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Tiebout Sorting in Metropolitan Areas
Author(s) -
Bickers Kenneth,
Engstrom Richard N.
Publication year - 2006
Publication title -
review of policy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.832
H-Index - 45
eISSN - 1541-1338
pISSN - 1541-132X
DOI - 10.1111/j.1541-1338.2006.00259.x
Subject(s) - metropolitan area , atlanta , tiebout model , homogeneity (statistics) , sorting , monte carlo method , geography , econometrics , regional science , statistics , economics , computer science , mathematics , public good , programming language , archaeology , microeconomics
In this article, we tackle the issue of sorting at the metropolitan area by utilizing an alternative methodological approach that permits us to avoid problems plaguing earlier studies. For this analysis, we take two Metropolitan Statistical Areas (MSAs) as our test cases: the Houston MSA and the Atlanta MSA. For each metropolitan area, we employ Monte Carlo computer simulations to randomly create a large number of metropolitan “jurisdictional” groupings. Based upon these Monte Carlos, we are able to estimate the level of jurisdictional homogeneity that is attributable to random chance. The observed levels of sorting, including the increasing homogeneity as populations decrease, are entirely consistent with what one might find if clusters of households were randomly grouped together into municipalities.