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Racial residential segregation in multiple neighborhood markets: a dynamic sorting study

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Abstract

Various degrees of residential segregation by income and race generally exist in U.S. cities. This study extends Sethi and Somanathan’s theoretical model (J Polit Econ 112:1296–1321, 2004) by presenting an agent-based sorting, repeated-game model to quantify the patterns of segregation from a broader perspective. Based on the belief that residential racial segregation is a probabilistic problem without assured results, a numerical model—calibrated to U.S. household income data—is proposed to examine residential segregation by income and racial preferences. Similar to the SimSeg model developed by Fosset (J Math Sociol 30:185–274, 2006a; J Math Sociol 35:114–145, 2011), the numerical model we construct is based on a simple format which also explores segregation dynamics. The simulation results exhibit various degrees of segregation probability in a hypothetical three-neighborhood scenario. It also reveals that although income plays an important role, racial consciousness—the measurement of an agent’s attitude toward the racial composition of the neighborhood—is the dominant factor in determining residential segregation.

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Data source: U.S. Bureau of the Census. Income, Poverty and Health Insurance Coverage in the United States: 2012. Web: www.census.gov

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Source: U.S. Bureau of the Census. Income, Poverty and Health Insurance Coverage in the United States: 2012. Web: www.census.gov

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Notes

  1. These are examples rather than a complete list.

  2. They are called standard capacities and are assigned as \(M_0^\mathrm{I} \), \(M_0^{\mathrm{II}} \) and \(M_0^{\mathrm{III}} \) for neighborhoods I, II and III, respectively.

  3. Bayer et al. (2014) also assumed an exogenous amenity level of the neighborhood in their neighborhood formation mechanism design.

  4. The results were not sensitive to these ratios. If the ratios were adjusted, the results would be scaled up or down proportionally.

  5. The results were not sensitive to rent.

  6. In our model, all agents whose utility provided by living in their present neighborhood was not the highest, needed to apply to relocate. So the number of agents who could simultaneously apply to relocate might range from 0 to 100. But only one agent randomly chosen by the program could successfully relocate in each round. The reason only one agent was permitted to relocate was the capacity limitation of a neighborhood. If all agents who applied to relocate were allowed to move to the new neighborhoods, the capacity limitation of neighborhoods might be broken. This also is true in the real world. A popular neighborhood has a capacity limitation and cannot accept all move-in applications from potential tenants. To simplify the issue, we assumed there was only one request for relocation that could be accepted for one neighborhood in one round.

  7. Some previous research provides empirical data on the distribution of racial preferences. Farley et al. (1994) found that blacks preferred integrated neighborhoods such as a neighborhood comprised of 50% whites and 50% blacks. Using an updated tool, Charles (2003) found that all groups drew quite integrated neighborhoods but also had a preference for at least a substantial number of co-ethnics exceeded the share of any other outgroup. However, we should realize that 50% is only the threshold for an ideally integrated neighborhood for blacks. As Clark (1991) demonstrated, although most blacks preferred an integrated black-white neighborhood, only 7% realized this preference. In the experiment, we set the threshold at 45% which is a little weaker condition than 50%. When we set the threshold a little higher—such as 50 or 53%—it does not change the results.

  8. The population ratios between blacks and whites are quite different from city to city. For 2010, the range was from 0 to 1.19 and the median was 0.1 in 366 metropolitan areas provided by https://s4.ad.brown.edu/projects/diversity/Data/Download3.htm. If we set the ratio at 0.1, the number of blacks was not big enough to fill in any one of the three neighborhoods. The utility of any black agent obtained from the integrated neighborhood \(\hbox {v}\left( \hbox {r}_{\mathrm{ij}} \right) \) was 0. Black households relied solely on their incomes to choose their dwellings. In this case, no racial segregation was observed.

  9. Note that the Y-axis of Fig. 7a is the upturned X-axis from Fig. 5.

  10. Any number of neighborhoods can be set in our model. To serve the purpose of introducing a new agent-based approach, we decided to present the results of our three-neighborhood sorting model with only four types which are relatively easy to understand. When the number of neighborhoods increases, the simulation results become much more complex.

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Acknowledgements

We are grateful to Prof. Thomas Lux and two anonymous referees for helpful comments and suggestions. All errors remain ours. Financial supported by “The Fundamental Research Funds for the Central Universities” is greatly acknowledged.

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Authors and Affiliations

  1. Central University of Finance and Economics, Beijing, 100081, China

    Sheng Li

  2. Agricultural Marketing Service, U.S. Department of Agriculture, 1400 Independence Ave., SW, Washington, DC, 20250, USA

    Kuo-Liang Chang

  3. China Academy of Public Finance and Public Policy, Central University of Finance and Economics, Beijing, 100081, China

    Lanlan Wang

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  1. Sheng Li
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Correspondence to Sheng Li.

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Li, S., Chang, KL. & Wang, L. Racial residential segregation in multiple neighborhood markets: a dynamic sorting study. J Econ Interact Coord 15, 363–383 (2020). https://doi.org/10.1007/s11403-017-0207-2

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