Empowerment Zone and Enterprise Community Program: Improvements Occurred in Communities, but the Effect of the Program Is Unclear
Published: 2006-09-22T00:00:00

Fast Facts:

Highlights:
section_title: Why GAO Did This Study
paragraphs: The Empowerment Zone/Enterprise Community (EZ/EC) program is one of the most recent large-scale federal effort intended to revitalize impoverished urban and rural communities. There have been three rounds of EZs and two rounds of ECs, all of which are scheduled to end no later than December 2009. The Community Renewal Tax Relief Act of 2000 mandated that GAO audit and report in 2004, 2007, and 2010 on the EZ/EC program and its effect on poverty, unemployment, and economic growth. This report, which focuses on the first round of the program starting in 1994, discusses program implementation; program oversight; data available on the use of program tax benefits; and the program's effect on poverty, unemployment, and economic growth. In conducting this work, GAO made site visits to all Round I EZs, conducted an e-mail survey of 60 Round I ECs, and used several statistical methods to analyze program effects.
section_title: What GAO Found
paragraphs: 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.

section_title: Letter
paragraphs: 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. 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. 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. 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. 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. 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.
subsections: section_title: Background
paragraphs: 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. 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.
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. 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.
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.
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.
section_title: Round I EZs and ECs Have Used Their Grant Funds to Implement a Wide Range of Program Activities
paragraphs: 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.
subsections: section_title: Most EZ/EC Grant Funds Have Been Expended, but Many EZs and Some ECs Received Grant Extensions
paragraphs: 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. 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.
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.
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.
section_title: EZs and ECs Implemented a Wide Variety of Activities, Most Related to Community Development
paragraphs: 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).
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.
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.
subsections: section_title: EZs and ECs Used Program Grants to Leverage Additional Funds, but Reliable Data on the Extent of Leveraging Are Not Available
paragraphs: 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.  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.
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. 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.
section_title: Designees Reported Other Accomplishments
paragraphs: 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.
section_title: Designees Reported Implementation Challenges
paragraphs: 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.
section_title: EZs and ECs Established a Variety of Governance Structures and Encouraged Community Participation
paragraphs: 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.
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.
section_title: Oversight Was Hindered by Limited Program Data and Variation in Monitoring
paragraphs: According to our federal standards, federal agencies should oversee the  use of public resources and ensure that ongoing monitoring occurs.  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.
subsections: section_title: Federal Agencies Are Required to Oversee the Use of Public Funds and Provide Ongoing Monitoring
paragraphs: 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. 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.
section_title: The Federal Agencies’ Oversight Efforts Had Shortcomings in Data Collection
paragraphs: 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.
section_title: Program Monitoring by State and Local Participants Varied
paragraphs: 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.
subsections: section_title: Limitations in EZ/EC Oversight May Have Resulted from the Program Design
paragraphs: 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.  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.
section_title: Lack of Detailed Tax Data Made It Difficult to Assess the Use of Program Tax Benefits
paragraphs: 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. 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. 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.
subsections: section_title: IRS Data on the Use of Program Tax Benefits Are Limited
paragraphs: 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. 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. 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.
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. 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. 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. 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.
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. 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.  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. 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. 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.
section_title: IRS Officials Reported that They Have Data Sufficient to Enforce the Tax Code, but This Information Is Insufficient for Assessing the Extent of Usage
paragraphs: 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.
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. 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.
section_title: In Aggregate, EZs and ECs Showed Some Improvements, but Our Analysis Did Not Definitively Link These Changes to the Program
paragraphs: 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. 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.
subsections: section_title: A Number of Challenges Affected Our Efforts to Measure the Effects of the EZ/EC Program
paragraphs: 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. 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. 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. 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.
section_title: In Some Cases, EZs and ECs Showed Improvements in Poverty, Unemployment, and Economic Growth
paragraphs: 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. 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.
section_title: Most EZs and ECs Saw Some Decrease in the Poverty Rate, but These Changes Could Not Be Tied Definitively to the EZ/EC Program
paragraphs: In most of the 11 EZs and 95 ECs, both urban and rural, poverty rates fell  between 1990 and 2000 (fig. 7). 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.
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.
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. 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. 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. 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.
section_title: Decreases in the Unemployment Rate in Some Communities Also Could Not Be Definitively Tied to the EZ/EC Program
paragraphs: 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. 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).
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.
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. 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.
section_title: Our Measures Showed that Some Economic Growth Occurred, but Results from Our Econometric Model Were Not Conclusive
paragraphs: 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. 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. 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.
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.
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. 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.
section_title: Additional Program Data Could Facilitate Evaluations of the Effects of the EZ/EC and Similar Programs
paragraphs: 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.
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. 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.
section_title: Observations
paragraphs: 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.
section_title: Agency Comments and Our Evaluation
paragraphs: We provided a draft of this report for review and comment to HHS, HUD,  IRS, and USDA. We received comments from HHS, HUD, and USDA. In  general, the agencies provided comments related to the oversight of the  program, the availability of data, and the methodology used to carry out the  work. Their written comments appear in appendixes V through VII,  respectively, and our responses to HUD’s more detailed comments also  appear in appendix VI. HHS, HUD, and USDA also provided technical  comments, which we have incorporated into the report where appropriate.
HHS commented that a statement made in our report—that the agency did  not provide guidance detailing the steps state and local authorities should  take to monitor the program—unfairly represented the relationship  between HHS and the other federal agencies that administered the EZ/EC  program. Specifically, HHS emphasized its responsibility for fiscal as  opposed to programmatic oversight of the program. We note in our report  that program design may have led to a lack of clarity in oversight, as no  single federal agency had sole oversight responsibility. While this lack of  clarity in oversight may be related in part to the design of the program,  which offered increased flexibility in the use of funds and relied on  multiple agencies for oversight, limited data and variation in monitoring  hindered federal oversight efforts. Moreover, we believe that, in  accordance with federal standards, each of the federal agencies that  administered the program bore at least some responsibility for ensuring  that public resources were being used effectively and that program goals  were being met.
HUD disagreed with GAO’s observation that there was a lack of data on the  use of program grant funds, the amount of funds leveraged, and the use of  the tax benefits. HUD indicated that we could obtain data on the use of  program funds and the amount of funds leveraged from its performance  reporting system. As we discussed in our report, we used information from  HUD’s reporting system to report on the types of activities that designated  communities implemented. We also noted that HUD maintained some  information on the amount of EZ/EC grants budgeted for specific activities.  Although we found evidence that activities were carried out with program  funds, information contained in the performance reporting system on the  amounts of funds used and the amount leveraged was not reliable. For  example, we found evidence that communities had undertaken certain  activities with program funding, but we were often unable to find  documentation of the actual amounts allocated or expended. HUD also  indicated that it did not agree that data on the use of the tax benefits were  lacking. However, HUD indicated that the agency itself had attempted to  gather such data by collaborating with IRS in identifying ways to collect  data on tax benefits, by developing a methodology to administer a survey to  businesses, and by compiling anecdotal evidence of the use of program tax  benefits. We continue to believe that the lack of data on program tax  benefits limits the ability of the agencies to administer and evaluate the  EZ/EC program. Further, the lack of such data is likely to become  increasingly problematic in light of the fact that future rounds of the EZ/EC  program and the Renewal Community program rely heavily on tax benefits  to achieve revitalization goals.
HUD concurred that limitations in the oversight of the EZ/EC program may  have resulted from the design of the program as no single federal agency  had sole responsibility for oversight. HUD also recommended that we  make clear that more oversight was not allowed in Round I and we include  a statement that it met agency requirements to undertake periodic  performance reviews and described some of its efforts to monitor the  program according to applicable regulations. We do not believe that more  oversight was not allowed. For example, early in the program HUD and  HHS made some efforts to share information. Specifically, HUD officials  said that they had received fiscal data from HHS and reconciled that  information with their program data on the activities implemented, but  these efforts to share information were not maintained. Further, as we  previously stated, while we recognize that program design may have led to  a lack of clarity in oversight, we believe that in accordance with federal  standards, each of the federal agencies that administered the program bore  at least some responsibility for ensuring that public resources were being  used effectively and that program goals were being met. HUD also  described changes it had made to ensure better oversight of program funds  for Round II. We acknowledge HUD’s efforts to improve oversight of the  program and, as discussed in our report, the oversight limitations that we  identified in Round I of the program may not apply to later rounds.
HUD provided several comments related to the methodology we used to  carry out our work. For example, HUD suggested that we measure the  successes of the Round I program in meeting the four key principles of the  program, which the designated communities were required to include in  their strategic plans. Additionally, HUD commented that the indices we  used to assess the effects of the EZ/EC program—poverty, unemployment  and economic growth—were used in the application process for the  program but were not intended to be used as performance measures. While  we appreciate HUD’s suggestions on our methodology, our congressional  mandate was to determine the effect of the EZ/EC program on poverty,  unemployment and economic growth. In designing our methodology, we  conducted extensive research on evaluations that had been conducted on  the EZ/EC program, including HUD’s 2001 Interim Assessment, and spoke  with several experts in the urban studies field.
USDA stated that data and analyses on the effectiveness of programs such  as EZ/EC were useful and offered areas to consider for future evaluations  of economic development programs involving rural areas. For example,  USDA mentioned issues involved in collecting data on rural areas, such as  the limited availability of economic and demographic data for small rural  populations, and discussed USDA’s efforts for developing a methodology  that focuses on economic impacts using county-level economic data. USDA  also said it is especially important in rural areas to have a clear and  adequately funded data collection process for program evaluations. In  addition, USDA noted that evaluations of the EZ/EC program could go  beyond the indicators of poverty, unemployment and economic growth to  include measures on economic development capacity and collaboration.  We agree that collecting data for rural areas is a challenge and appreciate  USDA’s effort to develop a methodology that focuses on economic impacts  using county-level economic data and captures the short-term Gross  Domestic Product changes in the impacted rural counties. Further, we  appreciate USDA’s suggestion that additional measures be considered in  future evaluations of economic development programs and that a broader  perspective on program results might be useful.
USDA also commented that its performance reporting system was intended  to be used as a management tool for both USDA and the individual EZs and  ECs. According to USDA, the system was not designed to be an accounting  tool but has been useful for providing a picture of each designated  community’s achievements. As we discussed in our report, we used  information from USDA’s reporting system to report on the types of  activities that designated communities implemented and also noted that  USDA maintained some information on the amounts of EZ/EC grants  budgeted for specific activities. Moreover, while we recognize the system  was not intended to be used as an accounting tool, we found that the data  on the amounts of the EZ/EC grant funding were not reliable. For example,  in our assessment of the reliability of data contained in USDA’s  performance reporting system, we were often unable to find  documentation of the actual amounts allocated or expended for specific  activities.
USDA further commented that it had encouraged designated communities  to report all investment that contributed to the EZ or EC in accomplishing  its strategic plan as leveraged funds. We recognize USDA’s efforts to  encourage leveraging in the designated communities and to report such  information in its performance reporting system. Our report notes that  stakeholders from all EZs and ECs we visited and EC survey respondents  reported having used their EZ/EC grants to leverage other resources.  However, we were unable to evaluate the amounts of funds leveraged  because the data contained in USDA’s performance reporting system were  not reliable. For example, USDA’s performance reporting system included  information on the amounts of funds leveraged for each activity, but for the  sample of activities we reviewed, either supporting documentation showed  an amount conflicting with the reported amount or documentation could  not be found. Moreover, as we discuss in our report, the definition of  leveraging used among the designated communities was inconsistent.
We are sending copies of this report to interested Members of Congress,  the Secretary of Health and Human Services, the Secretary of Housing and  Urban Development, the Secretary of Treasury, the Commissioner of the  Internal Revenue Service, and the Secretary of Agriculture. We will make  copies of this report available to others upon request. In addition, this  report will be available at no charge on the GAO Web site at  http://www.gao.gov.
Please contact me at (202) 512-8678 or 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.
section_title: Objectives, Scope, and Methodology
paragraphs: 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.
subsections: section_title: Methodology for Site Visits
paragraphs: To answer our objectives, we completed site visits to all 11 EZs and 2 of the  95 ECs, one urban and one rural. 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). 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.
section_title: Methodology for Survey of EC Officials
paragraphs: To gather similar information from the ECs, we administered an e-mail  survey to officials from the 60 Round I ECs that were still in operation as of  June 2005 and did not receive a subsequent designation. We chose to  exclude the 34 ECs that received subsequent designations, because we did  not want their responses to be influenced by those programs. A version of  the survey showing aggregated responses can be viewed at  www.gao.gov/cgi-bin/getrpt?GAO-06-734SP.
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. 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. 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.
section_title: Methodology for Qualitative Analysis of Site Visit and EC Survey Data
paragraphs: 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;  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.
section_title: Methodology for Review of Program Oversight
paragraphs: 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. 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.
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. 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.
section_title: Methodology for Survey of EZ Businesses
paragraphs: 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. 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. 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. 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.
section_title: Methodology for Assessing the Effect of the Program on Poverty, Unemployment, and Economic Growth
paragraphs: 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. 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.
subsections: section_title: Description of Data Sources
paragraphs: 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. 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.
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.
section_title: Choosing Comparison Areas Using the Propensity Score
paragraphs: 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.
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. 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. 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.
section_title: Our Descriptive and Econometric Analyses
paragraphs: 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. 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.
section_title: Methodology for and Results of Our Econometric Models
paragraphs: 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. 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. 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.
subsections: section_title: Description of Our Models
paragraphs: We used a weighted least square regression for our analyses. 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.
section_title: Results of Our Models for Poverty
paragraphs: 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.
section_title: Results of Our Models for Unemployment
paragraphs: 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.
section_title: Results of Our Models for Economic Growth
paragraphs: 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. 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). 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.
section_title: Other Variables Tested for Use in Our Econometric Models
paragraphs: 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.
section_title: List of Communities Designated in Round I of the EZ/EC Program
section_title: Description of the Empowerment Zones and Enterprise Communities We Visited
paragraphs: 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.
section_title: Comments from the Department of Health and Human Services
section_title: Comments from the Department of Housing and Urban Development
paragraphs: The following are GAO’s comments on the Department of Housing and  Urban Development’s letter dated August 17, 2006.
subsections: section_title: GAO Comments
paragraphs: 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.
section_title: Comments from the U.S. Department of Agriculture
section_title: GAO Contact and Staff Acknowledgments
subsections: section_title: GAO Contact
section_title: Acknowledgments
paragraphs: 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.