Estimating the Number of Unemployed Months for Individuals in Turkey with the Poisson and Negative Binomial Regression Models(Poisson ve Negatif Binom Regresyon Modelleri ile Türkiye’de Bireylerin İşsiz Kaldığı Ay Sayısının Tahmini)

Abstract

Unemployment is one of the greatest economic and social problems in Turkey, as well as it is in many other countries in the world. Unemployment is often explained by macroeconomic factors. However, demographic and individual characteristics also have an effect on the unemployment duration of individuals, in addition to the macroeconomic factors. The present study aims to find the factors that have an effect on the duration of unemployment of individuals in Turkey with count data regression models. Therefore, the present study examined Poisson Regression (PR) and Negative Binomial Regression (NBR) models, which are used in cases that the dependent variable is count data. The study also aims to determine the model with the best fit to the dataset among the estimated models. In the study, the number of months in which individuals were unemployed was modeled, using the data obtained from the Survey of Income and Living Conditions (SILC) micro dataset of the Turkish Statistical Institute (TURKSTAT) in 2019. 62713 people aged 15 and over participated in the SILC, of which 5889 reported that they were unemployed for one month or more. A model with the best fit and with the independent variables of marital status, education status, and general health status was determined among the seven models determined by the forward selection method. It has been determined that the model that best fits the dataset among the predicted models is the NBR model according to the Akaike Information Criterion (AIC).

Keywords

• count data
• poisson regression model
• negative binomial regression model