China Shadow Bank Credit, 1987-2018

Meenagh, David and Matthews, Kent (2022). China Shadow Bank Credit, 1987-2018. [Data Collection]. Colchester, Essex: UK Data Service. 10.5255/UKDA-SN-855434

The Chinese financial system has served the Chinese economy well in the early stages of development in channeling domestic savings to domestic investment. But, continued financial repression, along with a growing middle class and ageing population has created pressure on savings to 'search for yield'. At the same time, the dominance of lending to state-owned-enterprises, political constraints, inefficiencies and weak risk management practice by financial institutions (FI) have pushed SMEs to alternative sources of funding. The demand for yield from savers and funds from private investment has been met by the rapid growth in shadow banking. This study encompasses two of the identified themes of the research call. The research theme 'alternative strategies for reform and liberalization' covers the role of the Shadow Bank system in the credit intermediation process. This research is of critical importance because it informs the macroeconomic research necessary for investigating 'the role of the Chinese financial system in sustaining economic growth'. Addressing the first research theme we take a dual track approach to better understand the role of the financial system in sustaining in economic growth. The first track examines the role of bank and non-bank finance in promoting long-term economic growth at the regional level. The second track is aimed at the more short-term issue of identifying the potential frequency of macro-economic crises generated by a banking crisis. The finance-growth nexus is a well-established area of economic development, however the China experience questions the supposition that financial development is a necessary precondition. The empirical findings are mixed. Part of the reason for this could be the failure to distinguish between the quality of financial institutions across regions, and the openness of the local environment in terms of the balance between private and public enterprises. Our research would build on the existing literature in two ways. First, it would utilize imperfect but available data on informal finance to examine direct and spill-over effects on medium term growth from contiguous provinces. Second, primary data on the geographic dimension in shadow bank lending gleaned from Theme 2 research will be used to design a weighting system to adjust financial flows for the quality of the local financial environment. The second prong will develop a small macroeconomic model of a hybrid DSGE type that incorporates a banking sector including shadow banks. Such models have been developed for China in recent times but only a few have attempted to incorporate a banking sector.These models are mostly calibrated versions and make no attempt to test the structure against the data. Recent attempts to test a hybrid New Keynesian-RBC DSGE type model for the Chinese economy using the method of indirect inference have been successful and inclusion of a shadow banks have shown some success. The results of the Theme 2 study will inform the development of a fuller shadow banking sector in the macroeconomic model that will be used to estimate the frequency of economic crises generated by bank crises. Theme 2 research will examine the relationship between the banking system and the shadow banking system as complements or substitutes. It will aim to determine the variable interest rate on the P2P online lending platform on the basis of risk-return, the home bias in online investments, and the signaling and screening in the P2P online lending platform. Finally, it will aim to identify the impact of shadow banking on entrepreneurial activity, the industrial growth rate and regional housing investment and price differentials. These results would inform the theme 1 research on the interconnectedness of shadow banking with the mainstream and the fragility of the financial system to shocks and financial crises.

Data description (abstract)

Annual data on the size of China Shadow Bank credit was taken from two sources. Moodys (China) produces data 2000-2012 and Goldman Sachs 2013-2018. The average growth rate 2000-2015 was applied to generate data prior to 2000. Annual data was interpolated to produce quarterly estimates using the cubic match last function in EViews so that the integrity of the annual stock data is maintained for the 4th quarter.

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