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Published in: Public Choice 1-2/2024

18-10-2023

Political and racial neighborhood sorting: How is it changing?

Authors: Keith Ihlanfeldt, Cynthia Fan Yang

Published in: Public Choice | Issue 1-2/2024

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Abstract

It is well known that the racial composition of a neighborhood influences who chooses to live there. Less established is whether the political party mix of the neighborhood influences neighborhood choice. In this paper, we study racial and political neighborhood sorting, their interaction, and how they are changing over time. Our methodology involves the estimation of a conditional logit model with data on hundreds of thousands of homebuyers whose race and political affiliation are known. The neighborhood choices of homeowners categorized by race and party are explained by a typology that defines neighborhoods by their dominance of a particular party and race/ethnicity. We find that both Democrats and Republicans prefer living in a neighborhood that matches their race and political party, but both show an increased willingness to live in a non-matched neighborhood over the past decade. Our results are encouraging, suggesting that both political and racial/ethnicity neighborhood segregation may subside in the future.

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Appendix
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Footnotes
1
According to Google Scholar, Bishop’s book has been cited nearly 2000 times as of August, 2023. Abrams and Fiorina (2012) noted that the book was mentioned regularly during the 2008 presidential campaign, especially by former president Bill Clinton who urged audiences to read the book.
 
2
A comprehensive listing of these studies can be found in Gimpel and Hui (2017).
 
3
Gimpel and Hui (2017) chose the terms “inadvertent” and “intentional”, with the same meanings as direct and indirect.
 
4
Druckman and Levy (2022) review the extensive survey evidence documenting increasing affective political polarization in the U.S., where affective polarization is defined as dislike and distrust of political out-groups.
 
5
Survey evidence similar to that showing increasing political polarization is not available to measure increasing racial polarization. Strongly suggestive, however, are recent surveys by the Pew Research Center showing race relations in the U.S. have worsened in recent years (Horowitz et al., 2019). These data coincide with a more racially charged political environment around the country and a stream of high-profile police-related incidents resulting in the killing of black men, often by white officers.
 
6
All vectors in this paper are column vectors.
 
7
To be rigorous, the individual index \(i(t)\) should be time-dependent, but for ease of exposition, we suppress the parentheses and use subscript \(it\).
 
8
Metropolitan areas are commonly treated as separate markets in both the housing and labor economics literature, the assumption being that both the search for housing and employment will take place within the same metro area. According to the American Community Survey (ACS) 5-year estimate for the Miami-Dade Metropolitan Area, 97% of the people who live in the area also work within the area.
 
9
Formally, \({{\varvec{z}}}_{ijt}=({{\varvec{w}}}_{it}^{\prime}{d}_{j1},{{\varvec{w}}}_{it}^{\prime}{d}_{j2},\dots ,{{\varvec{w}}}_{it}^{\prime}{d}_{jG})^{\prime}\), where \({d}_{jg}\) is a dummy variable equal to unity if tract \(j\) belongs to neighborhood type \(g\), for \(g={1,2},\dots ,G\).
 
10
The coefficients \({\varvec{\beta}}\) can be identified by normalizing the scale of \({\varepsilon }_{ijt}\). We could also allow \({\varvec{\beta}}\) to be time-varying. However, as we have a large set of control variables in \({\varvec{x}}\) and 4 years of data, to limit the number of parameters, we assume \({\varvec{\beta}}\) to be constant and check the robustness of our results by estimating separate cross section models for each year.
 
11
Some authors also refer to the conditional logit model as the multinomial model, which, by convention, does not contain alternative-varying regressors.
 
12
See also Train (2009), Section 3.7, for a proof.
 
13
While statistical tests of IIA do exist, the literature does not recommend them due to their unreliability (Cheng & Long, 2007; Fry & Harris, 1996, 1998; Long & Freese, 2014).
 
14
See, for example, Wooldridge (2010), Section 13.11.
 
15
According to the ACS 5-year estimates (2016–2020), non-Hispanic whites (blacks) are 30 (20)% of the total population. The Hispanic percentage is 45. The Florida Division of Elections reports that 42, 26, and 30% of registered voters are Democrats, Republicans, and NPA, respectively.
 
16
The DOE voter registration data for 2020 was provided by them. The 2022 and 2017 voter registration data were provided by Harvard Dataverse. Other years were not available either from DOE or Harvard.
 
17
Only a small percentage of the home purchasers on the 2013 tax roll were not found on the 2017 voter roll. However, some misclassification results to the extent that homeowners changed their political affiliation between 2013 and 2017.
 
18
The use of consumption-based measures of permanent income is defended by Poterba (1989), and housing consumption is used empirically by Plummer (2003) and home value by Ihlanfeldt and Yang (2023).
 
19
The geocoding was implemented using the R package censusxy, which provides access to the Census Bureau’s Geocoding A.P.I. System (Prener & Fox, 2021).
 
20
A possible concern is that the association of political party with race/ethnicity would result in too few neighborhoods for home buyers to choose from in some of the other neighborhood groups defined by their dominant party and dominant race/ethnicity. There are 1197 total tracts in the Miami-Dade metro area. Each party and race/ethnicity combination exceeds 100 tracts in every year of our sample. In the pooled sample across years, there are 497 Republican/white, 575 Republican/Hispanic, 1447 Democratic/white, 1022 Democratic/Hispanic, and 856 Democratic/black neighborhoods.
 
21
Between the years 2012 and 2021, approximately 89% (91%) of all tracts retained their dominant race/ethnicity (political party). About 81% belonged to the same neighborhood type in 2012 and 2021.
 
22
Our neighborhood controls closely match those employed by Ioannides and Zabel (2008).
 
23
In most cases, the year with the fewest sales is 2018. The number of homes for sale equaled 3485 in Republican/white, 2335 in Republican/Hispanic, 8662 in Democratic/white, 6409 in Democratic/Hispanic, 3526 in Democratic/black, 237 in NPA/white, and 1613 in NPA/Hispanic neighborhoods.
 
24
Various pseudo-R2 measures have been proposed to extend the classical R2 to nonlinear models. McFadden's pseudo-R2 is a popular likelihood-based measure, with a higher value indicating a better model fit. Note that although it ranges from 0 to 1, McFadden's pseudo-R2 is typically considerably lower than the usual R2 (McFadden, 1979).
 
25
We also implemented an alternative approach to gauging the importance of the neighborhood types in explaining the neighborhood choices of homeowners. We compared the effects of the neighborhood types to the neighborhood control variables having the most significant effects. In order of importance, these are: (1) fraction of owner-occupied units, (2) fraction of households who moved in over the last 5 years, (3) the median age of homes, and (4) population density. The estimated average marginal effect of each of the latter control variables and the estimated average marginal effects of the neighborhood types are similar in magnitude.
 
26
With few exceptions, the neighborhood control variables for all three groups are highly statistically significant, with many of the variables having the same signs. To illustrate, we report in Table S.3 of the online supplement the results for white, black, and Hispanic Democrats. The results for Republicans mirrored those obtained for Democrats.
 
27
Generally, the estimates show a monotonic pattern, either up or down, over the 4 years, suggesting that changes between 2012 and 2021 are not the result of one of the years registering an outlier year.
 
28
Results for all years are in the online supplement (Tables S.4–S.9).
 
29
Tables S.10–S.15 in the online supplement report the results for all years for Hispanic home buyers.
 
30
Tables S.16–S.21 in the online supplement present the results for all years for black home buyers.
 
31
Note that some of the estimates for black homeowners are large negative numbers. This is due to observing very few black buyers within a group choosing these neighborhoods. Most frequently, the neighborhood is a white/NPA neighborhood.
 
32
We take up possible causal mechanisms underlying the NPA results below.
 
33
Exponentiated coefficients in multinomial logit models are also known as relative risk ratios or conditional odds ratios. Both interpretations are relative to the reference group.
 
34
We thank an anonymous referee for suggesting this mechanism.
 
35
We found no data on neighborhood clustering by Hispanic origin for the other two counties (Broward and Palm Beach) of the metro area.
 
36
The top four groups in Palm Beach County are Cubans, Mexicans, Puerto Ricans, and Columbians, with each group representing between 10 and 20% of the total number of Hispanics. In Broward County, the top four groups are Cubans, Puerto Ricans, Colombians, and Venezuelans, again with each representing between 10 and 20% of the Latino population.
 
38
Frey (2018) uses the index of dissimilarity to measure the degree of segregation, which compares the distribution of blacks across the metro area’s census tracts with the distribution of whites across the same tracts.
 
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Metadata
Title
Political and racial neighborhood sorting: How is it changing?
Authors
Keith Ihlanfeldt
Cynthia Fan Yang
Publication date
18-10-2023
Publisher
Springer US
Published in
Public Choice / Issue 1-2/2024
Print ISSN: 0048-5829
Electronic ISSN: 1573-7101
DOI
https://doi.org/10.1007/s11127-023-01120-6

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