인력수요 현황과 전망

Abstract

무형의 지식 정보 과학기술 등이 경제 성장의 원천이 되는 새로운 사회 이른바 지식기반사회로의 진입은 사람의 중요성을 부각시키고 있다 성장동력으로서의 사람 또는 인적자원의 중요성은 종래 자본과 노동의 양적 투입을 통한 경제 성장에서 새로운 지식의 창출과 활용에 의한 인적자원의 역량 강화를 무엇보다 강조하고 있다. 요컨대 21세기 지식기반사회에 있어 국가 경쟁력의 핵심은 사람과 지식이며 특히 인적자원개발정책 체계화를 위한 교육 및 노동 관련 기초통계의 정비 등을 통해 보다 정확한 인력수요 정보의 창출은 필수적인 과제라 할 수 있다. 본 연구는 이러한 배경과 필요성에 기초하여 국내외 경제 환경 및 노동시장의 주변 환경 변화 등을 고려하여(2010) 중기 년 의 산업별 직업별 구조변화에 따른 노동시장 수요 전망을 수행하였다 또한 직업을 4 개 직군으로 구분하여 학력수준별 산업별 교육투자수익률을 추정하고 이를 통하여 전 산업 또는 전 직업의 교육투자수익률 평균과 비교하여 부문간 노동시장상태의 인력수요 과부족을 추정하였으며 장기적으로는 시장이자율과의 비교를 통해 근로자 개인의 부문간 인적자본 투자 유무를 결정하게 하여 인력공급의 정도를 파악하는 질적 전망도 수행하였다.

In the knowledge based society in the 21st century the competitiveness of one economy is centrally determined by human capital and knowledge. It is necessary to have accurate creation and generation of statistic indices and information about education and labor in the construction of a human resources development policies system. Given this necessity, manpower demand & supply estimation is a very important issue for basic HRD policy. Such estimation methodologies are mainly divided into 2 ways of quantity and quality, the one is model specification technique and the other is market signaling technique. These 2 ways have been debated on usefulness. Because the objective of the model specification technique is to estimate the precisive number of manpower, it has the advantage of being transparent. However, due to the errors in the estimated forecasting, the international organizations like the World Bank recommend using the estimation attained based on market signaling technique. Against this backdrop, the central focus of this research was to estimate the mid-term (by 2010) industry×occupation manpower forecasting with using model specification technique and market signaling technique in the Korean labor market. For manpower forecasting, the classification of industry data used included second-digit level of the SITC, and occupations were divided into 4 groups (high skilled clerical workers, low skilled clerical workers, high skilled production workers and low skilled production workers). For those 4 groups, the investment return by educational attainment and industry was measured using Mincer's wage function. Based on this, a comparison was made with the average investment return of education for all industries, and for all occupations to estimate any surplus or shortage in the supply and demand for manpower in the sectoral labor market. In addition, a comparison with the market return allowed whether individual workers had made investment in human capital and this helped determine the degree of manpower supply and demand. And then this study tried to link model specification technique with market signaling technique to evaluate the credibility of the outcome. However, this linking trial study is only a first step and, because it had limited data and any other problems, future studies need to be undertaken continuously are necessary to link quality with quantity estimation. Included in the appendix are the forecasts for the supply and demand of manpower based on the 10 future economic growth engines. This appendix table will help human resources development policy makers and related interested parties as future information. Finally this study suggested policy issues for human resources development. This study placed a particular emphasis on higher education policy which was the fixed capacity number coordination issue. If the departments of universities provide excess supply of graduates relative to labor market demand, these fixed numbers should be coordinated. The problems of skill-mismatch and asymmetry of labor market information were also raised. Other policy issues raised in the study concerned problems associated with time series data and creation of essential data, and methodology development for manpower forecasting, linking quality with quantity for evaluating results, etc.