Die Arbeit entwickelt eine Architektur für einen EHDS-konformen Datenraum, der sektorübergreifende Forschungsdatenkooperationen im Gesundheitswesen unterstützt. Ausgangspunkt ist die Analyse rechtlicher, organisatorischer und technischer Anforderungen an Datennutzung und -austausch, die im Kontext fragmentierter IT-Landschaften, heterogener Prozesse und datenschutzrechtlicher Unsicherheiten besondere Bedeutung haben. Methodisch folgt die Arbeit dem Ansatz des Action Design Research und verbindet theoretische Meta- Anforderungen mit empirischen Erkenntnissen aus Interviews und Workshops im Projekt CaringS. Ergebnis ist ein Wegweiser, der Konsortien in fünf Phasen von der gemeinsamen Vision über die Identifikation relevanter Datenquellen und Fragen der Data-Governance bis zur technischen Architektur und langfristigen Nutzung begleitet. Wissenschaftlich leistet die Arbeit einen Beitrag zur Entwicklung von Gestaltungsprinzipien für Datenräume, die Zielkonflikte zwischen Generalisierbarkeit und Praxistauglichkeit sichtbar machen. Praktisch stellt sie ein anwendbares Instrument bereit, das EHDS-Vorgaben in konkrete Arbeitsschritte übersetzt und so Forschung und Versorgung verbindet.

This paper investigates how the ECB’s monetary policy affects consumers’ perceptions about the credibility of the inflation target. Monetary policy is assessed by the gap between the actual policy rate and a Taylor rate to approximate the interest rate expected by the public. Drawing on survey data for German consumers from 2019 to 2024, we find that the ECB’s interest rate policy contributes significantly to the credibility of the inflation target. In particular, the massive dent in inflation target credibility observed from 2021 to the end of 2023 could have been ameliorated by an earlier and more decisive tightening of monetary policy. This suggests that simple outcome-based Taylor rules may deserve more attention in the communication of the ECB’s monetary policy strategy.

Difference-in-differences (DiD) is one of the most popular approaches for empirical research in economics, political science, and beyond. Identification in these models is based on the conditional parallel trends assumption: In the absence of treatment, the average outcome of the treated and untreated group are assumed to evolve in parallel over time, conditional on pre-treatment covariates. We introduce a novel approach to sensitivity analysis for DiD models that assesses the robustness of DiD estimates to violations of this assumption due to unobservable confounders, allowing researchers to transparently assess and communicate the credibility of their causal estimation results. Our method focuses on estimation by Double Machine Learning and extends previous work on sensitivity analysis based on Riesz Representation in cross-sectional settings. We establish asymptotic bounds for point estimates and confidence intervals in the canonical 2 × 2 setting and group-time causal parameters in settings with staggered treatment adoption. Our approach makes it possible to relate the formulation of parallel trends violation to empirical evidence from (1) pre-testing, (2) covariate benchmarking and (3) standard reporting statistics and visualizations. We provide extensive simulation experiments demonstrating the validity of our sensitivity approach and diagnostics and apply our approach to two empirical applications.

The smearing effect of kernel estimates of the local density, local proportions and local means is used as a means for the construction of anonymized maps. The standard anonymization criteria were derived for the display of case numbers of a predefined area system. However, for kernel estimates there does not exist such a defined area system. We discuss the resulting difficulties of the application of these criteria for kernel estimates. Besides, there are some de-anonymization risks which are specific for kernel estimates. We discuss these topics for data from 1.9 million Berlin taxpayers with known exact address and taxable income. In the conclusions we vote for a much stronger emphasis on the output format of a map and the labelling of the displayed values in the map.

We study a principal who allocates a good to agents with private, independently distributed values through an optimal mechanism. The principal can strategically shape these value distributions by modifying the good’s features, which affect agents’ valuations. Our analysis reveals that optimal designs are frequently divisive—creating goods that appeal strongly to specific agents or agent types while being less valued by others. These divisive designs reduce information rents and increase total surplus, at the expense of competition. Even when total surplus is constrained, some divisiveness in designs remains optimal.

Tax evasion is associated with high social and fiscal costs. To address these, many governments employ behavioral interventions given their low implementation costs and high potential efficiency. Although many studies report positive effects of behavioral interventions to combat tax evasion, the effect sizes are often quite small. This may result from the partial cancellation of heterogeneous effects and prompts calls in the literature for individualized or group-tailored interventions. While classification approaches for taxpayer types exist, their practical implementation is limited by data availability. We systematically review 144 studies conducted between 1996 and 2024 and show that grouptailored interventions along key inequality dimensions—gender, income, age, and regionality—may not only enhance tax compliance but also help address inequality. Furthermore, our heterogeneity analysis shows that intervention effectiveness can be enhanced by the incorporation of specific characteristics related to framing, intervention frequency, and communication channels. Finally, we present a theoretical model to support group-tailored interventions and thus provide policymakers with an efficient strategy to combat tax evasion.

Ziel des Meilensteins M14 ist die Verstetigung der im Rahmen von Health-X entstandenen Strukturen und Geschäftsmodelle über die Projektlaufzeit hinaus. Für die nachhaltige Weiternutzung der Ergebnisse aus Health-X wurde ein aus drei Organisationen bestehendes „magisches Dreieck“ institutionalisiert, welches im Rahmen der European Health Data Space (EHDS) Gesetzgebung operieren wird. Als erste Institution wurde der European Health Data Alliance Verein (EHDA e.V.) gegründet, der als gemeinwohlorientierte Interessensvertretung für europäische Gesundheitsdatenräume agiert, Kooperationen fördert und die Weiterentwicklung des entstandenen Tech Stacks unterstützt. Somit treibt der EHDA e.V. die Entwicklung von bürgerzentrierten Gesundheitsdatenräumen in Europa voran, wobei er Regeln für Standardisierung und Interoperabilität definiert, um EHDS-konforme Gesundheitsprodukte und Services zu ermöglichen. Der EHDA e.V. legte außerdem 12 zentrale Regeln für unter anderem Datenschutz, -sicherheit und Governance fest, an die sich alle Vereinsmitglieder sowie Organisationen im magischen Dreieck bei der Nutzung des Tech Stacks halten müssen. Als zweite Institution ist derzeit die European Health Data Platform GmbH (EHDP) als mehrheitliche Tochter des EHDA e.V. in Gründung. Die EHDP wird den in Health-X initial entwickelten Tech Stack für föderierte Datenräume und sichere Verarbeitungsumgebungen im Sinne des EHDS kontinuierlich weiterentwickeln und gemeinwohlorientiert betreiben. Somit bietet die EHDP die technisch-organisatorische Grundlage für interoperable Gesundheitsdatenräume. Als Intermediär zwischen Datenanbietern und -nutzenden stellt sie notwendige Infrastruktur- und Managementservices bereit und ermöglicht den Aufbau und den Betrieb spezifischer EHDS-konformer Gesundheitsdatenräume. Durch den Aufbau solcher Datenräume werden als dritte Instanz verschiedene kommerzielle Servicegesellschaften entstehen, die EHDS-konforme sichere Verarbeitungsumgebungen für verschiedene Anwendungsfälle anbieten. Dieses Modell sichert eine dezentrale und partizipative Governance, die sich an Werten wie Datensouveränität, Fairness, Offenheit und Transparenz orientiert. Somit schafft das magische Dreieck die Basis für eine nachhaltige und föderierte Gesundheitsdatenökonomie im Sinne der Europäischen Union.

Gerschenkron (1962) argued that public institutions such as the State Bank of the Russian Empire spurred the country’s industrialization. We test this assertion by exploiting plant-level variation in access to State Bank branches using a unique geocoded factory data set. Employing an identification strategy based on geographical distances between banks and factories, our results show improved access to public banking encouraged faster growth in factory-level revenue, mechanization, and labor productivity. In line with theories of late industrialization, we also find evidence that public credit mattered more in regions where commercial banks were fewer and markets were smaller.

This paper investigates the determinants of inflation target credibility (ITC) using a unique survey we designed to measure the credibility of the ECB’s inflation target. Containing over 200,000 responses from German consumers collected between January 2019 and November 2024, our dataset enables us to estimate the effect of both positive and negative deviations of inflation from the 2% target on ITC. In contrast to the symmetry of the ECB’s inflation target, we find that ITC is asymmetric, i.e. consumers respond significantly and plausibly signed to target deviations only when inflation is above target. When inflation is below target, however, the credibility of the inflation target cannot be improved by raising the inflation rate to close the gap.

Motivated by the recent surge in union drives, we present a theoretical model of the factors that influence unionization. An employee seeking to unionize their workplace assembles organizers to persuade coworkers to vote in favor. If unionization benefits workers, it is more likely to succeed when the organizers are credible. Credibility depends on the organizers not being overly biased and/or bearing significant organizational costs. Our theory explains why grassroots movements, rather than established unions, often succeed in organizing workplaces. Interestingly, the likelihood of successful unionization, when it benefits workers, is non-monotonic with respect to organizational costs. When such costs are low, a firm that opposes unionization and targets organizers may paradoxically increase the chances of success. However, the unionization drive is ineffective if the firm’s opposition is sufficiently strong, as this makes organizational costs prohibitive.

This study explores the factors influencing household overcrowding using longitudinal survey data from Germany spanning the years 1985 to 2022. As average square meters per capita have declined for urban tenants, we find that overcrowding rates have substantially increased since 2012: By 2022, 11% of the population lived in overcrowded housing (Eurostat definition), while up to 19% of individuals subjectively felt overcrowded. At the same time, under-occupation also rose, with 39% of dwellings objectively classified as under-occupied, and 16% of residents subjectively perceiving their homes as under-occupied. We demonstrate that the likelihood of entering, experiencing, and remaining in overcrowded housing increases in early adulthood and decreases over the life cycle. Moreover, we find that, after controlling for socio-demographic characteristics such as the number of children or a migration background, economic factors contribute relatively little to explaining the likelihood of living in an overcrowded household. In policy terms, our paper highlights a misallocation of housing space and the need for housing policies to target particular vulnerable groups at high risk of overcrowding.

History shows militarily dominant states that pursue imperialism, relying on their might to extort resources from weaker states. Occasionally, the latter revolt and the dominant state suffers some casualties. This paper explores imperialism along steady-growth paths. If the dominant state maximizes domestic welfare, it should eventually abandon imperialism because its safety costs asymptotically overrun its material benefits. To shed light on diametrically opposed historical records, I propose a model of endogenous ideology and war bias in which the political elite cares about self-image. If that concern is strong enough, the political elite gradually identifies with its country’s mission of hegemony and imperialism persists. It is first driven by material concerns and later by ideal ones. Despite its divergent preferences, the population of a dominant state generally has little interest to oppose imperialism.

Recently, there is a growing interest in understanding how individuals adapt to changing climate conditions and climate-induced extreme weather events. An underexplored question is whether and how climate-related natural hazards affect household saving behavior. For this purpose, we exploit a natural experiment stemming from the European Flood of August 2002. Combining micro data with geo-coded flood maps allows us to analyze the causal impact of flood exposure on household savings within a differences-in-differences setting. We find that flood exposure depresses household saving behavior in the medium run. The most likely explanation is moral hazard induced by massive government support for affected households.

One of the primary objectives of protests and demonstrations is to bring social, political, or economic issues to the attention of politicians and the wider population. While protests can have a mobilizing and persuading effect, they may reduce support for their cause if they are perceived as a threat to public order. In this study, we look at how local or spontaneously organised xenophobic demonstrations affect concerns about hostility towards foreigners and worries about immigration among natives in Germany. We use a regression discontinuity design to compare the attitudes of individuals interviewed in the days immediately before a large far-right demonstration and individuals interviewed in the days immediately after that demonstration. Our results show that large right-wing demonstrations lead to a substantial increase in worries about hostility towards foreigners of 13.7% of a standard deviation. In contrast, worries about immigration are not affected by the demonstrations, indicating that the protesters are not successful in swaying public opinion in their favour. In the heterogeneity analyses, we uncover some polarisation in the population: While worries about hostility against foreigners increase and worries about immigration decrease in left-leaning regions, both types of worries increase in districts where centre-right parties are more successful. Lastly, we also show that people become more politically interested in response to protests, mainly benefiting left-wing parties, and are more likely to wish to donate money to help refugees.

Behavioral economics has so far largely avoided discussing the psychological origins of preferences, as well as their relation to needs. This has not only restricted interdisciplinary exchange, but also significantly limits the predictive capabilities of models. For example, the revealed preference approach can only reliably predict repeating choices, while needing large amounts of observations for calibration. In this paper, I show how unifying preferences with the psychological concept of needs strengthens economic models, by developing a decision-making framework for well-being assessment and choice prediction. To present the direct merit of this approach, I show how this framework yields a systematic approximation scheme, which is able to solve limitations of current approaches by describing new alternatives, non-repeating choices, or otherwise unobservable desires. Meanwhile, the approximation scheme requires less observations on an individual level than current approaches. I achieve this by constructing a hierarchical dependency between human motivations and preferences through the language of needs. I show the basic feasibility of the approximation scheme through simulations on random populations. In practice, the framework is applicable in situations where individuals exert choices only once and measuring preferences is expensive, like evaluating policy proposals or predicting decisions under technological change

Current time allocation and household production models face three major weaknesses: First, they only describe the average time allocation. Thus, information about the order of activities is lost. Therefore, it is impossible to describe the influence of activities on later ones. Such interactions are likely pervasive, and can significantly alter behavior. Second, they are unable to describe the effort allocation of individuals, although effort influences one’s time allocation. Thereby, they are either unable or very limited in describing labor productivity or multitasking although individuals frequently multitask. Through the omission of interactions and effort allocation, current models yield biased descriptions of e.g. price and time elasticities. Third, they require strong assumptions, such as perfect foresight or periodic environments, and thus cannot describe behavior in unpredictable environments, like reactions to external shocks. In this paper, I provide a remedy for these shortcomings by developing a dynamical model of procedurally rational decision making. The basic idea of the model is a feedback loop between experienced utility, decision utility, and activities. In applications of the model, I show how introducing a work-leisure interaction and multitasking significantly changes elasticities and how nonmarginal external shocks cause short-term demand surges, none of which can be described by current time allocation models.

What can be done to reduce the likelihood of future wars? While states’ decisions that bear on war are ultimately made by their political leaders, strengthening ordinary citizens’ control of those leaders is vital to reduce the risk of future wars. This thesis can be broken down into two claims: first, there is a war bias of political leaders; second, people’s control over those leaders may successfully counteract that bias. Claim n°1 has that in some situations political leaders and citizens have substantially divergent interests with respect to policies that risk the outbreak of war. Political leaders may have a war bias that is so strong that they prefer war occurring with high probability whereas citizens prefer peace occurring with high probability. Claim n°2 has that in some situations strengthening citizens’ control of political decision-makers may be decisive to prevent a war. As I’ll argue shortly, this empowerment can be beneficial only if sufficiently many citizens have previously become alert by productively engaging in a distinctive cognitive effort. This requirement points to a precise responsibility of intellectuals.

Die Einführung neuer Technologien stellt für Organisationen mit hoher Zuverlässigkeit (HROs) oder solche die danach streben (RSOs) eine erhebliche Herausforderung dar. Die vorliegende Arbeit analysiert die mit diesem Prozess verbundenen Herausforderungen, insbesondere in Hinblick auf mögliche Produktionsunterbrechungen, die Einführung neuer Arbeitslogiken und die Unsicherheit im Umgang mit neuer Technologie. In Zusam-menarbeit mit spezialisierten Partnern versuchen Organisationen, Fehler und Kontinui-tätsunterbrechungen während der Implementierung zu vermeiden. Die Arbeit fußt auf einer kritischen Literaturübersicht und untersucht 72 Forschungsbei-träge aus einer Reliabilitätsperspektive. Ziel ist die Identifikation relevanter Diskurse in der bestehenden Literatur sowie die Ableitung von Handlungsempfehlungen für die Technologieimplementierung. Die Ergebnisse deuten darauf hin, dass der Diskurs der Service-Dominant-Logic aus der Marketingforschung und die Reliabilitätsperspektive aus der Managementliteratur als kohärente Perspektiven betrachtet werden können. Diese Arbeit schlägt vor, diese Verbindung in zukünftigen Beiträgen zu überprüfen und trägt damit zur Integration von Erkenntnissen aus verschiedenen Forschungsdisziplinen bei.

Aufbauend auf im Rahmen eines Delphi-Verfahrens erhobenen Daten und Erkenntnissen werden ökonomische Verwertungs- und Geschäftsmodelloptionen für ein Blockchain-basiertes Gesundheitsdatenmanagement- und Zugriff-/Rechteverwaltungssystem zur Einbettung in den ersten deutschen Gesundheitsmarkt diskutiert und reflektiert. Im Fokus steht hierbei die im Rahmen eines BMBF-geförderten Verbundprojekts entwickelte und im Entlassmanagement für onkologische Patientinnen prototypisch umgesetzte BloG³-Lösung. Eine reibungslose sektorenübergreifende, interdisziplinäre Versorgung im Behandlungsprozess von Onkologie-Patientinnen ist als Anwendungsfall von besonderer Relevanz, da hier zahlreiche sowohl stationäre als auch ambulante Versorgungsanbieter eingebunden sind und es hier bei der Überleitung oft noch zu Versorgungs-, Medien- und Informationsbrüchen kommt. In dem Diskussionsbeitrag beschreiben und analysieren wir sowohl den aktuellen und zukünftig zu erwartenden Stand der digitalen Transformation des Gesundheitswesens, als auch die Besonderheiten des deutschen Gesundheitswesens mit Fokus auf die Möglichkeiten für digitale Innovationen profitabel in den regulierten ersten Gesundheitsmarkt in Form von erstattungsfähigen Gesundheitsleistungen zu gelangen. Unter Berücksichtigung der aktuellen und absehbaren zukünftigen Rahmenbedingungen werden mithilfe der Expertinnenmeinungen aus dem Delphi-Verfahren mögliche Szenarien für die Verwertung der BloG³-Lösung für verschiedene Zeithorizonte (kurz-, mittel-, langfristig) reflektiert und diskutiert.