中国影子银行的经济学分析：发展驱动因素
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| Title | Economics of China’s Shadow Banking: Driving Growth Factors
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| 作者 | 李文喆 |
| Author | LI Wenzhe |
| 作者单位 | 清华大学五道口金融学院；中国人民银行货币政策司 |
| Organization | PBC School of Finance, Tsinghua University;Monetary Policy Department, The People’s Bank of China |
| 作者Email | lwenzhe@pbc.gov.cn |
| 中文关键词 | 影子银行 宏观经济 货币政策 交易费用 金融监管 |
| Key Words | Shadow Banking; Macroeconomy; Monetary Policy; Transaction Cost; Financial Regulation |
| 内容提要 | 近年来，中国影子银行的迅速发展吸引了学术界和政策制订者的广泛关注。在传统的银行表内贷款和债券投资之外，存量高达50万亿元的影子银行如何发展起来？本文系统性回顾了影子银行自2002年以来的发展历程，认为其本质上是地方政府融资平台和房地产企业的融资工具，来自这两类企业的旺盛融资需求为影子银行发展提供了根本动力。为影子银行发展助力的是商业银行降低交易费用的动机，把表内业务挪到表外、内部交易外部化，资金成本高了一些，但显著降低了制度成本，从而降低了总交易费用。此外，监管政策对影子银行的发展也有影响。我们据此建立了局部均衡理论模型，并解出影子银行的存量方程和价格方程，首次采用李文喆（2019）测算的翔实的影子银行总量数据进行了实证检验。经工具变量法、广义矩估计、向量自回归等方法检验，发现本文的解释框架在较高的显著性下成立，模型解释力较强。 |
| Abstract | In recent years, the spectacular expansion of China’s shadow banking attracts broad attention from both academia and policy makers. Beyond traditional on-balance sheet bank loans and bond investment, how did shadow banking develop to a scale as high as RMB 5 trillion yuan? This paper systematically reviews development of shadow banking since 2002, and comes to conclude that shadow banking essentially is the financing vehicle of local government and real estate enterprises. Financing needs of these two types of enterprises are the essential driving factor of shadow banking growth. Incentive of commercial banks to lower transaction costs is also very important. Although shifting business off balance sheet, and externalizing internal transactions elevate funding cost, they substantially lower institutional cost and reduce total transaction cost. Besides, during this process, regulation policy also exerts influences. Based on the above analysis, we build partial equilibrium theoretical model and solve to obtain stock and price equations of shadow banking. For the first time, we utilize detailed aggregate data of China’s shadow banking from Li (2019) to conduct empirical tests of above analysis. The results from Instrumental Variable method, Generalized Method of Moments, and Vector Autoregression show that this explanatory framework stands at high significance level, and has strong explanatory power. |
| 文章编号 | WP1363 |
| 登载时间 | 2019-04-16 |
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