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The Influence of Crime on Economic Growth: Evidence from a Modified Support Vector Regression Model using Direct Search

Dan Li, Zhi Xiao, Reza Torkzadeh, Xin Dang


This paper examines the relationship between crime and economic growth in China using Least Squares Support Vector Regression (LS-SVR) with Direct Search (DS). As an economically relevant activity, crime has a significant impact on the economy although the exact nature of this impact has not always been clear. Previous studies that examined the
effect of crime on economics have primarily used qualitative approach. A few studies have been quantitative using large data that is not always available in all situations. The support vector regression (SVR) used in this study relies on small sample with high generalization performance to analyze the effect of crime on economics. We construct two models of least squares with a parameter optimization approach and a direct search; a basic model with no directional setting and a second model that specifies direction.Twenty two annual crime data from China (1978 to 2009)were used with these models. The results from both models suggest that crime has a significant negative effect on the economic growth in China. Based
on run time and error rate, the second model shows to be more robust. The findings from this study give us a better understanding of the impact of crime on economics and the significance of such impact.


Support vector regression, Criminal case, Economic growth, Direct search, Optimal parameters

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