High-bargaining-power (low-bargaining-power) customer (supplier) firms borrow (lend) more trade credit according to the literature. We study whether this bargaining power effect strengthens or weakens when the credit supply tightens. We construct a Nash bargaining model of trade credit and show that the bargaining power effect weakens if their financing costs increase more than that of the customers. We find support for our theory using a unique database of listed firms in China that discloses firms’ transaction information with important customers and suppliers. Interest-rate sensitive suppliers, proxied by a non-state ownership, a high debt rollover risk, and a high financial constraint index, reduce trade credit to their high-bargaining-power customers during credit contractions.

We study the income inequality among streamers using the administrative data of a leading Chinese live-streaming platform. The live-streaming technology enables a superstar to produce new entertainment products matched with demand and occupies a larger market share. Imagine an extreme case; the best streamer hosts live for 24 hours, earns all possible income, and leaves zero time for other streamers. Our data show that the income distribution of the highest-paid streamers follows Zipf’s Law and appears to be even more concentrated than any offline business: NBA top players, Forbes celebrities, and billionaires. Income inequality increased rapidly as the platform expanded from 2018 to 2020 — for example, the income share of the platform’s top 10 streamers increased from 14.82% to 45.15% as its revenue grew by 142%. To estimate inequality elasticity to the market size, we study four quasi-experimental shocks: potential market size proxied by economic development and Fintech coverage, quarter-end revenue spikes induced by the seasonal incentive regime, user surge induced by capital raising, and the Covid-19 lockdown in Wuhan. Gini coefficient elasticity ranges fromm1.3% to 10.6% estimated from the cross-city variations (local economic development and Covid-19 Wuhan lockdown); the time-series variations (quarter-end and user surge before capital raising) imply an elasticity ranging from 3.6% to 25.5%.

This paper investigates the effect on A–H premiums of the China Stock Connect, which allows the Mainland to invest in H-shares in Hong Kong (Southbound) and overseas to invest in A-shares in the Mainland (Northbound). It removes barriers to investor trading all crosslisted A- and H-shares but leads to them an enlarged premium. We develop the differential demand hypothesis of Stulz and Wasserfallen (1995) in China and identify the elasticities of Stock Connect relying on the demand asset pricing of Koijen and Yogo (2019). We ffnd that the average elasticity of Northbound (A-shares) is 0.18, and that of Southbound (H-shares) is 0.66, implying that A-H shares have different levels of substitute effect for investors on each side of Stock Connect, leading to the long-term premium. On a univariate basis, they explain 20% of the variation of the A-H premium and remain highly signiffcant when controlling other variables. We also estimate the cross-listed and time-varying elasticities of Stock Connect. They illustrate the strong positive spillover effect of A-H shares and check the robustness of our results.

The inelastic markets hypothesis states that the aggregate stock market price elasticity of demand is small, implying that flows have large impacts on prices. We exploit demand shocks created as investor funds are frozen and unfrozen during Chinese IPOs to estimate the impact of demand shocks on the Chinese stock market. Using brokerage account records, we observe the selling and buying as investors raise cash to subscribe for IPOs and then reinvest the funds that supported unsuccessful subscriptions. Using an instrumental variables estimator we find that a 10 bps demand shock increases stock prices by between 30 and 48 bps.

In this paper, I build a continuous-time macro-finance model in which firms can access both bond credit and bank credit. The model captures the simple idea that the presence of bond financing increases the price elasticity of demand for bank loans. I find that the optimal capital adequacy ratio is quantitatively sensitive to the presence of bond financing and that models would overstate the banking sector's recovery rate if they omit bond financing. Furthermore, the model highlights that an economy's optimal capital requirement highly depends on the efficiency of its bankruptcy procedure and the risk profile of its real sector.

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.

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.

Early-stage ?rms usually have a single large Research and Development (R&D) project that requires multi-stage investment. Firms? volatility can dramatically change due to the evolvement of R&D e¤orts and stage clearing. First, the success (failure) of R&D e¤orts within each stage (jump risk) decreases (increases) the un- certainty (i.e. volatility) level of the ?rms?future returns ?"jump e¤ect". Second, at the end of each stage, ?rms decide whether to continue next stage investment upon re-evaluating the project prospect conditional on the resolution of technical uncertainty and other information; as ?rms survive each investment stage and are becoming mature, the uncertainty level of their future returns should eventually decrease in later investment stages that lead to maturity ?"stage-clearing e¤ect". Ignoring these e¤ects results in incorrect estimation of ?rms?future volatility, an important element for early-stage ?rm valuation. In this paper, I develop a gener- alized Markov-Switching EARCH methodology for early-stage ?rms with discrete stage-clearing and jumps. My methodology can identify structural changes in the idiosyncratic volatility and also explore the relation between price changes and future volatility. Using a hand-collected dataset of early-stage biotech ?rms, I con?rmed the existence of the "stage-clearing e¤ect" and the "jump e¤ect". In the second part of my paper, I model early-stage ?rms as sequences of nested call options with jumps that lead to mature ?rms. "Jump e¤ect" arises because the early-stage ?rms are modeled as compound call options with jumps on the underly- ing cash ?ows, the volatility of the early-stage ?rms at each stage is determined by the compound call option elasticity to the underlying cash ?ows. If the downside (upside) jump happens, the value of the underlying cash ?ows decreases (increases), which makes the compound call option elasticity go up (down). As a result, the compound call option becomes riskier (less risky). "Stage-clearing e¤ect" arises because as ?rms exercise their option to continue investment, the new options that ?rms enter into will eventually become a less risky option.

he paper assesses the vulnerability of China to external shocks via the indirect negative effect of a slow-down in exports on domestic demand for investment. In the last decade China has increased its dependence on external demand, particularly from the advanced countries; at the same time it has become a primary destination market for goods produced in the rest of emerging Asia. Since 2001 investment expenditures have represented a key driver of Chinese GDP growth; as a very large share of activity in the manufacturing sector is export oriented, we expect fixed capital investment in this sector to be highly related to exports. Overcoming serious shortcomings in available data, we estimate an investment equation for the period 1993-2006 and find an elasticity of investment to exports in the manufacturing sector in the range between 0.9 and 1. Taking into account the dominant contribution of capital accumulation to Chinese GDP growth, we conclude that the growth effects of an external demand shock could become significant when taking into account the domestic investment channel.

Previous studies find mixed results on the relation between exchange rate movement and stock return. We revisit the issue by exploring the effect of market efficiency and heterogeneous investor’s reaction to exchange rate changes using the recent event of Chinese currency appreciation. Our results show that different investor groups react differently to the exchange rate appreciation and that this can be explained by the differences in information access and demand elasticity. In addition, we find that investors with limited investment opportunities react more positively to exchange rate appreciation. Our results suggest that it is important to consider the issues of market efficiency and the differences among investors when one analyzes the relation between exchange rate movement and stock return.