JOURNAL ARTICLE
The Damages and Distortions from Discrimination in the Rental Housing Market.
Published In: Quarterly Journal of Economics, 2023, v. 138, n. 4. P. 2505 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Christensen, Peter; Timmins, Christopher 3 of 3
Abstract
This article investigates the welfare costs of racial and ethnic discrimination in the U.S. rental housing market by combining a large-scale correspondence experiment with a structural residential sorting model across five metropolitan statistical areas (Atlanta, Houston, Philadelphia, Cleveland, and San Jose). The study finds that African American and Hispanic/Latinx renters face significant discriminatory constraints that reduce their access to rental units, particularly in neighborhoods with higher amenities such as better schools and lower pollution, resulting in welfare losses equivalent to 4.4% and 3.5% of annual income, respectively. These damages increase with income for African American renters, reflecting stronger discrimination in high-amenity, high-rent neighborhoods and higher marginal utility from such amenities. Additionally, renters of color must expend 10%–30% more search effort than comparable white renters to achieve similar utility levels, and ignoring discrimination in models biases estimates of willingness to pay for neighborhood amenities, potentially distorting public policy decisions. The study is limited to initial contact discrimination on a specific online platform and a narrow rental market segment but provides a utility-theoretic framework to quantify the economic impact of housing discrimination on minority renters.
Additional Information
- Source:Quarterly Journal of Economics. 2023/11, Vol. 138, Issue 4, p2505
- Document Type:Article
- Subject Area:Law
- Publication Date:2023
- ISSN:0033-5533
- DOI:10.1093/qje/qjad029
- Accession Number:172872635
- Copyright Statement:Copyright of Quarterly Journal of Economics is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.