Viral Acharya on COVID Lessons from India for Other Emerging Economies

Video & Slides

On Thursday, July 1, Viral Acharya joined Markus’ Academy for a lecture on COVID lessons from India for other emerging economies. Acharya is the C.V. Starr Professor of Economics in the Department of Finance at New York University Stern School of Business (NYU-Stern) and an Academic Advisor to the Federal Reserve Banks of New York and Philadelphia.

Watch the full presentation below and download the presentation slides. You can also watch all Markus’ Academy webinars on the Markus’ Academy YouTube channel.

Executive Summary

• [0:33] India is currently dealing with the more contagious Delta variant of the Covid virus, which raises the question: how should EMDE (Emerging, Developing Economies) and Africa prepare? A lot of emphasis is put on finance and production of vaccines, but not as much is put on the distribution and delivery. One possibility is vaccine production in India, or spending resources to produce vaccines in Africa. This would require the removal of cross-border export restrictions on raw materials. An alternative approach would be for advanced economies to produce the mRNA vaccines and distribute around the world, focusing resources on distribution and healthcare. 

• [3:29] Another question is how to distribute resources such as vaccines and oxygen. The traditional approach for vaccines is generally to give to those who are most vulnerable, but another approach would be to give to those who cause the greatest spread of the virus. Incentivizing vaccines also occurs in different ways, with pay incentives, mobile phone tracing, and “social signaling” encouraging the public to get vaccinated. Oxygen in India has been scarce in regions, but Ramanan Laxminarayanan has worked to deploy oxygen strategically, but data is imperative for successful implementation.

• [18:32] Looking at lessons from the past can shed light on the Coronavirus pandemic. The Spanish flu of 1918 occurred in 3 waves, with high infection and fatality rates. India was badly affected, with 12-18 million killed (>5% population), and the healthcare system was unable to meet the surge, leading to greater increase in emotion against colonial rule. Cities with multiple interventions were successful in preventing deaths during the peak of the pandemic. 

• [23:19] The coronavirus response could have worked in multiple ways, but there were a few key measures necessary in any plan. Suppression measures were necessary to contain the spread and buy time to avoid running out of hospital beds. Mitigation aims for herd immunity, but causes over-capacity problems. In March 2020, the measures were anticipated to cause global economic disruption, some of which are already being witnessed. Research shows the varying effects of suppression strategy scenarios on ICU bed requirements.

• [30:25] Flattening the curve was necessary, but in large part was dependent on capacity. A structural “patient-(over)flow” model shows the bed capacity, the number of patients needing beds, and the duration patients stay in beds. Subsequent day’s availability are controlled by prior days’ exits. Several inputs are required to “calibrate” the model, including the percent of daily new cases requiring hospitalization. The problem arises when reported cases understate true infections, so seroprevalence surveys (antibodies) may be helpful for estimation. 

• [43:07] Good calibration requires lots of data, disease-, wave-, or variant-specific. The Infection Fatality rate, hospitalization rate, and number of days in hospital all affect the public sentiments towards Covid precautions. SIR models can be adapted to factor in healthcare capacity, but must be applied granularly and aggregated to obtain the right dynamics. People were not worried about the virus until hospital beds were running out, which means that predicting shortages becomes even more important to prevent virus spread and hospitalizations. 

• [50:02] The dynamics of deaths and undercounts in a case study showed the glaring problems. Health care capacities are very low in India, and the undercount statistics show that the cumulative undercount could reach extreme levels. The daily death undercount went up and beyond 25x for a short period during April-May 2021. Cumulative death undercount increases 5-10x depending on district when averaging deaths. 

• [1:00:17] There are many implications of the overflow and death undercount. Deaths are undercounted because it is difficult to classify deaths into Covid/non-Covid, but also Covid deaths are highly politicized and there are (short-term) political rewards to understate Covid deaths. There has been an impact on the rich and formal economy; Covid is a tragically great equalizer once there is overflow, and the lack of testing becomes symptoms-based. Once higher income brackets cannot access healthcare, they withdraw discretionary expenditures. Poor people, women and the informal economy are, however, the most severely affected: the poor can’t get testing, medical care, doctors, or support, and their losses have scarring effects, many of which are not picked up in contemporaneous official statistics.

• [1:07:09] Uncertainty about virus outbreak and variants requires robust policy planning. Flattening the cases, tracking cases carefully, and encouraging access to healthcare are all imperative. Vaccinations are extremely important, otherwise herd immunity cannot develop without great losses. Healthcare capacity needs planning, and data is extremely important to facilitate data-dependent decision making. The pandemic is an opportunity for EMs to structurally shift the formalization of their large informal economies.