Abstract
Following the Covid-19 pandemic, governments all over the world have been forced to impose emergency measures such as lockdowns leading to a severe loss in economic output. Whereas standard models assume a progressive return to equilibrium output after a shock, we discuss the impact of a Covid-like shock on a simple toy economy, described by the Mark-0 Agent-Based Model. We consider a mixed supply and demand shock, and show that depending on the shock parameters (amplitude and duration), our minimal model can display V-shaped (quick recovery), U-shaped (rapid drop followed by slow recovery) or W-shaped (double dip) recoveries, and even an L-shaped output curve with permanent output loss. This latter scenario is due to the existence of a self-sustained ``bad’’ state of the economy. We then discuss two policies that attempt to moderate the impact of the shock: giving easy credit to firms, and the much-debated ‘helicopter money’ drop, i.e. injecting new money into the savings of households. We find that both policies are effective if strong enough, and we highlight their impact on inflation as well as the potential danger of terminating these policies too early. While we only discuss a limited number of scenarios, our model is flexible and versatile enough to allow for a much wider exploration, thus serving as a useful tool for the qualitative understanding of post-Covid recovery. Finally, our work highlights the importance of ABM as multi-purpose ‘scenario generators’, which produce outcomes that are difficult to foresee due to the intrinsic complexity of macro-economic dynamics.
This is a talk I gave at the Complex Systems Conference 2020 organized by the Complex Systems Society. Due to Covid restrictions, the conference was held remotely. I ended up presenting my work in a special session for papers researching on the impact of Covid-19 from a complexity perspective.
Dhruv Sharma
Researcher - Portfolio Construction, Risk Management, Agent-Based Models, Statistical Physics.
Physicist turned economic modeler trying to make sense of the world through ABMs.