53rd Congress of the European Regional Science Association: "Regional Integration: Europe, the Mediterranean and the World Economy", 27-31 August 2013, Palermo, Italy
Becker (1968) and Stigler (1970) provide the germinal works for an economic analysis of crime. The approach they outlined has been utilised to consider the response of crime rates to a range of economic, criminal and socioeconomic factors. Until recently however this did not extend to a consideration of the role of personal indebtedness in explaining the observed pattern of crime. This paper builds on a recent publication in the literature (McIntyre & Lacombe 2012), and using the Becker (1968) and Stigler (1970) framework, extends to a fuller consideration of the relationship between personal indebtedness and theft crimes. We also extend the existing empirical literature by building on recent extensions of the Becker (1968) model to the case of non-expected utility. This extension provides an interesting and useful way of motivating the observed level of crime, which better reflects the reality of criminality. We adopt the cumulative prospect theory approach in motivating the personal indebtedness and theft crime relationship. There has been a large increase in personal debt in the past decade, which combined with the recent global recession, has led to a large increase in personal insolvencies. This paper uses data available at the neighbourhood level for London, UK on county court judgments (CCJ?s) granted against residents in that neighbourhood for the years 2003 to 2005. We use this as our measure of personal indebtedness, and examine the relationship between a range of community characteristics (economic, socioeconomic, etc), including the number of CCJ?s granted against residents, and the observed pattern of theft crimes using spatial econometric methods. Specifically, we estimate three common spatial econometric models, the spatial error model, the spatial autoregressive model and the spatial Durbin model using Bayesian methods, before calculating posterior model probabilities to select the best model. Our results demonstrate the importance of personal indebtedness in explaining the observed pattern of theft crimes, as well as reinforcing a number of key conclusions in the existing literature. Our results are broadly consistent across time, but vary by crime type as is expected. Our results highlight a number of interesting areas for future research which we will pursue.
Spatial Econometrics Crime Personal Debt Economic Conditions