Using data from 56 nations over 45 years, we find that nations that are more likely to elect left wing governments face higher (and more volatile) sovereign spreads. To explain these facts, we build a sovereign default model in which two policymakers (left and right) alternate in power. The probability of an incumbent staying in power is increasing in the share of government spending. We parametrize the left policymaker as having a higher marginal political gain from increasing government spending than the right does, a feature found in our data. Model economies in which the left is more frequently in power face worse borrowing terms due to higher default risk, a greater reluctance for fiscal austerity in bad times, and a higher share of government spending on average. These features imply large welfare losses for households.

We examine the effect of board members with venture capital experience (i.e., VC directors) on executive incentives at publicly listed firms. VC directors serving on the compensation committee are associated with greater CEO risk-taking incentives (i.e., vega) and greater pay-for-performance sensitivity (i.e., delta). These effects are more substantial if VC directors are from highly reputable VC firms. Using Regulation S-K requirements to disclose attributes of nominated directors as an instrument, we show that these results are causal. We also document that prior finding of greater research intensity and innovation when VC directors serve on boards of public firms are in part explained by the presence of increased risk-taking incentives of the CEO instilled by such directors. Lastly, we find that having VC directors on nominating and/or governance committees is associated with a higher likelihood of forced CEO turnover.

Using a proprietary loan data set, we study how a large state-owned bank uses its internal ratings in loan granting decisions around China’s 2008 economic stimulus programme that relies on bank credit for financing. We find that there is little change in the rating process of the bank, and internal ratings remain a valid, albeit weaker, predictor of loan interest rates in the stimulus period. Weakened rating-interest rate relation is concentrated for borrowers from the industries that the stimulus programme focuses on, for state-owned enterprises (SOEs), for bank branches operating in provinces with a low level of credit market marketization, or when the credit rater and loan officer have no collaboration before. We also find that interest rates remain a valid predictor of ex-post loan outcomes in the stimulus period. Overall, there is no evidence that loan decisions of the state-owned bank are severely compromised in the economic stimulus period as speculated by some media. By showing how a state-owned bank maneuvers between supporting government stimulus initiative and maintaining market-based lending, we contribute to the limited literature on the roles of internal ratings in loan contracting decisions, and add to the debate over the roles of state-owned banks.

In a frictionless market where  rms can always raise capital, debtors default only if their total assets cannot cover their total liabilities. However, in the presence of market imperfection, debtors may default even while solvent if the cost of new capital outweighs the legal penalty on contract violation. Using a unique sample of Chinese bank loans over the period 2007-2012, we analyze the repayment decisions of borrowing rms whose cash holdings are high enough to cover the bank debt coming due. We  nd that poor legal enforcement signi cantly increases the likelihood of default. This positive association becomes stronger if  rms face tighter  nancing constraints, or when credit supply becomes more scarce. Our results illustrate the role of legal enforcement in determining  nancial risks and show that market imperfection strengthening the impact of legal enforcement on  nancial risks.

We study credit allocation across  rms and its real e ects during China's economic stimulus plan of 2009-2010. We match con dential loan-level data from the 19 largest Chinese banks with  rm-level data on manufacturing  rms. We document that the stimulus-driven credit expansion disproportionately favored state-owned rms and  rms with lower average product of capital, reversing the process of capital reallocation towards private  rms that characterized China's high growth before 2008. We argue that implicit government guarantees for state-connected  rms become more prominent during recessions and can explain this reversal.

Abstract: Traditional research about the passive investors and index were mainly focus on the tracking error and the performance of mutual funds. However, they ignored that, deceptive by name, the passive investors, such as index-tracking funds and ETFs, may have an active impact on the value of the company through large-scale transactions of these passive investors. Focused on the Chinese stock market, this paper investigates whether specific passive investors, the funds and ETFs that track MSCI China index, will actively influence the market valuation after MSCI Index Rebalance. When the passive shareholders, which are always the mutual funds, exceeds a threshold, I find that firms added to the index will have a significant positive return, about X%, to the index itself. Also, I find the firms eliminated out to the index have a significant negative return, about X%, to the index itself. One potential interpretation of these results is that index-rebalancing will lead the index-trackers to buy those stocks added to the index, and these transactions represent a large buy power that will lead the demanding of those stocks to exceed the selling power and this dynamic of trading plus the following transactions of other investors eventually cause a premium and positive return. The firm size will also have an impact on stock performance when the index get rebalanced, partially in that the weight of the index is calculated according to the market value, a calculate method that leads to the higher weight of large companies. If large companies are added to or removed from the index, the trading volume will be larger, causing more transactions dynamic on those stocks.

We provide nationwide causal estimates of digital financial inclusion’s (DFI) effect on air pollution in the short term for China from 2014 to 2018. Using distance to Xihu District as an instrument, 1% gain of DFI increases air pollution by 0.36%. The baseline result is strongly robust to various checks. The coverage breadth and usage depth of DFI increase pollution, with the elasticity of 0.39 and 0.37 respectively, whereas the digitization level of DFI lowers pollution, with the elasticity of -1.42. The heterogeneous short-run effect of DFI can be attributed to a multitude of channels, including pollution standard, geographical factors, population density, development gaps and international trade.

We examine the role of borrowers’ digital footprints in debt collection. Using a large sample of personal loans from a fintech lender in China, we find that the information acquired by the lender through borrowers’ digital footprints can increase the repayment likelihood on delinquent loans by 18.5%. The effect can be explained by two channels: bonding borrowers’ obligations with their social networks and locating borrowers’ physical locations. Moreover, the lender is more likely to approve loan applications from borrowers with digital footprints, even though these borrowers may occasionally have a higher likelihood of delinquency. The use of digital footprints can remain legitimate under stringent privacy protection regulations and fair debt collection practices. Our findings suggest that digital footprints, as a new type of collateral, can ultimately enhance financial inclusion by facilitating the lender’s collection of delinquent loans.

With growing awareness of sustainability, the field of Environmental, Social and Governance (ESG), has been attracting mainstream investors and researchers. Many previous studies have found inconclusive or mixed results on the relationship between ESG ratings and firms’ financial performance, which are mainly attributed to their varied markets, time horizons, and sources of ESG rating. Based on evidence from an emerging market, namely China, this paper examines whether ESG is an adequate indicator for firms’ future financial performance. Given the divergence in ESG rating methodologies, we use ESG data from two ESG rating agencies, one based in China (SynTao) and the other based in Switzerland (RepRisk), for robustness. Specifically, we investigate 377 China A-share companies covered by both agencies and find that ESG rating, albeit divergent due to disparate methodologies, is instrumental in predicting the trend of corporate financial performance (CFP). This work verifies that the forward-looking nature of ESG makes it crucial for firms’ long-term valuation and financial performance in emerging markets. Throughout the research, we observe four issues in the current ESG rating process: the opacity and inaccessibility of source data, the obscurity of ESG rating methodologies adopted by rating agencies, the lack of automated pipeline, and the unannounced historical data rewriting. We believe that the public blockchain ecosystem is promising to address these issues, and we propose future research on the ESG framework for blockchain to call for sustainability focus on this emerging technology.

We study how institutional investors utilize potentially biased information by analyzing the e ect of IPO underwriters' earnings forecasts on investors' bidding behaviors in Chinese IPO auctions. Despite the presence of upward biases in underwriters' earnings forecasts, we  nd that investors' bid prices are higher in IPOs with higher earnings forecasts. The investors' positive reaction to biased information can be explained in a rational expectation model where the underwriter has valuable information about the IPO but has a biased incentive in presenting the information to investors. Consistent with the model's predictions, we  nd that an investor's bid price is more sensitive to the underwriter's earnings forecast when the forecast bias is expected to be smaller, when the relative precision of the underwriter's information over the investor's information is higher, and when the investor has a higher valuation of the IPO.