This study examines the impact of climate-transition risks on trade credits for Chinese listed companies from 2007-2017. We develop an index of county-level climate-transition risks faced by Chinese-listed companies using data on local carbon emissions and carbon sequestration when moving towards net zero carbon emissions. Our two-way fixed effects OLS regression results find that local firms facing greater climate-transition risks significantly reduce their trade credit financing. Specifically, a one standard deviation of increase in Risk leads to a 0.73% decrease in trade credit. This reduction is more pronounced for state-owned enterprises (SOEs), firms operating in less competitive industries, and those headquartered in regions without carbon trading markets. Our main finding is robust to a battery of sensitivity tests including the use of alternative measures and lagged independent variables. Results on an Instrumental Variable (IV) method and a differences-in-difference (DiD) analysis suggest a causal relationship between climate-transition risks on trade credit. Further analyses reveal two plausible channels for the effect: increased financial distress risk and enhanced access to bank credit.

This paper comprehensively examines how subjective well-being (SWB) is influenced by various aspects of housing — tenure, living conditions, and housing values — based on an individual panel from the China Household Finance Survey. We employ a two-way fixed effects model to reduce the endogeneity problems of housing choices. Our findings suggest that housing plays a comparable role to income and wealth in SWB and that housing inequality and living experience both matter a great deal. Moreover, the positive impacts of home ownership on SWB reported by prior research are likely quality of life effects masked in home ownership. Results are robust to ordered logistic estimation with individual fixed effects. What we document carries important implications for housing policies, and these are generalizable to other countries.

Whilst there is increasing research attention on predicting financial distress, the existing literature is subject to two specific limitations. The first is that a firm can experience a financial distress event (e.g., loan default, bankruptcy) more than once, yet most studies that model corporate financial distress prediction treat financial distress as occurring only once. This approach leads to an inefficient use of data with all subsequent events being ignored and subsequently a decrease in statistical power. Second, to account for the lack of independence between observations of repeated event data, the extant research utilising hazard analysis either has a separate analysis for successive distressed events or relies upon robust standard errors. In addition to a much smaller sample, a separate analysis yields the models that can be used to predict the survival of a distressed firm rather than the survival of a firm generally. The method of robust standard errors, while innocuous to one-time event data, ignores the possible downward bias in coefficient estimates for repeated event data. To address these two limitations, we treat financial distress as repeated events and apply more advanced methods (generalised estimating equations, random effects, fixed effects, and a hybrid approach) to account for the lack of independence between observations in discrete time hazard analysis. These different approaches are applied to a sample of listed companies in China over the 2007‒2021 period. We find that variables that are not statistically significant in models based on one-time events data become statistically significant in the models based on repeated events data, and that coefficient estimates are larger in their magnitude with more advanced methods than with the method of robust standard errors. We also find that among the advanced methods, a hybrid approach achieves substantially better out-of-sample prediction, particularly over a long-term horizon than other approaches. Our results remain robust in tests of robustness.

We estimate the causal effect of income on happiness using a unique dataset of Chinese twins. This allows us to address omitted variable bias and measurement errors. Our findings show that individual income has a large positive effect on happiness, with a doubling of income resulting in an increase of 0.26 scales or 0.37 standard deviations in the four-scale happiness measure. We also find that income matters most for males and the middle-aged. Our results highlight the importance of accounting for various biases when studying the relationship between socioeconomic status and subjective well-being.

The foreign capital flight amid pandemic outbreaks can result in propagation effects in the equity market. With a daily shareholding dataset, this paper investigates the trading behavior of foreign mutual funds in China when it was the epicenter of COVID-19 outbreaks and the subsequent period with global spreads. Using fixed effects and panel structural VAR models, we confirm propagation effects caused by the capital flight of foreign mutual funds. Substantial heterogeneities across foreign funds affiliated and unaffiliated with commercial banks have been uncovered, though they are both found to withdraw from risky stocks as an indication of a "flight to quality." Without implicit guarantees, unaffiliated foreign mutual funds liquidated immediately and more when the pandemic hit China. The resulting price shocks led to further deleverage by bank-affiliated foreign funds on their pre-pandemic risk exposure stocks. Our results shed new light on the behavioral theory of stock market trading featuring fund and stock exposure channels.

This paper studies the information interpretation of return synchronicity in the context of corporate bond market and examines the specific event intensity rationale that underlie the relationship between bond synchronicity and bond level information environment. We find that investment-grade bonds, bonds without rating splits, and bonds issued by public firms present higher levels of bond return synchronicity. These results hold after we control for bond level characteristics, industry and year fixed effects. By using credit rating change announcements as a unique measure of bond specific event intensity, we corroborate that security under better information environment has lower likelihood of specific event surprise occurrence, and thus is more synchronous with the market. We also verify that once rating change announcements did take place, the corresponding return synchronicity would be lower. Such impact would be more pronounced when the rating is downgraded compared with upgrade rating changes.

We provide direct evidence of leverage-induced fire sales contributing to a market crash using account-level trading data for brokerage- and shadow-financed margin accounts during the Chinese stock market crash of 2015. Margin investors heavily sell their holdings when their account-level leverage edges toward their maximum leverage limits, controlling for stock-date and account fixed effects. Stocks that are disproportionately held by accounts close to leverage limits experience high selling pressure and abnormal price declines which subsequently reverse. Unregulated shadow-financed margin accounts, facilitated by FinTech lending platforms, contributed more to the crash despite their smaller asset holdings relative to regulated brokerage accounts.

We empirically evaluate 20 prominent contributions to a broad range of areas in the empirical corporate finance literature. We assemble the necessary data and then apply a single, simple econometric method, the connected-groups approach of Abowd, Karmarz, and Margolis (1999), to appraise the extent to which prevailing empirical specifications explain variation of the dependent variable, differ in composition of fit arising from various classes of independent variables, and exhibit resistance to omitted variable bias and other endogeneity problems. In particular, we identify and estimate the role of observed and unobserved firm- and manager-specific characteristics in determining primary features of corporate governance, financial policy, payout policy, investment policy, and performance. Observed firm characteristics do best in explaining market leverage and CEO pay level and worst for takeover defenses and outcomes. Observed manager characteristics have relatively high power to explain CEO contract design and low power for firm focus and investment policy. Estimated specifications without firm and manager fixed effects do poorly in explaining variation in CEO duality, corporate control variables, and capital expenditures, and best in explaining executive pay level, board size, market leverage, corporate cash holdings, and firm risk. Including manager and firm fixed effects, along with firm and manager observables, delivers the best fit for dividend payout, the propensity to adopt antitakeover defenses, firm risk, board size, and firm focus. In terms of source, unobserved manager attributes deliver a high proportion of explained variation in the dependent variable for executive wealth-performance sensitivity, board independence, board size, and sensitivity of expected executive compensation to firm risk. In contrast, unobserved firm attributes provide a high proportion of variation explained for dividend payout, antitakeover defenses, book and market leverage, and corporate cash holdings. In part, these results suggest where empiricists could look for better proxies for what current theory identifies as important and where theorists could focus in building new models that encompass economic forces not contained in existing models. Finally, we assess the relevance of omitted variables and endogeneity for conventional empirical designs in the various subfields. Including manager and firm fixed effects significantly alters inference on primary explanatory variables in 17 of the 20 representative subfield specifications.