Crime, Trust, and Quality of Life: Determinants of Perceived Insecurity across Italian Regions

Crime, Trust, and Quality of Life: Determinants of Perceived Insecurity across Italian Regions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Crime, Trust, and Quality of Life: Determinants of Perceived Insecurity across Italian Regions Massimo Arnone, Angelo Leogrande, Carlo Drago, Alberto Costantiello, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8889137/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The paper aims to investigate the determinants of the Perceived Risk of Crime (PRC) in Italian regions for the period 2004-2022, with data provided by the ISTAT-BES framework. The analysis relies on a regional panel dataset, which is somewhat unbalanced, with an extensive set of socio-institutional, crime, and subjective well-being variables, such as social participation, trust in people, trust in the judiciary, pickpocketing, fear of crime, life satisfaction, pessimism about the future, and dissatisfaction with the regional landscape. The analysis combines classical panel data methodologies with machine learning techniques to check the robustness of the results and to detect regional latent patterns. In all models, namely, fixed effects, random effects, dynamic panel, and weighted least squares, it is confirmed that objective crime variables, as well as subjective ones, play a crucial role in determining PRC. In particular, it is confirmed that, among the variables, pickpocketing and fear of crime are the most important positive determinants of PRC, while trust in people and trust in the judiciary have a significant mitigating effect on PRC. Variables concerning pessimism about the future and environmental dissatisfaction are also confirmed to have a positive effect on PRC. Among several machine learning alternatives, the regularized linear regression model is selected as the best-performing predictive model, which provides an interpretable and accurate representation of the relationships between the variables. In addition, model-based clustering allows us to detect different regional profiles characterized by different combinations of crime, trust, well-being, and security perceptions. In conclusion, the results confirm that PRC in Italian regions depends on the complex interaction between actual crime, emotional reactions, trust, and quality of life, suggesting that effective policies to address PRC should be based on the integrated action of crime control strategies, trust-building, social cohesion, and quality of the regional landscape. JEL codes: C23, C38, D74, I31, R11 Perceived risk of crime Social trust Fear of crime Quality of life Italian regions Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8889137","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591812323,"identity":"275dee2c-40fa-4620-a285-7fed5efa0c00","order_by":0,"name":"Massimo Arnone","email":"","orcid":"","institution":"University of Catania","correspondingAuthor":false,"prefix":"","firstName":"Massimo","middleName":"","lastName":"Arnone","suffix":""},{"id":591812324,"identity":"8918904e-76fd-408a-93d7-db4fe669a314","order_by":1,"name":"Angelo 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