jore

Journal of Research in Economics

2636-8307

Marmara University

Business Administration

İşletme

PREDICTION OF TRANSITION PROBABILITIES FROM UNEMPLOYMENT TO EMPLOYMENT FOR TURKEY VIA MACHINE LEARNING AND ECONOMETRICS: A COMPARATIVE STUDY

TÜRKİYE İÇİN EKONOMETRİ VE MAKİNE ÖĞRENMESİ YOLUYLA İŞSİZLİKTEN İSTİHDAMA GEÇİŞ OLASILIKLARININ TAHMİNİ: KARŞILAŞTIRMALI BİR ÇALIŞMA

https://orcid.org/0000-0003-2304-8309

Kütük

Yasin

Güloğlu

Bülent

03 15 2019

3 1 58 75 02 15 2019 03 01 2019

2016

Journal of Research in Economics

In this study, it is mainly aimed to predict transition probabilities of individuals who are previouslyunemployed and get employment or stay unemployed. In order to do that, Household Labor ForceSurveys conducted in Turkey are merged and matched from 2004 to 2016. Information aboutindividuals only consists of individual characteristics and qualifications since there should not be anyinformative clue about the present situation. To predict those probabilities, logistic regression analysisas econometric approach, a shallow neural network and machine learning classification algorithms arerun in order to compare them. The results indicate that classification in machine learning is slightlybetter than logistic regression and shallow neural network. While XGBoost classifier and RandomForest get 67% accuracy, logistic regression can predict only 63% of an individual’s transition andshallow neural network forecasts 51%.

Employment Transition Probability Machine Learning Classification

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