Working papers

Do common shocks drive changes in aggregate emissions intensity? (with Xiyu Ren and Fulvia Marotta)
INET Oxford Working paper 2025-15
We study co-movement in industry-level emissions intensity

Forecasting macroeconomic dynamics using a calibrated data-driven agent-based model (with Samuel Wiese, Jagoda Kaszowska-Mojsa, Joel Dyer, José Moran, Marco Pangallo, John Muellbauer, Anisoara Calinescu and J. Doyne Farmer)
INET Oxford Working paper 2024-06
We test the ability of a large-scale ABM to forecast macro-economic time series.

Firm-level production networks: what do we (really) know? (with Andrea Bacilieri, Pablo Astudillo-Estevez, András Borsos and Mads Hoefer)
INET Oxford Working paper 2025-14
Code
National firm-level production networks have remarkably similar structures.

Technological interdependencies predict innovation dynamics (with Anton Pichler and J. Doyne Farmer)
INET Oxford WP No. 2020-04
We predict yearly patenting rates by technological category using the citation and co-classification networks.

Published articles

Forecasting technological progress
chapter in The Economy as an evolving system complex system vol. IV, SFI Press, forthcoming.
Replication files
A review on forecasting technological progress, focusing on INET’s work.

Complex systems approaches to 21st century challenges: Introduction to the Special Issue
With Jenna Bednar, R. Maria del Rio Chanona,  J. Doyne Farmer, Jagoda Kaszowska-Mojsa, Penny Mealy, Marco Pangallo, and Anton Pichler.
Journal of Economic Behaviour & Organization
A brief summary of the papers in the Special Issue, which can be found here.

Measuring productivity dispersion: a parametric approach using the Lévy alpha-stable distribution
With Jangho Yang, Torsten Heinrich, Julian Winkler, Pantelis Koutroumpis, and J. Doyne Farmer Industrial and Corporate Change, 34:1, 79–117.
The Lévy alpha-stable distribution, a heavy-tailed distribution with infinite variance, is a good fit to labor productivity levels.

Reconstructing supply networks
With Luca Mungo, Alexandra Brintrup, and Diego Garlaschelli
Journal of Physics: Complexity, 2024.
We review methods to reconstruct missing data in firm-to-firm production networks.

Why is productivity slowing down?
With Ian Goldin, Pantelis Koutroumpis and Julian Winkler
Journal of Economic Literature 62(1) pp. 196-268, 2024
VoxEU column, accepted pdf.
The current productivity slowdown can largely be explained by mismeasurement and by a slowdown of capital deepening, allocative efficiency, trade and spillovers from intangibles.

The unequal effects of the health-economy tradeoff during the COVID-19 pandemic
With Marco Pangallo, Alberto Aleta, R. Maria del Rio Chanona, Anton Pichler, David Martín-Corral, Matteo Chinazzi, Marco Ajelli, Esteban Moro, Yamir Moreno, Alessandro Vespignani, J. Doyne Farmer
Nature Human Behaviour, 2023
Press release.
We calibrate a dynamic epi-macro model to a detailed synthetic population of the New York metropolitan area.

Building an alliance to map global supply networks
With Anton Pichler, Christian Diem, Alexandra Brintrup, Glenn Magerman, Gert Buiten, Thomas Y. Choi, Vasco M. Carvalho, J. Doyne Farmer, and Stefan Thurner
Science (Policy Forum) 382, no. 6668 (2023): 270-272
PDF. Press release.
To map the global firm-level supply chain, we need better data collection, data sharing, and reconstruction methods.

Reconstructing production networks using machine learning
With Luca Mungo, Pablo Astudillo-Estévez and J. Doyne Farmer
Journal of Economic Dynamics & Control 148, 104607, 2023.
We test the ability of machine learning classification techniques to predict links in production networks.

Forecasting the propagation of pandemic shocks with a dynamic input-output model
With Anton Pichler, Marco Pangallo, R. Maria del Rio-Chanona, and J. Doyne Farmer
Journal of Economic Dynamics & Control 144, 104527, 2022
Earlier version (May 2020), Data on Zenodo, Interactive simulator, VoxEU column, Twitter summary. Winner of the Rebuilding Macro Complexity in Macro 3rd prize. Press release.
Our forecasts for UK 2020-Q2 economic performance were fairly accurate. A post-mortem analysis finds that this is thanks to reasonable estimates of the severity of the shocks, associated with a bespoke production function.

Can stimulating demand drive costs down? World War II as a natural experiment
With Diana Greenwald and J. Doyne Farmer
Journal of Economic History, 82(3), 727-764, 2022.
Press summary. Data and Code. Journal version.
During WWII, the demand for weapons was not driven by prices. Yet, faster growth of cumulative output was associated with faster decline of unit costs.

Occupational mobility and automation: a data-driven network model
With R. Maria del Rio-Chanona, Penny Mealy, Mariano Beguerisse, J. Doyne Farmer
Journal of the Royal Society Interface 18(174), 2021.
Technical blog.
We develop a labor market model based on the occupational mobility network, and use it to assess the effects of the next wave of automation.

The rise of science in low-carbon energy technologies
With Kerstin Hötte and Anton Pichler
Data, INET Oxford WP No. 2020-10
Renewable & Sustainable Energy Reviews 139, 2021.
Low carbon energy technologies vary considerably in how science-intensive they are, but almost all have become more science intensive.

Supply and demand shocks in the COVID-19 pandemic: An industry and occupation perspective
with R. Maria del Rio-Chanona, Penny Mealy, Anton Pichler, and J. Doyne Farmer
Oxford Review of Economic Policy 36(Supplement_1), S94-S137,2020
Data on Zenodo, VoxEU column.
Pandemic-induced shocks affect low-wage occupations more, and vary widely across industries, which can be affected more by supply or demand shocks.

Early identification of important patents: design and validation of citation network metrics
With Manuel Mariani and Matúš Medo
Technological Forecasting and Social Change 146, pp. 644-654, 2019.
Publisher. 
A citation network-based indicator, time rescaled PageRank, can be used to detect important patents a few years only after that they are granted.

Wright meets Markowitz: How standard portfolio theory changes when assets are technologies following experience curves
With Rupert Way, Valentyn Panchenko, Fabrizio Lillo, and J. Doyne Farmer
Journal of Economic Dynamics & Control 101, pp. 211-238, 2019 
Open access at publisher
When there are increasing returns to investment, there may be multiple locally optimal diversified portfolios.

Long-run dynamics of the U.S. patent classification system
With Daniel Kim
Journal of Evolutionary Economics 29(2), pp. 631–664, 2019 
Data.
Patent classification systems change frequently and reflect the dynamics of technological change

How well do experience curves predict technological progress? A method for making distributional forecasts
With Aimee G. Bailey, Jan D. Bakker, Dylan Rebois, Rubina Zadourian, Patrick McSharry, and J. Doyne Farmer
Technological Forecasting and Social Change  128, pp 104-117, 2018
arXiv, Publisher, Data, Code.
We test and apply a simple method to make distributional forecasts for technological progress conditional on the growth of experience.

How predictable is technological progress?
With J. Doyne Farmer
Research Policy 45(3), 647–665, 2016
Open access at Publisher, Data, Code.
We test and apply a simple method to make distributional forecasts for technological progress.

Self-organisation of knowledge economies
Journal of Economic Dynamics and Control 52, 150-165, 2015.
Publisher.
The structure of agents-ideas networks depends on the innovation/diffusion trade-off.

Policy reports

Disruptive technologies and regional innovation policy
With Pantelis Koutroumpis
Background paper for an OECD/EC Workshop on 22 November 2018 within the workshop series “Broadening innovation policy: New insights for regions and cities”, Paris.

Advanced work in progress

Synthetic supply networks (with Galvin Ng, Damien Bertrand and Luca Mungo)

Why do supply networks have asymmetric degree distributions? (with Daniel Hoffman and Renaud Lambiotte)

The impact of the Net Zero transition on aggregate productivity (with Emilien Ravigné)

The empirics of economic volatility: Evidence from payments data (with Johannes Lumma and Glenn Magerman)

Global competitor networks (with Kieran Marray, Micheal Konig and Gordon Phillips)

The universality and predictability of technology diffusion (with Ben Wagenvoort, Joel Dyer and J. Doyne Farmer)

PhD Thesis

The evolution of knowledge systems, 2014, Maastricht University Press. PDF

Resting working papers

The size of patent categories: USPTO 1976-2006 (2014), UNU-MERIT WP #2014-060. Updated as “Long-run dynamics of the U.S. patent classification system”, with Daniel Kim.

Learning and the structure of citations networks (2012), UNU-MERIT WP #2012-071. Partly published as “Self-organization of knowledge economies”, and updated as “Knowledge diffusion and the structure of citations networks”, 2014.