Governance and Urban Revitalization: Lessons from the Urban Empowerment Zones Initiative

Michael J. Rich

Emory University Department of Political Science

Robert P. Stoker George Washington University Political Science Department

Prepared for the Conference on A Global Look at Urban and Regional Governance:

The State-Market-Civic Nexus

Sponsored by the Halle Program on Governance and the Department of Political Science

Emory University Atlanta, GA

January 18-19, 2007

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The Empowerment Zones and Enterprise Communities initiative was an innovative

federal policy to revitalize distressed communities. In the six original urban empowerment

zones, the initiative tried to spark economic and community development by combining several

market-oriented policy tools (such as special financing mechanisms, tax incentives, and

regulatory relief) with a ten-year, $100 million block grant to support local governance and

programs. The distinctive design of the initiative raises an important question: Are market-

oriented tools, when used in conjunction with intergovernmental block grants, effective means to

revitalize distressed urban communities?

Several recent evaluations, including one conducted by the Government Accountability

Office (GAO), have concluded that the Empowerment Zones and Enterprise Communities

initiative was not successful (GAO 2006; Oakley and Tsao 2006). This paper presents evidence

about local programs and outcomes in the six original urban empowerment zones (in Atlanta,

Baltimore, Chicago, Detroit, New York, and Philadelphia). The complexity of the initiative and

the unusual level of local discretion that it allowed make it difficult to arrive at summary

judgments about the performance of the initiative because local programs were often designed to

accomplish different objectives. However, if the evaluation is focused on the primary federal

objective of creating economic opportunity and jobs to assist needy zone residents, there is

evidence that some local empowerment zone programs created measurable benefits. Beyond

this, there is substantial evidence that empowerment zone programs created measurable benefits

when evaluation data are aggregated below the zone level.

Our research shows that important differences exist between and within empowerment

zones. This variation between and within localities suggests that an understanding of local

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context is essential to have an accurate interpretation of evaluation data. Consequently, we

complement our evaluation of the six original empowerment zones with qualitative evidence

about local context and programs in Atlanta and Baltimore.

Our evidence leads to three primary conclusions. First, the empowerment zone initiative

was not a national program that was implemented in several different sites; it was a set of local

programs that were sponsored by the same federal grants and made use of the same market-

oriented tools. Second, our evidence shows that some of the local programs were effective.

Third, the quality of local governance was a critical factor that distinguished successful from

unsuccessful programs. Our qualitative evidence suggests that successful local governance in

Baltimore integrated a variety of stakeholders into the policymaking process, coordinated their

actions within a focused economic development plan that reflected local opportunities and

constraints, and contributed to economic development by coordinating programs, increasing the

number of tools available to stimulate business and job growth, enhancing public services, and

informing businesses about market-oriented redevelopment incentives. By contrast, community

conflict in Atlanta undermined the empowerment zone program to such a great extent that little

was accomplished.

As federal urban policy has evolved over the past decade, efforts to assist distressed

communities have emphasized market-oriented tools and de-emphasized grants to support local

governance and programs (Stoker and Rich 2006). Our findings suggest that this policy shift is

wrong-headed: Market-oriented tools work better when they are used in conjunction with

effective local governance and complemented by effective local programs. Intergovernmental

block grants that support local governance and fund programs that reflect local conditions are an

essential part of successful efforts to assist distressed urban communities.

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Not Your Typical Federal Program

According to the Department of Housing and Urban Development (HUD), the

Empowerment Zones and Enterprise Communities initiative was “not a typical federal program”

(1994). The initiative was based on four key principles: (1) expanding economic opportunity, (2)

promoting sustainable community development, (3) fostering community-based partnerships,

and (4) crafting a strategic vision for change. Although the initiative’s application guide stated,

“the first priority in revitalizing distressed communities is to create economic opportunities –

jobs and work – for all residents,” the key principles implied that successful economic

development must be part of a broader agenda for community revitalization. The principles also

implied that economic development efforts were more likely to succeed when they were

grounded in a strategy that coordinated economic, environmental, and physical renewal with

community and human development programs. Finally, the principles implied that community

revitalization must be supported and driven by a broad coalition that includes local, state, and

national governments, the private sector, nonprofit agencies, community-based organizations,

and residents.

A distinctive feature of the Empowerment Zones and Enterprise Communities initiative

was its emphasis on promoting sustainable community development. According to the

Application Guide, “economic development can only be successful when part of a coordinated

and comprehensive strategy that includes physical development as well as human

development.”1 Thus, unlike previous federal enterprise zone proposals discussed in the 1980s,

which featured only tax incentives and regulatory relief, focused almost exclusively on

1 The President’s Community Enterprise Board, Building Communities: Together—Empowerment zones and Enterprise Communities Application Guide (Washington, D.C.: U.S. Department of Housing and Urban Development and U.S. Department of Agriculture, January 1994), p. 8.

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businesses, and provided little – if any – attention to the communities in which those businesses

were located, the Empowerment Zones and Enterprise Communities initiative had as a

fundamental objective the revitalization of distressed communities. According to the application

guide, “this initiative seeks to empower communities by supporting local plans that coordinate

economic, physical, environmental, community, and human development.”2

The application guidelines suggested that targeted economic development programs were

to be guided by a local process of comprehensive planning and service integration that attacked

problems systematically. In addition, “the community” was to be mobilized and included as

significant participants in the policymaking process. The guidelines also implied that the

problems of distressed urban communities require an ongoing, institutionalized response; it was

insufficient merely to have episodic programs to address discrete problems. To be effective, the

forces for change must be comprehensive and sustainable. However, rather than imposing a

standardized program in all sites, the guidelines encouraged the development of local strategic

plans that reflected local problems, resources, and opportunities.

Empowerment zones designees each received a $100 million Social Services Block Grant

(SSBG) that could be used for a broader range of activities than was normally permitted under

the Title XX SSBG program. The flexible funding provided over a ten-year period allowed local

actors to plan deliberately and to develop a variety of different tools for their empowerment zone

programs that reflected local needs. Empowerment zones also were eligible for $150 million in

federal tax credits including an empowerment zone wage tax credit that was worth as much as

$3,000 per employee, non-recognition of capital gains on the sale of assets and partial exclusion

of capital gains on stock sales, increased deductions for businesses that investment in machinery

2 Ibid.

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and equipment, and tax-favored bond financing (empowerment zone facility bonds). In addition,

federal agencies were directed to give special consideration to empowerment zone communities

that applied for other types of federal aid tied to any empowerment zone activity and to give

priority consideration to requests for relief from regulatory requirements of established federal

programs operating in the zones.

Seventy-four cities submitted applications to HUD in 1993 for designation of selected

areas within their borders as an urban empowerment zone. Atlanta, Baltimore, Chicago, Detroit,

New York, and Philadelphia/Camden were selected.3

Local Empowerment zone Programs

HUD managed the urban empowerment zone initiative in a manner that permitted

significant local variation in program content. To describe local programs, we analyze

information about local program content from the Performance, Monitoring, Review, and

Management (PERMS) reports for each empowerment zone city. Reflecting the influence of

holistic redevelopment initiatives that were popular in the early 1990s, each of the empowerment

zone cities adopted a multi-faceted strategy to encourage economic redevelopment that included

business services, workforce development, human services, public safety, housing, and physical

improvements. However, the Title XX budget allocations and program narratives submitted to

HUD by the local empowerment zone authorities as PERMS reposts suggest that the extent to

which these various elements were a priority did vary from one city to another.

Table 1 presents data gathered from the PERMS reports filed by each of the six original

empowerment zone cities.4 The data in the table are proportions of budget allocations from the

3 The composition of the empowerment zone in Philadelphia, Pennsylvania and Camden, New Jersey reflects a requirement in the enabling legislation that at least one of the Round I urban empowerment zones must be composed of an urban area that spans at least two states.

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$100 million Title XX block grant that was provided to each city to support local governance and

programs. The expenditure totals do not sum to $100 million because not all of the funds were

allocated by the closing date of the reports and some of the reports omit some expenditures (such

as administrative costs). Because the Title XX funds are directly controlled by the

empowerment zone implementing authority in each city, it is the best indicator of the priorities

and preferences of each governing authority.5 To identify the top local priorities, we selected the

categories that have the highest proportions of Title XX expenditures and summed until the total

proportion was at least fifty percent of the reported Title XX allocations.

In several cities, business development was the top priority. In Atlanta, New York, and

Philadelphia, a majority of the Title XX funds were allocated for business development. In

Baltimore, workforce development was the top priority, with business development second.

Within the cities where business development was a top priority, there was variation in the

means empowerment zone programs used to encourage business growth. In Baltimore and

Philadelphia, business development programs focused on providing access to capital by creating

loan programs. In New York, emphasis was placed on funding a variety of specific projects as

4 In all of the cities except Atlanta and Philadelphia, the reports are cumulative reports through the end of 2005. However, Atlanta sacrificed its empowerment zone designation in 2001 to receive designation s a Renewal Community. Consequently, the last annual empowerment zone report from Atlanta is for 2001. The data for Philadelphia are from the 2004 PERMS report (the last report available from Philadelphia in January 2007). 5 Two qualifications are in order here. First, the Government Accountability Office (2006) reviewed program budgetary data and deemed the data to be unreliable. Consequently, their program descriptions are based on an activity count rather than an analysis of budgetary data. However, our reading of the GAO report and our own field observations suggest that an important distinction exists between the Title XX money and monies that were claimed as “leveraged” funds. Our impression, consistent with the concern expressed in the GAO report, is that the data on leveraged funds are unreliable because of issues relating to poor accounting and credit-claiming. Consequently, we have limited this analysis to the Title XX money because these funds were closely monitored and controlled by virtue of the intergovernmental arrangements for implementation of the initiative. Second, New York had a distinctive local financing arrangement in which the $100 million federal grant was matched by state and local grants of equal amounts, making the allocation pot in New York’s empowerment zone $300 million. We have not included expenditures of the state and local funds in this analysis because with few exceptions, budgetary allocations in New York simply split the burden of program financing into three equal parts and drew the same amount from each of the three grants. Thus, accounting for state and city grants would do little to change our conclusions about the relative priority of different activities in New York.

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well as business finance. The largest single item in New York’s empowerment zone budget,

which accounted for more than $8 million in Title XX funding, established a loan fund targeting

small businesses in the Bronx. The largest development projects were located in Manhattan and

included budgeted items of more than $5.6 million for a General Motors Auto Center and more

than $3.7 million for the “Harlem USA Retail and Entertainment Complex.” Atlanta also

provided funds for business finance while underwriting two specific projects, specifically, the

“Historic Westside Village Development Project” and the “Southside/Pryor Road Development

Project.”

By contrast, the empowerment zone programs in Chicago and Detroit emphasized human

needs as reflected in budget allocations that prioritized human services and housing. A

distinctive feature of Chicago’s program is that empowerment zone officials there funded a

larger number of programs than was true in the other empowerment zone cities. For example,

Chicago financed seventy-two different human service initiatives. Large allocations were made

to support healthcare and childcare initiatives. The largest single initiative focused on

healthcare; approximately $5 million was allocated to underwrite construction and operation of a

health and wellness center. Chicago’s tendency to fund a large number of programs also is

evident in housing. In all, Chicago officials financed twenty-three housing initiatives with Title

XX funds totaling more than $19 million. Detroit established a distinctive approach to

empowerment by allocating a majority of the Title XX grant to human service programs. Ten

million dollars, the largest single allocation, were devoted to the “Innovation Fund,” a fund to

support existing community programs to stabilize families and support individual efforts to

become productive members of the community. Other large programs were “Schools as the

Heart of the Community” (an after-school activities initiative), “Homebound Elderly Care

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Management” (services to assist homebound elderly residents), and a “The Family Place” (an

early childhood support and resource center).

Baltimore’s empowerment zone program is distinctive because workforce development

received the largest proportion of Title XX allocations. The local program for workforce

development in Baltimore was unusual; more than $13 million in Title XX funds were used to

create “Career and Family Support Centers” in six Village Centers located throughout the

empowerment zone. These programs were designed to reflect community priorities and needs by

leaders in each of the Village Centers and integrated an array of services that could be

customized to meet the needs of different zone residents including, literacy training, job training,

and drug or alcohol rehabilitation. In addition, almost $8 million of Title XX funds were

allocated to finance “Customized Training,” a program that worked with zone employers to

recruit and train zone residents for specific job opportunities.

Table 1 also indicates some unusual local expenditure patterns. New York devoted a

considerable amount of its Title XX funds to the “other” category. Many of these expenditures

were grants to local cultural institutions (museums, performance arts centers, or performance

companies). Although this sort of expenditure is unusual compared to other empowerment zone

cities, it does reflect the emphasis HUD placed on local flexibility and the development of

strategies that reflect local strengths. Empowerment zone officials in New York are trying to

encourage tourism through support for museums, historic buildings, and the performing arts, a

strategy that reflects the distinctive history of some zone neighborhoods in New York. Second,

the public safety expenditures in Detroit were unusual. Detroit budgeted 11 percent of its Title

XX funds for this purpose, almost three times the proportion budgeted in Atlanta, the

empowerment zone that devoted the second largest proportion of funds for public safety. The

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primary public safety programs created in Detroit were a community policing initiative and a

program to integrate criminal justice services.

Although the narratives contained in the PERMS reports are more ambiguous than

budgetary figures, some of the narratives identify specific priorities that indicate differences in

local programs. Our analysis of Table 1 concluded that in Atlanta, New York, and Philadelphia,

economic development was the top priority. Though Atlanta’s original budget submitted with its

strategic plan identified “Lifting Youth and Families Out of Poverty” as the single largest

strategic theme ($36.3 million), most of the activities in this category identified in the strategic

plan were never implemented and the vast majority of funding for this category was redirected

for other uses. By the time the Atlanta empowerment zone initiative came to a close, more than

half of all the funds approved by the Atlanta Empowerment Zone Corporation ($50.3 million)

were earmarked for projects to support the strategic theme of “Expanding Employment and

Investment Opportunity.” More than $30 million of the Title XX funds Atlanta allocated for

economic development were awarded to four major redevelopment projects.

New York’s narrative identified three priorities in the zone’s Manhattan section:

development of anchor retail projects, support for small business, and support for cultural

enterprises. Although the goals listed for the zone’s Bronx section are more numerous and often

focus on process (such as sustaining progress or establishing strategic partnerships), the

outcomes that are mentioned as priorities are related to economic development. Philadelphia’s

report is more comprehensive than the others and reviews each program area, noting the number

of benchmarks pending and achieved. However, the two top priorities mentioned in the report

are economic and community development.

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The narratives from the zones in Chicago and Detroit, consistent with our analysis of

Table 1, emphasize human services and community development. Chicago’s report emphasized

services and opportunities afforded to zone residents and identified “sustainable community

development as the most important objective of this initiative” (Chicago PERMS, 2005, p.2).

Detroit’s report states: “economic development, despite its obvious importance, has never been

the primary focus of the Detroit Empowerment zone. The Strategic Plan of the Zone clearly

indicates that the major thrust of the Zone’s programs and projects was to be the delivery of

human services to Zone residents” (Detroit PERMS, 2005, p.5). These narratives are consistent

with our analysis of Title XX data that indicates that human services, rather than economic

development, were the highest priorities in Chicago and Detroit.

Baltimore’s program narrative is also consistent with the data presented in Table 1.

Empowerment zone leaders in Baltimore identified business development, workforce

development, and homeownership as the three key elements of their strategic plan. This is

consistent with our claim that Baltimore’s program emphasized workforce and business

development. Although home ownership was not identified as a key program area on the basis

of our analysis of Table 1, the emphasis on homeownership is consistent with the Title XX

budgetary data that indicate that housing was a secondary, though significant, priority.

Our analysis of program content leads to different conclusions than those presented by

Oakley and Tsao (2006). Their analysis was organized around three of the key principles that

were the basis of the empowerment zone initiative (community-based partnerships, economic

opportunity, and sustainable community development) and subcategories within each principle;

they concluded that the economic opportunity principle received the majority of funding in all of

the zones they examined except Chicago, and that most of the economic opportunity

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expenditures were devoted to economic development initiatives. Consequently, they conclude

that the innovative nature of the empowerment zone initiative may have been an exaggeration:

“despite the official rhetoric emphasizing the empowerment of people and communities, the

actual programmatic emphases reveal more traditional economic development strategies”

(Oakley and Tsao 2006, p. 456). Our view is that their conclusion is skewed by the leveraged

funds data reported in the PERMS. Because most of the leveraged funds identified in the

PERMS reports were devoted to private business investments, including these funds in the

analysis naturally leads to the conclusion that economic development was the highest priority.

Our view is that the leveraged funds do not reflect local priorities so much as differences in the

extent to which local empowerment zone authorities claimed credit for private investments that

occurred in the zones, investments that other reports have found suspect (e.g., GAO 2006).

The GAO report (2006, see Appendix IV) describes programs based on the number of

activities reported in each city, organized into eleven different programmatic areas. The GAO

report (2006) downplayed the value of data provided by the PERMS system because they

concluded that the financial data were unreliable. Consequently, although the GAO did use the

PERMS reports to sketch local program content, they counted the number of activities reported

rather than the resources allocated to each activity. In their reply to the GAO report (2006) HUD

disputed the contention that the financial data were not reliable. Our view (based on our field

observations in Atlanta and Baltimore) is that the data concerning the use of Title XX funds are

reliable, though we understand and agree with the concerns the GAO expressed about the

reliability of data concerning leveraged funds that were included in the PERMS reports.

The GAO report is consistent with our conclusions in that they observe significant

variation from one empowerment zone program to another in terms of programmatic and

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strategic focus. The GAO concluded that Atlanta emphasized housing and public safety;

Baltimore emphasized workforce development and business assistance; Chicago emphasized

workforce development, human services (including education) and housing; Detroit emphasized

human services; New York emphasized business assistance (including access to capital) and

workforce development; Philadelphia emphasized education and business development

(including access to capital). Only in Baltimore and New York were the majority of activities

oriented toward the economic opportunity principle. The GAO observed that the majority of

activities in Atlanta, Chicago, Detroit, and Philadelphia were oriented toward community

development. Our concern about the GAO’s conclusions is that counting activities gives equal

weight to small and large programmatic efforts and that can create misleading conclusions about

local priorities (compare, for example, GAO’s assessment of Atlanta’s emphasis on housing and

public safety with our classification of the Atlanta initiative being focused almost exclusively on

economic development).

The evidence presented in Table 1 reveals significant variation in local priorities and

shows that there was no national “Empowerment Zone Program;” rather, the empowerment zone

initiative sparked very different local programs that used different methods to achieve different

objectives. Thus, although it is true that all of the empowerment zones had the same tax

incentives and a $100 million block grant from the federal government, these resources were

used in very different ways in the six original zones. This is significant because rather than

thinking of the empowerment zone initiative as a single program that was implemented in several

different sites, it is more appropriate to think of it as a set of distinctive local programs. This

suggests that it may be inappropriate to use an evaluation strategy that pools observations across

the empowerment zone cities as was done in the recent Oakley and Tsao (2006) and GAO (2006)

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studies. Our data on program choices as well as our description of empowerment zone

governance entities strongly suggest that the construct validity of a national empowerment zone

strategy is extremely weak.

Evaluation and Program Design

It is extremely difficult to evaluate urban revitalization initiatives because urban

communities are open, dynamic systems that can be influenced by many different factors. To

evaluate targeted economic development initiatives, program effects must be distinguished from

the many confounding factors that also can influence urban economic performance. In addition,

the scale of urban revitalization initiatives is typically small in comparison to the scope of the

economy. Consequently, even if programs are effective, the magnitude of the program in

comparison to the overall size of the economy can make program effects difficult to detect. This

implies important concerns for evaluation design, and in particular, for the geographic level at

which program effects are likely to be evident. Finally, the local organizations that control

information about urban revitalization initiatives are more likely to be local boosters than tough-

minded critics; this makes performance data from disinterested third parties an essential

requirement for sound evaluation.

In addition to the usual difficulties, several features of the Empowerment Zones and

Enterprise Communities initiative further complicate the task of evaluation. First, local program

activities were designed to overlap and complement one another in order to compose a

comprehensive attack on numerous, related urban problems. Even when changes in urban

conditions occur in the zones, it will be difficult to know which program activities or

combination of program activities are responsible for the changes that are observed. Second, the

policy tools that are used to encourage revitalization are often indirect (and this is especially true

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when market-oriented tools are used). This makes it more difficult to forge a plausible link

between program activities and outcomes because the chain of causation can be long and

indirect. Third, in theory, local interventions were expected to produce synergy. For example,

reducing crime was expected to encourage investment; investment was expected to generate job

growth; job growth was expected to increase incomes and housing values as workers purchased

homes in the zone to live near where they work, encouraging the development of a stable

community and creating new markets for consumer-oriented businesses. The objectives of the

program are (by design) interrelated in complicated ways, making it difficult to establish the

trails of causation from program activities to outcomes.

Beyond this, the approach HUD embraced in developing and implementing the

empowerment zone initiative reflected a new community building paradigm for urban

revitalization that emphasized comprehensiveness, collaborative governance, community

participation, and strategic planning (Gibson, Kingsley, and McNeely 1997). When partners

participate in the development, financing, and implementation of local programs, empowerment

zone resources may be only one of many inputs that support local activities. It is unclear what

share of the credit should go to the empowerment zone program when resources to support its

activities come from several sources and what standards should be used to evaluate success when

empowerment zone resources may not be necessary or sufficient to achieve an objective but

make a contribution nonetheless. By encouraging local planning, HUD allowed significant

variation among local empowerment zone programs, making it difficult to have a common

evaluation framework that can be applied across sites. HUD also expected and permitted local

programs to change over time as strategies were developed and tested, making the content of

local programs a moving target. Finally, HUD expressed high expectations about community

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participation. Programs that “worked” (in the sense that they produced desirable outcomes) but

failed to mobilize the community may not have satisfied the federal objective to create

sustainable, community-based forces for change within the empowerment zones.

Some of these problems simply cannot be surmounted. In particular, the initiative’s

design precludes linking outcomes to discrete program activities because many different

programs that were expected to produce synergies were implemented simultaneously and the

level of information HUD required empowerment zone cities to report on individual activities

was not very detailed. Local programs must be evaluated as packages and this will make it

nearly impossible to establish links between program outcomes and specific programs or

activities within each city. In addition, it is not possible to untangle the effects of “empowerment

zone resources” (the Title XX block grants) and other leveraged funds that supported

empowerment zone programs, particularly given the uncertain standards that prevailed in local

accounting decisions about what counts as leveraged funds (GAO 2006).

However, some of the difficulties of evaluating the initiative can be addressed by paying

attention to local processes. Because HUD permitted local programs to develop independently

and evolve over time, program descriptions must account for local priorities. In addition, by

paying attention to local governance, inferences can be drawn about the extent to which

community mobilization was a priority and the extent to which such mobilization was achieved.

Although many aspects of the Empowerment Zones and Enterprise Communities

initiative complicated the task of evaluation, one aspect of its design did make evaluation more

feasible. Federal guidelines required that the borders of each empowerment zone correspond to

Census tracts. Although this requirement complicated the local problem of identifying

boundaries for the zones because it sometimes divided established neighborhoods, it also

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provided a basis for evaluation. By comparing information aggregated on the Census tract level

at different points in time within the zone tracts as a group (treatment) and a group of

comparison tracts (control), a quasi-experimental evaluation framework can be developed.

Our evaluation has two bases for comparison: (1) we compare empowerment zone tracts

to other eligible but not zone-designated high poverty tracts within the city and (2) we use

propensity score matching procedures to compare the mean difference between treatment and

control Census tracts after adjusting for covariate imbalance between the two groups. In

addition, we use outcome data from two different sources (the Census Bureau and Claritas6, a

commercial data provider) that have no material interest in the evaluation. Our outcomes

analysis focuses on three indicators that are direct measures of the economic opportunity goals of

the empowerment zone initiative: the number of jobs created between 1996 and 2004 (Claritas),

the percentage reduction in the number of persons below poverty between 1990 and 2000

(Census), and the percentage reduction in the number of persons unemployed between 1990 and

2000 (Census).

Our evaluation framework combines quantitative data about all six of the original urban

empowerment zones with qualitative data (based on participant observation, documentary

analysis, and interviews with key stakeholders) about governance and programs in Atlanta and

Baltimore, and from secondary analysis of data previously reported in HUD’s national

assessments of the Empowerment Zones and Enterprise Communities initiative conducted by the

Rockefeller Institute of Government at the State University of New York at Albany and Abt

6 A critical element needed to assess the impact of the Empowerment zone initiative is neighborhood-level business employment data. One of the primary objectives of the initiative was to enhance economic opportunities in the most distressed neighborhoods of the designated cities. Existing federal data (e.g., County Business Patterns, Economic Censuses) are inadequate for this task since these sources do not report data for a small enough geographic area to compile statistics and trends for zone neighborhoods versus non-zone neighborhoods. However, Claritas, a

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Associates. The authors served as field associates for Atlanta (Rich) and Baltimore (Stoker) for

both of those studies.

The Existing Evaluations

Our evaluation methods and conclusions about the empowerment zone initiative contrast

sharply with the existing evaluation literature. The GAO (2006) conducted an evaluation of the

eight Round I empowerment zone programs, including all six of the original empowerment zones

and also adding Cleveland and Los Angeles (cities that were initially designated as Supplemental

Empowerment zones). The GAO study concluded that although conditions did improve in many

empowerment zone communities, these changes may or may not have been a result of the

initiative. The evaluation by Oakley and Tsao (2006) examined four of the six original

empowerment zones and reached a similar conclusion: Although the empowerment zones did

experience economic and job growth and there were some isolated cases in which empowerment

zones performed better than comparison areas within their host cities, changing conditions in the

empowerment zones could not be attributed to the initiative. Although a number of details

distinguish our research from the existing evaluations, two points are especially important: (1)

we have a fundamentally different view of the nature of the empowerment zone initiative and (2)

we use methods that reflect our view that local program outcomes vary between and within each

of the empowerment zones.

The GAO (2006) and Oakley and Tsao (2006) studies treated the empowerment zone

initiative as a “program” that was implemented at several different sites across the country.

Consequently, their primary statistical models pooled observations across the different sites and

compared areas within the empowerment zones to matched comparison areas outside the zones.

nationally renowned provider of business and demographic data, has Census tract level business employment that

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Both of these evaluations also included local models that compared each local empowerment

zone and comparison tracts within each city. However, local program effects that were

inconsistent with the aggregate models were treated as aberrations rather than important findings

and the models for each city reflected the implicit premise that program effects were likely to be

uniform across each zone.

We think it is more accurate to treat the empowerment zone initiative as a series of local

programs sharing similar but not identical features. Consequently, we do not ask whether the

national empowerment zone program worked everywhere it was implemented, but rather

whether any one of the local programs worked and why. Beyond this, we do not expect the

effects of local empowerment zone programs to be uniform across each zone. Many local

programs also targeted specific geographic areas within the designated zones and were by no

means homogenous across zone neighborhoods in their application. Our expectation is that we

must look within each city at and below the zone level to detect program effects that may be

washed out when data are aggregated at the zone level or above. Consequently, our conclusion

is not that the empowerment zone initiative was successful (in fact we conclude that in many

localities it was not successful). However, we do contend that some local empowerment zone

programs did create measurable effects that can be detected and that the pattern of these effects

across cities suggests a relatively strong association with variations in local governance and civic

capacity.

A second, though related point, is that the application of the methods used by GAO

(2006) and Oakley and Tsao (2006) to organize and analyze the data is different from our

method. Reflecting our belief that program effects are likely to vary between and within local

empowerment zones, we do not create a statistical model that compares all of the empowerment

includes the number of establishments and number of employees.

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zone tracts to all of the matched tracts (as the existing evaluations do). In addition, we use

different approach for constructing the counterfactual comparisons within each city. We

compare the zone Census tracts to all other tracts within the city that were eligible for

designation. We also compare the zone tracts to selected tracts, matched on the basis of

propensity scores (as the existing evaluations do). However, we use a different method for

estimating effects – calculating the average treatment effect on the treated rather than using

regression techniques – that more closely approximates the real world contrasts between

treatment and control conditions than regression-adjusted estimates. Rather than compare the

mean for all of the zone tracts to the mean for all of the matched tracts (as the existing

evaluations do), we calculate the mean difference in outcomes for each matched pair of Census

tracts.

Third, the GAO and Oakley and Tsao studies examine a different set of cases than we do.

The GAO (2006) included Cleveland and Los Angeles as Round I Empowerment Zone

designees. While it is true that these sites were Round I designees, the programs in Cleveland

and Los Angeles were initiated in 1998, not 1995, and the program content was somewhat

different. Our decision to focus on the six original empowerment zones is more consistent in

terms of timing and program content. Oakley and Tsao (2006) examined four of the six original

empowerment zones, eliminating Atlanta and Philadelphia. Although (as Oakley and Tsao

observe) Atlanta’s empowerment zone program was formally terminated when the city received

designation as a Renewal Community before the original term of the empowerment zone

initiative had expired, field work (performed by Rich in Atlanta) indicates that implementation of

the empowerment zone continued by virtue of a special agreement with HUD following the

formal termination and that Atlanta’s renewal community program did not get off the ground

20

until 2005, nearly four years after the city was officially designated as a Renewal Community.

Consequently, we included Atlanta in our set of cases. Oakley and Tsao explained that they

eliminated the Philadelphia-Camden zone because, as a cross-state zone, it would have been

difficult to identify comparison tracts. We understand this difficulty but have taken a different

tack in addressing it; rather than eliminate the Philadelphia-Camden zone entirely we have

included only the Philadelphia part of the zone as one of our cases, due largely to the fact that the

two cities empowerment zone efforts were essentially separate (and independent) initiatives,

each with their own funding allocation (Philadelphia received $71 million) and governing

entities.

Finally, we use somewhat different indicators of program success. The GAO report

(2006) used two indicators, number of business establishments and number of jobs. Unlike the

GAO, we did not include the number of business establishments because that seemed to be an

intermediate objective (monitoring the number of business establishments was interesting

primarily because creating more businesses may result in the creation of more jobs) rather than

an outcome. Oakley and Tsao (2006) used only Census data for their evaluation and that is an

important limitation because Census data do not include indicators of jobs created and also do

not fully fit with the timing of the empowerment zone initiative (1990-2000 vs. 1995-2005).

Establishing the Counterfactual Framework

To estimate the effects of the empowerment zone intervention on zone neighborhoods we

need to establish a counterfactual framework that will allow one to examine what neighborhood

conditions would have been had there been no intervention. The empowerment zone initiative

provides an opportunity for what Campbell (1969) refers to as “reforms as experiments,” though

in this case we have a quasi-experiment in that cities did not randomly designate Census tracts as

21

empowerment zones. While randomization ensures that the treatment and control groups are

identical on all respects (observed and unobserved) except for the fact that the treatment group

was exposed to the intervention, the challenge for analysts of quasi-experiments is to “model the

selection process” in an effort to ensure group comparability. Then inferences about the effects

of social programs can be made on firmer grounds.

A typical strategy commonly employed in observational studies is to use regression

analysis to “control” for differences in covariates between the treatment and control groups and

then examine the coefficient of a dichotomous treatment variable (1 if in the treatment group; 0

otherwise) to determine whether the intervention had any effect on the outcome of interest.

However, as Oakes and Johnson (2006) point out, there are several concerns about the use of

regression analysis to determine causal effects in observational studies. The more obvious

concern is omitted variable bias: if the analyst fails to include all relevant confounding variables,

then the treatment and control groups will not be equivalent and the estimate of the effect of the

treatment on the outcome will be biased. A more subtle concern, according to Oakes and

Johnson, is the problem of off-support inference: “averages and other statistical procedures that

summarize information may end up obscuring fundamental differences between considered

objects.” They add that “it is not difficult to show that parameter estimates may be based not on

comparisons between actual persons but rather on extrapolation, interpolation, regression

smoothing, and imputation more generally.”

As an alternative to regression, analysts often use subclassification or matching to

determine the outcome differences between groups. While these techniques provide a more

direct comparison of empirically-observed subjects in the treatment and control groups, they

generally become quite difficult to implement when there are more than a couple of covariates to

22

“control” for. Oakes and Johnson point out that if subclassification needed to be based on five

dichotomous covariates, one would need 32 strata. They add that if the covariates needed for

subclassification were continuous variables or nominal variables with many categories,

subclassification would not be possible. Matching each subject in the treatment group to a

comparable subject in the control group to create a matched pair (a treatment subject and its

counterfactual substitute) presents the same dimensionality problems as subclassification.

A solution to the dimensionality problem is to use propensity score methods developed

by Rosenbaum and Rubin (1983; see also D’Agostino 1998; Oakes and Johnson 2006). The

propensity score is defined as the probability of a subject’s being exposed or unexposed to the

intervention. Propensity scores are generally calculated with a traditional logistic regression of

the treatment variable (1=exposed; 0=unexposed) on a set of covariates to explain exposure. The

propensity score thus reduces the dimensionality of potential confounding factors to a single

covariate.

Once calculated, propensity scores can then be used to match subjects in the exposed and

unexposed groups, which ensures that the matched exposed-unexposed subject pair consists of

subjects that had the same probability of exposure when, in fact, one subject was assigned to the

treatment group and the other was not. Given that the propensity score is based on a collection

of covariates thought to influence the assignment process, this ensures that the treatment and

control groups are comparable, which is what a randomized experiment does, except that it also

accounts for comparability based on unobservable confounding factors. Thus, the use of

propensity score matching in observation studies allows one to be more confident that the two

groups are comparable and thus attribute any difference in the outcome measure between the

treatment and control group to the treatment alone, as in a randomized experiment.

23

The critical factor for making causal inferences in an observational study using

propensity score matching is to ensure that there is overlap in the propensity scores for the two

groups of subjects. If all of the subjects in the treatment group have a high probability of

exposure and all of the subjects in the control group have a low probability of exposure, there is

no overlap and subjects are not exchangeable. On the other hand, if there is overlap in the

propensity scores between the two groups, then one can match a subject in the treatment group to

a subject in the control group based on propensity scores to construct a matched treatment-

control pair to establish a strong counterfactual. That is, there are subjects in the study that

provide “common support” for empirically observing what the effects would have been on a

treatment subject had it not been exposed to the treatment.

Though there are a variety of means for using propensity scores to assess the effects of

social interventions (for a review of these options see Oakes and Johnson 2006; D’Agostino

1998), we use propensity scores for direct matching using a “nearest neighbor within calipers”

procedure on the grounds that it provides for a more exact match between the two groups in

terms of covariate imbalance and it also strengthens common support inference.

For each of the six original urban empowerment zone cities, we included in our analysis

all Census tracts that were designated as part of an empowerment zone and all Census tracts that

were not included in an empowerment zone but met the federal requirements for empowerment

zone designation (i.e., a minimum Census tract level poverty rate of 20 percent; 35 percent for

Census tracts that include a portion of the central business district). Propensity scores were

calculated using Stata’s PSMATCH2 module for the included Census tracts in each city based on

the following covariates:

Population, 1990 Percent population change, 1980-1990

24

Percent nonwhite population, 1990 Percent Black (non Hispanic), 1990 Percent Hispanic, 1990 Percent female-headed households with children, 1990 Percent living in the same house as 1985, 1990 Percent of households with no car, 1990 Percent high school graduate or higher, 1990 Percent college graduate or higher, 1990 Percent unemployed, 1990 Percent of persons below poverty, 1989 Median family income, 1989 Percent of housing units vacant, 1990 Percent owner-occupied housing units, 1990 Percent of housing units built before 1940, 1990 Median value of owner-occupied housing units, 1990 Median rent, 1990 Housing units per square mile, 19907

We used a nearest neighbor matching algorithm with replacement and a caliper of ± .05

on the propensity score to construct our matched pairs. To assess covariate balance across the

treatment and control groups we rely on two measures, standardized differences and percent bias

reduction, as described by D’Agostino (1998) and Rosenbaum and Rubin (1985a) and

summarized by Oakes and Johnson (2006). The standardized difference is the mean difference as

a percentage of the average standard deviation:

{ }2/)(/)(100 cezcez vvxx +−

where x ez and x c are sample means for each covariate in the empowerment zone tracts and

control group tracts, respectively, and eez and vc are the corresponding variances. The percent

7 A reduced set of covariates was used for estimating the propensity scores for Atlanta Census tracts. This was due to the fact that for Atlanta the complete set of covariates completely determined the probabilities of assignment for most Census tracts (43 of the 52 tracts in the control group and 18 of the 23 tracts in the treatment group), yielding too many cases with identical propensity scores. The following covariates were included in the Atlanta estimation of propensity scores: population, percent nonwhite, percent poverty, percent unemployed, percent owner-occupied housing, median value of owner-occupied housing, and housing density. We report statistics for the full set of covariates in Table A-x to aid in assessing covariate imbalance between treatment and control groups before and after matching.

25

reduction in bias is represented by the percent reduction in the standardized differences before

and after matching:

% Bias Reduction = ⎟⎟⎠

⎞⎜⎜⎝

⎛−

tchedStdDifunmahedStdDifmatc

1

where the absolute value of the standardized difference in means for the matched sample is

divided by the absolute value of the standardized difference in means for the unmatched sample

and then subtracted from 1 to denote the percent reduction in covariate bias.

Table 2 summarizes the covariate imbalance after matching for selected covariates for the

six empowerment zone cities. A full list for all covariates included in the propensity score

matching for each of the six cities is reported in Appendix Tables A-1 to A-6. Maps A-1 to A-6

illustrate the empowerment zone tracts and their matched pairs for each city. Table 2 shows that

the propensity score matching procedures did a reasonably good job of reducing covariate

imbalance between the treatment and control groups for most covariates in most cities. In

Baltimore, Chicago, Detroit, and New York, most covariates are relatively well-balanced across

the two groups after matching with most indicators showing a standardized difference after

matching of 20 percent or less. Covariate imbalance after matching is relatively higher in Atlanta

and Philadelphia based on the standardized difference after matching score. However, after

matching only one of the 19 covariates shows a statistically significant difference between

groups at the .10 level in Philadelphia (p=.10 for the poverty rate, Table A-6). In Atlanta, 7 of

the 19 covariates show a statistically significant difference between groups after matching at the

.10 level, with the most problematic being percent high school graduate or higher (p=.01) (Table

A-1).

26

As shown below, none of the six empowerment zone cities show a statistically significant

difference at the .05 level between treatment and control groups after propensity score matching

for the 1990 (pre-treatment) measures for poverty rate and unemployment rate:

p-Value for differences in means City Poverty Unemployment Atlanta .26 .14 Baltimore .36 .20 Chicago .12 .20 Detroit .10 .29 New York .26 .17 Philadelphia .10 .21

Having established group comparability, the next step in the analysis is to calculate the

average treatment effect on the treated (ATT), which is estimated as follows:

)1()1()1( 0101 =−===− DYEDYEDYYE

Where Y1 and Y0 represent the mean outcome for the treatment (empowerment zone Census

tracts) and control (eligible non-empowerment zone tracts) groups and D represents a

dichotomous variable that indicates group status (1=treatment; 0=control). However, because it

is not possible for an empowerment zone Census tract to simultaneously be in both the treatment

and control groups, we substitute the outcome measure Y0 from each empowerment zone Census

tract’s matched pair for the term )1( 0 =DYE , which best estimates what the empowerment zone

Census tract’s outcome would likely have been had the tract been included in the control group

as opposed to the treatment group.

We present ATT estimates for three outcome measures: (1) the percentage change in the

number of jobs, 1996-2004; (2) the percentage change in the number of persons with income

below the poverty level, 1990-2000; and (3) the percent change in the number of persons

unemployed, 1990-2000. Table 3 summarizes these findings showing the differences between

empowerment zone tracts and non-empowerment zone tracts before and after matching for the

27

three outcome measures. Prior to matching five of the six cities (Atlanta, Baltimore, Chicago,

Detroit, and Philadelphia) show statistically significant differences (p < .10, one-tailed) in the

predicted direction for at least one of the three outcome measures. However, as noted in the

above discussion, these estimates are strongly biased by differences in group composition.

After propensity score matching to ensure covariate balance between treatment and

control groups, three cities (Baltimore, Detroit, and Philadelphia) show a statistically significant

difference (p < .10, one-tailed) in the predicted direction for at least one outcome measure and

two cities (Atlanta and New York) show a statistically significant difference in the opposite

direction, indicating that in those cities empowerment zone Census tracts fared worse than their

control group counterparts.

In Baltimore, the average increase in the number of jobs between 1996 and 2004 in

empowerment zone Census tracts was nearly five percent compared to a nearly twenty percent

decline in the number of jobs for Census tracts in the control group. New York (higher job

growth in empowerment zone tracts) and Detroit (smaller declines in empowerment zone tracts)

were the only other cities that reported better outcomes for job creation for empowerment zone

Census tracts than for control tracts, though those differences were not statistically significant.

The data for Atlanta, on the other hand, indicate that while the average job growth was about 10

percent in empowerment zone tracts between 1996 and 2004, the average number of jobs created

in control group Census tracts was more than double during that same time period (p=.02).

In terms of changes in poverty, the data for Detroit and Philadelphia show that in both

cities empowerment zone Census tracts had greater reductions in poverty, on average, between

1990 and 2000 than their respective control groups. In Detroit, the number of persons with

income below the poverty level declined by 37 percent, on average, in empowerment zone

28

Census tracts compared to an average decline of about 23 percent in control Census tracts

(p=.06). In Philadelphia, poverty declined in empowerment zone Census tracts at about the same

rate as Detroit (32 percent decline) whereas poverty actually increased slightly, on average, for

the control group Census tracts (p=.05). For the other cities, Baltimore and Chicago show

slightly larger declines for poverty, on average, in the empowerment zone Census tracts than the

control tracts and in New York both empowerment zone tracts and control tracts show an

increase in poverty, though the increase was slightly smaller in empowerment zone tracts than

control tracts. None of these differences, however, were statistically significant. In Atlanta, the

data show a much smaller decrease in poverty, on average, for empowerment zone tracts (-6.8%)

than for control tracts (-37.9%), though these differences were just beyond the bounds of

statistical significance (p=.11).

Finally, for the unemployment measure, New York was the only city to show a

statistically significant difference between empowerment zone tracts and control tracts, though

that difference was in the wrong direction. Unemployment, on average, increased by 45 percent

in empowerment zone tracts between 1990 and 2000 whereas the number of persons unemployed

in control tracts increased by about 15 percent during the same period (p=.07). The patterns

were mixed in the other cities. In Philadelphia, unemployment, on average, declined in the

empowerment zone tracts whereas it increased in the control tracts. In Detroit, unemployment

declined in empowerment zone and control tracts with the declines being much greater in the

empowerment zone tracts. In Chicago, both groups of Census reported similar declines in

unemployment. In Baltimore, the empowerment zone tracts showed a smaller decline in

unemployment than the control group tracts. And in Atlanta, both groups of Census tracts

29

reported an increase in unemployment though the average increase in the empowerment zone

tracts was only about one-third the rate of increase in the control tracts.

As noted above, we are also concerned about the level of geography one uses to

determine program effects and the fit between the geography of interventions and the geography

of analysis. Looking for effects at the zone level introduces two complications: first, it reduces

the sample to a small number of cases (13 zones in the 6 empowerment zone cities) and makes

the construction of comparable control groups problematic (i.e., it is much easier to identify a

matched pair at the Census tract level, where the average population is generally in the 3,000 –

4,000 range than at the zone level where population ranges from about 40,000 in Philadelphia to

200,000 in Chicago and New York). Second, looking for zone-wide effects may mask

differential effects in individual Census tracts. Consider Figures 1 and 2, which display the

percentage change in the number of jobs between 1996 and 2004 at the Census tract level for

Baltimore and Atlanta. In each map, Census tracts are grouped into quintiles based on their

employment change. In Baltimore, where the ATT analysis showed a statistically significant

effect in the predicted direction (empowerment zone tracts performed better than control tracts),

two of the three empowerment zone sectors each contain four Census tracts that ranked in the

bottom quintile for employment change and also contain three Census tracts each that ranked in

the top quintile. The third sector of the zone, comprised of a single Census tract, ranks in the

middle quintile. In Atlanta, where the data showed a statistically significant difference between

treatment and control groups in the wrong direction (empowerment zone neighborhoods fared

worse), five (of 23) Census tracts in the empowerment zone ranked in the top quintile for

employment change and seven ranked in the bottom quintile.

30

A similar pattern was observed in Atlanta for change in poverty (Figure 4): 3 Census

tracts in the empowerment zone ranked among the bottom two quintiles (greatest increases in

poverty) whereas 15 Census tracts ranked among the top two quintiles (greatest decreases in

poverty). In Philadelphia (Figure 3), on the other hand, nine of the twelve empowerment zone

Census tracts ranked in the top quintile (greatest decreases in poverty) and the other three Census

tracts ranked in the quintile with the second greatest decrease in poverty, suggesting that effects

on poverty reduction were more consistent across Philadelphia’s empowerment zone

neighborhoods than was the case in Atlanta.

As summarized below, our findings suggest that based on the three outcome measures we

examined, Baltimore, Detroit, and Philadelphia are the cities where the empowerment zone

initiative had the greatest (statistically significant) beneficial effects on neighborhood

revitalization, whereas Atlanta and New York are the cities where statistically significant

evidence suggests that empowerment zone neighborhoods did less well than their control group

counterparts on at least one outcome measure.

How EMPOWERMENT ZONE neighborhoods fared compared to their control group counterparts

Outcome measure City Jobs Poverty Unemployment Atlanta Worsened Worsened Improved Baltimore Improved Improved Worsened Chicago Worsened Improved About Same Detroit Improved Improved Improved New York Improved Improved Worsened Philadelphia About Same Improved Improved Note: Boldface type indicates statistically significant difference in group means.

In the next section we examine how – if at all – this pattern of findings can be explained

by variations in governance and civic capacity across the six cities.

31

The Significance of Governance

Earlier studies have pointed out that the Empowerment Zone and Enterprise Community

initiative was characterized by an unprecedented level of community involvement during the

planning phase. In their assessment of the strategic planning process utilized by empowerment

zone and enterprise community cities in developing their revitalization plans, the Rockefeller

Institute of Government (Wright, Nathan, and Rich 1996: 2) concluded that “the field associates

for this study (18 sites including all six empowerment zone cities) were nearly unanimous in

their assessment that the citizen participation that occurred during the development of their city’s

strategic plan was significantly and substantively greater than that which has taken place under

previous federal urban initiatives…outreach was more extensive and a wide group of community

stakeholders were involved in the planning process. This include[d] leading citizens in the

program areas, business groups, major nonprofit institutions (such as colleges, universities, and

hospitals), and a large number of government departments and agencies.” The authors added that

while “the public sector initiated the strategic planning process, played a major role in

structuring and designing the process and assumed responsibility for the day-to-day management

of the process…[community] input was substantial and surprisingly so, given the tight timelines

leading up to and following site designation.”

As summarized below, according to the Rockefeller Institute of Government analysis, in

most empowerment zone cities community residents determined or exercised major influence

regarding the content their city’s strategic plan:

32

Level of Community Influence on Content of Strategic Plan City

Revitalization

Strategies

Programs and Activities

Designating the Empowerment Zone

Neighborhoods Atlanta Determined Major Determined Baltimore Determined Major Minor Chicago Major Major Major Detroit Major Major Determined New York

Major

Minor (Upper Manhattan) Major (South Bronx)

Minor (Upper Manhattan) None (South Bronx)

Philadelphia Minor Major Minor

However, the Rockefeller Institute of Government’s analysis noted, as did other

subsequent studies (e.g., Chaskin and Peters 1997; Hebert et al 2001), that as the empowerment

zone initiative moved from planning to the establishment of formal governance entities to guide

the implementation of specific empowerment zone programs and activities, citizen input and

influence tended to decrease. For example, Table 4 points out that citizens and representatives of

community-based organizations held a majority of seats on the empowerment zone governing

body in only three empowerment zone cities: Chicago (62%), Detroit (52%), and Philadelphia

(54%).

The governing structures themselves varied widely across the six empowerment zone

cities, reflecting in part the substantial diversity in local government structures, prior experience

with federal community and economic development programs and other more recent community

building initiatives, and the number, character, and capacity of community-based organizations.

Despite these variations, patterns emerged across the empowerment zone cities regarding the

choices they made in the design of their governance entities.

One characteristic had to do with the relationship of the empowerment zone governance

entity to city government. Chicago, Detroit, and Philadelphia opted to operate their

33

empowerment zone initiatives under the formal auspices of city government. In Chicago, an

interagency coordinating committee was established to oversee the implementation of the city’s

strategic plan, the city council retained authority for the approval of the allocation of

empowerment zone funds, and the city’s budget office had day-to-day responsibility for

monitoring financial transactions and overseeing the monitoring of contracts with delegated

agencies and organizations. In Detroit, the city council also retained approval for funding

allocations, though a newly created nonprofit corporation – the Empowerment Zone

Development Corporation – had primary responsibility for the management of the city’s

empowerment zone initiative. Philadelphia also delegated day-to-day responsibility for planning

and implementation outside city government though the mayor retained authority for approving

empowerment zone funding allocations. In Philadelphia, three unincorporated Community Trust

Boards were created, one for each zone neighborhood. Each CTB was to be comprised of ten

community representatives who lived or worked in the neighborhood and five additional

representatives who brought specialized expertise to the CTB. Sixty percent of CTB members

were selected by community-based elections held in each zone neighborhood and the remaining

40 percent were appointed by the mayor.

In the three other empowerment zone cities new nonprofit corporations were established

outside of the city government. The Atlanta Empowerment Zone Corporation (AEZC) was a

newly created organization governed by a 17-member board of directors. The mayor appointed

the 17 members (6 members were nominated by the Community Empowerment Advisory Board,

which represented the 30 neighborhoods included in Atlanta’s empowerment zone and the 39

“linkage” neighborhoods which included neighborhoods eligible for designation but not

selected). The mayor also served as chairman of the AEZC. The Empower Baltimore

34

Management Corporation was also a newly created nonprofit corporation established outside the

formal auspices of the city government. Its board of directors was comprised of 30 members,

nine whom were selected by citizens, two by the governor, and nineteen by the mayor. New

York had perhaps the most complicated governance structure of the six empowerment zone

cities. The New York Empowerment Zone Corporation was established as a subsidiary of the

New York State Urban Development Corporation; two local development corporations were also

established to oversee the management and implementation of empowerment zone activities in

New York’s two empowerment zones (Upper Manhattan and the South Bronx). The NYEZ’s

board of directors includes six voting members, one appointed by each of five different elected

officials (the mayor, the governor, Congressman Rangel, Congressman Serrano, and the Bronx

Borough President). The sixth voting member is appointed by the Upper Manhattan

Empowerment Zone Corporation, the local development corporation responsible for overseeing

empowerment zone implementation in Harlem. A seventh member, appointed by the HUD

Secretary, serves in an ex officio capacity.

A second characteristic that distinguishes the governance entities of the empowerment

zone cities is whether the governing entity is a “single-tier” or “two-tier” structure. The former,

which characterizes the governance entities in Atlanta, Chicago, and Detroit, is a single structure

responsible for the governance of the empowerment zone initiative. Baltimore, New York, and

Philadelphia crafted two-tier structures that included one entity responsible for the overall

guidance and oversight of the empowerment zone initiative and a second set of zone-based

entities (Village Centers in Baltimore, Local Development Corporations in New York, and

Community Trust Boards in Philadelphia) that granted significant governance and

implementation responsibilities to the zone neighborhoods.

35

The breadth and depth of representation on the governance entity boards is a third

characteristic that distinguishes the empowerment zone cities. As Table 4 shows, Baltimore had

the most inclusive and balanced representation on its governing entity: 40 percent of the seats

were held by individual activists, zone residents, or representatives of community-based

organizations; 26 percent of the seats were held by public officials, 24 percent by representatives

from private businesses, and 10 percent other (e.g., unions, colleges and universities, foundations

and philanthropic organizations, etc.). Chicago (62%), Detroit (52%), and Philadelphia (54%) all

had governing boards where a majority of seats were held by individual activists, zone residents,

or CBO representatives. Government was the next highest category of representative in Detroit

(20%) and Philadelphia (26%) whereas business (21%) was the second highest category in

Chicago. In Atlanta, representation on the governing board was relatively evenly divided

between individual activists, zone residents, and CBO representatives (47%) and public officials

(42%), which proved to be a source of recurring tension throughout most of the city’s

empowerment zone experience (Rich 2003).

Overall, our results suggest that the cities where the empowerment zone initiative

produced the most efficacious results for empowerment zone neighborhoods were the cities that

had more effective governance structures and greater civic capacity to carry out programs and

projects. Our analysis of empowerment zone outcomes showed a statistically significant effect

in the predicted direction for at least one of our three outcome measures for Baltimore, Detroit,

and Philadelphia. All are cities that have received generally favorable publicity for their

empowerment zone initiatives. On the other hand, Atlanta and New York, the two cities where

we found at least one statistically significant effect in the wrong direction (i.e., empowerment

zone neighborhoods fared worse than their control counterparts) are cities where the

36

empowerment zone initiative was mired in conflict, deadlock, and delay over the ten-year history

of the empowerment zone program.

Effective governance is built upon collaboration among residents, community-based and

nonprofit organizations, the public sector, and local businesses. As Robert Chaskin and Sunil

Garg (1997) explain, “governance entails the creation or adoption of mechanisms and processes

to guide planning, decision making, and implementation as well as to identify and organize

accountability and responsibility for action undertaken. Thus governance is both process and

structure.” Effective governance contributes to the revitalization of distressed neighborhoods in

two ways. First, a collaborative, cross-sector system of governance can help to put into place a

comprehensive plan for neighborhood revitalization. Many different state, local, and regional

actors control resources that are vital to creating economic opportunity and fostering sustainable

community development. Governing arrangements can bring these participants together,

coordinate their actions, and create and sustain support for a revitalization plan. Second,

effective governance can make various aspects of a comprehensive neighborhood revitalization

strategy work better. Governance systems can coordinate programs, increase the number of

redevelopment tools available to stimulate business and job growth, enhance services, inform

businesses about redevelopment incentives, solve collective action problems, and address market

failures, all in a context tailored to the distinctive needs and opportunities that exist within local

communities.

The significance of governance for urban revitalization is illustrated by contrasting the

empowerment zone initiatives in Atlanta and Baltimore. The effectiveness of Baltimore’s

empowerment zone program in comparison to Atlanta’s reflects the emphasis officials in

Baltimore placed upon local governance. Atlanta failed and Baltimore succeeded due to the

37

strength and diversity of the policy regimes that local leaders constructed to oversee their local

programs. In Atlanta, the regime had little breadth or depth and did not extend significantly

beyond the mayor’s inner circle. Business involvement was nearly nonexistent, citywide and

neighborhood nonprofit organizations played a minor role at the start and were essentially absent

at the end, and government participation was largely limited to city agencies that the mayor

directly controlled. Hence, the art of governance – the process of building consensus across a

diverse group of interests, mobilizing the resources needed to pursue a shared vision, and tapping

the expertise of agencies to take the actions needed to execute that vision – never materialized in

Atlanta.

In Baltimore considerable attention was paid to governance. The governing Board of

Empower Baltimore Management Corporation (EBMC), the zone-wide, quasi-public, nonprofit

organization that coordinated Baltimore’s empowerment zone initiative, was composed of

influential members of the city’s business and philanthropic community along with government

and community leaders. Although this governing arrangement reduced the influence of zone

residents, EBMC leaders developed significant opportunities for the community to participate.

EBMC created an Advisory Council that was composed primarily of zone residents and

sponsored six community-based nonprofits called Village Centers to participate in zone

governance, plan community development initiatives, and design and implement programs to

serve zone residents. Reflecting the view that community revitalization is an ongoing process,

EBMC spawned community-based institutions that could sustain their mission of community

development beyond the scope of the empowerment zone initiative.

Effective local governance can help to extend the benefits of job growth to needy zone

residents. For example, the training program that was deemed most effective by leaders in

38

Baltimore’s empowerment zone was “Customized Training,” a program that trained workers for

specific job opportunities in the zone. Trainees were assured that the skills developed through

training would lead to a local job opportunity. Beyond this, customized training was one part of

a larger package of economic incentives to attract business investment for employment growth.

EBMC could offer low-interest loans to businesses in exchange for employment guarantees that

were conditioned on the ability EBMC enjoyed to recruit and train zone residents. The Village

Centers were vital partners in this initiative because they conducted outreach programs and pre-

screened applicants to assure that they were viable trainees.

In Atlanta, local elites and zone residents never reached consensus on what the

empowerment zone was supposed to accomplish or the means by which those goals would be

obtained. Atlanta was never able to successfully resolve this conflict, and as a result, the

implementation of the empowerment zone initiative was mired in deadlock and delay. Atlanta

had a weak policy regime in large part because it had weak institutions. It lacked a set of well-

established revitalization agencies that could bring representatives of the various sectors together

as well as agencies capable of completing the tasks needed to revitalize neighborhoods—e.g.,

manage revolving loan funds for business investment, job creation, and affordable housing;

design and administer employment and training programs. Baltimore, on the other hand, enjoyed

the experience and expertise of its leading EBMC Board members who were instrumental in

designing and managing zone programs. By working cooperatively with city agencies and non-

profits, EBMC managed the process while retaining its flexibility to use its resources effectively

as they learned about program performance and new opportunities emerged.

Atlanta had weak institutions – public, quasi-public, nonprofit – largely because the city

had never invested in the creation of strong institutions capable of fostering neighborhood

39

revitalization, out of fear that strong institutions – particularly neighborhood-based institutions –

would provide a serious countervailing force that would diminish the control city hall (i.e., the

mayor) could exercise over federal urban revitalization resources. Baltimore, in contrast, took

capacity building seriously and demonstrated that good governance and widespread community

participation can be integral parts of effective urban redevelopment programs.

This finding has important implications for the use of tax incentives and other market-

oriented tools as a basis for targeted economic development. Although all six of the original

empowerment zone cities had the same set of tax incentives and other market-oriented tools to

encourage job growth, job creation and employment outcomes varied, suggesting that market-

oriented tools alone are not enough to accomplish targeted economic development.

Conclusion

The existing evaluation literature downplays several important accomplishments of the

empowerment zone program. The program did create measurable local benefits in several cities,

despite significant variation in the outcomes from one city to another and within each of the

zones. Beyond this, the positive results of the program suggest that the combination of market-

oriented tools with grants to local government is a promising strategy to spur redevelopment in

distressed urban communities. Effective policy development and implementation can also

enhance and sustain community development by including residents of distressed communities

as important participants in the policymaking process.

As federal policy has evolved over the years during the transition from empowerment

zones to renewal communities, intergovernmental grants have been de-emphasized. The $100

million social service block grants that were provided to the original empowerment zone

designees were replaced by HUD empowerment zone grants in Round II. Although Round II

40

designees were authorized to receive as much as $100 million in grant support, the funds were

subjected to the annual appropriations process and lagged behind what was required to fully fund

the program (Burton 2005). Round III designees did not receive grants as part of the package of

empowerment zone benefits and no grant support was provided to renewal communities.

As a result of our evaluation, we can only conclude that current federal policy is wrong-

headed. Incentives are important, but market-oriented tools alone are not enough to spur urban

revitalization.8 The original empowerment zones experience has demonstrated that market-

oriented tools, when complemented by intergovernmental grants to develop local programs,

support community building initiatives, and fund technical assistance to develop effective

governance based on cross-sector collaboration, are an effective way to assist distressed urban

communities.

References

Burton, John. 2005. “The GO Zone Won’t Go: Lessons for Gulf Opportunity Zones.” Washington, D.C.: Center for American Progress.

Campbell, Donald T. 1969. “Reforms as Experiments.” American Psychologist 24: 409-429.

Chaskin, Robert J. and Sunil Garg. 1997. “The Issue of Governance in Neighborhood-Based Initiatives.” Urban Affairs Review 32 (May 1997).

Chaskin, Robert J. and Clark M. Peters. 1997. “Governance in Empowerment zone Communities: A Preliminary Examination of Governance in Fifteen Empowerment zone Communities.” Chicago: Chapin Hall Center for Children, University of Chicago.

D’Agostino, Jr., Ralph B. 1998. “Tutorial in Statistics: Propensity Score Methods for Bias Reduction in the Comparison of a Treatment to a Non-Randomized Control Group.” Statistics in Medicine 17: 2265-2281.

GAO. 2006. Empowerment zone and Enterprise Community Program: Improvements Occurred in Communities, but the Effect of the Program is Unclear. GAO report number, GAO-06-727 (September 2006). Washington, D.C.: Government Accountability Office.

8 We expand this point in a related paper assessing the Bush administration’s GO ZONES initiative for revitalizing the communities devastated by Hurricanes Katrina and Wilma during August 2005 (Stoker and Rich 2006)

41

Gibson, James O., Thomas Kingsley, Joseph B. McNeely. 1997. Community Building: Coming of Age. Washington, D.C.: Urban Institute. http://www.urban.org/url.cfm?ID=307016

Hebert, Scott, Avis Vidal, Greg Mills, Franklin James, and Debbie Gruenstein. 2001. Interim Assessment of the Empowerment zones and Enterprise Communities (EMPOWERMENT ZONE/EC) Program: A Progress Report. Cambridge, MA: Abt Associates.

Oakes, J. Michael and Pamela Jo Johnson. 2006. “Propensity Score Matching for Social Epidemiology,” pp. 370-392 in J. Michael Oakes and Jay S. Kaufman, eds., Methods in Social Epidemiology. San Francisco: Jossey-Bass.

Oakley, Dierdre and Hui-Shien Tsao. 2006. “A New Way of Revitalizing Distressed urban Communities? Assessing the Impact of the Federal Empowerment zone Program.” Journal of Urban Affairs 28, 5: 443-471.

Rich, Michael J. 2003. “Revitalizing Urban Communities: Lessons from Atlanta’s Empowerment zone Experience,” pp. 79-112 in Robert Holmes, ed., The Status of Black Atlanta 2003. Atlanta: Southern Center for Studies in Public Policy, Clark Atlanta University.

Rosenbaum, P.R. and Rubin, D.B. 1983. “The Central Role of the Propensity Score in Observational Studies for Causal Effects.” Biometrika 70: 41-55.

Stoker, Robert P. and Michael J. Rich. 2006. “Lessons and Limits: Tax Incentives and Rebuilding the Gulf Coast after Katrina.” Washington, D.C.: The Brookings Institution, Metropolitan Policy Program.

Wildavsky, Aaron. 1979. Speaking Truth to Power: The Art and Craft of Policy Analysis. Boston, Massachusetts: Little, Brown and Co.

Wright, David J., Richard P. Nathan, and Michael J. Rich. 1996. Building A Community Plan for Strategic Change: Findings from the First Round Assessment of the Empowerment zones/Enterprise Communities Initiative. Albany: Rockefeller Institute of Government, State University of New York at Albany.

Program Category ($000) Percent ($000) Percent ($000) Percent ($000) Percent ($000) Percent ($000) Percent

Business Development 48,909 57.3 25,909 31.7 13,097 13.5 - 0.0 30,448 69.8 43,838 63.1

Workforce Development 400 0.5 33,673 41.2 13,905 14.3 7,414 9.2 3,552 8.1 630 0.9

Human Services 2,700 3.2 3,257 4.0 34,410 35.5 48,507 60.1 2,764 6.3 1,950 2.8

Public Safety 3,561 4.2 2,235 2.7 1,309 1.3 8,990 11.1 300 0.7 1,899 2.7

Housing 23,000 26.9 8,014 9.8 19,532 20.1 12,278 15.2 333 0.8 10,284 14.8

Physical Improvements 49 0.1 91 0.1 8,308 8.6 3,460 4.3 1,359 3.1 5,553 8.0

Planning and Administration 5,988 7.0 8,413 10.3 - 0.0 - 0.0 351 0.8 2,249 3.2

Other 820 1.0 232 0.3 6,431 6.6 - 0.0 4,526 10.4 3,078 4.4

TOTAL 85,427 100.0 81,824 100.0 96,992 100.0 80,649 100.0 43,633 100.0 69,481 100.0

Table 1. Allocation of EZ Funds by Program Category.

Source: PERMS Reports submitted to HUD by each city. Dollar amounts in thousands.

New York PhiladelphiaChicago DetroitAtlanta Baltimore

Table 2. Percent Reduction in Bias for Selected Covariates.

StandardizedInitial Difference

Standardized After PercentDifference Matching Reduction

AtlantaPopulation, 1990 -101.6 -15.10 85.1Percent Population change, 1980-1990 -34.9 -56.84 -62.9Percent nonwhite, 1990 25.9 39.49 -52.2Percent of households with no car, 1990 166.8 59.31 64.4Percent high school graduate or higher, 1990 -127.4 -97.04 23.8Percent unemployed, 1990 82.9 44.04 46.9Percent of persons below poverty, 1990 118.9 23.86 79.9Percent of housing units vacant, 1990 43.6 65.34 -49.9Percent owner-occupied housing units, 1990 -124.1 -33.67 72.9Median rent, 1990 -83.8 -25.57 69.5Housing units per square mile, 1990 55.7 45.16 19.0

BaltimorePopulation, 1990 -62.3 6.85 89.0Percent Population change, 1980-1990 -24.5 -3.05 87.6Percent nonwhite, 1990 -0.4 -19.89 -4341.3Percent of households with no car, 1990 50.6 -25.80 49.0Percent high school graduate or higher, 1990 -63.2 -30.72 51.4Percent unemployed, 1990 33.5 -30.62 8.6Percent of persons below poverty, 1990 67.2 -12.50 81.4Percent of housing units vacant, 1990 84.0 11.00 86.9Percent owner-occupied housing units, 1990 -35.2 17.42 50.5Median rent, 1990 -37.3 10.05 73.0Housing units per square mile, 1990 5.9 2.20 62.7

ChicagoPopulation, 1990 -44.7 -17.12 61.7Percent Population change, 1980-1990 -37.0 -2.04 94.5Percent nonwhite, 1990 49.4 -1.93 96.1Percent of households with no car, 1990 76.5 28.32 63.0Percent high school graduate or higher, 1990 -24.1 -7.16 70.4Percent unemployed, 1990 52.0 15.35 70.5Percent of persons below poverty, 1990 61.3 21.30 65.2Percent of housing units vacant, 1990 53.8 30.42 43.4Percent owner-occupied housing units, 1990 -68.1 -21.38 68.6Median rent, 1990 -72.3 -22.19 69.3Housing units per square mile, 1990 -1.9 1.24 34.3

Table 2, cont'd.Standardized

Initial DifferenceStandardized After Percent

Difference Matching Reduction

DetroitPopulation, 1990 -76.7 7.67 90.0Percent Population change, 1980-1990 -98.1 -40.74 58.5Percent nonwhite, 1990 -23.6 11.25 52.4Percent of households with no car, 1990 109.2 21.16 80.6Percent high school graduate or higher, 1990 -112.2 19.29 82.8Percent unemployed, 1990 52.7 15.96 69.7Percent of persons below poverty, 1990 98.5 37.39 62.0Percent of housing units vacant, 1990 88.1 -5.76 93.5Percent owner-occupied housing units, 1990 -102.0 11.68 88.5Median rent, 1990 -61.4 23.93 61.0Housing units per square mile, 1990 20.2 -1.83 90.9

New YorkPopulation, 1990 -0.4 -5.77 -1468.9Percent Population change, 1980-1990 -31.5 -0.43 98.6Percent nonwhite, 1990 75.4 6.59 91.3Percent of households with no car, 1990 133.4 -4.12 96.9Percent high school graduate or higher, 1990 -42.6 18.82 55.8Percent unemployed, 1990 51.0 -22.10 56.6Percent of persons below poverty, 1990 75.9 -15.27 79.9Percent of housing units vacant, 1990 51.7 36.22 30.0Percent owner-occupied housing units, 1990 -91.4 3.52 96.1Median rent, 1990 -86.6 16.02 81.5Housing units per square mile, 1990 41.7 -3.44 91.8

PhiladelphiaPopulation, 1990 -62.0 35.15 43.3Percent Population change, 1980-1990 -37.7 25.27 33.0Percent nonwhite, 1990 76.3 25.43 66.7Percent of households with no car, 1990 101.5 17.49 82.8Percent high school graduate or higher, 1990 -80.9 -58.08 28.2Percent unemployed, 1990 122.1 43.86 64.1Percent of persons below poverty, 1990 174.2 74.04 57.5Percent of housing units vacant, 1990 62.0 -50.96 17.8Percent owner-occupied housing units, 1990 -85.2 2.41 97.2Median rent, 1990 -89.0 -31.78 64.3Housing units per square mile, 1990 -28.8 28.02 2.7

Table 3. Comparison of Neighborhood Effects for Empowerment Zone and EZ-Eligible Census Tracts Before and After Propensity Score Matching.

Eligible Eligible EZ Tracts EZ Tracts

(Mean) (Mean) Difference t p-Value (Mean) (Mean) Difference t p-Value

Atlanta n=23 n=52% Change no. of jobs, 1996-2004 4.29 7.72 -3.43 0.17 .43 9.4 117.9 -108.5 -2.11 .02% Change poverty, 1990-2000 -14.16 4.20 -18.36 1.29 .10 -6.84 -37.87 31.03 1.27 .11% Change unemployed, 1990-2000 78.94 202.73 -123.79 0.73 .23 56.00 180.95 -124.95 -0.45 .32

Baltimore n=25 n=70% Change no. of jobs, 1996-2004 6.66 -0.29 6.95 0.61 .27 4.88 -19.64 24.52 1.57 .07% Change poverty, 1990-2000 -33.48 -14.96 -18.52 2.59 .00 -34.46 -31.23 -3.23 0.44 .33% Change unemployed, 1990-2000 5.14 15.18 -10.04 0.39 .34 -10.74 -17.35 6.61 0.49 .32

Chicago n=96 n=324% Change no. of jobs, 1996-2004 13.4 10.66 2.74 0.35 .36 10.38 18.67 0.70 .24% Change poverty, 1990-2000 -24.33 -13.36 -10.97 1.79 .04 -19.81 -16.49 0.43 .33% Change unemployed, 1990-2000 -19.74 7.09 -26.83 1.29 .10 -16.43 -16.95 0.05 .48

Detroit n=41 n=193% Change no. of jobs, 1996-2004 12.46 -6.28 18.74 2.06 .02 -2.98 -8.85 5.87 0.40 .35% Change poverty, 1990-2000 -32.82 -25.56 -7.26 1.71 .04 -37.4 -22.88 -14.52 1.60 .06% Change unemployed, 1990-2000 -27.13 -30.14 3.01 0.27 .39 -39.5 -5.03 -34.47 0.67 .25

New York n=50 n=666% Change no. of jobs, 1996-2004 35.81 27.75 8.06 0.48 .31 31.93 9.26 22.67 1.05 .15% Change poverty, 1990-2000 6.83 10.05 -3.22 0.55 .29 9.18 10.55 -1.37 0.10 .46% Change unemployed, 1990-2000 39.3 31.16 8.14 0.33 .37 44.99 14.73 30.26 1.49 .07

Philadelphia n=12 n=138% Change no. of jobs, 1996-2004 13.59 21 -7.41 0.11 .45 6.1 6.65 -0.55 0.02 .49% Change poverty, 1990-2000 -30.23 -4.60 -25.63 2.55 .00 -32.22 2.37 -34.59 1.82 .05% Change unemployed, 1990-2000 -28.04 39.01 -67.05 1.34 .09 -30.54 157.85 -188.39 1.07 .15

n=19

Group comparisions prior to matching Group comparisions: matched pairs

n=27

n=43

n=7

n=19

n=71

Table 4. Governance Characteristics of Six Original Federal Urban Empowerment Zones.

Atlanta Baltimore Chicago Detroit New York Philadelphia

EZ Governance Entity (Decisionmaking Body)

Atlanta Empowerment

Zone Corporation

Empower Baltimore

Management Corporation

Empowerment Zone/Enterprise

Community Coordinating

Council

Empowerment Zone

Development Corporation

New York Empowerment

Zone CorporationCommunity Trust

Boards

Type of Governing Entity (relationship to city government)

Nonprofit corporation

independent of city government

Nonprofit Corporation

independent of city government

Interagency coordinating committee

Development Corporation

dependent on city government

Subsidiary of NY State Urban Development Corporation

Unincorporated local community

boards

Geographic Scope of Governing Entity

Single-Tier Structure: Zone-

wide only

Two-Tiered Structure: Zone-

wide and Individual Zones

Single-Tier Structure: Zone-

wide only

Single-Tier Structure: Zone-

wide only

Two-Tiered Structure: Zone-

wide and Individual Zones

Two-Tiered Structure: Zone-

wide and Individual Zones

Number of seats on governance entity 17 30 14 25 45 41

Selection method Appointed by

mayor

9 members appointed by

citizens; 2 members

appointed by governor; 19 appointed by

mayor Appointed by

mayor

EZ/EC Coordinating

Council selects; ratified by mayor and city council See note 1

60% selected in neighborhood elections/40% appointed by

mayor

Percentage distribution of seats by type of representative, June 2000 Individual activist/Zone resident 35 27 62 36 13 15

Community-based organization 12 13 0 16 9 39

Non-elected public official 24 23 8 20 4 24

Elected public official 18 3 0 0 2 2

Zone business person 0 7 21 0 18 7

Non-zone business representative 12 17 0 12 29 2

Other 0 10 15 16 24 10

Sources: Wright, Nathan and Rich (1996); Chaskin and Peters (1997); Hebert et al (2001); and HUD PERMS reports.

1 One member appointed by mayor; one appointed by Empire State Develop. Corp.; two appointed by Congressmen; one appointed by Upper Manhattan LDC; one appointed by Bronx Borough President; one ex officio appointment by HUD Secretary.

Figure 1. Percentage Change Number of Jobs, 1996-2004Baltimore Empowerment Zone

2606.05

2505

4304

2506

4303

2604.04

7502.01

4301.02

7503

2605.0

1304

2502.062501.03

7502.02

2502.05

2102

801.01

902

2006

2401

4302

2717

1511

2714

2602.01

1308.05

2705.02

1510

4301.01

260

2704.01

1509

1308.06

2719

2604

7501.01

2604.01

1202

2711.02

2101

2404

1608.02

2704.02

2504.01

2603.03

2007.01 401

2201

2703.012709.02

2503.01

2303

2709.03

1207

1307

1508

2008

2503.03

1201

2602.02

1605

2711.01

2701.02

1506

2701.01

2702

1607

1507.01

2710.02

02

2502.07

1513

2002

2502.03

2602.031505

2603.02

1306

802

2502.04

2716

04.04

1504

908

1512

2715.03

7501.02

903

905

2607

2501.02

1308.04

2503.02

1102

203

2504.02

1308.03

1401909

103

2709.01

2005

2603.01

901

501

2718.01

1606

2609

804.02

1205

2703.022718.02

302

1206

2710.01

2

402

803.02

2402

907

301

104

1507.02

1203

604

1503

101

1502

1604

2004

1402

1608.01

1903

906

1403

1702

1901

801.02

1602 1601

1501

102

8051303 1302

904

806

2301

808

1801

1001

7041101

1301

702

2608

1002

1703

807

1701

2610

2007.02

701

1803

605

1204

1902

803.01

2001

602

804

1603

601

2003

1802

2611

703

23022403

202

1004

603

201 105

1003

0 .5 1 1.5

Miles

Percent ChangeLess than -26.70 (40)-26.70 to -8.40 (40)-8.40 to 5.00 (40)5.00 to 30.70 (40)Greater than 30.70 (40)Other (425)

Figure 2. Percentage Change Number of Jobs, 1996-2004Atlanta Empowerment Zone

88

238.02

81.02

89.02

95

215.0

80

113.01

224.86.01

70.01

209

70.02

238.01

89.01

76.01

87.02

112.01

85

87.01

67

215.02

92

75

76.02

111

5

91

90

65

122

94.01

69

2

10

224.01

83.01

71

93

52

61

53

66.01

83.02

203

84

7

55.02

205

60

40

4

35

202

6

24

50

94.02

63

13

207

30

42

58

64

41

17

46

55.01

49

23

206

15

201

56

12

62

19

8

32

20416

26

18

81.01

31

25

39

2122

38

44

27

57

28

68.02

14

29

33

68.01

66.02

43

11

36

48

37

0 .6 1.2 1.8

Miles

Percent ChangeLess than -40.00 (23)-40.00 to -20.00 (23)-20.00 to 4.45 (24)4.45 to 24.00 (23)Greater than 24.00 (24)Other (543)

Figure 3. Percentage Change Number of Persons Below Poverty, 1990-2000Philadelphia Empowerment Zone

223

123

150182

184

183

43

292

35

170

217

122

191

207

205

338

289206

291

341

194

304

111

279

230

229

124

171

339

314

336

237310

305

189

366

215

76

306

67

236

273

252

247

193

335

300

297

266

208

302

121

36

210

173

317

89

201

197

256

282

181

231

125

274

180

234

187

290

245

142

264

235

308

195

238

232

190

255254

319

268

202

303

340

250

196

311

68

257

151

33

278

316

301

175

13

313

160

177

1

280

186

10

228

119179

263.02

161

293

69

38

286

283

243

7471

188

294

199

261

105

276

272

214

185

178

152

66

192

298

12

270

267

307

309

295

86

269

246

85

144

240

275

167

137

20

299

233

158

2728

176.01

258

168

44

253

141

88

209

163

139

3

169.02

172

108

262

143

321

277

265

211

34

213

87

198

260

24

61

242

159

251

203

4

92

169.01

281

45

212

32

157

239

110

164

284

318

30

104

39.02

70

153

107

26

146

288

90

149

42.02

78

132

5

39.01

156

140

80

134

244

91

155

75

312

204

263.01

241

135

2

136

93

271

165

8

2531

176.02

41.02

73

41.01

79

11

126

29

42.01

133

37.01

2

129

162

287

138

174

147

127

40.02

19

249

248

145

77

131

200

14

40.01

154

20

285

103

9

106

128

109

166

7

37.02

1518

21

296

2223

130

17

16

148

6

0 .7 1.4 2.1

Miles

Percent ChangeLess than -18.00 (76)-18.00 to 0.00 (82)0.00 to 22.30 (70)22.30 to 63.50 (76)Greater than 63.50 (77)

Figure 4. Percentage Change Number of Persons Below Poverty, 1990-2000Atlanta Empowerment Zone

238.02

89.02

95

215.01

238.03

224.03

237

70.01

209

70.02

238.01

223.01

89.01

216.03

67

215.02

92

75

111

5

91

90

65

1224.02

94.01

216.02

69

226

234.11

2

236

225

208.02

10

236.02

224.01

228

236.01

71

93

227

52

61

53

66.01

203

84

7

55.02

205

60

40

4

35

202

6

208.01

24

50

94.02

63

13

207

30

42

58

64

41

17

46

55.01

49

23

206

15

201

56

12

62

19

8

32

20416

26

18

31

25

39

2122

38

44

27

57

28

68.02

14

29

33

68.01

66.02

43

11

36

48

37

0 .6 1.2 1.8

Miles

Percent ChangeLess than -28.60 (23)-28.60 to -10.90 (23)-10.90 to 8.30 (24)8.30 to 38.00 (23)Greater than 38.00 (24)Other (543)

0 1 2 3

Miles

Census TractsEZ TractsComparison TractsCBD

0 1 2 3

Miles

Census TractsEZ TractsComparison TractsCBD

Map A-1. Map of Atlanta EZ and Comparision Area. Map A-2. Map of Baltimore EZ and Comparision Area.

Map A-3. Map of Chicago EZ and Comparision Area. Map A-4. Map of Detroit EZ and Comparision Area.

0 1 2 3

Miles

Census TractsEZ TractsComparison TractsCBD

0 2 4 6

Miles

Census TractsEZ TractsComparison TractsCBD

0 1 2 3

Miles

Census TractsEZ TractsComparison TractsCBD

0 1 2 3

Miles

Census TractsEZ TractsComparison TractsCBD

Map A-5. Map of New York EZ and Comparision Area. Map A-6. Map of Philadelphia EZ and Comparision Area.

Table A-1. Reduction in Covariate Imbalance After Matching on the Propensity Score: Atlanta.

EZ EZ Eligible Standardized % Bias(mean) (mean) Difference Reduction* t p-Value**

AtlantaPopulation, 1990 2079.2 2205.0 -15.1 85.1 -0.37 0.36Percent Population change, 1980-1990 -17.5 -8.3 -56.8 -62.9 -1.42 0.08Percent nonwhite, 1990 94.6 88.8 39.5 -52.2 1.11 0.14Percent Nonhispanic black, 1990 91.5 87.8 23.5 -21.6 0.63 0.26Percent Hispanic, 1990 2.5 0.4 43.9 -79.9 0.97 0.17Percent female-headed households with children, 1990 73.3 69.4 22.9 66.2 0.63 0.26Percent in same house as 1985, 1990 45.8 54.7 -74.0 1.6 -2.01 0.02Percent of households with no car, 1990 63.9 52.7 59.3 64.4 1.68 0.05Percent high school graduate or higher, 1990 35.7 42.5 -97.0 23.8 -2.52 0.01Percent college graduate or higher, 1990 5.2 9.2 -51.6 31.5 -1.32 0.09Percent unemployed, 1990 19.7 15.9 44.0 46.9 1.10 0.14Percent of persons below poverty, 1990 54.3 49.8 23.9 79.9 0.66 0.26Median family income, 1989 11121.0 13633.0 -37.5 64.3 -1.04 0.15Percent of housing units vacant, 1990 22.2 14.5 65.3 -49.9 1.55 0.07Percent owner-occupied housing units, 1990 18.4 24.4 -33.7 72.9 -0.94 0.18Percent of housing units built before 1940, 1990 26.8 20.2 49.2 27.3 1.28 0.10Median value of owner-occupied housing units, 1990 36273.0 38938.0 -14.3 77.1 -0.37 0.36Median rent, 1990 257.8 290.9 -25.6 69.5 -0.74 0.23Housing units per square mile, 1990 3659.3 2161.0 45.2 19.0 1.01 0.16

* Percent reduction in bias is represented by the percent reduction in the standardized differences before and after matching.

** One-tailed test.

Matched Pairs of Census Tracts

Table A-2. Reduction in Covariate Imbalance After Matching on the Propensity Score: Baltimore.

EZ EZ Eligible Standardized % Bias(mean) (mean) Difference Reduction* t p-Value**

Baltimore Population, 1990 3315.0 3253.0 6.9 89.0 0.20 0.42Percent Population change, 1980-1990 -6.0 -5.7 -3.1 87.6 -0.09 0.46Percent nonwhite, 1990 78.2 84.0 -19.9 -4341.3 -0.56 0.29Percent Nonhispanic black, 1990 76.3 82.3 -20.1 -3204.9 -0.57 0.29Percent Hispanic, 1990 0.5 0.5 4.6 83.0 0.15 0.44Percent female-headed households with children, 1990 69.6 72.8 -19.7 31.1 -0.55 0.29Percent in same house as 1985, 1990 53.7 54.1 -6.0 47.2 -0.16 0.44Percent of households with no car, 1990 62.2 65.8 -25.8 49.0 -0.73 0.23Percent high school graduate or higher, 1990 38.4 40.2 -30.7 51.4 -0.88 0.19Percent college graduate or higher, 1990 7.6 5.4 31.1 -83.7 0.87 0.20Percent unemployed, 1990 15.4 17.1 -30.6 8.6 -0.85 0.20Percent of persons below poverty, 1990 41.9 43.6 -12.5 81.4 -0.36 0.36Median family income, 1989 16698.0 15589.0 18.8 67.7 0.53 0.30Percent of housing units vacant, 1990 14.9 14.1 11.0 86.9 0.32 0.37Percent owner-occupied housing units, 1990 29.8 27.1 17.4 50.5 0.49 0.32Percent of housing units built before 1940, 1990 49.8 44.7 23.3 -105.1 0.68 0.25Median value of owner-occupied housing units, 1990 34268.0 33557.0 4.7 89.6 0.13 0.45Median rent, 1990 352.4 342.0 10.1 73.0 0.29 0.38Housing units per square mile, 1990 8794.0 8694.0 2.2 62.7 0.06 0.47

* Percent reduction in bias is represented by the percent reduction in the standardized differences before and after matching.

** One-tailed test.

Matched Pairs of Census Tracts

Table A-3. Reduction in Covariate Imbalance After Matching on the Propensity Score: Chicago.

EZ EZ Eligible Standardized % Bias(mean) (mean) Difference Reduction* t p-Value**

Chicago Population, 1990 2354.0 2691.0 -17.1 61.7 -0.94 0.17Percent Population change, 1980-1990 -18.6 -18.2 -2.0 94.5 -0.11 0.46Percent nonwhite, 1990 94.4 94.6 -1.9 96.1 -0.11 0.46Percent Nonhispanic black, 1990 72.5 69.6 7.2 80.5 0.40 0.35Percent Hispanic, 1990 21.2 23.8 -7.7 70.2 -0.42 0.34Percent female-headed households with children, 1990 63.5 61.2 10.4 77.4 0.57 0.28Percent in same house as 1985, 1990 58.4 57.3 10.5 62.4 0.59 0.28Percent of households with no car, 1990 54.7 50.3 28.3 63.0 1.56 0.06Percent high school graduate or higher, 1990 40.2 40.9 -7.2 70.4 -0.39 0.35Percent college graduate or higher, 1990 6.1 5.9 3.0 93.5 0.16 0.44Percent unemployed, 1990 24.2 22.7 15.3 70.5 0.94 0.20Percent of persons below poverty, 1990 44.8 41.8 21.3 65.2 1.18 0.12Median family income, 1989 15543.0 17854.0 -31.9 60.1 -1.79 0.04Percent of housing units vacant, 1990 16.6 14.4 30.4 43.4 1.63 0.05Percent owner-occupied housing units, 1990 21.1 23.9 -21.4 68.6 -1.17 0.12Percent of housing units built before 1940, 1990 45.6 47.0 -7.5 78.3 -0.40 0.34Median value of owner-occupied housing units, 1990 52999.0 52169.0 3.3 90.8 0.18 0.43Median rent, 1990 342.7 361.6 -22.2 69.3 -1.22 0.11Housing units per square mile, 1990 7177.5 7126.0 1.2 34.3 -0.06 0.47

* Percent reduction in bias is represented by the percent reduction in the standardized differences before and after matching.

** One-tailed test.

Matched Pairs of Census Tracts

Table A-4. Reduction in Covariate Imbalance After Matching on the Propensity Score: Detroit.

EZ EZ Eligible Standardized % Bias(mean) (mean) Difference Reduction* t p-Value**

Detroit Population, 1990 2790.0 2701.0 7.7 90.0 0.25 0.40Percent Population change, 1980-1990 -26.4 -16.9 -40.7 58.5 -1.46 0.08Percent nonwhite, 1990 83.4 81.1 11.2 52.4 -0.37 0.35Percent Nonhispanic black, 1990 78.5 74.3 14.4 62.5 0.49 0.31Percent Hispanic, 1990 3.4 4.0 -7.7 85.0 -0.26 0.40Percent female-headed households with children, 1990 71.3 65.5 34.8 23.1 1.21 0.12Percent in same house as 1985, 1990 57.8 56.0 16.4 57.0 0.54 0.29Percent of households with no car, 1990 51.0 48.6 21.2 80.6 0.70 0.24Percent high school graduate or higher, 1990 43.9 42.2 19.3 82.8 0.64 0.26Percent college graduate or higher, 1990 6.7 10.8 -30.9 -98.3 -1.11 0.14Percent unemployed, 1990 30.0 28.2 16.0 69.7 0.53 0.29Percent of persons below poverty, 1990 47.1 43.3 37.4 62.0 1.27 0.10Median family income, 1989 14899.7 17287.0 -29.8 24.8 -1.03 0.15Percent of housing units vacant, 1990 12.5 13.0 -5.8 93.5 -0.20 0.42Percent owner-occupied housing units, 1990 36.1 34.0 11.7 88.5 0.38 0.35Percent of housing units built before 1940, 1990 53.3 48.3 28.6 71.3 0.96 0.17Median value of owner-occupied housing units, 1990 18877.3 16443.1 29.9 -153.3 1.01 0.16Median rent, 1990 335.4 316.3 23.9 61.0 0.77 0.22Housing units per square mile, 1990 3486.4 3522.5 -1.8 90.9 -0.06 0.48

* Percent reduction in bias is represented by the percent reduction in the standardized differences before and after matching.

** One-tailed test.

Matched Pairs of Census Tracts

Table A-5. Reduction in Covariate Imbalance After Matching on the Propensity Score: New York.

EZ EZ Eligible Standardized % Bias(mean) (mean) Difference Reduction* t p-Value**

New York Population, 1990 4282.0 4461.0 -5.8 -1468.9 -0.25 0.40Percent Population change, 1980-1990 1.9 2.0 -0.4 98.6 -0.02 0.49Percent nonwhite, 1990 95.7 95.2 6.6 91.3 0.29 0.39Percent Nonhispanic black, 1990 52.0 45.2 22.6 51.2 0.96 0.17Percent Hispanic, 1990 42.2 47.0 -17.3 -121.8 -0.73 0.23Percent female-headed households with children, 1990 62.4 63.6 -7.8 87.5 -0.34 0.37Percent in same house as 1985, 1990 62.9 65.3 -22.3 -85.8 -0.96 0.17Percent of households with no car, 1990 84.1 84.4 -4.1 96.9 -0.17 0.43Percent high school graduate or higher, 1990 39.9 38.5 18.8 55.8 0.81 0.20Percent college graduate or higher, 1990 7.2 5.2 39.5 -9.6 1.70 0.05Percent unemployed, 1990 18.9 20.8 -22.1 56.6 -0.97 0.17Percent of persons below poverty, 1990 43.5 44.9 -15.3 79.9 -0.65 0.26Median family income, 1989 16799.0 16403.0 5.7 89.7 0.24 0.40Percent of housing units vacant, 1990 7.9 5.7 36.2 30.0 1.56 0.06Percent owner-occupied housing units, 1990 4.8 4.5 3.5 96.1 0.15 0.44Percent of housing units built before 1940, 1990 39.4 34.7 21.5 -607.6 0.93 0.18Median value of owner-occupied housing units, 1990 46146.0 22448.0 35.4 52.3 1.46 0.07Median rent, 1990 337.8 322.9 16.0 81.5 0.70 0.24Housing units per square mile, 1990 28169.0 28764.0 -3.4 91.8 -0.15 0.44

* Percent reduction in bias is represented by the percent reduction in the standardized differences before and after matching.

** One-tailed test.

Matched Pairs of Census Tracts

Table A-6. Reduction in Covariate Imbalance After Matching on the Propensity Score: Philadelphia.

EZ EZ Eligible Standardized % Bias(mean) (mean) Difference Reduction* t p-Value**

Philadelphia Population, 1990 3766.0 3089.0 35.2 43.3 0.66 0.26Percent Population change, 1980-1990 -11.2 -14.1 25.3 33.0 0.47 0.32Percent nonwhite, 1990 87.1 80.5 25.4 66.7 0.48 0.32Percent Nonhispanic black, 1990 69.9 72.5 -7.0 -285.7 -0.13 0.45Percent Hispanic, 1990 16.1 6.2 55.6 28.7 1.04 0.16Percent female-headed households with children, 1990 66.3 63.7 15.0 77.2 0.28 0.39Percent in same house as 1985, 1990 65.1 68.8 -32.1 -24.2 -0.60 0.28Percent of households with no car, 1990 66.1 63.9 17.5 82.8 0.33 0.37Percent high school graduate or higher, 1990 39.7 44.4 -58.1 28.2 -1.09 0.15Percent college graduate or higher, 1990 5.1 6.6 -21.2 68.3 -0.39 0.35Percent unemployed, 1990 22.9 19.2 43.9 64.1 0.82 0.21Percent of persons below poverty, 1990 48.5 39.8 74.0 57.5 1.38 0.10Median family income, 1989 13881.0 17497.0 -67.8 38.1 -1.27 0.11Percent of housing units vacant, 1990 17.0 20.8 -51.0 17.8 -0.95 0.18Percent owner-occupied housing units, 1990 40.8 40.3 2.4 97.2 0.04 0.48Percent of housing units built before 1940, 1990 53.7 57.4 -17.1 43.5 -0.32 0.37Median value of owner-occupied housing units, 1990 24200.0 28200.0 -29.9 50.2 -0.56 0.29Median rent, 1990 271.7 308.7 -31.8 64.3 -0.59 0.28Housing units per square mile, 1990 7307.0 6250.0 28.0 2.7 0.52 0.30

* Percent reduction in bias is represented by the percent reduction in the standardized differences before and after matching.

** One-tailed test.

Matched Pairs of Census Tracts