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.

Trust is a crucial component for successful transactions regardless of whether they are executed in physical or virtual spaces. Blockchain technology is often discussed in the context of trust and referred to as a trust-free, trustless, or trustworthy technology. However, the question of how the trustworthiness of blockchain platforms should be demonstrated and proven to end users still remains open. While there may be some genuine trust in the blockchain technology itself, on an application level trust in an IT artifact needs to be established. In this article, we examine how trust-supporting design elements may be implemented to foster an end user's trust in a blockchain platform. We follow the design science paradigm and suggest a practically useful set of design elements that can help designers of blockchain platforms to build more trustworthy systems.

This paper studies the impact of a key feature of competitive markets on moral behavior: the possibility that a competitor might step in and conclude the deal if a conscientious market actor forgoes a profitable business opportunity for ethical reasons. In a series of experiments, we study whether people invoke the replacement excuse, that is, the argument “if I don't do it, someone else will,” to justify narrowly self-interested actions. Our data are consistent with the possibility that the existence of a clear social norm of moral conduct can limit the impact of the availability of the replacement excuse on behavior.

In this paper, we address the problem of analyzing and computing all steady states of an overlapping generation (OLG) model with production and many generations. The characterization of steady states coincides with a geometrical representation of the algebraic variety of a polynomial ideal, and, in principle, one can apply computational algebraic geometry methods to solve the problem. However, it is infeasible for standard methods to solve problems with a large number of variables and parameters. Instead, we use the specific structure of the economic problem to develop a new algorithm that does not employ the usual steps for the computation of Grobner basis such as the computation of successive S-polynomial and expensive division.

Automatic medical text simplification can assist providers with patient-friendly communication and make medical texts more accessible, thereby improving health literacy. But curating a quality corpus for this task requires the supervision of medical experts. In this work, we present MedEASi (Medical dataset for Elaborative and Abstractive Simplification), a uniquely crowdsourced and finely annotated dataset for supervised simplification of short medical texts. Its expert-layman-AI collaborative annotations facilitate controllability over text simplification by marking four kinds of textual transformations: elaboration, replacement, deletion, and insertion. To learn medical text simplification, we fine-tune T5-large with four different styles of inputoutput combinations, leading to two control-free and two controllable versions of the model. We add two types of controllability into text simplification, by using a multi-angle training approach: position-aware, which uses in-place annotated inputs and outputs, and position-agnostic, where the model only knows the contents to be edited, but not their positions. Our results show that our fine-grained annotations improve learning compared to the unannotated baseline. Furthermore, position-aware control generates better simplification than the position-agnostic one. The data and code are available at https://github.com/Chandrayee/CTRL-SIMP.

We provide evidence that banks increase cross-border lending in response to higher climate policy stringency in their home countries. Saturating with granular set of fixed effects and including a rich set of control variables, we show that the increase in cross-border lending is not driven by loan demand and/or other bank home country characteristics. In line with banks use cross-border lending as a regulatory arbitrage tool, the increase in cross-border lending occurs only if banks' home countries have more stringent climate policy compared to their borrowers' countries. The effect is stronger for large, lowly capitalized banks with high NPL ratios and for banks with more experience in cross-border lending. Our results suggest that without a global cooperation, cross-border lending can be a channel that reduces the effectiveness of climate policies.

Information communication technology (ICT)’s environmental impact must be considered in digital innovation and associated policy to mitigate ICT’s climate change contribution. A pro- posed solution to reduce ICT emissions is by improving efficiency, yet this fails to consider rebound effects where efficiency improvements offset emissions savings or increase emissions. In this perspective, we reveal insights from a transdisciplinary workshop that identified challenges for why rebound effects are difficult to include in innovation and policy. From this, we call researchers to (1) find new ways of presenting rebound effects to digital innovators and policymakers; (2) gather cross-disciplinary evidence of ICT rebound effects; and (3) transparently analyze ICT’s environmental, societal, and economic impacts together. We also call for a systems thinking approach to addressing ICT’s environmental impacts, whereby a solution to rebound ef- fects becomes visible: efficiencies under emission constraints.

We study the combinatorial assignment domain, which includes combinatorial auctions and course allocation. The main challenge in this domain is that the bundle space grows exponentially in the number of items. To address this, several papers have recently proposed machine learning-based preference elicitation algorithms that aim to elicit only the most important information from agents. However, the main shortcoming of this prior work is that it does not model a mechanism's uncertainty over values for not yet elicited bundles. In this paper, we address this shortcoming by presenting a Bayesian optimization-based combinatorial assignment (BOCA) mechanism. Our key technical contribution is to integrate a method for capturing model uncertainty into an iterative combinatorial auction mechanism. Concretely, we design a new method for estimating an upper uncertainty bound that can be used to define an acquisition function to determine the next query to the agents. This enables the mechanism to properly explore (and not just exploit) the bundle space during its preference elicitation phase. We run computational experiments in several spectrum auction domains to evaluate BOCA's performance. Our results show that BOCA achieves higher allocative efficiency than state-of-the-art approaches.

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Managers of diversified equity funds aren’t bullish on the ability of sustainable and socially-conscious companies to generate superior returns, according to new research.