COVID-19 research working papers from MIT Economics
MIT Economics has launched a new webpage to aggregate the department's ongoing research into the economic impacts of the Covid-19 pandemic. Please refer to the page for all entries and updates as research continues:
MIT Economics: Working Papers on Covid-19 Research
INITIAL PAPERS | APRIL AND MAY 2020
Paying It Backward and Forward: Expanding Access to Convalescent Plasma Therapy Through Market Design
Scott Duke Kominers, Parag A. Pathak, Tayfun Sönmez, M. Utku Ünver
A Multi-Risk SIR Model with Optimally Targeted Lockdown | Online MR-SIR Simulator
Daron Acemoglu, Victor Chernozhukov, Iván Werning, Michael D. Whinston
Policy implications of models of the spread of coronavirus: Perspectives and opportunities for economists
Christopher Avery, William Bossert, Adam Clark, Glenn Ellison, Sara Fisher Ellison
A Model of Asset Price Spirals and Aggregate Demand Amplification of a "Covid-19" Shock
Ricardo J. Caballero and Alp Simsek
Triage Protocol Design for Ventilator Rationing in a Pandemic: Integrating Multiple Ethical Values through Reserves
Parag A. Pathak, Tayfun Sönmez, M. Utku Ünver, M. Bumin Yenmez
Reopening Under COVID-19: What to Watch For
Jeffrey E. Harris
Abstract. We critically analyze the currently available status indicators of the COVID-19 epidemic so that state governors will have the guideposts necessary to decide whether to further loosen or instead retighten controls on social and economic activity. To that end, we study the incidence of new COVID-19 infections in the state of Wisconsin and in San Antonio, Texas, numbers of deaths attributable to COVID-19 in Los Angeles County, the state of New Jersey, and New York City, hospitalization rates in New York City, and the daily patient census in intensive care units in Orange County, California. At least in some instances, enhanced availability of coronavirus testing has upwardly biased observed trends in infection rates. Data on numbers of deaths have, for the most part, been completely misinterpreted. Healthcare system-based indicators, such as rates of hospitalization or ICU census counts, are likely to be more reliable. Models to guide future policy decisions are severely limited by untested assumptions. Universal coronavirus testing may not on its own solve difficult problems of data interpretation and causal inference.
The Coronavirus Epidemic Curve is Already Flattening in New York City
Jeffrey E. Harris
New York City has been rightly characterized as the epicenter of the coronavirus pandemic in the United States. Just one month after the first cases of coronavirus infection were reported in the city, the burden of infected individuals with serious complications of COVID-19 has already outstripped the capacity of many of the city's hospitals. As in the case of most pandemics, scientists and public officials don't have complete, accurate, real-time data on the path of new infections. Despite these data inadequacies, there already appears to be sufficient evidence to conclude that the curve in New York City is indeed flattening. The purpose of this report is to set forth the evidence for- and against- this preliminary but potentially important conclusion. Having examined the evidence, we then inquire: in the curve is indeed flattening, do we know what caused it to level off?
Macroeconomic Implications of COVID-19: Can Negative Supply Shocks Cause Demand Shortages?
Veronica Guerrieri, Guido Lorenzoni, Ludwig Straub, and Ivan Werning
We present a theory of Keynesian supply shocks: supply shocks that trigger changes in aggregate demand larger than the shocks themselves. We argue that the economic shocks associated to the COVID-19 epidemic- shutdowns, layoffs, and firm exits- may have this feature. In one-sector economies, supply shocks are never Keynesian. We show that this is a general result that extends to economies with incomplete markets and liquidity constrained consumers. In economies with multiple sectors Keynesian supply shocks are possible, under some conditions. A 50% shock that hits all sectors is not the same as a 100% shock that hits half the economy. Incomplete markets make the conditions for Keynesian supply shocks more likely to be met. Firm exit and job destruction can amplify the initial effect, aggregating the recession. We discuss the effects of various policies. Standard fiscal stimulus can be less effective than usual because the fact that some sectors are shut down mutes the Keynesian multiplier feedback. Monetary policy, as long as it is unimpeded by the zero lower bound, can have magnified effects, by preventing firm exits. Turning to optimal policy, closing down contact-intensive sectors and providing full insurance payments to affected workers can achieve the first-best allocation, despite the lower per-dollar potency of fiscal policy.
The Geography of COVID-19 growth in the US: Counties and Metropolitan Areas
William C. Wheaton, Anne Kinsella Thompson
It has been 70 days since the first case of COVID-19 was detected in the US. Since then it has spread and grown in all but 2 of 376 MSAs and all but 45 of the 636 counties that are contained in these MSA. In this paper we examine the determinants of how rapidly the virus grows once it has been seeded within a MSA or county. We find virus cases can be well predicted by area population, as well as days-since-onset. In the data, virus cases scale almost proportionately with population, and excluding population significantly changes the impact of days-since-onset. Growth is also related to residential density and per capita income, particularly at the county level. There are weaker relationships to MSA average household size, per capita income, and the fraction of the population that is over 65. These results come from parameterizing a simple power function model of cumulative infections since onset. This is shifted proportionately by the various MSA/County covariates. We also experiment with restricting the sample of areas so as to have a minmum number of cases- equal to .01% of the area's population. This effectively focuses on the more advanced part of the virus growth curve. Here we find a significant further decrease in the coefficient of days-since-onset. This is preliminary evidence that the virus growth is tapering. We intend to repeat our analysis as time progresses.
Celebrities and Public Health Campaigns: A Nationwide Twitter Experiment Promoting Vaccination in Indonesia
Vivi Alatas, Arun Chandrasekhar, Markus Mobius, Benjamin Olken, and Cindy Paladines
We ask whether celebrities can help spread information about public health, above and beyond the fact that their statements are seen by many, and ask how they can be most effective in doing so. We conducted a nationwide Twitter experiment with 46 high profile Indonesian celebrities and organizations, with over 11 million followers, who agreed to randomly tweet or retweet content promoting immunization. Our design exploits the structure of what information is passed on along a retweet chain on Twitter to decompose how celebrities matter. We find that messages that can be identified as beinig authored by celebrities are 72 percent more likely to be passed or liked compared to similar messages without a celebrity imprimatur. Decomposing this effect, we find that 79 percent of the celebrity effect comes from the act of celebrity authorship itself, as opposed to merely passing on a message. Explicitly citing an external source decreases the likelihood of passing the message by 27 percent. The results suggest that celebrities have an outsize influence in shaping public opinion, particularly when they speak in their own voice.