摘要
本文基于中国工业企业数据库数据,采用双重差分倾向得分匹配法(PSM-DID)研究了我国国有企业混合所有制改革对全要素生产率的影响。研究发现:在消除选择性偏差以及异质性偏差后,国有企业改革后全要素生产率(TFP)显著提高,且国有控股型混合所有制改革效果还略高于完全私有化改革,而非国有控股型混改只在竞争性行业中对企业的TFP有提高作用。同时,与垄断性行业相比,竞争性行业的国有企业混合所有制改革对企业效率的提升作用更大。本文进一步发现,对于垄断性行业的国有企业,混合所有制改革对企业TFP的影响不受知识产权保护水平的影响,而对于竞争性行业的国有企业,知识产权保护程度对混合所有制改革的效应具有显著的正向影响。因此提高知识产权保护水平对于提高混合所有制改革的效果具有重要作用。基于上述实证结果,本文对当前我国国有企业混合所有制改革提出了相应的政策建议。
Based on the database of Chinese industrial enterprises, this paper studies the influence of the mixed ownership reform on the total factor productivity of China＇s state owned enterprises. After eliminating selection bias and heterogeneity bias by using the PSM-DID method, it is found that the total factor productivity （TFP） is significantly improved after the reform of the state-owned enterprises, and the mixed ownership reform effect of state holding type is slightly higher than the full privatization reform, the mixed ownership reform of non-state holding type improves the TFP of enterprises only in the competitive industries. At the same time, compared with the monopoly industries, the reform of state-owned enterprises in the competitive industries is more effective in improving the TFP of enterprises. This study also finds that the effect of mixed ownership reform on enterprises＇ TFP is not affected by the level of protection of intellectual property rights for monopoly of state-owned enterprises, but for the state owned enterprises in the competitive industries, the degree of protection of intellectual property rights has a positive impact on the effect of mixed ownership reform. Therefore, improving the level of intellectual property protection has an important role in improving the effect of mixed ownership reform. Based on the above empirical results, this paper puts forward the corresponding policy recommendations on the current reform of the state-owned enterprises in China.
作者
刘晔
张训常
蓝晓燕
Liu Ye Zhang Xunchang Lan Xiaoyan
出处
《财政研究》
CSSCI
北大核心
2016年第10期63-75,共13页
Public Finance Research
关键词
混合所有制改革
全要素生产率
倾向得分匹配
双重差分模型
Mixed Ownership Reform
Total Factor Productivity
Propensity Score Matching Difference-in- Difference Model
