Data Access for Researchers under the Digital Services Act: From Policy to Practice

The European Union’s Digital Services Act (DSA) aims to increase transparency and account-ability for Very Large Online Platforms and Search Engines (VLOPSEs) through a variety of measures and obligations. This policy paper focuses on the obligation of platforms to pro-vide data access to researchers as established in Art. 40 DSA, a significant shift from previ-ous, non-regulated access regimes, and the tensions and challenges resulting from its im-plementation, operating within existing power structures between platforms and govern-ments. A central element of the VLOP-specific obligations is the concept of systemic risk. It also serves as the foundation for data access requests under Article 40, as the requested data must be used for research that contributes to the understanding, identification, detection, or mitigation of such risks in the European Union. While researchers also need to meet other requirements (such as independence of commercial interests), the purpose limitation is the only factor that geographically restricts the scope of the research. This means that while the research itself is geographically limited in scope, access can in principle be granted to all re-searchers that meet the specified vetting criteria, independent of their location

Mallilaskelmien mukaan tämänhetkisten tuontitullien vaikutus euroalueeseen varsin pieni

Esitämme makrotaloudelliseen GIMF-malliin perustuvia vaikutusarvioita tämänhetkisten Yhdysvaltojen asettamien tuontitullikorotusten vaikutuksista päätalousalueiden kokonaistuotantoon. Lisätullit heikentävät kokonaistuotantoa kaikilla päätalousalueilla. Voimakkaimmat negatiiviset vaikutukset kohdistuvat simulaatioissa Yhdysvaltoihin ja Kiinaan. Euroalueen kokonaistuotantoa tämänhetkisten tietojen mukaiset lisätullit laskisivat noin 0,2 prosenttia, eli melko maltillisesti. Lopullisiin vaikutuksiin liittyy kuitenkin lukuisia epävarmuustekijöitä liittyen esimerkiksi kauppavirtojen uudelleenohjautumiseen ja kaupanesteiden vaikutuksiin investointeihin. Näissä arvioissa ei oteta huomioon edelleen mahdollisesti vallitsevaa epävarmuutta tullien tulevaisuudesta

The price of processing: Information frictions and market efficiency in DeFi

This paper investigates the speed of price discovery when information becomes publicly available but requires costly processing to become common knowledge. We exploit the unique institutional setting of hacks on decentralized finance (DeFi) protocols. Public blockchain data provides the precise time a hack's transactions are recorded - becoming public information - while subsequent social media disclosures mark the transition to common knowledge. This empirical design allows us to isolate the price impact occurring during the interval characterized by information asymmetry driven purely by differential processing capabilities. Our central empirical finding is that substantial price discovery precedes common knowledge: approximately 36 percent of the total 24-hour price decline (∼27 percent) materializes before the public announcement. This evidence suggests sophisticated traders rapidly exploit their ability to process complex, publicly available on-chain data, capturing informational rents. We develop a theoretical model of informed trading under processing costs which predicts strategic, slow information revelation, consistent with our empirical findings. Our results quantify the limits imposed by information processing costs on market efficiency, demonstrating that transparency alone does not guarantee immediate information incorporation into prices

From asessment to employment: The impact of skills tests on reemployment outcomes in Germany

This study examines the long-term effects of a standardized skills assessment test-MySkills-on the employment outcomes of jobseekers without formal vocational qualifications in Germany. In a labour market where certified qualifications are the key currency for access to stable jobs, individuals lacking such credentials face persistent structural disadvantages. This includes a large share of refugees, migrants, and native low-skilled workers who may possess substantial work experience but lack documented proof of their competencies. Public Employment Service (PES) caseworkers and employers often struggle to evaluate these jobseekers' true abilities, leading to mismatches in job referrals and limited access to Active Labour Market Programmes (ALMPs). To address these challenges, the German Federal Employment Agency introduced MySkills-a standardised, computer-based assessment tool designed to make informally acquired skills visible. After a test phase, MySkills was fully active 2019-2022, when low participation rates and high costs led to dropping the test. Unlike formal qualifications, MySkills does not confer certification but provides structured feedback across 30 occupational fields. The expectation was that this tool can improve the alignment between jobseekers and available training or employment measures, particularly for those outside the traditional education and training systems. Using rich administrative data from the German social security system spanning 2019 to 2022, we compare individuals who took the MySkills test to those who were referred but ultimately did not participate. To address selection bias, we apply inverse probability weighting based on a propensity score model, complemented by robustness checks and sensitivity analyses. Our results show that test participation does not lead to immediate improvements in employment - which confirms expectations from qualitative findings published earlier (Promberger and Kawalec 2024). In fact, a short-term decline in employment compared to the control group is observed, likely due to an increased enrolment in ALMPs. However, this pattern reverses over time. By the fourth year following the test referral, participants are up to six percentage points more likely to be in regular contributory employment than non-participants- equivalent to about 20 additional days of employment per year. The strongest positive effects are observed in vocational training and short-term skill-building programmes. The evidence suggests that the MySkills test enhances not the direct job transitions but the effectiveness of caseworker recommendations by offering objective signals of ability, thus improving programme targeting. However, the tool's limited uptake, long duration, and weak signalling value for employers curtailed its full potential. Qualitative insights indicate that, where applied, its greatest utility lay in aiding caseworkers rather than directly empowering jobseekers. In sum, MySkills functioned less as a standalone intervention and more as an institutional support mechanism within the Public Employment Services. When integrated properly, such tools can help reduce bias, improve matching quality, and promote long-term labour market integration for disadvantaged groups. To enhance impact, future initiatives should prioritise ease of use, broader recognition, and active integration into counselling routines.Diese Studie untersucht die mittel- und langfristigen Beschäftigungseffekte eines standardisierten Kompetenztests - MySkills - für Arbeitsuchende ohne formalen Berufsabschluss in Deutschland. In einem stark formalisierten Arbeitsmarkt, in dem Zertifikate als zentrale Signale für berufliche Eignung fungieren, haben Personen ohne anerkannte Abschlüsse erhebliche Schwierigkeiten beim Zugang zu regulärer Beschäftigung und arbeitsmarktpolitischen Maßnahmen. Dazu zählen Geflüchtete, Migrant*innen sowie einheimische Geringqualifizierte mit informell erworbenen Fähigkeiten. Der von der Bundesagentur für Arbeit entwickelte MySkills-Test zielt darauf ab, diese Kompetenzen sichtbar zu machen und so eine bessere Zuordnung zu Maßnahmen der aktiven Arbeitsmarktpolitik (AAMP) zu ermöglichen. Der Test liefert eine standardisierte, strukturierte Rückmeldung zu berufsbezogenen Fertigkeiten in 30 Berufsfeldern, ersetzt jedoch keinen formalen Abschluss. Unsere Analyse beruht auf administrativen Daten des deutschen Sozialversicherungssystems für den Zeitraum von 2019 bis 2022. Verglichen werden Personen, die den Test abgelegt haben, mit solchen, die zwar zugewiesen wurden, jedoch nicht teilgenommen haben. Die Ergebnisse zeigen ein differenziertes Bild: Kurzfristig sinkt bei Testteilnehmenden zunächst die Wahrscheinlichkeit regulärer Beschäftigung - vermutlich aufgrund verstärkter Teilnahme an Weiterbildungsmaßnahmen. Mittel- und langfristig kehrt sich dieser Trend jedoch um. Vier Jahre nach der Testzuweisung liegt die Beschäftigungswahrscheinlichkeit bei Testteilnehmenden um bis zu 6 Prozentpunkte höher als bei Nichtteilnehmenden - was etwa 20 zusätzlichen Tagen regulärer Beschäftigung pro Jahr entspricht. Der größte Effekt zeigt sich bei der Teilnahme an beruflicher Weiterbildung und kurzfristigen Qualifizierungsmaßnahmen. Dies deutet darauf hin, dass der Test vor allem die Fallsteuerung durch Vermittlungsfachkräfte verbessert, indem er objektive Informationen über die Fähigkeiten der Arbeitsuchenden bereitstellt. Die direkte Signalwirkung gegenüber Arbeitgebern bleibt hingegen begrenzt - nicht zuletzt wegen der fehlenden formalen Anerkennung des Tests. Insgesamt fungierte MySkills weniger als eigenständige Maßnahme, sondern eher als unterstützendes Instrument im Vermittlungsprozess. Wenn kompetenzbasierte Testverfahren systematisch in die Abläufe der Arbeitsvermittlung integriert werden, können sie helfen, Zugangsbarrieren abzubauen, Fehlentscheidungen zu reduzieren und die Integration benachteiligter Gruppen in den Arbeitsmarkt langfristig zu verbessern. Für eine breitere Wirkung sollten zukünftige Verfahren besser kommuniziert, praxisnäher gestaltet und stärker institutionell verankert werden

Consistent Commercial Real Estate Market Indicators: Methodology and an Application to the German Office Market

We develop a statistical‐methodological framework for a set of core commercial real estate market indicators, which consists of a market price index, a gross rent index, and a net rental yield index, as well as a vacancy rate. We argue that the indicators should be (macro‐)consistent, meaning that the asset valuation relation between the market price, rental income, yield, and vacant space of an individual property carries over to the macro indicators. In case of a bottom‐up compilation of all indicators, macro‐consistency is met if (1) target universes are common, (2) the granular data source is complete, and (3) price and rental yield indices are weighted with capital value shares while the rent index and the aggregate vacancy rate are weighted by rental income shares. We exemplify the established statistical‐methodological framework by compiling a consistent set of annual indicators of the German office market using appraisal data from a real estate consulting company

Unsupervised machine learning based anomaly detection in high frequency data: Evidence from Cryptocurrency Market

The rapid integration of cryptocurrencies into the global financial ecosystem has introduced unprecedented challenges in market surveillance, risk management, and anomaly detection. While conventional statistical models such as ARIMA (Autoregressive Integrated Moving Average) and GARCH (Generalized Autoregressive Conditional Heteroscedasticity) have been widely used for anomaly detection, their reliance on assumptions of normality and stationarity often fails to capture the complexities of high-frequency, non-linear cryptocurrency trading. Furthermore, traditional risk metrics including down-to-up volatility, negative conditional skewness, and relative frequency may overlook short-term anomalies due to data aggregation limitations. In order to address these issues, this paper proposes machine-learning model for detecting anomalies in cryptocurrency markets using Jupyter Notebook. We compare four advanced unsupervised machine learning models, i.e, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Isolation Forest (iForest), One-Class Support Vector Machine (OC-SVM), and Local Outlier Factor (LOF) for anomaly detection by using Monte Carlo simulations. The findings indicate that DBSCAN has the highest precision (79.7%) with the fewest false positives, making it ideal for supervisory monitoring. However, the high false positive rates of OC-SVM and Isolation Forest limit their use. By using data of six well-known cryptocurrencies at three different temporal resolutions (daily, hourly, and 15-minute) the performance of these four unsupervised learning techniques also examined and confirmed that the anomalies identified by DBSCAN are also consistent with the other three methods. Additionally, for robustness of results, we use UpSet Plots to incorporate the shared anomalies and found across the three unsupervised learning methods. Number of anomalies also depends on the volatility and time interval of cryptocurrencies, more volatile / high frequency more anomalies. The study presents sound methodological approach for facilitating financial monitoring and mitigating risks in the cryptocurrencies market, and provides useful information for market players, analysts and policymakers. These results emphasize the importance of choosing algorithms based on specific surveillance targets to promote greater stability in digital asset environments

Household chores, taxes, and the labor-supply elasticities of women and men

We study how the division of household chores and individual preferences contribute to gender differences in labor supply elasticities and examine the implications for optimal taxation. In a model of labor supply in dual-earner households, we show that elasticities and optimal income tax rates depend jointly on gender and the within-household allocation of chores. Using PSID data, we find that chore division substantially affects labor supply elasticities, whereas gender per se plays a smaller role. We then evaluate how well simple, feasible tax rules can approximate the optimal within-household tax structure. Gender-based taxation captures a sizable share of the potential efficiency gains, but gender-neutral rules with realistic levels of progressivity perform better.Wir untersuchen, wie die Aufteilung von Aufgaben im Haushalt sowie individuelle Präferenzen zu Geschlechterunterschieden in Arbeitsangebotselastizitäten beitragen und welche Konsequenzen sich daraus für die optimale Gestaltung der Einkommenssteuer ergeben. In einem Modell des Arbeitsangebots von Doppel-Verdiener-Paaren zeigen wir, dass Elastizitäten und optimale Einkommensteuersätze sowohl vom Geschlecht als auch von der innerfamiliären Aufgabenverteilung abhängen. Bei der Analyse von US-Mikrodaten aus dem PSID stellen wir fest, dass die innerfamiliäre Aufgabenverteilung Arbeitsangebotselastizitäten erheblich beeinflusst, während das Geschlecht an sich eine geringere Rolle spielt. Anschließend prüfen wir, inwieweit einfache und praktisch umsetzbare Steuerregeln die optimale innerfamiliäre Steuerstruktur approximieren können. Geschlechtsspezifische Besteuerung ("gender-based taxation") realisiert einen beträchtlichen Teil der potenziellen Effizienzgewinne, doch geschlechtsneutrale Regeln mit realistischen Progressivitätsgraden, aber ohne Splitting, schneiden besser ab

A data-driven deep learning approach incorporating investor sentiment and government interventions to predict post-crash stock return in China's A-share market

Global financial markets frequently experience extreme volatility, which poses significant challenges in forecasting stock returns, particularly following market crashes. Traditional models often falter under these conditions due to heightened investor sentiment and strong regulatory interventions. Predicting individual stock returns after a crash is especially challenging in China's A-share market, which is characterized by high volatility and active government involvement. Although deep learning has advanced stock return forecasting, most studies have focused on general market conditions or relied solely on sentiments extracted from texts, leaving firm-level government intervention metrics largely unaddressed. To bridge this gap, we propose a novel deep learning framework that leverages historical post-crash data ('distant relative data') to forecast future stock returns. Unlike conventional methods that rely on recent pre-crash data - often overlooking government interventions - our approach leverages post-crash data, where investor sentiment and regulatory responses are already reflected, to model stable relationships between financial and momentum factors and subsequent returns, thereby implicitly integrating the effects of government interventions on investor behavior. We validate our framework using data from four distinct 'thousand-stock limit-down' events in China's A-share market from 2018 to 2023. For the Fully Connected Neural Network (FCNN) model, training with close neighbor data yielded average F1-scores of 0.219 (2019), 0.106 (2020), and 0.282 (2022), whereas using distant relative data improved these to 0.571 (2019), 0.311 (2020), and 0.412 (2022). Notably, incorporating two distant relative datasets further boosted the FCNN F1-scores to 0.627 and 0.533 for 2020 and 2022, respectively. Additionally, Long Short-Term Memory (LSTM) networks consistently outperform FCNN models, underscoring their advantages in capturing temporal dependencies. Overall, our findings indicate that leveraging multiple historical crisis data sets significantly enhances post-crash stock return predictions. This data-driven approach, analogous to the stand-alone application of SMOTE for data balancing, offers a robust framework that can be integrated with other post-crisis models, thereby providing promising directions for future research and practical implementation

The Labour Market and Health Effects of a Diabetes Warning: Evidence of Gender and Age Differences from the Lifelines Cohort Study

To promote early detection of diabetes and ameliorate the negative consequences of diabetes, some governments provide diabetes screenings. This paper contributes to the literature by being the first to investigate whether an issued warning affects the individual's employment status. Additionally, our analysis also explores health effects, stratified by gender, age, and education , in order to receive indications for potential pathways of the employment effects. By doing so, we present the first results in the literature for individuals under 40. Using a multidimensional regression discontinuity design, we investigate the short- and long-run effects of a diabetes risk warning issued by Lifelines, a Dutch cohort study. In particular, low-educated individuals below 40 increase their labour market activities after a warning, which is generally more pronounced and also persistent for women. Surprisingly, this is not matched by similar strong effects on health outcomes by either gender. Health effects are very heterogeneous by gender, age and educational group. Older, highly educated women seem to benefit particularly strongly from a warning, as a significant reduction in the 4-year mortality rate indicates

Weighted Composite Diversification Index: A New Measure of Economic Diversification in Resource-Dependent Economies

This note introduces a Weighted Composite Diversification Index (WCDI) for resource-dependent economies that integrates fiscal, trade, and output diversification into a single measure, weighted by their relative economic size. Unlike existing indices, the WCDI endogenizes weights according to the macroeconomic structure: fiscal diversification is weighted by government size, trade diversification by export dependence, and output diversification by domestic demand as a share of GDP. To reflect the vulnerabilities of resource-dependent economies, the WCDI incorporates a penalty term that penalizes resource shares within each subindex, providing a context-sensitive gauge of diversification