Contributions in Merlin

In the battle against unethical behavior in organizations, fostering employees' moral courage proves vital beyond conventional regulation and compliance efforts. To propel this frontier and empower individuals to uphold moral values, a robust measure of workplace moral courage becomes imperative. Drawing upon a competency-based approach, this paper introduces the Workplace Moral Courage Scale (WMCS). Unlike previous measures, the WMCS stands out by acknowledging the diverse ways in which moral courage can manifest within workplace settings. Building on data of two diverse German employee samples (total N = 995), we unveil five distinct factors: challenging colleague and supervisor misconduct, opposing unethical orders, confessing mistakes, and initiating positive changes. The WMCS exhibits good psychometric properties and convergent and discriminant validity. Confirming its concurrent validity, the WMCS effectively predicts various forms of employee silence, even after controlling for organizational influences. The paper concludes with a discussion of the results and implications of the WMCS.

Moral sensitivity is one of the most important educational goals in the ethical domain. This paper offers a first review of attempts to train moral sensitivity from 1979 to 2018, including 45 studies. The studies were reviewed using a combination of qualitative and quantitative analyses. Our results show that longer courses are generally more successful in fostering moral sensitivity. However, this effect is relatively small. What ultimately appears to have mattered most, was whether learners received personal feedback on their awareness to ethical issues. Training methods that appeared to be of little value in promoting moral sensitivity including problem-based learning, the discussion of papers and texts (seminars) and a strong focus on critical thinking and challenging existing perspectives. A further major implication of our results is that reviews of ethics training ought to differentiate between learning outcomes (e.g., moral sensitivity vs. reasoning).

Education is widely recognized as structuring emerging political divides between the new left and the far right (Stubager 2009; Häusermann and Kriesi 2015; Abou-Chadi and Hix 2021; Marks et al. 2022). However, there is ongoing uncertainty about the mechanism through which the education cleavage operates, particularly in the absence of mobilizing organizations. We fielded a survey in Germany in October 2022 (to be followed by Switzerland and the UK) to explore the hypothesis that patterns of social segregation by education create social networks which foster common identities, political attitudes, and voting behavior (consolidating key aspects of an emerging cleavage). We offer descriptive evidence that individuals tend to be embedded in educationally-distinct social networks. In turn, our preliminary findings show that network composition in terms of both level and field are associated with social identities, political attitudes, and vote choice, above and beyond individual educational characteristics.

When firm-level information is not available, the greenness of financial portfolios, in terms of alignment to the EU Taxonomy, and their exposure to climate-related transition risk need to be estimated with a top-down approach. We improve the accuracy of available estimates by providing country-specific coefficients for both dimensions, based on homogeneous definitions of greenness and transition risk across countries. An application on confidential data from the European Central Bank shows that the exposure to transition risk of less regulated financial institutions has more than tripled from 2014 to 2023. Moreover, we show that the levels of Taxonomy alignment and transition risk exposure are largely heterogeneous across countries and sectors.

Credit Valuation Adjustment captures the difference in the value of derivative contracts when the counterparty default probability is taken into account. However, in the context of a network of contracts, the default probability of a direct counterparty can depend substantially on the default probabilities of indirect counterparties. We develop a model to clarify when and how these network effects matter for CVA, in particular in the presence of correlation among counterparties defaults. We provide an approximate analytical solution for the default probabilities. This solution allows for identifying conditions on key parameters such as network degree, leverage and correlation, where network effects yield large differences in CVA (e.g. above 50%), and thus relevant for practical applications. Moreover, we find evidence that network effects induce a multi-modal distribution of CVA values.

Much economic analysis derives policy recommendations based on social welfare criteria intended to model the preferences of a policy maker. Yet, little is known about policy maker’s normative views in a way amenable to this use. In a behavioral experiment, we elicit German legislators’ social welfare criteria unconfounded by political economy constraints. When resolving preference conflicts across individuals, politicians place substantially more importance on least-favored than on most-favored alternatives, contrasting with both common aggregation mechanisms and the equal weighting inherent in utilitarianism and the Kaldor-Hicks criterion. When resolving preference conflicts within individuals, we find no support for the commonly used “long-run criterion” which insists that choices merit intervention only if the lure of immediacy may bias intertemporal choice. Politicians’ and the public’s social welfare criteria largely coincide.

This paper studies how to incorporate observable factors in difference-in-differences and document their empirical relevance. We show that even under random assignment directly adding factors with unit-specific loadings into the difference-in-differences estimation results in biased estimates. This bias, which we term the “bad time control problem” arises when the treatment effect covaries with the factor variation. Researchers often control for factor structures by using: (i) unit time trends, (ii) pre-treatment covariates interacted with a time trend and (iii) group-time dummies. We show that all these methods suffer from the bad time control problem and/or omitted factor bias. We propose two solutions to the bad time control problem. To evaluate the relevance of the factor structure we study US housing returns. Adding macroeconomic factors shows that factors have additional explanatory power and estimated factor loadings differ systematically across geographic areas. This results in substantially altered treatment effects.

Expectancies, which are higher order prognostic beliefs, can have powerful effects on experiences, behavior and brain. However, it is unknown where, how, and when, in the brain, prognostic beliefs influence appetitive interoceptive experiences and related economic behavior. This study combined a placebo intervention on hunger with computational modelling and functional magnetic resonance imaging of value-based decision-making. The results show that prognostic beliefs about hunger shape hunger experiences, how much participants value food and food-value encoding in the prefrontal cortex. Computational modelling further revealed that these placebo effects were underpinned by how much and when during the decision process taste and health information are integrated into the accumulation of evidence toward a food choice. The drift weights of both sources of information further moderated ventromedial and dorsolateral prefrontal cortex interactions during choice formation. These findings provide novel insights into the neurocognitive mechanisms that translate higher order prognostic beliefs into non-aversive interoceptive sensitivity and shape decision-making.

Using cross-sectional data from 93 countries, we investigate the relationship between the desired level of redistribution among citizens from different socioeconomic backgrounds and the actual extent of government redistribution. Our focus on redistribution arises from the inherent class conflicts it engenders in policy choices, allowing us to examine whose preferences are reflected in policy formulation. Contrary to prevailing assumptions regarding political influence, we find that the preferences of the lower socioeconomic group, rather than those of the median or upper strata, are most predictive of realized redistribution. This finding contradicts the expectations of both leading experts and regular citizens.

This papers proposes a generic, high-level methodology for generating forecast combinations that would deliver the optimal linearly combined forecast in terms of the mean-squared forecast error if one had access to two population quantities: the mean vector and the covariance matrix of the vector of individual forecast errors. We point out that this problem is identical to a mean-variance portfolio construction problem, in which portfolio weights correspond to forecast combination weights. We allow negative forecast weights and interpret such weights as hedging over and under estimation risks across estimators. This interpretation follows directly as an implication of the portfolio analogy. We demonstrate our method's improved out-of-sample performance relative to standard methods in combining tree forecasts to form weighted random forests in 14 data sets.