In an interview with CBS News on 17 May 2020, Federal Reserve Chair Jay Powell emphasised the role an efficacious vaccine will play in ending the COVID-19 pandemic and allowing the economy to recover:
“For the economy to fully recover, people will have to be fully confident. And that may have to await the arrival of a vaccine.”
The next day, Moderna, one of the vaccine-developing companies, announced progress in its Phase I clinical trials and US stocks gained over $1 trillion in market capitalisation.
Quantifying the scale of the economic damage caused by the COVID-19 pandemic is a crucial step in assessing policy responses along social, medical, fiscal, and monetary dimensions. We hypothesise that stock markets may contain valuable information for gauging the value of ending the pandemic.
Stock markets, which corrected by as much as 40–50% at the outbreak of the coronavirus pandemic in February and March 2020, have rebounded robustly within six months. One market narrative relates to progress in vaccine development. On the one hand, only the arrival (and delivery) of an efficacious vaccine is considered as a definitive event that will end the pandemic and result in robust economic recovery. On the other hand, stock prices – by reflecting forward-looking expectations – should reflect the economic value of credible progress in the development of vaccines; this value arises from the ability of vaccines to end the pandemic and is naturally related to the scale of the economic damage caused by the pandemic.
Acharya et al. (2020) offer an asset-pricing perspective to estimate the value of a cure, i.e. the amount of wealth that a representative agent would be willing to pay to obtain a vaccine that puts an end to the pandemic. Our approach is directly analogous to the seminal work of Lucas (1987) in assessing the welfare costs associated with business cycle risk. Just as that paper provides a framework to assess the consequences of policy responses to mitigate output volatility, our work speaks to the cost-benefit analysis of potential public-sector investment to alleviate the threat of current and future pandemics.
While Lucas (1987) finds small welfare improvements to reducing this risk, Barro (2009) reports that in a model with rare disasters, moderate risk aversion, and an elasticity of intertemporal substitution greater than one, society would willingly pay up to 20% of permanent income to eliminate disaster risk. Our model of pandemics is close to that of rare disasters in asset-pricing literature (Barro 2006, Gabaix 2012, Tsai and Wachter 2015) but with endogenous exposure of the agent to disasters as well as featuring endogenous consumption, labour, and asset prices.
Vaccine progress indicator and its covariance with stock returns
To test our hypothesis that the stock market may convey important information about the social value of resolving the pandemic, we start by empirically documenting stock behaviour and expected time to vaccine deployment. We construct a novel ‘vaccine progress indicator’ that summarises the state of vaccine research throughout 2020. Then we estimate the stock market response to changes in the indicator.
Our indicator is based on the chronology of stage-by-stage progress of individual vaccines1 and related news.2 Using data on vaccine development for past epidemics and surveys during the current COVID-19 pandemic, we calibrate the probabilities of transition across different stages of vaccine development and use news to ‘tap’ these probabilities up or down. We then simulate over 200 vaccine ‘trials’ corresponding to the vaccines being developed, factoring in a correlation structure between trials based on relevant characteristics such as their approach (‘platform’), belonging to a common company, etc.
The result of this exercise is a vaccine progress indicator using all available information at a given point of time expressed in terms of expected time to deployment of a vaccine. The evolution of our indicator is shown in Figure 1.
Figure 1 Expected time to vaccine deployment
Notes: Figure shows our estimate of the expected time to widespread deployment of a COVID-19 vaccine in years. Dashed lines show one standard deviation bands.
Figure 2 plots the (inverted) vaccine progress indicator alongside the market portfolio’s year-to-date performance.
Figure 2 Vaccine progress and market performance
Notes: Figure plots vaccine progress (inverted and left axis) along with the cumulated year-to-date excess return on the value-weight CRSP index (right axis). The risk-free rate is the one-month Treasury bill rate.
We then relate stock market returns to changes in the expected time to deployment of a vaccine by regressing the returns on changes in our vaccine progress indicator, controlling for lagged returns as well as large moves attributable to the release of other macroeconomic news. Allowing for some lead-lag structure in the relationship – for example, due to leakage of news or dating noise in our news data – we estimate that a reduction in the expected time to deployment of a vaccine by a year results in an increase in the stock market return as a whole by between 4% and 8% on a daily basis. The joint relationship exhibits the anticipated cross-sectional properties, with the co-movement between returns and changes in the vaccine progress indicator being stronger for sectors most affected by the COVID-19 pandemic (see Figure 3).
Figure 3 Industry sensitivity to vaccine progress
Notes: Figure plots industry sensitivity to vaccine progress against exposure to COVID-19 as measured by cumulative returns. Cumulative returns are from 1 February 2020 to 22 March 2020. Sensitivity to vaccine progress is estimated from 23 March 2020 to 31 October 2020.
Value of a cure
We connect this empirical co-movement of stock returns and vaccine progress with the value of a cure using a general equilibrium regime-switching model of pandemics with asset-pricing implications. In our model, the economy can be ‘normal’, i.e. without a pandemic, or in a pandemic. Within the pandemic, there are several regimes mapping into the stages of vaccine development.
A key feature of our model is that the agent withdraws labour in the pandemic states in order to mitigate the economic exposure to a health shock. In other words, the arrival of a pandemic and the incidence of a health shock for the agent within the pandemic can be considered as rare disasters, the exposure to which is partly controlled by the agent.
A principal insight of our asset-pricing perspective is that the improvement in the welfare of the agent in switching out of a pandemic is related to the extent of contraction in labour in the pandemic state relative to the non-pandemic one; this same labour contraction is an important statistic (modulated by preference and pandemic parameters) that determines how sensitive stock prices are to progress towards vaccine deployment.
The model implies that the value of moving from a pandemic state to a non-pandemic state is simply the ratio of marginal propensity to consume in the pandemic state to that in the non-pandemic state, augmented by the intertemporal elasticity of substitution. Thus, the desire to resolve uncertainty sooner is informed by the endogenous consumption choices made by the household in pandemic states.
We can therefore readily connect our empirical work to the theoretical asset-pricing perspective. With standard preference parameters employed in the literature, the value of a cure turns out to be worth 5-15% of wealth (formally, capital stock in our model). At our baseline assumptions for other parameters, this corresponds to an approximately 25% contraction of labour during the pandemic relative to the non-pandemic state.
The reason why the economy would attach such a large value to the vaccine is that the pandemic causes a permanent loss of capital stock when it affects agents, which in turn is reflected in the significant precautionary contraction of labour during the pandemic.
Learning and uncertainty
Given the rare nature of pandemics and the evolving understanding of the connections between various pandemics (SARS, H1N1, COVID-19, etc.), we assess the effect of imperfect information on the value of a cure. We specialise our framework to just two states – non-pandemic and pandemic – but allow for uncertainty about the frequency and duration of pandemics.
It turns out that the value of the cure rises sharply when there is uncertainty about the frequency and duration of pandemics. Indeed, we find that the representative agent would be willing to pay as much for resolution of uncertainty as for the cure absent such uncertainty. This effect is stronger – not weaker – when agents have a preference for later resolution of uncertainty, as this induces a more significant contraction of labour during pandemics.
Extensions
An important policy implication is that understanding the fundamental biological and social determinants of future pandemics – for instance, whether pandemics are related to zoonotic diseases triggered more frequently by climate change – may be as important to mitigating their economic impact as resolving the immediate pandemic-induced crisis.
A number of papers have modelled climate risk using a similar approach we take to modelling pandemics. Pindyck and Wang (2013), for example, explore the welfare costs associated with climate risk and estimate the amount society should be willing to pay to reduce the probability or impact of a catastrophe. Our framework can also be helpful in the context of climate-risk-related issues, such as to how much society should be willing to incur as costs for adopting clean technologies.
References
Acharya, V, T Johnson, S Sundaresan and S Zheng (2020), “The value of a cure: An asset pricing perspective”, Covid Economics 61: 1–72.
Barro, R J (2006), “Rare disasters and asset markets in the twentieth century”, The Quarterly Journal of Economics 121(3): 823–66.
Barro, R J (2009), “Rare disasters, asset prices, and welfare costs”, American Economic Review 99(1): 243–64.
Gabaix, X (2012), “Variable rare disasters: An exactly solved framework for ten puzzles in macro-finance”, Quarterly Journal of Economics 127(2): 645–700.
Lucas, R (1987), Models of business cycles, Oxford: Blackwell.
Pindyck, r S, and N Wang (2013), “The economic and policy consequences of catastrophes”, American Economic Journal: Economic Policy 5(4): 306–39.
Tsai, J and J A Wachter (2015), “Disaster risk and its implications for asset pricing”, Annual Review of Financial Economics 7(1): 219–52.
Endnotes
1 Obtained from the Vaccine Centre at the London School of Hygiene and Tropical Medicine.
2 Obtained from FactSet.
