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.

Governments across the globe are facing pressure to increase the speed of their digital transformation to meet the needs of the digital society while fulfilling their primary task of delivering public value. While researchers agree on the importance of the public sector for public value creation, recently, more and more studies have recognized the criticality of collaboration between the public and private sectors for successful public value creation. In our research, we conduct a qualitative survey. We examine the idea of collaboration between the public and the private sectors in more detail and add the citizens' perspective. We highlight the need for joint forces for optimal public value creation, identify ways to achieve this, and determine what digital technologies can support this process.

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Fintech and big tech companies are making rapid inroads into credit markets. We hand construct a global database of fintech and big tech lending volumes for 79 countries over 2013-2018. Using a panel regression analysis, we find these new forms of digital lending are larger in countries with higher GDP per capita (albeit at a declining rate), where banking sector mark-ups are higher, and where banking regulation is less stringent. We also find that these alternative forms of credit are more developed where the ease of doing business is greater, investor protection disclosure and the efficiency of the judicial system are more advanced, and where bond and equity markets are more developed. Overall, fintech and big tech credit seem to complement other forms of credit, rather than substitute for them.

The Semantic Web is distributed yet interoperable: Distributed since resources are created and published by a variety of producers, tailored to their specific needs and knowledge; Interoperable as entities are linked across resources, allowing to use resources from different providers in concord. Complementary to the explicit usage of Semantic Web resources, embedding methods made them applicable to machine learning tasks. Subsequently, embedding models for numerous tasks and structures have been developed, and embedding spaces for various resources have been published. The ecosystem of embedding spaces is distributed but not interoperable: Entity embeddings are not readily comparable across different spaces. To parallel the Web of Data with a Web of Embeddings, we must thus integrate available embedding spaces into a uniform space. Current integration approaches are limited to two spaces and presume that both of them were embedded with the same method — both assumptions are unlikely to hold in the context of a Web of Embeddings. In this paper, we present FedCoder— an approach that integrates multiple embedding spaces via a latent space. We assert that linked entities have a similar representation in the latent space so that entities become comparable across embedding spaces. FedCoder employs an autoencoder to learn this latent space from linked as well as non-linked entities. Our experiments show that FedCoder substantially outperforms state-of-the-art approaches when faced with different embedding models, that it scales better than previous methods in the number of embedding spaces, and that it improves with more graphs being integrated whilst performing comparably with current approaches that assumed joint learning of the embeddings and were, usually, limited to two sources. Our results demonstrate that FedCoder is well adapted to integrate the distributed, diverse, and large ecosystem of embeddings spaces into an interoperable Web of Embeddings.

Corporate–startup collaboration (CSC) allows the co-development of innovations for pressing societal needs. Paradoxically, CSC is both fueled and challenged by diverging interests and approaches of the unequal actors. We apply a paradox lens to better understand the complex collaborative demands of CSC from the perspective of the corporate actors involved. Over the course of three years, we conducted 52 contextualized semi-structured interviews in a corporate-sponsored accelerator pursuing sustainability improvements. We identify five CSC paradoxes, which we translate into guiding principles for managing such paradoxes with a both/and mindset. Further, we show how these guiding principles help to address interdependencies between the CSC paradoxes. By disentangling the inherently paradoxical nature of the collaborative demands, we contribute to a fuller theoretical understanding of how organizational actors can manage these demands. We encourage companies engaging in CSC to use the guiding principles for empowering organizational actors’ understanding and approaches to CSC paradoxes.

Research on key determinants of negative attitudes toward immigration has often suggested that values held by individuals systematically explain such sentiments. Universalists appear to have more positive and conservatives more negative attitudes. So far, however, these insights are based on studies using adult samples. In our study, we analyze these relations among children and adolescents. For the analysis, we utilized a Swiss-Polish panel dataset (2015–2017, N = 5,332) with three time points collected among school children aged 8–19 years. We employed autoregressive cross-lagged models. The results indicated that while universalism decreased negative attitudes toward immigrants, the expected effect for conformity-tradition was not found.

We investigate the impact of the Bank of England's asset purchase program (APP) on the composition of assets of UK banks with unique data on the received reserves injections. The Monetary Policy Committee (MPC) didn’t expect there to be strong transmission of the APP’s impact through the bank lending channel. We find that compared to the control group, treated banks reallocated their assets towards lower risk-weighted investments, such as government securities, but did not provide more credit to the real economy. Overall, our findings suggest that when banks are not adequately capitalised, risk-based capital constraints can limit the effectiveness of expansionary unconventional monetary policies and provide incentives for carry trade activities.

Search engines serve as information gatekeepers on a multitude of topics that are prone to gender, ethnicity, and race misrepresentations. In this paper, we specifically look at the image search representation of migrant population groups that are often subjected to discrimination and biased representation in mainstream media, increasingly so with the rise of right-wing populist actors in the Western countries. Using multiple (n = 200) virtual agents to simulate human browsing behavior in a controlled environment, we collect image search results related to various terms referring to migrants (e.g., expats, immigrants, and refugees, seven queries in English and German used in total) from the six most popular search engines. Then, with the aid of manual coding, we investigate which features are used to represent these groups and whether the representations are subjected to bias. Our findings indicate that search engines reproduce ethnic and gender biases common for mainstream media representations of different subgroups of migrant population. For instance, migrant representations tend to be highly racialized, and female migrants as well as migrants at work tend to be underrepresented in the results. Our findings highlight the need for further algorithmic impact auditing studies in the context of representation of potentially vulnerable groups in web search results.