地方全要素生产率变动及财政支出对其影响的实证研究（1998-2015）

发布时间：2018-06-13 23:15

本文选题：全要素生产率 + 生产效率　；参考：《中国财政科学研究院》2017年硕士论文

【摘要】：近年来,随着我国经济步入“新常态”,经济发展方式转型的呼声日益强烈。在此背景下,习近平总书记提出“供给侧结构性改革”,其题中之意在于调整经济结构,从供给侧的科技进步、效率改进、要素配置等方面入手,全面提升中国经济增长的质量。而全要素生产率正是一国经济增长质量的集中体现。历史进程也说明,西方发达国家能够保持较高的发展水平有赖于全要素生产率的持续增长。由此看来,对于全要素生产率及其影响因素的深入研究对我国当前的发展具有重要的现实意义。基于以上认识,利用1998-2015年我国30个省级单位的数据,本文首先借助随机前沿分析方法,采取较为灵活的超越对数的生产函数形式,将我国各省份的经济增长率分解为要素投入变化、规模效应变化、技术进步及生产效率变化四部分,再利用后三部分加总得出我国各省份全要素生产率增长率,以期全面了解地方全要素生产率的历年变动情况。测度结果显示:1.我国各省份全要素生产率的平均增速为4.97%,并呈现先升后降,近两年企稳回升的趋势,全要素生产率的变动对经济增长的平均贡献为46.76%,略低于要素投入的贡献;2.我国地方全要素生产率增长主要依靠技术进步,但生产效率变化的贡献也较大,并且近年来伴随一系列全面深化改革措施的实施,其作用有逐步提升的趋势,而规模效应变化则对全要素生产率增长存在负向影响;3.东部地区全要素生产率的平均增速以及对经济增长的贡献程度要高于东北、中、西部地区,其次为西部地区和中部地区,东北地区最低。进而,本文先将财政支出进行分类并从理论上简要剖析了其对全要素生产率的影响,再利用依据此前模型测度的地方全要素生产率与财政总支出及不同类型财政支出占GDP比重构建一步系统GMM模型,估计后者对前者的影响效应。实证结果显示:1.财政总支出占比提升会显著降低全要素生产率;2.消费性支出占比提升会显著降低全要素生产率,滞后一期的物质资本投资性支出占比提升会显著提高全要素生产率,科技及教育支出占比提升对全要素生产率的影响不显著,转移性支出占比提升能够显著提升全要素生产率;3.沿海地区(东部、东北部)与内陆地区(中西部)在财政支出对全要素生产率的影响效应方面存在一定差异。最后,根据前文的分析论述,本文提出了包括控制财政支出总量、优化财政支出结构、提升政府机构自身效率在内的四条政策建议。
[Abstract]:In recent years, with China's economy stepping into the "new normal", the voice of economic development mode transformation is increasingly strong. In this context, General Secretary Xi Jinping put forward the "supply-side structural reform", which means to adjust the economic structure, starting with the scientific and technological progress, efficiency improvement, factor allocation on the supply-side, etc. Improve the quality of China's economic growth. The total factor productivity is a concentrated embodiment of the quality of a country's economic growth. The historical process also shows that the high level of development of western developed countries depends on the sustained growth of total factor productivity (TFP). In view of this, the in-depth study on total factor productivity and its influencing factors is of great practical significance to the current development of our country. Based on the above understanding, using the data of 30 provincial units in China from 1998 to 2015, this paper first uses the stochastic frontier analysis method to adopt a more flexible production function form that transcends logarithm. The economic growth rate of each province in China is divided into four parts: the change of factor input, the change of scale effect, the change of technological progress and the change of production efficiency. With a view to a comprehensive understanding of the local total factor productivity changes over the years. The result of the measure shows that: 1. The average growth rate of total factor productivity (TFP) in various provinces of China is 4.97, and shows the trend of rising first and then decreasing, and rising steadily in the last two years. The average contribution of TFP to economic growth is 46.76, which is slightly lower than that of factor input. The local total factor productivity growth in China mainly depends on technological progress, but the contribution of the change of production efficiency is also great. In recent years, with the implementation of a series of comprehensive deepening reform measures, the role of total factor productivity has gradually increased. On the other hand, the change of scale effect has a negative effect on total factor productivity growth. The average growth rate of total factor productivity and its contribution to economic growth in the eastern region are higher than those in the northeast, middle and western regions, followed by the western and central regions, and the lowest in Northeast China. Then, this paper classifies the financial expenditure and analyzes its influence on the total factor productivity in theory. Then the one-step system GMM model is constructed by using the local total factor productivity and total fiscal expenditure as well as the proportion of different types of fiscal expenditure to GDP measured according to the previous model to estimate the effect of the latter on the former. The empirical results show that: 1. An increase in the share of total fiscal expenditure would significantly reduce total factor productivity by 2. 5%. The proportion of consumption expenditure will significantly reduce the total factor productivity, the proportion of material capital investment expenditure will significantly increase the total factor productivity, and the proportion of science and technology and education expenditure will not significantly affect the total factor productivity. An increase in the share of transfer spending can significantly increase total factor productivity by 3. 5%. There are some differences in the effect of fiscal expenditure on TFP between coastal (eastern, northeast) and inland (central and western) regions. Finally, according to the previous analysis and discussion, this paper puts forward four policy recommendations, including controlling the total amount of fiscal expenditure, optimizing the structure of fiscal expenditure, and improving the efficiency of government agencies themselves.
【学位授予单位】：中国财政科学研究院
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F812.45

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