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.
Similar content being viewed by others
Notes
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.
A comprehensive listing of these studies can be found in Gimpel and Hui (2017).
Gimpel and Hui (2017) chose the terms “inadvertent” and “intentional”, with the same meanings as direct and indirect.
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.
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.
All vectors in this paper are column vectors.
To be rigorous, the individual index \(i(t)\) should be time-dependent, but for ease of exposition, we suppress the parentheses and use subscript \(it\).
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.
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 \(g={1,2},\dots ,G\).
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.
Some authors also refer to the conditional logit model as the multinomial model, which, by convention, does not contain alternative-varying regressors.
See also Train (2009), Section 3.7, for a proof.
See, for example, Wooldridge (2010), Section 13.11.
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.
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.
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.
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).
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.
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.
Our neighborhood controls closely match those employed by Ioannides and Zabel (2008).
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.
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).
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.
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.
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.
Results for all years are in the online supplement (Tables S.4–S.9).
Tables S.10–S.15 in the online supplement report the results for all years for Hispanic home buyers.
Tables S.16–S.21 in the online supplement present the results for all years for black home buyers.
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.
We take up possible causal mechanisms underlying the NPA results below.
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.
We thank an anonymous referee for suggesting this mechanism.
We found no data on neighborhood clustering by Hispanic origin for the other two counties (Broward and Palm Beach) of the metro area.
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.
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.
References
Abrams, S. J., & Fiorina, M. P. (2012). “The big sort” that wasn’t: A skeptical reexamination. PS: Political Science Politics, 45(2), 203–210.
Bayer, P., & McMillan, R. (2005). Racial sorting and neighborhood quality. National Bureau of Economic Research Working Paper 11813.
Bishop, B. (2008). The big sort: Why the clustering of like-minded American is tearing us apart. Houghton Mifflin Harcourt.
Blakley, P., & Ondrich, J. (1988). A limiting joint-choice model for discrete and continuous housing characteristics. The Review of Economics and Statistics, 70, 266–274.
Boustan, L. P. (2011). Racial residential segregation in American cities. In N. Brooks, K. Donaghy, & G. J. Knaap (Eds.), The Oxford handbook of urban economics and planning (pp. 318–339). Oxford University Press.
Boxell, L., Gentzkow, M., & Shapiro, J. M. (2017). Greater Internet use is not associated with faster growth in political polarization among US demographic groups. Proceedings of the National Academy of Sciences, 114(40), 10612–10617.
Brown, J. R., & Enos, R. D. (2021). The measurement of partisan sorting for 180 million voters. Nature Human Behaviour, 5(8), 998–1008.
Charles, C. Z. (2003). The dynamics of racial residential segregation. Annual Review of Sociology, 29(1), 167–207.
Cheng, S., & Long, J. S. (2007). Testing for IIA in the multinomial logit model. Sociological Methods & Research, 35(4), 583–600.
Cho, W. K. T., Gimpel, J. G., & Hui, I. S. (2013). Voter migration and the geographic sorting of the American electorate. Annals of the Association of American Geographers, 103(4), 856–870.
Clark, W. A. (1986). Residential segregation in American cities: A review and interpretation. Population Research and Policy Review, 5, 95–127.
Cutler, D. M., & Glaeser, E. L. (1997). Are ghettos good or bad? The Quarterly Journal of Economics, 112(3), 827–872.
Druckman, J. N., & Levy, J. (2022). Affective polarization in the American public. In R. Buchanan, M. Dias, & C. Wlezien (Eds.), Handbook on politics and public opinion (pp. 257–270). Edward Elgar Publishing.
Fay, B. (2020). Economic demographics of Republicans. Last updated May 18, 2020, from https://www.debt.org/faqs/americans-in-debt/economic-demographics-republicans/
Frey, W. H. (2018). Black-white segregation edges downward since 2000, Census shows. The Brookings Institution. Last updated December 17, 2018, from https://www.brookings.edu/articles/black-white-segregation-edges-downward-since-2000-census-shows/
Fry, T. R., & Harris, M. N. (1996). A Monte Carlo study of tests for the independence of irrelevant alternatives property. Transportation Research Part b: Methodological, 30(1), 19–30.
Fry, T. R., & Harris, M. N. (1998). Testing for independence of irrelevant alternatives: Some empirical results. Sociological Methods & Research, 26(3), 401–423.
Gimpel, J. G., & Hui, I. S. (2017). Inadvertent and intentional partisan residential sorting. The Annals of Regional Science, 58, 441–468.
Horowitz, J. M., Brown, A., & Cox, K. (2019). Race in America 2019. Pew Research Center.
Ihlanfeldt, K., & Mayock, T. (2009). Price discrimination in the housing market. Journal of Urban Economics, 66(2), 125–140.
Ihlanfeldt, K., & Scafidi, B. (2002). The neighbourhood contact hypothesis: Evidence from the multicity study of urban inequality. Urban Studies, 39(4), 619–641.
Ihlanfeldt, K., & Yang, C. F. (2023). Are the home values and property tax burdens of permanent homeowners affected by growth in housing rentals and second homes: Evidence based on big data from Florida. Journal of Regional Science, 63(2), 470–502.
Ioannides, Y. M., & Zabel, J. E. (2008). Interactions, neighborhood selection and housing demand. Journal of Urban Economics, 63(1), 229–252.
Kang, S. (2020). Warehouse location choice: A case study in Los Angeles. CA: Journal of Transport Geography, 88, 102297.
Kaysen, R. (2018). Buying into an urban retirement. Last updated February 20, 2018, accessed November, 2023. https://www.aarp.org/home-family/friends-family/info-2018/urban-retirement-fd.html
Long, J. S., & Freese, J. (2014). Regression models for categorical dependent variables using Stata (Vol. 7). London: Stata Press.
Martin, G. J., & Webster, S. W. (2020). Does residential sorting explain geographic polarization? Political Science Research Methods, 8(2), 215–231.
McCartney, W., Orellana-Li, J., & Zhang, C. (2023). Political polarization affects households’ financial decisions, evidence from home sales. Journal of Finance (forthcoming).
McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), Frontiers in econometrics (pp. 105–142). Academic Press.
McFadden, D. (1978). Modeling the choice of residential location. In A. Karlqvist (Ed.), Spatial interaction theory and planning models (pp. 75–96). North-Holland.
McFadden, D. (1979). Quantitative methods for analysing travel behaviour of individuals: some recent developments. In D. A. Hensher & P. R. Stopher (Eds.), Behavioural travel modelling (pp. 279–318). Routledge.
Motyl, M., Iyer, R., Oishi, S., Trawalter, S., & Nosek, B. A. (2014). How ideological migration geographically segregates groups. Journal of Experimental Social Psychology, 51, 1–14.
Mummolo, J., & Nall, C. (2017). Why partisans do not sort: The constraints on political segregation. The Journal of Politics, 79(1), 45–59.
Nerella, S., & Bhat, C. R. (2004). Numerical analysis of effect of sampling of alternatives in discrete choice models. Transportation Research Record, 1894(1), 11–19.
Pew Research Center. (2017). The partisan divide on political values grows even wider. Last updated October 5, 2017. https://www.pewresearch.org/politics/2017/10/05/the-partisan-divide-on-political-values-grows-even-wider/
Pew Research Center. (2022). As partisan hostility grows, signs of frustration with the two-party system. Last updated August 9, 2022. https://www.pewresearch.org/politics/2022/08/09/as-partisan-hostility-grows-signs-of-frustration-with-the-two-party-system/.
Plummer, E. (2003). Evidence on the incidence of residential property taxes across households. National Tax Journal, 56(4), 739–753.
Poterba, J. M. (1989). Lifetime incidence and the distributional burden of excise taxes. American Economic Review, 79(3), 325–330.
Prener, C. G., & Fox, B. (2021). Creating open source composite geocoders: Pitfalls and opportunities. Transactions in GIS, 25(4), 1868–1887.
Quigley, J. M. (1985). Consumer choice of dwelling, neighborhood and public services. Regional Science and Urban Economics, 15(1), 41–63.
Rodriguez, S. (2020). How Miami Cubans disrupted Biden’s path to a Florida win. Last updated November 4, 2020. https://www.politico.com/news/2020/11/04/biden-miami-cubans-election-2020-433999
Shertzer, A., & Walsh, R. P. (2019). Racial sorting and the emergence of segregation in American cities. The Review of Economics and Statistics, 101(3), 415–427.
Su, Y., Hua, Y., & Deng, L. (2021). Agglomeration of human capital: Evidence from city choice of online job seekers in China. Regional Science and Urban Economics, 91, 103621.
Su, Y., Hua, Y., & Liang, X. (2019). Toward job or amenity? Evaluating the locational choice of internal migrants in China. International Regional Science Review, 42(5–6), 400–430.
Toro, M. (2014). Maps: Los Latinos en Miami-Dade. Last updated April 17, 2014. https://miamigeographic.com/2014/04/17/maps-los-latinos-en-miami-dade/
Train, K. E. (2009). Discrete choice methods with simulation. Cambridge University Press.
Vigdor, J. L. (2009). The perplexing persistence of race: Segregation and the fortunes of African-Americans, 1968–2008. In R. P. Inman (Ed.), Making cities work: Prospects policies for Urban America (pp. 201–225). Princeton University Press.
Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT Press.
Acknowledgements
We wish to thank the Editor, Pete Leeson, the Associate Editor, Ennio E. Piano, and two anonymous referees for their valuable insights and constructive suggestions. We are also grateful for the helpful comments from the participants of the 2023 North America Summer Meeting of the Econometric Society and the Midwest Econometrics Group Conference. This paper supersedes a previous version entitled "Out-Group Dislike and Neighborhood Choice."
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors have no relevant financial or non-financial interests to disclose.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ihlanfeldt, K., Yang, C.F. Political and racial neighborhood sorting: How is it changing?. Public Choice 198, 161–187 (2024). https://doi.org/10.1007/s11127-023-01120-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11127-023-01120-6