中國大陸國家級高新技術開發區之績效分析

Abstract

本研究是應用Fried at el. (1999) 所提出的四階段資料包絡分析法，研究對象為中國53個大陸高新技術產業開發區，研究期間由2004年至2006年，主要探討中國大陸高新技術產業開發區的經營效率。主要的投入變數為各高新區的企業家數、員工人數、大專員工比例、R&D經費支出、科技活動人員比例等，而產出變數為各高新區之技術收入、產品銷售收入、商品銷售收入等，相關變數皆以2004年的GDP為基期做平減，轉換成實質變數。經過第一階段的分析後，再由所選入環境變數，地理位置環境變數(華東地區、華中地區及華南地區，並以華中地區作為對照群組)，高新區座落之省市鐵路密集度、公路密集度、固定資產投資、工業總產值、地方財政科技撥款以及教育程度來探討哪些會影響投入差額變數。經過本階段調整後的投入項，再重新計算一次經營效率，此時的效率值是排除相異客觀環境，能更準確反應使用狀況。主要的研究結果：第一階段分析時，包涵了北京、上海、南京等十一個高新區，皆擁有最佳的技術效率。經過Tobit迴歸後所得到的結果，發現鐵路密集度、固定資產投資、地方財政科技撥款對於高新區的經營效率有負向的影響。而座落於華東地區、工業總產值及高教育水準比例對於高新區之經營效率則有正向的影響。而經過環境因素調整後，擁有最佳的技術效率的高新區大幅減少為北京、上海、杭州共三個，擁有最佳效率的高新區數量銳減，且整體的效率值是降低的。

This research applies Fried at el. (1999) who brings up four-stage data envelopment analysis (DEA). Research objects are fifty-three high-tech industrial development zones in mainland China the sample period is from 2004 to 2006. This study uses five input variables, including the number of firms, number of employees, the percentage of employees graduate from university to total employees, R&D expenditure and the percentage of S&T personnel to total employees. And three output variables are technical income, products income and sales income. The high-tech industrial development zones are clustered into three locations and six environmental variables are introduced to analyze the effect. All nominal variables have been transformed into real variables at the 2004 price level by GDP deflators. The environmental variables are used in Tobit regressions to explain input slacks obtained in the first-stage DEA, including the location, railroad intensity, road intensity, fixed-asset investment, industrial output value, financial investment of science and technology. Environment-adjusted efficiency is computed in the fourth-stage by using the adjusted inputs. The major findings are as follows: (1) Eleven industrial zones continuously operate efficiently during 2004-2006. (2) Railroad density, fixed-asset investment and financial investment of science and technology have significantly positive impacts on some input slacks. (3) Industrial zones located in east China, industrial output values, and the college and educational level rate significantly reduce some input slacks. (4) After adjusting inputs to exclude influences of environment variables, only three industrial zones have the best technical efficiency and the average technical efficiency decreases.