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.

Are Shirking and Leisure Substitutable? An Empirical Test of Efficiency Wages Based on Urban Economic Theory

Recent theoretical work has examined the spatial distribution of unemployment using the efficiency wage model as the mechanism by which unemployment arises in the urban economy. This paper extends the standard efficiency wage model in order to allow for behavioral substitution between leisure time at home and effort at work. In equilibrium, residing at a location with a long commute affects the time available for leisure at home and therefore affects the trade-off between effort at work and risk of unemployment. This model implies an empirical relationship between expected commutes and labor market outcomes, which is tested using the Public Use Microdata sample of the 2000 U.S. Decennial Census. The empirical results suggest that efficiency wages operate primarily for blue collar workers, i.e. workers who tend to be in occupations that face higher levels of supervision. For this subset of workers, longer commutes imply higher levels of unemployment and higher wages, which are both consistent with shirking and leisure being substitutable.

Wage Premia in Employment Clusters: Agglomeration Economies or Worker Heterogeneity?

The correlation between wage premia and concentrations of firm activity may arise due to agglomeration economies or workers sorting by unobserved productivity. A worker's residential location is used as a proxy for their unobservable productivity attributes in order to test whether estimated work location wage premia are robust to the inclusion of these controls. Further, in a locational equilibrium, identical workers must receive equivalent compensation so that after controlling for residential location (housing prices) and commutes workers must be paid the same wages and only wage premia arising from unobserved productivity differences should remain unexplained. The models in this paper are estimated using a sample of male workers residing in 33 large metropolitan areas drawn from the 5% Public Use Microdata Sample (PUMS) from the 2000 U.S. Decennial Census. We find that wages are higher when an individual works in a location that has more workers or a greater density of workers. These agglomeration effects are robust to the inclusion of residential location controls and disappear with the inclusion of commute time suggesting that the effects are not caused by unobserved differences in worker productivity. Extended model specifications suggest that wages increase with the education level of nearby workers and the concentration of workers in an individual's own industry or occupation.