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An assessment of the impacts of relocation on public housing youth

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An assessment of the impacts of relocation on public housing youth
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English
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Zupo, Emily
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University of South Florida
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Deconcentration
Poverty
HOPE VI
GIS
Neighborhood effects
Dissertations, Academic -- Geography -- Masters -- USF   ( lcsh )
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non-fiction   ( marcgt )

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Abstract:
ABSTRACT: This paper will explore the social and economic impacts of public housing revitalization on households with minor children. The research traces the relocations of families from two public housing complexes to other public housing complexes or market housing, using Housing Choice formerly Section 8 vouchers. We contrast and compare the socioeconomic characteristics of the original neighborhoods to the relocation sites from the census tract level, exploring changes in resources available to families.
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Thesis (M.A.)--University of South Florida, 2009.
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Includes bibliographical references.
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by Emily Zupo.
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An Assessment of the Impacts of Re location on Public Housing Youth by Emily Zupo A thesis submitted in partial fulfillment of the requirements for the degree of Master of Arts Department of Geography College of Arts and Sciences University of South Florida Major Professor: Elizabeth Strom, Ph.D. Beverly Ward, Ph.D. Steven Reader, Ph.D. Date of Approval: April 6, 2009 Keywords: Deconcentration, Povert y, HOPE VI, GIS, Neighborhood Effects Copyright 2009, Emily Zupo

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Acknowledgements I would like to acknowledge Dr.’s Strom, Ward, and Greenbaum for giving me this opportunity to use my GIS skills for good and for giving me the opportunity to learn about this marginalized group. I would also like to thank my family. First, my love Louie: he is my support system and my punching bag. Secondly, my Mom who gave me my fascination for books and lear ning, to my Dad, who taught me to take pride in the work I do, an d my grandparents who have always encouraged me to continue to le arn. Finally, I would like to thank Robin Smith and Susan Popkin for gi ving me great feedback about my research and how to present my results.

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i Table of Contents List of Tables iv List of Figures vi Abstract ix Chapter 1 INTRODUCTION 1 1.1.1 Stereotypes 2 1.2 Neighborhood Effects 4 1.3 Literature Review 7 1.3.1 Assessing Concentrated Poverty 8 1.3.2 HOPE VI 14 Chapter 2 RESEARCH QUESTIONS 17 2.1.1 Hypothesis 17 2.2 Research Design 18 2.2.1 Study Area 18 2.2.2 Significance of the Problem 24 2.2.3 Problem Statement 24 2.2.4 Research Purpose 25 Chapter 3 DATA AND METHODS 26 3.1 Data, Data Sources, and Collection 27 3.1.1 Census 29

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ii 3.1.2 Crime 31 3.1.3 School 35 3.2 Methodology 38 3.2.1 Census 41 3.2.2 Crime 43 3.2.3 School Quality 45 Chapter 4 RESULTS / DISCUSSION 46 4.1 Relocation Statistics 50 4.2 Census 54 4.2.1 Race 55 4.2.2 Age 64 4.2.3 High School Graduate or Equivalent 71 4.2.4 Employment 75 4.2.5 Median Household Income 79 4.2.6 Poverty 83 4.2.7 Female Head of Household 87 4.2.8 Renter 90 4.2.9 Zero Vehicle Households 96 4.3 Crime 98 4.4 School Quality 103

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iii Chapter 5 CONCLUSIONS 114 5.1 Limitations 117 5.2 Future Research 121 REFERENCES 123

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iv List of Tables Table 1. Selected Socio-economic Characteristics of the Origin Neighborhood 23 Table 2. Origin Neighborhood: Total Crime Per Capita (1999) 35 Table 3. School Quality Grades for Origin Neighborhood 38 Table 4. Descriptive Statistics for Relocation Classifications 52 Table 5. White Population Comparison 57 Table 6. Black Population Comparison 60 Table 7. Hispanic Population Comparison 63 Table 8. Population under 18 Comparison 67 Table 9. Population 60 and over Comparison 70 Table 10. High School Degree Comparison 74 Table 11. Employment Comparison 78 Table 12. Median Household Income Comparison 82 Table 13. Poverty Comparison 85 Table 14. Female Head of Household Comparison 89 Table 15. Renters Comparison 92 Table 16. Zero Vehicle Households Comparison 95 Table 17. Crime per Capita Results (1999 and 2007) 102 Table 18. Elementary Sch ool Quality Comparison 106

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v Table 19. Middle School Quality Comparison 109 Table 20. High School Quality Comparison 112

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vi List of Figures Figure 1. Study Area Location 19 Figure 2. Family Location in Origin Census Tracts 21 Figure 3. Visual Flow Chart for Methodology 26 Figure 4. Family Locations in Origin Crime Grids 34 Figure 5. Relocation Neighborhoods by Census Tract 53 Figure 6. Choropleth of White Population (%) 56 Figure 7. Scatter plot of Dist ance from Origin Neighborhood And Change in White Population 57 Figure 8. Choropleth of Black Population (%) 59 Figure 9. Scatter plot of Dist ance from Origin Neighborhood And Change in Black Population 60 Figure 10. Choropleth of Hispanic Population (%) 62 Figure 11. Scatter plot of Dist ance from Origin Neighborhood And Change in Hispanic Population 63 Figure 12. Choropleth of Population under 18 (%) 66 Figure 13. Scatter plot of Dist ance from Origin Neighborhood And Change in Population under 18 67 Figure 14. Choropleth of Population 60 and over (%) 69

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vii Figure 15. Scatter plot of Dist ance from Origin Neighborhood And Change in Population 60 and over 70 Figure 16. Choropleth of High School Degree (%) 72 Figure 17. Scatter plot of Dist ance from Origin Neighborhood And Change in High School Degree 73 Figure 18. Choropleth of Employment (%) 77 Figure 19. Scatter plot of Dist ance from Origin Neighborhood And Change in Employment 78 Figure 20. Choropleth of Median Household Income ($) 81 Figure 21. Scatter plot of Dist ance from Origin Neighborhood And Change in Median Household Income 82 Figure 22. Choropleth of Poverty (%) 84 Figure 23. Scatter plot of Dist ance from Origin Neighborhood And Change in Poverty 85 Figure 24. Choropleth of Female Head of Household (%) 88 Figure 25. Scatter plot of Dist ance from Origin Neighborhood And Change in Female Head of Household 89 Figure 26. Choropleth of Renters (%) 91 Figure 27. Scatter plot of Dist ance from Origin Neighborhood And Change in Renters 92 Figure 28. Choropleth of Zero Vehicle Households (%) 94

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viii Figure 29. Scatter plot of Di stance from Origin Neighborhood And Change in Zero Vehicle Households 95 Figure 30. Relocation Neighb orhoods by Crime per Capita 101 Figure 31. Choropleth of Reloca tion Elementary School Quality 105 Figure 32. Choropleth of Relo cation Middle School Quality 108 Figure 33. Choropleth of Relo cation High School Quality 111

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ix An Assessment of the Impacts of Relocation on Public Housing Youth Emily Zupo ABSTRACT This paper will explore the social and economic impacts of public housing revitalization on households with minor children. The research traces the relocations of families fr om two public housing complexes to other public housing complexes or market housing, using Housing Choice formerly Section 8 vouchers. We contrast and compare the socioeconomic characteristics of th e original neighborhoods to the relocation sites from the census tract level, exploring changes in resources available to families.

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1 CHAPTER 1 INTRODUCTION The most distressing fact in th e present world is poverty; not absolute poverty, because some folks are rich and many are well-to-do; not poverty as great as some lands and other historical ages have known; but poverty more poignant and discouraging because it comes after a dream of wealth; of riotous, wasteful and even vulg ar accumulation of individual riches, which suddenly leaves the majority of mankind today without enough to eat; without pr oper shelter; without sufficient clothing. -W.E.B. Du Bois Poverty and its effects are experi enced differently in different environments. How a farmer in Indi a experiences poverty is different from how an American might feel th e effects of poverty. Likewise, how a poor family living in working cla ss neighborhood experiences poverty will be different from how an urban poor family experiences poverty in a public housing neighborhood (Jo hnston et al. 2000). Many scholars (Jargowsky and Bane 1990, Goering et al. 2003) chronicle the detrimental consequences of persiste nt poverty: families that remain poor for long periods of time and usually pass this financial state and

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2 resulting “behaviors” on to thei r children, a cycle which teaches children to grow up like their parent s and believe that living a ghetto lifestyle is acceptable. The harmful learned behaviors “appear especially severe for children whose behavior, choices, and prospects appear uniquely susceptible to neig hborhood characteristics, including limited resources, peer group infl uences, school quality, and violent crime” (Goering et al. 2003, 3). Children are influenced by the environment in which they grow up. Early behaviors of mimicking their elders, which at first help young children develop into functioning adults can backfire in these concen trated poverty settings: the results of which are not only passed on to the next generation and so on but are deleterious to current society (Case and Katz 1991). In his book The Truly Disadvantaged William Julius Wilson (1987) postulates that allowing youth to live in this cycle of poverty leads to many negative societal effects: youth becomes “not only a factor in crimes; it is also associated with out-of-wedlock births, female-headed homes, and welfare dependency” (14). 1.1.1 Stereotypes Increasingly the popular perc eption promoted by media and government, is that the persistent poverty of today is the result of largely atypical behavior by a mino rity group outside the mainstream society (Bane 1989). This perception is popularized by conservative

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3 think tanks, such as the Herita ge Foundation or the Manhattan Institute, and commonly influences bo th perception and policy. In their book The Urban Underclass Christopher Jencks an d Paul E. Peterson talk about what they feel are po pular misconceptions. They contest that many people commonly believe that the percentage of the population who live in persistent poverty is large and rapidly escalating, that more and more underage unmarri ed females are bearing children, and that “welfare rolls are expl oding” (Jencks and Peterson 1991, Preface). Jencks and Peterson (1991) claim that popular belief is that crime is on the rise, “young people ar e dropping out of school in record numbers, and higher percentages of the population are withdrawing from the labor force.” Additionally, the poor are said to be gradually more isolated by ghettos at the ce nters of our urban areas (Preface). “When figures on black crime, t eenage pregnancy, female-headed families, and welfare dependency ar e released to the public without sufficient explanation, racial ster eotypes are reinforced” (Wilson 1987, 21). This is the perception of po verty that people who follow stereotypes in the media mistaken ly share: lazy, immoral, or undeserving individuals who are resp onsible for their living conditions. The reasons for the urban poor’s persistent poverty are commonly misunderstood, and can even be misu nderstood by those that run the

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4 government: this, in turn affects the government’s view on poverty and, consequently, policies enforced to aid those living in poverty. In reality, there are many different an d unique theoretical explanations for the existence of poverty that have been offered by scholars (Wilson 1987, Glasmeier 2002), but an economis t, Schiller (2001) asserts that all these arguments can be brok en down into two categories: restricted opportunity and flawed char acter. Flawed character refers to those individuals who lack ambition or ability to move up from an impoverished state. Restricted o pportunity suggests that “the poor are poor because they do not have access to good schools, jobs, and income, because they are not fu rnished with a fair share of government protection, subsidy, or services” (Schiller 2001). 1.2 Neighborhood Effects Two schools of thought diverge on mitigation strategies to concentrated poverty. One answer is to bring more public services to public housing residents (Bennett et al. 2006, Greenbaum 2008); the second answer is to deconcentrate urban poor residents and encourage them to live better lives through example of low poverty neighborhoods. The first mitigation technique has long underlay government policy and some scholars believe this kept public housing residents from improving themse lves (Wilson 1987). The second mitigation technique mentioned above is somewhat more current

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5 government policy; however, it is st ill disputed in many locations as to whether or not it can be successful (Varady and Walker 2003, Greenbaum et al. 2008, and Popkin et al. 2008). The unfortunate truth of public housing is that it is the catch-all for America’s poorest citizens and it is probably for the best to deconcentrate poverty in order to give the urban poor an opportunity at a better quality of life. Public housing is cheaply made and most often in disrepair (Turner et al. 2007). Some scholars (Wilson 1987, Jargowsky and Bane 1990, Goering et al 2003, and Buron et al. 2007) believe that by deconcentrating the urba n poor and dispersing them to lower poverty neighborhoods, they will benefit in a number of ways. The theory of neighborhood effects states that fa milies that live near disadvantaged neighborhoods will experience adve rse effects on child development through exposure to crime and violence, poor peer influences, absence of positive role models, and lack of school, community, and health care resources (Wilson 1987, Goetz 2003, Kling and Leibman 2004). Conversely, if families live near affl uent neighborhoods, they will have the opportunity to experience posi tive effects though exposure to better job opportunity, less crime and violence, positive role models, and better quality of schools, communi ties, and health care resources. They have the potential to create social networks that “will be conductive to economic self-su fficiency” (Clampet-Lundquist 2004,

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6 415). According to Ellen and Turner (2003) neighborhood effects is facilitated in one of two ways: th e epidemic model or the relative deprivation model. The epidemic mo del assumes ‘like begets like.’ The relative deprivation model assumes that “people judge their success or failure by comparing themselves wi th others around them” (Jencks and Mayer 1990, 116). However, the neighborhood effects theory is controversial because it cannot be proven and cannot take into account personal or familial issues. Recent public policies, discusse d in more detail below, have aimed to move these residents out of concentrated poverty areas in the hope that they learn to impr ove their lives by the example of upper-class neighbors. In Clearing the Way: Deconcentrating the Poor in Urban America professor Edward Goetz (2003) questions this deconcentration strategy: “ is deco ncentration about moving people out of a particular neighborhood because the neighborhoods have been declared dysfunctional, or is it about providing housing choices for a class of people who have not had them in the past?” (Goetz 2003, 7) Varady and Walker (2003) would argue that it is about the latter: giving urban poor the opportunity to live in any ne ighborhood they choose regardless of racial or economic discrimination. The first section will review re search concerning impacts of public housing relocation, including the Moving to Opportunity social

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7 experiment, HOPE VI and Section 8/Housing Choice Vouchers. The second section will provide a summary of the research design including the research question this thesis seeks to answer as well as an overview of the study areas. The third section will describe the data gathering process and related method ology this paper will use in its assessment. The fourth section will report and discuss results. Finally, the fifth section will discuss conclusions, and limitations based on this particular case study. 1.3 Literature Review Impacts of relocation of public housing residents have been studied in social science (Dunc an and Rodgers 1991, Crane 1991, Clampet-Lundquist and Massey 2008), economic (Datcher 1982, Case and Katz 1991, Schiller 2001), anth ropological (Greenbaum et al. 2008), public policy (Kaufman and Rosenbaum 1992, Briggs 1997, Buron et al. 2007), law (Briggs and Turner 2006, Duncan and Zuberi 2006), and geographical (Jargowsky 1 997, Glasmeier 2002) literature. This literature can be generally classified into two distinct categories: qualitative assessment and quan titative assessment. Research concerned with qualitative assessm ent is usually conducted over extended time intervals to compare participant’s respon ses from public housing origins to relocation neighb orhoods using personal interviews, participant observation, surveys, and archival document analysis. This

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8 type of study evaluates primarily in tangible aspects such as thoughts and feelings that the researcher believes can contribute to a better understanding of quality of life im provements. Many scholars prefer this type of research method be cause conclusions can be drawn for specific individuals and individual analysis can be made. Research which focuses on quantitative meth ods focus on datasets which have been compiled and usually describe socio-economic characteristics: attributes that are usually more ta ngible such as median household income, which can be used to co mpare different neighborhoods and make generalized statements about a group of residents based on generic characteristics. Not as many scholars prefer this type of research method but it adds its ow n intrinsic value to an over all assessment of the research topic. Statements that can be made are not as specific as their qualitat ive counterpart, but conversely quantitative research can draw more general conclusions that qualitative research cannot. Both types of studies have merit, but quantitative research will be the focus for this study. 1.3.1 Assessing Concentrated Poverty Wilson’s (1987) controversial op inion is that the exodus of middleand working-class families fr om ghetto neighborhoods after the Fair Housing Act of 1968 removed an important “social buffer” that deflected the impact of unemployment that began to plague the inner

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9 city neighborhoods around the same time. In other words as antidiscrimination laws came into effect from the Fair Housing Act of 1968, it gave the opportunity for middleand working-class black families to leave inner-city neighborhoods where all black families were segregated. This left lowerand un der-class black residents without, as Wilson (1987) calls it, working ro le-models. In the United States, the Fair Housing Act of 1968 came fr om a political movement armed at outlawing discrimination in all aspect s of housing. The primary purpose of the Fair Housing Act of 1968 wa s to protect the individual from landlord discrimination. The goal wa s a united housing market in which a person's background, as opposed to financial resources, did not restrict access (Sidney 2001). Wilson argues for deconcentratio n because by relocating urban poor to a lower poverty neighborhood and the social buffer of working class and middle class residents were to be put back in place it would create more of a stable long term environment by providing contagious ideals: “mainstream role models that help keep alive the perception that education is meanin gful, that steady employment is a viable alternative to welfare, and th at family stability is the norm, not the exception” (56). In this manne r, the youth of the impoverished neighborhoods would not only s ee unemployed welfare dependant families but also families that ar e industrious, go to work every

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10 morning, and attend school regu larly thereby demonstrating a connection between “education and meaningful employment” (Wilson 1987, 56). But because this social buffer is lacking, this absence has the potential to create a myriad of social and economic problems that is more than the sum of its part s—a concentration of urban poor people that creates what Wilson calls “concentration effects.” Most scholars know it as “neighborhood effects” (Crane 1991, Goetz 2003, Kling et al. 2004). The idea of neig hborhoods effects, as it will be called in this paper, is the theo ry that a severe concentration of disadvantages and poor behavior choices will, in turn beget more neighborhood and individual dysfunc tion. This theory suggests that the neighborhood environment plays a critical role in determining individual opportunities, experien ces, and behaviors (Goetz 2003). These concentrated neighborhood s of urban poor families are inundated with these problems as determined by researchers (BrooksGunn et al. 1993, Kling and Liebman 2004) and as such “have become increasingly isolated from mainst ream patterns of behavior” (Wilson 1987, 58). In their book, Choosing a Better Life? Evaluating the Moving to Opportunity Social Experiment Goering et al. (2003) suggest that deconcentration through the Moving to Opportunity Experiment (MTO) may have important social, educational, and economic benefits. MTO

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11 was loosely based on the Gautreaux program in Chicago, IL. In fact, they begin their book with a look at the history of public housing policy and the Gautreaux program, a cour t-ordered racial desegregation program, which assisted racially isolated families with housing vouchers and counseling to move to lower poverty, racially mixed neighborhoods. Early results from this program suggested that children were the greatest beneficiar ies of this deconcentration effort: in moving to lower poverty neighb orhoods, they were less likely to drop out of school, were more lik ely to take college preparatory classes, and were also more likely to attend a four year college or become employed full time as opposed to their public housing peers. Other qualitative results of the Gautreaux program showed further evidence that deconcentrating the ur ban poor could lead to potentially successful outcomes for families and their children. The Gautreaux program was successful most likely be cause there were such stringent requirements on the relocation site s for the original public housing residents and a myriad of support se rvices for before and during the relocation. In his book, Clearing the Way: Deconcentrating the Poor in Urban America Goetz (2003) takes a co mprehensive look at concentrated urban poverty in Mi nneapolis. His assessment begins with a critical time in public hous ing policy and a turning point in the

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12 case of Hollman v. Cisneros: a case that not only had a huge impact on public housing policy but also ideas of voluntary and involuntary deconcentration. Hollman v. Cisneros was the first desegregation case in Minneapolis which argued that the city was deliberately building public housing in the most destitute parts of the city which reinforced segregation. Hollman v. Cisneros a lleged that the city was segregating public housing residents deliberate ly, not only from more affluent neighbors, but also segregating black public housing residents from white public housing residents. Studies conducted on Minneapolis housing at the time concluded that “concentrating and isolating low income families headed by primarily unemployed single parents intensified social problems” (G oetz 2003, 139). A settlement was reached which laid out an aggressive plan of deconcentration. Urban poor families were provided both monetary assistance and counseling in choosing their relocation neighb orhood and in the place of the former distressed public housing, a mix of public housing, subsidized housing and market rate housing wa s built (Goetz 2003). Those that chose not to relocate voluntarily we re eventually forcibly relocated when the distressed public housing communities were torn down in favor of mixed-income development. Goetz studied these two groups individually to asses if there were a difference in relocation outcomes.

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13 Other programs in other cities have tried to imitate the Gatreaux program’s success, but have expe rienced mixed results. These programs usually fall under the auspic es of the federally funded S8 / Housing Choice and HOPE VI (Hou sing Opportunities for People Everywhere). These programs have seen mixed results, for a number of reasons. One reason that th e Gautreaux program was successful was because it was court ordered and monitored closely by state agencies. These agencies set up the stringent application process, the relocation process, and the counse ling involved before these families could relocate. Secondly, these individuals were monitored as closely as possible to see how they adjusted to their new living conditions in the relocation areas: these fam ilies were counseled and monitored every step of the way to study the success of the move. And thirdly, they were asked to stay in thei r relocation neighborhood for the remainder of the redevelopment projec t on their former public housing. This allowed those monitoring the relocatees to assess the changes brought about by the new opportunities of the relocation neighborhoods. Ideally, every program wants the success that the Gautreaux program enjoyed, but that type of funding on the federal level is not always possible (Varady and Walker 2003). Local Housing Authority programs started with federal funding that try to imitate the

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14 Gautreaux program are usually not as thorough as the court-mandated based process in Chicago: whether du e to lax application guidelines, a lack of rigorous counseling, or lack of a requirement to stay in the relocation neighborhood for a set period of time to assess neighborhood impacts. “Physically redi stributing the poor [is] probably necessary. . but instead of coac hing them and then carefully spreading them out among many mo re-affluent neighborhoods, most cities gave them vouchers and told them to move in a rush with no support” (Rosin 2008, 17). 1.3.2 HOPE VI The federal program this case stud y will focus on is the HOPE VI program in Tampa, Florida. HO PE VI in Tampa endeavors to deconcentrate the urban poor much like any other federally funded HOPE VI program in other cities. HOPE VI has it origins in 1992 when Congress authorized $300 million to create the program whic h was meant to rebuild the most physically “distressed” public hous ing in the country. According to the U.S. Office of Management and Budget and Federal agencies distressed public housing in this case is defined as subjecting the families residing in them to extrem e poverty and intolerable conditions. It was anticipated that HOPE VI would reshape distressed neighborhoods and surrounding area s by changing the physical

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15 environment and the social classifi cation (Smith 2002). HOPE VI has a generic methodology followed in each city that gets funding: residents are relocated either to other pu blic housing complexes or lowerpoverty areas with a voucher, buildings are demolished or “substantial[ly] renovated” and a po rtion of the original residents are allowed to move back into the renovated housing (Smith 2002). The HOPE VI program was designed to alleviate the concentration of poverty and the resulting negati ve behaviors associated with concentrated poverty by not only di spersing impoverished households but also by assuring that original public housing residents are allowed the opportunity at a lower-povert y neighborhood (Clampet-Lundquist 2004). The HOPE VI program is a radical and ambitious urban redevelopment program with idealistic intentions. Since 1992, HUD has awarded 446 HOPE VI grants in 166 cities. To date, 63,100 severely distressed units have been demolished and another 20,300 units are slat ed for redevelopment. By the end of 2002, 15 of 165 HOPE VI programs were fully complete. The billions of federal dollars spent on this reconstruction have leveraged billions more in other public, private, and philanthropic investments. --Popkin et al. 2004, 15

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16 This program has “transformed the way public housing is designed, financed, and managed. Many of the new developments offer highquality, mixed-income living environm ents and are contributing to the health and vitality of surrounding ne ighborhoods. What happens to the former residents of the demolished HOPE VI projects is vital in understanding the success of this program” (Popkin et al. 2004, 19). Most scholars argue that there is a need for site-by-site analysis in order to understand the efficacy of the local programs in place to deconcentrate poverty. From th is overview of qualitative and quantitative research on public ho using resident relocation, it is evident that youth relocation can be nefit from a quantitative location assessment in Tampa, Florida.

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17 CHAPTER 2 RESEARCH QUESTIONS The purpose of this thesis is to asses the extent to which origin and relocation neighborhoods differ for housing authority participants. A second purpose is to determine whether the families who moved out of distressed public housing to re location neighborhoods indeed moved to areas with improved opportunity at a better qu ality of life measured by key census variables. A third objective of the research is to determine whether the families who moved out of public housing to relocation neighborhoods have bette r quality schools fo r their children and to determine if they relocated to areas with lower federally mandated Part 1 crime rates. 2.1.1 Hypothesis Deconcentrating poverty and relocating youth out of their original distressed public housin g neighborhood will improve their opportunity for a better quality of life by placing them in higher quality of life neighborhoods measured by variables such as racial heterogeneity, low poverty, high median income, low instances of female head of household, high empl oyment rates, low rates of renter occupancy, lower percentage of zero vehicles per household, better quality schools, and lower crime.

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18 2.2 Research Design 2.2.1 Study Area The study area that will be considered in this thesis is in the city of Tampa, illustrated in Figure 1, which belongs to the Metropolitan Statistical Area of Tampa Bay; the second largest metropolitan area in the State of Florida. The city of Tampa had a population of 303,447 in 2000. The U.S. census data estimate that there are approximately 18.1 percent of people living at or below the poverty level in 1999 (State of the Cities Data Syst em, 2005). The poverty field is an estimate of people for whom poverty status is determined to be living below the federally mandated poverty level. Poverty level is defined in 2001, as having two components: household income, and number of people living off that income in th e household. The 2000 census data for poverty is actually a measure of poverty based on 1999 income data (Dalaker 2001).

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19 Figure 1. Study Area Location Two neighborhoods in Tampa, FL are the main origin study areas in this assessment. The original ne ighborhoods are the sites of public housing where youths and their families in the Tampa Housing Authority database can be traced back to as early as 1999. These neighborhoods as revealed in Figure 2 are the Ponce de Leon and the College Hill public housing neighbor hoods. Further neighborhoods were defined by census tract, as the yo uths and their families are traced from the original public housing ne ighborhoods to the final relocation neighborhoods in 2007. In order to standardize comparison between

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20 origin neighborhoods and relocation neighborhoods in terms of socioeconomic characteristics census tracts are commonly considered acceptable (Jargowsky 1997), and w ill be used to compare origin neighborhoods, or the distressed public housing neighborhoods in which the youth were first located in 1999, to relocation neighborhoods, or the final neighbor hoods that the youth relocated to: through the Section 8 / Housing Choice Voucher program, or other public housing communities in 2007.

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21 Figure 2. Family Location in Origin Census Tracts

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22 Census tracts 31, 33, and 34 are associated with the origin dataset, which correspond to the Ponc e de Leon (census tracts 31 and 33) and College Hill (census tract 34) public housing neighborhoods, shown in Figure 2. Detailed in Ta ble 1, these neighborhoods had an approximate population of 6,873 in 2000, and, in Table 2, on average 36.4 percent of individuals living in this area were living in poverty. The overall poverty rate in the Ci ty of Tampa was 18.1 percent, and not shown here in a table, the overal l poverty rate in the United States as of the 2000 census was 11.3 percent. Table 1 and Table 2 below show the classification of each orig inal public housing neighborhood in terms of the socio-economic variable s that are discussed in this paper compared to overall City of Tampa characteristics.

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23 Table 1. Selected Socio-economic Charac teristics of the Origin Neighborhood TRACT Population % White % Black % Hispanic % Population Under 18 % Population Over 60 % High School Graduate % Employed Median Household Income % Poverty % Female Head of Houseold % Renter % Zero Vehicle Houseold 31 2,498 33.3 55.4 40.8 32.4 16.8 50.8 47.5 $22,177 30.2 37.1 39.4 20.2 33 1,987 21.0 68.2 27.0 28.5 18.7 38.8 47.1 $21,250 32.7 33.3 49.5 32.1 34 2,388 1.7 95.2 1.1 34.7 16.9 56.0 43.0 $14,538 46.2 42.7 47.6 39.2 Total 6,873 18.7* 73.0* 23.0* 31.9* 17.4* 48.5* 45.9* $19,322* 36.4* 37.7* 45.5* 30.5* City of Tampa 303,447 64.2 26.1 20.6 27.1 16.0 77.0 45.6 $34,415 18.1 17.5 42.8 12.9 *Average of the three tracts (Source: 2000 U.S. Census SF 1 and SF 3)

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24 2.2.2 Significance of the Problem Neighborhoods matter. Economic and social environments of concentrated poverty neighborhood s may have an ongoing influence on the life course of those who liv e there. Jargowsky (1997) believes that poor neighborhood s have an impact on social and economic outcomes of residents even after ta king into account their family and personal traits. “Of greatest co ncern are the effects that harsh neighborhood conditions have on children, whose choices in adolescence can have lifelong conseq uences. If teenagers drop out of school or bear children out of wedloc k in part because of neighborhood influences, then the study of neighb orhood poverty is important” (4). The study of neighborhood effects in Tampa, FL is equally important because not much is known about the effects of relocation on public housing youth. This suggests that researchers have limited knowledge of the overall success of Section 8 / Housing choice vouchers on a key age group in the Housing Authority program. 2.2.3 Problem Statement A poorly addressed area of public housing resident relocation is the impact on youth in Tampa, Florida. This assessment could potentially show, through the Neighborhood Effects argument, that relocation will improve the chance at a better quality of life by

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25 measuring quantitative variables such as a more racial heterogeneous mixture, lower instances of female head of household, lower rate of renters, higher median income, lower poverty rate, lower percentage of zero vehicle households, lower in stances of crime, and better quality schools. 2.2.4 Research Purpose My contribution will be an understanding of how relocation of urban poor youth and their families from distressed public housing to areas of improved opportunity will have the potential to improve the quality of life for these individual s. This will lead to a better understanding of the success of HOPE VI, and Section 8 / Housing Choice vouchers in Tampa, Florida.

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26 CHAPTER 3 DATA AND METHODS This section describes the data sources that will be used and the methodology that will be followed in order to assess the impacts of relocation on public hous ing youth in Tampa, Florida. First and foremost, the source of the resi dent data and the process that will be used to derive the cleaned data are outlined. Then the socioeconomic variables that will be us ed in this study are defined and described, along with their data sources. Finally, the use of census tracts for reporting results will be discussed and a methodology for the case study will be outlined to give an idea of how the assessment will be conducted. A visual flowchart of this process can be seen in Figure 3. Figure 3. Visual Flow Chart for Methodology

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27 3.1 Data, Data Sources, and Collection Quantitative data features were chosen by researching comparable case studies conducted by other scholars. Many other researchers, in the attempt to asse ss quality of life or well-being of relocated residents cannot definitive ly state whether these relocated residents are living with a better qu ality of life, but most agree that they are able to take quantita tive variables and through the neighborhoods effects argument say that these residents have been given the opportunity at a better quality of life because of these quantitative changes. For example, Larry Buron and hi s cohorts (2007) operationalize quality of life by housing quality, lower poverty, perceived safety in neighborhoods, financial burden mental or physical health, improvements in children’s behavi or, and job opportunity. Jeanne Brooks-Gunn, a formative author on children and poverty, operationalizes well-being improvements as being related to quality of schools, health factors including me ntal health, the quality of the neighborhood community, and inst ances of crime; she does not discount the fact that what goes on in a family situation might affect a child’s well-being as well (1995, 1997).

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28 A study by census tract was conducted with 1990 census data for all census tracts with 40 percen t or more residents living at or below the poverty level by Paul Jargowsky (1997) in his book, Poverty and Place: Ghettos, Barrios, and the American City Jargowsky conducted his research at the nation al level and reported his chosen variables at the individual census tract level. Researchers commonly use GIS-based methodology to delineate neighborhood conditions (Jargowsky and Bane 1991, Finkel and Buron 2001, Glasmeier 2002, Smith 2002, Ward and Spalding 2008). Th is case study differs in that rather than reporting every client th at can be traced from the origin neighborhood to a relocation neighb orhood in terms of census tracts; they will be compared by the distan ce they reside from the original ‘distressed’ public housing neig hborhood. This methodology was decided upon when it was determined that census variables were very different between those neighborhood s that were located inside the City of Tampa limits and outside city limits but still within Hillsborough County. This prompted two question s: how far did a family have to relocate to have the opportunity at a better quality of life, and along those same lines, does the potential for a better quality of life increase with distance from the origin ne ighborhood? This distance-based method, which will be described in greater detail shortly, not only

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29 accounts for all relocated families, bu t also attempts to answers these questions. 3.1.1 Census In keeping with a quantitative a ssessment focus, Paul Jargowsky has sought to assess the impacts of poverty by using all census indicators. He operationalizes quality of life by measures such as race / ethnicity concentrations, poverty rate median year built of all housing units, vacant units, percent home-ownership, percent employed, occupation classification, source of income, percent living with disability, average travel time to work, percent of female head of household, and highest level of education attained (1990, 1997). Jargowsky argues that all these attrib utes recorded by the census play key roles in the negative effects of persistent and concentrated poverty. This choice of data source will never provide a rich description of the urban poor like qualitativ e research would be able to do. Nevertheless, what this quantitative method “lacks in depth it makes up in breath” (Jargowsky 1997, 91). This broad range of variables paint a generalized picture for ea ch of the concentrated poverty neighborhoods-with startlingly si milar results across the county in Jargowsky’s study.

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30 For this case study, factors such as a more heterogeneous racial mixture, lower instances of female head of household, lower renter rates, higher median household in come, lower poverty rate, and lower zero vehicle households along with other variables will be used to assess the original public housing neighborhoods and the relocation neighborhoods. This case study w ill draw these variables from the 2000 census for its assessment. This available data has both merit for the wealth of information collected by the census and certain disadvantages: one disadvantage in particular, that will be mentioned on more than one occasion in this ca se study, which is that the census is only measured every ten years and in a way is a rather static type of data, that does not take into account the dynamic nature of a moving, changing population. Census respondents have the opti on to self-identify as any one of the races listed. Hispanic data are obtained as an ethnicity and census respondents have the option to choose some other category as their race. Hispanic totals are th erefore not reflective of total population in the neighborhood stud y area as they have already been counted racially elsewhere, but have merit in consideration. Poverty data, average median household income, single female head of household, employment status, number of renters, number of

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31 High School or equivalent gradua tes, and number of vehicles per household will be obtained from Summary Tape File 3 of the 2000 census. Approximately one-in-six census respondents self-report detailed population and housing da ta which are then weighted to represent the total population (Unite d States Census Bureau 2007). The poverty field is a percentage of people living in poverty divided by the total number of people living in the census tract. Median household income is measured as an average of self-reported total incomes as a response to open ende d questions that were given to one in six census respondents. Female head of household, employment status, and number of vehicles pe r household are measured as a percentage of those respondents who chose to identify themselves as single mothers, employed, and how many vehicles a household had access to (United States Census Bureau 2007). 3.1.2 Crime Hanratty et al. (2003) conducted a case study in Los Angeles with the Moving to Opportunity ex periment and found that studying crime in origin and relocation neig hborhood shed some light on not only the perception of safety, but im proved quality of life in terms of mental well-being, and quality of living environment (e.g. quality of housing, pride in neighborhood).

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32 Crime was chosen as a variable for this case study because it was available for the origin year, 1999, and the relocation year, 2007. It should give and idea of the relative safety of the relocation neighborhoods in comparison to the safety of the origin neighborhood. Like Hanratty’s (2003) study, this could show a relationship between resident’s choice of relocation neighborhood, and a safer community choice, which could lead to some ge neralized conclusions on resident quality of life, based on standardiz ed crime rates. Unlike the Hanratty (2003) study distance from the orig in neighborhood and the presence of city limits will be taken into acco unt in this assessment to determine if lower crime rates occur either farther away from the origin neighborhood or outs ide city limits. Crime Summary Statistics for the years 1999 and 2007 were obtained from the Tampa Police Department’s website in a portable document format. The year 1999 was the last complete year that all original public housing residents re sided in the ‘distressed’ origin neighborhood. The year 2 007 is the year when all the relocations for residents were complete. Data were collected by grid, and the origin neighborhood crime grids are illustrated in Figure 4. Crime in this case study is measured by standardized per capita figures expressed as per 1000 population. For the purposes of th is study, totals per capita of

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33 crime for the origin study neighborh ood are reported here in Table 2 and include all Part 1 crime, includ ing murder, sexual battery, robbery, aggravated assault, burglary, larcen y, and auto theft. Sexual battery includes rape, sodomy, and fondling. Larceny is comprised of pick pocketing, purse snatching, shop lifting, larceny from a building, larceny from coin operated machin ery, and larceny from a vehicle. Crime grids for the origin neighbor hood are 97, 98, and 108, and are located in Tampa Police Department crime grid. Crime summary statistics were also obtained from the Hillsborough County Sheriff’s Departme nt in shapefile format. Data are collected and organized into FBI-mand ated Part 1 crime classifications like the City of Tampa’s Police De partment: murder, sexual battery, robbery, aggravated assault, burglary, larceny, and auto theft. Total number of crimes per capita was ca lculated like the City of Tampa Police Department’s crime statistics.

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34 Figure 4. Family Locations in Origin Crime Grids

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35 Table 2. Origin Neighborhood : Total Crime per capita (1999) Crime Grid # of Families Total 1999 Crime Per Capita* Range 1999 Crime Per Capita* 97 128 109 98 63 100 108 104 296 Total (Average) 295 168 100 296 *Per capita expressed as per 1000 population (Source: TPD Deparment) The totals listed in Table 2 are a measure of the per capita total crime in the origin study by crime gr id and a total of per capita crime for the origin neighborhood. The tota l (average) is an average of the total per capita crime for that orig in neighborhood area and will be used to compare the relocation ne ighborhoods total crime per capita. 3.1.3 School Jargowsky (1997) attempted to assess socio-economic differences in high poverty ne ighborhoods by looking at the percentage of adults living in th at census tract over 25 who had graduated high school. While this fa ctor might be a good indicator of quality education and will be includ ed in this stud y, school quality grades for Hillsborough County migh t provide a better indicator of the quality of schools case study youth have the opportunity to attend. School data were collected from the Hillsborough County School Board based on catchment area (often ca lled school district or attendance

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36 boundaries) for origin neighbor hoods in 1999 and the relocation neighborhoods in 2007. Again, school quality grades were chosen for the complete year the residents occupied the origin neighborhood and the relocation neighborhood: school years 1999— 2000 and 2007—2008. This aspect of the case study will be treated slightly different, however, because while it’s possible to determine what schools these youth had access to, there is neither a guarantee that the school is located close to the neighborhood of residence (and therefore not determinate of the quality of the neighborhood), nor th at the youth chose to attend said school (e.g. satellite schools, which pl ay a large role in the initial results of the origin neighborhood school assessment). Therefore, the best possible means of determining school quality will be to assess school quality grades for every schoo l these youth had the opportunity to attend on an individual level. Wh ile the list is lengthy, in conjunction with the school quality grade ma ps, the results should illustrate neighborhood relocation versus qua lity of school. Thus it should be possible to generally conclude if school quality played a part in the relocation decision by how many families moved into what school districts. School quality grades are based on FCAT testing averages, and setting and making certain learning goals among specific groups of

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37 students: all students, students who are in the 25% lowest FCAT scores, and students who are minori ties. These school quality grades will be used to assess the types of school the urban poor youth had the opportunity to attend. School attendance boundaries were not available in shapefile format for the 1999 – 2000 school year, however, school quality grades were obtained in excel file format and address location of public housing reside nts was the determining factor in selecting out the appropriate schools. Table 3 shows the school qualit y grade from each school the youth from the origin neighborhood had the opportunity to attend. Most elementary schools, with the exception of one, were A and C quality grade schools. This is beca use the Hillsborough County School Board uses satellite school district s to evenly desegregate schools while offering low quality school neighborhoods the opportunity to have a better education at a higher school quality graded school. This origin neighborhood was a site of numerous satellite locations for elementary schools, sometimes as far away as a 30 minute or 40 minute ride by bus: in other word s, very few youth from the origin neighborhood would actually have had the chance to attend a local neighborhood school. Th e one exception, the only D quality grade school, was a local elementary sch ool. The middle schools and high schools that the youth were assigned to attend in the school year

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38 1999—2000 were also satellite schools bu t had less remarkable results, and were rated “C” or “B” quality. Table 3. School Quality Grad es for Origin Neighborhood Origin Neighborhood Elementary Schools Grade 99-00 # of Families BELLAMY ELEMENTARY A 70 ESSRIG ELEMENTARY SCHOOL A 63 LOCKHART ELEMENTARY MAGNET D 51 NORTHWEST ELEMENTARY A 34 CITRUS PARK ELEM C 26 MILES ELEM SCHOOL A 25 SCHWARZKOPF ELEMENTARY SCHOOL A 24 LITHIA SPRINGS ELEMENTARY C 2 *School Locations and number of families visually interpreted from image Origin Neighborhood Middle Schools Grade 99-00 # of Families OAK GROVE MAGNET SCHOOL unavailable 130 WALKER MIDDLE SCHOOL B 86 HILL MIDDLE SCHOOL C 73 MANN MIDDLE SCHOOL C 6 *School Locations and number of families visually interpreted from image Origin Neighborhood High Schools Grade 99-00 # of Families SICKLES HIGH SCHOOL C 204 GAITHER HIGH SCHOOL C 85 RIVERVIEW HIGH SCHOOL C 6 *School Locations and number of families visually interpreted from image 3.2 Methodology Empirical researchers typically measure neighborhoods by census tracts, well-defined units of spatial analysis through which much data are reported. However, census tracts may fail to accurately represent the neighborhood boundaries that make a difference in people’s lives. --Ellen and Turner 2003, 314

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39 This section addresses the question of the use of census tracts as neighborhoods and discusses the methodology that will be used for the rest of this case study. While most social scientists (Jargowsky 1997, Ward 2007) agree that the use of census tracts is acceptable, other researchers (Jencks and Ma yer 1990, Ellen and Turner 2003) recognize the limitations in using a system of aggregation that the common man knows little about, may not accurately represent a homogeneous population and that is essentially static—being measured only every ten years. And yet, the level of detail one gets at the census tract level is invaluable in assessing an area in which a person resides. How to combat this dilemma? One method would be to create an artificial neighborhood or “buffer” (in this case a 1 mile buffer) around an individual location and then take an average of a particular variable from all the census tracts that are located inside this buffer. This method was briefly examined and the attempt determined that this is not the method to use because the results are too similar to looking straight at census tract data and that is not the purpose of this study. To answer the questions above, and to think about neighborhood attributes as more than just charac teristics of a census tract, a few data classifications can be utiliz ed. First distance bands from the centroid (center) of the origin neig hborhood can be used to determine

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40 an average of an attribute, which co uld explain if there is a potential increase at a better quality of lif e the further one moves away from the origin neighborhood, for both ce nsus attributes and standardized per capita crime rates. This method ology uses descriptive statistics to show that census and crime variable s are more than ju st an attribute of a tract or a grid respectively. The benefit of this methodology is that this case study can investigate each relocated family in relation to the original neighborhood without putting them in a static box of their census tract / crime grid and without having to talk about each family individually. Some other ways to classi fy the data that will be used in this methodology are an organizati on of those families who relocated within the City of Tampa and those families who relocated outside city limits. This result can play an impo rtant role for the future of this program, when determining the reloca tion of future clients and their families. Finally, the last classification in this dataset will be to demarcate the variation of those families who relocated into census tracts with certain poverty characteristics. The idea for this final classification was taken from the Moving to Opportunity experiment in which clients were obligated to move to census tracts with a less than ten percent poverty rate. The relocated families in this case study will be examined similarly in terms of po verty rates-all relocation census tracts with: less than ten percent poverty, less than twenty percent

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41 poverty, and greater than 20 perc ent poverty to determine if the potential quality of life can be differe nt for those families that relocated into certain poverty classes. 3.2.1 Census To begin, the 1999 origin data set from the Tampa Housing Authority (THA) was cleaned to “e nsure consistencies in spellings, remove erroneous addresses beyond the boundaries of the study area, and to convert the data to a format that could be read by the GIS software” (Ward 2007, 2). It was then geo-coded a “process of matching an address with a geographic location,” by address to street centerlines from Hillsborough County to determine the actual location of the original public housin g residents and the surrounding neighborhood (Ward 2007, 2). “For the purposes of this research, address matching was limited to the boundaries of Hillsborough County” (Ward 2007, 2). In the study of the Moving to Opportunity program in New York City, Leventhal and Brooks-Gunn (2003) stated that almost all parents interviewed reported a strong desire to move away from neighborhoods with gangs, drugs, and violence. This too was an important aspect in the design of th is study, as the focus concentrated on families with children. All THA clients who had at least one child under 18 as of 2007 were to be consid ered a part of the family dataset.

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42 The THA dataset from 1999 with the or iginal public housing residents only had head of household listed in their data. The 2007 THA dataset had all family members and all thos e members shared a client number. We took those client numbers from the 2007 relocation dataset with a known set of children and compared them to names and client numbers from the 1999 origin da taset to determine origin and relocation neighborhoods. This wa s ultimately carried out through a “join by attribute” function. This brought a low success rate and it was necessary to manually review those records for which there were missing client numbers or an un-st andardized name: names could then be standardized and client number s carried over. Sometimes client names changed but birthdates and c lient numbers remained the same, while other times client names and birthdates remained the same and client numbers changed. Correctin g the data inconsistencies was a very arduous process that took a pproximately 6 months, tracing as many clients (and their families) as possible. It could finally then be determined what percentage of familie s with children stayed in public housing and what percentage of fam ilies chose to relocate elsewhere, how far away they relocated, and into what neighborhoods (census tracts and crime grids). Once the original public housin g neighborhood and relocation neighborhoods were determined, in order to make a concise

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43 comparison from the origin ne ighborhood to the relocation neighborhoods by distance, it was de termined that distance bands and cut distances had to be configured in such a way that the distance bands had approximately the same number of relocated families. These distance bands are used instea d of reporting every census tract in which these families relocated. Qu antitative variables of the census tracts which comprise these ne ighborhoods with children were assessed and compared (descriptive statistics such as range, average, weighted average, average percen t change from the origin, and standard deviation) in terms of soci al characteristics such as race / ethnicity, key population age grou ps (population of the age group under 18 and population of the age group 60 and over), family structure such as instances of single female head of house, educational attainment such as those with a Hi gh School degree or equivalent, and economic characteristics such as pe rcentage of individuals living in poverty, employment rate, median household income, percentage of renters living in occupied housing, and average number of vehicles owned. 3.2.2 Crime Crime is treated much like the census analysis. Standardized total per capita Part 1 FBI mandated crime was evaluated for both the origin neighborhood location and the relocation neighborhoods in a

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44 series of distance bands, within an d without city limits and by poverty classifications. The standardized tota l per capita crime rates of each of the crime grids falling into a particular distance band were described in terms of their descriptive statisti cs (range, average, percent change from the origin neighborhood) to ge t and understanding of the safety of a neighborhood. The benefit to this portion of the analysis, is that while the census results talk about origin and relocation yet only use one census year, the crime is measur ed at the actual year the families lived in the origin neighborhood (1999) and the final relocation neighborhoods (2007). Crime grids for the relocation ne ighborhood comp rise of over 100 crime grids: for a detailed listing of total standardized crime per capita by relocation crime grid, see Appendix A. Unfortunately, the Tampa Police Department (TPD) and Hillsborough County Sheriff’s Office (HCSO) divided the City of Tampa and the rest of Hillsborough County respectively into arbitrary gr ids that don’t really give crime data in any meaningful way. In order to determine the necessary grids for each study area, a GIS shapefile was downloaded from the City of Tampa’s GIS website and a shapefile was requested from HCSO: both were brought into Arc Map 9.2. Cr ime grids from TPD and HCSO are arbitrary grids, yet both agencies ta ke into account census boundaries, major roadways and natural features in determining crime grid

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45 boundaries. Crime grids were determin ed by selecting those grids that completely contained the relocated families in their census tract neighborhoods. Crime data from 1999 and 2007 was joined separately with the crime grid data and ex ported as shapefiles with grid information and crime detail for each year. 3.2.3 School Quality Finally, quality of schools meas ured by a school quality grade was examined on a county-wide scale visually to determine the quality of the schools that these youth had the opportunity to attend. This portion of the analysis cannot be conducted like the census or crime methodology for a few reasons: on e being that no shapefiles were available for the origin neighborh ood and another reason being that the Hillsborough County School Boar d approves the use of satellite school districts. While I can say with precision that a family in 2007 was assigned to a certain school district and that district has a certain school quality grade, this does not take into account whether the school is a local school or not, th erefore distance from the origin neighborhood or being wi thin or without city limits cannot ever be a determining factor in school quality grades.

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46 CHAPTER 4 RESULTS / DISCUSSION The purpose of this exercise was to provide an overall perspective of where original Ponce de Leon and College Hill residents relocated to within the bounds of Hillsborough County. The scope of this study was intended as a descr iptive assessment. Some scholars (Wilson 1987, Jargowsky and Bane 1990, Goering et al 2003, and Buron et al. 2007) believe that we ar e influenced by those people and experiences around us. They describe this idea as being the theory of ‘neighborhood effects’ in which a pe rson has the potential to adopt the dominant traits of the surrounding community. Likewise, the opposite can also be true, where deleterious attributes can have a pernicious effect on a person as well—especially in areas of concentrated poverty. The ‘distressed’ public housing projects of this case study, by definition, represent such harmful communities Most important is the effect these areas can have on a child’s development and the behaviors and attitudes that a child will come to fi nd acceptable will be influenced by whatever environment in which they are raised. The federal HOPE VI program wa s motivated by such concerns. HOPE VI emphasizes the benefits of poverty deconcentration for its

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47 participants. It is therefore wort hwhile to assess the differences between where these families come from and the new neighborhoods to which they relocate; whether they end up in neighborhoods in which the potential for a better quality of life (measured in this case by census variables, standardized cr ime statistics, and school quality) have improved. The extent to which origin and relocation neighborhoods differ for families warrants serious study for several reasons. First, there was little to no counseling for these fam ilies as they chose their relocation neighborhoods. Consequently, it is useful to ask whether the final location in 2007 was based on availab ility of housing (did the residents ‘hear it from the grapevine’ that available housing was located in certain neighborhoods?), or whether th ey actually sought to give youth a better opportunity at a better qualit y of life (i.e. safer neighborhoods, better schools, lower poverty)? These guidelines inform the design of the research as we query whether di stance from the original blighted neighborhood, being inside city limits versus outside, or relocating to certain areas with distinctly lower poverty rates might play a role in the opportunities that new neighbor hoods offer relocated families. Will certain characteristics of the relo cation neighborhoods enhance or impede opportunity for youth? My re search tracks these ‘distressed’

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48 public housing families, describ es conditions in their new neighborhoods to find any potent ial improvements and finds mixed results. Because the HOPE VI voucher pr ogram depends upon existing housing, rather than building new de velopments, it is the least costly approach for making housing affordab le to low-income families, and it has the added benefit of giving participants an extensive range of housing alternatives, and what location is most suitable for them. Unlike federal housing construction pr ograms such as public housing, which often have the effect of clus tering low-income families in a few distressed neighborhoods, vouchers generally allow participants to disperse more widely, and to live in potentially healthier neighborhoods (Popkin et al. 2004). However, due to the subjective nature of site-by-site assessments most researchers cannot come to a definitive conclusion about the bene fits of relocation. This further complicates the neighborhood e ffects argument because not all benefits of relocation are perceive d in every study area. Most studies on relocation assessment have also had mixed results (Katz et al. 2000, Smith 2002, Leventhal and Br ooks-Gunn 2003, Ludwig et al. 2003, Kling et al. 2004) while other researchers find clear positive results (Duncan and Zuberi 2006, Turner and Briggs 2008), and others

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49 find outcomes that seem dishearten ing to HOPE VI advocates (Goetz 2003, Greenbaum et al. 2008). This section describes the results of the Ponce de Leon and College Hills resident relocations as of 2007 and discusses them in order to compare the original ‘distr essed’ public housing neighborhood to relocation neighborhoods based on the distance from the origin neighborhood. First, for all variable s (census, crime and school quality) a map of the relocation census tracts detailing a particular variable is presented to give a visual repres entation of the diversity of the relocation neighborhoods. Then, for the census variables, a scatter plot of a variable versus the distance from the origin neighborhood is presented and discussed to attempt to detect general trends between distance from the origin neighborh ood and key census characteristics. Finally, for census variables and cr ime variables a table with detailed descriptive statistics is presented, which includes the key distance bands, the variable measurements in side and outside the city limits, and the variable differences within the key poverty classification groups. The descriptive statistics in clude: range, average, weighted average, average percent change fr om origin average, and standard deviation. Weighted average takes an average of a variable within a certain classification and weighs th e variable input from each census

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50 tract by how many families resided in said tract. So in a weighted average, in any given variable input, a census tract will naturally have more weight in the average by ho w many families reside in that census tract. This weighted averag e will only be calculated for the census variables because the point to this average is that by having more families move into a particular census tract it should give a variable that much more influence over a youth and their family. This cannot be said for standardized cr ime statistics, and school quality grades cannot be averaged at all. Results will be discussed in terms of these classifications, with the underlying assumption that the farther a family moves away from the blighted origin neighborhood, living outside the city limits, and living in a census tract with a low po verty rate will increase the chance at a better quality of life. 4.1 Relocation Statistics Christopher Jencks and Susa n E. Mayer (1990) have found through their evaluations that “children who live in affluent neighborhoods . get into less tr ouble with the law and have fewer illegitimate children than children who live in poor neighborhoods” (111). This seems like a very prom ising result. On the other hand, Popkin et al. (2008) have discover ed in their asse ssment of Boston,

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51 Baltimore, and New York City that the benefits of moving to lowpoverty neighborhoods could not be determined five years after relocation, and while they do argue that the feelings of safety and mental well-being have increased (f or women and girls only), they state that relocation may yet have some long term benefits that cannot be assessed at this time. The results of the relocation assessment reveal that 295 families were able to be traced to a final location in 2007 from the original public housing neighborhoods of Po nce de Leon and College Hill as seen in Figure 5. The 295 families all had at least one child under 18 as of 2007. These 295 families relocate d into 64 different census tracts, 101 different crime grids, and 83 diffe rent school districts. Of all the families I was able to trace, 23 fa milies or seven percent moved back to Belmont Heights, the HOPE VI ho using community that replaced the distressed public housing, and 12 families moved back to the surrounding area crime grids and ce nsus tracts but not into the Belmont Heights neighborhood. When the 295 families were summarized by census tract, the majo rity of families, or 229 families were still located inside city limits. Table 4 shows these neighborhood relocation results, based on the di stance moved in miles away from the origin neighborhood, classificati on by living inside city limits or

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52 outside city limits, and classificati on by key poverty groups (poverty less than ten percent, poverty le ss than 20 percent, and poverty greater than 20 percent). It was dete rmined that these classifications represented all 295 families or 100 percent of all relocated families. The number of dependents that a he ad of household was responsible for ranged between one and eigh t, with an average of three dependants. Table 4. Descriptive Statistics for Relocation Classifications Relocation by Distance from Origin Neighborhood Dependants Relocated # of Census Tracts Families Range Average Public Housing Section 8 / Housing Choice within 1 mile 9 56 (19%) 1 to 8 3 19 (6%) 37 (13%) 1 to 2 miles 15 69 (23%) 1 to 8 4 30 (10%) 39 (13%) 2 to 3 miles 10 52 (18%) 1 to 8 3 19 (6%) 33 (11%) 3 to 4 miles 14 50 (17%) 1 to 7 3 5 (2%) 45 (15%) 4 to 6 miles 11 35 (12%) 1 to 6 3 1 (<1%) 34 (12%) 6 or more miles 17 33 (11%) 1 to 5 3 0 (0%) 33 (11%) 295 (100%) 1 to 8 3 74 (25%) 221 (75%) within City of Tampa 229 (78%) 1 to 8 3 73 (32%) 156 (68%) Outside City Limits 66 (22%) 1 to 5 3 2 (3%) 64 (97%) Census Tracts < 10% Poverty 12 (4%) 1 to 5 2 0 12 (100%) Census Tracts < 20% Poverty 48 (16%) 1 to 6 3 3 (6%) 45 (94%) Census Tracts > 20% Poverty 247 (84%) 1 to 8 3 72 (29%) 175 (71%) (Source: Tampa Housing Authority)

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53 Figure 5. Relocation Neig hborhoods by Census Tract

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54 The most important aspect of thes e results to keep in mind in reviewing the following assessment is that, regardless of location, the census attributes were determined from the 2000 decennial census. At this time, there is no way to de termine how these census tracts changed from 1999 to 2007 with regards to the re-introduction of this public housing population, the addition of a mixed income community, or any other changes that may ha ve occurred during the 8 year interval of time. Sections 4.2.1 through 4.2.9 illustrate some key census variables and their differenc es among the origin neighborhood, and the relocation neighborhoods. 4.2 Census Many scholars place importance on different census variables. Brooks-Gunn et al. (1993) determined statistically that the most important variables in the neighb orhood effects argument were median household income, employment, and two parent households: those variables and more will be ex amined shortly. Some interesting variables for this case study have a wide range difference from origin neighborhood to relocation neighbor hoods, within and without the city, and within certain poverty classifications.

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55 4.2.1 Race The first census variable results to be extrapolated on will those dealing with race and ethnicity: whit e population (%), black population (%), and Hispanic ethnicity (%). In an ideal society, the optimal neighborhood to raise a family would be a neighborhood that isn’t ra cially segregated. Keeping this in mind, an ideal percentage of whit e population might be around 50 percent. Figure 6 shows that most of the relocation census tracts within the City of Tampa limits rema in low in the percentage of white population and it appears that a more optimal percentage is not reached until about 3 miles away from the origin neighborhood. This observation is backed by both th e scatter plot in Figure 7 and the Table 5. The scatter plot shows a general trend towards an increase in white population the further one ge ts from the origin neighborhood. The numbers look a bit confusing but think the bigger the increase in white population the more negative the number in this case and imagine the slightly parabolic lin e super-imposed on the plot.

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56 Figure 6. Choropleth of White Population (%)

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57 Figure 7. Scatter plot of Dist ance from Origin Neighborhood and Change in White Population Table 5. White Popula tion Comparison White Population Comparison # of Families Average (%) Weighted Average (%) Average Change Range (%) Std. Deviation Origin Neighborhood 295 18.7 25.6 1.7 33.3 13 Relocation by Distance from Origin Neighborhood within 1 mile 56 (19%) 21.0 20.3 +2.3 1.7 47.2 16.1 1 to 2 miles 69 (23%) 20.4 22.2 +1.7 3.1 74.6 16.2 2 to 3 miles 52 (18%) 23.3 30.5 +4.6 8.4 78.6 17.1 3 to 4 miles 50 (17%) 43.0 38.4 +24.2 27.7 81.8 18.3 4 to 6 miles 35 (12%) 54.4 46.0 +35.7 21.7 87.1 13.0 6 or more miles 33 (11%) 61.3 58.4 +42.6 29.6 92.8 20.5 Other Measures within City of Tampa 229 (78%) 44.4 +25.7 1.7 87.1 27.1 Outside City Limits 66 (22%) 58.1 +39.4 10.9 92.8 24.1 Census Tracts < 10% Poverty 12 (4%) 82.8 +64.1 72.3 92.8 7.7 Census Tracts < 20% Poverty 48 (16%) 70.4 +51.7 21.7 92.8 17.8 Census Tracts > 20% Poverty 247 (84%) 34 +15.3 1.7 76.8 20.4 (Source: United Stat es Census Bureau)

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58 In Table 5, both the average an d weighted average confirm that the further away from the origin neighborhood the higher the white population increases. Furthermore, wh ile living within the city limits or outside the city limits brought sim ilar results this time for white population, living in a low poverty census tract greatly increases the percent of white population in a ce nsus tract. However, more than 50 percent of the population relocated within three miles of the origin neighborhood, or in a census tract wi th a poverty rate of greater than 20 percent and are not living with a much larger white population percent than they started with. Again, in an ideal society, the optimal neighborhood to raise a family would be a neighborhood that isn’t racially segregated. So likewise, the ideal percentage of a black population should be around 50 percent. Figure 8 shows that most of the relocation census tracts within the City of Tampa limits have higher numbers of black population and it appears that a more optimal percentage is not reached until about 3 miles away from the origin neighborhood. This observation is backed by both th e scatter plot in Figure 9 and the Table 6.

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59 Figure 8. Choropleth of Black Population (%)

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60 Figure 9. Scatter plot of Dist ance from Origin Neighborhood and Change in Black Population Table 6. Black Popula tion Comparison Black Population Comparison # of Families Average (%) Weighted Average (%) Average Change Range (%) Std. Deviation Origin Neighborhood 295 73.0 77.4 55.4 95.2 16.6 Relocation by Distance from Origin Neighborhood within 1 mile 56 (19%) 70.9 72.4 -2.1 41.0 95.2 20.3 1 to 2 miles 69 (23%) 72.7 70.8 -0.3 6.0 94.3 18.8 2 to 3 miles 52 (18%) 68.9 61.4 -4.1 13.0 80.9 17.0 3 to 4 miles 50 (17%) 47.7 52.9 -25.3 6.1 66.6 19.5 4 to 6 miles 35 (12%) 33.8 43.9 -39.2 5.6 69.7 11.9 6 or more miles 33 (11%) 27.9 30.8 -45.1 2.6 67.3 19.3 Other Measures within City of Tampa 229 (78%) 45.6 -27.4 5.6 95.2 29.9 Outside City Limits 66 (22%) 32.9 -40.1 2.6 86.1 24.4 Census Tracts < 10% Poverty 12 (4%) 10.3 -62.7 2.6 19.1 6.3 Census Tracts < 20% Poverty 48 (16%) 20.6 -52.4 2.6 69.7 17.7 Census Tracts > 20% Poverty 247 (84%) 56.1 -16.9 6.0 95.2 24.4 (Source: United Stat es Census Bureau)

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61 The scatter plot in Figure 9 sh ows a general trend towards a decrease in black population the further one gets from the origin neighborhood. The numbers look a bit confusing but think the bigger the decrease in black population th e more positive the number in this case and imagine the slightly parabo lic line super-imposed on the plot. Much like the results from ex amining the table of white population, Table 6 for the black popu lation (%) shows similar results, leading to the conclusion that th ese two variables are probably in some way correlated. Statistical anal ysis shows a Pearson’s correlation coefficient of 0.59: a moderately strong correlation. As one moves away from the origin neighborhood the lower the percentage black population becomes. Also, being th at more than 50 percent of the families relocated within three mile s of the origin neighborhood (or similarly to a census tract with more than 20 percent poverty), they still moved to neighborhoods which had high percentages of black population. If neighborhood racial heterogeneity were an equal mix of white and black populations, over 50 percent of the families failed to move into an optimal living enviro nment with racial desegregation. These local results further confo und the possibility of a positive outcome because the majority of the families in this program were of black racial background.

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62 Figure 10. Choropleth of Hispanic Population (%)

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63 Figure 11. Scatter plot of Dist ance from Origin Neighborhood and Change in Hispanic Population Table 7. Hispanic Popu lation Comparison Hispanic Population Comparison # of Families Average (%) Weighted Average (%) Average Change Range (%) Std. Deviation Origin Neighborhood 295 23.0 31.8 1.1 40.8 16.5 Relocation by Distance from Origin Neighborhood within 1 mile 56 (19%) 21.1 17.2 -1.9 1.1 43.4 17.1 1 to 2 miles 69 (23%) 13.1 15.2 -9.9 3.4 61.8 9.8 2 to 3 miles 52 (18%) 13.0 14.3 -10.0 7.8 31.0 4.7 3 to 4 miles 50 (17%) 16.2 15.4 -6.8 7.8 64.9 10.0 4 to 6 miles 35 (12%) 19.4 16.6 -3.6 11.9 32.1 4.7 6 or more miles 33 (11%) 17.2 17.0 -5.8 4.1 30.1 7.7 Other Measures within City of Tampa 229 (78%) 22.8 -0.2 1.1 64.9 15.2 Outside City Limits 66 (22%) 15 -8.0 4.1 32.1 6.6 Census Tracts < 10% Poverty 12 (4%) 14.1 -8.9 8.8 31.0 7.1 Census Tracts < 20% Poverty 48 (16%) 18.3 -4.7 4.1 64.9 12 Census Tracts > 20% Poverty 247 (84%) 19.7 -3.3 1.1 61.8 12.9 (Source: United Stat es Census Bureau)

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64 The Hispanic ethnicity populati on from the choropleth map shows most of Hillsborough Count y to have a very low percent ethnicity in most census tracts and the rest of the population seems to be highly concentrated in certain tr acts. Visually, they appear to be located in the 3 to 4 mile distance ba nd, but this can be verified in the Hispanic comparison table shortly. The scatter plot in Figure 11 shows that there appears to be no trend in the change of Hispanic population the further one gets from the origin neighborhood. This ethnic variable in Table 7 has what some may construe as negative results were a good propor tion of Hispanic population be necessary for and optimal living envi ronment. Some may argue that a neighborhood that is not only raci ally diverse, but ethnically diverse should play a role in an optimal living environment to give youth the best possible chance at a better quality of life. All relocation classifications above experienced a decrease in the percentage of Hispanic ethnicity population. 4.2.2 Age Results The next set of census variables to examine will be the key age groups of population under 18(%) and population 60 years old and over (%). Figure 12 shows the variation in population of people under 18. There appears to be no visual patte rn to the concentration or absence

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65 of this key age group. In an optimal living environment, the percentage of individuals under 18, or youth, should never exceed one-third percent of the population This would give every one youth two adults ideally. Visually, there are very few relocation census tracts that exceed this percentage. The results in the scatter plot from Figure 13 show a wide variation of change in population values (%) within 20,000 feet, or 3 miles from the origin neighborhood This variation in the under 18 population change decreases the fu rther one gets from the origin neighborhood.

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66 Figure 12. Choropleth of Population under 18 (%)

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67 Figure 13. Scatter plot of Dist ance from Origin Neighborhood and Change in Population under 18 Table 8. Population under 18 Comparison Under 18 Population Comparison # of Families Average (%) Weighted Average (%) Average Change Range (%) Std. Deviation Origin Neighborhood 295 31.9 35.6 28.5 34.7 2.6 Relocation by Distance from Origin Neighborhood within 1 mile 56 (19%) 31.7 33.3 -0.2 27.1 34.7 2.3 1 to 2 miles 69 (23%) 35.3 33.4 +3.4 24.6 44.6 7.2 2 to 3 miles 52 (18%) 35.9 33.9 +4.0 15.9 45.9 7.9 3 to 4 miles 50 (17%) 33.4 32.3 +1.5 16.5 40.9 7.8 4 to 6 miles 35 (12%) 28.5 31.4 -3.4 20.7 34.4 3.9 6 or more miles 33 (11%) 27.5 27.5 -4.4 16.0 38.1 3.8 Other Measures within City of Tampa 229 (78%) 29 -2.9 16.5 45.9 7.2 Outside City Limits 66 (22%) 27.4 -4.5 15.9 38.1 4.8 Census Tracts < 10% Poverty 12 (4%) 23.6 -8.3 16.0 28.7 3.9 Census Tracts < 20% Poverty 48 (16%) 24.9 -7.0 15.9 38.1 5.2 Census Tracts > 20% Poverty 247 (84%) 31.1 -0.8 21.2 45.9 5.5 (Source: United Stat es Census Bureau)

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68 It appears from Table 8 that most relocation neighborhoods contained about the same percentage of under 18 population as the origin neighborhood. In fact, within all classifications, ther e is just a -8.3 to 4.0 percent range differenc e. Statistical analysis shows a Pearson’s correlation coefficient of 0.784 between percent population under 18 and percent poverty: a strong correlation. Figure 14 shows that most of the relocation census tracts had a very low population of people ag ed 60 and over, or the elderly. Visually, there appears to be no pa ttern to the percentage of elderly across the relocation census tracts. As for the scatter plot in Figure 15, there also appears to be not set pattern to the change in elderly population across the relocation census tracts. This is verified by Table 9 which shows that the average ch ange ranged from -6.2 to 0.3 percent. There appears to be very little difference in the average population of elderly from inside th e city limits to outside city limits. There also appears to be no pattern between the poverty classifications and the average percentage of elderly population. Statistical analysis shows a Pearson’ s correlation coefficient of 0.174 between percent population 60 and over and percent poverty: a weak negative correlation.

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69 Figure 14. Choropleth of Population 60 and over (%)

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70 Figure 15. Scatter plot of Dist ance from Origin Neighborhood and Change in Population 60 and over Table 9. Population 60 and over Comparison Over 60 Population Comparison # of Families Average (%) Weighted Average (%) Average Change Range (%) Std. Deviation Origin Neighborhood 295 17.4 20.0 16.8 18.7 0.9 Relocation by Distance from Origin Neighborhood within 1 mile 56 (19%) 17.7 16.3 +0.3 14.1 20.9 1.7 1 to 2 miles 69 (23%) 15.0 15.9 -2.4 7.8 31.5 6.2 2 to 3 miles 52 (18%) 12.3 13.0 -5.1 7.7 23.2 4.1 3 to 4 miles 50 (17%) 11.3 12.4 -6.1 7.4 33.1 5.0 4 to 6 miles 35 (12%) 12.3 11.3 -5.1 8.5 26.1 4.0 6 or more miles 33 (11%) 11.2 11.7 -6.2 4.2 19.9 4.7 Other Measures within City of Tampa 229 (78%) 17 -0.4 7.8 33.1 6.2 Outside City Limits 66 (22%) 12.4 -5.0 4.2 21.9 4.9 Census Tracts < 10% Poverty 12 (4%) 13.8 -3.6 5.2 23.2 6.4 Census Tracts < 20% Poverty 48 (16%) 14.9 -2.5 5.2 33.1 6.4 Census Tracts > 20% Poverty 247 (84%) 14.6 -2.8 4.2 31.5 5.8 (Source: United Stat es Census Bureau)

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71 4.2.3 High School Graduate or Equivalent Most scholars would agree that the presence of high school graduates (or equivalent degree) in a neighborhood is very important. This variable is a measure of the pe rcentage of individuals aged 25 or over who reported having at least obtained a High School diploma or equivalent degree (Dalaker 2001). In an ideal neighborhood, the optimal number of High School grad uates would be 100 percent. Sadly, this figure never seems to be reac hed within Hillsborough County, let alone the relocation census tracts. The origin neighborhood had a little over half of its residents who did not graduate high school. Figure 16 reveals a disproportionate number of relocation centrally located census tracts that also have that problem. Visually, there appears to be no pattern to the location of cens us tracts with a low percentage of High School graduates, but this can be verified shortly from Table 10.

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72 Figure 16. Choropleth of High School Degree (%)

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73 Figure 17. Scatter plot of Dist ance from Origin Neighborhood and Change in High School Degree The scatter plot in Figure 17 show s a positive result of moving away from the origin neighborhood location. There is a general trend in the increase in percentage of Hi gh School graduates (or equivalent) the further one moves out from the origin neighborhood. This shows that the further one moves the more likely it will be to give youth a positive role model of a high school graduate and this has been known to keep youth from dropping out of school (Jencks and Mayer 1990). These results are corroborated in Table 10.

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74 Table 10. High School Graduate or Equivalent Comparison High School Graduate Comparison # of Families Average (%) Weighted Average (%) Average Change Range (%) Std. Deviation Origin Neighborhood 295 48.5 32.7 38.8 56.0 4 Relocation by Distance from Origin Neighborhood within 1 mile 56 (19%) 54.3 56.5 +5.8 38.8 68.3 8.6 1 to 2 miles 69 (23%) 57.5 58.3 +9.0 40.6 79.4 7.3 2 to 3 miles 52 (18%) 57.5 62.1 +9.0 39.1 79.4 14.4 3 to 4 miles 50 (17%) 68.1 66.6 +19.6 60.5 87.5 8.2 4 to 6 miles 35 (12%) 74.3 70.8 +25.8 62.2 90.0 7.5 6 or more miles 33 (11%) 76.2 77.0 +27.7 59.7 94.0 8.0 Other Measures within City of Tampa 229 (78%) 62.8 +14.3 38.8 87.3 12.6 Outside City Limits 66 (22%) 75.1 +26.6 59.7 94.0 10.5 Census Tracts < 10% Poverty 12 (4%) 86.5 +38 79.4 94.0 5.2 Census Tracts < 20% Poverty 48 (16%) 77 +28.5 59.7 94.0 10.1 Census Tracts > 20% Poverty 247 (84%) 61.5 +13 38.8 79.1 11.1 (Source: United Stat es Census Bureau) Supposing that the ideal percentage of High School Graduates in a census tract would be 100 percent, Table 10 shows that none of the relocation census tracts reach that value. There does appear to be a positive relationship between the distance away from the origin neighborhood and increase in High School graduates (or equivalent). There also appears to be a positive relationship between living outside the city limits and having a higher average of High School graduates. Lastly, within the poverty classifications there too appears to be a positive relationship. Since this rela tionship can statistically be tested, a statistical analysis for multicolinearity between High School graduates (or equivalent) and povert y reveals a Pearson’s correlation coefficient of 0.075: a very weak corr elation. So statistically there is

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75 very little correlation between those High School graduates in a relocation census tract and those in dividuals who reported living at a certain poverty level (less than 10 percent, less than 20 percent, or over 20 percent). 4.2.4 Employment The next variable to be assessed is percent of employed individuals. This percentage is a measure of those aged 16 and over who reported being employed as of 1999 (Dalaker 2001). One surprising discovery was that the st ereo-typical idea that those who live in public housing communities ar e jobless is not as widespread as one might think. And yet, in an ideal neighborhood the number of employed individuals should be pre tty high. What would be an optimal percentage? That can probably not be quantified but as a generalize guess and to account for those that are unable to work, the stay at home parents, and those youth who choose not to work: an optimal number would maybe be around 70 percent. The origin neighborhood area, had on average a 45.9 percent employment rate, and while that means that a little over a half of th e residents in those origin census tracts reported being jobless in 2000, the results from the figures and tables will show that employment rates increased no matter what relocation classification one belonged to, another positive result in this census assessment.

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76 Figure 18 shows an alarming numbe r of centrally located census tracts with high percentages of unemployed individuals. Visually, it appears that the percentage of employ ed does not really increase past 50 percent until the 3 to 4 mile distance band. This can be verified in Table 11 below. The scatter plot in Figure 19 reve als a general trend towards an increase in employment rate in all relocation census tracts the further one gets from the origin neighborhood.

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77 Figure 18. Choropleth of Employment (%)

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78 Figure 19. Scatter plot of Dist ance from Origin Neighborhood and Change in Employment Table 11. Employment Comparison Employed Comparison # of Families Average (%) Weighted Average (%) Average Change Range (%) Std. Deviation Origin Neighborhood 295 45.9 53.0 43.0 47.5 2 Relocation by Distance from Origin Neighborhood within 1 mile 56 (19%) 48.4 50.4 +2.5 43.0 63.5 5.4 1 to 2 miles 69 (23%) 51.3 50.9 +5.4 40.5 67.5 6.5 2 to 3 miles 52 (18%) 51.8 57.1 +5.9 25.8 67.5 11.5 3 to 4 miles 50 (17%) 64.0 61.8 +18.1 52.0 75.3 4.5 4 to 6 miles 35 (12%) 66.5 64.4 +20.6 54.2 76.4 5.9 6 or more miles 33 (11%) 64.7 65.7 +18.8 57.5 85.5 6.4 Other Measures within City of Tampa 229 (78%) 56 +10.1 37.9 73.8 9.6 Outside City Limits 66 (22%) 64.7 +18.8 25.8 85.5 10.3 Census Tracts < 10% Poverty 12 (4%) 69.4 +23.5 59.2 85.5 8.5 Census Tracts < 20% Poverty 48 (16%) 64.8 +18.9 25.8 85.5 10.6 Census Tracts > 20% Poverty 247 (84%) 56.2 +10.3 37.9 71.4 9.4 (Source: United Stat es Census Bureau)

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79 A little more than half the origin census tracts were unemployed on average. From the results in Table 11 a move to any location outside the origin census tract wo uld have brought youth and their families into contact with a larger percentage in the workforce. As mentioned earlier, William Julius Wils on (1984) believes that these role models of the working class will ha ve a positive influence on these families. Within the breakdown of the distance bands the percentage of employed individuals increased as little as 2.5 percent and as great as 20.6 percent. Within the census tracts with less than 10 percent poverty, the percent of individuals who were employed increased on average 23.5 percent. Even a move to a census tract with less than 20 percent poverty would have increase d the percentage of the workforce by an average of 18.9 percent. Stat istical analysis for multi-colinearity between poverty and employment re turned a Pearson’s correlation coefficient of -0.703: a strong correlation. 4.2.5 Median Household Income The next variable to be discussed is Median Household income. This variable is measured in Unit ed States dollars. This is another variable that is obviously important for a better quality of life but that cannot be precisely quantified (though there may be a way around trying to guess at an optimal numbe r for and ideal living environment). This will be addressed when we di scuss the tabular results for this

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80 variable. Visually, the spatial variat ion in Figure 20 of the relocation census tracts show very low medi an households incomes centrally located to the origin neighborhood ag ain. This spatial concern persists until about the 2 to 3 mile distance band. The scatter plot in Figure 21 depicts a -$10,000 to $20,000 range of change in median household income, all within 20,000 feet or a pproximately 3 miles of the origin neighborhood. The positive result is that this range of change concentrates and increases the fu rther one moves away from the origin neighborhood.

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81 Figure 20. Choropleth of Median Household Income ($)

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82 Figure 21. Scatter plot of Dist ance from Origin Neighborhood and Change in Median Household Income Table 12. Median Househol d Income Comparison Median Household Income Comparison # of Families Average Weighted Average Average Change Range Std. Deviation Origin Neighborhood 295 $19,322 $23,210 $14,538 $22,177 $3,404 Relocation by Distance from Origin Neighborhood within 1 mile 56 (19%) $20,885 $20,667 $1,563 $14,538 $26,250 $3,969 1 to 2 miles 69 (23%) $19,477 $20,727 $155 $10,026 $35,625 $6,349 2 to 3 miles 52 (18%) $20,425 $23,411 $1,103 $9,461 $35,525 $8,221 3 to 4 miles 50 (17%) $26,786 $25,908 $7,464 $21,700 $38,164 $4,988 4 to 6 miles 35 (12%) $30,865 $28,488 $11,543 $20,789 $49,851 $8,238 6 or more miles 33 (11%) $33,725 $33,918 $14,403 $20,789 $56,699 $12,875 Other Measures within City of Tampa 229 (78%) $24,809 $5,487 $9,461 $35,625 $7,706 Outside City Limits 66 (22%) $35,305 $15,983 $19,708 $56,699 $11,724 Census Tracts < 10% Poverty 12 (4%) $48,658 $29,336 $35,525 $56,699 $8,051 Census Tracts < 20% Poverty 48 (16%) $37,984 $18,662 $19,708 $56,699 $9,671 Census Tracts > 20% Poverty 247 (84%) $22,753 $3,431 $9,461 $39,726 $6,455 (Source: United Stat es Census Bureau)

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83 The results of those census tracts within a certain poverty range are highly and obviously collinear, and therefore do not have applicable results to discuss. When a Pearson’s correlation coefficient test was conducted, the coefficien t returned for these two variables was -0.855 (at the o.o1 level of significance): a strong negative correlation, which is to be expected It would be lax not to mention this relationship and discuss the result s of the poverty classification in this case study. However, this corr elation can give us an idea of an ideal Median household income by lo oking at the poverty classifications and determining that an ideal povert y rate (be it less than 20 percent of individuals living in poverty, or even less than 10 percent of individuals living in poverty) would lead to an ideal median household income. 4.2.6 Poverty The next census variable to exam ine is poverty. Poverty is quite possibly the most important census va riable this case study assesses because so many scholars agree that the key to a chance at a better quality of life lies in deconcentrat ing poverty (Wilson 1987, Jargowsky and Bane 1990, Goering et al. 2003) ‘Distressed’ neighborhoods, concentrated poverty are mentioned in the literature and this variable is where that attribute comes from Most agree that approximately 40 percent poverty in a census tract determines concentrated poverty.

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84 Figure 22. Choropleth of Poverty (%)

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85 Figure 23. Scatter plot of Dist ance from Origin Neighborhood and Change in Poverty Table13 Poverty Comparison Poverty Comparison # of Families Average (%) Weighted Average (%) Average Change Range (%) Std. Deviation Origin Neighborhood 295 36.4 38.9 30.2 46.2 7 Relocation by Distance from Origin Neighborhood within 1 mile 56 (19%) 34.6 36.9 -1.8 22.2 46.2 7.5 1 to 2 miles 69 (23%) 42.4 37.1 6.0 14.8 71.9 17.7 2 to 3 miles 52 (18%) 38.4 33.3 +2.0 9.4 68 21.0 3 to 4 miles 50 (17%) 29.3 27.2 -7.1 10.5 43.1 12.5 4 to 6 miles 35 (12%) 20.9 25.5 -15.5 9.1 31.2 6.1 6 or more miles 33 (11%) 19.5 19.6 -16.9 1.8 31.8 11.3 Other Measures within City of Tampa 229 (78%) 29.8 -6.6 9.4 71.9 15.5 Outside City Limits 66 (22%) 16.7 -19.7 1.8 33.3 9.2 Census Tracts < 10% Poverty 12 (4%) Census Tracts < 20% Poverty 48 (16%) Census Tracts > 20% Poverty 247 (84%) (Source: United Stat es Census Bureau)

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86 Figure 22 shows the spatial locati on of all the relocation census tracts with different percentage poverty classifications. The census tracts with 30 percent poverty or greater are all centrally located within the relocation area. Most low poverty census tracts (at least less than 20 percent poverty) occur at least approximately two miles away from the origin neighborh ood. Most extremely low poverty census tracts (less than ten percent poverty) occur at least four miles away from the origin neighborhood. Th ere is a distinct difference in the percentage of people living in po verty who live in the city limits compared to those who live outside city limits. This can be verified by Table 13. Figure 23 is the scatter plot of the variation in change of poverty from the origin neighb orhood percentage. Within the first 20,000 feet or approximately three miles, the ch ange in poverty varies from the origin neighborhood value by -40 to +50 percent. This wide range of variation condenses the further one ge ts from the origin neighborhood and around 50,000 feet or approxim ately nine miles the range of change is between +20 to +30 percent. A shocking result located in Table 13, which confirms the visual estimation of location of poverty ce nsus tracts inside City of Tampa limits is the average percent of poverty. Some scholars (Bane and Elwood 1989, Jargowsky 1997) would most likely consider the City of

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87 Tampa to be living in a stressed co ndition, close to the concentrated poverty level with an average of 29.8 percent. 4.2.7 Female Head of Household Female head of household is another key variable that has significance in this research. Concen trated poverty areas tend to have higher percentages of female head of households. Visually, when comparing the spatial location of female head of household (%) in Figure 24, one can see the relationsh ip to the location of the higher poverty census tracts located in Figure 22. Beyond that relationship, there appears to be no pattern to wh ere the spatial variation of female head of household exists. The scatter plot of the change in female head of household (%) by distance from origin neighborh ood in Figure 25 appears to follow the same shape and curve as the pove rty scatter plot in Figure 23. Results from average change in Ta ble 14 confirm that there is no distance strong distance related patte rn to the location of percentage female head of household, though it still appears to be tied in some way to the poverty classifications (less than ten percent poverty, less than 20 percent poverty, and grea ter than 20 percent poverty). Statistical analysis reveals mult i-colinearity between poverty and female head of household. The Pe arson’s correlation coefficient was 0.850: a strong correlation.

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88 Figure 24. Choropleth of Fe male Head of Household (%)

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89 Figure 25. Scatter plot of Dist ance from Origin Neighborhood and Change in Female Head of Household Table 14. Female Head of Household Comparison Female Head of Household Comparison # of Families Average (%) Weighted Average (%) Average Change Range (%) Std. Deviation Origin Neighborhood 295 37.7 41.8 33.3 42.7 3.9 Relocation by Distance from Origin Neighborhood within 1 mile 56 (19%) 37.8 39.7 +0.1 30.2 44.9 4.6 1 to 2 miles 69 (23%) 42.7 40.2 +5.0 22.5 58.4 9.8 2 to 3 miles 52 (18%) 42.0 38.6 +4.3 20.4 53.6 9.1 3 to 4 miles 50 (17%) 35.3 35.5 -2.4 14.2 44.6 9.7 4 to 6 miles 35 (12%) 28.4 32.8 -9.3 17.9 34.6 4.6 6 or more miles 33 (11%) 25.2 26.0 -12.5 13.2 31.1 5.3 Other Measures within City of Tampa 229 (78%) 31.5 -6.2 14.2 58.4 10.6 Outside City Limits 66 (22%) 26.2 -11.5 13.2 40.7 6.9 Census Tracts < 10% Poverty 12 (4%) 18.3 -19.4 13.2 21.7 3 Census Tracts < 20% Poverty 48 (16%) 22.5 -15.2 13.2 31.1 4.7 Census Tracts > 20% Poverty 247 (84%) 34.6 -3.1 17.9 58.4 8.7 (Source: United Stat es Census Bureau)

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90 4.2.8 Renter The next key census variable to observe the changes in, is percent renters. Percent renter is reported as those people who identify themselves as renters of an occupied domicile (Dalaker 2001). No part of this variable takes into consideration un-occupied or abandoned homes. This variable has importance because renting is often associated with income le vel, and income level obviously determines poverty level. In an idea living environment the optimal percentage of renters would be low fo r this type of metropolitan area. Tampa is not as densely built as say New York City and so the opportunity to own your own home is greater in this type of sprawling life style. It is interesting to view the changes spatially and within the tabular classifications below to determine if youth and their families will be exposed to more home owne rs (more home owners may equal more responsible adults and better role models). Figure 26 delineates the spatial variation of percentage renters. It appears that there is no spatial va riation to the location of high or low percentages of renters. Surpri singly, there are a few centrally located census tracts with low numbers of renter (i.e. high numbers of home owners).

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91 Figure 26. Choropleth of Renters (%)

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92 Figure 27. Scatter plot of Dist ance from Origin Neighborhood and Change in Renters Table 15. Renters Comparison Renter Comparison # of Families Average (%) Weighted Average (%) Average Change Range (%) Std. Deviation Origin Neighborhood 295 45.5 51.1 39.4 49.5 4.4 Relocation by Distance from Origin Neighborhood within 1 mile 56 (19%) 41.6 45.0 -3.9 27.3 56.5 8.8 1 to 2 miles 69 (23%) 55.0 46.7 +9.5 18.1 93.8 24.1 2 to 3 miles 52 (18%) 56.7 50.4 +11.2 12.8 97.1 28.2 3 to 4 miles 50 (17%) 49.9 46.7 +4.4 25.0 72.5 11.3 4 to 6 miles 35 (12%) 52.2 51.8 +6.7 15.9 91.6 20.8 6 or more miles 33 (11%) 55.0 55.1 +9.5 7.0 98.3 29.7 Other Measures within City of Tampa 229 (78%) 48.3 +2.8 12.8 97.1 20.7 Outside City Limits 66 (22%) 43.8 -1.7 7.0 98.3 24.9 Census Tracts < 10% Poverty 12 (4%) 28.7 -16.8 7.0 65.2 22.8 Census Tracts < 20% Poverty 48 (16%) 35.8 -9.7 7.0 72.5 18.9 Census Tracts > 20% Poverty 247 (84%) 55.3 +9.8 22.1 98.3 22.1 (Source: United Stat es Census Bureau)

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93 The scatter plot in Figure 27 of percentage change in renters confirms the lack of pattern in loca tion of renters. And in fact when examining the location of percen tage renters in Table 15, the percentage of renters increases with distance from the origin neighborhood! The results of average percentage of renters within city limits versus outside the city are re markably about the same. The only interesting relationship from the tabl e is within the classifications of poverty relocation census tracts. Th e percentage of renters actually decreases in the relocation census tr acts with the decrease in poverty; the range also condenses slightly. When a Pearson’s correlation coefficient test was conducted betw een poverty and renters, it was discovered that the correlation was rather strong (0.704). What is strange about this number is that be ing a renter shouldn’t mean that one is impoverished. Perhaps this is a phenomenon caused by urban sprawl and the American dream of white picket fences and owning your own home. Renting do es not have to be associated with poverty: people can be successful financially and still rent their dwelling.

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94 Figure 28. Choropleth of Zero Vehicle Households (%)

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95 Figure 29. Scatter plot of Dist ance from Origin Neighborhood and Change in Zero Vehicle Households Table 16. Zero Vehicle Ho useholds Comparison Zero Vehicle Households Comparison # of Families Average (%) Weighted Average (%) Average Change Range (%) Std. Deviation Origin Neighborhood 295 30.5 32.4 20.2 39.2 7.8 Relocation by Distance from Origin Neighborhood within 1 mile 56 (19%) 26.4 26.6 -4.1 11.7 39.2 9.3 1 to 2 miles 69 (23%) 30.0 25.9 -0.5 5.3 55.2 15.0 2 to 3 miles 52 (18%) 29.4 22.9 -1.1 4.8 62.6 22.3 3 to 4 miles 50 (17%) 17.0 16.4 -13.5 6.8 31.6 5.5 4 to 6 miles 35 (12%) 14.6 15.7 -15.9 2.4 29.4 8.0 6 or more miles 33 (11%) 14.1 14.0 -16.4 1.4 29.4 10.7 Other Measures within City of Tampa 229 (78%) 22.5 -8.0 4.8 62.6 14.7 Outside City Limits 66 (22%) 11.3 -19.2 1.4 29.4 8.1 Census Tracts < 10% Poverty 12 (4%) 3.4 -27.1 1.4 5.5 1.5 Census Tracts < 20% Poverty 48 (16%) 8.8 -21.7 1.4 18.5 5.5 Census Tracts > 20% Poverty 247 (84%) 24.4 -6.1 6.2 62.6 13.6 (Source: United Stat es Census Bureau)

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96 4.2.9 Zero Vehicles per Household Results Percentage of zero vehicle househol ds is the last census variable to be examined and this variable is also closely tied to estimating the type of relocation neighborhood a family relocates to. In an ideal situation the most favorable percen tage of zero vehicle households would have to be pretty low. Tamp a, being the sprawling metropolitan area that it is, it is not feasible for most households to take public transportation, and therefore it is necessary to own at least one car per household. It was interesting to discover that there are there is no census tract in Hillsborough County wher e every household owns a car. Likewise, the highest rate of ze ro vehicle households was 62.6 percent: where well over half the peop le in a census tract do not have a vehicle in their household. Figure 28 illustrates the spatial variation to percentage of zero vehicle hous eholds and again, it appears that there is no pattern to the location of zero vehicle households, although the highest relocation census tracts with the highest percentage of zero vehicle census tracts were ce ntrally located. These results look very similar to the spatial variatio n of female head of household and poverty. The scatter plot in Figure 29 conf irms the shape and curve of the scatter plot of change in zero ve hicle households to the shape and

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97 curve of the poverty scatter plot. Within 20,000 feet or approximately three miles, the change in zero ve hicle households ranged from -40.0 to +40.0 percent. This scatter plot has a strange pattern to point out that occurs between 20,000 feet an d 40,000 or approximately 3 to 7.5 miles: the range of chan ge condenses greatly and then expands again. Table 16 shows the location of av erage percent of zero vehicle households behaving more normally than the scatter plot in Figure 29. As expected, the average percentage of zero vehicle households decreases with distance from the origin neighborhood. The average percent of zero vehicle households is noticeably smaller outside city limits than inside. Finally, like the fe male head of household results, zero vehicle households decrease gr eatly with the decrease of poverty census tracts. This seems logical and the Pearson’s correlation coefficient test resulted in the co rrelation between poverty and zero vehicle households being 0.893: a very strong association. When considering what zero vehicle hous eholds (%) is actually measuring— households with no vehicle this makes perfect sense. Briefly to make sense of all these census variables, it appears to have a great difference on the potential for a better neighborhood on how far away you moved from the origin neighborhood location, whether you lived inside or outside th e city limits, and not surprisingly, what the poverty level was in the ce nsus tract one relocated to. This

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98 will be discussed with the rest of th e variable results at the conclusion of Chapter 4. 4.3 Crime Another important variable to take into consideration is crime, specifically, standardized crime pe r capita. Examining crime for these relocation neighborhoods has ma ny benefits to understand the potential for a better quality of life. First and foremost, these data are more dynamic than the census data. While the census is recorded only every ten years, crime is recorded on a daily basis with a very specific location attributes. These data can be combined in multiple ways to create the outputs for different types of analysis. Another benefit to working with crim e data is that it captures the issue of resident safety. No cens us variable measures the safety aspect of a living environment. This is important to understand because an ideal neighborhood would be a neighborhood with little to no crime. The crime data used for this an alysis was taken from recorded instances of FBI-mandated Part 1 crime statistics measured by crime grid. Crime grids are arbitrary grid s that are places through out an area, and where its boundaries could either exactly square or follow features of some sort (census trac ts, popular streets, rivers etc.). Standardized crime for the origin neighborhood derived from in

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99 instances from the 1999 Tampa Polic e Department crime dataset. Standardized crime for all the relo cation neighborhoods was derived from the 2007 crime dataset. The 2007 crime dataset is a combination of city-wide data from the Tampa Po lice Department and outside city limits from the Hillsborough County Sherriff’s Office. Both of these datasets were measured according to the FBI-mandated Part 1 crime standards and so there was no need to standardize the way the crime was measured before combining the two datasets into one dataset. However upon examination, the diffe rence in the total instances of crime has greatly decreased for th e entirety of Hillsborough County from 1999 to 2007. This made comp aring instances of crime or even crime rates nearly useless from one year to another. A way to ameliorate this problem is to standardize the crime values. Commonly, crime is standardized as a per capita figure meaning it is standardized as a figure per 1000 people. This method would standardize the crime to a set numbe r (the population). This method was calculated through areal interpolation, a process by which population values from the 2000 cens us were interpolated to the size shape and location of the crime gr ids. The population numbers were then used to standardize total cr ime figures for each crime grid. Standardized crime per capita is comm only calculated for larger areas, such as a county or a ci ty, so this method coul d only feasibly work on

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100 the total numbers of crime rather th an the other detailed crime values. Never-the-less, once crime was stan dardized per capita, it was then possible to compare the new values across years to get an accurate idea of the standardized numbers of crimes within a particular location controlling for population. Goering et al. (2003) determin ed that for the Moving to Opportunity Experiment, not only did those families who moved to a lower poverty, more racially hetero geneous neighborhood feel safer, they also experienced less instance s of violent crime. “Given these extreme levels of violent crime, neig hborhood safety is arguably one of the most important metrics of the program’s impact on family wellbeing” (Hanratty et al. 2003, 255). Figure 30 shows the spatial variat ion in standardized crime per capita for the relocation census trac ts. There appears to be no pattern to the location of high crime area s, and low crime areas can be found as close to the origin neighborhood as one to two miles out. This can be verified by Table 17, where total standardized crime per capita in the relocation census tracts have appreciably lower results than the total standardized number of cr ime per capita for the origin neighborhood despite controlling by population to prevent a drastic difference.

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101 Figure 30. Relocation Neighb orhoods by Crime per Capita Table 17. Crime per Capi ta Results (1999 and 2007)

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102 Crime Comparison # of Families Average 1999 Crime Per Capita* Range 1999 Crime Per Capita* Origin Neighborhood 295 168 100 296 Relocation by Distance from Origin Neighborhood # of Families Average 2007 Crime Per Capita* Average Change in Crime Per Capita* Range 2007 Crime Per Capita* within 1 mile 56 58 -110 28 103 1 to 2 miles 69 45 -123 7 100 2 to 3 miles 52 34 -134 6 93 3 to 4 miles 50 22 -146 0 71 4 to 6 miles 35 18 -150 0 71 6 or more miles 33 29 -139 0 100 Within City of Tampa 229 (78%) 36 -132 0 103 Outside City Limits 66 (22%) 25 -143 6 100 Census Tracts < 10% Poverty 12 (4%) 26 -142 0 71 Census Tracts < 20% Poverty 48 (16%) 26 -142 0 93 Census Tracts > 20% Poverty 247 (84%) 41 -127 7 103 *Per capita expressed as per 1000 population (Source: TPD and HCSO Departments) Overall these results appear to ha ve more of an impact on the potential well-being of youth and fa milies than any other relocation result because this table shows actu al standardized crime rates in the neighborhoods they were living in as of 2007 compared to actual standardized crime rates in their or iginal neighborhood in 1999. This decrease in crime for all relocation neighborhoods shows definitively that these neighborhoods are safe r places to raise children in. Neighborhoods appear to be safer the farther out one moves from the origin neighborhood (the exceptio n being out past 6 miles). Living

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103 outside the City of Tampa appears sa fer than living inside city-limits, and living in a lower poverty area appears to definitely make a difference in the amount of crime th at occurs. Popkin et al.’s (2004) research on HOPE VI at the nation al level suggests that moving to neighborhoods with low levels of crime not only reduces stress, but promotes mental and physical health improves youths outcomes, “and ultimately leads to better educationa l and employment outcomes” (23). 4.4 School Quality Evaluating the school quality grad es relocation sites was very important to this study. Not only di d it give a clearer picture than the census variable of percentage of ad ults 25 or older with a High School degree or equivalent, it showed fo r all school levels, the quality of school these youth had the opportunity to attend. School districts (attendance boundaries) are such an important aspect of a child’s life in terms of learning opportunities, Jencks and Mayer (1990) actually defined local neighborhoods by elementary school attendance boundaries under the theory that th e boundaries, smaller than census tracts closely aligned with people ’s idea of a local neighborhood. Through a personal conversation with a member of the Hillsborough County School Board, it was estimated that about 75 to 80 percent of youth actually attend the school they are assigned to. School quality grades are a better reflection of potential education

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104 attainment than the static high school education census variable. School quality grades for the 1999 – 2000 school year were assessed for the origin neighborhood and school quality grad es for the 2007 – 2008 school year were assessed for the relocation neighborhoods. The reasoning behind that choice was that the children in the origin neighborhood were most likely still in the origin sch ool as of 2000 before they relocated and were definitely in the relocation schools by the end of the 2007 – 2008 school year for the fi nal relocation. Figures 31, 32, and 33 below show the loca tion and distribution of school attendance boundaries for elementary, middle, and high schools for all relocation neighborhoods. Table 18 had the most interesting differences between origin el ementary schools and relocation elementary schools that re quired further discussion with my contact at the Hillsborough County School Board.

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105 Figure 31. Choropleth of Reloca tion Elementary School Quality

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106 Table 18. Elementary School Quality Comparison Relocation Neighborhood Elementary Schools School Year 2007 2008 Grade # of Families Grade # of Families POTTER ELEMENTARY SCHOOL C 31 MORT ELEMENTARY SCHOOL C 10 JUST ELEMENTARY D 29 IPPOLITO ELEMENTARY SCHOOL C 6 OAK PARK ELEMENTARY SCHOOL C 23 BING ELEMENTARY SCHOOL A 5 BROWARD ELEMENTARY SCHOOL F 21 JAMES ELEMENTARY SCHOOL C 5 SULPHUR SPRINGS ELEMENTARY SCHOOL F 21 FOLSOM ELEMENTARY SCHOOL B 4 ROBLES ELEMENTARY SCHOOL D 20 HUNTER'S GREEN ELEMENTARY SCHOOL A 4 EDISON ELEMENTARY SCHOOL C 16 MILES ELEMENTARY SCHOOL C 3 GRAHAM ELEMENTARY SCHOOL C 14 SCHMIDT ELEMENTARY SCHOOL A 3 FOREST HILLS ELEMENTARY SCHOOL C 11 CLAIR-MEL ELEMENTARY SCHOOL C 2 BT WASHINGTON ELEMENTARY SCHOOL D 11 ELEMENTARY @ MOSI B 2 FOSTER ELEMENTARY SCHOOL C 10 TEMPLE TERRACE ELEMENTARY SCHOOL A 2 SHEEHY ELEMENTARY SCHOOL C 8 BAY CREST ELEMENTARY SCHOOL A 1 SHAW ELEMENTARY SCHOOL C 6 CITRUS PARK ELEMENTARY SCHOOL A 1 CLEVELAND ELEMENTARY SCHOOL C 4 COLLINS ELEMENTARY SCHOOL A 1 OAK GROVE ELEMENTARY SCHOOL B 3 CORR ELEMENTARY SCHOOL B 1 CLARK ELEMENTARY SCHOOL A 2 KENLY ELEMENTARY SCHOOL C 1 TAMPA BAY BOULEVARD ELEMENTARY SCHOOL C 2 KINGSWOOD ELEMENTARY SCHOOL B 1 DESOTO ELEMENTARY SCHOOL C 1 LOPEZ ELEMENTARY SCHOOL A 1 LANIER ELEMENTARY SCHOOL A 1 PALM RIVER ELEMENTARY SCHOOL C 1 MITCHELL ELEMENTARY SCHOOL A 1 SUMMERFIELD CROSSINGS ELEMENTARY A 1 PIZZO ELEMENTARY SCHOOL C 1 ROLAND PARK K-8 SCHOOL C 1 (Source: Hillsborough County School Board)

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107 It was determined that for the origin neighborhood that all schools in that area were satellite attendance neighborhoods with one exception: the only D quality school the children could have attended. Distance (measured by time) from the origin neighborhood to the actual elementary schools ranged from 2 minutes to the D school, and 30 to 40 minutes for the A and C quality schools. The quality of schools changed dramatically with the relocation elementary schools for the 2007 – 2008 school year. For the majority of the relocation neighborhoods, children were assign ed to local neighborhood schools. This produced a vivid difference fo r the families who relocated inside city limits: before youth had acce ss to A and C quality school, after they had access to primarily C, D, and F quality elementary schools. Outside city limits, relocated fam ilies had slightly better results: a variety of A, B, C, and one D qu ality grade elementary schools. It appears that 43 families or 15% of the relocated families were assigned to the two F quality Elementa ry schools in the relocation area.

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108 Figure 32. Choropleth of Relo cation Middle School Quality

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109 Table 19. Middle School Quality Comparison Middle School Quality Grades Origin Neighborhood Middle Schools Grade 99-00 # of Families OAK GROVE MAGNET SCHOOL unavailable 130 WALKER MIDDLE SCHOOL B 86 HILL MIDDLE SCHOOL C 73 MANN MIDDLE SCHOOL C 6 Relocation Neighborhood Middle Schools Grade 07-08 # of Families MCLANE MIDDLE SCHOOL C 61 SLIGH MIDDLE SCHOOL C 38 FRANKLIN MIDDLE MAGNET SCHOOL C 26 STEWART MIDDLE MAGNET SCHOOL B 26 MADISON MIDDLE SCHOOL C 25 VAN BUREN MIDDLE SCHOOL C 21 MONROE MIDDLE SCHOOL C 20 GRECO MIDDLE SCHOOL C 13 ADAMS MIDDLE SCHOOL A 11 BUCHANAN MIDDLE SCHOOL B 10 GIUNTA MIDDLE SCHOOL B 10 JENNINGS MIDDLE SCHOOL C 7 BARTELS MIDDLE SCHOOL A 5 LIBERTY MIDDLE SCHOOL A 4 MEMORIAL MIDDLE SCHOOL C 4 EISENHOWER MIDDLE SCHOOL B 3 BENITO MIDDLE SCHOOL A 2 DOWDELL MIDDLE SCHOOL C 2 BURNETT MIDDLE SCHOOL A 1 RODGERS MIDDLE SCHOOL A 1 ROLAND PARK K-8 SCHOOL C 1 WEBB MIDDLE SCHOOL C 1 WILSON MIDDLE SCHOOL A 1 Middle School evaluations did not seem to bring up and outstanding results. Most youth had the opportunity to attend a B or C grade quality school for the origin ne ighborhood, an A, B, or C grade quality school for the relocation neig hborhoods: althou gh it is worth mentioning that the only A grad e quality schools families had an

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110 opportunity at were for those familie s who relocated outside the city of Tampa. High School evaluations were al so an interesting attribute to review. Youth had the opportunity to attend C grade quality schools in the origin neighborhood and remarka ble range of grade quality schools for relocation neighborh oods. One very importan t factor to note for these results is that both Hillsboro ugh High School, in the city of Tampa, and King High School, outsid e city limits, contain International Baccalaureate Schools which will defi nitely skew the results of the FCAT and therefore the resulting sch ool quality grade. Taking these two schools out of the assessment leaves one C quality grade school and mostly D quality grade schools for those youth to potentially attend in the top ten relocation ne ighborhoods in the city of Tampa and mostly A and C quality grade schools for youth to have the opportunity to attend outside city limits.

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111 Figure 33. Choropleth of Relo cation High School Quality

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112 Table 20. High School Quality Comparison High School Quality Grades Origin Neighborhood High Schools Grade 99-00 # of Families SICKLES HIGH SCHOOL C 204 GAITHER HIGH SCHOOL C 85 RIVERVIEW HIGH SCHOOL C 6 Relocation Neighborhood High Schools Grade 07-08 # of Families MIDDLETON HIGH SCHOOL D 80 BLAKE HIGH SCHOOL-MAGNET D 49 HILLSBOROUGH HIGH SCHOOL A 39 CHAMBERLAIN HIGH SCHOOL C 34 KING HIGH SCHOOL B 28 FREEDOM HIGH SCHOOL A 19 WHARTON HIGH SCHOOL B 14 SPOTO HIGH SCHOOL C 11 BRANDON HIGH SCHOOL C 3 EAST BAY HIGH SCHOOL C 3 JEFFERSON HIGH SCHOOL B 3 PLANT HIGH SCHOOL A 3 ARMWOOD HIGH SCHOOL C 2 BLOOMINGDALE HIGH SCHOOL A 2 ALONSO HIGH SCHOOL A 1 RIVERVIEW HIGH SCHOOL A 1 ROBINSON HIGH SCHOOL B 1 SICKLES HIGH SCHOOL A 1 Jencks and Mayer (1990) discovered in their assessment that “children from affluent schools know more, stay in school longer, and end up with better jobs than child ren from schools that enroll mostly poor children” (111). While the school s in Hillsborough County are said to receive equal funding, a theory for the difference between city school quality and outside city limit s school quality may be due to the affluence of the neighborhood: affl uence could mean many things but in this case, with the census va riables at hand, could mean fewer renters, higher median household income, or lower poverty rates.

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113 When school quality grades were comp ared to percent renters, median household income, percent poverty for the same neighborhood, no conclusive results could be determ ined. Could percentage of people who live in poverty affect local qua lity of schools in some way? There is no way for this case study to determine this relationship. What is interesting is the high multi-colinearity between poverty and most of the other census variables. What can be determined is that is there a relation ship to distance from the origin neighborhood and more ideal neighborhood envi ronments, in terms of optimal census tract attributes. It also appears more likely that living outside the City of Tampa limits will improv e one’s chance at a better quality of life. Lastly, in conjunction with the results found in the Moving to Opportunity Experiment, living in lower poverty census tracts also appears to give optimal living conditio ns for all measured variables. If the neighborhood effects theory is correct, then some of these relocated families will “find social networks that encourage them to find employment” and the youth w ill live in a neighborhood that provides “role models that encourag e them to stay in school” (Popkin et al. 2004).

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114 CHAPTER 5 CONCLUSIONS Patterns of neighborhood effects, which have been purported to be found in other case studies (Jencks and Mayer 1990, Jargowsky 1997) have encouraged social scien tists, policy analysts, and other scholars that a neighborhood’s composition really may have an influence on a child’s life opportuniti es. The purpose of this exercise was to provide a quantitative perspective of where original Ponce de Leon and College Hill families relo cated to, within the bounds of Hillsborough County and the prospe ctive opportunities available to them through the argument of neighb orhood effects. Th e scope of this study was intended as a descript ive assessment and has found mixed results. “Even if better data were av ailable, the debate about resident outcomes would be difficult becaus e there is no consensus about how to define success” (Popkin et al. 2004). Qualitative assessment of the im pact of relocation on public housing youth can not really comp are generalized results from one census neighborhood to the other, nor can qualitative investigation determine how a relocation neighborhood has the potential to impact youth and their families: they ca n only gauge personal opinions, feelings, and beliefs (Jencks an d Mayer 1990). Ultimately, these

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115 results highlight the potential comp lexity of the relocated families’ experiences (Varady and Walker 2003). According to Varady and Walker (2003), in their assessment of federal policy, the goal of current policy has been to encourag e families to relocate low-poverty neighborhoods, but most often this goal has not been achieved. Many families make short-distance moves, often to areas of concentrated poverty with high proportions of mino rities. Because of re-clustering in particular communities, many resident s, civic leaders, and politicians have expressed concern “that cluste rs of Section 8 households can destabilize neighborhoods, bringing drugs, crime, and antisocial behavior and precipitating a cycle of neighborhood disinvestment and decline” (Turner et al. 2000, 9). “The Chicago HOPE VI research implies that the subgroup of re sidents who had the most complex personal problems are having difficu lty making the transition to either private housing or revitalized HO PE VI developments” (Varady and Walker 2003, 24). Families who relocated from the ‘distressed’ public housing of Ponce de Leon and College Hill relocated because Tampa Housing Authority believed they suffered intole rable conditions, and hopefully it was the intent of Tampa Housing Auth ority that they benefit from this relocation. Ultimately, “the housing authority—and society—has an obligation to ensure that at a minimum, original residents do no end

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116 up worse off than they were before ” (Popkin et al. 2004, 27). Clearly however, there seemed to be no rh yme or reason to the choice of relocation neighborhood: there certainly was no counseling on relocation neighborhoods—that is ce rtain from the results. Advocates for urban poor families and research for other HOPE VI studies “have cited issues regarding inadequate relo cation services, particularly lack of information and support during the relocation process that have resulted in residents ending up in less than ideal circumstances or experiencing hardship after they move” (Popkin et al. 2004, 33). Throughout this assessment, I searched for patterns. Did families choose lower poverty census tracts to raise their children in? It does not appear so. Did families choose neighborhoods with better schools or lower crime rates for their children? It does not appear so. And yet, I can say with some certainty, that however they arrived at their new neighborhoods, some families have a better opportunity for a better quality of life. Distance from the origin neighbor hood seemed to play a small role in more optimal living cond itions as best as I could estimate them along the way. Families that relocated outside city limits achieved noteworthy reductions in neighborhood crime instances, and increased opportunity at a better qu ality of life through better school districts and more ideal census variables. Lastly, there was a remarkable difference when one exam ined the variables associated

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117 with low poverty census tracts. This may be the key result in assessing the success of the program. “Housing programs that do not require families to move to lower poverty areas may condemn the children of movers to the leasteffective schools” (Ladd and Ludwig 2003). School results were mixed, but leaned towards better school grade quality outside city limits rather than inside the city of Tampa boundaries. Comparing school grade qualities to census variab les to determine if there was a connection between supposed affluent neighborhoods and quality of schools brought uncertain conclusions. 5.1 Limitations What exactly is the optimum scale for conducting this type of research? Do there appear to be meas urable positive benefits to these variables? It is too earl y to say and will definitely require further study, some aspects of which have already been discussed and will be discussed in the section below. This research is limited for a va riety of reasons from choice of data to methods employed to assump tions made with expected results. Firstly, the clearest limitation of th ese results is that the use of the static census data from 2000 limit s the census results. The origin neighborhood and the relocation ne ighborhoods all share the variables from the 2000 census, yet we know th e introduction of the HOPE VI

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118 mixed-income housing has dramatic ally changed the composition of those particular origin area census tracts. It will be interesting to see how this case study’s results would ch ange with the substitution of the 2010 census when it is released. An a dditional limitation is the use of a 40 percent poverty rate in the assessment of the poverty variable as concentrated poverty for the census tracts. This standard has the potential to be flawed because pove rty from the census is measured by a federal standard which does not take into account the difference in cost of living around the country. In place of the federal standard, Swanstrom et al. (2008) recommend using a relative measure of poverty which takes into account the median household income for a particular study area. Their analysis “ shows that using a relative standard generates a very different picture of the extent, geographic distribution, and trends in concentrated poverty” (287). A potential limitation is limited success at locating all housing authority youth throughout each database. The Tampa Housing Authority databases are poorly ma naged and not all include names and / or birthdates. Some families were probably unable to be located due to a variety of reasons: dropping out of the housing program, moving out of county, head of hous ehold death, homelessness, or geocoding error.

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119 Next, this case study is limited in that in order to assess the quantitative variables, it is necessary to use some sort of enumeration areas that are homogeneous in some fo rm or another: in this case, the census tract. It’s unfortunate that a study of this extent could not have been done on a more intimate basi s, but that would have changed the scope of the research and taken it in a completely different direction. Thus working with census tracts lim its the results in two related ways. The ecological fallacy seems to be th e necessary limitation to this case study and quite similar to the theory of neighborhood effects. In order to conduct this research, it was a ssumed that the individuals would or will exhibit characteristics of the gr oup they previously or currently belong to: it was essential in this case study to assume that the children from public housing disp layed the characteristics of the concentrated poverty neighborhood census tracts; likewise that they will adopt the behaviors of the new relocation neighborhoods. Crime grids also share in this limitation in terms of the Modifiable Area Unit Problem. The crime grids are based on census tract boundaries, major road boundaries and other landform boundaries, yet are for the most part square in shape and arbitr ary. Thus the crime results are dependent on the size and shape of the grid and may change in some way if crime were to be recorded in a different manner.

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120 This leads, of course, to the neighborhoods effects argument, which presents another yet similar limitation. Though popular with renowned scholars, neighborhood effects cannot explain all of the detrimental effects of concentrated poverty and cannot guarantee that an urban poor family will have a be tter quality of life if moved to a neighborhood far away from the or igin neighborhood, outside citylimits, or a lower poverty neighbor hood. “While most studies find evidence that neighborhoods matter, they suffer from data limitations that make it difficult to pinpoint causality” (Ellen and Turner 2003, 313). This assessment does not take into account the possibility that there are unobserved differences betw een the group that started off in public housing and the subsequent relocation group that might otherwise be related to their residential status (Jargowsky and Bane 1990). This was ameliorated by trying to simply describe differences in neighborhood characteristics, spec ulating on ideal conditions for a better quality of life, and possibl e reasons for these differences. “Understanding what is inside the ‘bla ck box’ of neighborhood effect is critical to evaluate” in order to de termine its efficacy (Ellen and Turner 2003, 313). From a scientific methods perspect ive, the best way to estimate neighborhood effects would be to conduct controlled experiments in which families were randomly assigned to different neighborhoods, to

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121 persuade each family to remain in its assigned neighborhood for an extended period, and then to measur e each neighborhood’s effects on the children involved (Goering et al. 2003). Moving to a lower-poverty ne ighborhood does not guarantee educational improvements. Another limitation to this data is the uncertainty that parents moved their children to their new assigned schools: “some parents may have found ways to send their children to schools outside local school districts, others may have rotated children among relatives living in different school districts, or children may have been expelled from school” (Ladd and Ludwig 2003). Even if children did enroll in schools with hi gher grades and more over all resources, their educational opport unities might not have improved. “They might have been placed in less demanding classes, been assigned to classes disproportiona tely attended by low-income or minority students, or been put in cl asses with less able teachers than the school’s average classroom” (Ladd and Ludwig 2003 119). 5.2 Future Research “Thus, a priority for future research should be to move beyond the question of whether neighborhoods matter and to attack the more difficult question of how they make a difference and for whom ” (Ellen and Turner 2003). This assessment tried to accomplish just that. However, so much more can be done to draw more definitive

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122 conclusions. The goal for this projec t was to assess living conditions by local neighborhood area (census trac t, crime grid, and school district) and potential outcomes for as many families that relocated as possible. It was determined that the best way to do this was to look not only at the census tract level, but also in distance based bands from the center of the origin neighborhood outwards, within and without the city limits, and in certain poverty classification census tracts. In terms of census variables; it would be interesting to see how the 2010 census will change the relo cation census tract outcomes, “because it may take some time for improvements to manifest themselves” (Popkin et al. 2004). It would be interesting as a continuation of this project to form ulate a study of neighborhoods with ideal conditions (low poverty, low cr ime, good schools) and to have it used in future HOPE VI relocations. Another area of interest would be to study the revitalization of the or iginal ‘distressed’ neighborhood quantitatively to assess if that port ion of HOPE VI was more successful than the mixed results found here.

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123 REFERENCES Bane, Mary Jo and David T. Ellwood 1989. One Fifth of the Nation's Children: Why are They Poor? Science, 245 1047-1053. Briggs, Xavier de Souza. 1997. Moving Up versus Moving Out: Neighborhood Effects in Ho using Mobility Programs. Housing Policy Debate, 8 195 234. --and Margery Austin Turner. 2006. Assisted Housing Mobility and the Success of Low-Income Minority Families: Lessons for Policy, Practice, and Future Research. Northwestern University Journal of Law and Social Policy, 1 25 61. --and Peter Dreier. 2008. Memphi s Murder Mystery? No, Just Mistaken Identity. In Shelterforce National Housing Institute. Brooks-Gunn, Jeanne, Greg J. Dunc an, Pamela Kato Klebanov and Naomi Sealand. 1993. Do Neighb orhoods Influence Child and Adolescent Development? The American Journal of Sociology, 99 353-395. ---. 1995. Strategies for altering th e outcomes of poor children and their families. In Escape from poverty: what makes a difference for children?, eds. J. Brooks Gunn, Chase Lansdale & P.Lindsay, 87 117. Cambridge, MA: Cambridge University Press.

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ABSTRACT: This paper will explore the social and economic impacts of public housing revitalization on households with minor children. The research traces the relocations of families from two public housing complexes to other public housing complexes or market housing, using Housing Choice formerly Section 8 vouchers. We contrast and compare the socioeconomic characteristics of the original neighborhoods to the relocation sites from the census tract level, exploring changes in resources available to families.
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