Research

Abstract: The technology revolution is transforming firm and industry dynamics, yet the roots of firm growth and dominance in the modern economy remain unclear. Is firm growth and dynamism driven by compute capabilities (AI), access to data, or the interaction between them? We develop a dynamic model in which firms gain knowledge from raw data using AI, but face ``informational entropy'': without sufficient AI, more raw data leads to information overload and has negative returns. The model predicts two key dynamics: (1) improvements in AI (compute) disproportionately benefit data-rich firms; and (2) access to processed data substitutes for compute, increasing industry dynamism. We confirm these predictions using novel data from 2000–2023 and two exogenous shocks: the 2006 launch of Amazon Web Services (AWS) and the 2017 introduction of transformer-based architectures. Our findings suggest that regulating data usability, not just AI models, is essential to preserving firm dynamism in the modern economy.

Supported by The Sandoz Family Foundation - Monique de Meuron Program for Academic Excellence 2022. 

Selected Presentations: Carnegie-Rochester-NYU 2025 Conference on Public Policy “The Consequences of AI use on Society and Policy”.

Abstract: In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

Abstract: We analyze the links between advances in financial technology, investors’ sophistication, and the composition and returns of their financial portfolios. We develop a portfolio choice model under asymmetric information and derive several theoretical predictions. Using detailed microdata from Banca d’Italia, we test these predictions for Italian households over the period 2004-2020. In general, heterogeneity in portfolio composition and in returns between sophisticated and unsophisticated investors grows with improvements in financial technology. This heterogeneity is reduced only if financial technology is accessible to everyone and if investors have a similar capacity to use it.

Supported by The Sandoz Family Foundation - Monique de Meuron Program for Academic Excellence 2022

Prepared for the RCFS 2022 Winter Conference and RCFS Special Issue on "Finance for the Greater Good"

Abstract: We study a model where firms accumulate data as a valuable intangible asset. Data accumulation affects firms' dynamics. It increases the skewness of the firm size distribution as large firms generate more data and invest more in active experimentation. On the other hand, small data-savvy firms can overtake more traditional incumbents, provided they can finance their initial money-losing growth. Our model can be used to estimate the market and social value of data.

Abstract: We analyze the expansion of Big Data and artificial intelligence technologies from the perspective of economic theory. We argue that these technologies can be viewed from three perspectives: (1) as an intangible asset; (2) as a search and matching technology; and (3) as a forecasting technology. These points of view shed light on how new technologies are likely to affect matching between firms and consumers, productivity growth, price discrimination, competition, inequality among firms, and inequality among workers.

Abstract: Macro-prudential policies aimed at mitigating systemic financial risks have become part of the policy toolkit in many emerging markets and some advanced countries. Their effectiveness and efficacy are not well-known, however. Using panel data regressions, we analyze how changes in balance sheets of some 2800 banks in 48 countries over 2000–2010 respond to specific policies. Controlling for endogeneity, we find that measures aimed at borrowers – caps on debt-to-income and loan-to-value ratios, and limits on credit growth and foreign currency lending – are effective in reducing leverage, asset and noncore to core liabilities growth during boom times. While countercyclical buffers (such as reserve requirements, limits on profit distribution, and dynamic provisioning) also help mitigate increases in bank leverage and assets, few policies help stop declines in adverse times, consistent with the ex-ante nature of macro-prudential tools.

Abstract: This paper investigates the effects of national culture on firm risk-taking, using a comprehensive dataset covering 50,000 firms in 400 industries in 51 countries. Risk-taking is found to be higher for domestic firms in countries with low uncertainty aversion, low tolerance for hierarchical relationships, and high individualism. Domestic firms in such countries tend to take substantially more risk in industries which are more informationally opaque (e.g., finance, mining, oil refinery, IT). Risk-taking by foreign firms is best explained by the cultural norms of their country of origin. These results hold even after controlling for legal constraints, insurance safety nets, and economic development.

Winner of the President's Prize by ACEI

Abstract: Data security is often viewed as a defensive cost. But in today’s data-driven economy, can it also create value? We propose a novel measure of "data innovation complementarity", capturing the extent to which firms have structurally integrated data-security expertise into their broader innovation. We show that such integration unlocks cross-domain knowledge spillovers, fueling firm-wide innovation and growth. Exploiting U.S. Data Breach Notification Laws as a quasi-exogenous increase in the perceived importance of data security, we find that only high-complementarity firms realize these gains. A calibrated real-options model shows that integrated data protection is a significant driver of innovation-led growth.

Supported by The Sandoz Family Foundation - Monique de Meuron Program for Academic Excellence 2022

Selected presentations: Wharton AI and the Future of Labor 2025*, AFA 2025*, Future of Financial Information 2024*, Toulouse Digital Economics 2024, WEIS 2024, GRETA 2024*, EEA 2024, University of Zurich, ETH Risk Center, Boston University.

Abstract: Does AI make firms vulnerable or resilient to cyberrisk? To answer this, we develop a measure of AI intensity for US public firms using patents and 10-K business descriptions. Cyberrisk suppresses innovation for non-AI firms, reducing patenting by 25--30\%, with the sharpest declines in data-management technologies. Frontier AI firms neutralise this effect: their patenting holds steady, and their valuations rise when cyberrisk is high. But not all AI capabilities provide protection. Firms that adopt external AI tools without building internal innovation capacity remain as exposed as non-AI firms. Cyberrisk acts as a tax on data-intensive innovation. Only firms with deep, internally developed AI capabilities can absorb it. Rising cyberrisk therefore widens the gap between AI frontier firms and the rest.

Supported by ArmaSuisse Cyberdefense Campus Grant 2024

Selected Presentations: RCFS 2025 Winter Conference; ETH Risk Center;

Abstract:  Information technology was supposed to democratize finance. Despite declines in onboarding, trading, and data costs, direct equity market participation inside and outside of retirement accounts has fallen. We offer and test a resolution to this participation paradox: cheaper data can reduce participation. When all participants can access data more cheaply, wealthier investors, with their larger portfolios, acquire relatively more data than others. Their informed trading intensifies adverse selection against less informed traders, lowering the marginal household’s gains from risky investing. The quantitative model shows that lower entry costs are not sufficient to overcome the adverse selection cost. Consistent with the model predictions, we find that returns and return premia accrue disproportionately to wealthier and more sophisticated investors. Finally, because informed investors both exhibit more skill and hold riskier portfolios, the framework reconciles competing explanation for financial income inequality.

Abstract:  We think big data advantages big firms because of the data feedback loop. But what if smaller firms can pool their data? Such services exist. Will these data pooling services undo the advantages of large firms?   

Abstract: If beliefs are sticky, households can experience inflation shocks while acting on stale perceptions, muting refinancing and spending responses. We back out household inflation attention from belief revisions in survey data and use the joint behavior of attention and uncertainty to distinguish whether uncertainty primarily reflects changes in perceived fundamentals (fragile priors) or changes in information quality (noisy news). Attention is not just a useful object to track due to its impact through expectations: higher attention strengthens the sensitivity of refinancing intentions and spending plans to stated inflation beliefs, conditional on belief levels. Given observed attention, subjective uncertainty helps infer whether that attention level is associated with fragile priors or with the quality of information, and thus, whether uncertainty is something policy communication can reduce (noise) or something agents must insure against and price (fundamentals). 

Supported by The Sandoz Family Foundation - Monique de Meuron Program for Academic Excellence 2022

Selected Presentations: Stockholm Backus Conference 2026, NTU Behavioral Macro 2026*, Salento Macro Meetings 2024, CEPR Monetary and Economic Fluctuations 2024, EEA-ESEM 2024, JME-SNB-SCG Conference* 2024, Sailing the Macro 2024*, 14th IFO Macro and Survey Data*.

Abstract: We leverage a novel dataset on U.S. data center energy loads, utility electricity prices, and establishment-level revenues, employment, and carbon emissions from 2010 to 2023 to examine whether rising data center demand affects local retail energy prices or other spillovers. For identification, we employ an instrumental variables continuous difference-in-differences design, exploiting exogenous variation in data center location attractiveness. We find no detectable local spillover effects from data center energy growth. A regional model calibrated to these null results suggests that shocks larger than those observed through 2024 could still result in noticeable increases in household utility bills if not offset by regulation or external supply.

Supported by The Sandoz Family Foundation - Monique de Meuron Program for Academic Excellence 2022

Selected Presentations: UNC Flagler Energy Finance and Climate Transition Risk (upcoming), IESE Economics of AI*, MRS -MIT AI and Climate Risk 2025*, CEBRA 2025*, EEA 2025*, Toulouse Digital Economy 2026 Conferences, AI Frontiers in Finance Webinar 2026 (upcoming), SGF Zurich 2026 (upcoming).

Abstract: We investigate how retail investors value privacy in financial markets by analyzing behavioral responses to information disclosure within a controlled trading simulation. Using a limit order book environment, we study whether awareness of trading history sharing alters order types, frequency, and risk-taking. We further quantify investors' willingness to pay (WTP) for complete anonymity. Our findings contribute to the literature on market microstructure, payment for order flow (PFOF), and the economics of privacy, offering insights for policymakers and platforms on fair data monetization on financial platforms.