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 naive estimates of both individual and group effects. This paper proposes a non-parametric 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 non- parametric 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 individual's 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 Effect of Classmate Characteristics on Individual Outcomes: Evidence from the Add Health

We use data from the National Longitudinal Study of Adolescent Health (Add Health) to examine the effects of classmate characteristics on economic and social outcomes of students. The unique structure of the Add Health allows us to estimate these effects using comparisons across cohorts within schools, and to examine a wider range of outcomes than other studies that have used this identification strategy. This strategy yields variation in cohort composition that is uncorrelated with student observables suggesting that our estimates are not biased by the selection of students into schools or grades based on classmate characteristics. We find that increases in the percent of classmates whose mother is college educated has significant, desirable effects on educational attainment and substance use. We do not find much evidence that the percent of classmates who are black or Hispanic has significant effects on individual outcomes, on average. Additional analyses suggest, however, that an increase in the percent black or Hispanic may increase dropout rates among black students and post-high school idleness among males.