|
|
|
| Monetary Transmission Heterogeneity and the “Matthew Effect” of Liquidity Allocation in the Real Economy |
| YANG Jisheng, XIANG Jingjie
|
School of Economics, Huazhong University of Science and Technology;
School of Economics and Business Administration, Central China Normal University |
|
|
|
Abstract The key for monetary policy to sustain real economic recovery is to improve the mechanism of monetary transmission to enhance liquidity in key areas and for weak links. Thus, allocative efficiency is more important than total supply. Typical symptoms of an inefficient transmission mechanism are the overallocation of resources to a virtual economy that squeezes out investment in the real economy or the coexistence of excess liquidity in the virtual economy and a liquidity shortage in the real economy. In addition, a liquidity imbalance in the real economy results in inefficiency. During the current transition from rapid growth to high-quality development, monetary policy is more targeted, so the heterogeneity of the micro transmission mechanisms of monetary policy are of concern to researchers.
This paper contributes to the literature by developing a quantile regression model with interactive effects based on data from Chinese A-share companies, and by conducting quantitative analysis on the effect of monetary policy and the virtual economy on firm financing, including internal and external financing. Empirical evidence suggests that during the 2009-2015 sample period, the economy faced a dilemma of “the richer the firm, the easier it is to get financed. The easier it is to get financed, the richer the firm,” also referred to as the “Matthew effect.” The Matthew effect was prominent in the effects of traditional monetary policy as the support given by releasing liquidity to poor firms did not reach half that of rich firms. At the same time, the virtual economy diverted liquidity in a way that strengthened the Matthew effect. The poorer the firm, the more likely it was to be negatively affected, with poor firms experiencing 3 times the diversion compared with rich firms.
During the sample period, the loose monetary policy when the interest rate and reserve ratio were lowered resulted in advantaged firms being more active, whereas disadvantaged firms were irrelevant with regulating intension. Specifically, the interest rate cut for external financing by head enterprises was 2.10 times that for tail enterprises, and the reserve ratio cut on external financing for head enterprises was 2.83 times that for tail enterprises. The traditional untargeted open market operation vehicles benefited head enterprises 6.87 times more than they benefited tail enterprises in terms of external financing.
In our study, the diversion to the virtual economy from the real economy was reflected in internal financing. The overheated stock market not only squeezed out business operations but also widened the gap between advantaged and disadvantaged firms. Similarly, firms in worse operating condition were more likely to be squeezed out by the housing boom. The diversion of the stock market on the liquidity of tail enterprises was 2.69 times that of head enterprises, and the diversion of the real estate market on tail enterprises was 2.30 times that of head enterprises.
According to the literature on the credit channel, rising housing prices are beneficial for external financing because mortgage values also increase. Our regression results are consistent with this view. However, the Matthew effect and increases in external financing have long been neglected. Our empirical findings suggest that during the 2009-2015 sample period, rising housing prices benefited the external financing of head enterprises 5.01 times more than that of tail enterprises. Therefore, efforts to stimulate the real estate market to fuel economic growth may be effective in the short run, but in the long run, stimulating the real estate market works against sustainable development.
Our empirical findings have the following profound implications for economic policy: (1) Rather than adopting strong stimulus policies that have an economy-wide effect, we should continue to move forward with structural reform and inject liquidity into key areas via the flexible manipulation of innovative and targeted instruments to create a proper monetary and financial environment for high-quality development. (2) The study of digital currency is of significant importance to gain more precise control over the currency flow between sectors. Therefore, big data technology is useful for customizing and adjusting dynamic strategies to strengthen areas of weakness. (3) As serving the real economy is the duty and purpose of the financial sector, China should guard against financial bubbles, be cautious of real economy hollowing arising from an overheated virtual economy, and guide liquidity back to the real economy. Benign interaction between the virtual and real economies is the long-term solution for high-quality development.
|
|
Received: 24 April 2019
Published: 02 December 2020
|
|
|
|
|
| [1] |
蔡卫星、曾诚和胡志颖,2015,《企业集团、货币政策与现金持有》,《金融研究》第2期,第114~130页。
|
| [2] |
方明月,2011,《资产专用性、融资能力与企业并购——来自中国A股工业上市公司的经验证据》,《金融研究》第5期,第156~170页。
|
| [3] |
李广子和刘力,2009,《债务融资成本与民营信贷歧视》,《金融研究》第12期,第137~150页。
|
| [4] |
李欣泽和陈言,2018,《金融摩擦与资源错配研究新进展》,《经济学动态》第9期,第100~114页。
|
| [5] |
林滨、王弟海和陈诗一,2018,《企业效率异质性、金融摩擦的资源再分配机制与经济波动》,《金融研究》第8期,第17~32页。
|
| [6] |
罗来军、蒋承和王亚章,2016,《融资歧视、市场扭曲与利润迷失——兼议虚拟经济对实体经济的影响》,《经济研究》第4期,第74~88页。
|
| [7] |
饶品贵和张会丽,2015,《通货膨胀预期与企业现金持有行为》,《金融研究》第1期,第101~116页。
|
| [8] |
孙大超、王博和Wang Gang,2014,《银行业垄断是导致货币政策抑制中小企业的原因吗》,《金融研究》第6期,第99~114页。
|
| [9] |
杨继生,2019,《宏观经济渐进式变革的动态识别与评价》,《中国社会科学报》第4期。
|
| [10] |
喻坤、李治国、张晓蓉和徐剑刚,2014,《企业投资效率之谜:融资约束假说与货币政策冲击》,《经济研究》第5期,第106~120页。
|
| [11] |
余明桂、钟慧洁和范蕊,2019,《民营化、融资约束与企业创新——来自中国工业企业的证据》,《金融研究》第4期,第75~91页。
|
| [12] |
于泽、钱智俊、方庆和罗瑜,2017,《数量管制、流动性错配和企业高额现金持有——来自上市公司的证据》,《管理世界》第2期,第67~84页。
|
| [13] |
曾爱民、张纯和魏志华,2013,《金融危机冲击、财务柔性储备与企业投资行为——来自中国上市公司的经验证据》,《管理世界》第4期,第107~120页。
|
| [14] |
战明华、汤颜菲、李帅,2020,《数字金融发展、渠道效应差异和货币政策传导效果》,《经济研究》第6期,第22~38页。
|
| [15] |
张成思和张步昙,2016,《中国实业投资率下降之谜:经济金融化视角》,《经济研究》第12期,第32~46页。
|
| [16] |
张成思和郑宁,2019,《中国实业部门金融化的异质性》,《金融研究》第7期,第1~18页。
|
| [17] |
张杰、刘元春、翟福昕和芦哲,2013,《银行歧视、商业信用与企业发展》,《世界经济》第9期,第94~126页。
|
| [18] |
Alfaro L., Kalemli-Ozcan S., Volosovych, and V. 2008. “Why doesn't Capital Flow from Rich to Poor Countries？ An Empirical Investigation”, The Review of Economics and Statistics, 90(2): 347～368.
|
| [19] |
Altman E.I. 1968. “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy”, Journal of Finance, 23(4): 589～609.
|
| [20] |
Bai J. 2009. “Panel Data Models with Interactive Fixed Effects”, Econometrica, 77(4): 1229～1279.
|
| [21] |
Berben R.P., Locarno A., and Morgan J.B. 2004. “Cross-country Differences in Monetary Policy Transmission”, Working Paper.
|
| [22] |
Bernanke B., and M. Gertler. 1995. “Inside the Black Box: the Credit Channel of Monetary Policy Transmission”, Journal of Economic Perspectives, 9(4): 27～48.
|
| [23] |
Berglof E., and Bolton P. 2002. “The Great Divide and Beyond: Financial Architecture in Transition”, Journal of Economic Perspectives, 16(1): 77～100.
|
| [24] |
Bleck A., Liu X. 2018. “Credit Expansion and Credit Misallocation”, Journal of Monetary Economics, 94: 27～40.
|
| [25] |
Clarida R., Gali J., and Gertler M. 1999. “The Science of Monetary Policy: a New Keynesian Perspective”, Journal of Economic Literature, 37(4): 1661～1707.
|
| [26] |
Cramer J.G. 1986. “The Volume of Transactions and the Circulation of Money in the United States, 1950～1979”, Journal of Business & Economic Statistics, 4(2): 225～232.
|
| [27] |
Harding M., and C. Lamarche. 2014. “Estimating and Testing a Quantile Regression Model with Interactive Effects”, Journal of Econometrics, 178: 101～113.
|
| [28] |
Hsieh C.T., and Klenow, P.J. 2009. “Misallocation and Manufacturing TFP in China and India”, The Quarterly Journal of Economics, 124(4): 1403～1448.
|
| [29] |
King R G, and Levine R. 1993. “Finance and Growth: Schumpeter Might be Right”, The Quarterly Journal of Economics, 108(3): 717～737.
|
| [30] |
Mishkin F.S. 2007. “Housing and the Monetary Transmission Mechanism”, NBER Working Paper.
|
| [31] |
Ouyang M. 2009. “The scarring Effect of ecessions”, Journal of Monetary Economics, 56(2): 184～199.
|
| [32] |
Peek J, Rosengren, and E.S. 2005. “Unnatural Selection: Perverse Incentives and the Misallocation of Credit in Japan”, American Economic Review, 95(4): 1144～1166.
|
| [33] |
Restuccia D., and Rogerson R. 2008. “Policy Distortions and Aggregate Productivity with Heterogeneous Establishments”, Review of Economic Dynamics, 11(4): 707～720.
|
| [34] |
Romer C.D., D. H. Romer, and S. M. Goldfeld. 1999. “New Evidence on the Monetary Transmission Mechanism”, Brookings Papers on Economic Activity, 1: 149～213.
|
| [35] |
Schivardi F, Sette E, Tabellini G. 2017. “Credit Misallocation During the European Financial Crisis”, Bank of Italy Temi di Discussione (Working Paper) No. 1139.
|
| [36] |
Taylor, and J.B. 1995. “The Monetary Transmission Mechanism: an Empirical Framework”, Journal of Economic Perspectives, 9(4): 11～26.
|
| [37] |
Whited T. M. and G. Wu. 2006. “Financial Constraints Risk”, Review of Financial Studies, 9(2): 531～559.
|
|
|
|
