What happens to crime after an increase in income inequality? The microeconomics literature that attempts to answer this question often employs identification strategies that exploit external sources of variation that provide quasi‐experiments to identify causal effects. In contrast, this paper tackles this question by using structural vector autoregressions (SVAR), a methodology typically employed in modern empirical macroeconomics to identify and estimate dynamic causal effects of exogenous shocks. Unlike the macroeconomic SVAR models that are often applied to time‐series data, we exploit the time series and cross‐sectional dimensions of our data, leading to the estimation of panel SVAR models. Using U.S. state‐level data for the period 1960–2015, our results indicate that structural shocks to inequality increase both violent and property crime. Variance decomposition analyses show that inequality has little explanatory power for movements in crime.

中文翻译：

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确定收入不平等对犯罪的动态影响

收入不平等加剧之后，犯罪将会如何？试图回答这个问题的微观经济学文献经常采用识别策略，这些策略利用外部变异源提供准实验来识别因果关系。相比之下，本文通过使用结构矢量自回归（SVAR）解决了这个问题，SVAR是现代经验宏观经济学中通常用来识别和估计外源冲击的动态因果关系的方法。与通常用于时间序列数据的宏观经济SVAR模型不同，我们利用数据的时间序列和横截面维度，从而估算了面板SVAR模型。使用美国1960年至2015年期间的州级数据，我们的结果表明，对不平等现象的结构性冲击加剧了暴力犯罪和财产犯罪。