DukeSpace

A Unified Framework for Measuring Preferences for Schools and Neighborhoods

DukeSpace

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dc.contributor.author Bayer, Dr Patrick en_US
dc.contributor.author Ferreira, Fernando V. en_US
dc.contributor.author McMillan, Robert en_US
dc.date.accessioned 2010-03-09T15:41:22Z
dc.date.available 2010-03-09T15:41:22Z
dc.date.issued 2007 en_US
dc.identifier.uri http://hdl.handle.net/10161/2014
dc.description.abstract This paper develops a comprehensive framework for estimating household preferences for school and neighborhood attributes in the presence of sorting. It embeds a boundary discontinuity design in a heterogeneous model of residential choice to address the endogeneity of school and neighborhood attributes. The model is estimated using restricted-access Census data from a large metropolitan area, yielding a number of new results. First, households are willing to pay less than one percent more in house prices -- substantially lower than previous estimates -- when the average performance of the local school increases by five percent. Second, much of the apparent willingness to pay for more educated and wealthier neighbors is explained by the correlation of these sociodemographic measures with unobserved neighborhood quality. Third, neighborhood race is not capitalized directly into housing prices; instead, the negative correlation of neighborhood race and housing prices is due entirely to the fact that blacks live in unobservably lower quality neighborhoods. Finally, there is considerable heterogeneity in preferences for schools and neighbors: in particular, we find that households prefer to self-segregate on the basis of both race and education. en_US
dc.format.extent 410146 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.publisher SSRN eLibrary en_US
dc.subject household preferences en_US
dc.subject self segregation en_US
dc.subject sorting en_US
dc.title A Unified Framework for Measuring Preferences for Schools and Neighborhoods en_US
dc.type Journal Article en_US
dc.department Economics

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