Are people willing to pay for less segregation? Evidence from U.S. internal migration
Junfu Zhang and
Liang Zheng
Regional Science and Urban Economics, 2015, vol. 53, issue C, 97-112
Abstract:
It is difficult to determine whether racial housing segregation is socially desirable, because segregation has some effects that are hard to measure. To overcome this challenge, we estimate a migration choice model to measure the willingness to pay for reduced segregation. The key idea underlying our empirical approach is that if segregation is undesirable, migrants should be willing to give up some earnings to avoid living in segregated cities. Using decennial census data from 1980 to 2000, we provide evidence that segregation is an urban disamenity. It is shown that both black and white migrants prefer to live in less segregated cities. For example, for a one percentage point reduction in the dissimilarity index, the estimated marginal willingness to pay of blacks is $436 (in 1999 dollars) in 2000. Among whites, this marginal willingness to pay is $301.
Keywords: Residential segregation; Willingness to pay; Internal migration; Discrete choice model (search for similar items in EconPapers)
JEL-codes: O15 R12 R23 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0166046215000435
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:regeco:v:53:y:2015:i:c:p:97-112
DOI: 10.1016/j.regsciurbeco.2015.05.002
Access Statistics for this article
Regional Science and Urban Economics is currently edited by D.P McMillen and Y. Zenou
More articles in Regional Science and Urban Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().