Contributions in Merlin

Between 2010 and 2012 and with bank stability as the ultimate target, five European countries implemented a tax levy on banks’ liabilities thereby decreasing the cost of equity relative to the cost of debt. Using a difference-in-differences approach we assess the impact of this tax levy on banks’ participation in the syndicated loan market. We further investigate the impact of the tax levy along bank size and capital structure. We find that banks located in countries where the tax levy was implemented supply more credit. This increase is more significant for larger lenders and banks that are more capital constrained.

Non-linear probability weighting is an integral part of descriptive theories of choice under risk such as prospect theory. But why do these objective errors in information processing exist? Should we try to help individuals overcome their mistake of overweighting small and underweighting large probabilities? In this paper, we argue that probability weighting can be seen as a compensation for preexisting biases in evaluating payoffs. In particular, inverse S-shaped probability weighting is a flipside of S-shaped payoff valuation. Probability distortions may thus have survived as a second-best solution to a fitness maximization problem, and it can be counter-productive to correct them while keeping the value function unchanged.

Deficits in neural processing of reward have been described in both bipolar disorder (BD) and schizophrenia (SZ), but it remains unclear to what extent these deficits are caused by similar mechanisms. Efficient reward processing relies on adaptive coding which allows representing large input spans by limited neuronal encoding ranges. Deficits in adaptive coding of reward have previously been observed across the SZ spectrum and correlated with total symptom severity. In the present work, we sought to establish whether adaptive coding is similarly affected in patients with BD. Twenty-five patients with BD, 27 patients with SZ and 25 healthy controls performed a variant of the Monetary Incentive Delay task during functional magnetic resonance imaging in two reward range conditions. Adaptive coding was impaired in the posterior part of the right caudate in BD and SZ (trend level). In contrast, BD did not show impaired adaptive coding in the anterior caudate and right precentral gyrus/insula, where SZ showed deficits compared to healthy controls. BD patients show adaptive coding deficits that are similar to those observed in SZ in the right posterior caudate. Adaptive coding in BD appeared more preserved as compared to SZ participants especially in the more anterior part of the right caudate and to a lesser extent also in the right precentral gyrus. Thus, dysfunctional adaptive coding could constitute a fundamental deficit in severe mental illnesses that extends beyond the SZ spectrum.

The television broadcasting industry is of crucial economic and social importance. Traditionally, this industry has been dominated by free-to-air TV (FTV) but due to technological progress, subscription-based pay TV (PTV) has emerged as a competing business model. A key question for the PTV broadcasters is whether to air commercials in addition to charging subscription fees. Based on a theoretical model of asymmetric competition between a PTV and an FTV broadcaster, we examine the effects of placing PTV advertising on broadcaster market strategies, viewer demands, broadcaster profits and consumer surplus. We find that introducing advertising on PTV can induce a higher viewer demand on this channel but a lower viewer demand on the FTV channel. Surprisingly, consumers can benefit through the introduction of advertising in PTV and broadcaster profits can increase if the viewer disutility of advertising is sufficiently large. Our study provides an analytical framework for choosing and implementing an optimal PTV strategy when an FTV competitor preexists in the market. Furthermore, our study derives implications for policymakers and regulatory authorities by showing that additional PTV advertising is not necessarily socially undesirable due to the strategic market reactions.

This paper examines the role of lifelong learning in counteracting skill depreciation and obsolescence. We differentiate between occupations with more hard skills versus more soft skills and draw on representative job advertisement data that contain machine-learning categorized skill requirements and cover the Swiss job market in great detail across occupations (from 1950 to 2019). We examine lifelong learning effects for “harder” versus “softer” occupations, thereby analyzing the role of training in counteracting skill depreciation in occupations that are differently affected by skill depreciation. Our results reveal novel empirical patterns regarding the benefits of lifelong learning, which are consistent with theoretical explanations based on structurally different skill depreciation rates: In harder occupations, with large shares of fast-depreciating hard skills, the role of lifelong learning is primarily as a hedge against unemployment risks rather than a boost to wages. By contrast, in softer occupations, in which workers build on more value-stable soft-skill foundations, the role of lifelong learning instead lies mostly in acting as a boost for upward career mobility and leads to larger wage gains.

This paper proposes a procedure for assessing sensitivity of inferential conclusions for functionals of sparse high-dimensional models following model selection. The proposed procedure is called targeted undersmoothing. Functionals considered include dense functionals that may depend on many or all elements of the highdimensional parameter vector. The sensitivity analysis is based on systematic enlargements of an initially selected model. By varying the enlargements, one can conduct sensitivity analysis about the strength of empirical conclusions to model selection mistakes. We illustrate the procedure's performance through simulation experiments and two empirical examples.

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Workplace learning is often used to train employees systematically. New in this context is workplace learning with the help of a pedagogical agent (PA). Following Actions Design Research (ADR), this paper describes organizational training for telephone service using such PA. To develop the training, existing employee telephone service problems were analyzed, and the content of the learning program was determined based on this analysis. Subsequently, a PA was developed, implemented, and used in three municipalities. The evaluation of the learning outcome shows promising results but also yields some challenges: even though the employees improved in various aspects of the learning, they also developed a perception of surveillance. This research concludes with the formulation of design principles and suggestions for the organizational embedding of a PA in a workplace setting.

As digital assets are constantly growing, stablecoins have attracted remarkable attention from central banks regarding financial stability risks. Focusing on 2018-2022, this study uses the DCC-GARCH (1,1) model to analyze the conditional dynamic correlation between stablecoins and traditional financial assets, specifically the energy market, Banks Index, DXY Index, and gold. The results imply that stablecoins significantly correlate with the energy market and the banking sector, while there is no significant co-movement with the DXY Index or gold. Furthermore, the results disclose a strongly increased correlation between all assets during financial market stress. These findings highlight that stablecoins are moving towards integration with the traditional financial market. As policymakers have not yet regulated stablecoins, this study suggests introducing appropriate regulation in the near future.

Climate policy uncertainty makes it difficult for investors to assess the impact of future climate regulations. This thesis investigates how uncertainty about carbon taxation is priced in the option market. The results show that the cost of protection against downside and jump risk is higher for firms with high carbon intensity, and firms that do not report carbon emissions. The value of option protection increases when attention to carbon taxation is high.