Tuesday, 23 August 2016

Distribution Dynamics of Property Crime Rates in the United States

Alessandro Moro - Ca’ Foscari University, Italy 

My paper “Distribution Dynamics of Property Crime Rates in the United States” analyses how property crime is distributed across the US states and how this distribution changes over time. This possibly explains why the perception of criminal activity is considerably different and changed in recent decades, especially in some states.

I’m a PhD student in Economics at Ca’ Foscari University in Venice, and I have been always interested in the study of different violence phenomena adopting quantitative methods and agent-based models.

In this work, in particular, I used a non-parametric statistical approach called “Distribution Dynamics” to investigate how property crimes changed across space and time. The paper detects two distinct phases in the evolution of the property crime recorded by FBI’s Uniform Crime Reports data on the 48 continental US states.

There is a phase of strong convergence, from 1971 to 1980, in which the property crime rates of the different US states tend to converge to a common value; but then we observe a period characterised by “divergence”, from 1981 to 2010, in which the differences between the states with the highest property crime rates and those with the lowest ones were exacerbated. These two distinct phases have not been highlighted by the existing literature and, in my view, there are technical merits in using flexible non-parametric methods in place of standard regressions. It’s not easy to provide reasons for what data tell us: the analysis hints that the increasing inequality across the US states in terms of income per capita and state police, started at the beginning of the 1980s, may play a role. This empirical evidence is consistent with the predictions of a proposed simple two-region model that explicitly outlines the channels through which the property crime dynamics is affected by economic inequality. I like simple models despite their obvious limitations: they are clean, sober and almost simplistic but help us in understanding data, that come with no life and meaning. Models enrich data with a story, which is really needed to draw implications and ponder possible actions.

An important policy insight that can be derived from this paper suggests that significant income disparities are translated into different concentrations of crime: poor states have lower resources to fight crime and, consequently, they exhibit higher crime rates. Since the presence of crime discourages investments and lowers income, these states are trapped in a vicious circle.  Therefore, mitigating the effects of inequality, say with cross-state compensations or other measures, in terms of financial and police resources, may help avoiding both the concentration of crime activities in specific regions and the emergence of self-reinforcing gaps between poor and rich states.

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