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This paper is the first to conduct an incentive-compatible experiment using real monetary payoffs to test the hypothesis of probabilistic insurance which states that willingness to pay for insurance decreases sharply in the presence of even small default probabilities as compared to a risk-free insurance contract. In our experiment, 181 participants state their willingness to pay for insurance contracts with different levels of default risk. We find that the willingness to pay sharply decreases with increasing default risk. Our results hence strongly support the hypothesis of probabilistic insurance. Furthermore, we study the impact of customer reaction to default risk on an insurer’s optimal solvency level using our experimentally obtained data on insurance demand. We show that an insurer should choose to be default-free rather than having even a very small default probability. This risk strategy is also optimal when assuming substantial transaction costs for risk management activities undertaken to achieve the maximum solvency level.
This dissertation consists of three essays, which study the implication of financial frictions in business cycles and monetary policy making. The first essay develops a Dynamic Stochastic General Equilibrium (DSGE) model to study how the instability of the banking sector can amplify and propagate business cycles. Model simulations show that in an economic down turn, in addition to credit demand contraction induced by low firm net worth, low bank capital
position can create strong credit supply contraction, and have a quantitatively significant effect on business cycle dynamics. The second essay studies the optimal Taylor-type monetary policy rules based on the model developed in the first chapter and find that with interest rate smoothing, 'leaning against the wind' can significantly dampen the procyclicality of financial distortions, and increase the welfare of the economy. The third chapter examines the role of households frugality in a financial crisis and finds that higher savings by more frugal households provide an important cushion for the fall in private investment funding.
Highlights
• Pathways for a circular economy towards the EU goals require policy support that, in turn, requires legitimacy.
• Legitimacy is often contested in the public discourse at all phases in the technological innovation system.
• Legitimacy remains poorly understood for ‘in-between’ technologies that struggle to move from the formative to the growth stage.
• The article explores legitimacy for chemical recycling primarily based on evidence from the UK, Germany, and Italy.
Abstract
The European Commission aims to increase the recycling of plastic packaging to 60% by 2025, requiring fundamental changes towards a more circular economy. Pathways for this transition require policy support that largely depends on their legitimacy in the public discourse. These normative aspects remain poorly understood for ‘in-between’ technologies, i.e., technologies that are no longer novel but struggle to move to the growth phase within the technological innovation system. Therefore, we ask: How do discourses shape technology legitimacy for in-between technologies? Drawing on the empirical example of chemical recycling, the analysis renders two principal findings. First, legitimising and delegitimising storylines present contesting views on in-between technologies regarding their technological aspects, environmental and social impacts, and economic and policy implications. Second, how discourses contribute to technology legitimacy depends on the actors and interests that drive the prevalent storylines in particular contexts.
Biodiversity loss poses a significant threat to the global economy and affects ecosystem services on which most large companies rely heavily. The severe financial implications of such a reduced species diversity have attracted the attention of companies and stakeholders, with numerous calls to increase corporate transparency. Using textual analysis, this study thus investigates the current state of voluntary biodiversity reporting of 359 European blue-chip companies and assesses the extent to which it aligns with the upcoming disclosure framework of the Task Force on Nature-related Financial Disclosures (TNFD). The descriptive results suggest a substantial gap between current reporting practices and the proposed TNFD framework, with disclosures largely lacking quantification, details and clear targets. In addition, the disclosures appear to be relatively unstandardized. Companies in sectors or regions exposed to higher nature-related risks as well as larger companies are more likely to report on aspects of biodiversity. This study contributes to the emerging literature on nature-related risks and provides detailed insights on the extent of the reporting gap in light of the upcoming standards.
Contemporary information systems make widespread use of artificial intelligence (AI). While AI offers various benefits, it can also be subject to systematic errors, whereby people from certain groups (defined by gender, age, or other sensitive attributes) experience disparate outcomes. In many AI applications, disparate outcomes confront businesses and organizations with legal and reputational risks. To address these, technologies for so-called “AI fairness” have been developed, by which AI is adapted such that mathematical constraints for fairness are fulfilled. However, the financial costs of AI fairness are unclear. Therefore, the authors develop AI fairness for a real-world use case from e-commerce, where coupons are allocated according to clickstream sessions. In their setting, the authors find that AI fairness successfully manages to adhere to fairness requirements, while reducing the overall prediction performance only slightly. However, they find that AI fairness also results in an increase in financial cost. Thus, in this way the paper’s findings contribute to designing information systems on the basis of AI fairness.
With free delivery of products virtually being a standard in E-commerce, product returns pose a major challenge for online retailers and society. For retailers, product returns involve significant transportation, labor, disposal, and administrative costs. From a societal perspective, product returns contribute to greenhouse gas emissions and packaging disposal and are often a waste of natural resources. Therefore, reducing product returns has become a key challenge. This paper develops and validates a novel smart green nudging approach to tackle the problem of product returns during customers’ online shopping processes. We combine a green nudge with a novel data enrichment strategy and a modern causal machine learning method. We first run a large-scale randomized field experiment in the online shop of a German fashion retailer to test the efficacy of a novel green nudge. Subsequently, we fuse the data from about 50,000 customers with publicly-available aggregate data to create what we call enriched digital footprints and train a causal machine learning system capable of optimizing the administration of the green nudge. We report two main findings: First, our field study shows that the large-scale deployment of a simple, low-cost green nudge can significantly reduce product returns while increasing retailer profits. Second, we show how a causal machine learning system trained on the enriched digital footprint can amplify the effectiveness of the green nudge by “smartly” administering it only to certain types of customers. Overall, this paper demonstrates how combining a low-cost marketing instrument, a privacy-preserving data enrichment strategy, and a causal machine learning method can create a win-win situation from both an environmental and economic perspective by simultaneously reducing product returns and increasing retailers’ profits.
By focusing on the cost conditions at issuance, I find that not only the Covid-19 pandemic effects were different across bonds and firms at different stages, but also that the market composition was significantly affected, collapsing on investment- grade bonds, a segment in which the share of bonds eligible to the ECB corporate programmes strikingly increased from 15% to 40%. At the same time the high-yield segment shrunk to almost disappear at 4%. In addition to a market segmentation along the bond grade and the eligibility to the ECB programmes, another source of risk detected in the pricing mechanism is the weak resilience to pandemic: the premium requested is around 30 basis points and started to be priced only after the early containment actions taken by the national authorities. On the contrary, I do not find evidence supporting an increased risk for corporations headquartered in countries with a reduced fiscal space, nor the existence of a premium in favour of green bonds, which should be the backbone of a possible “green recovery”.
We assess the degree of market fragmentation in the euro-area corporate bond market by disentangling the determinants of the risk premium paid on bonds at origination. By looking at over 2,400 bonds we are able to isolate the country-specific effects which are a suitable indicator of the market fragmentation. We find that, after peaking during the sovereign debt crisis, fragmentation shrank in 2013 and receded to pre-crisis levels only in 2014. However, the low level of estimated market fragmentation is coupled with a still high heterogeneity in actual bond yields, challenging the consistency of the new equilibrium.
We analyze the risk premium on bank bonds at origination with a special focus on the role of implicit and explicit public guarantees and the systemic relevance of the issuing institutions. By looking at the asset swap spread on 5,500 bonds, we find that explicit guarantees and sovereign creditworthiness have a substantial effect on the risk premium. In addition, while large institutions still enjoy lower issuance costs linked to the TBTF framework, we find evidence of enhanced market disciple for systemically important banks which face, since the onset of the financial crisis, an increased premium on bond placements.