In this study, it is mainly aimed to predict transition probabilities of individuals who are previously
unemployed and get employment or stay unemployed. In order to do that, Household Labor Force
Surveys conducted in Turkey are merged and matched from 2004 to 2016. Information about
individuals only consists of individual characteristics and qualifications since there should not be any
informative clue about the present situation. To predict those probabilities, logistic regression analysis
as econometric approach, a shallow neural network and machine learning classification algorithms are
run in order to compare them. The results indicate that classification in machine learning is slightly
better than logistic regression and shallow neural network. While XGBoost classifier and Random
Forest get 67% accuracy, logistic regression can predict only 63% of an individual’s transition and
shallow neural network forecasts 51%.
Employment Transition Probability Machine Learning Classification
Birincil Dil | İngilizce |
---|---|
Konular | İşletme |
Bölüm | Makaleler |
Yazarlar | |
Yayımlanma Tarihi | 15 Mart 2019 |
Yayımlandığı Sayı | Yıl 2019 Cilt: 3 Sayı: 1 |
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