Digital Crime: Tracking Sub-Indicator And Determinants

Abstract

Abstract This research focuses on three things, namely the digital world, crime, and the relationship of crime to the digital world. The measurement of the relationship of crime to the digital world consists of two steps of testing. First, examining the power of crime indicators with the digital world constructed on 16 correlations to find the most robust sub-indicators. Second, testing the most robust sub-indicators with determinants of control variables. The control variable determinants used in this study are economic group variables (GDP per capita and Gini ratio), geography (total natural resources rents), social (secondary enrollment), and politics (human freedom index). This research uses the Fixed Effect (FE) and Random Effect (RE) models based on panel data with Ordinary Least Square (OLS) method with 138 observations in the world in 2012-2016. The results obtained include: the crime sub-indicator that is the homicide variable, and the digital world sub-indicator namely individual using the internet, secure internet servers, and information technology export are most robust and consistency of the homicide variable. Furthermore, the determinant of control variables, namely GDP per capita, has a significant negative effect and the GINI ratio has a significant positive effect to homicide, gross secondary enrollment has no significant effect to homicide, the human freedom index has a significant negative relationship to homicide, and the total resources rents variable has a significantly positive effect to the homicide.