Past Events

We provide the first evidence on legal gender marker adjustments to fiscal incentives. In Austria, a current female marker lowers the retirement age by up to five years, increasing the expected lifetime pension income by 24 percent. Among individuals assigned male at birth, a female marker sharply raises earlier pension claiming. Yet marker changes around retirement age are negligible: we can reject cumulative increases above 0.008 percentage points. The introduction of legal gender self-identification does not affect responsiveness. These findings suggest that the costs of having an incongruent marker, or the social costs of changing it, are very high.

Jan Gromadzki is an Assistant Professor at the Department of Economics at the Vienna University of Economics and Business. He is also a Research Affiliate at INEQ and IZA@LISER. His interests lie in labor economics, public economics, and political economy.

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How does tax progressivity alter the macroeconomic impact of carbon revenue recycling? This talk examines the interplay between labor tax progressivity and carbon revenue recycling through an Environmental Two-Agent New Keynesian (TANK) DSGE model. By evaluating lump-sum transfers, capital tax cuts, and broad versus targeted labor tax cuts across low- and high-income households, our study reveals trade-offs between economic output and distributional equity. The results show that targeted low-income labor tax cuts effectively reconcile growth with equity, while demonstrating how baseline tax progressivity serves dual functions as a powerful incentive multiplier and an automatic dynamic stabilizer against carbon-shock downturns. 

Base-rate neglect—the failure of posterior beliefs to fully incorporate prior probabilities—is one of the most robust deviations from Bayesian updating. We argue that the literature has largely conflated two distinct psychological mechanisms that can generate this pattern. The first is the fallacy of the uniform prior (FUP), whereby individuals update as if the prior were uniform. The second is the fallacy of the transposed conditional (FTC), whereby individuals treat the precision of a signal as if it were the posterior probability. While both mechanisms imply base-rate neglect, they map information into beliefs in fundamentally different ways. We show that they are observationally equivalent under symmetric information structures, which dominate existing designs, but diverge under asymmetry. We develop a framework that nests both mechanisms and an experimental design that identifies them empirically.

Ro'i Zultan is a professor at the Department of Economics at Ben-Gurion University of the Negev and the director of the Negev Experimental Economics Laboratory. He is an experimental economist with a background in cognitive and experimental psychology. His research focuses on understanding how group dynamics influence cognition and behavior, as well as on broader questions of cooperation, coordination, and decision-making in teams. He is also interested in game theory, market efficiency, ethics, and the methodological foundations of experimental research.

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Why do shareholder activists disproportionately target female-led firms? Using US shareholder activism data from 2006 to 2018, we examine gender stereotyping and value creation as competing explanations for this phenomenon. To test these explanations and draw causal inferences, we employ an extended regression framework with instrumental variables. We find that CEO gender primarily affects activists’ targeting decisions rather than subsequent campaign dynamics. Specifically, after accounting for non-random CEO appointments and target selection, having a female CEO significantly increases the likelihood of activist targeting, but has little effect on campaign characteristics (e.g., activist hostility) or outcomes (e.g., activist success) once targeted. These findings are more consistent with gender stereotyping — particularly role congruity theory — than with value creation explanations. Overall, our results indicate gender bias in shareholder activism, suggesting that such bias may undermine the effectiveness of shareholder monitoring and distract female leaders from managing their firms.

Healthcare organizations increasingly invest in innovative technologies to improve quality, outcomes, and efficiency. Yet, the successful adoption of complex innovations depends not only on their technical performance but also on how they are integrated into organizational processes. Robot-Assisted Surgery provides an important example of this challenge, as implementation success is strongly influenced by organizational factors. This research examines how healthcare professionals’ involvement in technology evaluation influences the effective implementation of innovations. Building on organizational theory, Health Technology Assessment, and the implementation climate literature developed by Klein and Sorra in 1996 and Helfrich et al in 2007, the study argues that participation in assessment and decision-making fosters acceptance, legitimacy, and alignment between clinical and managerial priorities. Innovation adoption is therefore conceptualized as an organizational process in which professional involvement acts as a key governance mechanism.

The empirical setting is the IRCCS Sant’Orsola University Hospital of Bologna, where, in 2026, a participatory evaluation process was implemented to assess three robotic surgical platforms. Surgeons, anaesthesiologists, and operating room nurses were the protagonists in defining the evaluation criteria and designing the comparative testing protocols, which were implemented through both simulation-based exercises and clinical activities. The project led to the development of profession-specific questionnaires for the different stakeholder groups, resulting in the collection of 357 evaluations and the treatment of 39 patients over a 42-days period. The expected outcome is a healthcare-specific framework explaining how stakeholder involvement contributes to implementation effectiveness. The research highlights factors such as multidisciplinary coordination, shared responsibility, feedback integration, and alignment between managerial and clinical priorities, arguing that the value of innovation depends not only on technology itself but also on the organizational processes through which it is adopted and integrated into practice.

Tommaso Roccabianca is a PhD Candidate in Public Governance, Management and Policy at the Department of Management, University of Bologna, and a collaborator at IRCCS Sant'Orsola University Hospital since 2024. His research focuses on the adoption and implementation of technological innovations in healthcare organizations, with particular attention to robotic surgery, operating room management, and the organizational changes associated with the introduction of new technologies.

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Russia's 2022 invasion of Ukraine brought over 400,000 refugees to the Czech Republic within months. We ask whether this shock affected the mental health of the receiving population, using Czech GP prescription records. Because Czech registries do not link patients to providers, we simulate GP catchment areas from a congestion-sensitive choice model and build a physician-level exposure measure. Exposure raised antidepressant prescribing on the extensive margin — more patients treated, no rise in dose per patient — while anxiolytic prescribing and overall prescribing volume did not respond. The response is concentrated in the first months after the inflow began and leaves no persistent trace.

Population aging and declining internal mobility rates have been persistent demographic trends in the United States and are forecast to continue even into future decades. These trends have led to a significant increase in the share of internal migrants in the United States who are retired. Existing research has identified significant changes in consumption patterns and location choice associated with retirement which may induce significant demand changes in destination locations. This paper studies the effects of internal retiree migration flows in the United States in the 21st century and identifies effects of these flows on labor markets. Results suggest these flows induce significant changes in incomes, labor market activity, and educational attainment.

We investigate how social identity and social distance affect animosity towards out-group members, and the extent to which social desirability bias (SDB) distorts expressed preferences. A theoretical model predicts an inverted U-shaped relationship between social distance and out-group animosity, as well as the conditions for a negative relationship between social distance and SDB. We find that for far enough social distance, both animosity and SDB decline. To test these predictions, we conduct an online experiment in the United States implementing direct survey questions as well as a list experiment. We evaluate respondents’ willingness to accept an out-group member with less social distance (a Republican or a Democrat) versus with far social distance (a Chinese Communist Party sympathizer) as a neighbor or as a colleague. We find substantial true animosity toward domestic political opponents (45.6%), whereas true animosity toward CCP sympathizers is significantly lower (35.9%). Respondents exhibit strong positive SDB toward domestic opponents (16.1%), overreporting their animosity in direct settings. In contrast, SDB drops significantly regarding CCP sympathizers (6.8%). These findings closely align with our theoretical predictions, confirming that far social distance attenuates both out-group animosity and social image concerns. Heterogeneity analysis reveals that Democrats display significantly higher domestic animosity (59.7% true, 80.5% stated) and SDB (20.8%) than Republicans (31.5% true, 43.0% stated; 11.5% SDB). Furthermore, residing in politically competitive swing states markedly reduces expressed animosity and alters SDB.
This study contributes to the literature of identity economics and political polarization by providing a unified framework that disentangles genuine identity-driven hostility from social image concerns. It also shows that domestic partisan-divides generate far greater behavioral friction and social pressure than distant geopolitical rivalries, at least in the USA. 
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Subhasish Modak Chowdhury is a Professor at the University of Sheffield where he serves as the Director of the BRAINS research centre, and lead the Behavioural Economics Research Group. He is a microeconomist with research interests in contest theory, behavioral economics, industrial organization etc. Recently he joined the Journal of Competition Law & Economics as an Associate Editor.

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Our research measures emotions in US newspapers related to economic policy. The motivation of the study is to examine whether emotion decomposition provides information beyond conventional news-based uncertainty measures and sentiment indices, and could improve financial-market monitoring and forecasting. After emotion identification, we construct an emotion-related (sentiment) index and examine its impact on EPU, VIX, SP 500, and USD using the BVAR model. Preliminary results show that increases in negative emotions lead to persistent increases in the Economic Policy Uncertainty index and the VIX. In contrast, their effects on S&P 500 and US dollar returns are smaller, negative, and largely transitory. Positive-emotion shocks produce weaker and less precisely estimated responses.

Non-take-up (NTU) of social benefits is usually explained through barriers at the individual level, such as lack of information, stigma, or administrative burden. This study asks whether such household characteristics can account for differences in benefit take-up across countries, using harmonized EU-SILC 2022 data linked to EUROMOD microsimulation outputs reflecting the tax and benefit rules in place in 2021. Take-up of two benefit types is analyzed across European countries: housing- and family-related means-tested benefits, focusing on whether an eligible household claims the benefit at all. Firth penalized logistic regression is used to address convergence problems caused by very low variation in take-up in several countries, combining a broad set of household level predictors with country fixed effects. A McFadden pseudo R2 decomposition indicates that variation between countries accounts for roughly 60% of the explained variation in take-up for both benefit types, while household characteristics contribute comparatively little beyond this. Reported non-take-up of housing benefits is very high across most sampled countries; this may partly reflect genuine non-claiming behavior and partly known difficulties in simulating eligibility for housing benefits from survey data. Non-take-up of family benefits varies far more widely across countries, from around 5% to nearly 80%. The findings point to institutional and systemic factors, such as benefit design, administrative procedures, and national context, as the primary drivers of non-take-up, challenging accounts that locate the problem mainly in individual household behavior.

We investigate whether audit clients’ climate-related risks disclosure influence audit planning and fees. Using the Task Force on Climate-related Financial Disclosures (TCFD) framework in the UK, we distinguish between clients who mandatorily comply with TCFD (mandatory clients) and those who do not. We find that mandatory TCFD adoption is associated with higher audit fees, but fewer key audit matters disclosed by auditors, suggesting that enhanced climate-related disclosures reduce perceived audit risk through greater transparency, yet do not reduce audit pricing. Cross-sectional analyses reveal important heterogeneity in auditors’ responses to mandatory TCFD adoption. The increase in audit fees is concentrated among clients of small audit firms, while clients of large audit firms experience fee reductions. In addition, auditors apply higher materiality levels to clients with high climate impact, consistent with reduced audit effort. Further tests indicate that mandatory TCFD adoption does not translate into improvement in actual environmental performance. Given the growing interest in climate-related risks in practice and research, our study sheds light on how auditors respond to changes in climate-related disclosure requirements.

Does the legal protection of free speech change how managers use stock prices to guide corporate investment? We study this question using the staggered enactment of anti-SLAPP laws across U.S. states from 1992 to 2022, a setting that lowers the litigation cost of speaking out and empowers journalists, NGOs, employees, and short sellers to bring firm-specific information into public view. In a stacked difference-in-differences design, we find that stronger free-speech protections reduce the sensitivity of corporate investment to Tobin's Q by roughly 34%. We interpret this through revelatory price efficiency, the component of price informativeness that managers actually learn from: anti-SLAPP laws expand public information about matters managers already know, crowding out private information acquisition about the growth opportunities they most need prices to reveal. Consistent with this composition-shift mechanism, the effect is stronger for firms with higher pre-treatment informed trading and in states with broader statutory protections, and it survives controls for forecasting price efficiency. Channel tests show that anti-SLAPP enactment raises fundamental investor information acquisition, while analyst forecasts pin down the crowded-out information as growth-related: long-horizon forecast accuracy deteriorates even as short-horizon accuracy is unchanged. Cross-sectionally, the decline is most pronounced for firms with high discretionary accruals, low disclosure frequency, and high CEO ability, characteristics tied to greater pre-existing reliance on managerial learning from prices. Our findings reveal an unintended cost of transparency reform: laws that strengthen public discourse and corporate accountability can simultaneously weaken the informational feedback from stock prices to real investment decisions.

Eugen is an Associate Professor of Practice in Behavioral & Decision Sciences at the University of Pennsylvania and a Visiting Professor at Stanford University. His research lies at the intersection of behavioral economics, sociology, and political psychology, with a focus on social norms, polarization, and their effects on social preferences and cooperation. From 2022–2023, he served on the White House Behavioral Science Team, working on large-scale RCTs related to the opioid epidemic.

Therese Nilsson is a Professor of Economics affiliated with the Department of Economics at Lund University and the Research Institute of Industrial Economics (IFN). She also serves as an Academic Programme Advisor for the Trade Policy Training Institute in Africa (TRAPCA). Her research primarily lies at the intersection of health and education economics, with additional contributions to cultural and institutional economics.

This talk explores how implementation science and behavioral nudges can drive the successful design, adoption, and impact of digital health interventions for chronic disease management. Using the pediatric obesity pandemic as a focal point, we examine a novel eHealth solution launched within a major US safety-net hospital system for families with children ages 6–12 diagnosed with overweight or obesity. Key theoretical frameworks and evidence-based practices will be introduced alongside practical methodological considerations. The case study invites the audience to critically engage with real-world challenges—highlighting how behavioral insights, technology, and context-sensitive methods intersect to shape research outcomes, patient care experiences, and broader societal wellbeing. Participants will gain actionable strategies for integrating behavioral economics and scientific rigor into digital health, with lessons applicable to business, marketing, and health innovation.

Our cleantech startup strategy research examines how cleantech start-ups cope with resource constraints while pursuing both economic and environmental objectives. We focus on how firms can achieve balanced growth without mission drift. Building on bricolage theory, we conceptualize firms’ responses as configurations of hybrid harvesting strategies, including partnerships, networking, local resource utilisation, customer empowerment, and inclusive employment. Using fuzzy-set Qualitative Comparative Analysis (fsQCA) on data from a questionnaire survey and qualitative interviews with cleantech start-ups, we explore how different strategy configurations perform under varying resource constraints. The research is conducted within the project “Resilience and impact of clean-tech start-ups in Bavaria and the Czech Republic” in collaboration with the Technical University of Munich. Preliminary findings suggest multiple pathways to balanced growth, but also configurations associated with mission drift, indicating that outcomes depend on both strategy combinations and the context in which they are applied.

This lecture examines pharmaceutical regulation and the incentives for innovation from an international perspective, highlighting the public good nature of health care innovation and its cross-border diffusion. It summarizes empirical evidence on how push and pull incentives shape R&D investment, innovation, and global access. It emphasizes the role of strategic interdependencies and spillovers, including free riding in R&D financing, learning-by-doing effects, drug shortages, reference pricing, and parallel trade. We then provide new evidence on the international spillovers of pull incentives on innovation showing that international cooperation and innovative institutions are necessary to better align national regulations with the global objective of sustaining pharmaceutical innovation.

Before the lecture, there will be small refreshments at 4:00 pm. During this time, you can discuss with each other and meet the speaker.

When financial markets become turbulent, how do investors price the fear of uncertainty itself? Does that fear spread across different corners of the global economy? This research investigates the Variance Risk Premium (VRP), defined as the gap between what investors expect volatility to be and what it actually turns out to be, across five distinct asset classes: equities, fixed income, foreign exchange, cryptocurrencies, and commodities. While VRP has been studied extensively in stock markets, its behaviour in bond markets, currency markets, commodity markets, and the rapidly growing cryptocurrency space remains poorly understood — particularly in a unified, comparative framework.
Using intraday and high-frequency data spanning 2018–2025 — a period capturing COVID-19, the 2022 rate hiking cycle, and multiple crypto boom-bust episodes — the research employs advanced econometric models including GARCH, HAR, VAR, VECM, and Dynamic Conditional Correlation to examine how VRP behaves, what drives it, and whether volatility risk in one market spills over into others.

We consider the problem of identifying the optimal subset of past observations to be used for estimating the covariance matrix of asset returns when computing the global minimum variance portfolio. While the usual approach simply employs either all the available past returns or a rolling window of fixed size, we argue that such solutions are inefficient, and we instead propose to dynamically adjust the estimation window. To this end, we employ the PELT changepoints detection algorithm to identify outdated observations. We then set up a cross-validation procedure to determine the minimum number of upto-date observations that individual assets must have to be considered when estimating the covariance matrix. Assets that do not reach the selected threshold are excluded from the optimization and assigned equal weights. In an empirical application, our solution achieves a higher Sharpe ratio compared to both an expanding and rolling window estimation of the global minimum variance portfolio, and it also compares well with other benchmarks.