In this dissertation I analyze the economics of racial segregation. Each chapterprovides theoretical models and empirical methods to analyze the separation ofracial groups in several contexts.Chapter 2 propose a method for measuring residential segregation using techniquesfrom the spatial statistics literature. The available indices of segregationdepend on a partition of the city in neighborhoods: given the spatial distributionof racial groups, different partitions translate in different levels of measured segregation.I propose a location-specific index, that maps individual coordinates to locallevel of segregation. The segregation of the metropolitan area is measure as the averageindividual segregation. Therefore, the level of segregation measured according to myapproach is independent from arbitrary partitions.I show that this method provides a different ranking ofcities' segregation than the traditional neighborhood-based measures. The methodestimates the entire distribution of segregation across individuals and I provideevidence that high levels of aggregate segregation are the consequence of very fewhighly segregated neighborhoods. Using the spatial indices, I show evidence of thenegative effect of segregation on individual outcomes of minorities.Chapter 3 and 4 analyze segregation in social networks. In Chapter 3, I developand estimate a structural model of strategic network formation with heterogeneousagents. Structural estimation of strategic models of network formation is challenging, sincethese models usually have multiple equilibria. I present a dynamic model wherethe network is formed sequentially: each period an individual has the opportunityto update his linking strategy. This generates a sequence of networks that convergesto a unique stationary equilibrium. I characterize the equilibrium as providing thelikelihood of observing a specific network structure in the long run.However the estimation is complicated, since the likelihood is proportional toa normalizing constant that cannot be evaluated or approximated with precision.To overcome this problem, I propose a Bayesian Markov Chain Monte Carlo methodthat allows estimation of the posterior without evaluating the likelihood.I study segregation in social networks using data from Add Health, a surveyof US high schools, containing detailed information on school friendship networks.I find that students prefer interactions with individuals of the same race. The simulationof several busing programs shows that perfect integration across schools may not beoptimal. An equalization of racial shares across schools may increase segregation anddecrease welfare.In Chapter 4, I focus on an alternative estimation method. I proposean approximate Maximum likelihood estimation strategy. Assuming the utilitiesare linear in parameters, it can be shown that the Maximum likelihood maximizationproblem has the same solution of a system of nonlinear equations, which Isolve using a stochastic approximation algorithm. To perform the stochastic approximation,I develop an algorithm to generate samples from the stationary equilibrium of the model.The algorithm is a variant of the Simulated tempering and allows fast convergence to the equilibriumdistribution, decreasing the computational costs of estimation.Using Add Health data, I confirm the results of Chapter 3.
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