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Segregation and Tiebout Sorting: Investigating the Link between Investments in Public Goods and Neighborhood Tipping


H. Spencer Banzhaf


Georgia State University - Department of Economics; PERC - Property and Environment Research Center; National Bureau of Economic Research (NBER)

Randall Walsh


University of Pittsburgh - Department of Economics

June 2010

NBER Working Paper No. w16057

Abstract:     
Segregation has been a recurring social concern throughout human history. While much progress has been made to our understanding of the mechanisms driving segregation, work to date has ignored the role played by location-specific amenities. Nonetheless, policy remedies for reducing group inequity often involve place-based investments in minority communities. In this paper, we introduce an exogenous location-specific public good into a model of group segregation. We characterize the equilibria of the model and derive the comparative statics of improvements to the local public goods. We show that the dynamics of neighborhood tipping depend on the levels of public goods. We also show that investments in low-public good communities can actually increase segregation.

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Date posted: June 7, 2010  

Suggested Citation

Banzhaf , H. Spencer and Walsh, Randall, Segregation and Tiebout Sorting: Investigating the Link between Investments in Public Goods and Neighborhood Tipping (June 2010). NBER Working Paper No. w16057. Available at SSRN: http://ssrn.com/abstract=1620774

Contact Information

H. Spencer Banzhaf (Contact Author)
Georgia State University - Department of Economics ( email )
P.O. Box 3992
Atlanta, GA 30302-3992
United States
404-413-0252 (Phone)
HOME PAGE: http://www2.gsu.edu/~prchsb/
PERC - Property and Environment Research Center
2048 Analysis Drive
Suite A
Bozeman, MT 59718
United States

National Bureau of Economic Research (NBER) ( email )
1050 Massachusetts Avenue
Cambridge, MA 02138
United States
Randall Walsh
University of Pittsburgh - Department of Economics ( email )
Pittsburgh, PA 15260
United States
Feedback to SSRN (Beta)


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