The Spread of COVID-19 in India — An Economic and Geo-spatial Perspective
Aditya Murali
Email: aditya_murali@isb.edu
ISB Institute of Data Science, Hyderabad, India
Dr. Shruti Mantri
Email: shruti_mantri@isb.edu
ISB Institute of Data Science, Hyderabad, India
The covid-19 pandemic is a human disaster of 2020 affecting 200 countries and territories across globe. The exponential growth of the COVID-19 since its eruption, disrupted the supply chains, virtually shut down most economic verticals and took thousands of lives. This tough situation with no comparable template in generations resulted in the survival of only the fittest businesses. India, a developing economy expected to be affected through demand depression and high unemployment due to the pandemic. In the current study, the authors have analysed the movements in the National Stock Exchange, to understand and interpret sectoral performances and investor sentiments during the crisis and also analysed the spread of the virus at some of the major financial centres of India from a geo-spatial perspective.
We have used data visualization techniques and statistical methods to analyze the spread of covid-19 pandemic and its impact on the economy. The intersection of economics and geography (geo spatial-temporal data) allows us to understand the impact and risk certain industry sectors faced over the first two financial quarters of 2020 and mitigate those risks.
INTRODUCTION
COVID-19 is the greatest public health crisis witnessed in generations. Modern transportation systems facilitated the growth in the geographic spread of the virus from its original epicentre in Wuhan, China to countries across six continents. The exponential growth in the number of cases, that currently stands at over 7.91 million confirmed cases in India and over 43 million in the world [1] put an insurmountable strain on healthcare systems. Government-imposed lockdowns have been used as an approach to contain and slow down the spread. These lockdowns have consequently had a crippling effect on service-oriented industries such as Tourism and Aviation whilst on the other hand have given a new lease of life to industries such as Pharmaceuticals and Food Processing.
Given that India’s urban population is clustered in a few mega cities with high population densities, we have tried to empirically map the geographical spread of the virus and understand the common characteristics of localities that did not fare well. In this study, the authors have examined data from the financial capital Mumbai, the manufacturing hub of Chennai and try to decipher any patterns or trends that can be looked at, if such a situation were to ever arise again. We used data released on the Twitter handles of the municipal corporations of the cities, in order to understand a correlation if any between the spurt of cases and industries operating out of clusters in those regions.
In this study, we have also taken publicly available historic index price data [2] from the National Stock Exchange across industries to examine the impact of the economic headwinds because of the virus. The data spans over a range of six months and allows us to identify industries that have bucked the expectations and found opportunities to propel their growth.
PROBLEM DEFINITION AND OBJECTIVES
A shutdown that kept over a billion people at home for over three months would have had a profound behavioural impact on how customers consume products and how organizations run their businesses. With very likely threats of job loss and reduced pay, could 2020 represent a turning point in how people spend, save, and invest money? Have certain industries thrived in the challenging circumstances?
Not all states and union territories of India have been affected at the same scale. As of now only eight states have each had more than 300,000 confirmed cases. The Union Territory of Lakshadweep on the other hand has not recorded even a single case till date [3]. Hence, were certain companies exposed to much a higher degree of risk primarily due to their geographical location? The answer to this question brings forth the importance of geographical diversification in contingency planning and risk mitigation.
In a macroeconomic environment, every industry is interconnected if not interdependent on some or the other functioning industries of the economy. For example, the tourism industry in India is dependent on transportation and movement of people. Hence, the shutdown of mass transportation mechanisms would make locations out of bounds for tourists. These shutdowns would be directly proportional to the loss in revenue for hotels and restaurants in those regions. India’s economy is primarily a service driven one. Hence, we also focus on identifying clusters of industries that exhibited the same economic characteristics during the first half of 2020.
RESEARCH METHODOLOGY
The application of data visualization and statistics has allowed us to visualize trends and identify patterns. Given the geographic nature of the data, visualizing the metrics over geo-spatial objects like maps enables the comprehension of the spread and its scale. Time series plots on the other hand are best suited to understand the fluctuation in values over time. Heat maps have allowed the researchers to observe the density of cases across regions. These visuals allow us to clearly identify regions that have successfully flattened the growth in cases and those that still need to maintain a vigil.
When comparing the spurt in cases across regions, the development of benchmarks such as mean, and median is essential to judge performance. A broad indication of performance is if the metric is above or below the benchmark. Relative measures like population density and cases per million also allow for a fair comparison between regions. Correlation is another statistical measure that is used by us to understand the magnitude of the association between entities and their associated metrics.
Where x and y represent the data points of the two series whose strength of association is being measured and n represents the number of data points in the series. The value of
lies between -1 and +1. The closer the value is to 0 implies that the correlation is very weak. +1 indicates a strong positive correlation. This means that an increase in one variable leads to an increase in the other. On the other hand, -1 indicates a strong negative correlation. This means that an increase in one variable leads to a decrease in the other. In the context of this study, we have used correlation to measure the relationship between the number of cases of COVID-19 and the daily closing price of various sectoral indices at the National Stock Exchange.
Apart from using metrics released by the civic authorities, we developed a calculated field by analysing maps. The number of neighbouring geographic entities is a metric that can be applied at state, district or city levels.
FINDINGS AND RESULTS
A. MUMBAI REGION
As the most populated city of India, the financial capital and one of the key domestic transit hubs of the country, the impact of COVID-19 on Mumbai could significantly determine the overall impact of the virus on India. Local civic administration in the city is governed through a municipal corporation with a network of 24 wards [4]. The wards are broadly classified into three regions: Mumbai City, Western Suburbs and Eastern Suburbs.
We used data shared on the Twitter handle of the Brihanmumbai Municipal Corporation (BMC) [5] to analyze the spread over a period of three weeks from 4th April to 21st April 2020. This time-period starts about two weeks after a national lockdown was imposed by the Indian government [6]. At this point of time, there were only 330 cumulative positive cases in the city. A heat map using colour coding indicates the spread of the virus from the southern wards to the rest of the city. The colour code used is as follows:
Blue: 0 Cases
Dark Green: 1- 10 Cases
Light Green: 11–25 Cases
Yellow: 26–100 Cases
Orange: 101–200 Cases
Red: 201–300 Cases
Maroon: > 301 Cases
Figure 1. Heat map of active cases on a) 4th April b) 11th April c) 16th April d) 21st April
As can be observed in the figure above, Worli-Koliwada i.e. a community of fisherfolk emerged as one of the earliest hot spots of the virus in the city [7]. Whilst the data cannot explain how this locality emerged as a hotspot, it provided an insight with respect to possible containment strategies. A dense population in a small area meant that social distancing would have challenges in the face of physical constraints. The constraint of space allows us to ponder over the thought of enforcing tighter restrictions on regions that were known to have massive slums i.e. regions that exhibited characteristics similar to the Koliwada.
An inspection of the data two weeks later points to the emergence of a few more hotspots in the city. They include Dharavi and Kurla, two regions that have a significant slum population. Hence, population density is an important metric to be considered when dealing with future outbreaks. An analysis of data from the 2011 census [8] indicates that the five worst affected wards on 21st April could easily be clustered. All these wards had high population densities, with their corresponding metric higher than the mean and median density for the city.
Figure 2. Active Cases vs Number of neighbouring wards
Another calculated metric used to analyse the data was the number of neighbouring wards. It was obtained by interpreting the ward boundary layouts of the city. It was observed that wards with a higher number of positive cases shared their boundaries with at least four other wards. The reason this metric is important is because a greater number of neighbouring wards leads to a greater number of access routes for movement of people.
As COVID-19 is a very contagious virus that spreads exponentially [9], this metric underscores the importance of localized geo-spatial planning in order to maximize the benefits of social and physical distancing.
At the start of the year, on the basis of predictive models it was feared that several millions in India would be exposed to the virus and that the healthcare system would not be able to withstand the load. To allow state governments to better prepare and equip themselves, a country wide lockdown was announced on 24th March. The initial lockdown was meant to last only 21 days, but lasted much longer and the country has still not completely opened up over six months later. This abrupt shut down of the country virtually brought all economic activity to a standstill. The initial focus of the government was to save lives. The motto “Jaan hai toh Jahan hai” which means that life is paramount in order to have a vibrant world, was the message propagated by the government [10]. In this context, it can be seen through Figure 3 below that the multiple stages of lockdown had the desired effect in delaying the surge in cases.
Figure 3: Positive cases by Date and Phase of lockdown in Mumbai
From the figure above, it is visible that there is a progressive increase in the number of positive cases through each phase of the lockdown. However, the rate at which the cases grew was much higher in the third and fourth phases. This is evinced through the steep positive slopes between the start and end of the phases. A surge is clearly visible in the fourth phase. Hence, this visualization indicates that one of the goals of lockdown i.e. provide more time to prepare the healthcare system [11] for the onslaught of cases was achieved.
B. CHENNAI REGION
Tucked away in the southern part of the country, Chennai the capital of Tamil Nadu is the foremost auto mobile manufacturing cluster of the country. As a manufacturing-oriented economy, Chennai has several factories that employ a large workforce.
Figure 4: Total confirmed cases vs Number of neighbouring zones in Chennai
As was seen in Mumbai, we looked to at data from Chennai [12] to discover trends. This data is a snapshot in time, as it represents the metrics on a single date, i.e. 15th July 2020. Figure 4 plots the total number of confirmed cases (Active + Recovered + Deaths) against the number of neighbouring zones in Chennai. Through clustering, we can see that the zones with the highest total cases have at least four neighbouring zones. This pattern again brings to the fore the issue of mobility and movement of people.
It can also be noticed that these zones, that are part of the red cluster are in the core of the city. Hence, if a second wave were to occur, policy planners should pay closer attention to transit through zones that naturally facilitate movement of people across the city.
C. THE STOCK EXCHANGE — CORRELATION
The National Stock Exchange (NSE) is one of the largest stock exchanges in the world [13]. As one of the two major trading exchanges in India, it offers Indian investors a platform to invest in equities. The equity markets throughout the world never offer any guaranteed returns to investors and all investments are subject to risks. These markets are generally volatile and usually react to developments in the world.
One of the main reasons we considered the stock exchange is that the listed companies offer some of the highest avenues of employment and growth in the Indian economy. The price trends in the exchange can be considered as a pseudo barometer for the health of the formal corporate economy. This reason could be considered as price fluctuation of stocks is generally driven by news and events either positive or negative. COVID-19 was an event that disrupted almost every business. Only businesses with strong fundamentals have been able to withstand the headwinds of forced restrictions on mobility and consumption.
We collected open source historical data for the first six months of 2020 from the NSE website [2]. One of the reasons the data was taken for this snapshot of time was because local administrative bodies switched from reporting hyperlocal ward-based numbers to more aggregated measures for cities. Stock market metrics such as the Closing Price were collected for indices such as the NIFTY50 and sectoral indices such as NIFTY Auto, Bank, FMCG, IT and Pharma. We examined the data to interpret the movement in prices across industries and business verticals.
It is often said that the market metrics fluctuate with respect to economic and geo-political developments from around the world. We wanted to test this hypothesis by looking at the daily closing price of the NIFTY 50 from January to June in 2020. The first confirmed case of COVID-19 in India was recorded on 30th January 2020 [14] and the first nationwide lockdown began on 25th March 2020. Hence, this six-month window covers the onset of the virus, the responses of the government and the reaction witnessed in the stock market.
Figure 5: Number of confirmed COVID-19 cases vs Closing price of NIFTY 50 by date
From Figure 5 above, it is evident that the Indian markets tanked in their value through March up to the start of the series of lockdowns. The value of the NIFTY 50 on 25th March was 37.53% lower than its value on 1st January. Such a steep decline has not been observed in years. An intriguing trend is the gradual but positive increase over the next 60 days. That the markets did not fall further despite successive lockdowns is rather surprising. This leads us to ponder over a question: Are the markets in sync with what is happening on the ground?
The fall in value of 37.53% over the first three months is followed by an increase of 21.5% over the next two months. This is the period in which cases grew exponentially and four successive national lockdowns were enforced. Hence, we calculated the statistical correlation between the number of confirmed cases and the closing price over different time periods. For the period till the start of the first lockdown on 25th March, the correlation was -0.84. This strong negative correlation is on expected lines, as countries around the world stopped travel and announced lockdowns of their economies. This means as cases around the world were rising, the Indian markets fell in line with some of their global peers such as the United States of America [15]. However, after that the correlation is +0.779. Even though the cases increased exponentially, the indices gained significantly in their value. This dichotomy in correlation points to a recovery in challenging circumstances. This raises the question if the upward trend is sustainable or if the stock market is not in sync with the macro economic conditions of the country and the world at large?
D. THE STOCK EXCHANGE — SECTORAL PERFORMANCES
COVID-19 induced lockdowns affected the entire population in India. Non-essential industries had to shut down for over two months. At a first glance, it appears that sectors such as hospitality, tourism and services were the worst hit [16]. We looked at the sectoral indices from the NSE to identify if the trends are common across sectors such as Automobiles, Banking, FMCG, Pharma and IT.
From the table above, every sectoral index had a steep negative correlation with the number of cases and reached their peak fall at the end of March, when the nationwide lockdown was announced. By mid-June, when the number of cases crossed a few lakhs and India was firmly entrenched in the global pandemic, the expected steep negative correlation does not hold true.
Pharmaceutical companies did exceedingly well during the crisis, and it can be seen with a positive correlation of 0.712. The race to find vaccines and a greater focus on healthcare have provided an impetus for growth in this sector. IT companies started to buck the trend of negative correlation as the ability to work from home meant that businesses in this industry could continue working with a good level of productivity albeit remotely. FMCG companies started gaining value as packaged food consumption grew with the increase in duration of people working from home.
The banking index lost the most value as the crisis propelled bankruptcies and the resulting layoffs meant borrowers and businesses were unable to pay their loan instalments on time [17]. The fear that this would lead to a further deterioration of gross non-performing assets and render a collapse of the banking system. The sector was under stress even before COVID-19 affected the world and the weakening of the correlation coefficient would indicate that the economy has not been as badly impacted as anticipated. The Auto industry suffered initially as lockdowns meant there was a lack of manpower to implement production of vehicles at manufacturing plants. The economic stress due to job losses also meant that people would have postponed their decision of buying a vehicle. As months progressed, the exponential increase in number of cases and shutdown of public transportation systems could have had a behavioural shift in the mindset of customers. An increase in purchases was reported in rural areas, as people wanted to avoid public transportation [18]. This evolving situation represents a silver lining for the sector which showed a recovery of close to 50% in its index value.
The trends described are also evident in table 2 that looks at relative comparisons of index values over the months. It is clearly visible that investors who put in money at the start of the year, lost money in all sectors except Pharma by June. However, it should also be noticed that there was a steep correction witnessed across sectors. Figure 6 allows us to examine the recovery of the economy even as cases grew exponentially.
Figure 6: Performance of NIFTY sectoral indices w.r.t number of confirmed cases
The dichotomy of a recovering economy given the exponential growth in cases, offers insights to us that are significant in public policy planning. Unlike what was initially feared, the economy did not crash further beyond the lows in March. The recovery was not a sharp turnaround but appears to be slow and gradual.
The Pharma industry has made significant gains and provides an opportunity for the government to boost domestic manufacturing and enhance supply chains in this domain [19]. The IT industry on the other hand has been exposed to a viable work from home environment [20] and this could have long term implications on staffing policies and fixed operating costs.
The line chart shows a slow recovery for the auto index. One reason could be that most automobile factories were located near Chennai in Tamil Nadu and Pune in Maharashtra. Both states were badly affected in terms of the spread of the virus. The resulting lockdowns also significantly disrupted supply chains. Hence, hyperlocal geographic analysis plays an important role in understanding if industries and their supply chains are clustered in certain parts or are diversified and spread out across geographies. Such an exercise would help mitigate disruption risks in the future, if a second wave of the virus or any other pandemic were to strike the country.
CONCLUSION
COVID-19 has been a once in a lifetime event that triggered the brakes on almost every business vertical. It has reminded policy experts and business owners of some of the basic tenets such as diversification of products, alternative supply chain mechanisms and digitization especially in the 21st century. Instead of denser metros, the focus should be on providing more growth opportunities in cities across the country, so that situations where an entire industry or supply chain is concentrated in one geographic division are never repeated.
REFERENCES
[1] COVID-19 Dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, https://coronavirus.jhu.edu/map.html
[2] Historical Index Data from the National Stock Exchange, https://www1.nseindia.com/products/content/equities/indices/historical_index_data.htm
[3] India Covid Map and Case Count, The New York Times, https://www.nytimes.com/interactive/2020/world/asia/india-coronavirus-cases.html#states
[4] Wards Maps, Municipal Corporation of Greater Mumbai, http://dm.mcgm.gov.in/ward-maps
[5] Twitter account of the Brihanmumbai Municipal Corporation, @mybmc
[8] Census 2011, Public Health Department, Municipal Corporation of Greater Mumbai, https://portal.mcgm.gov.in/irj/go/km/docs/documents/MCGM%20Department%20List/Public%20Health%20Department/Docs/Census%20FAQ%20%26%20Answer.pdf
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[10] ‘Jaan bhi aur jahaan bhi’: PM Modi messages CMs at COVID-19 lockdown meet, Hindustan Times, https://www.hindustantimes.com/india-news/coronavirus-update-jaan-hai-toh-jahan-hai-modi-tells-cms-during-meeting-to-decide-on-covid-19-lockdown/story-uoUUG9tT5abXAYFOONX8SI.html
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[13] About Us, National Stock Exchange, https://www1.nseindia.com/global/content/about_us/about_us.htm
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[16] These industries suffered the biggest job losses in April 2020, CNBC, https://www.cnbc.com/2020/05/08/these-industries-suffered-the-biggest-job-losses-in-april-2020.html
[17] COVID-19 impact: SBI Cards, Live Mint, https://www.livemint.com/companies/company-results/covid-19-impact-sbi-cards-stock-plunges-7-as-q2-net-slips-46-to-rs-206-cr-11603363509706.html
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[19] Pharma PLI Scheme: 29 companies sign up to take benefits, more to apply soon, CNBC TV 18, https://www.cnbctv18.com/healthcare/pharma-pli-scheme-29-companies-sign-up-to-take-benefits-more-to-apply-soon-7130981.htm
[20] Companies see work-from-home as a viable long-term option if regulatory issues can be addressed, The Economic Times, https://economictimes.indiatimes.com/news/company/corporate-trends/companies-see-work-from-home-as-a-viable-long-term-option-if-regulatory-issues-can-be-addressed/articleshow/74985839.cms?from=mdr
