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http://dx.doi.org/10.5351/KJAS.2017.30.6.917

Bayesian analysis of Korean income data using zero-inflated Tobit model  

Hwang, Jisu (Department of Economics, Ewha Womans University)
Kim, Sei-Wan (Department of Economics, Ewha Womans University)
Oh, Man-Suk (Department of Statistics, Ewha Womans University)
Publication Information
The Korean Journal of Applied Statistics / v.30, no.6, 2017 , pp. 917-929 More about this Journal
Abstract
Korean income data obtained from Korea Labor Panel Survey shows excessive zeros, which may not be properly explained by the Tobit model. In this paper, we analyze the data using a zero-inflated Tobit model to incorporate excessive zeros. A zero-inflated Tobit model consists of two stages. In the first stage, individuals with 0 income are divided into two groups: genuine zero group and random zero group. Individuals in the genuine zero group did not participate labor market since they have no intention to do so. Individuals in the random zero group participated labor market but their incomes are very low and truncated at 0. In the second stage, the Tobit model is assumed to a subset of data combining random zeros and positive observations. Regression models are employed in both stages to obtain the effect of explanatory variables on the participation of labor market and the income amount. Markov chain Monte Carlo methods are applied for the Bayesian analysis of the data. The proposed zero-inflated Tobit model outperforms the Tobit model in model fit and prediction of zero frequency. The analysis results show strong evidence that the probability of participating in the labor market increases with age, decreases with education, and women tend to have stronger intentions on participating in the labor market than men. There also exists moderate evidence that the probability of participating in the labor market decreases with socio-economic status and reserved wage. However, the amount of monthly wage increases with age and education, and it is larger for married than unmarried and for men than women.
Keywords
Markov chain Monte Carlo; Tobit model; truncated data; zero-inflated data;
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