An international team of scientists developed a model in the early days of the pandemic to forecast its economic impact. Their predictions turned out to be highly accurate.
As the first wave of the Covid-19 pandemic hit Europe, Anton Pichler and his colleagues created a computer simulation of Britain’s economy to predict its economic impact ahead of time. “It turns out that our model was very successful in forecasting the UK economy,” says Pichler, a researcher at the Complexity Science Hub Vienna (CSH) and the first author of a study recently published in the Journal of Economic Dynamics and Control.
The new model predicted in May 2020 that the Gross Domestic Product (GDP) in the UK would contract by 21.5% in the second quarter of 2020, compared to the last quarter of 2019. That was remarkably close to the actual contraction of 22.1% estimated by the British Office of National Statistics. “The model was not only useful for predicting GDP,” points out Pichler. “It tracks most relevant economic variables on both levels: the entire economy and individual industries.”
The model is a milestone in making economic predictions and leaders around the world could use it as a tool during turbulent economic times, points out J. Doyne Farmer, CSH external faculty and one of the co-authors of the paper. As an example, governments could use this agent-based model to quantitatively analyze how the economy will react to different lockdown and re-opening scenarios.
An agent-based model is a computerized simulation of a number of decision-makers – or agents – and institutions, according to Farmer. Such a model doesn’t “rely on the assumption that the economy is in equilibrium. Instead, at any given time, each agent acts according to its current situation, the state of the world around it, and the rules governing its behavior,” states Farmer, who’s a professor at the University of Oxford.
Therefore, the new computer simulation incorporates the uncertain and changing nature of the real world. “We explored how consumers place their demands in the market, how businesses hire and fire employees, and how they deal with shocks to their inventory,” explains Pichler.
The model allowed the researchers to anticipate changes of several economic key variables. It correctly predicted a stronger reduction in private consumption and investment than in government consumption and inventories. It also forecasted a weaker decline in wages and salaries than in profits, due to job retention schemes promoted by the British government.
The researchers also modeled the dynamics of single industries. “Our research shows that several industries were affected by supply chain disruptions and including these effects has been key to making good forecasts,” says Pichler. Examples of industries being affected by supply chain disruptions range from ‘manufacturing of mineral products’ to ‘other scientific activities’.
The study “Forecasting the propagation of pandemic shocks with a dynamic input-output model,” by Anton Pichler, Marco Pangallo, R. Maria del Rio-Chanona, François Lafond, and J. Doyne Farmer, was published in the Journal of Economic Dynamics and Control (144) (Nov. 2022).
H. Kong, S. Martin-Gutierrez, F. Karimi
Influence of the first-mover advantage on the gender disparities in physics citations
Communications Physics 5 (243) (2022)
MAMMOth: KI-Tendenz zur Diskriminierung durchbrechen [feat.Fariba Karimi]
Brutkasten, Nov 22, 2022
CSH Talk by Meike Zehlike: "Beyond Incompatibility: Trade-offs between Mutually Exclusive Fairness Criteria in Machine Learning and Law"
Dec 16, 2022 | 15:00—15:30
N. Pontika, T. Klebel, A. Correia, H. Metzler, P. Knoth, T. Ross-Hellauer
Indicators of research quality, quantity, openness and responsibility in institutional review, promotion and tenure policies across seven countries
Quantitative Science Studies 1-49
A charada do desequilibrio de genero na ciencia [Portuguese] [feat.Fariba Karimi]
Pesquisa Fapesp, Nov 17, 2022
CSH Talk by Zlata Tabachová: "Supply chain shock propagation and financial systemic risk"
Dec 02, 2022 | 15:00—16:00
W. Schueller, J. Wachs, V. D. P. Servedio, S. Thurner, V. Loreto
Evolving collaboration, dependencies, and use in the Rust Open Source Software ecosystem
Scientific Data 9 (2022) 703
CSH Talk by Elma Dervic: "Unravelling cradle-to-grave disease trajectories from multilayer comorbidity networks"
Dec 16, 2022 | 15:30—16:00