Exploring Crime Problems from A Statistical Point of View with Negative Binomial Regression

Abstract

Criminality is a complex issue in Indonesia that is very important to the government, law enforcement agencies, and society. The underlying causes of Indonesia's crime problem are complex and impacted by various circumstances. The aim of this research is to model the crime problem in Indonesia and determine the influencing factors.  The method used in this research is Negative Binomial Regression. The results of the study show that the negative binomial regression model can be used to model criminal problems because the variance value is more significant than the average. Based on the parameter significance test results, both simultaneously and partially, the open unemployment rate, Gini ratio, average years of schooling, and prevalence of inadequate food consumption significantly affect the crime rate, with an Akaike’s Information Criterion Corrected (AICc) value of 698,098. These findings suggest that addressing economic inequality, unemployment, education, and food security could help reduce crime in Indonesia. Policies aimed at improving job opportunities, reducing income disparity, and enhancing education and food security are crucial in mitigating crime. This study provides valuable insights for policymakers and law enforcement agencies, offering a foundation for more targeted and effective crime prevention strategies. Future research could employ the robust Poisson Inverse Gaussian Regression method to avoid the overdispersion problem. Criminality is a complex problem in Indonesia that is of particular concern to the government, law enforcement officials and society. The root of the crime problem in Indonesia is diverse and influenced by various factors. Factors that affect the crime rate in Indonesia can be identified using negative binomial regression analysis, which can overcome overdispersion in the response variable. Based on the results of the parameter significance test, both simultaneously and partially, the predictor variables that have a significant effect on the crime rate are the open unemployment rate, gini ratio, average years of schooling, and prevalence of inadequate food consumption, with an AICc value of 698,098.