Empowerment Zone and Enterprise Community Program: Improvements
Occurred in Communities, but the Effect of the Program Is Unclear
(22-SEP-06, GAO-06-727).
The Empowerment Zone/Enterprise Community (EZ/EC) program is one
of the most recent large-scale federal effort 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.
-------------------------Indexing Terms-------------------------
REPORTNUM: GAO-06-727
ACCNO: A61305
TITLE: Empowerment Zone and Enterprise Community Program:
Improvements Occurred in Communities, but the Effect of the
Program Is Unclear
DATE: 09/22/2006
SUBJECT: Community development programs
Data collection
Economic analysis
Economic indicators
Federal aid to states
Federal grants
Grant monitoring
Grants to states
Internal controls
Locally administered programs
Performance measures
Program evaluation
Rural economic development
State-administered programs
Urban development programs
Urban economic development
Government agency oversight
Program implementation
HUD Empowerment Zones and Enterprise
Communities Program
USDA Empowerment Zones Program
USDA Rural Empowerment Zones and
Enterprise Communities Program
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GAO-06-727
* Report to Congressional Committees
* September 2006
* EMPOWERMENT ZONE AND ENTERPRISE COMMUNITY PROGRAM
* Improvements Occurred in Communities, but the Effect of the
Program Is Unclear
* Contents
* Results in Brief
* Background
* Round I EZs and ECs Have Used Their Grant Funds to Implement a
Wide Range of Program Activities
* Most EZ/EC Grant Funds Have Been Expended, but Many EZs and
Some ECs Received Grant Extensions
* EZs and ECs Implemented a Wide Variety of Activities, Most
Related to Community Development
* EZs and ECs Used Program Grants to Leverage Additional
Funds, but Reliable Data on the Extent of Leveraging
Are Not Available
* Designees Reported Other Accomplishments
* Designees Reported Implementation Challenges
* EZs and ECs Established a Variety of Governance Structures
and Encouraged Community Participation
* Oversight Was Hindered by Limited Program Data and Variation in
Monitoring
* Federal Agencies Are Required to Oversee the Use of Public
Funds and Provide Ongoing Monitoring
* The Federal Agencies' Oversight Efforts Had Shortcomings in
Data Collection
* Program Monitoring by State and Local Participants Varied
* Limitations in EZ/EC Oversight May Have Resulted from
the Program Design
* Lack of Detailed Tax Data Made It Difficult to Assess the Use of
Program Tax Benefits
* IRS Data on the Use of Program Tax Benefits Are Limited
* IRS Officials Reported that They Have Data Sufficient to
Enforce the Tax Code, but This Information Is Insufficient
for Assessing the Extent of Usage
* In Aggregate, EZs and ECs Showed Some Improvements, but Our
Analysis Did Not Definitively Link These Changes to the Program
* A Number of Challenges Affected Our Efforts to Measure the
Effects of the EZ/EC Program
* In Some Cases, EZs and ECs Showed Improvements in Poverty,
Unemployment, and Economic Growth
* Most EZs and ECs Saw Some Decrease in the Poverty Rate, but
These Changes Could Not Be Tied Definitively to the EZ/EC
Program
* Decreases in the Unemployment Rate in Some Communities Also
Could Not Be Definitively Tied to the EZ/EC Program
* Our Measures Showed that Some Economic Growth Occurred, but
Results from Our Econometric Model Were Not Conclusive
* Additional Program Data Could Facilitate Evaluations of the
Effects of the EZ/EC and Similar Programs
* Observations
* Agency Comments and Our Evaluation
* 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
* Description of Data Sources
* Choosing Comparison Areas Using the Propensity Score
* Our Descriptive and Econometric Analyses
* 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
* List of Communities Designated in Round I of the EZ/EC Program
* Description of the Empowerment Zones and Enterprise Communities We
Visited
* Atlanta Empowerment Zone
* How the EZ Was Governed
* Activities the EZ Implemented
* Changes in Poverty, Unemployment, and Economic Growth
* Stakeholder Perceptions of the Factors Influencing Changes
in Poverty, Unemployment, and Economic Growth
* Baltimore Empowerment Zone
* How the EZ Was Governed
* Activities the EZ Implemented
* Changes in Poverty, Unemployment, and Economic Growth
* Stakeholder Perceptions of the Factors Influencing Changes
in Poverty, Unemployment, and Economic Growth
* Chicago Empowerment Zone
* How the EZ Was Governed
* Activities the EZ Implemented
* Changes in Poverty, Unemployment, and Economic Growth
* Stakeholder Perceptions of the Factors Influencing Changes
in Poverty, Unemployment, and Economic Growth
* Detroit Empowerment Zone
* How the EZ Was Governed
* Activities the EZ Implemented
* Changes in Poverty, Unemployment, and Economic Growth
* Stakeholder Perceptions of the Factors Influencing Changes
in Poverty, Unemployment, and Economic Growth
* New York Empowerment Zone
* How the EZ Was Governed
* Activities the EZ Implemented
* Changes in Poverty, Unemployment, and Economic Growth
* Stakeholder Perceptions of the Factors Influencing Changes
in Poverty, Unemployment, and Economic Growth
* Philadelphia-Camden Empowerment Zone
* How the EZ Was Governed
* Activities the EZ Implemented
* Changes in Poverty, Unemployment, and Economic Growth
* Stakeholder Perceptions of the Factors Influencing Changes
in Poverty, Unemployment, and Economic Growth
* Cleveland Empowerment Zone
* How the EZ Was Governed
* Activities the EZ Implemented
* Changes in Poverty, Unemployment, and Economic Growth
* Stakeholder Perceptions of the Factors Influencing Changes
in Poverty, Unemployment, and Economic Growth
* Los Angeles Empowerment Zone
* How the EZ Was Governed
* Activities the EZ Implemented
* Changes in Poverty, Unemployment, and Economic Growth
* Stakeholder Perceptions of the Factors Influencing Changes
in Poverty, Unemployment, and Economic Growth
* Kentucky Highlands Empowerment Zone
* How the EZ Was Governed
* Activities the EZ Implemented
* Changes in Poverty, Unemployment, and Economic Growth
* Stakeholder Perceptions of the Factors Influencing Changes
in Poverty, Unemployment, and Economic Growth
* Mid-Delta Mississippi Empowerment Zone
* How the EZ Was Governed
* Activities the EZ Implemented
* Changes in Poverty, Unemployment, and Economic Growth
* Stakeholder Perceptions of the Factors Influencing Changes
in Poverty, Unemployment, and Economic Growth
* Rio Grande Valley, Texas Empowerment Zone
* How the EZ Was Governed
* Activities the EZ Implemented
* Changes in Poverty, Unemployment, and Economic Growth
* Stakeholder Perceptions of the Factors Influencing Changes
in Poverty, Unemployment, and Economic Growth
* Providence, Rhode Island Enterprise Community
* How the EC Was Governed
* Activities the EC Implemented
* Changes in Poverty, Unemployment, and Economic Growth
* Stakeholder Perceptions of the Factors Influencing Changes
in Poverty, Unemployment, and Economic Growth
* Fayette-Haywood, Tennessee Enterprise Community
* How the EC Was Governed
* Activities the EC Implemented
* Changes in Poverty, Unemployment, and Economic Growth
* Stakeholder Perceptions of the Factors Influencing Changes
in Poverty, Unemployment, and Economic Growth
* Comments from the Department of Health and Human Services
* Comments from the Department of Housing and Urban Development
* Comments from the U.S. Department of Agriculture
* GAO Contact and Staff Acknowledgments
Report to Congressional Committees
September 2006
EMPOWERMENT ZONE AND ENTERPRISE COMMUNITY PROGRAM
Improvements Occurred in Communities, but the Effect of the Program Is
Unclear
Contents
Tables
Figures
September 22, 2006Letter
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.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.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.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.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. 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.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.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.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.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.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.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.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.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.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
o had above-average poverty according to 1990 Census
data;
o had unemployment rates of at least the national
average according to 1990 Census data;
o met certain 1990 population and area criteria; and
o 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:
o economic opportunity,
o sustainable community development,
o community-based partnerships, and
o strategic vision for change.
EZ program benefits Round I EZs received Title XX Social Services Block
Grants (EZ/EC grants).
o Six urban EZs each received $100 million.a
o Three rural EZs each received $40 million.
Businesses located in EZs initially received three
tax benefits:
o a tax credit for wages paid to employees who both
live and work in an EZ,
o an increased expensing deduction for depreciable
property, and
o 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.
aThis 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.
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 2010.15 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
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 1990 1995 2000 2003 2004
Poverty 13.5% 13.8% 11.3% 12.5% 12.7%
Unemployment 5.6% 5.6% 4.0% 6.0% 5.5%
Number of 6.1 million 6.6 million 7.1 million 7.3 million a
businesses
Number of jobs 93.4 million 100.3 114.1 113.4 a
million million million
Sources: Census Bureau and Bureau of Labor Statistics.
aData were not yet available for 2004.
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.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
Total funding Amount remaining Percent remaining
EZs $720 million $146.6 million 20%
ECs $280 million $4.5 million 2%
Total $1 billion $151 million 15%
Source: GAO analysis of HHS data.
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
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.
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).17
Figure 3: Distribution of EZ and EC Activities by Key Program Principle
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.
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
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.
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.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.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.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
aThe 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.
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.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.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.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
1/2 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. 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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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.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:
o 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.
o 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.
o 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. 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.
o 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.
o 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.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.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
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.
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).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:
o 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.
o 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.
o 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.
o 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
Note: All poverty estimates had 95 percent confidence intervals of plus or
minus 5 percentage points or less.
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:
o 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).
o Rural designees experienced a larger significant decrease in poverty
than urban designees-7.2 and 5 percentage points, respectively.
o 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
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.
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.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.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.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.50 Many communities did not
experience a significant change. Specifically, our analysis showed the
following:
o 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.
o 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.
o 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.
o 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
Note: All unemployment estimates had 95 percent confidence intervals of
plus or minus 5 percentage points or less.
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:
o 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).
o 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.
o Urban EZs and ECs saw a greater decrease in unemployment than their
comparison areas, where the rates did not show a statistically significant
change.
o 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
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.
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.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.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:
o 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.
o 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.
o 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.53 However, 26 urban ECs saw a decrease in both measures.
o 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
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.
aData were not available for the Miami/Dade County, Florida EC.
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).
o 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.
o 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.
o 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.
o 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
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.
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.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.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.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.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
http://www.gao.gov .
Please contact me at (202) 512-8678 or [email protected] 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.
William B. Shear Director, Financial Markets and Community Investment
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.1 These EZs and ECs were located in:
o Atlanta, Georgia (EZ)
o Baltimore, Maryland (EZ)
o Chicago, Illinois (EZ)
o Cleveland, Ohio (EZ)
o Detroit, Michigan (EZ)
o Los Angeles, California (EZ)
o New York, New York (EZ)
o Philadelphia, Pennsylvania and Camden, New Jersey (EZ)
o rural Kentucky (Kentucky Highlands EZ)
o rural Mississippi (Mid-Delta EZ)
o rural Texas (Rio Grande Valley EZ)
o Providence, Rhode Island (EC)
o 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).2 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 EZ/EC Board Representatives Representatives Officials Other
officials members from subgrantee from the from the representatives
organizations Chamber of state a
Commerce or pass-through
other business entities
perspective
Urban
Atlanta EZ 2 2 6 2 4 10
Baltimore EZ 6 4 3 2 1 5
Chicago EZ 6 4 4 1 3 2
Cleveland EZ 5 4 3 0 4 8
Detroit EZ 7 4 3 1 2 9
Los Angeles EZ 1 2 2 0 0 12
New York EZ
Upper Manhattan 3 3 2 1 2 2
portion
Bronx portion 2 4 3 1 2 1
Philadelphia/Camden
EZ
Philadelphia 5 2 2 0 8 3
portion
Camden portion 1 3 2 1 2 2
Providence EC 2 3 3 0 2 1
Rural
Kentucky Highlands 4 6b 1 1 4 4
EZ
Mid-Delta 4 1 4 2 2 2
Mississippi EZ
Rio Grande Valley 4 3 3 1 3 1
EZ
Fayette Haywood EC 1 2 2 0 4 4
Source: GAO.
aIncludes local governmental officials, business owners, active community
members, and other representatives.
bIncludes directors of subzones.
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
www.gao.gov/cgi-bin/getrpt?GAO-06-734SP.
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.3 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 2005.4 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:
o strategic planning and census tract selection;
o goals, implemented activities, leveraging activities, and
sustainability;
o governance structure and process;
o program oversight;
o perceptions of the use of tax benefits; and
o 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.5 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 Description
2 Items with strong documentation, meaning that exact documentation
existed or could be easily inferred with the provided documentation.
1 Items with weak documentation, meaning that some evidence existed,
but numbers did not match.
0 Items for which no documentation existed.
Source: GAO.
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.6 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.7 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.8 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.9 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.10 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.11 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 Dollarsa
95 percent 95 percent 95 percent
confidence confidence confidence
interval interval interval
1990 From To 2000 From To Percent From To
estimate estimate change
Average
household
income
Atlanta EZ $18,343 $17,466 $19,220 $28,552 $27,205 $29,899 55.66 55.4 55.91
Comparison 30,567 29,741 31,393 39,500 38,328 40,672 29.23 28.99 29.46
Baltimore EZ 28,185 27,207 29,164 35,059 33,566 36,551 24.39 24.1 24.67
Comparison 27,931 27,316 28,546 31,367 30,511 32,223 12.3 12.05 12.56
Chicago EZ 23,097 22,636 23,559 34,718 33,868 35,567 50.31 50.13 50.49
Comparison 28,431 28,030 28,832 39,985 39,367 40,604 40.64 40.48 40.8
Detroit EZ 22,644 22,034 23,253 33,751 32,660 34,842 49.05 48.84 49.26
Comparison 25,609 25,197 26,021 36,200 35,523 36,877 41.36 41.19 41.52
1990 From To 2000 From To Percent From To
estimate estimate change
New York EZ 26,518 25,981 27,054 33,557 32,833 34,280 26.54 26.34 26.75
Comparison 26,993 26,714 27,272 31,247 30,872 31,622 15.76 15.59 15.93
Upper Manhattan 26,559 25,971 27,147 34,041 33,239 34,844 28.17 27.96 28.39
Bronx 26,294 24,983 27,606 30,842 29,238 32,446 17.29 16.95 17.64
Philadelphia- 23,188 22,259 24,117 28,562 27,197 29,927 23.17 22.87 23.48
Camden EZ
Comparison 27,292 26,031 28,553 31,318 29,718 32,918 14.75 14.4 15.1
Philadelphia 22,269 21,262 23,276 27,851 26,309 29,392 25.07 24.74 25.39
Camden 26,742 24,465 29,018 31,158 28,228 34,088 16.52 16.05 16.98
Cleveland EZ 20,535 19,730 21,340 28,781 27,524 30,038 40.16 39.9 40.42
Comparison 24,688 24,171 25,206 30,311 29,607 31,016 22.78 22.56 23
Los Angeles EZ 28,801 28,191 29,412 32,631 31,857 33,405 13.3 13.06 13.54
Comparison 34,087 33,478 34,696 37,843 37,058 38,628 11.02 10.79 11.25
Kentucky 23,304 22,043 24,565 31,064 29,520 32,608 33.3 32.99 33.61
Highlands EZ
Mid-Delta EZ 25,872 24,321 27,424 35,559 33,392 37,726 37.44 37.12 37.76
Rio Grande 25,093 23,626 26,560 32,763 30,920 34,606 30.57 30.24 30.9
Valley EZ
Providence EC 28,593 27,525 29,661 32,616 31,229 34,004 14.07 13.75 14.39
Fayette-Haywood 32,560 31,008 34,111 45,353 43,249 47,457 39.29 39.01 39.57
EC
Average
owner-occupied
housing value
Atlanta EZ $55,883 $52,688 $59,077 $117,869 $106,218 $129,519 110.92 110.68 111.17
Comparison 74,063 72,446 75,680 101,774 96,312 107,236 37.42 37.15 37.68
Baltimore EZ 53,714 51,381 56,048 62,219 58,659 65,779 15.83 15.48 16.19
Comparison 55,966 54,113 57,819 62,514 59,920 65,108 11.7 11.39 12.01
Chicago EZ 71,429 67,487 75,372 160,411 150,476 170,347 124.57 124.38 124.77
Comparison 88,445 85,343 91,548 167,015 159,548 174,482 88.83 88.64 89.03
Detroit EZ 23,114 22,153 24,075 52,234 49,362 55,106 125.99 125.81 126.16
Comparison 28,598 27,620 29,575 61,160 58,688 63,632 113.86 113.7 114.03
New York EZ 207,544 166,353 248,735 301,835 244,974 358,697 45.43 44.89 45.98
Comparison 177,446 167,025 187,867 209,423 198,465 220,380 18.02 17.66 18.38
Upper 238,864 188,845 288,882 384,155 308,848 459,462 60.83 60.32 61.33
Manhattan
Bronx 99,728 71,856 127,600 124,588 100,021 149,155 24.93 24.23 25.63
Philadelphia- 29,899 28,060 31,739 37,780 35,895 39,664 26.36 26.02 26.69
Camden EZ
Comparison 42,045 39,630 44,461 51,159 44,926 57,392 21.67 21.22 22.13
Philadelphia 28,288 26,263 30,313 37,353 35,178 39,528 32.04 31.7 32.39
1990 From To 2000 From To Percent From To
estimate estimate change
Camden 35,076 30,928 39,224 39,398 35,730 43,067 12.32 11.8 12.84
Cleveland EZ 38,071 36,277 39,866 75,186 71,537 78,835 97.49 97.29 97.69
Comparison 46,972 45,966 47,979 70,161 68,649 71,674 49.37 49.19 49.54
Los Angeles EZ 141,665 138,933 144,397 156,492 151,907 161,078 10.47 10.21 10.72
Comparison 160,090 157,393 162,787 165,180 161,599 168,761 3.18 2.94 3.42
Kentucky 43,392 38,713 48,071 65,815 62,527 69,104 51.68 51.35 52
Highlands EZ
Mid-Delta EZ 50,061 47,323 52,800 66,872 59,968 73,777 33.58 33.19 33.97
Rio Grande 46,100 42,654 49,546 61,450 55,970 66,929 33.3 32.91 33.69
Valley EZ
Providence EC 124,339 118,190 130,489 116,698 99,200 134,196 -6.15 -6.77 -5.52
Fayette-Haywood $68,945 $65,765 $72,125 $103,619 $97,144 $110,094 50.29 50.01 50.57
EC
Source: GAO analysis of Census data.
aOther 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.
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 Reason selected
1990 poverty rate a o EZ/EC program eligibility criteria
o Factor considered in a similar studyb, e
1990 unemployment ratec o EZ/EC program eligibility criteria
o Factor considered in a similar study b
1990 population density d o Calculation based on two EZ/EC program
eligibility criteria, population and area
o Factor considered in a similar studyb
1990 average household income o Factor considered in similar studies b, e
Percentage of minority o Factor considered in similar studies b, e
population in 1990f
Source: GAO.
aPercent based on individuals for whom poverty status has been determined.
bSee 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.
cPercent based on individuals 16 years of age or older.
dIndividuals per square mile.
eHebert and others, Interim Assessment.
fFor the purposes of this report, we calculated minority population by
subtracting the percent of white population from the total population.
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.12 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.13 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.14 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.
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.1 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.2 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.3 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:
o percent of minority population in 1990,
o average household income in 1990,
o population density in 1990,
o poverty rate in 1990, and
o 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.4
Table 8: Estimates of the Association between the EZ Program and the
Change in Poverty Rate, 1990-2000
Basic model Expanded
model
Variables Coefficient Standard Coefficient Standard
error error
EZ program 1.54 0.67
Atlanta EZ 3.41 2.69
Baltimore EZ -2.27 1.94
Chicago EZ 1.63 1.50
Cleveland EZ -2.43 2.10
Detroit EZ 2.25 1.54
Los Angeles EZ 3.07 1.20
New York EZ -1.87 1.23
Philadelphia-Camden -1.10 2.70
EZ
Atlanta EZ areaa -5.50 2.61
Baltimore EZ areaa 0.60 2.49
Chicago EZ areaa -5.08 2.23
Cleveland EZ areaa -4.65 2.36
Detroit EZ areaa -7.73 2.35
Los Angeles EZ 2.00 2.30
areaa
New York EZ areaa 0.56 2.40
Philadelphia-Camden b b
EZ areaa
Percent of high 0.080 0.026 0.017 0.024
school dropoutsc
Percent of vacant -0.064 0.042 0.046 0.044
housing units
Percent of 0.24 0.045 0.22 0.042
female-headed
households with
childrend
Percent employed in -0.0073 0.054 0.040 0.052
retail industrye
Percent housing -0.17 0.081 -0.29 0.082
units built between
1990 and 1994f
Percent minority 0.0045 0.022 -0.021 0.021
populationg
Average household -0.00046 0.00085 -0.00060 0.000081
income (in 2004
dollars)
Population densityh 0.000047 0.0000087 0.000024 0.000013
Poverty ratei -0.71 0.052 -0.79 0.047
Unemployment ratej -0.047 0.046 0.085 0.054
Constant 30.39 4.29 40.79 4.83
Number of tracts 851 851
R-sq 0.40 0.47
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.
aWe 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.
bResults 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.
cPercent based on the civilian population between ages 16 and 19 who are
not enrolled in school and are not high school graduates.
dPercent based on households headed by females without husbands present in
which there are children under 18 years of age.
ePercent based on individuals 16 and over.
fFrom the 2000 Census.
gWe calculated minority population by subtracting the percent of white
population from the total population.
hIndividuals per square mile.
iPercent based on individuals for whom poverty status has been determined.
jPercent based on individuals 16 years of age or older in the labor force.
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
Basic model Expanded
model
Variables Coefficient Standard Coefficient Standard
error error
EZ program -0.065 0.50
Atlanta EZ 2.56 1.95
Baltimore EZ -0.71 1.77
Chicago EZ -0.68 1.07
Cleveland EZ -3.65 1.40
Detroit EZ 1.76 1.09
Los Angeles EZ 0.092 1.20
New York EZ -1.57 0.88
Philadelphia-Camden EZ -0.87 1.83
Atlanta EZ areaa -2.48 1.71
Baltimore EZ areaa 2.35 1.71
Chicago EZ areaa 2.66 1.57
Cleveland EZ areaa -1.89 1.58
Detroit EZ areaa -2.59 1.55
Los Angeles EZ areaa 3.61 1.62
New York EZ areaa 3.13 1.62
Philadelphia-Camden EZ areaa b b
Percent of population of 0.068 0.072 0.093 0.068
working agec
Percent of population with a 0.11 0.039 0.20 0.044
high school diplomad
Percent of housing units -0.18 0.062 -0.23 0.062
built between 1990 and 1994e
Percent minority populationf 0.072 0.012 0.054 0.014
Average household income (in -0.00017 0.000068 -0.00032 0.000078
2004 dollars)
Population densityg 0.000032 0.0000066 0.0000064 0.000011
Poverty rateh 0.20 0.033 0.15 0.034
Unemployment ratei -0.90 0.039 -0.86 0.044
Constant -0.88 4.54 1.92 4.61
Number of tracts 866 866
R-sq 0.53 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.
aWe 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.
bResults 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.
cWe defined "working age" as between 16 and 64 years of age.
dPercent based on population 25 years of age and over.
eFrom the 2000 Census.
fFor the purposes of this report, we calculated minority population by
subtracting the percent of white population from the total population.
gIndividuals per square mile.
hPercent based on individuals for whom poverty status has been determined.
iPercent based on individuals 16 years of age or older in the labor force.
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.5 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). 6 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
Basic model Expanded
model
Variables Coefficient Standard Coefficient Standard
error error
EZ program -22.58 18.84
Atlanta EZ 24.59 26.62
Baltimore EZ -0.84 12.12
Chicago EZ 12.80 14.80
Cleveland EZ 4.95 7.05
Detroit EZ 13.68 13.05
Los Angeles EZ -113.70 91.86
New York EZ 10.80 10.92
Philadelphia-Camden EZ 1.03 21.50
Atlanta EZ areab -12.32 22.13
Baltimore EZ areab -23.11 17.86
Chicago EZ areab -17.65 16.25
Cleveland EZ areab 11.51 29.68
Detroit EZ areab 8.64 25.45
Los Angeles EZ areab -38.46 27.71
New York EZ areab -15.84 20.05
Variables Coefficient Standard Coefficient Standard
error error
Philadelphia-Camden EZ areab c c
Population -0.0027 0.0026 0.0011 0.0014
Percent vacant housing units 0.10 0.27 -1.09 1.01
Percent of housing units 0.95 0.58 1.77 1.37
built between 1990 and 1994d
Percent minority populatione 0.74 0.69 0.80 0.69
Average household income (in 0.0044 0.0051 0.0056 0.006
2004 dollars)
Population densityf 0.00027 0.00014 0.000056 0.00011
Poverty rateg 1.67 2.21 2.31 2.61
Unemployment rateh 0.13 0.35 -0.22 0.59
Constant -253.01 278.86 -300.97 314.17
Number of tracts 860 860
R-sq 0.042 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.
aExcluding establishments that were not eligible for the program tax
benefits, such as nonprofit and governmental organizations.
bWe 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.
cResults 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.
dFrom the 2000 Census.
eFor the purposes of this report, we calculated minority population by
subtracting the percent of white population from the total population.
fIndividuals per square mile.
gPercent based on individuals for whom poverty status has been determined.
hPercent based on individuals 16 years of age or older in the labor force.
Table 11: Estimates of the Association between the EZ Program and Economic
Growth, Measured by the Change in the Number of Jobs, 1995-1999
Basic model Expanded
model
Variables Coefficient Standard Coefficient Standard
error error
EZ program -68.86 196.83
Atlanta EZ 286.01 983.37
Baltimore EZ 1199.74 715.74
Chicago EZ -88.43 180.84
Cleveland EZ 102.85 293.30
Detroit EZ 199.16 181.98
Los Angeles EZ -438.63 632.33
New York EZ -288.08 231.26
Philadelphia-Camden EZ 197.87 631.54
Atlanta EZ areaa -318.84 883.10
Baltimore EZ areaa -819.50 810.54
Chicago EZ areaa 36.80 547.93
Cleveland EZ areaa 134.99 557.58
Detroit EZ areaa -61.44 576.67
Los Angeles EZ areaa -239.53 571.81
New York EZ areaa 270.91 553.59
Philadelphia-Camden EZ areaa b b
Percent of population of -21.04 28.00 -19.59 29.65
working aged
Percent of population with a 10.36 8.39 4.06 11.77
high school diplomad
Percent of housing units 10.57 29.50 5.09 29.33
built between 1990 and 1994e
Percent minority populationf 3.41 4.63 3.23 5.36
Average household income (in 0.022 0.038 0.032 0.048
2004 dollars)
Population densityg 0.0016 0.0023 -0.0019 0.0029
Poverty rateh -1.30 20.33 2.99 20.34
Unemployment ratei 3.10 15.33 -0.95 14.30
Constant -56.23 1483.38 -200.97 1695.03
Number of tracts 859 859
R-sq 0.016 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.
aWe 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.
bResults 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.
cWe defined "working age" as between 16 and 64 years of age.
dPercent based on population 25 years of age and over.
eFrom the 2000 Census.
fFor the purposes of this report, we calculated minority population by
subtracting the percent of white population from the total population.
gIndividuals per square mile.
hPercent based on individuals for whom poverty status has been determined.
iPercent based on individuals 16 years of age or older in the labor force.
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 Rationale Data sources
Dependent variables
Change in per-capita income An opposite measure of 1990 and 2000
between 1990 and 2000 poverty Census
Change in employment rate An opposite measure of 1990 and 2000
between 1990 and 2000 unemployment Census
Percent change in number of Alternative measure of Claritas 1995, 1999
businesses between 1995 and economic growth
1999
Percent change in the number Alternative measure of Claritas 1995, 1999
of jobs between 1995 and economic growth
1999
Percent change in number of Alternative measure of Claritas 1995, 2004
businesses between 1995 and economic growth
2004
Percent change in the number Alternative measure of Claritas 1995, 2004
of jobs between 1995 and economic growth
2004
Change in jobs per business Alternative measure of Claritas 1995, 1999
between 1995 and 1999 economic growth
Dependent variables
Change in aggregate sales Alternative measure of Claritas 1995, 1999
volume of businesses at each economic growth
tract between 1995 and 1999
Percent change in number of Alternative measure of Home Mortgage
loan originations for new economic growth Disclosure Act data
home purchases between 1995 1995, 1999
and 1999
Percent change in mean loan Alternative measure of Home Mortgage
amount for new home economic growth Disclosure Act data
purchases between 1995 and 1995, 1999
1999
Explanatory variables
Percent foreign-born Alternative indirect 1990 Census
population measure for minority
population
Adjusted per capita income Alternative indirect 1990 Census
in 2004 dollars measure household income
Percent of males aged 16 or Alternative measure for 1990 Census
greater working population
Percent of housing units Alternative measure to 1990 Census
built last 5 years before account for economic
census trend
Percent of persons aged 25 Alternative measure for 1990 Census
or greater with some college educational level
Percent of employment in Alternative measure of 1990 Census
manufacturing industry industry characteristics
Percent of female-headed Alternative measure for 1990 Census
single households household characteristics
Source: GAO.
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, Ohioa
Detroit, Michigan
Los Angeles, Californiaa
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, Massachusettsb
Bridgeport, Connecticut
Buffalo, New York
Burlington, Vermont
Charleston, South Carolina
Charlotte, North Carolina
Cleveland, Ohioc
Columbus, Ohio
Dallas, Texas
Denver, Colorado
Des Moines, Iowa
East St. Louis, Illinois
El Paso, Texas
Flint, Michigan
Harrisburg, Pennsylvania
Houston, Texasb
Huntington, West Virginia
Indianapolis, Indiana
Jackson, Mississippi
Kansas City, Missouri/Kansas City, Kansasb
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, Californiab
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
Source: HUD and USDA data.
aInitially designated as a Supplemental EZ
bAlso designated as an Enhanced EC
cAlso designated as a Supplemental EZ
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
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.1
Figure 14: Activities Implemented by the Atlanta EZ
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
1990 2000 Percent
changea
EZ Comparison EZ Comparison EZ Comparison
Poverty rate 54.67 30.15 44.82 28.02 -9.84b -2.12
(%)
Unemployment 17.48 11.36 23.44 11.88 5.96b 0.52
rate (%)
Average $18,343 $30,567 $28,552 $39,500 55.66b 29.23b
household
income
Percentage 24.62 20.02 21.26 19.95 -3.36b -0.07
of single
female
headed
households
with
children
Total 49,966 65,809 45,931 64,022 -8.07 -2.71
population
Total 5,408 2,671 4,972 2,756 -8.07 3.16
individuals
per square
mile
Percentage 50.87 46.01 53.32 52.52 2.45b 6.51b
of
households
that moved
in the last
5 years
Percentage 60.16 61.68 63.42 64.59 3.26b 2.92b
of
population
of working
age (16-64)
Percentage 43.10 60.53 58.96 69.3 15.86b 8.78b
of
population
with a high
school
diploma (or
equivalent)
Percentage 19.12 19.1 21.48 21.19 2.36b 2.08b
of high
school
dropouts
Percentage 20.79 14.65 13.30 7.43 -7.48b -7.22b
of vacant
housing
units
Average $55,883 $74,063 $117,869 $101,774 110.92b 37.42b
owner
occupied
housing
value
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.
aDifferences 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.
bThe change in estimates from 1990 to 2000 is statistically significant.
Table 14: Changes in Selected Economic Growth Variables Observed in the
Atlanta EZ and Its Comparison Area
1995 1999 2004 Percent
change
1995-2004a
EZ Comparison EZ Comparison EZ Comparison EZ Comparison
Number of 1,930 3,980 1,549 3,380 1,529 3,248 -20.78 -18.39
businesses
Number of 36,888 71,346 31,470 79,580 28,672 69,140 -22.27 -3.09
jobs
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.
aDifferences for the number of businesses and the number of jobs are
calculated as percent changes.
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.2
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
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
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
1990 2000 Percent
changea
EZ Comparison EZ Comparison EZ Comparison
Poverty rate 41.81 41.17 35.66 39.74 -6.16 -1.43
(%) b
Unemployment 15.00 14.55 16.48 17.58 1.49 3.03 b
rate (%)
Average $28,185 $27,931 $35,059 $31,367 24.39b 12.30b
household
income
Percentage 22.50 23.15 19.49 19.64 -3.01 -3.51b
of single b
female
headed
households
with
children
Total 72,725 150,507 54,657 113,052 -24.84 -24.89
population
Total 10,460 16,934 7,890 12,923 -24.57 -23.69
individuals
per square
mile
Percentage 41.07 42.98 41.00 44.90 -0.07 1.92b
of
households
that moved
in the last
5 years
Percentage 58.55 60.31 60.04 61.63 1.48 1.32
of
population
of working
age (16-64)
Percentage 45.69 49.86 56.44 58.50 10.74b 8.64b
of
population
with a high
school
diploma (or
equivalent)
Percentage 32.36 26.43 19.55 20.61 -12.81 -5.81b
of high b
school
dropouts
Percentage 17.59 12.67 26.22 23.63 8.62b 10.96b
of vacant
housing
units
Average $53,714 $55,966 $62,219 $62,514 15.83b 11.7b
owner
occupied
housing
value
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.
aDifferences 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.
bThe change in estimates from 1990 to 2000 is statistically significant.
Table 16: Changes in Selected Economic Growth Variables Observed in the
Baltimore EZ and Its Comparison Area
1995 1999 2004 Percent
change
1995-2004a
EZ Comparison EZ Comparison EZ Comparison EZ Comparison
Number of 2,797 3,481 2,399 2,930 2,487 3,005 -11.08 -13.67
businesses
Number of 41,837 61,519 53,732 35,268 47,504 36,860 13.55 -40.08
jobs
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.
aDifferences for the number of businesses and the number of jobs are
calculated as percent changes.
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
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
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
1990 2000 Percent
changea
EZ Comparison EZ Comparison EZ Comparison
Poverty rate 49.10 40.38 39.32 33.49 -9.77 b -6.89b
(%)
Unemployment 24.57 20.52 19.34 18.97 -5.23 b -1.54b
rate (%)
Average $23,097 $28,431 $34,718 $39,985 50.31b 40.64b
household
income
Percentage 25.64 23.07 21.59 19.69 -4.05b -3.38b
of single
female
headed
households
with
children
Total 200,182 377,580 177,309 369,343 -11.43 -2.18
population
Total 13,967 15,523 12,380 15,752 -11.36 1.47
individuals
per square
mile
Percentage 37.52 40.03 39.21 39.68 1.69b -0.35
of
households
that moved
in the last
5 years
Percentage 53.51 57.87 55.63 59.53 2.12b 1.66b
of
population
of working
age (16-64)
Percentage 44.04 54.24 54.30 63.58 10.26b 9.35b
of
population
with a high
school
diploma (or
equivalent)
Percentage 22.46 19.50 22.05 15.23 -0.41 -4.27 b
of high
school
dropouts
Percentage 19.69 13.54 18.23 12.44 -1.45b -1.1b
of vacant
housing
units
Average $71,429 $88,445 $160,412 $167,015 124.57b 88.83b
owner
occupied
housing
value
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.
aDifferences 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.
bThe change in estimates from 1990 to 2000 is statistically significant.
Table 18: Changes in Selected Economic Growth Variables Observed in the
Chicago EZ and Its Comparison Area
1995 1999 2004 Percent
change
1995-2004a
EZ Comparison EZ Comparison EZ Comparison EZ Comparison
Number of 5,089 10,567 4,614 9,582 4,496 9,211 -11.65 -12.83
businesses
Number of 83,935 183,369 80,294 169,741 69,767 162,541 -16.88 -11.36
jobs
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.
aDifferences for the number of businesses and the number of jobs are
calculated as percent changes.
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
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 fac,ade 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
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
1990 2000 Percent
changea
EZ Comparison EZ Comparison EZ Comparison
Poverty rate 47.63 42.72 36.73 32.38 -10.90b -10.34b
(%)
Unemployment 28.41 26.01 18.83 15.54 -9.58 b -10.47 b
rate (%)
Average $22,644 $25,609 $33,751 $36,200 49.05b 41.36b
household
income
Percentage of 17.30 20.94 15.88 18.77 -1.43 -2.18b
single female
headed
households with
children
Total 103,346 256,371 88,707 229,536 -14.16 -10.47
population
Total 5,547 6,923 4,762 6,200 -14.15 -10.45
individuals per
square mile
Percentage of 40.80 37.39 42.20 38.47 1.40 1.08
households that
moved in the
last 5 years
Percentage of 57.65 56.96 60.34 57.01 2.69b 0.05
population of
working age
(16-64)
Percentage of 49.34 53.63 58.06 60.87 8.72b 7.24b
population with
a high school
diploma (or
equivalent)
Percentage of 23.29 20.47 20.83 18.89 -2.46b -1.57b
high school
dropouts
Percentage of 18.26 11.22 17.46 13.98 -0.79 2.76b
vacant housing
units
Average owner $23,114 $28,598 $52,234 $61,160 125.99b 113.86b
occupied
housing value
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.
aDifferences 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.
bThe change in estimates from 1990 to 2000 is statistically significant.
Table 20: Changes in Selected Economic Growth Variables Observed in the
Detroit EZ and Its Comparison Area
1995 1999 2004 Percent
change
1995-2004a
EZ Comparison EZ Comparison EZ Comparison EZ Comparison
Number of 3,723 5,343 3,650 5,282 3,621 4,770 -2.74 -10.72
businesses
Number of 95,172 86,500 99,480 73,770 124,172 66,179 30.47 -23.49
jobs
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.
aDifferences for the number of businesses and the number of jobs are
calculated as percent changes.
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 fac,ade
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
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.3 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
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
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.)
1990 2000 Percent
changea
Entire Bronx UM Comp. Entire Bronx UM Comp. Entire Bronx UM Comp.
EZ EZ EZ
Poverty rate 42.68 44.2 42.38 42.5 38.62 41.59 38.02 41.75 -4.07b -2.61 -4.35b -0.75
(%)
Unemployment 17.45 15.36 17.86 17.17 19.46 20.96 19.18 20.04 2.00b 5.6b 1.32 2.87b
rate (%)
Average $26,518 $26,294 $26,559 $26,993 $33,557 $30,842 $34,041 $31,247 26.54b 17.29b 28.17b 15.76b
household
income
Percentage 20.19 25.55 19.2 25.91 19.6 23.15 18.97 25.26 -0.59 -2.40b -0.23 -0.64
of single
female
headed
households
with
children
Total 199,983 34,266 165,717 638,776 219,324 36,886 182,438 672,826 9.67 7.65 10.09 5.33
population
Total 31,890 11,651 49,763 58,404 35,286 12,553 55,672 67,150 10.65 7.73 11.88 14.97
individuals
per square
mile
Percentage 31.93 31.96 31.93 32.5 34.07 33.4 34.2 33.64 2.14b 1.45 2.28b 1.15b
of
households
that moved
in the last
5 years
Percentage 59.67 60.09 59.59 58.97 61.3 60.36 61.49 58.49 1.63b 0.28 1.9b -0.48
of
population
of working
age (16-64)
Percentage 47.74 44.43 48.37 48.4 55.16 51.25 55.9 51.66 7.42b 6.82b 7.53b 3.26b
of
population
with a high
school
diploma (or
equivalent)
Percentage 19.85 18.12 20.19 20.18 15.59 17.75 15.2 17.7 -4.26b -0.37 -4.99b -2.48b
of high
school
dropouts
Percentage 8.81 3.24 9.77 3.99 11.09 7.35 11.73 5.99 2.28b 4.11 b 1.96b 2.00b
of vacant
housing
units
Average $207,544 $99,728 $238,864 $177,446 $301,835 $124,588 $384,155 $209,423 45.43b 24.93b 60.83b 18.02b
owner
occupied
housing
value
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.
aDifferences 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.
bThe change in estimates from 1990 to 2000 is statistically significant.
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.)
1995
Entire EZ Bronx UM Comp.
Number of businesses 5,415 1,738 3,677 8,294
Number of jobs 96,228 32,243 63,985 108,785
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.
aDifferences for the number of businesses and the number of jobs are
calculated as percent changes.
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
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.4 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
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
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.5 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.)
1990 2000 Percent
change
a
Entire Cam. Phila. Comp. Entire Cam. Phila. Comp. Entire Cam. Phila. Comp.
EZ EZ EZ
Poverty rate 50.14 43 52.1 43.07 42.98 40.55 43.68 37.97 -7.16b -2.45 -8.42b -5.1b
(%)
Unemployment 22.21 18.09 23.6 18.81 19.3 19.08 19.38 17.91 -2.91b 0.99 -4.22b -0.9
rate (%)
Average $23,188 $26,742 $22,269 $27,292 $28,562 $31,158 $27,851 $31,318 23.17b 16.52b 25.07b 14.8b
household
income
Percentage 22.73 24.13 22.37 21.01 21.93 23.22 21.57 20.66 -0.81 -0.92 -0.8 -0.36
of single
female
headed
households
with
children
Total 52,440 13,332 39,108 38,520 45,725 12,749 32,976 35,827 -12.81 -4.37 -15.68 -6.99
population
Total 12,248 7,342 15,861 7,601 10,698 7,034 13,396 7,064 -12.66 -4.2 -15.54 -7.06
individuals
per square
mile
Percentage 34.71 41.93 32.25 40.69 34.05 44.42 30.04 44.05 -0.66 2.49 -2.21 3.36b
of
households
that moved
in the last
5 years
Percentage 57.58 63.02 55.72 59.76 58.85 66.86 55.75 61.93 1.27 3.84b 0.03 2.17
of
population
of working
age (16-64)
Percentage 42.79 44.51 42.21 54.04 51.82 50.43 52.38 63.19 9.04b 5.92b 10.17b 9.15b
of
population
with a high
school
diploma (or
equivalent)
Percentage 25.58 22.67 26.56 23.07 16.02 12.54 17.25 14.05 -9.56b -10.13b -9.3b -9.02b
of high
school
dropouts
Percentage 21.45 22.37 21.21 14.48 24.94 25.7 24.73 14.91 3.49b 3.33b 3.52b 0.43
of vacant
housing
units
Average $29,899 $35,076 $28,288 $42,045 $37,780 $39,398 $37,353 $51,159 26.36b 12.32b 32.04b 21.7b
owner
occupied
housing
value
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.
aDifferences 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.
bThe change in estimates from 1990 to 2000 is statistically significant.
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.)
1995
Entire EZ Cam. Phila. Comp.
Number of businesses 2,064 730 1,334 2,631
Number of jobs 35,867 14,430 21,437 55,071
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.
aDifferences for the number of businesses and the number of jobs are
calculated as percent changes.
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
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.6 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
Note: We were not able to determine specific types of activities the
Cleveland EZ implemented, because reliable data were not available.
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
1990 2000 Percent
changea
EZ Comparison EZ Comparison EZ Comparison
Poverty rate 46.85 39.41 36.03 35.70 -10.82b -3.71b
(%)
Unemployment 25.44 20.63 15.48 17.29 -9.96b -3.34b
rate (%)
Average $20,535 $24,688 $28,781 $30,311 40.16b 22.78b
household
income
Percentage of 19.07 23.24 18.24 23.19 -0.83 -0.05
single female
headed
households with
children
Total 50,724 153,578 43,694 141,465 -13.86 -7.89
population
Total 8,319 7,231 7,168 6,532 -13.84 -9.66
individuals per
square mile
Percentage of 36.13 36.04 39.55 39.70 3.41b 3.66b
households that
moved in the
last 5 years
Percentage of 55.01 56.80 53.08 55.54 -1.93 -1.26
population of
working age
(16-64)
Percentage of 47.14 54.84 61.82 65.13 14.68b 10.29b
population with
a high school
diploma (or
equivalent)
Percentage of 18.35 14.83 13.30 16.38 -5.05b 1.55b
high school
dropouts
Percentage of 14.68 14.70 18.82 15.13 4.14b 0.43
vacant housing
units
Average owner $38,071 $46,972 $75,186 $70,164 97.49b 49.37b
occupied
housing value
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.
aDifferences 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.
bThe change in estimates from 1990 to 2000 is statistically significant.
Table 26: Changes in Selected Economic Growth Variables Observed in the
Cleveland EZ and Its Comparison Area
1995 1999 2004 Percent
change
1995-2004a
EZ Comparison EZ Comparison EZ Comparison EZ Comparison
Number of 1,766 4,883 2,067 4,889 1,899 4,602 7.53 -5.75
businesses
Number of 42,087 87,334 58,679 102,996 38,023 84,064 -9.66 -3.74
jobs
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.
aDifferences for the number of businesses and the number of jobs are
calculated as percent changes.
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
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.7 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
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
1990 2000 Percent
changea
EZ Comparison EZ Comparison EZ Comparison
Poverty rate 40.24 31.52 41.49 33.14 1.25 1.61
(%)
Unemployment 18.39 15.07 18.61 15.47 0.22 0.40
rate (%)
Average $28,801 $34,087 $32,631 $37,843 13.30b 11.02b
household
income
Percentage of 18.32 18.64 16.90 17.40 -1.43b -1.24
single female
headed
households
with children
Total 211,365 221,657 225,591 219,001 6.73 -1.20
population
Total 11,082 12,918 11,836 13,170 6.81 1.95
individuals
per square
mile
Percentage of 46.12 44.06 43.53 41.20 -2.59b -2.86b
households
that moved in
the last 5
years
Percentage of 57.60 58.11 57.53 57.28 -0.06 -0.83
population of
working age
(16-64)
Percentage of 38.40 50.30 37.46 49.53 -0.94 -0.77
population
with a high
school diploma
(or
equivalent)
Percentage of 32.14 23.50 23.09 17.04 -9.04b -6.46b
high school
dropouts
Percentage of 6.33 6.31 9.65 8.11 3.31b 1.80b
vacant housing
units
Average owner $141,665 $160,090 $156,493 $165,180 10.47b 3.18b
occupied
housing value
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.
aDifferences 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.
bThe change in estimates from 1990 to 2000 is statistically significant.
Table 28: Changes in Selected Economic Growth Variables Observed in the
Los Angeles EZ and Its Comparison Area
1995 1999 2004 Percent
change
1995-2004a
EZ Comparison EZ Comparison EZ Comparison EZ Comparison
Number of 15,746 4,248 12,315 3,986 13,853 4,662 -12.02 9.75
businesses
Number of 165,457 52,973 153,340 55,627 156,793 66,783 -5.24 26.07
jobs
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.
aDifferences for the number of businesses and the number of jobs are
calculated as percent changes.
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
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
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
1990 2000 Percent changea
Poverty rate (%) 37.88 27.76 -10.12b
Unemployment rate (%) 9.76 7.75 -2.01b
Average household income $23,304 $31,064 33.3b
Percentage of single female headed 4.64 5.73 1.09
households with children
Total population 27,212 30,464 11.95
Total individuals per square mile 36 40 11.96
Percentage of households that moved in the 32.46 31.45 -1.01
last 5 years
Percentage of population of working age 59.04 61.98 2.93b
(16-64)
Percentage of population with a high 42.82 55.1 12.28b
school diploma (or equivalent)
Percentage of high school dropouts 15.80 16.47 0.67
Percentage of vacant housing units 16.74 18.97 2.23b
Average owner occupied housing value $43,392 $65,815 51.68b
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.
aDifferences 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.
bThe change in estimates from 1990 to 2000 is statistically significant.
Table 30: Changes in Selected Economic Growth Variables Observed in the
Kentucky Highlands EZ
1995 1999 2004 Percent change
1995-2004a
Number of businesses 609 691 810 33
Number of jobs 5,327 7,691 8,941 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.
aDifferences for the number of businesses and the number of jobs are
calculated as percent changes.
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
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
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
1990 2000 Percent changea
Poverty rate (%) 46.35 35.67 -10.68b
Unemployment rate (%) 14.31 17.38 3.07b
Average household income $25,872 $3,559 37.44b
Percentage of single female headed 16.51 17.31 0.80
households with children
Total population 29,494 29,770 0.94
Total individuals per square mile 30.06 30.34 0.95
Percentage of households that moved in the 34.75 31.00 -3.75b
last 5 years
Percentage of population of working age 51.71 57.36 5.65b
(16-64)
Percentage of population with a high 49.09 60.52 11.43b
school diploma (or equivalent)
Percentage of high school dropouts 14.66 12.62 -2.04b
Percentage of vacant housing units 8.08 9.41 1.33
Average owner occupied housing value $50,061 $66,872 33.58b
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.
aDifferences 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.
bThe change in estimates from 1990 to 2000 is statistically significant.
Table 32: Changes in Selected Economic Growth Variables Observed in the
Mid-Delta EZ
1995 1999 2004 Percent change 1995-2004a
Number of businesses 634 838 733 15.62
Number of jobs 9,415 12,694 9,884 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.
aDifferences for the number of businesses and the number of jobs are
calculated as percent changes.
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
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
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
1990 2000 Percent changea
Poverty rate (%) 49.65 42.34 -7.31b
Unemployment rate (%) 14.94 13.82 -1.12
Average household income $25,093 $32,763 30.57b
Percentage of single female headed 9.44 10.38 0.95
households with children
Total population 29,817 37,044 24.24
Total individuals per square mile 131 159 21.82
Percentage of households that moved in the 30.11 34.41 4.29b
last 5 years
Percentage of population of working age 55.47 55.48 0.01
(16-64)
Percentage of population with a high 41.51 46.80 5.29b
school diploma (or equivalent)
Percentage of high school dropouts 20.1 16.38 -3.72b
Percentage of vacant housing units 14.37 16.80 2.43b
Average owner occupied housing value $46,100 $61,450 33.3b
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.
aDifferences 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.
bThe change in estimates from 1990 to 2000 is statistically significant.
Table 34: Changes in Selected Economic Growth Variables Observed in the
Rio Grande Valley EZ
1995 1999 2004 Percent change 1995-2004a
Number of businesses 551 688 710 28.86
Number of jobs 6,025 6,548 7,427 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.
aDifferences for the number of businesses and the number of jobs are
calculated as percent changes.
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
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
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 2004.8 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
1990 2000 Percent changea
Poverty rate (%) 35.36 37.58 2.22
Unemployment rate (%) 13.63 11.90 -1.73
Average household income $28,593 $32,616 14.07b
Percentage of single female headed 21.64 21.96 0.31
households with children
Total population 48,789 53,845 10.36
Total individuals per square mile 9,179 10,141 10.48
Percentage of households that moved in 52.23 50.85 -1.38
the last 5 years
Percentage of population of working age 55.45 58.00 2.55b
(16-64)
Percentage of population with a high 48.69 53.51 4.82b
school diploma (or equivalent)
Percentage of high school dropouts 26.17 19.16 -7.01b
Percentage of vacant housing units 14.55 10.43 -4.13b
Average owner occupied housing value $124,339 $116,698 -6.15b
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.
aDifferences 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.
bThe change in estimates from 1990 to 2000 is statistically significant.
Table 36: Changes in Selected Economic Growth Variables Observed in the
Providence EC
1995 1999 2004 Percent change 1995-2004a
Number of businesses 2,714 2,426 2,200 -18.94
Number of jobs 37,724 34,763 33,545 -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.
aDifferences for the number of businesses and the number of jobs are
calculated as percent changes.
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
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.9 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
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
1990 2000 Percent changea
Poverty rate (%) 28.37 19.30 -9.07b
Unemployment rate (%) 9.75 7.02 -2.73b
Average household income $32,560 $45,353 39.29b
Percentage of single female headed 10.97 11.33 0.36
households with children
Total population 29,080 30,551 5.06
Total individuals per square mile 44 46 5.07
Percentage of households that moved in 34.49 36.48 1.99
the last 5 years
Percentage of population of working age 55.39 58.82 3.43b
(16-64)
Percentage of population with a high 53.57 65.81 12.24b
school diploma (or equivalent)
Percentage of high school dropouts 18.26 12.77 -5.49b
Percentage of vacant housing units 6.46 6.85 0.39
Average owner occupied housing value $68,945 $103,619 50.29b
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.
Table 38: Changes in Selected Economic Growth Variables Observed in the
Fayette-Haywood EC
1995 1999 2004 Percent change 1995-2004a
Number of businesses 892 921 1,128 26.46
Number of jobs 9,556 10,128 11,240 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.
aDifferences for the number of businesses and the number of jobs are
calculated as percent changes.
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.
Appendix V
Comments from the Department of Health and Human Services
Appendix VI
Comments from the Department of Housing and Urban Development
The following are GAO's comments on the Department of Housing and Urban
Development's letter dated August 17, 2006.
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.
Appendix VII
Comments from the U.S. Department of Agriculture
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)
transparent illustrator graphic
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: www.gao.gov/cgi-bin/getrpt? GAO-06-734SP .
For more information, contact William B. Shear at (202) 512-8678 or
[email protected].
Highlights of GAO-06-727 , a report to congressional committees
September 2006
EMPOWERMENT ZONE AND ENTERPRISE COMMUNITY PROGRAM
Improvements Occurred in Communities, but the Effect of the Program Is
Unclear
The EZ/EC program is one of the most recent large-scale federal effort
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 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.
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 GAOrecommended 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.
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