Empowerment Zone and Enterprise Community Program
Improvements Occurred in Communities, but the Effect of the Program Is Unclear Gao ID: GAO-06-727 September 22, 2006The 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.
Round I Empowerment Zones (EZ) and Enterprise Communities (EC) implemented a variety of activities using $1 billion in federal grant funding from the Department of Health and Human Services (HHS), and as of March 2006, the designated communities had expended all but 15 percent of this funding. Most of the activities that the grant recipients put in place were community development projects, such as projects supporting education and housing. Other activities included economic opportunity initiatives such as job training and loan programs. Although all EZs and ECs also reported using the program grants to leverage funds from other sources, reliable data on the extent of leveraging were not available. According to federal standards, agencies should oversee the use of public resources and ensure that ongoing monitoring occurs. However, none of the federal agencies that were responsible for program oversight--including HHS and the departments of Housing and Urban Development (HUD) and Agriculture (USDA)--collected data on the amount of program grant funds used to implement specific program activities. This lack of data limited both federal oversight and GAO's ability to assess the effect of the program. Moreover, because HHS did not provide the states and designated communities with clear guidance on how to monitor the program grant funds, the extent of monitoring varied across the sites. In addition, detailed Internal Revenue Service (IRS) data on the use of EZ/EC program tax benefits were not available. Previously, GAO cited similar challenges in assessing the use of tax benefits in other federal programs and stated that information on tax expenditures should be collected to ensure that these expenditures are achieving their intended purpose. Although GAO recommended in 2004 that HUD, USDA, and IRS work together to identify the data needed to assess the EZ/EC tax benefits and the cost effectiveness of collecting the information, the three agencies did not reach agreement on an approach. Without adequate data on the use of program grant funds or tax benefits, neither the responsible federal agencies nor GAO could determine whether the EZ/EC funds had been spent effectively or that the tax benefits had in fact been used as intended. Using the data that were available, GAO attempted to analyze changes in several indicators--poverty and unemployment rates and two measures of economic growth. Although improvements in poverty, unemployment, and economic growth had occurred in the EZs and ECs, our econometric analysis of the eight urban EZs could not tie these changes definitively to the EZ designation.
GAO-06-727, Empowerment Zone and Enterprise Community Program: Improvements Occurred in Communities, but the Effect of the Program Is Unclear
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Report to Congressional Committees:
September 2006:
Empowerment Zone And Enterprise Community Program:
Improvements Occurred in Communities, but the Effect of the Program Is
Unclear:
GAO-06-727:
GAO Highlights:
Highlights of GAO-06-727, a report to congressional committees
Why GAO Did This Study:
The EZ/EC program is one of the most recent large-scale federal efforts
intended to revitalize impoverished urban and rural communities. There
have been three rounds of EZs and two rounds of ECs, all of which are
scheduled to end no later than December 2009.
The Community Renewal Tax Relief Act of 2000 mandated that GAO audit
and report in 2004, 2007, and 2010 on the EZ/EC program and its effect
on poverty, unemployment, and economic growth. This report, which
focuses on the first round of the program starting in 1994, discusses
program implementation;
program oversight;
data available on the use of program tax benefits;
and the program‘s effect on poverty, unemployment, and economic growth.
In conducting this work, GAO made site visits to all Round I EZs,
conducted an e-mail survey of 60 Round I ECs, and used several
statistical methods to analyze program effects.
What GAO Found:
Round I Empowerment Zones (EZ) and Enterprise Communities (EC)
implemented a variety of activities using $1 billion in federal grant
funding from the Department of Health and Human Services (HHS), and as
of March 2006, the designated communities had expended all but 15
percent of this funding. Most of the activities that the grant
recipients put in place were community development projects, such as
projects supporting education and housing. Other activities included
economic opportunity initiatives such as job training and loan
programs. Although all EZs and ECs also reported using the program
grants to leverage funds from other sources, reliable data on the
extent of leveraging were not available.
According to federal standards, agencies should oversee the use of
public resources and ensure that ongoing monitoring occurs. However,
none of the federal agencies that were responsible for program
oversight”including HHS and the departments of Housing and Urban
Development (HUD) and Agriculture (USDA)”collected data on the amount
of program grant funds used to implement specific program activities.
This lack of data limited both federal oversight and GAO‘s ability to
assess the effect of the program. Moreover, because HHS did not provide
the states and designated communities with clear guidance on how to
monitor the program grant funds, the extent of monitoring varied across
the sites.
In addition, detailed Internal Revenue Service (IRS) data on the use of
EZ/EC program tax benefits were not available. Previously, GAO cited
similar challenges in assessing the use of tax benefits in other
federal programs and stated that information on tax expenditures should
be collected to ensure that these expenditures are achieving their
intended purpose. Although GAO recommended in 2004 that HUD, USDA, and
IRS work together to identify the data needed to assess the EZ/EC tax
benefits and the cost effectiveness of collecting the information, the
three agencies did not reach agreement on an approach.
Without adequate data on the use of program grant funds or tax
benefits, neither the responsible federal agencies nor GAO could
determine whether the EZ/EC funds had been spent effectively or that
the tax benefits had in fact been used as intended. Using the data that
were available, GAO attempted to analyze changes in several
indicators”poverty and unemployment rates and two measures of economic
growth. Although improvements in poverty, unemployment, and economic
growth had occurred in the EZs and ECs, our econometric analysis of the
eight urban EZs could not tie these changes definitively to the EZ
designation.
What GAO Recommends:
While not making recommendations, GAO makes observations that should be
considered if these or similar programs are authorized in the future.
HHS, HUD, and USDA provided comments. In particular, HUD disagreed with
the observation that there was a lack of data to perform program
oversight.
[Hyperlink, http://www.gao.gov/cgi-bin/getrpt?GAO-06-727.
To view the full product, including the scope and methodology, click on
the link above. To view the survey results, click on the following
link: [Hyperlink, http://www.gao.gov/cgi-bin/getrpt?GAO-06-734SP. For
more information, contact William B. Shear at (202) 512-8678 or
ShearW@gao.gov.
[End of Section]
Contents:
Letter:
Results in Brief:
Background:
Round I EZs and ECs Have Used Their Grant Funds to Implement a Wide
Range of Program Activities:
Oversight Was Hindered by Limited Program Data and Variation in
Monitoring:
Lack of Detailed Tax Data Made It Difficult to Assess the Use of
Program Tax Benefits:
In Aggregate, EZs and ECs Showed Some Improvements, but Our Analysis
Did Not Definitively Link These Changes to the Program:
Observations:
Agency Comments and Our Evaluation:
Appendixes:
Appendix I: Objectives, Scope, and Methodology:
Methodology for Site Visits:
Methodology for Survey of EC Officials:
Methodology for Qualitative Analysis of Site Visit and EC Survey Data:
Methodology for Review of Program Oversight:
Methodology for Survey of EZ Businesses:
Methodology for Assessing the Effect of the Program on Poverty,
Unemployment, and Economic Growth:
Appendix II: Methodology for and Results of Our Econometric Models:
Description of Our Models:
Results of Our Models for Poverty:
Results of Our Models for Unemployment:
Results of Our Models for Economic Growth:
Other Variables Tested for Use in Our Econometric Models:
Appendix III: List of Communities Designated in Round I of the EZ/EC
Program:
Appendix IV: Description of the Empowerment Zones and Enterprise
Communities We Visited:
Atlanta Empowerment Zone:
Baltimore Empowerment Zone:
Chicago Empowerment Zone:
Detroit Empowerment Zone:
New York Empowerment Zone:
Philadelphia-Camden Empowerment Zone:
Cleveland Empowerment Zone:
Los Angeles Empowerment Zone:
Kentucky Highlands Empowerment Zone:
Mid-Delta Mississippi Empowerment Zone:
Rio Grande Valley, Texas Empowerment Zone:
Providence, Rhode Island Enterprise Community:
Fayette-Haywood, Tennessee Enterprise Community:
Appendix V: Comments from the Department of Health and Human Services:
Appendix VI: Comments from the Department of Housing and Urban
Development:
Appendix VII: Comments from the U.S. Department of Agriculture:
Appendix VIII: GAO Contact and Staff Acknowledgments:
Tables:
Table 1: Round I EZ/EC Program Criteria and Benefits:
Table 2: National Poverty, Unemployment, Economic Growth Data for 1990
to 2004:
Table 3: Total EZ/EC Grant Funding Remaining as of March 31, 2006:
Table 4: Number of Stakeholders Interviewed for EZ and EC Site Visits,
by Type:
Table 5: Coding of Data Reliability of HUD and USDA Performance
Systems:
Table 6: Confidence Intervals for Average Household Income and Average
Housing Value in Constant 2004 Dollars:
Table 7: Factors Selected for Choosing Comparison Tracts:
Table 8: Estimates of the Association between the EZ Program and the
Change in Poverty Rate, 1990-2000:
Table 9: Estimates of the Association between the EZ Program and the
Change in Unemployment Rate, 1990-2000:
Table 10: Estimates of the Association between the EZ Program and
Economic Growth, Measured by the Change in the Number of Businesses,
from 1995-1999:
Table 11: Estimates of the Association between the EZ Program and
Economic Growth, Measured by the Change in the Number of Jobs, 1995-
1999:
Table 12: Alternative Variables Considered in Our Analyses:
Table 13: Changes in Selected Census Variables Observed in the Atlanta
EZ and Its Comparison Area:
Table 14: Changes in Selected Economic Growth Variables Observed in the
Atlanta EZ and Its Comparison Area:
Table 15: Changes in Selected Census Variables Observed in the
Baltimore EZ and Its Comparison Area:
Table 16: Changes in Selected Economic Growth Variables Observed in the
Baltimore EZ and Its Comparison Area:
Table 17: Changes in Selected Census Variables Observed in the Chicago
EZ and Its Comparison Area:
Table 18: Changes in Selected Economic Growth Variables Observed in the
Chicago EZ and Its Comparison Area:
Table 19: Changes in Selected Census Variables Observed in the Detroit
EZ and Its Comparison Area:
Table 20: Changes in Selected Economic Growth Variables Observed in the
Detroit EZ and Its Comparison Area:
Table 21: Changes in Selected Census Variables Observed in the New York
EZ, the Bronx and Upper Manhattan (UM) Portions, and the EZ Comparison
Area (Comp.):
Table 22: Changes in Selected Economic Growth Variables Observed in the
New York EZ, the Bronx and Upper Manhattan (UM) Portions, and the EZ
Comparison Area (Comp.):
Table 23: Changes in Selected Census Variables Observed in the
Philadelphia-Camden EZ, the Camden (Cam.) and Philadelphia (Phila.)
Portions, and the EZ Comparison Area (Comp.):
Table 24: Changes in Selected Economic Growth Variables Observed in the
Philadelphia-Camden EZ, the Camden (Cam.) and Philadelphia (Phila.)
Portions, and the EZ Comparison Area (Comp.):
Table 25: Changes in Selected Census Variables Observed in the
Cleveland EZ and Its Comparison Area:
Table 26: Changes in Selected Economic Growth Variables Observed in the
Cleveland EZ and Its Comparison Area:
Table 27: Changes in Selected Census Variables Observed in the Los
Angeles EZ and Its Comparison Area:
Table 28: Changes in Selected Economic Growth Variables Observed in the
Los Angeles EZ and Its Comparison Area:
Table 29: Changes in Selected Census Variables Observed in the Kentucky
Highlands EZ:
Table 30: Changes in Selected Economic Growth Variables Observed in the
Kentucky Highlands EZ:
Table 31: Changes in Selected Census Variables Observed in the Mid-
Delta EZ:
Table 32: Changes in Selected Economic Growth Variables Observed in the
Mid-Delta EZ:
Table 33: Changes in Selected Census Variables Observed in the Rio
Grande Valley EZ:
Table 34: Changes in Selected Economic Growth Variables Observed in the
Rio Grande Valley EZ:
Table 35: Changes in Selected Census Variables Observed in the
Providence EC:
Table 36: Changes in Selected Economic Growth Variables Observed in the
Providence EC:
Table 37: Changes in Selected Census Variables Observed in the Fayette-
Haywood EC:
Table 38: Changes in Selected Economic Growth Variables Observed in the
Fayette-Haywood EC:
Figures:
Figure 1: Oversight Responsibilities in Round I of the EZ/EC Program:
Figure 2: Remaining Grant Funds by EZ as of March 31, 2006:
Figure 3: Distribution of EZ and EC Activities by Key Program
Principle:
Figure 4: Types of Activities Implemented by Urban and Rural EZs and
ECs, by Percent of Total Activities:
Figure 5: Local Government Involvement in Decision Making in the Urban
EZs:
Figure 6: Changes in Poverty, Unemployment, and Two Measures of
Economic Growth Observed in Round I EZs:
Figure 7: Number and Percentage of EZs and ECs Experiencing a Decrease
in Poverty from 1990 to 2000:
Figure 8: Comparison of Decreases in Poverty in Urban and Rural
Designated Areas and Comparison Areas from 1990 to 2000:
Figure 9: Number and Percentage of EZs and ECs that Experienced a
Decrease in Unemployment from 1990 to 2000:
Figure 10: Comparison of Decreases in Unemployment in Urban and Rural
Designated Areas and Comparison Areas from 1990 to 2000:
Figure 11: Number and Percentage of EZs and ECs That Experienced an
Increase in One or Both Measures of Economic Growth between 1995 and
2004:
Figure 12: Comparison of Changes in the Number of Businesses and the
Number of Jobs in Urban and Rural Designated Areas and Comparison Areas
between 1995 and 2004:
Figure 13: Map of the Atlanta EZ and Its Comparison Area:
Figure 14: Activities Implemented by the Atlanta EZ:
Figure 15: Map of the Baltimore EZ and Its Comparison Area:
Figure 16: Activities Implemented by the Baltimore EZ:
Figure 17: Map of the Chicago EZ and Its Comparison Area:
Figure 18: Activities Implemented by the Chicago EZ:
Figure 19: Map of the Detroit EZ and Its Comparison Area:
Figure 20: Activities Implemented by the Detroit EZ:
Figure 21: Map of the New York EZ and Its Comparison Area:
Figure 22: Activities Implemented by the Upper Manhattan portion of the
New York EZ:
Figure 23: Activities Implemented by the Bronx portion of the New York
EZ:
Figure 24: Map of the Philadelphia-Camden EZ and Its Comparison Area:
Figure 25: Activities Implemented by the Philadelphia Portion of the
Philadelphia-Camden EZ:
Figure 26: Activities Implemented by the Camden Portion of the
Philadelphia-Camden EZ:
Figure 27: Map of the Cleveland EZ and Its Comparison Area:
Figure 28: Activity Implemented by the Cleveland EZ:
Figure 29: Map of the Los Angeles EZ and Its Comparison Area:
Figure 30: Activity Implemented by the Los Angeles EZ:
Figure 31: Map of the Kentucky Highlands EZ:
Figure 32: Activities Implemented by the Kentucky Highlands EZ:
Figure 33: Map of the Mid-Delta EZ:
Figure 34: Activities Implemented by the Mid-Delta EZ:
Figure 35: Map of the Rio Grande Valley EZ:
Figure 36: Activities Implemented by the Rio Grande Valley EZ:
Figure 37: Map of the Providence EC:
Figure 38: Activities Implemented by the Providence EC:
Figure 39: Map of the Fayette-Haywood EC:
Figure 40: Activities Implemented by the Fayette-Haywood EC:
Abbreviations:
EC: Enterprise Community:
EZ: Empowerment Zone:
HHS: Department of Health and Human Services:
HUD: Department of Housing and Urban Development:
IRS: Internal Revenue Service:
USDA: U.S. Department of Agriculture:
September 22, 2006:
The Honorable Charles E. Grassley:
Chairman:
The Honorable Max Baucus:
Ranking Minority Member:
Committee on Finance:
United States Senate:
The Honorable William M. Thomas:
Chairman:
The Honorable Charles B. Rangel:
Ranking Minority Member:
Committee on Ways and Means:
House of Representatives:
The Empowerment Zone and Enterprise Community (EZ/EC) program is one of
the most recent in a series of large-scale federal efforts intended to
address one of the nation's most persistent challenges--the
revitalization of impoverished urban and rural communities. When it was
enacted in 1993, the EZ/EC program provided grants to public and
private entities for social services and community redevelopment and
tax benefits to local businesses to attract or retain jobs and
businesses in distressed communities. The program differs from earlier
initiatives with similar goals in that it emphasizes the role of local
communities in identifying solutions and the use of public-private
partnerships to attract the investment necessary for sustainable
economic and community development. To date, Congress has authorized
three rounds of EZs and two rounds of ECs. Communities designated under
Round I of the program shared a total of $1 billion in federal grant
funding and also received tax and other benefits. The EZs received the
bulk of this funding--$720 million in total--as well as more extensive
tax benefits than the ECs. Communities designated in the two subsequent
rounds of the program received a smaller amount of federal funding and
more tax benefits. All three rounds of the EZ/EC program are scheduled
to end no later than December 31, 2009.
The Community Renewal Tax Relief Act of 2000 mandated that we audit and
report in 2004, 2007, and 2010 on the EZ/EC program and a later
initiative, the Renewal Community program, and their effect on poverty,
unemployment, and economic growth.[Footnote 1] This report, the second
of the mandated series, focuses on the first round of communities
designated as EZs and ECs in 1994. It (1) describes how the designated
communities implemented Round I of the EZ/EC program; (2) evaluates the
extent of federal, state, and local oversight of the program; (3)
examines the extent to which data are available to assess the use of
program tax benefits; and (4) analyzes the effects that the Round I EZs
and ECs had on poverty, unemployment, and economic growth in their
communities.
To address our first three objectives, we made site visits to all 11
Round I EZs and 2 of the 95 ECs--1 urban and 1 rural--to interview
stakeholders and review documentation.[Footnote 2] To gather
information from the ECs, we administered an e-mail survey to officials
from the 60 Round I ECs that were still in operation as of June 2005
and did not receive a subsequent designation.[Footnote 3] We chose to
exclude the 34 ECs that received subsequent designations, because we
did not want their responses to be influenced by those programs.
Because the states distributed the federal funding to the communities,
we conducted telephone interviews with state officials in the 13 states
containing the EZs and ECs that we visited. In addition, we interviewed
officials from the federal agencies with primary responsibility for the
program--the Department of Health and Human Services (HHS), the
Department of Housing and Urban Development (HUD), the Internal Revenue
Service (IRS), and the U.S. Department of Agriculture (USDA). We also
analyzed fiscal and program data from the agencies and assessed the
reliability of these data.[Footnote 4] To address our fourth objective,
that is, the effect of the program on poverty, unemployment, and
economic growth, we used several methods. First, we calculated the
changes in the poverty and unemployment rates from 1990 to 2000 and
measures of economic growth from 1995 to 2004 in the designated EZs and
ECs and in comparison areas selected for their similarities to the
designated communities.[Footnote 5] Then, we used econometric models to
assess the effects of the program. Finally, we used testimonial
information gathered during our site visits and our survey results to
help put these changes in context.
We conducted our work between November 2004 and July 2006 in accordance
with generally accepted government auditing standards. Appendix I lists
the communities we visited. Appendixes I and II provide details on our
methodology, and appendix III shows a list of communities designated in
Round I of the EZ/EC program. Appendix IV provides details on each of
the sites we visited.
Results in Brief:
Round I EZs and ECs used most of the $1 billion in program grant funds
to implement a wide range of activities designed to help revitalize the
designated communities. As of March 31, 2006, 20 percent of the $720
million that EZs received and 2 percent of the $280 million that ECs
received remained unspent, and some designees had received extensions
of the original 10-year grant period that was set to expire in 2004. In
general, EZs and ECs undertook more community development activities in
areas such as education, housing, and infrastructure than they did
economic opportunity activities such as job training and assistance to
businesses. Although stakeholders from all EZs and ECs reported using
the program grants to leverage funds from other sources and some said
that they had required subgrantees to obtain other funds as a condition
of receiving EZ/EC funds, reliable data on the extent of leveraging
were not available. EZ and EC designees also reported other
accomplishments and challenges and utilized a variety of governance
structures to implement these activities.
Data were not collected on program benefits for specific activities,
limiting the ability of federal agencies to oversee the program, and
the monitoring performed at the state and local levels varied.
According to our Standards for Internal Control in the Federal
Government, federal agencies should oversee the use of public resources
and ensure that ongoing monitoring occurs.[Footnote 6] However, the
three agencies responsible for overseeing the program--HHS, HUD, and
USDA--did not collect data on how program funds were used. For
instance, HHS data show that EZs and ECs have used most of the EZ/EC
grant funds but do not show the specific activities or types of
activities for which the funds were used. And, although the performance
reporting systems maintained by HUD and USDA do contain some
information on activities that were carried out, they do not contain
information on how much of the EZ/EC funds actually were used for
specific activities or types of activities.[Footnote 7] Further, HHS
did not provide the states, EZs, and ECs with clear guidance on how to
monitor the program grant funds, so the types and extent of monitoring
performed by state and local participants varied. To some degree, the
lack of reporting requirements may be an outcome of the program's
design, which was intended to give communities flexibility in using
program funds and relied on multiple agencies for oversight. But the
result has been that little information is available on the amount of
funds spent on specific activities, hindering the agencies' efforts to
oversee the program.
Similarly, only limited data are available on the use of EZ/EC tax
benefits, which were estimated to be much more substantial than the
amount of program grant funds. We have stated that information on tax
expenditures should be collected to ensure that these expenditures are
achieving their intended purpose.[Footnote 8] In 2004, we reported that
IRS collected data on some but not all of the program tax benefits and
that the data could not be linked to the individual
communities.[Footnote 9] We also recommended that HUD, USDA, and IRS
work together to identify the data needed to measure the use of EZ/EC
tax benefits and the cost-effectiveness of collecting the information,
but the three agencies did not reach agreement on a cost-effective
approach.[Footnote 10] During our work for this report, officials from
some EZs and ECs told us that some local businesses were using the tax
benefits. However, these testimonial data were neither sufficient to
allow us to determine the actual amount of tax benefits used by EZs and
ECs nationwide nor to assess the extent to which the program tax
benefits contributed to the achievement of program goals.
Although improvements in poverty, unemployment, and economic growth had
occurred in the EZs and ECs, our econometric analysis of the eight
urban EZs could not tie these changes definitively to the EZ
designation.[Footnote 11] As mentioned in our previous report,
measuring the effect of initiatives such as the EZ/EC program is
difficult for a number of reasons, such as data limitations and the
difficulty of determining what would have happened in the absence of
the program.[Footnote 12] Given these limitations, the effects of the
EZ/EC program remain unclear. In some cases, communities did see
decreases in poverty and unemployment and increases in economic growth.
However, when we used econometric analyses to separate the effect of
the program from other nonprogram factors we found that the comparison
tracts we selected showed changes that were similar to those in the
urban EZs. Further, EZ stakeholders and EC survey respondents said that
program-related factors had influenced changes in their communities but
noted that other unrelated factors had also had an effect. For example,
stakeholders who observed a decrease in poverty in their communities
believed that this change had resulted in part from EZ/EC activities,
but they also noted that the population in their communities had
changed, with original EZ/EC residents moving out of the area and
individuals with higher incomes moving in. Ultimately, the evaluation
techniques we developed were limited by the absence of data on the use
of program grants and tax benefits.
While all three rounds of the EZ/EC program are scheduled to end no
later than December 31, 2009, we observe two limitations that should be
considered if these or similar programs are authorized in the future.
These include (1) oversight limitations that occurred because data were
not collected on how program grant funds were used for specific
activities and (2) the limited ability to evaluate the effect of the
program due to the lack of data on the use of program grant funds, the
extent of leveraging, and the extent to which program tax benefits were
used. Given the magnitude of federal grant funds and tax benefits
provided for the program, more should be done to better understand the
extent to which these federal expenditures are having the desired
effect.
We provided a draft of this report to HHS, HUD, IRS, and USDA. We
received comments from HHS, HUD, and USDA. HHS commented that a
statement made in our report--that the agency did not provide guidance
detailing the steps state and local authorities should take to monitor
the program--unfairly represented the relationship between HHS and the
other federal agencies that administered the EZ/EC program. However, we
note in our report that the program's design may have led to a lack of
clarity in oversight since no single federal agency had sole oversight
responsibility. Nonetheless, we believe that, in accordance with
federal standards, each of the federal agencies that administered the
program bore at least some responsibility for ensuring that public
resources were being used effectively and that program goals were being
met. HUD disagreed with our observation that there was a lack of data
on the use of program grant funds, the amount of funds leveraged, and
the use of tax benefits. However, although we found evidence that
activities were carried out with program funds, information contained
in HUD's performance reporting system on the amounts of funds used and
the amounts leveraged was not reliable. Both HUD and USDA provided
suggestions for future evaluations of similar programs. The agencies'
comments are discussed later in the report and are reproduced in
appendixes V through VII. HHS, HUD and USDA also provided technical
comments that we incorporated into the report where appropriate.
Background:
The concept behind the EZ/EC program originated in Great Britain in
1978 with the inception of the Enterprise Zone program. The main
objective of the Enterprise Zone program was to foster an attractive
business environment in specific areas where economic growth was
lacking. In the United States, some states began to administer similar
state Enterprise Zones in the 1980s. In 1993, the federal government
established the federal EZ/EC program to help reduce unemployment and
revitalize economically distressed areas. The authorizing legislation
established the eligibility requirements and the package of grants and
tax benefits for the EZ/EC program (table 1). Multiagency teams from
HHS, HUD, USDA, and other federal agencies reviewed the applications in
Round I, and HUD and USDA issued designations based on the
effectiveness of communities' strategic plans, assurances that the
plans would be implemented, and geographic diversity.[Footnote 13] In
Round I, HUD designated a total of 8 urban EZs and 65 urban ECs, and
USDA designated 3 rural EZs and 30 rural ECs.[Footnote 14]
Table 1: Round I EZ/EC Program Criteria and Benefits:
Eligibility criteria;
To be considered for the program, communities were required to select
census tracts that;
* had above-average poverty according to 1990 Census data;;
* had unemployment rates of at least the national average according to
1990 Census data;;
* met certain 1990 population and area criteria;
and;
* exhibited other conditions of distress, such as high crime,
deteriorating infrastructure, or population decline.
In addition, they were required to submit a strategic plan that
addressed the four key principles of the program:
* economic opportunity,;
* sustainable community development,;
* community-based partnerships, and;
* strategic vision for change.
EZ program benefits;
Round I EZs received Title XX Social Services Block Grants (EZ/EC
grants);
* Six urban EZs each received $100 million.[A];
* Three rural EZs each received $40 million.
Businesses located in EZs initially received three tax benefits:
* a tax credit for wages paid to employees who both live and work in an
EZ,;
* an increased expensing deduction for depreciable property, and;
* tax- exempt bonds that could be used to issue loans to qualified
businesses for financing certain property;
By 2002, businesses in EZs also became eligible for two additional tax
benefits related to the treatment of gains on the sale of EZ assets and
stock.
EC program benefits;
95 Round I ECs each received $2.95 million in EZ/ EC grants;
Businesses located in ECs were eligible for one program tax benefit,
the tax-exempt bond financing.
Source: GAO.
[A] This does not include two additional urban communities--Cleveland
and Los Angeles--that initially received Supplemental EZ designations
and received full Round I EZ status in 1998, because they did not
receive EZ/EC grant funds.
[End of table]
HHS provided Round I EZs and ECs with a total of $1 billion in EZ/EC
grant funds. EZs and ECs were allowed to use the EZ/EC grants for a
broader range of activities than was generally allowed with those types
of HHS funds. For instance, EZs and ECs could use funding for
"traditional" activities, such as skills training programs for
disadvantaged youth or drug and alcohol treatment programs, as well as
for additional activities, such as the purchase of land or facilities
related to an eligible program or the capitalization of a revolving
loan fund. EZs and ECs were also permitted to use grant funds to cover
some administrative costs and to change their goals and activities over
time, with approval from HUD or USDA. In addition, HUD and USDA
expected EZs and ECs to use the EZ/EC grant to leverage additional
investment.
Businesses operating in EZs and ECs were eligible for a substantial
amount of program tax benefits. In 1993, the Joint Committee on
Taxation estimated that the tax benefits available to businesses in
Round I communities would result in a $2.5 billion reduction in tax
revenues between 1994 and 1998. In 2000, the committee estimated that
the combination of EZ/EC program tax benefits and the Renewal Community
tax benefits would reduce tax revenues by a total of $10.9 billion
between 2001 and 2010.[Footnote 15] The tax benefits for ECs expired in
2004, and the tax benefits for all EZs and Renewal Communities are
currently set to expire at the end of 2009.
Four federal agencies are responsible for administering the program in
Round I. Oversight responsibilities for Round I were divided among
three agencies, with HHS providing fiscal oversight and HUD and USDA
providing program oversight (fig. 1). HHS issued grants to the states,
which served as pass-through entities--that is, they distributed funds
to individual EZs and ECs. According to their regulations, HUD and USDA
are required to evaluate the progress each EZ and EC made on its
strategic plan based on information gathered on site visits and on
information reported to them by the designated communities. In
addition, IRS is responsible for administering the program tax
benefits.
Figure 1: Oversight Responsibilities in Round I of the EZ/EC Program:
[See PDF for image]
Source: GAO analysis.
[End of figure]
In assessing the extent of EZ/EC program improvements, it is useful to
understand the overall national trends in poverty, unemployment, and
economic growth. National trends in these indicators have varied since
Round I of the program was established. As shown in table 2, the
national poverty and unemployment rates showed improvements (i.e.,
declines) in 2000 compared with 1990, but both were somewhat higher in
2004. In 1990, Round I EZs and ECs had poverty and unemployment rates
that exceeded these national averages, as was required for program
eligibility.
Table 2: National Poverty, Unemployment, Economic Growth Data for 1990
to 2004:
Indicator: Poverty;
1990: 13.5%;
1995: 13.8%;
2000: 11.3%;
2003: 12.5%;
2004: 12.7%.
Indicator: Unemployment;
1990: 5.6%;
1995: 5.6%;
2000: 4.0%;
2003: 6.0%;
2004: 5.5%.
Indicator: Number of businesses;
1990: 6.1 million;
1995: 6.6 million;
2000: 7.1 million;
2003: 7.3 million;
2004: [A].
Indicator: Number of jobs;
1990: 93.4 million;
1995: 100.3 million;
2000: 114.1 million;
2003: 113.4 million;
2004: [A].
Sources: Census Bureau and Bureau of Labor Statistics.
[A] Data were not yet available for 2004.
[End of table]
In terms of economic growth, the table shows that the number of
businesses increased gradually between 1990 and 2003, and the number of
jobs increased from 1990 to 2000 but fell slightly between 2000 and
2003.
Round I EZs and ECs Have Used Their Grant Funds to Implement a Wide
Range of Program Activities:
EZs and ECs used most of the program grant funds to implement a wide
range of activities to carry out their respective revitalization
strategies. In total, as of March 31, 2006, EZs and ECs had used all
but 15 percent of the available grants. EZs and ECs implemented a
variety of activities, but, in general, focused more on community
development than economic opportunity. In addition, all designated
communities reported leveraging additional resources, though a lack of
reliable data prevented us from determining how much. Several designees
also noted other accomplishments, such as increasing local coordination
and capacity. The governance structures that Round I EZs and ECs
established to implement these activities varied and included
organizations to manage the day-to-day operations of the EZs, boards,
and advisory committees.
Most EZ/EC Grant Funds Have Been Expended, but Many EZs and Some ECs
Received Grant Extensions:
As of March 31, 2006, Round I EZs and ECs had spent all but 15 percent
of the program grant funds they received. HHS data show that 20 percent
of the program grant funds provided to EZs and 2 percent of the funds
provided to ECs were unspent (table 3). In addition, HUD data show that
the Cleveland and Los Angeles EZs, which originally received
Supplemental EZ designations, had used significant portions of the
Economic Development Initiative grants and Section 108 Loan Guarantees
that came with their designations.[Footnote 16] Specifically, each of
them had spent slightly more than 70 percent of their grants;
Cleveland had used 72 percent of its loan guarantees, but Los Angeles
had used less--about 33 percent.
Table 3: Total EZ/EC Grant Funding Remaining as of March 31, 2006:
EZs;
Total funding: $720 million;
Amount remaining: $146.6 million;
Percent remaining: 20%.
ECs;
Total funding: $280 million;
Amount remaining: $4.5 million;
Percent remaining: 2%.
Total;
Total funding: $1 billion;
Amount remaining: $151 million;
Percent remaining: 15%.
Source: GAO analysis of HHS data.
[End of table]
Most of the remaining $151 million in EZ/EC grants consists of the
funds of four urban EZs: Atlanta, New York, Philadelphia-Camden, and
Chicago, with Atlanta and New York accounting for the majority of the
unspent funds (fig. 2). When the Atlanta EZ received a Renewal
Community designation from HUD in 2002, the EZ designation was
terminated, but HHS allowed the city of Atlanta to continue spending
its remaining EZ grant funds through December 2009. The city of Atlanta
elected to administer its remaining EZ grants in conjunction with its
Renewal Community initiative, and prepared a strategic plan to address
administration of both the remaining HHS funds and the HUD-designated
Renewal Community. The Atlanta Renewal Community officials told us that
they did not use the EZ funds for about 4 years after receiving the
designation because of the time required for start-up but added that
they planned to begin utilizing the funds soon. The New York EZ
received matching funds from both the state and city governments, for a
total of $300 million. New York EZ officials stated that they used
equal parts of funding from these three sources for each activity,
potentially explaining why they have drawn down funds at a slower rate
than other EZs.
Figure 2: Remaining Grant Funds by EZ as of March 31, 2006:
[See PDF for image]
Source: GAO analysis of HHS data.
Note: Two urban EZs--Philadelphia-Camden and New York--implemented the
program through two separate entities that split the $100 million
grant. These separate entities are represented above for Philadelphia-
Camden, but separate data for the New York EZ were not available from
HHS. The Cleveland and Los Angeles EZs did not receive EZ grant funds.
[End of figure]
Although the grant period for Round I EZs and ECs was originally
scheduled to end December 21, 2004, several EZs and some ECs received
extensions from HHS to continue drawing down their remaining funds. The
recipients had to demonstrate a legitimate need to complete project
activities outlined in their strategic plans. Eight of the 11 EZs (6
urban, 2 rural) and 17 of the 95 ECs (11 urban and 6 rural) received
extensions of their grants until December 31, 2009. In addition, 1
urban EZ and 9 ECs (6 urban and 3 rural) received extensions for a
shorter time frame, such as 2005, 2006, or 2007.
EZs and ECs Implemented a Wide Variety of Activities, Most Related to
Community Development:
The designated communities were encouraged to implement both community
and economic development activities as part of their revitalization
strategies. The EZ/EC program was designed to be tailored to address
local needs, and the type of grant funds most EZs and ECs received from
HHS allowed them to implement a wide range of activities. Overall, both
EZs and ECs used the program grants to implement a larger number of
community development activities--such as education, health care, and
infrastructure--than economic opportunity activities--such as workforce
development and providing assistance to businesses (fig. 3).[Footnote
17]
Figure 3: Distribution of EZ and EC Activities by Key Program
Principle:
[See PDF for image]
Source: GAO analysis of HUD and USDA data.
Note: This figure shows the percent of the total number of activities
implemented, not the funds devoted to those activity types. The
Cleveland and Los Angeles EZs are not included in this graphic because
they did not receive EZ grant funds. The numbers do not always add up
to 100 due to rounding.
[End of figure]
The activities most often implemented by urban EZs and ECs were
workforce development, human services, education, and assistance to
businesses, which accounted for more than 50 percent of the activities
in urban EZs and 60 percent of the activities in urban ECs (fig. 4).
For example, the Baltimore EZ implemented a customized training program
that provided EZ residents with individualized training and a stipend
during the training period. In the Bronx portion of the New York EZ,
stakeholders explained that they had funded an organization that
trained women to become child care providers, a program that not only
provided job skills and employment opportunities but also improved the
availability of child care in the area. In addition, the Atlanta EZ and
the Camden portion of the Philadelphia-Camden EZ implemented
educational programs for EZ youth, such as after-school or summer
programs. Also, stakeholders from the Upper Manhattan portion of the
New York EZ mentioned contributing financial assistance to the business
development of the Harlem USA project, a 275,000-square-foot retail
development located in the EZ. Moreover, stakeholders from the
Providence EC said they provided grants to a nonprofit that offered job
training to youth and business development programs, such as "business
incubators" that offered office space and technical assistance to new
small businesses.
Figure 4: Types of Activities Implemented by Urban and Rural EZs and
ECs, by Percent of Total Activities:
[See PDF for image]
Source: GAO analysis of HUD and USDA data.
Note: This figure shows the percent of the total number of activities
implemented, not the funds devoted to those activity types. The data
reporting systems for urban and rural designees used slightly different
categories of activities. The Cleveland and Los Angeles EZs are not
included in this graphic because they did not receive EZ grant funds.
[End of figure]
Rural EZs and ECs implemented many of the same types of activities as
urban designees, such as business development and job training, but
often included activities related to health care and public
infrastructure. For example, stakeholders from the Kentucky Highlands
and Mid-Delta Mississippi EZs said that they had attracted businesses
to the areas using EZ loans, grants, or tax benefits, and stakeholders
from the Rio Grande Valley EZ reported funding job training for EZ
residents. In addition, stakeholders from Kentucky Highlands said the
EZ purchased ambulances for an area that previously did not have those
services. All three rural EZs reported using the EZ/EC grant to improve
the water or sewerage infrastructure in their EZs, which some said was
needed to foster additional economic development. Finally, stakeholders
from the Fayette-Haywood EC reported having implemented several
activities related to health care, such as recruiting doctors and
providing funding to reopen a clinic that had been closed for several
years. For more information on the types of activities implemented by
the individual communities we visited, see appendix IV.
EZs and ECs Used Program Grants to Leverage Additional Funds, but
Reliable Data on the Extent of Leveraging Are Not Available:
HUD and USDA also expected designees to use their grants to leverage
additional investment. Stakeholders from all EZs and ECs we visited and
all EC survey respondents reported having used their EZ/EC grants to
leverage other resources, including both monetary and in-kind
donations. EZs and ECs developed different policies that may have
affected the extent to which they leveraged funds. For example, the Mid-
Delta EZ required that direct grant recipients obtain at least 65
percent of their funding from other sources. Some other communities,
such as the Atlanta EZ, did not have similar requirements for
subgrantees, although in some cases subgrantees did leverage funds on
their own initiative. EC survey respondents reported using the EZ/EC
grants to leverage additional resources for capital improvements,
social services, and funding for businesses, among other things. Some
EC survey respondents also mentioned that the designation had helped
them to leverage funds to implement additional programs or to expand EC
programs.
All EZs and ECs that provided us with a definition of leveraging said
that they included all non-EZ/EC grant funds that were used in EZ/EC-
funded programs. But only two of the four EZs that used the program tax-
exempt bond included the amount of the bonds in their total leveraged
funds. In addition, some EZs reported as leveraged funds other
investments made in the EZ area, aside from those directly funded with
the EZ/EC grant funds, although other designated communities did not.
For example, the Baltimore EZ included all business investments made
subsequent to infrastructure improvements the EZ made to an industrial
park.
USDA encouraged rural EZs and ECs to report all investment in the EZ as
leveraged funds, not only those projects that received EZ/EC funds. For
example, at USDA's instruction, the Fayette-Haywood EC included funding
from other USDA programs operating in the EC, even when EC funds were
not involved. However, not all rural sites used this broad definition
of leveraging. Similarly, at one HUD official's instruction, the
Cleveland EZ included as leveraged funds other investments made within
the EZ, such as city Community Development Block Grant funds invested
in the area.[Footnote 18] However, there was no written guidance
telling the Cleveland EZ to include other investments, and it no longer
includes these other investments as leveraged funds in performance
reports.[Footnote 19]
Although communities reported using the EZ/EC grants to leverage
additional resources, we could not verify the actual amounts. HUD's and
USDA's performance reporting systems include information on the amount
of funds leveraged for each activity, but for the sample of activities
we reviewed, either supporting documentation showed an amount
conflicting with the reported amount or documentation could not be
found.[Footnote 20] In addition, the definition of "leveraged" varied
across sites, as the federal agencies did not provide EZs and ECs with
a consistent definition of what leveraged funds should include. As a
result, designated communities included different types of funds in the
amounts they reported as leveraged.
Designees Reported Other Accomplishments:
In addition to the activities that were implemented, EZ and EC
stakeholders with whom we spoke mentioned other accomplishments that
were not as easy to quantify and report in the performance systems. For
example, one of the aims of the EZ/EC program was to increase
collaboration among local governments, nonprofits, community members,
and the business community. Stakeholders from several sites we visited
commented on how the designation facilitated increased collaboration
among different groups of people and organizations. For instance,
several stakeholders from the Rio Grande Valley EZ noted the value of
having different communities and people work together, something that
had not happened prior to the EZ/EC program. Several EC survey
respondents also mentioned the importance of collaboration and
partnerships in carrying out the EC program. Stakeholders from some
sites we visited mentioned that the EZ/EC program had helped to empower
local residents by giving them a better understanding of how government
worked. In addition, stakeholders from some EZs said that the EZ/EC
program had helped to build the capacity of local organizations. In
Cleveland, local stakeholders said that the funding provided by the EZ
had helped increase the organizational capacity of four local community
development corporations and that participation in the governance of
the EZ helped to foster communication between the groups.
Designees Reported Implementation Challenges:
EZ stakeholders also mentioned some issues that had made implementing
the EZ/EC program more challenging. Stakeholders from some EZs noted
that an initial lack of experience or expertise on the part of EZ
officials had made it difficult to implement the program. In addition,
stakeholders from the Camden portion of the Philadelphia-Camden EZ and
the Rio Grande Valley EZ said that local subgrantee organizations
generally had a low level of organizational capacity, which sometimes
made it difficult to choose qualified applicants to implement EZ
programs. Stakeholders from several sites also said that it was
difficult to manage the expectations of both the EZ community and of
residents and businesses that were not located in the zones and were
not eligible for EZ/EC program benefits, especially when the
individuals and businesses were located just across the street from the
designated area.
EZs and ECs Established a Variety of Governance Structures and
Encouraged Community Participation:
In addition to choosing the activities that their EZs or ECs
implemented, designated communities were permitted to determine the
structure they would use to govern and operate the program. Generally,
these structures included an EZ/EC management entity--either a
nonprofit organization or an entity that was part of the local
government. Two urban EZs--New York and Philadelphia-Camden--became two
separate entities that were managed by different types of organizations
that split the $100 million EZ grant. In the Philadelphia-Camden EZ,
for example, the Philadelphia portion was run by the city of
Philadelphia and the Camden portion by a nonprofit organization. All
designees had at least one board, and, in some cases, EZs included
community advisory groups or separate "subzone" boards, which
represented specific areas of the EZ in their governance structures.
All three rural EZ boards made decisions about EZ activities without
the direct involvement of local government entities. However, the
extent of government involvement in urban EZ boards varied, regardless
of whether the EZ was managed by a nonprofit or local government
organization (fig. 5). For example, in two EZs, Cleveland and Chicago,
local government had extensive control of the program, but in other
EZs, such as Detroit, the board of the nonprofit organization that
managed the EZ shared partial decision-making authority with the mayor
and city council. Other EZs were operated with minimal local government
involvement, with the boards determining which activities to implement,
allocating resources, and deciding which entities would implement the
programs. Appendix IV provides more details on the governance
structures of the EZs we visited.
Figure 5: Local Government Involvement in Decision Making in the Urban
EZs:
[See PDF for image]
Source: GAO analysis of EZ documentation and interview data.
[A] The Los Angeles EZ was operated by a for profit organization--the
Los Angeles Community Development Bank--until 2002 when it filed for
bankruptcy. Since then, a Los Angeles city department has continued its
operations; however, the mayor and city council are not directly
involved.
[End of figure]
Another program expectation was to encourage community participation
within the designated communities. Regardless of the type of governance
structure they used, EZs and ECs involved community participants in the
planning and carrying out of program activities. According to
stakeholders from all the EZs and the ECs we visited, residents were
involved in meetings such as "visioning sessions" and town hall
gatherings during the strategic planning process. Community groups,
such as local colleges and universities, development corporations, and
businesses, were also involved prior to designation. In addition, 56
out of 58 ECs responding to our survey reported that EC residents
attended listening sessions, generated ideas for activities, or helped
to establish priorities. Respondents also indicated that a variety of
other groups participated in the strategic planning process for the
ECs, including local government officials and representatives from
community-based organizations.
After designation, stakeholders from the EZs and ECs we visited said
that residents often served on boards, and some stakeholders noted they
relied on the boards to capture a wide range of viewpoints. Most EZs
and ECs we visited also included as participants business
representatives, officials from nonprofits, and clergy, among others.
Some EZs and ECs also included residents from specific neighborhoods
within the designated area or individuals with special expertise, such
as in the areas of health care and housing.
Oversight Was Hindered by Limited Program Data and Variation in
Monitoring:
According to our federal standards, federal agencies should oversee the
use of public resources and ensure that ongoing monitoring
occurs.[Footnote 21] However, HHS, HUD, and USDA did not collect data
on how program funds were spent. In addition, HHS did not provide the
states, EZs, and ECs with clear guidance on how to monitor the program
grant funds, and the types and extent of monitoring performed by state
and local participants varied. The lack of reporting requirements may
be related to the program's design, which was intended to give
communities flexibility in using program funds and relied on multiple
agencies for oversight. However, these limitations have hindered the
agencies' efforts to determine whether the public resources are being
used effectively and program goals are met.
Federal Agencies Are Required to Oversee the Use of Public Funds and
Provide Ongoing Monitoring:
According to federal standards established in the Standards for
Internal Control in the Federal Government, program managers need both
program and fiscal data to determine whether public resources are being
used effectively and program goals are being met.[Footnote 22] In the
case of the EZ/EC program, fiscal data would include not only the
aggregate amount of program grant funding designated communities spent,
but also data on the amount of funds spent on specific types of
activities. Program data would include descriptions of the activities
implemented and program outputs, such as the number of individuals
trained in a job training program. The standards also state that
federal agencies should ensure that ongoing monitoring occurs in the
course of normal operations. For instance, the federal agencies should
provide guidelines on what monitoring should occur, including whether
on-site reviews or reporting are required. For the EZ/EC program, HHS
regulations require states, EZs, and ECs to maintain fiscal control of
program funds and accounting procedures sufficient to enable them to
prepare reports and ensure the funds were not used in violation of the
applicable statute.
The Federal Agencies' Oversight Efforts Had Shortcomings in Data
Collection:
None of the federal agencies collected data showing how program funds
had been spent. As we have noted, the EZ/EC grants were special Social
Services Block Grants that gave recipients expanded flexibility in
using the funds. The regulations for most grants of this type require
states to report on, among other things, the amount of funding spent on
each type of activity. However, because HHS did not require this level
of reporting for the EZ/EC program, the agency's data show how much of
each grant was used but not how much was spent on specific activities
or types of activities. Further, HHS's data sometimes do not show how
much of the grant a specific EC used, since states could aggregate
drawdowns for multiple communities. For example, there are five urban
ECs in Texas, but the data reported to HHS show only the aggregate
amount of funds these ECs used, not the amount used by each.
Similarly, although HUD's and USDA's reporting systems contained some
information on the amount of EZ/EC grants budgeted for specific
activities, the systems did not account for the amounts actually spent
on those activities. Moreover, we found that the data on the amount of
EZ/EC grant funding were often not reliable, as some EZs and ECs
reported budgeted amounts and others reported actual amounts spent.
Further, in our assessments of the reliability of these data, we found
documentation showing that the designated communities had undertaken
certain activities with program funding, but we were often unable to
find documentation of the actual amounts allocated or
expended.[Footnote 23]
Program Monitoring by State and Local Participants Varied:
Although HHS regulations require states, EZs, and ECs to maintain
fiscal control of program grant funds, the agency also did not provide
guidance detailing the steps state and local authorities should take to
monitor the program. In the absence of clear guidance, the type and
level of monitoring conducted at the state and local levels varied. For
example, some state and EZ/EC officials applied guidelines from other
programs, such as the Community Development Block Grant program, or
developed their own policies. Officials from almost all states we
interviewed said they reviewed audits of the EZs and ECs and were
required to submit aggregate data to HHS, and most had performed site
visits at least once during the program. State officials also said they
reviewed requests to draw down grant funds, approving expenditures if
the requests met the goals outlined in the strategic plans. However,
most states did not maintain records showing the types of activities
designated communities undertook. Some states said that they had taken
corrective actions, such as withholding payments when designated
communities had not properly reported how funds were used. However,
only a few states also completed program monitoring activities, such as
reviewing whether a project took place or benefited EZ or EC residents,
in conjunction with their fiscal reviews. Most of the EZs and ECs we
visited conducted on-site monitoring of subgrantees and reviewed their
financial and performance data, and some communities required annual
audits of their subgrantees. For example, the Rio Grande Valley EZ
assigned a program staff member to monitor each subgrantee activity and
required annual audits. In contrast, the Fayette-Haywood EC did not
perform any site visits and relied on other funding organizations to
monitor subgrantees.
Some instances of misuse of program funds did occur during the EZ/EC
program. For example, officials at the Mid-Delta EZ reported two cases
of embezzlement by EZ personnel.According to an EZ official, in one
case that was discovered through an independent audit, an individual
was prosecuted for embezzling $28,000 in 1996 (only $1,800 was
recouped). The second case of embezzlement of $31,000 by two EZ staff,
discovered when the staff turned themselves in, is currently under
joint State of Mississippi and FBI investigation as part of a larger
investigation of misuse of EZ funds starting as early as 1996. In
addition, three audits by the state of Georgia found that almost all
the administrative funds designated for the Atlanta EZ ($4 million) had
been used in the first 3 ½ years of the program, including
approximately $44,000 used for questionable costs related to personnel
and travel expenditures. To address this issue, the Atlanta EZ repaid
some of the costs in question, provided additional documentation, and
instituted better recordkeeping procedures. The city of Atlanta also
initiated a restructuring of the EZ and fired the majority of EZ staff.
Limitations in EZ/EC Oversight May Have Resulted from the Program
Design:
As discussed earlier, the EZ/EC program was designed to give the
designated communities increased flexibility in deciding how to use
program funds and used states as pass-through entities for providing
funds. Part of the philosophy behind the program was to relieve states
and localities of the burden of excessive reporting requirements.
Furthermore, no single federal agency had sole responsibility for
oversight of Round I of the EZ/EC program, although federal standards
require that agencies provide adequate oversight over public resources.
In the beginning, the agencies made some efforts to share information,
but these efforts were not maintained. For example, HUD officials said
that they had received fiscal data from HHS and reconciled that
information with their program data on the activities implemented in
the early years of the program.[Footnote 24] According to HUD, the
agency made additional attempts to obtain data from HHS but only
recently received a report. An HHS official said the agency no longer
regularly shared detailed data with HUD and USDA, which the official
said was likely due to a lack of program staff.
These limitations do not necessarily apply to Rounds II and III of the
EZ/EC program. For example, both fiscal and program oversight of the
urban and rural EZs and ECs were provided directly through HUD and USDA
in Round II because the program funding came directly through HUD and
USDA appropriations. Officials from both agencies explained that
information on the activity for which funds were used was linked to
each drawdown of program funds. In addition, a HUD official said they
had issued improved monitoring guidance in Round II, since designees
receive funds directly from HUD. However, a USDA official said that
they provided similar monitoring guidance to designees in Rounds I, II,
and III. Because this report focuses on Round I of the program, we did
not determine the effectiveness of the oversight of future rounds of
the program.
Lack of Detailed Tax Data Made It Difficult to Assess the Use of
Program Tax Benefits:
A lack of detailed tax data limited our ability to assess the extent to
which businesses in the EZs and ECs used program tax benefits. We have
previously reported that information on tax expenditures should be
collected to ensure that these expenditures are achieving their
intended purpose.[Footnote 25] IRS collects data on the use of some of
the program tax benefits, but not all of them, and none of the data can
be linked to the individual communities where the benefits were
claimed. We also recommended that HUD, USDA, and IRS work together to
identify the data needed to measure the use of EZ/EC tax benefits and
the cost-effectiveness of collecting the information, but the three
agencies did not reach agreement on a cost-effective approach.[Footnote
26] Officials from some EZs and ECs reported that some of the tax
benefits were being used, but this information was not sufficient to
allow us to determine the actual extent of usage.
IRS Data on the Use of Program Tax Benefits Are Limited:
Previously, we have noted that information on tax expenditures should
be collected in order to evaluate their effectiveness as a means of
accomplishing federal objectives and to ensure that they are achieving
their intended purpose.[Footnote 27] Inadequate or missing data can
impede such studies, especially given the difficulties in quantifying
the benefits of tax expenditures. Nevertheless, we have stated that the
nation's current and projected fiscal imbalance serves to reinforce the
importance of engaging in such evaluations.
However, as described in our 2004 report, the IRS collects limited data
on the EZ/EC tax benefits. It does not collect data on benefits used in
individual designated sites and for some benefits it does not have any
data.[Footnote 28] For example, the IRS collects some information on EZ
businesses' use of tax credits for employing EZ residents. However, the
data cannot be separated to show how much was claimed in individual
EZs. In addition, IRS does not have data on the use of the increased
expensing deduction for depreciable property, because taxpayers do not
report this benefit as a separate line item on their returns. The lack
of data on the use of program tax benefits is consistent with findings
of other reports we prepared citing data challenges in other similar
community and economic development programs, such as the Liberty Zone
program.[Footnote 29]
Our 2004 report recommended that HUD, IRS, and USDA collaborate to
identify a cost-effective means of collecting the data needed to assess
the use of the tax benefits.[Footnote 30] In response, HUD, IRS, and
USDA identified two methods for collecting the information--through a
national survey or by modifying the tax forms. However, the three
agencies did not reach agreement on a cost-effective method for
collecting additional data. Given the lack of information at the
federal level, we, some EZs, and other researchers have tried to assess
the use of EZ/EC tax benefits by surveying businesses.[Footnote 31]
However, these surveys have had low response rates and a high number of
undeliverable surveys, suggesting that the results might not be
representative. Reasons associated with the low response rates were
cited in previous reports, including the difficulty of locating someone
at the businesses who knew whether the tax benefit had been claimed and
issues associated with multiple business locations.[Footnote 32] In
addition, some EZ officials said that businesses were not willing to
share their tax information. Further, a high rate of small business
closures was determined to be a contributing factor to the high number
of undeliverable surveys. We initiated a survey of businesses as a part
of the audit work for this engagement, but discontinued the survey due
to a low response rate.[Footnote 33]
In the absence of other data, we relied on testimonial information to
assess how often the EZ tax benefits were used and who used them.
Although stakeholders from all EZs told us that they did not have any
data on the extent to which EZ businesses had used program tax
benefits, they provided us with some information that was consistent
with the findings of past studies.[Footnote 34] For example, during our
site visits, EZ stakeholders told us that they believed large
businesses, which tend to use tax professionals who know and understand
the benefits, were more likely to use the tax benefits than small
businesses. They also noted that small businesses were less likely to
make enough in profits to take advantage of the tax benefits.[Footnote
35] The stakeholders stated further that the credit for employing EZ
residents was the most frequently used of the three original tax
benefits. A few EZ officials commented that retail businesses were more
likely to use the employment credit and manufacturing businesses were
more likely to use the increased expensing deduction.
Stakeholders from only 4 of the 11 EZs and 2 of the 58 ECs that
responded to our EC survey told us that the tax-exempt bond benefit had
been used in their communities. EZ stakeholders and EC survey
respondents cited a variety of reasons that the tax-exempt bond
financing had not been more widely used. For instance, some said that
the bonds were not used because of the availability of the Industrial
Development Revenue Bond, which EZ stakeholders explained had fewer
restrictions and could be issued for larger amounts.[Footnote 36] In
addition, some EZ stakeholders and one EC survey respondent said that
it was difficult to find a large pool of qualified EZ residents to
satisfy the employment requirement for the bond, which required that at
least 35 percent of the workforce be EZ residents. Some EZ stakeholders
also told us that the legal fees for an EZ bond were higher than for
other types of bonds because the restrictions made the EZ bond more
complex. For this reason, stakeholders explained, the cost of issuing
the EZ bond was high relative to the bond cap, particularly early in
the program.[Footnote 37] Finally, some EC survey respondents noted
other reasons for not using the bond, such as the complicated nature of
the bond or a lack of interested businesses or viable projects.
IRS Officials Reported that They Have Data Sufficient to Enforce the
Tax Code, but This Information Is Insufficient for Assessing the Extent
of Usage:
IRS officials said that the limited data the agency collected did not
affect its ability to enforce compliance with the tax code. They told
us that IRS's role is to administer tax laws and said that collecting
more comprehensive data on the use of program tax benefits would not
help the agency to achieve this objective. Further, they said that they
allocate their resources based on the potential effect of abuse on
federal revenue and noted that these tax benefits are not considered
high risk, since the amount claimed is small compared with revenues
collected from other tax provisions or the amount of potential losses
from abusive tax schemes. Furthermore, both IRS officials and our
previous reports have suggested that IRS generally does not collect
information on the frequency of use or types of businesses claiming tax
benefits unless legislatively mandated to do so.[Footnote 38]
Although the total program tax benefits were estimated to be much
larger than the federal grant funding--over $2.5 billion compared with
the $1 billion in EZ/EC grants--we do not, as we have noted, know the
actual amount of tax benefits claimed by Round I EZs and ECs nationwide
or the amounts used in individual communities.[Footnote 39] As a
result, we could not assess differences in the rates of usage among the
designated communities. Although we understand IRS's concerns, the lack
of data is likely to become increasingly problematic in light of the
fact that future rounds of the EZ/EC program and the Renewal Community
program rely heavily on tax benefits to achieve revitalization goals.
It may also be a concern with the Gulf Opportunity Zone Act, which
provides tax benefits in counties and parishes affected by the 2005
Gulf Coast hurricanes.[Footnote 40]
In Aggregate, EZs and ECs Showed Some Improvements, but Our Analysis
Did Not Definitively Link These Changes to the Program:
Although EZs and ECs showed some improvements in poverty, unemployment,
and economic growth, we did not find a definitive connection between
these changes and the EZ/EC program. As mentioned in our previous
report, measuring the effect of initiatives such as the EZ/EC program
is difficult for a number of reasons, such as data limitations and the
difficulty of determining what would have happened in the absence of
the program.[Footnote 41] In some cases, communities saw decreases in
poverty and unemployment and increases in economic growth. But, we
could not conclusively determine whether these changes were a response
to the EZ/EC program or to other economic conditions. EZ stakeholders
and EC survey respondents said that program-related factors had
influenced changes in their communities but that other unrelated
factors also had an effect. Although the overall effects of the EZ/EC
program remain unclear, having data on the use of program grants and
tax benefits would have allowed for a richer assessment of the program.
A Number of Challenges Affected Our Efforts to Measure the Effects of
the EZ/EC Program:
We attempted to assess the effects of the program on four indicators:
poverty, unemployment, and two measures of economic growth--the number
of businesses and the number of jobs.[Footnote 42] Although we used
several quantitative and qualitative methods, including an econometric
analysis to try to isolate the EZ/EC program's effect, we could not
differentiate between the effects of the program and other factors.
Among the challenges we encountered were the following:
* A lack of adequate data on the use of program benefits. As mentioned
earlier, data on the use of EZ/EC grant funds and tax benefits were
very limited.
* Limited demographic data. We used poverty and unemployment data from
the 1990 and 2000 censuses, but these dates do not correspond well to
the program dates, as communities were designated in 1994 and in some
cases are still operating.
* Demonstrating what would have happened in the absence of the program.
For example, we attempted to identify comparison areas that did not
receive EZ or EC designations and that reflected similar community
characteristics of EZs and ECs.[Footnote 43] However, the designated
communities sometimes had the highest poverty levels in the area,
making it difficult to find exact matches among nearby census tracts.
* Accounting for the spillover effects of the program to other areas,
the effects of similar public and private programs, and the effects of
regional and local economic trends.
* Accounting for bias in the choice of program areas. For example, if
program officials tended to pick census tracts that were already
experiencing gentrification prior to 1994, we may be overstating the
effect of the EZ designation.[Footnote 44] Conversely, if officials
tended to choose census tracts that were experiencing economic declines
prior to 1994, such as areas affected by the loss of major employers,
we may be understating the program's impact.
Several program-specific factors also limited our ability to assess the
effects of the program. First, the program was designed to be tailored
to the local sites, and each community was given broad latitude to
determine its own needs and the program activities it thought would
address those needs. Thus, each designee may or may not have selected
program activities that directly related to the three factors--poverty,
unemployment, and economic growth--mandated for our evaluation. Second,
the time frame of actual program implementation may have varied among
the designees. For instance, some EZ stakeholders mentioned that their
programs took 2 or 3 years to get started, while others were able to
begin drawing down funds in the first year. Third, the nature of the
EZ/EC program, which focuses on changes in geographic areas rather than
on individuals, makes it difficult to determine how the program
affected residents who lived in an EZ/EC in 1994 but later moved.
Stakeholders from most of the EZs and ECs we visited said that
residents were moving out of the designated areas, often after finding
a job. If true, this phenomenon may have masked some of the program's
effects on poverty and unemployment, since these individuals would not
be captured in the 2000 data.
In Some Cases, EZs and ECs Showed Improvements in Poverty,
Unemployment, and Economic Growth:
Some EZs and ECs saw improvements in poverty, unemployment, and
economic growth. Four of the 11 EZs--Cleveland, Detroit, Philadelphia-
Camden, and Kentucky Highlands--showed improvements in both poverty and
unemployment between 1990 and 2000 and at least one measure of economic
growth between 1995 and 2004 (fig. 6). Some ECs also experienced
similar improvements. For example, 25 out of 95 ECs saw positive
changes in poverty and unemployment and at least one measure of
economic growth.[Footnote 45] None of the EZs and ECs experienced
negative changes in all three indicators, but many experienced negative
changes in at least one. For instance, the Atlanta EZ experienced
negative changes in unemployment and both measures of economic growth.
However, the extent of these changes varied, particularly in our two
measures of economic growth. For those EZs that saw improvements in the
number of jobs, the increases ranged from a low of 2.6 percent in the
Philadelphia-Camden EZ to a high of 67.8 percent in the Kentucky
Highlands EZ. Of those EZs that saw decreases in the number of
businesses, the amount varied from 2.7 percent in the Detroit EZ to
20.8 percent in the Atlanta EZ.
Figure 6: Changes in Poverty, Unemployment, and Two Measures of
Economic Growth Observed in Round I EZs:
[See PDF for image]
Source: GAO analysis of Census and Claritas data.
Note: The changes in poverty and unemployment rates are based on the
difference between 1990 and 2000 Census data, and the changes in the
number of businesses and jobs are based on the difference between 1995
and 2004 data from a private data vendor, Claritas. All poverty and
unemployment estimates had 95 percent confidence intervals of plus or
minus 5 percentage points or less. For the change in the number of
businesses and jobs, we did not consider a change of plus or minus one
percent or less as being significant.
[End of figure]
Most EZs and ECs Saw Some Decrease in the Poverty Rate, but These
Changes Could Not Be Tied Definitively to the EZ/EC Program:
In most of the 11 EZs and 95 ECs, both urban and rural, poverty rates
fell between 1990 and 2000 (fig. 7).[Footnote 46] Most communities
experienced statistically significant decreases in the poverty rate
that ranged from 2.6 to 14.6 percent. Specifically, our analysis showed
the following:
* Almost all urban EZs experienced significant decreases ranging from a
low of 4.1 percentage points in the New York EZ to 10.9 percentage
points in the Detroit EZ.
* All three rural EZs showed significant decreases--7.3 percentage
points in the Rio Grande Valley EZ, 10.1 percentage points in the
Kentucky Highlands EZ, and 10.7 percentage points the Mid-Delta EZ.
* 44 out of the 65 urban ECs also saw significant decreases in poverty,
with declines ranging from 2.6 percentage points in the Boston,
Massachusetts EC to 14.6 percentage points in the Minneapolis,
Minnesota EC.
* Most rural ECs saw significant decreases, ranging from 3.4 percentage
points in the Imperial County, California EC to 12.2 percentage points
in the Eastern Arkansas EC.
Figure 7: Number and Percentage of EZs and ECs Experiencing a Decrease
in Poverty from 1990 to 2000:
[See PDF for image]
Source: GAO analysis of Census data.
Note: All poverty estimates had 95 percent confidence intervals of plus
or minus 5 percentage points or less.
[End of figure]
We also compared changes in poverty in designated areas and comparison
areas and across urban and rural communities for both EZs and ECs. Our
analysis showed the following:
* When combining urban and rural areas, the poverty rate in the
designated areas fell more than in the comparison areas--5.4 percentage
points overall, compared with 3.9 percentage points in the comparison
areas (fig. 8).
* Rural designees experienced a larger significant decrease in poverty
than urban designees--7.2 and 5 percentage points, respectively.
* Urban and rural EZs experienced greater decreases in poverty than
both their comparison areas and the ECs.
Figure 8: Comparison of Decreases in Poverty in Urban and Rural
Designated Areas and Comparison Areas from 1990 to 2000:
[See PDF for image]
Source: GAO analysis of Census data.
Note: There are 1,557 census tracts in the designated areas and 1,504
in the comparison areas. All poverty estimates had 95 percent
confidence intervals of plus or minus 5 percentage points or less.
[End of figure]
Because we could not separate the program's effects from other factors
in these analyses, we developed an econometric model for the eight
urban EZs and their comparison areas that considered a variety of
factors related to the poverty rate.[Footnote 47] Among the nonprogram
factors we considered were high school dropouts, the presence of
households headed by females, and vacant housing units as reported in
the 1990 Census. Our models indicated that the poverty rate in the
comparison areas fell slightly more than in the EZs themselves (app.
II). This result did not demonstrate that the declines in poverty in
the EZs were directly associated with the EZ program.
Finally, we conducted interviews of EZ stakeholders and surveyed EC
officials to determine their views of the effects of the EZ/EC program
on their communities. Their responses were consistent with the
inconclusive results of our other analyses: in general, they believed
that both the EZ/EC program and additional factors had affected the
prevalence of poverty in their communities.[Footnote 48] Some EZ and EC
stakeholders said that the EZ/EC designation and program activities had
addressed poverty by bringing in jobs and helping to stabilize the
area. For instance, stakeholders from several EZs, including the
Chicago, Mid-Delta, and Kentucky Highlands EZs, mentioned the role of
the EZ in job creation. In addition, stakeholders from other EZs, such
as Detroit and Rio Grande Valley, mentioned the role of EZ programs
that were related to housing. EC survey respondents commented that the
EC designation gave them the opportunity to focus on initiatives that
could improve poverty in the area, such as job creation, infrastructure
and physical improvements, and housing.
However, EZ and EC stakeholders also mentioned external factors that
may have affected the changes in poverty, such as changes in the local
population when original residents moved away and gentrification. In
addition, stakeholders from three EZs mentioned the positive effects of
changes to welfare policy during the EZ/EC program.[Footnote 49] In ECs
where our data showed that the poverty rate fell, some EC survey
respondents also mentioned an increase in the availability of social
services as a contributing factor. At EZs where stakeholders had mixed
opinions on the changes in poverty, some cited a loss of industry or
shifts in the national economy. Of the three EC survey respondents in
areas where poverty either remained the same or increased, respondents
mentioned the decrease in the number of jobs, increase in housing and
utility costs, and the out-migration of residents with middle or high
incomes.
Decreases in the Unemployment Rate in Some Communities Also Could Not
Be Definitively Tied to the EZ/EC Program:
As we did for the poverty rate, we analyzed changes in the unemployment
rate in EZs and ECs, using the same quantitative and qualitative
methods. We found an overall decline in unemployment across
communities; but, once again we could not tie the decrease definitively
to the program's presence. Further, fewer than half of the individual
EZs and ECs experienced a decrease in unemployment (fig. 9), with
declines ranging from 1.5 to 11.7 percentage points, and a number saw
significant increases--up to 6.5 percentage points.[Footnote 50] Many
communities did not experience a significant change. Specifically, our
analysis showed the following:
* Four of the eight urban EZs saw unemployment fall, with rates
declining from 2.9 percentage points in the Philadelphia-Camden EZ to
10 percentage points in the Cleveland EZ. Two of the EZs saw
unemployment rise--2 percentage points in New York and 6 percentage
points in Atlanta--and two did not see a statistically significant
change.
* Changes in the unemployment rates of the rural EZs were also mixed.
For example, unemployment in the Kentucky Highlands EZ fell 2
percentage points, but it rose 3.1 percentage points in the Mid-Delta
EZ and did not change significantly in the Rio Grande Valley EZ.
* Twenty-seven, or fewer than half, of the 65 urban ECs saw significant
decreases from 1.5 percentage points (San Diego, California) to 8.7
percentage points (Flint, Michigan). Eleven saw a significant increase
of between 2.1 percentage points (Rochester, New York) and 6.5
percentage points (Charlotte, North Carolina), while 27 did not
experience a significant change.
* Almost half of the rural ECs saw significant decreases, with declines
ranging from 2.7 percentage points (Fayette-Haywood, Tennessee) to 11.7
percentage points (Lake County, Michigan). The unemployment rate
remained about the same in 12 rural ECs, but 4 showed increases of
between 2.8 and 3.5 percentage points (Williamsburg-Lake City, South
Carolina and Central Savannah River Area, Georgia, respectively).
Figure 9: Number and Percentage of EZs and ECs that Experienced a
Decrease in Unemployment from 1990 to 2000:
[See PDF for image]
Source: GAO analysis of Census data.
Note: All unemployment estimates had 95 percent confidence intervals of
plus or minus 5 percentage points or less.
[End of figure]
Our analysis also looked at changes in unemployment across urban and
rural communities and compared changes in designated areas and
comparison areas for both EZs and ECs. The analysis showed the
following results:
* The designated areas saw a statistically significant decrease in
unemployment of 1.4 percentage points, compared with a decrease of just
under 1 percentage point in the comparison areas (fig. 10).
* In general, rural designees saw unemployment fall more than urban
designees, although these differences were not as marked as those we
identified in our analysis of the changes in poverty.
* Urban EZs and ECs saw a greater decrease in unemployment than their
comparison areas, where the rates did not show a statistically
significant change.
* Unemployment in rural EZs and their comparison areas remained about
the same, while rural ECs and their comparison areas both experienced a
significant decrease of about 2 percentage points.
Figure 10: Comparison of Decreases in Unemployment in Urban and Rural
Designated Areas and Comparison Areas from 1990 to 2000:
[See PDF for image]
Source: GAO analysis of Census data.
Note: Areas for which there was no statistically significant change are
not shown. There are 1,557 census tracts in the designated areas and
1,504 in the comparison areas. All unemployment estimates had 95
percent confidence intervals of plus or minus 5 percentage points or
less.
[End of figure]
Although our analyses of changes again showed that EZs experienced a
larger decrease in unemployment than the comparison areas, these
analyses did not separate the effect of the program from other factors.
We again used an econometric model for the eight urban EZs that
considered other factors, such as average household income and the
presence of individuals with a high school diploma as reported in the
1990 Census. This analysis showed that the EZs experienced a decrease
that was slightly greater than in the comparison areas, but the
difference was not statistically significant (app. II).
We also looked at the observations of EZ stakeholders that we
interviewed and the responses to our EC survey. Once again, these
observations generally saw both program and external factors as
affecting the changes in unemployment.[Footnote 51] Some EZ
stakeholders cited EZ programs--such as providing financial assistance
to EZ businesses, fostering job creation, and offering job training--as
helping to reduce unemployment. For example, the Upper Manhattan and
Bronx portions of the New York EZ and the Chicago EZ required
subgrantees and borrowers to create a certain number of jobs based on
the size of the EZ grant or loan received. Similarly, EC survey
respondents also mentioned the EC's involvement in creating jobs,
attracting new businesses, and offering loans and technical assistance
to businesses, along with a variety of social service programs designed
to support employment.
EZ stakeholders and EC survey respondents also noted additional factors
that may have been associated with changes in unemployment. For
example, some EZs cited the availability of social services not
sponsored by the EZ as factors that influenced unemployment--for
instance, daycare, transportation, and adult education or job placement
programs. Some EZ stakeholders also suggested that changes in the
national economy and in welfare policy had helped to reduce
unemployment. Many survey respondents in ECs where unemployment fell
reported that the decreases could be attributed to activities that may
or may not have been part of the EC program, including adult
educational services, higher skill levels among area residents, and
social services such as childcare, programs for the homeless, and
substance abuse treatment. Stakeholders from EZs where unemployment did
not change or rose explained that EZ residents faced barriers to
employment such as a lack of education or job skills, drug dependency,
and criminal histories.
Our Measures Showed that Some Economic Growth Occurred, but Results
from Our Econometric Model Were Not Conclusive:
A number of indicators can be used to measure economic growth,
including data on the change in the number of local businesses, sales
volumes, or home values. Our poverty and unemployment analyses used
specific variables available in Census data, but to measure economic
growth, we chose two measures--the number of businesses and the number
of jobs.[Footnote 52] Overall, our analysis showed that most EZs and
ECs experienced an increase in at least one measure of economic growth
between 1995 and 2004 (fig. 11). Specifically:
* Two of the eight urban EZs experienced significant increases in the
number of both businesses and jobs, and three more experienced
significant increases in one measure. The increases in businesses
ranged from 4.2 percent in the Philadelphia-Camden EZ to 23.6 percent
in the New York EZ. The increases in jobs ranged from 2.6 percent in
the Philadelphia-Camden EZ to 30.5 percent in the Detroit EZ. However,
some urban EZs experienced decreases in the number of businesses or
jobs, some of which were large. Five experienced decreases in the
number of businesses, ranging from 2.7 percent in the Detroit EZ to
20.8 percent in the Atlanta EZ, and four experienced decreases in the
number of jobs, from 5.2 percent in the Los Angeles EZ to 22.3 percent
in the Atlanta EZ.
* All three rural EZs experienced increases in both businesses and
jobs, with businesses increasing between 15.6 percent in the Mid-Delta
EZ and 33 percent in the Kentucky Highlands EZ, and jobs rising between
5 and 67.8 percent in the same two EZs, respectively.
* Fourteen of the 64 urban ECs experienced an increase in both economic
growth measures, and an additional 24 saw an increase in one of the
measures.[Footnote 53] However, 26 urban ECs saw a decrease in both
measures.
* Like rural EZs, the majority of the rural ECs experienced an increase
in both measures of economic growth.
Figure 11: Number and Percentage of EZs and ECs That Experienced an
Increase in One or Both Measures of Economic Growth between 1995 and
2004:
[See PDF for image]
Source: GAO analysis of Claritas data.
Note: We excluded establishments that were not eligible for program tax
benefits, such as nonprofit and governmental organizations, from our
analysis of the change in the number of businesses. However, we
included jobs at those businesses in our analysis of the change in the
number of jobs.
[A] Data were not available for the Miami/Dade County, Florida EC.
[End of figure]
Like the analyses of poverty and unemployment, our analysis of the
changes in economic growth compared urban and rural designees,
designated and comparison areas, and EZs and ECs (fig. 12).
* In aggregate, both designated and comparison areas saw little change
in the number of businesses, and both experienced an increase in the
number of jobs of about 7 percent.
* Overall, urban designees saw a decrease in the number of businesses,
while rural designees saw a substantial increase. Both urban and rural
designees saw an increase in the number of jobs, but the aggregate
increase in rural areas was much greater (23.6 percent) than in urban
areas (5.7 percent). Urban and rural comparison areas generally
experienced changes similar to the designated areas.
* Urban EZs experienced a decrease in the number of businesses, while
the number in comparison areas remained about the same. But urban EZs
saw an increase in the number of jobs, while their comparison areas saw
a decrease.
* Rural EZs fared better than their comparison areas in both measures
of economic growth.
Figure 12: Comparison of Changes in the Number of Businesses and the
Number of Jobs in Urban and Rural Designated Areas and Comparison Areas
between 1995 and 2004:
[See PDF for image]
Source: GAO analysis of Claritas data.
Note: There are 1,557 census tracts in the designated areas and 1,504
in the comparison areas. We excluded establishments that were not
eligible for program tax benefits, such as nonprofit and governmental
organizations, from our analysis of the change in the number of
businesses. However, we included jobs at those businesses in our
analysis of the change in the number of jobs. These analyses do not
include data for the Miami/Dade County, Florida EC.
[End of figure]
As explained earlier, our descriptive analyses could not isolate the
effects of the EZ/EC program from other factors affecting the
designated and comparison areas. We conducted an econometric analysis
that incorporated other factors, such as the percentage of vacant
housing units and population density as reported in the 1990 Census.
However, the results of our models explained little of the relative
changes in the number of businesses or jobs in the urban EZs with
respect to their comparison areas (app. II). Because our proxy
measures--the number of businesses and jobs--were not the only
indicators representative of economic growth, we tested our models
using different measures, such as the number of home mortgage
originations, but found similar results. As a result, we could not
determine with a reasonable degree of confidence the role that the EZs
might have played in the changes in economic growth that we observed.
We also reviewed the perceptions of EZ stakeholders interviewed and
respondents to our survey of ECs on economic growth in their
communities.[Footnote 54] These observations cited several aspects of
the program that contributed to economic growth, including loan
programs and other benefits that aided small businesses, infrastructure
improvements, and tax benefits, especially when the tax benefits were
combined with other federal, state, and local benefits. Additionally,
several stakeholders mentioned that their EZ or EC had acted as a
catalyst for other local development. EZ stakeholders also noted
several external factors that affected the change in economic growth,
such as the increase of jobs in businesses located within the EZ or EC,
the role of other state and local initiatives in attracting businesses,
and trends in the national economy. In ECs where our data showed an
increase in the number of businesses or jobs, some survey respondents
reported that the result was due to an increase in technical assistance
for area businesses, such as entrepreneurial training programs, and
others reported that financial assistance to businesses contributed to
the growth, both of which may or may not have been EC programs. EZ
stakeholders also mentioned challenges facing their communities,
including the lack of infrastructure and residents with incomes that
were not high enough to support local businesses. In ECs where our data
showed a decrease in the number of businesses or jobs, survey
respondents pointed to a decrease in the number of area businesses and
downsizing of existing businesses as contributing factors.
Additional Program Data Could Facilitate Evaluations of the Effects of
the EZ/EC and Similar Programs:
Our efforts to analyze the effects of Round I designation on poverty,
unemployment, and economic growth were limited by the absence of data
on the use of program grant funds, the amount of funds leveraged, and
the use of tax benefits. Without these data, we could not account for
the amount of funds EZs used to carry out specific activities, the
extent to which they leveraged other resources, or how extensively
businesses used the tax benefits. As a result, we could not assess
differences in program implementation. In addition, as we reported in
2004, we could not evaluate the effectiveness of the tax benefits,
although later rounds of the EZ/EC program have relied heavily on
them.[Footnote 55]
While we recognize, and discussed in our prior report on the EZ/EC
program, the difficulties inherent in evaluating economic development
programs, having more specific data would facilitate evaluations of
this and similar programs.[Footnote 56] For example, the precision of
our econometric models might have been improved by combining data on
how program funds were used--such as the amounts used for assisting
businesses--and the use of program tax benefits with other data we
obtained, such as data on businesses and area jobs. Also, additional
data would have allowed us to do in-depth evaluations of the extent to
which various tax benefits were being used within each community, the
size and type of businesses utilizing them, and the potential
competitive advantages of using these benefits. Our previous reports
have recommended that information on outlay programs and tax
expenditures be collected to evaluate the most effective methods for
accomplishing federal objectives.[Footnote 57]
Observations:
The EZ/EC program, one of the most recent large-scale federal programs
aimed at revitalizing distressed urban and rural communities, resulted
in a variety of activities intended to improve social and economic
conditions in the nation's high-poverty communities. As of March 31,
2006, all but 15 percent of the $1 billion in program grant funds
provided to Round I communities had been expended, and the program was
reaching its end. All three rounds of the EZ/EC program are scheduled
to end no later than December 31, 2009. However, given our findings
from this evaluation of Round I EZs and ECs, the following observations
should be considered if these or similar programs are authorized in the
future.
Based on our review, we found that oversight for Round I of the program
was limited because the three agencies--HHS, HUD, and USDA--did not
collect data on how program funds were used, and HHS did not provide
state and local entities with guidance sufficient to ensure monitoring
of the program. These limitations may be related in part to the design
of the program, which offered increased flexibility in the use of funds
and relied on multiple agencies for oversight. However, limited data
and variation in monitoring hindered federal oversight efforts.
In addition, the lack of data on the use of program grant funds, the
extent of leveraging, and extent to which program tax benefits were
used also limited our ability and the ability of others to evaluate the
effects of the program. The lack of data on the use of tax benefits is
of particular concern, since the estimated amount of the tax benefits
was far greater than the amount of grant funds dedicated to the
program. In response to the recommendation in our 2004 report, HUD,
IRS, and USDA discussed options for collecting additional data on
program tax benefits and determined two methods for collecting the
information--through a national survey or the modification of tax
forms. The three agencies, however, did not reach agreement on a cost-
effective method for collecting the additional data. In our and others'
prior attempts to obtain this information using surveys, survey
response rates were low and thus did not produce reliable information
on the use of program tax benefits.
We acknowledge that the collection of additional tax data by IRS would
introduce additional costs to both IRS and taxpayers. Nonetheless, a
lack of data on tax benefits is significant given that subsequent
rounds of the EZ/EC program and the Renewal Community program rely
almost exclusively on tax benefits, and other federal economic
development programs, such as the recent Gulf Opportunity Zone
initiative, involve substantial amounts of tax benefits. Furthermore,
the nation's current and projected fiscal imbalance serves to reinforce
the importance of understanding the benefits of such tax expenditures.
If Congress authorizes similar programs that rely heavily on tax
benefits in the future, it would be prudent for federal agencies
responsible for administering the program to collect information
necessary for determining whether the tax benefits are effective in
achieving program goals.
Agency Comments and Our Evaluation:
We provided a draft of this report for review and comment to HHS, HUD,
IRS, and USDA. We received comments from HHS, HUD, and USDA. In
general, the agencies provided comments related to the oversight of the
program, the availability of data, and the methodology used to carry
out the work. Their written comments appear in appendixes V through
VII, respectively, and our responses to HUD's more detailed comments
also appear in appendix VI. HHS, HUD, and USDA also provided technical
comments, which we have incorporated into the report where appropriate.
HHS commented that a statement made in our report--that the agency did
not provide guidance detailing the steps state and local authorities
should take to monitor the program--unfairly represented the
relationship between HHS and the other federal agencies that
administered the EZ/EC program. Specifically, HHS emphasized its
responsibility for fiscal as opposed to programmatic oversight of the
program. We note in our report that program design may have led to a
lack of clarity in oversight, as no single federal agency had sole
oversight responsibility. While this lack of clarity in oversight may
be related in part to the design of the program, which offered
increased flexibility in the use of funds and relied on multiple
agencies for oversight, limited data and variation in monitoring
hindered federal oversight efforts. Moreover, we believe that, in
accordance with federal standards, each of the federal agencies that
administered the program bore at least some responsibility for ensuring
that public resources were being used effectively and that program
goals were being met.
HUD disagreed with GAO's observation that there was a lack of data on
the use of program grant funds, the amount of funds leveraged, and the
use of the tax benefits. HUD indicated that we could obtain data on the
use of program funds and the amount of funds leveraged from its
performance reporting system. As we discussed in our report, we used
information from HUD's reporting system to report on the types of
activities that designated communities implemented. We also noted that
HUD maintained some information on the amount of EZ/EC grants budgeted
for specific activities. Although we found evidence that activities
were carried out with program funds, information contained in the
performance reporting system on the amounts of funds used and the
amount leveraged was not reliable. For example, we found evidence that
communities had undertaken certain activities with program funding, but
we were often unable to find documentation of the actual amounts
allocated or expended. HUD also indicated that it did not agree that
data on the use of the tax benefits were lacking. However, HUD
indicated that the agency itself had attempted to gather such data by
collaborating with IRS in identifying ways to collect data on tax
benefits, by developing a methodology to administer a survey to
businesses, and by compiling anecdotal evidence of the use of program
tax benefits. We continue to believe that the lack of data on program
tax benefits limits the ability of the agencies to administer and
evaluate the EZ/EC program. Further, the lack of such data is likely to
become increasingly problematic in light of the fact that future rounds
of the EZ/EC program and the Renewal Community program rely heavily on
tax benefits to achieve revitalization goals.
HUD concurred that limitations in the oversight of the EZ/EC program
may have resulted from the design of the program as no single federal
agency had sole responsibility for oversight. HUD also recommended that
we make clear that more oversight was not allowed in Round I and we
include a statement that it met agency requirements to undertake
periodic performance reviews and described some of its efforts to
monitor the program according to applicable regulations. We do not
believe that more oversight was not allowed. For example, early in the
program HUD and HHS made some efforts to share information.
Specifically, HUD officials said that they had received fiscal data
from HHS and reconciled that information with their program data on the
activities implemented, but these efforts to share information were not
maintained. Further, as we previously stated, while we recognize that
program design may have led to a lack of clarity in oversight, we
believe that in accordance with federal standards, each of the federal
agencies that administered the program bore at least some
responsibility for ensuring that public resources were being used
effectively and that program goals were being met. HUD also described
changes it had made to ensure better oversight of program funds for
Round II. We acknowledge HUD's efforts to improve oversight of the
program and, as discussed in our report, the oversight limitations that
we identified in Round I of the program may not apply to later rounds.
HUD provided several comments related to the methodology we used to
carry out our work. For example, HUD suggested that we measure the
successes of the Round I program in meeting the four key principles of
the program, which the designated communities were required to include
in their strategic plans. Additionally, HUD commented that the indices
we used to assess the effects of the EZ/EC program--poverty,
unemployment and economic growth--were used in the application process
for the program but were not intended to be used as performance
measures. While we appreciate HUD's suggestions on our methodology, our
congressional mandate was to determine the effect of the EZ/EC program
on poverty, unemployment and economic growth. In designing our
methodology, we conducted extensive research on evaluations that had
been conducted on the EZ/EC program, including HUD's 2001 Interim
Assessment, and spoke with several experts in the urban studies field.
USDA stated that data and analyses on the effectiveness of programs
such as EZ/EC were useful and offered areas to consider for future
evaluations of economic development programs involving rural areas. For
example, USDA mentioned issues involved in collecting data on rural
areas, such as the limited availability of economic and demographic
data for small rural populations, and discussed USDA's efforts for
developing a methodology that focuses on economic impacts using county-
level economic data. USDA also said it is especially important in rural
areas to have a clear and adequately funded data collection process for
program evaluations. In addition, USDA noted that evaluations of the
EZ/EC program could go beyond the indicators of poverty, unemployment
and economic growth to include measures on economic development
capacity and collaboration. We agree that collecting data for rural
areas is a challenge and appreciate USDA's effort to develop a
methodology that focuses on economic impacts using county-level
economic data and captures the short-term Gross Domestic Product
changes in the impacted rural counties. Further, we appreciate USDA's
suggestion that additional measures be considered in future evaluations
of economic development programs and that a broader perspective on
program results might be useful.
USDA also commented that its performance reporting system was intended
to be used as a management tool for both USDA and the individual EZs
and ECs. According to USDA, the system was not designed to be an
accounting tool but has been useful for providing a picture of each
designated community's achievements. As we discussed in our report, we
used information from USDA's reporting system to report on the types of
activities that designated communities implemented and also noted that
USDA maintained some information on the amounts of EZ/EC grants
budgeted for specific activities. Moreover, while we recognize the
system was not intended to be used as an accounting tool, we found that
the data on the amounts of the EZ/EC grant funding were not reliable.
For example, in our assessment of the reliability of data contained in
USDA's performance reporting system, we were often unable to find
documentation of the actual amounts allocated or expended for specific
activities.
USDA further commented that it had encouraged designated communities to
report all investment that contributed to the EZ or EC in accomplishing
its strategic plan as leveraged funds. We recognize USDA's efforts to
encourage leveraging in the designated communities and to report such
information in its performance reporting system. Our report notes that
stakeholders from all EZs and ECs we visited and EC survey respondents
reported having used their EZ/EC grants to leverage other resources.
However, we were unable to evaluate the amounts of funds leveraged
because the data contained in USDA's performance reporting system were
not reliable. For example, USDA's performance reporting system included
information on the amounts of funds leveraged for each activity, but
for the sample of activities we reviewed, either supporting
documentation showed an amount conflicting with the reported amount or
documentation could not be found. Moreover, as we discuss in our
report, the definition of leveraging used among the designated
communities was inconsistent.
We are sending copies of this report to interested Members of Congress,
the Secretary of Health and Human Services, the Secretary of Housing
and Urban Development, the Secretary of Treasury, the Commissioner of
the Internal Revenue Service, and the Secretary of Agriculture. We will
make copies of this report available to others upon request. In
addition, this report will be available at no charge on the GAO Web
site at [Hyperlink, http://www.gao.gov].
Please contact me at (202) 512-8678 or ShearW@gao.gov if you or your
staff have any questions about this report. Contact points for our
Offices of Congressional Relations and Public Affairs may be found on
the last page of this report. Key contributors to this report are
listed in appendix VIII.
Signed by:
William B. Shear:
Director, Financial Markets and Community Investment:
[End of section]
Appendix I: Objectives, Scope, and Methodology:
The objectives of this study were to (1) describe how Round I of the
Empowerment Zone and Enterprise Community (EZ/EC) program was
implemented by the designated communities; (2) evaluate the extent of
federal, state, and local oversight of the program; (3) examine the
extent to which data are available to assess the use of program tax
benefits; and (4) analyze the effects the Round I EZs and ECs had on
poverty, unemployment, and economic growth in their communities. To
address each of our objectives, we completed site visits to all Round I
EZs and two Round I ECs and administered a survey to all ECs that did
not receive subsequent designations, such as a Round II EZ designation.
At each site, we asked uniform questions on implementation, oversight,
tax benefits, and changes observed in the EZ and ECs. We also surveyed
60 ECs that were in operation as of June 2005 and did not receive later
designations and asked about similar topics. We performed a qualitative
analysis to identify common themes from our interview data and open-
ended survey responses. To address our second objective, we also
interviewed federal and state program participants, reviewed oversight
guidance and documentation, and verified a sample of reported
performance data by tracing it to EZ and EC records. To address our
third objective, we attempted to administer a survey of EZ businesses,
but discontinued it due to a low response rate. To address our fourth
objective, we obtained demographic and socioeconomic data from the 1990
and 2000 decennial censuses and business data for 1995, 1999, and 2004
from a private data vendor, Claritas. We used 1990 Census data to
select areas similar to the EZ and EC areas for purposes of comparison.
We then calculated the percent changes in poverty, unemployment, and
economic growth observed in the EZs and ECs and their comparison areas.
In addition, for the eight urban EZs, we used an econometric model to
estimate the effect of the program, by controlling for certain factors,
such as average household income, in the EZs and their comparison
areas. Finally, we used information gathered from our qualitative
analysis to provide context for the changes observed in the EZs and
ECs.
Methodology for Site Visits:
To answer our objectives, we completed site visits to all 11 EZs and 2
of the 95 ECs, one urban and one rural.[Footnote 58] These EZs and ECs
were located in:
* Atlanta, Georgia (EZ):
* Baltimore, Maryland (EZ):
* Chicago, Illinois (EZ):
* Cleveland, Ohio (EZ):
* Detroit, Michigan (EZ):
* Los Angeles, California (EZ):
* New York, New York (EZ):
* Philadelphia, Pennsylvania and Camden, New Jersey (EZ):
* rural Kentucky (Kentucky Highlands EZ):
* rural Mississippi (Mid-Delta EZ):
* rural Texas (Rio Grande Valley EZ):
* Providence, Rhode Island (EC):
* rural Tennessee (Fayette-Haywood EC):
We interviewed stakeholders from each site on the implementation,
governance, oversight, and tax benefits of the EZ or EC and asked about
the changes the stakeholders had observed in their communities. Using a
standardized interview guide, we interviewed some combination of the
following program stakeholders at each location: EZ/EC officials, board
members (including some EZ/EC residents), representatives of subgrantee
organizations, and Chamber of Commerce representatives or individuals
able to provide the perspective of the business community (table
4).[Footnote 59] We identified participants to interview at each site
by soliciting opinions from EZ/EC officials and the current board
chair. For each site, we reviewed strategic plans, organizational
charts, and documentation on oversight procedures. In addition, we
toured the EZ/EC to see some of activities implemented.
Table 4: Number of Stakeholders Interviewed for EZ and EC Site Visits,
by Type:
EZ/EC: Urban: Atlanta EZ;
EZ/EC officials: Urban: 2;
Board members: Urban: 2;
Representatives from subgrantee organizations: Urban: 6;
Representatives from the Chamber of Commerce or other business
perspective: Urban: 2;
Officials from the state pass-through entities: Urban: 4;
Other representatives[A]: Urban: 10.
EZ/EC: Urban: Baltimore EZ;
EZ/EC officials: Urban: 6;
Board members: Urban: 4;
Representatives from subgrantee organizations: Urban: 3;
Representatives from the Chamber of Commerce or other business
perspective: Urban: 2;
Officials from the state pass-through entities: Urban: 1;
Other representatives[A]: Urban: 5.
EZ/EC: Urban: Chicago EZ;
EZ/EC officials: Urban: 6;
Board members: Urban: 4;
Representatives from subgrantee organizations: Urban: 4;
Representatives from the Chamber of Commerce or other business
perspective: Urban: 1;
Officials from the state pass-through entities: Urban: 3;
Other representatives[A]: Urban: 2.
EZ/EC: Urban: Cleveland EZ;
EZ/EC officials: Urban: 5;
Board members: Urban: 4;
Representatives from subgrantee organizations: Urban: 3;
Representatives from the Chamber of Commerce or other business
perspective: Urban: 0;
Officials from the state pass-through entities: Urban: 4;
Other representatives[A]: Urban: 8.
EZ/EC: Urban: Detroit EZ;
EZ/EC officials: Urban: 7;
Board members: Urban: 4;
Representatives from subgrantee organizations: Urban: 3;
Representatives from the Chamber of Commerce or other business
perspective: Urban: 1;
Officials from the state pass-through entities: Urban: 2;
Other representatives[A]: Urban: 9.
EZ/EC: Urban: Los Angeles EZ;
EZ/EC officials: Urban: 1;
Board members: Urban: 2;
Representatives from subgrantee organizations: Urban: 2;
Representatives from the Chamber of Commerce or other business
perspective: Urban: 0;
Officials from the state pass-through entities: Urban: 0;
Other representatives[A]: Urban: 12.
EZ/EC: Urban: New York EZ;
EZ/EC officials: Urban: [Empty];
Board members: Urban: [Empty];
Representatives from subgrantee organizations: Urban: [Empty];
Representatives from the Chamber of Commerce or other business
perspective: Urban: [Empty];
Officials from the state pass-through entities: Urban: [Empty];
Other representatives[A]: Urban: [Empty].
EZ/EC: Urban: Upper Manhattan portion;
EZ/EC officials: Urban: 3;
Board members: Urban: 3;
Representatives from subgrantee organizations: Urban: 2;
Representatives from the Chamber of Commerce or other business
perspective: Urban: 1;
Officials from the state pass-through entities: Urban: 2;
Other representatives[A]: Urban: 2.
EZ/EC: Urban: Bronx portion;
EZ/EC officials: Urban: 2;
Board members: Urban: 4;
Representatives from subgrantee organizations: Urban: 3;
Representatives from the Chamber of Commerce or other business
perspective: Urban: 1;
Officials from the state pass-through entities: Urban: 2;
Other representatives[A]: Urban: 1.
EZ/EC: Urban: Philadelphia/Camden EZ;
EZ/EC officials: Urban: [Empty];
Board members: Urban: [Empty];
Representatives from subgrantee organizations: Urban: [Empty];
Representatives from the Chamber of Commerce or other business
perspective: Urban: [Empty];
Officials from the state pass-through entities: Urban: [Empty];
Other representatives[A]: Urban: [Empty].
EZ/EC: Urban: Philadelphia portion;
EZ/EC officials: Urban: 5;
Board members: Urban: 2;
Representatives from subgrantee organizations: Urban: 2;
Representatives from the Chamber of Commerce or other business
perspective: Urban: 0;
Officials from the state pass-through entities: Urban: 8;
Other representatives[A]: Urban: 3.
EZ/EC: Urban: Camden portion;
EZ/EC officials: Urban: 1;
Board members: Urban: 3;
Representatives from subgrantee organizations: Urban: 2;
Representatives from the Chamber of Commerce or other business
perspective: Urban: 1;
Officials from the state pass-through entities: Urban: 2;
Other representatives[A]: Urban: 2.
EZ/EC: Urban: Providence EC;
EZ/EC officials: Urban: 2;
Board members: Urban: 3;
Representatives from subgrantee organizations: Urban: 3;
Representatives from the Chamber of Commerce or other business
perspective: Urban: 0;
Officials from the state pass-through entities: Urban: 2;
Other representatives[A]: Urban: 1.
EZ/EC: Rural: Kentucky Highlands EZ;
EZ/EC officials: Urban: 4;
Board members: Urban: 6[B];
Representatives from subgrantee organizations: Urban: 1;
Representatives from the Chamber of Commerce or other business
perspective: Urban: 1;
Officials from the state pass-through entities: Urban: 4;
Other representatives[A]: Urban: 4.
EZ/EC: Rural: Mid-Delta Mississippi EZ;
EZ/EC officials: Urban: 4;
Board members: Urban: 1;
Representatives from subgrantee organizations: Urban: 4;
Representatives from the Chamber of Commerce or other business
perspective: Urban: 2;
Officials from the state pass-through entities: Urban: 2;
Other representatives[A]: Urban: 2.
EZ/EC: Rural: Rio Grande Valley EZ;
EZ/EC officials: Urban: 4;
Board members: Urban: 3;
Representatives from subgrantee organizations: Urban: 3;
Representatives from the Chamber of Commerce or other business
perspective: Urban: 1;
Officials from the state pass-through entities: Urban: 3;
Other representatives[A]: Urban: 1.
EZ/EC: Rural: Fayette Haywood EC;
EZ/EC officials: Urban: 1;
Board members: Urban: 2;
Representatives from subgrantee organizations: Urban: 2;
Representatives from the Chamber of Commerce or other business
perspective: Urban: 0;
Officials from the state pass-through entities: Urban: 4;
Other representatives[A]: Urban: 4.
Source: GAO.
[A] Includes local governmental officials, business owners, active
community members, and other representatives.
[B] Includes directors of subzones.
[End of table]
Methodology for Survey of EC Officials:
To gather similar information from the ECs, we administered an e-mail
survey to officials from the 60 Round I ECs that were still in
operation as of June 2005 and did not receive a subsequent designation.
We chose to exclude the 34 ECs that received subsequent designations,
because we did not want their responses to be influenced by those
programs. A version of the survey showing aggregated responses can be
viewed at [Hyperlink, http://www.gao.gov/cgi-bin/getrpt?GAO-06-734SP].
We developed survey questions from existing program literature and
interview data collected from Department of Housing and Urban
Development (HUD) and U.S. Department of Agriculture (USDA)
headquarters officials as well as our site visits to Round I EZs and
ECs. The questionnaire items covered the implementation of the program,
the types of governance structures used, usage of the program tax-
exempt bond, and stakeholders' views of factors that influenced the
changes they observed in poverty, unemployment, and economic growth in
their ECs. We created two versions of the questionnaire, one for urban
ECs and another for rural ECs, in order to tailor items to urban or
rural sites. Department of Health and Human Services (HHS), HUD, and
USDA officials reviewed the survey for content, and we conducted
pretests at four urban and two rural ECs.[Footnote 60] Since the survey
was administered by e-mail, a usability pretest was conducted at one
urban EC (Akron, Ohio) to observe the respondent answering the
questionnaire as it would appear when opened and displayed on their
computer screen.
In administering the survey, we took the following steps to increase
the response rate. To identify survey participants, we obtained contact
information for the Round I ECs that did not receive a subsequent
designation from HUD and USDA in April 2005.[Footnote 61] We then sent
a notification e-mail to inform the ECs of the survey, to identify the
correct point of contact, and to ensure the e-mail account was active.
Those who did not respond to the first e-mail received follow up e-
mails and telephone calls. The questionnaire was e-mailed on August 25,
2005 to 27 rural ECs and 33 urban ECs, and participants were given the
option to respond via e-mail, fax, or post mail. Between September and
December 2005, multiple follow up e-mails and calls were made to
increase the response rate. When the survey closed on December 20,
2005, all of the rural ECs and 31 of the 33 urban ECs had completed it.
The overall response rate was high at 97 percent, with the response
rates for the rural ECs at 100 percent and urban ECs at 94 percent. We
did not attempt to verify the respondents' answers against an
independent source of information. However, we used two techniques to
verify the reliability of questionnaire items. First, we used in-depth
interviewing techniques to evaluate the answers of pretest
participants, and interviewers judged that all the respondents' answers
to the questions were based on reliable information. Second, for the
items that asked about changes to poverty, unemployment, and economic
growth in the EC, we asked respondents to provide a source of data for
their response. Responses to those questions that did not include a
data source were excluded from our analysis of those items.
The practical difficulties of conducting any survey may introduce
certain types of errors, commonly referred to as nonsampling errors.
For example, differences in how a particular question is interpreted,
the sources of information available to respondents, or the types of
people who do not respond can introduce unwanted variability into the
survey results. We sought to minimize these errors by taking the
following steps: conducting pretests, making follow-up contacts with
participants to increase response rates, performing statistical
analyses to identify logical inconsistencies, and having a second
independent analyst review the statistical analyses. Returned surveys
were reviewed for consistency before the data were entered into an
electronic database. All keypunched or inputted data were 100-percent
verified--that is, the data were electronically entered twice. Further,
a random sample of the surveys was verified for completeness and
accuracy. We used statistical software to analyze responses to close-
ended questions and performed a qualitative analysis on open-ended
questions to identify common themes.
Methodology for Qualitative Analysis of Site Visit and EC Survey Data:
To summarize the information collected at our site visits, we conducted
a qualitative analysis of interview data. The goal of the analysis was
to create a summary that would produce an overall "story" or brief
description of the program as implemented in each site. In this
process, we reviewed data from over 200 interviews to identify
information pertaining to the following six broad topics:
* strategic planning and census tract selection;
* goals, implemented activities, leveraging activities, and
sustainability;
* governance structure and process;
* program oversight;
* perceptions of the use of tax benefits; and:
* perceptions of poverty, unemployment, economic growth, and other
changes within the zone.
Based on initial reviews of the interview data, we produced general
outlines for each topic. For example, a description of the governance
structure and process included identifying the type of governance
structure used, roles within the structure, opportunities for community
involvement, the process for decision making, and successes and
challenges related to governance. One reviewer was assigned to each of
the six topics for an individual site. The reviewer examined all
interviews completed at an individual site and created a topical
summary based on interview data. Each summary was verified by (1)
presenting the summaries to the group of six interview reviewers to
ensure accuracy, clarity, and completeness and (2) having a second
reviewer trace the summaries back to source documents.
We also performed a qualitative analysis of the open-ended responses in
the EC survey to determine reasons why the tax-exempt bond was not more
widely used; why poverty, unemployment, and economic growth may have
remained the same over the designation; and what role the EC played in
changes in poverty, unemployment and economic growth, as well as
obtaining general comments about the program. Responses to these
questions were first reviewed by an analyst to identify common
categories within the responses and then independently verified by a
second analyst.
Methodology for Review of Program Oversight:
We interviewed and obtained documentation from federal, state, and
local program participants regarding program oversight. We interviewed
officials from the federal agencies involved with the program and
obtained and analyzed fiscal and program data from the agencies. In
addition, since the states were the pass-through entities for grant
funds provided to the EZs and ECs--that is, they distributed federal
funding to the communities--we conducted telephone interviews with
state officials and obtained relevant documents in the 13 states
containing EZs and ECs we visited. Finally, we interviewed EZ and EC
officials on their oversight of subgrantees as well as the oversight
they received from federal and state entities. We did not perform
financial audits of the EZs and ECs.
To determine the reliability of data in HUD and USDA Internet-based
performance reporting systems, we randomly selected activities at each
EZ and EC we visited and conducted a file review to determine the
accuracy of:
the data.[Footnote 62] In the files, we searched related documentation
for the amounts reported in the system for certain categories,
including EZ/EC grant funding, leveraged funds, and program outputs. We
also determined whether, at a minimum, documentation existed to support
that the activity was implemented. We then assigned each item we
verified a code (table 5). Finally, we averaged the information for
each site by category and calculated the average score for each urban
and rural community.
Table 5: Coding of Data Reliability of HUD and USDA Performance
Systems:
Code: 2;
Description: Items with strong documentation, meaning that exact
documentation existed or could be easily inferred with the provided
documentation.
Code: 1;
Description: Items with weak documentation, meaning that some evidence
existed, but numbers did not match.
Code: 0;
Description: Items for which no documentation existed.
Source: GAO.
V
We found sufficient documentation that most EZ/EC activities contained
in the Internet-based reporting systems had occurred, with average
codes of 2.0 for urban areas and 1.9 for rural areas.[Footnote 63] We
found that data on EZ/EC grant funding, leveraged funds, and program
outputs were not sufficiently reliable for our purposes because only
weak or no documentation could be found at most sites.
Methodology for Survey of EZ Businesses:
To assess the use of program tax benefits, we attempted to administer a
survey to EZ businesses; however, we discontinued the survey due to a
very low response rate. Based on past post-mailed and phone-
administered surveys of EZ businesses, we knew that this would be a
challenging population to survey. In fact, surveys we and Abt
Associates conducted in 1998 obtained response rates of only 42 and 35
percent, respectively.[Footnote 64] In addition, both surveys had a
relatively high number of undeliverable surveys. In anticipation of
these issues, we attempted to administer a concise, high-level survey
via mail to a stratified random sample (n=517) of EZ
businesses.[Footnote 65] We implemented a sampling procedure using the
2004 Claritas Business Facts dataset that stratified businesses located
in the EZ by three strata: urban small businesses (less than 50
employees), urban large businesses (50 or more employees), and rural
businesses. The survey was targeted to private businesses rather than
public and nonprofit businesses, since these for-profit businesses were
the ones eligible for the tax benefits.[Footnote 66] Public and
nonprofit businesses were excluded from the sample by the primary
industry code identifier included in the Claritas data. A few of these
types of businesses that were not initially excluded based on their
industry code were later removed from the sample because the
respondents said that they were not eligible for the tax benefits.
We developed our survey after reviewing surveys used in previous
studies, interviewing business owners, and conducting pretests with EZ
businesses. The questionnaire was brief--containing 21 closed-ended
items and 1 optional open-ended item--and took most pretest respondents
approximately five minutes to complete. When we conducted pretests with
10 businesses from Baltimore, Philadelphia, and rural Kentucky, all
pretest participants found the survey to be easy to complete and said
that it did not ask for sensitive information. These business owners,
however, often lacked complete information about their company's tax
filings and were not always able to answer all of the survey questions.
Several indicated that they would be unlikely to complete the survey
because the topic was not relevant to them.
We administered the survey according to standard survey data collection
practices. We sent a letter notifying the 517 businesses of our survey
about a week prior to the survey mailing, mailed a copy of the survey,
and followed that mailing with a reminder postcard. We received a total
of 63 responses after our initial mailing, a response rate of 12
percent. Our mailings to 104 businesses (20 percent) could not be
delivered and were returned because of incorrect addresses or contact
information.
Methodology for Assessing the Effect of the Program on Poverty,
Unemployment, and Economic Growth:
To determine the effect of the EZ/EC program on changes in poverty,
unemployment, and economic growth, we used a variety of quantitative
methods that examined changes in the designated program areas and areas
we identified as comparison areas. In addition, we incorporated
interview data in our qualitative analysis to provide context for the
changes observed. We calculated percent changes of demographic,
socioeconomic, and business data between two points in time for the all
Round I EZs and ECs.[Footnote 67] However, we used only urban EZs in
our econometric analysis because of data limitations in rural areas and
the amount of funds awarded to ECs.
Description of Data Sources:
To assess the changes in poverty and unemployment, we used census tract-
level data on poverty rates and unemployment rates from the 1990 and
2000 decennial censuses. To determine changes in economic growth in EZ
and ECs, we defined economic growth in terms of the number of private
businesses created and the total number of jobs in the areas.[Footnote
68] We obtained year-end data on these variables for years 1995, 1999,
and 2004 from the Business-Facts Database maintained by Claritas, a
private data processing company. We explored several public and private
data sources that contained the number of businesses and jobs at the
census tract level and selected Claritas because it (1) maintained
archival data, (2) provided data with a high level of reliability at
the census tract level, and (3) used techniques to ensure the
representation of small businesses. We also explored a variety of other
data options to enhance our analysis, but were ultimately not able to
use them. For example, we tried to acquire data throughout the period
of the program, such as state unemployment data, local building permit
and crime data, and data on students receiving free or reduced lunches.
However, we were not able to use these data because they were not
captured consistently across sites, not available at the census tract
level, or not sufficiently reliable for our purposes.
The decennial census data used are from the census long form that is
administered to a sample of respondents. Because census data used in
this analysis are estimated based on a probability sample, each
estimate is based on just one of a large number of samples that could
have been drawn. Since each sample could have produced different
estimates, we express our confidence in the precision of our particular
sample's results as a 95 percent confidence interval. For example, the
estimated percent change in the poverty rate of EZs is a decrease of
6.1 percent, and the 95 percent confidence interval for this estimate
ranges from 4.9 to 7.2 percent. This is the interval that would contain
the actual population value for 95 percent of the samples that could
have been drawn. As a result, we are 95 percent confident that each of
the confidence intervals in this report will include the true values in
the study population. All Census variables based on percentages, such
as poverty rate and unemployment rate, have 95 percent confidence
intervals of plus or minus 5 percentage points or less. The confidence
intervals for average household income and average owner-occupied
housing value are shown in table 6.
Table 6: Confidence Intervals for Average Household Income and Average
Housing Value in Constant 2004 Dollars[A]:
Average household income: Atlanta EZ;
95 percent confidence interval: 1990 estimate: $18,343;
95 percent confidence interval: From: $17,466;
95 percent confidence interval: To: $19,220;
95 percent confidence interval: 2000 estimate: $28,552;
95 percent confidence interval: From: $27,205;
95 percent confidence interval: To: $29,899;
95 percent confidence interval: Percent change: 55.66;
95 percent confidence interval: From: 55.4;
95 percent confidence interval: To: 55.91.
Average household income: Atlanta EZ: Comparison;
95 percent confidence interval: 1990 estimate: 30,567;
95 percent confidence interval: From: 29,741;
95 percent confidence interval: To: 31,393;
95 percent confidence interval: 2000 estimate: 39,500;
95 percent confidence interval: From: 38,328;
95 percent confidence interval: To: 40,672;
95 percent confidence interval: Percent change: 29.23;
95 percent confidence interval: From: 28.99;
95 percent confidence interval: To: 29.46.
Average household income: Baltimore EZ;
95 percent confidence interval: 1990 estimate: 28,185;
95 percent confidence interval: From: 27,207;
95 percent confidence interval: To: 29,164;
95 percent confidence interval: 2000 estimate: 35,059;
95 percent confidence interval: From: 33,566;
95 percent confidence interval: To: 36,551;
95 percent confidence interval: Percent change: 24.39;
95 percent confidence interval: From: 24.1;
95 percent confidence interval: To: 24.67.
Average household income: Baltimore EZ; Comparison;
95 percent confidence interval: 1990 estimate: 27,931;
95 percent confidence interval: From: 27,316;
95 percent confidence interval: To: 28,546;
95 percent confidence interval: 2000 estimate: 31,367;
95 percent confidence interval: From: 30,511;
95 percent confidence interval: To: 32,223;
95 percent confidence interval: Percent change: 12.3;
95 percent confidence interval: From: 12.05;
95 percent confidence interval: To: 12.56.
Average household income: Chicago EZ;
95 percent confidence interval: 1990 estimate: 23,097;
95 percent confidence interval: From: 22,636;
95 percent confidence interval: To: 23,559;
95 percent confidence interval: 2000 estimate: 34,718;
95 percent confidence interval: From: 33,868;
95 percent confidence interval: To: 35,567;
95 percent confidence interval: Percent change: 50.31;
95 percent confidence interval: From: 50.13;
95 percent confidence interval: To: 50.49.
Average household income: Chicago EZ: Comparison;
95 percent confidence interval: 1990 estimate: 28,431;
95 percent confidence interval: From: 28,030;
95 percent confidence interval: To: 28,832;
95 percent confidence interval: 2000 estimate: 39,985;
95 percent confidence interval: From: 39,367;
95 percent confidence interval: To: 40,604;
95 percent confidence interval: Percent change: 40.64;
95 percent confidence interval: From: 40.48;
95 percent confidence interval: To: 40.8.
Average household income: Detroit EZ;
95 percent confidence interval: 1990 estimate: 22,644;
95 percent confidence interval: From: 22,034;
95 percent confidence interval: To: 23,253;
95 percent confidence interval: 2000 estimate: 33,751;
95 percent confidence interval: From: 32,660;
95 percent confidence interval: To: 34,842;
95 percent confidence interval: Percent change: 49.05;
95 percent confidence interval: From: 48.84;
95 percent confidence interval: To: 49.26.
Average household income: Detroit EZ: Comparison;
95 percent confidence interval: 1990 estimate: 25,609;
95 percent confidence interval: From: 25,197;
95 percent confidence interval: To: 26,021;
95 percent confidence interval: 2000 estimate: 36,200;
95 percent confidence interval: From: 35,523;
95 percent confidence interval: To: 36,877;
95 percent confidence interval: Percent change: 41.36;
95 percent confidence interval: From: 41.19;
95 percent confidence interval: To: 41.52.
Average household income:
New York EZ;
95 percent confidence interval: 1990 estimate: 26,518;
95 percent confidence interval: From: 25,981;
95 percent confidence interval: To: 27,054;
95 percent confidence interval: 2000 estimate: 33,557;
95 percent confidence interval: From: 32,833;
95 percent confidence interval: To: 34,280;
95 percent confidence interval: Percent change: 26.54;
95 percent confidence interval: From: 26.34;
95 percent confidence interval: To: 26.75.
Average household income: New York: Comparison;
95 percent confidence interval: 1990 estimate: 26,993;
95 percent confidence interval: From: 26,714;
95 percent confidence interval: To: 27,272;
95 percent confidence interval: 2000 estimate: 31,247;
95 percent confidence interval: From: 30,872;
95 percent confidence interval: To: 31,622;
95 percent confidence interval: Percent change: 15.76;
95 percent confidence interval: From: 15.59;
95 percent confidence interval: To: 15.93.
Average household income: Upper Manhattan;
95 percent confidence interval: 1990 estimate: 26,559;
95 percent confidence interval: From: 25,971;
95 percent confidence interval: To: 27,147;
95 percent confidence interval: 2000 estimate: 34,041;
95 percent confidence interval: From: 33,239;
95 percent confidence interval: To: 34,844;
95 percent confidence interval: Percent change: 28.17;
95 percent confidence interval: From: 27.96;
95 percent confidence interval: To: 28.39.
Average household income: Bronx;
95 percent confidence interval: 1990 estimate: 26,294;
95 percent confidence interval: From: 24,983;
95 percent confidence interval: To: 27,606;
95 percent confidence interval: 2000 estimate: 30,842;
95 percent confidence interval: From: 29,238;
95 percent confidence interval: To: 32,446;
95 percent confidence interval: Percent change: 17.29;
95 percent confidence interval: From: Average household income: 16.95;
95 percent confidence interval: To: 17.64.
Average household income: Philadelphia-Camden EZ;
95 percent confidence interval: 1990 estimate: 23,188;
95 percent confidence interval: From: 22,259;
95 percent confidence interval: To: 24,117;
95 percent confidence interval: 2000 estimate: 28,562;
95 percent confidence interval: From: 27,197;
95 percent confidence interval: To: 29,927;
95 percent confidence interval: Percent change: 23.17;
95 percent confidence interval: From: 22.87;
95 percent confidence interval: To: 23.48.
Average household income: Philadelphia-Camden EZ: Comparison;
95 percent confidence interval: 1990 estimate: 27,292;
95 percent confidence interval: From: 26,031;
95 percent confidence interval: To: 28,553;
95 percent confidence interval: 2000 estimate: 31,318;
95 percent confidence interval: From: 29,718;
95 percent confidence interval: To: 32,918;
95 percent confidence interval: Percent change: 14.75;
95 percent confidence interval: From: 14.4;
95 percent confidence interval: To: 15.1.
Average household income: Philadelphia;
95 percent confidence interval: 1990 estimate: 22,269;
95 percent confidence interval: From: 21,262;
95 percent confidence interval: To: 23,276;
95 percent confidence interval: 2000 estimate: 27,851;
95 percent confidence interval: From: 26,309;
95 percent confidence interval: To: 29,392;
95 percent confidence interval: Percent change: 25.07;
95 percent confidence interval: From: 24.74;
95 percent confidence interval: To: 25.39.
Average household income: Camden;
95 percent confidence interval: 1990 estimate: 26,742;
95 percent confidence interval: From: 24,465;
95 percent confidence interval: To: 29,018;
95 percent confidence interval: 2000 estimate: 31,158;
95 percent confidence interval: From: 28,228;
95 percent confidence interval: To: 34,088;
95 percent confidence interval: Percent change: 16.52;
95 percent confidence interval: From: 16.05;
95 percent confidence interval: To: 16.98.
Average household income: Cleveland EZ;
95 percent confidence interval: 1990 estimate: 20,535;
95 percent confidence interval: From: 19,730;
95 percent confidence interval: To: 21,340;
95 percent confidence interval: 2000 estimate: 28,781;
95 percent confidence interval: From: 27,524;
95 percent confidence interval: To: 30,038;
95 percent confidence interval: Percent change: 40.16;
95 percent confidence interval: From: 39.9;
95 percent confidence interval: To: 40.42.
Average household income: Cleveland EZ: Comparison;
95 percent confidence interval: 1990 estimate: 24,688;
95 percent confidence interval: From: 24,171;
95 percent confidence interval: To: 25,206;
95 percent confidence interval: 2000 estimate: 30,311;
95 percent confidence interval: From: 29,607;
95 percent confidence interval: To: 31,016;
95 percent confidence interval: Percent change: 22.78;
95 percent confidence interval: From: 22.56;
95 percent confidence interval: To: 23.
Average household income: Los Angeles EZ;
95 percent confidence interval: 1990 estimate: 28,801;
95 percent confidence interval: From: 28,191;
95 percent confidence interval: To: 29,412;
95 percent confidence interval: 2000 estimate: 32,631;
95 percent confidence interval: From: 31,857;
95 percent confidence interval: To: 33,405;
95 percent confidence interval: Percent change: 13.3;
95 percent confidence interval: From: 13.06;
95 percent confidence interval: To: 13.54.
Average household income: Los Angeles EZ: Comparison;
95 percent confidence interval: 1990 estimate: 34,087;
95 percent confidence interval: From: 33,478;
95 percent confidence interval: To: 34,696;
95 percent confidence interval: 2000 estimate: 37,843;
95 percent confidence interval: From: 37,058;
95 percent confidence interval: To: 38,628;
95 percent confidence interval: Percent change: 11.02;
95 percent confidence interval: From: 10.79;
95 percent confidence interval: To: 11.25.
Average household income: Kentucky Highlands EZ;
95 percent confidence interval: 1990 estimate: 23,304;
95 percent confidence interval: From: 22,043;
95 percent confidence interval: To: 24,565;
95 percent confidence interval: 2000 estimate: 31,064;
95 percent confidence interval: From: 29,520;
95 percent confidence interval: To: 32,608;
95 percent confidence interval: Percent change: 33.3;
95 percent confidence interval: From: 32.99;
95 percent confidence interval: To: 33.61.
Average household income: Mid-Delta EZ;
95 percent confidence interval: 1990 estimate: 25,872;
95 percent confidence interval: From: 24,321;
95 percent confidence interval: To: 27,424;
95 percent confidence interval: 2000 estimate: 35,559;
95 percent confidence interval: From: 33,392;
95 percent confidence interval: To: 37,726;
95 percent confidence interval: Percent change: 37.44;
95 percent confidence interval: From: 37.12;
95 percent confidence interval: To: 37.76.
Average household income: Rio Grande Valley EZ;
95 percent confidence interval: 1990 estimate: 25,093;
95 percent confidence interval: From: 23,626;
95 percent confidence interval: To: 26,560;
95 percent confidence interval: 2000 estimate: 32,763;
95 percent confidence interval: From: 30,920;
95 percent confidence interval: To: 34,606;
95 percent confidence interval: Percent change: 30.57;
95 percent confidence interval: From: 30.24;
95 percent confidence interval: To: 30.9.
Average household income: Providence EC;
95 percent confidence interval: 1990 estimate: 28,593;
95 percent confidence interval: From: 27,525;
95 percent confidence interval: To: 29,661;
95 percent confidence interval: 2000 estimate: 32,616;
95 percent confidence interval: From: 31,229;
95 percent confidence interval: To: 34,004;
95 percent confidence interval: Percent change: 14.07;
95 percent confidence interval: From: 13.75;
95 percent confidence interval: To: 14.39.
Average household income: Fayette-Haywood EC;
95 percent confidence interval: 1990 estimate: 32,560;
95 percent confidence interval: From: 31,008;
95 percent confidence interval: To: 34,111;
95 percent confidence interval: 2000 estimate: 45,353;
95 percent confidence interval: From: 43,249;
95 percent confidence interval: To: 47,457;
95 percent confidence interval: Percent change: 39.29;
95 percent confidence interval: From: 39.01;
95 percent confidence interval: To: 39.57.
Average owner-occupied housing value: Atlanta EZ;
95 percent confidence interval: 1990 estimate: $55,883;
95 percent confidence interval: From: $52,688;
95 percent confidence interval: To: $59,077;
95 percent confidence interval: 2000 estimate: $117,869;
95 percent confidence interval: From: $106,218;
95 percent confidence interval: To: $129,519;
95 percent confidence interval: Percent change: 110.92;
95 percent confidence interval: From: 110.68;
95 percent confidence interval: To: 111.17.
Average owner-occupied housing value: Atlanta EZ: Comparison;
95 percent confidence interval: 1990 estimate: 74,063;
95 percent confidence interval: From: 72,446;
95 percent confidence interval: To: 75,680;
95 percent confidence interval: 2000 estimate: 101,774;
95 percent confidence interval: From: 96,312;
95 percent confidence interval: To: 107,236;
95 percent confidence interval: Percent change: 37.42;
95 percent confidence interval: From: 37.15;
95 percent confidence interval: To: 37.68.
Average owner-occupied housing value: Baltimore EZ;
95 percent confidence interval: 1990 estimate: 53,714;
95 percent confidence interval: From: 51,381;
95 percent confidence interval: To: 56,048;
95 percent confidence interval: 2000 estimate: 62,219;
95 percent confidence interval: From: 58,659;
95 percent confidence interval: To: 65,779;
95 percent confidence interval: Percent change: 15.83;
95 percent confidence interval: From: 15.48;
95 percent confidence interval: To: 16.19.
Average owner-occupied housing value: Baltimore EZ: Comparison;
95 percent confidence interval: 1990 estimate: 55,966;
95 percent confidence interval: From: 54,113;
95 percent confidence interval: To: 57,819;
95 percent confidence interval: 2000 estimate: 62,514;
95 percent confidence interval: From: 59,920;
95 percent confidence interval: To: 65,108;
95 percent confidence interval: Percent change: 11.7;
95 percent confidence interval: From: 11.39;
95 percent confidence interval: To: 12.01.
Average owner-occupied housing value: Chicago EZ;
95 percent confidence interval: 1990 estimate: 71,429;
95 percent confidence interval: From: 67,487;
95 percent confidence interval: To: 75,372;
95 percent confidence interval: 2000 estimate: 160,411;
95 percent confidence interval: From: 150,476;
95 percent confidence interval: To: 170,347;
95 percent confidence interval: Percent change: 124.57;
95 percent confidence interval: From: 124.38;
95 percent confidence interval: To: 124.77.
Average owner-occupied housing value: Chicago EZ: Comparison;
95 percent confidence interval: 1990 estimate: 88,445;
95 percent confidence interval: From: 85,343;
95 percent confidence interval: To: 91,548;
95 percent confidence interval: 2000 estimate: 167,015;
95 percent confidence interval: From: 159,548;
95 percent confidence interval: To: 174,482;
95 percent confidence interval: Percent change: 88.83;
95 percent confidence interval: From: 88.64;
95 percent confidence interval: To: 89.03.
Average owner-occupied housing value: Detroit EZ;
95 percent confidence interval: 1990 estimate: 23,114;
95 percent confidence interval: From: 22,153;
95 percent confidence interval: To: 24,075;
95 percent confidence interval: 2000 estimate: 52,234;
95 percent confidence interval: From: 49,362;
95 percent confidence interval: To: 55,106;
95 percent confidence interval: Percent change: 125.99;
95 percent confidence interval: From: 125.81;
95 percent confidence interval: To: 126.16.
Average owner-occupied housing value: Detroit EZ: Comparison;
95 percent confidence interval: 1990 estimate: 28,598;
95 percent confidence interval: From: 27,620;
95 percent confidence interval: To: 29,575;
95 percent confidence interval: 2000 estimate: 61,160;
95 percent confidence interval: From: 58,688;
95 percent confidence interval: To: 63,632;
95 percent confidence interval: Percent change: 113.86;
95 percent confidence interval: From: 113.7;
95 percent confidence interval: To: 114.03.
Average owner-occupied housing value: New York EZ;
95 percent confidence interval: 1990 estimate: 207,544;
95 percent confidence interval: From: 166,353;
95 percent confidence interval: To: 248,735;
95 percent confidence interval: 2000 estimate: 301,835;
95 percent confidence interval: From: 244,974;
95 percent confidence interval: To: 358,697;
95 percent confidence interval: Percent change: 45.43;
95 percent confidence interval: From: 44.89;
95 percent confidence interval: To: 45.98.
Average owner-occupied housing value: New York EZ: Comparison;
95 percent confidence interval: 1990 estimate: 177,446;
95 percent confidence interval: From: 167,025;
95 percent confidence interval: To: 187,867;
95 percent confidence interval: 2000 estimate: 209,423;
95 percent confidence interval: From: 198,465;
95 percent confidence interval: To: 220,380;
95 percent confidence interval: Percent change: 18.02;
95 percent confidence interval: From: 17.66;
95 percent confidence interval: To: 18.38.
Average owner-occupied housing value: Upper Manhattan;
95 percent confidence interval: 1990 estimate: 238,864;
95 percent confidence interval: From: 188,845;
95 percent confidence interval: To: 288,882;
95 percent confidence interval: 2000 estimate: 384,155;
95 percent confidence interval: From: 308,848;
95 percent confidence interval: To: 459,462;
95 percent confidence interval: Percent change: 60.83;
95 percent confidence interval: From: 60.32;
95 percent confidence interval: To: 61.33.
Average owner-occupied housing value: Bronx;
95 percent confidence interval: 1990 estimate: 99,728;
95 percent confidence interval: From: 71,856;
95 percent confidence interval: To: 127,600;
95 percent confidence interval: 2000 estimate: 124,588;
95 percent confidence interval: From: 100,021;
95 percent confidence interval: To: 149,155;
95 percent confidence interval: Percent change: 24.93;
95 percent confidence interval: From: 24.23;
95 percent confidence interval: To: 25.63.
Average owner-occupied housing value: Philadelphia-Camden EZ;
95 percent confidence interval: 1990 estimate: 29,899;
95 percent confidence interval: From: 28,060;
95 percent confidence interval: To: 31,739;
95 percent confidence interval: 2000 estimate: 37,780;
95 percent confidence interval: From: 35,895;
95 percent confidence interval: To: 39,664;
95 percent confidence interval: Percent change: 26.36;
95 percent confidence interval: From: 26.02;
95 percent confidence interval: To: 26.69.
Average owner-occupied housing value: Philadelphia-Camden EZ:
Comparison;
95 percent confidence interval: 1990 estimate: 42,045;
95 percent confidence interval: From: 39,630;
95 percent confidence interval: To: 44,461;
95 percent confidence interval: 2000 estimate: 51,159;
95 percent confidence interval: From: 44,926;
95 percent confidence interval: To: 57,392;
95 percent confidence interval: Percent change: 21.67;
95 percent confidence interval: From: 21.22;
95 percent confidence interval: To: 22.13.
Average owner-occupied housing value: Philadelphia;
95 percent confidence interval: 1990 estimate: 28,288;
95 percent confidence interval: From: 26,263;
95 percent confidence interval: To: 30,313;
95 percent confidence interval: 2000 estimate: 37,353;
95 percent confidence interval: From: 35,178;
95 percent confidence interval: To: 39,528;
95 percent confidence interval: Percent change: 32.04;
95 percent confidence interval: From: 31.7;
95 percent confidence interval: To: 32.39.
Average owner-occupied housing value: Camden;
95 percent confidence interval: 1990 estimate: 35,076;
95 percent confidence interval: From: 30,928;
95 percent confidence interval: To: 39,224;
95 percent confidence interval: 2000 estimate: 39,398;
95 percent confidence interval: From: 35,730;
95 percent confidence interval: To: 43,067;
95 percent confidence interval: Percent change: 12.32;
95 percent confidence interval: From: 11.8;
95 percent confidence interval: To: 12.84.
Average owner-occupied housing value: Cleveland EZ;
95 percent confidence interval: 1990 estimate: 38,071;
95 percent confidence interval: From: 36,277;
95 percent confidence interval: To: 39,866;
95 percent confidence interval: 2000 estimate: 75,186;
95 percent confidence interval: From: 71,537;
95 percent confidence interval: To: 78,835;
95 percent confidence interval: Percent change: 97.49;
95 percent confidence interval: From: 97.29;
95 percent confidence interval: To: 97.69.
Average owner-occupied housing value: Cleveland EZ: Comparison;
95 percent confidence interval: 1990 estimate: 46,972;
95 percent confidence interval: From: 45,966;
95 percent confidence interval: To: 47,979;
95 percent confidence interval: 2000 estimate: 70,161;
95 percent confidence interval: From: 68,649;
95 percent confidence interval: To: 71,674;
95 percent confidence interval: Percent change: 49.37;
95 percent confidence interval: From: 49.19;
95 percent confidence interval: To: 49.54.
Average owner-occupied housing value: Los Angeles EZ;
95 percent confidence interval: 1990 estimate: 141,665;
95 percent confidence interval: From: 138,933;
95 percent confidence interval: To: 144,397;
95 percent confidence interval: 2000 estimate: 156,492;
95 percent confidence interval: From: 151,907;
95 percent confidence interval: To: 161,078;
95 percent confidence interval: Percent change: 10.47;
95 percent confidence interval: From: 10.21;
95 percent confidence interval: To: 10.72.
Average owner-occupied housing value: Los Angeles EZ: Comparison;
95 percent confidence interval: 1990 estimate: 160,090;
95 percent confidence interval: From: 157,393;
95 percent confidence interval: To: 162,787;
95 percent confidence interval: 2000 estimate: 165,180;
95 percent confidence interval: From: 161,599;
95 percent confidence interval: To: 168,761;
95 percent confidence interval: Percent change: 3.18;
95 percent confidence interval: From: 2.94;
95 percent confidence interval: To: 3.42.
Average owner-occupied housing value: Kentucky Highlands EZ;
95 percent confidence interval: 1990 estimate: 43,392;
95 percent confidence interval: From: 38,713;
95 percent confidence interval: To: 48,071;
95 percent confidence interval: 2000 estimate: 65,815;
95 percent confidence interval: From: 62,527;
95 percent confidence interval: To: 69,104;
95 percent confidence interval: Percent change: 51.68;
95 percent confidence interval: From: 51.35;
95 percent confidence interval: To: 52.
Average owner-occupied housing value: Mid-Delta EZ;
95 percent confidence interval: 1990 estimate: 50,061;
95 percent confidence interval: From: 47,323;
95 percent confidence interval: To: 52,800;
95 percent confidence interval: 2000 estimate: 66,872;
95 percent confidence interval: From: 59,968;
95 percent confidence interval: To: 73,777;
95 percent confidence interval: Percent change: 33.58;
95 percent confidence interval: From: 33.19;
95 percent confidence interval: To: 33.97.
Average owner-occupied housing value: Rio Grande Valley EZ;
95 percent confidence interval: 1990 estimate: 46,100;
95 percent confidence interval: From: 42,654;
95 percent confidence interval: To: 49,546;
95 percent confidence interval: 2000 estimate: 61,450;
95 percent confidence interval: From: 55,970;
95 percent confidence interval: To: 66,929;
95 percent confidence interval: Percent change: 33.3;
95 percent confidence interval: From: 32.91;
95 percent confidence interval: To: 33.69.
Average owner-occupied housing value: Providence EC;
95 percent confidence interval: 1990 estimate: 124,339;
95 percent confidence interval: From: 118,190;
95 percent confidence interval: To: 130,489;
95 percent confidence interval: 2000 estimate: 116,698;
95 percent confidence interval: From: 99,200;
95 percent confidence interval: To: 134,196;
95 percent confidence interval: Percent change: -6.15;
95 percent confidence interval: From: -6.77;
95 percent confidence interval: To: - 5.52.
Average owner-occupied housing value: Fayette-Haywood EC;
95 percent confidence interval: 1990 estimate: $68,945;
95 percent confidence interval: From: $65,765;
95 percent confidence interval: To: $72,125;
95 percent confidence interval: 2000 estimate: $103,619;
95 percent confidence interval: From: $97,144;
95 percent confidence interval: To: $110,094;
95 percent confidence interval: Percent change: 50.29;
95 percent confidence interval: From: 50.01;
95 percent confidence interval: To: 50.57.
Source: GAO analysis of Census data.
[A] Other variables used in the model and shown in the site visit
descriptions that were based on percentages, such as the poverty rate,
had confidence intervals of less than +/-5 percentage points.
[End of table]
In addition to sampling errors, Census data (both sampled and 100
percent data) are subject to nonsampling errors that may occur during
the operations used to collect and process census data. Examples of
nonsampling errors are not enumerating every housing unit or person in
the sample, failing to obtain all required information from a
respondent, obtaining incorrect information, and recording information
incorrectly. Operations such as field review of enumerator's work,
clerical handling of questionnaires, and electronic processing of
questionnaires also may introduce nonsampling errors in the data. The
Census Bureau discusses sources of nonsampling errors and makes
attempts to limit them.
Choosing Comparison Areas Using the Propensity Score:
To provide context for the changes we observed in the EZs and ECs, we
calculated the percent change of the designated areas as well as areas,
called comparison areas, that most closely resembled the EZ/EC program
areas. To select comparison areas for our analysis, we used a
statistical matching method called the propensity score. The propensity
score predicts the probability that a tract could have been designated
based on having characteristics similar to those found in the tracts
selected for the program. We used five factors to calculate the
propensity scores, as shown in table 7.
Table 7: Factors Selected for Choosing Comparison Tracts:
Factor: 1990 poverty rate[ A];
Reason selected:
* EZ/EC program eligibility criteria;
* Factor considered in a similar study[B, E].
Factor: 1990 unemployment rate[C];
Reason selected:
* EZ/EC program eligibility criteria;
* Factor considered in a similar study[ B].
Factor: 1990 population density[ D];
Reason selected:
* Calculation based on two EZ/EC program eligibility criteria,
population and area;
* Factor considered in a similar study[B].
Factor: 1990 average household income;
Reason selected:
* Factor considered in similar studies[ B, E].
Factor: Percentage of minority population in 1990[F];
Reason selected:
* Factor considered in similar studies[ B, E].
Source: GAO.
[A] Percent based on individuals for whom poverty status has been
determined.
[B] See Bondonio, Daniele and John Engberg (2000), "Enterprise Zones
and Local Employment: Evidence from the States' Programs," Regional
Science and Urban Economics, Vol. 30, No.5, pp. 519-549.
[C] Percent based on individuals 16 years of age or older.
[D] Individuals per square mile.
[E] Hebert and others, Interim Assessment.
[F] For the purposes of this report, we calculated minority population
by subtracting the percent of white population from the total
population.
[End of table]
To ensure that our comparison areas were similar to the designated
areas in terms of geography, we explored two selection methods, one
that included tracts in the same county as the EZ/EC and in adjacent
counties, and another that selected tracts within a 5-mile radius of
the EZ/EC.[Footnote 69] We excluded tracts that received a subsequent
designation in the EZ/EC or Renewal Community programs in 1998 and 2002
in order to remove the possibility of tracts that may have received
similar benefits affecting our analysis. After mapping the resulting
comparison tracts using these two methods, we decided to use tracts
selected within a 5-mile radius of the EZs and ECs because this method
provided more contiguous areas, while the results of the county and the
adjacent counties method yielded comparison tracts in other states
where political structures and types of funds could differ.
Using the computed propensity scores, we selected comparison tracts
whose scores were greater than 0.1. This threshold was chosen because
most EZ tracts had propensity scores of 0.1 or higher;
therefore, comparison tracts with propensity scores of at least 0.1
were the most similar to the EZ tracts. This threshold also yielded
approximately the same number of comparison tracts as EZ tracts in most
of the eight urban EZs. In addition, we tested this threshold by
running our models with comparison tracts whose propensity scores were
greater than 0.05 or 0.15 and found that the results did not change
significantly.[Footnote 70] Some limitations exist with this method.
For example, since many of census tracts chosen for the program may
have had the highest level of poverty, it was difficult to find tracts
with the same level of poverty.
Our Descriptive and Econometric Analyses:
We calculated the percent changes at the program wide level for our
four indicators of poverty, unemployment, and economic growth for both
designated and comparison areas.[Footnote 71] We also calculated the
changes for urban and rural designees and EZs and ECs separately, so
that we could make comparisons between those groups. In addition, for
the eight urban Round I EZs, we calculated the percentages separately
for each EZ and EZ comparison area to show differences between zones.
Although the comparison areas were sufficient to use in our program
wide analyses, for rural EZs and urban and rural ECs, we did not use
comparison areas for site-level analyses because there were too few
comparison tracts. For example, the Providence, Rhode Island EC
consisted of 13 tracts, but the area had only four eligible comparison
tracts.
We also completed an econometric analysis of the eight urban EZs. We
used a standard econometric approach, the weighted least squares model,
which allowed us to analyze the change from 1990 to 2000 and compare it
with the 1990 value of several explanatory variables. The benefit of
this approach is that the program, officially implemented in 1994,
would not affect the 1990 values of the explanatory variables. In
addition, we spoke with several experts in the urban studies field on
our methodology. For more information on the methods used in our
econometric analysis and a full discussion of our results, please see
appendix II.
[End of section]
Appendix II: Methodology for and Results of Our Econometric Models:
This appendix describes our efforts to isolate the effect of the EZ/EC
program on the changes in poverty, unemployment, and economic growth,
by conducting an econometric analysis of all urban EZ census
tracts.[Footnote 72] In our analysis of percent changes, we found that
poverty and unemployment had decreased and that some economic growth
had occurred. However, when we used the econometric models to control
for other area characteristics, our results did not definitively
suggest that the observed changes in poverty and unemployment were
associated with the EZ program in urban areas. In addition, our models
did not adequately explain the observed changes in the proxy measures
we used for economic growth; thus, the results did not allow us to
conclude whether there is an association between the EZ program and
economic growth.
As mentioned in the report, there were several challenges that limited
our ability to determine the effect of the program. First, data at the
census tract-level for the program years were limited. We used data
from the 1990 and 2000 decennial censuses to show the changes in
poverty and unemployment. In addition, we primarily used two measures
for economic growth--the number of businesses and the number of jobs
from Claritas Business-Facts dataset for years 1995, 1999, and 2004--in
our models of economic growth.[Footnote 73] Second, we were not able to
account for the spillover effects of EZ designation into their
neighboring areas. For example, if the EZ/EC program affected
comparison tracts as well as the designated communities, our analyses
would not find any significant differences between the designated and
comparison tracts. The result may be an obscuring of the extent of the
statistical association between the urban EZ program and the study
variables. Third, the analyses did not account for the confounding
effects of other public or private programs, such as those intended to
reduce poverty or unemployment or increase the number of area jobs. As
a result, estimates for the EZ program in our analyses may under or
overstate the extent of EZ program's correlation with poverty,
unemployment, and economic growth. Fourth, our estimations did not
fully account for the economic trends that were affecting the choice of
areas selected for the program. For example, if program officials
tended to pick census tracts that were already experiencing
gentrification prior to 1994, our estimations could overstate the
effect of the EZ designation. Conversely, if officials tended to choose
census tracts that were experiencing economic declines prior to 1994,
such as those in which major employers had closed, we might understate
the program's impact. We did include a variable from Census data--new
housing construction between 1990 and 1994--that measured one dimension
of economic trends prior to EZ designation, but we did not include
other dimensions, such as employment trends at the tract level, in the
models.
Description of Our Models:
We used a weighted least square regression for our analyses.[Footnote
74] Our dependent variables were (1) the difference in the poverty rate
between 1990 and 2000, (2) the difference in the unemployment rate
between 1990 and 2000 (3) the difference in the number of businesses
between 1995 and 1999, and (4) the number of jobs between 1995 and
1999. For the basic model, we measured the difference in each dependent
variable against the 1990 value of some explanatory variables. The
benefit of this approach is that 1990 values of the explanatory
variables would not have been affected by the program, which was
implemented in 1994. We also ran an expanded version of the model that
included variables for each of the EZs to determine whether there were
differences among the EZs, and we included variables for the EZs and
their surrounding areas to account for economic trends at the
metropolitan level, such as the growing or declining output of local
industries.
Some of the explanatory variables for which we controlled included
socioeconomic factors, such as percent of population with a high school
diploma. In addition to these socioeconomic factors, we also considered
the five factors we used to select the comparison tracts:
* percent of minority population in 1990,
* average household income in 1990,
* population density in 1990,
* poverty rate in 1990, and:
* unemployment rate in 1990.
We included these variables because the comparison tracts may not be
perfectly matched to the EZ tracts; including these factors allowed us
to further account for differences between EZ and comparison tracts.
Moreover, we weighted the estimations by the geometric mean of 1990 and
2000 household counts of each tract to account for differences in the
number of households in each tract. The purpose of this decision was to
put more weight on the tracts with large numbers of households, because
these tracts would tend to have smaller sampling errors.
The coefficients for the EZ program variables represent the EZs with
respect to the comparison areas, and the positive or negative values
suggest whether the EZs fared better or worse than the comparison
areas. For instance, a positive coefficient in the models for poverty
and unemployment would mean that the EZs did not fare as well as the
comparison areas--that is, they had either a greater increase or a
smaller decrease in poverty or unemployment. See our discussion of the
results of each model for more information.
Results of Our Models for Poverty:
Although our comparison of the percentage change between 1990 and 2000
showed that poverty decreased in most urban EZs, the results of our
models did not conclusively suggest that the change in poverty was
associated with the EZ program. Our analysis of the percentage changes
showed that the poverty rate fell more in the EZs than in the
comparison areas. But, when we controlled for other factors in our
models, we found in the basic model that poverty decreased less in the
EZs than in the comparison areas, although the difference was very
small (table 8). In addition, many of the variables used in selection
of comparison tracts were significant, suggesting that the choice of
areas selected for the program might have affected the differences
between the urban EZs and the comparison areas in the change in
poverty. When accounting for the different urban EZs and their
comparison tracts, the poverty rate decreased more in some urban EZs
but less in others with respect to the comparison tracts, although the
only significant result was in the Los Angeles EZ, which experienced a
greater increase in poverty than the comparison areas. The differences
among EZs may be a result of the local factors. In addition, one
researcher found that there was a nationwide decrease in the number of
people living in high poverty neighborhoods, defined as census tracts
with poverty rates of 40 percent or higher, between 1990 and 2000--a
trend that might be a factor affecting our results.[Footnote 75]
Table 8: Estimates of the Association between the EZ Program and the
Change in Poverty Rate, 1990-2000:
Variables: EZ program;
Basic model: Coefficient: 1.54;
Basic model: Standard error: 0.67;
Expanded model: Coefficient: [Empty];
Expanded model: Standard error: [Empty].
Variables: Atlanta EZ;
Basic model: Coefficient: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 3.41;
Expanded model: [Empty];
Expanded model: Standard error: 2.69.
Variables: Baltimore EZ;
Basic model: Coefficient: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -2.27;
Expanded model: Standard error: 1.94.
Variables: Chicago EZ;
Basic model: Coefficient: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 1.63;
Expanded model: Standard error: 1.50.
Variables: Cleveland EZ;
Basic model: Coefficient: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -2.43;
Expanded model: Standard error: 2.10.
Variables: Detroit EZ;
Basic model: Coefficient: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 2.25;
Expanded model: Standard error: 1.54.
Variables: Los Angeles EZ;
Basic model: Coefficient: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 3.07;
Expanded model: Standard error: 1.20.
Variables: New York EZ;
Basic model: Coefficient: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -1.87;
Expanded model: Standard error: 1.23.
Variables: Philadelphia-Camden EZ;
Basic model: Coefficient: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -1.10;
Expanded model: Standard error: 2.70.
Variables: Atlanta EZ area[A];
Basic model: Coefficient: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -5.50;
Expanded model: Standard error: 2.61.
Variables: Baltimore [A];
Basic model: Coefficient: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 0.60;
Expanded model: Standard error: 2.49.
Variables: Chicago EZ area[A];
Basic model: Coefficient: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -5.08;
Expanded model: Standard error: 2.23.
Variables: Cleveland EZ area[A];
Basic model: Coefficient: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -4.65;
Expanded model: Standard error: 2.36.
Variables: Detroit EZ area[A];
Basic model: Coefficient: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -7.73;
Expanded model: Standard error: 2.35.
Variables: Los Angeles EZ area[A];
Basic model: Coefficient: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 2.00;
Expanded model: Standard error: 2.30.
Variables: New York EZ area[A];
Basic model: Coefficient: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 0.56;
Expanded model: Standard error: 2.40.
Variables: Philadelphia-Camden EZ area[A];
Basic model: Coefficient: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: [ B];
Expanded model: Standard error: [B].
Variables: Percent of high school dropouts[C];
Basic model: Coefficient: 0.080;
Basic model: Standard error: 0.026;
Expanded model: Coefficient: 0.017;
Expanded model: Standard error: 0.024.
Variables: Percent of vacant housing units;
Basic model: Coefficient: -0.064;
Basic model: Standard error: 0.042;
Expanded model: Coefficient: 0.046;
Expanded model: Standard error: 0.044.
Variables: Percent of female-headed households with children[D];
Basic model: Coefficient: 0.24;
Basic model: Standard error: 0.045;
Expanded model: Coefficient: 0.22;
Expanded model: Standard error: 0.042.
Variables: Percent employed in retail industry[E];
Basic model: Coefficient: -0.0073;
Basic model: Standard error: 0.054;
Expanded model: Coefficient: 0.040;
Expanded model: Standard error: 0.052.
Variables: Percent housing units built between 1990 and 1994[F];
Basic model: Coefficient: -0.17;
Basic model: Standard error: 0.081;
Expanded model: Coefficient: - 0.29;
Expanded model: Standard error: 0.082.
Variables: Percent minority population[G];
Basic model: Coefficient: 0.0045;
Basic model: Standard error: 0.022;
Expanded model: Coefficient: -0.021;
Expanded model: Standard error: 0.021.
Variables: Average household income (in 2004 dollars);
Basic model: Coefficient: -0.00046;
Basic model: Standard error: 0.00085;
Expanded model: Coefficient: - 0.00060;
Expanded model: Standard error: 0.000081.
Variables: Population density[H];
Basic model: Coefficient: 0.000047;
Basic model: Standard error: 0.0000087;
Expanded model: Coefficient: 0.000024;
Expanded model: Standard error: 0.000013.
Variables: Poverty rate[I];
Basic model: Coefficient: -0.71;
Basic model: Standard error: 0.052;
Expanded model: Coefficient: -0.79;
Expanded model: Standard error: 0.047.
Variables: Unemployment rate[J];
Basic model: Coefficient: -0.047;
Basic model: Standard error: 0.046;
Expanded model: Coefficient: 0.085;
Expanded model: Standard error: 0.054.
Variables: Constant;
Basic model: Coefficient: 30.39;
Basic model: Standard error: 4.29;
Expanded model: Coefficient: 40.79;
Expanded model: Standard error: 4.83.
Variables: Number of tracts;
Basic model: Coefficient: [Empty];
Basic model: 851;
Basic model: Standard error: [Empty];
Expanded model: Coefficient: [Empty];
Expanded model: 851;
Expanded model: Standard error: [Empty].
Variables: R-sq;
Basic model: Coefficient: [Empty];
Basic model: 0.40;
Basic model: Standard error: [Empty];
Expanded model: Coefficient: [Empty];
Expanded model: 0.47;
Expanded model: Standard error: [Empty].
Source: GAO analysis of Census data.
Notes: Coefficients significant at the 5 percent level are in bold. All
variables are from the 1990 Census unless otherwise noted. We weighted
the regressions by the geometric mean of 1990 and 2000 household counts
of each tract.
[A] We defined the EZ area to include both the EZ tracts and comparison
tracts that were selected from within a 5-mile boundary of the EZ.
[B] Results for the Philadelphia-Camden EZ area are not listed, because
we used them as a reference group for the other seven EZs and their
surrounding areas.
[C] Percent based on the civilian population between ages 16 and 19 who
are not enrolled in school and are not high school graduates.
[D] Percent based on households headed by females without husbands
present in which there are children under 18 years of age.
[E] Percent based on individuals 16 and over.
[F] From the 2000 Census.
[G] We calculated minority population by subtracting the percent of
white population from the total population.
[H] Individuals per square mile.
[I] Percent based on individuals for whom poverty status has been
determined.
[J] Percent based on individuals 16 years of age or older in the labor
force.
[End of table]
Results of Our Models for Unemployment:
Like our models for poverty, our models for the unemployment did not
conclusively suggest that the changes in unemployment were associated
with the EZ program. The results of our basic model suggested that
unemployment decreased more in the EZs than in the comparison areas,
but the difference was very small and was not statistically significant
(table 9). All five of the variables we used to select comparison
tracts were statistically significant, suggesting that the choice of
areas selected for the program might have affected the difference in
the change in unemployment rate between EZ and comparison tracts. Like
the model for poverty, our model showed that the unemployment rate
decreased more in some urban EZs but less in others, although the only
EZ that experienced a significant change was the Cleveland EZ, which
showed a significantly greater decrease in unemployment than the
comparison areas. As with poverty rate, local factors may have
accounted for the difference between the various urban EZs with respect
to the comparison tracts.
Table 9: Estimates of the Association between the EZ Program and the
Change in Unemployment Rate, 1990-2000:
Variables: EZ program;
Basic model: Coefficient: -0.065;
Basic model: [Empty];
Basic model: Standard error: 0.50;
Expanded model: Standard Error: [Empty].
Variables: Atlanta EZ;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 2.56;
Expanded model: Standard error: 1.95.
Variables: Baltimore EZ;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -0.71;
Expanded model: Standard error: 1.77.
Variables: Chicago EZ;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -0.68;
Expanded model: Standard error: 1.07.
Variables: Cleveland EZ;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -3.65;
Expanded model: Standard error: 1.40.
Variables: Detroit EZ;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 1.76;
Expanded model: Standard error: 1.09.
Variables: Los Angeles EZ;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 0.092;
Expanded model: Standard error: 1.20.
Variables: New York EZ;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -1.57;
Expanded model: Standard error: 0.88.
Variables: Philadelphia-Camden EZ;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -0.87;
Expanded model: Standard error: 1.83.
Variables: Atlanta EZ area[A];
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -2.48;
Expanded model: Standard error: 1.71.
Variables: Baltimore EZ area[A];
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 2.35;
Expanded model: Standard error: 1.71.
Variables: Chicago EZ area[A];
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 2.66;
Expanded model: Standard error: 1.57.
Variables: Cleveland EZ area[A];
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -1.89;
Expanded model: Standard error: 1.58.
Variables: Detroit EZ area[A];
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -2.59;
Expanded model: Standard error: 1.55.
Variables: Los Angeles EZ area[A];
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 3.61;
Expanded model: Standard error: 1.62.
Variables: New York EZ area[A];
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 3.13;
Expanded model: Standard error: 1.62.
Variables: Philadelphia-Camden EZ area[A];
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: [ B];
Expanded model: Standard error: [ B].
Variables: Percent of population of working age[C];
Basic model: Coefficient: 0.068;
Basic model: [Empty];
Basic model: Standard error: 0.072;
Expanded model: Coefficient: 0.093;
Expanded model: Standard error: 0.068.
Variables: Percent of population with a high school diploma[D];
Basic model: Coefficient: 0.11;
Basic model: [Empty];
Basic model: Standard error: 0.039;
Expanded model: Coefficient: 0.20;
Expanded model: Standard error: 0.044.
Variables: Percent of housing units built between 1990 and 1994[E];
Basic model: Coefficient: -0.18;
Basic model: [Empty];
Basic model: Standard error: 0.062;
Expanded model: Coefficient: -0.23;
Expanded model: Standard error: 0.062.
Variables: Percent minority population[F];
Basic model: Coefficient: 0.072;
Basic model: [Empty];
Basic model: Standard error: 0.012;
Expanded model: Coefficient: 0.054;
Expanded model: Standard error: 0.014.
Variables: Average household income (in 2004 dollars);
Basic model: Coefficient: -0.00017;
Basic model: [Empty];
Basic model: Standard error: 0.000068;
Expanded model: Coefficient: -0.00032;
Expanded model: Standard error: 0.000078.
Variables: Population density[G];
Basic model: Coefficient: 0.000032;
Basic model: [Empty];
Basic model: Standard error: 0.0000066;
Expanded model: Coefficient: 0.0000064;
Expanded model: Standard error: 0.000011.
Variables: Poverty rate[H];
Basic model: Coefficient: 0.20;
Basic model: [Empty];
Basic model: Standard error: 0.033;
Expanded model: Coefficient: 0.15;
Expanded model: Standard error: 0.034.
Variables: Unemployment rate[I];
Basic model: Coefficient: -0.90;
Basic model: [Empty];
Basic model: Standard error: 0.039;
Expanded model: Coefficient: -0.86;
Expanded model: Standard error: 0.044.
Variables: Constant;
Basic model: Coefficient: -0.88;
Basic model: [Empty];
Basic model: Standard error: 4.54;
Expanded model: Coefficient: 1.92;
Expanded model: Standard error: 4.61.
Variables: Number of tracts;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: 866;
Expanded model: Standard error: 866.
Variables: R-sq;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: 0.53;
Expanded model: Standard error: 0.57.
Source: GAO analysis of Census data:
Notes: Coefficients significant at the 5 percent level are in bold. All
variables are from the 1990 Census unless otherwise noted. We weighted
the regressions by the geometric mean of 1990 and 2000 household counts
of each tract.
[A] We defined the EZ area to include both the EZ tracts and comparison
tracts that were selected from within a 5-mile boundary of the EZ.
[B] Results for the Philadelphia-Camden EZ area are not listed, because
we used them as a reference group for the other seven EZs and their
surrounding areas.
[C] We defined "working age" as between 16 and 64 years of age.
[D] Percent based on population 25 years of age and over.
[E] From the 2000 Census.
[F] For the purposes of this report, we calculated minority population
by subtracting the percent of white population from the total
population.
[G] Individuals per square mile.
[H] Percent based on individuals for whom poverty status has been
determined.
[I] Percent based on individuals 16 years of age or older in the labor
force.
[End of table]
Results of Our Models for Economic Growth:
To estimate the statistical relationship between the EZ program and
economic growth, we used two proxy measures: (1) the number of
businesses excluding establishments that were not eligible for program
tax benefits such as nonprofit and governmental organizations and (2)
the number of jobs in the EZ. In order to be consistent with our
analyses of poverty rate and unemployment, which covered the time
period between 1990 and 2000, we used 1995 and 1999 data for our models
of economic growth.[Footnote 76] We also tested the model using Home
Mortgage Disclosure Act data on the number of loan originations for new
home purchases and the mean loan amount for new home purchases as other
possible measures of economic growth, but found consistent results,
which are not presented here.
On the basis of the results of our models, we were not able to
determine whether there is a statistical association between the EZ
program and economic growth because the explanatory variables we used
explained little of the variation in the changes in the number of
businesses or jobs between 1995 and 1999 (tables 10 and 11).[Footnote
77] Not surprisingly, most explanatory variables were also not
significant. The low explanatory power of our models could be the
result of not having considered the right variables;
however, we explored many combinations of variables, all of which
yielded consistent results. This lack of explanatory power might also
be the result of the fact that our proxy measures--the number of
businesses and jobs--were not strongly representative of economic
growth. Nevertheless, similar to the models of the change in poverty
and unemployment, the models of the change in economic growth reflect
variation between the EZs with respect to the comparison areas, but
none of the results were statistically significant.
Table 10: Estimates of the Association between the EZ Program and
Economic Growth, Measured by the Change in the Number of Businesses,
from 1995-1999A:
Variables: EZ program;
Basic model: Coefficient: -22.58;
Basic model: [Empty];
Basic model: Standard error: 18.84;
Expanded model: Standard error: [Empty].
Variables: Atlanta EZ;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 24.59;
Expanded model: Standard error: 26.62.
Variables: Baltimore EZ;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -0.84;
Expanded model: Standard error: 12.12.
Variables: Chicago EZ;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 12.80;
Expanded model: Standard error: 14.80.
Variables: Cleveland EZ;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 4.95;
Expanded model: Standard error: 7.05.
Variables: Detroit EZ;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 13.68;
Expanded model: Standard error: 13.05.
Variables: Los Angeles EZ;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -113.70;
Expanded model: Standard error: 91.86.
Variables: New York EZ;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 10.80;
Expanded model: Standard error: 10.92.
Variables: Philadelphia-Camden EZ;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 1.03;
Expanded model: Standard error: 21.50.
Variables: Atlanta EZ area[B];
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -12.32;
Expanded model: Standard error: 22.13.
Variables: Baltimore EZ area[B];
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -23.11;
Expanded model: Standard error: 17.86.
Variables: Chicago EZ area[B];
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -17.65;
Expanded model: Standard error: 16.25.
Variables: Cleveland EZ area[B];
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 11.51;
Expanded model: Standard error: 29.68.
Variables: Detroit EZ area[B];
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 8.64;
Expanded model: Standard error: 25.45.
Variables: Los Angeles EZ area[B];
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -38.46;
Expanded model: Standard error: 27.71.
Variables: New York EZ area[B];
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -15.84;
Expanded model: Standard error: 20.05.
Variables: Philadelphia-Camden EZ area[B];
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: [C];
Expanded model: Standard error: [ C].
Variables: Population;
Basic model: Coefficient: -0.0027;
Basic model: [Empty];
Basic model: Standard error: 0.0026;
Expanded model: Coefficient: 0.0011;
Expanded model: Standard error: 0.0014.
Variables: Percent vacant housing units;
Basic model: Coefficient: 0.10;
Basic model: [Empty];
Basic model: Standard error: 0.27;
Expanded model: Coefficient: -1.09;
Expanded model: Standard error: 1.01.
Variables: Percent of housing units built between 1990 and 1994[D];
Basic model: Coefficient: 0.95;
Basic model: [Empty];
Basic model: Standard error: 0.58;
Expanded model: Coefficient: 1.77;
Expanded model: Standard error: 1.37.
Variables: Percent minority population[E];
Basic model: Coefficient: 0.74;
Basic model: [Empty];
Basic model: Standard error: 0.69;
Expanded model: Coefficient: 0.80;
Expanded model: Standard error: 0.69.
Variables: Average household income (in 2004 dollars);
Basic model: Coefficient: 0.0044;
Basic model: [Empty];
Basic model: Standard error: 0.0051;
Expanded model: Coefficient: 0.0056;
Expanded model: Standard error: 0.006.
Variables: Population density[F];
Basic model: Coefficient: 0.00027;
Basic model: [Empty];
Basic model: Standard error: 0.00014;
Expanded model: Coefficient: 0.000056;
Expanded model: Standard error: 0.00011.
Variables: Poverty rate[G];
Basic model: Coefficient: 1.67;
Basic model: [Empty];
Basic model: Standard error: 2.21;
Expanded model: Coefficient: 2.31;
Expanded model: Standard error: 2.61.
Variables: Unemployment rate[H];
Basic model: Coefficient: 0.13;
Basic model: [Empty];
Basic model: Standard error: 0.35;
Expanded model: Coefficient: -0.22;
Expanded model: Standard error: 0.59.
Variables: Constant;
Basic model: Coefficient: -253.01;
Basic model: [Empty];
Basic model: Standard error: 278.86;
Expanded model: Coefficient: -300.97;
Expanded model: Standard error: 314.17.
Variables: Number of tracts;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: 860;
Expanded model: Standard error: 860.
Variables: R-sq;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: 0.042;
Expanded model: Standard error: 0.11.
Source: GAO analysis of Census and Claritas data.
Notes: Coefficients significant at the 5 percent level are in bold. All
variables are from the 1990 Census unless otherwise noted. We weighted
the regressions by the geometric mean of 1990 and 2000 household counts
of each tract.
[A] Excluding establishments that were not eligible for the program tax
benefits, such as nonprofit and governmental organizations.
[B] We defined the EZ area to include both the EZ tracts and comparison
tracts that were selected from within a 5-mile boundary of the EZ.
[C] Results for the Philadelphia-Camden EZ area are not listed, because
we used them as a reference group for the other seven EZs and their
surrounding areas.
[D] From the 2000 Census.
[E] For the purposes of this report, we calculated minority population
by subtracting the percent of white population from the total
population.
[F] Individuals per square mile.
[G] Percent based on individuals for whom poverty status has been
determined.
[H] Percent based on individuals 16 years of age or older in the labor
force.
[End of table]
Table 11: Estimates of the Association between the EZ Program and
Economic Growth, Measured by the Change in the Number of Jobs, 1995-
1999:
Variables: EZ program;
Basic model: Coefficient: -68.86;
Basic model: [Empty];
Basic model: Standard error: 196.83;
Expanded model: Standard error: [Empty].
Variables: Atlanta EZ;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 286.01;
Expanded model: Standard error: 983.37.
Variables: Baltimore EZ;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 1199.74;
Expanded model: Standard error: 715.74.
Variables: Chicago EZ;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -88.43;
Expanded model: Standard error: 180.84.
Variables: Cleveland EZ;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 102.85;
Expanded model: Standard error: 293.30.
Variables: Detroit EZ;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 199.16;
Expanded model: Standard error: 181.98.
Variables: Los Angeles EZ;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -438.63;
Expanded model: Standard error: 632.33.
Variables: New York EZ;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -288.08;
Expanded model: Standard error: 231.26.
Variables: Philadelphia-Camden EZ;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 197.87;
Expanded model: Standard error: 631.54.
Variables: Atlanta EZ area[A];
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -318.84;
Expanded model: Standard error: 883.10.
Variables: Baltimore EZ area[A];
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -819.50;
Expanded model: Standard error: 810.54.
Variables: Chicago EZ area[A];
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 36.80;
Expanded model: Standard error: 547.93.
Variables: Cleveland EZ area[A];
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 134.99;
Expanded model: Standard error: 557.58.
Variables: Detroit EZ area[A];
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -61.44;
Expanded model: Standard error: 576.67.
Variables: Los Angeles EZ area[A];
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: -239.53;
Expanded model: Standard error: 571.81.
Variables: New York EZ area[A];
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: 270.91;
Expanded model: Standard error: 553.59.
Variables: Philadelphia-Camden EZ area[A];
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: [Empty];
Expanded model: Coefficient: [B];
Expanded model: Standard error: [ B].
Variables: Percent of population of working age[D];
Basic model: Coefficient: -21.04;
Basic model: [Empty];
Basic model: Standard error: 28.00;
Expanded model: Coefficient: -19.59;
Expanded model: Standard error: 29.65.
Variables: Percent of population with a high school diploma[D];
Basic model: Coefficient: 10.36;
Basic model: [Empty];
Basic model: Standard error: 8.39;
Expanded model: Coefficient: 4.06;
Expanded model: Standard error: 11.77.
Variables: Percent of housing units built between 1990 and 1994[E];
Basic model: Coefficient: 10.57;
Basic model: [Empty];
Basic model: Standard error: 29.50;
Expanded model: Coefficient: 5.09;
Expanded model: Standard error: 29.33.
Variables: Percent minority population[F];
Basic model: Coefficient: 3.41;
Basic model: [Empty];
Basic model: Standard error: 4.63;
Expanded model: Coefficient: 3.23;
Expanded model: Standard error: 5.36.
Variables: Average household income (in 2004 dollars);
Basic model: Coefficient: 0.022;
Basic model: [Empty];
Basic model: Standard error: 0.038;
Expanded model: Coefficient: 0.032;
Expanded model: Standard error: 0.048.
Variables: Population density[G];
Basic model: Coefficient: 0.0016;
Basic model: [Empty];
Basic model: Standard error: 0.0023;
Expanded model: Coefficient: -0.0019;
Expanded model: Standard error: 0.0029.
Variables: Poverty rate[H];
Basic model: Coefficient: -1.30;
Basic model: [Empty];
Basic model: Standard error: 20.33;
Expanded model: Coefficient: 2.99;
Expanded model: Standard error: 20.34.
Variables: Unemployment rate[I];
Basic model: Coefficient: 3.10;
Basic model: [Empty];
Basic model: Standard error: 15.33;
Expanded model: Coefficient: -0.95;
Expanded model: Standard error: 14.30.
Variables: Constant;
Basic model: Coefficient: -56.23;
Basic model: [Empty];
Basic model: Standard error: 1483.38;
Expanded model: Coefficient: -200.97;
Expanded model: Standard error: 1695.03.
Variables: Number of tracts;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: 859;
Expanded model: Standard error: 859.
Variables: R-sq;
Basic model: Coefficient: [Empty];
Basic model: [Empty];
Basic model: Standard error: 0.016;
Expanded model: Standard error: 0.043.
Source: GAO analysis of Census and Claritas data.
Notes: Coefficients significant at the 5 percent level are in bold. All
variables are from the 1990 Census unless otherwise noted. We weighted
the regressions by the geometric mean of 1990 and 2000 household counts
of each tract.
[A] We defined the EZ area to include both the EZ tracts and comparison
tracts that were selected from within a 5-mile boundary of the EZ.
[B] Results for the Philadelphia-Camden EZ area are not listed, because
we used them as a reference group for the other seven EZs and their
surrounding areas.
[C] We defined "working age" as between 16 and 64 years of age.
[D] Percent based on population 25 years of age and over.
[E] From the 2000 Census.
[F] For the purposes of this report, we calculated minority population
by subtracting the percent of white population from the total
population.
[G] Individuals per square mile.
[H] Percent based on individuals for whom poverty status has been
determined.
[I] Percent based on individuals 16 years of age or older in the labor
force.
[End of table]
Other Variables Tested for Use in Our Econometric Models:
In addition to the variables presented in the models above, we explored
many alternative dependent variables and explanatory variables to test
the robustness of the models we used (table 12). In particular, we
experimented with several alternative measures for economic growth. To
test how our results might change in response to the selection of
comparison tracts, we also reestimated the models using comparison
tracts selected with different propensity scores. We also ran the
models excluding the Los Angeles and Cleveland EZs, because these EZs
received a slightly different package of benefits when they were
initially designated as Supplemental EZs. These tests all yielded
results consistent with our models, so they are not presented here.
Table 12: Alternative Variables Considered in Our Analyses:
Definition of variables: Dependent variables: Change in per-capita
income between 1990 and 2000;
Rationale: An opposite measure of poverty;
Data sources: 1990 and 2000 Census.
Definition of variables: Dependent variables: Change in employment
rate between 1990 and 2000;
Rationale: An opposite measure of unemployment;
Data sources: 1990 and 2000 Census.
Definition of variables: Dependent variables: Percent change in number
of businesses between 1995 and 1999;
Rationale: Alternative measure of economic growth;
Data sources: Claritas 1995, 1999.
Definition of variables: Dependent variables: Percent change in the
number of jobs between 1995 and 1999;
Rationale: Alternative measure of economic growth;
Data sources: Claritas 1995, 1999.
Definition of variables: Dependent variables: Percent change in number
of businesses between 1995 and 2004;
Rationale: Alternative measure of economic growth;
Data sources: Claritas 1995, 2004.
Definition of variables: Dependent variables: Percent change in the
number of jobs between 1995 and 2004;
Rationale: Alternative measure of economic growth;
Data sources: Claritas 1995, 2004.
Definition of variables: Dependent variables: Change in jobs per
business between 1995 and 1999;
Rationale: Alternative measure of economic growth;
Data sources: Claritas 1995, 1999.
Definition of variables: Dependent variables: Change in aggregate
sales volume of businesses at each tract between 1995 and 1999;
Rationale: Alternative measure of economic growth;
Data sources: Claritas 1995, 1999.
Definition of variables: Dependent variables: Percent change in number
of loan originations for new home purchases between 1995 and 1999;
Rationale: Alternative measure of economic growth;
Data sources: Home Mortgage Disclosure Act data 1995, 1999.
Definition of variables: Dependent variables: Percent change in mean
loan amount for new home purchases between 1995 and 1999;
Rationale: Alternative measure of economic growth;
Data sources: Home Mortgage Disclosure Act data 1995, 1999.
Definition of variables: Explanatory variables: Percent foreign-born
population;
Rationale: Alternative indirect measure for minority population;
Data sources: 1990 Census.
Definition of variables: Explanatory variables: Adjusted per capita
income in 2004 dollars;
Rationale: Alternative indirect measure household income;
Data sources: 1990 Census.
Definition of variables: Explanatory variables: Percent of males aged
16 or greater;
Rationale: Alternative measure for working population;
Data sources: 1990 Census.
Definition of variables: Explanatory variables: Percent of housing
units built last 5 years before census;
Rationale: Alternative measure to account for economic trend;
Data sources: 1990 Census.
Definition of variables: Explanatory variables: Percent of persons aged
25 or greater with some college;
Rationale: Alternative measure for educational level;
Data sources: 1990 Census.
Definition of variables: Explanatory variables: Percent of employment
in manufacturing industry;
Rationale: Alternative measure of industry characteristics;
Data sources: 1990 Census.
Definition of variables: Explanatory variables: Percent of female-
headed single households;
Rationale: Alternative measure for household characteristics;
Data sources: 1990 Census.
Source: GAO.
[End of table]
[End of section]
Appendix III: List of Communities Designated in Round I of the EZ/EC
Program:
Round I:Urban EZs (8):
Atlanta, Georgia:
Baltimore, Maryland:
Chicago, Illinois:
Cleveland, Ohio[A]:
Detroit, Michigan:
Los Angeles, California[A]:
New York, New York:
Philadelphia, Pennsylvania/Camden, New Jersey:
Round I Urban ECs (65):
Akron, Ohio:
Albany, Georgia:
Albany/Schenectady/Troy, New York:
Albuquerque, New Mexico:
Birmingham, Alabama:
Boston, Massachusetts[B]:
Bridgeport, Connecticut:
Buffalo, New York:
Burlington, Vermont:
Charleston, South Carolina:
Charlotte, North Carolina:
Cleveland, Ohio[C]:
Columbus, Ohio:
Dallas, Texas:
Denver, Colorado:
Des Moines, Iowa:
East St. Louis, Illinois:
El Paso, Texas:
Flint, Michigan:
Harrisburg, Pennsylvania:
Houston, Texas[B]:
Huntington, West Virginia:
Indianapolis, Indiana:
Jackson, Mississippi:
Kansas City, Missouri/Kansas City, Kansas[B]:
Las Vegas, Nevada:
Little Rock/Pulaski, Arkansas:
Los Angeles, California:
Louisville, Kentucky:
Lowell, Massachusetts:
Manchester, New Hampshire:
Memphis, Tennessee:
Miami/Dade County, Florida:
Milwaukee, Wisconsin:
Minneapolis, Minnesota:
Muskegon, Michigan:
Nashville/Davidson, Tennessee:
New Haven, Connecticut:
New Orleans, Louisiana:
Newark, New Jersey:
Newburgh/Kingston, New York:
Norfolk, Virginia:
Oakland, California[B]:
Ogden, Utah:
Oklahoma City, Oklahoma:
Omaha, Nebraska:
Ouachita Parish, Louisiana:
Phoenix, Arizona:
Pittsburgh, Pennsylvania:
Portland, Oregon:
Providence, Rhode Island:
Rochester, New York:
San Antonio, Texas:
San Diego, California:
San Francisco, California:
Seattle, Washington:
Springfield, Illinois:
Springfield, Massachusetts:
St. Louis, Missouri:
St. Paul, Minnesota:
Tacoma, Washington:
Tampa, Florida:
Waco, Texas:
Washington, District of Columbia:
Wilmington, Delaware:
Round I Rural EZs (3):
Kentucky Highlands, Kentucky:
Mid-Delta, Mississippi:
Rio Grande Valley, Texas:
Round I Rural ECs (30):
Accomack and Northampton County, Virginia:
Arizona Border Region, Arizona:
Beadle/Spink Counties, South Dakota:
Central Appalachia, West Virginia:
Central Savannah River Area, Georgia:
Chambers County, Alabama:
City of East Prairie, Missouri:
City of Lock Haven, Pennsylvania:
City of Watsonville, California:
Crisp/ Dooly County, Georgia:
East Arkansas, Arkansas:
Fayette/Haywood County, Tennessee:
Greater Portsmouth, Ohio:
Greene-Sumter, Alabama:
The Halifax/ Edgecombe/Wilson Empowerment Alliance, North Carolina:
Imperial County, California:
Jackson County, Florida:
Josephine County, Oregon:
La Jicarita, New Mexico:
Lake County, Michigan:
Lower Yakima County, Washington:
Macon Ridge, Louisiana:
McDowell County, West Virginia:
Mississippi County, Arkansas:
North Delta Mississippi, Mississippi:
Northeast Louisiana Delta, Louisiana:
Robeson County, North Carolina:
Scott, Tennessee/McCreary, Kentucky:
Southeast Oklahoma, Oklahoma:
Williamsburg-Lake City, South Carolina:
[A] Initially designated as a Supplemental EZ:
[B] Also designated as an Enhanced EC:
[C] Also designated as a Supplemental EZ:
Source: HUD and USDA data.
[End of section]
Appendix IV: Description of the Empowerment Zones and Enterprise
Communities We Visited:
This appendix contains detailed information we gathered from our site
visits to the 11 Round I EZs and 2 ECs. The appendix describes how the
EZs and ECs were governed; the activities they implemented; changes in
poverty, unemployment, and economic growth; and stakeholders'
perceptions of factors influencing those changes. It also includes the
percent changes in variables used in the econometric model.
Atlanta Empowerment Zone:
Figure 13: Map of the Atlanta EZ and Its Comparison Area:
[See PDF for image]
Source: GAO analysis of Census and HUD data.
[End of figure]
How the EZ Was Governed:
The city of Atlanta established the nonprofit Atlanta Empowerment Zone
Corporation to operate the EZ. The corporation had two boards: the
Executive Board and the Community Empowerment Advisory Board, which
included representatives of each of the EZ neighborhoods. According to
EZ stakeholders we interviewed, the EZ Executive Board gave final
approval on activities the EZ implemented. However, EZ stakeholders
also mentioned that the intended process was not always followed and
that the board was not always able to approve activities due to
difficulties reaching consensus.
Activities the EZ Implemented:
According to HUD data, most of the Atlanta EZ's activities related to
community development, but the EZ also implemented some activities
related to economic opportunity, such as making loans to EZ businesses.
Initiatives involving housing, public safety, and assistance to
businesses were the most frequently implemented types of activities
(fig. 14). In our interviews, EZ stakeholders mentioned initiatives
they saw as particularly useful, including housing programs for seniors
and low-income EZ residents--for example, a program that helped to
repair code violations in homes of senior citizens. Stakeholders also
said that the EZ provided funds to after-school and health-related
programs, such as one that provided children and adults with asthma
with needed resources and education. Some EZ stakeholders suggested
that the loan program lacked positive results, because many of the
businesses that received the loans failed.[Footnote 78]
Figure 14: Activities Implemented by the Atlanta EZ:
[See PDF for image]
Sources: GAO (photos); GAO analysis of HUD data (charts).
[End of figure]
Changes in Poverty, Unemployment, and Economic Growth:
Poverty declined in the Atlanta EZ, but unemployment did not, and
measures of economic growth did not show improvement. Atlanta had the
highest poverty rate of any EZ in 1990 (55 percent). By 2000, this rate
had fallen by around 10 percentage points, while the rate of its
comparison area remained the same. Conversely, the unemployment rate
went from one of the lowest of the urban EZs in 1990 to one of the
highest in 2000, and the increase was greater than in its comparison
area. Similarly, the Atlanta EZ and its comparison area experienced a
large decline--more than 20 percent--in total number of businesses from
1995 to 2004. The Atlanta EZ had the second largest decline in the
number of jobs of any EZ, which was also more than in its comparison
area. Tables 13 and 14 show the changes in poverty, unemployment, and
economic growth in the EZ and its comparison area. Table 13 also
includes data on the changes in other variables included in our models.
Table 13: Changes in Selected Census Variables Observed in the Atlanta
EZ and Its Comparison Area:
Poverty rate (%);
1990: EZ: 54.67;
1990: Comparison: 30.15;
2000: EZ: 44.82;
2000: Comparison: 28.02;
Percent change[A]: EZ: -9.84[B];
Percent change[A]: Comparison: -2.12.
Unemployment rate (%);
1990: EZ: 17.48;
1990: Comparison: 11.36;
2000: EZ: 23.44;
2000: Comparison: 11.88;
Percent change[A]: EZ: 5.96[B];
Percent change[A]: Comparison: 0.52.
Average household income;
1990: EZ: $18,343;
1990: Comparison: $30,567;
2000: EZ: $28,552;
2000: Comparison: $39,500;
Percent change[A]: EZ: 55.66[B];
Percent change[A]: Comparison: 29.23[B].
Percentage of single female headed households with children;
1990: EZ: 24.62;
1990: Comparison: 20.02;
2000: EZ: 21.26;
2000: Comparison: 19.95;
Percent change[A]: EZ: -3.36[B];
Percent change[A]: Comparison: -0.07.
Total population;
1990: EZ: 49,966;
1990: Comparison: 65,809;
2000: EZ: 45,931;
2000: Comparison: 64,022;
Percent change[A]: EZ: -8.07;
Percent change[A]: Comparison: - 2.71.
Total individuals per square mile;
1990: EZ: 5,408;
1990: Comparison: 2,671;
2000: EZ: 4,972;
2000: Comparison: 2,756;
Percent change[A]: EZ: -8.07;
Percent change[A]: Comparison: 3.16.
Percentage of households that moved in the last 5 years;
1990: EZ: 50.87;
1990: Comparison: 46.01;
2000: EZ: 53.32;
2000: Comparison: 52.52;
Percent change[A]: EZ: 2.45[B];
Percent change[A]: Comparison: 6.51[B].
Percentage of population of working age (16-64);
1990: EZ: 60.16;
1990: Comparison: 61.68;
2000: EZ: 63.42;
2000: Comparison: 64.59;
Percent change[A]: EZ: 3.26[B];
Percent change[A]: Comparison: 2.92[B].
Percentage of population with a high school diploma (or equivalent);
1990: EZ: 43.10;
1990: Comparison: 60.53;
2000: EZ: 58.96;
2000: Comparison: 69.3;
Percent change[A]: EZ: 15.86[B];
Percent change[A]: Comparison: 8.78[B].
Percentage of high school dropouts;
1990: EZ: 19.12;
1990: Comparison: 19.1;
2000: EZ: 21.48;
2000: Comparison: 21.19;
Percent change[A]: EZ: 2.36[B];
Percent change[A]: Comparison: 2.08[B].
Percentage of vacant housing units;
1990: EZ: 20.79;
1990: Comparison: 14.65;
2000: EZ: 13.30;
2000: Comparison: 7.43;
Percent change[A]: EZ: -7.48[B];
Percent change[A]: Comparison: -7.22[B].
Average owner occupied housing value;
1990: EZ: $55,883;
1990: Comparison: $74,063;
2000: EZ: $117,869;
2000: Comparison: $101,774;
Percent change[A]: EZ: 110.92[B];
Percent change[A]: Comparison: 37.42[B].
Source: GAO analysis of Census data.
Note: There are 23 census tracts in the designated area and 16 in the
comparison area. Estimates for all census variables based on
percentages had 95 percent confidence intervals of plus or minus 5
percentage points or less. For the confidence intervals for average
household income and average owner-occupied housing estimates, see
appendix I.
[A] Differences in poverty rate, unemployment rate, and other variables
shown as percentages are based upon percentage point differences.
Differences for average household income, population, individuals per
square mile, and average housing value are calculated as percent
changes.
[B] The change in estimates from 1990 to 2000 is statistically
significant.
[End of table]
Table 14: Changes in Selected Economic Growth Variables Observed in the
Atlanta EZ and Its Comparison Area:
Number of businesses;
1995: EZ: 1,930;
1995: Comparison: 3,980;
1999: EZ: 1,549;
1999: Comparison: 3,380;
2004: EZ: 1,529;
2004: Comparison: 3,248;
Percent change 1995-2004[A]: EZ: -20.78;
Percent change 1995-2004[A]: Comparison: -18.39.
Number of jobs;
1995: EZ: 36,888;
1995: Comparison: 71,346;
1999: EZ: 31,470;
1999: Comparison: 79,580;
2004: EZ: 28,672;
2004: Comparison: 69,140;
Percent change 1995-2004[A]: EZ: - 22.27;
Percent change 1995-2004[A]: Comparison: -3.09.
Source: GAO analysis of Claritas data.
Note: There are 23 census tracts in the designated area and 16 in the
comparison area. We excluded establishments that were not eligible for
program tax benefits, such as nonprofit and governmental organizations,
from our analysis of the change in the number of businesses. However,
we included jobs at those businesses in our analysis of the change in
the number of jobs.
[A] Differences for the number of businesses and the number of jobs are
calculated as percent changes.
[End of table]
Stakeholder Perceptions of the Factors Influencing Changes in Poverty,
Unemployment, and Economic Growth:
In our interviews, stakeholders said that changes in the poverty rate
may have been due to changes in the EZ population and the demolition of
public housing. They explained that residents with lower incomes had
left the EZ and that households with higher incomes were moving in
because of changes in the EZ as a result of development from the
Olympics and the demolition of public housing through the HOPE VI
program.[Footnote 79]
Commenting on unemployment, stakeholders suggested that EZ residents
had benefited from EZ job training and placement programs but that a
mismatch still existed between residents' skills and some of the new
jobs available in the EZ.
Although our economic growth data suggested a decrease in the number of
businesses and number of jobs, stakeholders suggested that the EZ had
helped to foster economic growth in some of the commercial corridors by
helping to fund neighborhood plans. Two stakeholders also mentioned the
1996 Olympics as a factor in bringing jobs and development to the EZ
and the city of Atlanta, although one stakeholder noted that several
businesses had closed down after the Olympics. This loss of businesses
potentially helps explain the significant decrease in the number of
businesses between 1995 and 2004.
Baltimore Empowerment Zone:
Figure 15: Map of the Baltimore EZ and Its Comparison Area:
[See PDF for image]
Source: GAO analysis of Census and HUD data.
[End of figure]
How the EZ Was Governed:
The nonprofit Empower Baltimore Management Corporation was created
specifically to manage Baltimore's EZ program. The EZ was governed by a
board composed of community leaders, three committees (one for each
core strategic goal), an executive committee of the three committee
chairs, and an advisory council of individuals from all areas of the
EZ. Governance of the Baltimore EZ also included six "Village Centers"-
-community groups that applied to be the implementing agencies of EZ
programs in their local communities. EZ activities were vetted through
the advisory council and sent to the executive committee and full board
for final approval.
Activities the EZ Implemented:
Unlike most EZs, the Baltimore EZ implemented a higher number of
economic opportunity activities than community development activities.
The three types of activities most often implemented were workforce
development, access to capital, and assistance to businesses (fig. 16).
Most stakeholders described the EZ's workforce training activities,
such as the customized training program that provided EZ residents with
individualized instruction and a stipend during the training period. In
addition, the EZ operated several loan funds and partially funded the
Bank One check processing center and the Montgomery Park business
incubator, two business developments. The EZ also ran a lead paint
abatement program and a homeownership program. The Baltimore EZ
received a grant extension through June 2006.
Figure 16: Activities Implemented by the Baltimore EZ:
[See PDF for image]
Source: GAO (photo); GAO analysis of HUD data (charts).
[End of figure]
Changes in Poverty, Unemployment, and Economic Growth:
Poverty decreased in the Baltimore EZ and economic growth improved
somewhat, but unemployment stayed the same. The poverty rate in the EZ
fell between 1990 and 2000, while its comparison area stayed about the
same. However, the unemployment rate, which was one of the lowest of
the urban EZs in 1990, stayed the same between 1990 and 2000, while the
rate in its comparison area increased. In terms of economic growth, the
results were mixed, with the EZ doing somewhat better than its
comparison area. The number of businesses in the EZ fell from 1995 to
2004, but the number of jobs increased. In its comparison area, the
number of businesses also fell, but the number of jobs fell
substantially. Tables 15 and 16 show the changes in poverty,
unemployment, and economic growth in the EZ and its comparison area.
Table 15 also includes data on the changes in other variables included
in our models.
Table 15: Changes in Selected Census Variables Observed in the
Baltimore EZ and Its Comparison Area:
Poverty rate (%);
1990: EZ: 41.81;
1990: Comparison: 41.17;
2000: EZ: 35.66;
2000: Comparison: 39.74;
Percent change[A]: EZ: - 6.16[ B];
Percent change[A]: Comparison: -1.43.
Unemployment rate (%);
1990: EZ: 15.00;
1990: Comparison: 14.55;
2000: EZ: 16.48;
2000: Comparison: 17.58;
Percent change[A]: EZ: 1.49;
Percent change[A]: Comparison: 3.03[ B].
Average household income;
1990: EZ: $28,185;
1990: Comparison: $27,931;
2000: EZ: $35,059;
2000: Comparison: $31,367;
Percent change[A]: EZ: 24.39[B];
Percent change[A]: Comparison: 12.30[B].
Percentage of single female headed households with children;
1990: EZ: 22.50;
1990: Comparison: 23.15;
2000: EZ: 19.49;
2000: Comparison: 19.64;
Percent change[A]: EZ: -3.01[ B];
Percent change[A]: Comparison: -3.51[B].
Total population;
1990: EZ: 72,725;
1990: Comparison: 150,507;
2000: EZ: 54,657;
2000: Comparison: 113,052;
Percent change[A]: EZ: -24.84;
Percent change[A]: Comparison: -24.89.
Total individuals per square mile;
1990: EZ: 10,460;
1990: Comparison: 16,934;
2000: EZ: 7,890;
2000: Comparison: 12,923;
Percent change[A]: EZ: -24.57;
Percent change[A]: Comparison: -23.69.
Percentage of households that moved in the last 5 years;
1990: EZ: 41.07;
1990: Comparison: 42.98;
2000: EZ: 41.00;
2000: Comparison: 44.90;
Percent change[A]: EZ: -0.07;
Percent change[A]: Comparison: 1.92[B].
Percentage of population of working age (16-64);
1990: EZ: 58.55;
1990: Comparison: 60.31;
2000: EZ: 60.04;
2000: Comparison: 61.63;
Percent change[A]: EZ: 1.48;
Percent change[A]: Comparison: 1.32.
Percentage of population with a high school diploma (or equivalent);
1990: EZ: 45.69;
1990: Comparison: 49.86;
2000: EZ: 56.44;
2000: Comparison: 58.50;
Percent change[A]: EZ: 10.74[B];
Percent change[A]: Comparison: 8.64[B].
Percentage of high school dropouts;
1990: EZ: 32.36;
1990: Comparison: 26.43;
2000: EZ: 19.55;
2000: Comparison: 20.61;
Percent change[A]: EZ: -12.81[ B];
Percent change[A]: Comparison: -5.81[B].
Percentage of vacant housing units;
1990: EZ: 17.59;
1990: Comparison: 12.67;
2000: EZ: 26.22;
2000: Comparison: 23.63;
Percent change[A]: EZ: 8.62[B];
Percent change[A]: Comparison: 10.96[B].
Average owner occupied housing value;
1990: EZ: $53,714;
1990: Comparison: $55,966;
2000: EZ: $62,219;
2000: Comparison: $62,514;
Percent change[A]: EZ: 15.83[B];
Percent change[A]: Comparison: 11.7[B].
Source: GAO analysis of Census data.
Note: There are 25 census tracts in the designated area and 41 in the
comparison area. Estimates for all census variables based on
percentages had 95 percent confidence intervals of plus or minus 5
percentage points or less. For the confidence intervals for average
household income and average owner-occupied housing estimates, see
appendix I.
[A] Differences in poverty rate, unemployment rate, and other variables
shown as percentages are based upon percentage point differences.
Differences for average household income, population, individuals per
square mile, and average housing value are calculated as percent
changes.
[B] The change in estimates from 1990 to 2000 is statistically
significant.
[End of table]
Table 16: Changes in Selected Economic Growth Variables Observed in the
Baltimore EZ and Its Comparison Area:
Number of businesses;
1995: EZ: 2,797;
1995: Comparison: 3,481;
1999: EZ: 2,399;
1999: Comparison: 2,930;
2004: EZ: 2,487;
2004: Comparison: 3,005;
Percent change 1995-2004[A]: EZ: -11.08;
Percent change 1995-2004[A]: Comparison: -13.67.
Number of jobs;
1995: EZ: 41,837;
1995: Comparison: 61,519;
1999: EZ: 53,732;
1999: Comparison: 35,268;
2004: EZ: 47,504;
2004: Comparison: 36,860;
Percent change 1995-2004[A]: EZ: 13.55;
Percent change 1995-2004[A]: Comparison: -40.08.
Source: GAO analysis of Claritas data.
Note: There are 25 census tracts in the designated area and 41 in the
comparison area. We excluded establishments that were not eligible for
program tax benefits, such as nonprofit and governmental organizations,
from our analysis of the change in the number of businesses. However,
we included jobs at those businesses in our analysis of the change in
the number of jobs.
[A] Differences for the number of businesses and the number of jobs are
calculated as percent changes.
[End of table]
Stakeholder Perceptions of the Factors Influencing Changes in Poverty,
Unemployment, and Economic Growth:
In our interviews, several stakeholders from the Baltimore EZ said that
changes in the population of the zone had influenced the change in
poverty rate. They said that a local HOPE VI project had relocated many
of the original EZ residents and that rising property values may have
caused some original residents to move out of the zone. Four
stakeholders also mentioned lower crime rates in the EZ, which three of
them linked to the decrease in poverty.
Two stakeholders mentioned trends in the national economy that
influenced the change in unemployment, and some said that population
changes in the zone had affected unemployment as well as poverty.
Stakeholders cited both EZ-related and external factors as affecting
economic growth. For example, some said that the EZ created economic
growth with its entrepreneurial programs, loan funds, and businesses
developments, such as the Montgomery Park business incubator.
Stakeholders offered mixed perceptions on the impact of the EZ tax
benefits on economic growth. Some believed that tax benefits were
helpful to economic growth, while others did not. In addition, one
stakeholder said that the waterfront area of the EZ was a natural place
for development and that the designated area might have experienced
economic growth in the absence of the program.
Chicago Empowerment Zone:
Figure 17: Map of the Chicago EZ and Its Comparison Area:
[See PDF for image]
Source: GAO analysis of Census and HUD data.
[End of figure]
How the EZ Was Governed:
The city of Chicago operated its EZ program, incorporating an EZ
coordinating council and advisory subgroups called "community
clusters." Both the coordinating council and community clusters were
made up of EZ residents and local officials. All proposals for EZ
activities were submitted through a request-for-proposal process, made
available for comment by the coordinating council, and were reviewed
and approved by the Chicago City Council.
Activities the EZ Implemented:
The Chicago EZ implemented more community development than economic
opportunity activities. The activities it implemented most often were
related to workforce development, education, and human services, and
stakeholders said that the EZ was also active in the area of housing
development (fig. 18). EZ stakeholders also noted that the EZ had
helped to improve health care for individuals without insurance by
contributing to the renovation or expansion of local medical
facilities. In addition, businesses in the Chicago EZ used six program
tax-exempt bonds. The Chicago EZ received a grant extension through
2009.
Figure 18: Activities Implemented by the Chicago EZ:
[See PDF for image]
Sources: GAO (photo); GAO analysis of HUD data (charts).
[End of figure]
Changes in Poverty, Unemployment, and Economic Growth:
Our analyses showed improvements in the Chicago EZ in the poverty and
unemployment rates, but not in economic growth. Both the EZ and its
comparison area saw a decrease in poverty from 1990 to 2000. The EZ
also experienced a decrease in unemployment that was considerably
greater than that of its comparison area in that time period. In terms
of economic growth, the Chicago EZ and its comparison area saw
decreases in the numbers of businesses and jobs between 1995 and 2004,
with the EZ seeing a larger decline in the number of jobs but less of a
decline in the number of businesses than its comparison area. Tables 17
and 18 show the changes in poverty, unemployment, and economic growth
in the EZ and its comparison area. Table 17 also includes data on the
changes in other variables included in our models.
Table 17: Changes in Selected Census Variables Observed in the Chicago
EZ and Its Comparison Area:
Poverty rate (%);
1990: EZ: 49.10;
1990: Comparison: 40.38;
2000: EZ: 39.32;
2000: Comparison: 33.49;
Percent change[A]: EZ: - 9.77[ B];
Percent change[A]: Comparison: -6.89[B].
Unemployment rate (%);
1990: EZ: 24.57;
1990: Comparison: 20.52;
2000: EZ: 19.34;
2000: Comparison: 18.97;
Percent change[A]: EZ: -5.23[ B];
Percent change[A]: Comparison: -1.54[B].
Average household income;
1990: EZ: $23,097;
1990: Comparison: $28,431;
2000: EZ: $34,718;
2000: Comparison: $39,985;
Percent change[A]: EZ: 50.31[B];
Percent change[A]: Comparison: 40.64[B].
Percentage of single female headed households with children;
1990: EZ: 25.64;
1990: Comparison: 23.07;
2000: EZ: 21.59;
2000: Comparison: 19.69;
Percent change[A]: EZ: -4.05[B];
Percent change[A]: Comparison: -3.38[B].
Total population;
1990: EZ: 200,182;
1990: Comparison: 377,580;
2000: EZ: 177,309;
2000: Comparison: 369,343;
Percent change[A]: EZ: -11.43;
Percent change[A]: Comparison: -2.18.
Total individuals per square mile;
1990: EZ: 13,967;
1990: Comparison: 15,523;
2000: EZ: 12,380;
2000: Comparison: 15,752;
Percent change[A]: EZ: -11.36;
Percent change[A]: Comparison: 1.47.
Percentage of households that moved in the last 5 years;
1990: EZ: 37.52;
1990: Comparison: 40.03;
2000: EZ: 39.21;
2000: Comparison: 39.68;
Percent change[A]: EZ: 1.69[B];
Percent change[A]: Comparison: -0.35.
Percentage of population of working age (16-64);
1990: EZ: 53.51;
1990: Comparison: 57.87;
2000: EZ: 55.63;
2000: Comparison: 59.53;
Percent change[A]: EZ: 2.12[B];
Percent change[A]: Comparison: 1.66[B].
Percentage of population with a high school diploma (or equivalent);
1990: EZ: 44.04;
1990: Comparison: 54.24;
2000: EZ: 54.30;
2000: Comparison: 63.58;
Percent change[A]: EZ: 10.26[B];
Percent change[A]: Comparison: 9.35[B].
Percentage of high school dropouts;
1990: EZ: 22.46;
1990: Comparison: 19.50;
2000: EZ: 22.05;
2000: Comparison: 15.23;
Percent change[A]: EZ: -0.41;
Percent change[A]: Comparison: -4.27[ B].
Percentage of vacant housing units;
1990: EZ: 19.69;
1990: Comparison: 13.54;
2000: EZ: 18.23;
2000: Comparison: 12.44;
Percent change[A]: EZ: -1.45[B];
Percent change[A]: Comparison: -1.1[B].
Average owner occupied housing value;
1990: EZ: $71,429;
1990: Comparison: $88,445;
2000: EZ: $160,412;
2000: Comparison: $167,015;
Percent change[A]: EZ: 124.57[B];
Percent change[A]: Comparison: 88.83[B].
Source: GAO analysis of Census data.
Note: There are 96 census tracts in the designated area and 146 in the
comparison area. Estimates for all census variables based on
percentages had 95 percent confidence intervals of plus or minus 5
percentage points or less. For the confidence intervals for average
household income and average owner-occupied housing estimates, see
appendix I.
[A] Differences in poverty rate, unemployment rate, and other variables
shown as percentages are based upon percentage point differences.
Differences for average household income, population, individuals per
square mile, and average housing value are calculated as percent
changes.
[B] The change in estimates from 1990 to 2000 is statistically
significant.
[End of table]
Table 18: Changes in Selected Economic Growth Variables Observed in the
Chicago EZ and Its Comparison Area:
Number of businesses;
1995: EZ: 5,089;
1995: Comparison: 10,567;
1999: EZ: 4,614;
1999: Comparison: 9,582;
2004: EZ: 4,496;
2004: Comparison: 9,211;
Percent change 1995-2004[A]: EZ: -11.65;
Percent change 1995-2004[A]: Comparison: -12.83.
Number of jobs;
1995: EZ: 83,935;
1995: Comparison: 183,369;
1999: EZ: 80,294;
1999: Comparison: 169,741;
2004: EZ: 69,767;
2004: Comparison: 162,541;
Percent change 1995-2004[A]: EZ: - 16.88;
Percent change 1995-2004[A]: Comparison: -11.36.
Source: GAO analysis of Claritas data.
Note: There are 96 census tracts in the designated area and 146 in the
comparison area. We excluded establishments that were not eligible for
program tax benefits, such as nonprofit and governmental organizations,
from our analysis of the change in the number of businesses. However,
we included jobs at those businesses in our analysis of the change in
the number of jobs.
[A] Differences for the number of businesses and the number of jobs are
calculated as percent changes.
[End of table]
Stakeholder Perceptions of the Factors Influencing Changes in Poverty,
Unemployment, and Economic Growth:
Asked about factors influencing the change in poverty, stakeholders
pointed to both EZ activities and external factors. Among the EZ
activities they cited were projects promoting homeownership or
providing educational training. However, some stakeholders mentioned
changes in the EZ population as an external factor that may have
affected the changes in poverty, noting the demolition of several
public housing buildings in the EZ and the addition of individuals with
higher incomes moving into new housing built on those sites.
Some stakeholders attributed a decrease in unemployment to the zone's
focus on creating jobs and the requirement that subgrantees demonstrate
that they had created jobs for EZ residents. Some stakeholders also
noted that the EZ's provision of services, such as childcare, after-
school programs, and job training, provided opportunities for more
residents to obtain jobs. But some stakeholders believed that the
decreases in unemployment were due to external economic forces, such as
changes in the population of the EZ and more jobs available due to
changes in the national economy.
In terms of economic growth, some EZ stakeholders observed that the EZ
had provided some initial investment in the zone and that private
investment had followed. However, some stakeholders noted that the EZ
had not done enough in the area of economic development. In addition,
stakeholders said that not all the jobs from new businesses in the zone
had gone to zone residents and that the number of new businesses did
not meet the zone's employment needs.
Detroit Empowerment Zone:
Figure 19: Map of the Detroit EZ and Its Comparison Area:
[See PDF for image]
Source: GAO analysis of Census and HUD data.
[End of figure]
How the EZ Was Governed:
The nonprofit Detroit Empowerment Zone Development Corporation ran the
Detroit EZ and included an executive committee, a board made up of
residents and other local officials, and three neighborhood review
panels representing neighborhoods in the EZ. Each review panel had an
advisory role in determining how a portion of the EZ funds would be
spent. The EZ was required to obtain the approval of the Detroit City
Council and Mayor for many EZ-funded activities.
Activities the EZ Implemented:
The Detroit EZ implemented mostly community development activities. The
two most common types of activities were in the areas of human services
and education (fig. 20). In addition, EZ stakeholders explained that
the EZ had helped to spur housing development in the east and southwest
areas of the zone by providing funds to community development
corporations. Detroit EZ stakeholders also highlighted a business
façade improvement program during our tour of the EZ. Although they
focused mainly on community development, the Detroit EZ did implement
some economic opportunity activities. Some EZ stakeholders said that
the Financial Institutions Consortium, which set lending goals within
the EZ, had helped EZ businesses. The Detroit EZ did not request a
grant extension because it had used nearly all of the EZ grant funds.
Figure 20: Activities Implemented by the Detroit EZ:
[See PDF for image]
Source: GAO (photo); GAO analysis of HUD data (charts).
[End of figure]
Changes in Poverty, Unemployment, and Economic Growth:
The Detroit EZ experienced positive changes in poverty, unemployment,
and one measure of economic growth. Of the urban EZs, the Detroit EZ
had the largest decrease in poverty and the second largest decrease in
unemployment from 1990 to 2000. Although the decrease in the EZ was
slightly greater than in its comparison area in poverty, the decrease
in unemployment was less than in its comparison area. Between 1995 and
2004, the EZ generally fared better than its comparison area in our
measures of economic growth; however, the changes were not always
positive. The number of businesses declined slightly, but the decrease
was notably smaller than the decline in its comparison area. In
addition, the EZ saw a greater increase in the number of jobs than in
either its comparison area or most urban EZs. Tables 19 and 20 show the
changes in poverty, unemployment, and economic growth in the EZ and its
comparison area. Table 19 also includes data on the changes in other
variables included in our models.
Table 19: Changes in Selected Census Variables Observed in the Detroit
EZ and Its Comparison Area:
Poverty rate (%);
1990: EZ: 47.63;
1990: Comparison: 42.72;
2000: EZ: 36.73;
2000: Comparison: 32.38;
Percent change[A]: EZ: -10.90[B];
Percent change[A]: Comparison: -10.34[B].
Unemployment rate (%);
1990: EZ: 28.41;
1990: Comparison: 26.01;
2000: EZ: 18.83;
2000: Comparison: 15.54;
Percent change[A]: EZ: -9.58[ B];
Percent change[A]: Comparison: -10.47[ B].
Average household income;
1990: EZ: $22,644;
1990: Comparison: $25,609;
2000: EZ: $33,751;
2000: Comparison: $36,200;
Percent change[A]: EZ: 49.05[B];
Percent change[A]: Comparison: 41.36[B].
Percentage of single female headed households with children;
1990: EZ: 17.30;
1990: Comparison: 20.94;
2000: EZ: 15.88;
2000: Comparison: 18.77;
Percent change[A]: EZ: -1.43;
Percent change[A]: Comparison: -2.18[B].
Total population;
1990: EZ: 103,346;
1990: Comparison: 256,371;
2000: EZ: 88,707;
2000: Comparison: 229,536;
Percent change[A]: EZ: -14.16;
Percent change[A]: Comparison: -10.47.
Total individuals per square mile;
1990: EZ: 5,547;
1990: Comparison: 6,923;
2000: EZ: 4,762;
2000: Comparison: 6,200;
Percent change[A]: EZ: -14.15;
Percent change[A]: Comparison:
-10.45.
Percentage of households that moved in the last 5 years;
1990: EZ: 40.80;
1990: Comparison: 37.39;
2000: EZ: 42.20;
2000: Comparison: 38.47;
Percent change[A]: EZ: 1.40;
Percent change[A]: Comparison: 1.08.
Percentage of population of working age (16-64);
1990: EZ: 57.65;
1990: Comparison: 56.96;
2000: EZ: 60.34;
2000: Comparison: 57.01;
Percent change[A]: EZ: 2.69[B];
Percent change[A]: Comparison: 0.05.
Percentage of population with a high school diploma (or equivalent);
1990: EZ: 49.34;
1990: Comparison: 53.63;
2000: EZ: 58.06;
2000: Comparison: 60.87;
Percent change[A]: EZ: 8.72[B];
Percent change[A]: Comparison: 7.24[B].
Percentage of high school dropouts;
1990: EZ: 23.29;
1990: Comparison: 20.47;
2000: EZ: 20.83;
2000: Comparison: 18.89;
Percent change[A]: EZ: -2.46[B];
Percent change[A]: Comparison: -1.57[B].
Percentage of vacant housing units;
1990: EZ: 18.26;
1990: Comparison: 11.22;
2000: EZ: 17.46;
2000: Comparison: 13.98;
Percent change[A]: EZ: -0.79;
Percent change[A]: Comparison: 2.76[B].
Average owner occupied housing value;
1990: EZ: $23,114;
1990: Comparison: $28,598;
2000: EZ: $52,234;
2000: Comparison: $61,160;
Percent change[A]: EZ: 125.99[B];
Percent change[A]: Comparison: 113.86[B].
Source: GAO analysis of Census data.
Note: There are 49 census tracts in the designated area and 86 in the
comparison area. Estimates for all census variables based on
percentages had 95 percent confidence intervals of plus or minus 5
percentage points or less. For the confidence intervals for average
household income and average owner-occupied housing estimates, see
appendix I.
[A] Differences in poverty rate, unemployment rate, and other variables
shown as percentages are based upon percentage point differences.
Differences for average household income, population, individuals per
square mile, and average housing value are calculated as percent
changes.
[B] The change in estimates from 1990 to 2000 is statistically
significant.
[End of table]
Table 20: Changes in Selected Economic Growth Variables Observed in the
Detroit EZ and Its Comparison Area:
Number of businesses;
1995: EZ: 3,723;
1995: Comparison: 5,343;
1999: EZ: 3,650;
1999: Comparison: 5,282;
2004: EZ: 3,621;
2004: Comparison: 4,770;
Percent change 1995-2004[A]: EZ: -2.74;
Percent change 1995-2004[A]: Comparison: -10.72.
Number of jobs;
1995: EZ: 95,172;
1995: Comparison: 86,500;
1999: EZ: 99,480;
1999: Comparison: 73,770;
2004: EZ: 124,172;
2004: Comparison: 66,179;
Percent change 1995-2004[A]: EZ: 30.47;
Percent change 1995-2004[A]: Comparison: -23.49.
Source: GAO analysis of Claritas data.
Note: There are 49 census tracts in the designated area and 86 in the
comparison area. We excluded establishments that were not eligible for
program tax benefits, such as nonprofit and governmental organizations,
from our analysis of the change in the number of businesses. However,
we included jobs at those businesses in our analysis of the change in
the number of jobs.
[A] Differences for the number of businesses and the number of jobs are
calculated as percent changes.
[End of table]
Stakeholder Perceptions of the Factors Influencing Changes in Poverty,
Unemployment, and Economic Growth:
An EZ stakeholder said that the population of the zone had changed,
possibly affecting the changes in the poverty rate. For example, the
stakeholder noted that many of the initial EZ residents had moved out
of the zone since designation, and that other individuals with higher
incomes had moved into the zone.
Stakeholders noted that EZ programs in job training, youth education,
supportive services, and health care had helped some EZ residents to
gain employment. However, some stakeholders also mentioned the lack of
a skilled workforce in the EZ and the need for more job training. In
addition, some stakeholders thought that the changes in the zone's
population might also have influenced the change in unemployment.
In terms of economic growth, EZ stakeholders noted that their façade
improvement program had contributed to business growth in the EZ. One
stakeholder also suggested that the EZ tax benefits and financing from
the Financial Institutions Consortium had provided incentives to
attract businesses to locate in the EZ. Another stakeholder mentioned
external challenges to economic growth that included the loss of the
automobile industry and the poor national economy over the time period
of the EZ.
New York Empowerment Zone:
Figure 21: Map of the New York EZ and Its Comparison Area:
[See PDF for image]
Source: GAO analysis of Census and HUD data.
[End of figure]
How the EZ Was Governed:
The New York EZ was governed by three boards: an overarching board and
two subzone boards representing the Upper Manhattan and Bronx
neighborhoods. The overarching board, which included officials from the
city, state, and each subzone board, as well as local congressional
representatives, provided final funding approval for all EZ
projects.[Footnote 80] However, the program was managed locally by the
two subzones, which had separate management organizations, boards, and
budgets and made decisions about the activities that would be funded
and the organizations that would implement them. The Upper Manhattan
subzone received the bulk of the EZ grant ($83 million), and the Bronx
portion received the remaining $17 million. The New York EZ also
received matching funds from the city and state, bringing the total
funding for the EZ to $300 million.
The EZ created the nonprofit Upper Manhattan Empowerment Zone to manage
the Upper Manhattan portion of the zone. This EZ is governed by a board
that includes community members, at-large members selected for their
expertise, and representatives from city community planning boards. The
board also has seven committees. Activities proposed in this portion of
the EZ were reviewed by the committees, approved by the Upper Manhattan
board, and finally approval by the overarching EZ board.
The Bronx Overall Economic Development Corporation, a part of the Bronx
Borough President's office, managed the Bronx portion of the New York
EZ. The board of the Bronx Overall Economic Development Corporation
covered both EZ and non-EZ activities but included an EZ committee.
Although the board did not include any EZ residents, an EZ stakeholder
explained that it included some residents of other areas of the Bronx.
In general, the board decided on activities, encouraged local
nonprofits to submit proposals, and chose the organizations to
implement the activities. Then the activities went before the New York
EZ board for final approval.
Activities the EZ Implemented:
Unlike most EZs, both portions of the New York EZ implemented more
economic opportunity activities than community development activities.
However, the Upper Manhattan and Bronx portions of the EZ differed
somewhat in the types of activities they implemented. The New York EZ
as a whole received a grant extension until 2009.
The types of activities most commonly implemented by the Upper
Manhattan portion were assistance to businesses, workforce development,
access to capital, and infrastructure (fig. 22). Several EZ
stakeholders mentioned the business developments in Harlem USA or along
125TH Street as major accomplishments of their program. Stakeholders
also noted that the EZ had assisted small businesses, successfully
sponsored a restaurant initiative that provided local restaurants with
loan capital and technical assistance, and facilitated the use of an EZ
tax-exempt bond to finance a new car dealership. In addition, an EZ
stakeholder said that the EZ fostered job growth by requiring
recipients of EZ grants and loans to employ a certain number of EZ
residents.
Figure 22: Activities Implemented by the Upper Manhattan portion of the
New York EZ:
[See PDF for image]
Source: GAO (photo); GAO analysis of HUD data(charts).
[End of figure]
The types of activities most commonly implemented by the Bronx portion
of the New York EZ included workforce development, education, access to
capital, and human services (fig. 23). EZ stakeholders explained that
the EZ had funded several workforce training activities, such as a
program to train women to become childcare providers. However, several
stakeholders also said that as the program progressed more funds were
used to provide loans to EZ businesses, an activity that was felt to
provide the best return on investment.
Figure 23: Activities Implemented by the Bronx portion of the New York
EZ:
[See PDF for image]
Sources: GAO (photos); GAO analysis of HUD data (charts).
[End of figure]
Changes in Poverty, Unemployment, and Economic Growth:
Overall, the New York EZ saw poverty fall and economic growth improve,
but unemployment increase. The changes in the Upper Manhattan portion
of the EZ followed this pattern, but the Bronx portion of the EZ also
showed a decrease in one of the measures of economic growth, the number
of total jobs. Tables 21 and 22 show the changes in poverty,
unemployment, and economic growth in the EZ, the Upper Manhattan and
Bronx portions of the EZ, and the EZ comparison area. Table 21 also
includes data on the changes in other variables included in our models.
Indicators for the Upper Manhattan portion of the New York EZ were
mixed compared with the New York comparison area. The poverty rate in
the Upper Manhattan portion of the EZ fell between 1990 and 2000, while
the unemployment rate stayed the same. The New York comparison area
stayed about the same in poverty, and its unemployment rate rose. In
economic growth, between 1995 and 2004 the Upper Manhattan portion of
the EZ had the largest increase in total number of businesses and the
second-largest increase in jobs of any urban EZ. The comparison area
saw a slightly smaller increase in businesses and a larger increase in
jobs.
Like the Upper Manhattan portion of the New York EZ, the Bronx portion
showed mixed results relative to the New York comparison area. Its
poverty rate stayed the same between 1990 and 2000 as did the New York
comparison area. Between 1990 and 2000, it experienced a greater
increase in unemployment than the New York comparison area. In terms of
economic growth, the area did show an increase in the number of
businesses from 1995 to 2004, but its comparison area showed a larger
increase. However, in the same time period, the Bronx experienced a
slight decrease in the number of jobs, while the comparison area
experienced a large increase.
Table 21: Changes in Selected Census Variables Observed in the New York
EZ, the Bronx and Upper Manhattan (UM) Portions, and the EZ Comparison
Area (Comp.)
Poverty rate (%);
1990: Entire EZ: 42.68;
1990: Bronx: 44.2;
1990: UM: 42.38;
1990: Comp.: 42.5;
2000: Entire EZ: 38.62;
2000: Bronx: 41.59;
2000: UM: 38.02;
2000: Comp.: 41.75;
Percent change[A]: Entire EZ: -4.07[B];
Percent change[A]: Bronx: -2.61;
Percent change[A]: UM: -4.35[B];
Percent change[A]: Comp.: -0.75.
Unemployment rate (%);
1990: Entire EZ: 17.45;
1990: Bronx: 15.36;
1990: UM: 17.86;
1990: Comp.: 17.17;
2000: Entire EZ: 19.46;
2000: Bronx: 20.96;
2000: UM: 19.18;
2000: Comp.: 20.04;
Percent change[A]: Entire EZ: 2.00[B];
Percent change[A]: Bronx: 5.6[B];
Percent change[A]: UM: 1.32;
Percent change[A]: Comp.: 2.87[B].
Average household income;
1990: Entire EZ: $26,518;
1990: Bronx: $26,294;
1990: UM: $26,559;
1990: Comp.: $26,993;
2000: Entire EZ: $33,557;
2000: Bronx: $30,842;
2000: UM: $34,041;
2000: Comp.: $31,247;
Percent change[A]: Entire EZ: 26.54[B];
Percent change[A]: Bronx: 17.29[B];
Percent change[A]: UM: 28.17[B];
Percent change[A]: Comp.: 15.76[B].
Percentage of single female headed households with children;
1990: Entire EZ: 20.19;
1990: Bronx: 25.55;
1990: UM: 19.2;
1990: Comp.: 25.91;
2000: Entire EZ: 19.6;
2000: Bronx: 23.15;
2000: UM: 18.97;
2000: Comp.: 25.26;
Percent change[A]: Entire EZ: -0.59;
Percent change[A]: Bronx: -2.40[B];
Percent change[A]: UM: -0.23;
Percent change[A]: Comp.: -0.64.
Total population;
1990: Entire EZ: 199,983;
1990: Bronx: 34,266;
1990: UM: 165,717;
1990: Comp.: 638,776;
2000: Entire EZ: 219,324;
2000: Bronx: 36,886;
2000: UM: 182,438;
2000: Comp.: 672,826;
Percent change[A]: Entire EZ: 9.67;
Percent change[A]: Bronx: 7.65;
Percent change[A]: UM: 10.09;
Percent change[A]: Comp.: 5.33.
Total individuals per square mile;
1990: Entire EZ: 31,890;
1990: Bronx: 11,651;
1990: UM: 49,763;
1990: Comp.: 58,404;
2000: Entire EZ: 35,286;
2000: Bronx: 12,553;
2000: UM: 55,672;
2000: Comp.: 67,150;
Percent change[A]: Entire EZ: 10.65;
Percent change[A]: Bronx: 7.73;
Percent change[A]: UM: 11.88;
Percent change[A]: Comp.: 14.97.
Percentage of households that moved in the last 5 years;
1990: Entire EZ: 31.93;
1990: Bronx: 31.96;
1990: UM: 31.93;
1990: Comp.: 32.5;
2000: Entire EZ: 34.07;
2000: Bronx: 33.4;
2000: UM: 34.2;
2000: Comp.: 33.64;
Percent change[A]: Entire EZ: 2.14[B];
Percent change[A]: Bronx: 1.45;
Percent change[A]: UM: 2.28[B];
Percent change[A]: Comp.: 1.15[B].
Percentage of population of working age (16-64);
1990: Entire EZ: 59.67;
1990: Bronx: 60.09;
1990: UM: 59.59;
1990: Comp.: 58.97;
2000: Entire EZ: 61.3;
2000: Bronx: 60.36;
2000: UM: 61.49;
2000: Comp.: 58.49;
Percent change[A]: Entire EZ: 1.63[B];
Percent change[A]: Bronx: 0.28;
Percent change[A]: UM: 1.9[B];
Percent change[A]: Comp.: -0.48.
Percentage of population with a high school diploma (or equivalent);
1990: Entire EZ: 47.74;
1990: Bronx: 44.43;
1990: UM: 48.37;
1990: Comp.: 48.4;
2000: Entire EZ: 55.16;
2000: Bronx: 51.25;
2000: UM: 55.9;
2000: Comp.: 51.66;
Percent change[A]: Entire EZ: 7.42[B];
Percent change[A]: Bronx: 6.82[B];
Percent change[A]: UM: 7.53[B];
Percent change[A]: Comp.: 3.26[B].
Percentage of high school dropouts;
1990: Entire EZ: 19.85;
1990: Bronx: 18.12;
1990: UM: 20.19;
1990: Comp.: 20.18;
2000: Entire EZ: 15.59;
2000: Bronx: 17.75;
2000: UM: 15.2;
2000: Comp.: 17.7;
Percent change[A]: Entire EZ: -4.26[B];
Percent change[A]: Bronx: -0.37;
Percent change[A]: UM: - 4.99[B];
Percent change[A]: Comp.: -2.48[B].
Percentage of vacant housing units;
1990: Entire EZ: 8.81;
1990: Bronx: 3.24;
1990: UM: 9.77;
1990: Comp.: 3.99;
2000: Entire EZ: 11.09;
2000: Bronx: 7.35;
2000: UM: 11.73;
2000: Comp.: 5.99;
Percent change[A]: Entire EZ: 2.28[B];
Percent change[A]: Bronx: 4.11[ B];
Percent change[A]: UM: 1.96[B];
Percent change[A]: Comp.: 2.00[B].
Average owner occupied housing value;
1990: Entire EZ: $207,544;
1990: Bronx: $99,728;
1990: UM: $238,864;
1990: Comp.: $177,446;
2000: Entire EZ: $301,835;
2000: Bronx: $124,588;
2000: UM: $384,155;
2000: Comp.: $209,423;
Percent change[A]: Entire EZ: 45.43[B];
Percent change[A]: Bronx: 24.93[B];
Percent change[A]: UM: 60.83[B];
Percent change[A]: Comp.: 18.02[B].
Source: GAO analysis of Census data.
Note: There are 65 census tracts in the designated area and 160 in the
comparison area. Estimates for all census variables based on
percentages had 95 percent confidence intervals of plus or minus 5
percentage points or less. For the confidence intervals for average
household income and average owner-occupied housing estimates, see
appendix I.
[A] Differences in poverty rate, unemployment rate, and other variables
shown as percentages are based upon percentage point differences.
Differences for average household income, population, individuals per
square mile, and average housing value are calculated as percent
changes.
[B] The change in estimates from 1990 to 2000 is statistically
significant.
[End of table]
Table 22: Changes in Selected Economic Growth Variables Observed in the
New York EZ, the Bronx and Upper Manhattan (UM) Portions, and the EZ
Comparison Area (Comp.)
Number of businesses;
1995: Entire EZ: 5,415;
1995: Bronx: 1,738;
1995: UM: 3,677;
1995: Comp.: 8,294;
1999: Entire EZ: 6,203;
1999: Bronx: 1,750;
1999: UM: 4,453;
1999: Comp.: 9,400;
2004: Entire EZ: 6,691;
2004: Bronx: 1,840;
2004: UM: 4,851;
2004: Comp.: 10,719;
Percent change 1995- 2004[A]: Entire EZ: 23.6;
Percent change 1995-2004[A]: Bronx: 5.87;
Percent change 1995-2004[A]: UM: 31.9;
Percent change 1995-2004[A]: Comp.: 29.2.
Number of jobs;
1995: Entire EZ: 96,228;
1995: Bronx: 32,243;
1995: UM: 63,985;
1995: Comp.: 108,785;
1999: Entire EZ: 101,462;
1999: Bronx: 28,696;
1999: UM: 72,766;
1999: Comp.: 122,447;
2004: Entire EZ: 121,550;
2004: Bronx: 30,137;
2004: UM: 91,413;
2004: Comp.: 162,360;
Percent change 1995- 2004[A]: Entire EZ: 26.3;
Percent change 1995-2004[A]: Bronx: -6.53;
Percent change 1995-2004[A]: UM: 42.9;
Percent change 1995-2004[A]: Comp.: 49.3.
Source: GAO analysis of Claritas data.
Note: There are 65 census tracts in the designated area and 160 in the
comparison area. We excluded establishments that were not eligible for
program tax benefits, such as nonprofit and governmental organizations,
from our analysis of the change in the number of businesses. However,
we included jobs at those businesses in our analysis of the change in
the number of jobs.
[A] Differences for the number of businesses and the number of jobs are
calculated as percent changes.
[End of table]
Stakeholder Perceptions of the Factors Influencing Changes in Poverty,
Unemployment, and Economic Growth:
Many Upper Manhattan stakeholders we interviewed attributed the change
in poverty to the higher incomes from the jobs the EZ helped create. In
addition, several stakeholders discussed changes in the zone's
population, as low-income residents were displaced by increases in
property values and rental costs and employed residents with higher
incomes moved into the area. One stakeholder attributed some of the
decrease in poverty to welfare reform.
For unemployment, some stakeholders said that it was difficult to
improve the unemployment rate in the Upper Manhattan portion of the EZ
due to a lack of residents with needed job skills. Stakeholders also
noted that the change in the zone's population had affected
unemployment as well as poverty.
Several stakeholders observed that the Upper Manhattan EZ had helped
foster economic growth, citing its role in the creation of retail areas
and real estate development as examples. They also said that it had
helped small businesses by providing technical assistance, training,
and loans.
Bronx stakeholders noted that the program had helped to influence
poverty and unemployment through the resident employment requirements
it had for businesses that received loans and the center it had created
to match residents to jobs. However, some EZ stakeholders said that the
EZ had had trouble getting EZ residents jobs, since there were few
residents living in the Bronx portion of the EZ and many of them lacked
necessary job skills. Further affecting the changes in poverty and
unemployment, one stakeholder perceived that some original EZs
residents had relocated and that new residents had moved into the EZ.
Bronx stakeholders also said that access to capital for businesses
resulted in an increase in businesses moving into the EZ, but one
stakeholder noted that few jobs had been created. Another stakeholder
said that some zone businesses were downsizing as a result of changes
in the national economy.
Philadelphia-Camden Empowerment Zone:
Figure 24: Map of the Philadelphia-Camden EZ and Its Comparison Area:
[See PDF for image]
Source: GAO analysis of Census and HUD data.
[End of figure]
How the EZ Was Governed:
Although Philadelphia and Camden were designated as one EZ and their
original strategic plan envisioned a central board to oversee the EZ
operations, the Philadelphia and Camden portions operated completely
independently. Of the $100 million EZ grant, the Philadelphia portion
of the EZ received about $79 million, and the Camden portion of the EZ
received about $21 million.
The Philadelphia Empowerment Zone Office that oversees the EZ program
is part of the city government. The EZ created three subzones, each of
which had its own Community Trust Board to identify the needs of the
community, select activities to implement, and allocate resources to
the activities. However, the EZ did not create an overarching board to
oversee the three Philadelphia subzones. To select entities to
implement activities, the EZ issued requests for proposals. A panel of
community members, experts, and officials selected the best
applications, and the city approved the funding. The mayor required
that more than half of EZ/EC grant be spent on economic development
(including job training) and retained the right to veto decisions by
the Community Trust Boards, although this rarely happened.
The Camden portion of the EZ was managed by a nonprofit entity called
the Camden Empowerment Zone Corporation and was governed by a board,
which included residents as well as "block captains" who were residents
that had been elected to represent their communities, and other
individuals from the business, cultural, religious, and nonprofit
community.[Footnote 81] Under the board, there was a subcommittee
structure. The EZ issued requests for proposals to identify
organizations to implement the programs, which were reviewed by a
subcommittee and then forwarded to the full board for approval.
Activities the EZ Implemented:
Both portions of the Philadelphia-Camden EZ implemented more community
development activities than economic opportunity activities. Officials
from the Philadelphia portion of the EZ explained that, while they
spent more than half of their program grant funding on economic
development as required by their mayor, the number of community
development programs implemented was greater than the number of
economic opportunity programs. In addition, both portions of the EZ
received grant extensions until 2009.
Activities related to education, access to capital, and assisting
businesses were the most common in the Philadelphia portion of the
Philadelphia-Camden EZ (fig. 25). The two activities most often cited
by stakeholders in our interviews were the program to clean up vacant
lots and the community lending institutions. EZ stakeholders also noted
that the EZ had helped to organize the business community in each
neighborhood.
Figure 25: Activities Implemented by the Philadelphia Portion of the
Philadelphia-Camden EZ:
[See PDF for image]
Sources: GAO (photo); GAO analysis of HUD data (charts).
[End of figure]
The activities most frequently implemented in the Camden portion of the
EZ were housing, capacity building, access to capital, and
infrastructure (fig. 26). In addition, most EZ stakeholders described
the U.S.S. New Jersey, a new tourist attraction for which the EZ funded
a portion of the application and the visitors' center, as a success of
the EZ program. During our interviews, stakeholders also pointed out
activities such as a summer youth program, the refurbishing of a local
park, physical improvements to the streets and sidewalks, and an EZ
program designed to make loans and grants to EZ businesses.
Figure 26: Activities Implemented by the Camden Portion of the
Philadelphia-Camden EZ:
[See PDF for image]
Source: GAO (photo); GAO analysis of HUD data (charts).
[End of figure]
Changes in Poverty, Unemployment, and Economic Growth:
Overall, the Philadelphia-Camden EZ was the only urban EZ to see
positive changes in poverty, unemployment, and both measures of
economic growth. However, changes in the Philadelphia and Camden
portions varied, and were not always positive. Tables 23 and 24 show
the changes in poverty, unemployment, and economic growth in the EZ,
the Philadelphia and Camden portions of the EZ, and the EZ comparison
area. Table 23 also includes data on the changes in other variables
included in our models.
The Philadelphia portion of the EZ experienced decreases in poverty and
unemployment and little change or a decrease in our measures of
economic growth. Its declines in the poverty and unemployment rates
from 1990 to 2000 outpaced those in the Philadelphia-Camden comparison
area.[Footnote 82] In economic growth, the Philadelphia portion of the
EZ experienced little change in the number of businesses between 1995
and 2004, while its comparison area experienced a large increase. Both
the Philadelphia portion of the EZ and the EZ comparison area saw a
similar decline in the number of jobs available.
In contrast, the Camden portion of the Philadelphia-Camden EZ
experienced little change in poverty or the unemployment rate, but it
experienced positive changes in both measures of economic growth. Its
poverty rate from 1990 to 2000 stayed about the same, while its
comparison area decreased. Both the Camden portion of the EZ and the EZ
comparison area saw little change in the unemployment rate. For
economic growth, the Camden portion of the EZ had one of the highest
increases in the number of businesses of any EZ from 1995 to 2004,
slightly better than its comparison area. It also saw a large increase
in the number of jobs. In contrast, the EZ comparison area experienced
a large decline in number of jobs over the same time period.
Table 23: Changes in Selected Census Variables Observed in the
Philadelphia-Camden EZ, the Camden (Cam.) and Philadelphia (Phila.)
Portions, and the EZ Comparison Area (Comp.)
Poverty rate (%);
1990: Entire EZ: 50.14;
1990: Cam.: 43;
1990: Phila.: 52.1;
1990: Comp.: 43.07;
2000: Entire EZ: 42.98;
2000: Cam.: 40.55;
2000: Phila.: 43.68;
2000: Comp.: 37.97;
Percent change[ A]: Entire EZ: -7.16[B];
Percent change[ A]: Cam.: -2.45;
Percent change[ A]: Phila.: -8.42[B];
Percent change[ A]: Comp.: -5.1[B].
Unemployment rate (%);
1990: Entire EZ: 22.21;
1990: Cam.: 18.09;
1990: Phila.: 23.6;
1990: Comp.: 18.81;
2000: Entire EZ: 19.3;
2000: Cam.: 19.08;
2000: Phila.: 19.38;
2000: Comp.: 17.91;
Percent change[ A]: Entire EZ: -2.91[B];
Percent change[ A]: Cam.: 0.99;
Percent change[ A]: Phila.: -4.22[B];
Percent change[ A]: Comp.: -0.9.
Average household income;
1990: Entire EZ: $23,188;
1990: Cam.: $26,742;
1990: Phila.: $22,269;
1990: Comp.: $27,292;
2000: Entire EZ: $28,562;
2000: Cam.: $31,158;
2000: Phila.: $27,851;
2000: Comp.: $31,318;
Percent change[ A]: Entire EZ: 23.17[B];
Percent change[ A]: Cam.: 16.52[B];
Percent change[ A]: Phila.: 25.07[B];
Percent change[ A]: Comp.: 14.8[B].
Percentage of single female headed households with children;
1990: Entire EZ: 22.73;
1990: Cam.: 24.13;
1990: Phila.: 22.37;
1990: Comp.: 21.01;
2000: Entire EZ: 21.93;
2000: Cam.: 23.22;
2000: Phila.: 21.57;
2000: Comp.: 20.66;
Percent change[ A]: Entire EZ: -0.81;
Percent change[ A]: Cam.: -0.92;
Percent change[ A]: Phila.: -0.8;
Percent change[ A]: Comp.: -0.36.
Total population;
1990: Entire EZ: 52,440;
1990: Cam.: 13,332;
1990: Phila.: 39,108;
1990: Comp.: 38,520;
2000: Entire EZ: 45,725;
2000: Cam.: 12,749;
2000: Phila.: 32,976;
2000: Comp.: 35,827;
Percent change[ A]: Entire EZ: -12.81;
Percent change[ A]: Cam.: -4.37;
Percent change[ A]: Phila.: -15.68;
Percent change[ A]: Comp.: -6.99.
Total individuals per square mile;
1990: Entire EZ: 12,248;
1990: Cam.: 7,342;
1990: Phila.: 15,861;
1990: Comp.: 7,601;
2000: Entire EZ: 10,698;
2000: Cam.: 7,034;
2000: Phila.: 13,396;
2000: Comp.: 7,064;
Percent change[ A]: Entire EZ: -12.66;
Percent change[ A]: Cam.: -4.2;
Percent change[ A]: Phila.:
-15.54;
Percent change[ A]: Comp.: -7.06.
Percentage of households that moved in the last 5 years;
1990: Entire EZ: 34.71;
1990: Cam.: 41.93;
1990: Phila.: 32.25;
1990: Comp.: 40.69;
2000: Entire EZ: 34.05;
2000: Cam.: 44.42;
2000: Phila.: 30.04;
2000: Comp.: 44.05;
Percent change[ A]: Entire EZ: -0.66;
Percent change[ A]: Cam.: 2.49;
Percent change[ A]: Phila.: -2.21;
Percent change[ A]: Comp.: 3.36[B].
Percentage of population of working age (16-64);
1990: Entire EZ: 57.58;
1990: Cam.: 63.02;
1990: Phila.: 55.72;
1990: Comp.: 59.76;
2000: Entire EZ: 58.85;
2000: Cam.: 66.86;
2000: Phila.: 55.75;
2000: Comp.: 61.93;
Percent change[ A]: Entire EZ: 1.27;
Percent change[ A]: Cam.: 3.84[B];
Percent change[ A]: Phila.: 0.03;
Percent change[ A]: Comp.: 2.17.
Percentage of population with a high school diploma (or equivalent);
1990: Entire EZ: 42.79;
1990: Cam.: 44.51;
1990: Phila.: 42.21;
1990: Comp.: 54.04;
2000: Entire EZ: 51.82;
2000: Cam.: 50.43;
2000: Phila.: 52.38;
2000: Comp.: 63.19;
Percent change[ A]: Entire EZ: 9.04[B];
Percent change[ A]: Cam.: 5.92[B];
Percent change[ A]: Phila.: 10.17[B];
Percent change[ A]: Comp.: 9.15[B].
Percentage of high school dropouts;
1990: Entire EZ: 25.58;
1990: Cam.: 22.67;
1990: Phila.: 26.56;
1990: Comp.: 23.07;
2000: Entire EZ: 16.02;
2000: Cam.: 12.54;
2000: Phila.: 17.25;
2000: Comp.: 14.05;
Percent change[ A]: Entire EZ: - 9.56[B];
Percent change[ A]: Cam.: -10.13[B];
Percent change[ A]: Phila.: -9.3[B];
Percent change[ A]: Comp.: -9.02[B].
Percentage of vacant housing units;
1990: Entire EZ: 21.45;
1990: Cam.: 22.37;
1990: Phila.: 21.21;
1990: Comp.: 14.48;
2000: Entire EZ: 24.94;
2000: Cam.: 25.7;
2000: Phila.: 24.73;
2000: Comp.: 14.91;
Percent change[ A]: Entire EZ: 3.49[B];
Percent change[ A]: Cam.: 3.33[B];
Percent change[ A]: Phila.: 3.52[B];
Percent change[ A]: Comp.: 0.43.
Average owner occupied housing value;
1990: Entire EZ: $29,899;
1990: Cam.: $35,076;
1990: Phila.: $28,288;
1990: Comp.: $42,045;
2000: Entire EZ: $37,780;
2000: Cam.: $39,398;
2000: Phila.: $37,353;
2000: Comp.: $51,159;
Percent change[ A]: Entire EZ: 26.36[B];
Percent change[ A]: Cam.: 12.32[B];
Percent change[ A]: Phila.: 32.04[B];
Percent change[ A]: Comp.: 21.7[B].
Source: GAO analysis of Census data.
Note: There are 18 census tracts in the designated area and 16 in the
comparison area. Estimates for all census variables based on
percentages had 95 percent confidence intervals of plus or minus 5
percentage points or less. For the confidence intervals for average
household income and average owner-occupied housing estimates, see
appendix I.
[A] Differences in poverty rate, unemployment rate, and other variables
shown as percentages are based upon percentage point differences.
Differences for average household income, population, individuals per
square mile, and average housing value are calculated as percent
changes.
[B] The change in estimates from 1990 to 2000 is statistically
significant.
[End of table]
Table 24: Changes in Selected Economic Growth Variables Observed in the
Philadelphia-Camden EZ, the Camden (Cam.) and Philadelphia (Phila.)
Portions, and the EZ Comparison Area (Comp.)
Number of businesses;
1995: Entire EZ: 2,064;
1995: Cam.: 730;
1995: Phila.: 1,334;
1995: Comp.: 2,631;
1999: Entire EZ: 2,078;
1999: Cam.: 720;
1999: Phila.: 1,358;
1999: Comp.: 2,768;
2004: Entire EZ: 2,150;
2004: Cam.: 806;
2004: Phila.: 1,344;
2004: Comp.: 2,821;
Percent change 1995- 2004[A]: Entire EZ: 4.17;
Percent change 1995-2004[A]: Cam.: 10.41;
Percent change 1995-2004[A]: Phila.: 0.75;
Percent change 1995-2004[A]: Comp.: 7.22.
Number of jobs;
1995: Entire EZ: 35,867;
1995: Cam.: 14,430;
1995: Phila.: 21,437;
1995: Comp.: 55,071;
1999: Entire EZ: 35,904;
1999: Cam.: 16,702;
1999: Phila.: 19,202;
1999: Comp.: 53,702;
2004: Entire EZ: 36,789;
2004: Cam.: 21,032;
2004: Phila.: 15,757;
2004: Comp.: 39,720;
Percent change 1995-2004[A]: Entire EZ: 2.57;
Percent change 1995-2004[A]: Cam.: 45.75;
Percent change 1995-2004[A]: Phila.: -26.5;
Percent change 1995-2004[A]: Comp.: -27.87.
Source: GAO analysis of Claritas data.
Note: There are 18 census tracts in the designated area and 16 in the
comparison area. We excluded establishments that were not eligible for
program tax benefits, such as nonprofit and governmental organizations,
from our analysis of the change in the number of businesses. However,
we included jobs at those businesses in our analysis of the change in
the number of jobs.
[A] Differences for the number of businesses and the number of jobs are
calculated as percent changes.
[End of table]
Stakeholder Perceptions of the Factors Influencing Changes in Poverty,
Unemployment, and Economic Growth:
Stakeholders in the Philadelphia portion of the EZ mentioned the change
in the zone's population as having an effect on the changes in the
poverty and unemployment rates. They noted that the number of poor
households had decreased, in part due to the HOPE VI housing program,
which had demolished some area public housing, and in part because some
individuals who had obtained jobs had also moved out of the zone
neighborhoods. In addition, some stakeholders noted that the change in
welfare policy over the course of the EZ/EC program had an effect on
poverty and unemployment by moving former welfare recipients into jobs.
In describing the changes in economic growth, Philadelphia stakeholders
said that community lending institutions had provided loans to
businesses and that the vacant lot improvement program had helped
retain and attract businesses to the EZ. However, one stakeholder noted
that EZ lending had not resulted in many new jobs.
EZ stakeholders in the Camden portion of the EZ said that EZ programs
such as the Battleship New Jersey and housing and after school
initiatives may have contributed to the slight decrease in poverty
rate. They also noted that there had probably been a change in the EZ
population, since Camden's population was transient and individuals
often left the area when they found a job. In addition, some
stakeholders also mentioned changes in the national economy and a high
homeless population as challenges to improving the area's poverty and
unemployment rates.
Camden stakeholders noted that the EZ had influenced economic growth
through the improvements it had made to the physical appearance of
certain commercial corridors, as well as through its loans and grants
to small businesses. Stakeholders also said that the development of
market-rate housing had helped to increase the customer base for local
businesses. Finally, one stakeholder said that the state's expansion of
the light rail to Camden had influenced economic growth by improving
transportation to the area.
Cleveland Empowerment Zone:
Figure 27: Map of the Cleveland EZ and Its Comparison Area:
[See PDF for image]
Source: GAO analysis of Census and HUD data.
[End of figure]
HUD initially designated Cleveland as a Supplemental EZ, which provided
it with Economic Development Initiative grants and Section 108 Loan
Guarantees rather than EZ/EC grant funds. The area received full Round
I EZ status in 1998, and businesses in the EZ could claim the program
tax benefits starting in 2000.
How the EZ Was Governed:
The EZ was operated by the city of Cleveland Department of Economic
Development and included the Community Advisory Committee, an advisory
board made up of EZ residents, business owners, bank representatives,
and representatives from four local community development corporations.
Although the Community Advisory Committee was involved in the decision-
making process, the mayor and Cleveland City Council made all final
decisions about EZ funding.
Activities the EZ Implemented:
Cleveland EZ stakeholders said that they focused mainly on economic
development activities, largely because of the type of benefits they
received with the Supplemental EZ designation.[Footnote 83]
Stakeholders explained that most of their funds had been used to fund
loans to EZ businesses. They also said that the EZ also worked to build
the capacity of four community development corporations helping each of
them complete a major project in their neighborhood--for example, the
Quincy Place building in the Fairfax neighborhood (fig. 28). EZ
stakeholders noted that the EZ had implemented some successful job
training programs. The EZ received an extension of its grants and loan
guarantees through 2009.
Figure 28: Activity Implemented by the Cleveland EZ:
[See PDF for image]
Source: GAO.
Note: We were not able to determine specific types of activities the
Cleveland EZ implemented, because reliable data were not available.
[End of figure]
Changes in Poverty, Unemployment, and Economic Growth:
The Cleveland EZ experienced positive changes in poverty, unemployment,
and one measure of economic growth. From 1990 to 2000, Cleveland had
one of the sharpest reductions in both poverty and unemployment of the
urban EZs, and these changes outpaced those of its comparison area.
Between 1995 and 2004, the EZ experienced an increase in economic
growth as measured by the number of businesses, while its comparison
area experienced a decrease. However, the EZ experienced a decrease in
the number of jobs that was greater than the decrease experienced in
its comparison area. Tables 25 and 26 show the changes in poverty,
unemployment, and economic growth in the EZ and its comparison area.
Table 25 also includes data on the changes in other variables included
in our models.
Table 25: Changes in Selected Census Variables Observed in the
Cleveland EZ and Its Comparison Area:
Poverty rate (%);
1990: EZ: 46.85;
1990: Comparison: 39.41;
2000: EZ: 36.03;
2000: Comparison: 35.70;
Percent change[A]: EZ: -10.82[B];
Percent change[A]: Comparison: -3.71[B].
Unemployment rate (%);
1990: EZ: 25.44;
1990: Comparison: 20.63;
2000: EZ: 15.48;
2000: Comparison: 17.29;
Percent change[A]: EZ: -9.96[B];
Percent change[A]: Comparison: -3.34[B].
Average household income;
1990: EZ: $20,535;
1990: Comparison: $24,688;
2000: EZ: $28,781;
2000: Comparison: $30,311;
Percent change[A]: EZ: 40.16[B];
Percent change[A]: Comparison: 22.78[B].
Percentage of single female headed households with children;
1990: EZ: 19.07;
1990: Comparison: 23.24;
2000: EZ: 18.24;
2000: Comparison: 23.19;
Percent change[A]: EZ: -0.83;
Percent change[A]: Comparison: -0.05.
Total population;
1990: EZ: 50,724;
1990: Comparison: 153,578;
2000: EZ: 43,694;
2000: Comparison: 141,465;
Percent change[A]: EZ: -13.86;
Percent change[A]: Comparison: -7.89.
Total individuals per square mile;
1990: EZ: 8,319;
1990: Comparison: 7,231;
2000: EZ: 7,168;
2000: Comparison: 6,532;
Percent change[A]: EZ: -13.84;
Percent change[A]: Comparison:
-9.66.
Percentage of households that moved in the last 5 years;
1990: EZ: 36.13;
1990: Comparison: 36.04;
2000: EZ: 39.55;
2000: Comparison: 39.70;
Percent change[A]: EZ: 3.41[B];
Percent change[A]: Comparison: 3.66[B].
Percentage of population of working age (16-64);
1990: EZ: 55.01;
1990: Comparison: 56.80;
2000: EZ: 53.08;
2000: Comparison: 55.54;
Percent change[A]: EZ: -1.93;
Percent change[A]: Comparison: -1.26.
Percentage of population with a high school diploma (or equivalent);
1990: EZ: 47.14;
1990: Comparison: 54.84;
2000: EZ: 61.82;
2000: Comparison: 65.13;
Percent change[A]: EZ: 14.68[B];
Percent change[A]: Comparison: 10.29[B].
Percentage of high school dropouts;
1990: EZ: 18.35;
1990: Comparison: 14.83;
2000: EZ: 13.30;
2000: Comparison: 16.38;
Percent change[A]: EZ: -5.05[B];
Percent change[A]: Comparison: 1.55[B].
Percentage of vacant housing units;
1990: EZ: 14.68;
1990: Comparison: 14.70;
2000: EZ: 18.82;
2000: Comparison: 15.13;
Percent change[A]: EZ: 4.14[B];
Percent change[A]: Comparison: 0.43.
Average owner occupied housing value;
1990: EZ: $38,071;
1990: Comparison: $46,972;
2000: EZ: $75,186;
2000: Comparison: $70,164;
Percent change[A]: EZ: 97.49[B];
Percent change[A]: Comparison: 49.37[B].
Source: GAO analysis of Census data.
Note: There are 32 census tracts in the designated area and 68 in the
comparison area. Estimates for all census variables based on
percentages had 95 percent confidence intervals of plus or minus 5
percentage points or less. For the confidence intervals for average
household income and average owner-occupied housing estimates, see
appendix I.
[A] Differences in poverty rate, unemployment rate, and other variables
shown as percentages are based upon percentage point differences.
Differences for average household income, population, individuals per
square mile, and average housing value are calculated as percent
changes.
[B] The change in estimates from 1990 to 2000 is statistically
significant.
[End of table]
Table 26: Changes in Selected Economic Growth Variables Observed in the
Cleveland EZ and Its Comparison Area:
Number of businesses;
1995: EZ: 1,766;
1995: Comparison: 4,883;
1999: EZ: 2,067;
1999: Comparison: 4,889;
2004: EZ: 1,899;
2004: Comparison: 4,602;
Percent change 1995-2004[A]: EZ: 7.53;
Percent change 1995-2004[A]: Comparison: -5.75.
Number of jobs;
1995: EZ: 42,087;
1995: Comparison: 87,334;
1999: EZ: 58,679;
1999: Comparison: 102,996;
2004: EZ: 38,023;
2004: Comparison: 84,064;
Percent change 1995-2004[A]: EZ: - 9.66;
Percent change 1995-2004[A]: Comparison: -3.74.
Source: GAO analysis of Claritas data.
Note: There are 32 census tracts in the designated area and 68 in the
comparison area. We excluded establishments that were not eligible for
program tax benefits, such as nonprofit and governmental organizations,
from our analysis of the change in the number of businesses. However,
we included jobs at those businesses in our analysis of the change in
the number of jobs.
[A] Differences for the number of businesses and the number of jobs are
calculated as percent changes.
[End of table]
Stakeholder Perceptions of the Factors Influencing Changes in Poverty,
Unemployment, and Economic Growth:
When asked about factors that had affected the changes observed in the
Cleveland EZ, stakeholders said that factors related to poverty and
unemployment were intertwined. For example, EZ stakeholders felt that
EZ training programs had helped prepare residents for jobs, potentially
affecting both poverty and unemployment. Stakeholders also cited
changes in the zone population that had affected both factors, noting
that as residents obtained jobs, they left the zone, and that some
individuals with higher incomes had moved in, particularly in areas
where new housing had been built. EZ stakeholders also mentioned the
effect of general economic trends on poverty and unemployment.
In terms of economic growth, EZ stakeholders noted that the majority of
the businesses that had received EZ loans were still operating, that
the number of businesses had increased in some areas of the EZ, and
that these businesses had brought new jobs to the community. However,
some EZ stakeholders commented that the EZ's strict underwriting
standards made it less successful in helping new or less sophisticated
businesses. In addition, although the EZ had helped to create some
jobs, some stakeholders felt that the jobs created were going to new
residents rather than to original EZ residents. EZ staff also observed
that regional trends such as the overall loss of jobs in the city of
Cleveland had an effect on economic growth in the Cleveland EZ.
Los Angeles Empowerment Zone:
Figure 29: Map of the Los Angeles EZ and Its Comparison Area:
[See PDF for image]
Source: GAO analysis of Census and HUD data.
[End of figure]
HUD initially designated Los Angeles as a Supplemental EZ, which
provided it with Economic Development Initiative grants and Section 108
Loan Guarantees rather than EZ/EC grant funds. The area received full
Round I EZ status in 1998, and businesses in the EZ could claim the
program tax benefits starting in 2000.
How the EZ Was Governed:
The Los Angeles EZ created the Los Angeles Community Development Bank
to administer its EZ program. The Community Development Bank was a
"wholesale" rather than a conventional bank that entered into
partnerships with other economic development entities that were already
delivering services and operating loan programs. The EZ was autonomous
from the city and had its own board of directors predominately made up
of private sector members with one seat for a community representative.
However, EZ stakeholders told us that this seat usually remained
vacant. The board had a committee structure that included an audit
committee, credit committee, and venture capital committee. The EZ
board made all funding decisions. Any transaction over $1 million
required full board approval, but smaller amounts could be approved by
a committee of the board. In an effort to involve community members,
the city created an advisory council called EZ Oversight Committee,
which was filled through appointments made by the mayor and county
board of supervisors. However, EZ stakeholders said that the EZ
oversight committee never had a formal role in decision making or
oversight.
Activities the EZ Implemented:
Los Angeles EZ stakeholders said that they focused mainly on economic
development activities, largely due to the type of benefits they
received with the Supplemental EZ designation.[Footnote 84]
Stakeholders noted that the job requirements attached to loans from the
EZ and the six tax-exempt bonds had helped create jobs in the zone
(fig. 30). In addition to providing loans to several businesses, the EZ
helped fund a shopping complex and other development. One stakeholder
felt the EZ did not lend enough funds to small businesses and pointed
out that some of the loans to large businesses, such as a large dairy,
had defaulted. The EZ bank filed for bankruptcy in 2002 due to a high
level of loan defaults and the remaining funds were transferred to the
city of Los Angeles. The city of Los Angeles received an extension for
the grant and loan guarantees through 2009.
Figure 30: Activity Implemented by the Los Angeles EZ:
[See PDF for image]
Source: GAO.
[End of figure]
Changes in Poverty, Unemployment, and Economic Growth:
Unlike the other EZs, both poverty and unemployment in the Los Angeles
EZ largely remained the same between 1990 and 2000, and measures of
economic growth declined from 1995 to 2004. The comparison area also
saw little change in poverty and unemployment, but economic growth in
the comparison area increased in that time period. Tables 27 and 28
show the changes in poverty, unemployment, and economic growth in the
EZ and its comparison area. Table 27 also includes data on the changes
in other variables included in our models.
Table 27: Changes in Selected Census Variables Observed in the Los
Angeles EZ and Its Comparison Area:
Poverty rate (%);
1990: EZ: 40.24;
1990: Comparison: 31.52;
2000: EZ: 41.49;
2000: Comparison: 33.14;
Percent change[A]: EZ: 1.25;
Percent change[A]: Comparison: 1.61.
Unemployment rate (%);
1990: EZ: 18.39;
1990: Comparison: 15.07;
2000: EZ: 18.61;
2000: Comparison: 15.47;
Percent change[A]: EZ: 0.22;
Percent change[A]: Comparison: 0.40.
Average household income;
1990: EZ: $28,801;
1990: Comparison: $34,087;
2000: EZ: $32,631;
2000: Comparison: $37,843;
Percent change[A]: EZ: 13.30[B];
Percent change[A]: Comparison: 11.02[B].
Percentage of single female headed households with children;
1990: EZ: 18.32;
1990: Comparison: 18.64;
2000: EZ: 16.90;
2000: Comparison: 17.40;
Percent change[A]: EZ: -1.43[B];
Percent change[A]: Comparison: -1.24.
Total population;
1990: EZ: 211,365;
1990: Comparison: 221,657;
2000: EZ: 225,591;
2000: Comparison: 219,001;
Percent change[A]: EZ: 6.73;
Percent change[A]: Comparison: -1.20.
Total individuals per square mile;
1990: EZ: 11,082;
1990: Comparison: 12,918;
2000: EZ: 11,836;
2000: Comparison: 13,170;
Percent change[A]: EZ: 6.81;
Percent change[A]: Comparison: 1.95.
Percentage of households that moved in the last 5 years;
1990: EZ: 46.12;
1990: Comparison: 44.06;
2000: EZ: 43.53;
2000: Comparison: 41.20;
Percent change[A]: EZ: -2.59[B];
Percent change[A]: Comparison: -2.86[B].
Percentage of population of working age (16-64);
1990: EZ: 57.60;
1990: Comparison: 58.11;
2000: EZ: 57.53;
2000: Comparison: 57.28;
Percent change[A]: EZ: -0.06;
Percent change[A]: Comparison: -0.83.
Percentage of population with a high school diploma (or equivalent);
1990: EZ: 38.40;
1990: Comparison: 50.30;
2000: EZ: 37.46;
2000: Comparison: 49.53;
Percent change[A]: EZ: -0.94;
Percent change[A]: Comparison: -0.77.
Percentage of high school dropouts;
1990: EZ: 32.14;
1990: Comparison: 23.50;
2000: EZ: 23.09;
2000: Comparison: 17.04;
Percent change[A]: EZ: -9.04[B];
Percent change[A]: Comparison: -6.46[B].
Percentage of vacant housing units;
1990: EZ: 6.33;
1990: Comparison: 6.31;
2000: EZ: 9.65;
2000: Comparison: 8.11;
Percent change[A]: EZ: 3.31[B];
Percent change[A]: Comparison: 1.80[B].
Average owner occupied housing value;
1990: EZ: $141,665;
1990: Comparison: $160,090;
2000: EZ: $156,493;
2000: Comparison: $165,180;
Percent change[A]: EZ: 10.47[B];
Percent change[A]: Comparison: 3.18[B].
Source: GAO analysis of Census data.
Note: There are 41 census tracts in the designated area and 43 in the
comparison area. Estimates for all census variables based on
percentages had 95 percent confidence intervals of plus or minus 5
percentage points or less. For the confidence intervals for average
household income and average owner-occupied housing estimates, see
appendix I.
[A] Differences in poverty rate, unemployment rate, and other variables
shown as percentages are based upon percentage point differences.
Differences for average household income, population, individuals per
square mile, and average housing value are calculated as percent
changes.
[B] The change in estimates from 1990 to 2000 is statistically
significant.
[End of table]
Table 28: Changes in Selected Economic Growth Variables Observed in the
Los Angeles EZ and Its Comparison Area:
Number of businesses;
1995: EZ: 15,746;
1995: Comparison: 4,248;
1999: EZ: 12,315;
1999: Comparison: 3,986;
2004: EZ: 13,853;
2004: Comparison: 4,662;
Percent change 1995-2004[A]: EZ: -12.02;
Percent change 1995-2004[A]: Comparison: 9.75.
Number of jobs;
1995: EZ: 165,457;
1995: Comparison: 52,973;
1999: EZ: 153,340;
1999: Comparison: 55,627;
2004: EZ: 156,793;
2004: Comparison: 66,783;
Percent change 1995- 2004[A]: EZ: -5.24;
Percent change 1995-2004[A]: Comparison: 26.07.
Source: GAO analysis of Claritas data.
Note: There are 41 census tracts in the designated area and 43 in the
comparison area. We excluded establishments that were not eligible for
program tax benefits, such as nonprofit and governmental organizations,
from our analysis of the change in the number of businesses. However,
we included jobs at those businesses in our analysis of the change in
the number of jobs.
[A] Differences for the number of businesses and the number of jobs are
calculated as percent changes.
[End of table]
Stakeholder Perceptions of the Factors Influencing Changes in Poverty,
Unemployment, and Economic Growth:
Los Angeles EZ stakeholders we interviewed suggested that the EZ was
not as likely as other factors to have effected changes in poverty and
unemployment because they could not address those issues directly with
the benefits they received. One stakeholder did not believe that the EZ
had met its goals of increasing job training and employment
opportunities, but other stakeholders believed that it had helped to
assist and retain businesses and redevelop the area. Stakeholders
mentioned external factors that influenced changes in poverty and
unemployment, such as shifts in demographics with the influx of new
immigrants and the outmigration of EZ residents as they obtained jobs
or their incomes increased. In addition, some said that the EZ's high
concentration of homeless individuals and the lack of available public
transportation in the EZ could be additional reasons that poverty and
unemployment rates did not improve.
One stakeholder noted that, because the original strategic plan was
designed to focus on social services, the census tracts chosen were not
well-suited for economic development. However, stakeholders mentioned
that the EZ had helped to stabilize the area, since a large number of
businesses had been leaving the Los Angeles area for advantages offered
in other locations.
Kentucky Highlands Empowerment Zone:
Figure 31: Map of the Kentucky Highlands EZ:
[See PDF for image]
Source: GAO analysis of USDA data.
[End of figure]
How the EZ Was Governed:
The EZ was managed by the Kentucky Highlands Investment Corporation, a
nonprofit that had been operating in the area for over 25 years. There
were subzone boards in each of the three counties that became separate
nonprofit entities and had funds to hire staff, manage the board, and
conduct fiscal oversight. An overarching steering committee, which
included representatives of the subzone boards, directed the EZ's
activities in the entire zone by providing oversight, making financial
decisions, and implementing certain activities, such as the revolving
loan fund. EZ stakeholders suggested that most of the decision making
occurred at the subzone level, although the steering committee gave
final approval to all projects. The EZ used about half of the available
funds, and the rest was distributed among the three subzones.
Activities the EZ Implemented:
Almost two-thirds of the EZ's activities involved community
development. Initiatives involving business development and job
training; resources for communities, youth and families; and education
were the most common activities (fig. 32). In addition, each county
implemented different types of activities from the strategic plan. For
example, stakeholders from the Clinton County subzone funded a library,
a learning center, and health care initiatives--such as helping to fund
the expansion of an emergency room and surgical wing at the local
hospital--and attracted businesses from the houseboat industry.
Stakeholders from the Jackson County subzone said that they had
provided funds for a community center, which housed vocational training
classes and a community theatre. Stakeholders from the Wayne County
subzone said that they completed a water infrastructure project that
they said was critical to attracting businesses and brought in jobs in
the houseboat industry. The Kentucky Highlands EZ received a grant
extension until 2009.
Figure 32: Activities Implemented by the Kentucky Highlands EZ:
[See PDF for image]
Sources: GAO (photo); GAO analysis of USDA data (charts).
[End of figure]
Changes in Poverty, Unemployment, and Economic Growth:
Not only did the Kentucky Highlands EZ experience positive changes in
all indicators, it experienced the largest decrease in unemployment
between 1990 and 2000 and the largest increases in the number of
businesses and jobs between 1995 and 2004 of any rural EZ. Tables 29
and 30 show the changes in poverty, unemployment, and economic growth
in the EZ. Table 29 also includes data on the changes in other
variables included in our models of the urban EZs.
Table 29: Changes in Selected Census Variables Observed in the Kentucky
Highlands EZ:
Poverty rate (%);
1990: 37.88;
2000: 27.76;
Percent change[A]: - 10.12[B].
Unemployment rate (%);
1990: 9.76;
2000: 7.75;
Percent change[A]: - 2.01[B].
Average household income;
1990: $23,304;
2000: $31,064;
Percent change[A]: 33.3[B].
Percentage of single female headed households with children;
1990: 4.64;
2000: 5.73;
Percent change[A]: 1.09.
Total population;
1990: 27,212;
2000: 30,464;
Percent change[A]: 11.95.
Total individuals per square mile;
1990: 36;
2000: 40;
Percent change[A]: 11.96.
Percentage of households that moved in the last 5 years;
1990: 32.46;
2000: 31.45;
Percent change[A]: -1.01.
Percentage of population of working age (16-64);
1990: 59.04;
2000: 61.98;
Percent change[A]: 2.93[B].
Percentage of population with a high school diploma (or equivalent);
1990: 42.82;
2000: 55.1;
Percent change[A]: 12.28[B].
Percentage of high school dropouts;
1990: 15.80;
2000: 16.47;
Percent change[A]: 0.67.
Percentage of vacant housing units;
1990: 16.74;
2000: 18.97;
Percent change[A]: 2.23[B].
Average owner occupied housing value;
1990: $43,392;
2000: $65,815;
Percent change[A]: 51.68[B].
Source: GAO analysis of Census data.
Note: There are seven census tracts in the designated area;
we did not use comparison areas for rural EZs. For more information on
our methodology, see appendix I. Estimates for all census variables
based on percentages had 95 percent confidence intervals of plus or
minus 5 percentage points or less. For the confidence intervals for
average household income and average owner-occupied housing estimates,
see appendix I.
[A] Differences in poverty rate, unemployment rate, and other variables
shown as percentages are based upon percentage point differences.
Differences for average household income, population, individuals per
square mile, and average housing value are calculated as percent
changes.
[B] The change in estimates from 1990 to 2000 is statistically
significant.
[End of table]
Table 30: Changes in Selected Economic Growth Variables Observed in the
Kentucky Highlands EZ:
Number of businesses;
1995: 609;
1999: 691;
2004: 810;
Percent change: 1995-2004[A]: 33.
Number of jobs;
1995: 5,327;
1999: 7,691;
2004: 8,941;
Percent change: 1995-2004[A]: 67.84.
Source: GAO analysis of Claritas data.
Note: There are seven census tracts in the designated area;
we did not use comparison areas for rural EZs. For more information on
our methodology, see appendix I. We excluded establishments that were
not eligible for program tax benefits, such as nonprofit and
governmental organizations, from our analysis of the change in the
number of businesses. However, we included jobs at those businesses in
our analysis of the change in the number of jobs.
[A] Differences for the number of businesses and the number of jobs are
calculated as percent changes.
[End of table]
Stakeholder Perceptions of the Factors Influencing Changes in Poverty,
Unemployment, and Economic Growth:
In our interviews, stakeholders said that changes in the poverty rate
may have been the result of new jobs created by EZ projects, many of
which offered benefits such as health insurance that helped to
stabilize families. However, EZ staff and other stakeholders
acknowledged that external factors, such as welfare reform and general
economic trends, also could have contributed to poverty reduction.
Stakeholders also attributed the reduction in unemployment to the job
creation efforts, saying that the EZ had helped stabilize the area when
a key employer, a sewing plant, closed prior to designation.
In terms of economic growth, stakeholders felt that the EZ had played a
role in the change in economic growth, citing infrastructure
improvements and zone workshops on how to start new businesses. In
addition, some EZ stakeholders noted that the economic growth that had
occurred was due in part to the EZ program tax benefits, although not
all stakeholders agreed.
Mid-Delta Mississippi Empowerment Zone:
Figure 33: Map of the Mid-Delta EZ:
[See PDF for image]
Source: GAO analysis of USDA data.
[End of figure]
How the EZ Was Governed:
The nonprofit Mid-Delta Empowerment Zone Alliance was created to manage
the EZ. It included a board that consisted of city and county elected
officials and representatives from community organizations, plus
subzones boards in each of the six counties. Most decisions were made
by the committees and brought to the full board for approval. However,
several stakeholders noted that this formal process was not always
followed and that some board decisions appeared to favor large
businesses over community groups.
Activities the EZ Implemented:
Most of the activities the Mid-Delta EZ implemented were related to
community development. Initiatives involving business development and
job training; resources for communities, youth, and families;
education; and housing accounted for the bulk of the activities (fig.
34). In our interviews, stakeholders noted that EZ funds were used for
a variety of community-and family-oriented projects. These included
helping a small municipality purchase needed police and fire equipment,
partially funding a mortgage assistance program that moved 20 people
into houses, and implementing some health care programs, such as a
substance abuse treatment center for women. Also, one business in the
Mid-Delta EZ used a program tax-exempt bond. In addition, stakeholders
mentioned that EZ funds were used to attract major corporations, such
as an automobile parts manufacturer and retail distribution center.
However, several stakeholders also noted that some programs were
unsuccessful, and an EZ official said that approximately 16 projects
were under review for possible misuse of funds. The Mid-Delta EZ
received a grant extension until 2009.
Figure 34: Activities Implemented by the Mid-Delta EZ:
[See PDF for image]
Sources: GAO (photo); GAO analysis of USDA data (charts).
[End of figure] - graphic text:
Changes in Poverty, Unemployment, and Economic Growth:
The Mid-Delta EZ saw positive changes in two indicators: poverty and
economic growth. Between 1990 and 2000, the poverty rate in the Mid-
Delta EZ decreased more than any rural EZ. However, the Mid-Delta EZ
experienced a small increase in unemployment over that time period. For
economic growth, the EZ saw an increase in both measures from 1995 to
2004, but the changes were significantly less than in the other two
rural EZs. Tables 31 and 32 show the changes in poverty, unemployment,
and economic growth in the EZ. Table 31 also includes data on the
changes in other variables included in our models of the urban EZs.
Table 31: Changes in Selected Census Variables Observed in the Mid-
Delta EZ:
Poverty rate (%);
1990: 46.35;
2000: 35.67;
Percent change[A]: - 10.68[B].
Unemployment rate (%);
1990: 14.31;
2000: 17.38;
Percent change[A]: 3.07[B].
Average household income;
1990: $25,872;
2000: $3,559;
Percent change[A]: 37.44[B].
Percentage of single female headed households with children;
1990: 16.51;
2000: 17.31;
Percent change[A]: 0.80.
Total population;
1990: 29,494;
2000: 29,770;
Percent change[A]: 0.94.
Total individuals per square mile;
1990: 30.06;
2000: 30.34;
Percent change[A]: 0.95.
Percentage of households that moved in the last 5 years;
1990: 34.75;
2000: 31.00;
Percent change[A]: -3.75[B].
Percentage of population of working age (16-64);
1990: 51.71;
2000: 57.36;
Percent change[A]: 5.65[B].
Percentage of population with a high school diploma (or equivalent);
1990: 49.09;
2000: 60.52;
Percent change[A]: 11.43[B].
Percentage of high school dropouts;
1990: 14.66;
2000: 12.62;
Percent change[A]: -2.04[B].
Percentage of vacant housing units;
1990: 8.08;
2000: 9.41;
Percent change[A]: 1.33.
Average owner occupied housing value;
1990: $50,061;
2000: $66,872;
Percent change[A]: 33.58[B].
Source: GAO analysis of Census data.
Note: There are eight census tracts in the designated area;
we did not use comparison areas for rural EZs. For more information on
our methodology, see appendix I. Estimates for all census variables
based on percentages had 95 percent confidence intervals of plus or
minus 5 percentage points or less. For the confidence intervals for
average household income and average owner-occupied housing estimates,
see appendix I.
[A] Differences in poverty rate, unemployment rate, and other variables
shown as percentages are based upon percentage point differences.
Differences for average household income, population, individuals per
square mile, and average housing value are calculated as percent
changes.
[B] The change in estimates from 1990 to 2000 is statistically
significant.
[End of table]
Table 32: Changes in Selected Economic Growth Variables Observed in the
Mid-Delta EZ:
Number of businesses;
1995: 634;
1999: 838;
2004: 733;
Percent change 1995-2004[A]: 15.62.
Number of jobs;
1995: 9,415;
1999: 12,694;
2004: 9,884;
Percent change 1995-2004[A]: 4.98.
Source: GAO analysis of Claritas data.
Note: There are eight census tracts in the designated area;
we did not use comparison areas for rural EZs. For more information on
our methodology, see appendix I. We excluded establishments that were
not eligible for program tax benefits, such as nonprofit and
governmental organizations, from our analysis of the change in the
number of businesses. However, we included jobs at those businesses in
our analysis of the change in the number of jobs.
[A] Differences for the number of businesses and the number of jobs are
calculated as percent changes.
[End of table]
Stakeholder Perceptions of the Factors Influencing Changes in Poverty,
Unemployment, and Economic Growth:
In our interviews, EZ stakeholders credited the EZ with improving
poverty and unemployment by helping bring in higher paying jobs with
benefits. However, some suggested that increases in unemployment were
not the same for each county, and added that the Mississippi Delta
region overall had a low educational level that limited some residents'
ability to participate in the workforce.
In terms of economic growth, EZ stakeholders noted the EZ's efforts to
attract large businesses through grants and loans had brought in new
companies that provided jobs with relatively high wages and benefits.
One stakeholder said that the EZ's efforts helped to stabilize the area
during a period when several large manufacturing plants relocated to
other countries.
Rio Grande Valley, Texas Empowerment Zone:
Figure 35: Map of the Rio Grande Valley EZ:
[See PDF for image]
Source: GAO analysis of USDA data.
[End of figure]
How the EZ Was Governed:
The EZ was managed by the nonprofit Rio Grande Valley Empowerment Zone,
which was created specifically for the EZ. EZ stakeholders explained
that the EZ board included an executive committee of members
representing each of the four counties in the EZ and four subzone
boards, one for each county. Both the EZ and the subzone boards were
involved in selecting activities for implementation. Subzone members
reviewed proposals and then forwarded their recommendations to a
project review committee, which reviewed the activities for feasibility
and sustainability. Once this process was complete, the activity was
sent to the full board for approval.
Activities the EZ Implemented:
The Rio Grande Valley EZ implemented mostly community development
activities, most commonly education, public infrastructure, and
business development and job training initiatives (fig. 36). In our
interviews, stakeholders mentioned that the EZ had provided funds to
several projects sponsored by the school districts, focusing its
funding on improving the well-being of children. For example, the EZ
provided computers and technical assistance to local Boys and Girls
Clubs. EZ stakeholders also cited several infrastructure improvements,
such as a water plant, a water tower, the expansion of a fire
department facility, and the creation of community centers. In terms of
economic opportunity initiatives, three counties provided loans through
a revolving loan program, and one county created a small business
incubator. In addition, the EZ provided funding to a community-based
organization to provide low-skilled workers with training for jobs in
the health care field.
Figure 36: Activities Implemented by the Rio Grande Valley EZ:
[See PDF for image]
Sources: GAO (photo); GAO analysis of USDA data (charts).
[End of figure]
Changes in Poverty, Unemployment, and Economic Growth:
The Rio Grande Valley EZ experienced positive changes in poverty and
economic growth. The EZ had the highest poverty and unemployment rates
in 1990 of any of the rural EZs. Between 1990 and 2000, the EZ
experienced a decrease in poverty;
however, the unemployment rate did not show a significant change. For
economic growth, the EZ experienced an increase in the number of
businesses and jobs between 1995 and 2004. Tables 33 and 34 show the
changes in poverty, unemployment, and economic growth in the EZ. Table
33 also includes data on the changes in other variables included in our
models of the urban EZs.
Table 33: Changes in Selected Census Variables Observed in the Rio
Grande Valley EZ:
Poverty rate (%);
1990: 49.65;
2000: 42.34;
Percent change[A]: - 7.31[B].
Unemployment rate (%);
1990: 14.94;
2000: 13.82;
Percent change[A]: - 1.12.
Average household income;
1990: $25,093;
2000: $32,763;
Percent change[A]: 30.57[B].
Percentage of single female headed households with children;
1990: 9.44;
2000: 10.38;
Percent change[A]: 0.95.
Total population;
1990: 29,817;
2000: 37,044;
Percent change[A]: 24.24.
Total individuals per square mile;
1990: 131;
2000: 159;
Percent change[A]: 21.82.
Percentage of households that moved in the last 5 years;
1990: 30.11;
2000: 34.41;
Percent change[A]: 4.29[B].
Percentage of population of working age (16-64);
1990: 55.47;
2000: 55.48;
Percent change[A]: 0.01.
Percentage of population with a high school diploma (or equivalent);
1990: 41.51;
2000: 46.80;
Percent change[A]: 5.29[B].
Percentage of high school dropouts;
1990: 20.1;
2000: 16.38;
Percent change[A]: -3.72[B].
Percentage of vacant housing units;
1990: 14.37;
2000: 16.80;
Percent change[A]: 2.43[B].
Average owner occupied housing value;
1990: $46,100;
2000: $61,450;
Percent change[A]: 33.3[B].
Source: GAO analysis of Census data.
Note: There are six census tracts in the designated area;
we did not use comparison areas for rural EZs. For more information on
our methodology, see appendix I. Estimates for all census variables
based on percentages had 95 percent confidence intervals of plus or
minus 5 percentage points or less. For the confidence intervals for
average household income and average owner-occupied housing estimates,
see appendix I.
[A] Differences in poverty rate, unemployment rate, and other variables
shown as percentages are based upon percentage point differences.
Differences for average household income, population, individuals per
square mile, and average housing value are calculated as percent
changes.
[B] The change in estimates from 1990 to 2000 is statistically
significant.
[End of table]
Table 34: Changes in Selected Economic Growth Variables Observed in the
Rio Grande Valley EZ:
Number of businesses;
1995: 551;
1999: 688;
2004: 710;
Percent change 1995-2004[A]: 28.86.
Number of jobs;
1995: 6,025;
1999: 6,548;
2004: 7,427;
Percent change 1995-2004[A]: 23.27.
Source: GAO analysis of Claritas data.
Note: There are six census tracts in the designated area;
we did not use comparison areas for rural EZs. For more information on
our methodology, see appendix I. We excluded establishments that were
not eligible for program tax benefits, such as nonprofit and
governmental organizations, from our analysis of the change in the
number of businesses. However, we included jobs at those businesses in
our analysis of the change in the number of jobs.
[A] Differences for the number of businesses and the number of jobs are
calculated as percent changes.
[End of table]
Stakeholder Perceptions of the Factors Influencing Changes in Poverty,
Unemployment, and Economic Growth:
In our interviews, EZ stakeholders suggested that EZ programs may have
helped to improve residents' quality of life through programs that
provided employment opportunities or taught residents skills to improve
their income. One stakeholder mentioned a health clinic that was
partially funded by the EZ that had helped to provide additional jobs
in the area. However, another stakeholder mentioned the large number of
migrant farm workers in the area make tracking these changes difficult.
In terms of changes in economic growth, EZ stakeholders noted the
initial lack of public infrastructure in the zone and mentioned that
the EZ infrastructure development helped to prepare the area for future
economic development and growth. Stakeholders credited EZ activities
with helping to attract tourism to areas of the EZ and said that
efforts to help businesses through revolving loan funds in some of the
EZ counties had fostered economic growth. Some EZ stakeholders added
that some of the growth of cities surrounding the EZ also might be due
to an increase in trade across the border with Mexico.
Providence, Rhode Island Enterprise Community:
Figure 37: Map of the Providence EC:
[See PDF for image]
Source: GAO analysis of HUD data.
[End of figure]
How the EC Was Governed:
The Providence EC was managed by the nonprofit Providence Plan and
included a board called the Oversight Committee that included EC
residents from each neighborhood, small business owners, and two city
council members. Unlike many of the EZs, the EC allocated most of its
grant funds during the strategic planning process, so there were few
funds for the board to approve during the course of the program.
However, in those cases, the board reviewed background information on
the organizations that requested funds, discussed the applicants at
their meetings, and then chose the applicants to fund. The board also
reviewed the routine reporting by subgrantees and participated in site
visits.
Activities the EC Implemented:
The Providence EC implemented four types of activities--workforce
development, assistance to businesses, access to capital, and human
services--most of which were related to economic opportunity (fig. 38).
According to stakeholders, the EC's largest subgrantee was a community
development corporation, which implemented workforce training, a summer
youth program, and business development programs. It also funded the
renovation and development of some small business incubators that
offered space and technical assistance to new small businesses. In
addition, stakeholders noted that the EC implemented some Community
Opportunity Zones, which were designed to provide integrated access to
education, health, and social services for families with children. An
EC official explained that most of the EC funds were spent in the first
5 years of the program and that all EC funds had been spent by June
2004.
Figure 38: Activities Implemented by the Providence EC:
[See PDF for image]
Sources: GAO (photo); GAO analysis of HUD data (charts).
[End of figure]
Changes in Poverty, Unemployment, and Economic Growth:
In the Providence EC, poverty and unemployment stayed about the same
from 1990 to 2000 and the number of businesses and jobs decreased
between 1995 and 2004.[Footnote 85] Tables 35 and 36 show the changes
in poverty, unemployment, and economic growth in the EC. Table 35 also
includes data on the changes in other variables included in our models
of the urban EZs.
Table 35: Changes in Selected Census Variables Observed in the
Providence EC:
Poverty rate (%);
1990: 35.36;
2000: 37.58;
Percent change[A]: 2.22.
Unemployment rate (%);
1990: 13.63;
2000: 11.90;
Percent change[A]: - 1.73.
Average household income;
1990: $28,593;
2000: $32,616;
Percent change[A]: 14.07[B].
Percentage of single female headed households with children;
1990: 21.64;
2000: 21.96;
Percent change[A]: 0.31.
Total population;
1990: 48,789;
2000: 53,845;
Percent change[A]: 10.36.
Total individuals per square mile;
1990: 9,179;
2000: 10,141;
Percent change[A]: 10.48.
Percentage of households that moved in the last 5 years;
1990: 52.23;
2000: 50.85;
Percent change[A]: -1.38.
Percentage of population of working age (16-64);
1990: 55.45;
2000: 58.00;
Percent change[A]: 2.55[B].
Percentage of population with a high school diploma (or equivalent);
1990: 48.69;
2000: 53.51;
Percent change[A]: 4.82[B].
Percentage of high school dropouts;
1990: 26.17;
2000: 19.16;
Percent change[A]: -7.01[B].
Percentage of vacant housing units;
1990: 14.55;
2000: 10.43;
Percent change[A]: -4.13[B].
Average owner occupied housing value;
1990: $124,339;
2000: $116,698;
Percent change[A]: -6.15[B].
Source: GAO analysis of Census data.
Note: There are 13 census tracts in the designated area;
we did not use comparison areas for individual ECs. For more
information on our methodology, see appendix I. Estimates for all
census variables based on percentages had 95 percent confidence
intervals of plus or minus 5 percentage points or less. For the
confidence intervals for average household income and average owner-
occupied housing estimates, see appendix I.
[A] Differences in poverty rate, unemployment rate, and other variables
shown as percentages are based upon percentage point differences.
Differences for average household income, population, individuals per
square mile, and average housing value are calculated as percent
changes.
[B] The change in estimates from 1990 to 2000 is statistically
significant.
[End of table]
Table 36: Changes in Selected Economic Growth Variables Observed in the
Providence EC:
Number of businesses;
1995: 2,714;
1999: 2,426;
2004: 2,200;
Percent change 1995-2004[A]: -18.94.
Number of jobs;
1995: 37,724;
1999: 34,763;
2004: 33,545;
Percent change 1995-2004[A]: -11.08.
Source: GAO analysis of Claritas data.
Note: There are 13 census tracts in the designated area;
we did not use comparison areas for individual ECs. For more
information on our methodology, see appendix I. We excluded
establishments that were not eligible for the program tax benefit, such
as nonprofit and governmental organizations, from our analysis of the
change in the number of businesses. However, we included jobs at those
businesses in our analysis of the change in the number of jobs.
[A] Differences for the number of businesses and the number of jobs are
calculated as percent changes.
[End of table]
Stakeholder Perceptions of the Factors Influencing Changes in Poverty,
Unemployment, and Economic Growth:
In our interviews, stakeholders cited several factors that they thought
had influenced changes in poverty in the EC, including the increased
costs of housing and utilities, growth in the foreign-born population,
the loss of manufacturing jobs, and changes to welfare reform. In
addition, one EC stakeholder noted that many residents were working but
not earning high enough incomes to move them out of poverty. Although
the EC experienced a decline in unemployment, stakeholders noted that
barriers to employment remained, including limited job and language
skills and records of incarceration.
With respect to economic growth, EC stakeholders said that businesses
began working together as a result of the EC. However, one stakeholder
suggested that the EC was influenced by the slow Rhode Island economy
and that the EC should have done more to foster economic growth.
Fayette-Haywood, Tennessee Enterprise Community:
Figure 39: Map of the Fayette-Haywood EC:
[See PDF for image]
Source: GAO analysis of USDA data.
[End of figure]
How the EC Was Governed:
Three entities shared responsibility for operating the Fayette-Haywood
EC. Haywood County administered the EC grant funds, a local development
district was in charge of the EC's reporting to USDA, and a board that
represented both counties in the EC made funding decisions.[Footnote
86] To make decisions about what activities to fund, each county held
separate meetings to discuss projects that pertained to their community
and sought final approval at a meeting of the full board. EC
stakeholders mentioned that USDA officials played an active role in the
EC and attended most board meetings.
Activities the EC Implemented:
The majority of the activities implemented by the Fayette-Haywood EC
were in community development, mainly in the areas of health care and
housing (fig. 40). In our interviews, stakeholders mentioned benefits
of the EC that included health care-related activities, such as
recruiting doctors and nurses to the area and the reopening a medical
clinic that had been closed for 10 years. Stakeholders also noted that
new housing projects had been also built with the help of EC funds. The
EC also conducted activities related to public infrastructure, such as
helping to build a YMCA in Haywood County and other community centers
in both counties. The EC did not request a grant extension, because it
had used all of its grant funds.
Figure 40: Activities Implemented by the Fayette-Haywood EC:
[See PDF for image]
Source: GAO (photo); GAO analysis of USDA data (charts).
[End of figure]
Changes in Poverty, Unemployment, and Economic Growth:
The Fayette-Haywood EC experienced positive changes in poverty and
unemployment between 1990 and 2000 and both measures of economic growth
between 1995 and 2004. Tables 37 and 38 show the changes in poverty,
unemployment, and economic growth in the EC. Table 37 also includes
data on the changes in other variables included in our models of the
urban EZs.
Table 37: Changes in Selected Census Variables Observed in the Fayette-
Haywood EC:
Poverty rate (%);
1990: 28.37;
2000: 19.30;
Percent change[A]: - 9.07[B].
Unemployment rate (%);
1990: 9.75;
2000: 7.02;
Percent change[A]: - 2.73[B].
Average household income;
1990: $32,560;
2000: $45,353;
Percent change[A]: 39.29[B].
Percentage of single female headed households with children;
1990: 10.97;
2000: 11.33;
Percent change[A]: 0.36.
Total population;
1990: 29,080;
2000: 30,551;
Percent change[A]: 5.06.
Total individuals per square mile;
1990: 44;
2000: 46;
Percent change[A]: 5.07.
Percentage of households that moved in the last 5 years;
1990: 34.49;
2000: 36.48;
Percent change[A]: 1.99.
Percentage of population of working age (16-64);
1990: 55.39;
2000: 58.82;
Percent change[A]: 3.43[B].
Percentage of population with a high school diploma (or equivalent);
1990: 53.57;
2000: 65.81;
Percent change[A]: 12.24[B].
Percentage of high school dropouts;
1990: 18.26;
2000: 12.77;
Percent change[A]: -5.49[B].
Percentage of vacant housing units;
1990: 6.46;
2000: 6.85;
Percent change[A]: 0.39.
Average owner occupied housing value;
1990: $68,945;
2000: $103,619;
Percent change[A]: 50.29[B].
Source: GAO analysis of Census data.
Note: There are eight census tracts in the designated area;
we did not use comparison areas for individual ECs. For more
information on our methodology, see appendix I. Differences in poverty
rate, unemployment rate, and other variables shown as percentages are
based upon percentage point differences. Differences for average
household income, population, individuals per square mile, and average
housing value are calculated as percent changes. Estimates for all
census variables based on percentages had 95 percent confidence
intervals of plus or minus 5 percentage points or less. For the
confidence intervals for average household income and average owner-
occupied housing estimates, see appendix I.
[End of table]
Table 38: Changes in Selected Economic Growth Variables Observed in the
Fayette-Haywood EC:
Number of businesses;
1995: 892;
1999: 921;
2004: 1,128;
Percent change 1995-2004[A]: 26.46.
Number of jobs;
1995: 9,556;
1999: 10,128;
2004: 11,240;
Percent change 1995-2004[A]: 17.62.
Source: GAO analysis of Claritas data.
Note: There are eight census tracts in the designated area;
we did not use comparison areas for individual ECs. For more
information on our methodology, see appendix I. We excluded
establishments that were not eligible for the program tax benefit, such
as nonprofit and governmental organizations, from our analysis of the
change in the number of businesses. However, we included jobs at those
businesses in our analysis of the change in the number of jobs.
[A] Differences for the number of businesses and the number of jobs are
calculated as percent changes.
[End of table]
Stakeholder Perceptions of the Factors Influencing Changes in Poverty,
Unemployment, and Economic Growth:
In our interviews, stakeholders said that changes in the poverty rate
may have been due to changes in demographics as higher-income residents
from neighboring counties moved into the EC, which had lower property
taxes. In addition, stakeholders suggested that EC residents benefited
from new affordable housing partially funded by the EC.
When discussing changes in unemployment and economic growth,
stakeholders mentioned that one factor was the designated area's
proximity to a growing city 25 miles away that provided additional job
opportunities. In addition, stakeholders mentioned that the EC
designation had helped the Haywood county government win grants to
build infrastructure, such as a rail spur that attracted large
industries to the EC. These industries offered jobs with higher wages
and provided water lines with potable water for EC residents.
[End of section]
Appendix V: Comments from the Department of Health and Human Services:
Department Of Health & Human Services:
Office of Inspector General:
Washington, D.C. 20201:
AUG 18 2006:
Mr. William B. Shear:
Director, Financial Markets and Community Investment:
U.S. Government Accountability Office:
Washington, DC 20548:
Dear Mr. Shear:
Enclosed are the Department's comments on the U.S. Government
Accountability Office's (GAO) draft report entitled, "Empowerment Zone
And Enterprise Community Program-Improvements Occurred in Communities,
but the Effect of the Program Is Unclear" (GAO-06-727), before its
publication. These comments represent the tentative position of the
Department and are subject to reevaluation when the final version of
this report is received.
The Department provided several technical comments directly to your
staff.
The Department appreciates the opportunity to comment on this draft
report before its publication.
Sincerely,
Signed by:
Daniel R. Levinson:
Inspector General:
Enclosure:
The Office of Inspector General (OIG) is transmitting the Department's
response to this draft report in our capacity as the Department's
designed focal point and coordinator for U.S. Government Accountability
Office reports. OIG has not conducted an independent assessment of
these comments and therefore expresses no opinion on them.
Comments Of Department Of Health And Human Services On The U.S.
Government Accountability Office's (GAO) Draft Report Entitled,
"Empowerment Zone And Enterprise Community Program-Improvements
Occurred In Communities, But The Effect Of The Program Is Unclear" (GAO-
06-727):
The Department of Health and Human Services (HHS) appreciates the
opportunity to comment on the U.S. Government Accountability Office's
(GAO) draft report.
GAO Observations:
The EZ/EC program, one of the most recent large-scale federal programs
aimed at revitalizing distressed urban and rural communities, resulted
in a variety of activities intended to improve social and economic
conditions in the nation's high poverty communities. As of March 31,
2006, all but 1 S percent of the $1 billion in program grant funds
provided to Round I communities had been expended, and the program is
reaching its end. All three rounds of the EZIEC program are scheduled
to end no later than December 31, 2009. However, given our findings
from this evaluation of Round I EZs and ECs, the following observations
should be considered if these or similar programs are authorized in the
future.
Based on our review, we found that oversight for Round I of the program
was limited because the three agencies HHS, HUD, and USDA-did not
collect data on how program funds were used, and HHS did not provide
state and local entities with guidance sufficient to ensure monitoring
of the program. These limitations maybe related in part to the design
of the program, which offered increased flexibility in the use of funds
and relied on multiple agencies for oversight. However, limited data
and variation in monitoring hindered federal oversight efforts.
In addition, the lack of data on the use of program grant funds, the
extent of leveraging, and extent to which program tax benefits were
used also limited our ability and the ability of others to evaluate the
effect of the program. The lack of data on the use of tax benefits is
of particular concern, since the estimated amount of the tax benefits
was far greater than the amount of grant funds dedicated to the
program. In response to the recommendation in our 2004 report, HUD,
IRS, and USDA discussed options for collecting additional data on
program tax benefits and determined two methods for collecting the
information-through a national survey or the modification of tax forms.
The three agencies, however, did not reach agreement on a cost-
effective method for collecting the additional data. In our and others'
prior attempts to obtain this information using surveys, survey
response rates were low and thus did not produce reliable information
on the use of program tax benefits.
We acknowledge that the collection of additional tax data by IRS would
introduce additional costs to both IRS and taxpayers. Nonetheless, a
lack of data on tax benefits is significant given that subsequent
rounds of the EZ/EC program and the Renewal Community program rely
almost exclusively on tax benefits, and other federal economic
development programs, such as the recent Gulf Opportunity Zone
initiative, involve substantial amounts of tax benefits. Furthermore,
the nation's current and projected fiscal imbalance serves to reinforce
the importance of understanding the benefits of such tax expenditures.
If Congress authorizes similar programs in the future that rely heavily
on tax benefits, it would be prudent for federal agencies responsible
for administering the program to collect information necessary to
determine whether the tax benefits are effective in achieving program
goals.
HHS Comments:
The GAO report of the EZ/EC program asserts that HHS did not provide
States and designated communities with clear guidance regarding how to
monitor the program. It also noted that HHS did not receive or review
required financial data, nor specify or require that financial and
program information should be segregated by specific activities.
We believe the statement concerning monitoring unfairly represents the
relationship between HHS and the other Federal agencies in the
administration of the EZ/EC program. HHS is cited (on pages 20 and 21)
along with HUD and USDA for not collecting data on how program funds
were spent: "Although HHS regulations require States, EZs, and ECs to
maintain fiscal control of program grant funds, the agency also did not
provide guidance detailing the steps state and local authorities should
take to monitor the program." Based on our review of a number of
documents, including a Policy Memorandum from HUD dated July 16, 1996;
Memoranda of Agreement between HUD and EZ/EC Communities; EZ/EC Terms
and Conditions; an August 5, 1998, ACF Memorandum; and the Guidance for
Auditors with respect to the EZ/EC program issued February 12, 1996,
HUD and USDA are indicated as the lead Federal agencies with
programmatic oversight and management responsibilities for the overall
EZ/EC program, while HHS has fiscal responsibility for the EZ/EC Social
Services Block Grant (SSBG) program.
[End of section]
Appendix VI: Comments from the Department of Housing and Urban
Development:
U.S. Department Of Housing And Urban Development:
Washington, D.C. 20410-7000:
Office Of The Assistant Secretary:
For Community Planning And Development:
AUG 17 2006:
Mr. Charles Wilson:
Assistant Director:
Financial Markets & Community Investment Team:
U.S. Government Accounting Office:
441 G. Street, NW, Room 2B17:
Washington, D.C. 20548:
Dear Mr. Wilson:
Thank you for the opportunity to review and comment on the Government
Accounting Office's (GAO) proposed report entitled, Empowerment Zones
and Enterprise Community Program: Improvements Occurred in Communities,
but the Effect of the Program is Unclear (GAO-06-727).
We disagree with GAO's observations that there was an "absence of data
on the use of program grant funds, the amount of funds leveraged, and
the use of tax benefits." While we acknowledge that the division of
program authority and responsibilities among HUD, USDA, HHS and
Treasury has resulted in several unforeseen and unintended
consequences, HUD collected as much data as possible on the use of
program grant funds and leveraging data in the Performance Measurement
System.
Thank you for your work on reviewing the Round I EZs/ECs. We believe
that Appendix IV provides valuable information on changes to poverty,
unemployment, and economic growth rates occurring in a sample of EZ/EC
designated areas as well as stakeholders' perception of factors
influencing those changes. This appendix through its onsite case
studies will be useful in providing Congress an understanding of how
Round I EZs/ECs were governed.
Since many of our enclosed comments touch upon and, in certain
instances, overlap in several report areas, we decided that for the
sake of clarity, we would present them by the report's four objectives:
1. Describes how the designated communities implemented Round I of EZ/
EC program.
2. Evaluates the extent of federal, state, and local oversight of the
program.
3. Examines the extent to which data are available to assess the use of
program tax benefits.
4. Analyzes the effects that the Round I EZs and ECs had on poverty,
unemployment, and economic growth in their communities.
In you have questions or would like to discuss our comments, please
contact Pamela Glekas, Director, Office of Community Renewal, at (202)
708-6339.
Sincerely yours,
Signed by:
Nelson R. Bregon:
General Deputy Assistant Secretary For CPD:
Enclosure:
Comments to Draft Report # GAO-06-727, Entitled "Empowerment Zone And
Enterprise Community Program, Improvements Occurred In Communities, But
The Effect Of The Program Is Unclear"
The following HUD comments on the above GAO draft report are
categorized by the report's four objectives.
Objective #1: Describes how the designated communities implemented
Round I of EZ/EC program.
* The report needs to make clear that each Round 11 urban EZs received
a total amount of $25.6 million in EZ HUD grant funds plus tax
incentives (much less than that of Round 1) and Round III urban EZs
received no funding, but did receive tax incentives for their EZ
businesses. Reference page l, end of the first paragraph of the report.
* Four Key Principles: Highlighting the Round I achievements in meeting
the key principles would provide Congress with a balanced view of the
effectiveness of the Round I EZ/EC program that is more consistent with
the statute. Reference CFR 24 Section 597.200 (c) (1)(2)(3) and (4). It
would be useful for the report to address the Round I successes in
meeting the four key principles of 1) economic development, 2)
sustainable community development, 3) community-based partnerships and
4) strategic vision for change.
Highlighting the key principles also helps to characterize in a more
positive light, one of the report's conclusions that in general, EZs
and ECs used program grants to implement a larger number of community
development activities in areas such as education, housing and
infrastructure than economic opportunity activities, such as job
training and assistance to businesses. Reference page 13 of the report.
The report's subtle message is that Round I EZ/ECs inappropriately
focused on community development rather than economic opportunity
activities. The report does not point out that the key principles allow
for a wide range of SSBG supported activities to be undertaken and that
the implementation of community development activities meets the
principle of sustainable community development.
Recommendation: Given the fact that the four key principles are central
to the development of the strategic plan, HUD believes an evaluation of
how well the EZ/ECs did in meeting the key principles of the strategic
plan would be a significant performance standard for GAO evaluation of
the Round II and Round III EZs. Reference the Omnibus Budget
Reconciliation Act of 1995, Sec. 1391 (f)(2)(A) through (F), and CFR 24
Section 597.200 (c) of the Round I EZ/EC governing regulation.
* Strategic Plan: Statutorily, the quality of an application's
strategic plan was the essential document in the rating and ranking of
EZ/EC applications and in the selection of the Round I designees.
Because of the key principles significance and strong influence in the
development of the strategic plan, a discussion in the report of the
designees' success or lack thereof in meeting these principles would be
a reasonable addition to the report. Examples of the urban Round I EZs
and ECs successes in carrying out their programs are captured in HUD's
2005 publication "Spotlight on Results." Section 6 of the publication
consists of interviews with key administrators of Round I urban
designated areas that demonstrate the achievements of these cities in
empowering their residents to act on the four key principles.
Reporting on how well Round I EZ/ECs did in implementing their
strategic plan is also a critical factor in measuring Round I
performance although the Community Renewal Tax Relief Act of 2000, Sec.
1400J, requires a Congressional report only on the EZs/ECs impact on
poverty, unemployment and economic growth. It is clear that GAO's Round
I EZ/EC report evaluating the program performance in conjunction with
these indices was not achievable. However, we disagree that this
failure was due to a lack of program data but rather from a need for a
more inclusive methodology, such as more emphasis on the strategic plan
and a better method to track performance.
* PERMS & Strategic Plans: In view of the GAO conclusion that it was
unable to determine the impact because of the lack of financial data to
effectively tie the projects/activities to the SSBG funding source, HUD
recommends an approach to compensate for the lack of data on the use of
program funds. This approach would be based on information emanating
from the strategic plan and HUD's Performance Measurement System
(PERMS), consisting of budgets, implementation plans (IPs) and annual
reports. A separate discussion on PERMS is presented under Objective
#2.
Recommendation: HUD asserts that an assessment of designees'
performance in carrying out their strategic plans be included in the
reports on evaluating Round II and Round III EZs as a standard of
performance.
Objective #2: Evaluates the extent of federal, state, and local
oversight of the program.
* PERMS & Leveraging: A function of HUD's PERMS allows for ad hoc
reports on a variety of funding and program analyses, including the
extent and degree of leveraging occurring in Round I EZ/EC designated
areas. For example in the 2001 reporting year alone, Round I urban EZs
leveraged approximately $4.155 billion in non-Social Service Block
Grants (SSBG) for $365 million in SSBG assisted projects in the EZ
designated areas. Thus, for every $1 dollar SSBG investment there was
approximately $11+ million in leveraged funds.
Table #5 "Coding of Data Reliability of HUD/USDA Performance Systems."
Reference page 55 of the report indicates that the systems received a
code of #2.0 indicating, "items had strong documentation." This
suggests a contradiction in information and that the leveraging
information is more reliable than the last two sentences on page 55
suggests.
Recommendation: The amount of leveraging for each of the urban Round I
EZs and ECs can be tracked through PERMS. HUD would like the
opportunity to demonstrate how the information is recorded and tracked
in PERMS and believe that a demonstration of this system may help to
alleviate GAO's concern that "reliable data on the extent of leveraging
were not available." Reference page 3, "Results in Brief' and page 55
of the report.
* HUD PERMS: HUD's belief is that the report did not adequately address
PERMS and its role in HUD's oversight of the urban Round I EZs/ECs.
PERMS primary function allows the user to enter transmit and share
their program information easily and in a consistent fashion. HUD
recommends that PERMS be fully addressed in the next GAO reports. Among
PERMS informational and data elements are: executive summaries for
field review and coordination with designees for needed review changes
and additional information. For example, the summaries narrate the
accomplishments and concerns in the areas of community-based
partnerships, economic opportunity, sustainable community development
and Round III and Renewal Community tax incentive utilization plans.
Recommendation: HUD requests that the report acknowledge that HUD met
its obligation to compile program data through PERMS (that includes a
description of the activities implemented, program outputs and budgets
for projects) and include a statement such as the following, in the
final report on Round I EZs/ECs:
PERMS allow Round I EZ/EC to submit annual reports on their progress in
achieving goals, milestones, outputs, and implementing their activities
and projects. Although PERMS is not considered a financial system per
se, it is considered an informational management system consisting of
elements useful for measuring program effectiveness. HUD is
congressionally mandated to obtain performance reports from the EZs/ECs
to evaluate their performance and to undertake program oversight/
monitoring.
Furthermore, HUD notes that activities and the fiscal data discussed on
page 20 of the report were HHS responsibility. Reference page 20 of the
report.
HUD also requests that the following statement be added to the final
report:
Reliability of PERMS Data: Designees are responsible for providing
accurate and complete PERMS data that enables HUD to determine whether
designees are carrying out its strategic plan. HUD relies on the
designee's written assurances certifying that it will carry out its
strategic plan in accordance with the provisions of CFR Section 597.200
(c) and (d), including a certification that the designee will provide
periodic reports on the use of and how funds will be allocated/
budgeted.
* Monitoring and Performance Reviews: The report criticizes the lack of
monitoring guidance pointing out that to some degree the lack of
reporting requirements may be an outcome of program design. This point
is significant because it provides insight about the nature and extent
of the federal, state, and local attitude that existed at the time of
the first Round of EZ/ECs. That attitude was based on the premise of
maximum local flexibility and control and limited government intrusion.
Monitoring: HUD did not conduct monitoring of the SSBG funds because
monitoring those funds came under the purview of HHS since this agency
was responsible for the allocation, tracking and monitoring of those
funds. You may recall we responded to GAO in a written document, dated
December 8, 2004, on questions submitted by the GAO team. The following
points were made:
1. HUD Office of Inspector General, (OIG) audited a sample of urban
Round I EZs consisting of Atlanta, Philadelphia, Los Angeles,
Cleveland, and Detroit.
2. HHS informed HUD that it acts as a pass through for the SSBG
mandatory grants to the states and provides technical assistance on
compliance.
3. HUD conducted a "standard" grants monitoring procedure consisting of
a field review rating the grantees in their jurisdiction by risk
analysis resulting in a selection of high risk grantees for monitoring
The risk analysis for Round I would have been limited to EDI/108 funds
awarded to Round II EZ which compete with all other CPD competitive
grants for monitoring.
4. The HUD OIG found disallowed and questioned costs in some Round I
EZ's use of SSBG funds and EDI/108 funds in audits performed in 1998-
1999 and 2002-2003. The OIG also raised issues regarding management
controls, lack of progress and benefit of the program to residents. In
1997, HUD warned five EZs/ECs of possible revocation of their
designation regarding deficiencies after field visits. The warnings
related to adequacy of progress in implementing the strategic plans and
did not involve the use of funds.
5. Round I urban challenges involved having one-agency award funds,
another agency track progress and having different Federal benefits in
different types of designations for which different rules apply. In the
case of Round I urban communities large funding sources included SSBG
funds, HUD's 108 grantees, and Economic Development Initiative funds.
Periodic Performance Reviews: HUD's oversight role stems from Section
1391 (d) (2) of the Omnibus Budget Reconciliation Act of 1993 and CFR
Part 597.400, 597.401, 597.402, and 597.403, all of which provide for
reporting, periodic performance reviews, validation of designation and
revocation. According to limited legislative provisions, performance
reviews required only an evaluation of progress in carrying out an EZ/
EC strategic plan. In particular, the funding aspects of the strategic
plan were confined to identifying the "funding requested" under any
Federal program in support of the proposed economic, human, community
and physical development and related activities. Reference Omnibus
Budget Reconciliation Act of 1993, Sec. 1391, Designation Procedures.
Furthermore, for the purpose of the strategic plan "funding requested"
was identified as amounts budgeted for the proposed projects and
activities and did not include provisions for obligations, and/
expenditures. The limited statutory provisions were the basis for
determining the degree and extent for Round I EZ/EC monitoring and the
related collection of data as well. HUD agrees with the GAO's report
statement on page 6 urging, "more should have been done." We appreciate
GAO surfacing this issue. HUD, too believes, that it is important that
Congress be aware of the program's imperfections associated with the
oversight of Round I EZ/EC federal expenditures, particularly when
developing legislation for future programs.
Recommendation:
1. The report's observation that "more should have been done" should
also make clear that "more" was not allowed in Round I.
2. In conjunction with this issue, HUD requests that the report include
an accompanying statement that HUD met its agency requirement to
undertake periodic performance reviews on Round I urban EZ/ECs and did
so based on legislative provisions.
* Division of Authority and Responsibilities: As mentioned in the cover
letter, a substantial flaw in the administration of the Round I was the
division of authority and responsibilities among HUD, USDA, HHS, and
Treasury. This division has resulted in unforeseen and unintended
consequences that affected financial oversight of the Social Service
Block Grant (SSBG) funds.
HUD recognized in its administration of Round I urban EZ/ECs that we
had no control over the State's distribution of SSBG funds leading to a
vacuum of information on how the funds were being expended. After
numerous attempts to collect SSBG data, it was not until in 2004/2005
that we were provided a report of the expenditures and remaining Round
I urban EZ unallocated/unspent funds.
* Line of Credit Control System (LOCCS) Administration: The issues
originating from the division of authority among the four agencies led
HUD to closely examine its in-place financial and management systems
when Congress instituted a second round of urban EZs in 1998. In its
direct responsibility of administering the 15 Round II urban EZs and
their HUD EZ grant funds, HUD coupled PERMS and LOCCS. HUD further made
changes to these systems so that Round II HUD EZ grant funds could be
tracked and tied to Zone activities and projects.
LOCCS Administrator: To ensure better oversight of the HUD EZ grant
funds, HUD assigned a LOCCS administrator at the level of senior
professional, and set forth the duties assigned to the administrator as
well as Desk Officers in drawing and reconciling EZ grant funds on a
monthly basis. Additionally, HUD has assigned a PERMS administrator to
manage the timely execution of annual performance reviews. Another
significant HUD action was developing a Round II Zone guidebook
containing post designation policies and procedures with sections
covering drawdowns and tracking the use of HUD EZ grants.
Objective #3: Examines the extent to which data are available to assess
the use of program tax benefits.
* HUD and Treasury Discussions on Tax Incentives: It should be noted
that HUD took immediate action in response to the GAO recommendations
in the March 2004 report "Federal Revitalization Programs Are Being
Implemented, but Data on the Use of Tax Benefits are Limited." The
specific GAO recommendations were:
1. Identify the data needed to assess the use of the tax benefits:
2. Determine the cost effectiveness of collecting these data:
3. Document the finds of their analysis:
4. If necessary seek the authority to collect data, if a cost-effective
means is available:
* HUD Actions: In addressing the above, HUD initiated actions that
included a May 10, 2004, meeting with Treasury and IRS managers to
discuss the GAO recommendations. At that meeting, HUD suggested changes
to the IRS form 8844, compiled by Zip Codes by adding a line to the
form that would allow an understanding of the utilization of the EZ/
Renewal Community (RC) employment credits, the largest used incentive
out of the total incentives provided in the $11 billion tax incentive
package.
In a follow-up email in November 8, 2004, from the IRS and in a
Memorandum GAO-04-306, HUD was informed that changing form 8844 was not
an option largely because of the costs associated with such a change
and the number of other tax forms that would have to be changed in
meeting HUD's suggestion requiring additional appropriated funds. IRS
also stated in that email that changes to these forms would result in
at least $1 million hours of taxpayer burden annually. The memorandum
also stated that surveying business located in RCs/EZs would require
that HUD request that grant recipients compile lists identifying
businesses that benefited from the credits. IRS further stated, "No
data are collected on additional 179 expensing and capital gains
relief."
* HUD Survey: The IRS response lead HUD to undertake its own survey
through contractor resources. This resulted in the development of a
methodology, business tax incentive questionnaire and a random sample
of businesses selected from the latest Dun & Bradstreet CD ROM
identifying and providing relevant information on approximately 300,000
EZ/RC businesses. Because the survey had not been included in the
latest HUD budgets, the completion of the survey is on hold. HUD's
Office of Policy, Development & Research have included the completion
of the survey methodology as part of its 2007 research agenda.
* Treasury/HUD Partnership: Through this partnership, Treasury has
provided assistance and data on the use of the New Markets Tax
Incentives (NMTC) and on the Commercial Revitalization Deductions
available to RCs. In December 2006, Treasury will provide HUD with
national level data on RC/EZ wage credits from the IRS Form 8844
relating to individual and partnership data for 2004 with corporation
data expected in September 2007. Businesses benefit from the NMTC
program, as private sector resources are made available to them to
better meet their short and long term financial needs through increased
loans and financial technical assistance.
* HUD Action on Tax Incentives includes a recent HUD publication
"Spotlight on Results, Capturing Successes in Renewal Communities and
Empowerment Zones." This publication contains anecdotal evidence of the
utilization of tax incentives by EZ/RC businesses in the areas of wage
credits, expensing/deductions, bond financing, & capital gains.
Recommendation: To meet the GAO recommendations made in its March 2004
report, report needs to include information on the above HUD efforts.
* Round I versus Rounds II & III use of Tax Incentives: Although there
was little programmatic emphasis on the use of tax incentives until the
second round of EZs, there are at least two studies that addressed the
likelihood and significance of their use in the first round, one GAO
and the other a HUD-Policy Development and Research study. Reference
pie 45 of the report.
Both studies suggested significant limitations on the use of tax
incentives in the first round, which led HUD to provide much more
explicit emphasis on and in support of the incentives by aggressively
marketing them. HUD held workshops conducted by IRS representatives and
a nationally known tax attorney, national hearings by the Secretary's
Advisory Council where businesses, community leaders, and designees
gave testimonials on how tax incentives were being used and their
economic impact in job creation and business expansion in their
communities. These groups published three publications on tax
incentives with the "Tax Incentive Guide for Businesses" distributing
60,000 copies nationwide, and a grass root campaign representing the
best efforts by 16 EZ/RC in promoting tax incentives.
* HUD asserts that the policy issue to consider in the remaining GAO
reports is what impact more explicit emphasis on tax incentives has had
on the Rounds II and III and on RCs. The Round I urban expectation was
an assumption that the use of tax incentives would proceed more or less
automatically without federal encouragement or technical support.
Objective #4: Analyzes the effects that the Round I EZs and ECs had on
poverty, unemployment and economic growth in their communities:
* Appendix IV: We believe that the report's Appendix IV should be
useful in assisting Congress to make judgments on how individual Round
I EZs/ ECs did in changing poverty rates, unemployment rates and
increasing and economic growth. Moreover, Appendix IV presents a
valuable assessment of individual urban Round I EZs and ECs governance,
the activities implemented and other related operational issues of the
program.
* The indices of poverty, unemployment, and economic growth were used
in the application process as eligibility thresholds in identifying the
most distressed communities. These indices were never intended to be
used as a performance measure, except in the broadest sense of
comparing indice changes over a particular timeframe.
* You may want to consider HUD's parallel research and related
methodology used in its econometric model. As we understand, the GAO
model used 1994 to 2000 as a single time period to measure the changes
in poverty, unemployment and economic growth rather than a time line of
measuring change over two five-year periods, starting five years before
designation (1990-1995) and five years into designation (1995-2000) as
HUD's research had done.
It is not surprising that it is tough to tease out changes in
neighborhoods over time. The successes of EZs varied by things like
staff expertise, institutionalization of administrative structure, and
whether the program was government administered. For example, the
groups named in Figure #5 pg. 19 (Managed by another type of
organizations; Managed by local government; Managed by nonprofit
organizations) might suggest a way to cut the outcome data. Reference
pages 18 and 19 of the report.
Theory of Change Approach: Certainly having data on the use of program
grants and tax incentives would have allowed for a richer assessment of
the program, seemingly as such data could be tied to the induces of
poverty, unemployment and economic growth. On the other hand, it might
be worth citing some other methodological issues. This was intended to
be a ten-year intervention, which has been statutorily extended for
another five years for the purposes of utilizing tax incentives and the
remaining unspent funds. How activities are sequenced over ten years is
undoubtedly important as well as the intention of policy makers.
Even with so called "good data on expenditures", a longitudinal case
study approach might be the best way to assess the effectiveness of
this type of intervention. There are scholars who think a "theory of
change" approach to this subject matter is more compelling than using
an artificially constructed comparison area even in circumstances where
"good data" is available. Reference pages 28-29.
The following are GAO's comments on the Department of Housing and Urban
Development's letter dated August 17, 2006.
GAO Comments:
1. HUD commented that GAO should include details on the amount of
funding and tax incentives provided for Rounds II and III of the EZ/EC
program. We noted in our report that communities designated in Rounds
II and III received a smaller amount of funding and more tax benefits
than those designated in Round I. Our statement does not provide
further details on Rounds II and III because the focus of the report is
Round I.
2. We recognize that Round I designees were required to address four
key principles as part of their strategic plans. However, our mandate
was to assess the effectiveness of the EZ/EC program on poverty,
unemployment, and economic growth. Assessing the extent to which
communities addressed the key principles would not have been useful in
meeting our mandate because, among other things, there is not a clear
relationship between the key principles and poverty, unemployment, and
economic growth. Further, while the report did not evaluate the extent
to which communities met the key principles, it included many examples
of activities carried out under them. The report also indicated that
communities had implemented a larger percentage of community
development activities than economic opportunity activities but did not
comment on the appropriateness of the distribution of activities.
3. Our mandate was to assess the effects of the EZ/EC program on
poverty, unemployment, and economic growth. Our report stated that
communities were required to submit strategic plans that addressed the
four key principles. However, because communities were able to modify
their strategic plans over time, it would have been difficult to
establish set criteria for assessing performance. Nonetheless, our
report does contain numerous examples of activities undertaken by the
communities, including examples mentioned in a separate appendix
focusing on the 13 designated communities we visited.
4. HUD commented that because GAO found that a lack of data on how
program funds were used was a limiting factor in determining the
effectiveness of the EZ/EC program, we should make use of information
in the agency's performance reporting system and in communities'
strategic plans. However, we reported that our file review to determine
the accuracy of data in HUD's performance reporting system found that
the data were not sufficiently reliable for our purposes. For example,
we found evidence that communities had undertaken certain activities
with program funding, but we were often unable to find documentation of
the actual amounts allocated or expended. As a result, we were unable
to rely on information contained in the agency's performance reporting
system on the amounts of program funds allocated or expended on
specific activities.
5. We found that data in HUD's performance reporting system on the
amounts of funds used and the amounts leveraged were not reliable. For
example, we found that HUD's system included information on the amount
of funds leveraged. But for the sample of activities we reviewed, the
supporting documentation either showed an amount conflicting with the
reported amount or was not available. Moreover, we found that the
definition of "leveraging" varied across EZ and EC sites. HUD further
commented that Table 5 in the report showed that the agency's
performance reporting system received a code of 2.0, showing that
leveraging data had strong documentation. However, HUD appears to have
misinterpreted the information we presented on this matter. We found
that HUD's data on leveraging received an average code of 1.0,
indicating that such information had weak documentation. Lastly, HUD
recommended that it be allowed to alleviate GAO's concerns about the
reliability of its leveraging data by demonstrating how the data were
tracked and recorded in its performance reporting system. However, the
data reliability problems we found during the course of this work were
due not to concerns about the system used to track and record the data,
but rather to the frequent lack of supporting documentation for the
data entered into the system.
6. HUD commented that our report did not adequately address HUD's
performance reporting system and its role in HUD's oversight of the
urban Round I EZ and ECs. We acknowledge that HUD established the
system in response to an earlier GAO recommendation and has since used
it to oversee Round I EZs and ECs. Moreover, we agree that the system
contains a variety of information and data elements, including
activities implemented and program outputs. We also acknowledge that
the performance reporting system is not intended to be a financial
system for Round I. However, as discussed in our report, we found that
because the system did not always contain information on what was spent
on activities and did not always contain reliable information, HUD and
the other federal agencies were limited in their ability to oversee the
program.
7. HUD commented that the program's design was significant because it
provided insight about the nature and extent of the federal, state, and
local attitudes that existed at the time of the first Round of EZs/ECs.
HUD also stated that it did not conduct monitoring of the SSBG funds
because monitoring those funds was the responsibility of HHS. HUD's
statement further supports our discussion on the limitation in the
oversight of the EZ/EC program that may have resulted from the
program's design. Although we found program oversight was hindered, we
also reported that no single federal agency had sole responsibility for
oversight. We do not agree with HUD's recommendation that we make clear
that more oversight was not allowed in Round I. For example, early in
the program HUD and HHS made some efforts to share information.
Specifically, HUD officials said that they had received fiscal data
from HHS and reconciled that information with their program data on the
activities implemented, but these efforts to share information were not
maintained. Regarding the second recommendation, although HUD described
some of its efforts to monitor the program according to applicable
regulations, the oversight concerns we identified in the report remain.
8. We reported that limitations in the oversight of the EZ/EC program
may have resulted from the design of the program.
9. We stated in the report that the concerns raised about program
oversight for the Round I EZ/EC program may not apply to future rounds
of the EZ/EC program. We also acknowledge that HUD may have made
changes in its oversight of later rounds of the program. However, an
evaluation of later rounds of the EZ/EC and Renewal Community programs
is beyond the scope of this report.
10. In our report, we acknowledged HUD's as well as the other agencies'
response to the recommendation in our 2004 report to identify a cost-
effective means of collecting the data needed to assess the use of the
tax benefits.
11. Our report acknowledged the collaboration among HUD, IRS, and USDA
in addressing our previous recommendation and summarizes the outcome of
their discussions, including the identification of two data collection
methods--through a national survey or by modifying the tax forms. In
addition, our report also acknowledged that IRS did not have any data
for some program tax benefits. The lack of data on the use of tax
benefits continues to be a source of concern that limits an assessment
of the effect of the EZ/EC program.
12. We agree that HUD's efforts to develop a methodology to administer
a survey to businesses to assess the use of the program tax benefits is
a useful step in gathering such information.
13. We recognize the efforts between HUD and Treasury on sharing
national-level data on EZ businesses' use of tax credits for employing
EZ residents. However, as we mention in our report, data on the EZ
employment tax benefit were limited because they could not be linked to
the specific EZ claiming the benefit.
14. In the absence of other data, we acknowledge HUD's efforts to
capture anecdotal information on the use of program tax benefits by EZ
businesses.
15. We recognize HUD's efforts to market the EZ/EC program tax
benefits.
16. We appreciate HUD's suggestion on how to approach evaluations of
later rounds of the EZ/EC and Renewal Community programs and welcome
the opportunity to discuss these ideas.
17. We appreciate HUD's comments on the descriptive information on EZs
and ECs we visited that are discussed in appendix IV.
18. HUD commented that the measures used in our report---poverty,
unemployment and economic growth--were used in the application process
and were not intended to be used as performance measures. However, as
mentioned earlier our mandate was to assess the effects of the EZ/EC
program on poverty, unemployment, and economic growth.
19. HUD suggested that we consider additional methodologies for
measuring the effects of the EZ/EC program, such as trend analysis
using data from 1990 through 1995 and 1995 through 2000. To conduct our
work, we used 1990 and 2000 data to measure changes in poverty and
unemployment and 1995, 1999, and 2004 data to measure changes in
economic growth. We chose these dates because data were available at
the census tract level for these years. Moreover, in designing our
methodology for our econometric analysis, we conducted a literature
review and discussed our methodology with several experts in the urban
studies field and determined that the approach presented in this report
was effective in answering the objectives of our mandate. As mentioned
in Appendix II, we also conducted different tests to ensure the
robustness of our models, which all yielded results consistent with our
models. The approach that HUD suggested controlled for trends that
began before the EZs were designated in 1994. Because we did not have
data on poverty or unemployment for 1995 we were unable to use this
approach. However, our use of housing trends between 1990 and 1994 in
our econometric model controlled for some trends that were in place
prior to EZ designation.
HUD also suggested a longitudinal case study approach might be the best
way to assess the effectiveness of this type of program. Although a
longitudinal case study approach would be informative, it is unlikely
that a successful retrospective longitudinal study could be designed at
the end of the program. As HUD noted, this intervention was intended to
be implemented over a ten-year period. However, a longitudinal case
study approach would necessitate data collection beginning at the
inception of the program and continuing for the duration of the program
as well as some period of time after it ends.
[End of section]
Appendix VII: Comments from the U.S. Department of Agriculture:
United States Department of Agriculture:
Office of the Secretary:
Washington, D.C. 20250:
AUG 22 2006:
William B. Shear:
Director, Financial Markets and Community Investment:
United States Government Accountability Office:
441 G Street, NW, Room 2A10:
Washington, DC 20548:
Dear Mr. Shear:
Thank you for providing the United States Department of Agriculture
(USDA) and Rural Development with your Government Accountability Office
(GAO) draft report entitled, "Empowerment Zone and Enterprise Community
Program: Improvements Occurred in Communities, but the Effect of the
Program is Unclear," Report Number GAO-06-727. For your consideration,
Rural Development offers the following comments to the draft report and
requests that a copy of these comments be included in your final
report. Rural Development's response is limited to a discussion of the
rural Empowerment Zones and Enterprise Communities (EZ/ECs).
USDA concurs that data and analyses on the effectiveness of programs,
such as the EZ/EC, are useful. Our long experience working with rural
communities, operating economic development programs, and attempting to
evaluate those programs may be instructive to future evaluations. Rural
Development suggests three general areas to consider:
First, rural communities are substantially different from each other
and from urban and metropolitan areas, and many performance metrics
have been designed with the urban model and data in mind. Rural
measurements need to acknowledge different realities. For instance,
small populations mean that some statistically meaningful economic,
demographic, and education data are not always available in targeted
rural communities. Currently, USDA is developing a methodology that
focuses on economic impacts using county-level economic data and
captures the short-term Gross Domestic Product changes in the impacted
rural counties. However, this system can not be applied retroactively
to communities receiving grants or other economic stimulation in the
past. In addition, this system can not be used to measure impacts at
the sub-county level.
Second, it is especially important in rural areas to build in up front
a clear and adequately funded data collection process for program
evaluation. When that data collection process is not in place at the
start, the appropriate resources and attention required will often be
difficult to obtain. This problem is likely to be particularly acute in
rural areas, where local governments and key economic development
institutions frequently have limited financial capacity and
professional staff.
The collection of baseline and on-going data should enable construction
of a program evaluation that will examine the program's success in
achieving intended results. Beyond data on the participating
communities, if a comparison is desired, the appropriate data should be
obtained for comparison communities from the outset.
Third, performance metric design is dynamic, and Rural Development
encourages an expansion of the discussion, as well as the use of
appropriate methodologies that recognize data conditions at the time
grants are made. The new collaborations at the local level among key
economic and community development institutions, which were required by
the EZ/EC program, are worthy of careful examination within a full
program evaluation.
We commend GAO for looking at program impacts in critical areas such as
poverty, unemployment, and economic growth. Program evaluation might,
however, go beyond these measures to look at the construction of
stronger economic development capacity and more effective collaborative
networks. Such a broader perspective on program results might be
particularly pertinent for rural EZ/ECs since GAO's econometric
analysis was applied only to urban EZ/ECs.
A major portion of the report is devoted to comparing the change in the
number of jobs and businesses between EZ/ECs and comparison areas. We
do not have a good understanding of how these comparison areas were
chosen; therefore, we can not comment on this portion of the report.
GAO's report states on page 4, "Data were not collected on program
benefits for specific activities." and also ".USDA - did not collect
data on how program funds were used." USDA did, however, design and
implement a Benchmark Management System (BMS), intended as a management
tool for both USDA and the individual EZ/ECs. The BMS requires each of
the EZ/EC's major activities to be tracked over time by the community
and verified by USDA. The BMS was not designed to be an accounting
tool, but it has proven useful for providing a good picture of each
community's achievements.
GAO also states in its report that, "USDA encouraged rural EZ and ECs
to report all investment in the EZ as leveraged funds, not only those
projects that received EZ/EC funds." In fact, USDA encouraged each
community to report all investments that contributed to accomplishing
the community's strategic plan, the guiding document for the
community's revitalization strategy. Most EZ/ECs developed ambitious
plans that could be realized only by leveraging resources beyond the
core EZ/EC funding. In addition to the resources marshaled through
leveraging, the communities were able to build a strong network of
partners, with a collaborative track record that will likely be in
place long after the last core EZ/EC dollar has been spent.
There are also some technical details that need correction and/or
further explanation:
* Page 10. The statement, "In addition, all designated communities
reported leveraging additional resources, though a lack of reliable
data prevented us from determining how much." USDA's BMS does include
data on leveraged resources, as self-reported by the communities.
Anecdotal evidence indicates that communities generally underreport
such leveraged resources.
* Page 64. The first paragraph concludes, ".the results did not allow
us to conclude whether there is an association between the EZ program
and economic growth." The word "urban" should be inserted before the
EZ, as the econometric analysis only dealt with urban EZ and ECs. As
GAO notes in its report, there are differences in how the program was
implemented and the results achieved. Rural EZ and ECs generally had a
greater per capita number of jobs created and new businesses formed
than urban EZ and ECs.
* Page 133. Rio Grande Valley EZ, Texas, received an extension,
according to information USDA received from the Department of Health
and Human Services.
Thank you for this opportunity to comment on the report. If you have
any questions, please contact John Dunsmuir, Acting Director, Financial
Management Division, at (202) 692-0080.
Sincerely,
Signed by:
Thomas C. Dorr:
Under Secretary Rural Development:
[End of section]
Appendix VIII GAO Contact and Staff Acknowledgments:
GAO Contact:
William B. Shear (202) 512-8678:
Acknowledgments:
In addition to the individual named above, Charles Wilson, Jr.,
Assistant Director, Carl Barden, Mark Braza, Marta Chaffee, Emily
Chalmers, Nadine Garrick, Kenrick Isaac, DuEwa Kamara, Austin Kelly,
Terence Lam, John Larsen, Alison Martin, Denise McCabe, John McGrail,
John Mingus, Jr., Marc Molino, Gretchen Maier Pattison, James
Vitarello, and Daniel Zeno made key contributions to this report.
(250221):
FOOTNOTES
[1] Since its enactment in 2000, the Renewal Community program has
focused on providing tax benefits to businesses in designated
communities to attract or retain jobs and businesses.
[2] For the purposes of this report, EZ/EC stakeholders include EZ/EC
officials, board members, subgrantees, local chamber of commerce
representatives, and other local officials recommended by EZ or EC
officials as having a role in the program. See appendix I for
information on the types of stakeholders we interviewed at each site.
[3] Survey results can be viewed at [Hyperlink, http://www.gao.gov/cgi-
bin/getrpt?GAO-06-734SP].
[4] Program data include information on how activities were implemented
and outputs.
[5] We calculated confidence intervals around estimates derived from
Census data. In this report, all estimates shown for a percentage have
a 95 percent confidence interval of less than plus or minus 5
percentage points, unless otherwise noted.
[6] GAO, Standards for Internal Control in the Federal Government, GAO/
AIMD-00-21.3.1 (Washington, D.C.: November 1, 1999).
[7] HUD's performance reporting system is known as Performance
Measurement System while USDA's is called the Benchmark Management
System. For the purposes of this report, we refer to them as
performance reporting systems.
[8] GAO, Government Performance and Accountability: Tax Expenditures
Represent a Substantial Federal Commitment and Need to Be Reexamined,
GAO-05-690 (Washington, D.C.: September 23, 2005).
[9] GAO, Community Development: Federal Revitalization Programs Are
Being Implemented, but Data on the Use of Tax Benefits Are Limited, GAO-
04-306, (Washington, D.C.: March 5, 2004).
[10] Our earlier recommendation was not directed to HHS because its
role was limited to distribution and oversight of the EZ/EC grant
funds.
[11] We were able to use statistical modeling techniques for the eight
Round I urban EZs only, because the rural EZs were made up of too few
census tracts to perform these analyses, and because the ECs received
such small amounts of money over the 10-year period that we could not
separate the program's effects from those of other programs.
[12] GAO-04-306.
[13] Multiagency teams also included officials from the Department of
Justice, the Environmental Protection Agency, and the Small Business
Administration, among other agencies.
[14] Two urban EZs--Cleveland and Los Angeles--were originally
designated as Supplemental EZs and received a combination of Economic
Development Initiative grants and Section 108 Loan Guarantees, both of
which could only be used for certain economic development or
revitalization projects. The Supplemental EZs were given full Round I
EZ status in 1998, and local businesses were allowed to use the program
tax benefits starting in 2000. However, they did not receive the grants
the other Round I EZs received. Four urban ECs also received Enhanced
EC designations, which provided them with some Economic Development
Initiative grants and Section 108 Loan Guarantees.
[15] This number represents businesses' use of tax benefits in all
Round I EZs and ECs, as well as 20 Round II EZs, 10 Round III EZs, and
40 Renewal Communities.
[16] The Cleveland EZ received $87 million in grants and an equal
amount of loan guarantees, while the Los Angeles EZ received $125
million in grants and $325 million in loan guarantees.
[17] EZs and ECs self-determined the categories for their activities,
so it is possible that a similar activity implemented in two sites
could be categorized differently.
[18] The federal Community Development Block Grant program supports a
wide array of local community development activities that are primarily
to benefit low-and moderate-income individuals.
[19] However, past reported amounts remain in their performance data in
HUD's system.
[20] This finding is consistent with findings of the HUD and USDA
Offices of Inspector General.
[21] GAO/AIMD-00-21.3.1.
[22] GAO/AIMD-00-21.3.1.
[23] Because data on the amount of funds used for specific activities
was not reliable, this report only includes information on the number
of activities implemented. We were able to find complete documentation
for our sample of activities for the amount of EZ grant funding
reported by the Baltimore and Detroit EZs and the Camden portion of the
Philadelphia-Camden EZ.
[24] Neither HHS nor USDA officials told us that they had made any
efforts to reconcile fiscal and program data on the EZ/EC program.
[25] GAO-05-690.
[26] GAO-04-306.
[27] GAO-05-690.
[28] GAO-04-306.
[29] GAO-05-690;
GAO, 21ST Century Challenges: Reexamining the Case of the Federal
Government, GAO-05-325SP (Washington, D.C.: February 1, 2005); and GAO,
Tax Policy: Tax Expenditures Deserve More Scrutiny, GAO/GGD/AIMD-94-122
(Washington, D.C.: June 3, 1994). The New York Liberty Zone was created
in response to the September 11, 2001 terrorist attacks and created
seven tax benefits designed to assist in the recovery efforts,
including a tax credit for hiring employees who are Liberty Zone
residents.
[30] GAO-04-306. Our earlier recommendation was not directed to HHS
because its role was limited to distribution and oversight of the EZ/EC
grant funds.
[31] GAO, Community Development: Businesses' Use of Empowerment Zone
Tax Incentives, GAO/RCED-99-253 (Washington, D.C.: September 30, 1999)
and Scott Hebert and others, Interim Assessment of the Empowerment
Zones and Enterprise Communities (EZ/EC) Program: A Progress Report,
prepared for the U.S. Department of Housing and Urban Development
(Washington, D.C.: November 2001).
[32] GAO/RCED-99-253 and Hebert and others, Interim Assessment.
[33] For more information on our survey methods, see appendix I.
[34] GAO/RCED-99-253 and Hebert and others, Interim Assessment.
[35] The EZ/EC tax benefits were nonrefundable, which means that a
business can only claim them if it has a tax liability.
[36] An Industrial Revenue Bond is a bond used to finance the
construction of manufacturing or commercial facilities for a private
user. This bond is available to businesses in general and is not part
of the EZ program benefits package.
[37] A bond cap is the maximum dollar amount available for issuing the
tax exempt bond. Prior to January 1, 2002, the cap for bonds was $3
million per borrower for activities in any EZ or EC, with a nationwide
limit of $20 million per borrower. After 2002, the bond cap was removed
for all EZs.
[38] GAO-05-690 and GAO-04-306.
[39] The Joint Committee on Taxation estimated that businesses in Round
I communities would claim about $2.5 billion in tax benefits in the
first four years of the program alone. Information for later years of
the Round I program is not available because subsequent estimates by
the committee did not separate benefits claimed by Round I communities
from benefits claimed by later round communities.
[40] The Gulf Opportunity Zone Act of 2005 provides tax benefits to
assist in the recovery and rebuilding of areas affected by Hurricanes
Katrina, Rita, and Wilma.
[41] GAO-04-306.
[42] The indicators of poverty, unemployment, and economic growth were
specifically identified in our mandate.
[43] We selected comparison areas through a statistical technique
called the propensity score, which allowed us to identify the census
tracts that were most similar to the census tracts selected in the
original EZ and EC designations based on a set of factors, such as 1990
poverty and unemployment rates. For more information on how we chose
the comparison areas, see appendix I.
[44] Gentrification or economic displacement refers to the
transformation of a relatively low-income neighborhood into a more
affluent neighborhood through redevelopment, usually in conjunction
with changing demographics and an influx of wealthier residents.
[45] Our analysis of changes in economic growth only included 94 ECs,
since we did not have data for the Miami/Dade County, Florida EC.
[46] Changes in poverty rate are based upon percentage point
differences.
[47] We were able to use statistical modeling techniques only for the
eight Round I urban EZs, because the rural EZs were made up of too few
census tracts to perform these analyses and because the ECs received
such small amounts of money that we could not separate the program's
effects from those of other programs. In addition, we could not isolate
the effects of the program on individual EZs because the number of
census tracts in some urban EZs was not large enough to provide
reliable results. Appendix II describes our methodology and the results
of the econometric analysis.
[48] These stakeholders did not comment on the changes that occurred in
our comparison areas.
[49] The Personal Responsibility and Work Opportunity Reconciliation
Act of 1996 instituted a change in welfare policies, establishing work
requirements and time limits for receiving benefits.
[50] Changes in unemployment rate are based upon percentage point
differences.
[51] These stakeholders did not comment on the changes that occurred in
our comparison areas.
[52] We obtained these data for 1995, 1999, and 2004 from a private
data vendor, Claritas. Changes in the number of businesses and number
of jobs are based upon percent changes.
[53] Data were not available for the Miami/Dade County, Florida EC.
[54] These stakeholders did not comment on the changes that occurred in
our comparison areas.
[55] GAO-04-306.
[56] GAO-04-306.
[57] GAO-05-690 and GAO/GGD/AIMD-94-122.
[58] Of the 61 ECs that had not received additional designations, we
selected a judgmental sample of 8 ECs, 4 urban and 4 rural, that
represented geographic diversity and also factored in each EC's
combined poverty and unemployment rates in 1990. From this sample, we
chose two ECs to visit--Providence, Rhode Island (urban) and Fayette-
Haywood, Tennessee (rural)--that were similar in terms of their poverty
and unemployment rates.
[59] In the New York and Philadelphia-Camden EZs, we implemented two
separate site visit protocols, due to the split governance structures
in those locations.
[60] The four urban pretests were conducted in the Akron, Ohio EC;
Bridgeport, Connecticut EC; Providence, Rhode Island EC; and the
Albuquerque, New Mexico EC. The two rural pretests occurred in the
Central Appalachia EC located in West Virginia and the Williamsburg/
Lake City EC in South Carolina.
[61] There were 61 ECs that did not receive subsequent designations,
but we excluded 1 urban EC from our sample because it was no longer in
operation as of June 2005.
[62] We selected the lesser of 10 activities or half of an EZ's or EC's
activities using a systematic random sample. We use the term "activity"
to describe the units of information reported in the HUD and USDA
systems, called "implementation plans" in HUD's system and "benchmarks"
in USDA's system.
[63] We excluded the Cleveland and Los Angeles EZs from our discussion,
because they did not receive EZ/EC grant funds.
[64] GAO/RCED-99-253 and Hebert and others, Interim Assessment.
[65] Businesses in the Atlanta EZ were excluded from the analysis,
since the same area received a Renewal Community designation in 2002
and we did not want businesses to confuse the different tax benefits
available for each designation.
[66] We also excluded private businesses that were not eligible for the
tax benefits, such as gambling establishments and liquor stores.
[67] The points in time for Census data were 1990 and 2000 and for the
business data were 1995 and 2004.
[68] We determined this definition based on research done on the EZ
program and similar programs, such as the state Enterprise Zone
initiative. Similar to the EZ program, state Enterprise Zones are state
run programs that offer certain tax benefits within an established
area. We excluded establishments that were not eligible for program tax
benefits, such as nonprofit and governmental organizations, from our
analysis of the change in the number of businesses. However, we
included jobs at those businesses in our analysis of the change in the
number of jobs.
[69] Other researchers have used similar approaches, such as Greenbaum
and Engberg in "The Impact of State Urban Enterprise Zones on Business
Outcomes," December 1998, Carnegie Melon University. p. 24.
[70] Generally, increasing the propensity score for selecting
comparison tracts has the effect of reducing the sample of comparison
tracts and decreasing the propensity score has the effect of increasing
the sample of comparison tracts.
[71] Due to changes in the census tract boundaries for some EZs and ECs
from 1990 to 2000, we used the 2000 census block group to recreate the
initial 1990 boundaries and ensure that our analysis remained
consistent. For the census variables based on percentage
characteristics, like poverty and unemployment rates, we calculated the
change in percentage points by finding the difference between the 1990
sample estimate and the 2000 sample estimate. For census variables
based on average characteristics, such as average household income, we
calculated the percent change by finding the difference between the
1990 sample estimate and the 2000 sample estimate and then dividing the
difference by the 1990 sample estimate. Census variables using dollar
amounts like average household income were adjusted for inflation to
2004 dollars. We calculated the percent change for our economic growth
measures.
[72] We completed econometric analyses of the eight urban EZs only,
because the amount of program grant funding for ECs was too small to
separate the program's effects from other programs. In addition, we
excluded the rural EZs because they are made up of too few census
tracts to perform these analyses.
[73] We excluded establishments that were not eligible for program tax
benefits, such as nonprofit and governmental organizations, from our
analysis of the change in the number of businesses. However, we
included jobs at those establishments in our analysis of the change in
the number of jobs.
[74] We also tested use of a fixed effect model, which allowed us to
account for some tract-specific factors that may not vary over time but
might be correlated with the designation of a tract, called "fixed
effects." Other researchers used fixed effects regression techniques to
control for area-specific unchanging factors, such as industry
composition. (See, for example, Leslie E. Papke, "What Do We Know About
Enterprise Zones?" NBER Working Paper No. 4251. Cambridge, Mass.,
National Bureau of Economic Research, 1993.) The analysis in this
report predicts first differences in the dependent variables, that is,
the difference between the value in the 2000 Census and the value in
the 1990 Census. When only 2 years of data are analyzed, regressions
based on first differences are equivalent to fixed effect models. (See
Zvi Griliches and Jerry Hausman, "Errors in Variables in Panel Data,"
Journal of Econometrics, 31 (1986), pp. 93-118.)
[75] Paul A. Jargowsky, Stunning Progress, Hidden Problems: The
Dramatic Decline of Concentrated Poverty in the 1990s, (Washington,
D.C.: The Brookings Institution, May 2003).
[76] We also tested the models using the longer time period of 1995 to
2004, but the results were consistent with those using the time period
from 1995 to 1999.
[77] This low explanatory power is indicated in the low R-square
statistics in tables 10 and 11.
[78] In 2002, the Atlanta EZ was designated as a Renewal Community, and
by 2003 the Atlanta EZ was no longer in operation. HHS transferred the
remaining EZ/EC funds (over $53 million) to the Renewal Community, but
as of March 2006, these funds had not been used.
[79] Congress established the HOPE VI program in 1992 to revitalize
severely distressed public housing by demolition, rehabilitation, or
replacement of sites.
[80] The overarching board also included a nonvoting HUD official.
[81] The Camden portion of the EZ was initially managed by the city of
Camden, but HUD officials fostered the change to a nonprofit to deal
with tensions between the city of Camden and state of New Jersey.
[82] All tracts that qualified as comparison tracts for Philadelphia-
Camden were located in Philadelphia.
[83] The grants and loan guarantees the EZ received could only be used
for certain economic development or revitalization projects. When
Cleveland received the Supplemental EZ instead of the regular EZ
designation, the officials modified their strategic plan.
[84] When Los Angeles received the Supplemental EZ instead of the
regular EZ designation, the officials modified their strategic plan
from having some social service initiatives to focusing only on
activities directly related to economic development.
[85] We did not use comparison areas for individual ECs. For more
information on our methodology, see appendix I.
[86] Local development districts are unique to the state of Tennessee,
and the majority of their funding comes from the Tennessee General
Assembly.
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