What the Demolition of Public Housing Teaches Us About the Impact of Racial Threat on Political Behavior”

Home , Dearborn Homes, Stateway Gardens

Online Appendix for “What the Demolition of Public Housing Teaches Us About the Impact of Racial Threat on Political Behavior”

Details of the Process of Demolition of Public Housing in Chicago Residents of public housing who were relocated were moved to what is known as “Section 8” housing, which is where residents are provided with vouchers to help pay for housing in private facilities. In demolishing the projects, HUD and the CHA were following the law from Section 202 of the Omnibus Consolidated Rescissions and Appropriations Act of 1996. An internal HUD audit describes the relevant part of Section 202 as follows: The public housing developments that were subject to Section 202 must have been on the same or contiguous sites, must have been more expensive than Section 8 tenant-based assistance, could not have been revitalized through reasonable programs, and must have had 300 or more dwelling units and had a vacancy rate of at least 10 percent for dwelling units not in funded on-schedule modernization programs. Section 202 requires that a public housing development’s cost of operations be analyzed to determine whether it is more expensive to renovate and operate the low-income housing units than it is to provide Section 8 assistance to current residents and operate the low-income housing units than it is to provide Section 8 assistance to current residents and relocate them to other available developments. For a public housing development for which revitalization is deemed the more expensive option, additional analysis is undertaken to assess its long-term viability if revitalized. Under current deﬁnitions, viability includes achieving structural/system soundness and full occupancy. These factors, along with an analysis of market support, must be considered to establish the appropriate long- range plan for the use or reuse of a public housing development. Low-income housing units that do not pass the tests involved in this analysis and that cannot be revitalized through reasonable programs are required by Section 202 to be removed from HUD’s public housing inventory (Wolfe 2006). The housing projects eventually demolished under these rules were: Frances Cabrini Extension North Ida B. Wells Homes and Wells Extension/ Madden Park Homes/ Clarence Darrow Homes Frances Cabrini Extension South William Green Homes Rockwell Gardens Stateway Gardens Robert Taylor Homes (A and B) Washington Park Homes (high-rises) Henry Horner Homes ABLA (Jane Addams Homes, Robert H. Brooks Homes and Brooks Extension, Loomis Courts, and the Grace Abbott Apartments)

1 Table A.1: Pre-treatment Characteristics of Precincts within 1km of Demolished and Non- Demolished Projects

Demolished Non-Demolished All Others White (> 18 years) 0.27 0.17 0.44 Black (> 18 years) 0.60 0.64 0.32 Hispanic (> 18 years) 0.08 0.12 0.20 White Median Income (dollars) 31296 30837 32414 Black Median Income (dollars) 26517 26653 28369 White Vote Bush 2000 0.40 0.38 0.45 Black Vote Bush 2000 0.01 0.01 0.01 Mean levels of pre-treatment variables within 1 kilometer of demolished projects (column 1), non-demolished projects (column 2), and all other precincts in the city (column 3). Vote by race is calculated using the ecological inference technique described in King (1997).

In Table A.1 I display the pre-treatment levels of variables within 1 kilometer of demolished and non-demolished projects. In general, the precincts are similar, although precincts near demolished projects had more white residents over 18 years old. This is expected given that most (but not all) of the projects near largely white areas were demolished. It is unlikely that this difference in the concentration of white voters is consequential and matching voters near demolished and non-demolished projects on percent white at the Census Block Group level yields similar results. Moreover, the greater percentage of Blacks near non-demolished projects would conservatively bias the findings on racial threat because the threat for whites would likely be greater in the areas with higher concentrations of blacks, as demonstrated in the results of the article. Importantly, there is not any substantial difference in income or vote tendency among the white voters near demolished and non-demolished projects.

Imputation of Race from Names This is an explanation of how I impute the race of voters based on their names. The probability that a voter on the voter list is a particular race or ethnicity can be determined by ﬁrst looking at the probability that their surname is associated with a particular race or ethnicity according to U.S. Census Bureau counts of names by race. The Census Bureau tabulates the frequencies of races/ethnicities associated with last names in the United States. For example, Smith, still the most common name in the United States, is 73.35% white, 22.22% black, 0.4% Asian and Paciﬁc Islander, 0.85% American Indian, 1.63% mixed race, and 1.56% Hispanic. I take these frequencies for the name of each voter and combine them with the racial demographics of Census Blocks. I use a simple application of Bayes’ rule to update the name’s initial probability of belonging to a given racial group with the probability that a voter is of a certain race based on their census block demographics.1

1Note that the “Confidentiality Edit” performed by the Census Bureau does not affect the accuracy of these estimates. The Confidentiality Edit is a data scrambling procedure performed by the Census to “assure published data do not disclose information about specific individuals” but is “controlled so that the counts

2 In applying Bayes’ Rule, the probability that a person with a given surname, S, is of a particular racial group gi is:

P r(S|gi)pi P r(gi|S) = X (1) P r(S|gj)pj j∈G where P r(gi|S) is the probability that any given name belongs to racial group gi, so that P r(S|gi) is the probability that any given individual from racial group gi has the name S. And pi is the probability that any given individual living within a Census Block is of racial group gi. G is the set of all available racial groups. There are six racial/ethnic categories for which the Census Bureau collects surname counts. So, for example, to calculate the probability that a person with name S is African American, j ∈ G would consist gj = African American, plus the five other available racial group designations from the Census that are not African American. These would be white, American Indian or Alaskan Native, Asian or Pacific Islander, two or more races, and Hispanic. The Census only lists name frequencies for names for which occur at least 100 times in the decennial census. I discarded all voters for which I could not calculate a race probability based on their name. Because of this, I was probably more likely to discard voters that came from small ethnic or immigrant minorities. Some voters could also not have their name probability calculated because I could not match their address with a Census Block. As an example of this method, I randomly selected five voters from my sample. They are listed in Table A.2. The probability that each of these voters is either white or black is quite high. I include here only the probabilities for black and white, although the frequencies of all five of the census designated racial groups and Hispanic are used to estimate the probabilities. The probabilities for selected names are typical for a segregated city, such as Chicago, and show how updating on the name and the census demographic information gives more certain probabilities than if location or name were used alone. Looking at an example from the Table A.2 can help to demonstrate how this method improves estimates of race/ethnicity for individuals where using either just census demographics or just their name might be misleading. For example, the first voter in Table A.2 has the last name Collins. Using only the name Collins, this voter would likely be white, with over 73% of people in the United States with the name Collins being white. However, looking at where this voter lives, we would be more likely to say that she is African American, with 96.39% of her census block being African American. After combining these two pieces of information, the updated probability of this voter being African American is improved to .9886.2 of total persons, totals by race and American Indian tribe, Hispanic origin, and age 18 years and over are not affected” (http://www.census.gov/geo/lv4help/nonsamp err.html). 2Note that some probabilities in Table A.2 are listed as zero. Zero probability events are, rightly, often viewed with suspicion. However, in this case, if the census has perfect measurement and there is no movement, the probability is zero. Take, for example, the voter named Reid. There are no whites in her census block. If this count is correct, then the probability that she is white is certainly zero. Of more interest, however, is that the probability of her being black is more certain than what could be determined by only looking at the census block demographics. Similarly, with the voter named Mogel, there are African Americans in her

3 Table A.2: Sample Probabilities: Race Given Name and Location

Name Census Block Bayesian Probability Name %White %Black %White %Black White Black Collins 73.92 21.70 1.71 96.39 .0010 .9886 Mogel 95.38 0.00 72.71 9.43 .9883 .0000 Reid 63.24 32.01 0.00 94.59 .0000 .9816 Haile 51.48 41.10 64.74 2.10 .7624 .1121 Gorman 91.48 3.11 55.26 24.56 .8163 .0698

Five names drawn randomly from the voter ﬁle to demonstrate my method for identifying voters’ race. The ﬁrst two columns are the frequencies of the name by race group in the United States. The next two columns are the demographics of the Census Block in which the voter lives and the last two columns are the updated probability that a voter belongs to a each racial group.

Sample Selection Here I describe the technical details of how the sample was selected. Using the 2004 Illinois voter file, I identified the race/ethnicity of each voter, as described in the previous section. However, to do this, I first had to match each voter with the demographics of their Census Block. I did this by writing a computer program that sent each unique address in the voter file to the Census Bureau website’s address search service. This returned the Census Block for each address, which I then combined with the voter file. I then matched the Census Blocks with demographic data from Summary File 1 from the 2000 Census. I obtained the block group-level income information in the same way. This process, in addition to discarding voters because p(race|name) could not be calculated, led to a total of about 150,000 voters being discarded. Each voter was geocoded by obtaining a latitude and longitude for each address from a geocoding computer program. I was unable to geocode less than 1% of addresses. However, each address represented, on average, about 2.5 voters, so about 2% of the original voters were discarded during this process. I obtained an archived GIS shape file of public housing in 2000 from the City of Chicago, Department of Innovation and Technology. This was valuable because the shape files provide the two-dimensional perimeter of the entire project, rather than just a one dimensional point like an address or geographic coordinate. Using GIS software that is integrated with a Structured Query Language database, I measured the census block, but her probability of being black is zero because the Census Bureau has counted no African Americans named Mogel in the United States. Even if the Census Bureau is wrong about this count of the name Mogel, the probability calculated here is probably a fairly accurate assessment of the likelihood that this particular individual is white or black. Additionally, Some voters live in census blocks that are perfectly racially homogeneous, therefore the estimates generated by my updates have probability 1 that the voter is of a certain race. These estimates could be wrong, due to measurement error by the Census and demographic change over time. However, even self-identification of race in surveys has associated error due to coding error and misreporting.

4 distance between every voter and the projects. This is where the two-dimensional feature of the GIS shape files is important because it allows me to identify voters for whom the distance from the project is zero, meaning that they live or lived in the project itself. These voters were discarded from the sample. However, GIS data is sometimes inaccurate. GIS coordinates can deviate from the actual physical location of the geocoded object. In order to avoid including in the sample voters that lived in the projects but because of measurement error did not have a distance of zero from the projects, I created a boundary of 10 meters around the perimeter of each housing project shape and discarded all voters within this boundary. The logic in this was that it would discard voters that actually lived in the projects but for which geocoding errors had placed them outside of the projects’ GIS shape. This removed several hundred voters, identified as African American, from the sample. I think there is little danger of false positive error in this case. These projects were free- standing structures, unattached to other buildings and often separated by streets. As such, there seems to be little reason why a person would reside within 10 meters of the project if they did not actually live in the project. Leaving these discarded voters in the sample makes little difference in the estimates. Finally, I discarded all voters that had moved or newly registered after October 10, 2000, the closing date for registration prior to the November 2000 election. This is to eliminate voters that might have moved into the area of treatment after the projects were demolished and to keep the same samples for 2000 and 2004. With all the discarded voters, the final sample was reduced from 1,366,357 to about 848,066 voters.

Home-ownership data I obtained data on homeownership and property values by matching addresses to the web database maintained by the Cook County Registrar. Data on property values, zoning classifications, tax status, and deed transactions were recorded. The voterfile and registrar data are maintained by different sources so matching is imperfect, but 95.6% of voters were success- fully matched. Some matches return multiple properties3 With multiple matches, values of homes and homeownership were measured by both averaging across properties, eliminating properties based on zoning classifications, and by taking high and low values. All strategies yield similar estimates in the analysis presented in the paper. The value of the home was used to proxy for wealth for homeowners. I measured homeownership using two different methods. Deeds were matched to the voterfile by just matching to last name or matching both the first and last name of the parties to transactions in the deeds. Homeownership was also measured by whether properties were listed as being eligible for homeowner tax exemptions. All methods yield substantively similar results.

Treatment Effects with Different P r(race|name) In Figure A.1, I demonstrate changes in the ATE, estimated in Figure 1, as P r(race|name) goes from .91 to 1.00. The white treatment effect and difference between the effect on white

3This is likely due to imperfections in both the voterfile and the registrar data. The registrar data would occasionally list multiple properties at the same site that were, upon inspection and consultation with staff at the Registrar, identified as errors.

5 and black subjects is correlated with the threshold of P r(race|name) used to define the subjects This is consistent with different behavior between white and black voters and with higher name thresholds more strictly distinguishing the groups. When the P r(race|name) is smaller, there is more error in name classification, but the N increases, so the estimates become more precisely defined. The differences in treatment effects between groups also diminishes, which is consistent with individuals being placed in the wrong racial category as the threshold of P r(race|name) becomes smaller.

Parallel Trends In Figure A.2, I demonstrate that treatment and control groups, both black and white, represented in Figure 1 had parallel trends in turnout between 1996 and 2000.4 Note that that all groups followed a similar trend from 1996 to 2000 and, except for the white treatment group, all groups followed a similar trend from 2000 to 2004. This analysis demonstrates that the change in turnout behavior between 2000 and 2004 was unlikely to have been caused by an unmeasured change occurring prior to the demolition of the housing projects.

Other Robustness Checks This section presents a series of robustness checks, estimating models similar to those used in Figures 2 and 3. In the following figures, triangles represent coefficient estimates obtained by OLS regression, squares and circles represent estimates from difference-in-differences es- timators obtained by matching with voters near non-demolished projects and other voters close to the demolished projects, respectively. I match white voters living near demolished projects with white voters living d∗ or greater from the project, using the same variables available for matching in Figure 2. The coefficients on treatment from these regressions are displayed using triangles in Figure A.3. The effects are very similar and even a little stronger to those in Figure 1 using unmatched voters. With these estimates, the effect of treatment is larger than 15 percentage points at d∗ = 100. These demonstrate that the effect in Figure 1 is unlikely to be the result of unmeasured differences between treatment and control among white subjects. Figure A.4 is the same as Figure A.3, except I match on home ownership, rather than Census Income. Figure A.5 is the turnout of the a treatment group of white voters living near demolished projects, compared to a matched control group of white voters living near projects that were not demolished. These are the same voters used in Figure 2. In this figure the estimates are from a difference-in-differences estimator, rather than an OLS estimator as in Figure 2. Figure A.6 represents the same process used in Figure 2 except matching on home ownership, rather than Census Block Group income. The voters in the treatment group are matched with voters living near projects that were not demolished. Triangles represent the coefficients estimated by OLS regression of turnout on treatment. In this case, treatment is a dummy variable for living near a demolished, rather than a non-demolished project. Figure A.7 is the same matching process, but using a difference-in-differences estimator, rather than an OLS estimator. 4Elections prior to 1996 are not available on the Illinois voter file to which I have access.

6 Figure A.8 represents the same process used in Figure 2 except also matching on local Census Block Group percent black for each white voter. The voters in the treatment group are matched with voters living near projects that were not demolished. Triangles represent the coefficients estimated by OLS regression of turnout on treatment. In this case, treatment is a dummy variable for living near a demolished, rather than a non-demolished project. Figure A.9 is the same matching process, but using a difference-in-differences estimator, rather than a regression. Figure A.10 displays the treatment effect when estimated using a matched white treatment group and black control group and using Census data to measure income, rather than matching income using property as in Figure 3. Triangles represent the coefficients estimated by OLS regression of turnout on treatment. In this case, the treatment variable in the regression is the equivalent to a dummy variable for white, since all white voters are in the treatment group and all black voters are in the matched control group. Figure A.11 is the same voters as Figure A.10 except using a difference-in-differences estimator. Figure A.12 is the turnout of the a treatment group of white voters living near demolished projects, compared to a matched control group of black voters living near the same demolished projects and matched on home ownership. These are the same voters used in Figure 3, except in this figure the estimates are difference-in-differences estimates.

Location of Housing Projects and Electoral Districts Figures A.13 and A.14 represent housing project locations and the boundaries of Aldermanic Wards and Congressional Districts in central Chicago. The wards and districts are represented by black lines and housing projects are black shapes. Notice that ward and district boundaries are drawn in a manner which largely isolates residents of the projects from other nearby voters. As noted in the article, only 22% of the voters used in this study lived in the same aldermanic wards as a housing project. If voters living in the same wards as the housing projects are excluded, all analysis presented is substantively unchanged and larger average treatment eﬀects are sometimes obtained. Similarly, while 57% of the voters used were in the same congressional district as a housing project, using only those voters not in the same congressional district as a housing project yields the same substantive results.

7 Figure A.1: Treatment Eﬀects with Diﬀerent P r(race|name)

(a) .91 (b) .92 (c) .93 (d) .94

0.10 0.10 0.10 0.10

0.05 0.05 0.05 0.05

(2774) (2770) (2765) (2756) 0.00 ● (12825) (16834) (20286) 0.00 ● (12760) (16741) (20171) 0.00 ● (12683) (16630) (20035) 0.00 ● (12604) (16515) (19896) (7251) ● (24053) (27463) (33356) (35700) (7219) ● (23919) (27308) (33174) (35491) (7170) ● (23764) (27125) (32928) (7122) ● (23593) (26911) (32658) ● ● ● ● (30718) ● ● ● ● (30547) ● ● ● ● (30324) (35211) ● ● ● (30074) (34884) (135) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● (124) ● (114) ● ● ● ● (12269) ● ● (11888) ● ● (11429) ● (10943) (9030) (8385) ● ● (6012) (8026) ● (6791) ● (6545) (8730) ● (6299) −0.05 −0.05 −0.05 −0.05 (104) ● (3898) ● (4438) ● ● (4259) ● ● (4091) ● (2951) (367) (2815) (2695) (2559) (400) ● ● (347) ● ● ● ● ● (319) ● (1562) ● (1462) ● ● ● ● (839) (763) (703) (1382) (1297) −0.10 (2148) −0.10 (2029) −0.10 (1926) −0.10 (651) (1809)

−0.15 −0.15 −0.15 −0.15 Treatment Effect Treatment Effect Treatment Effect Treatment Effect Treatment

−0.20 −0.20 −0.20 −0.20

−0.25 −0.25 −0.25 −0.25

−0.30 −0.30 −0.30 −0.30

−0.35 −0.35 −0.35 −0.35

100 200 300 400 500 600 700 800 900 1000 100 200 300 400 500 600 700 800 900 1000 100 200 300 400 500 600 700 800 900 1000 100 200 300 400 500 600 700 800 900 1000

Treated Group Distance from Projects Treated Group Distance from Projects Treated Group Distance from Projects Treated Group Distance from Projects

(e) .95 (f) .96 (g) .99 (h) 1.00

8 0.10 0.10 0.10 0.10

0.05 0.05 0.05 0.05

(2740) (2711) (2044) ● ● ● (9223) 0.00 (12437) (16302) (19647) 0.00 (12198) (15956) (19219) 0.00 (11576) (13791) 0.00 (7023) (23302) (6860) (16122) ● (26583) (29674) (32231) (34382) ● (22770) (25994) (28990) (31509) (33572) (5109) ● ● (18364) (22263) (23625) (650) ● ● ● ● ● ● (20392) (2839) (3627) (4431) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● (5095) (5823) ●(6448) (7070) (7411) ● ● ● ● ● ● (1588) ● ● ● ● ● ●● ●● ● ● (10261) ● ● (2218) (4083) (1058)● ● ● (9408) (1401) (2947) ● ● (1372) (1767) −0.05 ● (5619) (7527) −0.05 (73) (5122) (6898) −0.05 ● −0.05 ● ● (3631) ● (3291) (922) (741) (2097) ● (89) (2353) ● ● ● (502) (85) ● (381) ● ● (1011) ● ● (71) ● −0.10 ● (1161) −0.10 (1443) −0.10 ● (586) −0.10 (280) ● (1641) ● (405) ● (291) (581) ● ● (228) (495) (195) (154)

−0.15 −0.15 −0.15 ● −0.15 ● (29) (25) Treatment Effect Treatment Effect Treatment Effect Treatment Effect Treatment

−0.20 −0.20 −0.20 −0.20

−0.25 −0.25 −0.25 −0.25

−0.30 −0.30 −0.30 −0.30

−0.35 −0.35 −0.35 −0.35

100 200 300 400 500 600 700 800 900 1000 100 200 300 400 500 600 700 800 900 1000 100 200 300 400 500 600 700 800 900 1000 100 200 300 400 500 600 700 800 900 1000

Treated Group Distance from Projects Treated Group Distance from Projects Treated Group Distance from Projects Treated Group Distance from Projects

Difference-in-differences results for treatment groups defined by increasing distances from projects for different levels of P r(race|name). Difference are for the mean turnout in 2004 minus 2000 for the treatment group minus control group. White circles represent the mean effect on white voters, black circles represent the mean effect on black voters. P r(race|name) is listed above each figure. The N in each treatment group is in parentheses next to the mean effect. Vertical lines represent the 95% confidence intervals generated by bootstrapped standard errors of the difference between treatment and control. Figure A.2: Parallel Trends Test

0.9

W 0.85 WCT

0.8 W BT C B 0.75 W C T WC WC 0.7 WC BT WT BC BT BC 0.65 Percent Voter Turnout Voter Percent BC WBTT WT 0.6 BT BC

0.55

0.5

1996 1998 2000 2002 2004

Year

Turnout by experimental group in 1996, 2000, and 2004. Wc = white control, Wt = white treatment, Bc = black control, Bt = black treatment. Treatment groups have solid lines, while control groups have dashed lines.

9 Figure A.3: Treatment Eﬀects Using Matched White Control Group

0.10

0.05

0.00 (15166) (11070) (5160) (3262) −0.05 (8180) (1518)

(2192) −0.10 (728) (322) (90) −0.15 Treatment Effect Treatment

−0.20

−0.25

−0.30

−0.35

100 200 300 400 500 600 700 800 900 1000

Treated Group Distance from Projects

Coefficients on treatment as defined by increasing distances from projects from linear regressions on change in turnout from 2004 to 2000 (triangles). Treatment group is matched to a control group of voters living more than d∗ from the demolished projects using nearest neighbor matching. Regressions include variables used in matching as controls. N for the regression using matched groups is next to the point representing the coefficient. Vertical lines represent the 95% confidence intervals generated by bootstrapped standard errors on the treatment coefficient.

10 Figure A.4: Treatment Eﬀects Using Matched White Control Group and Controlling for Home Ownership

0.10

0.05

(12598) 0.00 (9182) (4166) (6734)

(2606) −0.05 (1644)

(74) (1136) −0.10 (248) (598)

−0.15 Treatment Effect Treatment

−0.20

−0.25

−0.30

−0.35

100 200 300 400 500 600 700 800 900 1000

Treated Group Distance from Projects

Coefficients on treatment as defined by increasing distances from projects from linear regressions on change in turnout from 2004 to 2000 (triangles). Treatment group is matched to a control group of voters living more than d∗ from the demolished projects using nearest neighbor matching and matching for home-ownership. Regressions include variables used in matching as controls. N for the regression using matched groups is next to the point representing the coefficient. Vertical lines represent the 95% confidence intervals generated by bootstrapped standard errors on the treatment coefficient.

11 Figure A.5: Diﬀerence-in-Diﬀerences Estimates for Treatment Group and Control Group of Matched White Voters Near Non-demolished Projects

0.10

0.05

0.00 (6579) (8763)

(3297) (5050) −0.05 (2147)

−0.10 (1475)

(523) −0.15 (1013) Treatment Effect Treatment (224) −0.20

−0.25

−0.30 (64)

−0.35

100 200 300 400 500 600 700 800 900 1000

Treated Group Distance from Projects

Difference-in-Differences Estimates for groups used in regression represented in Figure 2. The treatment group is matched to a control group of white voters living near projects that were not demolished, using nearest neighbor matching. Estimates are represented by squares. The N in each treatment group is in parentheses next to the mean effect. Vertical lines represent the 95% confidence intervals generated by bootstrapped standard errors on the difference.

12 Figure A.6: Treatment Eﬀects Using Matched White Voters Near Non-demolished Projects for Control Group and Controlling for Home Ownership

0.10

0.05

0.00 (5467) (7306)

−0.05 (4137) (2645)

−0.10 (1662)

−0.15 (1056)

−0.20 (417) (157) (713) Treatment Effect Treatment −0.25

−0.30

−0.35 (48)

−0.40

−0.45

100 200 300 400 500 600 700 800 900 1000

Treated Group Distance from Projects

Coefficients on treatment as defined by increasing distance from the demolished projects from OLS regressions on change in turnout from 2004 to 2000 (triangles). Based on same matching process used to generate Figure 2, but now also matching on home ownership. N for the regression using matched groups is next to the point representing the coefficient. The treatment group is matched to a control group of white voters living near projects that were not demolished, using nearest neighbor matching. Regressions include variables used in matching as controls. Vertical lines represent the 95% confidence intervals generated by bootstrapped standard errors on treatment coefficient.

13 Figure A.7: Diﬀerence-in-Diﬀerences Estimates for Treatment Group and Control Group of Matched White Voters Near Non-demolished Projects and Controlling for Home Ownership

0.10

0.05

0.00 (5467) (7306)

−0.05 (2645) (4137)

−0.10 (1662)

−0.15 (157) (1056)

(713) −0.20 (417) Treatment Effect Treatment −0.25

−0.30

−0.35 (48) −0.40

−0.45

100 200 300 400 500 600 700 800 900 1000

Treated Group Distance from Projects

Difference-in-Differences Estimates for groups used in regression represented in Figure A.6. The treatment group is matched to a control group of white voters living near projects that were not demolished, using nearest neighbor matching. Estimates are represented by squares. The N in each treatment group is in parentheses next to the mean effect. Vertical lines represent the 95% confidence intervals generated by bootstrapped standard errors on the difference.

14 Figure A.8: Treatment Eﬀects Using Matched White Voters Near Non-demolished Projects for Control Group, with Matching on Block Group Percent Black

0.10

0.05

0.00 (6597) (8747) (5074) (3334) −0.05 (2161) −0.10 (1013) (1488) (218) −0.15 (516)

−0.20 Treatment Effect Treatment −0.25

−0.30

−0.35

−0.40

−0.45 (67)

100 200 300 400 500 600 700 800 900 1000

Treated Group Distance from Projects

Coefficients on treatment as defined by increasing distance from the demolished projects from OLS regressions on change in turnout from 2004 to 2000 (triangles). Based on same matching process used to generate Figure 2, except now also matching on percent black in the Census Block Group. N for the regression using matched groups is next to the point representing the coefficient. The treatment group is matched to a control group of white voters living near projects that were not demolished, using nearest neighbor matching. Regressions include variables used in matching as controls. Vertical lines represent the 95% confidence intervals generated by bootstrapped standard errors on the treatment coefficient.

15 Figure A.9: Diﬀerence-in-Diﬀerences Estimates for Treatment Group and Control Group of Matched White Voters Near Non-demolished Projects, with Matching on Block Group Percent Black

0.10

0.05

0.00 (6597) (5074) (8747) −0.05 (3334)

(2161) −0.10 (218) (1488) −0.15 (1013)

(516) −0.20 Treatment Effect Treatment −0.25

−0.30

−0.35

(67) −0.40

−0.45

100 200 300 400 500 600 700 800 900 1000

Treated Group Distance from Projects

Difference-in-Differences Estimates for groups used in regression represented in Figure A.8. The treatment group is matched to a control group of white voters living near projects that were not demolished, using nearest neighbor matching. Estimates are represented by squares. The N in each treatment group is in parentheses next to the mean effect. Vertical lines represent the 95% confidence intervals generated by bootstrapped standard errors on the difference.

16 Figure A.10: Treatment Eﬀects Using Matched Black Control Group and Using Census Income

0.10

0.05

0.00 (15166) (11070) (728)

(8180) −0.05 (1518) (5160) (3262) (322) −0.10 (2192)

−0.15

−0.20

Treatment Effect Treatment (90) −0.25

−0.30

−0.35

−0.40

−0.45

100 200 300 400 500 600 700 800 900 1000

Treated Group Distance from Projects

Coefficients on treatment as defined by increasing distances from projects from OLS regressions on change in turnout from 2004 to 2000 (triangles). The white treatment group is matched to a black control group of the same N using nearest neighbor matching, including Census income. Regressions include variables used in matching as controls. N used in regression is listed above the points. Vertical lines represent the 95% confidence intervals generated by bootstrapped standard errors on treatment coefficient.

17 Figure A.11: Diﬀerence-in-Diﬀerences Estimates for Treatment Group and Control Group of Matched Black Voters and Using Census Income

0.10

0.05

0.00

(15166) (11070) ● −0.05 (8180) ● ● (5160)● (3262) −0.10 (1518)● ● (728)● (322) (2192) −0.15 ● ●

−0.20 Treatment Effect Treatment −0.25

−0.30

−0.35 (90)● −0.40

−0.45

100 200 300 400 500 600 700 800 900 1000

Treated Group Distance from Projects

Difference-in-Differences Estimates for groups used in regression represented in Figure A.10. The white treatment group is matched to a black control group of the same N using nearest neighbor matching, including Census income. Estimates are represented by circles. The N in each treatment group is in parentheses next to the mean effect.Vertical lines represent the 95% confidence intervals generated by bootstrapped standard errors on the difference.

18 Figure A.12: Diﬀerence-in-Diﬀerences Estimates for Treatment Group and Control Group of Matched Black Voters and Controlling for Home Ownership

0.10

0.05

0.00 (12598) (9182) ● (6734) ● −0.05 ● (4166)● (2606)● −0.10 (598) (1136) (74) (248) ● ● ● ● (1644)● −0.15 Treatment Effect Treatment

−0.20

−0.25

−0.30

−0.35

100 200 300 400 500 600 700 800 900 1000

Treated Group Distance from Projects

Difference-in-Differences Estimates for groups used in regression represented in Figure 3. Estimates are represented by circles.The N in each treatment group is in parentheses next to the mean effect. Vertical lines represent the 95% confidence intervals generated by bootstrapped standard errors on the difference.

19 Figure A.13: Housing Projects and Aldermanic wards in Central Chicago Julia Lathrop Homes ¯ 0 1 2 William Green Homes Kilometers Francis Cabrini Homes

Henry Horner Homes

Rockwell Gardens

Jane Addams Houses

Ogden Courts Loomis Courts

Archer Courts Harold Ickes Homes

Lawndale Gardens Prairie Courts Dearborn Homes Madden Park Homes

Stateway Gardens Wentworth GardensIda B. Wells Homes

Le Claire Courts

Robert Taylor Homes

Midway Gardens Washington Park Homes

Housing projects in central Chicago are represented by solid black shapes and are labeled. Dark lines show aldermanic wards in 2000.

20 Figure A.14: Housing Projects and Aldermanic wards in Central Chicago Julia Lathrop Homes ¯ 0 1 2 William Green Homes Kilometers Francis Cabrini Homes

Henry Horner Homes

Rockwell Gardens

Jane Addams Houses

Ogden Courts Loomis Courts

Archer Courts Harold Ickes Homes

Lawndale Gardens Prairie Courts Dearborn Homes Madden Park Homes

Stateway Gardens Wentworth GardensIda B. Wells Homes

Le Claire Courts

Robert Taylor Homes

Midway Gardens Washington Park Homes

Housing projects in central Chicago are represented by solid black shapes and are labeled. Dark lines show Congressional Districts in 2000.

21 References

King, Gary. 1997. A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data. Princeton, NJ: Princeton University Press.

Wolfe, Heath. 2006. The Oﬃce of Public and Indian Housing is Taking Action to Overse the Section 202 Mandatory Conversion Program. Technical report Oﬃce of Inspector General, Department of Housing and Urban Development.