Get access Abstract This study examines how changes in state corporate tax rates affect corporate social responsibility (CSR) performance among U.S. firms. Using staggered state-level tax reforms and a difference-in-differences (DiD) design, we identify an asymmetric causal effect: tax cuts significantly enhance CSR performance by reducing concerns, whereas tax increases only marginally weaken CSR strengths. Drawing primarily on signaling theory, complemented by slack resource and stakeholder perspectives, we argue that tax cuts expand financial slack, enabling firms to use CSR as a positive signal of financial strength, long-term orientation, and responsible use of tax savings. In contrast, firms avoid cutting CSR significantly after tax hikes to prevent negative signaling. In support of the theories, our heterogeneity analyses show that these effects are stronger among financially constrained firms and are concentrated in material CSR issues that are financially relevant to investors. A domain-level analysis further reveals that tax increases reduce environmental strengths, while tax cuts lower concerns related to employee relations, diversity, and environmental practices. These findings highlight how tax policy shapes CSR through its impact on financial flexibility and stakeholder expectation, offering implications for corporate strategy and public policy.

We examine whether combining factual information on inflation levels and forecasts with a narrative can persistently shape consumers’ inflation expectations. In a preregistered randomized controlled trial with a representative sample of 3,000 German consumers, participants received either numerical or textual information about inflation rates, with or without an accompanying narrative. All treatments immediately lower inflation expectations, with numerical information eliciting stronger adjustments. Adding a narrative produces no additional immediate effect, confirming that it conveys no new information. However, only the combination of numerical information with a narrative yields a lasting reduction in inflation expectations and forecast uncertainty still observable after four weeks. Our results suggest that combining precise information with a narrative enhances information retention and can lead to more persistent shifts in consumers’ beliefs. The effects are strongest when respondents perceive the narrative as relatable and emotionally engaging, and among those with low financial literacy and limited knowledge of inflation.

Drawing on detailed career and biographical data of Chinese politicians, this study builds a dynamic social network for all political elites in China and examines the selection process of provincial-level politicians. Using regression and tree-based machine learning techniques and leveraging individuals’ global centrality within political networks, we unveil the relative importance of economic performance, political networks, and career trajectory in determining the selection of provincial leaders. Our findings highlight the critical role of network embeddedness.

Using lenders who become members of the Task Force on Climate-Related Financial Disclosures (TCFD) as an exogenous shock, we examine whether and how lenders’ commitment to transparent climate-related disclosures affects borrowers’ environmental performance. We find that borrowers of TCFD-member lenders, relative to control firms, significantly improve their environmental performance after the TCFD launch. Lenders’ disclosure commitments influence borrowers through credit rationing and monitoring. Specifically, polluting borrowers face higher borrowing costs, reduced access to credit, and greater incorporation of environmental action covenants in loan agreements. Additionally, polluting borrowers of TCFD-member lenders experience heightened financial constraints. Finally, borrowers of TCFD-member lenders are more likely to adopt the TCFD framework for climate-related disclosure after the TCFD establishment. Together, these findings illuminate the role of lenders in driving corporate environmental performance improvement through their commitment to transparent climate-related disclosures.

While the drastic physical impacts of climate change and related natural hazards are increasingly apparent, little is known about the long-term behavioral consequences of climate change-related experiences. Psychological evidence suggests that climate change (CC)-related experiences induce people to make more climate-friendly choices. Building on Upper Echelons Theory and relevant psychological literature, we investigate whether early-life natural hazard experiences of Chief Executive Officers (CEOs) are associated with more climate-friendly policies during their tenure. Our sample covers decisions taken between 1991 and 2018 by 447 US-born CEOs. While we observe an effect of hazard experiences on climate policies, we do not observe the same effect when focusing only on CC-related experiences. This result is robust across different measures of corporate climate performance.

Predicting IPO first-day returns is inherently challenging due to the wide range of contributing factors, each with distinct statistical properties. We assess the performance of several machine learning (ML) techniques and identify XGBoost as the most statistically effective model for forecasting first-day returns. Using a comprehensive set of 863 pre-IPO variables, our high-performing predictive model accurately estimates both the direction and magnitude of IPO first-day returns. The most influential predictors include underwriter agency measures, price revision, and the free-float fraction. Using a rolling-window predictive approach, the model demonstrates substantial practical value, generating approximately $300 billion in gains from IPOs with positive first-day returns and avoiding more than $22 billion in losses from those with negative returns over the 2000–2016 period.

DThe interactions between supply chains and sustainable finance have become a key area of research in financial markets, driven by growing global awareness of environmental and social challenges. Article 1 examines how lenders use sustainability clauses to monitor borrowers with negative environmental incidents and compares the use of this unique loan agreement design with conventional loan terms, financial and balance sheet-related clauses. We show that lenders are less inclined to include sustainability clauses in the loan agreement if a borrower has a history of negative environmental incidents. In contrast, lenders use sustainability clauses to attract institutional investors to participate in syndication rather than as monitoring tools for borrowers' environmental performance. Article 2 examines whether banks associated with biodiversity loss in the Amazon region experience a withdrawal of deposits when depositors become aware of their financing activities. I find empirical evidence that so-called ‘Amazon carbon banks’ experience slower growth in deposits once depositors learn about their financing activities. This effect is particularly pronounced when Amazon carbon banks have branches in counties that experience greater biodiversity loss compared to other branches. Article 3, how European companies that are heavily integrated into global supply chains (GSC) are affected by a supply chain disruption (Covid-19). We show that Covid-19 negatively affects the revenue growth of companies that are heavily dependent on GSC in their home country. Crucially, we uncover the role of banking relationships in mitigating the disruptive effects.