College of Behavioral & Social Sciences
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Item ESSAYS ON NONPARAMETRIC ESTIMATION OF HETEROGENEOUS CAUSAL EFFECTS(2018) Noh, Sungho; Kuersteiner, Guido M; Economics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)My dissertation studies semi- and non-parametric estimation strategies for the distribution of heterogeneous causal effects with applications to labor economics and macroeconomics. \par In the first chapter, I propose a nonparametric strategy to identify the distribution of heterogeneous causal effects. A set of identifying restrictions proposed in this chapter differs from existing approaches in three ways. First, it extends the random coefficient model by allowing potentially non-linear interaction between distributional parameters and the set of covariates. Second, the treatment effect distribution identified in this chapter offers an alternative interpretation to that of the the rank invariance assumption. Third, the identified distribution lies within a sharp bound of distributions of the treatment effect. An estimator exploiting the identifying restriction is developed by extending the classical version of statistical deconvolution method to the Rubin causal framework. I show that the estimator is uniformly consistent for the distribution of causal effects. \par In chapter two, I apply the nonparametric method developed in the previous chapter to the estimation of heterogeneous effects of displacement on earnings losses. Using the Current Population Survey (CPS) individual-level data from 1996 to 2016, I show that the decline in labor incomes of displaced workers is not only substantial in magnitude compared to their non-displaced counterparts, but also varies significantly within groups characterized by, for example, tenure and educational attainment. I find that displaced workers, on average, lose 19\% of their potential earnings while the dispersion of losses among workers is wide. In addition, estimated quantile effects of displacement are more dispersed when the local unemployment rate is higher. \par In the third chapter, co-authored with Guido Kuersteiner, we develop a new asymptotic theory for flexible semi-parametric estimators of dynamic causal effects in data with discrete policy interventions. Our framework extends existing theory of propensity score weighted estimators to weakly dependent processes. We show uniform consistency and asymptotic normality by applying a newly-developed asymptotic theory for the series estimator over a non-compact support. The estimator proposed in this chapter captures non-linear and asymmetric impulse response functions that are often difficult to be accommodated in parametric models.Item Moving Social Disorder Around Which Corner? A Case Study of Spatial Displacement and Diffusion of Benefits(2011) Wyckoff, Laura Ann; Paternoster, Ray; Criminology and Criminal Justice; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Prior research seeking to understand the spatial displacement of crime and diffusion of intervention benefits has suggested that place-based opportunities - levels and types of guardianship, offenders, and targets - explain spatial intervention effects to places proximate to a targeted intervention area. However, there has been no systematic test of this relationship. This dissertation uses observational and interview data to examine the relationship, in two street-level markets, between place-based opportunities and spatial displacement and diffusion of social disorder. The street segment is the unit of analysis for this study, since research shows crime clusters at this level and it is a unit small enough to accurately represent the context for street-level crime opportunities. The study begins by investigating if catchment area (an area proximate to an intervention area) segments with similar opportunities to the target area segments differentially experienced parallel intervention effects as compared to segments with dissimilar opportunity factors. These analyses resulted in null findings. The second set of analyses examined if place-based opportunities predicted the segments which fall into a high diffusion group or a displacement group, as compared to a low/moderate group. These analyses resulted in primarily null findings, except for the measures of public flow and the average level of place manager responsibility which positively predicted the segments in the high diffusion group, as compared to the low/moderate diffusion group. A third set of analyses was also performed where the outcome measure was the odds of the occurrence of a social disorder incident in a measured situation period in the segment during the intervention. These analyses revealed that the situations within segments which had a greater number of possible targets and offenders with a lack of guardianship were more likely to experience incidents of social disorder, reinforcing past findings about the relationship between social disorder and opportunities at place. Place-based opportunity factors are likely important factors in understanding parallel spatial intervention effects, but the null findings suggest additional research is needed to better understand these effects.