Julia M. Schmidt

🧪 Work in Progress

Two Faces of Data: Productivity and Markups in the Digital Economy

Coauthored with Isaac Baley and Alejandro Rábano

Presented at: PSL Paris Dauphine PhD Retreat, Cévennes, 2026.

The rise of data-intensive production has coincided with growing dispersion in firm productivity and markups. Yet most frameworks model data and information as affecting either productivity or market power, not both simultaneously. We build a heterogeneous-firm general equilibrium model in which firms invest in two distinct information channels: innovation data, which improves physical productivity through better knowledge of production techniques, and consumer data, which reduces demand elasticity and raises markups through improved product differentiation. Firms differ in their ability to exploit each channel, generating joint heterogeneity in productivity and market power that is endogenously determined in equilibrium. Three types of workers, production, R&D, and marketing labour, are demanded by firms and supplied by a representative household, so that firm-level data investment decisions aggregate into labour market outcomes. We use this framework to ask: how does heterogeneity in data technologies shape the cross-sectional distribution of productivity and markups? And to what extent are observed differences in firm size driven by specialisation in data use rather than differences in underlying production technology?

Productivity in Europe: Data-related Investment and Bank Lending Constraints

Coauthored with Marine Charlotte André, Julie Delanote, Lise Patureau and Fabien Tripier

Presented at:

• 3rd Global INTAN-Invest Conference, co-organized by Luiss Business School and the World Intellectual Property Organization (WIPO), in person, 7-8 May 2026.
• 24th ZEW Conference on the Economics of Information and Communication Technologies, in person, 25-26 June 2026.
• EEA-ESEM 2026, in person, 17-21 August 2026.

This paper empirically quantifies how financial frictions shape firms’ investment in artificial intelligence and the associated productivity gains. We use an original firm-level dataset that combines balance-sheet information from Orbis with investment and technology-adoption data from the European Investment Bank Investment Survey, covering firms across 27 EU countries over the period 2015-2023. Our empirical analysis proceeds in two steps. First, we estimate the effect of data-related investment on firm productivity. The results show that data-related investment is associated with economically meaningful and statistically significant productivity gains. Second, we examine how financial constraints affect firms’ engagement with AI and data-related technologies. We find that financially constrained firms appear to reallocate investment away from tangible assets toward AI and other intangibles, relying more heavily on internal liquidity.

The Carbon Cost of Compute: AI, Data Externalities, and Climate Policy

Presented at: UPF-CREI Macroeconomic Lunch, UPF Barcelona, invited by Prof. Isaac Baley, in person, 26 November 2025.

What is the optimal carbon tax in a data-driven economy? We develop a dynamic general-equilibrium model that embeds data-economy features into a macro-environmental framework to study how AI-intensive production alters emissions and welfare. In the model, AI services combine unpriced, non-rival data with energy-intensive compute. The productivity response of AI can shape the economy’s response to climate policy.

🧩 Policy Work

📊 AI & Data

• Schmidt, J., G. Pilgrim and A. Mourougane (2024), “Measuring the demand for AI skills in the United Kingdom”, OECD Artificial Intelligence Papers, No. 25, OECD Publishing, Paris.

• Schmidt, J., G. Pilgrim and A. Mourougane (2024), “Towards a better understanding of data-intensive firms in the United Kingdom”, OECD Statistics Working Papers, No. 2024/07, OECD Publishing, Paris.

• Schmidt, J., G. Pilgrim and A. Mourougane (2023), “What is the role of data in jobs in the United Kingdom, Canada, and the United States?: A natural language processing approach”, OECD Statistics Working Papers, No. 2023/05, OECD Publishing, Paris.

• Schmidt, J. and G. Zappalà (2021), Statistics about statistics: How do we measure statistical performance?. World Bank Data Blog.

• Misra, A. and J. Schmidt (2020), Enhancing Trust in Data: Participatory Data Ecosystems for the Post-Covid Society. OECD Shaping the COVID-19 Recovery Working Paper.

📈 Nowcasting

• Dorville, Y. et al. (2025), “Towards more timely measures of labour productivity growth”, OECD Statistics Working Papers, No. 2025/01, OECD Publishing, Paris.

• Mourougane, A. et al. (2023), “Nowcasting trade in value added indicators”, OECD Statistics Working Papers, No. 2023/03, OECD Publishing, Paris.

🌍 Regional Development & Inequality

• Schmidt, J. (2019), EU Cohesion Policy: A suitable tool to foster innovation?. Policy Brief, Bertelsmann Stiftung.

• Kastrop, C., D. Ponattu, J. Schmidt and S. Schmidt (2019), The Urban-Rural Divide and Regionally Inclusive Growth in the Digital Age. G20 Insights Policy Brief.

• Kastrop, C., D. Ponattu and J. Schmidt (2019), Inequality and the productivity slowdown: The growing gap between the most productive and the rest dampens social cohesion. Global Solutions Journal, Issue 4, pp. 30-33 (PDF).