This paper defines and studies the determinants of good job based on good job index which was created using KLIPS data. I examined the distribution of good jobs by sex, education, age, regions and etc. Good job indices of male, the thirties, those wit...
This paper defines and studies the determinants of good job based on good job index which was created using KLIPS data. I examined the distribution of good jobs by sex, education, age, regions and etc. Good job indices of male, the thirties, those with higher education level, and in financial sector, public services and manager-specialist group are found to be higher than that of other groups in the analysis. I ran the binary logit regression of the probability of having a good job, and analyzed which group can have good jobs. Regression results show that sex, age, education level, and industry category significantly affect the probability of holding a good job. First, I found that women are less likely than men to hold a good job. Second, as education level rises, it is likely to increase the probability of holding a good job and the gap of probability becomes larger as well. Third, the probability of having a good job in manufacturing industry is much less likely than financial and public service sector. Forth, the probability of having a good job in wholesale and retail food business, electric gas and water supply service are much less likely than manufacturing industry. At last, the probability of having a good job in management sector is higher than other occupations.