Ekonomi ve Finans Bölümü Koleksiyonu
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- ItemBridging the Education–Employment Gap in Europe: An AI-Driven Approach to Skill Matching(MDPI, 2025-10) Sanguino, Ramón; Çağlarırmak Uslu, Nilgün; Karahan Dursun, Pınar; Özdemir, Caner; Barroso, Ascensión; Sánchez-Hernández, María Isabel; Gaga, Eftade O.; 414023Education–employment mismatch represents a persistent structural issue across Europe, especially among young people. In line with the digital transformation, green transformation and population aging, new jobs are emerging every day, and some of the older jobs are disappearing. However, existing skills of job seekers may not fit these new jobs. This article presents results from the EMLT + AI project, which aimed to explore how artificial intelligence (AI) tools could contribute to reducing such mismatches and supporting inclusive labor market integration. Based on a sample of 1039 participants across European countries, we analyzed the alignment between individuals’ educational background and their current employment, as well as their willingness to reskill. Using binary logistic regression models, the study identifies key factors influencing mismatch and reskilling motivation, including educational level, type of occupation, the presence of meaningful career guidance, and AI-based job search practices. The results indicate that individuals who hold a master’s degree and work in positions requiring at least bachelor’s level degrees are more likely to be matched with jobs that align with their field of study. However, access to mentoring remains limited. The paper concludes by proposing an AI-supported training model integrating career recommendation systems, flexible learning modules, and structured mentoring. These findings provide empirical evidence on how emerging technologies can foster more responsive and adaptive education-to-employment transitions, contributing to policy innovation and the development of inclusive digital labor ecosystems in Europe.
- ItemSpatial analysis of the macroeconomic determinants of crime: Evidence from regions of Türkiye(Elsevier, 2025-09) Şengül, Serkan; Canbay, Şerif; 355807This study investigates the macroeconomic and spatial determinants of crime rates in Türkiye by employing spatial panel data models. Using annual data from 12 NUTS-1 regions over the period 2009–2022, the analysis examines the effects of unemployment rate, income inequality (Gini coefficient), inflation rate, per capita income (GDP), public education expenditures, and urbanization on crime rates. Recognizing the spatial dependence structure of regional data, spatial error models are preferred over classical panel estimators. In addition to total crime, the study disaggregates crime data into two major categories—assault and theft—to assess the robustness of results and explore type-specific dynamics. The findings reveal that income inequality, per capita income, and inflation have positive and statistically significant effects on crime rates across all models. While public education expenditures do not exhibit a significant direct effect, their spatially lagged values show a negative and significant relationship with both total and disaggregated crime categories. The urbanization variable is found to reduce theft rates, although its effect on assault is weaker and not consistently significant. The unemployment rate does not appear to be a significant determinant in most specifications. These results highlight the importance of spatially informed, region-specific crime prevention strategies. In particular, the findings emphasize the relevance of interregional spillover effects of education policy and the differentiated impact of urbanization on distinct types of crime. The study contributes to the literature by offering both empirical and methodological advancements through the use of disaggregated crime data and spatial econometric techniques.
