剥离环境因素的高技术产业技术创新效率评价 ——以我国西部地区为例

The Evaluation of Innovation Efficiency of High-tech Industries Based on Environmental Factors：The Case of Western Region of China

摘要:

传统数据包络分析（DEA）和随机前沿分析( SFA )由于无法消除环境因素和随机误差的影响从而导致测评结果不真实。采用二者相结合的三阶段DEA模型，对我国西部地区高技术产业创新效率及其影响因素进行测度研究。研究结果表明：我国西部地区高技术产业技术创新效率总体处于较低水平且创新投入规模不足是制约创新效率提升的主要瓶颈；政府支持和地区经济发展水平不利于创新资源冗余减少，而市场开放度可显著促进创新效率提升，同时企业规模的扩大有利于减少人力投入冗余，对财力、物力投入冗余的减少有负向促进作用。进一步按纯技术效率和规模效率将我国西部地区高技术产业技术创新效率划分为4种不同类型。最后，就分析结果提出相应的对策建议。

关键词:三阶段DEA模型;西部地区;高技术产业;创新效率;环境因素

Abstract:

Traditional data envelopment analysis (DEA) and stochastic frontier analysis (SFA) can not eliminate the influence of environmental factors and random errors, so the evaluation results are not true.This paper studies the innovation efficiency of high-tech industries and its influencing factors in the western regions of China by using the three-stage DEA model combined with the two, the results show that the overall technological innovation efficiency of high-tech industries in the western region of China is at a low level, and the insufficient scale of innovation investment is the main bottleneck restricting the improvement of innovation efficiency, the level of government support and regional economic development are not conducive to the reduction of innovation resource redundancy, while market openness can significantly promote the improvement of innovation efficiency, at the same time, the expansion of enterprise scale is favorable to reduce the redundancy of manpower input but has a negative effect on reducing the redundancy of financial and material input. Further, according to pure technical efficiency and scale efficiency, the technological innovation efficiency of high-tech industry in the western region of China can be divided into four different types. Finally, the paper puts forward corresponding countermeasures and suggestions according to the analysis results.

Key words:three stage data envelopment analysis (DEA) model;Western Region;High-tech Industry;Innovation Efficiency; Envirnmental Factors