Contributions in Merlin

We use three natural field experiments to examine anti-Semitism in Italy by sending email inquiries to amateur football clubs, landlords, and employers and comparing the response rates to emails sent with Jewish- and non-Jewish-sounding names. Italy is an interesting country as discrimination was heterogeneous and geographically unevenly distributed during World War II. We analyze if today's anti-Semitism in Italy is geographically correlated to the deportations and killings of Jews during the Holocaust. The results show significant discrimination when looking for football club and an apartment, but not when seeking a job. We find markedly different results for women. Comparing areas with different societal and economic implications provides us with a more informed perspective about the extent of discrimination.

What is the impact of policy interventions on the systemic risk of banks? To answer this question, we analyze a comprehensive sample that combines an original set of bank-specific bailout events with the balance sheets of key affected and nonaffected European banks between 2005 and 2014. We find a positive and significant association of guarantees with systemic risk that is somewhat weaker in the long run when the regulator appoints members to the supervisory board. The short run association between recapitalizations and systemic risk is also positive for large and less capitalized banks, while in the long run, recapitalizations are linked with reduced systemic importance, especially for less profitable banks and in cases when the regulator limits management pay. Liquidity injections are positively linked with systemic risk, but the long-run effect is mitigated for small or better capitalized banks. In the short run, injecting liquidity is associated with reduced systemic risk when the regulator imposes restrictions on supervisory board composition or on management pay or capital payouts.

Research suggests that firms with family CEOs differ from other types of businesses, yet surprisingly little is known about how employees in these firms feel and behave compared to those working in other firms. We draw from family science and management research to suggest that family CEOs, because of their emotion-evoking double role as family members and business leaders, are, on average, more likely to infuse employees with positive emotions, such as enthusiasm and excitement, than hired professional CEOs. We suggest that these emotions spread through firms by way of emotional contagion during interactions with employees, thereby setting the organizational affective tone. In turn, we hypothesize that in firms with family CEOs the voluntary turnover rate is lower. In considering structural features as boundary conditions, we propose that family CEOs have stronger effects in smaller and centralized firms, and weaker effects in formalized firms. Multilevel data from 41,200 employees and 2,246 direct reports of CEOs from 497 firms with and without family CEOs provide support for our model. This research suggests that firms managed by family CEOs, despite often being criticized as nepotistic relics of the past, tend to offer pleasant work environments.

We consider the design of contests when the principal can choose both the prize profile and how the prizes are allocated as a function of a possibly noisy signal about the agents' efforts. We provide sufficient conditions that guarantee optimality of a contest. Optimal contests have a minimally competitive prize profile and an intermediate degree of competitiveness in the contest success function. Whenever observation is not too noisy, the optimum can be achieved by an all-pay contest with a cap. When observation is perfect, the optimum can also be achieved by a nested Tullock contest. We relate our results to a recent literature which has asked similar questions but has typically focused on the design of either the prize profile or the contest success function.

We identify the effects of negative rates on bank behavior using difference-in-differences identification. First, we find that going negative can interrupt not only the pass-through from policy to deposit but also to mortgage rates. To preserve their deposit franchise, banks finance negative deposit with increased mortgage spreads, the more the bigger their market power. Second, negative rates on reserves induce banks to cut some reserves without replacement and replace others with riskier assets. Together with increased mortgage spreads, balance sheet restructuring preserves profits but risk-taking increases. Third, pass-through interruption and risk-taking can be reduced through tiered remuneration.

In this paper, we introduce a general concept for the analysis of temporal and multivariate data and the system LFPeers that applies this concept to temporal similarity search and results exploration. The conceptual workflow divides the analysis in two phases: a search phase to find the most similar objects to a query object before a time point in the temporal data, and an exploration phase to analyze and contextualize this subset of objects after . LFPeers enables users to search for peers through interactive similarity search and filtering, explore interesting behavior of this peer group, and learn from peers through the assessment of diverging behaviors. We present the conceptual workflow to learn from peers and the LFPeers system with novel interfaces for search and exploration in temporal and multivariate data. An earlier workshop publication for LFPeers included a usage scenario targeting epidemiologists and the public who want to learn from the Covid-19 pandemic and distinguish successful and ineffective measures. In this extended paper, we now show how our concept is generalized and applied by domain experts in two case studies, including a novel case on stocks data. Finally, we reflect on the new state of development and on the insights gained by the experts in the case studies on the search and exploration of temporal data to learn from peers.

Event-based cameras are ideal for line-based motion estimation, since they predominantly respond to edges in the scene. However, accurately determining the camera displacement based on events continues to be an open problem. This is because line feature extraction and dynamics estimation are tightly coupled when using event cameras, and no precise model is currently available for describing the complex structures generated by lines in the space-time volume of events. We solve this problem by deriving the correct non-linear parametrization of such manifolds, which we term eventails, and demonstrate its application to eventbased linear motion estimation, with known rotation from an Inertial Measurement Unit. Using this parametrization, we introduce a novel minimal 5-point solver that jointly estimates line parameters and linear camera velocity projections, which can be fused into a single, averaged linear velocity when considering multiple lines. We demonstrate on both synthetic and real data that our solver generates more stable relative motion estimates than other methods while capturing more inliers than clustering based on spatiotemporal planes. In particular, our method consistently achieves a 100% success rate in estimating linear velocity where existing closed-form solvers only achieve between 23% and 70%. The proposed eventails contribute to a better understanding of spatio-temporal event-generated geometries and we thus believe it will become a core building block of future event-based motion estimation algorithms.

The development of effective vision-based algorithms has been a significant challenge in achieving autonomous drones, which promise to offer immense potential for many real-world applications. This paper investigates learning deep sensorimotor policies for vision-based drone racing, which is a particularly demanding setting for testing the limits of an algorithm. Our method combines feature representation learning to extract task-relevant feature representations from high-dimensional image inputs with a learning-by-cheating framework to train a deep sensorimotor policy for vision-based drone racing. This approach eliminates the need for globally-consistent state estimation, trajectory planning, and handcrafted control design, allowing the policy to directly infer control commands from raw images, similar to human pilots. We conduct experiments using a realistic simulator and show that our vision-based policy can achieve state-of-the-art racing performance while being robust against unseen visual disturbances. Our study suggests that consistent feature embeddings are essential for achieving robust control performance in the presence of visual disturbances. The key to acquiring consistent feature embeddings is utilizing contrastive learning along with data augmentation.

What is the long-term impact of negative interest rates on bank lending? To answer this question, we construct a unique summary measure of negative rate exposure by individual banks based on exclusive survey data and banks’ balance sheets and couple it with the credit register of Spain and firms’ balance sheets to identify this impact on the supply of credit to firms. We find that only when deposit rates reached the zero lower bound did affected banks (relative to non-affected banks) decrease their supply, especially when undercapitalized and lending to risky firms. The adverse effects of the negative rates on banks’ intermediation capacity only took place after a protracted period of time.

Scatterplot sampling has long been an efficient and effective way to resolve the overplotting issues commonly occurring in large-scale scatterplot visualization applications. However, it is challenging to preserve the existence of low-density points or outliers after sampling for a sub-sampling algorithm if, at the same time, faithfully representing the relative data densities is of importance. In this work, we propose to address this issue in a visual-assisted manner. While the whole dataset is sub-sampled, the density of the outliers is modeled and visually integrated into the final scatterplot together with the sub-sampled point data. We showcase the effectiveness of our proposed method in various cases and user studies.