摘
要
摘 要
我国的经济重心已经迈入高质量发展阶段，对全要素生产率的依赖性更强。对于
企业而言，早期依赖要素禀赋投入拉动经济增长的方式不能持续，人口红利不断消失，
为维持高质量的经济增长，必须重视我国各地区企业全要素生产率的发展。金融要素
在经济发展中占据重要地位，近些年金融集聚现象不断显现，有利于优化资本配置，
促进企业创新行为。但是，我国金融市场在发挥功能时仍受到政府的干预，分税制改
革政策提出后，在以 GDP为考核标准的压力下，地方政府偏向于投机性的项目，长久
以来金融市场自身的资本配置效率会下降。因此，研究经济新常态下金融集聚、政府
干预与企业全要素生产率三者之间的关系，对于更准确地把握金融集聚、优化各地区
全要素生产率、加快实现协同发展规划和一体化发展战略具有重要意义。
本文以企业全要素生产率为切入点，选取我国 2009-2019年 A股上市公司为研究对
象。使用区位熵和 LP方法科学研究各地区金融集聚现状和企业全要素生产率水平，深
入讨论金融集聚与企业全要素生产率的内在联系，并考虑政府对金融的干预如何影响
企业全要素生产率。主要研究内容与结论如下：
1.金融集聚发展现状及时空差异分析。本文基于区位熵和标准差椭圆方法分析各
省域金融业集聚程度，结果表明：（1）金融集聚核心区域明显，2009-2019年整体金融
集聚水平呈现稳定增长的态势。（2）分三大地区看，东部地区金融业集聚水平位于全
国前列，中部和西部地区集聚水平相差无几。（3）从空间演化格局来看，我国金融业
整体集聚特征是“东北-西南”格局，集聚中心向西南方向偏移，空间分布呈现向西南
方向转移的趋势。
2.企业全要素生产率的时空差异分析。基于LP方法计算各省域的企业全要素生产
率，结果表明：（1）我国整体企业全要素生产率呈现利好态势。（2）分地区来看，企
业全要素生产率呈现出“东高西低”的空间格局。东部地区上市公司数量多且质量高，
从东部向西部生产率水平逐渐下降，西部地区企业发展条件有限，生产率始终低于全
国平均值。
3.金融集聚、政府干预与企业全要素生产率的关系研究。根据面板数据采用系统
GMM方法进行实证分析，结果表明：（ 1）金融业集聚有利于提高企业全要素生产率。
（2）集聚效应具有行业异质性。银行业和证券业对企业全要素生产率表现出正向促进
作用，保险业则具有显著的抑制效应，保险市场尚未发挥有效作用与各地区保险业分
布不均衡有关。（3）地方政府干预会阻碍金融集聚对企业全要素生产率的推动作用。
细分行业来看，政府干预与银行业交互项结果显著为负，表明政府对银行业的干预造
成资源“扭曲”程度更高；政府干预与保险业、证券业交互项结果显著为正，表明政
府干预有利于缓解保险业集聚的抑制作用，提高对证券业的促进作用。（ 4）金融集聚
效应存在区域异质性，东部和中部地区金融集聚有利于提升企业全要素生产率，而西
部地区金融未发挥集聚效应。东部地区政府转型进程较快，市场化程度不断深化，政
I
摘
要
府干预具有促进作用，中部和西部地区市场化水平较低，政府干预具有抑制作用。
4.提升企业全要素生产率，提出推动企业高质量发展的对策建议。本文从金融集
聚与企业全要素生产率的影响结果的分析结果入手，从“因地制宜共建金融，完善体
系互惠互利”、“有的放矢干预金融，协调政府市场关系”、“增加区域要素投入，
齐驱并进发挥效应”三个角度，为引导推动各地区金融集聚、促进区域经济协调发展
提供对策建议。
关键词：金融集聚；企业全要素生产率；政府干预；系统广义矩阵
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Abstract
Abstract
Chinese economy center has entered the stage of high-quality development, which is more
dependent on total factor productivity. For enterprises, relying on factor endowment to drive
economic growth cannot be sustained and the demographic dividend is disappearing. In order
to maintain high-quality economic development, it is necessary to focus on enterprise total
factor productivity. Financial factor plays an important role in economic development. The
phenomenon of financial agglomeration has emerged recently, which is beneficial to optimizing
capital allocation and promoting corporate innovation. However, Chinese financial market is
still subject to government intervention. After the tax-sharing system reform policy was
proposed, under the pressure of assessment using GDP as standard, local governments prefer
speculative projects and the financial market’s allocation efficiency will decline in the long run.
Therefore, analyzing the relationship among financial agglomeration, government intervention
and enterprise total factor productivity is significant to realize collaborative development and
integration strategy.
This paper uses total factor productivity as hitting point and chooses Chinese A-share
listed companies from 2009 to 2019 as the research object. This paper also uses location entropy
and LP methods to analysis the current situation of financial agglomeration and total factor
productivity in China, then deeply discusses the internal relationship between them and
analyzes the impactof government intervention ontotal factor productivity. Themain
conclusions are as follows:
1. The analysis of the status quo and space-time evolution of financial agglomeration,
which based on the location entropy and standard deviation ellipse method. (1) The core area
of financial agglomeration is obvious and the overall level of financial agglomeration in China
shows a steady upward trend. (2) From the perspective of three major regions, the eastern region
has a high level of financial agglomeration and ranks among the top in China, while the central
and western regions have similar agglomeration levels. (3) From the perspective of spatial
evolution pattern, Chinese financial industry agglomeration presents a “northeast southwest”
pattern. The agglomeration center shifts to the southwest and the spatial distribution has a trend
of changing to the southwest.
2. The analysis of the status quo and space-time evolution of the enterprise total factor
productivity, which based on the LP method. (1) Total factor productivity in China presents a
positive form. (2) The enterprise total factor productivity shows an obvious pattern of “high in
the east and low in the west”. The number and quality of enterprise in east are relatively high
and the productivity gradually declined from the east to the west. The enterprises’ development
conditions in west are limited and the productivity is always lower than the national average.
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