부트스트래핑 기법에 의한 전남지역 농협의 효율성 분석
An Analysis of the Efficiency of Agricultural Cooperatives Using the Stochastic DEA Model
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The purpose of this study is to estimate efficiency of agricultural cooperatives using Data Envelopment Analysis. A proposed method employs a bootstrapping approach to generating efficiency estimates through Monte Carlo simulation resampling process. The technical efficiency, pure technical efficiency, and scale efficiency measure of the corporations is 0.749, 0.790, 0.948 respectively. Among the 132 agricultural cooperatives, the number of Increasing Returns to Scale (IRS)-type cooperatives was analyzed to be 82 (62.12%). The number of Constant Returns to Scale (CRS)-type cooperatives was 5 (3.79%), and that of Decreasing Returns to Scale (DRS)-type cooperatives was 45 (34.09%). Since an increase in input is lower than an increase in output in IRS, an increase in input factors such as new investments is required. We know that the DEA estimator is an upward biased estimator. In technical efficiency, average lower and upper confidence bounds of 0.698 and 0.814. According to these results, the DEA bootstrapping model used here provides bias-corrected and confidence intervals for the point estimates, it is more preferable.
Abstract
The purpose of this study is to estimate efficiency of agricultural cooperatives using Data Envelopment Analysis. A proposed method employs a bootstrapping approach to generating efficiency estimates through Monte Carlo simulation resampling process. The technical efficiency, pure technical efficiency, and scale efficiency measure of the corporations is 0.749, 0.790, 0.948 respectively. Among the 132 agricultural cooperatives, the number of Increasing Returns to Scale (IRS)-type cooperatives was analyzed to be 82 (62.12%). The number of Constant Returns to Scale (CRS)-type cooperatives was 5 (3.79%), and that of Decreasing Returns to Scale (DRS)-type cooperatives was 45 (34.09%). Since an increase in input is lower than an increase in output in IRS, an increase in input factors such as new investments is required. We know that the DEA estimator is an upward biased estimator. In technical efficiency, average lower and upper confidence bounds of 0.698 and 0.814. According to these results, the DEA bootstrapping model used here provides bias-corrected and confidence intervals for the point estimates, it is more preferable.
참고문헌 (19)
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