도시가스 업체의 효율성 측정과 영향 요인 분석 = Measuring the Efficiency of the City Gas Firms and Analysis of Impact Factors

다국어 초록 (Multilingual Abstract)

The purpose of this paper is to measure the efficiency and impact factors of city gas firms in S. Korea using DEA (Data Envelopment Analysis) and SFA (Stochastic Frontier Analysis). The analysis period is from 2009 to 2012 and the number of sample firms is thirty-two.
Empirical result shows that; first, four years average efficiency of city gas firms in S. Korea is estimated low. Second, city gas firms in S. Korea have big difference between firms to have a minimum efficiency and maximum efficiency. Third, the gap of efficiency by region or listed company had decreased since 2009. The city gas companies in non-metropolitan area or unlisted gas companies had caught up metropolitan city gas companies or listed gas companies. Also, the ranking of companies’ efficiency is almost similar based on Spearman's rank correlation coefficient.
Tobit model is used to examine impact factors of efficiency. The dependent variable is city gas firms’ efficiency in S. Korea. The independent variables are population density, proportion of industrial supply, labor costs per total number of employees, current ratio, ratio of gross profit, ratio of operating profit, turnover ratio of capital, gross profit per total number of employees, metropolitan dummy, and listed dummy. impact factors of efficiency are industry supply, liquidity ratio and whether the company is listed or not and so on. Efficiency is increased by the higher industry supply, liquidity ratio and metropolitan area.
Based on the empirical results, we can try to draw following implications. First of all, this measurement results can be the opposite evidence of arguing that the City Gas Firms reached the critical point with efficient self-management. The average efficiency of city gas firms is estimated low.
And based on analysis with DEA and SFA, the efficiency can be improved. Therefore, Non-metropolitan companies and listed companies are required to focus on improving the efficiency.
Looking at the empirical results, metropolitan dummy and listed one have a negative influence on efficiency. Because the population density per pipe length in non-metropolitan areas is lower than metropolitan ones.
So the government should try to improve the efficiency of these companies. In addition, In order to facilitate supply of city gas, the government's support is needed. And for supporting non-metropolitan areas, an element of the external force should be removed. Because these things can lead to inefficiencies. In the case of listed companies, CEOs are more likely to focus on company's appearance like focusing on gross profit and sales.
Thus individual shareholders should lead them not only to focus on company appearance but also increase efficiency.
In this study, we measured the efficiency with DEA and SFA simultaneously because measuring only with DEA has several disadvantages and those two ways are complementary.
Accordingly if you use both methods at the same time, you can obtain more reliable information.

국문 초록 (Abstract)

본 연구의 목적은 소매 도시가스 업체의 효율성을 계측하고 영향요인을 분석하고자함이다. 본 연구에서 자료포락분석(DEA)과 확률변경분석(SFA)을 이용하여 효율성을 추정하였다. 분석기간은 2009년부터 2012년까지의 4년간이었으며, 표본기업의 수는 32개 업체이다. 추정 결과, 첫째, 32개 소매도시가스 업체의 4개년 평균효율은 낮게 측정되었다. 둘째, 최소효율을 가지는 기업과 최대효율을 가지는 기업의 차이가 아주 크게 나타났다. 셋째, 2009년 이후로 지역별, 상장여부에 따른 효율성 차이가 점점 줄어드는 것으로 나타났다. 이는 지역적으로 비수도권에 위치하는 소매도시가스업체들 또는 비상장 소매도시가스업체들이 상대적으로 효율성이 높은 수도권 지역 소매도시가스업체들 또는 상장 소매도시가스업체들을 추격(catching-up)하고 있는 것으로 볼 수도 있다. 또한 두 측정결과의 일관성을 보기 위하여 Spearman의 순위상관계수를 확인한 결과, 두 측정결과는 효율 수준의 값에서 다소 차이를 보이나 소매도시가스 32개 업체의 두 효율 순위는 대체로 유사하게 나타났다. 이러한 효율성에 대한 영향요인을 살펴보기 위하여 Tobit 모형을 이용하여 분석하였다. 종속변수로는 자료포락분석(DEA)과 확률변경분석(SFA)을 이용하여 도출한 효율성을, 설명변수로는 인구밀집도, 산업용비중, 총직원수 당 인건비, 유동비율, 매출총이익률, 영업이익률, 총자본회전율, 총직원수 당 매출총이익, 수도권․비수도권 더미, 상장․비상장 더미를 사용하였다. 그 결과, 효율성 영향요인은 산업용 공급량이 높을수록, 유동성비율이 높을수록, 수도권 지역에 위치할수록 효율성이 증가하는 것으로 나타났다.

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