COVID-19, Economics, Tipping Points – Socioeconomic networks

Tags

COVID-19, Network theory, networks, socioeconomics

Previous posts in this series:
1. COVID-19, Economics, Tipping Points – Part I

Roopnarine, P. D., M. Abarca, D. Goodwin and J. Russack. 2023. Economic cascades, tipping points, and the costs of a business-as-usual approach to COVID-19. Frontiers in Physics. 11:1074704. doi: 10.3389/fphy.2023.1074704

Complex Systems

An aquatic food web. doi:10.3389/frym.2018.00004

A system, meaning a group of interacting elements (in turn meaning anything really), is considered to be complex if it displays certain properties that make its behaviour hard to predict, including adaptation, path-dependence (history matters) and emergence. Elements, entities, agents, whatever you wish to call them, are often adaptable to changing conditions. For example, one of the core discoveries of evolutionary theory is that populations of species are capable of adapting to a changing environment via natural selection, if there is sufficient genetic variation. The adaptability of a complex system is a bit more difficult to understand because, while it is rooted in the adaptive dynamics (I prefer “dynamics” to the more often used “behaviours”; it is more accurate) of its elements, it is at least the sum of all those individual adaptive dynamics, and quite often is more: “the whole is more than the sum of its parts” (or, as Aristotle said it originally: “the whole is something besides the parts). That is, the system will display properties that are neither shared with any of its constituent elements, nor are they easily predicted from elemental properties. Such system-level properties are termed “emergent”.

There are unfortunate tendencies on the one hand to treat emergent properties as mysterious or inaccessible, or on the other, as the failure of an observer to reduce them to the level of the elements. Neither is correct. An “elementary” example is water: the macroscopic properties of water, e.g. wetness, or turbulence, or being denser in its liquid form compared to its solid form (ice floats; see this blog’s banner image!), are not easily predicted from the properties of atomic hydrogen and oxygen. Yet great progress can be made from understanding how the molecular combination of two hydrogen atoms and one oxygen atom interact with each other. Complex systems can be very sensitive to initial conditions, meaning both that limited precision in understanding those initial conditions, and that their histories can dictate to a great extent their futures, places limits on our ability to simply list the parts and relationships of its system and then predict its possible future. (I refer readers interested in complexity and complex systems to the excellent and accessible volume, “Signs of Life” by Ricard Sole and Brian Goodwin).

What does this all have to do with economics during the time of COVID-19? In my previous post I explained that our investigation was motivated by prior work on ecosystems and mass extinctions. Ecosystems are complex systems, and their responses to extreme stresses, such as during times of mass extinction, can appear to be unpredictable. Nevertheless, our treatment of ancient ecosystems as complex networks of interacting species have allowed us to make predictions consistent with the fossil record. For example, we have uncovered multiple phases of the end Permian extinction in both terrestrial and marine systems, and the unique dynamics of systems in the aftermath of the extinction. The key to unlocking ecosystem properties lies in treating the systems as complex networks of interactions, and there is a long history in theoretical ecology rooted in the foundational work by Robert May back in the 1970s. May was also a proponent of treating economic systems in the same manner, recognizing that such systems possess complex dynamics.

An Economic Network

It was with this in mind that we approached our counterfactual question of whether shutting down economies early in the COVID-19 pandemic would have avoided the subsequent extreme reductions of productivity and losses of employment. To my knowledge, studies of the economy during this time have followed three general paths. First, there have been fascinating studies at the level of individuals, facilitated by both “big data” and computational power. For example, Serina Chang (Stanford University) and colleagues examined responses to the chaos of those months early in 2020 by accessing population movement based on cell phone signals. A second path appears, to my untrained economist eye, as more conventional analyses of economic indicator data, such as tracking monetary or products flows and making predictions based on economic theory. I will confess that I am uncomfortable with conventional theory which treats any complex system, including the economy, with notions of equilibria, predictability, and linear mathematics; see work by Brian Arthur. They don’t appear to correspond very closely to the real world.

The third approach, ours, was to combine network views of economies with the type of mathematical modeling that has been used to examine complex systems in other areas, including ecology and physical systems. Our goals were straightforward. First, construct a network of relationships among various economic entities; second, develop a mathematical model of the economic interactions among those entities; and third, simulate the response of the network to an outbreak of COVid-19 in which no attempt was made to tackle the outbreak by disrupting the economic interactions.

A California Network

We decided to focus on the US state of California for several reasons. First, three of us live in California, and back in 2020 were experiencing first hand the impact of the pandemic and economic shutdown here. California also has a very diverse economy, being both the largest agricultural producer in the nation, and a hub for financial, technological and entertainment industries. Finally, it possesses one of the world’s largest economies, ranking fifth in 2019, ahead of many nation states. Therefore, we felt that California could be representative of a broad range of other places. This diversity in California, however, also meant that it would be a mistake to treat the state as a single, homogeneous system. We got around this by taking advantage of a system of geo-economic partitioning used by the United States Bureau of Labor Statistics (USBLS), wherein the United States is divided into metropolitan divisions or metropolitan statistical areas for the purposes of collecting Federal statistics, including economic. In total we considered ten major metropolitan areas in California (see map), and for each we compiled population size and the size of the employed labour force. We referred to each area as a socio-economic system (SES).

We divided the economy within each SES into a set of 15 major economic or industrial sectors, following a major classification system used by both the USBLS and the California Employment and Development Department (CAEDD). All these sectors are networked, or connected to each other because each sector both depends on the others, as well as provided goods or services to others. For example, the agricultural sector obviously provides food to workers in all other sectors, but it needs goods produced by Manufacturing, and it depends on Transportation, as well as Health and Leisure. In ecology we would consider this to be a mutualistic network. The connections or links between the networks are more than just symbolic of these exchanges though; they also possess magnitude, that is, the strengths of the inter-sector dependencies. We measured these using the average monetary exchanges between sectors, or inter-industry exchanges, as measured by the US Bureau of Economic Analysis.

The end result was that we now had a network showing the breakdown of our economy into semi-independent entities, as well as the relationships among those entities. That network, when populated with employees and monetary values from a particular place, becomes a model of the economic system of that place. All that was needed next was to make the system dynamic, basically bringing it to life with mathematics, and initiating the pandemic. I’ll describe those steps in the next posts!