Identifying Individual and Group Effects in the Presence of Sorting: A Neighborhood Effects Application

Researchers have long recognized that the non-random sorting of individuals into groups generates correlation between individual and group attributes that is likely to bias naı̈ve estimates of both individual and group effects. This paper proposes a nonparametric strategy for identifying these effects in a model that allows for both individual and group unobservables, applying this strategy to the estimation of neighborhood effects on labor market outcomes. The first part of this strategy is guided by a robust feature of the equilibrium in the canonical vertical sorting model of Epple and Platt (1998), that there is a monotonic relationship between neighborhood housing prices and neighborhood quality. This implies that under certain conditions a nonparametric function of neighborhood housing prices serves as a suitable control function for the neighborhood unobservable in the labor market outcome regression. The second part of the proposed strategy uses aggregation to develop suitable instruments for both exogenous and endogenous group attributes. Instrumenting for each individuals observed neighborhood attributes with the average neighborhood attributes of a set of observationally identical individuals eliminates the portion of the variation in neighborhood attributes due to sorting on unobserved individual attributes. The neighborhood effects application is based on confidential microdata from the 1990 Decennial Census for the Boston MSA. The results imply that the direct effects of geographic proximity to jobs, neighborhood poverty rates, and average neighborhood education are substantially larger than the conditional correlations identified using OLS, although the net effect of neighborhood quality on labor market outcomes remains small. These findings are robust across a wide variety of specifications and robustness checks. The authors are grateful to the Department of Housing and Urban Development, Federal Reserve Bank of New York, and the Center for Real Estate and Urban Economic Studies at the University of Connecticut for financial support. We are especially thankful to Steven Durlauf for extensive comments on an earlier draft and to William Brock, Fernando Ferreira, Albert Saiz, Holger Sieg, Todd Sinai and seminar participants at UC Berkeley, NYU, Penn-Wharton, and Wisconsin for many helpful comments and suggestions. The research in this paper was conducted while the authors were Special Sworn Status researchers of the U.S. Census Bureau at the Boston Research Data Center (BRDC). The research results and conclusions expressed are those of the authors and do not necessarily reflect the views of the U.S. Census Bureau, the Department of Housing and Urban Development or any other agency of the U.S. Government. This paper was screened to insure that no confidential data are revealed.