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

Abstract

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%.

Keywords

• Employment,Transition Probability,Machine Learning,Classification

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

Öz

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