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Questionable research practices have generated considerable recent interest throughout and beyond the scientific community. We subsume such practices involving secret data snooping that influences subsequent statistical inference under the term MESSing (manipulating evidence subject to snooping) and discuss, illustrate and quantify the possibly dramatic effects of several forms of MESSing using an empirical and a simple theoretical example. The empirical example uses numbers from the most popular German lottery, which seem to suggest that 13 is an unlucky number.
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%.
The present study investigates the moderating effect of usage intensity of the social networking site (SNS) Instagram (IG) on the influence of advertisement disclosure types on advertising performance. A national sample (N = 566) participated in a randomized online experiment including a real influencer and followers in order to investigate how different advertisement disclosure types affect advertising performance and how usage intensity moderates this effect. We find that disclosing an influencer’s postings with “#ad” increases the trustworthiness of the influencer and the general credibility of the posting for heavy users, but not for light users. Followership of a user has been found to strongly improve all researched variables (attitude toward product placement, trustworthiness of the spokesperson and general credibility of the posting). This study adds to literature the first distinction on heavy and light usage intensity, and on followership of an IG user when regarding the effects of advertisement disclosure types on advertising performance. To conclude, we present a number of recommendations regarding how advertisers, influencers, and SNS providers should develop strategies for monitoring, understanding, and responding to different social media users, e.g., to closely monitor an influencer’s audience to identify heavy users and optimally target them.
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.
A person's intelligence level positively influences his or her professional success. Gifted and highly intelligent individuals should therefore be successful in their careers. However, previous findings on the occupational situation of gifted adults are mainly known from popular scientific sources in the fields of coaching and self-help groups and confirm prevailing stereotypes that gifted people have difficulties at work. Reliable studies are scarce. This systematic literature review examines 40 studies with a total of 22 job-related variables. Results are shown in general for (a) the employment situation and more specific for the occupational aspects (b) career, (c) personality and behavior, (d) satisfaction, (e) organization, and (f) influence of giftedness on the profession. Moreover, possible differences between female and male gifted individuals and gifted and non-gifted individuals are analyzed. Based on these findings, implications for practice as well as further research are discussed.
The importance of agile methods has increased in recent years, not only to manage IT projects but also to establish flexible and adaptive organisational structures, which are essential to deal with disruptive changes and build successful digital business strategies. This paper takes an industry-specific perspective by analysing the dissemination, objectives and relative popularity of agile frameworks in the German banking sector. The data provides insights into expectations and experiences associated with agile methods and indicates possible implementation hurdles and success factors. Our research provides the first comprehensive analysis of agile methods in the German banking sector. The comparison with a selected number of fintechs has revealed some differences between banks and fintechs. We found that almost all banks and fintechs apply agile methods in IT projects. However, fintechs have relatively more experience with agile methods than banks and use them more intensively. Scrum is the most relevant framework used in practice. Scaled agile frameworks are so far negligible in the German banking sector. Acceleration of projects is apparently the most important objective of deploying agile methods. In addition, agile methods can contribute to cost savings and lead to improved quality and innovation performance, though for banks it is evidently more challenging to reach their respective targets than for fintechs. Overall our findings suggest that German banks are still in a maturing process of becoming more agile and that there is room for an accelerated adoption of agile methods in general and scaled agile frameworks in particular.
This paper examines rent sharing in private investments in public equity (PIPEs) between newly public firms and private investors. The evidence suggests highly asymmetric rent sharing. Newly public firms earn a negative return of up to −15% in the first post-PIPE year, while investors benefit due to the ability to dictate transaction terms. The results are economically relevant because newly public firms are, at least in recent years, more likely to tap private rather than public markets for follow-on financing shortly after the initial public offering (IPO), and because the results for newly public firms contrast with those for the broad PIPE market in Lim et al. (2021). The study also contributes to the PIPE literature by offering an integrative view of competing theories of the cross-section of post-PIPE stock returns. We simultaneously test proxies for corporate governance, asymmetric information, bargaining power, and managerial entrenchment. While all explanations have univariate predictive power for the post-PIPE performance, only the proxies for corporate governance and asymmetric information are robust in ceteris-paribus tests.
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.
We use census data to show that structural transformation reflects a fundamental reallocation of labour from goods to services, instead of a relabelling that occurs when goods-producing firms outsource their in-house service production. The novelty of our approach is that it categorizes labour by occupations, which are invariant to outsourcing. We find that the reallocation of labour from goods-producing to service-producing occupations is a robust feature in censuses from around the world and different time periods. To understand the underlying forces, we propose a tractable model in which uneven occupation-specific technological change generates structural transformation of occupation employment.
We propose a novel approach to the study of international trade based on a theory of country integration that embodies a broad systemic viewpoint on the relationship between trade and growth. Our model leads to an indicator of country openness that measures a country's level of integration through the full architecture of its connections in the trade network. We apply our methodology to a sample of 204 countries and find a sizable and significant positive relationship between our integration measure and a country's growth rate, while that of the traditional measures of outward orientation is only minor and statistically insignificant.
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.
Sample-based longitudinal discrete choice experiments: preferences for electric vehicles over time
(2021)
Discrete choice experiments have emerged as the state-of-the-art method for measuring preferences, but they are mostly used in cross-sectional studies. In seeking to make them applicable for longitudinal studies, our study addresses two common challenges: working with different respondents and handling altering attributes. We propose a sample-based longitudinal discrete choice experiment in combination with a covariate-extended hierarchical Bayes logit estimator that allows one to test the statistical significance of changes. We showcase this method’s use in studies about preferences for electric vehicles over six years and empirically observe that preferences develop in an unpredictable, non-monotonous way. We also find that inspecting only the absolute differences in preferences between samples may result in misleading inferences. Moreover, surveying a new sample produced similar results as asking the same sample of respondents over time. Finally, we experimentally test how adding or removing an attribute affects preferences for the other attributes.
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.
Crowdfunding platforms offer project initiators the opportunity to acquire funds from the Internet crowd and, therefore, have become a valuable alternative to traditional sources of funding. However, some processes on crowdfunding platforms cause undesirable external effects that influence the funding success of projects. In this context, we focus on the phenomenon of project overfunding. Massively overfunded projects have been discussed to overshadow other crowdfunding projects which in turn receive less funding. We propose a funding redistribution mechanism to internalize these overfunding externalities and to improve overall funding results. To evaluate this concept, we develop and deploy an agent-based model (ABM). This ABM is based on a multi-attribute decision-making approach and is suitable to simulate the dynamic funding processes on a crowdfunding platform. Our evaluation provides evidence that possible modifications of the crowdfunding mechanisms bear the chance to optimize funding results and to alleviate existing flaws.
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.
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 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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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’.
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.
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.