By the time this paper is being written, the World Health Organization (WHO) COVID 19 dashboard is displaying 11.12 million confirmed cases and 0.52 million deaths due to the current pandemic globally. The figure seems to increase further and most probably in increasing rate since developing countries like USA, India, Brazil and Nepal are reporting higher numbers of cases despite most of these countries after many weeks of complete lockdown have already lifted up and in the next phase of lockdown. Countries like Nepal and India are in phase two of opening by which the number was expected to decrease but unfortunately not.

Beside these health hazards globally, the pandemic carries huge economic shock on global economy with several collateral damages as IMF has already reported that most of economies around the world will face severe negative growth up to minus 3% for advance economies and retarded growth for emerging economies. Hence, the economic analysis of this issue has been talk of the town as it carries a potential significance to be reviewed among economists, scholars and academia around the world and thousands of working papers are already in queue to be published.

It is worth looking at historical pandemics, especially Spanish flu that spread in 1918. Though the origin of Spanish flu was controversial like COVID, due to media coverage of a number of cases in Spain the country was associated with that great influenza. The spread occurred during time of World War I which caused 110 million deaths which was 2.1% of the world population at that time. Though the current Pandemic cannot be compared to the Spanish flu since by then the medical science has made tremendous progress, economic impact during that flu was a 6% decline in GDP and 8% in consumption and that offers valuable relevance to the current situation.

Correia, Luck and Verner (2020) in their papers has a convincing argument to refer to US intervention at present to compare with Spanish flu since the non-pharmaceutical public health intervention (NPI) that was taken during 1918 has been adopted by many countries now. Hence, in that term it can be compared to Spanish flu though there is substantial variation in degree in speed and aggressiveness of measure across cities and countries. The finding of paper recommends taking early measures in terms of NPI since those measures were proven effective not only to control mortality but also to bounce back the economy in a post pandemic situation.

Jorda, Singh and Taylor (2020) in different paper “Pandemic since 14th century” has studied the rate of return on assets during 12 major pandemics including arm conflict which caused more than 10000 death. The researchers have found that the saving increases as perception of risk and to replace the lost wealth during the calamity. In addition, they found that there was reduced investment demand as labor shortage in the economy exceeds the need for higher investment.

In addition to these, there are some theoretical models described by Atkinson (2020) that have introduced the SIR model to explain the progression of COVID-19 in the United States. This model has tried to explain how the development of disease depends upon transition from one state to another such as Susceptibility, Infection and Recovery. Atkinson has tried to base his research on that Markova model to make quantitative analysis between tradeoff of the severity and timing of the disease through NPI such as social distancing and spread of disease in the population.

Similarly, Alvarez, Argente and Lippi (2020) in their working paper a simple planning problem for COVID lock down has tried to reveal the optimal lockdown policy which can minimize the fatality and optimize the output cost. The SIR model has been used in this paper and the dynamic problem of planner control has been tried to formalize. This paper further has suggested the impact of testing on economic cost and fatality. It has quantified the benefit of effective testing and hazards of absence of effective costing.

Further profound results on economics of COVID lockdown have been analyzed by Acemoglu Chernozhukov, Werning, Whinston (2020) in their working paper on optimal target lockdown using the Multi-group SIR model. This study has tried to study the lockdown policy where impacts and incidence vary across groups of different ages. This paper has found that optimal policy targeting different groups outperforms optimal lockdown policy targeting every group optimally. Hence, targeted policies can be the most effective tool to minimize the fatality and optimize the economic cost as recommended by this paper.

In this way, historical approach reveals that economics of pandemic are significant in three ways. First, pandemics can cause great damage to the economy. Second, Non-pharmaceutical Interventions may help to recover the economy after a pandemic. Third, the natural rate of interest may decline for a long period of time. Similarly, a theoretical approach depicts that uniform optimal lockdown can cause economic loss in terms of GDP which can be minimized through targeted optimal lockdown policy. Thus, this paper has tried to gauge the impact of lockdown on the economy in terms of per capita GDP for Nepal and has used the exchange rate and GDP approach for the analysis.

This paper has used the Undervaluation and Growth model used by Dani Rodrik at Harvard University in his paper “The Real Exchange Rate and Economic Growth (2008)”. According to that model, the regression of log of real exchange rate on log of GDP per capita yields the estimate of the coefficient of -0.24. This gives the elasticity of changes in real exchange rate with changes in per unit GDP per capita of a country. Thus, using the same relation, if we analyze the data for nominal exchange rate of NPR with USD on March 23, 2020, the day before lockdown and the nominal exchange rate of NPR with respect to USD on June 15 when the lockdown lifted, it yields the elasticity of -0.02004 for the relation. This implies the conclusion that the GDP per capita has decreased by 2.004% for the period of lockdown and the same figure can be converted into total loss in GDP given the total population and figure of GDP.

But the result should be generalized with precaution since the model used in this paper has not included all the exhaustive list of variables that affect GDP. Similarly, there are other limitations such as there may be fixed effect and lagged variable effect which has not been considered in this paper. Further the result should be analyzed within the given period and results may vary if taken for longer consequence. Despite all these limitations, the results has a preliminary merit for policy maker for more comprehensive study in the future and to design the immediate intervention in lack of other studies.

Hitotsubashi University