Celbiş Mehmet Güney
Department of Economics, Yeditepe University, Istanbul, Turkey.
Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT), Maastricht, The Netherlands.
Appl Spat Anal Policy. 2022 Jun 2:1-25. doi: 10.1007/s12061-022-09464-0.
This paper aims to provide policy-relevant findings that can contribute to the resilience of rural regions by discovering the main individual-level factors related to unemployment in those areas through the use of a set of machine learning techniques. Unemployment status is predicted using tree-based classification models: namely, classification tree, bootstrap aggregation, random forest, gradient boosting, and stochastic gradient boosting. The results are further analyzed using inferential techniques such as SHAP value analysis. Results suggest that access to training programmes can mitigate the labor market inequalities caused by differences in education levels, gender, age, alongside with parental education levels. The results also show how such inequalities are even larger for various subgroups detected by the employed algorithms.
本文旨在通过运用一系列机器学习技术，找出与农村地区失业相关的主要个体层面因素，从而提供与政策相关的研究结果，以增强农村地区的恢复力。使用基于树的分类模型预测失业状况，即分类树、装袋法、随机森林、梯度提升和随机梯度提升。使用诸如SHAP值分析等推理技术进一步分析结果。结果表明，参加培训项目可以缓解由教育水平、性别、年龄以及父母教育水平差异所导致的劳动力市场不平等现象。结果还显示，对于所采用算法检测出的不同亚组而言，这种不平等现象甚至更为严重。
