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.

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.

We examine how institutional saving mechanisms influence retirement saving decisions under bounded rationality and income risk. Using a life-cycle experiment with habit formation and loss aversion, we test mandatory and voluntary binding savings under deterministic and stochastic income. Voluntary commitment improves saving performance only when income is predictable; under uncertainty, it fails to improve performance. Mandatory savings do not raise total saving, as participants reduce voluntary contributions. These results emphasize the role of income smoothing in enabling behavioral interventions to improve long-term financial outcomes.

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.

Institutional common ownership of firm pairs in the same industry increases the likelihood of a preexisting social connection among their CEOs. We establish this relationship using a quasi-natural experiment that exploits institutional mergers combined with firms’ hiring events and detailed information on CEO biographies. In addition, for peer firms, gaining a CEO connection from a hiring firm’s CEO appointment correlates with higher returns on assets, stock market returns, and decreasing product similarity between companies. We find evidence consistent with common owners allocating CEO connections to shape managerial decisionmaking and increase portfolio firms’ performance.

In this paper, we study champions of corporate social responsibility (CSR) performance among the U.S. publicly traded firms and their common characteristics by utilizing machine learning algorithms to identify predictors of firms’ CSR activity. We contribute to the CSR and leadership determinants literature by introducing the first comprehensive framework for analyzing the factors associated with corporate engagement with socially responsible behaviors by grouping all relevant predictors into four broad categories: corporate governance, managerial incentives, leadership, and firm characteristics. We find that strong corporate governance characteristics, as manifested in board member heterogeneity and managerial incentives, are the top predictors of CSR performance. Our results suggest policy implications for providing incentives and fostering characteristics conducive to firms “doing good.”

The European Commission’s growth forecasts play a crucial role in shaping policies and provide a benchmark for many (national) forecasters. The annual forecasts are built on quarterly estimates, which do not receive much attention and are hardly known. Therefore, this paper provides a comprehensive analysis of multi-period ahead quarterly GDP growth forecasts for the European Union (EU), euro area, and several EU member states with respect to first-release and current-release data. Forecast revisions and forecast errors are analyzed, and the results show that the forecasts are not systematically biased. However, GDP forecasts for several member states tend to be overestimated at short-time horizons. Furthermore, the final forecast revision in the current quarter is generally downward biased for almost all countries. Overall, the differences in mean forecast errors are minor when using real-time data or pseudo-real-time data and these differences do not significantly impact the overall assessment of the forecasts’ quality. Additionally, the forecast performance varies across countries, with smaller countries and Central and Eastern European countries (CEECs) experiencing larger forecast errors. The paper provides evidence that there is still potential for improvement in forecasting techniques both for nowcasts but also forecasts up to eight quarters ahead. In the latter case, the performance of the mean forecast tends to be superior for many countries.