Shadow financing through off-balance sheet wealth management products (WMPs) has become increasingly important besides deposits in China. We quantify the economic magnitude of the effect of WMPs on banking stability in an equilibrium model calibrated to Chinese banking sector data. Alternative equilibria emerge, which deviate substantially from the observed banking system and lead to severe financial distress and large welfare losses. Rollover costs from the WMP market and negative shocks to the asset market underlying WMPs can exacerbate banking instability. Moreover, we show that smaller and medium sized banks are comparably relevant for financial stability as the systemically important big 4 banks in China.

We investigate the impact of state ownership structure on asset prices and corporate policies. By primarily focusing on China’s corporations, we show that the relationship between expected returns and capital investment varies significantly across state owned enterprises (SOE) and private owned enterprises (POE). A portfolio that longs low investment and shorts high investment firms earns an average annual excess stock return of 5% in the SOE sector. In contrast, there is no relationship between investment and expected returns in the POE sector. We show that the difference in the link between expected returns and investment across SOE and POE firms is driven by their differential exposures to the debt issuance shocks, which captures the monetary supply shocks in China. As SOE firms have easier access to bank loans, the high investment firms in the SOE sector are more able to raise debt despite that debt supply is shrinking, and hence they are less risky. We develop a dynamic model with SOE and POE firms facing different frictions in debt markets. The economic mechanism emphasizes that heterogeneous access to the debt market is an important determinant of equilibrium risk premiums across sectors with different state ownership.

This paper examines how FinTech can lower investment barriers and help households move toward optimal risk-taking, using a unique account-level data on consumption, investments, and FinTech usage from Ant Group. During our sample period, China ex- perienced a rapid increase in FinTech penetration in the form of offline digital payment, and our measure of FinTech adoption is constructed relative to this fast-developing trend of new technology. Taking advantage of our consumption data, we further infer individuals’ risk tolerance from their consumption volatility. We find that, while Fin- Tech adoption improves risk-taking for all, the more risk-tolerant individuals benefit more from FinTech advancement. The magnitude of FinTech improvement is further quantified relative to the optimal alignment of risk-taking and consumption prescribed by Merton (1971). Aggregating to the city-level, we find significant variations in Fin- Tech adoption across cities in China, owing to the gradual spread of the new technology from Hangzhou to inner China. Examining the enhancement in risk-taking across ge- ographical locations, we find that cities with low financial-service coverage benefit the most from FinTech penetration. Overall, our results show that, by unshackling the traditional constraints, FinTech improves risk-taking for individuals who need it the most.

We provide the first direct evidence on how issuers choose a credit rating agency (CRA). Using rating data from a leading CRA in China, we find that although in most cases the issuers publish more favourable ratings, in some cases issuers just select the ratings provided by CRAs they have business relationships with, especially when the more favourable ratings are above issuers’ prior ratings. Our further analysis suggests that this phenomenon is driven by the switching cost arising from the issuer being considered as a rating shopper when it obtains an upgrade from a CRA without a business relationship.

Over the past 150 years, finance has evolved into one of the world’s most globalized, digitized and regulated industries. Digitalization has transformed finance but also enabled new entrants over the past decade in the form of technology companies, especially FinTechs and BigTechs. As a highly digitized industry, incumbents and new entrants are increasingly pursuing similar approaches and models, focusing on the economies of scope and scale typical of finance and the network effects typical of data, with the predictable result of the emergence of increasingly large digital finance platforms. We argue that the combination of digitization, new entrants (especially BigTechs) and platformization of finance – which we describe as FinTech 4.0 and mark as beginning in 2019-2020 – brings massive benefits and an increasing range of risks to broader sustainable development. The platformization of finance poses challenges for societies and regulators around the world, apparent most clearly to date in the US and China. Existing regulatory frameworks for finance, competition, data, and technology are not designed to comprehensively address the challenges to these trends to broader sustainable development. We need to build new approaches domestically and internationally to maximize the benefits of network effects and economies of scope and scale in digital finance while monitoring and controlling the attendant risks of platformization of finance across the existing regulatory silos. We argue for a principles-based approach that brings together regulators responsible for different sectors and functions, regulating both on a functional activities based approach but also – as scale and interconnectedness increase – addressing specific entities as they emerge: a graduated proportional hybrid approach, appropriate both domestically in the US, China and elsewhere, as well as for cross-border groups, building on experiences of supervisory colleges and lead supervision developed for Globally Systemically Important Financial Institutions (G-SIFIs) and Financial Market Infrastructures (FMIs). This will need to be combined with an appropriate strategic approach to data in finance, to enable the maximization of data benefits while constraining related risks.

We develop a portfolio-choice model to investigate how regulatory reforms influence the risk-taking behavior of financial institutions with different capital adequacy levels. The model predicts that either all firms reduce their risk-taking, or there exists a capital-adequacy threshold below which risk-taking increases as regulation becomes more stringent. The Chinese insurance solvency regulatory reform provides a unique natural experiment to test our theory. In 2015, each insurer reported two solvency ratios under the original and the new regulatory systems. The difference between them produces an exogenous and insurer-specific measure of the regulatory pressure shock. Consistent with our theoretical predictions, we find that increasing regulatory pressure induces greater risk-taking for less capital-adequate insurers, of which the regulator should want to reduce risk-taking mostly. We show that increasing the penalties of insolvency, increasing the risk sensitivity of capital requirements, and reinforcing the qualitative risk assessment are effective policy remedies for this backfiring problem.

In geographically segmented credit markets, local real estate booms can divert capital away from manufacturing firms, create capital scarcity, increase local real interest rates, lower real wages, and cause underinvestment and relative decline in the industrial sector. Using exogenous variation in the administrative land supply across 172 Chinese cities, we show that the predicted variation in real estate prices does indeed cause substantially higher capital costs for manufacturing firms, reduce their bank lending, lower their capital intensity and labor productivity, weaken firms' financial performance, and reduce their TFP growth by economically significant magnitudes. This evidence highlights macroeconomic stability concerns associated with real estate booms.

This paper studies the effect of housing wealth shocks on workplace shirking. We use the type and actual time stamps of credit card transactions to detect non-work-related behavior during work hours. After positive shocks to house prices, affected homeowners experienced a fast and persistent increase (by 19% per month) in their propensity to use work hours to attend to personal needs. The post-shock response is more pronounced among homeowners with a greater wealth increase, with poorer career potential, or for occupations with higher monitoring costs. Our estimate implies an elasticity of shirking propensity with respect to house price of 3.8.

Using proprietary individual level loan data, this paper explores the economic consequences of the 2009 bank entry deregulation in China. Such deregulation leads to higher screening standards, lower interest rates, and lower delinquency rates for corporate loans from entrant banks. Consequently, in deregulated cities, private firms with bank credit access increase asset investments, employment, net income, and ROA. In contrast, the performance of state-owned enterprises (SOEs) does not improve following deregulation. Deregulation also amplifies bank credit from productive private firms to inefficient SOEs due mainly to SOEs’ soft budget constraints. This adverse effect accounts for 0.31% annual GDP losses.

We document that monetary policy has asymmetric effects on investments by large and small firms in China. Large firms’ investment are highly responsive to monetary expansions, but less affected by monetary contractions. In contrast, small firms’ investments are less responsive to monetary expansions, but significantly affected by monetary contractions. We argue that this asymmetric responses of large and small firms stem from their differential access to credits in a two-tiered banking system. Large firms borrow from the big state-owned banks, which have a strong depositor base, whereas small firms borrow mainly from small banks which does not have a large depositor base and therefore rely heavily on the inter-bank market for financing their loans to small firms. We build a DSGE model with heterogeneous banks, heterogeneous firms, and an inter-bank market that is calibrated to the Chinese data. We show that the model’s quantitative predictions about the effects of monetary policy on large and small firms are consistent with the facts we documented.