ArticleApplied spatial analysis and policy2022
Unemployment in Rural Europe: A Machine Learning Perspective.
Article in Applied spatial analysis and policy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- A comparative analysis of variants of machine learning and time series models in predicting women's participation in the labor force.PeerJ. Computer science · 2024Article
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Abstract
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.
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Registered trials
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