Using a market-indicator-based approach, this paper empirically examines whether the stability of the US and EU financial systems is affected by the digital finance revolution driven by BigTechs, FinTechs, and crypto-assets. These three sectors display different downside volatility profiles, with financial intermediaries being particularly sensitive to shocks from the crypto ecosystem only under extremely severe downturns, which are prevented in regulated equity markets. In that vein, we provide evidence that the Markets in Crypto Assets Regulation reduced financial systemic risk in EU. Overall, our empirical analysis shows that markets perceive the performance and riskiness of tech-driven companies and assets in differentiated ways, and that the transmission of shocks from digital finance ecosystems operates uniquely under varying conditions of systemic stress. Finally, we also document asymmetric spillover effects between advanced and emerging economies, with shock transmission from the US and EU to emerging markets being systematically stronger than in the reverse direction.

This paper analyzes the distribution and composition of pre-tax national income in Germany since 1992, combining personal income tax returns, household survey data, and national accounts. Inequality rose from the 1990s to the late 2000s due to falling labor incomes among the bottom 50% and rising incomes in the top 10%. This trend reversed after 2007 as labor incomes across the bottom 90% increased. The top 1% income share, dominated by business income, remained relatively stable between 1992 and 2019. A large share of Germany’s top 1% earners are non-corporate business owners in labor-intensive professions. At least half of the business owners in P99-99.9 and a quarter in the top 0.1% operate firms in professional services – a pattern mirroring the United States. From 1992 to 2019, Germany’s top 0.1% income concentration exceeded France’s and matched U.S. levels until the late 2000s.

This paper investigates the impact of manager-investor interaction quality on stock returns by utilizing an online IPO roadshow dataset and leveraging a word-embedding model. We find that such interactions are positively valued, as reflected in initial returns. The effect is particularly pronounced for firms characterized by higher levels of information asymmetry, greater investor attention, increased question uncertainty, or discussions on topics not covered in prospectus. Additionally, our research reveals that effective management communication leads to increased first-day turnover rates and thus higher returns. These heightened returns persist up to 180 days following the IPO, without displaying a significant long-term reversal associated with interaction quality. These findings underscore the meaningful impact of the quality of manager-investor interactions on firm valuation.

We assemble data about executives on the boards of national supervisory authorities (NSA) in European banking, which reveal that 40% have an employment history in the financial industry. Appointment patterns to NSA boards differ substantially across countries. Generally, the appointment of former finance professionals as prudential supervisors is inconsequential for banks‘ valuations, capitalization, and asset growth. The exception are positive, but short-lived valuation effects for those banks whose alumni are appointed as executives of directly responsible NSA boards. Circumstantial evidence suggests that a “proximity bias“ is a more likely driver of this effect instead of superior financial expertise or intrinsic skills of the designated NSA executives.

We study the firm-level and aggregate effects of government-imposed employment targets. We develop a dynamic general equilibrium model with heterogeneous firms and endogenous productivity growth in which penalties for below-target hiring generate a polarization mechanism: low-productivity firms exit, while others expand employment beyond efficient levels, and firms invest in productivity to avoid future penalties. We test and confirm the model’s firm level predictions using unique contractual data on more than 18,000 employment commitments from the East German privatization, exploiting quasi-random variation in the assignment of privatizers to firms. Quantitatively, employment targets reduce unemployment in the short run, but these gains reverse over time as distorted labor allocations and weakened investment incentives slow aggregate productivity growth and reduce welfare. We also evaluate how alternative designs for employment-protection (e.g., the choice between mandates and subsidies, the structure of targets) impact misallocation and the resulting short- and long-run outcomes.

We develop a general approach for stress testing correlations of financial asset portfolios. The correlation matrix of asset returns is specified in a parametric form, where correlations are represented as a function of risk factors, such as country and industry factors. A sparse factor structure linking assets and risk factors is built using Bayesian variable selection methods. Regular calibration yields a joint distribution of economically meaningful stress scenarios of the factors. As such, the method also lends itself as a reverse stress testing framework: using the Mahalanobis distance or Highest Density Regions (HDR) on the joint risk factor distribution allows to infer worst-case correlation scenarios. We give examples of stress tests on a large portfolio of European and North American stocks.