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우리나라의 이직과 해고에 대한 연구

Title
우리나라의 이직과 해고에 대한 연구
Other Titles
(The) Analysis of layoffs and quits in Korea : used by the panel data
Authors
高允貞
Issue Date
1997
Department/Major
대학원 경제학과
Keywords
이직해고경제학panel
Publisher
이화여자대학교 대학원
Degree
Master
Abstract
This paper discusses determinants of layoffs and quits and how rent affects layoffs and quits There are not many researches on layoffs and quits in Korea. because layoffs rarely happens in Korea, if it would be happened it would not mean firm-initiated separations. So I divided esparations into two groups according to their wage growth; that is wage growth between jobs is higher for quits than for layoffs. The model is consistent with mant empirical regularities of the quit-layoff disticttion. Test results based on structural turn over model are as follows. In the study of empirical analysis, the result came out that education experience, union, race, marital status, children and unemployment rate affect layoffs and quits as I expected before; however industry and occupation do not affect layoffs and quits. In the , government has positive effects on layoffs and quits, but in the
, it has negative effects on layoffs after I added outside wage offer to the variables in order to correct results. The firm size has negative effects on layoffs and quits. In the real statistics , it is also came out that the separation rate is 35% in the small firms (less than 199 people) but the separation rate is 19.6% in the large ones (more than 200 people). The main focus of my paper is how rent affects layoffs and quits. The parameter estimates in the
indicate that the separation rate is decreasing in the difference between opportunities within and outside the incumbent firm. The empirical problem in testing this implication of the theory arises because the data (KHPS-Korean household Panel Study) used in this research consist of data for only 4 years. In fact, four years is not enough time to see this data as the panel data. And another problem is that I used the proxy instead of experience because of data problem. I used age-education-8 instead of experience because there was no information about experience in the KHPS. And the way I divided separations into layoffs and quits according to wage growth could be a problem, because there might be a number of other reasons that cause layoffs and quits besides wage growth. Job search period can be another factor to divide separation, but I didin't deal with it inthis paper.
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