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A common element of market structure analysis is the spatial representation of firms’ competitive positions on maps. Such maps typically capture static snapshots in time. Yet, competitive positions tend to change. Embedded in such changes are firms’ trajectories, that is, the series of changes in firms’ positions over time relative to all other firms in a market. Identifying these trajectories contributes to market structure analysis by providing a forward-looking perspective on competition, revealing firms’ (re)positioning strategies and indicating strategy effectiveness. To unlock these insights, we propose EvoMap, a novel dynamic mapping framework that identifies firms’ trajectories from high-frequency and potentially noisy data. We validate EvoMap via extensive simulations and apply it empirically to study the trajectories of more than 1,000 publicly listed firms over 20 years. We find substantial changes in several firms’ positioning strategies, including Apple, Walmart, and Capital One. Because EvoMap accommodates a wide range of mapping methods, analysts can easily apply it in other empirical settings and to data from various sources.
Regulators worldwide have been implementing different privacy laws. They vary in their impact on the value for advertisers, publishers and users, but not much is known about these differences. This article focuses on three important privacy laws (i.e., General Data Protection Regulation [GDPR], California Consumer Privacy Act [CCPA] and Personal Information Protection Law [PIPL]) and compares their impact on the value for the three primary actors of the online advertising market, namely, advertisers, publishers and users. This article first compares these three privacy laws by developing a legal strictness score. It then uses the existing literature to derive the effects of the legal strictness of each privacy law on each actor’s value. Finally, it quantifies the three privacy laws’ impact on each actor’s value. The results show that GDPR and PIPL are similar and stricter than CCPA. Stricter privacy laws bring larger negative changes to the value for actors. As a result, both GDPR and PIPL decrease the actors’ value more substantially than CCPA. These value declines are the largest for publishers and are rather similar for users and advertisers. Scholars and practitioners can use our findings to explore ways to create value for multiple actors under various privacy laws.
For many services, consumers can choose among a range of optional tariffs that differ in their access and usage prices. Recent studies indicate that tariff-specific preferences may lead consumers to choose a tariff that does not minimize their expected billing rate. This study analyzes how tariff-specific preferences influence the responsiveness of consumers’ usage and tariff choice to changes in price. We show that consumer heterogeneity in tariff-specific preferences leads to heterogeneity in their sensitivity to price changes. Specifically, consumers with tariff-specific preferences are less sensitive to price increases of their preferred tariff than other consumers. Our results provide an additional reason why firms should offer multiple tariffs rather than a uniform nonlinear pricing plan to extract maximum consumer surplus.
Digitale Technologien begünstigen den Einsatz einer dynamischen Preisgestaltung, also von Preisen, die für ein prinzipiell gleiches Produkt unangekündigt variieren. Dabei werden in der öffentlichen Diskussion unterschiedliche Ausgestaltungsformen dynamischer Preise oftmals vermischt, was eine sinnvolle Analyse der Vor- und Nachteile der dynamischen Preisgestaltung erschwert. Das Ziel des Beitrags ist die Darstellung der ökonomischen Grundlagen und die Diskussion sowie Klassifikation der Ausgestaltungsmöglichkeiten der dynamischen Preisgestaltung. Darüber hinaus erfolgt eine Bewertung der Vor- und Nachteile der dynamischen Preisgestaltung aus Käufer- und Verkäufersicht. Abschließend werden Implikationen für die betriebswirtschaftliche Forschung diskutiert.
Highlights
• The 1986 Immigration Reform and Control Act legalized millions of Hispanic migrants.
• The IRCA receive significantly increases state-to-county fiscal transfers.
• Electoral incentives of the state governor drive the fiscal response of the IRCA.
• Legalization increases Hispanic turnout and political engagement.
Abstract
We study the impact of immigrant legalization on fiscal transfers from state to local governments in the United States, exploiting variation in legal status from the 1986 Immigration Reform and Control Act (IRCA). State governments allocate more resources to IRCA counties, an allocation that is responsive to the electoral incentives of the governor. Importantly, the effect emerges prior to the enfranchisement of the IRCA migrants and we argue it is driven by the IRCA’s capacity to politically empower already legal Hispanic migrants in mixed legal status communities. The IRCA increases turnout in large Hispanic communities as well as Hispanic political engagement, without detectably triggering anti-migrant sentiment.
With adequate support for the learner, errors can have high learning potential. This study investigates rather unsuitable action patterns of teachers in dealing with errors. Teachers rarely investigate the causes that evoke the occurrence of individual students’ errors, but instead often change addressees immediately after an error occurs. Such behavior is frequent in the classroom, leaving unexploited, yet important potential to learn from errors. It has remained unexplained why teachers act the way they do in error situations. Using video-stimulated recalls, I investigate the reasons for teachers’ behavior in students’ error situations by confronting them with recorded episodes from their own teaching. Error situations are analyzed (within-case) and teachers’ beliefs are classified in an explanatory model (cross-case) to illustrate patterns across teachers. Results show that teachers refer to an interaction of student attributes, their own attributes, and error attributes when reasoning their own behavior. I find that reference to specific attributes varies depending on the situation, and so do the described reasons that led to a particular behavior as a spontaneous or more reflective decision.
The crowdfunding of altruism
(2022)
This paper introduces a machine learning approach to quantify altruism from the linguistic style of textual documents. We apply our method to a central question in (social) entrepreneurship: How does altruism impact entrepreneurial success? Specifically, we examine the effects of altruism on crowdfunding outcomes in Initial Coin Offerings (ICOs). The main result suggests that altruism and ICO firm valuation are negatively related. We, then, explore several channels to shed some light on whether the negative altruism-valuation relation is causal. Our findings suggest that it is not altruism that causes lower firm valuation; rather, low-quality entrepreneurs select into altruistic projects, while the marginal effect of altruism on high-quality entrepreneurs is actually positive. Altruism increases the funding amount in ICOs in the presence of high-quality projects, low asymmetric information, and strong corporate governance.
Detailed feedback on exercises helps learners become proficient but is time-consuming for educators and, thus, hardly scalable. This manuscript evaluates how well Generative Artificial Intelligence (AI) provides automated feedback on complex multimodal exercises requiring coding, statistics, and economic reasoning. Besides providing this technology through an easily accessible web application, this article evaluates the technology’s performance by comparing the quantitative feedback (i.e., points achieved) from Generative AI models with human expert feedback for 4,349 solutions to marketing analytics exercises. The results show that automated feedback produced by Generative AI (GPT-4) provides almost unbiased evaluations while correlating highly with (r = 0.94) and deviating only 6 % from human evaluations. GPT-4 performs best among seven Generative AI models, albeit at the highest cost. Comparing the models’ performance with costs shows that GPT-4, Mistral Large, Claude 3 Opus, and Gemini 1.0 Pro dominate three other Generative AI models (Claude 3 Sonnet, GPT-3.5, and Gemini 1.5 Pro). Expert assessment of the qualitative feedback (i.e., the AI’s textual response) indicates that it is mostly correct, sufficient, and appropriate for learners. A survey of marketing analytics learners shows that they highly recommend the app and its Generative AI feedback. An advantage of the app is its subject-agnosticism—it does not require any subject- or exercise-specific training. Thus, it is immediately usable for new exercises in marketing analytics and other subjects.
This paper studies discrete time finite horizon life-cycle models with arbitrary discount functions and iso-elastic per period power utility with concavity parameter θ. We distinguish between the savings behavior of a sophisticated versus a naive agent. Although both agent types have identical preferences, they solve different utility maximization problems whenever the model is dynamically inconsistent. Pollak (1968) shows that the savings behavior of both agent types is nevertheless identical for logarithmic utility (θ = 1). We generalize this result by showing that the sophisticated agent saves in every period a greater fraction of her wealth than the naive agent if and only if θ ≥ 1. While this result goes through for model extensions that preserve linearity of the consumption policy function, it breaks down for non-linear model extensions.
Homeownership rates differ widely across European countries. We document that part of this variation is driven by differences in the fraction of adults co-residing with their parents. Comparing Germany and Italy, we show that in contrast to homeownership rates per household, homeownership rates per individual are very similar during the first part of the life cycle. To understand these patterns, we build an overlapping-generations model where individuals face uninsurable income risk and make consumption-saving and housing tenure decisions. We embed an explicit intergenerational link between children and parents to capture the three-way trade-off between owning, renting, and co-residing. Calibrating the model to Germany we explore the role of income profiles, housing policies, and the taste for independence and show that a combination of these factors goes a long way in explaining the differential life-cycle patterns of living arrangements between the two countries.