Research Article: 2021 Vol: 25 Issue: 3

Thematic Extraction of the Worldwide Economic Financial and Managerial Effects of Covid-1

Diya Guha Roy, Assistant Professor, Goa Institute of Management


The research article has derived the thematic analysis from thirty-three Newspaper articles by Nobel Laureates and eminent economists, and financial analysts. The resulting collage of the themes-subthemes identifies the issues that are widely being discussed and floated around the globe for scholarly as well as political consultations. The thematic analysis has consolidated the issues discussed during the first two weeks of April, 2020 in a tabular, coherent manner. The outcome simply gives a clear picture what impending crisis oriented research facilities are required to counteract the precise problems at the peak of this pandemic. The Isolated discussions are often very brief and covers certain problems, or policies. However, this research assemblage itself will help to identify the bigger picture by narrating the primary issues in one explicit pail of information.


Covid-19, Coronavirus, Text Mining, Financial Impact, Economic Impact.


According to The Union Health Ministry, India, as of on 18 April, 2020 eleven thousand six hundred sixteen people in India got inflicted by coronavirus infection and four hundred fifty-two people succumbed to death. On 17 April, 2020, the WHO (2020) reports that there are 2,074,529 confirmed cases, and the death toll is estimated as 139,378. Amidst the other rumours such as Covid-19 is global bio war tool such as SARS that out broke in China during end of November, 2002 and finally had spread to 26 countries. As SARS spread from the epicentre there were news of new economic ruptures after the bank failures and currency devaluations five years ago. We can see a similar cycle in 2020 when the mass economic initiatives by the World Bank and individual government bodies are being assessed to counteract the economic massacres on the account of reduced manpower and working hours as well as collapse of trade in general. There are ongoing economic negotiations for financial moratoriums to be distributed among various states in India. According to a report by The Times of India Covid-19 will make 195 million full-time jobs or 6.7 % working hours wiped out. There are debates going on about borrowing, lending, credit deferments, economic stimulus packages to combat the upcoming recession etc.

The report by International Labour Organization ILO (2020) classified issues such as solvency, income loss, layoffs, unprotected workers, changes in working hours, annual job losses. workforce displacement, limited access to health services and social protection, looming risk of escalated poverty PTI (2020), policy responses, scarcity public resources as major indexes for the economic deterioration in the present financial quarter. The International Labour Organization ILO (2020) also did a thematic analysis to create a documentation on “Interventions to support enterprises during the COVID-19 pandemic and recovery”.

This article analyses thirty important economics and financial market related newspaper articles by eminent analysts and did a content analysis to identify the main themes and subthemes associated. This research bring paper brings forth the main financial and economic themes at one vessel to be used for future researches by market analysts and researchers.

The value lies in the consolidation of the major setbacks during this pandemic, itemizing them in one list, and thereby essentially creating a vessel where all the peak economic and financial crisis had been accumulated.


Thirty-Three articles comprising of a total of thirty thousand fifty-nine words were collected from sources such as BBC, The Times, Aljazeera, The Economics Time, The New York Times, The Wall Street, Bloomberg, The Guardian, The Telegraph, The Washington Post, Lancet etc. Most of the Articles were composed by eminent scholars such as Nobel Laureates as well as other famous economists, and financial analysts. The articles talk about the global economic situations due to the outbreak of Covid-19 as well as emphasise the geographical markets such as Vietnam, China, Korea, India, The USA, Europe, The Middle East etc. The articles were collected between first and second week of April, 2020.


A simple analytic consolidation of main themes and associated subthemes were conducted using Nvivo 12 plus software. NVivo 12 is software provided by QSR International, which is effective in analysing the thematic analysis as well as hierarchical clustering using word similarity and Pearson's coefficient (Azeem, 2012). The thirty-three articles comprising of 67 pages and 30059 words were put into a Microsoft word document and analysed for thematic extraction by machine algorithm supported automatic coding.


Initially we “auto-coded” (software algorithm generated coding) the document using NVivo 12. The NVivo software uses a semi-supervised “machine-learning” algorithm to automatically code (Hai-Jew, 2014). Considering the length (67 pages) of the whole document we found the relying on the machine algorithm was more realistic and scientific approach rather than coding it through manual open/axial coding for identification of themes-subthemes. Simultaneously different levels of sentiment analysis were performed using the software as discussed in the following section.

The Sentiment count of identified “auto-coded” words are shown in Table 1. The total number of relevant key words with associated sentiments were 718, out of which only 64 words had positive sentiments associated with them; and 237 words were neutral. 286 of the identified keywords had Negative sentiments, and 131 words reflected Mixed (both positive and negative) sentiments. Therefore, only 9% of total number of identified words had positive sentiments Figure 1. The Neutral and Mixed sentiments were 33% and 18% respectively (See Figure 1). The Negative sentiments occurred for 40% and had the maximum share (Figure 1). Figure 2 explicitly shows the data in Figure 1 in a 2 dimensional matrix for an alternative appeal to the visual pallet.

Table 1 Number of Coding References for Coded Sentiments
Codes Number of coding references
data_192 718
data_192 - Mixed 131
data_192 - Negative 286
data_192 - Neutral 237
data_192 - Positive 64

Figure 1 % Occurrence of Sentiments for Software Identified Keywords

Figure 2 Matrix of Sentiments for Software Identified Keywords

The main themes with more than 2% occurrences are tabulated in Table 2. The main themes identified are: supply, products, chains, companies, market, payment, unemployment, shocks, consumers, email, systems, coronavirus, online, delivery, debt. Table 3 has given the full list of the themes and associated subthemes. The main themes that appeared for 1% or so are: advertising, growth, benefits, chains, challenge, health, risk, retailers, strategy, credit, services, unemployment, policy, and debt.

Table 2 % Occurrence of Coded Themes
supply 4.53%
products 3.79%
chains 3.41%
companies 3.2%
market 3.07%
payment 2.61%
unemployment 2.51%
shocks 2.43%
consumers 2.36%
email 2.36%
systems 2.3%
coronavirus 2.05%
online 2.05%
delivery 2.04%
debt 2.01%
Table 3 Themes and Subthemes from the Thirty Three Articles
Auto-coded Themes
advertising growth products
advertising arrangements economic growth rate dairy products
advertising commitments economic growth targets factory production
advertising partners forecast growth finished product
digital advertising initial growth path health-relevant products
asset target growth rate hygiene products
asset valuations health medicine production
equity asset class global health product groups
risk assets health conditions productive activity
safe assets health crisis scheduling products
wide asset classification forbearance health infrastructure protections
benefits mental health support anti-virus protection
awarding benefits public health policies coronavirus protections
core benefit hit protective gear
unemployment benefits direct hit rate
capacity financial hit economic growth rate
adding capacity hitting manufacturing household savings rates
excess capacity indirect hit key interest rate
logistical capacities household occupancy rates
chains household exposure policy rates
2016 value chain model household savings rates target growth rate
fair share household wealth contracts recession
global value chains industry global recession
share values banking industry last recession
supermarket chain extractive industries policy recessions
supply chains particular industry recession risk
challenge theatre industry recovery
challenging categories infrastructure recovery cycle
challenging proposition city infrastructures recovery dynamics
current coronavirus challenge health infrastructure recovery patterns
legal challenge road infrastructure recovery strategy
major challenge instructions response
companies changed payment instructions bungled response
aviation companies urgent instructions crisis response
big companies wiring instructions monetary policy response
chinese companies internet retailers
company guidelines internet router bricks-and-mortar retail
german company internet use electronic retailer
gig economy companies password-protected internet connections large retailers
large companies legacy risk
lingerie company macroeconomic legacy additional transmission risk
luxury goods company microeconomic legacy recession risk
several pharma companies loan risk assets
tourism companies bad loans risk profiles
consumers bank loan repayment services
chinese consumers loan covenants backup child care service
consumer comfort past loans cleaning services
consumer confidence correlate market delivery services
keeping consumers financial market performance remote meeting services
coronavirus financial market sell-offs shocks
coronavirus crisis key markets demand shocks
coronavirus governments key stock market economic shock
coronavirus protections market intelligence services    portal exogenous shocks
coronavirus strain market signals prior shocks
coronavirus struggle market worldwide supply shocks
current coronavirus challenge wholesale money market supply-side shock
credit media shopping
corporate credit media giant bricks-and-mortar shopping
credit intermediation social media accounts offline shopping worlds
credit spreads social media content online shopping
crisis models store
coronavirus crisis 2016 value chain model physical stores
crisis planning door-to-door models store cleaning
crisis response economic model store closing
health crisis mechanical models strategy
cut national post-recovery strategy
cut backs foreign nationals recovery strategy
surprise cut national concerns supply
temporary pay cuts national developments critical supplies
damage national governments essential supplies
actual damage oil requisition supplies
economic damage crude oil futures sufficient supplies
structural damage global oil demand supply chains
debt mentha oil futures supply disruption
bank holiday oil importer supply shocks
debt holiday provision online supply shortage
generous provision online purchases three-year supply pact
government debt online sales channels support
delivery online shopping fiscal support
delivery services online spaces mental health support
efficient delivery system transacting online policy support
enabled delivery systems packaging taking support
home delivery economic rescue package systems
demand economic stimulus packages company systems
demand shocks payment digital inventory system
exogenous demand changed payment instructions efficient delivery system
global oil demand paid time-off enabled delivery systems
economy payment leeway intuitive reasoning system
advanced economies post-holiday payments political systems
gig economy companies resumed payments transmission
major economies temporary pay cuts additional transmission risk
real economy shock planning classic transmission
email crisis planning plausible transmission channels
365 email accounts planned broadcast transmission mechanism
business email interruption scams post-recovery planning unemployment
internal company email policy unemployment benefits
phishing email monetary policy response unemployment claims
goods policy action unemployment insurance appeal board
ancillary goods chemicals policy error unemployment insurance fund
luxury goods company policy interventions  
material goods policy rates  
government policy recessions  
government debt policy support  
individual governments public health policies  
national governments    

A deep look into Figure 3 will divulge that Mixed sentiments are actually dominating most of the thematic words. For a few words (companies, systems, unemployment, challenge, products, market, benefits, loan, industry, economy, protect, support, delivery, internet, infrastructure, media, advertising, consumers, email, capacity etc.) the Neutral sentiments governed the second biggest chunk after Mixed sentiment. So even when the death toll is rising and it seems that the world is coming to a standstill, the economic measures and media news seem to have kept a neutral and mixed sentiment ambience for the most deeply economically hit sectors. The Negative sentiments are observed for words such as supply, products, coronavirus, payment, rate, stocks, growth, debt, risk, recession, demand, damage, good, retailers, credit, cut, crisis, transmission, national, models, planning, response. A focused review of these words shows that the direct economic and financial components, and the retail industry had been shaken badly and the fear is emanating from and resonating in these domains.

Figure 3 Matrix of Sentiments for Software Identified Individual Themes

The only holistically positive and mixed sentiments are ruling the following themes: infrastructure, advertising, apps, internet, capacity, loan, and protection. Figure 4 provides the most dominating hundred words in a machine generated basic word cloud.

Figure 4 World Cloud for Software Identified Keywords

The reason advertising was seen to be in a mixed and positive zone due to digital applications Table 3. Infrastructure seems to be more of an established component with three sub-components as represented in Table 3: city infrastructures, health infrastructure, and road infrastructure. The loan market shows a better mood due to bank loan repayment, loan covenants, and past loans. The protection key word Table 3 has a better impact as it talks about anti-virus protection, coronavirus protections, and protective gear. However, economic protection is totally neglected. The logistical capacities are the main reason why we had a primacy dominance of mixed feeling and secondary dominance of positive sentiment in the keyword “capacity”.


The scope of this article, as stated before, to identify the opportunities during the mightiest pandemic crisis in the last century. The article simply finds the right scope of in-depth research problems for better analytically presented managerial solutions. In this line this research found certain scopes for researchers to target certain issues.

The auto-coded themes and subthemes in Table 3 highlighted a few important points as follows:

1. Growth: The growth is related to forecasting as well as the growth cycle, growth target and rate. There are abundant scopes for research in term of the whole economic cycle at this phase.

2. Assets: The asset (financials) need to be explored more crucially from the point of risk, safe, equity etc. How these have long term individual and collective impact should reach the research community for better management system for future crisis onsets.

3. Chains: The values chain models, global and individual retail chains ask for magnified and micro analysis.

4. Industry: Beyond banking, how the theatre industry is taking the hit is also of concern.

5. Recession: Are the current researchers put adequate weightage of importance for policy recession as the onset of any global war, bio-warfare, and pandemics?

6. Health: How the global health, health crisis, health infrastructure, and public health policies are adequate? Is there gap in the mental health support system?

7. Advertising: How are the advertisement arrangements, commitments are proceeding? How the advertising partners are getting affected due to this pandemic? Is there more scope for digital advertising as the physical advertisements is facing an impediment?

8. Products: The research expresses to specifically pour more focus on the industries such as dairy products, factory production system, health-relevant, medicine, hygiene products. The production activity and scheduling of production system also stood out.

9. Recovery: the text mining specifically pointed towards the recovery cycle, recovery dynamics, recovery patterns, and recovery strategy.

Table 3 also points to the specific possibilities of explorations of various companies affected by the covid-19 pandemic, consumers, retailers, shopping, services that are crucial during the pandemic, and economics facets such as debt, loan, shocks, risk, credit, crisis, cut, supply, demand, unemployment, policy, and payment.

The research itself does not provide any solution, nor it does explore any gap in the current economic analysis. It simply explores the possibilities of certain themes and related sub-themes and point at the major research needs under the spell of the pandemic. This crisis can be a learning phase in industrial 4.0 era in order to control, plan, and find feasible solutions at the quickest time. And the research simply put the major issue, that are observed at this point of time, upfront for concise and precise outlooks at one go for immediate concentration.


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