Models of Local Interactions in Economics

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Scheinkman 9601920 The high degree of variance of crime rates across space and across time is one of the oldest puzzles in the social sciences. Under his previous NSF grant the investigator demonstrated that the variation of crime rates across U.S. cities with a population of at least 25,000, is too large to be explained by independent decisions across agents, even after considering a series of urban characteristics. A model is developed where social interactions create enough covariance across individuals to explain the high cross-city variance of crime rates. This model provides a natural index of social interactions which can compare the degree of social interaction across crimes, across geographic units and across time. This empirical framework allows the decomposition of the gap between actual crime rates and the crime rates predicted using observable characteristics, into a portion due to omitted city characteristics, and a portion due to social interactions. The empirical estimates of the index of social interactions gives similar results for different data samples, and suggests that the amount of social interactions are highest in petty crimes (such as larceny and auto theft), moderate in more serious crimes (assault, burglary and robbery) and almost negligible in murder and rape. Part of the work continues research on understanding and measuring the role of social interactions in the determination of crime rates. The index of social interactions is also applied to non-criminal choices such as teenage pregnancies and schooling choice. The proposal also deals with dynamic models of social interaction with an aim to modeling and measuring the role of social interactions in the determination of the behavior of migration over time and in the distribution of migration across communities.