This project investigates complex system approaches for modelling the economy. Our models are based on fine-grained data sets at the level of firms, products and industries, with the goal of providing a detailed understanding of the rich and heterogeneous behaviour underlying business cycles, inflation and interest rates, innovation, and long-run growth.
The importance of empirically supported microfoundations is well acknowledged in modern macroeconomics, but traditional models struggle to go beyond perfect rationality, or to incorporate more than one form of bounded rationality. Our goal is to develop approaches such as agent-based modelling, because they are capable of incorporating an ecosystem of behavioural models and make it possible to characterise the resulting non-equilibrium dynamics.
The project covers several areas:
- Structure and dynamics of the global production and credit network
- Understanding the economic effects of Covid-related lockdown.
- Heterogeneous dynamics of inflation at a fine-grained level
Structure and dynamics of the global production and credit network
We are developing models of the global production and credit networks. This involves developing methods to reconstruct missing data and agent-based modelling of supply chain and trade credit.
Understanding the economic effects of Covid-related lockdown
During the first wave of Covid-19, we developed methods and models to understand the economic effects of lockdown, by evaluating the shocks at the industry and occupation level and by evaluating the effects of these shocks in a non-equilibrium network model of the economy.
Heterogeneous dynamics of inflation at a fine-grained level
Inflation rates at the product level are highly heterogeneous and highly persistent. We are developing methods to characterise groups of products or services that have similar patterns of price changes.
Key publications:
- Bacilieri, A., Borsos, A., Astudillo-Estevez, P. & Lafond, F. (2023). Firm-level production networks: what do we (really) know? INET Oxford Working Paper No. 2023-08.
- Mungo, L. & Moran, J. (2023). Revealing production networks from firm growth dynamics. INET Oxford Working Paper No. 2023-02.
- Mungo, L., Lafond, F., Astudillo-Estévez, P., Farmer, J.D. (2023). Reconstructing production networks using machine learning. Journal of Economic Dynamics and Control, Volume 148.
- Pangallo, M., Aleta, A., Chanona, R., Pichler, A., Martín-Corral, D., Chinazzi, M., Lafond, F., Ajelli, M., Moro, E., Moreno, Y., Vespignani, A., and Farmer, J.D. 2022. The unequal effects of the health-economy tradeoff during the COVID-19 pandemic. arXiv:2212.03567.
- Pichler, A., Pangallo, M., del Rio-Chanona, R.M., Lafond, F. & Farmer, J.D. (2022). Forecasting the propagation of pandemic shocks with a dynamic input-output model. Journal of Economic Dynamics and Control, Volume 144.
- Yang, J., Heinrich, T., Winkler, J., Lafond, F., Koutroumpis, P., & Farmer, J.D. (2022). Measuring productivity dispersion: a parametric approach using the Lévy alpha-stable distribution. INET Oxford Working Paper No. 2019-14
- McNerney, J., Savoie, C., Caravelli, F., Carvalho, V.M., Farmer, J.D (2022). How production networks amplify economic growth. Proceedings of the National Academy of Sciences
- Pichler, A. & Farmer, J. D. (2021). Simultaneous supply and demand constraints in input–output networks: the case of Covid-19 in Germany, Italy, and Spain. Economic Systems Research
- Asano, Y. M., Kolb, J. J., Heitzig, J., & Farmer, J. D. (2021). Emergent inequality and business cycles in a simple behavioral macroeconomic model. Proceedings of the National Academy of Sciences
- Goldin, I., Koutroumpis, P., Lafond, F., & Winkler, J. (2021). Why is productivity slowing down? INET Oxford Working Paper No. 2021-12
- del Rio-Chanona, R. M., Mealy, P., Pichler, A., Lafond, F., & Farmer, J. D. (2020). Supply and demand shocks in the COVID-19 pandemic: an industry and occupation perspective. Oxford Review of Economic Policy
- Pichler, A., Pangallo, M., del Rio-Chanona, R.M., Lafond, F., & Farmer, J.D. (2020). Production networks and epidemic spreading: How to restart the UK economy? INET Oxford Working Paper No. 2020-12.
- Pichler, A., Lafond, F., & Farmer, J. D. (2020). Technological interdependencies predict innovation dynamics. INET Oxford Working Paper, No.2020-04
- Yang, J. (2018). A Quantal Response Statistical Equilibrium Model of Induced Technical Change in an Interactive Factor Market: Firm-Level Evidence in the EU Economies. Entropy, 20-3, p.156.
- Mealy, P., Farmer, J.D., & Teytelboym, A. (2018). A New Interpretation of the Economic Complexity Index. INET Oxford Working Paper No. 2018-04
Other outputs:
- Applied Complexity Network Conversation with J. Doyne Farmer (video by Santa Fe Institute)
- Doyne Farmer: How complexity can resolve the crisis in economics (video by Oxford Martin School)
- Production networks and epidemic spreading: Re-opening the UK economy. VoxEU.org Column by Pichler, A., Pangallo, M., del Rio-Chanona, R. M., Lafond, F., & Farmer, J.D. (2020).
Impact:
Rebuilding Macroeconomics prize awarded for Propagation of supply and demand shocks in a dynamic input-output models
Funders include:
Baillie Gifford, IARPA
Researchers involved:
Maria del Rio Chanona, Doyne Farmer, François Lafond, Penny Mealy, José Moran, Luca Mungo, Anton Pichler, Will Scarrold, Valentina Semenova, Julian Winkler, Samuel Wiese, Jangho Yang