Models of Local Interactions in Economics

经济学中的地方互动模型

• 批准号: 9601920
• 负责人: Lars Hansen
• 金额: $ 11.13万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-08-15 至 2000-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9601920&HistoricalAwards=false
• 关键词: Models; Local; Interactions; Economics

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.

Scheinkman 9601920 犯罪率在空间和时间上的高度差异是社会科学中最古老的难题之一。 在他之前的NSF资助下，研究人员证明，美国人口至少为25，000的城市犯罪率的变化太大，无法通过跨代理商的独立决策来解释，即使在考虑了一系列城市特征之后。 建立了一个模型，在这个模型中，社会互动在个体之间产生了足够的协方差，以解释犯罪率的高跨城市方差。 该模型提供了一个自然的社会互动指数，可以比较不同犯罪、不同地理单位和不同时间的社会互动程度。 该实证框架允许将实际犯罪率与使用可观察特征预测的犯罪率之间的差距分解为由于遗漏的城市特征而导致的部分和由于社会互动而导致的部分。 社会互动指数的经验估计对不同的数据样本给出了类似的结果，并表明社会互动的数量在轻微犯罪（如盗窃和汽车盗窃）中最高，在更严重的犯罪（袭击，入室盗窃和抢劫）中中等，在谋杀和强奸中几乎可以忽略不计。 这项工作的一部分是继续研究如何理解和衡量社会互动在确定犯罪率方面的作用。 社会互动指数也适用于非犯罪选择，如少女怀孕和学校选择。 该提案还涉及社会互动的动态模型，目的是建立模型和衡量社会互动在确定一段时间内的移徙行为和在各社区之间的移徙分布方面的作用。