THE EFFECTIVENESS OF ENTERPRISE ZONES AS A TOOL FOR COMMUNITY REDEVELOPMENT IN FLORIDA: AN ANALYSIS OF THE IMPACT OF ZONE
DESIGNATION ON SOCIOECONOMIC INDICATORS
By
MARIO F. DURON, JR.
A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF URBAN AND REGIONAL PLANNING
UNIVERSITY OF FLORIDA
2016
© 2016 Mario F. Duron, Jr.
To all of those that helped me on this journey
4
ACKNOWLEDGMENTS
I thank my committee members for their guidance and support of my research. I
thank my parents for their unwavering support and encouragement. I thank Alba Allison
for her motivation and help to achieve this milestone. I thank Virginia Alberts for
providing clarity and focus to my research. And finally, I thank Raisa for her patience
and love.
5
TABLE OF CONTENTS page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES ............................................................................................................ 7
LIST OF FIGURES .......................................................................................................... 8
LIST OF ABBREVIATIONS ............................................................................................. 9
ABSTRACT ................................................................................................................... 10
CHAPTER
1 INTRODUCTION .................................................................................................... 12
2 LITERATURE REVIEW .......................................................................................... 15
History of Enterprise Zones .................................................................................... 15 The British Experience ............................................................................................ 16 The US Interpretation.............................................................................................. 17
A Tool for Community Redevelopment ................................................................... 20 The Miami Riots ...................................................................................................... 21
The Florida Program ............................................................................................... 24
Program Administration and Objectives .................................................................. 24
Evaluation of the Florida Program .......................................................................... 26
3 THE IMPACT OF ENTERPRISE ZONES ............................................................... 30
Measuring the Impact ............................................................................................. 30
Tax Incentives and Job Growth .............................................................................. 31 Impact on Property ................................................................................................. 33
Impact on Zone Residents ...................................................................................... 34 Limitations of the Methods ...................................................................................... 35
4 METHODOLOGICAL FRAMEWORK ..................................................................... 37
Approach ................................................................................................................ 37 County Profiles ....................................................................................................... 38 Enterprise Zone Program Perception ..................................................................... 39
Duval County .................................................................................................... 40
Hillsborough County ......................................................................................... 41 Miami-Dade County .......................................................................................... 42
Summary of Program Priorities ............................................................................... 43 Data ........................................................................................................................ 44 Limitations ............................................................................................................... 46
6
5 RESULTS ............................................................................................................... 48
Inferred Findings ..................................................................................................... 48 Regression Analysis ............................................................................................... 50
Hot Spot Analysis ................................................................................................... 52 Discussion .............................................................................................................. 54 Conclusion .............................................................................................................. 55
Policy Recommendations ................................................................................. 56 Future Studies .................................................................................................. 58
APPENDIX
A MEANS ANALYSIS ................................................................................................ 59
B REGRESSION ANALYSIS ..................................................................................... 68
C HOT SPOT ANALYSIS ........................................................................................... 77
LIST OF REFERENCES ............................................................................................... 80
BIOGRAPHICAL SKETCH ............................................................................................ 84
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LIST OF TABLES
Table page 1-1 Nomination criteria for the designation of Florida’s Urban Enterprise Zones. ..... 28
1-2 State and Local Incentives and Job Growth from 2006 to 2014 ......................... 29
A-1 Duval County Means Analysis ............................................................................ 59
A-2 Hillsborough County Means Analysis ................................................................. 62
A-3 Miami-Dade County Means Analysis .................................................................. 65
B-1 Coefficient analysis of Duval EZ Predictors for 1990 .......................................... 68
B-2 Coefficient analysis of Hillsborough EZ Predictors for 19900 ............................. 69
B-3 Coefficient analysis of Miami-Dade EZ Predictors for 1990 ................................ 70
B-4 Coefficient analysis of Duval EZ Predictors for 2000 .......................................... 71
B-5 Coefficient analysis of Hillsborough EZ Predictors for 2000 ............................... 72
B-6 Coefficient analysis of Miami-Dade EZ Predictors for 2000 ................................ 73
B-7 Coefficient analysis of Duval EZ Predictors for 2010 .......................................... 74
B-8 Coefficient analysis of Hillsborough EZ Predictors for 2010 ............................... 75
B-9 Coefficient analysis of Miami-Dade EZ Predictors for 2010 ................................ 76
8
LIST OF FIGURES
Figure page 4-1 Selection of EZ CBGs… ..................................................................................... 47
C-1 Hot spot analysis for Poverty .............................................................................. 77
C-2 Hot spot analysis for Households Receiving Public Assistance.......................... 78
C-3 Hot spot analysis for Employed Residents. ........................................................ 79
9
LIST OF ABBREVIATIONS
ACS American Community Survey
CBG Census Block Group
DEO Department of Economic Opportunity
EDR Office of Economic and Demographic Research
EZ Enterprise Zone
FAC Florida Association of Counties
FGDL Florida Geographic Data Library
NHGIS National Historical Geographic Information System
OTTED Governor’s Office of Tourism, Trade and Economic Development
ROI Return-on-Investment
UK United Kingdom
US United States
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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Urban and Regional Planning
THE EFFECTIVENESS OF ENTERPRISE ZONES AS A TOOL FOR COMMUNITY REDEVELOPMENT IN FLORIDA: AN ANALYSIS OF THE IMPACT OF ZONE
DESIGNATION ON SOCIOECONOMIC INDICATORS
By
Mario F. Duron, Jr.
August 2016
Chair: Abhinav Alakshendra Cochair: Joseli Macedo Major: Urban and Regional Planning
The state of Florida was an early pioneer of enterprise zone policy in 1980,
responding to economic inequality observed in urban areas. The zones were
designated in distressed areas of the state that demonstrated factors like high rates of
poverty, unemployment, and poor infrastructure. The theory holds that the zone’s
incentives and reduced operational costs attracts businesses to the area, promotes job
growth for zone residents, and eliminates slum and blight. In December 2015, the
Florida Enterprise Zone program expired after twenty-five years. This study uses
decennial census data from Duval, Hillsborough, and Miami-Dade Counties to
determine whether census block groups designated as enterprise zones improve as a
result of the group’s status. The research finds that census block groups designated as
enterprise zones in 1995 remained more distressed than non-zone areas by 2010. A
regression analysis confirms that vacant housing and low educational achievements
remain characteristic of Duval and Hillsborough’s zones. Miami-Dade’s analysis
suggests that zones have improved with more renters and individuals above poverty,
11
however a more robust analysis will be needed to determine if the changes are a result
of the policy. This paper concludes that enterprise zone policy was not an effective
community redevelopment tool to overcome the social and economic barriers of
Florida’s distressed communities.
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CHAPTER 1 INTRODUCTION
The Florida Legislature enacted the Enterprise Zone (EZ) Act in 19951 as a tool
for the redevelopment of economically distressed urban and rural communities of the
state. The State Program’s objective is to create employment opportunities and
enhance the zone residents’ economic and social well-being (Florida Enterprise Zone
Act, 2015). The theory supporting EZs maintains that credits and incentives attract firms
to designated zones, credits reduce participating firms’ operational costs, and inversely
increase output and employment opportunities in targeted areas (Beck, 2001; Bostic &
Prohofsky, 2006; Hirasuna & Michael, 2005).
During the last ten years of the Program’s run, approximately $500 million of
state incentives and local credits were provided to businesses in Florida’s EZs (Florida
Department of Economic Opportunity, 2013). During that time, zones reported increases
in both firm establishment and job growth. However, considering the Program’s impact
on fiscal variable fails to capture the effects on zone properties and resident’s welfare.
Using socioeconomic variables provided by the US census, I augment the literature of
EZs by providing an analysis of Florida’s program in terms of community
redevelopment. Analyzing the policy from its enactment in 1995 to five years before its
repeal in 2015, the research investigates the social, economic, and housing differences
between EZ and non-EZ census block groups during thirty years.
The research begins with the history of EZ policy, with its conceptualization by
Peter Hall and experimentation in the United Kingdom under the Land Act of 1980, to its
1 At the second ten year review of the Enterprise Zone Act, the Florida Legislature decided not to take further action to extend the law. As a result, as of December 31st, 2015 it is no longer active.
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introduction and interpretation in the United States. In the US, state and federal
governments amended the policy for their objectives, resulting in varying expression of
EZ policy among the state programs. Yet, among all interpretations of EZ policy, they all
have the underlying program objective of serving as a tool for community development.
In Florida, the policy for enterprise zones was prompted by riots in Miami during the
early 1980’s.
The following chapter presents an overview of research done by economists and
social scientists to investigate the efficacy of EZs. Various metrics, from firm
establishment and job growth studies to property values, and resident welfare have
been undertaken. As varied as the parameters used to measure the Program’s impact,
so have been the conclusions made about EZ effects. The literature often cites
methodology, control groups, and data availability as factors producing inconclusive
results. I end the chapter with a discussion of common approaches employed to
overcome the cited limitations.
In chapter four of this study, I present my research by first introducing the three
counties and the respective enterprise zone programs used in the analysis. Then, I
discuss my approach to determine the impact of EZ status on Florida’s urban zones.
Using census block group data from 1990, 2000, and 2010 for Duval, Hillsborough, and
Miami –Dade Counties and the respective enterprise zone boundaries, I am able to
identify and label the census block groups that encompassed EZs within each county
for the time referenced. I consider the changes in socioeconomic factors between the
EZ and non EZ census block groups, and employ a regression analysis to determine if
the changes are attributable to EZ policy.
14
I conclude my research discussing the findings provided by the analysis. In
Florida, EZs were established in distressed areas of the state, but appear to have done
little to improve the socioeconomic conditions of distressed area residents. Two of the
three counties showed little improvement, these findings are well documented. I
conclude with policy recommendations and suggestions for future analyses of EZs in
Florida.
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CHAPTER 2 LITERATURE REVIEW
Before delving into the analysis of Florida’s EZ program, the paper begins with
an overview of the enterprise zone concept developed in the United Kingdom (UK), and
moves on through its adoption by federal and local governments in the United States
(US). The chapter provides a definition towards community redevelopment in Florida
and supports the argument that the State policy was motivated by the social and
economic barriers observed in distressed areas in the 1980’s. The chapter concludes
with an overview of the Florida program and its evaluation. But first, the history of
enterprise zones.
History of Enterprise Zones
Peter Hall is often recognized for developing the concept of EZs in the 1970’s
(Boarnet, 2001; Hall & Squires, 2013; Hyman, 1998; Williams, 1990). Hall, a British
professor of Urban Planning, theorized that spatially-targeted economic policy in
depressed urban areas can help stimulate private market forces. Strategies like reduced
regulation, lower taxes, and financial incentives entice private firms to establish in
designated zones (Hyman, 1998; Williams, 1990). In exchange, dormant industrial
areas regenerate and employment increases. According to Hall’s theory, EZs can
improve economic conditions through a two-fold process. First, firms looking for reduced
operational costs establish in zones, and provide area residents with low-wage, low-skill
employment opportunities. As these firms become specialized and new markets open,
wages and demand for skilled-employment opportunities increase (Hyman, 1998;
Williams, 1990).
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The British Experience
The Docklands project is regarded as a successful venture resulting from the
1980 law adopted in the UK (Fainstein, 1991). In 1981, the British Parliament
established the London Docklands Development Corporation (LDDC), to carry out
redevelopment initiatives in a central area of London comprised of approximately 20
square miles. Program initiatives included a mixed development of 12.5 square miles for
commercial space, and approximately 8.5 square miles dedicated to residential, light
manufacturing, and public spaces like schools and parks (Fainstein, 1991). Within the
scope of the LDDC’s role, the project resulted in an economically mixed area and was
able to successfully raise approximately $8 billion in private investments within the first
year of its inception (Fainstein, 1991).
Yet, as successful as the project appeared, it was not free of criticism. Fainstein
argues that the UK project prioritized the interests of private investors before those of
the general public (1991). She adds, that while the Docklands project presented a
narrative of a planned community, the juxtaposition and stark contrast between the
quality of space between commercial and lower-income residential areas confirms there
was no master plan (Fainstein, 1991). She was critical of the change occurring within
the planning profession, as the enterprise zone policies allowed planners to map land
for the most marketable uses, and make the sites available to private developers
(Fainstein, 1991).
In another analysis of British zones, researchers argue that zone administrators
overestimated the positive effects. The Land Act which was intended to attract new
firms to establish designated zones resulted in the relocation of existing ventures into
EZs. New firms created by EZs accounted for 12% of the firms participating in the
17
program (Gunther & Leathers, 1987). More detrimental was EZ’s impact on
employment, as the authors cite less than 1, 000 jobs were created by the policy. In
fact, when taking into account the expenditures of local authorities made to reclaim
land and then develop it, each new job created was at a public cost of $250, 000
(Gunther & Leathers, 1987). On the other side of the Atlantic, EZ policy was being
touted as the solution for failing Inner Cities beginning in the early 1980s.
The US Interpretation
Though its effectiveness as an economic development policy was still
questionable, in 1979, Stuart Butler, a champion of EZs, introduced the concept to the
US in a report published by the Heritage Foundation (Williams, 1990). Butler analyzed
the EZ concept from its development by Hall, to its implementation by Thatcher. He
extolled the benefits of EZ policy, citing its ability to foster the creation of new small
businesses and facilitate the establishment of community-based development
corporations in distressed Inner Cities (Mounts, 1981). His report sparked an interest in
the approach to urban renewal programs in the US, and, by the end of 1979, EZ policy
was being proposed in five federal bills and in more than 20 state legislations
(Goldsmith, 1982).
The 1980’s brought a tide of change to the United States. Newly elected
Republican President Ronald Reagan entered the White House amidst a national
recession. Anti-government sentiments after President Carter’s Administration helped
elect President Reagan, a supporter of free markets (Engberg & Greenbaum, 2000).
Meanwhile, a Republican Representative from New York, Jack Kemp struggled to
introduce EZ policy at the federal level. Opponents argued that Kemp’s proposal was
merely an attempt to lure businesses into impoverished inner city areas at the expense
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of the general public’s taxes (U.S. Senate, 1992). Yet, Kemp argued that EZs
encouraged entrepreneurship and in particular provided minority men and women
access to venture capital (U.S. Senate, 1992).
President Reagan, an admirer of Supply-Side Economics, did away with many
federal urban renewal programs and instated policies that limited government oversight
and subsidized private markets (Gunther & Leathers, 1987; Riposa, 1996). In 1982, in
his State of the Union address, the President called on the American people to free the
economy by supporting a national federal EZ policy (Williams, 1990). Ronald Reagan
considered Representative Kemp’s bill innovative, as it addressed unemployment in
Inner Cities, limited government intervention, and supported private enterprise. The
urban renewal language and funding mechanism made the US EZ policy palatable to
the Republican administration and the American people because it was a means to
improve the Inner Cities at the expense of private markets. In fact, EZ policy was the
only urban renewal program supported by President Reagan under his federalism
policies (Boeck, 1984; Gunther & Leathers, 1987; Mounts, 1981).
At the national level, the EZ program faced resistance from Congress as many
policy makers and tax payers were suspicious that EZs would replace existing social
welfare programs (Hyman, 1998). Representative Kemp, with bipartisan support from
Robert Garcia, a Democrat from New York, eventually succeeded in getting Congress
to pass House Resolution 3824 Urban Jobs and Enterprise Zone Act. However, it did
not pass in the Senate; even President Reagan failed to obtain support for federal EZ
policy (Boeck, 1984; Gunther & Leathers, 1987). In 1994, two years after the Los
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Angeles Riots, the bill succeeded in becoming federal policy under Empowerment Zone
and Enterprise Communities Act (Hyman, 1998).
As the concept grappled to hold ground at the national level, EZs quickly became
popular among states. Florida was one of the first states to introduce EZ legislation in
1980, which preceded federal EZ policy (Rogers & Tao, 2004). Other states followed
suite; Connecticut which implemented EZ legislation in 1982 as did Indiana, Colorado,
and New Jersey. By the time the national government enacted the Empowerment Zone
and Enterprise Community program in 1994, approximately 40 states and the District of
Columbia had already adopted their own interpretations of EZ policy (Bondonio &
Enberg, 2000).
Today, Enterprise Zone Programs vary at the state level, as each state adopted
the concept of EZs, but amended EZ participation requirements, eligibility requirements,
and incentives based on the state’s general goals. In all states however, EZ policy
shares the common objective of reducing operational costs and red tape for businesses
so they are motivated to establish themselves in a geographically targeted area, and in
turn provide employment opportunities and higher wages to zone residents (Bondonio &
Enberg, 2000; Gunn, 1993). The variety of objectives and incentives offered makes
evaluations and monitoring of zone performance a difficult task; yet, states continue to
support these programs without determining the policy’s impact.
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A Tool for Community Redevelopment
Community is a social unit with place and purpose. Not simply locations containing habitation activities, communities are sociospaces where inhabitants create a built environment to encompass political, social and economic activities to meet their needs and desires. The extent to which a community succeeds in this effort depends on the context in which it exists and its ability to develop the capacity to participate in determining its needs and programs through which those needs are met (as cited in Riposa, 1996, p. 537).
Community redevelopment is an “outcome that improves the physical, social,
economic, and environmental conditions in a community” (Brennan et al., 2014, n.p.).
When carried out successfully, redevelopment efforts incorporate the collective
participation of community members, maximizing local resources and development
impact. There are three approaches in which these relationships manifest themselves:
traditional, intermediate, and direct participation (McSwain & Welbaum, 1989).
Traditional and direct participation are the two extremes of the relationship. In a
traditional approach, redevelopment employs the use of police powers like eminent
domain, deregulation, and tax abatements. While in a direct approach tools like quasi-
public equity financing allows the public the greatest level of control over redevelopment
projects. Between the two forms lies the intermediate approach. This partnership
towards redevelopment requires the equal input of public and private markets to enact
redevelopment policy. Tools like tax increment financing ad bonds are common. Florida
carries out redevelopment using a combination of the three approaches. However, the
most popular redevelopment programs in the state have typically been defined as
intermediary policy.
In Florida, enterprise zones are a type of intermediary public/private partnership
used to remove economic barriers in distressed areas of the state (Hirasuna & Michael,
2005; McSwain & Welbaum, 1989). Zones were designed to promote equitable access
21
to skill-matched employment opportunities while, also, attempting to eliminate the
spatial mismatch between residents and employers. The Spatial Mismatch Hypothesis
suggests that the location of jobs have increasingly moved away from the inner cities
towards the suburbs, while lower-income minorities remain in the inner city (Lubuele &
Mills, 1997).
The Enterprise Zone Program in Florida also distinguishes from other economic
development programs by focusing on the social and economic conditions of residents
as opposed to similar in targeted communities and not spaces. Moreover, EZs are
redistributive forms of socioeconomic development policy, because they facilitate the
process of transitioning resources from the private sector (financial investments) and
redistributing them (jobs, higher wages) to targeted distressed areas (Cassell & Turner,
2007; Roger & Tao, 2004). The following section provides a synopsis of Florida’ s 1980
Enterprise Zone Program which was designated to address community redevelopment
initiatives in the Inner Cities.
The Miami Riots
Social disturbances stemming from racial tensions in Miami during the early
1980’s were the catalyst for the passage of EZ policy in Florida. In the wake of racial
strife across major US cities, Miami’s black population took to the streets in protest of a
verdict acquitting four white Miami police officers for the manslaughter of Arthur
McDuffie—a 33 year old black man (Chase et al., 1986). McDuffie, an insurance agent
ran a red light on a borrowed motorcycle; after an eight minute police chase, he was
apprehended and beaten to death. In May 1980, an all-white jury provided a not guilty
verdict for the accused policemen, and within three hours of the decision, riots began in
Miami. Protests turned violent resulting in eighteen deaths and 283 looted business
22
properties (Boswell et al., 1986). The damages, translated to roughly a $100 million and
the loss of 3,000 jobs. All occurred in a predominately Black community encompassing
roughly 2.5 square miles of the City’s Inner City (Whitefield, 2000).
Following the disturbances, the Miami Herald newspaper surveyed African-
American residents of Miami-Dade County to determine the reasoning for the riots and
violence. The respondents ranked unemployment, police brutality, and poor housing as
the top three issues challenging their community (Bowell et al., 1986). A similar survey
was conducted by the Miami Herald after riots in 1968. The Miami-Dade Black
community was polled and asked to identify the top three challenges. At that time,
respondents cited dropout rates of black children, dirty neighborhoods, and parents who
do not control unruly children (Bowell et al., 1986). Researchers of the Miami riots noted
the differences in the catalyst for the protests. In 1968, riots were motivated by internal
pressures, which were ascribed to vestiges of the civil rights movement, while in 1980,
problems stemmed from external sources and current events. Researchers argued that
the solutions were external to the Black community as most Blacks had White
employers, the majority of police where White men, and much of the predominately
Black housing was owned and operated by Whites (Bowell et al., 1986).
Black unemployment was a critical catalyst for the 1980 Riot as expressed by
Miami-Dade’s Black population. Social theory argues that a major stressor for rioting is
often times attributable to employment access, or lack of it. The Blocked Opportunities
Model suggests that Backs were “excluded from full integration into American society
because of white-controlled economic institutions (Bowell et al., 1986, p. 5). Overt
racism, labor-skill inequality, and distancing employment opportunities from Black
23
communities were some of the tactics that resulted in the systematic exclusion of Blacks
(Bowell et al., 1986).
Poor housing conditions was third on the list of factors said to prompt the riots.
Miami’s Black neighborhoods followed the national trends of the time, with symptoms of
declining populations and outdated or poor housing stock (Bowell et al., 1986). Because
local residents were not able to pay high rents, the lack of finance availability, and high
insurance rates, private developers were hesitant to invest in housing within
predominately Black communities. In Liberty City, the epicenter of the Miami riots,
housing stock was older or uninhabitable, and about a third of the residents were living
in overcrowded housing (Bowell et al., 1986).
In response to the social disturbances, and with the understanding of the factors
that plagued the Miami Black community, State Representative Barry Kutun along with
Dade County delegates presented the Slum & Blight Bill to the state legislation. The
goal of the legislation was to “encourage employment of unemployed residents,
economic development of businesses, participation of the businesses sector in
community improvement projects, and formation of community development
corporations” (U.S. Department of Housing and Urban Development [HUD], 1987, p.
196). There was hesitation to pass the Bill, as lawmakers argued that the bill rewarded
the rioters, but, by the end of 1980, the Bill passed with the support of Florida Governor
Graham (Bowell et al., 1986). The State acknowledged that it was failing Black
communities by not providing equitable access to education, employment, and housing.
As a result, the Florida Legislature’s motive to enact enterprise zones was not solely
fiscal, but it was a policy responding to social deficiencies within urban communities.
24
The Slum & Blight Bill served as the foundation for the Florida Enterprise Zone policy;
and, it was, eventually, influenced by President Reagan’s federal EZ proposal. In 1980,
Florida’s targeted areas were referred to as “Slum and Blight Areas”, but by 1982 they
had come to be known as “Enterprise Zones”, or “Free Enterprise Zones” (HUD, 1987,
p. 170).
The Florida Program
In 1995, the Florida Legislature (Florida Enterprise Zone Act, 2015) declared it
was the policy of the State to provide the necessary means to assist local communities:
Create viable communities
Eliminate slum blight
Provide decent housing and suitable living environments
Expand economic opportunities, principally for persons of low or moderate income
The legislature declared that “the development, redevelopment, preservation,
and revitalization of communities in this State… are public purposes for which public
money may be borrowed, expended, loaned, pledged to guarantee loans, and
granted”(Florida Enterprise Zone Act, 290 Fl. Stat. § .002, 2015). The following section
provides a brief overview of the Florida program.
Program Administration and Objectives
After the 1995 state legislature enacted the Enterprise Zone Act, all existing
zones established under the Slum & Blight Bill in the 1980s were repealed, abandoned,
or re-designated. A total of 19 EZs were established in 1995, including zones in
Hillsborough, Duval and Miami-Dade County. The new policy was modeled after the
Clinton administration’s Empowerment Zone and Enterprise Community initiatives which
focused on small businesses and the employment of minority residents living in inner-
25
cities (Hyman, 1998). In addition, the federal programs developed designation criteria
based on census data which the new legislation adopted. Factors like housing,
population, poverty rates, and economic distress were some of the metrics used for
Florida’s zone designation. Table 1-1 outlines the nominating procedure for urban EZs
as adapted from the Florida Enterprise Zone Act.
In 2005, the legislation was extended, and allowed for boundary amendments
and the addition of EZs in urban and rural areas of the State. By 2015, prior to the
Program’s discontinuation in December, a total of 65 urban and rural enterprise zones
existed throughout the State (Florida Department of Economic Opportunity, 2014). The
Governor’s Office of Tourism, Trade and Economic Development (OTTED)
administered the EZ Program from 1996 to 2011 until it was dissolved and incorporated
into the new Department of Economic Opportunity. The Florida Department of
Economic Opportunity (DEO) is tasked with administering the State’s tax credits and
economic incentives provided by state and federal agencies, and redistributes funds to
state and local programs in order to guide the economic vision of the State (Florida
Department of Economic Opportunity, 2013).
State and Local Incentives
From 2006 to 2014 a combined total of $500 million state and local EZ incentives
were granted to private firms. In return, approximately 90,000 new jobs were created by
participating businesses. If we only consider the Program’s cost and return of
investment based on job creation, each job created came at a cost of $5,252.65.
However, the Florida EZ program provides credits and incentives for more than just
employing zone residents; when all credits are considered, the value for each job
created may actually be less. Table 1-2 outlines the incentives provided, a breakdown
26
of the Program’s budget for the past ten years, and the number of businesses and jobs
created. Regardless, public and private agencies have concluded that irrespective of
the cost of incentives to local and state government, the EZ Program’s budget was
relatively low (Florida Department of Economic Opportunity, 2013).
Evaluation of the Florida Program
In 2014, Florida’s Office of Economic and Demographic Research (EDR)
conducted a study of 18 state economic development programs, including Enterprise
Zones. The Return-on-Investment (ROI) was used to measure the Program’s
effectiveness. It strictly evaluated the program on a monetary basis by calculating state
incentive expenditures. The study concluded that the Program’s negative impact was
based on the fiscal variable. First, it found that previously taxable activity was converted
to be non-taxable by the policy. Secondly, while the purpose of the policy was new job
creation and employment expansion, EZ’s were mostly shifting jobs from one region of
the state and relocating to EZs (Office of Economic and Demographic Research [EDR],
2014). The authors made special note that their approach excluded any analysis of
external or social benefits, and added that, “For some programs, the ROI may not be
the principal purpose of the program or even a secondary goal. This applies to
Brownfield Redevelopment Bonus Tax Refund, Innovation incentive, and Enterprise
Zone programs” (EDR, p. 4, 2014).
The 2014 Study also cites a 2010 and 2013 EDR investigation of the impact of
EZs on slum and blight, using changes in property values as indicators. The 2010 Study
analyzed three county programs over a five year period (1999 to 2004), and could not
determine a relationship between EZ Program and property values. But the follow-up
2014 Study, using property tax data from 1999 to 2012, found positive property
27
appreciation growth in two of the three counties analyzed. In January 2015, EDR
released a study expanding on methods used in 2010 and 2014 to analyze the growth
of property values for forty Florida EZs and the surrounding areas. The study found that
of the top five EZs receiving almost 80% of state incentives, only Miami-Dade, which
accounts for 58% of incentives, showed an average Just Value Growth at 0.1% higher
than the growth observed in surrounding non-EZ areas (EDR, 2015). The investigation
infers that more credit and incentives does not guarantee better results.
The Florida Tax Watch, a non-profit group, independently evaluated the program
and found that policy filled a void for various inadequacies in the State (2005). Most
notably, the study found that EZ areas provided access to food and medication to
Floridians, that otherwise would be living in areas which do not have healthy food
options (Florida Tax Watch, 2005). Furthermore, one national pharmacy chain, CVS,
established approximately 10% of their 850 Florida stores within EZ areas, improving
access to health services such as, but not limited to, immunizations, blood pressure
screening, and diabetes awareness (Florida Tax Watch, 2005). Florida EZs were
established in socially and economically distressed areas of the State and as other
research has suggested, the Program’s ROI may not be the best indicator for the
Program’s evaluation. Findings like those made by the Florida Tax Watch are difficult to
interpret when only using economic metrics.
This chapter has provided a brief overview of the evolution of the Enterprise
Zone concept. While the British EZ Program emphasized rejuvenating dormant
industries and increasing employment capacity, the US interpretation of the British
Program focused on improvements to people and places. In particular, Florida’s Zones
28
attempted to overcome the social and economic barriers characteristic of distressed
communities in South Florida. Evaluations of the Program made by state and
independent agencies resulted in divergent conclusions, and suggest that pecuniary
indicators may not adequately measure the policy. In the following chapter, I discuss the
literature of the methods and present the indicators used in research of Enterprise Zone
Programs.
Table 2-1: Nomination criteria for the designation of Florida’s Urban Enterprise Zones.
Source: Adapted from Florida Enterprise Zone Act. Fl. Stat. ch. 290 § .001-.016 (2015)
Indicator Criteria
Pervasive Poverty
Census block group Poverty Rate may not be less than 20%
In at least 50 % of the census geographic block groups within the nominated area, the poverty rate may not be less than 30%
Unemployment
Average rate of unemployment for the nominated area is not less than the state’s average of unemployment Evidence of especially severe economic conditions which have brought about significant job dislocation within the nominated area
General Distress A high incidence of crime, abandoned structures, and deteriorated infrastructure or substantial population decline are examples of appropriate indicators
29
Table 2-2: State and Local Incentives and Job Growth from 2006 to 2014
Source: Adapted from Florida Department of Economic Opportunity. (2013). 2013 Annual Incentives Report
Fiscal Year Businesses
Receiving Technical Assistance
New Businesses Created
New Jobs Created
State EZ Incentives Approved
Local EZ Incentives Approved
2013/2014 11,151 6,065 12,982 $ 15,767,116.00 $ 11,373,610.00 2012/2013 6,989 5,306 16,640 $ 16,299,681.00 $ 53,586,962.00 2011/2012 9,014 4,500 11,602 $ 1,795,594.00 $ 56,586,962.00 2010/2011 5,618 4,103 11,559 $ 29,577,795.00 $ 33,091,214.00 2009/2010 9,056 7,559 6,784 $ 67,602,482.00 $ 19,975,176.00 2008/2009 11,708 3,104 9,073 $ 45,351,441.00 $ 11,577,541.00 2007/2008 10,850 2,719 9,600 $ 40,359,538.00 $ 22,470,601.00 2006/2007 16,170 4,976 11,456 $ 35,718,744.00 $ 10,006,935.00
Total: 80,556 38,332 89,696 $ 252,472,391.00 $ 218,669,001.00
30
CHAPTER 3 THE IMPACT OF ENTERPRISE ZONES
Methodology is an important factor in determining the impact of Enterprise
Zones. This chapter provides an overview of EZ investigations and the evolution of the
study designs used. Because EZs are not randomly selected and are based on
designation criteria, experimental research cannot be applied toward their investigation.
In the following sections, I present the prominent social and economic measures used
to investigate the impact and then highlight the approaches used to overcome the
limitations in order to develop a definitive conclusion of the Program’s effectiveness.
Measuring the Impact
Similar to Florida’s evaluations, studies of other state EZ programs have not
provided definitive results. Peters and Fisher identified two of the most common
hurdles EZ researchers face when determining the Program’s effectiveness. These
include how to properly measure the effectiveness of the program and how to assess
the impact of its incentives (Fisher & Peters, 2002). Luckily, EZs can be measured using
a variety of means, including an economic return on investment (ROI) or on a
socioeconomic index. Researchers have qualified the EZ program looking at factors like
EZ firm establishment, changes in income status, growth of property values, poverty
levels and employment trends. The varied body of literature provides insight into those
significant variables and econometric research methods used to measure the Program’s
impact. The following presents some of the results found in EZ research and the variety
of social and economic measures used to determine their effectiveness.
31
Tax Incentives and Job Growth
Tax credits and incentives can be effective measures of the Program’s success,
as these can indicate whether incentives are sufficient to overcome the Zone’s
economic barriers (Hirasuna & Michael, 2005). In their analysis of EZs, Enberg and
Greenbaumb measure the impact EZ policy and tax incentives have on the decision to
establish firms with EZs (2004). Five states programs, including Florida’s, were used to
investigate the Program’s impact on establishment, employment, and capital spending
outcomes by EZ businesses. Initial findings confirmed that EZ programs were
established in distressed areas, and incentives used to reduce economic barriers. A set
of comparable non-EZ ZIP codes, were developed to control social and geographic
variables. Result of the sample difference-in-difference analysis show that after zones
designation, manufacturing sector observed significant increase in the number of new
firm establishment, employment opportunities, payroll, and shipments. Conversely,
existing businesses saw increases in the operational costs of the aforementioned
business activity. Similar to results from less reliable surveying methods, the team
concluded that EZs facilitated the growth of employment among new firms, however,
when compared to results from robust econometric EZ analyses, the Zones have no
substantial effect on overall employment growth (Enberg & Greenbaumb, 2004).
Bondonio and Greenbaumb (2007) expanded on the Enberg and Greenbaumb
2004 approach by employing establishment-specific panel data to compare the impact
of eleven state EZ Program policies with the same measures used in the 2004 Study to
determine economic growth. The State EZ Programs demonstrate varying objectives,
designation criteria, and varying types of incentives. The macro analysis of the diverse
program policy allows for a comparative analysis of incentives, and offers best practices
32
for programs. Like the 2004 Study, the economic growth measures show that zone
status positively affected employment and capital expenditures for new establishments
while conversely increasing costs for existing business in EZs. Findings from the policy
impact analysis determined that incentives associated with job creation had more
positive impact than credits for capital goods. The team argues that credits for capital
purchases can have substitution effects resulting in firms substituting labor for capital
purchases. The authors find that programs with strategic local development plans and
incentives tied to job creation were more successful and had the greatest impact on
new and existing enterprises (Bondonio & Greenbaumb, 2007).
Similar to the measures listed above, Beck analyzed 51 EZs throughout the US
to determine their effectiveness based on job growth and firm establishment (2001). His
study adds an additional development to the EZ literature by including an analysis
isolating the factors that promote the most growth in EZs. Beck looked at incentives,
land-use, and characteristics of local EZ programs. A regression analysis was used to
compare the relationship of the aforementioned factors to business and job growth
within EZs. Beck concluded that enterprise zones benefit firms more than residents. He
also found that the improvements to services and quality of life in EZs positively
impacted job growth. Zones that provided more incentives had higher rates of new firm
establishment (Beck, 2001).
33
Impact on Property
In 2006, economist, Landers, asked “Why don’t Enterprise Zones Work?”. He
attempted to answer this question by looking at the impact of Ohio’s Urban Jobs and
Enterprise Zone Program on commercial and industrial property values using property
sales data from 1984 through 1993 (2006). This method differs from the typical
approach of EZ analysis because it takes a micro-level view of the economic effect of
EZs by using parcel data. Landers uses a special pricing model (hedonic) to estimate
the property values of parcels within designated EZs and of those non-EZ properties
nearby. The results of Landers’ study did not provide a definitive outcome. In some Ohio
counties, EZs had a positive impact; but, Landers, also, found that as more EZs were
designated in close proximity to other EZ locations, the price effect diminished. Landers
suggests that competitiveness among counties for EZ funds may be a primary cause of
his indefinite findings while the discretionary tax abatements provided by local
governments to selected businesses may be another major cause (Landers, 2006).
In “An Evaluation of State Enterprise Zone Policies”, undertaken by Enberg and
Greenbaum, analyzed the impact of EZs on housing values, occupancy rates, and
employment for six states, including California, Florida, and New Jersey (2000). They
determined that, overall, EZ programs did not significantly improve housing markets,
income, or employment for EZ residents. They reached their conclusion using decennial
census data that was aggregated by ZIP code level; the data provided details on
housing, income, and demographics. Then, they employed a stepwise regression
analysis to determine the variables that best predict EZ status. Greenbaum and Enberg
continued their analysis by estimating designation probability to develop a set of
comparable non-EZ ZIP codes resembling the social and economic pre-designation
34
conditions of EZ areas although they not been officially designated as an EZ. Enberg
and Greenbaum’s results for their ten year analysis of the six state programs confirmed
great variations in programmatic impact. Particularly, Florida’s EZ Program increased
poverty and unemployment rates while simultaneously increasing home ownership and
occupancy rates. The authors theorize that Florida’s incentives are more greatly valued
by homeowners than business. Suggesting that within Florida’s EZ, more funds are
appropriated toward public services and local capital improvement projects (Enberg &
Greenbaum, 2000).
Impact on Zone Residents
A recent study, undertaken by David Lynch uses GIS and Census Block Group
data to analyze impact of Colorado’s EZ status on per capita income, poverty and
unemployment (2010). An initial aggregate summary of 1990 and 2000 census data
demonstrated that EZ designation provides improvements to the poverty and
unemployment rates of zone residents compared to those in non-zones. Particularly,
poverty rates fell faster in EZs and the unemployment gap between EZ and non-EZs
narrowed during the ten year study. However, when the unemployment rate, poverty
rate, and per capita income, were analyzed using a regression analysis under the
constraints of social and economic dependent variables, the results showed that
Colorado’s EZ status had no impact on poverty rates. Income levels in EZ and non-EZ
areas of the state increased, yet per capita income remained lower in EZs. Moreover,
the EZ designation appeared to increase, rather than decrease, unemployment levels
for those living within urban EZs (Lynch, 2010).
In their study of California’s EZ on the individual welfare, EZ researchers, Bostic
and Prohofsky, examined the policy and its benefits to individuals who were hired under
35
the Program (2006). In order for firms to receive hiring credits, they should hire
employees living in designated enterprise zone areas. The authors used tax returns to
identify EZ firms and the employees that qualify the firm to receive EZ benefits. Then,
using tax return data, Bostic and Prohofsky developed a set of economically
comparable non-EZ control group employees and documented the income changes
among the groups from 1993 to 1997. The researchers concluded that EZ status
positively impacted wages and income because those hired under the Program had
higher wages and were more likely to file an annual income tax return, than those in the
control groups (Bostic & Prohofsky, 2006).
Limitations of the Methods
In the analysis of the EZ tax incentives and program objectives, Engberg and
Greenbaumb’s results demonstrated the importance of methodology to understand EZs
(2004). The team observed that aligning their conclusions with survey results showed
zones have created many new jobs, while their analytic study measuring net changes
attributed no significant employment growth to zone impact (Enberg and Greenbaumb,
2004). The team’s analytic results coincided with the findings of more statistically robust
EZ research. While other studies have employed surveying methods, they are often
considered a poor approach because they provide little guarantee that the impact can
be attributed to the zone’s designation (Hirasuma & Michael, 2005).
Plus, surveying can produce a non-response bias, as made evident in Beck’s
2001 study. Beck only received a 33% response rate from his surveys, and found that
administrators of successful EZs are more likely to respond and complete surveys,
while respondents of zones that do not do as well typically omitted information and
36
returned incomplete surveys (Beck, 2001). Because of these concerns, surveying is not
a popular method for EZ research.
Another popular approach for EZ research has been Shift-share analysis. This
approach isolates job growth in zones from regional or national trends, and develops a
conclusion based on the proportional growth between the two areas of interest. It is
more sophisticated than surveying, although it only analyzes industry and employment
factors. However, this is a faulty assumption, as job growth can be affected by many
variables, including the work-readiness of EZ employees and the availability of land in
EZs where firms can be established (Hirasuma & Michael, 2005).
One of the most common methodologies employed in EZ research is the use of
regression analyses. This approach, measures the statistical relationship a variety of
factors has on a dependent variable which, then, allows researchers to determine if, in
fact, the variables used are good predictors of the dependent variable (Hirasuna &
Michael, 2005). Regression analyses do not completely control for all endogenous bias,
it is the best available method to isolate some of confounding factors that may be
manipulating the variables under study (Elwert & Winship, 2014). If nothing else, the
regression analysis can help justify salient findings in the research.
The literature of EZ programs continues to grow as improvements to previous
methods are developed. However, researchers have failed to provide a concrete
approach that distinguishes zone variables from uncontrollable factors. Moreover, the
heterogeneity of the policy observed among state EZs makes it virtually impossible to
develop a measurement design standard for all of the programs. In the next chapter, I
present my approach to determine the impact of Florida’s EZ programs.
37
CHAPTER 4 METHODOLOGICAL FRAMEWORK
In the fourth chapter, the methodology of my investigation is introduced. I present
the selected study areas and their respective EZs. A survey by the Florida Association
of Counties (FAC) is also highlighted to demonstrate the different program perceptions
and objectives within each zone. This chapter also discusses the data sources,
methods, and limitations of this study.
Approach
Three Florida counties and their corresponding Enterprise Zones were selected
for my investigation of the Florida EZ program from its official enactment in 1995 (it had
been used unofficially since 1980 under the Slum and Blight Bill) through 2010. Duval,
Hillsborough, Miami-Dade Counties were selected because of their diverse and realistic
characteristics that are easily comparable to the entire State.
For the purposes of this study, I will be employing the use of socioeconomic
variables to analyze the Florida program. Social and economic data is gathered from
the decennial censuses to provide information regarding education, employment,
income, housing, and demographics for each of the three counties. Due to the fact that,
the intent of the Program is for it to primarily serve as a community redevelopment tool
to rejuvenate blighted areas and encourage employment of zone residents, I analyze
the Florida EZ under socioeconomic constraints. Moreover, Florida’s selective
designation process to establish EZs requires the analysis of socioeconomic conditions
like vacancy rates, unemployment, change in property values and per capita local taxes
(Enberg & Greenbaum, 2000; Florida Enterprise Zone Act, 2015). The following section
provides a brief overview for each county.
38
County Profiles
Duval has approximately 900,000 residents; and population growth is almost half
of what other counties in the State experience. Of the three areas, Duval County had
the highest rate of residents identified as White (at 55%). While the minority population,
made up of Blacks (30%) and Hispanics (8%) accounted for less than 40% (Office of
Economic and Demographic Research [EDR], 2016a). Duval has the highest
homeownership rate and, also, has affordable rent averaging less than the State rate.
The County also leads in having the lowest number of uninsured residents. Almost 40%
of government expenditures go toward improvements to the physical environment, and
less than 5% toward cultural, recreational, and human services combined (EDR,
2016a). Commute to work time in Duval was less than the other counties averaging
23.5 minutes.
In Hillsborough, approximately 30% of its 1.3 million residents has a college
degree. And, its trade sector and professional services account for nearly 40% of
employment (EDR, 2016b). The median household income is approximately $50,000
and the poverty rate is lower than in Duval or Miami-Dade. Hillsborough’s crime rate is
the lowest in Florida, but this can likely be explained by the county’s investment in
public safety, which accounts for 20% of its government expenditures (EDR, 2016b). Of
the three counties, Hillsborough has the most favorable conditions.
Miami-Dade is the most populous county with an estimated population of 2.6
million. Approximately 65% of its residents identify as Hispanic, 18.9% as Black, and
14.8% White; and more than half of all residents are foreign born. Owner-occupied
housing values in Miami-Dade are almost $50,000 greater than state values; however, it
has the lowest per capita income and homeownership rates, in addition to having the
39
highest poverty rate (at approximately 20%) (EDR, 2016c). Roughly 20% of government
expenditures in Miami-Dade County go toward human services; the high rate of
residents without health insurance may possibly account for this (EDR, 2016c). Of the
three counties used in this study, Miami-Dade has the highest crime rates and longest
travel commute to work (averaging 30 minutes).
Enterprise Zone Program Perception
The Florida Association of Counties (FAC) is the only organization that
represents Florida’s counties. It serves as a collective platform for county officials to
“speak with a unified voice on behalf of Floridians” (Florida Association of Counties
[FAC], 2009). For more than 80 years it has championed home rule in the State,
supporting the belief that decision making should be left to the communities that are
directly impacted by the decisions.
In October 2014, the FAC surveyed program coordinators asking them to
evaluate their local EZ based on questions provided by the Office of Program Policy
Analysis and Government Accountability (OPPAGA). The survey asked EZ program
coordinators to describe their understanding of as well as the intent of the enterprise
zone program, the Program’s advantages and disadvantages, and its perceived impact
on factors like crime rates, infrastructure, and property values. The EDR and OPPAGA
were directed to preform studies evaluating the program, and the FAC assisted with the
survey component. The Survey achieved nearly 30 responses or an approximate 50%
response rate.
40
Duval County
According to Paul Crawford, Deputy Director for City of Jacksonville’s Office of
Economic Development, the intent of the policy is to “provide disadvantaged areas of
the community with an enhanced opportunity to attract businesses and commerce;
therefore offsetting the risk businesses take in locating in these areas” (FAC, 2014,
p.15). Crawford added that risks typically associated with doing business in Duval’s EZs
include high crime rates, little private capital investments, lower than average wages,
and poor appreciation of zone property values. However, the tax relief afforded to
businesses locating in EZs via hiring credits and reimbursement programs help offsets
risks and make zones more enticing for firms.
Crawford reported that zone designation, had little, if any, impact on crime rates
in the area and he called for more educational and job training opportunities for zone
residents as a probable solution. He stressed the importance of zones needing to be
areas of the county that are more attractive than other areas and are equipped with
infrastructure that will entice firms to establish in the zone. Interestingly, Crawford noted
that the impact of zone designation has had some spillover effects, reducing blight in
adjacent properties not located inside the EZ boundaries.
According to Crawford, the most impactful result of the EZ program in Duval is
that it brings attention to distressed communities and facilitates the funneling of
resources to EZs (FAC, 2014). Once the Program is no longer in use, Crawford fears
that distressed areas will rapidly decay as firms will move to lower risk areas when they
have no EZ incentives or tax credits.
41
Hillsborough County
In Hillsborough, Elizabeth Pytlik with the Hillsborough County Office of
Operations and Legislative Affairs provided the survey responses. Hillsborough EZs are
considered “underutilized, distressed areas targeted for economic revitalization” and are
intended to “stabilize needy areas… as well as position the area for growth and
investment to improve overall conditions” (FAC, 2014, p. 26). No disadvantages to the
Program were cited, and was touted by Pytlik for its success in improving physical
conditions and crime rates within those areas designated as EZs.
In Hillsborough, the Program’s primary purpose is to assist small, local
businesses in areas designated as EZs. They found that many small businesses utilized
the Building Materials Incentive in Hillsborough; and, that this incentive has caused a
‘domino effect’ on other local businesses prompting a general physical improvement of
EZs. However, Pytlik recommends reducing minimum purchase requirement of $5,000
in building materials to make it easier for small businesses to benefit from this EZ
incentive (FAC, 2014).
Pytlik concludes by citing the important contribution EZs provide to small
businesses. She stated that the value of one job opportunity created by a small
business in an EZ can be compared to a large venture like Walmart that may provide
fifty plus jobs; this comparison may be different in the size of the contribution to the
community but both are critical to maintaining stable socioeconomic environments
(FAC, 2014).
42
Miami-Dade County
In Miami-Dade Lori Weldon, the former EZ Program Coordinator, responded to
the FAC Survey. Based on her answers, there appears to be a distinct difference in
Miami-Dade’s EZ objectives in comparison to the Duval and Hillsborough Programs.
Weldon describes the primary objective of the EZ Program as being “to improve socio-
economic conditions reflected in poverty and unemployment rates, household median
incomes, labor force participation, and affordable housing” (FAC, 2014, p.44). These
factors have been recognized as issues prevalent in EZs and throughout the County;
and, are seen as roadblocks to new businesses ventures.
However, when trying to determine the positive impacts on EZs on crime and
property values in Miami-Dade, there’s skepticism. Although, crime rates have fallen in
the County as a whole, it is difficult to determine the effect of EZ programs on property
values. “The results show that between 2000 and 2010, the share of homes valued at
less than $100,000 in the Miami-Dade County Enterprise Zone declined from 65% to
15%, while the percentage of homes valued at less $100,000 in the non-EZ area fell to
22%.” (FAC, 2014, p.46).
An OPPAGA report released in 2014 showed improvement in poverty rates of
those living within in EZs as compared to those living in non-EZ areas of the County.
Between 2000 and 2010 poverty “fell slightly in Miami-Dade County’s Enterprise Zone,
while increasing in a comparable non-EZ area; and the Median household income in
Miami-Dade’s Enterprise Zone increased by 43% from 2000 to 2010 versus 23% in a
comparable non-EZ area in the county” (FAC, 2014, p.46).
In addition to State incentives, Miami-Dade also provides waivers for road impact
fees, water and sewer connection fees, and a 50% abatement of local business tax to
43
firms located within EZs. Weldon recommends supporting small businesses by lowering
threshold requirements to accommodate the behavior and economic practices of small
ventures. To paraphrase, it means, employment requirements and material purchase
requirements should be changed to facilitate small businesses receiving credits. CRA
areas within the EZ have effectively increased the population in the County’s urban core
because CRA’s promote significant revitalization improvement in the areas, especially
with regard to street and façade renovations (FAC, 2014).
Summary of Program Priorities
In Hillsborough and Duval responses emphasized the Program’s objective of
attracting businesses to establish in zones, while in Miami-Dade the Program’s
objective appears to seek to improve socio-economic conditions.
The EZ coordinators of the three respective counties have acknowledged the
importance of the Program and have suggested a replacement rather than a repeal of
the Program. Other than EZs, economic development planners have no comparable
tools to promote economic redevelopment policy.
Survey respondents acknowledged the need to support small businesses within
EZs, and the contributions these firms make to communities. EZ representatives agree
that the program is more beneficial when combined with other community
redevelopment initiatives, because it allows for funding from multiple sources to a
targeted area.
EZ coordinator responses exemplify the diversification of the Program perception
and objectives even within the same state.
44
Data
The analysis begins by gathering US Census spatial (GIS) data from the Florida
Geographic Data Library (FGDL). Files provided by the FGDL included US census
urban area boundaries, census block group data, and enterprise zone boundaries for
the entire state of Florida in a format compatible with ESRI GIS mapping programs.
Census Block Group (CBG) data from 1990, 2000, and 2010 was released in
2014 by FGDL. It was developed using data from Summary File 1, and sample data
from both Summary File 3 and from the American Community Survey (ACS) 2006 -
2010. Summary File 1 and 3 contain population and housing data that provides details
like income, occupancy status, demographics, and educational levels. The ACS data
provides insight into the employment and poverty status of those surveyed. This
information was spatially mapped using block group level boundary data provided by
National Historical Geographic Information System (NHGIS). The NHGIS provides
historical US census data and boundary shapefiles to the public at no cost. The process
of compiling and joining tabular data from census files with spatial block group data is
time consuming and requires precision to overcome the difficulty and to ensure the
integrity of the data is upheld. Therefore, researchers are lucky that the FGDL facilitates
this task by developing and easily providing such data.
Using ArcGIS, I ran a selection process to identify and label the CBGs that
intercepted the zone boundaries within each county, and repeated the process for the
three decennial datasets. Next, I manually omitted CBGs that were minimally
intercepted by the EZ boundary but had been picked up through the automatic selection
process and included those that had a population of 500 or fewer. Typically, mapping
software overcomes the concerns of confounding or over quantifying the effects of EZs
45
by precisely identifying zone areas, however the irregular shape of Florida’s EZs made
the process challenging for analysis (Boarnet, 2001; Lynch, 2010). The CBG data was
also a concern as the State’s Block Boundaries had been modified during each
decennial dataset. Figures 4 -1 shows the selection of the CBGs in each county
identified as EZ areas for the purposes of this analysis.
Once I identified the EZ CBGs, I gathered all the county CBG data and used IBM
SPSS to perform an initial analysis of means to compare the socioeconomic conditions
between EZ and non-EZ CBGs within Hillsborough, Duval, and Miami-Dade Counties
for 1990, 2000, and 2010. This analysis is a simple approach and fails to identify the
relationships between zones and the socioeconomic variables within each Zone. To
further understand the relationship, I developed simple linear regression models, using
a combination of variables identified in the Florida EZ language as criteria for distressed
areas, and tested these models against the decennial datasets. Using the findings form
the regression analysis, I was able to determine if the null hypothesis could be rejected,
and therefore confirming whether the social and economic variables can be attributed to
EZS.
Geospatially mapping the variables provided an interesting insight into the
designation of EZs. For the three counties, I mapped the poverty rates, households
receiving public assistance, and the employed population from the decennial data
records. I mapped the data using a Getis-Ord Gi* or hot spot analysis. The spatial
analyst tool maps statistically significant clusters based on an algorithm of the Z-score
and p-value. The Z-score is the numerical value of the standard deviations and the p-
value is the probability. When displayed on a map, statistically significant positive Z-
46
scores with high p-values are represented as hot spots on the map (red), while
statistically significant negative z-scores with high p-values are the cold spots (green)
(ESRI, 2016). Once the socioeconomic variables were mapped for 1990, 2000, and
2010, I overlaid the enterprise zone boundary and compared the hot spot analysis
outcomes to the EZ boundaries.
Limitations
Typical limitations of my study included the need for a control group. This is not a
random experiment, because EZs are not randomly designated. Another concern is that
EZs do not match census block group boundaries, as these have changed from 1990
through 2010. In an effort to overcome the selection, I manually selected CBGs that are
partially covered by EZs and I removed any CBGs with populations less than 5,000
people in Hillsborough and Duval counties, and less than 10,000 for Miami-Dade
County to prevent skewing my findings. The next section discusses the results and
conclusions of this investigation.
47
A.
B.
C.
Figure 4-1. Selection of EZ CBGs. EZ areas are displayed in red while the black areas represent the selected CBGs from 1990, 2000, and 2010.2
2 World street map, ESRI, retrieved from
http://www.arcgis.com/home/item.html?id=3b93337983e9436f8db950e38a8629af, accessed August 20, 2015. US Census shapefiles (1990, 2000, and 2010), Florida Geographic Digital Library, retrieved from http://www.fgdl.org/metadataexplorer/explorer.jsp, accessed August 20, 2015.
48
CHAPTER 5 RESULTS
An initial analysis of the summary statistics found in Appendix A demonstrates
that census block groups (CBGs) designated as EZs are more distressed than non-EZ
CBGs; these findings are congruent with the State’s. Moreover, minorities are usually
more likely to live in EZ areas; the majority of these EZ residents are Black or Hispanic.
In addition, there is greater number of non-family households as opposed to family
households within EZs, and these non-family households are typically renter occupied.
Older housing stock tends to define EZs. In addition, public transportation use was
greater among EZ residents while vehicle ownership was greater among those living
outside the EZs. This finding shows the importance of EZs being able to provide
equitable access to employment opportunities.
Inferred Findings
In Duval, designated EZ CBGs did not appear to show an improvement as a
result of having EZ status. For instance, the reported median household incomes of EZ
residents during the decennial censuses has historically remained half of non-zone
residents. Investment in new housing development was also less in EZ CBGs as
demonstrated by higher vacancy rates and aged housing stock which was typically 30
years older than the housing stock in non-zone areas.
The inferred findings also shed light on the employment behaviors of Duval’s
residents. In non-EZ areas, most residents owned an average of two or more vehicles
and were more likely to drive to work; however, there has been a growing number of
non-zone residents working from home instead. In EZ designated CBGs, residents
49
responded to having only one or no vehicle per household and; consequently were
more likely to rely on public transportation as an alternative to driving to work.
Among Miami-Dade’s EZ CBGs, the majority of residents identified as Black or
Hispanic. The White, non-Hispanic population has declined throughout the county since
1990, but data shows that those who are living in the county are more likely to live in
non-EZ areas. The non-EZ designated areas of the county have experienced more
housing demand. And when comparing the housing stock of EZ and non-EZ CBGs,
non-EZ CBGs housing is typically 20 years newer than housing found within the EZs.
Similar to what we have seen in Duval, residents of Miami-Dade’s EZs preferred public
transportation as a means to get to and from work, second only to driving their own
personal vehicles.
Additional disparities between EZ and non-EZ areas of the county continued to
grow from 1990 to 2010. Overall, the County’s residents achieved higher educational
levels; however, those living in EZs had higher rates of residents with only a 9th grade
education or less. I am inclined to attribute this finding to the County’s large foreign-born
population and the lower rents found within the County’s EZs.
In 1990, Hillsborough’s EZ CBGs had an almost equal distribution of non-
Hispanic White and Black residents. As of 2010, however, the non-EZ areas of
Hillsborough grew to be majority White, while EZ areas have maintained the highest
concentrations of Black residents. Typically, residents of Hillsborough County are more
educated than the residents in the other areas of the State; moreover, in both EZ and
non-EZ areas of the County, education had progressively improved. Residents living
within both areas achieved some college education; however, non-EZ residents more
50
often pursued degrees requiring more than four years of college. The data also
suggested that residents of Hillsborough are becoming wealthier; yet, median
household incomes of those in EZs have remained almost half of the earnings reported
by non-EZ residents. Throughout Hillsborough, poverty declined along with demand for
public assistance; however, these concerns remained prevalent for residents of
Hillsborough’s EZs. The difference in the age of available housing stock was less than
what was observed in the other counties. EZ housing was only 10 years older than non-
EZ housing; nonetheless, vacancy rates were higher in designated EZ CBGs. Similar to
travel to work patterns observed in the non-EZ CBGs of Duval, a growing number of
Hillsborough’s non-EZ residents have opted to work from home while EZ residents
continue to rely on public transportation as an alternative to driving to work.
Regression Analysis
Regression analyses were made to identify the statistically significant predictors
for Census Block Groups (CGBs) designated as Enterprise Zones (EZs). The model for
the regression analysis considered education, poverty, housing data, transportation to
work, and demographics using data provided by the 1990, 2000, and 2010 censuses for
Duval, Hillsborough and Miami-Dade counties. The decennial censuses have provided
an insight into the conditions of EZ CBGs prior to the enactment of the EZ Program in
1995. A narrative of the Program’s short-term impact is emphasized in the 2000
regression analysis. And, finally, a 15 year analysis of the Program was made using the
2010 data. The findings regarding the three counties cited below are taken from the
results of the analysis found in Appendix B of this report.
In Duval, the 1990 results suggest that the EZ CBGs may have been selected as
a result of the low educational levels of its residents. In 2000, the data showed that EZ
51
CBGs continued to decline facing concerns of vacant housing and low educational
levels. By 2010, issues of vacant housing and low high school graduation rates
persisted; but, in 2010, the variable, households living below the poverty level, was not
present as it had been in the 2000 results.
Hillsborough had the highest Regression Analysis Model Predictors (R2 Value),
showing an almost 40% confidence that the variables entered into the Model are good
predictors of EZ status. For the other two counties the probability of the models’ fit or R2
Value averaged around 33%. In Hillsborough, the most significant predictor variables
prior to zone designation were vacant housing and public transportation as mode to
work. In 2000, the predictor variables were households on public assistance, the Black
population, and residents with college degrees that showed the strongest relationships.
By 2010, the vacancy predictor variable reappeared, and even had a greater significant
value. This may suggest that EZs have a short term impact on housing.
The Miami-Dade 1990 analysis of the regression model provides an insight to
conditions of CBGs prior to the enactment of the EZ Program; the EZ Program was
launched in 1995. In fact, the results mostly demonstrate conditions preceding the 1990
census. The findings suggest that the areas that were to eventually become EZs were
already experiencing a lack of investment as evidenced by the vacant housing variable
referenced in Table B-3. Noteworthy is the EZ outcome variable that shows a significant
and positive relationship to the non-Hispanic White population, and a significant; but,
negative, relationship to individuals living above the poverty level.
In 2000, the regression results for Miami-Dade County show households at
below the poverty level as the only significant relationship in the Model. The narrative
52
developed by these results coincides with the transformation of Miami’s communities
stemming from the conditions leading to the 1980 Miami Riots. By 2010, the strongest
positive predictors of EZ outcomes were minorities (Black and Hispanic) and Renters;
this suggests an evolution of conditions within EZ CBGs. Also noteworthy is the fact that
the 2010 regression shows individuals living above the poverty level are more likely to
walk to work or use public transportation to get there. This observation may be the
result of job growth within Miami-Dade’s EZ.
Based on the regression analysis of the Duval, Hillsborough and Miami-Dade
EZs, we can infer that Florida EZ’s were established in areas where the Program would
benefit both people and the location, itself. Typically, an area that received EZ
designation in 1995 had been experiencing higher housing vacancy rates and lower
educational levels. Less education also equates to lower wages as education is a good
indicator of wages and income. In Hillsborough and Duval, the housing vacancy
variable appeared in the 2010 regressions which suggests that EZ status may have
made only temporary improvements in Hillsborough and, ultimately, continued to
worsen conditions as it had done in Duval.
Hot Spot Analysis
According to the Hot Spot Analysis, the designation of EZs, particularly in Duval
and in Hillsborough counties, coincided with areas where poverty was concentrated.
These special economic zones are situated in the urban centers of most Florida
counties. In Duval, the Hot Spot Analysis showed that poverty is concentrated around
downtown Jacksonville; but, while poverty remains prevalent within the core, it appears
that from 2000 to 2010, those living in poverty moved to the western and to the northern
areas of the county. Those least likely to be living in poverty, those who are employed,
53
and non-Hispanic Whites typically resided in non-EZ areas located adjacent to the
Atlantic coast or along the St. Johns River. Furthermore, since 1990, the maps have
shown how the employment cold spot (area of Duval where residents are more likely to
be unemployed) has continued to grow larger from the urban center and Duval’s EZ
CBGs, suggesting that employment conditions have actually worsened for residents of
EZs. The finding parallels Lubuele and Mill’s research on the Spatial Mismatch Theory
suggesting that more affluent residents were able to move to areas for employment that
provided higher wages, while low-income residents and minorities were relegated to the
Inner Cities (1997).
In Hillsborough, EZs are located in areas that have higher levels of poverty and
demand for public assistance. But, it appears that hot spots for these two indicators
have begun to move away from the EZ area covering the city of Tampa’s urban core
and toward the surrounding areas. The analysis showed the hot spots for public
assistance and poverty were growing larger outside of designated EZ areas; and, they
expanded toward the north and inland portions of the County. When mapping the
concentration of Hillsborough’s employed population, the maps suggest that employed
residents are more likely to live outside EZ areas. That trend continued into 2010 with
the employed population hot spots appearing in regions surrounding the EZ area and
downtown Tampa.
Mapping the Miami-Dade hot spots provided puzzling results for the County’s EZ
analysis. In 1990 and 2000, poverty was concentrated in areas outside of the EZ
boundary; and, only, by 2010 did poverty and the EZ boundary coincide. Similarly, hot
spots for household recipients of public assistance were typically outside EZ boundaries
54
for 1990, 2000, and 2010. The final variable, the concentration of the employed
population in the County, showed that since 1990 a greater number of employed
residents were living outside the EZ and that there was a migration of employed
residents to the western suburbs of the County. It was only in the 2010 Hot Spot
Analysis that poverty and those least likely to be employed correlated with the EZ
boundary.
Discussion
Though typically analysis of this kind is measured with mathematical return of
investment or other statistical indicators, this study has been built around the
socioeconomic impact of EZs. Utilizing demographics, education levels, housing
vacancies, rents, transportation (modes to work), and other results provided by
analyzing the 1990, 2000, and 2010 Duval, Hillsborough, and Miami-Dade census data,
I was able to formulate my own analysis of the benefits of Enterprise Zones (EZs) in
Florida. My findings suggest that EZs may not have accomplished all of the objectives
the Program expected to achieve.
Vacant housing remained a constant significant variable for two of the three EZs
which suggests that the Program was not able to consistently improve the communities
in which it was being used. It was only in Miami-Dade County that EZ CBGs resulted in
income gains, greater access to employment, and fewer housing vacancies. There was
a turnover predictor variable, however, that showed vacant housing turned into more
renters (meaning fewer buyers). Due to the fact that Miami-Dade is the State’s largest
and fastest growing county (meaning many people continuously moving in), this salient
finding may not be due to the implementation of EZs and, therefore, may require more
investigation before crediting zone designation for the improvements noted.
55
Nonetheless, in Duval and Hillsborough counties this was not true. The evidence
indicates that vacant housing within EZs remained a concern in both counties into 2010
(vacant housing was a major reason for establishing EZs in Duval and Hillsborough).
Moreover, in Duval County, the age of available housing stock within EZs was
approximately 30 years older than housing found in non-EZ CBGs. This finding shows
that more investment was made in developing homes outside of Duval’s EZ CBGs
rather than refurbishing existing homes within EZ areas. This made be due to the fact
that EZ CBGs were not as attractive as non-EZ CBGs for private developers. Although,
Hillsborough’s EZ housing stock is only 10 years older than housing in non-EZ CBGs,
the same reluctance toward investment is also prevalent.
Conclusion
My research set out to investigate the impact of Enterprise Zones (EZs) as a
viable redevelopment tool for Florida communities. A historical review of the EZ
Concept, from its origins in the UK and adoption by the US, demonstrated the different
interpretations of Hall’s EZ Concept (in the UK, it focused on renewing urban dormant
industrial areas while in the US it focused on bettering inner cities and improving the
welfare of the residents living there). I presented the Program’s adoption by the State of
Florida; it was presented under the Slum and Blight Bill and eventually became the
State’s 1995 Florida Enterprise Zone Act. The Act was in its heyday when I began my
research in 2013, and as referenced herein above it was not reenacted when it came up
for renewal in December 2015. The literature of EZ investigations, the various indicators
used to measure and identify results, and the diverging conclusions of the Zones’
impact has also been presented. Findings from the review of the literature note that a
conclusive impact of the Program is difficult to make, as the methods, variables, and
56
data had conflicting results. Following the literature review, this research found that
Program results in Florida’s distressed areas was dubious.
Although the Program did not improve the conditions of the CBGs designated as
EZs in 1995, many benefits can be cited as result of the EZs. For more than 25 years,
the policy associated with the Program promoted public-private partnerships and helped
direct attention and resources to the economically challenged and distressed areas of
the State. Furthermore, the Program has increased access to healthy food options and
medical supplies and services; as well as to more and better employment opportunities.
Future studies should employ statistical methods to expand this research. They
should measure the Program’s impact to determine how much the changes observed
can be attributed to the implementation of EZs and/or the policies surrounding EZs.
Based on lessons learned, this researcher recommends utilizing a similar economic
development program but one that better supports small businesses and promotes
resident welfare.
Policy Recommendations
While the Program’s impact may have been shown to be conflicting, EZ policy
did serve a critical role toward community redevelopment in Florida. Regardless of
whether the EZs failed or succeeded, the Program allowed communities to collectively
target areas, and directly funnel resources toward distressed areas (Cassell & Turner,
2007). Commonly cited is the ability of the Program to bring together various segments
of the community; it is especially known for uniting private markets and local
governments to support and carry out redevelopment efforts (HUD, 1997; McSwain &
Welbaum, 1989). In addition, Program administrators should employ Program policy
that constantly reevaluates expected results and pinpoints EZ areas where the Program
57
can be modified or phased out. A review process for the Program’s renewal will also
help to hold local program coordinators accountable for their efforts in marketing the
Program.
Several EZ researchers argue that programmatic outcomes are more effective
when combined with ongoing redevelopment initiatives (Bondonio & Greenbaumb,
2007). This process allows for more funding channels and increases the awareness of
existing redevelopment programs for residents and businesses. Based on the literature,
I recommend that incentives be modified to support small firms establishing within EZ
areas. Originally enacted as a policy to help small businesses, the incentives and
purchasing credit limitations have demonstrated to be too cumbersome to readily
promote small businesses gaining significant benefits. In accordance with the literature,
credits for capital purchases made by all businesses (small and large) may substitute
labor for more capital goods (i.e.: purchasing machines that replace workers).
Removing the purchasing policy or modifying it so that I can be utilized by small
businesses while solely instating hiring credits for larger firms, should ensure funds are
not substituted for goods; and, will promote the establishment of new, yet, smaller firms.
In Duval, Hillsborough, and Miami-Dade counties, public transportation was cited
as a common mode to work just after private vehicles. Public transportation is critical for
the access of employment, education, and obtaining healthy food options as well as
medical supplies and services by Florida’s EZ residents. As cities continue to expand,
and the distances between work and home grows, public transportation becomes more
of a concern. If the State cannot guarantee employment opportunities, it should, at
least, support equitable access with more or better public transportation.
58
Future Studies
Future studies of EZs may benefit from an aggregate analysis of the State
Program. An analysis of the entire state of Florida can help develop a stronger
conclusion of the Program’s impact during its 25 year history. Only EZs that are
concentrated within a centralized area of a county should be analyzed (noting that
extracting data from zones with irregular boundaries or from those that may extend
throughout the entire jurisdiction, may over quantify or confound the results).
Although I used a linear regression analysis, I propose a multistep regression be
used by those more adept at statistical analyses. The multistep regression will add
another analytical layer to the study and will produce more robust findings of the
predictor variables for Enterprise Zone outcomes. Finally, I recommend the use of an
econometric tool to promote going beyond basic inferences and producing quantifiable
results regarding the Program’s impact.
59
APPENDIX A MEANS ANALYSIS
The following tables are the results of the means analysis comparing the non-EZ and EZ designated census block groups in 1990, 2000, and 2010 for each County. Table A-1. Duval County Means Analysis
Duval County 1990 Duval County 2000 Duval County 2010
Non-EZ (n=295)
EZ (n=65) Non-EZ (n=299)
EZ (n=73)
Non-EZ (n=405)
EZ (n=67)
Employment
Labor Force Employed over 16
757.61 374.35 795.19 347.04 862.82 396.03
Demographics
Total Population 1571.87 1039.2 1663.69 1074.53 1763.86 1143.94
Male 766.28 462.45 807.27 502.85 908.51 588.87
Female 805.59 576.75 856.41 571.68 451.08 248.9
Population Density 4.62 8.27 4.04 7.03 4.74 6.5
Families 419.05 249.82 446.73 251.88 451.08 248.9
Average Family Size 3.1 3.34 3.1 3.38 3 3
Households 604.32 401.8 646.51 408.38 700.42 440.45
Average Household Sized
2.62 2.66 2.51 2.56 2.5 2.43
Race and Ethnicity
White (Not Hispanic) 1216.09 220.31 1147.34 181.79 1064.96 211.04
Black 285.87 809.02 389.95 865.12 447.98 888.21
Hispanic 41.17 9.31 59.88 12.75 139.42 26.75
Minorities all 355.78 818.89 516.35 892.74 698.9 932.9
Ages
Under 5 124.81 88.28 114.92 79.25 118.83 83.67
5 to 17 277.33 211.85 320.83 236.38 292.76 201.07
18 to 21 95.55 59.58 85.63 61.78 104.12 69.97
22 to 29 229.62 116.65 174.04 105.3 218.85 134.21
30 to 39 275.79 151.57 266.83 138.19 236.04 135.72
40 to 49 199.6 99.14 264.32 157.33 254.91 154.22
50 to 64 199.77 140.37 253.31 136.11 338.47 218.13
65 and up 169.39 171.77 183.82 160.19 199.87 146.94
Education
College and above 180.77 47.48 218.03 55.53 294.59 81.27
Some College 282.92 116.18 340.23 140.85 377.28 183.97
High School 784.79 357.52 895.44 402.07 1020.07 534.6
Some High School not graduated
153.57 171.4 142.33 183.6 93.21 152.75
Less than 9th grade 61.92 113.46 45.53 69.05 39.61 52.66
60
Table A-1. Continued
Duval County 1990 Duval County 2000 Duval County 2010
Non-EZ (n=295)
EZ (n=65) Non-EZ (n=299)
EZ (n=73) Non-EZ (n=405)
EZ (n=67)
Income
Less than 10K 76.13 155.31 53.91 107.73 46.35 93.15
10k to 14 53.85 54.43 37.98 45.22 30 55.1
15k to 19 59.95 40.42 38.23 43.32 35.04 38.21
20k to 24 61.21 31.54 45.92 35.4 37.39 40.3
25k to 29 55.02 26.82 46.74 29.78 38.71 31.1
30k to 34 51.64 23.15 44.05 28.92 41.44 27.73
35k to 39 46.33 20.11 42.71 21.62 34.57 25.57
40k to 44 40.04 14.32 43.02 19.56 38.12 16.72
45k to 49 31.12 10.74 36.84 15.64 31.58 12.63
50k to 59 46.51 10.72 62.91 20.53 63.66 31.42
60k to 74 38.24 7.51 69.97 19.84 78.1 27.9
75k to 99k 25.36 4.28 60.81 12.16 89.72 22.1
100k to 124 8.61 0.72 29 4.36 48.76 9.58
125k to 149 3.14 0.2 11.87 2.36 26.12 6.64
150k and more 8.08 0.75 10.04 1.23 22.45 3.7
Median Household Income
31243.3 15099.95 43763.1 21226.33 53520.12 25971.66
Median Family Income
36045.55 19020.63 50895.87 28849.18 62845.36 34061.28
Poverty
Individuals Below Poverty
157.66 338.12 175.98 327.25 217.95 367.73
Individuals Above Poverty
1382.4 673.6 1462.48 705.44 1508.04 746.27
Households Above Poverty
543.05 271.52 580.04 282.68 600.89 304.16
Households Below Poverty
605.24 401.02 67.03 127.16 80.79 140.69
Households with Public Assistance
30 72.22 14.59 27.81 10.71 20.18
Households No Public Assistance
575.24 328.8 632.48 382.04 670.97 424.67
Housing
Total Number Housing Units
659.09 471.06 699.93 478.45 786.58 557.21
Occupied 604.32 401.8 646.51 408.38 700.42 440.45
Vacant 54.77 69.26 53.42 70.07 86.16 116.76
Housing Units Owner Occupied
391.28 211.45 447.01 204.92 440.99 200.97
Housing Units Renter Occupied
213.03 190.35 199.5 203.47 259.43 239.48
61
Table A-1. Continued Duval County 1990 Duval County 2000 Duval County 2010
Non-EZ (n=295)
EZ (n=65) Non-EZ (n=299)
EZ (n=73) Non-EZ (n=405)
EZ (n=67)
Housing Multi-Family Units
144.58 132.06 149.09 155.66 219.26 167.84
Housing Single Family Units
399.02 262.12 476.94 314.08 519 387.96
Median Owner Occupied Housing Value
$68,338.31 $30,990.77 $94,870.57 $64,012.34 $188,792.84 $102422.39
Housing Structures by Year Built
Before 1939 36.51 70.86 34.17 70.18 30.43 111
1940 to 1949 55.95 85.05 51.54 78.95 36.05 83.52
1950 to 1959 125.23 129.32 118.29 114.9 88.91 114.85
1960 to 1969 140.87 97.89 123.93 97.18 92.68 88.78
1970 to 1979 142.81 60.06 133.08 63.88 123.43 64.85
1980 to 1989 157.76 27.17 130.72 31.18 157.43 31.84
1990 to 1999 108.2 22.19 137.39 27.3
2000 to 2010 116.23 46.12
Transportation to Work
Walk 19.23 16.52 12.07 12.29 14.29 13.3
Bike 5.47 2.95 3.91 3.22 4.57 2.9
Motorcycle 2.82 0.82 1.89 0.29 2.52 0.55
Car 727.15 295.72 746.17 276.25 797.74 323.25
Public Transportation
15.53 42.34 12.49 32.07 10.84 29.81
Work from Home 12.16 3.74 15.12 3.44 30.23 11.09
Vehicle Ownership
None 46.82 141.98 47.35 121.27 43.06 109.61
1 224.44 148.08 241.17 173.92 262.83 200.15
2 243.41 80.15 263.48 87.41 271.37 98.31
3 68.74 25.57 74.35 19.4 79.24 23.66
4 16.14 4.14 15.65 3.85 19.73 9.04
5 or more 4.43 1.38 4.51 2.53 5.45 4.07
62
Table A-2. Hillsborough County Means Analysis
Hillsborough County 1990 Hillsborough County 2000 Hillsborough County
2010 Non-EZ
(n=456) EZ (n=78)
Non-EZ (n=622)
EZ (n=89)
Non-EZ (n=737)
EZ (n=98)
Employment
Labor Force Employed over 16
716.97 423.91 688.4 387.69 690.48 395.79
Demographics
Total Population 1403.86 1106.69 1417.38 1119.84 1433.31 1069.47
Male 685.16 528.49 695.88 535.43 698.92 521.1
Female 718.71 578.21 721.5 584.42 734.39 548.37
Population Density 8.42 10.8 5.15 9.31 5.8 9.9
Families 375.81 248.95 372.21 246.62 362.8 220.56
Average Family Size 3.07 3.33 3.14 3.5 3.02 3.15
Households 545.43 422.88 557.61 420.81 559.55 399.85
Average Household Sized
2.63 2.75 2.59 2.65 2.59 2.67
Race and Ethnicity
White (Not Hispanic) 1090.65 413.37 970.45 250.87 816.41 222.21
Black 112.82 538.47 147.73 599.48 186.69 552.63
Hispanic 180.45 154.51 243.64 242.56 359.61 283.28
Minorities all 313.21 693.32 446.93 868.98 616.9 847.26
Ages
Under 5 98.5 113.08 92.22 97.19 89.88 89.63
5 to 17 235.22 215.22 254.88 245.35 246.12 194.03
18 to 21 83.52 86.41 71.19 83.16 78.2 99.89
22 to 29 200.16 172.6 155.35 134.94 166.18 147
30 to 39 248.09 164.85 233.11 159.42 195.78 138.41
40 to 49 188.98 101.91 218.93 141.82 215.29 132.09
50 to 64 185.89 117.24 214.56 128.9 264.77 162.05
65 and up 163.5 135.38 177.15 129.07 177.1 106.37
Education
College and above 195.42 56.69 245.51 63.48 282.13 77.01
Some College 253.78 126.81 279.5 140.16 270.6 140.69
High School Graduate or equivalent
717.25 364.49 778.63 395.58 809.31 439.97
Some High School not graduated
134.13 158.78 112.81 161.62 77.06 104
Less than 9th grade 72.68 105.9 53.59 83.93 46.93 66.88
Income
Less than 10K 63.66 162.4 40.31 107.7 32.86 81.66
10k to 14 49.22 62.17 31.11 51.79 26.05 42.94
15k to 19 52.15 53.88 33.34 41.61 27.59 38.77
20k to 24 53.05 34.46 37.78 43.29 29.83 29.22
25k to 29 49.1 30.81 39.46 31.62 30.61 27.08
63
Table A-2. Continued Hillsborough County
1990 Hillsborough County
2000 Hillsborough County
2010 Non-EZ
(n=456) EZ
(n=78) Non-EZ (n=622)
EZ (n=89)
Non-EZ (n=737)
EZ (n=98)
30k to 34 47 23.08 38.98 28.39 31.69 27.33
35k to 39 40.43 16.15 36.67 22.04 30.15 22.62
40k to 44 35.24 11.47 33.31 20.01 30.66 16.61
45k to 49 29.28 6.67 29.27 15.03 25.18 13.98
50k to 59 43.3 11.13 52.91 22.51 48.26 20.59
60k to 74 36.99 6.28 59.93 15.39 56.36 27
75k to 99k 25.42 2.71 55.97 10.83 66.33 21.88
100k to 124 9 0.67 29.11 4.42 41.5 10.29
125k to 149 4 0 13.65 1.45 23.6 3.62
150k and more 7.91 0.79 12.03 1.34 23 1.81
Median Household Income 31842.89 14666.99 47097.53 21488.63 58762.34 27974.52
Median Family Income 36451.23 17739.53 53577.22 25219.99 67713.25 30977.03
Poverty
Individuals Below Poverty 141.63 408.94 136.89 393.31 170.58 375.12
Individuals Above Poverty 1239.11 647.83 1256.28 694.11 1222.55 626.77
Households Above Poverty 492.75 267.62 506.58 282.54 253.47 484.88
Households Below Poverty 52.98 155.05 50.84 137.69 61.87 134.15
Households with Public Assistance
23.86 77.65 13.36 36.57 9.09 24.51
Households No Public Assistance
521.88 345.01 544.05 383.65 537.66 363.11
Housing
Total Number Housing Units 611.18 515.32 609.96 475.58 626.42 488.59
Occupied 545.43 422.88 557.61 420.49 559.55 399.85
Vacant 65.75 92.44 52.35 55.09 66.87 88.74
Housing Units Owner Occupied
358.28 152.35 379.84 160.91 353.19 129.62
Housing Units Renter Occupied
187.15 270.54 177.77 259.58 206.36 270.22
Housing Multi-Family Units 133.65 220.37 160.14 232.7 171.23 226.78
Housing Single Family Units 342.75 176.15 376.75 223.17 392.51 229.22
Median Owner Occupied Housing Value
80960.09 44080.78 104378.62 55852.78 220792.13 129858.16
Housing Units per Acre 3.07 4.83 2.26 3.8 2.77 4.6
Housing Structures by Year Built
Before 1939 24.44 61.41 20.05 59.48 13.38 45.72
1940 to 1949 20.86 50.58 18.53 45.54 14.69 33.2
1950 to 1959 69.51 75.9 55.16 71.38 47.37 64.69
1960 to 1969 95.72 96 71.82 88.81 58.51 84.21
1970 to 1979 164.9 119.45 124.83 99.99 104.08 94.26
64
Table A-2. Continued Hillsborough County
1990 Hillsborough County
2000 Hillsborough County
2010 Non-EZ
(n=456) EZ
(n=78) Non-EZ (n=622)
EZ (n=89)
Non-EZ (n=737)
EZ (n=98)
1980 to 1989 235.58 114.77 167.54 79.61 146.2 69.94
1990 to 1999 152.03 30.78 113.83 28.51
2000 to 2010 118.23 52.69
Transportation to Work
Walk 13.52 21.24 9.7 17.15 9.42 16.08
Bike 3.63 4.56 3.19 6.3 3.45 7.58
Motorcycle 2.38 1.9 1.25 0.85 1.18 0.77
Car 666.47 347.44 634.84 317.72 616.78 316.15
Public Transportation 8.74 28.63 6.55 23.45 7.96 27.24
Work from Home 16.02 5.82 20.8 4.71 35.64 9.92
Vehicle Ownership
None 30.87 126.92 33.28 112.62 27.89 92.72
1 206.53 177.54 219.84 193.36 213.43 170.51
2 227.21 93.04 232 86.33 224.88 91.86
3 62.09 22.18 56.67 22.7 61.75 24.88
4 14.38 5.19 12.1 3.26 15.25 5.32
5 or more 3.77 1.47 3.72 2.24 3.55 2.34
65
Table A-3. Miami-Dade County Means Analysis
Miami-Dade 1990 Miami-Dade 2000 Miami-Dade County
2010 Non-EZ
(n=603) EZ
(n=241) Non-EZ (n=883)
EZ (n=254)
Non-EZ (n=1266)
EZ (n=251)
Employment
Labor Force Employed over 16
948.09 636.15 875.08 528.74 763.99 602.72
Demographics
Total Population 1976.45 1629.52 2040.45 1621.48 1653.18 1536.1
Male 945.83 784.08 977.68 794.49 795.86 763.73
Female 1030.62 845.44 1062.77 826.99 857.33 772.37
Population Density 12.43 18.3 13.86 16.99 17.32 19.04
Families 499.12 372.42 510.88 365.93 406.1 337.38
Average Family Size 3.43 3.68 3.48 3.67 3.27 3.35
Households 709.81 577.06 712.56 557.46 568.65 551.91
Average Household Sized 3.06 3.06 2.97 2.95 2.93 2.81
Race and Ethnicity
White (Not Hispanic) 712.4 214.33 495.12 138.08 276.53 109.78
Black 313.88 695.65 310.84 698.48 247.38 628.16
Hispanic 941.35 748.96 1188.99 768.81 1112.81 818.58
Minorities all 1264.05 1415.2 1545.33 1483.41 1376.65 1426.32
Ages
Under 5 131.45 139.02 126.04 118.58 96.61 106.94
5 to 17 322.63 308.66 360.98 332.13 263.05 242.88
18 to 21 110.28 91.54 103.56 93.54 93.16 93.61
22 to 29 248.24 207.97 220.31 186.22 177.6 200.06
30 to 39 313.52 249.43 334.81 251.49 232.91 220
40 to 49 255.88 176.94 298.85 220.04 259.85 215.55
50 to 64 303.25 224.08 313.39 214.28 293.79 258.04
65 and up 291.21 231.89 282.52 205.2 236.2 199.03
Education
College and above 283.72 81.74 334.7 97.34 314.02 125.92
Some College 324.46 160.75 347.05 174.78 265.7 183.73
High School Graduate or equivalent
920.98 470.42 985.28 514.98 873.58 620.2
Some High School not graduated
207.78 251.98 213.49 258.39 109.68 148.59
Less than 9th grade 207.26 298.76 174.76 237.76 118.93 200.38
Age 3 and older enrolled in school
517.79 437.68 574.63 481.09 423.78 361.15
Age 3 and older not enrolled in school
1380.54 1113.05 1390.32 1067.26 1137.93 1050.07
Income
Less than 10K 109.87 204.24 78.57 149.61 45.18 102.71
10k to 14 61.91 78.87 47.67 64.26 32.99 59.19
66
Table A-3. Continued
Miami-Dade 1990 Miami-Dade 2000 Miami-Dade County
2010 Non-EZ
(n=603) EZ
(n=241) Non-EZ (n=883)
EZ (n=254)
Non-EZ (n=1266)
EZ (n=251)
15k to 19 61.21 65.4 47.21 54.44 30.81 52.35
20k to 24 59.71 51.5 49.42 51.41 31.71 44.08
25k to 29 56.49 41.95 46.98 40.97 29.71 39.06
30k to 34 51.15 33.12 45.16 34.15 30.07 33.68
35k to 39 44.49 23.36 41.98 28.3 27.57 26.83
40k to 44 40.44 19.9 40.37 24.02 28.22 25.14
45k to 49 34.06 13.63 33.22 18.43 22.9 21.97
50k to 59 54.81 17.48 60.77 28.09 45.52 33.15
60k to 74 50.64 14.43 67.88 26.26 53.06 31.84
75k to 99k 40.32 8.18 64.91 19.51 59.68 27.55
100k to 124 17.45 2.22 33.73 8.34 38.77 11.84
125k to 149 8.17 0.55 17.14 2.96 21.27 6.24
150k and more 19.54 0.9 16.21 2.53 21.19 4.54
Median Household Income 34801.93
16207.18
46257.11 22268.6
7 58217.39
28099.89
Median Family Income 38667.57
18171.54
50995.67 24905.5
5 65233.37
32184.04
Poverty
Individuals Below Poverty 269.79 543.39 289.43 532.71 231.86 452.79
Individuals Above Poverty 1672.99 1045.61 1040.24 1718.5 1355.67 983.66
Households Above Poverty 613.11 380.59 610.64 364.76 460.48 351.92
Households Below Poverty 97.14 195.13 102.77 191.7 82.86 171.37
Households with Public Assistance
56.64 107.06 34.04 62.33 8.69 12.94
Households No Public Assistance
653.6 468.66 679.36 494.13 534.65 510.35
Housing
Total Number Housing Units
789.14 645.83 785.7 620.11 643.53 629.57
Occupied 709.81 577.06 712.56 557.46 568.65 551.91
Vacant 79.33 68.77 73.14 62.65 74.88 77.66
Housing Units Owner Occupied
434.34 184.01 449.61 184.51 346.28 165.08
Housing Units Renter Occupied
275.47 393.05 262.95 372.95 222.37 386.83
Housing Multi-Family Units 266.19 345.61 342.53 365.51 275.54 386.48
Housing Single Family Units
420.03 204.98 429 233.41 354.97 227.64
Median Owner Occupied Housing Value
108754.62
53172.2 143778.1
5 76252.7
5 322851.6
181025.1
Housing Units per Acre 5.1 7.9 5.58 7.12 8.29 9
Housing Structures by Year Built
Before 1939 34.5 52.57 26.61 44.69 19.26 50.12
1940 to 1949 56.39 74.45 44.28 65.31 33.85 85.26
1950 to 1959 162.74 141.15 123.77 123.11 93.18 140.51
67
Table A-3. Continued
Miami-Dade 1990 Miami-Dade 2000 Miami-Dade County
2010 Non-EZ
(n=603) EZ
(n=241) Non-EZ (n=883)
EZ (n=254)
Non-EZ (n=1266)
EZ (n=251)
1960 to 1969 162.63 130.37 128.84 132.78 84.92 91.52
1970 to 1979 225.01 154.88 187.48 126.6 136.81 99
1980 to 1989 147.2 93.06 154.03 70.33 107.32 55.99
1990 to 1999 114.13 75.44 86.13 37.92
2000 to 2010 78.11 65.39
Transportation to Work
Walk 21.72 25.01 15.86 20.27 13.34 26.3
Bike 4.52 3.56 3.31 4.31 2.91 4.43
Motorcycle 1.51 0.73 0.83 0.5 1.44 1.71
Car 835.57 495.03 769.09 408.78 661.76 444.08
Public Transportation 42.42 80.79 34.11 63.72 30.6 85.45
Work from Home 20.53 9.49 25.02 7.04 29.21 16.45
Vehicle Ownership
None 88.05 188.69 78.68 164.33 44.93 132.71
1 261.49 225.39 277.2 232.49 205.89 235.02
2 250.4 119.79 257.28 119.46 206.06 115.88
3 78.76 33.13 72.4 29.78 63.12 30.17
4 23.45 8.25 20.43 7.81 18.37 7.55
5 or more 7.07 2.54 6.56 3.6 4.96 1.95
68
APPENDIX B REGRESSION ANALYSIS
The following tables are the results of the regression analyses used to determine the socioeconomic predictor coefficients of EZ outcome for each County, for 1990, 2000, and 2010. Table B-1. Coefficient analysis of Duval EZ Predictors for 1990; Model R2=0.34
Unstandardized
Coefficients Standardized Coefficients
t Sig.
B Std. Error Beta
(Constant) 0.14 0.04 4.07 0.00 Bicycle as primary transportation mode to work 0.00 0.00 -0.09 -1.51 0.13 Black population 0.00 0.00 -0.62 -0.82 0.42 Car as primary transportation mode to work 0.00 0.00 -0.66 -1.60 0.11 Education bachelor's degree 0.00 0.00 0.29 1.80 0.07 Education high school graduate
0.00 0.00 0.11 0.20 0.84 Education less than 9th grade 0.00 0.00 0.24 3.09 0.00 Education some high school but no degree 0.00 0.00 -0.07 -0.66 0.51 Families 0.00 0.00 -0.08 -0.12 0.91 Hispanic population 0.00 0.00 -0.13 -0.52 0.60 Households above poverty 0.00 0.00 -0.45 -0.63 0.53 Households below poverty 0.00 0.00 -0.09 -0.54 0.59 Households receiving public assistance 0.00 0.00 -0.02 -0.25 0.80 Individuals above poverty 0.00 0.00 -0.05 -0.06 0.95 Individuals below poverty 0.00 0.00 0.17 0.88 0.38 Minority population 0.00 0.00 0.84 0.96 0.34 Motorcycle as primary transportation mode to work 0.00 0.00 0.04 0.63 0.53 Other as primary transportation mode to work 0.00 0.00 0.00 0.04 0.97 Owners 0.00 0.00 0.23 0.49 0.63 Public transportation as primary mode to work 0.00 0.00 0.15 2.23 0.03 Renters 0.00 0.00 0.24 0.59 0.55 Vacant housing 0.00 0.00 0.13 1.72 0.09 Walking as primary transportation mode to work 0.00 0.00 0.03 0.37 0.71 White non-Hispanic population 0.00 0.00 0.07 0.10 0.92 Work from home 0.00 0.00 -0.09 -0.77 0.44
a. Dependent Variable: Enterprise Zone
69
Table B-2. Coefficient analysis of Hillsborough EZ Predictors for 1990; Model R2=0.40
Unstandardized Coefficients
Standardized Coefficients
t Sig.
B Std. Error Beta
(Constant) 0.12 0.02 4.97 0.00 Bicycle as primary transportation mode to work 0.00 0.00 -0.04 -0.91 0.36 Black population 0.00 0.00 0.46 0.74 0.46 Car as primary transportation mode to work 0.00 0.00 -0.11 -0.53 0.60 Education bachelor's degree
0.00 0.00 0.15 1.44 0.15 Education less than 9th grade
0.00 0.00 0.02 0.31 0.76 Education some high school but no degree 0.00 0.00 0.13 1.60 0.11
Families 0.00 0.00 -0.92 -2.65 0.01
High school graduate 0.00 0.00 0.63 1.81 0.07 Hispanic population 0.00 0.00 0.19 0.39 0.69
Households above poverty 0.00 0.00 -1.20 -2.54 0.01
Households below poverty 0.00 0.00 0.18 1.27 0.20 Households receiving public assistance 0.00 0.00 0.12 1.59 0.11 Individuals above poverty 0.00 0.00 0.90 1.80 0.07 Individuals below poverty 0.00 0.00 0.01 0.08 0.94 Minority population 0.00 0.00 -0.48 -0.60 0.55 Motorcycle as primary transportation mode to work 0.00 0.00 -0.01 -0.16 0.88 Other as primary transportation mode to work 0.00 0.00 -0.03 -0.70 0.48 Owners 0.00 0.00 0.56 1.47 0.14 Public transportation as primary mode to work 0.00 0.00 0.19 4.15 0.00 Renters 0.00 0.00 0.31 1.10 0.27 Vacant housing 0.00 0.00 0.12 2.22 0.03 Walking as primary transportation mode to work 0.00 0.00 0.08 1.31 0.19 White non-Hispanic population
0.00 0.00 -0.51 -1.39 0.17 Work from home 0.00 0.00 -0.01 -0.23 0.82
a. Dependent Variable: Enterprise Zone
70
Table B-3. Coefficient analysis of Miami-Dade EZ Predictors for 1990; Model R2=0.30
Unstandardized
Coefficients Standardized Coefficients
t Sig.
B Std. Error Beta
(Constant) 0.20 0.02 9.05 0.00 Bicycle as primary transportation mode to work 0.00 0.00 -0.03 -0.88 0.38 Black population 0.00 0.00 -0.25 -0.53 0.60 Car as primary transportation mode to work 0.00 0.00 0.47 1.53 0.13 Education bachelor's degree 0.00 0.00 0.12 0.93 0.35 Education high school graduate
0.00 0.00 -1.34 -3.07 0.00 Education less than 9th grade
0.00 0.00 -0.16 -1.67 0.10 Education some high school but no degree 0.00 0.00 -0.11 -1.01 0.31 Families 0.00 0.00 0.42 0.92 0.36 Hispanic population 0.00 0.00 -1.07 -0.75 0.45 Households above poverty 0.00 0.00 -0.21 -0.30 0.77 Households below poverty 0.00 0.00 0.20 1.09 0.28 Households receiving public assistance 0.00 0.00 0.10 1.14 0.25 Individuals above poverty 0.00 0.00 -1.56 -2.68 0.01 Individuals below poverty 0.00 0.00 -0.05 -0.34 0.74 Minority population 0.00 0.00 2.21 1.45 0.15 Motorcycle as primary transportation mode to work 0.00 0.00 -0.03 -0.77 0.44 Other as primary transportation mode to work 0.00 0.00 -0.07 -1.71 0.09 Owners 0.00 0.00 0.23 0.54 0.59 Public transportation as primary mode to work 0.00 0.00 0.21 4.37 0.00 Renters 0.00 0.00 0.19 0.47 0.64 Vacant housing 0.00 0.00 0.19 2.99 0.00 Walking as primary transportation mode to work 0.00 0.00 -0.13 -2.79 0.01 White non-Hispanic population 0.00 0.00 0.71 2.95 0.00 Work from home 0.00 0.00 -0.02 -0.45 0.65
a. Dependent Variable: Enterprise Zone
71
Table B-4. Coefficient analysis of Duval EZ Predictors for 2000; Model R2=0.36
Unstandardized
Coefficients Standardized Coefficients
t Sig.
B Std. Error Beta
(Constant) 0.10 0.03 3.38 0.00 Bicycle as primary transportation mode to work 0.00 0.00 -0.07 -1.39 0.17 Black population 0.00 0.00 -0.25 -0.62 0.54 Car as primary transportation mode to work 0.00 0.00 -0.60 -1.19 0.23 Education bachelor's degree 0.00 0.00 0.60 3.60 0.00 Education high school graduate 0.00 0.00 1.45 1.79 0.07 Education less than 9th grade 0.00 0.00 0.04 0.51 0.61 Education some high school but no degree 0.00 0.00 0.23 2.03 0.04 Families 0.00 0.00 -2.10 -2.75 0.01 Hispanic population 0.00 0.00 -0.25 -1.39 0.17 Households above poverty 0.00 0.00 0.29 0.22 0.83 Households below poverty 0.00 0.00 0.38 2.02 0.04 Households receiving public assistance 0.00 0.00 0.03 0.40 0.69 Housing Units 0.00 0.00 -2.62 -1.79 0.07 Individuals above poverty 0.00 0.00 3.09 2.38 0.02 Individuals below poverty 0.00 0.00 0.29 1.46 0.14 Minority population 0.00 0.00 0.18 0.30 0.77 Motorcycle as primary transportation mode to work 0.00 0.00 -0.03 -0.67 0.50 Other as primary transportation mode to work 0.00 0.00 0.00 -0.02 0.98 Public transportation as primary mode to work 0.00 0.00 0.13 2.27 0.02 Renters 0.00 0.00 -0.03 -0.17 0.86 Vacant housing 0.00 0.00 0.35 2.48 0.01 Walking as primary transportation mode to work 0.00 0.00 0.05 0.49 0.62 White non-Hispanic population 0.00 0.00 -0.78 -0.86 0.39 Work from home 0.00 0.00 -0.01 -0.11 0.91
a. Dependent Variable: Enterprise Zone
72
Table B-5. Coefficient analysis of Hillsborough EZ Predictors for 2000; Model R2=0.41
Unstandardized
Coefficients Standardized Coefficients
t Sig.
B Std. Error Beta
(Constant) 0.09 0.02 5.05 0.00 Bicycle as primary transportation mode to work 0.00 0.00 0.05 1.62 0.10 Black population 0.00 0.00 0.63 3.44 0.00 Car as primary transportation mode to work 0.00 0.00 -0.04 -0.24 0.81 Education bachelor's degree 0.00 0.00 0.42 4.16 0.00 Education high school graduate 0.00 0.00 0.48 1.66 0.10 Education less than 9th grade 0.00 0.00 0.02 0.40 0.69 Education some high school but no degree 0.00 0.00 0.12 1.84 0.07 Families 0.00 0.00 0.09 0.35 0.73 Hispanic population 0.00 0.00 0.26 1.44 0.15 Households above poverty 0.00 0.00 -0.53 -1.18 0.24 Households below poverty 0.00 0.00 0.21 1.76 0.08 Households receiving public assistance 0.00 0.00 0.10 2.00 0.05 Individuals above poverty 0.00 0.00 -0.03 -0.06 0.95 Individuals below poverty 0.00 0.00 0.20 1.82 0.07 Minority population 0.00 0.00 -0.76 -2.52 0.01 Motorcycle as primary transportation mode to work 0.00 0.00 0.03 0.96 0.34 Other as primary transportation mode to work 0.00 0.00 0.01 0.25 0.80 Owners 0.00 0.00 -0.06 -0.17 0.87 Public transportation as primary mode to work 0.00 0.00 0.06 1.40 0.16 Renters 0.00 0.00 -0.14 -0.52 0.61 Vacant housing 0.00 0.00 0.05 1.16 0.25 Walking as primary transportation mode to work 0.00 0.00 -0.01 -0.28 0.78 White non-Hispanic population 0.00 0.00 -0.51 -2.21 0.03 Work from home 0.00 0.00 -0.03 -0.65 0.51
a. Dependent Variable: Enterprise Zone
73
Table B-6. Coefficient analysis of Miami-Dade EZ Predictors for 2000; Model R2=0.31
Unstandardized
Coefficients Standardized Coefficients
t Sig.
B Std. Error Beta
(Constant) 0.16 0.02 7.70 0.00 Bicycle as primary transportation mode to work 0.00 0.00 -0.02 -0.59 0.56 Black population 0.00 0.00 0.30 1.31 0.19 Car as primary transportation mode to work 0.00 0.00 -0.24 -1.35 0.18 Education bachelor's degree 0.00 0.00 0.17 1.86 0.06 Education high school graduate
0.00 0.00 -0.01 -0.05 0.96 Education less than 9th grade 0.00 0.00 -0.04 -0.61 0.54 Education some high school but no degree 0.00 0.00 0.01 0.09 0.93 Families 0.00 0.00 -0.08 -0.30 0.77 Hispanic population 0.00 0.00 0.35 0.75 0.46 Households above poverty 0.00 0.00 -0.69 -1.49 0.14 Households below poverty 0.00 0.00 0.40 3.21 0.00 Households receiving public assistance 0.00 0.00 0.05 1.03 0.30 Housing Units 0.00 0.00 0.05 0.08 0.94 Individuals above poverty 0.00 0.00 0.48 1.19 0.23 Individuals below poverty 0.00 0.00 0.10 0.89 0.37 Minority population 0.00 0.00 -0.44 -0.76 0.45 Motorcycle as primary transportation mode to work 0.00 0.00 0.01 0.21 0.83 Other as primary transportation mode to work 0.00 0.00 0.01 0.37 0.71 Public transportation as primary mode to work 0.00 0.00 0.11 2.97 0.00 Renters 0.00 0.00 -0.08 -1.17 0.24 Vacant housing 0.00 0.00 0.08 0.41 0.68 Walking as primary transportation mode to work 0.00 0.00 0.00 0.08 0.94 White non-Hispanic population
0.00 0.00 -0.09 -0.70 0.48 Work from home 0.00 0.00 0.04 0.84 0.40
a. Dependent Variable: Enterprise Zone
74
Table B-7. Coefficient analysis of Duval EZ Predictors for 2010; Model R2=0.36
Unstandardized Coefficients
Standardized Coefficients
t Sig.
B Std. Error Beta
(Constant) 0.14 0.03 4.38 0.00 Bicycle as primary transportation mode to work 0.00 0.00 0.01 0.25 0.80 Black population 0.00 0.00 -0.22 -0.87 0.38 Car as primary transportation mode to work 0.00 0.00 -0.35 -1.75 0.08 Education bachelor's degree
0.00 0.00 0.30 3.19 0.00 Education high school graduate
0.00 0.00 -0.24 -0.99 0.32 Education less than 9th grade
0.00 0.00 0.00 -0.01 0.99 Education some high school but no degree 0.00 0.00 0.15 2.62 0.01 Hispanic population 0.00 0.00 -0.39 -3.20 0.00 Households above poverty 0.00 0.00 -0.31 -1.15 0.25 Households below poverty 0.00 0.00 0.16 1.56 0.12 Households receiving public assistance 0.00 0.00 0.05 1.22 0.22 Individuals above poverty 0.00 0.00 0.31 1.26 0.21 Individuals below poverty 0.00 0.00 -0.09 -0.95 0.34 Minority population 0.00 0.00 0.61 1.88 0.06 Motorcycle as primary transportation mode to work 0.00 0.00 -0.02 -0.62 0.54 Other as primary transportation mode to work 0.00 0.00 0.04 1.03 0.31
Owners 0.00 0.00 -0.11 -0.57 0.57 Public transportation as primary mode to work 0.00 0.00 0.15 3.53 0.00 Renters 0.00 0.00 -0.05 -0.37 0.71 Vacant housing 0.00 0.00 0.17 2.92 0.00 Walking as primary transportation mode to work 0.00 0.00 -0.04 -0.81 0.42 White non-Hispanic population 0.00 0.00 0.03 0.21 0.84 Work from home 0.00 0.00 0.04 0.85 0.39
a. Dependent Variable: Enterprise Zone
75
Table B-8. Coefficient analysis of Hillsborough EZ Predictors for 2010; Model R2=0.38
Unstandardized
Coefficients Standardized Coefficients
t Sig.
B Std. Error Beta
(Constant) 0.14 0.02 7.02 0.00 Individuals above poverty 0.00 0.00 0.15 0.88 0.38 Education bachelor's degree 0.00 0.00 0.23 3.49 0.00 Individuals below poverty 0.00 0.00 -0.02 -0.30 0.76 Black population 0.00 0.00 0.83 5.55 0.00 Education some high school but no degree 0.00 0.00 0.02 0.51 0.61 Education less than 9th grade 0.00 0.00 0.08 1.99 0.05 Families 0.00 0.00 0.10 0.60 0.55 Households above poverty 0.00 0.00 -0.61 -3.61 0.00 Households below poverty 0.00 0.00 0.06 0.87 0.39 Households receiving public assistance 0.00 0.00 0.06 1.89 0.06 Hispanic population 0.00 0.00 0.41 2.37 0.02 Households 0.00 0.00 -0.05 -0.26 0.80 Education high school graduate 0.00 0.00 0.22 1.47 0.14 Minority population 0.00 0.00 -0.85 -3.18 0.00 Renters 0.00 0.00 0.12 1.67 0.10 Bicycle as primary transportation mode to work 0.00 0.00 0.05 1.83 0.07 Car as primary transportation mode to work 0.00 0.00 -0.25 -2.51 0.01 Work from home 0.00 0.00 0.01 0.30 0.76 Motorcycle as primary transportation mode to work
0.00 0.00 0.02 0.60 0.55 Other as primary transportation mode to work 0.00 0.00 0.01 0.23 0.82 Public transportation as primary mode to work 0.00 0.00 0.09 2.97 0.00 Walking as primary transportation mode to work 0.00 0.00 0.06 1.59 0.11 Vacant housing 0.00 0.00 0.10 2.54 0.01 White non-Hispanic population 0.00 0.00 -0.12 -1.10 0.27 a. Dependent Variable: Enterprise Zone
76
Table B-9. Coefficient analysis of Miami-Dade EZ Predictors for 2010; Model R2=0.30
Unstandardized
Coefficients Standardized Coefficients
t Sig.
B Std. Error
Beta
(Constant) 0.18 0.02 8.69 0.00 Individuals above poverty 0.00 0.00 0.20 2.09 0.04 Education bachelor's degree
0.00 0.00 0.05 1.27 0.20 Individuals below poverty 0.00 0.00 0.03 0.69 0.49 Black population 0.00 0.00 1.16 3.86 0.00 Education some high school but no degree 0.00 0.00 0.02 0.54 0.59 Education less than 9th grade
0.00 0.00 0.00 0.11 0.91 Families 0.00 0.00 -0.21 -1.91 0.06 Households above poverty
0.00 0.00 -0.16 -1.90 0.06 Households below poverty 0.00 0.00 0.09 1.78 0.08 Households receiving public assistance 0.00 0.00 0.01 0.61 0.54 Hispanic population 0.00 0.00 1.57 3.38 0.00 Education high school graduate
0.00 0.00 -0.08 -1.11 0.27 Minority population 0.00 0.00 -1.51 -3.15 0.00 Owners 0.00 0.00 0.03 0.38 0.71 Renters 0.00 0.00 0.21 3.09 0.00 Bicycle as primary transportation mode to work
0.00 0.00 -0.04 -1.69 0.09 Car as primary transportation mode to work
0.00 0.00 -0.22 -2.74 0.01 Work from home 0.00 0.00 0.01 0.22 0.82 Motorcycle as primary transportation mode to work
0.00 0.00 0.01 0.67 0.50 Other as primary transportation mode to work
0.00 0.00 -0.01 -0.49 0.63 Public transportation as primary mode to work 0.00 0.00 0.16 5.77 0.00 Walking as primary transportation mode to work
0.00 0.00 0.07 2.49 0.01 Vacant housing 0.00 0.00 0.00 -0.05 0.96 White non-Hispanic population
0.00 0.00 -0.03 -0.64 0.52 a. Dependent Variable: Enterprise Zone
77
APPENDIX C HOT SPOT ANALYSIS
A.
B.
C.
Figure C-1. Hot Spot Analysis for Poverty. A) Duval County, B) Hillsborough County, C) Miami-Dade County.3
3 World street map, ESRI, retrieved from http://www.arcgis.com/home/item.html?id=3b93337983e9436f8db950e38a8629af, accessed August 20, 2015. US Census shapefiles (1990, 2000, and 2010), Florida Geographic Digital Library, retrieved from http://www.fgdl.org/metadataexplorer/explorer.jsp, accessed August 20, 2015.
78
A.
B.
C.
Figure C-2. Hot Spot Analysis for Households Receiving Public Assistance. A) Duval County, B) Hillsborough County, C) Miami-Dade County. 4
4 World street map, ESRI, retrieved from http://www.arcgis.com/home/item.html?id=3b93337983e9436f8db950e38a8629af, accessed August 20, 2015. US Census shapefiles (1990, 2000, and 2010), Florida Geographic Digital Library, retrieved from http://www.fgdl.org/metadataexplorer/explorer.jsp, accessed August 20, 2015.
79
A.
B.
C. Figure C-3. Hot Spot Analysis for Employed Residents. A) Duval County, B) Hillsborough County, C) Miami-Dade County.5
5 World street map, ESRI, retrieved from http://www.arcgis.com/home/item.html?id=3b93337983e9436f8db950e38a8629af, accessed August 20, 2015. US Census shapefiles (1990, 2000, and 2010), Florida Geographic Digital Library, retrieved from http://www.fgdl.org/metadataexplorer/explorer.jsp, accessed August 20, 2015.
80
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from the states’ programs. Regional Science and Urban Economics, 30(5), 519–549. doi:10.1016/s0166-0462(00)00042-9
Bondonio, D., & Greenbaum, R. (2007). Do local tax incentives affect economic growth?
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Bostic, R. W., & Prohofsky, A. C. (2006). Enterprise zones and individual welfare: A
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BIOGRAPHICAL SKETCH
Mario F. Duron, Jr. was born in Miami, Florida in 1987. He graduated from the
University of Florida in 2009 with his bachelor’s degree in anthropology. After
graduation, Mario worked in the non-profit sector helping engage residents of Miami’s
underserved communities carry-out redevelopment initiatives. Inspired by the
transformation he witnessed, he decided to pursue his master’s degree in urban and
regional planning. Upon completion of his master’s, Mario would like to remain in Florida
helping support and develop local communities.
