[NEW VERSION!] Why is productivity slowing down? (with Ian Goldin, Pantelis Koutroumpis and Julian Winkler), INET Oxford WP No. 2021-12, VoxEU column.
The current productivity slowdown can largely be explained by mismeasurement and by a slowdown of capital deepening, allocative efficiency, trade and spillovers from intangibles.
In and out of lockdown: Propagation of supply and demand shocks in a dynamic input-output model (with Anton Pichler, Marco Pangallo, R. Maria del Rio-Chanona, and J. Doyne Farmer, Earlier version: Production networks and epidemic spreading: How to restart the UK economy? INET Oxford WP 2020-12 Data on Zenodo, Interactive simulator, VoxEU column, Twitter summary. Winner of the Rebuilding Macro Complexity in Macro 3rd prize.
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.
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.
Can stimulating demand drive costs down? World War II as a natural experiment (with Diana Greenwald and J. Doyne Farmer), INET Oxford WP No.2020-02. Press summary
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.
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), INET Oxford WP No. 2019-14
The Lévy alpha-stable distribution, a heavy-tail distribution with infinite variance, is a good fit to labor productivity levels and change.
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.
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.
Advanced work in progress
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.