Classified Regulatory Policy and the Perform ance of Local Government Financing Platforms—Evidence from the Exit of Financing Platforms from List-based Management System
FANG Jiayu, LU Yi
School of Economics and Management, Tsinghua University
Summary:
In order to categorize and transform local government financing platforms (LGFPs) and modernize China's financial governance, it is imperative to improve the performance of LGFPs. China has implemented a number of regulatory policies to standardize LGFPs' management and encourage the transformation of companies with stable operating income and cash flow. One important regulatory tool representing the classified regulatory policy is the China Banking Regulatory Commission's (CBRC) LGFP list-based management system. We employ the staggered difference-in-differences model to estimate the effect of CBRC's regulatory policy on the performance of LGFPs using manually collected data from 2010 to 2018, with the withdrawal from the list-based management system as a quasi-natural experiment. Additionally, we explore mechanisms through which classified regulatory policy influences the performance of LGFPs and the heterogeneous effects from the perspectives of regional marketization, debt ratio, credit rating, and business type of LGFPs. Our main findings are as follows. (1) Classified regulatory policy is conducive to promoting the performance of LGFPs. The aforementioned conclusion holds after a number of tests, including parallel trend tests, exclusion of samples from municipalities directly under the central government, exclusion of other policies, alternative estimation methods, and placebo tests. In terms of the calendar effect, this positive association becomes apparent primarily since 2015. (2) The classified regulatory policy significantly improves the performance of LGFPs located in more marketized provinces, as well as LGFPs with higher debt ratios, credit ratings, and operational business, but not conducive to improving the performance of LGFPs for public welfare activities. (3) The mechanism analysis demonstrates that the classified regulatory policy can promote the performance of LGFPs through revenue improvement, cost control and operating efficiency enhancement. (4) The extended analysis reveals that LFGPs have optimized the debt structure following their departure from the list-based management system, with an increase in the percentage of long-term debt but a loss in their long-term solvency. The conclusions carry significant policy implications. (1) The classified regulatory policy needs to be further improved in order to support LGFPs' development and transformation. (2) It is imperative to investigate multifaceted approaches to improve LGFPs' capacity for sustainable development and to encourage the transformation of their classification through performance-targeted assessment management. (3) It is crucial to strengthen the debt risk management of LGFPs, prevent and resolve such risks. We make three potential contributions to the literature. (1) This study assesses the impact of CBRC’s regulatory policy on the performance of LGFPs from the perspective of classified regulation, providing empirical evidence for understanding the effectiveness of classified regulatory policy. (2) While most previous studies have focused on the causes of the debt risk of LGFPs and the impact of regulatory policies on their debt risk, we examine the impact of classified regulatory policy on the performance of LGFPs and explore its intrinsic mechanism. Moreover, we investigate the heterogeneous effects of classified regulatory policy from regional marketization, debt ratio, credit level, and business type of LGFPs. (3) The results of this study not only reveal the effectiveness of classified regulatory policy on the long-term development of LGFPs, but also examine the impact of classified regulatory policy on the debt scale and solvency of LGFPs. Our findings also provide insightful policy recommendations for further understanding the effectiveness of regulatory policies and accelerating the pace of classified transformation of LGFPs.
方嘉宇, 陆毅. 分类监管政策与地方融资平台绩效——来自融资平台退出名单制管理系统的经验证据[J]. 金融研究, 2024, 526(4): 75-93.
FANG Jiayu, LU Yi. Classified Regulatory Policy and the Perform ance of Local Government Financing Platforms—Evidence from the Exit of Financing Platforms from List-based Management System. Journal of Financial Research, 2024, 526(4): 75-93.
[1]曹光宇、刘晨冉、周黎安和刘畅,2020,《财政压力与地方政府融资平台的兴起》,《金融研究》第5期,第59～76页。 [2]曹婧、毛捷和薛熠,2019,《城投债为何持续增长:基于新口径的实证分析》,《财贸经济》第5期,第5～22页。 [3]郭玉清、姜晓妮、毛捷和汪昊,2022,《债权方信贷约束的风险治理效应:基于信号传递视角》,《世界经济》第9期,第57～82页。 [4]郭玉清、刘俊现和姜晓妮,2021,《转型视域下的融资平台风险治理:政策评估与战略取向》,《财政研究》第6期,第55～69页。 [5]韩鹏飞和胡奕明,2015,《政府隐性担保一定能降低债券的融资成本吗？——关于国有企业和地方融资平台债券的实证研究》,《金融研究》第3期,第116～130页。 [6]刘晓蕾、吕元稹和余凡,2021,《地方政府隐性债务与城投债定价》,《金融研究》第12期,第170～188页。 [7]罗党论和佘国满,2015,《地方官员变更与地方债发行》,《经济研究》第6期,第131～146页。 [8]罗荣华和刘劲劲,2016,《地方政府的隐性担保真的有效吗？——基于城投债发行定价的检验》,《金融研究》第4期,第83～98页。 [9]毛捷、韩瑞雪和刘冲,2024,《融资平台债务增长的新机理研究:担保网络的视角》,《经济研究》第1期,第72～92页。 [10]毛捷、刘潘和吕冰洋,2019,《地方公共债务增长的制度基础——兼顾财政和金融的视角》,《中国社会科学》第9期,第45～67+205页。 [11]毛捷和徐军伟,2021,《地方融资平台公司的市场化转型研究——制度溯源、个性刻画与实现路径》,《财贸经济》第3期,第28～43页。 [12]聂卓、刘松瑞和玄威,2023,《从“主动负债”到“被动负债”:中央监管转变下的隐性债务扩张变化》,《经济学(季刊)》第6期,第2136～2155页。 [13]邱志刚、王子悦和王卓,2022,《地方政府债务置换与新增隐性债务——基于城投债发行规模与定价的分析》,《中国工业经济》第4期,第42～60页。 [14]沈红波、华凌昊和张金清,2018,《城投债发行与地方融资平台主动债务置换——基于银行授信视角》,《金融研究》第12期,第91～104页。 [15]宋傅天和姚东旻,2021,《“城投部门”议价能力与地方政府债务扩张》,《管理世界》第12期,第92～110页。 [16]汪莉和陈诗一,2015,《政府隐性担保、债务违约与利率决定》,《金融研究》第9期,第66～81页。 [17]王小鲁、胡李鹏和樊纲,2021,《中国分省份市场化指数报告(2021)》,社会科学文献出版社,2021年10月。 [18]王彦超、吴雨珊、刘芷蕙和白璐,2023,《宏观审慎、地方政府隐性债务监管与系统性风险防范——基于微观企业风险的网络传导机制研究》,《中国工业经济》第8期,第23～41页。 [19]吴文锋和胡悦,2022,《财政金融协同视角下的地方政府债务治理——来自金融市场的证据》,《中国社会科学》第8期,第143～162+207页。 [20]熊琛、周颖刚和金昊,2022,《地方政府隐性债务的区域间效应:银行网络关联视角》,《经济研究》第7期,第153～171页。 [21]徐军伟、毛捷和管星华,2020,《地方政府隐性债务再认识——基于融资平台公司的精准界定和金融势能的视角》,《管理世界》第9期,第37～59页。 [22]郁芸君、张一林、陈卓和蒲明,2022,《缓兵之计？地方债务展期与隐性违约风险——来自地方融资平台“借新还旧”的经验证据》,《经济学(季刊)》第3期,第955～976期。 [23]张莉、年永威和刘京军,2018,《土地市场波动与地方债——以城投债为例》,《经济学(季刊)》第3期,第1103～1126页。 [24]张牧扬、潘妍和余泳泽,2022,《社会信用、刚兑信仰与地方政府隐性债务》,《金融研究》第10期,第1～19页。 [25]钟辉勇和陆铭,2015,《财政转移支付如何影响了地方政府债务？》,《金融研究》第9期,第1～16页。 [26]钟宁桦、陈姗姗、马惠娴和王姝晶,2021,《地方融资平台债务风险的演化——基于对“隐性担保”预期的测度》,《中国工业经济》第4期,第5～23页。 [27]朱莹和王健,2018,《市场约束能够降低地方债风险溢价吗？——来自城投债市场的证据》,《金融研究》第6期,第56～72页。 [28]祝小全、陈卓、施展和何治国,2022,《违约风险传染的避险效应与溢出效应:隐性担保预期的视角》,《经济研究》第11期,第174～190页。 [29]Borusyak, K., X. Jaravel, and J. Spiess, 2024, “Revisiting Event Study Designs: Robust and Efficient Estimation”, Review of Economic Studies, pp.1~33. [30]Callaway, B., and P. H. C. Sant’Anna, 2021, “Difference-in-differences with Multiple Time Periods”, Journal of Econometrics, 225(2), pp. 200～230. [31]Chen, Z., Z. He, and C. Liu, 2020, “The Financing of Local Government in China: Stimulus Loan Wanes and Shadow Banking Waxes”, Journal of Financial Economics, 137(1), pp. 42～71. [32]De Chaisemartin, C., and X. d’Haultfoeuille, 2020, “Two-way Fixed Effects Estimators with Heterogeneous Treatment Effects”, American Economic Review, 110(9), pp. 2964～2996. [33]Goodman-Bacon, A., 2021, “Difference-in-differences with Variation in Treatment Timing”, Journal of Econometrics, 225(2), pp. 254～277. [34]Li, P., Y. Lu, and J. Wang, 2016, “Does Flattening Government Improve Economic Performance？ Evidence from China”, Journal of Development Economics, 123, pp. 18～37. [35]Roth, J., P. H. C. Sant’Anna, A. Bilinski, and J. Poe,2023, “What's Trending in Difference-in-differences？ A Synthesis of the Recent Econometrics Literature”, Journal of Econometrics, 235(2), pp. 2218～2244. [36]Tao, K., 2015, “Assessing Local Government Debt Risks in China: A Case Study of Local Government Financial Vehicles”, China & World Economy, 23(5), pp. 1～25.
