Essays in applied microeconomics and microeconometrics

First, I investigate the change in hate crime targeting race or religion after the Brexit vote. My results reveal a substantial and transitory increase in such hate crime following the vote. The focus of my analysis is the considerable spatial heterogeneity of this increase. Areas with a greater increase in hate crime are characterized by both a greater immigrant share and higher income proxies. Issues of multiple hypothesis testing and model selection limit the use of classic methods; therefore, I apply and adapt recent machine learning methods to uncover patterns in the spatial heterogeneity. I then focus on the question how to utilize data from randomized control trials to obtain an optimal dynamic treatment rule. Consider a situation wherein individuals arrive sequentially - for example when becoming unemployed - to a social planner. Once each individual arrives, the planner decides instantaneously on a treatment assignment - for example job training - while taking into account the characteristics of the individual and the remaining capacity to offer training. In order to decide optimally, expectations over the dynamic process of unemployment patterns are required. Reinforcement learning methods can be used to solve this dynamic optimization problem and the resulting algorithm has a number of desirable properties. Finally, I study the creation of not-for-profit firms. Reputation is key for high-quality producers when quality is only observed after the time of purchase. For companies that potentially enter several markets, I show that the concern for reputation affects both the optimal organizational form and the decision which markets to enter. Specifically, a market with poor customers that would be ignored in isolation can be served for signaling purposes. The optimal organizational forms in that case are a not-for-profit firm used for signaling in the “market for the poor” and an associated for-profit firm in the “market for the rich”.