What does Bayesian probit regression tell us about Turkish female- and male-headed households poverty?

Abstract

The objectives of the study are to examine the determinants of the poverty status and to illustrate the probabilities of household poverty in Turkey using the Household Budget Survey which was prepared by the Turkish Statistical Institute, 2013. The data is reorganized as rural and urban area considering female- and male-headed households so that to analyze the determinants of household poverty. Bayesian probit regression is applied using a Markov Chain algorithm, Gibbs sampler. The results of the study show that the most effective variables, which cause a decrease of the probability of living under poverty line, are education level of bachelor for 4 years, master and PhD for female-headed households and household type of being single adult for male-headed households in urban area, working full time for male- and female-headed households in rural area. However, other most remarkable variables, which cause an increased risk of poverty, are being elderly, disabled or inoperable for male-headed households, being illiterate for female-headed households in urban area and for rural area, being elderly, disabled or inoperable for male- and marital status of being single for female-headed households.

Keywords

• poverty
• household poverty
• probit
• Bayesian probit regression