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.

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.

This paper examines the relation between corporate social responsibility (CSR) performance and tax–motivated income shifting. Using a profit–shifting measure estimated from multinational enterprises (MNEs) data, we find that parent firms with higher CSR scores shift significantly more profits to their low-tax foreign subsidiaries. Overall, our evidence suggests that MNEs engaging in CSR activities acquire legitimacy and moral capital that temper negative responses by stakeholders and thus have greater scope and chance to engage in unethical profit-shifting activities, consistent with the legitimacy theory.

In Chapter 1, I combine longitudinal administrative firm-level data from Germany with 8,000 local tax changes for identification to show that local tax hikes (cuts) increase (decrease) the local manufacturing share. Firm-level results reveal that this is due to wage, employment, firm entry, and labor productivity in the service sector being more responsive to a tax shock than in manufacturing. With this evidence in mind, I calibrate a two-sector model with heterogeneous firms and profit tax to show that, owing to different structural parameters, a corporate tax cut disproportionately benefits service firms, contributing to the sectoral reallocation from manufacturing to service. In Chapter 2, we derive a European Herfindahl-Hirschman concentration index from 15 micro-aggregated country datasets. We show that European concentration rose due to a reallocation of economic activity towards large and concentrated industries. Over the same period, productivity gains from an increasing allocative efficiency of the European market accounted for 50% of European productivity growth while markups stayed constant. Using country-industry variation, we show that changes in concentration are positively associated with changes in productivity and allocative efficiency. This holds across most sectors and countries and supports the notion that rising concentration in Europe reflects a more efficient market environment rather than weak competition and rising market power. In chapter 3, We study the consequences of the Covid-19 pandemic and related policy support on productivity. We employ an extensive micro-distributed exercise to access otherwise unavailable individual data on firm performance and government subsidies. Our cross-country evidence for five EU countries shows that the pandemic led to a significant short-term decline in aggregate productivity and the direct support to firms had only a limited positive effect on productivity developments.

This paper provides a comprehensive analysis of the forecastability of the real price of natural gas in the United States at the monthly frequency considering a universe of models that differ in their complexity and economic content. Our key finding is that considerable reductions in mean-squared prediction error relative to a random walk benchmark can be achieved in real time for forecast horizons of up to two years. A particularly promising model is a six-variable Bayesian vector autoregressive model that includes the fundamental determinants of the supply and demand for natural gas. To capture real-time data constraints of these and other predictor variables, we assemble a rich database of historical vintages from multiple sources. We also compare our model-based forecasts to readily available model-free forecasts provided by experts and futures markets. Given that no single forecasting method dominates all others, we explore the usefulness of pooling forecasts and find that combining forecasts from individual models selected in real time based on their most recent performance delivers the most accurate forecasts.

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.