EZs used to carry out specific activities, the 
extent to which they leveraged other resources, or how extensively 
businesses used the tax benefits. As a result, we could not assess 
differences in program implementation. In addition, as we reported in 
2004, we could not evaluate the effectiveness of the tax benefits, 
although later rounds of the EZ/EC program have relied heavily on 
them.[Footnote 55] 

While we recognize, and discussed in our prior report on the EZ/EC 
program, the difficulties inherent in evaluating economic development 
programs, having more specific data would facilitate evaluations of 
this and similar programs.[Footnote 56] For example, the precision of 
our econometric models might have been improved by combining data on 
how program funds were used--such as the amounts used for assisting 
businesses--and the use of program tax benefits with other data we 
obtained, such as data on businesses and area jobs. Also, additional 
data would have allowed us to do in-depth evaluations of the extent to 
which various tax benefits were being used within each community, the 
size and type of businesses utilizing them, and the potential 
competitive advantages of using these benefits. Our previous reports 
have recommended that information on outlay programs and tax 
expenditures be collected to evaluate the most effective methods for 
accomplishing federal objectives.[Footnote 57] 

Observations: 

The EZ/EC program, one of the most recent large-scale federal programs 
aimed at revitalizing distressed urban and rural communities, resulted 
in a variety of activities intended to improve social and economic 
conditions in the nation's high-poverty communities. As of March 31, 
2006, all but 15 percent of the $1 billion in program grant funds 
provided to Round I communities had been expended, and the program was 
reaching its end. All three rounds of the EZ/EC program are scheduled 
to end no later than December 31, 2009. However, given our findings 
from this evaluation of Round I EZs and ECs, the following observations 
should be considered if these or similar programs are authorized in the 
future. 

Based on our review, we found that oversight for Round I of the program 
was limited because the three agencies--HHS, HUD, and USDA--did not 
collect data on how program funds were used, and HHS did not provide 
state and local entities with guidance sufficient to ensure monitoring 
of the program. These limitations may be related in part to the design 
of the program, which offered increased flexibility in the use of funds 
and relied on multiple agencies for oversight. However, limited data 
and variation in monitoring hindered federal oversight efforts. 

In addition, the lack of data on the use of program grant funds, the 
extent of leveraging, and extent to which program tax benefits were 
used also limited our ability and the ability of others to evaluate the 
effects of the program. The lack of data on the use of tax benefits is 
of particular concern, since the estimated amount of the tax benefits 
was far greater than the amount of grant funds dedicated to the 
program. In response to the recommendation in our 2004 report, HUD, 
IRS, and USDA discussed options for collecting additional data on 
program tax benefits and determined two methods for collecting the 
information--through a national survey or the modification of tax 
forms. The three agencies, however, did not reach agreement on a cost- 
effective method for collecting the additional data. In our and others' 
prior attempts to obtain this information using surveys, survey 
response rates were low and thus did not produce reliable information 
on the use of program tax benefits. 

We acknowledge that the collection of additional tax data by IRS would 
introduce additional costs to both IRS and taxpayers. Nonetheless, a 
lack of data on tax benefits is significant given that subsequent 
rounds of the EZ/EC program and the Renewal Community program rely 
almost exclusively on tax benefits, and other federal economic 
development programs, such as the recent Gulf Opportunity Zone 
initiative, involve substantial amounts of tax benefits. Furthermore, 
the nation's current and projected fiscal imbalance serves to reinforce 
the importance of understanding the benefits of such tax expenditures. 
If Congress authorizes similar programs that rely heavily on tax 
benefits in the future, it would be prudent for federal agencies 
responsible for administering the program to collect information 
necessary for determining whether the tax benefits are effective in 
achieving program goals. 

Agency Comments and Our Evaluation: 

We provided a draft of this report for review and comment to HHS, HUD, 
IRS, and USDA. We received comments from HHS, HUD, and USDA. In 
general, the agencies provided comments related to the oversight of the 
program, the availability of data, and the methodology used to carry 
out the work. Their written comments appear in appendixes V through 
VII, respectively, and our responses to HUD's more detailed comments 
also appear in appendix VI. HHS, HUD, and USDA also provided technical 
comments, which we have incorporated into the report where appropriate. 

HHS commented that a statement made in our report--that the agency did 
not provide guidance detailing the steps state and local authorities 
should take to monitor the program--unfairly represented the 
relationship between HHS and the other federal agencies that 
administered the EZ/EC program. Specifically, HHS emphasized its 
responsibility for fiscal as opposed to programmatic oversight of the 
program. We note in our report that program design may have led to a 
lack of clarity in oversight, as no single federal agency had sole 
oversight responsibility. While this lack of clarity in oversight may 
be related in part to the design of the program, which offered 
increased flexibility in the use of funds and relied on multiple 
agencies for oversight, limited data and variation in monitoring 
hindered federal oversight efforts. Moreover, we believe that, in 
accordance with federal standards, each of the federal agencies that 
administered the program bore at least some responsibility for ensuring 
that public resources were being used effectively and that program 
goals were being met. 

HUD disagreed with GAO's observation that there was a lack of data on 
the use of program grant funds, the amount of funds leveraged, and the 
use of the tax benefits. HUD indicated that we could obtain data on the 
use of program funds and the amount of funds leveraged from its 
performance reporting system. As we discussed in our report, we used 
information from HUD's reporting system to report on the types of 
activities that designated communities implemented. We also noted that 
HUD maintained some information on the amount of EZ/EC grants budgeted 
for specific activities. Although we found evidence that activities 
were carried out with program funds, information contained in the 
performance reporting system on the amounts of funds used and the 
amount leveraged was not reliable. For example, we found evidence that 
communities had undertaken certain activities with program funding, but 
we were often unable to find documentation of the actual amounts 
allocated or expended. HUD also indicated that it did not agree that 
data on the use of the tax benefits were lacking. However, HUD 
indicated that the agency itself had attempted to gather such data by 
collaborating with IRS in identifying ways to collect data on tax 
benefits, by developing a methodology to administer a survey to 
businesses, and by compiling anecdotal evidence of the use of program 
tax benefits. We continue to believe that the lack of data on program 
tax benefits limits the ability of the agencies to administer and 
evaluate the EZ/EC program. Further, the lack of such data is likely to 
become increasingly problematic in light of the fact that future rounds 
of the EZ/EC program and the Renewal Community program rely heavily on 
tax benefits to achieve revitalization goals. 

HUD concurred that limitations in the oversight of the EZ/EC program 
may have resulted from the design of the program as no single federal 
agency had sole responsibility for oversight. HUD also recommended that 
we make clear that more oversight was not allowed in Round I and we 
include a statement that it met agency requirements to undertake 
periodic performance reviews and described some of its efforts to 
monitor the program according to applicable regulations. We do not 
believe that more oversight was not allowed. For example, early in the 
program HUD and HHS made some efforts to share information. 
Specifically, HUD officials said that they had received fiscal data 
from HHS and reconciled that information with their program data on the 
activities implemented, but these efforts to share information were not 
maintained. Further, as we previously stated, while we recognize that 
program design may have led to a lack of clarity in oversight, we 
believe that in accordance with federal standards, each of the federal 
agencies that administered the program bore at least some 
responsibility for ensuring that public resources were being used 
effectively and that program goals were being met. HUD also described 
changes it had made to ensure better oversight of program funds for 
Round II. We acknowledge HUD's efforts to improve oversight of the 
program and, as discussed in our report, the oversight limitations that 
we identified in Round I of the program may not apply to later rounds. 

HUD provided several comments related to the methodology we used to 
carry out our work. For example, HUD suggested that we measure the 
successes of the Round I program in meeting the four key principles of 
the program, which the designated communities were required to include 
in their strategic plans. Additionally, HUD commented that the indices 
we used to assess the effects of the EZ/EC program--poverty, 
unemployment and economic growth--were used in the application process 
for the program but were not intended to be used as performance 
measures. While we appreciate HUD's suggestions on our methodology, our 
congressional mandate was to determine the effect of the EZ/EC program 
on poverty, unemployment and economic growth. In designing our 
methodology, we conducted extensive research on evaluations that had 
been conducted on the EZ/EC program, including HUD's 2001 Interim 
Assessment, and spoke with several experts in the urban studies field. 

USDA stated that data and analyses on the effectiveness of programs 
such as EZ/EC were useful and offered areas to consider for future 
evaluations of economic development programs involving rural areas. For 
example, USDA mentioned issues involved in collecting data on rural 
areas, such as the limited availability of economic and demographic 
data for small rural populations, and discussed USDA's efforts for 
developing a methodology that focuses on economic impacts using county- 
level economic data. USDA also said it is especially important in rural 
areas to have a clear and adequately funded data collection process for 
program evaluations. In addition, USDA noted that evaluations of the 
EZ/EC program could go beyond the indicators of poverty, unemployment 
and economic growth to include measures on economic development 
capacity and collaboration. We agree that collecting data for rural 
areas is a challenge and appreciate USDA's effort to develop a 
methodology that focuses on economic impacts using county-level 
economic data and captures the short-term Gross Domestic Product 
changes in the impacted rural counties. Further, we appreciate USDA's 
suggestion that additional measures be considered in future evaluations 
of economic development programs and that a broader perspective on 
program results might be useful. 

USDA also commented that its performance reporting system was intended 
to be used as a management tool for both USDA and the individual EZs 
and ECs. According to USDA, the system was not designed to be an 
accounting tool but has been useful for providing a picture of each 
designated community's achievements. As we discussed in our report, we 
used information from USDA's reporting system to report on the types of 
activities that designated communities implemented and also noted that 
USDA maintained some information on the amounts of EZ/EC grants 
budgeted for specific activities. Moreover, while we recognize the 
system was not intended to be used as an accounting tool, we found that 
the data on the amounts of the EZ/EC grant funding were not reliable. 
For example, in our assessment of the reliability of data contained in 
USDA's performance reporting system, we were often unable to find 
documentation of the actual amounts allocated or expended for specific 
activities. 

USDA further commented that it had encouraged designated communities to 
report all investment that contributed to the EZ or EC in accomplishing 
its strategic plan as leveraged funds. We recognize USDA's efforts to 
encourage leveraging in the designated communities and to report such 
information in its performance reporting system. Our report notes that 
stakeholders from all EZs and ECs we visited and EC survey respondents 
reported having used their EZ/EC grants to leverage other resources. 
However, we were unable to evaluate the amounts of funds leveraged 
because the data contained in USDA's performance reporting system were 
not reliable. For example, USDA's performance reporting system included 
information on the amounts of funds leveraged for each activity, but 
for the sample of activities we reviewed, either supporting 
documentation showed an amount conflicting with the reported amount or 
documentation could not be found. Moreover, as we discuss in our 
report, the definition of leveraging used among the designated 
communities was inconsistent. 

We are sending copies of this report to interested Members of Congress, 
the Secretary of Health and Human Services, the Secretary of Housing 
and Urban Development, the Secretary of Treasury, the Commissioner of 
the Internal Revenue Service, and the Secretary of Agriculture. We will 
make copies of this report available to others upon request. In 
addition, this report will be available at no charge on the GAO Web 
site at [Hyperlink, http://www.gao.gov]. 

Please contact me at (202) 512-8678 or ShearW@gao.gov if you or your 
staff have any questions about this report. Contact points for our 
Offices of Congressional Relations and Public Affairs may be found on 
the last page of this report. Key contributors to this report are 
listed in appendix VIII. 

Signed by: 

William B. Shear: 
Director, Financial Markets and Community Investment: 

[End of section] 

Appendix I: Objectives, Scope, and Methodology: 

The objectives of this study were to (1) describe how Round I of the 
Empowerment Zone and Enterprise Community (EZ/EC) program was 
implemented by the designated communities; (2) evaluate the extent of 
federal, state, and local oversight of the program; (3) examine the 
extent to which data are available to assess the use of program tax 
benefits; and (4) analyze the effects the Round I EZs and ECs had on 
poverty, unemployment, and economic growth in their communities. To 
address each of our objectives, we completed site visits to all Round I 
EZs and two Round I ECs and administered a survey to all ECs that did 
not receive subsequent designations, such as a Round II EZ designation. 
At each site, we asked uniform questions on implementation, oversight, 
tax benefits, and changes observed in the EZ and ECs. We also surveyed 
60 ECs that were in operation as of June 2005 and did not receive later 
designations and asked about similar topics. We performed a qualitative 
analysis to identify common themes from our interview data and open- 
ended survey responses. To address our second objective, we also 
interviewed federal and state program participants, reviewed oversight 
guidance and documentation, and verified a sample of reported 
performance data by tracing it to EZ and EC records. To address our 
third objective, we attempted to administer a survey of EZ businesses, 
but discontinued it due to a low response rate. To address our fourth 
objective, we obtained demographic and socioeconomic data from the 1990 
and 2000 decennial censuses and business data for 1995, 1999, and 2004 
from a private data vendor, Claritas. We used 1990 Census data to 
select areas similar to the EZ and EC areas for purposes of comparison. 
We then calculated the percent changes in poverty, unemployment, and 
economic growth observed in the EZs and ECs and their comparison areas. 
In addition, for the eight urban EZs, we used an econometric model to 
estimate the effect of the program, by controlling for certain factors, 
such as average household income, in the EZs and their comparison 
areas. Finally, we used information gathered from our qualitative 
analysis to provide context for the changes observed in the EZs and 
ECs. 

Methodology for Site Visits: 

To answer our objectives, we completed site visits to all 11 EZs and 2 
of the 95 ECs, one urban and one rural.[Footnote 58] These EZs and ECs 
were located in: 

* Atlanta, Georgia (EZ): 

* Baltimore, Maryland (EZ): 

* Chicago, Illinois (EZ): 

* Cleveland, Ohio (EZ): 

* Detroit, Michigan (EZ): 

* Los Angeles, California (EZ): 

* New York, New York (EZ): 

* Philadelphia, Pennsylvania and Camden, New Jersey (EZ): 

* rural Kentucky (Kentucky Highlands EZ): 

* rural Mississippi (Mid-Delta EZ): 

* rural Texas (Rio Grande Valley EZ): 

* Providence, Rhode Island (EC): 

* rural Tennessee (Fayette-Haywood EC): 

We interviewed stakeholders from each site on the implementation, 
governance, oversight, and tax benefits of the EZ or EC and asked about 
the changes the stakeholders had observed in their communities. Using a 
standardized interview guide, we interviewed some combination of the 
following program stakeholders at each location: EZ/EC officials, board 
members (including some EZ/EC residents), representatives of subgrantee 
organizations, and Chamber of Commerce representatives or individuals 
able to provide the perspective of the business community (table 
4).[Footnote 59] We identified participants to interview at each site 
by soliciting opinions from EZ/EC officials and the current board 
chair. For each site, we reviewed strategic plans, organizational 
charts, and documentation on oversight procedures. In addition, we 
toured the EZ/EC to see some of activities implemented. 

Table 4: Number of Stakeholders Interviewed for EZ and EC Site Visits, 
by Type: 



EZ/EC: Urban: Atlanta EZ; 
EZ/EC officials: Urban: 2; 
Board members: Urban: 2; 
Representatives from subgrantee organizations: Urban: 6; 
Representatives from the Chamber of Commerce or other business 
perspective: Urban: 2; 
Officials from the state pass-through entities: Urban: 4; 
Other representatives[A]: Urban: 10. 

EZ/EC: Urban: Baltimore EZ; 
EZ/EC officials: Urban: 6; 
Board members: Urban: 4; 
Representatives from subgrantee organizations: Urban: 3; 
Representatives from the Chamber of Commerce or other business 
perspective: Urban: 2; 
Officials from the state pass-through entities: Urban: 1; 
Other representatives[A]: Urban: 5. 

EZ/EC: Urban: Chicago EZ; 
EZ/EC officials: Urban: 6; 
Board members: Urban: 4; 
Representatives from subgrantee organizations: Urban: 4; 
Representatives from the Chamber of Commerce or other business 
perspective: Urban: 1; 
Officials from the state pass-through entities: Urban: 3; 
Other representatives[A]: Urban: 2. 

EZ/EC: Urban: Cleveland EZ; 
EZ/EC officials: Urban: 5; 
Board members: Urban: 4; 
Representatives from subgrantee organizations: Urban: 3; 
Representatives from the Chamber of Commerce or other business 
perspective: Urban: 0; 
Officials from the state pass-through entities: Urban: 4; 
Other representatives[A]: Urban: 8. 

EZ/EC: Urban: Detroit EZ; 
EZ/EC officials: Urban: 7; 
Board members: Urban: 4; 
Representatives from subgrantee organizations: Urban: 3; 
Representatives from the Chamber of Commerce or other business 
perspective: Urban: 1; 
Officials from the state pass-through entities: Urban: 2; 
Other representatives[A]: Urban: 9. 

EZ/EC: Urban: Los Angeles EZ; 
EZ/EC officials: Urban: 1; 
Board members: Urban: 2; 
Representatives from subgrantee organizations: Urban: 2; 
Representatives from the Chamber of Commerce or other business 
perspective: Urban: 0; 
Officials from the state pass-through entities: Urban: 0; 
Other representatives[A]: Urban: 12. 

EZ/EC: Urban: New York EZ; 
EZ/EC officials: Urban: [Empty]; 
Board members: Urban: [Empty]; 
Representatives from subgrantee organizations: Urban: [Empty]; 
Representatives from the Chamber of Commerce or other business 
perspective: Urban: [Empty]; 
Officials from the state pass-through entities: Urban: [Empty]; 
Other representatives[A]: Urban: [Empty]. 

EZ/EC: Urban: Upper Manhattan portion; 
EZ/EC officials: Urban: 3; 
Board members: Urban: 3; 
Representatives from subgrantee organizations: Urban: 2; 
Representatives from the Chamber of Commerce or other business 
perspective: Urban: 1; 
Officials from the state pass-through entities: Urban: 2; 
Other representatives[A]: Urban: 2. 

EZ/EC: Urban: Bronx portion; 
EZ/EC officials: Urban: 2; 
Board members: Urban: 4; 
Representatives from subgrantee organizations: Urban: 3; 
Representatives from the Chamber of Commerce or other business 
perspective: Urban: 1; 
Officials from the state pass-through entities: Urban: 2; 
Other representatives[A]: Urban: 1. 

EZ/EC: Urban: Philadelphia/Camden EZ; 
EZ/EC officials: Urban: [Empty]; 
Board members: Urban: [Empty]; 
Representatives from subgrantee organizations: Urban: [Empty]; 
Representatives from the Chamber of Commerce or other business 
perspective: Urban: [Empty]; 
Officials from the state pass-through entities: Urban: [Empty]; 
Other representatives[A]: Urban: [Empty]. 

EZ/EC: Urban: Philadelphia portion; 
EZ/EC officials: Urban: 5; 
Board members: Urban: 2; 
Representatives from subgrantee organizations: Urban: 2; 
Representatives from the Chamber of Commerce or other business 
perspective: Urban: 0; 
Officials from the state pass-through entities: Urban: 8; 
Other representatives[A]: Urban: 3. 

EZ/EC: Urban: Camden portion; 
EZ/EC officials: Urban: 1; 
Board members: Urban: 3; 
Representatives from subgrantee organizations: Urban: 2; 
Representatives from the Chamber of Commerce or other business 
perspective: Urban: 1; 
Officials from the state pass-through entities: Urban: 2; 
Other representatives[A]: Urban: 2. 

EZ/EC: Urban: Providence EC; 
EZ/EC officials: Urban: 2; 
Board members: Urban: 3; 
Representatives from subgrantee organizations: Urban: 3; 
Representatives from the Chamber of Commerce or other business 
perspective: Urban: 0; 
Officials from the state pass-through entities: Urban: 2; 
Other representatives[A]: Urban: 1. 

EZ/EC: Rural: Kentucky Highlands EZ; 
EZ/EC officials: Urban: 4; 
Board members: Urban: 6[B]; 
Representatives from subgrantee organizations: Urban: 1; 
Representatives from the Chamber of Commerce or other business 
perspective: Urban: 1; 
Officials from the state pass-through entities: Urban: 4; 
Other representatives[A]: Urban: 4. 

EZ/EC: Rural: Mid-Delta Mississippi EZ; 
EZ/EC officials: Urban: 4; 
Board members: Urban: 1; 
Representatives from subgrantee organizations: Urban: 4; 
Representatives from the Chamber of Commerce or other business 
perspective: Urban: 2; 
Officials from the state pass-through entities: Urban: 2; 
Other representatives[A]: Urban: 2. 

EZ/EC: Rural: Rio Grande Valley EZ; 
EZ/EC officials: Urban: 4; 
Board members: Urban: 3; 
Representatives from subgrantee organizations: Urban: 3; 
Representatives from the Chamber of Commerce or other business 
perspective: Urban: 1; 
Officials from the state pass-through entities: Urban: 3; 
Other representatives[A]: Urban: 1. 

EZ/EC: Rural: Fayette Haywood EC; 
EZ/EC officials: Urban: 1; 
Board members: Urban: 2; 
Representatives from subgrantee organizations: Urban: 2; 
Representatives from the Chamber of Commerce or other business 
perspective: Urban: 0; 
Officials from the state pass-through entities: Urban: 4; 
Other representatives[A]: Urban: 4. 

Source: GAO. 

[A] Includes local governmental officials, business owners, active 
community members, and other representatives. 

[B] Includes directors of subzones. 

[End of table] 

Methodology for Survey of EC Officials: 

To gather similar information from the ECs, we administered an e-mail 
survey to officials from the 60 Round I ECs that were still in 
operation as of June 2005 and did not receive a subsequent designation. 
We chose to exclude the 34 ECs that received subsequent designations, 
because we did not want their responses to be influenced by those 
programs. A version of the survey showing aggregated responses can be 
viewed at [Hyperlink, http://www.gao.gov/cgi-bin/getrpt?GAO-06-734SP]. 

We developed survey questions from existing program literature and 
interview data collected from Department of Housing and Urban 
Development (HUD) and U.S. Department of Agriculture (USDA) 
headquarters officials as well as our site visits to Round I EZs and 
ECs. The questionnaire items covered the implementation of the program, 
the types of governance structures used, usage of the program tax- 
exempt bond, and stakeholders' views of factors that influenced the 
changes they observed in poverty, unemployment, and economic growth in 
their ECs. We created two versions of the questionnaire, one for urban 
ECs and another for rural ECs, in order to tailor items to urban or 
rural sites. Department of Health and Human Services (HHS), HUD, and 
USDA officials reviewed the survey for content, and we conducted 
pretests at four urban and two rural ECs.[Footnote 60] Since the survey 
was administered by e-mail, a usability pretest was conducted at one 
urban EC (Akron, Ohio) to observe the respondent answering the 
questionnaire as it would appear when opened and displayed on their 
computer screen. 

In administering the survey, we took the following steps to increase 
the response rate. To identify survey participants, we obtained contact 
information for the Round I ECs that did not receive a subsequent 
designation from HUD and USDA in April 2005.[Footnote 61] We then sent 
a notification e-mail to inform the ECs of the survey, to identify the 
correct point of contact, and to ensure the e-mail account was active. 
Those who did not respond to the first e-mail received follow up e- 
mails and telephone calls. The questionnaire was e-mailed on August 25, 
2005 to 27 rural ECs and 33 urban ECs, and participants were given the 
option to respond via e-mail, fax, or post mail. Between September and 
December 2005, multiple follow up e-mails and calls were made to 
increase the response rate. When the survey closed on December 20, 
2005, all of the rural ECs and 31 of the 33 urban ECs had completed it. 
The overall response rate was high at 97 percent, with the response 
rates for the rural ECs at 100 percent and urban ECs at 94 percent. We 
did not attempt to verify the respondents' answers against an 
independent source of information. However, we used two techniques to 
verify the reliability of questionnaire items. First, we used in-depth 
interviewing techniques to evaluate the answers of pretest 
participants, and interviewers judged that all the respondents' answers 
to the questions were based on reliable information. Second, for the 
items that asked about changes to poverty, unemployment, and economic 
growth in the EC, we asked respondents to provide a source of data for 
their response. Responses to those questions that did not include a 
data source were excluded from our analysis of those items. 

The practical difficulties of conducting any survey may introduce 
certain types of errors, commonly referred to as nonsampling errors. 
For example, differences in how a particular question is interpreted, 
the sources of information available to respondents, or the types of 
people who do not respond can introduce unwanted variability into the 
survey results. We sought to minimize these errors by taking the 
following steps: conducting pretests, making follow-up contacts with 
participants to increase response rates, performing statistical 
analyses to identify logical inconsistencies, and having a second 
independent analyst review the statistical analyses. Returned surveys 
were reviewed for consistency before the data were entered into an 
electronic database. All keypunched or inputted data were 100-percent 
verified--that is, the data were electronically entered twice. Further, 
a random sample of the surveys was verified for completeness and 
accuracy. We used statistical software to analyze responses to close- 
ended questions and performed a qualitative analysis on open-ended 
questions to identify common themes. 

Methodology for Qualitative Analysis of Site Visit and EC Survey Data: 

To summarize the information collected at our site visits, we conducted 
a qualitative analysis of interview data. The goal of the analysis was 
to create a summary that would produce an overall "story" or brief 
description of the program as implemented in each site. In this 
process, we reviewed data from over 200 interviews to identify 
information pertaining to the following six broad topics: 

* strategic planning and census tract selection; 

* goals, implemented activities, leveraging activities, and 
sustainability; 

* governance structure and process; 

* program oversight; 

* perceptions of the use of tax benefits; and: 

* perceptions of poverty, unemployment, economic growth, and other 
changes within the zone. 

Based on initial reviews of the interview data, we produced general 
outlines for each topic. For example, a description of the governance 
structure and process included identifying the type of governance 
structure used, roles within the structure, opportunities for community 
involvement, the process for decision making, and successes and 
challenges related to governance. One reviewer was assigned to each of 
the six topics for an individual site. The reviewer examined all 
interviews completed at an individual site and created a topical 
summary based on interview data. Each summary was verified by (1) 
presenting the summaries to the group of six interview reviewers to 
ensure accuracy, clarity, and completeness and (2) having a second 
reviewer trace the summaries back to source documents. 

We also performed a qualitative analysis of the open-ended responses in 
the EC survey to determine reasons why the tax-exempt bond was not more 
widely used; why poverty, unemployment, and economic growth may have 
remained the same over the designation; and what role the EC played in 
changes in poverty, unemployment and economic growth, as well as 
obtaining general comments about the program. Responses to these 
questions were first reviewed by an analyst to identify common 
categories within the responses and then independently verified by a 
second analyst. 

Methodology for Review of Program Oversight: 

We interviewed and obtained documentation from federal, state, and 
local program participants regarding program oversight. We interviewed 
officials from the federal agencies involved with the program and 
obtained and analyzed fiscal and program data from the agencies. In 
addition, since the states were the pass-through entities for grant 
funds provided to the EZs and ECs--that is, they distributed federal 
funding to the communities--we conducted telephone interviews with 
state officials and obtained relevant documents in the 13 states 
containing EZs and ECs we visited. Finally, we interviewed EZ and EC 
officials on their oversight of subgrantees as well as the oversight 
they received from federal and state entities. We did not perform 
financial audits of the EZs and ECs. 

To determine the reliability of data in HUD and USDA Internet-based 
performance reporting systems, we randomly selected activities at each 
EZ and EC we visited and conducted a file review to determine the 
accuracy of: 

the data.[Footnote 62] In the files, we searched related documentation 
for the amounts reported in the system for certain categories, 
including EZ/EC grant funding, leveraged funds, and program outputs. We 
also determined whether, at a minimum, documentation existed to support 
that the activity was implemented. We then assigned each item we 
verified a code (table 5). Finally, we averaged the information for 
each site by category and calculated the average score for each urban 
and rural community. 

Table 5: Coding of Data Reliability of HUD and USDA Performance 
Systems: 

Code: 2; 
Description: Items with strong documentation, meaning that exact 
documentation existed or could be easily inferred with the provided 
documentation. 

Code: 1; 
Description: Items with weak documentation, meaning that some evidence 
existed, but numbers did not match. 

Code: 0; 
Description: Items for which no documentation existed. 

Source: GAO. 

V

We found sufficient documentation that most EZ/EC activities contained 
in the Internet-based reporting systems had occurred, with average 
codes of 2.0 for urban areas and 1.9 for rural areas.[Footnote 63] We 
found that data on EZ/EC grant funding, leveraged funds, and program 
outputs were not sufficiently reliable for our purposes because only 
weak or no documentation could be found at most sites. 

Methodology for Survey of EZ Businesses: 

To assess the use of program tax benefits, we attempted to administer a 
survey to EZ businesses; however, we discontinued the survey due to a 
very low response rate. Based on past post-mailed and phone- 
administered surveys of EZ businesses, we knew that this would be a 
challenging population to survey. In fact, surveys we and Abt 
Associates conducted in 1998 obtained response rates of only 42 and 35 
percent, respectively.[Footnote 64] In addition, both surveys had a 
relatively high number of undeliverable surveys. In anticipation of 
these issues, we attempted to administer a concise, high-level survey 
via mail to a stratified random sample (n=517) of EZ 
businesses.[Footnote 65] We implemented a sampling procedure using the 
2004 Claritas Business Facts dataset that stratified businesses located 
in the EZ by three strata: urban small businesses (less than 50 
employees), urban large businesses (50 or more employees), and rural 
businesses. The survey was targeted to private businesses rather than 
public and nonprofit businesses, since these for-profit businesses were 
the ones eligible for the tax benefits.[Footnote 66] Public and 
nonprofit businesses were excluded from the sample by the primary 
industry code identifier included in the Claritas data. A few of these 
types of businesses that were not initially excluded based on their 
industry code were later removed from the sample because the 
respondents said that they were not eligible for the tax benefits. 

We developed our survey after reviewing surveys used in previous 
studies, interviewing business owners, and conducting pretests with EZ 
businesses. The questionnaire was brief--containing 21 closed-ended 
items and 1 optional open-ended item--and took most pretest respondents 
approximately five minutes to complete. When we conducted pretests with 
10 businesses from Baltimore, Philadelphia, and rural Kentucky, all 
pretest participants found the survey to be easy to complete and said 
that it did not ask for sensitive information. These business owners, 
however, often lacked complete information about their company's tax 
filings and were not always able to answer all of the survey questions. 
Several indicated that they would be unlikely to complete the survey 
because the topic was not relevant to them. 

We administered the survey according to standard survey data collection 
practices. We sent a letter notifying the 517 businesses of our survey 
about a week prior to the survey mailing, mailed a copy of the survey, 
and followed that mailing with a reminder postcard. We received a total 
of 63 responses after our initial mailing, a response rate of 12 
percent. Our mailings to 104 businesses (20 percent) could not be 
delivered and were returned because of incorrect addresses or contact 
information. 

Methodology for Assessing the Effect of the Program on Poverty, 
Unemployment, and Economic Growth: 

To determine the effect of the EZ/EC program on changes in poverty, 
unemployment, and economic growth, we used a variety of quantitative 
methods that examined changes in the designated program areas and areas 
we identified as comparison areas. In addition, we incorporated 
interview data in our qualitative analysis to provide context for the 
changes observed. We calculated percent changes of demographic, 
socioeconomic, and business data between two points in time for the all 
Round I EZs and ECs.[Footnote 67] However, we used only urban EZs in 
our econometric analysis because of data limitations in rural areas and 
the amount of funds awarded to ECs. 

Description of Data Sources: 

To assess the changes in poverty and unemployment, we used census tract-
level data on poverty rates and unemployment rates from the 1990 and 
2000 decennial censuses. To determine changes in economic growth in EZ 
and ECs, we defined economic growth in terms of the number of private 
businesses created and the total number of jobs in the areas.[Footnote 
68] We obtained year-end data on these variables for years 1995, 1999, 
and 2004 from the Business-Facts Database maintained by Claritas, a 
private data processing company. We explored several public and private 
data sources that contained the number of businesses and jobs at the 
census tract level and selected Claritas because it (1) maintained 
archival data, (2) provided data with a high level of reliability at 
the census tract level, and (3) used techniques to ensure the 
representation of small businesses. We also explored a variety of other 
data options to enhance our analysis, but were ultimately not able to 
use them. For example, we tried to acquire data throughout the period 
of the program, such as state unemployment data, local building permit 
and crime data, and data on students receiving free or reduced lunches. 
However, we were not able to use these data because they were not 
captured consistently across sites, not available at the census tract 
level, or not sufficiently reliable for our purposes. 

The decennial census data used are from the census long form that is 
administered to a sample of respondents. Because census data used in 
this analysis are estimated based on a probability sample, each 
estimate is based on just one of a large number of samples that could 
have been drawn. Since each sample could have produced different 
estimates, we express our confidence in the precision of our particular 
sample's results as a 95 percent confidence interval. For example, the 
estimated percent change in the poverty rate of EZs is a decrease of 
6.1 percent, and the 95 percent confidence interval for this estimate 
ranges from 4.9 to 7.2 percent. This is the interval that would contain 
the actual population value for 95 percent of the samples that could 
have been drawn. As a result, we are 95 percent confident that each of 
the confidence intervals in this report will include the true values in 
the study population. All Census variables based on percentages, such 
as poverty rate and unemployment rate, have 95 percent confidence 
intervals of plus or minus 5 percentage points or less. The confidence 
intervals for average household income and average owner-occupied 
housing value are shown in table 6. 

Table 6: Confidence Intervals for Average Household Income and Average 
Housing Value in Constant 2004 Dollars[A]: 

Average household income: Atlanta EZ; 
95 percent confidence interval: 1990 estimate: $18,343; 
95 percent confidence interval: From: $17,466; 
95 percent confidence interval: To: $19,220; 
95 percent confidence interval: 2000 estimate: $28,552; 
95 percent confidence interval: From: $27,205; 
95 percent confidence interval: To: $29,899; 
95 percent confidence interval: Percent change: 55.66; 
95 percent confidence interval: From: 55.4; 
95 percent confidence interval: To: 55.91. 

Average household income: Atlanta EZ: Comparison; 
95 percent confidence interval: 1990 estimate: 30,567; 
95 percent confidence interval: From: 29,741; 
95 percent confidence interval: To: 31,393; 
95 percent confidence interval: 2000 estimate: 39,500; 
95 percent confidence interval: From: 38,328; 
95 percent confidence interval: To: 40,672; 
95 percent confidence interval: Percent change: 29.23; 
95 percent confidence interval: From: 28.99; 
95 percent confidence interval: To: 29.46. 

Average household income: Baltimore EZ; 
95 percent confidence interval: 1990 estimate: 28,185; 
95 percent confidence interval: From: 27,207; 
95 percent confidence interval: To: 29,164; 
95 percent confidence interval: 2000 estimate: 35,059; 
95 percent confidence interval: From: 33,566; 
95 percent confidence interval: To: 36,551; 
95 percent confidence interval: Percent change: 24.39; 
95 percent confidence interval: From: 24.1; 
95 percent confidence interval: To: 24.67. 

Average household income: Baltimore EZ; Comparison; 
95 percent confidence interval: 1990 estimate: 27,931; 
95 percent confidence interval: From: 27,316; 
95 percent confidence interval: To: 28,546; 
95 percent confidence interval: 2000 estimate: 31,367; 
95 percent confidence interval: From: 30,511; 
95 percent confidence interval: To: 32,223; 
95 percent confidence interval: Percent change: 12.3; 
95 percent confidence interval: From: 12.05; 
95 percent confidence interval: To: 12.56. 

Average household income: Chicago EZ; 
95 percent confidence interval: 1990 estimate: 23,097; 
95 percent confidence interval: From: 22,636; 
95 percent confidence interval: To: 23,559; 
95 percent confidence interval: 2000 estimate: 34,718; 
95 percent confidence interval: From: 33,868; 
95 percent confidence interval: To: 35,567; 
95 percent confidence interval: Percent change: 50.31; 
95 percent confidence interval: From: 50.13; 
95 percent confidence interval: To: 50.49. 

Average household income: Chicago EZ: Comparison; 
95 percent confidence interval: 1990 estimate: 28,431; 
95 percent confidence interval: From: 28,030; 
95 percent confidence interval: To: 28,832; 
95 percent confidence interval: 2000 estimate: 39,985; 
95 percent confidence interval: From: 39,367; 
95 percent confidence interval: To: 40,604; 
95 percent confidence interval: Percent change: 40.64; 
95 percent confidence interval: From: 40.48; 
95 percent confidence interval: To: 40.8. 

Average household income: Detroit EZ; 
95 percent confidence interval: 1990 estimate: 22,644; 
95 percent confidence interval: From: 22,034; 
95 percent confidence interval: To: 23,253; 
95 percent confidence interval: 2000 estimate: 33,751; 
95 percent confidence interval: From: 32,660; 
95 percent confidence interval: To: 34,842; 
95 percent confidence interval: Percent change: 49.05; 
95 percent confidence interval: From: 48.84; 
95 percent confidence interval: To: 49.26. 

Average household income: Detroit EZ: Comparison; 
95 percent confidence interval: 1990 estimate: 25,609; 
95 percent confidence interval: From: 25,197; 
95 percent confidence interval: To: 26,021; 
95 percent confidence interval: 2000 estimate: 36,200; 
95 percent confidence interval: From: 35,523; 
95 percent confidence interval: To: 36,877; 
95 percent confidence interval: Percent change: 41.36; 
95 percent confidence interval: From: 41.19; 
95 percent confidence interval: To: 41.52. 
Average household income: 
New York EZ; 
95 percent confidence interval: 1990 estimate: 26,518; 
95 percent confidence interval: From: 25,981; 
95 percent confidence interval: To: 27,054; 
95 percent confidence interval: 2000 estimate: 33,557; 
95 percent confidence interval: From: 32,833; 
95 percent confidence interval: To: 34,280; 
95 percent confidence interval: Percent change: 26.54; 
95 percent confidence interval: From: 26.34; 
95 percent confidence interval: To: 26.75. 

Average household income: New York: Comparison; 
95 percent confidence interval: 1990 estimate: 26,993; 
95 percent confidence interval: From: 26,714; 
95 percent confidence interval: To: 27,272; 
95 percent confidence interval: 2000 estimate: 31,247; 
95 percent confidence interval: From: 30,872; 
95 percent confidence interval: To: 31,622; 
95 percent confidence interval: Percent change: 15.76; 
95 percent confidence interval: From: 15.59; 
95 percent confidence interval: To: 15.93. 

Average household income: Upper Manhattan; 
95 percent confidence interval: 1990 estimate: 26,559; 
95 percent confidence interval: From: 25,971; 
95 percent confidence interval: To: 27,147; 
95 percent confidence interval: 2000 estimate: 34,041; 
95 percent confidence interval: From: 33,239; 
95 percent confidence interval: To: 34,844; 
95 percent confidence interval: Percent change: 28.17; 
95 percent confidence interval: From: 27.96; 
95 percent confidence interval: To: 28.39. 

Average household income: Bronx; 
95 percent confidence interval: 1990 estimate: 26,294; 
95 percent confidence interval: From: 24,983; 
95 percent confidence interval: To: 27,606; 
95 percent confidence interval: 2000 estimate: 30,842; 
95 percent confidence interval: From: 29,238; 
95 percent confidence interval: To: 32,446; 
95 percent confidence interval: Percent change: 17.29; 
95 percent confidence interval: From: Average household income: 16.95; 
95 percent confidence interval: To: 17.64. 

Average household income: Philadelphia-Camden EZ; 
95 percent confidence interval: 1990 estimate: 23,188; 
95 percent confidence interval: From: 22,259; 
95 percent confidence interval: To: 24,117; 
95 percent confidence interval: 2000 estimate: 28,562; 
95 percent confidence interval: From: 27,197; 
95 percent confidence interval: To: 29,927; 
95 percent confidence interval: Percent change: 23.17; 
95 percent confidence interval: From: 22.87; 
95 percent confidence interval: To: 23.48. 

Average household income: Philadelphia-Camden EZ: Comparison; 
95 percent confidence interval: 1990 estimate: 27,292; 
95 percent confidence interval: From: 26,031; 
95 percent confidence interval: To: 28,553; 
95 percent confidence interval: 2000 estimate: 31,318; 
95 percent confidence interval: From: 29,718; 
95 percent confidence interval: To: 32,918; 
95 percent confidence interval: Percent change: 14.75; 
95 percent confidence interval: From: 14.4; 
95 percent confidence interval: To: 15.1. 

Average household income: Philadelphia; 
95 percent confidence interval: 1990 estimate: 22,269; 
95 percent confidence interval: From: 21,262; 
95 percent confidence interval: To: 23,276; 
95 percent confidence interval: 2000 estimate: 27,851; 
95 percent confidence interval: From: 26,309; 
95 percent confidence interval: To: 29,392; 
95 percent confidence interval: Percent change: 25.07; 
95 percent confidence interval: From: 24.74; 
95 percent confidence interval: To: 25.39. 

Average household income: Camden; 
95 percent confidence interval: 1990 estimate: 26,742; 
95 percent confidence interval: From: 24,465; 
95 percent confidence interval: To: 29,018; 
95 percent confidence interval: 2000 estimate: 31,158; 
95 percent confidence interval: From: 28,228; 
95 percent confidence interval: To: 34,088; 
95 percent confidence interval: Percent change: 16.52; 
95 percent confidence interval: From: 16.05; 
95 percent confidence interval: To: 16.98. 

Average household income: Cleveland EZ; 
95 percent confidence interval: 1990 estimate: 20,535; 
95 percent confidence interval: From: 19,730; 
95 percent confidence interval: To: 21,340; 
95 percent confidence interval: 2000 estimate: 28,781; 
95 percent confidence interval: From: 27,524; 
95 percent confidence interval: To: 30,038; 
95 percent confidence interval: Percent change: 40.16; 
95 percent confidence interval: From: 39.9; 
95 percent confidence interval: To: 40.42. 

Average household income: Cleveland EZ: Comparison; 
95 percent confidence interval: 1990 estimate: 24,688; 
95 percent confidence interval: From: 24,171; 
95 percent confidence interval: To: 25,206; 
95 percent confidence interval: 2000 estimate: 30,311; 
95 percent confidence interval: From: 29,607; 
95 percent confidence interval: To: 31,016; 
95 percent confidence interval: Percent change: 22.78; 
95 percent confidence interval: From: 22.56; 
95 percent confidence interval: To: 23. 

Average household income: Los Angeles EZ; 
95 percent confidence interval: 1990 estimate: 28,801; 
95 percent confidence interval: From: 28,191; 
95 percent confidence interval: To: 29,412; 
95 percent confidence interval: 2000 estimate: 32,631; 
95 percent confidence interval: From: 31,857; 
95 percent confidence interval: To: 33,405; 
95 percent confidence interval: Percent change: 13.3; 
95 percent confidence interval: From: 13.06; 
95 percent confidence interval: To: 13.54. 

Average household income: Los Angeles EZ: Comparison; 
95 percent confidence interval: 1990 estimate: 34,087; 
95 percent confidence interval: From: 33,478; 
95 percent confidence interval: To: 34,696; 
95 percent confidence interval: 2000 estimate: 37,843; 
95 percent confidence interval: From: 37,058; 
95 percent confidence interval: To: 38,628; 
95 percent confidence interval: Percent change: 11.02; 
95 percent confidence interval: From: 10.79; 
95 percent confidence interval: To: 11.25. 

Average household income: Kentucky Highlands EZ; 
95 percent confidence interval: 1990 estimate: 23,304; 
95 percent confidence interval: From: 22,043; 
95 percent confidence interval: To: 24,565; 
95 percent confidence interval: 2000 estimate: 31,064; 
95 percent confidence interval: From: 29,520; 
95 percent confidence interval: To: 32,608; 
95 percent confidence interval: Percent change: 33.3; 
95 percent confidence interval: From: 32.99; 
95 percent confidence interval: To: 33.61. 

Average household income: Mid-Delta EZ; 
95 percent confidence interval: 1990 estimate: 25,872; 
95 percent confidence interval: From: 24,321; 
95 percent confidence interval: To: 27,424; 
95 percent confidence interval: 2000 estimate: 35,559; 
95 percent confidence interval: From: 33,392; 
95 percent confidence interval: To: 37,726; 
95 percent confidence interval: Percent change: 37.44; 
95 percent confidence interval: From: 37.12; 
95 percent confidence interval: To: 37.76. 

Average household income: Rio Grande Valley EZ; 
95 percent confidence interval: 1990 estimate: 25,093; 
95 percent confidence interval: From: 23,626; 
95 percent confidence interval: To: 26,560; 
95 percent confidence interval: 2000 estimate: 32,763; 
95 percent confidence interval: From: 30,920; 
95 percent confidence interval: To: 34,606; 
95 percent confidence interval: Percent change: 30.57; 
95 percent confidence interval: From: 30.24; 
95 percent confidence interval: To: 30.9. 

Average household income: Providence EC; 
95 percent confidence interval: 1990 estimate: 28,593; 
95 percent confidence interval: From: 27,525; 
95 percent confidence interval: To: 29,661; 
95 percent confidence interval: 2000 estimate: 32,616; 
95 percent confidence interval: From: 31,229; 
95 percent confidence interval: To: 34,004; 
95 percent confidence interval: Percent change: 14.07; 
95 percent confidence interval: From: 13.75; 
95 percent confidence interval: To: 14.39. 

Average household income: Fayette-Haywood EC; 
95 percent confidence interval: 1990 estimate: 32,560; 
95 percent confidence interval: From: 31,008; 
95 percent confidence interval: To: 34,111; 
95 percent confidence interval: 2000 estimate: 45,353; 
95 percent confidence interval: From: 43,249; 
95 percent confidence interval: To: 47,457; 
95 percent confidence interval: Percent change: 39.29; 
95 percent confidence interval: From: 39.01; 
95 percent confidence interval: To: 39.57. 

Average owner-occupied housing value: Atlanta EZ; 
95 percent confidence interval: 1990 estimate: $55,883; 
95 percent confidence interval: From: $52,688; 
95 percent confidence interval: To: $59,077; 
95 percent confidence interval: 2000 estimate: $117,869; 
95 percent confidence interval: From: $106,218; 
95 percent confidence interval: To: $129,519; 
95 percent confidence interval: Percent change: 110.92; 
95 percent confidence interval: From: 110.68; 
95 percent confidence interval: To: 111.17. 

Average owner-occupied housing value: Atlanta EZ: Comparison; 
95 percent confidence interval: 1990 estimate: 74,063; 
95 percent confidence interval: From: 72,446; 
95 percent confidence interval: To: 75,680; 
95 percent confidence interval: 2000 estimate: 101,774; 
95 percent confidence interval: From: 96,312; 
95 percent confidence interval: To: 107,236; 
95 percent confidence interval: Percent change: 37.42; 
95 percent confidence interval: From: 37.15; 
95 percent confidence interval: To: 37.68. 

Average owner-occupied housing value: Baltimore EZ; 
95 percent confidence interval: 1990 estimate: 53,714; 
95 percent confidence interval: From: 51,381; 
95 percent confidence interval: To: 56,048; 
95 percent confidence interval: 2000 estimate: 62,219; 
95 percent confidence interval: From: 58,659; 
95 percent confidence interval: To: 65,779; 
95 percent confidence interval: Percent change: 15.83; 
95 percent confidence interval: From: 15.48; 
95 percent confidence interval: To: 16.19. 

Average owner-occupied housing value: Baltimore EZ: Comparison; 
95 percent confidence interval: 1990 estimate: 55,966; 
95 percent confidence interval: From: 54,113; 
95 percent confidence interval: To: 57,819; 
95 percent confidence interval: 2000 estimate: 62,514; 
95 percent confidence interval: From: 59,920; 
95 percent confidence interval: To: 65,108; 
95 percent confidence interval: Percent change: 11.7; 
95 percent confidence interval: From: 11.39; 
95 percent confidence interval: To: 12.01. 

Average owner-occupied housing value: Chicago EZ; 
95 percent confidence interval: 1990 estimate: 71,429; 
95 percent confidence interval: From: 67,487; 
95 percent confidence interval: To: 75,372; 
95 percent confidence interval: 2000 estimate: 160,411; 
95 percent confidence interval: From: 150,476; 
95 percent confidence interval: To: 170,347; 
95 percent confidence interval: Percent change: 124.57; 
95 percent confidence interval: From: 124.38; 
95 percent confidence interval: To: 124.77. 

Average owner-occupied housing value: Chicago EZ: Comparison; 
95 percent confidence interval: 1990 estimate: 88,445; 
95 percent confidence interval: From: 85,343; 
95 percent confidence interval: To: 91,548; 
95 percent confidence interval: 2000 estimate: 167,015; 
95 percent confidence interval: From: 159,548; 
95 percent confidence interval: To: 174,482; 
95 percent confidence interval: Percent change: 88.83; 
95 percent confidence interval: From: 88.64; 
95 percent confidence interval: To: 89.03. 

Average owner-occupied housing value: Detroit EZ; 
95 percent confidence interval: 1990 estimate: 23,114; 
95 percent confidence interval: From: 22,153; 
95 percent confidence interval: To: 24,075; 
95 percent confidence interval: 2000 estimate: 52,234; 
95 percent confidence interval: From: 49,362; 
95 percent confidence interval: To: 55,106; 
95 percent confidence interval: Percent change: 125.99; 
95 percent confidence interval: From: 125.81; 
95 percent confidence interval: To: 126.16. 

Average owner-occupied housing value: Detroit EZ: Comparison; 
95 percent confidence interval: 1990 estimate: 28,598; 
95 percent confidence interval: From: 27,620; 
95 percent confidence interval: To: 29,575; 
95 percent confidence interval: 2000 estimate: 61,160; 
95 percent confidence interval: From: 58,688; 
95 percent confidence interval: To: 63,632; 
95 percent confidence interval: Percent change: 113.86; 
95 percent confidence interval: From: 113.7; 
95 percent confidence interval: To: 114.03. 

Average owner-occupied housing value: New York EZ; 
95 percent confidence interval: 1990 estimate: 207,544; 
95 percent confidence interval: From: 166,353; 
95 percent confidence interval: To: 248,735; 
95 percent confidence interval: 2000 estimate: 301,835; 
95 percent confidence interval: From: 244,974; 
95 percent confidence interval: To: 358,697; 
95 percent confidence interval: Percent change: 45.43; 
95 percent confidence interval: From: 44.89; 
95 percent confidence interval: To: 45.98. 

Average owner-occupied housing value: New York EZ: Comparison; 
95 percent confidence interval: 1990 estimate: 177,446; 
95 percent confidence interval: From: 167,025; 
95 percent confidence interval: To: 187,867; 
95 percent confidence interval: 2000 estimate: 209,423; 
95 percent confidence interval: From: 198,465; 
95 percent confidence interval: To: 220,380; 
95 percent confidence interval: Percent change: 18.02; 
95 percent confidence interval: From: 17.66; 
95 percent confidence interval: To: 18.38. 

Average owner-occupied housing value: Upper Manhattan; 
95 percent confidence interval: 1990 estimate: 238,864; 
95 percent confidence interval: From: 188,845; 
95 percent confidence interval: To: 288,882; 
95 percent confidence interval: 2000 estimate: 384,155; 
95 percent confidence interval: From: 308,848; 
95 percent confidence interval: To: 459,462; 
95 percent confidence interval: Percent change: 60.83; 
95 percent confidence interval: From: 60.32; 
95 percent confidence interval: To: 61.33. 

Average owner-occupied housing value: Bronx; 
95 percent confidence interval: 1990 estimate: 99,728; 
95 percent confidence interval: From: 71,856; 
95 percent confidence interval: To: 127,600; 
95 percent confidence interval: 2000 estimate: 124,588; 
95 percent confidence interval: From: 100,021; 
95 percent confidence interval: To: 149,155; 
95 percent confidence interval: Percent change: 24.93; 
95 percent confidence interval: From: 24.23; 
95 percent confidence interval: To: 25.63. 

Average owner-occupied housing value: Philadelphia-Camden EZ; 
95 percent confidence interval: 1990 estimate: 29,899; 
95 percent confidence interval: From: 28,060; 
95 percent confidence interval: To: 31,739; 
95 percent confidence interval: 2000 estimate: 37,780; 
95 percent confidence interval: From: 35,895; 
95 percent confidence interval: To: 39,664; 
95 percent confidence interval: Percent change: 26.36; 
95 percent confidence interval: From: 26.02; 
95 percent confidence interval: To: 26.69. 

Average owner-occupied housing value: Philadelphia-Camden EZ: 
Comparison; 
95 percent confidence interval: 1990 estimate: 42,045; 
95 percent confidence interval: From: 39,630; 
95 percent confidence interval: To: 44,461; 
95 percent confidence interval: 2000 estimate: 51,159; 
95 percent confidence interval: From: 44,926; 
95 percent confidence interval: To: 57,392; 
95 percent confidence interval: Percent change: 21.67; 
95 percent confidence interval: From: 21.22; 
95 percent confidence interval: To: 22.13. 

Average owner-occupied housing value: Philadelphia; 
95 percent confidence interval: 1990 estimate: 28,288; 
95 percent confidence interval: From: 26,263; 
95 percent confidence interval: To: 30,313; 
95 percent confidence interval: 2000 estimate: 37,353; 
95 percent confidence interval: From: 35,178; 
95 percent confidence interval: To: 39,528; 
95 percent confidence interval: Percent change: 32.04; 
95 percent confidence interval: From: 31.7; 
95 percent confidence interval: To: 32.39. 

Average owner-occupied housing value: Camden; 
95 percent confidence interval: 1990 estimate: 35,076; 
95 percent confidence interval: From: 30,928; 
95 percent confidence interval: To: 39,224; 
95 percent confidence interval: 2000 estimate: 39,398; 
95 percent confidence interval: From: 35,730; 
95 percent confidence interval: To: 43,067; 
95 percent confidence interval: Percent change: 12.32; 
95 percent confidence interval: From: 11.8; 
95 percent confidence interval: To: 12.84. 

Average owner-occupied housing value: Cleveland EZ; 
95 percent confidence interval: 1990 estimate: 38,071; 
95 percent confidence interval: From: 36,277; 
95 percent confidence interval: To: 39,866; 
95 percent confidence interval: 2000 estimate: 75,186; 
95 percent confidence interval: From: 71,537; 
95 percent confidence interval: To: 78,835; 
95 percent confidence interval: Percent change: 97.49; 
95 percent confidence interval: From: 97.29; 
95 percent confidence interval: To: 97.69. 

Average owner-occupied housing value: Cleveland EZ: Comparison; 
95 percent confidence interval: 1990 estimate: 46,972; 
95 percent confidence interval: From: 45,966; 
95 percent confidence interval: To: 47,979; 
95 percent confidence interval: 2000 estimate: 70,161; 
95 percent confidence interval: From: 68,649; 
95 percent confidence interval: To: 71,674; 
95 percent confidence interval: Percent change: 49.37; 
95 percent confidence interval: From: 49.19; 
95 percent confidence interval: To: 49.54. 

Average owner-occupied housing value: Los Angeles EZ; 
95 percent confidence interval: 1990 estimate: 141,665; 
95 percent confidence interval: From: 138,933; 
95 percent confidence interval: To: 144,397; 
95 percent confidence interval: 2000 estimate: 156,492; 
95 percent confidence interval: From: 151,907; 
95 percent confidence interval: To: 161,078; 
95 percent confidence interval: Percent change: 10.47; 
95 percent confidence interval: From: 10.21; 
95 percent confidence interval: To: 10.72. 

Average owner-occupied housing value: Los Angeles EZ: Comparison; 
95 percent confidence interval: 1990 estimate: 160,090; 
95 percent confidence interval: From: 157,393; 
95 percent confidence interval: To: 162,787; 
95 percent confidence interval: 2000 estimate: 165,180; 
95 percent confidence interval: From: 161,599; 
95 percent confidence interval: To: 168,761; 
95 percent confidence interval: Percent change: 3.18; 
95 percent confidence interval: From: 2.94; 
95 percent confidence interval: To: 3.42. 

Average owner-occupied housing value: Kentucky Highlands EZ; 
95 percent confidence interval: 1990 estimate: 43,392; 
95 percent confidence interval: From: 38,713; 
95 percent confidence interval: To: 48,071; 
95 percent confidence interval: 2000 estimate: 65,815; 
95 percent confidence interval: From: 62,527; 
95 percent confidence interval: To: 69,104; 
95 percent confidence interval: Percent change: 51.68; 
95 percent confidence interval: From: 51.35; 
95 percent confidence interval: To: 52. 

Average owner-occupied housing value: Mid-Delta EZ; 
95 percent confidence interval: 1990 estimate: 50,061; 
95 percent confidence interval: From: 47,323; 
95 percent confidence interval: To: 52,800; 
95 percent confidence interval: 2000 estimate: 66,872; 
95 percent confidence interval: From: 59,968; 
95 percent confidence interval: To: 73,777; 
95 percent confidence interval: Percent change: 33.58; 
95 percent confidence interval: From: 33.19; 
95 percent confidence interval: To: 33.97. 

Average owner-occupied housing value: Rio Grande Valley EZ; 
95 percent confidence interval: 1990 estimate: 46,100; 
95 percent confidence interval: From: 42,654; 
95 percent confidence interval: To: 49,546; 
95 percent confidence interval: 2000 estimate: 61,450; 
95 percent confidence interval: From: 55,970; 
95 percent confidence interval: To: 66,929; 
95 percent confidence interval: Percent change: 33.3; 
95 percent confidence interval: From: 32.91; 
95 percent confidence interval: To: 33.69. 

Average owner-occupied housing value: Providence EC; 
95 percent confidence interval: 1990 estimate: 124,339; 
95 percent confidence interval: From: 118,190; 
95 percent confidence interval: To: 130,489; 
95 percent confidence interval: 2000 estimate: 116,698; 
95 percent confidence interval: From: 99,200; 
95 percent confidence interval: To: 134,196; 
95 percent confidence interval: Percent change: -6.15; 
95 percent confidence interval: From: -6.77; 
95 percent confidence interval: To: - 5.52. 

Average owner-occupied housing value: Fayette-Haywood EC; 
95 percent confidence interval: 1990 estimate: $68,945; 
95 percent confidence interval: From: $65,765; 
95 percent confidence interval: To: $72,125; 
95 percent confidence interval: 2000 estimate: $103,619; 
95 percent confidence interval: From: $97,144; 
95 percent confidence interval: To: $110,094; 
95 percent confidence interval: Percent change: 50.29; 
95 percent confidence interval: From: 50.01; 
95 percent confidence interval: To: 50.57. 

Source: GAO analysis of Census data. 

[A] Other variables used in the model and shown in the site visit 
descriptions that were based on percentages, such as the poverty rate, 
had confidence intervals of less than +/-5 percentage points. 

[End of table] 

In addition to sampling errors, Census data (both sampled and 100 
percent data) are subject to nonsampling errors that may occur during 
the operations used to collect and process census data. Examples of 
nonsampling errors are not enumerating every housing unit or person in 
the sample, failing to obtain all required information from a 
respondent, obtaining incorrect information, and recording information 
incorrectly. Operations such as field review of enumerator's work, 
clerical handling of questionnaires, and electronic processing of 
questionnaires also may introduce nonsampling errors in the data. The 
Census Bureau discusses sources of nonsampling errors and makes 
attempts to limit them. 

Choosing Comparison Areas Using the Propensity Score: 

To provide context for the changes we observed in the EZs and ECs, we 
calculated the percent change of the designated areas as well as areas, 
called comparison areas, that most closely resembled the EZ/EC program 
areas. To select comparison areas for our analysis, we used a 
statistical matching method called the propensity score. The propensity 
score predicts the probability that a tract could have been designated 
based on having characteristics similar to those found in the tracts 
selected for the program. We used five factors to calculate the 
propensity scores, as shown in table 7. 

Table 7: Factors Selected for Choosing Comparison Tracts: 

Factor: 1990 poverty rate[ A]; 
Reason selected: 
* EZ/EC program eligibility criteria; 
* Factor considered in a similar study[B, E]. 

Factor: 1990 unemployment rate[C]; 
Reason selected: 
* EZ/EC program eligibility criteria; 
* Factor considered in a similar study[ B]. 

Factor: 1990 population density[ D]; 
Reason selected: 
* Calculation based on two EZ/EC program eligibility criteria, 
population and area; 
* Factor considered in a similar study[B]. 

Factor: 1990 average household income; 
Reason selected: 
* Factor considered in similar studies[ B, E]. 

Factor: Percentage of minority population in 1990[F]; 
Reason selected: 
* Factor considered in similar studies[ B, E]. 

Source: GAO. 

[A] Percent based on individuals for whom poverty status has been 
determined. 

[B] See Bondonio, Daniele and John Engberg (2000), "Enterprise Zones 
and Local Employment: Evidence from the States' Programs," Regional 
Science and Urban Economics, Vol. 30, No.5, pp. 519-549. 

[C] Percent based on individuals 16 years of age or older. 

[D] Individuals per square mile. 

[E] Hebert and others, Interim Assessment. 

[F] For the purposes of this report, we calculated minority population 
by subtracting the percent of white population from the total 
population. 

[End of table] 

To ensure that our comparison areas were similar to the designated 
areas in terms of geography, we explored two selection methods, one 
that included tracts in the same county as the EZ/EC and in adjacent 
counties, and another that selected tracts within a 5-mile radius of 
the EZ/EC.[Footnote 69] We excluded tracts that received a subsequent 
designation in the EZ/EC or Renewal Community programs in 1998 and 2002 
in order to remove the possibility of tracts that may have received 
similar benefits affecting our analysis. After mapping the resulting 
comparison tracts using these two methods, we decided to use tracts 
selected within a 5-mile radius of the EZs and ECs because this method 
provided more contiguous areas, while the results of the county and the 
adjacent counties method yielded comparison tracts in other states 
where political structures and types of funds could differ. 

Using the computed propensity scores, we selected comparison tracts 
whose scores were greater than 0.1. This threshold was chosen because 
most EZ tracts had propensity scores of 0.1 or higher; 
therefore, comparison tracts with propensity scores of at least 0.1 
were the most similar to the EZ tracts. This threshold also yielded 
approximately the same number of comparison tracts as EZ tracts in most 
of the eight urban EZs. In addition, we tested this threshold by 
running our models with comparison tracts whose propensity scores were 
greater than 0.05 or 0.15 and found that the results did not change 
significantly.[Footnote 70] Some limitations exist with this method. 
For example, since many of census tracts chosen for the program may 
have had the highest level of poverty, it was difficult to find tracts 
with the same level of poverty. 

Our Descriptive and Econometric Analyses: 

We calculated the percent changes at the program wide level for our 
four indicators of poverty, unemployment, and economic growth for both 
designated and comparison areas.[Footnote 71] We also calculated the 
changes for urban and rural designees and EZs and ECs separately, so 
that we could make comparisons between those groups. In addition, for 
the eight urban Round I EZs, we calculated the percentages separately 
for each EZ and EZ comparison area to show differences between zones. 
Although the comparison areas were sufficient to use in our program 
wide analyses, for rural EZs and urban and rural ECs, we did not use 
comparison areas for site-level analyses because there were too few 
comparison tracts. For example, the Providence, Rhode Island EC 
consisted of 13 tracts, but the area had only four eligible comparison 
tracts. 

We also completed an econometric analysis of the eight urban EZs. We 
used a standard econometric approach, the weighted least squares model, 
which allowed us to analyze the change from 1990 to 2000 and compare it 
with the 1990 value of several explanatory variables. The benefit of 
this approach is that the program, officially implemented in 1994, 
would not affect the 1990 values of the explanatory variables. In 
addition, we spoke with several experts in the urban studies field on 
our methodology. For more information on the methods used in our 
econometric analysis and a full discussion of our results, please see 
appendix II. 

[End of section] 

Appendix II: Methodology for and Results of Our Econometric Models: 

This appendix describes our efforts to isolate the effect of the EZ/EC 
program on the changes in poverty, unemployment, and economic growth, 
by conducting an econometric analysis of all urban EZ census 
tracts.[Footnote 72] In our analysis of percent changes, we found that 
poverty and unemployment had decreased and that some economic growth 
had occurred. However, when we used the econometric models to control 
for other area characteristics, our results did not definitively 
suggest that the observed changes in poverty and unemployment were 
associated with the EZ program in urban areas. In addition, our models 
did not adequately explain the observed changes in the proxy measures 
we used for economic growth; thus, the results did not allow us to 
conclude whether there is an association between the EZ program and 
economic growth. 

As mentioned in the report, there were several challenges that limited 
our ability to determine the effect of the program. First, data at the 
census tract-level for the program years were limited. We used data 
from the 1990 and 2000 decennial censuses to show the changes in 
poverty and unemployment. In addition, we primarily used two measures 
for economic growth--the number of businesses and the number of jobs 
from Claritas Business-Facts dataset for years 1995, 1999, and 2004--in 
our models of economic growth.[Footnote 73] Second, we were not able to 
account for the spillover effects of EZ designation into their 
neighboring areas. For example, if the EZ/EC program affected 
comparison tracts as well as the designated communities, our analyses 
would not find any significant differences between the designated and 
comparison tracts. The result may be an obscuring of the extent of the 
statistical association between the urban EZ program and the study 
variables. Third, the analyses did not account for the confounding 
effects of other public or private programs, such as those intended to 
reduce poverty or unemployment or increase the number of area jobs. As 
a result, estimates for the EZ program in our analyses may under or 
overstate the extent of EZ program's correlation with poverty, 
unemployment, and economic growth. Fourth, our estimations did not 
fully account for the economic trends that were affecting the choice of 
areas selected for the program. For example, if program officials 
tended to pick census tracts that were already experiencing 
gentrification prior to 1994, our estimations could overstate the 
effect of the EZ designation. Conversely, if officials tended to choose 
census tracts that were experiencing economic declines prior to 1994, 
such as those in which major employers had closed, we might understate 
the program's impact. We did include a variable from Census data--new 
housing construction between 1990 and 1994--that measured one dimension 
of economic trends prior to EZ designation, but we did not include 
other dimensions, such as employment trends at the tract level, in the 
models. 

Description of Our Models: 

We used a weighted least square regression for our analyses.[Footnote 
74] Our dependent variables were (1) the difference in the poverty rate 
between 1990 and 2000, (2) the difference in the unemployment rate 
between 1990 and 2000 (3) the difference in the number of businesses 
between 1995 and 1999, and (4) the number of jobs between 1995 and 
1999. For the basic model, we measured the difference in each dependent 
variable against the 1990 value of some explanatory variables. The 
benefit of this approach is that 1990 values of the explanatory 
variables would not have been affected by the program, which was 
implemented in 1994. We also ran an expanded version of the model that 
included variables for each of the EZs to determine whether there were 
differences among the EZs, and we included variables for the EZs and 
their surrounding areas to account for economic trends at the 
metropolitan level, such as the growing or declining output of local 
industries. 

Some of the explanatory variables for which we controlled included 
socioeconomic factors, such as percent of population with a high school 
diploma. In addition to these socioeconomic factors, we also considered 
the five factors we used to select the comparison tracts: 

* percent of minority population in 1990, 

* average household income in 1990, 

* population density in 1990, 

* poverty rate in 1990, and: 

* unemployment rate in 1990. 

We included these variables because the comparison tracts may not be 
perfectly matched to the EZ tracts; including these factors allowed us 
to further account for differences between EZ and comparison tracts. 
Moreover, we weighted the estimations by the geometric mean of 1990 and 
2000 household counts of each tract to account for differences in the 
number of households in each tract. The purpose of this decision was to 
put more weight on the tracts with large numbers of households, because 
these tracts would tend to have smaller sampling errors. 

The coefficients for the EZ program variables represent the EZs with 
respect to the comparison areas, and the positive or negative values 
suggest whether the EZs fared better or worse than the comparison 
areas. For instance, a positive coefficient in the models for poverty 
and unemployment would mean that the EZs did not fare as well as the 
comparison areas--that is, they had either a greater increase or a 
smaller decrease in poverty or unemployment. See our discussion of the 
results of each model for more information. 

Results of Our Models for Poverty: 

Although our comparison of the percentage change between 1990 and 2000 
showed that poverty decreased in most urban EZs, the results of our 
models did not conclusively suggest that the change in poverty was 
associated with the EZ program. Our analysis of the percentage changes 
showed that the poverty rate fell more in the EZs than in the 
comparison areas. But, when we controlled for other factors in our 
models, we found in the basic model that poverty decreased less in the 
EZs than in the comparison areas, although the difference was very 
small (table 8). In addition, many of the variables used in selection 
of comparison tracts were significant, suggesting that the choice of 
areas selected for the program might have affected the differences 
between the urban EZs and the comparison areas in the change in 
poverty. When accounting for the different urban EZs and their 
comparison tracts, the poverty rate decreased more in some urban EZs 
but less in others with respect to the comparison tracts, although the 
only significant result was in the Los Angeles EZ, which experienced a 
greater increase in poverty than the comparison areas. The differences 
among EZs may be a result of the local factors. In addition, one 
researcher found that there was a nationwide decrease in the number of 
people living in high poverty neighborhoods, defined as census tracts 
with poverty rates of 40 percent or higher, between 1990 and 2000--a 
trend that might be a factor affecting our results.[Footnote 75] 

Table 8: Estimates of the Association between the EZ Program and the 
Change in Poverty Rate, 1990-2000: 

Variables: EZ program; 
Basic model: Coefficient: 1.54; 
Basic model: Standard error: 0.67; 
Expanded model: Coefficient: [Empty]; 
Expanded model: Standard error: [Empty]. 

Variables: Atlanta EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 3.41; 
Expanded model: [Empty]; 
Expanded model: Standard error: 2.69. 

Variables: Baltimore EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -2.27; 
Expanded model: Standard error: 1.94. 

Variables: Chicago EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 1.63; 
Expanded model: Standard error: 1.50. 

Variables: Cleveland EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -2.43; 
Expanded model: Standard error: 2.10. 

Variables: Detroit EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 2.25; 
Expanded model: Standard error: 1.54. 

Variables: Los Angeles EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 3.07; 
Expanded model: Standard error: 1.20. 

Variables: New York EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -1.87; 
Expanded model: Standard error: 1.23. 

Variables: Philadelphia-Camden EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -1.10; 
Expanded model: Standard error: 2.70. 

Variables: Atlanta EZ area[A]; 
Basic model: Coefficient: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -5.50; 
Expanded model: Standard error: 2.61. 

Variables: Baltimore [A]; 
Basic model: Coefficient: [Empty]; 
Basic model: Standard error: [Empty];  
Expanded model: Coefficient: 0.60; 
Expanded model: Standard error: 2.49. 

Variables: Chicago EZ area[A]; 
Basic model: Coefficient: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -5.08; 
Expanded model: Standard error: 2.23. 

Variables: Cleveland EZ area[A]; 
Basic model: Coefficient: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -4.65; 
Expanded model: Standard error: 2.36. 

Variables: Detroit EZ area[A]; 
Basic model: Coefficient: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -7.73; 
Expanded model: Standard error: 2.35. 

Variables: Los Angeles EZ area[A]; 
Basic model: Coefficient: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 2.00; 
Expanded model: Standard error: 2.30. 

Variables: New York EZ area[A]; 
Basic model: Coefficient: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 0.56; 
Expanded model: Standard error: 2.40. 

Variables: Philadelphia-Camden EZ area[A]; 
Basic model: Coefficient: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: [ B]; 
Expanded model: Standard error: [B]. 

Variables: Percent of high school dropouts[C]; 
Basic model: Coefficient: 0.080; 
Basic model: Standard error: 0.026; 
Expanded model: Coefficient: 0.017; 
Expanded model: Standard error: 0.024. 

Variables: Percent of vacant housing units; 
Basic model: Coefficient: -0.064; 
Basic model: Standard error: 0.042; 
Expanded model: Coefficient: 0.046; 
Expanded model: Standard error: 0.044. 

Variables: Percent of female-headed households with children[D]; 
Basic model: Coefficient: 0.24; 
Basic model: Standard error: 0.045; 
Expanded model: Coefficient: 0.22; 
Expanded model: Standard error: 0.042. 

Variables: Percent employed in retail industry[E]; 
Basic model: Coefficient: -0.0073; 
Basic model: Standard error: 0.054; 
Expanded model: Coefficient: 0.040; 
Expanded model: Standard error: 0.052. 

Variables: Percent housing units built between 1990 and 1994[F]; 
Basic model: Coefficient: -0.17; 
Basic model: Standard error: 0.081; 
Expanded model: Coefficient: - 0.29; 
Expanded model: Standard error: 0.082. 

Variables: Percent minority population[G]; 
Basic model: Coefficient: 0.0045; 
Basic model: Standard error: 0.022; 
Expanded model: Coefficient: -0.021; 
Expanded model: Standard error: 0.021. 

Variables: Average household income (in 2004 dollars); 
Basic model: Coefficient: -0.00046; 
Basic model: Standard error: 0.00085; 
Expanded model: Coefficient: - 0.00060; 
Expanded model: Standard error: 0.000081. 

Variables: Population density[H]; 
Basic model: Coefficient: 0.000047; 
Basic model: Standard error: 0.0000087; 
Expanded model: Coefficient: 0.000024; 
Expanded model: Standard error: 0.000013. 

Variables: Poverty rate[I]; 
Basic model: Coefficient: -0.71; 
Basic model: Standard error: 0.052; 
Expanded model: Coefficient: -0.79; 
Expanded model: Standard error: 0.047. 

Variables: Unemployment rate[J]; 
Basic model: Coefficient: -0.047; 
Basic model: Standard error: 0.046; 
Expanded model: Coefficient: 0.085; 
Expanded model: Standard error: 0.054. 

Variables: Constant; 
Basic model: Coefficient: 30.39; 
Basic model: Standard error: 4.29; 
Expanded model: Coefficient: 40.79; 
Expanded model: Standard error: 4.83. 

Variables: Number of tracts; 
Basic model: Coefficient: [Empty]; 
Basic model: 851; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: [Empty]; 
Expanded model: 851; 
Expanded model: Standard error: [Empty]. 

Variables: R-sq; 
Basic model: Coefficient: [Empty]; 
Basic model: 0.40; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: [Empty]; 
Expanded model: 0.47; 
Expanded model: Standard error: [Empty]. 

Source: GAO analysis of Census data. 

Notes: Coefficients significant at the 5 percent level are in bold. All 
variables are from the 1990 Census unless otherwise noted. We weighted 
the regressions by the geometric mean of 1990 and 2000 household counts 
of each tract. 

[A] We defined the EZ area to include both the EZ tracts and comparison 
tracts that were selected from within a 5-mile boundary of the EZ. 

[B] Results for the Philadelphia-Camden EZ area are not listed, because 
we used them as a reference group for the other seven EZs and their 
surrounding areas. 

[C] Percent based on the civilian population between ages 16 and 19 who 
are not enrolled in school and are not high school graduates. 

[D] Percent based on households headed by females without husbands 
present in which there are children under 18 years of age. 

[E] Percent based on individuals 16 and over. 

[F] From the 2000 Census. 

[G] We calculated minority population by subtracting the percent of 
white population from the total population. 

[H] Individuals per square mile. 

[I] Percent based on individuals for whom poverty status has been 
determined. 

[J] Percent based on individuals 16 years of age or older in the labor 
force. 

[End of table] 

Results of Our Models for Unemployment: 

Like our models for poverty, our models for the unemployment did not 
conclusively suggest that the changes in unemployment were associated 
with the EZ program. The results of our basic model suggested that 
unemployment decreased more in the EZs than in the comparison areas, 
but the difference was very small and was not statistically significant 
(table 9). All five of the variables we used to select comparison 
tracts were statistically significant, suggesting that the choice of 
areas selected for the program might have affected the difference in 
the change in unemployment rate between EZ and comparison tracts. Like 
the model for poverty, our model showed that the unemployment rate 
decreased more in some urban EZs but less in others, although the only 
EZ that experienced a significant change was the Cleveland EZ, which 
showed a significantly greater decrease in unemployment than the 
comparison areas. As with poverty rate, local factors may have 
accounted for the difference between the various urban EZs with respect 
to the comparison tracts. 

Table 9: Estimates of the Association between the EZ Program and the 
Change in Unemployment Rate, 1990-2000: 

Variables: EZ program; 
Basic model: Coefficient: -0.065; 
Basic model: [Empty]; 
Basic model: Standard error: 0.50; 
Expanded model: Standard Error: [Empty]. 

Variables: Atlanta EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 2.56; 
Expanded model: Standard error: 1.95. 

Variables: Baltimore EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -0.71; 
Expanded model: Standard error: 1.77. 

Variables: Chicago EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -0.68; 
Expanded model: Standard error: 1.07. 

Variables: Cleveland EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -3.65; 
Expanded model: Standard error: 1.40. 

Variables: Detroit EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 1.76; 
Expanded model: Standard error: 1.09. 

Variables: Los Angeles EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 0.092; 
Expanded model: Standard error: 1.20. 

Variables: New York EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -1.57; 
Expanded model: Standard error: 0.88. 

Variables: Philadelphia-Camden EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -0.87; 
Expanded model: Standard error: 1.83. 

Variables: Atlanta EZ area[A]; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -2.48; 
Expanded model: Standard error: 1.71. 

Variables: Baltimore EZ area[A]; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 2.35; 
Expanded model: Standard error: 1.71. 

Variables: Chicago EZ area[A]; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 2.66; 
Expanded model: Standard error: 1.57. 

Variables: Cleveland EZ area[A]; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -1.89; 
Expanded model: Standard error: 1.58. 

Variables: Detroit EZ area[A]; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -2.59; 
Expanded model: Standard error: 1.55. 

Variables: Los Angeles EZ area[A]; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 3.61; 
Expanded model: Standard error: 1.62. 

Variables: New York EZ area[A]; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 3.13; 
Expanded model: Standard error: 1.62. 

Variables: Philadelphia-Camden EZ area[A]; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: [ B]; 
Expanded model: Standard error: [ B]. 

Variables: Percent of population of working age[C]; 
Basic model: Coefficient: 0.068; 
Basic model: [Empty]; 
Basic model: Standard error: 0.072; 
Expanded model: Coefficient: 0.093; 
Expanded model: Standard error: 0.068. 

Variables: Percent of population with a high school diploma[D]; 
Basic model: Coefficient: 0.11; 
Basic model: [Empty]; 
Basic model: Standard error: 0.039; 
Expanded model: Coefficient: 0.20; 
Expanded model: Standard error: 0.044. 

Variables: Percent of housing units built between 1990 and 1994[E]; 
Basic model: Coefficient: -0.18; 
Basic model: [Empty]; 
Basic model: Standard error: 0.062; 
Expanded model: Coefficient: -0.23; 
Expanded model: Standard error: 0.062. 

Variables: Percent minority population[F]; 
Basic model: Coefficient: 0.072; 
Basic model: [Empty]; 
Basic model: Standard error: 0.012; 
Expanded model: Coefficient: 0.054; 
Expanded model: Standard error: 0.014. 

Variables: Average household income (in 2004 dollars); 
Basic model: Coefficient: -0.00017; 
Basic model: [Empty]; 
Basic model: Standard error: 0.000068; 
Expanded model: Coefficient: -0.00032; 
Expanded model: Standard error: 0.000078. 

Variables: Population density[G]; 
Basic model: Coefficient: 0.000032; 
Basic model: [Empty]; 
Basic model: Standard error: 0.0000066; 
Expanded model: Coefficient: 0.0000064; 
Expanded model: Standard error: 0.000011. 

Variables: Poverty rate[H]; 
Basic model: Coefficient: 0.20; 
Basic model: [Empty]; 
Basic model: Standard error: 0.033; 
Expanded model: Coefficient: 0.15; 
Expanded model: Standard error: 0.034. 

Variables: Unemployment rate[I]; 
Basic model: Coefficient: -0.90; 
Basic model: [Empty]; 
Basic model: Standard error: 0.039; 
Expanded model: Coefficient: -0.86; 
Expanded model: Standard error: 0.044. 

Variables: Constant; 
Basic model: Coefficient: -0.88; 
Basic model: [Empty]; 
Basic model: Standard error: 4.54; 
Expanded model: Coefficient: 1.92; 
Expanded model: Standard error: 4.61. 

Variables: Number of tracts; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: 866; 
Expanded model: Standard error: 866. 

Variables: R-sq; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: 0.53; 
Expanded model: Standard error: 0.57. 

Source: GAO analysis of Census data: 

Notes: Coefficients significant at the 5 percent level are in bold. All 
variables are from the 1990 Census unless otherwise noted. We weighted 
the regressions by the geometric mean of 1990 and 2000 household counts 
of each tract. 

[A] We defined the EZ area to include both the EZ tracts and comparison 
tracts that were selected from within a 5-mile boundary of the EZ. 

[B] Results for the Philadelphia-Camden EZ area are not listed, because 
we used them as a reference group for the other seven EZs and their 
surrounding areas. 

[C] We defined "working age" as between 16 and 64 years of age. 

[D] Percent based on population 25 years of age and over. 

[E] From the 2000 Census. 

[F] For the purposes of this report, we calculated minority population 
by subtracting the percent of white population from the total 
population. 

[G] Individuals per square mile. 

[H] Percent based on individuals for whom poverty status has been 
determined. 

[I] Percent based on individuals 16 years of age or older in the labor 
force. 

[End of table] 

Results of Our Models for Economic Growth: 

To estimate the statistical relationship between the EZ program and 
economic growth, we used two proxy measures: (1) the number of 
businesses excluding establishments that were not eligible for program 
tax benefits such as nonprofit and governmental organizations and (2) 
the number of jobs in the EZ. In order to be consistent with our 
analyses of poverty rate and unemployment, which covered the time 
period between 1990 and 2000, we used 1995 and 1999 data for our models 
of economic growth.[Footnote 76] We also tested the model using Home 
Mortgage Disclosure Act data on the number of loan originations for new 
home purchases and the mean loan amount for new home purchases as other 
possible measures of economic growth, but found consistent results, 
which are not presented here. 

On the basis of the results of our models, we were not able to 
determine whether there is a statistical association between the EZ 
program and economic growth because the explanatory variables we used 
explained little of the variation in the changes in the number of 
businesses or jobs between 1995 and 1999 (tables 10 and 11).[Footnote 
77] Not surprisingly, most explanatory variables were also not 
significant. The low explanatory power of our models could be the 
result of not having considered the right variables; 
however, we explored many combinations of variables, all of which 
yielded consistent results. This lack of explanatory power might also 
be the result of the fact that our proxy measures--the number of 
businesses and jobs--were not strongly representative of economic 
growth. Nevertheless, similar to the models of the change in poverty 
and unemployment, the models of the change in economic growth reflect 
variation between the EZs with respect to the comparison areas, but 
none of the results were statistically significant. 

Table 10: Estimates of the Association between the EZ Program and 
Economic Growth, Measured by the Change in the Number of Businesses, 
from 1995-1999A: 

Variables: EZ program; 
Basic model: Coefficient: -22.58; 
Basic model: [Empty]; 
Basic model: Standard error: 18.84; 
Expanded model: Standard error: [Empty]. 

Variables: Atlanta EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 24.59; 
Expanded model: Standard error: 26.62. 

Variables: Baltimore EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -0.84; 
Expanded model: Standard error: 12.12. 

Variables: Chicago EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 12.80; 
Expanded model: Standard error: 14.80. 

Variables: Cleveland EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 4.95; 
Expanded model: Standard error: 7.05. 

Variables: Detroit EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 13.68; 
Expanded model: Standard error: 13.05. 

Variables: Los Angeles EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -113.70; 
Expanded model: Standard error: 91.86. 

Variables: New York EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 10.80; 
Expanded model: Standard error: 10.92. 

Variables: Philadelphia-Camden EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 1.03; 
Expanded model: Standard error: 21.50. 

Variables: Atlanta EZ area[B]; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -12.32; 
Expanded model: Standard error: 22.13. 

Variables: Baltimore EZ area[B]; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -23.11; 
Expanded model: Standard error: 17.86. 

Variables: Chicago EZ area[B]; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -17.65; 
Expanded model: Standard error: 16.25. 

Variables: Cleveland EZ area[B]; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 11.51; 
Expanded model: Standard error: 29.68. 

Variables: Detroit EZ area[B]; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 8.64; 
Expanded model: Standard error: 25.45. 

Variables: Los Angeles EZ area[B]; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -38.46; 
Expanded model: Standard error: 27.71. 

Variables: New York EZ area[B]; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -15.84; 
Expanded model: Standard error: 20.05. 

Variables: Philadelphia-Camden EZ area[B]; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: [C]; 
Expanded model: Standard error: [ C]. 

Variables: Population; 
Basic model: Coefficient: -0.0027; 
Basic model: [Empty]; 
Basic model: Standard error: 0.0026; 
Expanded model: Coefficient: 0.0011; 
Expanded model: Standard error: 0.0014. 

Variables: Percent vacant housing units; 
Basic model: Coefficient: 0.10; 
Basic model: [Empty]; 
Basic model: Standard error: 0.27; 
Expanded model: Coefficient: -1.09; 
Expanded model: Standard error: 1.01. 

Variables: Percent of housing units built between 1990 and 1994[D]; 
Basic model: Coefficient: 0.95; 
Basic model: [Empty]; 
Basic model: Standard error: 0.58; 
Expanded model: Coefficient: 1.77; 
Expanded model: Standard error: 1.37. 

Variables: Percent minority population[E]; 
Basic model: Coefficient: 0.74; 
Basic model: [Empty]; 
Basic model: Standard error: 0.69; 
Expanded model: Coefficient: 0.80; 
Expanded model: Standard error: 0.69. 

Variables: Average household income (in 2004 dollars); 
Basic model: Coefficient: 0.0044; 
Basic model: [Empty]; 
Basic model: Standard error: 0.0051; 
Expanded model: Coefficient: 0.0056; 
Expanded model: Standard error: 0.006. 

Variables: Population density[F]; 
Basic model: Coefficient: 0.00027; 
Basic model: [Empty]; 
Basic model: Standard error: 0.00014; 
Expanded model: Coefficient: 0.000056; 
Expanded model: Standard error: 0.00011. 

Variables: Poverty rate[G]; 
Basic model: Coefficient: 1.67; 
Basic model: [Empty]; 
Basic model: Standard error: 2.21; 
Expanded model: Coefficient: 2.31; 
Expanded model: Standard error: 2.61. 

Variables: Unemployment rate[H]; 
Basic model: Coefficient: 0.13; 
Basic model: [Empty]; 
Basic model: Standard error: 0.35; 
Expanded model: Coefficient: -0.22; 
Expanded model: Standard error: 0.59. 

Variables: Constant; 
Basic model: Coefficient: -253.01; 
Basic model: [Empty]; 
Basic model: Standard error: 278.86; 
Expanded model: Coefficient: -300.97; 
Expanded model: Standard error: 314.17. 

Variables: Number of tracts; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: 860; 
Expanded model: Standard error: 860. 

Variables: R-sq; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: 0.042; 
Expanded model: Standard error: 0.11. 

Source: GAO analysis of Census and Claritas data. 

Notes: Coefficients significant at the 5 percent level are in bold. All 
variables are from the 1990 Census unless otherwise noted. We weighted 
the regressions by the geometric mean of 1990 and 2000 household counts 
of each tract. 

[A] Excluding establishments that were not eligible for the program tax 
benefits, such as nonprofit and governmental organizations. 

[B] We defined the EZ area to include both the EZ tracts and comparison 
tracts that were selected from within a 5-mile boundary of the EZ. 

[C] Results for the Philadelphia-Camden EZ area are not listed, because 
we used them as a reference group for the other seven EZs and their 
surrounding areas. 

[D] From the 2000 Census. 

[E] For the purposes of this report, we calculated minority population 
by subtracting the percent of white population from the total 
population. 

[F] Individuals per square mile. 

[G] Percent based on individuals for whom poverty status has been 
determined. 

[H] Percent based on individuals 16 years of age or older in the labor 
force. 

[End of table] 

Table 11: Estimates of the Association between the EZ Program and 
Economic Growth, Measured by the Change in the Number of Jobs, 1995- 
1999: 

Variables: EZ program; 
Basic model: Coefficient: -68.86; 
Basic model: [Empty]; 
Basic model: Standard error: 196.83; 
Expanded model: Standard error: [Empty]. 

Variables: Atlanta EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 286.01; 
Expanded model: Standard error: 983.37. 

Variables: Baltimore EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 1199.74; 
Expanded model: Standard error: 715.74. 

Variables: Chicago EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -88.43; 
Expanded model: Standard error: 180.84. 

Variables: Cleveland EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 102.85; 
Expanded model: Standard error: 293.30. 

Variables: Detroit EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 199.16; 
Expanded model: Standard error: 181.98. 

Variables: Los Angeles EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -438.63; 
Expanded model: Standard error: 632.33. 

Variables: New York EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -288.08; 
Expanded model: Standard error: 231.26. 

Variables: Philadelphia-Camden EZ; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 197.87; 
Expanded model: Standard error: 631.54. 

Variables: Atlanta EZ area[A]; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -318.84; 
Expanded model: Standard error: 883.10. 

Variables: Baltimore EZ area[A]; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -819.50; 
Expanded model: Standard error: 810.54. 

Variables: Chicago EZ area[A]; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 36.80; 
Expanded model: Standard error: 547.93. 

Variables: Cleveland EZ area[A]; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 134.99; 
Expanded model: Standard error: 557.58. 

Variables: Detroit EZ area[A]; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -61.44; 
Expanded model: Standard error: 576.67. 

Variables: Los Angeles EZ area[A]; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: -239.53; 
Expanded model: Standard error: 571.81. 

Variables: New York EZ area[A]; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: 270.91; 
Expanded model: Standard error: 553.59. 

Variables: Philadelphia-Camden EZ area[A]; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: [Empty]; 
Expanded model: Coefficient: [B]; 
Expanded model: Standard error: [ B]. 

Variables: Percent of population of working age[D]; 
Basic model: Coefficient: -21.04; 
Basic model: [Empty]; 
Basic model: Standard error: 28.00; 
Expanded model: Coefficient: -19.59; 
Expanded model: Standard error: 29.65. 

Variables: Percent of population with a high school diploma[D]; 
Basic model: Coefficient: 10.36; 
Basic model: [Empty]; 
Basic model: Standard error: 8.39; 
Expanded model: Coefficient: 4.06; 
Expanded model: Standard error: 11.77. 

Variables: Percent of housing units built between 1990 and 1994[E]; 
Basic model: Coefficient: 10.57; 
Basic model: [Empty]; 
Basic model: Standard error: 29.50; 
Expanded model: Coefficient: 5.09; 
Expanded model: Standard error: 29.33. 

Variables: Percent minority population[F]; 
Basic model: Coefficient: 3.41; 
Basic model: [Empty]; 
Basic model: Standard error: 4.63; 
Expanded model: Coefficient: 3.23; 
Expanded model: Standard error: 5.36. 

Variables: Average household income (in 2004 dollars); 
Basic model: Coefficient: 0.022; 
Basic model: [Empty]; 
Basic model: Standard error: 0.038; 
Expanded model: Coefficient: 0.032; 
Expanded model: Standard error: 0.048. 

Variables: Population density[G]; 
Basic model: Coefficient: 0.0016; 
Basic model: [Empty]; 
Basic model: Standard error: 0.0023; 
Expanded model: Coefficient: -0.0019; 
Expanded model: Standard error: 0.0029. 

Variables: Poverty rate[H]; 
Basic model: Coefficient: -1.30; 
Basic model: [Empty]; 
Basic model: Standard error: 20.33; 
Expanded model: Coefficient: 2.99; 
Expanded model: Standard error: 20.34. 

Variables: Unemployment rate[I]; 
Basic model: Coefficient: 3.10; 
Basic model: [Empty]; 
Basic model: Standard error: 15.33; 
Expanded model: Coefficient: -0.95; 
Expanded model: Standard error: 14.30. 

Variables: Constant; 
Basic model: Coefficient: -56.23; 
Basic model: [Empty]; 
Basic model: Standard error: 1483.38; 
Expanded model: Coefficient: -200.97; 
Expanded model: Standard error: 1695.03. 

Variables: Number of tracts; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: 859; 
Expanded model: Standard error: 859. 

Variables: R-sq; 
Basic model: Coefficient: [Empty]; 
Basic model: [Empty]; 
Basic model: Standard error: 0.016; 
Expanded model: Standard error: 0.043. 

Source: GAO analysis of Census and Claritas data. 

Notes: Coefficients significant at the 5 percent level are in bold. All 
variables are from the 1990 Census unless otherwise noted. We weighted 
the regressions by the geometric mean of 1990 and 2000 household counts 
of each tract. 

[A] We defined the EZ area to include both the EZ tracts and comparison 
tracts that were selected from within a 5-mile boundary of the EZ. 

[B] Results for the Philadelphia-Camden EZ area are not listed, because 
we used them as a reference group for the other seven EZs and their 
surrounding areas. 

[C] We defined "working age" as between 16 and 64 years of age. 

[D] Percent based on population 25 years of age and over. 

[E] From the 2000 Census. 

[F] For the purposes of this report, we calculated minority population 
by subtracting the percent of white population from the total 
population. 

[G] Individuals per square mile. 

[H] Percent based on individuals for whom poverty status has been 
determined. 

[I] Percent based on individuals 16 years of age or older in the labor 
force. 

[End of table] 

Other Variables Tested for Use in Our Econometric Models: 

In addition to the variables presented in the models above, we explored 
many alternative dependent variables and explanatory variables to test 
the robustness of the models we used (table 12). In particular, we 
experimented with several alternative measures for economic growth. To 
test how our results might change in response to the selection of 
comparison tracts, we also reestimated the models using comparison 
tracts selected with different propensity scores. We also ran the 
models excluding the Los Angeles and Cleveland EZs, because these EZs 
received a slightly different package of benefits when they were 
initially designated as Supplemental EZs. These tests all yielded 
results consistent with our models, so they are not presented here. 

Table 12: Alternative Variables Considered in Our Analyses: 

Definition of variables:  Dependent variables: Change in per-capita 
income between 1990 and 2000; 
Rationale: An opposite measure of poverty; 
Data sources: 1990 and 2000 Census. 

Definition of variables:  Dependent variables: Change in employment 
rate between 1990 and 2000; 
Rationale: An opposite measure of unemployment; 
Data sources: 1990 and 2000 Census. 

Definition of variables:  Dependent variables: Percent change in number 
of businesses between 1995 and 1999; 
Rationale: Alternative measure of economic growth; 
Data sources: Claritas 1995, 1999. 

Definition of variables:  Dependent variables: Percent change in the 
number of jobs between 1995 and 1999; 
Rationale: Alternative measure of economic growth; 
Data sources: Claritas 1995, 1999. 

Definition of variables:  Dependent variables: Percent change in number 
of businesses between 1995 and 2004; 
Rationale: Alternative measure of economic growth; 
Data sources: Claritas 1995, 2004. 

Definition of variables:  Dependent variables: Percent change in the 
number of jobs between 1995 and 2004; 
Rationale: Alternative measure of economic growth; 
Data sources: Claritas 1995, 2004. 

Definition of variables:  Dependent variables: Change in jobs per 
business between 1995 and 1999; 
Rationale: Alternative measure of economic growth; 
Data sources: Claritas 1995, 1999. 

Definition of variables:  Dependent variables: Change in aggregate 
sales volume of businesses at each tract between 1995 and 1999; 
Rationale: Alternative measure of economic growth; 
Data sources: Claritas 1995, 1999. 

Definition of variables:  Dependent variables: Percent change in number 
of loan originations for new home purchases between 1995 and 1999; 
Rationale: Alternative measure of economic growth; 
Data sources: Home Mortgage Disclosure Act data 1995, 1999. 

Definition of variables:  Dependent variables: Percent change in mean 
loan amount for new home purchases between 1995 and 1999; 
Rationale: Alternative measure of economic growth; 
Data sources: Home Mortgage Disclosure Act data 1995, 1999. 

Definition of variables: Explanatory variables: Percent foreign-born 
population; 
Rationale: Alternative indirect measure for minority population; 
Data sources: 1990 Census. 

Definition of variables: Explanatory variables: Adjusted per capita 
income in 2004 dollars; 
Rationale: Alternative indirect measure household income; 
Data sources: 1990 Census. 

Definition of variables: Explanatory variables: Percent of males aged 
16 or greater; 
Rationale: Alternative measure for working population; 
Data sources: 1990 Census. 

Definition of variables: Explanatory variables: Percent of housing 
units built last 5 years before census; 
Rationale: Alternative measure to account for economic trend; 
Data sources: 1990 Census. 

Definition of variables: Explanatory variables: Percent of persons aged 
25 or greater with some college; 
Rationale: Alternative measure for educational level; 
Data sources: 1990 Census. 

Definition of variables: Explanatory variables: Percent of employment 
in manufacturing industry; 
Rationale: Alternative measure of industry characteristics; 
Data sources: 1990 Census. 

Definition of variables: Explanatory variables: Percent of female-
headed single households; 
Rationale: Alternative measure for household characteristics; 
Data sources: 1990 Census. 

Source: GAO. 

[End of table] 

[End of section] 

Appendix III: List of Communities Designated in Round I of the EZ/EC 
Program: 

Round I:Urban EZs (8): 
Atlanta, Georgia: 
Baltimore, Maryland: 
Chicago, Illinois: 
Cleveland, Ohio[A]: 
Detroit, Michigan: 
Los Angeles, California[A]: 
New York, New York: 
Philadelphia, Pennsylvania/Camden, New Jersey: 

Round I Urban ECs (65): 
Akron, Ohio: 
Albany, Georgia: 
Albany/Schenectady/Troy, New York: 
Albuquerque, New Mexico: 
Birmingham, Alabama: 
Boston, Massachusetts[B]: 
Bridgeport, Connecticut: 
Buffalo, New York: 
Burlington, Vermont: 
Charleston, South Carolina: 
Charlotte, North Carolina: 
Cleveland, Ohio[C]: 
Columbus, Ohio: 
Dallas, Texas: 
Denver, Colorado: 
Des Moines, Iowa: 
East St. Louis, Illinois: 
El Paso, Texas: 
Flint, Michigan: 
Harrisburg, Pennsylvania: 
Houston, Texas[B]: 
Huntington, West Virginia: 
Indianapolis, Indiana: 
Jackson, Mississippi: 
Kansas City, Missouri/Kansas City, Kansas[B]: 
Las Vegas, Nevada: 
Little Rock/Pulaski, Arkansas: 
Los Angeles, California: 
Louisville, Kentucky: 
Lowell, Massachusetts: 
Manchester, New Hampshire: 
Memphis, Tennessee: 
Miami/Dade County, Florida: 
Milwaukee, Wisconsin: 
Minneapolis, Minnesota: 
Muskegon, Michigan: 
Nashville/Davidson, Tennessee: 
New Haven, Connecticut: 
New Orleans, Louisiana: 
Newark, New Jersey: 
Newburgh/Kingston, New York: 
Norfolk, Virginia: 
Oakland, California[B]: 
Ogden, Utah: 
Oklahoma City, Oklahoma: 
Omaha, Nebraska: 
Ouachita Parish, Louisiana: 
Phoenix, Arizona: 
Pittsburgh, Pennsylvania: 
Portland, Oregon: 
Providence, Rhode Island: 
Rochester, New York: 
San Antonio, Texas: 
San Diego, California: 
San Francisco, California: 
Seattle, Washington: 
Springfield, Illinois: 
Springfield, Massachusetts: 
St. Louis, Missouri: 
St. Paul, Minnesota: 
Tacoma, Washington: 
Tampa, Florida: 
Waco, Texas: 
Washington, District of Columbia: 
Wilmington, Delaware: 

Round I Rural EZs (3): 
Kentucky Highlands, Kentucky: 
Mid-Delta, Mississippi: 
Rio Grande Valley, Texas: 

Round I Rural ECs (30): 
Accomack and Northampton County, Virginia: 
Arizona Border Region, Arizona: 
Beadle/Spink Counties, South Dakota: 
Central Appalachia, West Virginia: 
Central Savannah River Area, Georgia: 
Chambers County, Alabama: 
City of East Prairie, Missouri: 
City of Lock Haven, Pennsylvania: 
City of Watsonville, California: 
Crisp/ Dooly County, Georgia: 
East Arkansas, Arkansas: 
Fayette/Haywood County, Tennessee: 
Greater Portsmouth, Ohio: 
Greene-Sumter, Alabama: 
The Halifax/ Edgecombe/Wilson Empowerment Alliance, North Carolina: 
Imperial County, California: 
Jackson County, Florida: 
Josephine County, Oregon: 
La Jicarita, New Mexico: 
Lake County, Michigan: 
Lower Yakima County, Washington: 
Macon Ridge, Louisiana: 
McDowell County, West Virginia: 
Mississippi County, Arkansas: 
North Delta Mississippi, Mississippi: 
Northeast Louisiana Delta, Louisiana: 
Robeson County, North Carolina: 
Scott, Tennessee/McCreary, Kentucky: 
Southeast Oklahoma, Oklahoma: 
Williamsburg-Lake City, South Carolina: 

[A] Initially designated as a Supplemental EZ: 

[B] Also designated as an Enhanced EC: 

[C] Also designated as a Supplemental EZ: 

Source: HUD and USDA data. 

[End of section] 

Appendix IV: Description of the Empowerment Zones and Enterprise 
Communities We Visited: 

This appendix contains detailed information we gathered from our site 
visits to the 11 Round I EZs and 2 ECs. The appendix describes how the 
EZs and ECs were governed; the activities they implemented; changes in 
poverty, unemployment, and economic growth; and stakeholders' 
perceptions of factors influencing those changes. It also includes the 
percent changes in variables used in the econometric model. 

Atlanta Empowerment Zone: 

Figure 13: Map of the Atlanta EZ and Its Comparison Area: 

[See PDF for image] 

Source: GAO analysis of Census and HUD data. 

[End of figure] 

How the EZ Was Governed: 

The city of Atlanta established the nonprofit Atlanta Empowerment Zone 
Corporation to operate the EZ. The corporation had two boards: the 
Executive Board and the Community Empowerment Advisory Board, which 
included representatives of each of the EZ neighborhoods. According to 
EZ stakeholders we interviewed, the EZ Executive Board gave final 
approval on activities the EZ implemented. However, EZ stakeholders 
also mentioned that the intended process was not always followed and 
that the board was not always able to approve activities due to 
difficulties reaching consensus. 

Activities the EZ Implemented: 

According to HUD data, most of the Atlanta EZ's activities related to 
community development, but the EZ also implemented some activities 
related to economic opportunity, such as making loans to EZ businesses. 
Initiatives involving housing, public safety, and assistance to 
businesses were the most frequently implemented types of activities 
(fig. 14). In our interviews, EZ stakeholders mentioned initiatives 
they saw as particularly useful, including housing programs for seniors 
and low-income EZ residents--for example, a program that helped to 
repair code violations in homes of senior citizens. Stakeholders also 
said that the EZ provided funds to after-school and health-related 
programs, such as one that provided children and adults with asthma 
with needed resources and education. Some EZ stakeholders suggested 
that the loan program lacked positive results, because many of the 
businesses that received the loans failed.[Footnote 78] 

Figure 14: Activities Implemented by the Atlanta EZ: 

[See PDF for image] 

Sources: GAO (photos); GAO analysis of HUD data (charts). 

[End of figure] 

Changes in Poverty, Unemployment, and Economic Growth: 

Poverty declined in the Atlanta EZ, but unemployment did not, and 
measures of economic growth did not show improvement. Atlanta had the 
highest poverty rate of any EZ in 1990 (55 percent). By 2000, this rate 
had fallen by around 10 percentage points, while the rate of its 
comparison area remained the same. Conversely, the unemployment rate 
went from one of the lowest of the urban EZs in 1990 to one of the 
highest in 2000, and the increase was greater than in its comparison 
area. Similarly, the Atlanta EZ and its comparison area experienced a 
large decline--more than 20 percent--in total number of businesses from 
1995 to 2004. The Atlanta EZ had the second largest decline in the 
number of jobs of any EZ, which was also more than in its comparison 
area. Tables 13 and 14 show the changes in poverty, unemployment, and 
economic growth in the EZ and its comparison area. Table 13 also 
includes data on the changes in other variables included in our models. 

Table 13: Changes in Selected Census Variables Observed in the Atlanta 
EZ and Its Comparison Area: 

Poverty rate (%); 
1990: EZ: 54.67; 
1990: Comparison: 30.15; 
2000: EZ: 44.82; 
2000: Comparison: 28.02; 
Percent change[A]: EZ: -9.84[B]; 
Percent change[A]: Comparison: -2.12. 

Unemployment rate (%); 
1990: EZ: 17.48; 
1990: Comparison: 11.36; 
2000: EZ: 23.44; 
2000: Comparison: 11.88; 
Percent change[A]: EZ: 5.96[B]; 
Percent change[A]: Comparison: 0.52. 

Average household income; 
1990: EZ: $18,343; 
1990: Comparison: $30,567; 
2000: EZ: $28,552; 
2000: Comparison: $39,500; 
Percent change[A]: EZ: 55.66[B]; 
Percent change[A]: Comparison: 29.23[B]. 

Percentage of single female headed households with children; 
1990: EZ: 24.62; 
1990: Comparison: 20.02; 
2000: EZ: 21.26; 
2000: Comparison: 19.95; 
Percent change[A]: EZ: -3.36[B]; 
Percent change[A]: Comparison: -0.07. 

Total population; 
1990: EZ: 49,966; 
1990: Comparison: 65,809; 
2000: EZ: 45,931; 
2000: Comparison: 64,022; 
Percent change[A]: EZ: -8.07; 
Percent change[A]: Comparison: - 2.71. 

Total individuals per square mile; 
1990: EZ: 5,408; 
1990: Comparison: 2,671; 
2000: EZ: 4,972; 
2000: Comparison: 2,756; 
Percent change[A]: EZ: -8.07; 
Percent change[A]: Comparison: 3.16. 

Percentage of households that moved in the last 5 years; 
1990: EZ: 50.87; 
1990: Comparison: 46.01; 
2000: EZ: 53.32; 
2000: Comparison: 52.52; 
Percent change[A]: EZ: 2.45[B]; 
Percent change[A]: Comparison: 6.51[B]. 

Percentage of population of working age (16-64); 
1990: EZ: 60.16; 
1990: Comparison: 61.68; 
2000: EZ: 63.42; 
2000: Comparison: 64.59; 
Percent change[A]: EZ: 3.26[B]; 
Percent change[A]: Comparison: 2.92[B]. 

Percentage of population with a high school diploma (or equivalent); 
1990: EZ: 43.10; 
1990: Comparison: 60.53; 
2000: EZ: 58.96; 
2000: Comparison: 69.3; 
Percent change[A]: EZ: 15.86[B]; 
Percent change[A]: Comparison: 8.78[B]. 

Percentage of high school dropouts; 
1990: EZ: 19.12; 
1990: Comparison: 19.1; 
2000: EZ: 21.48; 
2000: Comparison: 21.19; 
Percent change[A]: EZ: 2.36[B]; 
Percent change[A]: Comparison: 2.08[B]. 

Percentage of vacant housing units; 
1990: EZ: 20.79; 
1990: Comparison: 14.65; 
2000: EZ: 13.30; 
2000: Comparison: 7.43; 
Percent change[A]: EZ: -7.48[B]; 
Percent change[A]: Comparison: -7.22[B]. 

Average owner occupied housing value; 
1990: EZ: $55,883; 
1990: Comparison: $74,063; 
2000: EZ: $117,869; 
2000: Comparison: $101,774; 
Percent change[A]: EZ: 110.92[B]; 
Percent change[A]: Comparison: 37.42[B]. 

Source: GAO analysis of Census data. 

Note: There are 23 census tracts in the designated area and 16 in the 
comparison area. Estimates for all census variables based on 
percentages had 95 percent confidence intervals of plus or minus 5 
percentage points or less. For the confidence intervals for average 
household income and average owner-occupied housing estimates, see 
appendix I. 

[A] Differences in poverty rate, unemployment rate, and other variables 
shown as percentages are based upon percentage point differences. 
Differences for average household income, population, individuals per 
square mile, and average housing value are calculated as percent 
changes. 

[B] The change in estimates from 1990 to 2000 is statistically 
significant. 

[End of table] 

Table 14: Changes in Selected Economic Growth Variables Observed in the 
Atlanta EZ and Its Comparison Area: 

Number of businesses; 
1995: EZ: 1,930; 
1995: Comparison: 3,980; 
1999: EZ: 1,549; 
1999: Comparison: 3,380; 
2004: EZ: 1,529; 
2004: Comparison: 3,248; 
Percent change 1995-2004[A]: EZ: -20.78; 
Percent change 1995-2004[A]: Comparison: -18.39. 

Number of jobs; 
1995: EZ: 36,888; 
1995: Comparison: 71,346; 
1999: EZ: 31,470; 
1999: Comparison: 79,580; 
2004: EZ: 28,672; 
2004: Comparison: 69,140; 
Percent change 1995-2004[A]: EZ: - 22.27; 
Percent change 1995-2004[A]: Comparison: -3.09. 

Source: GAO analysis of Claritas data. 

Note: There are 23 census tracts in the designated area and 16 in the 
comparison area. We excluded establishments that were not eligible for 
program tax benefits, such as nonprofit and governmental organizations, 
from our analysis of the change in the number of businesses. However, 
we included jobs at those businesses in our analysis of the change in 
the number of jobs. 

[A] Differences for the number of businesses and the number of jobs are 
calculated as percent changes. 

[End of table] 

Stakeholder Perceptions of the Factors Influencing Changes in Poverty, 
Unemployment, and Economic Growth: 

In our interviews, stakeholders said that changes in the poverty rate 
may have been due to changes in the EZ population and the demolition of 
public housing. They explained that residents with lower incomes had 
left the EZ and that households with higher incomes were moving in 
because of changes in the EZ as a result of development from the 
Olympics and the demolition of public housing through the HOPE VI 
program.[Footnote 79] 

Commenting on unemployment, stakeholders suggested that EZ residents 
had benefited from EZ job training and placement programs but that a 
mismatch still existed between residents' skills and some of the new 
jobs available in the EZ. 

Although our economic growth data suggested a decrease in the number of 
businesses and number of jobs, stakeholders suggested that the EZ had 
helped to foster economic growth in some of the commercial corridors by 
helping to fund neighborhood plans. Two stakeholders also mentioned the 
1996 Olympics as a factor in bringing jobs and development to the EZ 
and the city of Atlanta, although one stakeholder noted that several 
businesses had closed down after the Olympics. This loss of businesses 
potentially helps explain the significant decrease in the number of 
businesses between 1995 and 2004. 

Baltimore Empowerment Zone: 

Figure 15: Map of the Baltimore EZ and Its Comparison Area: 

[See PDF for image] 

Source: GAO analysis of Census and HUD data. 

[End of figure] 

How the EZ Was Governed: 

The nonprofit Empower Baltimore Management Corporation was created 
specifically to manage Baltimore's EZ program. The EZ was governed by a 
board composed of community leaders, three committees (one for each 
core strategic goal), an executive committee of the three committee 
chairs, and an advisory council of individuals from all areas of the 
EZ. Governance of the Baltimore EZ also included six "Village Centers"-
-community groups that applied to be the implementing agencies of EZ 
programs in their local communities. EZ activities were vetted through 
the advisory council and sent to the executive committee and full board 
for final approval. 

Activities the EZ Implemented: 

Unlike most EZs, the Baltimore EZ implemented a higher number of 
economic opportunity activities than community development activities. 
The three types of activities most often implemented were workforce 
development, access to capital, and assistance to businesses (fig. 16). 
Most stakeholders described the EZ's workforce training activities, 
such as the customized training program that provided EZ residents with 
individualized instruction and a stipend during the training period. In 
addition, the EZ operated several loan funds and partially funded the 
Bank One check processing center and the Montgomery Park business 
incubator, two business developments. The EZ also ran a lead paint 
abatement program and a homeownership program. The Baltimore EZ 
received a grant extension through June 2006. 

Figure 16: Activities Implemented by the Baltimore EZ: 

[See PDF for image] 

Source: GAO (photo); GAO analysis of HUD data (charts). 

[End of figure] 

Changes in Poverty, Unemployment, and Economic Growth: 

Poverty decreased in the Baltimore EZ and economic growth improved 
somewhat, but unemployment stayed the same. The poverty rate in the EZ 
fell between 1990 and 2000, while its comparison area stayed about the 
same. However, the unemployment rate, which was one of the lowest of 
the urban EZs in 1990, stayed the same between 1990 and 2000, while the 
rate in its comparison area increased. In terms of economic growth, the 
results were mixed, with the EZ doing somewhat better than its 
comparison area. The number of businesses in the EZ fell from 1995 to 
2004, but the number of jobs increased. In its comparison area, the 
number of businesses also fell, but the number of jobs fell 
substantially. Tables 15 and 16 show the changes in poverty, 
unemployment, and economic growth in the EZ and its comparison area. 
Table 15 also includes data on the changes in other variables included 
in our models. 

Table 15: Changes in Selected Census Variables Observed in the 
Baltimore EZ and Its Comparison Area: 

Poverty rate (%); 
1990: EZ: 41.81; 
1990: Comparison: 41.17; 
2000: EZ: 35.66; 
2000: Comparison: 39.74; 
Percent change[A]: EZ: - 6.16[ B]; 
Percent change[A]: Comparison: -1.43. 

Unemployment rate (%); 
1990: EZ: 15.00; 
1990: Comparison: 14.55; 
2000: EZ: 16.48; 
2000: Comparison: 17.58; 
Percent change[A]: EZ: 1.49; 
Percent change[A]: Comparison: 3.03[ B]. 

Average household income; 
1990: EZ: $28,185; 
1990: Comparison: $27,931; 
2000: EZ: $35,059; 
2000: Comparison: $31,367; 
Percent change[A]: EZ: 24.39[B]; 
Percent change[A]: Comparison: 12.30[B]. 

Percentage of single female headed households with children; 
1990: EZ: 22.50; 
1990: Comparison: 23.15; 
2000: EZ: 19.49; 
2000: Comparison: 19.64; 
Percent change[A]: EZ: -3.01[ B]; 
Percent change[A]: Comparison: -3.51[B]. 

Total population; 
1990: EZ: 72,725; 
1990: Comparison: 150,507; 
2000: EZ: 54,657; 
2000: Comparison: 113,052; 
Percent change[A]: EZ: -24.84; 
Percent change[A]: Comparison: -24.89. 

Total individuals per square mile; 
1990: EZ: 10,460; 
1990: Comparison: 16,934; 
2000: EZ: 7,890; 
2000: Comparison: 12,923; 
Percent change[A]: EZ: -24.57; 
Percent change[A]: Comparison: -23.69. 

Percentage of households that moved in the last 5 years; 
1990: EZ: 41.07; 
1990: Comparison: 42.98; 
2000: EZ: 41.00; 
2000: Comparison: 44.90; 
Percent change[A]: EZ: -0.07; 
Percent change[A]: Comparison: 1.92[B]. 

Percentage of population of working age (16-64); 
1990: EZ: 58.55; 
1990: Comparison: 60.31; 
2000: EZ: 60.04; 
2000: Comparison: 61.63; 
Percent change[A]: EZ: 1.48; 
Percent change[A]: Comparison: 1.32. 

Percentage of population with a high school diploma (or equivalent); 
1990: EZ: 45.69; 
1990: Comparison: 49.86; 
2000: EZ: 56.44; 
2000: Comparison: 58.50; 
Percent change[A]: EZ: 10.74[B]; 
Percent change[A]: Comparison: 8.64[B]. 

Percentage of high school dropouts; 
1990: EZ: 32.36; 
1990: Comparison: 26.43; 
2000: EZ: 19.55; 
2000: Comparison: 20.61; 
Percent change[A]: EZ: -12.81[ B]; 
Percent change[A]: Comparison: -5.81[B]. 

Percentage of vacant housing units; 
1990: EZ: 17.59; 
1990: Comparison: 12.67; 
2000: EZ: 26.22; 
2000: Comparison: 23.63; 
Percent change[A]: EZ: 8.62[B]; 
Percent change[A]: Comparison: 10.96[B]. 

Average owner occupied housing value; 
1990: EZ: $53,714; 
1990: Comparison: $55,966; 
2000: EZ: $62,219; 
2000: Comparison: $62,514; 
Percent change[A]: EZ: 15.83[B]; 
Percent change[A]: Comparison: 11.7[B]. 

Source: GAO analysis of Census data. 

Note: There are 25 census tracts in the designated area and 41 in the 
comparison area. Estimates for all census variables based on 
percentages had 95 percent confidence intervals of plus or minus 5 
percentage points or less. For the confidence intervals for average 
household income and average owner-occupied housing estimates, see 
appendix I. 

[A] Differences in poverty rate, unemployment rate, and other variables 
shown as percentages are based upon percentage point differences. 
Differences for average household income, population, individuals per 
square mile, and average housing value are calculated as percent 
changes. 

[B] The change in estimates from 1990 to 2000 is statistically 
significant. 

[End of table] 

Table 16: Changes in Selected Economic Growth Variables Observed in the 
Baltimore EZ and Its Comparison Area: 

Number of businesses; 
1995: EZ: 2,797; 
1995: Comparison: 3,481; 
1999: EZ: 2,399; 
1999: Comparison: 2,930; 
2004: EZ: 2,487; 
2004: Comparison: 3,005; 
Percent change 1995-2004[A]: EZ: -11.08; 
Percent change 1995-2004[A]: Comparison: -13.67. 

Number of jobs; 
1995: EZ: 41,837; 
1995: Comparison: 61,519; 
1999: EZ: 53,732; 
1999: Comparison: 35,268; 
2004: EZ: 47,504; 
2004: Comparison: 36,860; 
Percent change 1995-2004[A]: EZ: 13.55; 
Percent change 1995-2004[A]: Comparison: -40.08. 

Source: GAO analysis of Claritas data. 

Note: There are 25 census tracts in the designated area and 41 in the 
comparison area. We excluded establishments that were not eligible for 
program tax benefits, such as nonprofit and governmental organizations, 
from our analysis of the change in the number of businesses. However, 
we included jobs at those businesses in our analysis of the change in 
the number of jobs. 

[A] Differences for the number of businesses and the number of jobs are 
calculated as percent changes. 

[End of table] 

Stakeholder Perceptions of the Factors Influencing Changes in Poverty, 
Unemployment, and Economic Growth: 

In our interviews, several stakeholders from the Baltimore EZ said that 
changes in the population of the zone had influenced the change in 
poverty rate. They said that a local HOPE VI project had relocated many 
of the original EZ residents and that rising property values may have 
caused some original residents to move out of the zone. Four 
stakeholders also mentioned lower crime rates in the EZ, which three of 
them linked to the decrease in poverty. 

Two stakeholders mentioned trends in the national economy that 
influenced the change in unemployment, and some said that population 
changes in the zone had affected unemployment as well as poverty. 

Stakeholders cited both EZ-related and external factors as affecting 
economic growth. For example, some said that the EZ created economic 
growth with its entrepreneurial programs, loan funds, and businesses 
developments, such as the Montgomery Park business incubator. 
Stakeholders offered mixed perceptions on the impact of the EZ tax 
benefits on economic growth. Some believed that tax benefits were 
helpful to economic growth, while others did not. In addition, one 
stakeholder said that the waterfront area of the EZ was a natural place 
for development and that the designated area might have experienced 
economic growth in the absence of the program. 

Chicago Empowerment Zone: 

Figure 17: Map of the Chicago EZ and Its Comparison Area: 

[See PDF for image] 

Source: GAO analysis of Census and HUD data. 

[End of figure] 

How the EZ Was Governed: 

The city of Chicago operated its EZ program, incorporating an EZ 
coordinating council and advisory subgroups called "community 
clusters." Both the coordinating council and community clusters were 
made up of EZ residents and local officials. All proposals for EZ 
activities were submitted through a request-for-proposal process, made 
available for comment by the coordinating council, and were reviewed 
and approved by the Chicago City Council. 

Activities the EZ Implemented: 

The Chicago EZ implemented more community development than economic 
opportunity activities. The activities it implemented most often were 
related to workforce development, education, and human services, and 
stakeholders said that the EZ was also active in the area of housing 
development (fig. 18). EZ stakeholders also noted that the EZ had 
helped to improve health care for individuals without insurance by 
contributing to the renovation or expansion of local medical 
facilities. In addition, businesses in the Chicago EZ used six program 
tax-exempt bonds. The Chicago EZ received a grant extension through 
2009. 

Figure 18: Activities Implemented by the Chicago EZ: 

[See PDF for image] 

Sources: GAO (photo); GAO analysis of HUD data (charts). 

[End of figure] 

Changes in Poverty, Unemployment, and Economic Growth: 

Our analyses showed improvements in the Chicago EZ in the poverty and 
unemployment rates, but not in economic growth. Both the EZ and its 
comparison area saw a decrease in poverty from 1990 to 2000. The EZ 
also experienced a decrease in unemployment that was considerably 
greater than that of its comparison area in that time period. In terms 
of economic growth, the Chicago EZ and its comparison area saw 
decreases in the numbers of businesses and jobs between 1995 and 2004, 
with the EZ seeing a larger decline in the number of jobs but less of a 
decline in the number of businesses than its comparison area. Tables 17 
and 18 show the changes in poverty, unemployment, and economic growth 
in the EZ and its comparison area. Table 17 also includes data on the 
changes in other variables included in our models. 

Table 17: Changes in Selected Census Variables Observed in the Chicago 
EZ and Its Comparison Area: 

Poverty rate (%); 
1990: EZ: 49.10; 
1990: Comparison: 40.38; 
2000: EZ: 39.32; 
2000: Comparison: 33.49; 
Percent change[A]: EZ: - 9.77[ B]; 
Percent change[A]: Comparison: -6.89[B]. 

Unemployment rate (%); 
1990: EZ: 24.57; 
1990: Comparison: 20.52; 
2000: EZ: 19.34; 
2000: Comparison: 18.97; 
Percent change[A]: EZ: -5.23[ B]; 
Percent change[A]: Comparison: -1.54[B]. 

Average household income; 
1990: EZ: $23,097; 
1990: Comparison: $28,431; 
2000: EZ: $34,718; 
2000: Comparison: $39,985;  
Percent change[A]: EZ: 50.31[B]; 
Percent change[A]: Comparison: 40.64[B]. 

Percentage of single female headed households with children; 
1990: EZ: 25.64; 
1990: Comparison: 23.07; 
2000: EZ: 21.59; 
2000: Comparison: 19.69; 
Percent change[A]: EZ: -4.05[B]; 
Percent change[A]: Comparison: -3.38[B]. 

Total population; 
1990: EZ: 200,182; 
1990: Comparison: 377,580; 
2000: EZ: 177,309; 
2000: Comparison: 369,343; 
Percent change[A]: EZ: -11.43; 
Percent change[A]: Comparison: -2.18. 

Total individuals per square mile; 
1990: EZ: 13,967; 
1990: Comparison: 15,523; 
2000: EZ: 12,380; 
2000: Comparison: 15,752; 
Percent change[A]: EZ: -11.36; 
Percent change[A]: Comparison: 1.47. 

Percentage of households that moved in the last 5 years; 
1990: EZ: 37.52; 
1990: Comparison: 40.03; 
2000: EZ: 39.21; 
2000: Comparison: 39.68; 
Percent change[A]: EZ: 1.69[B]; 
Percent change[A]: Comparison: -0.35. 

Percentage of population of working age (16-64); 
1990: EZ: 53.51; 
1990: Comparison: 57.87; 
2000: EZ: 55.63; 
2000: Comparison: 59.53; 
Percent change[A]: EZ: 2.12[B]; 
Percent change[A]: Comparison: 1.66[B]. 

Percentage of population with a high school diploma (or equivalent); 
1990: EZ: 44.04; 
1990: Comparison: 54.24; 
2000: EZ: 54.30; 
2000: Comparison: 63.58; 
Percent change[A]: EZ: 10.26[B]; 
Percent change[A]: Comparison: 9.35[B]. 

Percentage of high school dropouts; 
1990: EZ: 22.46; 
1990: Comparison: 19.50; 
2000: EZ: 22.05; 
2000: Comparison: 15.23; 
Percent change[A]: EZ: -0.41; 
Percent change[A]: Comparison: -4.27[ B]. 

Percentage of vacant housing units; 
1990: EZ: 19.69; 
1990: Comparison: 13.54; 
2000: EZ: 18.23; 
2000: Comparison: 12.44; 
Percent change[A]: EZ: -1.45[B]; 
Percent change[A]: Comparison: -1.1[B]. 

Average owner occupied housing value; 
1990: EZ: $71,429; 
1990: Comparison: $88,445; 
2000: EZ: $160,412; 
2000: Comparison: $167,015; 
Percent change[A]: EZ: 124.57[B]; 
Percent change[A]: Comparison: 88.83[B]. 

Source: GAO analysis of Census data. 

Note: There are 96 census tracts in the designated area and 146 in the 
comparison area. Estimates for all census variables based on 
percentages had 95 percent confidence intervals of plus or minus 5 
percentage points or less. For the confidence intervals for average 
household income and average owner-occupied housing estimates, see 
appendix I. 

[A] Differences in poverty rate, unemployment rate, and other variables 
shown as percentages are based upon percentage point differences. 
Differences for average household income, population, individuals per 
square mile, and average housing value are calculated as percent 
changes. 

[B] The change in estimates from 1990 to 2000 is statistically 
significant. 

[End of table] 

Table 18: Changes in Selected Economic Growth Variables Observed in the 
Chicago EZ and Its Comparison Area: 

Number of businesses; 
1995: EZ: 5,089; 
1995: Comparison: 10,567; 
1999: EZ: 4,614; 
1999: Comparison: 9,582; 
2004: EZ: 4,496; 
2004: Comparison: 9,211; 
Percent change 1995-2004[A]: EZ: -11.65; 
Percent change 1995-2004[A]: Comparison: -12.83. 

Number of jobs; 
1995: EZ: 83,935; 
1995: Comparison: 183,369; 
1999: EZ: 80,294; 
1999: Comparison: 169,741; 
2004: EZ: 69,767; 
2004: Comparison: 162,541; 
Percent change 1995-2004[A]: EZ: - 16.88; 
Percent change 1995-2004[A]: Comparison: -11.36. 

Source: GAO analysis of Claritas data. 

Note: There are 96 census tracts in the designated area and 146 in the 
comparison area. We excluded establishments that were not eligible for 
program tax benefits, such as nonprofit and governmental organizations, 
from our analysis of the change in the number of businesses. However, 
we included jobs at those businesses in our analysis of the change in 
the number of jobs. 

[A] Differences for the number of businesses and the number of jobs are 
calculated as percent changes. 

[End of table] 

Stakeholder Perceptions of the Factors Influencing Changes in Poverty, 
Unemployment, and Economic Growth: 

Asked about factors influencing the change in poverty, stakeholders 
pointed to both EZ activities and external factors. Among the EZ 
activities they cited were projects promoting homeownership or 
providing educational training. However, some stakeholders mentioned 
changes in the EZ population as an external factor that may have 
affected the changes in poverty, noting the demolition of several 
public housing buildings in the EZ and the addition of individuals with 
higher incomes moving into new housing built on those sites. 

Some stakeholders attributed a decrease in unemployment to the zone's 
focus on creating jobs and the requirement that subgrantees demonstrate 
that they had created jobs for EZ residents. Some stakeholders also 
noted that the EZ's provision of services, such as childcare, after- 
school programs, and job training, provided opportunities for more 
residents to obtain jobs. But some stakeholders believed that the 
decreases in unemployment were due to external economic forces, such as 
changes in the population of the EZ and more jobs available due to 
changes in the national economy. 

In terms of economic growth, some EZ stakeholders observed that the EZ 
had provided some initial investment in the zone and that private 
investment had followed. However, some stakeholders noted that the EZ 
had not done enough in the area of economic development. In addition, 
stakeholders said that not all the jobs from new businesses in the zone 
had gone to zone residents and that the number of new businesses did 
not meet the zone's employment needs. 

Detroit Empowerment Zone: 

Figure 19: Map of the Detroit EZ and Its Comparison Area: 

[See PDF for image] 

Source: GAO analysis of Census and HUD data. 

[End of figure] 

How the EZ Was Governed: 

The nonprofit Detroit Empowerment Zone Development Corporation ran the 
Detroit EZ and included an executive committee, a board made up of 
residents and other local officials, and three neighborhood review 
panels representing neighborhoods in the EZ. Each review panel had an 
advisory role in determining how a portion of the EZ funds would be 
spent. The EZ was required to obtain the approval of the Detroit City 
Council and Mayor for many EZ-funded activities. 

Activities the EZ Implemented: 

The Detroit EZ implemented mostly community development activities. The 
two most common types of activities were in the areas of human services 
and education (fig. 20). In addition, EZ stakeholders explained that 
the EZ had helped to spur housing development in the east and southwest 
areas of the zone by providing funds to community development 
corporations. Detroit EZ stakeholders also highlighted a business 
façade improvement program during our tour of the EZ. Although they 
focused mainly on community development, the Detroit EZ did implement 
some economic opportunity activities. Some EZ stakeholders said that 
the Financial Institutions Consortium, which set lending goals within 
the EZ, had helped EZ businesses. The Detroit EZ did not request a 
grant extension because it had used nearly all of the EZ grant funds. 

Figure 20: Activities Implemented by the Detroit EZ: 

[See PDF for image] 

Source: GAO (photo); GAO analysis of HUD data (charts). 

[End of figure] 

Changes in Poverty, Unemployment, and Economic Growth: 

The Detroit EZ experienced positive changes in poverty, unemployment, 
and one measure of economic growth. Of the urban EZs, the Detroit EZ 
had the largest decrease in poverty and the second largest decrease in 
unemployment from 1990 to 2000. Although the decrease in the EZ was 
slightly greater than in its comparison area in poverty, the decrease 
in unemployment was less than in its comparison area. Between 1995 and 
2004, the EZ generally fared better than its comparison area in our 
measures of economic growth; however, the changes were not always 
positive. The number of businesses declined slightly, but the decrease 
was notably smaller than the decline in its comparison area. In 
addition, the EZ saw a greater increase in the number of jobs than in 
either its comparison area or most urban EZs. Tables 19 and 20 show the 
changes in poverty, unemployment, and economic growth in the EZ and its 
comparison area. Table 19 also includes data on the changes in other 
variables included in our models. 

Table 19: Changes in Selected Census Variables Observed in the Detroit 
EZ and Its Comparison Area: 

Poverty rate (%); 
1990: EZ: 47.63; 
1990: Comparison: 42.72; 
2000: EZ: 36.73; 
2000: Comparison: 32.38; 
Percent change[A]: EZ: -10.90[B]; 
Percent change[A]: Comparison: -10.34[B]. 

Unemployment rate (%); 
1990: EZ: 28.41; 
1990: Comparison: 26.01; 
2000: EZ: 18.83; 
2000: Comparison: 15.54; 
Percent change[A]: EZ: -9.58[ B]; 
Percent change[A]: Comparison: -10.47[ B]. 

Average household income; 
1990: EZ: $22,644; 
1990: Comparison: $25,609; 
2000: EZ: $33,751; 
2000: Comparison: $36,200; 
Percent change[A]: EZ: 49.05[B]; 
Percent change[A]: Comparison: 41.36[B]. 

Percentage of single female headed households with children; 
1990: EZ: 17.30; 
1990: Comparison: 20.94; 
2000: EZ: 15.88; 
2000: Comparison: 18.77; 
Percent change[A]: EZ: -1.43; 
Percent change[A]: Comparison: -2.18[B]. 

Total population; 
1990: EZ: 103,346; 
1990: Comparison: 256,371; 
2000: EZ: 88,707; 
2000: Comparison: 229,536; 
Percent change[A]: EZ: -14.16; 
Percent change[A]: Comparison: -10.47. 

Total individuals per square mile; 
1990: EZ: 5,547; 
1990: Comparison: 6,923; 
2000: EZ: 4,762; 
2000: Comparison: 6,200; 
Percent change[A]: EZ: -14.15; 
Percent change[A]: Comparison: 
-10.45. 

Percentage of households that moved in the last 5 years; 
1990: EZ: 40.80; 
1990: Comparison: 37.39; 
2000: EZ: 42.20; 
2000: Comparison: 38.47; 
Percent change[A]: EZ: 1.40; 
Percent change[A]: Comparison: 1.08. 

Percentage of population of working age (16-64); 
1990: EZ: 57.65; 
1990: Comparison: 56.96;  
2000: EZ: 60.34; 
2000: Comparison: 57.01; 
Percent change[A]: EZ: 2.69[B]; 
Percent change[A]: Comparison: 0.05. 

Percentage of population with a high school diploma (or equivalent); 
1990: EZ: 49.34; 
1990: Comparison: 53.63; 
2000: EZ: 58.06; 
2000: Comparison: 60.87; 
Percent change[A]: EZ: 8.72[B]; 
Percent change[A]: Comparison: 7.24[B]. 

Percentage of high school dropouts; 
1990: EZ: 23.29; 
1990: Comparison: 20.47; 
2000: EZ: 20.83; 
2000: Comparison: 18.89; 
Percent change[A]: EZ: -2.46[B]; 
Percent change[A]: Comparison: -1.57[B]. 

Percentage of vacant housing units; 
1990: EZ: 18.26; 
1990: Comparison: 11.22; 
2000: EZ: 17.46; 
2000: Comparison: 13.98; 
Percent change[A]: EZ: -0.79; 
Percent change[A]: Comparison: 2.76[B]. 

Average owner occupied housing value; 
1990: EZ: $23,114; 
1990: Comparison: $28,598; 
2000: EZ: $52,234; 
2000: Comparison: $61,160; 
Percent change[A]: EZ: 125.99[B]; 
Percent change[A]: Comparison: 113.86[B]. 

Source: GAO analysis of Census data. 

Note: There are 49 census tracts in the designated area and 86 in the 
comparison area. Estimates for all census variables based on 
percentages had 95 percent confidence intervals of plus or minus 5 
percentage points or less. For the confidence intervals for average 
household income and average owner-occupied housing estimates, see 
appendix I. 

[A] Differences in poverty rate, unemployment rate, and other variables 
shown as percentages are based upon percentage point differences. 
Differences for average household income, population, individuals per 
square mile, and average housing value are calculated as percent 
changes. 

[B] The change in estimates from 1990 to 2000 is statistically 
significant. 

[End of table] 

Table 20: Changes in Selected Economic Growth Variables Observed in the 
Detroit EZ and Its Comparison Area: 

Number of businesses; 
1995: EZ: 3,723; 
1995: Comparison: 5,343; 
1999: EZ: 3,650; 
1999: Comparison: 5,282; 
2004: EZ: 3,621; 
2004: Comparison: 4,770; 
Percent change 1995-2004[A]: EZ: -2.74; 
Percent change 1995-2004[A]: Comparison: -10.72. 

Number of jobs; 
1995: EZ: 95,172; 
1995: Comparison: 86,500; 
1999: EZ: 99,480; 
1999: Comparison: 73,770; 
2004: EZ: 124,172; 
2004: Comparison: 66,179; 
Percent change 1995-2004[A]: EZ: 30.47; 
Percent change 1995-2004[A]: Comparison: -23.49. 

Source: GAO analysis of Claritas data. 

Note: There are 49 census tracts in the designated area and 86 in the 
comparison area. We excluded establishments that were not eligible for 
program tax benefits, such as nonprofit and governmental organizations, 
from our analysis of the change in the number of businesses. However, 
we included jobs at those businesses in our analysis of the change in 
the number of jobs. 

[A] Differences for the number of businesses and the number of jobs are 
calculated as percent changes. 

[End of table] 

Stakeholder Perceptions of the Factors Influencing Changes in Poverty, 
Unemployment, and Economic Growth: 

An EZ stakeholder said that the population of the zone had changed, 
possibly affecting the changes in the poverty rate. For example, the 
stakeholder noted that many of the initial EZ residents had moved out 
of the zone since designation, and that other individuals with higher 
incomes had moved into the zone. 

Stakeholders noted that EZ programs in job training, youth education, 
supportive services, and health care had helped some EZ residents to 
gain employment. However, some stakeholders also mentioned the lack of 
a skilled workforce in the EZ and the need for more job training. In 
addition, some stakeholders thought that the changes in the zone's 
population might also have influenced the change in unemployment. 

In terms of economic growth, EZ stakeholders noted that their façade 
improvement program had contributed to business growth in the EZ. One 
stakeholder also suggested that the EZ tax benefits and financing from 
the Financial Institutions Consortium had provided incentives to 
attract businesses to locate in the EZ. Another stakeholder mentioned 
external challenges to economic growth that included the loss of the 
automobile industry and the poor national economy over the time period 
of the EZ. 

New York Empowerment Zone: 

Figure 21: Map of the New York EZ and Its Comparison Area: 

[See PDF for image] 

Source: GAO analysis of Census and HUD data. 

[End of figure] 

How the EZ Was Governed: 

The New York EZ was governed by three boards: an overarching board and 
two subzone boards representing the Upper Manhattan and Bronx 
neighborhoods. The overarching board, which included officials from the 
city, state, and each subzone board, as well as local congressional 
representatives, provided final funding approval for all EZ 
projects.[Footnote 80] However, the program was managed locally by the 
two subzones, which had separate management organizations, boards, and 
budgets and made decisions about the activities that would be funded 
and the organizations that would implement them. The Upper Manhattan 
subzone received the bulk of the EZ grant ($83 million), and the Bronx 
portion received the remaining $17 million. The New York EZ also 
received matching funds from the city and state, bringing the total 
funding for the EZ to $300 million. 

The EZ created the nonprofit Upper Manhattan Empowerment Zone to manage 
the Upper Manhattan portion of the zone. This EZ is governed by a board 
that includes community members, at-large members selected for their 
expertise, and representatives from city community planning boards. The 
board also has seven committees. Activities proposed in this portion of 
the EZ were reviewed by the committees, approved by the Upper Manhattan 
board, and finally approval by the overarching EZ board. 

The Bronx Overall Economic Development Corporation, a part of the Bronx 
Borough President's office, managed the Bronx portion of the New York 
EZ. The board of the Bronx Overall Economic Development Corporation 
covered both EZ and non-EZ activities but included an EZ committee. 
Although the board did not include any EZ residents, an EZ stakeholder 
explained that it included some residents of other areas of the Bronx. 
In general, the board decided on activities, encouraged local 
nonprofits to submit proposals, and chose the organizations to 
implement the activities. Then the activities went before the New York 
EZ board for final approval. 

Activities the EZ Implemented: 

Unlike most EZs, both portions of the New York EZ implemented more 
economic opportunity activities than community development activities. 
However, the Upper Manhattan and Bronx portions of the EZ differed 
somewhat in the types of activities they implemented. The New York EZ 
as a whole received a grant extension until 2009. 

The types of activities most commonly implemented by the Upper 
Manhattan portion were assistance to businesses, workforce development, 
access to capital, and infrastructure (fig. 22). Several EZ 
stakeholders mentioned the business developments in Harlem USA or along 
125TH Street as major accomplishments of their program. Stakeholders 
also noted that the EZ had assisted small businesses, successfully 
sponsored a restaurant initiative that provided local restaurants with 
loan capital and technical assistance, and facilitated the use of an EZ 
tax-exempt bond to finance a new car dealership. In addition, an EZ 
stakeholder said that the EZ fostered job growth by requiring 
recipients of EZ grants and loans to employ a certain number of EZ 
residents. 

Figure 22: Activities Implemented by the Upper Manhattan portion of the 
New York EZ: 

[See PDF for image] 

Source: GAO (photo); GAO analysis of HUD data(charts). 

[End of figure] 

The types of activities most commonly implemented by the Bronx portion 
of the New York EZ included workforce development, education, access to 
capital, and human services (fig. 23). EZ stakeholders explained that 
the EZ had funded several workforce training activities, such as a 
program to train women to become childcare providers. However, several 
stakeholders also said that as the program progressed more funds were 
used to provide loans to EZ businesses, an activity that was felt to 
provide the best return on investment. 

Figure 23: Activities Implemented by the Bronx portion of the New York 
EZ: 

[See PDF for image] 

Sources: GAO (photos); GAO analysis of HUD data (charts). 

[End of figure] 

Changes in Poverty, Unemployment, and Economic Growth: 

Overall, the New York EZ saw poverty fall and economic growth improve, 
but unemployment increase. The changes in the Upper Manhattan portion 
of the EZ followed this pattern, but the Bronx portion of the EZ also 
showed a decrease in one of the measures of economic growth, the number 
of total jobs. Tables 21 and 22 show the changes in poverty, 
unemployment, and economic growth in the EZ, the Upper Manhattan and 
Bronx portions of the EZ, and the EZ comparison area. Table 21 also 
includes data on the changes in other variables included in our models. 

Indicators for the Upper Manhattan portion of the New York EZ were 
mixed compared with the New York comparison area. The poverty rate in 
the Upper Manhattan portion of the EZ fell between 1990 and 2000, while 
the unemployment rate stayed the same. The New York comparison area 
stayed about the same in poverty, and its unemployment rate rose. In 
economic growth, between 1995 and 2004 the Upper Manhattan portion of 
the EZ had the largest increase in total number of businesses and the 
second-largest increase in jobs of any urban EZ. The comparison area 
saw a slightly smaller increase in businesses and a larger increase in 
jobs. 

Like the Upper Manhattan portion of the New York EZ, the Bronx portion 
showed mixed results relative to the New York comparison area. Its 
poverty rate stayed the same between 1990 and 2000 as did the New York 
comparison area. Between 1990 and 2000, it experienced a greater 
increase in unemployment than the New York comparison area. In terms of 
economic growth, the area did show an increase in the number of 
businesses from 1995 to 2004, but its comparison area showed a larger 
increase. However, in the same time period, the Bronx experienced a 
slight decrease in the number of jobs, while the comparison area 
experienced a large increase. 

Table 21: Changes in Selected Census Variables Observed in the New York 
EZ, the Bronx and Upper Manhattan (UM) Portions, and the EZ Comparison 
Area (Comp.) 

Poverty rate (%); 
1990: Entire EZ: 42.68; 
1990: Bronx: 44.2; 
1990: UM: 42.38; 
1990: Comp.: 42.5; 
2000: Entire EZ: 38.62; 
2000: Bronx: 41.59; 
2000: UM: 38.02; 
2000: Comp.: 41.75; 
Percent change[A]: Entire EZ: -4.07[B]; 
Percent change[A]: Bronx: -2.61; 
Percent change[A]: UM: -4.35[B]; 
Percent change[A]: Comp.: -0.75. 

Unemployment rate (%); 
1990: Entire EZ: 17.45; 
1990: Bronx: 15.36; 
1990: UM: 17.86; 
1990: Comp.: 17.17; 
2000: Entire EZ: 19.46; 
2000: Bronx: 20.96; 
2000: UM: 19.18; 
2000: Comp.: 20.04; 
Percent change[A]: Entire EZ: 2.00[B]; 
Percent change[A]: Bronx: 5.6[B]; 
Percent change[A]: UM: 1.32; 
Percent change[A]: Comp.: 2.87[B]. 

Average household income; 
1990: Entire EZ: $26,518; 
1990: Bronx: $26,294; 
1990: UM: $26,559; 
1990: Comp.: $26,993; 
2000: Entire EZ: $33,557; 
2000: Bronx: $30,842; 
2000: UM: $34,041; 
2000: Comp.: $31,247; 
Percent change[A]: Entire EZ: 26.54[B]; 
Percent change[A]: Bronx: 17.29[B]; 
Percent change[A]: UM: 28.17[B]; 
Percent change[A]: Comp.: 15.76[B]. 

Percentage of single female headed households with children; 
1990: Entire EZ: 20.19; 
1990: Bronx: 25.55; 
1990: UM: 19.2; 
1990: Comp.: 25.91; 
2000: Entire EZ: 19.6; 
2000: Bronx: 23.15; 
2000: UM: 18.97; 
2000: Comp.: 25.26; 
Percent change[A]: Entire EZ: -0.59; 
Percent change[A]: Bronx: -2.40[B]; 
Percent change[A]: UM: -0.23; 
Percent change[A]: Comp.: -0.64. 

Total population; 
1990: Entire EZ: 199,983; 
1990: Bronx: 34,266; 
1990: UM: 165,717; 
1990: Comp.: 638,776; 
2000: Entire EZ: 219,324; 
2000: Bronx: 36,886; 
2000: UM: 182,438; 
2000: Comp.: 672,826; 
Percent change[A]: Entire EZ: 9.67; 
Percent change[A]: Bronx: 7.65; 
Percent change[A]: UM: 10.09; 
Percent change[A]: Comp.: 5.33. 

Total individuals per square mile; 
1990: Entire EZ: 31,890; 
1990: Bronx: 11,651; 
1990: UM: 49,763; 
1990: Comp.: 58,404; 
2000: Entire EZ: 35,286; 
2000: Bronx: 12,553; 
2000: UM: 55,672; 
2000: Comp.: 67,150; 
Percent change[A]: Entire EZ: 10.65; 
Percent change[A]: Bronx: 7.73; 
Percent change[A]: UM: 11.88; 
Percent change[A]: Comp.: 14.97. 

Percentage of households that moved in the last 5 years; 
1990: Entire EZ: 31.93; 
1990: Bronx: 31.96; 
1990: UM: 31.93; 
1990: Comp.: 32.5; 
2000: Entire EZ: 34.07; 
2000: Bronx: 33.4; 
2000: UM: 34.2; 
2000: Comp.: 33.64; 
Percent change[A]: Entire EZ: 2.14[B]; 
Percent change[A]: Bronx: 1.45; 
Percent change[A]: UM: 2.28[B]; 
Percent change[A]: Comp.: 1.15[B]. 

Percentage of population of working age (16-64); 
1990: Entire EZ: 59.67; 
1990: Bronx: 60.09; 
1990: UM: 59.59; 
1990: Comp.: 58.97; 
2000: Entire EZ: 61.3; 
2000: Bronx: 60.36; 
2000: UM: 61.49; 
2000: Comp.: 58.49; 
Percent change[A]: Entire EZ: 1.63[B]; 
Percent change[A]: Bronx: 0.28; 
Percent change[A]: UM: 1.9[B]; 
Percent change[A]: Comp.: -0.48. 

Percentage of population with a high school diploma (or equivalent); 
1990: Entire EZ: 47.74; 
1990: Bronx: 44.43; 
1990: UM: 48.37; 
1990: Comp.: 48.4; 
2000: Entire EZ: 55.16; 
2000: Bronx: 51.25; 
2000: UM: 55.9; 
2000: Comp.: 51.66; 
Percent change[A]: Entire EZ: 7.42[B]; 
Percent change[A]: Bronx: 6.82[B]; 
Percent change[A]: UM: 7.53[B]; 
Percent change[A]: Comp.: 3.26[B]. 

Percentage of high school dropouts; 
1990: Entire EZ: 19.85; 
1990: Bronx: 18.12; 
1990: UM: 20.19; 
1990: Comp.: 20.18; 
2000: Entire EZ: 15.59; 
2000: Bronx: 17.75; 
2000: UM: 15.2; 
2000: Comp.: 17.7; 
Percent change[A]: Entire EZ: -4.26[B]; 
Percent change[A]: Bronx: -0.37; 
Percent change[A]: UM: - 4.99[B]; 
Percent change[A]: Comp.: -2.48[B]. 

Percentage of vacant housing units; 
1990: Entire EZ: 8.81; 
1990: Bronx: 3.24; 
1990: UM: 9.77; 
1990: Comp.: 3.99; 
2000: Entire EZ: 11.09; 
2000: Bronx: 7.35; 
2000: UM: 11.73; 
2000: Comp.: 5.99; 
Percent change[A]: Entire EZ: 2.28[B]; 
Percent change[A]: Bronx: 4.11[ B]; 
Percent change[A]: UM: 1.96[B]; 
Percent change[A]: Comp.: 2.00[B]. 

Average owner occupied housing value; 
1990: Entire EZ: $207,544; 
1990: Bronx: $99,728; 
1990: UM: $238,864; 
1990: Comp.: $177,446; 
2000: Entire EZ: $301,835; 
2000: Bronx: $124,588; 
2000: UM: $384,155; 
2000: Comp.: $209,423; 
Percent change[A]: Entire EZ: 45.43[B]; 
Percent change[A]: Bronx: 24.93[B]; 
Percent change[A]: UM: 60.83[B]; 
Percent change[A]: Comp.: 18.02[B]. 

Source: GAO analysis of Census data. 

Note: There are 65 census tracts in the designated area and 160 in the 
comparison area. Estimates for all census variables based on 
percentages had 95 percent confidence intervals of plus or minus 5 
percentage points or less. For the confidence intervals for average 
household income and average owner-occupied housing estimates, see 
appendix I. 

[A] Differences in poverty rate, unemployment rate, and other variables 
shown as percentages are based upon percentage point differences. 
Differences for average household income, population, individuals per 
square mile, and average housing value are calculated as percent 
changes. 

[B] The change in estimates from 1990 to 2000 is statistically 
significant. 

[End of table] 

Table 22: Changes in Selected Economic Growth Variables Observed in the 
New York EZ, the Bronx and Upper Manhattan (UM) Portions, and the EZ 
Comparison Area (Comp.) 

Number of businesses; 
1995: Entire EZ: 5,415; 
1995: Bronx: 1,738; 
1995: UM: 3,677; 
1995: Comp.: 8,294; 
1999: Entire EZ: 6,203; 
1999: Bronx: 1,750; 
1999: UM: 4,453; 
1999: Comp.: 9,400; 
2004: Entire EZ: 6,691; 
2004: Bronx: 1,840; 
2004: UM: 4,851; 
2004: Comp.: 10,719; 
Percent change 1995- 2004[A]: Entire EZ: 23.6; 
Percent change 1995-2004[A]: Bronx: 5.87; 
Percent change 1995-2004[A]: UM: 31.9; 
Percent change 1995-2004[A]: Comp.: 29.2. 

Number of jobs; 
1995: Entire EZ: 96,228; 
1995: Bronx: 32,243; 
1995: UM: 63,985; 
1995: Comp.: 108,785; 
1999: Entire EZ: 101,462; 
1999: Bronx: 28,696; 
1999: UM: 72,766; 
1999: Comp.: 122,447; 
2004: Entire EZ: 121,550; 
2004: Bronx: 30,137; 
2004: UM: 91,413; 
2004: Comp.: 162,360; 
Percent change 1995- 2004[A]: Entire EZ: 26.3; 
Percent change 1995-2004[A]: Bronx: -6.53; 
Percent change 1995-2004[A]: UM: 42.9; 
Percent change 1995-2004[A]: Comp.: 49.3. 

Source: GAO analysis of Claritas data. 

Note: There are 65 census tracts in the designated area and 160 in the 
comparison area. We excluded establishments that were not eligible for 
program tax benefits, such as nonprofit and governmental organizations, 
from our analysis of the change in the number of businesses. However, 
we included jobs at those businesses in our analysis of the change in 
the number of jobs. 

[A] Differences for the number of businesses and the number of jobs are 
calculated as percent changes. 

[End of table] 

Stakeholder Perceptions of the Factors Influencing Changes in Poverty, 
Unemployment, and Economic Growth: 

Many Upper Manhattan stakeholders we interviewed attributed the change 
in poverty to the higher incomes from the jobs the EZ helped create. In 
addition, several stakeholders discussed changes in the zone's 
population, as low-income residents were displaced by increases in 
property values and rental costs and employed residents with higher 
incomes moved into the area. One stakeholder attributed some of the 
decrease in poverty to welfare reform. 

For unemployment, some stakeholders said that it was difficult to 
improve the unemployment rate in the Upper Manhattan portion of the EZ 
due to a lack of residents with needed job skills. Stakeholders also 
noted that the change in the zone's population had affected 
unemployment as well as poverty. 

Several stakeholders observed that the Upper Manhattan EZ had helped 
foster economic growth, citing its role in the creation of retail areas 
and real estate development as examples. They also said that it had 
helped small businesses by providing technical assistance, training, 
and loans. 

Bronx stakeholders noted that the program had helped to influence 
poverty and unemployment through the resident employment requirements 
it had for businesses that received loans and the center it had created 
to match residents to jobs. However, some EZ stakeholders said that the 
EZ had had trouble getting EZ residents jobs, since there were few 
residents living in the Bronx portion of the EZ and many of them lacked 
necessary job skills. Further affecting the changes in poverty and 
unemployment, one stakeholder perceived that some original EZs 
residents had relocated and that new residents had moved into the EZ. 

Bronx stakeholders also said that access to capital for businesses 
resulted in an increase in businesses moving into the EZ, but one 
stakeholder noted that few jobs had been created. Another stakeholder 
said that some zone businesses were downsizing as a result of changes 
in the national economy. 

Philadelphia-Camden Empowerment Zone: 

Figure 24: Map of the Philadelphia-Camden EZ and Its Comparison Area: 

[See PDF for image] 

Source: GAO analysis of Census and HUD data. 

[End of figure] 

How the EZ Was Governed: 

Although Philadelphia and Camden were designated as one EZ and their 
original strategic plan envisioned a central board to oversee the EZ 
operations, the Philadelphia and Camden portions operated completely 
independently. Of the $100 million EZ grant, the Philadelphia portion 
of the EZ received about $79 million, and the Camden portion of the EZ 
received about $21 million. 

The Philadelphia Empowerment Zone Office that oversees the EZ program 
is part of the city government. The EZ created three subzones, each of 
which had its own Community Trust Board to identify the needs of the 
community, select activities to implement, and allocate resources to 
the activities. However, the EZ did not create an overarching board to 
oversee the three Philadelphia subzones. To select entities to 
implement activities, the EZ issued requests for proposals. A panel of 
community members, experts, and officials selected the best 
applications, and the city approved the funding. The mayor required 
that more than half of EZ/EC grant be spent on economic development 
(including job training) and retained the right to veto decisions by 
the Community Trust Boards, although this rarely happened. 

The Camden portion of the EZ was managed by a nonprofit entity called 
the Camden Empowerment Zone Corporation and was governed by a board, 
which included residents as well as "block captains" who were residents 
that had been elected to represent their communities, and other 
individuals from the business, cultural, religious, and nonprofit 
community.[Footnote 81] Under the board, there was a subcommittee 
structure. The EZ issued requests for proposals to identify 
organizations to implement the programs, which were reviewed by a 
subcommittee and then forwarded to the full board for approval. 

Activities the EZ Implemented: 

Both portions of the Philadelphia-Camden EZ implemented more community 
development activities than economic opportunity activities. Officials 
from the Philadelphia portion of the EZ explained that, while they 
spent more than half of their program grant funding on economic 
development as required by their mayor, the number of community 
development programs implemented was greater than the number of 
economic opportunity programs. In addition, both portions of the EZ 
received grant extensions until 2009. 

Activities related to education, access to capital, and assisting 
businesses were the most common in the Philadelphia portion of the 
Philadelphia-Camden EZ (fig. 25). The two activities most often cited 
by stakeholders in our interviews were the program to clean up vacant 
lots and the community lending institutions. EZ stakeholders also noted 
that the EZ had helped to organize the business community in each 
neighborhood. 

Figure 25: Activities Implemented by the Philadelphia Portion of the 
Philadelphia-Camden EZ: 

[See PDF for image] 

Sources: GAO (photo); GAO analysis of HUD data (charts). 

[End of figure] 

The activities most frequently implemented in the Camden portion of the 
EZ were housing, capacity building, access to capital, and 
infrastructure (fig. 26). In addition, most EZ stakeholders described 
the U.S.S. New Jersey, a new tourist attraction for which the EZ funded 
a portion of the application and the visitors' center, as a success of 
the EZ program. During our interviews, stakeholders also pointed out 
activities such as a summer youth program, the refurbishing of a local 
park, physical improvements to the streets and sidewalks, and an EZ 
program designed to make loans and grants to EZ businesses. 

Figure 26: Activities Implemented by the Camden Portion of the 
Philadelphia-Camden EZ: 

[See PDF for image] 

Source: GAO (photo); GAO analysis of HUD data (charts).  

[End of figure] 

Changes in Poverty, Unemployment, and Economic Growth: 

Overall, the Philadelphia-Camden EZ was the only urban EZ to see 
positive changes in poverty, unemployment, and both measures of 
economic growth. However, changes in the Philadelphia and Camden 
portions varied, and were not always positive. Tables 23 and 24 show 
the changes in poverty, unemployment, and economic growth in the EZ, 
the Philadelphia and Camden portions of the EZ, and the EZ comparison 
area. Table 23 also includes data on the changes in other variables 
included in our models. 

The Philadelphia portion of the EZ experienced decreases in poverty and 
unemployment and little change or a decrease in our measures of 
economic growth. Its declines in the poverty and unemployment rates 
from 1990 to 2000 outpaced those in the Philadelphia-Camden comparison 
area.[Footnote 82] In economic growth, the Philadelphia portion of the 
EZ experienced little change in the number of businesses between 1995 
and 2004, while its comparison area experienced a large increase. Both 
the Philadelphia portion of the EZ and the EZ comparison area saw a 
similar decline in the number of jobs available. 

In contrast, the Camden portion of the Philadelphia-Camden EZ 
experienced little change in poverty or the unemployment rate, but it 
experienced positive changes in both measures of economic growth. Its 
poverty rate from 1990 to 2000 stayed about the same, while its 
comparison area decreased. Both the Camden portion of the EZ and the EZ 
comparison area saw little change in the unemployment rate. For 
economic growth, the Camden portion of the EZ had one of the highest 
increases in the number of businesses of any EZ from 1995 to 2004, 
slightly better than its comparison area. It also saw a large increase 
in the number of jobs. In contrast, the EZ comparison area experienced 
a large decline in number of jobs over the same time period. 

Table 23: Changes in Selected Census Variables Observed in the 
Philadelphia-Camden EZ, the Camden (Cam.) and Philadelphia (Phila.) 
Portions, and the EZ Comparison Area (Comp.) 

Poverty rate (%); 
1990: Entire EZ: 50.14; 
1990: Cam.: 43; 
1990: Phila.: 52.1; 
1990: Comp.: 43.07; 
2000: Entire EZ: 42.98; 
2000: Cam.: 40.55; 
2000: Phila.: 43.68; 
2000: Comp.: 37.97; 
Percent change[ A]: Entire EZ: -7.16[B]; 
Percent change[ A]: Cam.: -2.45; 
Percent change[ A]: Phila.: -8.42[B]; 
Percent change[ A]: Comp.: -5.1[B]. 

Unemployment rate (%); 
1990: Entire EZ: 22.21; 
1990: Cam.: 18.09; 
1990: Phila.: 23.6; 
1990: Comp.: 18.81; 
2000: Entire EZ: 19.3; 
2000: Cam.: 19.08; 
2000: Phila.: 19.38; 
2000: Comp.: 17.91; 
Percent change[ A]: Entire EZ: -2.91[B]; 
Percent change[ A]: Cam.: 0.99; 
Percent change[ A]: Phila.: -4.22[B]; 
Percent change[ A]: Comp.: -0.9. 

Average household income; 
1990: Entire EZ: $23,188; 
1990: Cam.: $26,742; 
1990: Phila.: $22,269; 
1990: Comp.: $27,292; 
2000: Entire EZ: $28,562; 
2000: Cam.: $31,158; 
2000: Phila.: $27,851; 
2000: Comp.: $31,318; 
Percent change[ A]: Entire EZ: 23.17[B]; 
Percent change[ A]: Cam.: 16.52[B]; 
Percent change[ A]: Phila.: 25.07[B]; 
Percent change[ A]: Comp.: 14.8[B]. 

Percentage of single female headed households with children; 
1990: Entire EZ: 22.73; 
1990: Cam.: 24.13; 
1990: Phila.: 22.37; 
1990: Comp.: 21.01; 
2000: Entire EZ: 21.93; 
2000: Cam.: 23.22; 
2000: Phila.: 21.57; 
2000: Comp.: 20.66; 
Percent change[ A]: Entire EZ: -0.81; 
Percent change[ A]: Cam.: -0.92; 
Percent change[ A]: Phila.: -0.8; 
Percent change[ A]: Comp.: -0.36. 

Total population; 
1990: Entire EZ: 52,440; 
1990: Cam.: 13,332; 
1990: Phila.: 39,108; 
1990: Comp.: 38,520; 
2000: Entire EZ: 45,725; 
2000: Cam.: 12,749; 
2000: Phila.: 32,976; 
2000: Comp.: 35,827; 
Percent change[ A]: Entire EZ: -12.81; 
Percent change[ A]: Cam.: -4.37; 
Percent change[ A]: Phila.: -15.68; 
Percent change[ A]: Comp.: -6.99. 

Total individuals per square mile; 
1990: Entire EZ: 12,248; 
1990: Cam.: 7,342; 
1990: Phila.: 15,861; 
1990: Comp.: 7,601; 
2000: Entire EZ: 10,698; 
2000: Cam.: 7,034; 
2000: Phila.: 13,396; 
2000: Comp.: 7,064; 
Percent change[ A]: Entire EZ: -12.66; 
Percent change[ A]: Cam.: -4.2; 
Percent change[ A]: Phila.: 
-15.54; 
Percent change[ A]: Comp.: -7.06. 

Percentage of households that moved in the last 5 years; 
1990: Entire EZ: 34.71; 
1990: Cam.: 41.93; 
1990: Phila.: 32.25; 
1990: Comp.: 40.69; 
2000: Entire EZ: 34.05; 
2000: Cam.: 44.42; 
2000: Phila.: 30.04; 
2000: Comp.: 44.05; 
Percent change[ A]: Entire EZ: -0.66; 
Percent change[ A]: Cam.: 2.49; 
Percent change[ A]: Phila.: -2.21; 
Percent change[ A]: Comp.: 3.36[B]. 

Percentage of population of working age (16-64); 
1990: Entire EZ: 57.58; 
1990: Cam.: 63.02; 
1990: Phila.: 55.72; 
1990: Comp.: 59.76; 
2000: Entire EZ: 58.85; 
2000: Cam.: 66.86; 
2000: Phila.: 55.75; 
2000: Comp.: 61.93; 
Percent change[ A]: Entire EZ: 1.27; 
Percent change[ A]: Cam.: 3.84[B]; 
Percent change[ A]: Phila.: 0.03; 
Percent change[ A]: Comp.: 2.17. 

Percentage of population with a high school diploma (or equivalent); 
1990: Entire EZ: 42.79; 
1990: Cam.: 44.51; 
1990: Phila.: 42.21; 
1990: Comp.: 54.04; 
2000: Entire EZ: 51.82; 
2000: Cam.: 50.43; 
2000: Phila.: 52.38; 
2000: Comp.: 63.19; 
Percent change[ A]: Entire EZ: 9.04[B]; 
Percent change[ A]: Cam.: 5.92[B]; 
Percent change[ A]: Phila.: 10.17[B]; 
Percent change[ A]: Comp.: 9.15[B]. 

Percentage of high school dropouts; 
1990: Entire EZ: 25.58; 
1990: Cam.: 22.67; 
1990: Phila.: 26.56; 
1990: Comp.: 23.07; 
2000: Entire EZ: 16.02; 
2000: Cam.: 12.54; 
2000: Phila.: 17.25; 
2000: Comp.: 14.05; 
Percent change[ A]: Entire EZ: - 9.56[B]; 
Percent change[ A]: Cam.: -10.13[B]; 
Percent change[ A]: Phila.: -9.3[B]; 
Percent change[ A]: Comp.: -9.02[B]. 

Percentage of vacant housing units; 
1990: Entire EZ: 21.45; 
1990: Cam.: 22.37; 
1990: Phila.: 21.21; 
1990: Comp.: 14.48; 
2000: Entire EZ: 24.94; 
2000: Cam.: 25.7; 
2000: Phila.: 24.73; 
2000: Comp.: 14.91; 
Percent change[ A]: Entire EZ: 3.49[B]; 
Percent change[ A]: Cam.: 3.33[B]; 
Percent change[ A]: Phila.: 3.52[B]; 
Percent change[ A]: Comp.: 0.43. 

Average owner occupied housing value; 
1990: Entire EZ: $29,899; 
1990: Cam.: $35,076; 
1990: Phila.: $28,288; 
1990: Comp.: $42,045; 
2000: Entire EZ: $37,780; 
2000: Cam.: $39,398; 
2000: Phila.: $37,353; 
2000: Comp.: $51,159; 
Percent change[ A]: Entire EZ: 26.36[B]; 
Percent change[ A]: Cam.: 12.32[B]; 
Percent change[ A]: Phila.: 32.04[B]; 
Percent change[ A]: Comp.: 21.7[B]. 

Source: GAO analysis of Census data. 

Note: There are 18 census tracts in the designated area and 16 in the 
comparison area. Estimates for all census variables based on 
percentages had 95 percent confidence intervals of plus or minus 5 
percentage points or less. For the confidence intervals for average 
household income and average owner-occupied housing estimates, see 
appendix I. 

[A] Differences in poverty rate, unemployment rate, and other variables 
shown as percentages are based upon percentage point differences. 
Differences for average household income, population, individuals per 
square mile, and average housing value are calculated as percent 
changes. 

[B] The change in estimates from 1990 to 2000 is statistically 
significant. 

[End of table] 

Table 24: Changes in Selected Economic Growth Variables Observed in the 
Philadelphia-Camden EZ, the Camden (Cam.) and Philadelphia (Phila.) 
Portions, and the EZ Comparison Area (Comp.) 

Number of businesses; 
1995: Entire EZ: 2,064; 
1995: Cam.: 730; 
1995: Phila.: 1,334; 
1995: Comp.: 2,631; 
1999: Entire EZ: 2,078; 
1999: Cam.: 720; 
1999: Phila.: 1,358; 
1999: Comp.: 2,768; 
2004: Entire EZ: 2,150; 
2004: Cam.: 806; 
2004: Phila.: 1,344; 
2004: Comp.: 2,821; 
Percent change 1995- 2004[A]: Entire EZ: 4.17; 
Percent change 1995-2004[A]: Cam.: 10.41; 
Percent change 1995-2004[A]: Phila.: 0.75; 
Percent change 1995-2004[A]: Comp.: 7.22. 

Number of jobs; 
1995: Entire EZ: 35,867; 
1995: Cam.: 14,430; 
1995: Phila.: 21,437; 
1995: Comp.: 55,071; 
1999: Entire EZ: 35,904; 
1999: Cam.: 16,702; 
1999: Phila.: 19,202; 
1999: Comp.: 53,702; 
2004: Entire EZ: 36,789; 
2004: Cam.: 21,032; 
2004: Phila.: 15,757; 
2004: Comp.: 39,720; 
Percent change 1995-2004[A]: Entire EZ: 2.57; 
Percent change 1995-2004[A]: Cam.: 45.75; 
Percent change 1995-2004[A]: Phila.: -26.5; 
Percent change 1995-2004[A]: Comp.: -27.87. 

Source: GAO analysis of Claritas data. 

Note: There are 18 census tracts in the designated area and 16 in the 
comparison area. We excluded establishments that were not eligible for 
program tax benefits, such as nonprofit and governmental organizations, 
from our analysis of the change in the number of businesses. However, 
we included jobs at those businesses in our analysis of the change in 
the number of jobs. 

[A] Differences for the number of businesses and the number of jobs are 
calculated as percent changes. 

[End of table] 

Stakeholder Perceptions of the Factors Influencing Changes in Poverty, 
Unemployment, and Economic Growth: 

Stakeholders in the Philadelphia portion of the EZ mentioned the change 
in the zone's population as having an effect on the changes in the 
poverty and unemployment rates. They noted that the number of poor 
households had decreased, in part due to the HOPE VI housing program, 
which had demolished some area public housing, and in part because some 
individuals who had obtained jobs had also moved out of the zone 
neighborhoods. In addition, some stakeholders noted that the change in 
welfare policy over the course of the EZ/EC program had an effect on 
poverty and unemployment by moving former welfare recipients into jobs. 

In describing the changes in economic growth, Philadelphia stakeholders 
said that community lending institutions had provided loans to 
businesses and that the vacant lot improvement program had helped 
retain and attract businesses to the EZ. However, one stakeholder noted 
that EZ lending had not resulted in many new jobs. 

EZ stakeholders in the Camden portion of the EZ said that EZ programs 
such as the Battleship New Jersey and housing and after school 
initiatives may have contributed to the slight decrease in poverty 
rate. They also noted that there had probably been a change in the EZ 
population, since Camden's population was transient and individuals 
often left the area when they found a job. In addition, some 
stakeholders also mentioned changes in the national economy and a high 
homeless population as challenges to improving the area's poverty and 
unemployment rates. 

Camden stakeholders noted that the EZ had influenced economic growth 
through the improvements it had made to the physical appearance of 
certain commercial corridors, as well as through its loans and grants 
to small businesses. Stakeholders also said that the development of 
market-rate housing had helped to increase the customer base for local 
businesses. Finally, one stakeholder said that the state's expansion of 
the light rail to Camden had influenced economic growth by improving 
transportation to the area. 

Cleveland Empowerment Zone: 

Figure 27: Map of the Cleveland EZ and Its Comparison Area: 

[See PDF for image] 

Source: GAO analysis of Census and HUD data. 

[End of figure] 

HUD initially designated Cleveland as a Supplemental EZ, which provided 
it with Economic Development Initiative grants and Section 108 Loan 
Guarantees rather than EZ/EC grant funds. The area received full Round 
I EZ status in 1998, and businesses in the EZ could claim the program 
tax benefits starting in 2000. 

How the EZ Was Governed: 

The EZ was operated by the city of Cleveland Department of Economic 
Development and included the Community Advisory Committee, an advisory 
board made up of EZ residents, business owners, bank representatives, 
and representatives from four local community development corporations. 
Although the Community Advisory Committee was involved in the decision- 
making process, the mayor and Cleveland City Council made all final 
decisions about EZ funding. 

Activities the EZ Implemented: 

Cleveland EZ stakeholders said that they focused mainly on economic 
development activities, largely because of the type of benefits they 
received with the Supplemental EZ designation.[Footnote 83] 
Stakeholders explained that most of their funds had been used to fund 
loans to EZ businesses. They also said that the EZ also worked to build 
the capacity of four community development corporations helping each of 
them complete a major project in their neighborhood--for example, the 
Quincy Place building in the Fairfax neighborhood (fig. 28). EZ 
stakeholders noted that the EZ had implemented some successful job 
training programs. The EZ received an extension of its grants and loan 
guarantees through 2009. 

Figure 28: Activity Implemented by the Cleveland EZ: 

[See PDF for image] 

Source: GAO. 

Note: We were not able to determine specific types of activities the 
Cleveland EZ implemented, because reliable data were not available. 

[End of figure] 

Changes in Poverty, Unemployment, and Economic Growth: 

The Cleveland EZ experienced positive changes in poverty, unemployment, 
and one measure of economic growth. From 1990 to 2000, Cleveland had 
one of the sharpest reductions in both poverty and unemployment of the 
urban EZs, and these changes outpaced those of its comparison area. 
Between 1995 and 2004, the EZ experienced an increase in economic 
growth as measured by the number of businesses, while its comparison 
area experienced a decrease. However, the EZ experienced a decrease in 
the number of jobs that was greater than the decrease experienced in 
its comparison area. Tables 25 and 26 show the changes in poverty, 
unemployment, and economic growth in the EZ and its comparison area. 
Table 25 also includes data on the changes in other variables included 
in our models. 

Table 25: Changes in Selected Census Variables Observed in the 
Cleveland EZ and Its Comparison Area: 

Poverty rate (%); 
1990: EZ: 46.85; 
1990: Comparison: 39.41; 
2000: EZ: 36.03; 
2000: Comparison: 35.70; 
Percent change[A]: EZ: -10.82[B]; 
Percent change[A]: Comparison: -3.71[B]. 

Unemployment rate (%); 
1990: EZ: 25.44; 
1990: Comparison: 20.63; 
2000: EZ: 15.48; 
2000: Comparison: 17.29; 
Percent change[A]: EZ: -9.96[B]; 
Percent change[A]: Comparison: -3.34[B]. 

Average household income; 
1990: EZ: $20,535; 
1990: Comparison: $24,688; 
2000: EZ: $28,781; 
2000: Comparison: $30,311; 
Percent change[A]: EZ: 40.16[B]; 
Percent change[A]: Comparison: 22.78[B]. 

Percentage of single female headed households with children; 
1990: EZ: 19.07; 
1990: Comparison: 23.24; 
2000: EZ: 18.24; 
2000: Comparison: 23.19; 
Percent change[A]: EZ: -0.83; 
Percent change[A]: Comparison: -0.05. 

Total population; 
1990: EZ: 50,724; 
1990: Comparison: 153,578; 
2000: EZ: 43,694; 
2000: Comparison: 141,465; 
Percent change[A]: EZ: -13.86; 
Percent change[A]: Comparison: -7.89. 

Total individuals per square mile; 
1990: EZ: 8,319; 
1990: Comparison: 7,231; 
2000: EZ: 7,168; 
2000: Comparison: 6,532; 
Percent change[A]: EZ: -13.84; 
Percent change[A]: Comparison: 
-9.66. 

Percentage of households that moved in the last 5 years; 
1990: EZ: 36.13; 
1990: Comparison: 36.04; 
2000: EZ: 39.55; 
2000: Comparison: 39.70; 
Percent change[A]: EZ: 3.41[B]; 
Percent change[A]: Comparison: 3.66[B]. 

Percentage of population of working age (16-64); 
1990: EZ: 55.01; 
1990: Comparison: 56.80; 
2000: EZ: 53.08; 
2000: Comparison: 55.54; 
Percent change[A]: EZ: -1.93; 
Percent change[A]: Comparison: -1.26. 

Percentage of population with a high school diploma (or equivalent); 
1990: EZ: 47.14; 
1990: Comparison: 54.84; 
2000: EZ: 61.82; 
2000: Comparison: 65.13; 
Percent change[A]: EZ: 14.68[B]; 
Percent change[A]: Comparison: 10.29[B]. 

Percentage of high school dropouts; 
1990: EZ: 18.35; 
1990: Comparison: 14.83; 
2000: EZ: 13.30; 
2000: Comparison: 16.38; 
Percent change[A]: EZ: -5.05[B]; 
Percent change[A]: Comparison: 1.55[B]. 

Percentage of vacant housing units; 
1990: EZ: 14.68; 
1990: Comparison: 14.70; 
2000: EZ: 18.82; 
2000: Comparison: 15.13; 
Percent change[A]: EZ: 4.14[B]; 
Percent change[A]: Comparison: 0.43. 

Average owner occupied housing value; 
1990: EZ: $38,071; 
1990: Comparison: $46,972; 
2000: EZ: $75,186; 
2000: Comparison: $70,164; 
Percent change[A]: EZ: 97.49[B]; 
Percent change[A]: Comparison: 49.37[B]. 

Source: GAO analysis of Census data. 

Note: There are 32 census tracts in the designated area and 68 in the 
comparison area. Estimates for all census variables based on 
percentages had 95 percent confidence intervals of plus or minus 5 
percentage points or less. For the confidence intervals for average 
household income and average owner-occupied housing estimates, see 
appendix I. 

[A] Differences in poverty rate, unemployment rate, and other variables 
shown as percentages are based upon percentage point differences. 
Differences for average household income, population, individuals per 
square mile, and average housing value are calculated as percent 
changes. 

[B] The change in estimates from 1990 to 2000 is statistically 
significant. 

[End of table] 

Table 26: Changes in Selected Economic Growth Variables Observed in the 
Cleveland EZ and Its Comparison Area: 

Number of businesses; 
1995: EZ: 1,766; 
1995: Comparison: 4,883; 
1999: EZ: 2,067; 
1999: Comparison: 4,889;  
2004: EZ: 1,899; 
2004: Comparison: 4,602; 
Percent change 1995-2004[A]: EZ: 7.53; 
Percent change 1995-2004[A]: Comparison: -5.75. 

Number of jobs; 
1995: EZ: 42,087; 
1995: Comparison: 87,334; 
1999: EZ: 58,679; 
1999: Comparison: 102,996; 
2004: EZ: 38,023; 
2004: Comparison: 84,064; 
Percent change 1995-2004[A]: EZ: - 9.66; 
Percent change 1995-2004[A]: Comparison: -3.74. 

Source: GAO analysis of Claritas data. 

Note: There are 32 census tracts in the designated area and 68 in the 
comparison area. We excluded establishments that were not eligible for 
program tax benefits, such as nonprofit and governmental organizations, 
from our analysis of the change in the number of businesses. However, 
we included jobs at those businesses in our analysis of the change in 
the number of jobs. 

[A] Differences for the number of businesses and the number of jobs are 
calculated as percent changes. 

[End of table] 

Stakeholder Perceptions of the Factors Influencing Changes in Poverty, 
Unemployment, and Economic Growth: 

When asked about factors that had affected the changes observed in the 
Cleveland EZ, stakeholders said that factors related to poverty and 
unemployment were intertwined. For example, EZ stakeholders felt that 
EZ training programs had helped prepare residents for jobs, potentially 
affecting both poverty and unemployment. Stakeholders also cited 
changes in the zone population that had affected both factors, noting 
that as residents obtained jobs, they left the zone, and that some 
individuals with higher incomes had moved in, particularly in areas 
where new housing had been built. EZ stakeholders also mentioned the 
effect of general economic trends on poverty and unemployment. 

In terms of economic growth, EZ stakeholders noted that the majority of 
the businesses that had received EZ loans were still operating, that 
the number of businesses had increased in some areas of the EZ, and 
that these businesses had brought new jobs to the community. However, 
some EZ stakeholders commented that the EZ's strict underwriting 
standards made it less successful in helping new or less sophisticated 
businesses. In addition, although the EZ had helped to create some 
jobs, some stakeholders felt that the jobs created were going to new 
residents rather than to original EZ residents. EZ staff also observed 
that regional trends such as the overall loss of jobs in the city of 
Cleveland had an effect on economic growth in the Cleveland EZ. 

Los Angeles Empowerment Zone: 

Figure 29: Map of the Los Angeles EZ and Its Comparison Area: 

[See PDF for image] 

Source: GAO analysis of Census and HUD data. 

[End of figure] 

HUD initially designated Los Angeles as a Supplemental EZ, which 
provided it with Economic Development Initiative grants and Section 108 
Loan Guarantees rather than EZ/EC grant funds. The area received full 
Round I EZ status in 1998, and businesses in the EZ could claim the 
program tax benefits starting in 2000. 

How the EZ Was Governed: 

The Los Angeles EZ created the Los Angeles Community Development Bank 
to administer its EZ program. The Community Development Bank was a 
"wholesale" rather than a conventional bank that entered into 
partnerships with other economic development entities that were already 
delivering services and operating loan programs. The EZ was autonomous 
from the city and had its own board of directors predominately made up 
of private sector members with one seat for a community representative. 
However, EZ stakeholders told us that this seat usually remained 
vacant. The board had a committee structure that included an audit 
committee, credit committee, and venture capital committee. The EZ 
board made all funding decisions. Any transaction over $1 million 
required full board approval, but smaller amounts could be approved by 
a committee of the board. In an effort to involve community members, 
the city created an advisory council called EZ Oversight Committee, 
which was filled through appointments made by the mayor and county 
board of supervisors. However, EZ stakeholders said that the EZ 
oversight committee never had a formal role in decision making or 
oversight. 

Activities the EZ Implemented: 

Los Angeles EZ stakeholders said that they focused mainly on economic 
development activities, largely due to the type of benefits they 
received with the Supplemental EZ designation.[Footnote 84] 
Stakeholders noted that the job requirements attached to loans from the 
EZ and the six tax-exempt bonds had helped create jobs in the zone 
(fig. 30). In addition to providing loans to several businesses, the EZ 
helped fund a shopping complex and other development. One stakeholder 
felt the EZ did not lend enough funds to small businesses and pointed 
out that some of the loans to large businesses, such as a large dairy, 
had defaulted. The EZ bank filed for bankruptcy in 2002 due to a high 
level of loan defaults and the remaining funds were transferred to the 
city of Los Angeles. The city of Los Angeles received an extension for 
the grant and loan guarantees through 2009. 

Figure 30: Activity Implemented by the Los Angeles EZ: 

[See PDF for image] 

Source: GAO. 

[End of figure] 

Changes in Poverty, Unemployment, and Economic Growth: 

Unlike the other EZs, both poverty and unemployment in the Los Angeles 
EZ largely remained the same between 1990 and 2000, and measures of 
economic growth declined from 1995 to 2004. The comparison area also 
saw little change in poverty and unemployment, but economic growth in 
the comparison area increased in that time period. Tables 27 and 28 
show the changes in poverty, unemployment, and economic growth in the 
EZ and its comparison area. Table 27 also includes data on the changes 
in other variables included in our models. 

Table 27: Changes in Selected Census Variables Observed in the Los 
Angeles EZ and Its Comparison Area: 

Poverty rate (%); 
1990: EZ: 40.24; 
1990: Comparison: 31.52; 
2000: EZ: 41.49; 
2000: Comparison: 33.14; 
Percent change[A]: EZ: 1.25; 
Percent change[A]: Comparison: 1.61. 

Unemployment rate (%); 
1990: EZ: 18.39; 
1990: Comparison: 15.07; 
2000: EZ: 18.61; 
2000: Comparison: 15.47; 
Percent change[A]: EZ: 0.22; 
Percent change[A]: Comparison: 0.40. 

Average household income; 
1990: EZ: $28,801; 
1990: Comparison: $34,087; 
2000: EZ: $32,631; 
2000: Comparison: $37,843; 
Percent change[A]: EZ: 13.30[B]; 
Percent change[A]: Comparison: 11.02[B]. 

Percentage of single female headed households with children; 
1990: EZ: 18.32; 
1990: Comparison: 18.64; 
2000: EZ: 16.90; 
2000: Comparison: 17.40; 
Percent change[A]: EZ: -1.43[B]; 
Percent change[A]: Comparison: -1.24. 

Total population; 
1990: EZ: 211,365; 
1990: Comparison: 221,657; 
2000: EZ: 225,591; 
2000: Comparison: 219,001; 
Percent change[A]: EZ: 6.73; 
Percent change[A]: Comparison: -1.20. 

Total individuals per square mile; 
1990: EZ: 11,082; 
1990: Comparison: 12,918; 
2000: EZ: 11,836; 
2000: Comparison: 13,170; 
Percent change[A]: EZ: 6.81; 
Percent change[A]: Comparison: 1.95. 

Percentage of households that moved in the last 5 years; 
1990: EZ: 46.12; 
1990: Comparison: 44.06; 
2000: EZ: 43.53; 
2000: Comparison: 41.20; 
Percent change[A]: EZ: -2.59[B]; 
Percent change[A]: Comparison: -2.86[B]. 

Percentage of population of working age (16-64); 
1990: EZ: 57.60; 
1990: Comparison: 58.11; 
2000: EZ: 57.53; 
2000: Comparison: 57.28; 
Percent change[A]: EZ: -0.06; 
Percent change[A]: Comparison: -0.83. 

Percentage of population with a high school diploma (or equivalent); 
1990: EZ: 38.40; 
1990: Comparison: 50.30; 
2000: EZ: 37.46; 
2000: Comparison: 49.53; 
Percent change[A]: EZ: -0.94; 
Percent change[A]: Comparison: -0.77. 

Percentage of high school dropouts; 
1990: EZ: 32.14; 
1990: Comparison: 23.50; 
2000: EZ: 23.09; 
2000: Comparison: 17.04; 
Percent change[A]: EZ: -9.04[B]; 
Percent change[A]: Comparison: -6.46[B]. 

Percentage of vacant housing units; 
1990: EZ: 6.33; 
1990: Comparison: 6.31; 
2000: EZ: 9.65; 
2000: Comparison: 8.11; 
Percent change[A]: EZ: 3.31[B]; 
Percent change[A]: Comparison: 1.80[B]. 

Average owner occupied housing value; 
1990: EZ: $141,665; 
1990: Comparison: $160,090; 
2000: EZ: $156,493; 
2000: Comparison: $165,180; 
Percent change[A]: EZ: 10.47[B]; 
Percent change[A]: Comparison: 3.18[B]. 

Source: GAO analysis of Census data. 

Note: There are 41 census tracts in the designated area and 43 in the 
comparison area. Estimates for all census variables based on 
percentages had 95 percent confidence intervals of plus or minus 5 
percentage points or less. For the confidence intervals for average 
household income and average owner-occupied housing estimates, see 
appendix I. 

[A] Differences in poverty rate, unemployment rate, and other variables 
shown as percentages are based upon percentage point differences. 
Differences for average household income, population, individuals per 
square mile, and average housing value are calculated as percent 
changes. 

[B] The change in estimates from 1990 to 2000 is statistically 
significant. 

[End of table] 

Table 28: Changes in Selected Economic Growth Variables Observed in the 
Los Angeles EZ and Its Comparison Area: 

Number of businesses; 
1995: EZ: 15,746; 
1995: Comparison: 4,248; 
1999: EZ: 12,315; 
1999: Comparison: 3,986; 
2004: EZ: 13,853; 
2004: Comparison: 4,662; 
Percent change 1995-2004[A]: EZ: -12.02; 
Percent change 1995-2004[A]: Comparison: 9.75. 

Number of jobs; 
1995: EZ: 165,457; 
1995: Comparison: 52,973; 
1999: EZ: 153,340; 
1999: Comparison: 55,627; 
2004: EZ: 156,793; 
2004: Comparison: 66,783; 
Percent change 1995- 2004[A]: EZ: -5.24; 
Percent change 1995-2004[A]: Comparison: 26.07. 

Source: GAO analysis of Claritas data. 

Note: There are 41 census tracts in the designated area and 43 in the 
comparison area. We excluded establishments that were not eligible for 
program tax benefits, such as nonprofit and governmental organizations, 
from our analysis of the change in the number of businesses. However, 
we included jobs at those businesses in our analysis of the change in 
the number of jobs. 

[A] Differences for the number of businesses and the number of jobs are 
calculated as percent changes. 

[End of table] 

Stakeholder Perceptions of the Factors Influencing Changes in Poverty, 
Unemployment, and Economic Growth: 

Los Angeles EZ stakeholders we interviewed suggested that the EZ was 
not as likely as other factors to have effected changes in poverty and 
unemployment because they could not address those issues directly with 
the benefits they received. One stakeholder did not believe that the EZ 
had met its goals of increasing job training and employment 
opportunities, but other stakeholders believed that it had helped to 
assist and retain businesses and redevelop the area. Stakeholders 
mentioned external factors that influenced changes in poverty and 
unemployment, such as shifts in demographics with the influx of new 
immigrants and the outmigration of EZ residents as they obtained jobs 
or their incomes increased. In addition, some said that the EZ's high 
concentration of homeless individuals and the lack of available public 
transportation in the EZ could be additional reasons that poverty and 
unemployment rates did not improve. 

One stakeholder noted that, because the original strategic plan was 
designed to focus on social services, the census tracts chosen were not 
well-suited for economic development. However, stakeholders mentioned 
that the EZ had helped to stabilize the area, since a large number of 
businesses had been leaving the Los Angeles area for advantages offered 
in other locations. 

Kentucky Highlands Empowerment Zone: 

Figure 31: Map of the Kentucky Highlands EZ: 

[See PDF for image] 

Source: GAO analysis of USDA data. 

[End of figure] 

How the EZ Was Governed: 

The EZ was managed by the Kentucky Highlands Investment Corporation, a 
nonprofit that had been operating in the area for over 25 years. There 
were subzone boards in each of the three counties that became separate 
nonprofit entities and had funds to hire staff, manage the board, and 
conduct fiscal oversight. An overarching steering committee, which 
included representatives of the subzone boards, directed the EZ's 
activities in the entire zone by providing oversight, making financial 
decisions, and implementing certain activities, such as the revolving 
loan fund. EZ stakeholders suggested that most of the decision making 
occurred at the subzone level, although the steering committee gave 
final approval to all projects. The EZ used about half of the available 
funds, and the rest was distributed among the three subzones. 

Activities the EZ Implemented: 

Almost two-thirds of the EZ's activities involved community 
development. Initiatives involving business development and job 
training; resources for communities, youth and families; and education 
were the most common activities (fig. 32). In addition, each county 
implemented different types of activities from the strategic plan. For 
example, stakeholders from the Clinton County subzone funded a library, 
a learning center, and health care initiatives--such as helping to fund 
the expansion of an emergency room and surgical wing at the local 
hospital--and attracted businesses from the houseboat industry. 
Stakeholders from the Jackson County subzone said that they had 
provided funds for a community center, which housed vocational training 
classes and a community theatre. Stakeholders from the Wayne County 
subzone said that they completed a water infrastructure project that 
they said was critical to attracting businesses and brought in jobs in 
the houseboat industry. The Kentucky Highlands EZ received a grant 
extension until 2009. 

Figure 32: Activities Implemented by the Kentucky Highlands EZ: 

[See PDF for image] 

Sources: GAO (photo); GAO analysis of USDA data (charts). 

[End of figure] 

Changes in Poverty, Unemployment, and Economic Growth: 

Not only did the Kentucky Highlands EZ experience positive changes in 
all indicators, it experienced the largest decrease in unemployment 
between 1990 and 2000 and the largest increases in the number of 
businesses and jobs between 1995 and 2004 of any rural EZ. Tables 29 
and 30 show the changes in poverty, unemployment, and economic growth 
in the EZ. Table 29 also includes data on the changes in other 
variables included in our models of the urban EZs. 

Table 29: Changes in Selected Census Variables Observed in the Kentucky 
Highlands EZ: 

Poverty rate (%); 
1990: 37.88; 
2000: 27.76; 
Percent change[A]: - 10.12[B]. 

Unemployment rate (%); 
1990: 9.76; 
2000: 7.75; 
Percent change[A]: - 2.01[B]. 

Average household income; 
1990: $23,304; 
2000: $31,064; 
Percent change[A]: 33.3[B]. 

Percentage of single female headed households with children; 
1990: 4.64; 
2000: 5.73; 
Percent change[A]: 1.09. 

Total population; 
1990: 27,212; 
2000: 30,464; 
Percent change[A]: 11.95. 

Total individuals per square mile; 
1990: 36; 
2000: 40; 
Percent change[A]: 11.96. 

Percentage of households that moved in the last 5 years; 
1990: 32.46; 
2000: 31.45; 
Percent change[A]: -1.01. 

Percentage of population of working age (16-64); 
1990: 59.04; 
2000: 61.98; 
Percent change[A]: 2.93[B]. 

Percentage of population with a high school diploma (or equivalent); 
1990: 42.82; 
2000: 55.1; 
Percent change[A]: 12.28[B]. 

Percentage of high school dropouts; 
1990: 15.80; 
2000: 16.47; 
Percent change[A]: 0.67. 

Percentage of vacant housing units; 
1990: 16.74; 
2000: 18.97; 
Percent change[A]: 2.23[B]. 

Average owner occupied housing value; 
1990: $43,392; 
2000: $65,815; 
Percent change[A]: 51.68[B]. 

Source: GAO analysis of Census data. 

Note: There are seven census tracts in the designated area; 
we did not use comparison areas for rural EZs. For more information on 
our methodology, see appendix I. Estimates for all census variables 
based on percentages had 95 percent confidence intervals of plus or 
minus 5 percentage points or less. For the confidence intervals for 
average household income and average owner-occupied housing estimates, 
see appendix I. 

[A] Differences in poverty rate, unemployment rate, and other variables 
shown as percentages are based upon percentage point differences. 
Differences for average household income, population, individuals per 
square mile, and average housing value are calculated as percent 
changes. 

[B] The change in estimates from 1990 to 2000 is statistically 
significant. 

[End of table] 

Table 30: Changes in Selected Economic Growth Variables Observed in the 
Kentucky Highlands EZ: 

Number of businesses; 
1995: 609; 
1999: 691; 
2004: 810; 
Percent change: 1995-2004[A]: 33. 

Number of jobs; 
1995: 5,327; 
1999: 7,691; 
2004: 8,941; 
Percent change: 1995-2004[A]: 67.84. 

Source: GAO analysis of Claritas data. 

Note: There are seven census tracts in the designated area; 
we did not use comparison areas for rural EZs. For more information on 
our methodology, see appendix I. We excluded establishments that were 
not eligible for program tax benefits, such as nonprofit and 
governmental organizations, from our analysis of the change in the 
number of businesses. However, we included jobs at those businesses in 
our analysis of the change in the number of jobs. 

[A] Differences for the number of businesses and the number of jobs are 
calculated as percent changes. 

[End of table] 

Stakeholder Perceptions of the Factors Influencing Changes in Poverty, 
Unemployment, and Economic Growth: 

In our interviews, stakeholders said that changes in the poverty rate 
may have been the result of new jobs created by EZ projects, many of 
which offered benefits such as health insurance that helped to 
stabilize families. However, EZ staff and other stakeholders 
acknowledged that external factors, such as welfare reform and general 
economic trends, also could have contributed to poverty reduction. 
Stakeholders also attributed the reduction in unemployment to the job 
creation efforts, saying that the EZ had helped stabilize the area when 
a key employer, a sewing plant, closed prior to designation. 

In terms of economic growth, stakeholders felt that the EZ had played a 
role in the change in economic growth, citing infrastructure 
improvements and zone workshops on how to start new businesses. In 
addition, some EZ stakeholders noted that the economic growth that had 
occurred was due in part to the EZ program tax benefits, although not 
all stakeholders agreed. 

Mid-Delta Mississippi Empowerment Zone: 

Figure 33: Map of the Mid-Delta EZ: 

[See PDF for image] 

Source: GAO analysis of USDA data. 

[End of figure] 

How the EZ Was Governed: 

The nonprofit Mid-Delta Empowerment Zone Alliance was created to manage 
the EZ. It included a board that consisted of city and county elected 
officials and representatives from community organizations, plus 
subzones boards in each of the six counties. Most decisions were made 
by the committees and brought to the full board for approval. However, 
several stakeholders noted that this formal process was not always 
followed and that some board decisions appeared to favor large 
businesses over community groups. 

Activities the EZ Implemented: 

Most of the activities the Mid-Delta EZ implemented were related to 
community development. Initiatives involving business development and 
job training; resources for communities, youth, and families; 
education; and housing accounted for the bulk of the activities (fig. 
34). In our interviews, stakeholders noted that EZ funds were used for 
a variety of community-and family-oriented projects. These included 
helping a small municipality purchase needed police and fire equipment, 
partially funding a mortgage assistance program that moved 20 people 
into houses, and implementing some health care programs, such as a 
substance abuse treatment center for women. Also, one business in the 
Mid-Delta EZ used a program tax-exempt bond. In addition, stakeholders 
mentioned that EZ funds were used to attract major corporations, such 
as an automobile parts manufacturer and retail distribution center. 
However, several stakeholders also noted that some programs were 
unsuccessful, and an EZ official said that approximately 16 projects 
were under review for possible misuse of funds. The Mid-Delta EZ 
received a grant extension until 2009. 

Figure 34: Activities Implemented by the Mid-Delta EZ: 

[See PDF for image] 

Sources: GAO (photo); GAO analysis of USDA data (charts). 

[End of figure] - graphic text: 

Changes in Poverty, Unemployment, and Economic Growth: 

The Mid-Delta EZ saw positive changes in two indicators: poverty and 
economic growth. Between 1990 and 2000, the poverty rate in the Mid- 
Delta EZ decreased more than any rural EZ. However, the Mid-Delta EZ 
experienced a small increase in unemployment over that time period. For 
economic growth, the EZ saw an increase in both measures from 1995 to 
2004, but the changes were significantly less than in the other two 
rural EZs. Tables 31 and 32 show the changes in poverty, unemployment, 
and economic growth in the EZ. Table 31 also includes data on the 
changes in other variables included in our models of the urban EZs. 

Table 31: Changes in Selected Census Variables Observed in the Mid- 
Delta EZ: 

Poverty rate (%); 
1990: 46.35; 
2000: 35.67; 
Percent change[A]: - 10.68[B]. 

Unemployment rate (%); 
1990: 14.31; 
2000: 17.38; 
Percent change[A]: 3.07[B]. 

Average household income; 
1990: $25,872; 
2000: $3,559; 
Percent change[A]: 37.44[B]. 

Percentage of single female headed households with children; 
1990: 16.51; 
2000: 17.31; 
Percent change[A]: 0.80. 

Total population; 
1990: 29,494; 
2000: 29,770; 
Percent change[A]: 0.94. 

Total individuals per square mile; 
1990: 30.06; 
2000: 30.34; 
Percent change[A]: 0.95. 

Percentage of households that moved in the last 5 years; 
1990: 34.75; 
2000: 31.00; 
Percent change[A]: -3.75[B]. 

Percentage of population of working age (16-64); 
1990: 51.71; 
2000: 57.36; 
Percent change[A]: 5.65[B]. 

Percentage of population with a high school diploma (or equivalent); 
1990: 49.09; 
2000: 60.52; 
Percent change[A]: 11.43[B]. 

Percentage of high school dropouts; 
1990: 14.66; 
2000: 12.62; 
Percent change[A]: -2.04[B]. 

Percentage of vacant housing units; 
1990: 8.08; 
2000: 9.41; 
Percent change[A]: 1.33. 

Average owner occupied housing value; 
1990: $50,061; 
2000: $66,872; 
Percent change[A]: 33.58[B]. 

Source: GAO analysis of Census data. 

Note: There are eight census tracts in the designated area; 
we did not use comparison areas for rural EZs. For more information on 
our methodology, see appendix I. Estimates for all census variables 
based on percentages had 95 percent confidence intervals of plus or 
minus 5 percentage points or less. For the confidence intervals for 
average household income and average owner-occupied housing estimates, 
see appendix I. 

[A] Differences in poverty rate, unemployment rate, and other variables 
shown as percentages are based upon percentage point differences. 
Differences for average household income, population, individuals per 
square mile, and average housing value are calculated as percent 
changes. 

[B] The change in estimates from 1990 to 2000 is statistically 
significant. 

[End of table] 

Table 32: Changes in Selected Economic Growth Variables Observed in the 
Mid-Delta EZ: 

Number of businesses; 
1995: 634; 
1999: 838; 
2004: 733; 
Percent change 1995-2004[A]: 15.62. 

Number of jobs; 
1995: 9,415; 
1999: 12,694; 
2004: 9,884; 
Percent change 1995-2004[A]: 4.98. 

Source: GAO analysis of Claritas data. 

Note: There are eight census tracts in the designated area; 
we did not use comparison areas for rural EZs. For more information on 
our methodology, see appendix I. We excluded establishments that were 
not eligible for program tax benefits, such as nonprofit and 
governmental organizations, from our analysis of the change in the 
number of businesses. However, we included jobs at those businesses in 
our analysis of the change in the number of jobs. 

[A] Differences for the number of businesses and the number of jobs are 
calculated as percent changes. 

[End of table] 

Stakeholder Perceptions of the Factors Influencing Changes in Poverty, 
Unemployment, and Economic Growth: 

In our interviews, EZ stakeholders credited the EZ with improving 
poverty and unemployment by helping bring in higher paying jobs with 
benefits. However, some suggested that increases in unemployment were 
not the same for each county, and added that the Mississippi Delta 
region overall had a low educational level that limited some residents' 
ability to participate in the workforce. 

In terms of economic growth, EZ stakeholders noted the EZ's efforts to 
attract large businesses through grants and loans had brought in new 
companies that provided jobs with relatively high wages and benefits. 
One stakeholder said that the EZ's efforts helped to stabilize the area 
during a period when several large manufacturing plants relocated to 
other countries. 

Rio Grande Valley, Texas Empowerment Zone: 

Figure 35: Map of the Rio Grande Valley EZ: 

[See PDF for image] 

Source: GAO analysis of USDA data. 

[End of figure] 

How the EZ Was Governed: 

The EZ was managed by the nonprofit Rio Grande Valley Empowerment Zone, 
which was created specifically for the EZ. EZ stakeholders explained 
that the EZ board included an executive committee of members 
representing each of the four counties in the EZ and four subzone 
boards, one for each county. Both the EZ and the subzone boards were 
involved in selecting activities for implementation. Subzone members 
reviewed proposals and then forwarded their recommendations to a 
project review committee, which reviewed the activities for feasibility 
and sustainability. Once this process was complete, the activity was 
sent to the full board for approval. 

Activities the EZ Implemented: 

The Rio Grande Valley EZ implemented mostly community development 
activities, most commonly education, public infrastructure, and 
business development and job training initiatives (fig. 36). In our 
interviews, stakeholders mentioned that the EZ had provided funds to 
several projects sponsored by the school districts, focusing its 
funding on improving the well-being of children. For example, the EZ 
provided computers and technical assistance to local Boys and Girls 
Clubs. EZ stakeholders also cited several infrastructure improvements, 
such as a water plant, a water tower, the expansion of a fire 
department facility, and the creation of community centers. In terms of 
economic opportunity initiatives, three counties provided loans through 
a revolving loan program, and one county created a small business 
incubator. In addition, the EZ provided funding to a community-based 
organization to provide low-skilled workers with training for jobs in 
the health care field. 

Figure 36: Activities Implemented by the Rio Grande Valley EZ: 

[See PDF for image] 

Sources: GAO (photo); GAO analysis of USDA data (charts). 

[End of figure] 

Changes in Poverty, Unemployment, and Economic Growth: 

The Rio Grande Valley EZ experienced positive changes in poverty and 
economic growth. The EZ had the highest poverty and unemployment rates 
in 1990 of any of the rural EZs. Between 1990 and 2000, the EZ 
experienced a decrease in poverty; 
however, the unemployment rate did not show a significant change. For 
economic growth, the EZ experienced an increase in the number of 
businesses and jobs between 1995 and 2004. Tables 33 and 34 show the 
changes in poverty, unemployment, and economic growth in the EZ. Table 
33 also includes data on the changes in other variables included in our 
models of the urban EZs. 

Table 33: Changes in Selected Census Variables Observed in the Rio 
Grande Valley EZ: 

Poverty rate (%); 
1990: 49.65; 
2000: 42.34; 
Percent change[A]: - 7.31[B]. 

Unemployment rate (%); 
1990: 14.94; 
2000: 13.82; 
Percent change[A]: - 1.12. 

Average household income; 
1990: $25,093; 
2000: $32,763; 
Percent change[A]: 30.57[B]. 

Percentage of single female headed households with children; 
1990: 9.44; 
2000: 10.38; 
Percent change[A]: 0.95. 

Total population; 
1990: 29,817; 
2000: 37,044; 
Percent change[A]: 24.24. 

Total individuals per square mile; 
1990: 131; 
2000: 159; 
Percent change[A]: 21.82. 

Percentage of households that moved in the last 5 years; 
1990: 30.11; 
2000: 34.41; 
Percent change[A]: 4.29[B]. 

Percentage of population of working age (16-64); 
1990: 55.47; 
2000: 55.48; 
Percent change[A]: 0.01. 

Percentage of population with a high school diploma (or equivalent); 
1990: 41.51; 
2000: 46.80; 
Percent change[A]: 5.29[B]. 

Percentage of high school dropouts; 
1990: 20.1; 
2000: 16.38; 
Percent change[A]: -3.72[B]. 

Percentage of vacant housing units; 
1990: 14.37; 
2000: 16.80; 
Percent change[A]: 2.43[B]. 

Average owner occupied housing value; 
1990: $46,100; 
2000: $61,450; 
Percent change[A]: 33.3[B]. 

Source: GAO analysis of Census data. 

Note: There are six census tracts in the designated area; 
we did not use comparison areas for rural EZs. For more information on 
our methodology, see appendix I. Estimates for all census variables 
based on percentages had 95 percent confidence intervals of plus or 
minus 5 percentage points or less. For the confidence intervals for 
average household income and average owner-occupied housing estimates, 
see appendix I. 

[A] Differences in poverty rate, unemployment rate, and other variables 
shown as percentages are based upon percentage point differences. 
Differences for average household income, population, individuals per 
square mile, and average housing value are calculated as percent 
changes. 

[B] The change in estimates from 1990 to 2000 is statistically 
significant. 

[End of table] 

Table 34: Changes in Selected Economic Growth Variables Observed in the 
Rio Grande Valley EZ: 

Number of businesses; 
1995: 551; 
1999: 688; 
2004: 710; 
Percent change 1995-2004[A]: 28.86. 

Number of jobs; 
1995: 6,025; 
1999: 6,548; 
2004: 7,427; 
Percent change 1995-2004[A]: 23.27. 

Source: GAO analysis of Claritas data. 

Note: There are six census tracts in the designated area; 
we did not use comparison areas for rural EZs. For more information on 
our methodology, see appendix I. We excluded establishments that were 
not eligible for program tax benefits, such as nonprofit and 
governmental organizations, from our analysis of the change in the 
number of businesses. However, we included jobs at those businesses in 
our analysis of the change in the number of jobs. 

[A] Differences for the number of businesses and the number of jobs are 
calculated as percent changes. 

[End of table] 

Stakeholder Perceptions of the Factors Influencing Changes in Poverty, 
Unemployment, and Economic Growth: 

In our interviews, EZ stakeholders suggested that EZ programs may have 
helped to improve residents' quality of life through programs that 
provided employment opportunities or taught residents skills to improve 
their income. One stakeholder mentioned a health clinic that was 
partially funded by the EZ that had helped to provide additional jobs 
in the area. However, another stakeholder mentioned the large number of 
migrant farm workers in the area make tracking these changes difficult. 

In terms of changes in economic growth, EZ stakeholders noted the 
initial lack of public infrastructure in the zone and mentioned that 
the EZ infrastructure development helped to prepare the area for future 
economic development and growth. Stakeholders credited EZ activities 
with helping to attract tourism to areas of the EZ and said that 
efforts to help businesses through revolving loan funds in some of the 
EZ counties had fostered economic growth. Some EZ stakeholders added 
that some of the growth of cities surrounding the EZ also might be due 
to an increase in trade across the border with Mexico. 

Providence, Rhode Island Enterprise Community: 

Figure 37: Map of the Providence EC: 

[See PDF for image] 

Source: GAO analysis of HUD data. 

[End of figure] 

How the EC Was Governed: 

The Providence EC was managed by the nonprofit Providence Plan and 
included a board called the Oversight Committee that included EC 
residents from each neighborhood, small business owners, and two city 
council members. Unlike many of the EZs, the EC allocated most of its 
grant funds during the strategic planning process, so there were few 
funds for the board to approve during the course of the program. 
However, in those cases, the board reviewed background information on 
the organizations that requested funds, discussed the applicants at 
their meetings, and then chose the applicants to fund. The board also 
reviewed the routine reporting by subgrantees and participated in site 
visits. 

Activities the EC Implemented: 

The Providence EC implemented four types of activities--workforce 
development, assistance to businesses, access to capital, and human 
services--most of which were related to economic opportunity (fig. 38). 
According to stakeholders, the EC's largest subgrantee was a community 
development corporation, which implemented workforce training, a summer 
youth program, and business development programs. It also funded the 
renovation and development of some small business incubators that 
offered space and technical assistance to new small businesses. In 
addition, stakeholders noted that the EC implemented some Community 
Opportunity Zones, which were designed to provide integrated access to 
education, health, and social services for families with children. An 
EC official explained that most of the EC funds were spent in the first 
5 years of the program and that all EC funds had been spent by June 
2004. 

Figure 38: Activities Implemented by the Providence EC: 

[See PDF for image] 

Sources: GAO (photo); GAO analysis of HUD data (charts). 

[End of figure] 

Changes in Poverty, Unemployment, and Economic Growth: 

In the Providence EC, poverty and unemployment stayed about the same 
from 1990 to 2000 and the number of businesses and jobs decreased 
between 1995 and 2004.[Footnote 85] Tables 35 and 36 show the changes 
in poverty, unemployment, and economic growth in the EC. Table 35 also 
includes data on the changes in other variables included in our models 
of the urban EZs. 

Table 35: Changes in Selected Census Variables Observed in the 
Providence EC: 

Poverty rate (%); 
1990: 35.36; 
2000: 37.58; 
Percent change[A]: 2.22. 

Unemployment rate (%); 
1990: 13.63; 
2000: 11.90; 
Percent change[A]: - 1.73. 

Average household income; 
1990: $28,593; 
2000: $32,616; 
Percent change[A]: 14.07[B]. 

Percentage of single female headed households with children; 
1990: 21.64; 
2000: 21.96; 
Percent change[A]: 0.31. 

Total population; 
1990: 48,789; 
2000: 53,845; 
Percent change[A]: 10.36. 

Total individuals per square mile; 
1990: 9,179; 
2000: 10,141; 
Percent change[A]: 10.48. 

Percentage of households that moved in the last 5 years; 
1990: 52.23; 
2000: 50.85; 
Percent change[A]: -1.38. 

Percentage of population of working age (16-64); 
1990: 55.45; 
2000: 58.00; 
Percent change[A]: 2.55[B]. 

Percentage of population with a high school diploma (or equivalent); 
1990: 48.69; 
2000: 53.51; 
Percent change[A]: 4.82[B]. 

Percentage of high school dropouts; 
1990: 26.17; 
2000: 19.16; 
Percent change[A]: -7.01[B]. 

Percentage of vacant housing units; 
1990: 14.55; 
2000: 10.43; 
Percent change[A]: -4.13[B]. 

Average owner occupied housing value; 
1990: $124,339; 
2000: $116,698; 
Percent change[A]: -6.15[B]. 

Source: GAO analysis of Census data. 

Note: There are 13 census tracts in the designated area; 
we did not use comparison areas for individual ECs. For more 
information on our methodology, see appendix I. Estimates for all 
census variables based on percentages had 95 percent confidence 
intervals of plus or minus 5 percentage points or less. For the 
confidence intervals for average household income and average owner-
occupied housing estimates, see appendix I. 

[A] Differences in poverty rate, unemployment rate, and other variables 
shown as percentages are based upon percentage point differences. 
Differences for average household income, population, individuals per 
square mile, and average housing value are calculated as percent 
changes. 

[B] The change in estimates from 1990 to 2000 is statistically 
significant. 

[End of table] 

Table 36: Changes in Selected Economic Growth Variables Observed in the 
Providence EC: 

Number of businesses; 
1995: 2,714; 
1999: 2,426; 
2004: 2,200; 
Percent change 1995-2004[A]: -18.94. 

Number of jobs; 
1995: 37,724; 
1999: 34,763; 
2004: 33,545; 
Percent change 1995-2004[A]: -11.08. 

Source: GAO analysis of Claritas data. 

Note: There are 13 census tracts in the designated area; 
we did not use comparison areas for individual ECs. For more 
information on our methodology, see appendix I. We excluded 
establishments that were not eligible for the program tax benefit, such 
as nonprofit and governmental organizations, from our analysis of the 
change in the number of businesses. However, we included jobs at those 
businesses in our analysis of the change in the number of jobs. 

[A] Differences for the number of businesses and the number of jobs are 
calculated as percent changes. 

[End of table] 

Stakeholder Perceptions of the Factors Influencing Changes in Poverty, 
Unemployment, and Economic Growth: 

In our interviews, stakeholders cited several factors that they thought 
had influenced changes in poverty in the EC, including the increased 
costs of housing and utilities, growth in the foreign-born population, 
the loss of manufacturing jobs, and changes to welfare reform. In 
addition, one EC stakeholder noted that many residents were working but 
not earning high enough incomes to move them out of poverty. Although 
the EC experienced a decline in unemployment, stakeholders noted that 
barriers to employment remained, including limited job and language 
skills and records of incarceration. 

With respect to economic growth, EC stakeholders said that businesses 
began working together as a result of the EC. However, one stakeholder 
suggested that the EC was influenced by the slow Rhode Island economy 
and that the EC should have done more to foster economic growth. 

Fayette-Haywood, Tennessee Enterprise Community: 

Figure 39: Map of the Fayette-Haywood EC: 

[See PDF for image] 

Source: GAO analysis of USDA data. 

[End of figure] 

How the EC Was Governed: 

Three entities shared responsibility for operating the Fayette-Haywood 
EC. Haywood County administered the EC grant funds, a local development 
district was in charge of the EC's reporting to USDA, and a board that 
represented both counties in the EC made funding decisions.[Footnote 
86] To make decisions about what activities to fund, each county held 
separate meetings to discuss projects that pertained to their community 
and sought final approval at a meeting of the full board. EC 
stakeholders mentioned that USDA officials played an active role in the 
EC and attended most board meetings. 

Activities the EC Implemented: 

The majority of the activities implemented by the Fayette-Haywood EC 
were in community development, mainly in the areas of health care and 
housing (fig. 40). In our interviews, stakeholders mentioned benefits 
of the EC that included health care-related activities, such as 
recruiting doctors and nurses to the area and the reopening a medical 
clinic that had been closed for 10 years. Stakeholders also noted that 
new housing projects had been also built with the help of EC funds. The 
EC also conducted activities related to public infrastructure, such as 
helping to build a YMCA in Haywood County and other community centers 
in both counties. The EC did not request a grant extension, because it 
had used all of its grant funds. 

Figure 40: Activities Implemented by the Fayette-Haywood EC: 

[See PDF for image] 

Source: GAO (photo); GAO analysis of USDA data (charts). 

[End of figure] 

Changes in Poverty, Unemployment, and Economic Growth: 

The Fayette-Haywood EC experienced positive changes in poverty and 
unemployment between 1990 and 2000 and both measures of economic growth 
between 1995 and 2004. Tables 37 and 38 show the changes in poverty, 
unemployment, and economic growth in the EC. Table 37 also includes 
data on the changes in other variables included in our models of the 
urban EZs. 

Table 37: Changes in Selected Census Variables Observed in the Fayette- 
Haywood EC: 

Poverty rate (%); 
1990: 28.37; 
2000: 19.30; 
Percent change[A]: - 9.07[B]. 

Unemployment rate (%); 
1990: 9.75; 
2000: 7.02; 
Percent change[A]: - 2.73[B]. 

Average household income; 
1990: $32,560; 
2000: $45,353; 
Percent change[A]: 39.29[B]. 

Percentage of single female headed households with children; 
1990: 10.97; 
2000: 11.33; 
Percent change[A]: 0.36. 

Total population; 
1990: 29,080; 
2000: 30,551; 
Percent change[A]: 5.06. 

Total individuals per square mile; 
1990: 44; 
2000: 46; 
Percent change[A]: 5.07. 

Percentage of households that moved in the last 5 years; 
1990: 34.49; 
2000: 36.48; 
Percent change[A]: 1.99. 

Percentage of population of working age (16-64); 
1990: 55.39; 
2000: 58.82; 
Percent change[A]: 3.43[B]. 

Percentage of population with a high school diploma (or equivalent); 
1990: 53.57; 
2000: 65.81; 
Percent change[A]: 12.24[B]. 

Percentage of high school dropouts; 
1990: 18.26; 
2000: 12.77; 
Percent change[A]: -5.49[B]. 

Percentage of vacant housing units; 
1990: 6.46; 
2000: 6.85; 
Percent change[A]: 0.39. 

Average owner occupied housing value; 
1990: $68,945; 
2000: $103,619; 
Percent change[A]: 50.29[B]. 

Source: GAO analysis of Census data. 

Note: There are eight census tracts in the designated area; 
we did not use comparison areas for individual ECs. For more 
information on our methodology, see appendix I. Differences in poverty 
rate, unemployment rate, and other variables shown as percentages are 
based upon percentage point differences. Differences for average 
household income, population, individuals per square mile, and average 
housing value are calculated as percent changes. Estimates for all 
census variables based on percentages had 95 percent confidence 
intervals of plus or minus 5 percentage points or less. For the 
confidence intervals for average household income and average owner-
occupied housing estimates, see appendix I. 

[End of table] 

Table 38: Changes in Selected Economic Growth Variables Observed in the 
Fayette-Haywood EC: 

Number of businesses; 
1995: 892; 
1999: 921; 
2004: 1,128; 
Percent change 1995-2004[A]: 26.46. 

Number of jobs; 
1995: 9,556; 
1999: 10,128; 
2004: 11,240; 
Percent change 1995-2004[A]: 17.62. 

Source: GAO analysis of Claritas data. 

Note: There are eight census tracts in the designated area; 
we did not use comparison areas for individual ECs. For more 
information on our methodology, see appendix I. We excluded 
establishments that were not eligible for the program tax benefit, such 
as nonprofit and governmental organizations, from our analysis of the 
change in the number of businesses. However, we included jobs at those 
businesses in our analysis of the change in the number of jobs. 

[A] Differences for the number of businesses and the number of jobs are 
calculated as percent changes. 

[End of table] 

Stakeholder Perceptions of the Factors Influencing Changes in Poverty, 
Unemployment, and Economic Growth: 

In our interviews, stakeholders said that changes in the poverty rate 
may have been due to changes in demographics as higher-income residents 
from neighboring counties moved into the EC, which had lower property 
taxes. In addition, stakeholders suggested that EC residents benefited 
from new affordable housing partially funded by the EC. 

When discussing changes in unemployment and economic growth, 
stakeholders mentioned that one factor was the designated area's 
proximity to a growing city 25 miles away that provided additional job 
opportunities. In addition, stakeholders mentioned that the EC 
designation had helped the Haywood county government win grants to 
build infrastructure, such as a rail spur that attracted large 
industries to the EC. These industries offered jobs with higher wages 
and provided water lines with potable water for EC residents. 

[End of section] 

Appendix V: Comments from the Department of Health and Human Services: 

Department Of Health & Human Services: 
Office of Inspector General: 
Washington, D.C. 20201: 

AUG 18 2006: 

Mr. William B. Shear: 
Director, Financial Markets and Community Investment: 
U.S. Government Accountability Office: 
Washington, DC 20548: 

Dear Mr. Shear: 

Enclosed are the Department's comments on the U.S. Government 
Accountability Office's (GAO) draft report entitled, "Empowerment Zone 
And Enterprise Community Program-Improvements Occurred in Communities, 
but the Effect of the Program Is Unclear" (GAO-06-727), before its 
publication. These comments represent the tentative position of the 
Department and are subject to reevaluation when the final version of 
this report is received. 

The Department provided several technical comments directly to your 
staff. 

The Department appreciates the opportunity to comment on this draft 
report before its publication. 

Sincerely, 

Signed by: 

Daniel R. Levinson: 
Inspector General: 

Enclosure: 

The Office of Inspector General (OIG) is transmitting the Department's 
response to this draft report in our capacity as the Department's 
designed focal point and coordinator for U.S. Government Accountability 
Office reports. OIG has not conducted an independent assessment of 
these comments and therefore expresses no opinion on them. 

Comments Of Department Of Health And Human Services On The U.S. 
Government Accountability Office's (GAO) Draft Report Entitled, 
"Empowerment Zone And Enterprise Community Program-Improvements 
Occurred In Communities, But The Effect Of The Program Is Unclear" (GAO-
06-727): 

The Department of Health and Human Services (HHS) appreciates the 
opportunity to comment on the U.S. Government Accountability Office's 
(GAO) draft report. 

GAO Observations: 

The EZ/EC program, one of the most recent large-scale federal programs 
aimed at revitalizing distressed urban and rural communities, resulted 
in a variety of activities intended to improve social and economic 
conditions in the nation's high poverty communities. As of March 31, 
2006, all but 1 S percent of the $1 billion in program grant funds 
provided to Round I communities had been expended, and the program is 
reaching its end. All three rounds of the EZIEC program are scheduled 
to end no later than December 31, 2009. However, given our findings 
from this evaluation of Round I EZs and ECs, the following observations 
should be considered if these or similar programs are authorized in the 
future. 

Based on our review, we found that oversight for Round I of the program 
was limited because the three agencies HHS, HUD, and USDA-did not 
collect data on how program funds were used, and HHS did not provide 
state and local entities with guidance sufficient to ensure monitoring 
of the program. These limitations maybe related in part to the design 
of the program, which offered increased flexibility in the use of funds 
and relied on multiple agencies for oversight. However, limited data 
and variation in monitoring hindered federal oversight efforts. 

In addition, the lack of data on the use of program grant funds, the 
extent of leveraging, and extent to which program tax benefits were 
used also limited our ability and the ability of others to evaluate the 
effect of the program. The lack of data on the use of tax benefits is 
of particular concern, since the estimated amount of the tax benefits 
was far greater than the amount of grant funds dedicated to the 
program. In response to the recommendation in our 2004 report, HUD, 
IRS, and USDA discussed options for collecting additional data on 
program tax benefits and determined two methods for collecting the 
information-through a national survey or the modification of tax forms. 

The three agencies, however, did not reach agreement on a cost- 
effective method for collecting the additional data. In our and others' 
prior attempts to obtain this information using surveys, survey 
response rates were low and thus did not produce reliable information 
on the use of program tax benefits. 

We acknowledge that the collection of additional tax data by IRS would 
introduce additional costs to both IRS and taxpayers. Nonetheless, a 
lack of data on tax benefits is significant given that subsequent 
rounds of the EZ/EC program and the Renewal Community program rely 
almost exclusively on tax benefits, and other federal economic 
development programs, such as the recent Gulf Opportunity Zone 
initiative, involve substantial amounts of tax benefits. Furthermore, 
the nation's current and projected fiscal imbalance serves to reinforce 
the importance of understanding the benefits of such tax expenditures. 
If Congress authorizes similar programs in the future that rely heavily 
on tax benefits, it would be prudent for federal agencies responsible 
for administering the program to collect information necessary to 
determine whether the tax benefits are effective in achieving program 
goals. 

HHS Comments: 

The GAO report of the EZ/EC program asserts that HHS did not provide 
States and designated communities with clear guidance regarding how to 
monitor the program. It also noted that HHS did not receive or review 
required financial data, nor specify or require that financial and 
program information should be segregated by specific activities. 

We believe the statement concerning monitoring unfairly represents the 
relationship between HHS and the other Federal agencies in the 
administration of the EZ/EC program. HHS is cited (on pages 20 and 21) 
along with HUD and USDA for not collecting data on how program funds 
were spent: "Although HHS regulations require States, EZs, and ECs to 
maintain fiscal control of program grant funds, the agency also did not 
provide guidance detailing the steps state and local authorities should 
take to monitor the program." Based on our review of a number of 
documents, including a Policy Memorandum from HUD dated July 16, 1996; 
Memoranda of Agreement between HUD and EZ/EC Communities; EZ/EC Terms 
and Conditions; an August 5, 1998, ACF Memorandum; and the Guidance for 
Auditors with respect to the EZ/EC program issued February 12, 1996, 
HUD and USDA are indicated as the lead Federal agencies with 
programmatic oversight and management responsibilities for the overall 
EZ/EC program, while HHS has fiscal responsibility for the EZ/EC Social 
Services Block Grant (SSBG) program. 

[End of section] 

Appendix VI: Comments from the Department of Housing and Urban 
Development: 

U.S. Department Of Housing And Urban Development: 
Washington, D.C. 20410-7000: 

Office Of The Assistant Secretary: 
For Community Planning And Development: 

AUG 17 2006: 

Mr. Charles Wilson: 
Assistant Director: 
Financial Markets & Community Investment Team: 
U.S. Government Accounting Office: 
441 G. Street, NW, Room 2B17: 
Washington, D.C. 20548: 

Dear Mr. Wilson: 

Thank you for the opportunity to review and comment on the Government 
Accounting Office's (GAO) proposed report entitled, Empowerment Zones 
and Enterprise Community Program: Improvements Occurred in Communities, 
but the Effect of the Program is Unclear (GAO-06-727). 

We disagree with GAO's observations that there was an "absence of data 
on the use of program grant funds, the amount of funds leveraged, and 
the use of tax benefits." While we acknowledge that the division of 
program authority and responsibilities among HUD, USDA, HHS and 
Treasury has resulted in several unforeseen and unintended 
consequences, HUD collected as much data as possible on the use of 
program grant funds and leveraging data in the Performance Measurement 
System. 

Thank you for your work on reviewing the Round I EZs/ECs. We believe 
that Appendix IV provides valuable information on changes to poverty, 
unemployment, and economic growth rates occurring in a sample of EZ/EC 
designated areas as well as stakeholders' perception of factors 
influencing those changes. This appendix through its onsite case 
studies will be useful in providing Congress an understanding of how 
Round I EZs/ECs were governed. 

Since many of our enclosed comments touch upon and, in certain 
instances, overlap in several report areas, we decided that for the 
sake of clarity, we would present them by the report's four objectives: 

1. Describes how the designated communities implemented Round I of EZ/ 
EC program. 

2. Evaluates the extent of federal, state, and local oversight of the 
program. 

3. Examines the extent to which data are available to assess the use of 
program tax benefits. 

4. Analyzes the effects that the Round I EZs and ECs had on poverty, 
unemployment, and economic growth in their communities. 

In you have questions or would like to discuss our comments, please 
contact Pamela Glekas, Director, Office of Community Renewal, at (202) 
708-6339. 

Sincerely yours, 

Signed by: 

Nelson R. Bregon: 
General Deputy Assistant Secretary For CPD: 

Enclosure: 

Comments to Draft Report # GAO-06-727, Entitled "Empowerment Zone And 
Enterprise Community Program, Improvements Occurred In Communities, But 
The Effect Of The Program Is Unclear" 

The following HUD comments on the above GAO draft report are 
categorized by the report's four objectives. 

Objective #1: Describes how the designated communities implemented 
Round I of EZ/EC program. 

* The report needs to make clear that each Round 11 urban EZs received 
a total amount of $25.6 million in EZ HUD grant funds plus tax 
incentives (much less than that of Round 1) and Round III urban EZs 
received no funding, but did receive tax incentives for their EZ 
businesses. Reference page l, end of the first paragraph of the report. 

* Four Key Principles: Highlighting the Round I achievements in meeting 
the key principles would provide Congress with a balanced view of the 
effectiveness of the Round I EZ/EC program that is more consistent with 
the statute. Reference CFR 24 Section 597.200 (c) (1)(2)(3) and (4). It 
would be useful for the report to address the Round I successes in 
meeting the four key principles of 1) economic development, 2) 
sustainable community development, 3) community-based partnerships and 
4) strategic vision for change. 

Highlighting the key principles also helps to characterize in a more 
positive light, one of the report's conclusions that in general, EZs 
and ECs used program grants to implement a larger number of community 
development activities in areas such as education, housing and 
infrastructure than economic opportunity activities, such as job 
training and assistance to businesses. Reference page 13 of the report. 

The report's subtle message is that Round I EZ/ECs inappropriately 
focused on community development rather than economic opportunity 
activities. The report does not point out that the key principles allow 
for a wide range of SSBG supported activities to be undertaken and that 
the implementation of community development activities meets the 
principle of sustainable community development. 

Recommendation: Given the fact that the four key principles are central 
to the development of the strategic plan, HUD believes an evaluation of 
how well the EZ/ECs did in meeting the key principles of the strategic 
plan would be a significant performance standard for GAO evaluation of 
the Round II and Round III EZs. Reference the Omnibus Budget 
Reconciliation Act of 1995, Sec. 1391 (f)(2)(A) through (F), and CFR 24 
Section 597.200 (c) of the Round I EZ/EC governing regulation. 

* Strategic Plan: Statutorily, the quality of an application's 
strategic plan was the essential document in the rating and ranking of 
EZ/EC applications and in the selection of the Round I designees. 
Because of the key principles significance and strong influence in the 
development of the strategic plan, a discussion in the report of the 
designees' success or lack thereof in meeting these principles would be 
a reasonable addition to the report. Examples of the urban Round I EZs 
and ECs successes in carrying out their programs are captured in HUD's 
2005 publication "Spotlight on Results." Section 6 of the publication 
consists of interviews with key administrators of Round I urban 
designated areas that demonstrate the achievements of these cities in 
empowering their residents to act on the four key principles. 

Reporting on how well Round I EZ/ECs did in implementing their 
strategic plan is also a critical factor in measuring Round I 
performance although the Community Renewal Tax Relief Act of 2000, Sec. 
1400J, requires a Congressional report only on the EZs/ECs impact on 
poverty, unemployment and economic growth. It is clear that GAO's Round 
I EZ/EC report evaluating the program performance in conjunction with 
these indices was not achievable. However, we disagree that this 
failure was due to a lack of program data but rather from a need for a 
more inclusive methodology, such as more emphasis on the strategic plan 
and a better method to track performance. 

* PERMS & Strategic Plans: In view of the GAO conclusion that it was 
unable to determine the impact because of the lack of financial data to 
effectively tie the projects/activities to the SSBG funding source, HUD 
recommends an approach to compensate for the lack of data on the use of 
program funds. This approach would be based on information emanating 
from the strategic plan and HUD's Performance Measurement System 
(PERMS), consisting of budgets, implementation plans (IPs) and annual 
reports. A separate discussion on PERMS is presented under Objective 
#2. 

Recommendation: HUD asserts that an assessment of designees' 
performance in carrying out their strategic plans be included in the 
reports on evaluating Round II and Round III EZs as a standard of 
performance. 

Objective #2: Evaluates the extent of federal, state, and local 
oversight of the program. 

* PERMS & Leveraging: A function of HUD's PERMS allows for ad hoc 
reports on a variety of funding and program analyses, including the 
extent and degree of leveraging occurring in Round I EZ/EC designated 
areas. For example in the 2001 reporting year alone, Round I urban EZs 
leveraged approximately $4.155 billion in non-Social Service Block 
Grants (SSBG) for $365 million in SSBG assisted projects in the EZ 
designated areas. Thus, for every $1 dollar SSBG investment there was 
approximately $11+ million in leveraged funds. 

Table #5 "Coding of Data Reliability of HUD/USDA Performance Systems." 
Reference page 55 of the report indicates that the systems received a 
code of #2.0 indicating, "items had strong documentation." This 
suggests a contradiction in information and that the leveraging 
information is more reliable than the last two sentences on page 55 
suggests. 

Recommendation: The amount of leveraging for each of the urban Round I 
EZs and ECs can be tracked through PERMS. HUD would like the 
opportunity to demonstrate how the information is recorded and tracked 
in PERMS and believe that a demonstration of this system may help to 
alleviate GAO's concern that "reliable data on the extent of leveraging 
were not available." Reference page 3, "Results in Brief' and page 55 
of the report. 

* HUD PERMS: HUD's belief is that the report did not adequately address 
PERMS and its role in HUD's oversight of the urban Round I EZs/ECs. 
PERMS primary function allows the user to enter transmit and share 
their program information easily and in a consistent fashion. HUD 
recommends that PERMS be fully addressed in the next GAO reports. Among 
PERMS informational and data elements are: executive summaries for 
field review and coordination with designees for needed review changes 
and additional information. For example, the summaries narrate the 
accomplishments and concerns in the areas of community-based 
partnerships, economic opportunity, sustainable community development 
and Round III and Renewal Community tax incentive utilization plans. 

Recommendation: HUD requests that the report acknowledge that HUD met 
its obligation to compile program data through PERMS (that includes a 
description of the activities implemented, program outputs and budgets 
for projects) and include a statement such as the following, in the 
final report on Round I EZs/ECs: 

PERMS allow Round I EZ/EC to submit annual reports on their progress in 
achieving goals, milestones, outputs, and implementing their activities 
and projects. Although PERMS is not considered a financial system per 
se, it is considered an informational management system consisting of 
elements useful for measuring program effectiveness. HUD is 
congressionally mandated to obtain performance reports from the EZs/ECs 
to evaluate their performance and to undertake program oversight/ 
monitoring. 

Furthermore, HUD notes that activities and the fiscal data discussed on 
page 20 of the report were HHS responsibility. Reference page 20 of the 
report. 

HUD also requests that the following statement be added to the final 
report: 

Reliability of PERMS Data: Designees are responsible for providing 
accurate and complete PERMS data that enables HUD to determine whether 
designees are carrying out its strategic plan. HUD relies on the 
designee's written assurances certifying that it will carry out its 
strategic plan in accordance with the provisions of CFR Section 597.200 
(c) and (d), including a certification that the designee will provide 
periodic reports on the use of and how funds will be allocated/ 
budgeted. 

* Monitoring and Performance Reviews: The report criticizes the lack of 
monitoring guidance pointing out that to some degree the lack of 
reporting requirements may be an outcome of program design. This point 
is significant because it provides insight about the nature and extent 
of the federal, state, and local attitude that existed at the time of 
the first Round of EZ/ECs. That attitude was based on the premise of 
maximum local flexibility and control and limited government intrusion. 

Monitoring: HUD did not conduct monitoring of the SSBG funds because 
monitoring those funds came under the purview of HHS since this agency 
was responsible for the allocation, tracking and monitoring of those 
funds. You may recall we responded to GAO in a written document, dated 
December 8, 2004, on questions submitted by the GAO team. The following 
points were made: 

1. HUD Office of Inspector General, (OIG) audited a sample of urban 
Round I EZs consisting of Atlanta, Philadelphia, Los Angeles, 
Cleveland, and Detroit. 

2. HHS informed HUD that it acts as a pass through for the SSBG 
mandatory grants to the states and provides technical assistance on 
compliance. 

3. HUD conducted a "standard" grants monitoring procedure consisting of 
a field review rating the grantees in their jurisdiction by risk 
analysis resulting in a selection of high risk grantees for monitoring 
The risk analysis for Round I would have been limited to EDI/108 funds 
awarded to Round II EZ which compete with all other CPD competitive 
grants for monitoring. 

4. The HUD OIG found disallowed and questioned costs in some Round I 
EZ's use of SSBG funds and EDI/108 funds in audits performed in 1998- 
1999 and 2002-2003. The OIG also raised issues regarding management 
controls, lack of progress and benefit of the program to residents. In 
1997, HUD warned five EZs/ECs of possible revocation of their 
designation regarding deficiencies after field visits. The warnings 
related to adequacy of progress in implementing the strategic plans and 
did not involve the use of funds. 

5. Round I urban challenges involved having one-agency award funds, 
another agency track progress and having different Federal benefits in 
different types of designations for which different rules apply. In the 
case of Round I urban communities large funding sources included SSBG 
funds, HUD's 108 grantees, and Economic Development Initiative funds. 

Periodic Performance Reviews: HUD's oversight role stems from Section 
1391 (d) (2) of the Omnibus Budget Reconciliation Act of 1993 and CFR 
Part 597.400, 597.401, 597.402, and 597.403, all of which provide for 
reporting, periodic performance reviews, validation of designation and 
revocation. According to limited legislative provisions, performance 
reviews required only an evaluation of progress in carrying out an EZ/ 
EC strategic plan. In particular, the funding aspects of the strategic 
plan were confined to identifying the "funding requested" under any 
Federal program in support of the proposed economic, human, community 
and physical development and related activities. Reference Omnibus 
Budget Reconciliation Act of 1993, Sec. 1391, Designation Procedures. 

Furthermore, for the purpose of the strategic plan "funding requested" 
was identified as amounts budgeted for the proposed projects and 
activities and did not include provisions for obligations, and/ 
expenditures. The limited statutory provisions were the basis for 
determining the degree and extent for Round I EZ/EC monitoring and the 
related collection of data as well. HUD agrees with the GAO's report 
statement on page 6 urging, "more should have been done." We appreciate 
GAO surfacing this issue. HUD, too believes, that it is important that 
Congress be aware of the program's imperfections associated with the 
oversight of Round I EZ/EC federal expenditures, particularly when 
developing legislation for future programs. 

Recommendation: 

1. The report's observation that "more should have been done" should 
also make clear that "more" was not allowed in Round I. 

2. In conjunction with this issue, HUD requests that the report include 
an accompanying statement that HUD met its agency requirement to 
undertake periodic performance reviews on Round I urban EZ/ECs and did 
so based on legislative provisions. 

* Division of Authority and Responsibilities: As mentioned in the cover 
letter, a substantial flaw in the administration of the Round I was the 
division of authority and responsibilities among HUD, USDA, HHS, and 
Treasury. This division has resulted in unforeseen and unintended 
consequences that affected financial oversight of the Social Service 
Block Grant (SSBG) funds. 

HUD recognized in its administration of Round I urban EZ/ECs that we 
had no control over the State's distribution of SSBG funds leading to a 
vacuum of information on how the funds were being expended. After 
numerous attempts to collect SSBG data, it was not until in 2004/2005 
that we were provided a report of the expenditures and remaining Round 
I urban EZ unallocated/unspent funds. 

* Line of Credit Control System (LOCCS) Administration: The issues 
originating from the division of authority among the four agencies led 
HUD to closely examine its in-place financial and management systems 
when Congress instituted a second round of urban EZs in 1998. In its 
direct responsibility of administering the 15 Round II urban EZs and 
their HUD EZ grant funds, HUD coupled PERMS and LOCCS. HUD further made 
changes to these systems so that Round II HUD EZ grant funds could be 
tracked and tied to Zone activities and projects. 

LOCCS Administrator: To ensure better oversight of the HUD EZ grant 
funds, HUD assigned a LOCCS administrator at the level of senior 
professional, and set forth the duties assigned to the administrator as 
well as Desk Officers in drawing and reconciling EZ grant funds on a 
monthly basis. Additionally, HUD has assigned a PERMS administrator to 
manage the timely execution of annual performance reviews. Another 
significant HUD action was developing a Round II Zone guidebook 
containing post designation policies and procedures with sections 
covering drawdowns and tracking the use of HUD EZ grants. 

Objective #3: Examines the extent to which data are available to assess 
the use of program tax benefits. 

* HUD and Treasury Discussions on Tax Incentives: It should be noted 
that HUD took immediate action in response to the GAO recommendations 
in the March 2004 report "Federal Revitalization Programs Are Being 
Implemented, but Data on the Use of Tax Benefits are Limited." The 
specific GAO recommendations were: 

1. Identify the data needed to assess the use of the tax benefits: 

2. Determine the cost effectiveness of collecting these data: 

3. Document the finds of their analysis: 

4. If necessary seek the authority to collect data, if a cost-effective 
means is available: 

* HUD Actions: In addressing the above, HUD initiated actions that 
included a May 10, 2004, meeting with Treasury and IRS managers to 
discuss the GAO recommendations. At that meeting, HUD suggested changes 
to the IRS form 8844, compiled by Zip Codes by adding a line to the 
form that would allow an understanding of the utilization of the EZ/ 
Renewal Community (RC) employment credits, the largest used incentive 
out of the total incentives provided in the $11 billion tax incentive 
package. 

In a follow-up email in November 8, 2004, from the IRS and in a 
Memorandum GAO-04-306, HUD was informed that changing form 8844 was not 
an option largely because of the costs associated with such a change 
and the number of other tax forms that would have to be changed in 
meeting HUD's suggestion requiring additional appropriated funds. IRS 
also stated in that email that changes to these forms would result in 
at least $1 million hours of taxpayer burden annually. The memorandum 
also stated that surveying business located in RCs/EZs would require 
that HUD request that grant recipients compile lists identifying 
businesses that benefited from the credits. IRS further stated, "No 
data are collected on additional 179 expensing and capital gains 
relief." 

* HUD Survey: The IRS response lead HUD to undertake its own survey 
through contractor resources. This resulted in the development of a 
methodology, business tax incentive questionnaire and a random sample 
of businesses selected from the latest Dun & Bradstreet CD ROM 
identifying and providing relevant information on approximately 300,000 
EZ/RC businesses. Because the survey had not been included in the 
latest HUD budgets, the completion of the survey is on hold. HUD's 
Office of Policy, Development & Research have included the completion 
of the survey methodology as part of its 2007 research agenda. 

* Treasury/HUD Partnership: Through this partnership, Treasury has 
provided assistance and data on the use of the New Markets Tax 
Incentives (NMTC) and on the Commercial Revitalization Deductions 
available to RCs. In December 2006, Treasury will provide HUD with 
national level data on RC/EZ wage credits from the IRS Form 8844 
relating to individual and partnership data for 2004 with corporation 
data expected in September 2007. Businesses benefit from the NMTC 
program, as private sector resources are made available to them to 
better meet their short and long term financial needs through increased 
loans and financial technical assistance. 

* HUD Action on Tax Incentives includes a recent HUD publication 
"Spotlight on Results, Capturing Successes in Renewal Communities and 
Empowerment Zones." This publication contains anecdotal evidence of the 
utilization of tax incentives by EZ/RC businesses in the areas of wage 
credits, expensing/deductions, bond financing, & capital gains. 

Recommendation: To meet the GAO recommendations made in its March 2004 
report, report needs to include information on the above HUD efforts. 

* Round I versus Rounds II & III use of Tax Incentives: Although there 
was little programmatic emphasis on the use of tax incentives until the 
second round of EZs, there are at least two studies that addressed the 
likelihood and significance of their use in the first round, one GAO 
and the other a HUD-Policy Development and Research study. Reference 
pie 45 of the report. 

Both studies suggested significant limitations on the use of tax 
incentives in the first round, which led HUD to provide much more 
explicit emphasis on and in support of the incentives by aggressively 
marketing them. HUD held workshops conducted by IRS representatives and 
a nationally known tax attorney, national hearings by the Secretary's 
Advisory Council where businesses, community leaders, and designees 
gave testimonials on how tax incentives were being used and their 
economic impact in job creation and business expansion in their 
communities. These groups published three publications on tax 
incentives with the "Tax Incentive Guide for Businesses" distributing 
60,000 copies nationwide, and a grass root campaign representing the 
best efforts by 16 EZ/RC in promoting tax incentives. 

* HUD asserts that the policy issue to consider in the remaining GAO 
reports is what impact more explicit emphasis on tax incentives has had 
on the Rounds II and III and on RCs. The Round I urban expectation was 
an assumption that the use of tax incentives would proceed more or less 
automatically without federal encouragement or technical support. 

Objective #4: Analyzes the effects that the Round I EZs and ECs had on 
poverty, unemployment and economic growth in their communities: 

* Appendix IV: We believe that the report's Appendix IV should be 
useful in assisting Congress to make judgments on how individual Round 
I EZs/ ECs did in changing poverty rates, unemployment rates and 
increasing and economic growth. Moreover, Appendix IV presents a 
valuable assessment of individual urban Round I EZs and ECs governance, 
the activities implemented and other related operational issues of the 
program. 

* The indices of poverty, unemployment, and economic growth were used 
in the application process as eligibility thresholds in identifying the 
most distressed communities. These indices were never intended to be 
used as a performance measure, except in the broadest sense of 
comparing indice changes over a particular timeframe. 

* You may want to consider HUD's parallel research and related 
methodology used in its econometric model. As we understand, the GAO 
model used 1994 to 2000 as a single time period to measure the changes 
in poverty, unemployment and economic growth rather than a time line of 
measuring change over two five-year periods, starting five years before 
designation (1990-1995) and five years into designation (1995-2000) as 
HUD's research had done. 

It is not surprising that it is tough to tease out changes in 
neighborhoods over time. The successes of EZs varied by things like 
staff expertise, institutionalization of administrative structure, and 
whether the program was government administered. For example, the 
groups named in Figure #5 pg. 19 (Managed by another type of 
organizations; Managed by local government; Managed by nonprofit 
organizations) might suggest a way to cut the outcome data. Reference 
pages 18 and 19 of the report. 

Theory of Change Approach: Certainly having data on the use of program 
grants and tax incentives would have allowed for a richer assessment of 
the program, seemingly as such data could be tied to the induces of 
poverty, unemployment and economic growth. On the other hand, it might 
be worth citing some other methodological issues. This was intended to 
be a ten-year intervention, which has been statutorily extended for 
another five years for the purposes of utilizing tax incentives and the 
remaining unspent funds. How activities are sequenced over ten years is 
undoubtedly important as well as the intention of policy makers. 

Even with so called "good data on expenditures", a longitudinal case 
study approach might be the best way to assess the effectiveness of 
this type of intervention. There are scholars who think a "theory of 
change" approach to this subject matter is more compelling than using 
an artificially constructed comparison area even in circumstances where 
"good data" is available. Reference pages 28-29. 

The following are GAO's comments on the Department of Housing and Urban 
Development's letter dated August 17, 2006. 

GAO Comments: 

1. HUD commented that GAO should include details on the amount of 
funding and tax incentives provided for Rounds II and III of the EZ/EC 
program. We noted in our report that communities designated in Rounds 
II and III received a smaller amount of funding and more tax benefits 
than those designated in Round I. Our statement does not provide 
further details on Rounds II and III because the focus of the report is 
Round I. 

2. We recognize that Round I designees were required to address four 
key principles as part of their strategic plans. However, our mandate 
was to assess the effectiveness of the EZ/EC program on poverty, 
unemployment, and economic growth. Assessing the extent to which 
communities addressed the key principles would not have been useful in 
meeting our mandate because, among other things, there is not a clear 
relationship between the key principles and poverty, unemployment, and 
economic growth. Further, while the report did not evaluate the extent 
to which communities met the key principles, it included many examples 
of activities carried out under them. The report also indicated that 
communities had implemented a larger percentage of community 
development activities than economic opportunity activities but did not 
comment on the appropriateness of the distribution of activities. 

3. Our mandate was to assess the effects of the EZ/EC program on 
poverty, unemployment, and economic growth. Our report stated that 
communities were required to submit strategic plans that addressed the 
four key principles. However, because communities were able to modify 
their strategic plans over time, it would have been difficult to 
establish set criteria for assessing performance. Nonetheless, our 
report does contain numerous examples of activities undertaken by the 
communities, including examples mentioned in a separate appendix 
focusing on the 13 designated communities we visited. 

4. HUD commented that because GAO found that a lack of data on how 
program funds were used was a limiting factor in determining the 
effectiveness of the EZ/EC program, we should make use of information 
in the agency's performance reporting system and in communities' 
strategic plans. However, we reported that our file review to determine 
the accuracy of data in HUD's performance reporting system found that 
the data were not sufficiently reliable for our purposes. For example, 
we found evidence that communities had undertaken certain activities 
with program funding, but we were often unable to find documentation of 
the actual amounts allocated or expended. As a result, we were unable 
to rely on information contained in the agency's performance reporting 
system on the amounts of program funds allocated or expended on 
specific activities. 

5. We found that data in HUD's performance reporting system on the 
amounts of funds used and the amounts leveraged were not reliable. For 
example, we found that HUD's system included information on the amount 
of funds leveraged. But for the sample of activities we reviewed, the 
supporting documentation either showed an amount conflicting with the 
reported amount or was not available. Moreover, we found that the 
definition of "leveraging" varied across EZ and EC sites. HUD further 
commented that Table 5 in the report showed that the agency's 
performance reporting system received a code of 2.0, showing that 
leveraging data had strong documentation. However, HUD appears to have 
misinterpreted the information we presented on this matter. We found 
that HUD's data on leveraging received an average code of 1.0, 
indicating that such information had weak documentation. Lastly, HUD 
recommended that it be allowed to alleviate GAO's concerns about the 
reliability of its leveraging data by demonstrating how the data were 
tracked and recorded in its performance reporting system. However, the 
data reliability problems we found during the course of this work were 
due not to concerns about the system used to track and record the data, 
but rather to the frequent lack of supporting documentation for the 
data entered into the system. 

6. HUD commented that our report did not adequately address HUD's 
performance reporting system and its role in HUD's oversight of the 
urban Round I EZ and ECs. We acknowledge that HUD established the 
system in response to an earlier GAO recommendation and has since used 
it to oversee Round I EZs and ECs. Moreover, we agree that the system 
contains a variety of information and data elements, including 
activities implemented and program outputs. We also acknowledge that 
the performance reporting system is not intended to be a financial 
system for Round I. However, as discussed in our report, we found that 
because the system did not always contain information on what was spent 
on activities and did not always contain reliable information, HUD and 
the other federal agencies were limited in their ability to oversee the 
program. 

7. HUD commented that the program's design was significant because it 
provided insight about the nature and extent of the federal, state, and 
local attitudes that existed at the time of the first Round of EZs/ECs. 
HUD also stated that it did not conduct monitoring of the SSBG funds 
because monitoring those funds was the responsibility of HHS. HUD's 
statement further supports our discussion on the limitation in the 
oversight of the EZ/EC program that may have resulted from the 
program's design. Although we found program oversight was hindered, we 
also reported that no single federal agency had sole responsibility for 
oversight. We do not agree with HUD's recommendation that we make clear 
that more oversight was not allowed in Round I. For example, early in 
the program HUD and HHS made some efforts to share information. 
Specifically, HUD officials said that they had received fiscal data 
from HHS and reconciled that information with their program data on the 
activities implemented, but these efforts to share information were not 
maintained. Regarding the second recommendation, although HUD described 
some of its efforts to monitor the program according to applicable 
regulations, the oversight concerns we identified in the report remain. 

8. We reported that limitations in the oversight of the EZ/EC program 
may have resulted from the design of the program. 

9. We stated in the report that the concerns raised about program 
oversight for the Round I EZ/EC program may not apply to future rounds 
of the EZ/EC program. We also acknowledge that HUD may have made 
changes in its oversight of later rounds of the program. However, an 
evaluation of later rounds of the EZ/EC and Renewal Community programs 
is beyond the scope of this report. 

10. In our report, we acknowledged HUD's as well as the other agencies' 
response to the recommendation in our 2004 report to identify a cost- 
effective means of collecting the data needed to assess the use of the 
tax benefits. 

11. Our report acknowledged the collaboration among HUD, IRS, and USDA 
in addressing our previous recommendation and summarizes the outcome of 
their discussions, including the identification of two data collection 
methods--through a national survey or by modifying the tax forms. In 
addition, our report also acknowledged that IRS did not have any data 
for some program tax benefits. The lack of data on the use of tax 
benefits continues to be a source of concern that limits an assessment 
of the effect of the EZ/EC program. 

12. We agree that HUD's efforts to develop a methodology to administer 
a survey to businesses to assess the use of the program tax benefits is 
a useful step in gathering such information. 

13. We recognize the efforts between HUD and Treasury on sharing 
national-level data on EZ businesses' use of tax credits for employing 
EZ residents. However, as we mention in our report, data on the EZ 
employment tax benefit were limited because they could not be linked to 
the specific EZ claiming the benefit. 

14. In the absence of other data, we acknowledge HUD's efforts to 
capture anecdotal information on the use of program tax benefits by EZ 
businesses. 

15. We recognize HUD's efforts to market the EZ/EC program tax 
benefits. 

16. We appreciate HUD's suggestion on how to approach evaluations of 
later rounds of the EZ/EC and Renewal Community programs and welcome 
the opportunity to discuss these ideas. 

17. We appreciate HUD's comments on the descriptive information on EZs 
and ECs we visited that are discussed in appendix IV. 

18. HUD commented that the measures used in our report---poverty, 
unemployment and economic growth--were used in the application process 
and were not intended to be used as performance measures. However, as 
mentioned earlier our mandate was to assess the effects of the EZ/EC 
program on poverty, unemployment, and economic growth. 

19. HUD suggested that we consider additional methodologies for 
measuring the effects of the EZ/EC program, such as trend analysis 
using data from 1990 through 1995 and 1995 through 2000. To conduct our 
work, we used 1990 and 2000 data to measure changes in poverty and 
unemployment and 1995, 1999, and 2004 data to measure changes in 
economic growth. We chose these dates because data were available at 
the census tract level for these years. Moreover, in designing our 
methodology for our econometric analysis, we conducted a literature 
review and discussed our methodology with several experts in the urban 
studies field and determined that the approach presented in this report 
was effective in answering the objectives of our mandate. As mentioned 
in Appendix II, we also conducted different tests to ensure the 
robustness of our models, which all yielded results consistent with our 
models. The approach that HUD suggested controlled for trends that 
began before the EZs were designated in 1994. Because we did not have 
data on poverty or unemployment for 1995 we were unable to use this 
approach. However, our use of housing trends between 1990 and 1994 in 
our econometric model controlled for some trends that were in place 
prior to EZ designation. 

HUD also suggested a longitudinal case study approach might be the best 
way to assess the effectiveness of this type of program. Although a 
longitudinal case study approach would be informative, it is unlikely 
that a successful retrospective longitudinal study could be designed at 
the end of the program. As HUD noted, this intervention was intended to 
be implemented over a ten-year period. However, a longitudinal case 
study approach would necessitate data collection beginning at the 
inception of the program and continuing for the duration of the program 
as well as some period of time after it ends. 

[End of section] 

Appendix VII: Comments from the U.S. Department of Agriculture: 

United States Department of Agriculture: 
Office of the Secretary: 
Washington, D.C. 20250: 

AUG 22 2006: 

William B. Shear: 
Director, Financial Markets and Community Investment: 
United States Government Accountability Office: 
441 G Street, NW, Room 2A10: 
Washington, DC 20548: 

Dear Mr. Shear: 

Thank you for providing the United States Department of Agriculture 
(USDA) and Rural Development with your Government Accountability Office 
(GAO) draft report entitled, "Empowerment Zone and Enterprise Community 
Program: Improvements Occurred in Communities, but the Effect of the 
Program is Unclear," Report Number GAO-06-727. For your consideration, 
Rural Development offers the following comments to the draft report and 
requests that a copy of these comments be included in your final 
report. Rural Development's response is limited to a discussion of the 
rural Empowerment Zones and Enterprise Communities (EZ/ECs). 

USDA concurs that data and analyses on the effectiveness of programs, 
such as the EZ/EC, are useful. Our long experience working with rural 
communities, operating economic development programs, and attempting to 
evaluate those programs may be instructive to future evaluations. Rural 
Development suggests three general areas to consider: 

First, rural communities are substantially different from each other 
and from urban and metropolitan areas, and many performance metrics 
have been designed with the urban model and data in mind. Rural 
measurements need to acknowledge different realities. For instance, 
small populations mean that some statistically meaningful economic, 
demographic, and education data are not always available in targeted 
rural communities. Currently, USDA is developing a methodology that 
focuses on economic impacts using county-level economic data and 
captures the short-term Gross Domestic Product changes in the impacted 
rural counties. However, this system can not be applied retroactively 
to communities receiving grants or other economic stimulation in the 
past. In addition, this system can not be used to measure impacts at 
the sub-county level. 

Second, it is especially important in rural areas to build in up front 
a clear and adequately funded data collection process for program 
evaluation. When that data collection process is not in place at the 
start, the appropriate resources and attention required will often be 
difficult to obtain. This problem is likely to be particularly acute in 
rural areas, where local governments and key economic development 
institutions frequently have limited financial capacity and 
professional staff. 

The collection of baseline and on-going data should enable construction 
of a program evaluation that will examine the program's success in 
achieving intended results. Beyond data on the participating 
communities, if a comparison is desired, the appropriate data should be 
obtained for comparison communities from the outset. 

Third, performance metric design is dynamic, and Rural Development 
encourages an expansion of the discussion, as well as the use of 
appropriate methodologies that recognize data conditions at the time 
grants are made. The new collaborations at the local level among key 
economic and community development institutions, which were required by 
the EZ/EC program, are worthy of careful examination within a full 
program evaluation. 

We commend GAO for looking at program impacts in critical areas such as 
poverty, unemployment, and economic growth. Program evaluation might, 
however, go beyond these measures to look at the construction of 
stronger economic development capacity and more effective collaborative 
networks. Such a broader perspective on program results might be 
particularly pertinent for rural EZ/ECs since GAO's econometric 
analysis was applied only to urban EZ/ECs. 

A major portion of the report is devoted to comparing the change in the 
number of jobs and businesses between EZ/ECs and comparison areas. We 
do not have a good understanding of how these comparison areas were 
chosen; therefore, we can not comment on this portion of the report. 

GAO's report states on page 4, "Data were not collected on program 
benefits for specific activities." and also ".USDA - did not collect 
data on how program funds were used." USDA did, however, design and 
implement a Benchmark Management System (BMS), intended as a management 
tool for both USDA and the individual EZ/ECs. The BMS requires each of 
the EZ/EC's major activities to be tracked over time by the community 
and verified by USDA. The BMS was not designed to be an accounting 
tool, but it has proven useful for providing a good picture of each 
community's achievements. 

GAO also states in its report that, "USDA encouraged rural EZ and ECs 
to report all investment in the EZ as leveraged funds, not only those 
projects that received EZ/EC funds." In fact, USDA encouraged each 
community to report all investments that contributed to accomplishing 
the community's strategic plan, the guiding document for the 
community's revitalization strategy. Most EZ/ECs developed ambitious 
plans that could be realized only by leveraging resources beyond the 
core EZ/EC funding. In addition to the resources marshaled through 
leveraging, the communities were able to build a strong network of 
partners, with a collaborative track record that will likely be in 
place long after the last core EZ/EC dollar has been spent. 

There are also some technical details that need correction and/or 
further explanation: 

* Page 10. The statement, "In addition, all designated communities 
reported leveraging additional resources, though a lack of reliable 
data prevented us from determining how much." USDA's BMS does include 
data on leveraged resources, as self-reported by the communities. 
Anecdotal evidence indicates that communities generally underreport 
such leveraged resources. 

* Page 64. The first paragraph concludes, ".the results did not allow 
us to conclude whether there is an association between the EZ program 
and economic growth." The word "urban" should be inserted before the 
EZ, as the econometric analysis only dealt with urban EZ and ECs. As 
GAO notes in its report, there are differences in how the program was 
implemented and the results achieved. Rural EZ and ECs generally had a 
greater per capita number of jobs created and new businesses formed 
than urban EZ and ECs. 

* Page 133. Rio Grande Valley EZ, Texas, received an extension, 
according to information USDA received from the Department of Health 
and Human Services. 

Thank you for this opportunity to comment on the report. If you have 
any questions, please contact John Dunsmuir, Acting Director, Financial 
Management Division, at (202) 692-0080. 

Sincerely, 

Signed by: 

Thomas C. Dorr: 
Under Secretary Rural Development: 

[End of section] 

Appendix VIII GAO Contact and Staff Acknowledgments: 

GAO Contact: 

William B. Shear (202) 512-8678: 

Acknowledgments: 

In addition to the individual named above, Charles Wilson, Jr., 
Assistant Director, Carl Barden, Mark Braza, Marta Chaffee, Emily 
Chalmers, Nadine Garrick, Kenrick Isaac, DuEwa Kamara, Austin Kelly, 
Terence Lam, John Larsen, Alison Martin, Denise McCabe, John McGrail, 
John Mingus, Jr., Marc Molino, Gretchen Maier Pattison, James 
Vitarello, and Daniel Zeno made key contributions to this report. 

(250221): 

FOOTNOTES 

[1] Since its enactment in 2000, the Renewal Community program has 
focused on providing tax benefits to businesses in designated 
communities to attract or retain jobs and businesses. 

[2] For the purposes of this report, EZ/EC stakeholders include EZ/EC 
officials, board members, subgrantees, local chamber of commerce 
representatives, and other local officials recommended by EZ or EC 
officials as having a role in the program. See appendix I for 
information on the types of stakeholders we interviewed at each site. 

[3] Survey results can be viewed at [Hyperlink, http://www.gao.gov/cgi-
bin/getrpt?GAO-06-734SP]. 

[4] Program data include information on how activities were implemented 
and outputs. 

[5] We calculated confidence intervals around estimates derived from 
Census data. In this report, all estimates shown for a percentage have 
a 95 percent confidence interval of less than plus or minus 5 
percentage points, unless otherwise noted. 

[6] GAO, Standards for Internal Control in the Federal Government, GAO/ 
AIMD-00-21.3.1 (Washington, D.C.: November 1, 1999). 

[7] HUD's performance reporting system is known as Performance 
Measurement System while USDA's is called the Benchmark Management 
System. For the purposes of this report, we refer to them as 
performance reporting systems. 

[8] GAO, Government Performance and Accountability: Tax Expenditures 
Represent a Substantial Federal Commitment and Need to Be Reexamined, 
GAO-05-690 (Washington, D.C.: September 23, 2005). 

[9] GAO, Community Development: Federal Revitalization Programs Are 
Being Implemented, but Data on the Use of Tax Benefits Are Limited, GAO-
04-306, (Washington, D.C.: March 5, 2004). 

[10] Our earlier recommendation was not directed to HHS because its 
role was limited to distribution and oversight of the EZ/EC grant 
funds. 

[11] We were able to use statistical modeling techniques for the eight 
Round I urban EZs only, because the rural EZs were made up of too few 
census tracts to perform these analyses, and because the ECs received 
such small amounts of money over the 10-year period that we could not 
separate the program's effects from those of other programs. 

[12] GAO-04-306. 

[13] Multiagency teams also included officials from the Department of 
Justice, the Environmental Protection Agency, and the Small Business 
Administration, among other agencies. 

[14] Two urban EZs--Cleveland and Los Angeles--were originally 
designated as Supplemental EZs and received a combination of Economic 
Development Initiative grants and Section 108 Loan Guarantees, both of 
which could only be used for certain economic development or 
revitalization projects. The Supplemental EZs were given full Round I 
EZ status in 1998, and local businesses were allowed to use the program 
tax benefits starting in 2000. However, they did not receive the grants 
the other Round I EZs received. Four urban ECs also received Enhanced 
EC designations, which provided them with some Economic Development 
Initiative grants and Section 108 Loan Guarantees. 

[15] This number represents businesses' use of tax benefits in all 
Round I EZs and ECs, as well as 20 Round II EZs, 10 Round III EZs, and 
40 Renewal Communities. 

[16] The Cleveland EZ received $87 million in grants and an equal 
amount of loan guarantees, while the Los Angeles EZ received $125 
million in grants and $325 million in loan guarantees. 

[17] EZs and ECs self-determined the categories for their activities, 
so it is possible that a similar activity implemented in two sites 
could be categorized differently. 

[18] The federal Community Development Block Grant program supports a 
wide array of local community development activities that are primarily 
to benefit low-and moderate-income individuals. 

[19] However, past reported amounts remain in their performance data in 
HUD's system. 

[20] This finding is consistent with findings of the HUD and USDA 
Offices of Inspector General. 

[21] GAO/AIMD-00-21.3.1. 

[22] GAO/AIMD-00-21.3.1. 

[23] Because data on the amount of funds used for specific activities 
was not reliable, this report only includes information on the number 
of activities implemented. We were able to find complete documentation 
for our sample of activities for the amount of EZ grant funding 
reported by the Baltimore and Detroit EZs and the Camden portion of the 
Philadelphia-Camden EZ. 

[24] Neither HHS nor USDA officials told us that they had made any 
efforts to reconcile fiscal and program data on the EZ/EC program. 

[25] GAO-05-690. 

[26] GAO-04-306. 

[27] GAO-05-690. 

[28] GAO-04-306. 

[29] GAO-05-690; 
GAO, 21ST Century Challenges: Reexamining the Case of the Federal 
Government, GAO-05-325SP (Washington, D.C.: February 1, 2005); and GAO, 
Tax Policy: Tax Expenditures Deserve More Scrutiny, GAO/GGD/AIMD-94-122 
(Washington, D.C.: June 3, 1994). The New York Liberty Zone was created 
in response to the September 11, 2001 terrorist attacks and created 
seven tax benefits designed to assist in the recovery efforts, 
including a tax credit for hiring employees who are Liberty Zone 
residents. 

[30] GAO-04-306. Our earlier recommendation was not directed to HHS 
because its role was limited to distribution and oversight of the EZ/EC 
grant funds. 

[31] GAO, Community Development: Businesses' Use of Empowerment Zone 
Tax Incentives, GAO/RCED-99-253 (Washington, D.C.: September 30, 1999) 
and Scott Hebert and others, Interim Assessment of the Empowerment 
Zones and Enterprise Communities (EZ/EC) Program: A Progress Report, 
prepared for the U.S. Department of Housing and Urban Development 
(Washington, D.C.: November 2001). 

[32] GAO/RCED-99-253 and Hebert and others, Interim Assessment. 

[33] For more information on our survey methods, see appendix I. 

[34] GAO/RCED-99-253 and Hebert and others, Interim Assessment. 

[35] The EZ/EC tax benefits were nonrefundable, which means that a 
business can only claim them if it has a tax liability. 

[36] An Industrial Revenue Bond is a bond used to finance the 
construction of manufacturing or commercial facilities for a private 
user. This bond is available to businesses in general and is not part 
of the EZ program benefits package. 

[37] A bond cap is the maximum dollar amount available for issuing the 
tax exempt bond. Prior to January 1, 2002, the cap for bonds was $3 
million per borrower for activities in any EZ or EC, with a nationwide 
limit of $20 million per borrower. After 2002, the bond cap was removed 
for all EZs. 

[38] GAO-05-690 and GAO-04-306. 

[39] The Joint Committee on Taxation estimated that businesses in Round 
I communities would claim about $2.5 billion in tax benefits in the 
first four years of the program alone. Information for later years of 
the Round I program is not available because subsequent estimates by 
the committee did not separate benefits claimed by Round I communities 
from benefits claimed by later round communities. 

[40] The Gulf Opportunity Zone Act of 2005 provides tax benefits to 
assist in the recovery and rebuilding of areas affected by Hurricanes 
Katrina, Rita, and Wilma. 

[41] GAO-04-306. 

[42] The indicators of poverty, unemployment, and economic growth were 
specifically identified in our mandate. 

[43] We selected comparison areas through a statistical technique 
called the propensity score, which allowed us to identify the census 
tracts that were most similar to the census tracts selected in the 
original EZ and EC designations based on a set of factors, such as 1990 
poverty and unemployment rates. For more information on how we chose 
the comparison areas, see appendix I. 

[44] Gentrification or economic displacement refers to the 
transformation of a relatively low-income neighborhood into a more 
affluent neighborhood through redevelopment, usually in conjunction 
with changing demographics and an influx of wealthier residents. 

[45] Our analysis of changes in economic growth only included 94 ECs, 
since we did not have data for the Miami/Dade County, Florida EC. 

[46] Changes in poverty rate are based upon percentage point 
differences. 

[47] We were able to use statistical modeling techniques only for the 
eight Round I urban EZs, because the rural EZs were made up of too few 
census tracts to perform these analyses and because the ECs received 
such small amounts of money that we could not separate the program's 
effects from those of other programs. In addition, we could not isolate 
the effects of the program on individual EZs because the number of 
census tracts in some urban EZs was not large enough to provide 
reliable results. Appendix II describes our methodology and the results 
of the econometric analysis. 

[48] These stakeholders did not comment on the changes that occurred in 
our comparison areas. 

[49] The Personal Responsibility and Work Opportunity Reconciliation 
Act of 1996 instituted a change in welfare policies, establishing work 
requirements and time limits for receiving benefits. 

[50] Changes in unemployment rate are based upon percentage point 
differences. 

[51] These stakeholders did not comment on the changes that occurred in 
our comparison areas. 

[52] We obtained these data for 1995, 1999, and 2004 from a private 
data vendor, Claritas. Changes in the number of businesses and number 
of jobs are based upon percent changes. 

[53] Data were not available for the Miami/Dade County, Florida EC. 

[54] These stakeholders did not comment on the changes that occurred in 
our comparison areas. 

[55] GAO-04-306. 

[56] GAO-04-306. 

[57] GAO-05-690 and GAO/GGD/AIMD-94-122. 

[58] Of the 61 ECs that had not received additional designations, we 
selected a judgmental sample of 8 ECs, 4 urban and 4 rural, that 
represented geographic diversity and also factored in each EC's 
combined poverty and unemployment rates in 1990. From this sample, we 
chose two ECs to visit--Providence, Rhode Island (urban) and Fayette- 
Haywood, Tennessee (rural)--that were similar in terms of their poverty 
and unemployment rates. 

[59] In the New York and Philadelphia-Camden EZs, we implemented two 
separate site visit protocols, due to the split governance structures 
in those locations. 

[60] The four urban pretests were conducted in the Akron, Ohio EC; 
Bridgeport, Connecticut EC; Providence, Rhode Island EC; and the 
Albuquerque, New Mexico EC. The two rural pretests occurred in the 
Central Appalachia EC located in West Virginia and the Williamsburg/ 
Lake City EC in South Carolina. 

[61] There were 61 ECs that did not receive subsequent designations, 
but we excluded 1 urban EC from our sample because it was no longer in 
operation as of June 2005. 

[62] We selected the lesser of 10 activities or half of an EZ's or EC's 
activities using a systematic random sample. We use the term "activity" 
to describe the units of information reported in the HUD and USDA 
systems, called "implementation plans" in HUD's system and "benchmarks" 
in USDA's system. 

[63] We excluded the Cleveland and Los Angeles EZs from our discussion, 
because they did not receive EZ/EC grant funds. 

[64] GAO/RCED-99-253 and Hebert and others, Interim Assessment. 

[65] Businesses in the Atlanta EZ were excluded from the analysis, 
since the same area received a Renewal Community designation in 2002 
and we did not want businesses to confuse the different tax benefits 
available for each designation. 

[66] We also excluded private businesses that were not eligible for the 
tax benefits, such as gambling establishments and liquor stores. 

[67] The points in time for Census data were 1990 and 2000 and for the 
business data were 1995 and 2004. 

[68] We determined this definition based on research done on the EZ 
program and similar programs, such as the state Enterprise Zone 
initiative. Similar to the EZ program, state Enterprise Zones are state 
run programs that offer certain tax benefits within an established 
area. We excluded establishments that were not eligible for program tax 
benefits, such as nonprofit and governmental organizations, from our 
analysis of the change in the number of businesses. However, we 
included jobs at those businesses in our analysis of the change in the 
number of jobs. 

[69] Other researchers have used similar approaches, such as Greenbaum 
and Engberg in "The Impact of State Urban Enterprise Zones on Business 
Outcomes," December 1998, Carnegie Melon University. p. 24. 

[70] Generally, increasing the propensity score for selecting 
comparison tracts has the effect of reducing the sample of comparison 
tracts and decreasing the propensity score has the effect of increasing 
the sample of comparison tracts. 

[71] Due to changes in the census tract boundaries for some EZs and ECs 
from 1990 to 2000, we used the 2000 census block group to recreate the 
initial 1990 boundaries and ensure that our analysis remained 
consistent. For the census variables based on percentage 
characteristics, like poverty and unemployment rates, we calculated the 
change in percentage points by finding the difference between the 1990 
sample estimate and the 2000 sample estimate. For census variables 
based on average characteristics, such as average household income, we 
calculated the percent change by finding the difference between the 
1990 sample estimate and the 2000 sample estimate and then dividing the 
difference by the 1990 sample estimate. Census variables using dollar 
amounts like average household income were adjusted for inflation to 
2004 dollars. We calculated the percent change for our economic growth 
measures. 

[72] We completed econometric analyses of the eight urban EZs only, 
because the amount of program grant funding for ECs was too small to 
separate the program's effects from other programs. In addition, we 
excluded the rural EZs because they are made up of too few census 
tracts to perform these analyses. 

[73] We excluded establishments that were not eligible for program tax 
benefits, such as nonprofit and governmental organizations, from our 
analysis of the change in the number of businesses. However, we 
included jobs at those establishments in our analysis of the change in 
the number of jobs. 

[74] We also tested use of a fixed effect model, which allowed us to 
account for some tract-specific factors that may not vary over time but 
might be correlated with the designation of a tract, called "fixed 
effects." Other researchers used fixed effects regression techniques to 
control for area-specific unchanging factors, such as industry 
composition. (See, for example, Leslie E. Papke, "What Do We Know About 
Enterprise Zones?" NBER Working Paper No. 4251. Cambridge, Mass., 
National Bureau of Economic Research, 1993.) The analysis in this 
report predicts first differences in the dependent variables, that is, 
the difference between the value in the 2000 Census and the value in 
the 1990 Census. When only 2 years of data are analyzed, regressions 
based on first differences are equivalent to fixed effect models. (See 
Zvi Griliches and Jerry Hausman, "Errors in Variables in Panel Data," 
Journal of Econometrics, 31 (1986), pp. 93-118.) 

[75] Paul A. Jargowsky, Stunning Progress, Hidden Problems: The 
Dramatic Decline of Concentrated Poverty in the 1990s, (Washington, 
D.C.: The Brookings Institution, May 2003). 

[76] We also tested the models using the longer time period of 1995 to 
2004, but the results were consistent with those using the time period 
from 1995 to 1999. 

[77] This low explanatory power is indicated in the low R-square 
statistics in tables 10 and 11. 

[78] In 2002, the Atlanta EZ was designated as a Renewal Community, and 
by 2003 the Atlanta EZ was no longer in operation. HHS transferred the 
remaining EZ/EC funds (over $53 million) to the Renewal Community, but 
as of March 2006, these funds had not been used. 

[79] Congress established the HOPE VI program in 1992 to revitalize 
severely distressed public housing by demolition, rehabilitation, or 
replacement of sites. 

[80] The overarching board also included a nonvoting HUD official. 

[81] The Camden portion of the EZ was initially managed by the city of 
Camden, but HUD officials fostered the change to a nonprofit to deal 
with tensions between the city of Camden and state of New Jersey. 

[82] All tracts that qualified as comparison tracts for Philadelphia- 
Camden were located in Philadelphia. 

[83] The grants and loan guarantees the EZ received could only be used 
for certain economic development or revitalization projects. When 
Cleveland received the Supplemental EZ instead of the regular EZ 
designation, the officials modified their strategic plan. 

[84] When Los Angeles received the Supplemental EZ instead of the 
regular EZ designation, the officials modified their strategic plan 
from having some social service initiatives to focusing only on 
activities directly related to economic development. 

[85] We did not use comparison areas for individual ECs. For more 
information on our methodology, see appendix I. 

[86] Local development districts are unique to the state of Tennessee, 
and the majority of their funding comes from the Tennessee General 
Assembly. 

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