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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. The following are GAO’s comments on the Department of Housing and Urban Development’s letter dated August 17, 2006. 1. HUD commented that GAO should include details on the amount of funding and tax incentives provided for Rounds II and III of the EZ/EC program. We noted in our report that communities designated in Rounds II and III received a smaller amount of funding and more tax benefits than those designated in Round I. Our statement does not provide further details on Rounds II and III because the focus of the report is Round I. 2. We recognize that Round I designees were required to address four key principles as part of their strategic plans. However, our mandate was to assess the effectiveness of the EZ/EC program on poverty, unemployment, and economic growth. Assessing the extent to which communities addressed the key principles would not have been useful in meeting our mandate because, among other things, there is not a clear relationship between the key principles and poverty, unemployment, and economic growth. Further, while the report did not evaluate the extent to which communities met the key principles, it included many examples of activities carried out under them. The report also indicated that communities had implemented a larger percentage of community development activities than economic opportunity activities but did not comment on the appropriateness of the distribution of activities. 3. Our mandate was to assess the effects of the EZ/EC program on poverty, unemployment, and economic growth. Our report stated that communities were required to submit strategic plans that addressed the four key principles. However, because communities were able to modify their strategic plans over time, it would have been difficult to establish set criteria for assessing performance. Nonetheless, our report does contain numerous examples of activities undertaken by the communities, including examples mentioned in a separate appendix focusing on the 13 designated communities we visited. 4. HUD commented that because GAO found that a lack of data on how program funds were used was a limiting factor in determining the effectiveness of the EZ/EC program, we should make use of information in the agency’s performance reporting system and in communities’ strategic plans. However, we reported that our file review to determine the accuracy of data in HUD’s performance reporting system found that the data were not sufficiently reliable for our purposes. For example, we found evidence that communities had undertaken certain activities with program funding, but we were often unable to find documentation of the actual amounts allocated or expended. As a result, we were unable to rely on information contained in the agency’s performance reporting system on the amounts of program funds allocated or expended on specific activities. 5. We found that data in HUD’s performance reporting system on the amounts of funds used and the amounts leveraged were not reliable. For example, we found that HUD’s system included information on the amount of funds leveraged. But for the sample of activities we reviewed, the supporting documentation either showed an amount conflicting with the reported amount or was not available. Moreover, we found that the definition of “leveraging” varied across EZ and EC sites. HUD further commented that Table 5 in the report showed that the agency’s performance reporting system received a code of 2.0, showing that leveraging data had strong documentation. However, HUD appears to have misinterpreted the information we presented on this matter. We found that HUD’s data on leveraging received an average code of 1.0, indicating that such information had weak documentation. Lastly, HUD recommended that it be allowed to alleviate GAO’s concerns about the reliability of its leveraging data by demonstrating how the data were tracked and recorded in its performance reporting system. However, the data reliability problems we found during the course of this work were due not to concerns about the system used to track and record the data, but rather to the frequent lack of supporting documentation for the data entered into the system. 6. HUD commented that our report did not adequately address HUD’s performance reporting system and its role in HUD’s oversight of the urban Round I EZ and ECs. We acknowledge that HUD established the system in response to an earlier GAO recommendation and has since used it to oversee Round I EZs and ECs. Moreover, we agree that the system contains a variety of information and data elements, including activities implemented and program outputs. We also acknowledge that the performance reporting system is not intended to be a financial system for Round I. However, as discussed in our report, we found that because the system did not always contain information on what was spent on activities and did not always contain reliable information, HUD and the other federal agencies were limited in their ability to oversee the program. 7. HUD commented that the program’s design was significant because it provided insight about the nature and extent of the federal, state, and local attitudes that existed at the time of the first Round of EZs/ECs. HUD also stated that it did not conduct monitoring of the SSBG funds because monitoring those funds was the responsibility of HHS. HUD’s statement further supports our discussion on the limitation in the oversight of the EZ/EC program that may have resulted from the program’s design. Although we found program oversight was hindered, we also reported that no single federal agency had sole responsibility for oversight. We do not agree with HUD’s recommendation that we make clear that more oversight was not allowed in Round I. For example, early in the program HUD and HHS made some efforts to share information. Specifically, HUD officials said that they had received fiscal data from HHS and reconciled that information with their program data on the activities implemented, but these efforts to share information were not maintained. Regarding the second recommendation, although HUD described some of its efforts to monitor the program according to applicable regulations, the oversight concerns we identified in the report remain. 8. We reported that limitations in the oversight of the EZ/EC program may have resulted from the design of the program. 9. We stated in the report that the concerns raised about program oversight for the Round I EZ/EC program may not apply to future rounds of the EZ/EC program. We also acknowledge that HUD may have made changes in its oversight of later rounds of the program. However, an evaluation of later rounds of the EZ/EC and Renewal Community programs is beyond the scope of this report. 10. In our report, we acknowledged HUD’s as well as the other agencies’ response to the recommendation in our 2004 report to identify a cost- effective means of collecting the data needed to assess the use of the tax benefits. 11. Our report acknowledged the collaboration among HUD, IRS, and USDA in addressing our previous recommendation and summarizes the outcome of their discussions, including the identification of two data collection methods—through a national survey or by modifying the tax forms. In addition, our report also acknowledged that IRS did not have any data for some program tax benefits. The lack of data on the use of tax benefits continues to be a source of concern that limits an assessment of the effect of the EZ/EC program. 12. We agree that HUD’s efforts to develop a methodology to administer a survey to businesses to assess the use of the program tax benefits is a useful step in gathering such information. 13. We recognize the efforts between HUD and Treasury on sharing national-level data on EZ businesses’ use of tax credits for employing EZ residents. However, as we mention in our report, data on the EZ employment tax benefit were limited because they could not be linked to the specific EZ claiming the benefit. 14. In the absence of other data, we acknowledge HUD’s efforts to capture anecdotal information on the use of program tax benefits by EZ businesses. 15. We recognize HUD’s efforts to market the EZ/EC program tax benefits. 16. We appreciate HUD’s suggestion on how to approach evaluations of later rounds of the EZ/EC and Renewal Community programs and welcome the opportunity to discuss these ideas. 17. We appreciate HUD’s comments on the descriptive information on EZs and ECs we visited that are discussed in appendix IV. 18. HUD commented that the measures used in our report---poverty, unemployment and economic growth—were used in the application process and were not intended to be used as performance measures. However, as mentioned earlier our mandate was to assess the effects of the EZ/EC program on poverty, unemployment, and economic growth. 19. HUD suggested that we consider additional methodologies for measuring the effects of the EZ/EC program, such as trend analysis using data from 1990 through 1995 and 1995 through 2000. To conduct our work, we used 1990 and 2000 data to measure changes in poverty and unemployment and 1995, 1999, and 2004 data to measure changes in economic growth. We chose these dates because data were available at the census tract level for these years. Moreover, in designing our methodology for our econometric analysis, we conducted a literature review and discussed our methodology with several experts in the urban studies field and determined that the approach presented in this report was effective in answering the objectives of our mandate. As mentioned in Appendix II, we also conducted different tests to ensure the robustness of our models, which all yielded results consistent with our models. The approach that HUD suggested controlled for trends that began before the EZs were designated in 1994. Because we did not have data on poverty or unemployment for 1995 we were unable to use this approach. However, our use of housing trends between 1990 and 1994 in our econometric model controlled for some trends that were in place prior to EZ designation. HUD also suggested a longitudinal case study approach might be the best way to assess the effectiveness of this type of program. Although a longitudinal case study approach would be informative, it is unlikely that a successful retrospective longitudinal study could be designed at the end of the program. As HUD noted, this intervention was intended to be implemented over a ten-year period. However, a longitudinal case study approach would necessitate data collection beginning at the inception of the program and continuing for the duration of the program as well as some period of time after it ends. 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.

Summary: 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. 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.