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Identifying the Dynamic Effects of Income Inequality on Crime
Author(s) -
Atems Bebonchu
Publication year - 2020
Publication title -
oxford bulletin of economics and statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.131
H-Index - 73
eISSN - 1468-0084
pISSN - 0305-9049
DOI - 10.1111/obes.12359
Subject(s) - economics , econometrics , exploit , inequality , variance decomposition of forecast errors , variance (accounting) , explanatory power , identification (biology) , contrast (vision) , economic inequality , panel data , estimation , mathematics , computer science , mathematical analysis , philosophy , botany , computer security , accounting , epistemology , management , artificial intelligence , biology
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.