Abstract

This paper empirically examines the impact of various variables on Perceived Risk of Crime (PRC) for Italian regions in the period of 2004–2022 using data collected in the framework of the Benessere Equo e Sostenibile (BES, Equitable and Sustainable Well-being) questionnaire conducted by the Italian National Institute of Statistics (ISTAT). The econometric analysis of the relationship is based on panel regressions (including fixed-, random-, and dynamic-effects estimations). Moreover, weighted least squares regression and machine learning algorithms are used as robustness checks. The results of the empirical estimation reveal the importance of both objective and subjective factors as drivers of PRC. The main positive influences on PRC are provided by pickpocketing cases and fear of crime, while generalized and judicial systems’ trust is negatively related to PRC. Additionally, future pessimism and dissatisfaction with the local environment contribute to higher levels of perceived risk. The analysis of the machine learning model confirms the reliability of the principal outcomes and shows the high accuracy of regularized linear regression as a forecasting tool. The clusters identified through the cluster analysis reflect region-specific differences in crime exposure, institutional trust, well-being, and perceptions of security. In summary, perceived insecurity depends not only on actual crimes but also on the combination of crime exposure, emotions and feelings, institutional trust, and the environment. Therefore, a multidimensional approach should be applied for developing an effective strategy aimed at preventing the risks associated with crimes.

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