学术论文

Environmental protection is widely perceived as a state responsibility, but market-based solutions such as green investing have emerged in the financial sector. Little research has addressed whether green investing can affect corporate environmental performance and how the state would moderate such an impact. Using an institutional logics perspective, we extend the literature on institutional complexity by exploring the factors leading to compatibility of logics and practices. We theorize that the success of green investing as a novel hybrid practice combining financial means and environmental goals depends on the legitimacy it achieves as an appropriate solution to the stated goal, and this legitimacy can be boosted or dampened by other hybrid practices in the field. Analyzing a panel dataset of 3,706 firms from 20 countries between 2002 and 2013, we find a positive relationship between the relative size of green investment in the economy and firm-level environmental performance in that country. This relationship is moderated by state policies: a strong environmental protection policy weakens the positive relationship between green investing and corporate environmental performance, and a strong shareholder protection policy strengthens the relationship. We contribute to research on institutional complexity, logic compatibility, and public–private cooperation in pursuing the common good.

We study the global diffusion of culture through multinationals, focusing on gender norms. Using data on manufacturing firms in China from 2004 to 2007, we find that foreign affiliates from countries with a more gender-equal culture tend to employ proportionally more women and appoint more female managers. They also generate cultural spillovers, as we find that domestic firms' female labor share increases with the prevalence of foreign affiliates in the same industry or city. Based on a multi-sector model that accounts for firm heterogeneity in productivity, gender bias, and learning, we perform counterfactual exercises. By hypothetically eliminating firms' gender biases, we observe a 5% increase in China's aggregate total factor productivity, 19% of which is due to spillovers from foreign affiliates.

Inspired by the recent health science findings that air pollution affects mental health and cognition, we examine whether air pollution can intensify the cognitive bias observed in the financial markets. Based on a proprietary data set obtained from a large Chinese mutual fund family consisting of complete trading information for more than 773,198 accounts in 247 cities, we find that air pollution significantly increases investors’ disposition effects. Analysis based on two plausible exogenous variations in air quality (the vast dissipation of air pollution caused by strong winds and the Huai River policy) supports a causal interpretation. Mood regulation provides a potential mechanism.

Why did banks experience massive deposit inflows during the pandemic? We discover that deposit interest rates at bank branches in counties with higher COVID-19 infection rates fell by more than rates at branches—even branches of the same bank—in counties with lower infection rates. Credit drawdowns, national policies, such as the Payment Protection Program, and a flight-to-safety do not account for these cross-branch changes in deposit rates. Evidence suggests that higher local COVID-19 infection rates are associated with households’ greater anxiety about future job and income losses, anxiety that induces households to reduce spending and increase deposits.

Video advertisements often show actors and influence agents consuming and enjoying products in slow motion. By prolonging depictions of influence agents’ consumption utility, slow motion cinematographic effects ostensibly enhance social proof and signal product qualities that are otherwise difficult to infer visually (e.g., pleasant tastes, smells, haptic sensations, etc.). Seven studies including an eye-tracking study, a Facebook Ads field experiment, and lab and online experiments—all using real ads across diverse contexts—demonstrate that slow motion (vs. natural speed) can backfire and undercut product appeal by making the influence agent’s behavior seem more intentional and extrinsically motivated. The authors rule out several alternative explanations by showing that the effect attenuates for individuals with lower intentionality bias, is mitigated under cognitive load, and reverses when ads use non-human influence agents. The authors conclude by highlighting the potential for cross-pollination between visual information processing and social cognition research, particularly in contexts such as persuasion and trust, and discuss managerial implications for visual marketing, especially on digital and social platforms.

We introduce the concept of risk entanglement in a preference-free setting to jointly explain the exchange rate volatility, cyclicality, and currency risk premia in the data. Risk entanglement specifies a subset of incomplete market models, in which nondiffusive or nonlog-normal shocks to exchange rates are not fully spanned by asset returns. When risks are entangled, there exist multiple pricing-consistent exchange rates, but none of them are equal to the ratio of the stochastic discount factors (SDFs) or their projections. Decoupling the exchange rate from the SDFs allows us to address key FX market patterns that are puzzling in international finance.

We provide a psychological explanation for the delayed price response to news about economically linked firms. We show that the return predictability of economically linked firms depends on the nearness to the 52-week high stock price. The interaction between news about economically linked firms and the nearness to the 52-week high can partially explain the underreaction to news about customers, geographic neighbors, industry peers, or foreign industries. We also find that analysts react to news about economically linked firms but the 52-week high effect reduces such reactions, providing direct evidence that the 52-week high affects the belief-updating process.

We provide direct evidence that governments selectively default on debt when they can identify creditors. Analyzing a comprehensive data set of subnational debt, we show that Chinese local governments choose to default on banks with weaker political power. A reduction in a bank's political power relative to other banks increases the likelihood of selective default by local governments. Such default selections are driven by banks’ influence over politician promotion. When local politicians are highly ranked or connected to national leaders, they engage less in selective default as their promotion is less affected by bank loan defaults. Our findings suggest a politics-finance nexus through which government defaults are restrained.

A common problem in econometrics, statistics, and machine learning is to estimate and make inference on functions that satisfy shape restrictions. For example, distribution functions are nondecreasing and range between zero and one, height growth charts are nondecreasing in age, and production functions are nondecreasing and quasi-concave in input quantities. We propose a method to enforce these restrictions ex post on generic unconstrained point and interval estimates of the target function by applying functional operators. The interval estimates could be either frequentist confidence bands or Bayesian credible regions. If an operator has reshaping, invariance, order-preserving, and distance-reducing properties, the shape-enforced point estimates are closer to the target function than the original point estimates and the shape-enforced interval estimates have greater coverage and shorter length than the original interval estimates. We show that these properties hold for six different operators that cover commonly used shape restrictions in practice: range, convexity, monotonicity, monotone convexity, quasi-convexity, and monotone quasi-convexity, with the latter two restrictions being of paramount importance. The main attractive property of the post-processing approach is that it works in conjunction with any generic initial point or interval estimate, obtained using any of parametric, semi-parametric or nonparametric learning methods, including recent methods that are able to exploit either smoothness, sparsity, or other forms of structured parsimony of target functions. The post-processed point and interval estimates automatically inherit and provably improve these properties in finite samples, while also enforcing qualitative shape restrictions brought by scientific reasoning. We illustrate the results with two empirical applications to the estimation of a height growth chart for infants in India and a production function for chemical firms in China.