• 한국조사연구학회지:조사연구
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• 제12권1호
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• pp.119-139
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• 2011

본 연구에서는 대학생의 공무원시험 준비가 그들의 취업 및 직장 생활에 미치는 영향을 분석했다. 이를 위해 한국고용정보원의 대졸자 직업이동 경로조사(Graduates Occupational Mobility Survey: GOMS) 자료를 사용하였다. 본 연구의 주요 분석결과를 요약하면 다음과 같다. (1) 대학생의 공무원시험 준비는 대학재학기간을 다소 연장시킨다. (2) 대학생의 공무원시험 준비는 취업을 지연시키는 효과를 갖는다. (3) 민간기업 취업자만을 대상으로 할 때, 공무원시험 준비 경험은 보수에 다소 불리한 영향을 미친다. (4) 공무원시험 준비를 하다가 민간기업에 취업한 경우는 공공부문에 취업한 경우 및 처음부터 공무원시험 준비를 하지 않고 민간기업에 취업한 경우에 비하여 직업만족도가 낮다. 이와 같은 본 연구의 결과는, 기본적으로 대학생들의 공무원시험 준비에 대해 주변에서 갖는 일반적인 우려와 부합한다. 그러나 그 크기에 있어서는 일반적으로 염려하는 것보다는 작음을 보여 준다.

• 한국콘텐츠학회논문지
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• 제17권7호
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• pp.679-695
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• 2017

본 연구에서는 2014년도 대졸자직업이동경로조사 자료를 활용하여 대졸 신입사원의 구직경로가 첫 일자리 직업적응에 어떠한 영향을 미치는 지를 검증하고자 하였다. 주요 분석 결과는 다음과 같다. 첫째, 구직경로에서 인적 네트워크를 활용했을 때 비인적 네트워크에서보다 교육수준 적합성과 전공 일치도가 높게 나타났으며, 또한 직장만족도와 업무만족도도 높게 나타났다. 둘째, Sobel-Test를 통해 매개효과를 분석한 결과, 기술수준 적합성을 제외한 교육수준 적합성과 전공 일치도가 구직경로와 직업적응 사이에서 매개효과를 갖는 것으로 나타났다. 따라서 인적 네트워크가 구인과 구직 사이에 직무적합성에 대한 정보전달 통로로 작용함으로써 첫 일자리 직업적응도를 높이는데 기여하고 있음이 확인되었다. 이러한 분석결과는 구직 활동에서 인적 네트워크의 전략적인 활용이 중요하며, 대학 또는 고용서비스기관의 진로관련 프로그램에서도 인적 네트워크 활용의 중요성과 방법 등을 체계적으로 교육하고 경험할 수 있도록 하여야 함을 시사하고 있다.

• 응용통계연구
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• 제25권6호
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• pp.959-975
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• 2012

Each year research institutions such as the Korea Employment Information Service(KEIS), a government institution established for the advancement of employment support services, and Job Korea, a popular Korean job website, announce first job waiting times after college graduation. This provides useful information understand and resolve youth unemployment problems. However, previous reports deal with the time as a completely observed one and are not appropriate. This paper proposes a new study on first job waiting times after college graduation set to 4 months prior to graduation. In Korea, most college students hunt for jobs before college graduation in addition, the full-fledged job markets also open before graduation. In this case the exact waiting time of college graduates can be right-censored. We apply a Cox proportional hazards model to evaluate the associations between first job waiting times and risk factors. A real example is based on the 2008 Graduates Occupational Mobility Survey(GOMS).

• Communications for Statistical Applications and Methods
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• 제15권6호
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• pp.859-873
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• 2008

근로자의 직무만족도는 이직의사와 높은 연관성을 갖고 이직의사는 이직행동에 영향을 주고 있는데 본 논문에서는 직무만족도가 이직행동에 영향을 주는가를 조사하였다. 이직행동에 영향을 주는 변인에 대한 실증적인 연구에서 이직행동을 종속변수로 하고 이직의사를 독립변인으로 모형화 하였을 때, 이직의사는 직무만족도, 급여, 근로형태 등의 다른 독립변수들과 높은 상관관계를 갖기 때문에 이직행동에 유의한 변인을 검출하는데 주의가 필요하다. 본 논문에서는 한국고용정보원의 "대졸자 직업이동 경로조사"자료에서 이항 변수인 이직의사를 도구변수에 의해 확률값으로 추정하고 이를 이직행동 모형에 독립변인으로 대입하는 2단계 로지스틱 회귀모형을 사용함으로써 이직행동에 영향을 주는 유의한 요인을 검출하였다.

• 응용통계연구
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• 제23권2호
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• pp.235-247
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• 2010

본 연구에서는 대졸자가 노동시장에 진입하여 구직 및 이직 등을 통해 자신의 경력을 쌓는 과정 중에서 발생하게 되는 일자리 경험 횟수 분포를 살펴보았다. 일자리 경험 유형을 다양하게 분류한 후, 이해하기 쉽게 도식화하였다. 일자리 경험 횟수에 따른 최종일자리의 월평균임금 차이 분석과 동일한 일자리 횟수에서 일자리 유형에 따른 월평균 임금 차이 분석을 위해 일원배치 분산분석(one way ANOVA)을 실시하였다. 이를 위해 2006년에 조사된 GOMS 1차년도 자료와 2007년에 조사된 GOMS 2차년도 자료를 연계하여 분석하였다.

• 디지털산업정보학회논문지
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• 제19권3호
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• pp.177-192
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• 2023

The employment rate of university graduates has been decreasing significantly recently. With the advent of the Fourth Industrial Revolution, the demand for software employment has increased. It is necessary to analyze the factors for software employment of university graduates. This paper proposes explainable software employment model of university graduates using machine learning and explainable AI. The Graduates Occupational Mobility Survey(GOMS) provided by the Korea Employment Information Service is used. The employment model uses boosting machine learning. Then, performance evaluation is performed with four algorithms of boosting model. Moreover, it explains the factors affecting the employment using SHAP. The results indicates that the top 3 factors are major, employment goal setting semester, and vocational education and training.

• International Journal of Knowledge Content Development & Technology
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• 제13권1호
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• pp.101-109
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• 2023

Fifteen years have passed since the enactment of the Industrial Technology Innovation Promotion Act, which promoted industry cooperation activities for universities. Therefore, the study analyzes the relationship between the university's industrial cooperation activities and the college graduates' starting salaries and provides policy suggestions on improving the direction of university-industry cooperation. The study used nine-year panel data from Graduates Occupational Mobility Survey (GOMS) to conduct an empirical analysis and found that starting salaries of college graduates were not significantly higher if the university only participated in basic industry cooperation activities. On the other hand, when the quality of university-industry cooperation activities was higher, such as job search support, the starting salary of college graduates was higher. The findings suggest that university-industry cooperation activities must focus on qualitative performances rather than quantitative approaches.

• 패션비즈니스
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• 제19권3호
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• pp.73-87
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• 2015

The purpose of this study is 1) to compare the employment status and wage level of "Clothing and Fashion" major (CF major) graduates with those from other majors and 2) to examine factors affecting the employment status and wage level of CF major university graduates. The data were pooled from the 2009-2012 Graduates Occupational Mobility Survey Data, conducted by Korea Employment Information Service. First, when graduates of CF major were compared with other majors in the same academic area-natural science and arts-, the rate of employment was higher for CF majors than that of other majors; on the other hand, there is no difference in the wage levels between those with graduates in CF majors and those with other majors. Second, we examined factors affecting CF major graduates' employment and wage level based on graduates' individual factors, university factor, and job preparation factors. Employment status of graduates in CF major was predicted by respondent's gender and university type, and work experience. The wage level of CF major graduates was significantly predicted by individual factors (e.g., gender and age, university factors (e.g., university type, university program, location), and job preparation factors (e.g., certificates, overseas experience of foreign language training, English test scores). The results of this study would provide a guide to direct university educational program in order to assess the current capabilities in the field of clothing and fashion.

• 한국간호교육학회지
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• 제23권3호
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• pp.353-362
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• 2017

Purpose: This study aimed to analyze the mobility of newly graduated nurses from regions where their nursing schools were located to regions where they took up their first jobs, and to identify factors influencing nurses' mobility. Methods: Data from the Graduates Occupational Mobility Survey, collected annually from 2010 to 2014 by the Korea Employment Information Service, were analyzed. The sample consisted of 1,488 graduates and 1,229 nurses who were employed on a full-time basis in hospitals. Multiple logistic regression analysis was conducted to identify factors associated with geographic mobility. Results: Among the nurses working in hospitals, 69.2% had their first jobs in their nursing school regions and 11.3% in their high school regions. Fifty-two percent of the nurses worked in the capital region; 47.2% thereof had moved from a non-capital region. Nurses were more likely to work in their nursing school region when they were female, were older, graduated from a high school located in their nursing school region, graduated from a college (vs. university), had a lower nursing school performance, and expected lower monthly wage, compared with those who left their nursing school region. Conclusion: Education and remuneration policies are required to reduce geographical mobility to the capital region.

• 디지털산업정보학회논문지
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• 제20권1호
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• pp.31-45
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• 2024

The purpose of this study is to explain employment factors in the software field. For it, the Graduates Occupational Mobility Survey by the Korea employment information service is used. This paper proposes employment models in the software field using machine learning. Then, it explains employment factors of the models using explainable artificial intelligence. The models focus on both university graduates and vocational college graduates. Our works explain and interpret both black box model and glass box model. The SHAP and EBM explanation are used to interpret black box model and glass box model, respectively. The results describes that positive employment impact factors are major, vocational education and training, employment preparation setting semester, and intern experience in the employment models. This study provides a job preparation guide to universitiy and vocational college students that want to work in software field.