Please find a brief description of selected research projects below. A list of preprints and publications is available here.

Production networks and epidemic spreading: How to restart the UK economy?

We analyse the economics and epidemiology of different scenarios for a phased restart of the UK economy. Our economic model is designed to address the unique features of the COVID-19 pandemic. Social distancing measures affect both supply and demand, and input-output constraints play a key role in restricting economic output. Standard models for production functions are not adequate to model the short-term effects of lockdown. A survey of industry analysts conducted by IHS Markit allows us to evaluate which inputs for each industry are absolutely necessary for production over a two month period. Our model also includes inventory dynamics and feedback between unemployment and consumption. We demonstrate that economic outcomes are very sensitive to the choice of production function, show how supply constraints cause strong network effects, and find some counter-intuitive effects, such as that reopening only a few industries can actually lower aggregate output. Occupation-specific data and contact surveys allow us to estimate how different industries affect the transmission rate of the disease. We investigate six different re-opening scenarios, presenting our best estimates for the increase in R0 and the increase in GDP. Our results suggest that there is a reasonable compromise that yields a relatively small increase in R0 and delivers a substantial boost in economic output. This corresponds to a situation in which all non-consumer facing industries reopen, schools are open only for workers who need childcare, and everyone who can work from home continues to work from home.

Supply and demand shocks in the COVID-19 pandemic: An industry and occupation 

We provide quantitative predictions of first-order supply and demand shocks for the US economy associated with the COVID-19 pandemic at the level of individual occupations and indus-tries. To analyse the supply shock, we classify industries as essential or non-essential and construct a Remote Labour Index, which measures the ability of different occupations to work from home. Demand shocks are based on a study of the likely effect of a severe influenza epidemic developed by the US Congressional Budget Office. Compared to the pre-COVID period, these shocks would threaten around 20 per cent of the US economy’s GDP, jeopardize 23 per cent of jobs, and reduce total wage in-come by 16 per cent. At the industry level, sectors such as transport are likely to be output-constrained by demand shocks, while sectors relating to manufacturing, mining, and services are more likely to be constrained by supply shocks. Entertainment, restaurants, and tourism face large supply and demand shocks. At the occupation level, we show that high-wage occupations are relatively immune from ad-verse supply- and demand-side shocks, while low-wage occupations are much more vulnerable. We should emphasize that our results are only first-order shocks—we expect them to be substantially amp-lified by feedback effects in the production network. Read more

This work was done in collaboration with Penny Mealy, Anton Pichler, François Lafond and J. Doyne Farmer.

Fraction employed in an essential industry vs Remote Labor Index for each occupation. Omitting
the effect of demand reduction, the occupations in the lower left corner, with a small proportion of workers in essential
industries and a low Remote Labor Index, are the most vulnerable to loss of employment due to social distancing

Occupational mobility and Automation.

A data-driven network model to study the impact of automation on employment. Read more

This work was done in collaboration with Penny Mealy, Mariano Beguerisse-Diaz, François Lafond and J. Doyne Farmer.

Estimates of automatability in the occupational mobility network. Panel (A) is a
histogram of the probability of computerization for different occupations as estimated by Frey
and Osborne
. Noticeably, the probability of computerization has a bimodal distribution.
Panel (B) shows the occupational mobility network, where nodes represent occupations and
links represent possible worker transitions between occupations. The color of the nodes indicates
the estimated probability of computerization. Red nodes have high automatability and
blue nodes have low automatability. The size of the nodes indicates the number of employees
in each occupation.

Financial contagion and Multiplex networks

A collaboration with the International Monetary Fund. We use multiplex networks to study global financial contagion. Our current manuscript is under revision. Please find an earlier version of this work here

This work was done in collaboration with Yevgeniya Korniyenko, Manasa Patnam, and Mason Porter.

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