The Spatial Concentration of Subsidized Housing - Poverty & Race ...

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MSA /PMSA Code MSA / PMSA Name PopulationBlack-Non-BlackHispanic-Non-HispanicPoor-Non-PoorSubsidized-Non-Subsidized8760 Vineland-Millville-Bridgeton, NJ PMSA 146,438 0.3425 0.4340 0.2538 0.42438780 Visalia-Tulare-Porterville, CA MSA 367,137 0.3872 0.3804 0.2591 0.42388800 Waco, TX MSA 213,517 0.4521 0.3919 0.4550 0.58348840 Washington, DC-MD-VA-WV PMSA 4,922,060 0.5892 0.4332 0.3777 0.61508880 Waterbury, CT PMSA 229,064 0.5498 0.5513 0.4191 0.59498920 Waterloo-Cedar Falls, IA MSA 128,012 0.6658 0.3991 0.3084 0.47848940 Wausau, WI MSA 125,834 0.3931 0.3355 0.2820 0.66758960 West Palm Beach-Boca Raton, FL MSA 1,128,748 0.6091 0.3814 0.3652 0.74209000 Wheeling, WV-OH MSA 153,172 0.5425 0.3503 0.1834 0.49959040 Wichita, KS MSA 545,220 0.5345 0.4077 0.3629 0.51539080 Wichita Falls, TX MSA 140,518 0.4929 0.3194 0.2546 0.55009140 Williamsport, PA MSA 120,044 0.6104 0.4200 0.2662 0.63049160 Wilmington-Newark, DE-MD PMSA 586,216 0.5165 0.3951 0.4047 0.60439200 Wilmington, NC MSA 233,450 0.4697 0.2508 0.2842 0.67909240 Worcester, MA-CT PMSA 508,982 0.5043 0.5703 0.4175 0.60509260 Yakima, WA MSA 222,581 0.3626 0.4799 0.3269 0.47259270 Yolo, CA PMSA 168,660 0.2633 0.3802 0.3216 0.36779280 York, PA MSA 381,751 0.6507 0.5525 0.3291 0.68509320 Youngstown-Warren, OH MSA 594,746 0.7155 0.4494 0.3361 0.57199340 Yuba City, CA MSA 139,149 0.3304 0.2649 0.2815 0.48619360 Yuma, AZ MSA 160,026 0.3456 0.4491 0.2807 0.5409All MSA’s (331) 225,666,050 0.4836 0.3747 0.3280 0.5600Source: Population by Race, Ethnicity and Income: U.S. Census, 2000. Subsidized Housing: HUD, A Picture of Subsidized Housing 2000.Segregation Index calculated using the Index of Dissimilarity (D).182
APPENDIX BLiterature Review on Subsidized HousingAuthor Date Data GeographyType ofProgramOutcomeMeasures Method FindingsRoncek, Belland Francik 1981 1970Goldstein andYancey 1986Massey andKanaiaupuni 19931950-19801950-1990Cleveland,OHPhiladelphia,PAChicagoMSAPublic Housing(17 projects)Public HousingPublic HousingPUBLIC HOUSINGViolent andproperty crime onblockssurroundingpublic housingsites.Racial change atthe census tractlevelChange inneighborhoodpoverty rateComparison of blocks withpublic housing to blocks withoutpublic housing. Correlation andregression analysis on thenumber of public housing unitson a block and distance to publichousing sites and 13 otherfactors.Regression on changes in racialcomposition from 1950 to 1980in one city controlling for otherecological characteristics of thecensus tract. No data onresidents of the public housingor on the beginning racialcomposition of theneighborhood.A four stage regression thatlooks at the effect of publichousing built in the 1950's and1960's on neighborhoods in1980 and 1990.Blocks with public housing havesignificantly more violent and propertycrimes. However, after controlling for SESand housing stock characteristics adjacent tothe public housing, location adjacent topublic housing has the least importance inpredicting violent crime and had noimportance in predicting property crime.No association was found between pre-1950public housing and increased black racialconcentration. Post-1950 public housingwas placed in neighborhoods with higherblack populations and areas where the blackpopulation was increasing. The authorsconclude that racial change in census tractswas related to the ecological history of theneighborhood and not related to the siting ofpublic housing.Public housing causes concentrated povertythrough three processes: 1) structural effectof concentrating poor families by virtue ofthe public housing income restrictions; 2)the continuing effects over time of earlierconstruction; and 3) migration rates(although this last effect is small)183
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THE SPATIAL CONCENTRATION OF SUBSID
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I certify that I have read this dis
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TABLE OF CONTENTSLIST OF TABLES ...
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6.4 Summary of Cluster Analysis Res
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Table 5.2 Range of MSA Segregation
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ABSTRACTSubsidized housing has been
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Chapter 1INTRODUCTIONPublic housing
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Data on subsidized housing prior to
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subsidy programs in that rents are
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Chapter 2LITERATURE REVIEWA compreh
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from the nine matched neighborhood
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Overall, it is clear that there are
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limited the study to city vs. subur
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to the public housing, location adj
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hardship; and 2) public housing wea
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deconcentrated over time is whether
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deconcentration. In fact, a higher
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in the same neighborhood). On avera
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Concentration of Tenant-Based Subsi
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Wang, Varady and Wang (2008) studie
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from the vouchers. However, there w
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consisting of single family zones,
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early-mid 1990’s consisting of pu
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Recent studies of individual housin
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One of the criticisms of the HOPE V
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that they are smaller scale, better
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Chapter 3METHODOLOGYWhile the conce
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Data AvailabilityA limitation in th
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just coming on line in the 1990’s
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with 1,500 to 12,000 the minimum an
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exclusion of these units is not pro
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Unduplication of Subsidized UnitsDu
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projects between 35.2 and 46.6 perc
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Resulting Data for AnalysisAs a res
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Lack of household level data will l
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TABLE 4.2Mean Subsidized Housing Un
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Measures of ConcentrationThree meas
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TABLE 4.6Subsidized Units as a Perc
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would have to be to be considered t
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mean of 82 subsidized units per tra
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Subsidized Housing by Type and Pove
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TABLE 4.9Subsidized Units by Subsid
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unemployment rate (.427), less than
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TABLE 4.12Correlation Matrix (page
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TABLE 4.12Correlation Matrix (page
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spatial sensitivity because many di
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It is possible that these census tr
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only 8 MSA’s). The segregation in
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The correlation between the subsidi
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developing strategies to deconcentr
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y subsidy type. The correlation bet
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with a poverty rate of 9.2 percent.
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The cluster is relatively small con
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Cluster 7: Other Site-Based Units -
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the only cluster that had a signifi
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alter the perception of public hous
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VouchersVoucher type tracts are dom
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TABLE 6.1Subsidized Units by Cluste
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FIGURE 6.1Percent Census Tracts by
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FIGURE 6.5Census Tract Percent Rent
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Cluster - Concentration -PovertyCen
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strategies. The cluster map shows t
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FIGURE 6.9Map of Public Housing Uni
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Cluster 1: Voucher/No Subsidized Un
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considered moderately concentrated
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Cluster Analysis ResultsThe cluster
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tracts than other subsidy types it
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ecommend efforts to reduce the leve
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scale at which the impacts occur; 2
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units. Given the high cost of these
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REFERENCESAbt Associates, I. (2006)
Page 144 and 145: Briggs, X. d. S. (Ed.). (2005). The
Page 146 and 147: Deng, L. (2007). Comparing the effe
Page 148 and 149: Ellen, I. G., & Voicu, I. (2005). N
Page 150 and 151: Galster, G. C. (2005). Consequences
Page 152 and 153: Harris, L. E. (1999). A home is mor
Page 154 and 155: Johnson, M. P. (2006). Single-perio
Page 156 and 157: Lee, B. A., Reardon, S. F., Firebau
Page 158 and 159: Nguyen, M. T. (2005). Does Affordab
Page 160 and 161: implementing eight consent decrees.
Page 162 and 163: Schwartz, A. (1999). New York City
Page 164 and 165: Varady, D. P., & Walker, C. C. (200
Page 166 and 167: APPENDIX A.1Downloadable Databases
Page 168 and 169: APPENDIX A.4Missing DataPublicHousi
Page 170 and 171: APPENDIX A.6Subsidized Housing Unit
Page 172 and 173: APPENDIX A.8Subsidized Housing Unit
Page 174 and 175: APPENDIX A.9Demographics by Cluster
Page 176 and 177: MSAFIPS MSA Name Population Voucher
Page 186 and 187: MSA /PMSA Code MSA / PMSA Name Popu
Page 196 and 197: Author Date Data GeographyGalster a
Page 198 and 199: Author Date Data GeographyHolloway,
Page 200 and 201: Author Date Data GeographyType ofPr
Page 202 and 203: Author Date Data GeographyMurray 19
Page 204 and 205: Author Date Data GeographyLee 20081
Page 206 and 207: Author Date Data GeographyOakley 20
Page 208 and 209: Devine, Gray,Rubin andTaghavi (HUD)
Page 210 and 211: Carlson,Haveman,Kaplan andWolfe 200
Page 212 and 213: Newman andSchnare 1997Rohe andFreem
Page 214: Galster andZobel 1998Freeman andBot
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