Institutes
Refine
Year of publication
Document Type
- Article (62)
- Doctoral Thesis (8)
- Working Paper (6)
- Bachelor Thesis (2)
- Review (2)
- Preprint (1)
- Report (1)
Language
- English (82) (remove)
Has Fulltext
- yes (82)
Is part of the Bibliography
- no (82)
Keywords
- Artificial intelligence (3)
- Machine learning (3)
- Retirement (3)
- Life insurance (2)
- Stock market (2)
- machine learning (2)
- 401(k) plan (1)
- AI fairness (1)
- Accounting (1)
- Adoption (1)
Institute
- Wirtschaftswissenschaften (82)
- House of Finance (HoF) (6)
- Center for Financial Studies (CFS) (4)
- Sustainable Architecture for Finance in Europe (SAFE) (4)
- Institute for Monetary and Financial Stability (IMFS) (2)
- Exzellenzcluster Die Herausbildung normativer Ordnungen (1)
- Informatik und Mathematik (1)
The mobile games business is an ever-increasing sub-sector of the entertainment industry. Due to its high profitability but also high risk and competitive atmosphere, game publishers need to develop strategies that allow them to release new products at a high rate, but without compromising the already short lifespan of the firms' existing games. Successful game publishers must enlarge their user base by continually releasing new and entertaining games, while simultaneously motivating the current user base of existing games to remain active for more extended periods. Since the core-component reuse strategy has proven successful in other software products, this study investigates the advantages and drawbacks of this strategy in mobile games. Drawing on the widely accepted Product Life Cycle concept, the study investigates whether the introduction of a new mobile game built with core-components of an existing mobile game curtails the incumbent's product life cycle. Based on real and granular data on the gaming activity of a popular mobile game, the authors find that by promoting multi-homing (i.e., by smartly interlinking the incumbent and new product with each other so that users start consuming both games in parallel), the core-component reuse strategy can prolong the lifespan of the incumbent game.
Recent advances in natural language processing have contributed to the development of market sentiment measures through text content analysis in news providers and social media. The effectiveness of these sentiment variables depends on the imple- mented techniques and the type of source on which they are based. In this paper, we investigate the impact of the release of public financial news on the S&P 500. Using automatic labeling techniques based on either stock index returns or dictionaries, we apply a classification problem based on long short-term memory neural networks to extract alternative proxies of investor sentiment. Our findings provide evidence that there exists an impact of those sentiments in the market on a 20-minute time frame. We find that dictionary-based sentiment provides meaningful results with respect to those based on stock index returns, which partly fails in the mapping process between news and financial returns.
The term structure of interest rates is crucial for the transmission of monetary policy to financial markets and the macroeconomy. Disentangling the impact of monetary policy on the components of interest rates, expected short rates, and term premia is essential to understanding this channel. To accomplish this, we provide a quantitative structural model with endogenous, time-varying term premia that are consistent with empirical findings. News about future policy, in contrast to unexpected policy shocks, has quantitatively significant effects on term premia along the entire term structure. This provides a plausible explanation for partly contradictory estimates in the empirical literature.
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.
This paper uses historical monthly temperature level data for a panel of 114 countries to identify the effects of within year temperature level variability on productivity growth in five different macro regions, i.e., (1) Africa, (2) Asia, (3) Europe, (4) North America and (5) South America. We find two primary results. First, higher intra-annual temperature variability reduces (increases) productivity in Europe and North America (Asia). Second, higher intra-annual temperature variability has no significant effects on productivity in Africa and South America. Additional empirical tests indicate also the following: (1) rising intra-annual temperature variability reduces productivity (even thought less significantly)in both tropical and non-tropical regions, (2) inter-annual temperature variability reduces (increases) productivity in North America (Europe) and (3) winter and summer inter-annual temperature variability generates a drop in productivity in both Europe and North America. Taken together, these findings indicate that temperature variability shocks tend to have stronger adverse economic effects among richer economies. In a production economy featuring long-run productivity and temperature volatility shocks, we quantify these negative impacts and find welfare losses of 2.9% (1%) in Europe (North America).
Solving High-Dimensional Dynamic Portfolio Choice Models with Hierarchical B-Splines on Sparse Grids
(2021)
Discrete time dynamic programming to solve dynamic portfolio choice models has three immanent issues: firstly, the curse of dimensionality prohibits more than a handful of continuous states. Secondly, in higher dimensions, even regular sparse grid discretizations need too many grid points for sufficiently accurate approximations of the value function. Thirdly, the models usually require continuous control variables, and hence gradient-based optimization with smooth approximations of the value function is necessary to obtain accurate solutions to the optimization problem. For the first time, we enable accurate and fast numerical solutions with gradient-based optimization while still allowing for spatial adaptivity using hierarchical B-splines on sparse grids. When compared to the standard linear bases on sparse grids or finite difference approximations of the gradient, our approach saves an order of magnitude in total computational complexity for a representative dynamic portfolio choice model with varying state space dimensionality, stochastic sample space, and choice variables.
The aim of this study was to identify and evaluate different de-identification techniques that may be used in several mobility-related use cases. To do so, four use cases have been defined in accordance with a project partner that focused on the legal aspects of this project, as well as with the VDA/FAT working group. Each use case aims to create different legal and technical issues with regards to the data and information that are to be gathered, used and transferred in the specific scenario. Use cases should therefore differ in the type and frequency of data that is gathered as well as the level of privacy and the speed of computation that is needed for the data. Upon identifying use cases, a systematic literature review has been performed to identify suitable de-identification techniques to provide data privacy. Additionally, external databases have been considered as data that is expected to be anonymous might be reidentified through the combination of existing data with such external data.
For each case, requirements and possible attack scenarios were created to illustrate where exactly privacy-related issues could occur and how exactly such issues could impact data subjects, data processors or data controllers. Suitable de-identification techniques should be able to withstand these attack scenarios. Based on a series of additional criteria, de-identification techniques are then analyzed for each use case. Possible solutions are then discussed individually in chapters 6.1 - 6.2. It is evident that no one-size-fits-all approach to protect privacy in the mobility domain exists. While all techniques that are analyzed in detail in this report, e.g., homomorphic encryption, differential privacy, secure multiparty computation and federated learning, are able to successfully protect user privacy in certain instances, their overall effectiveness differs depending on the specifics of each use case.
Strict environmental regulation may deter foreign direct investment (FDI). The paper develops the hypothesis that regulation predominantly discourages FDI that is conducted as Greenfield investment rather than mergers and acquisitions (M&A). The hypothesis is tested with German firm-level FDI data. Empirically, stricter regulation reduces new Greenfield projects in polluting industries, but indeed has a much smaller impact on the number of M&As. This significant difference is compatible with the fact that existing operations often benefit from grandfathering rules, which provide softer regulation for pre-exisiting plants, and with the expectation that for M&As part of the regulation is capitalized in the purchase price. The heterogeneous effects help explaining mixed results in previous studies that have neglected the mode of entry.
We analyze the joint dynamics of prices, productivity, and employment across firms, building a dynamic equilibrium model of heterogeneous firms who compete for workers and customers in frictional labor and product markets. Using panel data on prices and output for German manufacturing firms, the model is calibrated to evaluate the quantitative contributions of productivity and demand for the labor market. Product market frictions decisively dampen the firms' employment adjustments to productivity shocks. We further analyze the impact of aggregate shocks to the first and second moments of productivity and demand and relate them to business-cycle features in our data.
Over the course of the last financial crises, retail investors have been identified to bear a major share of the invoked financial losses. As a consequence, financial market regulators put major effort on retail investor protection, especially following the Great Financial Crisis of 2007-2009. The major legislative initiatives, such as in the Dodd-Frank Act in the United States, seemingly manifest retail investors’ overly fragile role among the variety of professional investors in the financial market by establishing additional protection requirements for retail investors. A vast majority of related international academic literature is supporting those steps. However, considering the most recent developments that occurred in the US financial markets, the dogma of the lamb-like retail investor seems to be crumbling: In 2021, under the synonym “WallStreetBets” retail investors systematically colluded in investment bets which eventually disrupted not only financial markets by distorting stock price formation of single firms but also systematically squeezed sizeable positions of institutional investors. The key question arises, how retail investors have changed, such that they not only became a source of price distortions and market turmoil but also endanger professional institutional investors. In this thesis, I study this changing role and investment behavior of retail investors, taking into account the retail investor’s wellestablished and researched behavioral characteristics to the changing environmental aspects such as regulation and the adaption and usage of technology for information gathering and collaboration. Based on the combination of those different research streams, I am able to deduct the sequential consequences of these developments for financial markets.
Vehicle registrations have been shown to strongly react to tax reforms aimed at reducing CO2 emissions from passengers’ cars, but are the effects equally strong for positive and negative tax changes? The literature on asymmetric reactions to price and tax changes has documented asymmetries for everyday goods but has not yet considered durables. We leverage multiple vehicle registration tax (VRT) reforms in Norway and estimate their impact on within car-model substitutions. We estimate stronger effects for cars receiving tax cuts and rebates than for those affected by tax increases. The corresponding estimated elasticity is − 1.99 for VRT decreases and 0.77 for increases. As consumers may also substitute across car models, our estimates represent a lower bound.
This study explores the implications of rising markups for optimal Mirrleesian income and profit taxation. Using a stylized model with two individuals, the main forces shaping welfare-optimal policies are analytically characterized. Although a higher profit tax has redistributive benefits, it adversely affects market competition, leading to a greater equilibrium cost-of-living. Rising markups directly contribute to a decline in optimal marginal taxes on labor income. The optimal policy response to higher markups includes increasingly relying on the profit tax to fund redistribution. Declining optimal marginal income taxes assists the redistributive function of the profit tax by contributing to the expansion of the profit tax base. This response alone considerably increases the equilibrium cost-of-living. Nevertheless, a majority of the individuals become better off with the optimal policy. If it is not possible to tax profits optimally, due, for example, to profit shifting, increasing redistribution via income taxes is not optimal; every individual is worse off relative to the scenario with optimal profit taxation.
Small businesses face major challenges to becoming more innovative. These challenges are particularly prevalent in emerging economies where high uncertainties are a barrier to innovation. We know from previous studies that linkages to universities, on the one hand, and public procurement, on the other, support large and innovative firms in their efforts to become more innovative. However, we do not know whether these positive effects also hold true for small businesses. In this paper, we focus on how policy strategies reducing information, market and financial uncertainties shape small businesses’ innovation in China. Based on a sample of 926 small businesses derived from the World Bank Enterprises Survey in China (2012), we find that university-industry linkages enhance innovation, though only when it comes to minor forms of innovation. In line with the resource-based view of the firm, this effect is stronger for small businesses with higher capabilities. Moreover, we show that bidding for or delivering contracts to public sector clients has a positive effect on innovation, and in particular of major forms of innovation. In the bidding selection process, private firms and firms with higher capabilities are selected. Our findings show that both policy strategies have enhanced innovation, though with different effects on the degree of novelty. We attribute this finding to the different degrees of uncertainties they address.
Having a gatekeeper position in a collaborative network offers firms great potential to gain competitive advantages. However, it is not well understood what kind of collaborations are associated with such a position. Conceptually grounded in social network theory, this study draws on the resource-based view and the relational factors view to investigate which types of collaboration characterize firms that are in a gatekeeper position, which ultimately could improve firm performance in subsequent periods. The empirical analysis utilizes a unique longitudinal data set to examine dynamic network formation. We used a data crawling approach to reconstruct collaboration networks among the 500 largest companies in Germany over nine years and matched these networks with performance data. The results indicate that firms in gatekeeper positions often engage in medium-intensity collaborations and less likely weak-intensity collaborations. Strong-intensity collaborations are not related to the likelihood of being a gatekeeper. Our study further reveals that a firm's knowledge base is an important moderator and that this knowledge base can increase the benefits of having a gatekeeper position in terms of firm performance.
Nations are imposing unprecedented measures at a large scale to contain the spread of the COVID-19 pandemic. While recent studies show that non-pharmaceutical intervention measures such as lockdowns may have mitigated the spread of COVID-19, those measures also lead to substantial economic and social costs, and might limit exposure to ultraviolet-B radiation (UVB). Emerging observational evidence indicates the protective role of UVB and vitamin D in reducing the severity and mortality of COVID-19 deaths. This observational study empirically outlines the protective roles of lockdown and UVB exposure as measured by the ultraviolet index (UVI). Specifically, we examine whether the severity of lockdown is associated with a reduction in the protective role of UVB exposure. We use a log-linear fixed-effects model on a panel dataset of secondary data of 155 countries from 22 January 2020 until 7 October 2020 (n = 29,327). We use the cumulative number of COVID-19 deaths as the dependent variable and isolate the mitigating influence of lockdown severity on the association between UVI and growth rates of COVID-19 deaths from time-constant country-specific and time-varying country-specific potentially confounding factors. After controlling for time-constant and time-varying factors, we find that a unit increase in UVI and lockdown severity are independently associated with − 0.85 percentage points (p.p) and − 4.7 p.p decline in COVID-19 deaths growth rate, indicating their respective protective roles. The change of UVI over time is typically large (e.g., on average, UVI in New York City increases up to 6 units between January until June), indicating that the protective role of UVI might be substantial. However, the widely utilized and least severe lockdown (governmental recommendation to not leave the house) is associated with the mitigation of the protective role of UVI by 81% (0.76 p.p), which indicates a downside risk associated with its widespread use. We find that lockdown severity and UVI are independently associated with a slowdown in the daily growth rates of cumulative COVID-19 deaths. However, we find evidence that an increase in lockdown severity is associated with significant mitigation in the protective role of UVI in reducing COVID-19 deaths. Our results suggest that lockdowns in conjunction with adequate exposure to UVB radiation might have even reduced the number of COVID-19 deaths more strongly than lockdowns alone. For example, we estimate that there would be 11% fewer deaths on average with sufficient UVB exposure during the period people were recommended not to leave their house. Therefore, our study outlines the importance of considering UVB exposure, especially while implementing lockdowns, and could inspire further clinical studies that may support policy decision-making in countries imposing such measures.
Digital wealth and its necessary regulation have gained prominence in recent years. The European Commission has published several documents and policy proposals relating, directly or indirectly, to the data economy. A data economy can be defined as an ecosystem of different types of market players collaborating to ensure that data is accessible and usable in order to extract value from data through, for example, creating a variety of applications with great potential to improve daily life. The value of data can increase from EUR 257 billion (1.85 of EU Gross Domestic Product (GDP)) to EUR 643 billion by 2020 (3.17% of EU GDP), according to the EU Commission. The legal implications of the increasing value of the data economy are clear; hence the need to address the challenges presented by its legal regulation.
This paper explores entrepreneurs’ initially intended exit strategies and compares them to their final exit paths using an inductive approach that builds on the grounded theory methodology. Our data shows that initially intended and final exit strategies differ among entrepreneurs. Two groups of entrepreneurs emerged from our data. The first group comprises entrepreneurs who financed their firms through equity investors. The second group is made up of entrepreneurs who financed their businesses solely with their own equities. Our data shows that the first group originally intended a financial harvest exit strategy and settled with this harvest exit strategy. The second group initially intended a stewardship exit strategy but did not succeed. We used the theory of planned behavior and the behavioral agency model to analyze our data. By examining our results from these two theoretical perspectives, our study explains how entrepreneurs’ exit intentions lead to their actual exit strategies.
Ownership of databases: personal data protection and intellectual property rights on databases
(2021)
When we think on initiatives on access to and reuse of data, we must consider both the European Intellectual Property Law and the General Data Protection Regulation (GDPR). The first one provides a special intellectual property (IP) right – the sui generis right – for those makers that made a substantial investment when creating the database, whether it contains personal or non-personal data. That substantial investment can be made by just one person, but, in many cases, it is the result of the activities of many people and/or some undertakings processing and aggregating data. In the modern digital economy, data are being dubbed the ‘new oil’ and the sui generis right might be con- sidered a right to control any access to the database, thus having an undeniable relevance. Besides, there are still important inconsistences between IP Law and the GDPR, which must be removed by the European legislator. The genuine and free consent of the data subject for the use of his/her data must remain the first step of the legal analysis.
Commercialization of consumers’ personal data in the digital economy poses serious, both conceptual and practical, challenges to the traditional approach of European Union (EU) Consumer Law. This article argues that mass-spread, automated, algorithmic decision-making casts doubt on the foundational paradigm of EU consumer law: consent and autonomy. Moreover, it poses threats of discrimination and under- mining of consumer privacy. It is argued that the recent legislative reaction by the EU Commission, in the form of the ‘New Deal for Consumers’, was a step in the right direction, but fell short due to its continued reliance on consent, autonomy and failure to adequately protect consumers from indirect discrimination. It is posited that a focus on creating a contracting landscape where the consumer may be properly informed in material respects is required, which in turn necessitates blending the approaches of competition, consumer protection and data protection laws.
The quality of life: protecting non-personal interests and non-personal data in the age of big data
(2021)
Under the current legal paradigm, the rights to privacy and data protection provide natural persons with subjective rights to protect their private interests, such as related to human dignity, individual autonomy and personal freedom. In principle, when data processing is based on non-personal or aggregated data or when such data pro- cesses have an impact on societal, rather than individual interests, citizens cannot rely on these rights. Although this legal paradigm has worked well for decades, it is increasingly put under pressure because Big Data processes are typically based indis- criminate rather than targeted data collection, because the high volumes of data are processed on an aggregated rather than a personal level and because the policies and decisions based on the statistical correlations found through algorithmic analytics are mostly addressed at large groups or society as a whole rather than specific individuals. This means that large parts of the data-driven environment are currently left unregu- lated and that individuals are often unable to rely on their fundamental rights when addressing the more systemic effects of Big Data processes. This article will discuss how this tension might be relieved by turning to the notion ‘quality of life’, which has the potential of becoming the new standard for the European Court of Human Rights (ECtHR) when dealing with privacy related cases.
The health and genetic data of deceased people are a particularly important asset in the field of biomedical research. However, in practice, using them is compli- cated, as the legal framework that should regulate their use has not been fully developed yet. The General Data Protection Regulation (GDPR) is not applicable to such data and the Member States have not been able to agree on an alternative regulation. Recently, normative models have been proposed in an attempt to face this issue. The most well- known of these is posthumous medical data donation (PMDD). This proposal supports an opt-in donation system of health data for research purposes. In this article, we argue that PMDD is not a useful model for addressing the issue at hand, as it does not consider that some of these data (the genetic data) may be the personal data of the living relatives of the deceased. Furthermore, we find the reasons supporting an opt-in model less convincing than those that vouch for alternative systems. Indeed, we propose a normative framework that is based on the opt-out system for non-personal data combined with the application of the GDPR to the relatives’ personal data.
Tail-correlation matrices are an important tool for aggregating risk measurements across risk categories, asset classes and/or business segments. This paper demonstrates that traditional tail-correlation matrices—which are conventionally assumed to have ones on the diagonal—can lead to substantial biases of the aggregate risk measurement’s sensitivities with respect to risk exposures. Due to these biases, decision-makers receive an odd view of the effects of portfolio changes and may be unable to identify the optimal portfolio from a risk-return perspective. To overcome these issues, we introduce the “sensitivity-implied tail-correlation matrix”. The proposed tail-correlation matrix allows for a simple deterministic risk aggregation approach which reasonably approximates the true aggregate risk measurement according to the complete multivariate risk distribution. Numerical examples demonstrate that our approach is a better basis for portfolio optimization than the Value-at-Risk implied tail-correlation matrix, especially if the calibration portfolio (or current portfolio) deviates from the optimal portfolio.
Nowadays, digitalization has an immense impact on the landscape of jobs. This technological revolution creates new industries and professions, promises greater efficiency and improves the quality of working life. However, emerging technologies such as robotics and artificial intelligence (AI) are reducing human intervention, thus advancing automation and eliminating thousands of jobs and whole occupational images. To prepare employees for the changing demands of work, adequate and timely training of the workforce and real-time support of workers in new positions is necessary. Therefore, it is investigated whether user-oriented technologies, such as augmented reality (AR) and virtual reality (VR) can be applied “on-the-job” for such training and support—also known as intelligence augmentation (IA). To address this problem, this work synthesizes results of a systematic literature review as well as a practically oriented search on augmented reality and virtual reality use cases within the IA context. A total of 150 papers and use cases are analyzed to identify suitable areas of application in which it is possible to enhance employees' capabilities. The results of both, theoretical and practical work, show that VR is primarily used to train employees without prior knowledge, whereas AR is used to expand the scope of competence of individuals in their field of expertise while on the job. Based on these results, a framework is derived which provides practitioners with guidelines as to how AR or VR can support workers at their job so that they can keep up with anticipated skill demands. Furthermore, it shows for which application areas AR or VR can provide workers with sufficient training to learn new job tasks. By that, this research provides practical recommendations in order to accompany the imminent distortions caused by AI and similar technologies and to alleviate associated negative effects on the German labor market.
By computing a volatility index (CVX) from cryptocurrency option prices, we analyze this market’s expectation of future volatility. Our method addresses the challenging liquidity environment of this young asset class and allows us to extract stable market implied volatilities. Two alternative methods are considered to compute volatilities from granular intra-day cryptocurrency options data, which spans over the COVID-19 pandemic period. CVX data therefore capture ‘normal’ market dynamics as well as distress and recovery periods. The methods yield two cointegrated index series, where the corresponding error correction model can be used as an indicator for market implied tail-risk. Comparing our CVX to existing volatility benchmarks for traditional asset classes, such as VIX (equity) or GVX (gold), confirms that cryptocurrency volatility dynamics are often disconnected from traditional markets, yet, share common shocks.
Artificial Intelligence (AI) and Machine Learning (ML) are currently hot topics in industry and business practice, while management-oriented research disciplines seem reluctant to adopt these sophisticated data analytics methods as research instruments. Even the Information Systems (IS) discipline with its close connections to Computer Science seems to be conservative when conducting empirical research endeavors. To assess the magnitude of the problem and to understand its causes, we conducted a bibliographic review on publications in high-level IS journals. We reviewed 1,838 articles that matched corresponding keyword-queries in journals from the AIS senior scholar basket, Electronic Markets and Decision Support Systems (Ranked B). In addition, we conducted a survey among IS researchers (N = 110). Based on the findings from our sample we evaluate different potential causes that could explain why ML methods are rather underrepresented in top-tier journals and discuss how the IS discipline could successfully incorporate ML methods in research undertakings.
This paper analyzes the scope of the private market for pandemic insurance. We develop a framework that explains theoretically how the equilibrium price of pandemic insurance depends on accumulation risk, covariance between pandemic claims and other claims, and covariance between pandemic claims and the stock market performance. Using the natural catastrophe (NatCat) insurance market as a laboratory, we estimate the relationship between the insurance price markup and the tail characteristics of the loss distribution. Then, by using the high-frequency data tracking the economic impact of the COVID-19 pandemic in the United States, we calibrate the loss distribution of a hypothetical insurance contract designed to alleviate the impact of the pandemic on small businesses. The pandemic insurance contract price markup corresponds to the top 20% markup observed in the NatCat insurance market. Then we analyze an intertemporal risk-sharing scheme that can reduce the expected shortfall of the loss distribution by 50%.
When requesting a web-based service, users often fail in setting the website’s privacy settings according to their self privacy preferences. Being overwhelmed by the choice of preferences, a lack of knowledge of related technologies or unawareness of the own privacy preferences are just some reasons why users tend to struggle. To address all these problems, privacy setting prediction tools are particularly well-suited. Such tools aim to lower the burden to set privacy preferences according to owners’ privacy preferences. To be in line with the increased demand for explainability and interpretability by regulatory obligations – such as the General Data Protection Regulation (GDPR) in Europe – in this paper an explainable model for default privacy setting prediction is introduced. Compared to the previous work we present an improved feature selection, increased interpretability of each step in model design and enhanced evaluation metrics to better identify weaknesses in the model’s design before it goes into production. As a result, we aim to provide an explainable and transparent tool for default privacy setting prediction which users easily understand and are therefore more likely to use.
Public kindergarten, maternal labor supply, and earnings in the longer run: too little too late?
(2021)
By facilitating early re-entry to the labor market after childbirth, public kindergarten might positively affect maternal human capital and labor market outcomes: Are such effects long-lasting? Can we rely on between-individuals differences in quarter of birth to identify them? I isolate the effects of interest from spurious associations through difference-in-difference, exploiting across-states and over-time variation in public kindergarten eligibility regulations in the United States. The estimates suggest a very limited impact in the first year, and no longer-run impacts. Even in states where it does not affect kindergarten eligibility, quarter of birth is strongly and significantly correlated with maternal outcomes.
Correction to: Computational Economics https://doi.org/10.1007/s10614-020-10061-x
The original publication has been updated. In the original publication of this article, under the Introduction heading section, the corrections to the second paragraph’s inline equation were not incorporated. The author’s additional corrections have also been incorporated. The publisher apologizes for the error made during production.
Vulnerability comes, according to Orio Giarini, with two risks: human-made risks, also called entrepreneurial risks, and natural or pure risks such as accidents and earthquakes. Both types of risk are growing in dimension and are increasingly interrelated. To control the vulnerability, sophisticated insurance products are called for. Here, mutual insurance is relevant, in particular when risks are large, probabilities uncertain or unknown, and events interrelated or correlated. In this paper the following three examples are discussed and the advantages of mutual insurance are shown: unknown probabilities connected with unforeseeable events, correlated risks and macroeconomic or demographic risks.
The current economic landscape is complex and globalized, and it imposes on individuals the responsibility for their own financial security. This situation has been intensified by the COVID-19 crisis, since short-time work and layoffs significantly limit the availability of financial resources for individuals. Due to the long duration of the lockdown, these challenges will have a long-term impact and affect the financial well-being of many citizens. Moreover, it can be assumed that the consequences of this crisis will once again particularly affect groups of people who have already frequently been identified as having low financial literacy. Financial literacy is therefore an important target for educational measures and interventions. However, it cannot be considered in isolation but must take into account the many potential factors that influence financial literacy alone or in combination. These include personality traits and socio-demographic factors as well as the (in)ability to defer gratification. Against this background, individualized support offers can be made. With this in mind, in the first step of this study, we analyze the complex interaction of personality traits, socio-demographic factors, the (in-)ability to delay gratification, and financial literacy. In the second step, we differentiate the identified effects regarding different groups to identify moderating effects, which, in turn, allow conclusions to be drawn about the need for individualized interventions. The results show that gender and educational background moderate the effects occurring between self-reported financial literacy, financial learning opportunities, delay of gratification, and financial literacy.
We investigate how financial literacy shapes older Americans’ demand for financial advice. Using an experimental module fielded in the Health and Retirement Study, we show that financial literacy strongly improves the quality but not the quantity of financial advice sought. In particular, more financially literate people seek financial help from professionals. This effect is more pronounced among older people and those with more wealth and more complex financial positions. Our analysis result implies that financial literacy and financial advisory services are complementary with, rather than substitutes for, each other.
We empirically examine how systemic risk in the banking sector leads to correlated risk in office markets of global financial centers. In so doing, we compute an aggregated measure of systemic risk in financial centers as the cumulated expected capital shortfall of local financial institutions. Our identification strategy is based on a double counterfactual approach by comparing normal with financial distress periods as well as office with retail markets. We find that office market interconnectedness arises from systemic risk during financial turmoil periods. Office market performance in a financial center is affected by returns of systemically linked financial center office markets only during a systemic banking crisis. In contrast, there is no evidence of correlated risk during normal times and among the within-city counterfactual retail sector. The decline in office market returns during a banking crisis is larger in financial centers compared to non-financial centers.
For private investors it is imperative to a) understand and define their own, individual risk preferences, b) assess their financial and demographic circumstances to determine the individual risk-taking potential, and c) form and maintain a well-diversified risky portfolio. The three chapters of my thesis each match one of these three tasks. \\ \noindent The first chapter of my thesis presents novel experimental evidence to test the existence of a potential projection bias in loss aversion, a significant determinant of investor preferences, thus matching task a). The second chapter is devoted to the determination of private investors' risk-taking potential based on their financial and socio-demographic circumstances, matching task b): In a large portfolio experiment, we examine the ability and heterogeneity of lay and professional advisors in matching investor demographics, such as age and income, with risky asset portfolio shares. The third and final chapter addresses the question on how to reach and maintain an efficient risky portfolio, therefore matching task c): It analyzes a decision support system for private investors that allows its users to simulate any arbitrary set of securities, and by reporting aggregated expected return and risk, to optimize their current portfolio.
With the rapid growth of technology in recent years, we are surrounded by or even dependent on the use of technological devices such as smartphones as they are now an indispensable part of our life. Smartphone applications (apps) provide a wide range of utilities such as navigation, entertainment, fitness, etc. To provide such context-sensitive services to users, apps need to access users' data including sensitive ones, which in turn, can potentially lead to privacy invasions. To protect users against potential privacy invasions in such a vulnerable ecosystem, legislation such as the European Union General Data Protection Regulation (EU GDPR) demands best privacy practices. Therefore, app developers are required to make their apps compatible with legal privacy principles enforced by law. However, this is not an easy task for app developers to comprehend purely legal principles to understand what needs to be implemented. Similarly, bridging the gap between legal principles and technical implementations to understand how legal principles need to be implemented is another barrier to develop privacy-friendly apps. To this end, this paper proposes a privacy and security design guide catalog for app developers to assist them in understanding and adopting the most relevant privacy and security principles in the context of smartphone apps. The presented catalog is aimed at mapping the identified legal principles to practical privacy and security solutions that can be implemented by developers to ensure enhanced privacy aligned with existing legislation. Through conducting a case study, it is confirmed that there is a significant gap between what developers are doing in reality and what they promise to do. This paper provides researchers and developers of privacy-related technicalities an overview of the characteristics of existing privacy requirements needed to be implemented in smartphone ecosystems, on which they can base their work.
The modern tontine : an innovative instrument for longevity risk management in an aging society
(2020)
We investigate whether a historical pension concept, the tontine, yields enough innovative potential to extend and improve the prevailing privately funded pension solutions in a modern way. The tontine basically generates an age-increasing cash flow, which can help to match the increasing financing needs at old ages. In contrast to traditional pension products, however, the tontine generates volatile cash flows, which means that the insurance character of the tontine cannot be guaranteed in every situation. By employing Multi Cumulative Prospect Theory (MCPT) we answer the question to what extent tontines can be a complement to or a substitute for traditional annuities. We find that it is only optimal to invest in tontines for a certain range of initial wealth. In addition, we investigate in how far the tontine size, the volatility of individual liquidity needs and expected mortality rates contribute to the demand for tontines.
Background: Nations are imposing unprecedented measures at large-scale to contain the spread of COVID-19 pandemic. Recent studies indicate that measures such as lockdowns may have slowed down the growth of COVID-19. However, in addition to substantial economic and social costs, these measures also limit the exposure to Ultraviolet-B radiation (UVB). Emerging observational evidence indicate the protective role of UVB and vitamin D in reducing the severity and mortality of COVID-19 deaths. In this observational study, we empirically outline the independent protective roles of lockdown and UVB exposure as measured by ultraviolet index (UVI), whilst also examining whether the severity of lockdown is associated with a reduction in the protective role.
Methods: We apply a log-linear fixed-effects model to a panel dataset of 162 countries over a period of 108 days (n=6049). We use the cumulative number of COVID-19 deaths as the dependent variable and isolate the mitigating influence of lockdown severity on the association between UVI and growth-rates of COVID-19 deaths from time-constant country-specific and time-varying country-specific potentially confounding factors.
Findings: After controlling for time-constant and time-varying factors, we find that a unit increase in UVI and lockdown severity are independently associated with 17% [-1.8 percentage points] and 77% [-7.9 percentage points] decline in COVID-19 deaths growth rate, indicating their respective protective roles. However, the widely utilized and least severe lockdown (recommendation to not leave the house) already fully mitigates the protective role of UVI by 95% [1.8 percentage points] indicating its downside.
Interpretation: We find that lockdown severity and UVI are independently associated with a slowdown in the daily growth rates of cumulative COVID-19 deaths. However, we find consistent evidence that increase in lockdown severity is associated with a significant reduction in the protective role of UVI in reducing COVID-19 deaths. Our results suggest that lockdowns in conjunction with adequate exposure to UVB radiation might have provided even more substantial health benefits, than lockdowns alone. For example, we estimate that there would be 21% fewer deaths on average with sufficient UVB exposure while people were recommended not to leave their house. Therefore, our study outlines the importance of considering UVB exposure, especially while implementing lockdowns and may support policy decision making in countries imposing such measures.
Competing Interest Statement: RKM is a PhD researcher at Goethe University, Frankfurt. He also is an employee of a multinational chemical company involved in vitamin D business and holds the shares of the company. This study is intended to contribute to the ongoing COVID-19 crisis and is not sponsored by his company. All other authors declare no competing interests. The views expressed in the paper are those of the authors and do not represent that of any organization. No other relationships or activities that could appear to have influenced the submitted work.
This paper defends The Transformation of Values into Prices on the Basis of Random Systems, published in EIER, by answering to the Comments made in the same journal by Professors Mori, Morioka and Yamazaki. The clarifications mainly concern the justification of the randomness assumptions, the conditions needed to obtain the equality of total profit with total surplus value in the simplified one-industry system and the invariance of the results to changes in the units of measurement.
This article investigates the roles of psychological biases for deviations between subjective survival beliefs (SSBs) and objective survival probabilities. We model these deviations through age-dependent inverse S-shaped probability weighting functions. Our estimates suggest that implied measures for cognitive weakness increase and relative optimism decrease with age. Direct measures of cognitive weakness and optimism share these trends. Our regression analyses confirm that these factors play strong quantitative roles in the formation of SSBs. Our main finding is that cognitive weakness instead of optimism becomes with age an increasingly important contributor to the well-documented overestimation of survival chances in old age.
External linkages allow nascent ventures to access crucial resources during the process of new product development. Forming external linkages can substantially contribute to a venture’s performance. However, little is known about the paths of external linkage formation, as well as the circumstances that drive the choice to pursue one rather than another path. This gap deserves further investigation, because we do not know whether insights developed for incumbent firms also apply to nascent ventures: To address this gap, we explore a novel dataset of 370 venture creation processes. Using sequence analyses based on optimal matching techniques and cluster analyses, we reveal that nascent ventures pursue one of overall four distinct paths of linkage formation activities during new product development. Contrary to the findings of the strategy literature, we find that if nascent ventures engage in external linkages at all, they do not combine exploration- and exploitation-oriented linkages but form either exploration- or exploitation-oriented linkages. Additional regression analyses highlight the circumstances that lead nascent ventures to pursue one rather than the other pathways. Taken together, our analyses point out that resource scarcity constitutes an important factor shaping the linkage formation activities of nascent ventures. Accordingly, we show that nascent ventures tend not to optimize by adding complementary knowledge to the firm’s knowledge base but rather to extend the existing knowledge base—a strategy which we call bricolage.
This research examines the impact of online display advertising and paid search advertising relative to offline advertising on firm performance and firm value. Using proprietary data on annualized advertising expenditures for 1651 firms spanning seven years, we document that both display advertising and paid search advertising exhibit positive effects on firm performance (measured by sales) and firm value (measured by Tobin's q). Paid search advertising has a more positive effect on sales than offline advertising, consistent with paid search being closest to the actual purchase decision and having enhanced targeting abilities. Display advertising exhibits a relatively more positive effect on Tobin's q than offline advertising, consistent with its long-term effects. The findings suggest heterogeneous economic benefits across different types of advertising, with direct implications for managers in analyzing advertising effectiveness and external stakeholders in assessing firm performance.
The US Treasury recently permitted deferred longevity income annuities to be included in pension plan menus as a default payout solution, yet little research has investigated whether more people should convert some of the $18 trillion they hold in employer-based defined contribution plans into lifelong income streams. We investigate this innovation using a calibrated lifecycle consumption and portfolio choice model embodying realistic institutional considerations. Our welfare analysis shows that defaulting a modest portion of retirees’ 401(k) assets (over a threshold) is an attractive way to enhance retirement security, enhancing welfare by up to 20% of retiree plan accruals.
Knowledge of consumers' willingness to pay (WTP) is a prerequisite to profitable price-setting. To gauge consumers' WTP, practitioners often rely on a direct single question approach in which consumers are asked to explicitly state their WTP for a product. Despite its popularity among practitioners, this approach has been found to suffer from hypothetical bias. In this paper, we propose a rigorous method that improves the accuracy of the direct single question approach. Specifically, we systematically assess the hypothetical biases associated with the direct single question approach and explore ways to de-bias it. Our results show that by using the de-biasing procedures we propose, we can generate a de-biased direct single question approach that is accurate enough to be useful for managerial decision-making. We validate this approach with two studies in this paper.
This dissertation consists of four self-contained chapters in the overlapping fields of industrial organization and organizational economics on the topics pricing, careers and supervision. Each chapter is the result of an independent research project. The dissertation analyzes empirical research topics by exploring novel observational data sets. It sheds light on open questions in the economic profession by extending fundamental models on pricing in the first two chapters and by challenging conventional explanations and methods on careers and supervision in the last two chapters.
- Chapter 1:
The first chapter is based on joint work with Steffen Eibelshäuser. It models price competition among brick-and-mortar retailers with business hours. Specifically, we propose a dynamic model of intraday price competition featuring spatial differentiation and firm size heterogeneity. The model makes detailed predictions concerning equilibrium-pricing patterns. When spatial differentiation is high and consumers cannot easily switch between retailers, equilibrium prices are stable at oligopoly levels. When differentiation is low, equilibrium prices fluctuate in cycles. The shapes of the cycles depend on the level of differentiation and on retailers’ reaction times. When reaction times decrease, the number of price cycles increases. In a second step, we apply the model to the German retail gasoline market. Gasoline retailers have been using digital price tags for decades and fast-paced price competition with more than ten price changes per day is no exception. Our model has successfully predicted the emergence of an additional intraday subcycle in April 2017. Moreover, we were able to confirm several detailed predictions concerning the shape of equilibrium price paths and individual firm behavior. Finally, we calibrate the model using a generalized method of moments. The model fits the data remarkably well, with coefficients of determination ranging from 60% to 80%. We use the fitted model to evaluate a number of policy counterfactuals. Restricting price increases results in higher prices and decreased welfare, leading us to conclude that regulation of dynamic markets is highly complex and can easily backfire.
- Chapter 2:
The second chapter analyzes the price-matching policies of two gasoline retailers. Customers of these retailers that are able to provide evidence of competitors posting lower prices have the ability to claim price matches. As shown in the first chapter, the Edgeworth Cycle model rationalizes price fluctuations in the German gasoline retail market. To determine policy interactions in cycling markets, this chapter extends the classical Edgeworth Cycle model by price-matching. The model predicts that price-matching retailers post higher prices and initiate price increases. The price-consulted firm anticipates this strategy, posts lower prices, and provokes the implementing firm to restore the price more frequently. Consulted stations also anticipate earlier price restoration reactions from implementing stations and, thus, provoke restorations earlier. This effect dominates in welfare calculations, such that price matching has positive welfare implications.
The second part of the chapter tests the hypotheses with price data on the German gasoline retail market. The estimation exploits a discontinuity in the policy-affected retailers. Therefore, the analysis disentangles the competitive effects of implementing and price-consulted market participants in comparison to retailers that are not affected. As predicted, the posted average and minimum prices of one implementing retailer and its consulted competitors increase. For the other price-matching retailer, I find reduced prices that contradict the model. The last part of the chapter relates the empirics to static models and shows that the dynamic component provides previously undiscovered insights.
- Chapter 3:
The third chapter is based on joint work with Emmanuelle Auriol and Guido Friebel. It represents the subtopic of careers in this dissertation. Specifically, the chapter provides the first comprehensive data collection analysis of women’s careers in all European research institutions in the field of economics. Using a web-scraping algorithm that constantly accesses position information on institutions’ websites, we collect a novel data set on researchers in Europe. These details entail information on researchers’ gender obtained by the first name and a face recognition. Similar to survey data on U.S. institutions, we identify a leaky pipeline, as women are less likely to become professors than men are. The situation is very heterogeneous across Europe. The gap is substantially larger in Western and Southern Europe than in Central and Eastern Europe. Furthermore, we identify institutions with a higher research output and a better research-ranking having a systematically lower share of females in full professor positions as well as entry-level positions for Ph.D. graduates. Austria, Belgium, Italy, Portugal, and Spain are the drivers for this correlation. All these results are in line with the “leaky pipeline” hypothesis, in which, over the different stages of a career, the attrition of women is higher than the one of men. We show that the cohort hypothesis arguing that the lag effect between the time of Ph.D. completion and the time of promotion to a full professorship is unable to explain the current low number of females.
- Chapter 4:
The fourth and last chapter "What does Mystery Shopping do?" is based on joint work with Sidney Block, Guido Friebel, Matthias Heinz, and Nick Zubanov. It addresses an auditing practice with a yearly U.S.-turnover of 19.5 billion USD in 2016 (European Society for Opinion and Market Research, 2017: Global Market Research 2017). The term mystery represents the key aspect of the tool. During an anonymous visit, so-called mystery shoppers perform certain predefined tasks such as purchasing a product, asking questions, registering complaints, or behaving in a certain way. Following their visit, the shoppers provide detailed reports about their experiences to the evaluated firms. The chapter investigates whether the practice is suitable to determine employees’ pay. Contrary to the general understanding that firms are able to observe service quality and, in turn, can proxy for business success with mystery shopping, we do not observe mystery-shopping evaluations to correlate positively with firm performance. A decomposition of the evaluation reports indicates that mystery-shopping scores are biased and the shopper’s identity explains up to 20% of the score’s variance. Thus, the shopper’s identity has the largest impact out of all observable characteristics. With the results that mystery-shopping scores are noisy and biased, we conclude that they are not suitable for performance pay in the context of our study. In addition, we show that if the number of observations is sufficiently large, aggregated scores relate to business success. The required number of shops per evaluation period must be, however, larger by a factor between 3 and 30 per evaluated subject. Hence, cost advantages of mystery shopping diminish such that the cost benefits to customer assessments could vanish completely. The current methodology, however, may still be useful for other employee-related purposes like monitoring, which is in line with the policies of the considered firms.
This paper examines heterogeneity in time discounting among a representative sample of elderly Americans, as well as its role in explaining key economic behaviors at older ages. We show how older Americans evaluate simple (hypothetical) inter-temporal choices in which payments today are compared with payments in the future. Using the indicators derived from this measure, we then demonstrate that differences in discounting patterns are associated with characteristics of particular importance in elderly populations. For example, cognitive deficits are associated with greater impatience, whereas bequest motives are associated with less impatience. We then relate our discounting measure to key economic outcomes and find that impatience is associated with lower wealth, fewer investments in health, and less planning for end of life care.
This study explores anomalies in stock returns found in their seasonal patterns. These are verified through multiple trading strategies based on past-performance returns that require information up to 20 years in the past. Some of the presented strategies deliver relatively high performance, especially for those strategies based on returns in the same calendar month from past years. In order to minimize any possible bias due to omitted delisting returns, those are incorporated into the monthly returns. Furthermore, to find an explanation for this seasonal effect, behavioral theories are discussed and the returns are controlled for risk and mispricing factors. However, empirical evidence indicates no evidence of explanation based on these factors for the seasonal patterns. Furthermore, possible reasons why the returns persist are discussed.
The Judgement of the EGC in the Case T-122/15 – Landeskreditbank Baden-Württemberg - Förderbank v European Central Bank is the first statement of the European judiciary on the sub-stantive law of the Banking Union. Beyond its specific holding, the decision is of great importance, because it hints at the methodological approach the EGC will take in interpreting prudential banking regulation in the appeals against supervisory measures that fall in its jurisdiction under TFEU, arts. 256(1) subpara 1 and 263(4). Specifically, the case pertained to the scope of direct ECB oversight of significant banks in the euro area and the reassignment of this competence to national competent authorities (NCAs) in individual circumstances (Single Supervisory Mechanism (SSM) Regulation, art. 6(4) subpara 2; SSM Framework Regulation, arts. 70, 71).
Most event studies rely on cumulative abnormal returns, measured as percentage changes in stock prices, as their dependent variable. Stock price reflects the value of the operating business plus non-operating assets minus debt. Yet, many events, in particular in marketing, only influence the value of the operating business, but not non-operating assets and debt. For these cases, the authors argue that the cumulative abnormal return on the operating business, defined as the ratio between the cumulative abnormal return on stock price and the firm-specific leverage effect, is a more appropriate dependent variable. Ignoring the differences in firm-specific leverage effects inflates the impact of observations pertaining to firms with large debt and deflates those pertaining to firms with large non-operating assets. Observations of firms with high debt receive several times the weight attributed to firms with low debt. A simulation study and the reanalysis of three previously published marketing event studies shows that ignoring the firm-specific leverage effects influences an event study's results in unpredictable ways.
Life insurers use accounting and actuarial techniques to smooth reporting of firm assets and liabilities, seeking to transfer surpluses in good years to cover benefit payouts in bad years. Yet these techniques have been criticized as they make it difficult to assess insurers’ true financial status. We develop stylized and realistically-calibrated models of a participating life annuity, an insurance product that pays retirees guaranteed lifelong benefits along with variable non-guaranteed surplus. Our goal is to illustrate how accounting and actuarial techniques for this type of financial contract shape policyholder wellbeing, along with insurer profitability and stability. Smoothing adds value to both the annuitant and the insurer, so curtailing smoothing could undermine the market for long-term retirement payout products.
Challenging voluntary CSR-initiatives – a case study on the effectiveness of the Equator Principles
(2015)
The Equator Principles (EPs) are a voluntary and self-regulatory Corporate Social Responsibility (CSR) initiative in the field of project finance. The EPs provide a number of principles to businesses to reduce the negative impacts of lending practices linked to environment-damaging projects. The paper argues that the actual impact of the EPs even now as revised version is still limited. This is due to their voluntary nature and their lack of adequate governance mechanisms, that is, enforcement, monitoring and sanctioning. With the help of RepRisk, which provides a database capturing third-party criticism as well as a company’s or project’s exposure to controversial socio-environmental issues, the paper evaluates the on-the-ground performances of the two ‘Equator banks’ Barclays and JPMorgan Chase and compares their performance with the one of the two non-Equator banks Deutsche Bank and UBS. The paper shows that the EPs do not have a substantial influence on the broader CSR-performance of multinational banks due to the EPs’ limited scope – focusing mainly on project finance – and the (still) existing various loopholes, grey areas and discretionary leeway. The paper also gives an overview of the main institutional shortcomings of the EPs and their association and discusses some potential reform steps which should be taken to further strengthen and ‘harden’ this ‘soft law’ EP-framework. The paper thus argues in favor of (more) mandatory and legally binding rules and standards at the transnational level to overcome the EPs’ ‘voluntariness bias’.
Demographic change belongs to the mega-trends of the 20th and the 21st century. The ongoing aging process in major industrialized countries gives rise to the relative scarcity of raw labor and the relative abundance of physical capital. Standard macroeconomic models suggest that this depresses asset returns and increases wages which, in turn, provides incentives for more human capital accumulation. This thesis quantifies the macroeconomic effects of demographic change and reveals the importance of human capital adjustments for price and welfare effects within and across generations. Chapter 1 investigates the distributions of income, skills, and welfare in the German economy along the inter- and the intra-generational dimension. It shows that demographic change leads to a more capital- and skill-intensive economy and that high-school households loose compared to college households in terms of welfare. Chapter 2 disentangles the effect of demographic change on returns to risk-free and risky assets in the U.S. and measures the net effect on the equity premium. It shows that both returns decline while the equity premium increases slightly. Endogenous human capital adjustments are crucial for relatively small effects. Chapter 3 develops a method for computing transitional dynamics in heterogeneous agent models with aggregate risk if these transitions are induced by exogenous deterministic dynamics such as demographic change. The application of the method to a simple illustrative example shows a large reduction in total computing time while approximation errors are small.
IT-driven trading innovations offer institutional investors alternative trading channels to broker delegated order handling. Motivated by the impact on intermediation relationships in securities trading and the adoption rate of such trading channels, the new option of self-directed order handling is analyzed. To capture the prerequisites for institutional investors to insource their order handling, an order-channel management (OCM) framework is introduced. It is based on a structural approach to account for the increasing complexity in comparison to traditional intermediary services. Drivers for the adoption of an OCM framework are investigated from the strategic perspective. Operational OCM is based on the business value of IT analysis of distinct trading innovations. It includes smart order router technology, low latency technology as an upgrade for existing IT-driven trading channels as well as negotiation dark pools, representing alternative trading venues. Evidence that all investigated IT-driven trading innovations generate additional business value is provided as one result. However, it is also shown that they exhibit entry barriers tightly related to investor size. Further, Task-Technology Fit is proven to be the major driver for the adoption decision. Consequently, IT-driven trading innovations should increase trading control, satisfy high anonymity and varying urgency demands.
Die Dissertation besteht aus drei thematisch zusammenhängenden Forschungspapieren, in denen zeitstetige Konsum-, Investment- und Versicherungsprobleme über den Lebenszyklus betrachtet werden. Ein besonderer Fokus liegt auf realistischen Features wie stochastischem Sterberisiko und nicht-replizierbarem Einkommen. In der ersten Forschungsarbeit untersuche ich die Relevanz von stochastischem Sterberisiko. Dabei zeige ich, dass eine Sprungkomponente in der Sterberate die optimalen Entscheidungen der Agenten und das Wohlfahrtslevel signifikant beeinflusst. Eine Diffusionskomponente ist hingegen vernachlässigbar. In dem zweiten Forschungspapier untersuchen wir die Risikolebensversicherungsnachfrage einer Familie, dessen Alleinverdiener stochastischem Sterberisiko ausgesetzt ist. Wir achten insbesondere auf eine realistische Modellierung der Versicherung. Wir zeigen, dass dadurch junge Agenten dem Versicherungsmarkt fern bleiben und die Versicherungsnachfrage mit dem Alter steigt, im Gegensatz zu Modellen mit einfachen stetig-veränderbaren Versicherungen. Weiterhin verstärken langlaufende Versicherungsverträge die negativen Effekte von Einkommensschocks und werden daher von risikoaversen Agenten weniger abgeschlossen. In der dritten Forschungsarbeit untersuche ich die Critical Illness Versicherungsnachfrage eines Agenten in einem Modell mit stochastischem Sterberisiko und Gesundheitsausgaben. Die Versicherung übernimmt dabei die zusätzlichen Gesundheitskosten, die bei einem Sprung entstehen. Fast alle Agenten schließen solch eine Versicherung vor dem Rentenalter ab, selbst wenn diese sehr kostspielig ist. Insbesondere Agenten mit geringen Gesundheitsausgaben und hohem Einkommen haben eine hohe Versicherungsnachfrage.
Essays in behavioral economics - evidence on self-selection into jobs, social networks and leniency
(2013)
Die Dissertation mit dem Titel „Essays in Behavioral Economics – Evidence on Self-Selection into Jobs, Social Networks and Leniency“ besteht aus einer Sammlung von vier wissenschaftlichen Abhandlungen. Alle Arbeiten verbindet die Analyse von theoretischen Konzepten und Erkenntnissen der Verhaltensökonomie unter Verwendung der experimentellen Methode. Die erste wissenschaftliche Abhandlung trägt den Titel „Sorting of Motivated Agents - Empirical Evidence on Self-Selection into the German Police“ und untersucht Selbstselektion bestimmter Individuen in den Polizeiberuf. Die experimentelle Studie untersucht die Frage, ob Polizeibewerber sich hinsichtlich ihrer Präferenzen in Bezug auf ihr Normdurchsetzungsverhalten in den Polizeiberuf selektieren. Die zweite Abhandlung greift diese Erkenntnisse auf und untersucht Polizeianwärter in ihrer Berufsausbildung ebenfalls hinsichtlich ihrer Normdurchsetzungsbereitschaft. Die Arbeit trägt den Titel „Selection and formation of motivated agents -- empirical evidence from the German Police”. In der dritten wissenschaftlichen Abhandlung werden geschlechterspezifische Unterschiede bei der Wahl von Partnern und dem Aufbau des sozialen Netzwerkes untersucht. Diese trägt den Titel „Selectivity and opportunism: two dimensions of gender differences in trust games and network formation“ und wurde zusammen mit Guido Friebel, Marie Lalanne, Paul Seabright und Peter Schwardmann verfasst. Die vierte Abhandlung geht einer aktuellen Fragestellung der Industrieökonomie nach und trägt den Titel „Antitrust, auditing and leniency programs: evidence from the laboratory“, verfasst mit Mehdi Feizi and Ali Mazyaki. In ihrer Gesamtheit liefert meine Dissertation Antworten auf personalpolitische, soziale und industrieökonomische Fragestellungen.
In this thesis the behavior of banks in financial markets which banks frequently use to obtain short-term as well as long-term financing is studied. In the first chapter we incorporate an interbank market for collateralized lending among banks into a dynamic, stochastic, general equilibrium (DSGE) framework to analyze the impact of variations in the expected value of the collateral on the interbank lending volume. We find that a central bank which decides to lower the haircut on eligible collateral in repurchase agreements is able to stimulate interbank markets. In the second chapter a microeconomic model of bank behavior on the interbank market is set up to analyze the impact of risk-taking behavior of interbank borrowing banks and uncertainty about their balance sheet quality on the lending behavior of interbank lending banks. It is found that the disruptions on the interbank market are the result of optimal behavior on the part of interbank lending banks in response to the uncertainty about the balance sheet quality of an interbank borrowing bank. In the third chapter we use monthly data on German bank bond spreads and regress it on bank-specific risk factors to assess the degree of market discipline in the German bank bond market. The regression results for the whole German bank bond market indicate that the bond spread does not show signs of market discipline. However, a structural break analysis uncovers that since the beginning of the financial crisis the German bank bond market exhibits at least a weak form of market discipline for bonds issued by medium-size and large banks.
This paper examines whether an exogenous anticipated monetary shock causes real economic effects, i.e. whether anticipated money is neutral. A major finding is that an anticipated monetary shock can in fact be massively non-neutral in the shortrun, if the economic environment is characterized by strategic complementarity. If the environment is characterized by strategic substitutability, anticipated monetary shocks are largely neutral.
Compliance with prevailing accounting standards is induced if the expected disadvantage due to sanctions imposed if non-compliance is detected outweighs the advantage of noncompliant accounting choices. The expected disadvantage materialises the threat potential of sanctions imposed by an enforcement agency. The capital market mechanism unfolds an important threat potential if companies expect an adverse share price reaction suite to enforcement actions. Enforcement agencies in turn can make use of this capital market related sanction by releasing information on defections to the market after the settlement of an investigation. The present contribution analyses the capital market reaction on accounting standards enforcement activities of the British Financial Reporting Review Panel (FRRP). After a brief introduction into the legal basis and working procedure of the Panel, the analysis of its activities will serve a dual purpose: firstly, the significance of capital market related sanctions for the overall enforcement regime will be elaborated upon. Secondly, the extent to which capital market related sanctions accomplish their function within the overall enforcement regime will be assessed empirically. The results of the empirical analysis suggest that the capital market related sanctioning by the FRRP may not unfold a sufficient threat potential which is a prerequisite for compliance enhancement.