Objectives: Criminologists have long studied the relationship between economic conditions and crime. Empirical evidence is inconclusive, pointing at different directions. This may reflect the conflicting theoretical predictions on the relationship between these phenomena, but also the prevailing methodological choice which focuses on linear relationships even though nonlinearities are plausible theoretically. Methods: In this paper, we revisit the empirical relationship between economic conditions and crime by exploring potential nonlinearities. We look at flexible parametric specifications that include up to a cubic term of per capita income (or one dummy for each income quintile) and nonparametric and semi-parametric specifications (such as General Additive Models). Our results are robust to controlling for the standard socioeconomic, demographic, and policy determinants of crime, as well as to including a lagged dependent variable or state and time fixed effects. Results: We document the existence of an inverted U-shaped relationship between crime and income within US states for the period 1970-2011. Crime increases with per capita income until it reaches a maximum, and then decreases as income keeps rising. This “Crime Kuznets Curve” (CKC) exists for property crime and for categories of violent crime that can be related to economic appropriation, like robbery, and is less robust for violent crimes not connected to economic incentives. We show that this pattern cannot be explained by correlated changes in economic inequality or by changes in law enforcement. Conclusions: In addition to providing robust evidence of the existence of a CKC, our findings lay the groundwork for studies exploring the underlying theoretical mechanisms. These should go beyond income inequality or law enforcement, and should explain why the results hold more clearly for property than for violent crime. Our findings and subsequent research to understand the underlying drivers are relevant for policy, as they suggest that violent conflict cannot be tackled solely by the trickle-down forces of economic growth.
The Crime Kuznets Curve
Vargas J. F.
Last
2017-01-01
Abstract
Objectives: Criminologists have long studied the relationship between economic conditions and crime. Empirical evidence is inconclusive, pointing at different directions. This may reflect the conflicting theoretical predictions on the relationship between these phenomena, but also the prevailing methodological choice which focuses on linear relationships even though nonlinearities are plausible theoretically. Methods: In this paper, we revisit the empirical relationship between economic conditions and crime by exploring potential nonlinearities. We look at flexible parametric specifications that include up to a cubic term of per capita income (or one dummy for each income quintile) and nonparametric and semi-parametric specifications (such as General Additive Models). Our results are robust to controlling for the standard socioeconomic, demographic, and policy determinants of crime, as well as to including a lagged dependent variable or state and time fixed effects. Results: We document the existence of an inverted U-shaped relationship between crime and income within US states for the period 1970-2011. Crime increases with per capita income until it reaches a maximum, and then decreases as income keeps rising. This “Crime Kuznets Curve” (CKC) exists for property crime and for categories of violent crime that can be related to economic appropriation, like robbery, and is less robust for violent crimes not connected to economic incentives. We show that this pattern cannot be explained by correlated changes in economic inequality or by changes in law enforcement. Conclusions: In addition to providing robust evidence of the existence of a CKC, our findings lay the groundwork for studies exploring the underlying theoretical mechanisms. These should go beyond income inequality or law enforcement, and should explain why the results hold more clearly for property than for violent crime. Our findings and subsequent research to understand the underlying drivers are relevant for policy, as they suggest that violent conflict cannot be tackled solely by the trickle-down forces of economic growth.
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