Intergenerational justice is a core principle of sustainability, yet empirical metrics on the impact of business on future generations remain scarce. Moreover, evidence suggests that different ESG scores capture distinct dimensions of corporate responsibility, highlighting the need for more targeted assessments. This study examines the relationship between corporate engagement with children’s rights and financial performance using a dataset of 1672 firm-year observations, combining a novel children’s rights benchmark with Refinitiv’s financial and sustainability metrics. Results indicate a negative association between marketplace ratings, assessing firms’ child welfare considerations in marketing, and accounting-based profitability, even when controlling for ESG subscores. However, no similar relationship emerges in stock market performance. These findings highlight potential tensions between corporate responsibility and short-term financial outcomes, emphasizing the role of regulatory frameworks and stakeholder engagement in balancing financial and social objectives.

We investigate the relationship between county-level social capital in the U.S. and asymmetric earnings timeliness (accounting conservatism). We measure social capital by the strength of civic norms and the density of social networks in a community. We find that firms headquartered in regions with higher social capital have earnings that reflect bad news more quickly than good news. Two potential mechanisms driving this connection are evident in our findings. First, the positive link between social capital and asymmetric earnings timeliness is more pronounced in firms with weaker external oversight, suggesting that social capital compensates for weaknesses in these mechanisms by discouraging managers from delaying the recognition of bad news. Second, we illustrate that firms in high social capital regions are more likely to recruit senior executives with higher asymmetric earnings timeliness coefficients. This result implies a preference for managers who adopt more conservative accounting practices. We find similar results using an international sample of firms from 21 countries. Our findings offer new insights into how local social norms influence corporate financial reporting.

Firm training is widely regarded as crucial for protecting workers from automation, yet there is a lack of empirical evidence to support this belief. Using internationally harmonized data from over 90,000 workers across 37 industrialized countries, we construct an individual-level measure of automation risk based on tasks performed at work. Our analysis reveals substantial within-occupation variation in automation risk, overlooked by existing occupation-level measures. To assess whether firm training mitigates automation risk, we exploit within-occupation and within-industry variation. Additionally, we employ entropy balancing to re-weight workers without firm training based on a rich set of background characteristics, including tested numeracy skills as a proxy for unobserved ability. We find that training reduces workers’ automation risk by 3.8 percentage points, equivalent to 8% of the average automation risk. The training-induced reduction in automation risk accounts for 15% of the wage returns to firm training. Firm training is effective in reducing automation risk and increasing wages across nearly all countries, underscoring the external validity of our findings. Training is similarly effective across gender, age, and education groups, suggesting widely shared benefits rather than gains concentrated in specific demographic segments.

This study examines the institutional demand for mispriced stocks with incongruent expectations implied by the book-to-market (BM) ratio and financial strength. Institutional trading (or institutional demand) is calculated by both changes in institutional ownership (percentage of shares held) and the number of institutional investors from the previous to the current quarter. Market mispricing and expectation errors in value/glamour stocks can be identified by analysing firms’ recent financial strength (measured by FSCORE). Firms are sorted into value stocks (top 30%), middle stocks (between 30% and 70%) and glamour stocks (bottom 30%) by distribution of BM ratios at the end of the previous fiscal year. Firms in the sample are then double sorted by FSCORE and BM: in each BM portfolio, firms are further classified into high-, mid- and low-FSCORE groups. Consistent with the argument of expectation errors in value/glamour stocks (Piotroski and So, 2012), institutional investors buy value stocks with strong fundamentals (underpriced) and sell glamour stocks with weak fundamentals (overpriced). Independent institutions are more likely to take advantage of the mispricing in value/glamour firms than passive institutions. Institutional trading on expectation errors could reduce the abnormal returns to mispriced stocks. Institutional trading patterns on mispriced value/glamour stocks are also documented in global markets. Our research provides new evidence that the institutional investors do exploit the BM anomalies if the mispricing can be identified by both the BM and the recent financial strength. Our study differs from Caglayan, Celiker and Sonaer (2018) as we emphasise that financial institutions, in addition to relying on only the BM values, process information from financial statements to infer firms’ financial strength. This study is also the first to document that institutional demand on mispricing could attenuate the BM anomaly.

We examine the effects of the big five personalities of CEOs (openness, conscientiousness, extroversion, agreeableness, and neuroticism) on their annual compensation. We hand-collect the tweets of S&P 1500 CEOs and use IBM's Watson Personality Insights to measure their personalities. CEOs with high ratings of agreeableness and conscientiousness get more compensation. We further find that the firms with these CEOs outperform their peers due to better investment efficiency. Firms are willing to pay higher compensation for talent, especially for firms with better operations, located in states with higher labor unionization, or facing higher competition in the product market. Overall, CEO personality is a valid predictor of CEOs' compensation.

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.

Based on a sufficient statistics approach, we show how the state of technology of European industries relative to the rest of the world can be empirically assessed in a way that is simple in terms of computation, parsimonious in terms of data requirements, but still comprehensive in terms of information. The lack of systematic cross-industry correlation between export specialization and technological advantage suggests that standard measures of revealed comparative advantage only imperfectly capture a country’s technological prowess due to the concurrent influences of factor prices, market size, markups, firm selection and market share reallocation. These findings offer policy insights relevant to the EU’s external competitiveness debate, echoing several recommendations from the Draghi report. Achieving export specialization in key sectors requires more than just technological superiority.

How durable are the political accountability effects of the worst pandemic in a century? We track the effects of the COVID-19 pandemic on political preferences through its “high” and “low” phases in the Czech Republic. Uniquely, we ask about the effects of both the health and the economic costs of the pandemic measured at both personal and municipality levels. Consistent with the literature, we estimate effects suggestive of political accountability of leaders during “high” pandemic phases without higher support for non-democratic alternatives. However, we also find that the pandemic political accountability effects are mostly short-lived, and do not extend to the first post-pandemic elections.

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.