Healing the Economy: Estimating the Economic Impact of India’s Vaccination and Related Measures†
Amit Kapoor1 Richard Dasher2
† The authors would like to thank Rishi Jain, Shivani Kowadkar, Sheen Zutshi, and Teesta Bose for their research assistance.
* The views presented here are those of the authors and do not necessarily represent the position of either Institute for Competitiveness or Stanford University. Working papers are in draft form. This working paper is distributed for purposes of comment and discussion only.
1 Honorary Chairman at the Institute for Competitiveness India; Lecturer at Stanford University 2 Director of the US-Asia Technology Management Centre at Stanford University
Table of Contents
Introduction …………………………………………………………………………………………………….. 3 India’s COVID-19 Containment Strategy………………………………………………………………. 5 Background…………………………………………………………………………………………………………………. 5 India’s Strategy ……………………………………………………………………………………………………………. 6 India’s COVID-19 Relief Package and its Impact on Economic Recovery ……………….. 13 Literature Review…………………………………………………………………………………………………………13
Potential Economic Impact of the Relief Package……………………………………………………………15
Impact on MSMEs……………………………………………………………………………………………………………………….15 Impact on Social Sector………………………………………………………………………………………………………………… 17 Impact on Employment Generation………………………………………………………………………………………………..18 Impact on Agriculture ………………………………………………………………………………………………………………….. 19
India’s COVID-19 Vaccination ………………………………………………………………………….. 23 Background……………………………………………………………………………………………………………….. 23 Literature Review……………………………………………………………………………………………………….. 29 The Elderly………………………………………………………………………………………………………………… 44 Lifetime Earnings ………………………………………………………………………………………………………. 45 The Social Aspect……………………………………………………………………………………………………….. 46 Future Work ………………………………………………………………………………………………………………. 47
Way Forward ………………………………………………………………………………………………….. 48 References ……………………………………………………………………………………………………… 49
Healing the Economy: Estimating the Economic Impact of India’s Vaccination and Related Measures
The COVID-19-induced pandemic impacted the world economy to a degree not caused by a disease since the Spanish Flu of 1918-20. Both Spanish Flu and Covid-19 wreaked havoc to the global economy. Pushing people into poverty and increasing inequality were some of the common features that both came with (Sharma et al. 2021). Back then, the Spanish Flu infected about 500 million people – about one-third of the global population. The current pandemic infected more than 730 million people3 – about 9% of the world’s population4. While the Spanish Flu reduced the world GDP to a great extent5, the Covid pandemic is expected to cost about $8.5 trillion to the world.6 Starting in a province of China, the Covid virus slowly proliferated to the rest of the world. It caused global panic due to its adverse effects. Halting economic activity and causing mass damage to people’s lives, the pandemic accelerated the hunt for a solution. According to April 2021 World Economic Outlook Report, the global economy contracted by 3.5% in 2020 (Yeyatti & Filippini, 2021). Inequalities within nations were further reinforced in more ways than one. The outbreak had an impact on all segments of the population but with pronounced effects on the lives of those belonging to vulnerable social groups.
Against this backdrop, the development of a vaccine was regarded early on as essential, but such development takes time. Accordingly, in the interim, the global response was to contain the spread via isolation that ranged from tracking and quarantining, especially in earlier stages of the pandemic, to simple mask-wearing and social distancing that persisted throughout most of 2022. Solutions to reduce the impact of the catastrophe were decided on and implemented primarily at the national level. Isolation strategies were accompanied by economic relief package(s) that nations rolled out to reduce economic and social damage. Finally, with the development of the vaccine, the focus shifted to vaccinating the masses to develop immunity against the virus. By the end of 2022, the number of confirmed cases and confirmed deaths globally surpassed 730 million and 6.7 million, respectively, and the cumulative global vaccination doses crossed 13 billion7 (WHO). According to the World Bank’s future estimates, all advanced economies will have attained a full output recovery by 2023, and output in emerging and developing economies is expected to remain 4% below its pre-pandemic trend. As a major developing economy with increasing geopolitical clout, India’s post-COVID-19 progress has been a subject of much discussion regarding its Covid management, vaccine administration, and economic growth, to name a few. India has registered a positive development on multiple economic parameters. The Goods and Services Tax (GST)8 revenue collection was the highest at ₹1.68 lakh crore for April 2022 (PIB, 2022). The Second
3 By end of 2022. For more information see https://covid19.who.int/
4 Considering global population of 8 billion.
5 Although data is scant for that time, some estimates like Liang et al. (2021) suggests that the Flu contracted the Mexican economy suffered a $9 billion economic loss. Other estimates like Barro et al. (2020) suggests that it reduced the GDP per capita by about 6% for a typical country.
6 COVID-19 to slash global economic output by $8.5 trillion over next two years. See more at https://www.un.org/en/desa/covid-19-slash-global-economic-output-85-trillion-over-next-two-years
7 Vaccine data is as on 5th January, 2023.
8 GST is a form of indirect tax levied on goods and services in India.
Advance Estimates of National Income released in February 2022 showed that the economy surpassed its pre-COVID level in 2021-22. The country also witnessed a strong export performance that outperformed its pre-pandemic level.
However, the impact of the pandemic is an area that needs attention. The pandemic’s effects are reflected in the recalibrated relationship between the market economy, state, and society in multiple countries. In their efforts to combat the pandemic’s adverse impact, the state and civil society came to be perceived in a different light, altering the public’s perception of the role of markets, government, and society (Miranda & Snower, 2021). A substantial body of work has developed in the past couple of years studying the impact of the pandemic on different areas, including political, environmental, economic, and social matters. In conjunction with this branch of study, there is another area that calls for a deeper analysis studying the impact of a country’s strategy in combating COVID-19. How the world’s second most populous country embarked on a strategic battle against the virus is an area that warrants deeper investigation.
India systematically tackled COVID-19 using a strategy based on three cornerstones – Containment, Vaccination, and a comprehensive yet targeted Relief Package. The uniqueness of each of the three approaches is in the manner in which they were implemented. The calibrated approach used by India in formulating a relief package, as opposed to a front-loaded stimulus package, played a major role in bringing a resilient post-pandemic trajectory. This paper will attempt to study the impact of the three cornerstones. The Economic Survey (2021-22), for instance, suggested that in the context of macroeconomic stability indicators, vaccination progress should be perceived as much more than just a health indicator. It must be considered a buffer against economic disruptions as well. Vaccinating hundreds of millions of people belonging to different social groups across the country was a herculean task that India managed to perform successfully. India attained a milestone when it delivered the highest single-day vaccinations of 25 million doses on 17 September 2021. The country emerged as an example for the world for harnessing cooperative federalism in tackling the virus, with every state playing a vital role in combating the pandemic as per its own strength. India’s strategic choices and their implementation have global ramifications. Moreover, while times have improved, understanding how the three strategies led to the revival of the economy and society is imperative. There has been little empirical analysis of the effects of vaccination, containment, and relief measures on the economic activity in India. Against this backdrop, assessing its strategy against COVID-19 and its impact is a prerequisite for greater crisis preparedness in the future.
India’s COVID-19 Containment Strategy
Early on in the pandemic, the question was how to save lives. Given the uncertainties that the pandemic brought with its onset, the larger goal was to save lives by somehow containing the virus. In epidemiology, two factors are particularly important for evaluating the severity of a contagious disease: first, the basic reproduction number R0 – the expected number of new cases of the disease caused by a single individual; second, case fatality rate CFR or the fraction of individuals infected who lose their life due to the disease. Another key factor is regarding the ‘uncertainty’ in CFR and R0 since the two also get affected by asymptomatic patients who are initially hard to detect. This makes it very difficult to ascertain the true number of individuals infected with COVID-19, and hence determine the CFR and R0. Thus, the reported indicators were uncertain at the onset of the pandemic and showed wide variation. For example, the CFR was as high as 9% in Italy as on 17th March 2020. Other countries, especially the European Union region, were seeing the proliferation of the pandemic with CFR being about 2.25% for France and 3.36% for Spain, for example.9
With a lack of information on the trajectory and intensity of the virus, the only option that seemed relevant was a lockdown. However, containing the spread of the virus meant closing down spaces and restricting the movement of individuals. This meant a significant impact on economic activity and the countless livelihoods dependent on it. But given the priority was saving lives and containing the spread of the virus, lockdown became a viable option. Stringency measures are non- pharmaceutical interventions (NPI) associated with reduced hospitalization rates. With this, they help prevent the health infrastructure from reeling under pressure. Acting as a suppression strategy against COVID-19, stringency measures are applied with the purpose of flattening the pandemic’s curve in a particular region.
Studies have highlighted the importance of an early lockdown to curb the spread of the virus. Evidence suggests that stringent lockdowns if introduced early on in a country during the pandemic, were effective in limiting the spread of the virus. Lockdowns came with short-term economic costs, but their imposition was necessary to put economic activity back on track in the long-term (Caselli et al., 2020). Cerqueti et al. (2022) calculated the number of lives saved due to non-pharmaceutical intervention to be more than 21,000 in Italy. Moreover, Ruktanonchai et al. (2020) conducted a study in the European context and concluded that the effect of premature termination of countries’ stringency measures could trigger a resurgence of the epidemic five weeks after the early termination.
The question was whether the mitigation efforts that involved restricting mobility were worth the economic costs. While stalling the spread of the virus was imperative, the economic imperative of livelihoods weighed heavy. Therefore, while initially the debate was to save lives, it slowly traversed into the policy debate of trade-off between lives and livelihoods. As a result, it was increasingly perceived by governments worldwide that they needed to strike a balance. It was not a matter of choosing one of the two but striking an appropriate balance. While it is known that a lockdown
imposes substantial economic and humanitarian costs on a country, several studies point to the decrease in the number of cases, delays in peak, and the reduced pressure on health infrastructure as evidence of its significance. Moreover, the lockdown and containment strategy were the only tools governments had to combat virus transmission in the initial period in the absence of vaccination.
In the Indian context, the focus at the outset of the pandemic outbreak was on border control. A strict lockdown in the initial period was announced, after which three more lockdown phases followed in succession. A containment strategy followed this in the unlock phases that got decentralized gradually, with specific regions gaining more control over its implementation (see table below). Both strategies were equally important in tackling the virus. Against this backdrop, we lay out some of the strategies India followed to contain the spread of the virus. The idea is to approach it from a theoretical perspective since doing a cost-benefit analysis in something beyond the scope of this paper since the objective of this section is to see things at the central government level and not at the state government level. Moreover, it is learned that containment was mainly at the discretion of the state level, which requires disaggregate data in terms of, say, red, orange, green zones10, essential and non-essential goods, number of hospitalisations, number of home isolations etc., something not available in a uniform format at the state level. Therefore, the idea is to comprehend different strategies that were adopted by local authorities to balance lives and livelihoods.
Phases of Lockdown and Unlock in India (2020)
Lockdown Unlock 1 Unlock 2 Unlock 3 Unlock 4 Unlock 5 Unlock 6
1 2 3 4
25-Mar-20 15-Apr-20 4-May-20
18-May-20 1-Jun-20 1-Jul-20 1-Aug-20 1-Sep-20 1-Oct-20 1-Nov-20
14-Apr-20 3-May-20 17-May-20
31-May-20 30-Jun-20 31-Jul-20 31-Aug-20 30-Sep-20 31-Oct-20 30-Nov-20
After detecting the first case of COVID-19 in Kerala, the authorities became vigilant and undertook precautionary measures, such as screening passengers arriving from particular countries. This got complemented by a quarantine period of 14 days for people who returned to India from COVID-19 affected countries. The culmination point was reached with the
10 In the vein of containing the spread of the virus, colour coded zonal classification was implemented across the country. These classifications differed across states relative number of cases, hospitalisations, CFR, etc.
announcement of a complete lockdown with limited mobility. However, before delving into the economic aspect, a brief discussion on the decision to have a lockdown follows.
We have already seen why lockdown was viable, given that the initial intent was to contain the spread. Moreover, studies show that density and city size aggravate the spread of the virus (Stier et al., 2020). This had significant policy implications in terms of early measures to prevent the spread in India, given it has a population density of 382 persons per square km versus the global average of 58 persons per square km.11 Thus, having no lockdown would have been potentially very dangerous, given that both CFR and R0 could have risen substantially. Also, the government was aware of the fact that it needed to rigorously revamp its health infrastructure given India’s relatively insufficient health infrastructure (Patnaik & Sharma, 2020) coupled with the destruction that the virus was causing even in nations with a relatively nuanced health infrastructure (such as Italy and Germany). Therefore, the decision to lockdown became all the more relevant so that time could be bought to enhance the healthcare infrastructure.
Studies in the Indian context also suggest the benefits of having a lockdown in terms of saved lives, reduced cases, or providing the necessary time to enhance the health infrastructure. For example, Ray et al. (2020) calculated the impact on the number of cases by varying the length of the lockdown in their paper. They deduce that having a lockdown would reduce the number of cases and provide valuable time to enhance the health infrastructure. Likewise, Agrawal et al. (2021) tried to predict the number of cases and deaths in counterfactual scenarios of no lockdown and a delayed lockdown (i.e., lockdown on different dates as against the actual date of 24th March 2020). They predicted that the peak would get realised in September 2020. With a no-lockdown scenario, however, the peak would have been much larger and preponed to around mid-May. The authors also concluded that the difference in terms of the number of deaths – between the counterfactual scenario of no lockdown against the actual scenario – would have been about 2 million.
The Economic Survey (2020-21) also highlighted that India was able to save more than 100,000 (0.1 million) lives through the lockdown in March-April 2020. Moreover, the country took around 175 days to reach the peak from its first 100 cases, while most countries reached their first peak in less than 50 days (Russia, Canada, France, Italy, Germany, etc.). India thus benefited from successfully pushing the peak of the pandemic curve to September 2020 through the lockdown and buying the necessary time to revamp its health infrastructure.
According to the Ministry of Health and Family Welfare statistical analysis, the COVID-19 tally could have reached about 2 lakhs (0.2 million) without lockdown by April 11, 2020, with a presumed R0 value of 2.5 and a peak growth rate of 28.9%.12 However, due to lockdown measures, the actual cases only went up to about 7500 by April 11, 2020, making a case for lockdown stronger.13
11 As per the Economic Survey 2020-21
Given that the lockdown was implemented, the government had a myriad task to increase the capacity of the existing healthcare infrastructure. To this end, looking at certain factors gives some context. For example, more than 1900 dedicated COVID-19 hospitals were identified at both Centre and State levels, having more than 1.7 lakhs (0.17 million) isolation beds and more than 21,000 ICU beds.14 With the passage of time, where less than 100 tests per day were being conducted at the beginning of the pandemic, the country was able to increase the same to 100,000 (0.1 million) tests per day before May end. Moreover, having just one COVID-19 testing laboratory, the capacity was increased to more than 550 by mid-May.15 The result was also reflective in the doubling rate16 which increased to about 6 days by mid-April from about 3 days before any such lockdown was announced. Therefore, India performed well, particularly in how it scaled up testing and treatment facilities during the national lockdown in 2020 (Babu, et al. 2021).
14 https://pib.gov.in/PressReleaseIframePage.aspx?PRID=1615405 15https://main.icmr.nic.in/sites/default/files/press_realease_files/ICMR_Press_Release_India_testing_story_20052 020.pdf
16 Number of days it takes for the number of cases to double.
Box 1: Containing the Virus: Role of the Society
Containing the spread of the virus was not just in the hands of the authorities. This involved community participation at all levels – local people, civil societies, NGOs, among others. Although the role of the government involved ensuring social distancing, ensuring that people wear masks, spreading awareness about personal hygiene, augmenting healthcare services, among others, the role of the community cannot be negated. It was on the people and other stakeholders to adhere to the safety protocols and follow other community guidelines.
Nonetheless, it cannot be taken away that the people faced multiple hassles like getting supplies of essential goods and services like consumer goods, medicine supplies, and even ready ambulance services. The role of NGOs and other voluntary organisations were crucial in this regard. Media reports suggests that there were multiple such organisations that helped people avail such services. Many organisations ensured that ready ambulance services were available both for hospitalisation and cremation. Moreover, there were examples where resources were mobilised in ensuring essential goods reached people’s home, along with basic medical equipment like masks and sanitisers.
Voluntary associations were also formed in preventing the spread of fake news relating to vaccination. Many local doctors volunteered for free for to take virtual sessions within the community that guided people in developing better immunity and also suggested them in taking preventive action. All of these examples, not only complemented the efforts of the government in containing the virus, but also showed the willingness of people to help and participate at the community level.
Source: Media Reports
However, to move towards containment, the government devised a strategy to unlock the nation in a phased manner. This meant opening certain regions and allowing the economic activity. The broader idea was to pass the authority of locking and unlocking to state authorities since they had a better understanding of their local regions and better control over the health authorities. The states had a mammoth task to ensure that the lives and livelihoods of the people were saved. In this regard, containment becomes extremely important since until early 2021, i.e., when the vaccine became available, containment was the sole factor that could strike a balance between lives and livelihoods. Containment worked as a double-edged sword since it not only ensured some economic activity but also reduced the burden on health infrastructure by measures such as quarantining17 and home isolation18 which restricted potential patient traffic to their respective homes. Therefore, it is essential to understand some of the successful containment strategies implemented by the local authorities that also serve as a reference point for future pandemic
17 Quarantine refers to separation of individuals who are not yet ill but have been exposed to COVID-19 and therefore have a potential to become ill. There will be voluntary home quarantine of contacts of suspect /confirmed cases.
18 Isolation refers to separation of individuals who are ill and suspected or confirmed of COVID-19. Pre-symptomatic cases/ very mild/ mild cases can opt for home isolation.
response preparedness. Being a diverse country in terms of demographics, density, health infrastructure, and awareness, the containment strategies differed as per native circumstances.
The case study of the Bhilwara district of Rajasthan serves as one example of how effective and quick response coupled with the delegation of power can act as a significant weapon in containing the spread of the virus. Bhilwara, where by the end of March 2020, there were about 26 new cases, got reduced to just 10 new cases by the end of May 2020 (for the whole of May). Although the number is small, the amount of effort put into was myriad. By imposing an effective lockdown and delegating the power to the district administrator, the district could contain the spread of the virus. Some of the measures under the containment was constant co-ordination among public health department, police, local municipal corporation, community leaders, supplier, etc., cluster mapping of positive cases, rigorous phase-wise screening in affected epicentres with clear demarcation of areas along constant follow-up, enhancing the capacity of health infrastructure – adding beds, training staff, conducting sanitation drives, etc., and continuous delivery of information and services concerning cases, beds, protocols, etc. simultaneously ensuring door step delivery of essential commodities (Golechha, 2021). The Bhilwara model might look typical, but the learning remains in its quick response action and the ability to comprehend that local authorities can best handle local containment against the top-down approach. By this approach, a district like Bhilwara – whose population is almost equivalent to Namibia (a Southern African country) – was able to contain the virus effectively. The figure below depicts the Indian strategy of delegating power towards the lower level.
Another example that stands out is the one of Dharavi, Mumbai. Being one of the largest urban slum settlements of the world, with a population density of more than 2.2 lakh people per square kilometre19, the Dharavi model (chasing the virus) serves as an example of how community engagement, constant coordination and collaboration, pervasive commitment, and quick response
action can help prevent the spread of the virus and loss of livelihoods (Golechha, 2020). The model was based on four T’s – tracing, tracking, testing, and treating under which thousands of people were screened in clinics, homes, and even mobile vans. To facilitate things, the process was based on a PPP model wherein gloves, sanitisers, and PPE kits, among other necessary equipment, were provided by the BMC to private doctors. Moreover, the fact that with such a dense population – where about 8-10 people live in a 10×10 room – home quarantine becomes unfeasible; efforts were made to enhance the health infrastructure capacities wherein institutional quarantine facilities were created in available schools, marriage halls, sports complexes, etc. which were supplemented with community kitchens. As a result of the above measures, the COVID-19 growth rate was reduced from about 12% in April to about 4% in May to about 1% in June 2020.
Yet another prominent example is the Kerala model. The state’s effective COVID-19 management is another example of how good governance can help contain the spread of the virus. One of the biggest strengths of Kerala is its available health infrastructure which enabled it to conduct mass screening and training of its health care professionals. This was complemented by basic training of local representatives of self-help groups known as ‘Kudumbashree’ to develop a response action (Rahim, 2020). As Kerala witnesses the movement of people from in and outside the country, one of the response actions included airport screening and sending symptomatic patients to the nearest health facility where they would be further admitted or referred for home quarantine. This was accompanied by active contact tracing and mapping of all potential patients. Despite an already robust health infrastructure, the same was enhanced further by adding hospital beds and ICU units. The state also ensured the use of ICT services to communicate information regarding hygiene, free consultation and psychological support. Therefore, a collaborative effort at the state and local level was able to contain the spread and serve as a guiding tool for its peers.
Other studies have also focused on the overall efficacy of containment. For instance, a study by Chowdhry & Jain (2021) attempted to construct a stringency index at the State level in India to assess the impact of stringent lockdown/containment policy on health and economic outcomes. The study explored the effect of lockdown on Covid-19 spread and deaths using the index. The results confirmed that higher stringency reduced cases and deaths per capita. Findings revealed that a 1% increase in the stringency index in the previous month led to a 4.6% lower virus spread in the current month. Similarly, 5.9% fewer deaths per capita got reported when stringency was higher by 1% in the previous month. India’s strategy of imposition of a stringent lockdown in the initial stages to control the spread and focus on ramping up testing infrastructure and health facilities is validated by this analysis. The lockdown, therefore, was a critical instrument in ‘flattening the curve’ and saving lives.
From the above examples, it can be argued that although centralized coordinated policies may be necessary, their effectiveness is highly dependent on states’ capacities. Therefore, given the regional disparities in the country, it is necessary to look into sub-national-level mitigation strategies to provide an overview of the growth rate of the epidemic during different phases of the mitigation strategies deployed by states for sound decision-making and learnings which can be applied in future pandemics.
In this section, we have tried to develop an argument for why lockdown was a plausible and effective strategy for containing the spread of the pernicious virus. The Indian government was witnessing the damage in other parts of the world and therefore had to ensure minimizing it in the country. However, as highlighted in the economic survey, the government took the firm stand of a lockdown with the vision that a short-term restriction would be more prudent as against a permanent damage in terms of loss of lives. We have seen evidence of how various models have come up with different numbers of confirmed cases and deaths that happened and would have happened in the counterfactual scenario of delayed or no lockdown. It can be ascertained that the numbers show variation due to their different methodologies adopted, varying time periods, and data sources, to mention a few. However, as a caveat, it should be kept in mind that all models are susceptible to change with the availability of more reliable data.
Moreover, no model is a panacea and cannot precisely predict an active virus such as COVID (Babu et al. 2021). Also, most models that have tried to predict the COVID trajectory are prone to an underestimate since these models have not fully captured cases like underreporting or false negatives. India is no exception, with scholars and media highlighting the loopholes in reporting actual numbers (Bhaduri et al., 2020; Chatterjee, 2020). Therefore, although various estimates can be arrived at, the broader learning is that lockdown and containment measures were essential in containing the spread of the virus. Lastly, as can be ascertained from various examples mentioned above, the key to containing the spread of the virus lies in a bottom-up approach as against a top- down approach. India was quick to realise this and therefore delegated the power to the local authorities. Thus, the lockdown and subsequent containment measures helped flatten the curve and struck a balance between saving lives and sustained economic activity.
Although containment can be thought of as a useful measure, given the magnitude of the pandemic, it was expected that other supportive measures would be required. This was essential given that a balance between lives and livelihoods needed to be maintained. In this regard, the government came up with a targeted fiscal relief package where the idea was to sustain the economic activity that came to a sudden halt due to the pandemic. We unfold the relief package in the next chapter.
India’s COVID-19 Relief Package and its Impact on Economic Recovery
The impact of the pernicious COVID-19 virus and the fight against it to save lives has been discussed in the previous chapter. This chapter moves beyond containing the spread of the virus and focuses more on the nation’s response in dealing with the halted economic cycle that the pandemic perforce at its outset. The COVID virus being new to the world did not have an immediate response function. Therefore, as the cases started to increase, governments across the globe moved towards locking down the economy, leading to the cessation of economic activity in virtually all sectors. Although the impact was heterogeneous across sectors, the overall effect was reflected in numbers whereby Q1 GDP for 2020-21 dipped by about 24% relative to the previous year and about 29% sequentially. However, the phase-wise opening up of the economy, after about 45 days of the complete lockdown, brought about challenges to provide the kick start to the engine. Against this backdrop, the revival of economic activity and its growth would arguably not have been possible without the government’s proactive measures in the form of economic ‘relief packages’.
The relief package under the ‘Aatmanirbhar Abhiyan’ was a comprehensive package spanning across sectors with a vision to revitalise the growth cycle of the nation along with empowering the relatively lower strata of society and small businesses that required that initial boost or support to restart their businesses. The myriad package was designed to take care of the country’s heterogeneity and diversity in terms of people, businesses, location, and income, among other paradigms. Being implemented stage-wise, the package was a kind of a learning or outcome-based model under which subsequent phases were launched by taking into account the feedback of the former ones. Whether the entire credit should be given to the relief package can be of debate, the numbers show that the Q2 GDP for 2020-21 saw an increase of about 22% sequentially20, which carried forward to other quarters as well. Against this backdrop, the chapter first delves into the literature to comprehend the effect of some of the relief measures (global and local) that helped in the resurgence of the economic activity. This would also entail developing a mechanism to trace the impact of such measures in the Indian scenario. The second section will try to map the impact of relief measures by assessing the economic and social impact of the same, along with drawing some inferences.
Although the literature is relatively new regarding COVID-19 relief measures, it captures their brief account of how they were infused to combat the effects of the virus at the health and economic level. There have been efforts in other directions whereby dashboards and data points can be accessed that map the COVID atrocities via numbers. International Monetary Fund (IMF) has made one such data point available. The IMF has provided a policy tracker summarizing the fiscal measures and relief packages rolled out by different economies to combat the adverse effects of COVID-19 on society. It provides an overview of the measures that India has initiated. In May
20 Although this was still lower by about 6.6% relative to Q2 of previous year.
2020, India announced a relief package for around 10% of its GDP – about ₹20 lakh crores (about $ 282 billion)21. IMF Policy tracker states that India’s central government fiscal support measures can be divided into two broad categories: (i) above-the-line measures, which consist of government spending, foregone or deferred revenues and (ii) below-the-line measures formulated to support businesses and increase credit provision to several sectors. In the initial months of the pandemic, the relief response was more in the area of above-the-line measures providing support in-kind in the form of food and cooking gas, cash transfers to low-income households, employment provision to low-wage workers, insurance coverage for workers in the healthcare sector, etc. Later in the pandemic, in October and November 2020, the measures announced included support schemes for certain sectors, some of which included credit support to businesses, and poor households and targeted support for some sectors.
Although literature is scant, some studies have tried to map the potential impact of the relief package(s). For instance, Deb et al. (2021) investigated the effects of COVID-19 related fiscal policy measures based on the daily fiscal policy announcements, classifying them by the type of fiscal measure, and high-frequency economic indicators. They conducted this study for 52 countries for the year 2020 and concluded that fiscal policy measures positively impacted economic activity and reduced unemployment. However, the study adds that the degree of this effect depends on certain country specific characteristics like the stringency in containment policies, among others.
Similarly, Gourinchas et al. (2021), by studying the effects of fiscal policy at the firm, sector, country and global level, deduced that business failures would have amplified in the absence of policy support. Moreover, fiscal policy does have the capacity to reallocate resources to demand deficient sectors and also exert a positive influence on employment. Walmsley et. al (2022), using a computable general equilibrium model also explored the effect of fiscal stimulus in the context of US economy. Their results indicated that the initial rounds of the fiscal stimulus were more effective relative to the later rounds. Moreover, they found that unemployment benefits had a greater impact than direct or indirect benefits since these benefits gets saved rather than getting expended.
As for the Indian context, Varshney et al. (2021) examine the impact of India’s Pradhan Mantri Garib Kalyan Yojana, implemented after the COVID-19 lockdown, on the procurement of agricultural inputs for the farming season. Based on a quasi-experimental method and survey data from about 1800 households in the states of Rajasthan, Madhya Pradesh, and Uttar Pradesh, the study concluded that about 89-94% of households in the survey benefited from the direct cash transfer in the package and thus observed a positive impact of the government’s package on easing credit constraints for the agricultural sector. Similarly, Prusty et al. (2022) attempt to explore the impact of India’s monetary and fiscal stimulus package measures by analysing the feedback from respondents using structural equation modelling (SEM) during the post-lockdown period. Their results showed a positive perception among stakeholders (academicians, professionals from
Taken average exchange rate for the year 2019-20. See
corporates and entrepreneurs) with regard to the impact of the stimulus measures on the Indian economy’s revival.
A study by Das & Mishra (2021) based on a telephonic interview with a sample of around 200 people in Delhi (largely migrant workers), found that almost all workers did not have a job in the first phase of the national lockdown and relied on savings, borrowings and other sources for finance. They thus calculated the actual and potential impact of relief measures given by the state and central government and found that 75% of the respondents could get some assistance.
Against this backdrop, we attempt to map the impact of the COVID-19 relief package on the Indian economy. The Indian government launched the relief package under the ambit of ‘Aatmanirbhar Abhiyan’ with the motive to not only lubricate the engine of growth but with the ideology of making India self-reliant. The package was comprehensive, encompassing sectors from farming, MSMEs, tourism, health, insurance, and employment, among others. Having reached about 10% of the GDP, the package was a combination of direct transfers, loan extensions, subsidies, tax reliefs, moratoriums, etc. Given the magnitude of the relief package, it becomes essential to map its impact on the economy since fiscal stimulus working through an income multiplier can potentially improve consumption in the near term (Rao et al., 2021). Although studies have tried to assess the impact of relief package(s) in the Indian or global context, they have done it at a macro level by focusing on either just a few aspects of the entire package or specifically on a particular policy under the package. As highlighted above, Varshney et al. (2021), for example, studied the impact of India’s Pradhan Mantri Garib Kalyan Yojana on the farm sector. Others have also focused on specific outcomes either through telephonic surveys or other primary survey techniques. Although the available literature ascertains the impact of particular arms of the relief package(s), evaluating the entire package in a granular way spanning across sectors is something that has not been explored at much length.
Therefore, this study attempts to assess the impact of the Indian relief package by classifying it into four components: MSMEs, social sector, employment, and agriculture, and studying the schemes revolving around a particular component by calculating their potential economic impact. Since covering all schemes or the broad components under one realm is not possible given the heterogeneity in terms of area and their respective targets, a detailed descriptive methodology is developed separately for all components to ascertain their potential economic impact. In this regard, all data pertaining to the number of beneficiaries, the amount disbursed, wages, etc. have been sourced from the government. As a corollary, while citing government data makes the whole exercise reliable, it restrains the study so that not all measures undertaken can be evaluated since data pertaining to those measures are either available in discrete format or are not available altogether. Thus, the study only assessed those measures on which data was available in a complete format.
Potential Economic Impact of the Relief Package Impact on MSMEs
MSME is one of the most pivotal pillars of the Indian economy. For long, MSME has contributed to the Indian economy both in terms of generating output and in terms of employment. Being the heartland of the Indian manufacturing and services sector millions are associated with it for their
livelihoods. As per government data, the sector contributes roughly 30% to the Indian GDP and employs approximately 11 crore people.22 With such magnitude, the sector is bound to make an impact if provided with adequate support. This has exactly been the case whereby we discuss two schemes that the government launched for the promotion and well-being of the sector; Subordinate Debt for Stressed MSMEs, and Guaranteed Emergency Credit Line (GECL) / Emergency Credit Line Guarantee Scheme (ECLGS) for MSMEs. The former essentially entails providing credit facility through lending institutions to stressed MSMEs, and the latter involves providing 100% guarantee to lenders (banks and NBFCs) so that they can extend credit to businesses and MSMEs to meet their working capital requirements. The figure below gives the number of beneficiaries that availed the scheme’s benefit.
Schemes and Number of Beneficiaries under MSME Relief Package 23 24
To calculate the economic impact, we first evaluate the contribution of MSMEs to Indian GDP. We take the figure of 2019-20 for GDP since that was just before the pandemic hit and thus captures the true essence of the contribution of MSMEs.
Contribution of MSMEs to the GDP is about 30%25. Therefore, for 2019-20, MSMEs contributed 1,45,15,95826 crore * 30% = ₹ 43,54,787,40,00,000 (approx. ₹ 43.5 lakh crore) or $ 614 billion.
Assuming the total number of MSMEs in India to be about 6.3 crore27, we get the contribution of one MSME to the above figure as 43,54,787,40,00,000 / 6,30,00,000 = ₹ 6,91,236
Assuming the relief package helped sustain operations for the aforementioned beneficiaries in the figure (1,13,00,782), we can say that the economic impact is –
1,13,00,782 * 6,91,236 = ₹ 7,81,150,73,46,552 (approx. ₹ 7,81,150 crore) or $ 110.18 billion.
This comes out to be roughly 5.38% of the GDP. However, if we apply a shutdown rate of 9%28
to the number of beneficiaries, the number of such beneficiaries would then reduce to 1,02,83,711
22 National Sample Survey 73rd Round (2015-16).
25 https://www.pib.gov.in/PressReleasePage.aspx?PRID=1744032 26https://www.mospi.gov.in/documents/213904/416359//Press%20Note_PE%20FY22m1653998874449.pdf/96 16eef9-71b9-7522-808a-5fd438857454
27 National Sample Survey 73rd Round (2015-16).
since 9% of the total number of beneficiaries would have shut down. This then gives the economic impact as: –
1,02,83,711 * 6,91,236 = ₹ 7,10,847,12,56,796 (approx. ₹ 7,10,847 crore) or $ 100.26 billion, which comes out to be about 4.90% of the GDP.
Therefore, one can argue that the relief package brought respite to the MSME sector by ensuring their working capital requirements. It is not unknown that the more than 90% of the MSMEs belong to micro and small enterprises29 who generally face the problems pertaining to regular cash flows. These cash flows often become part of machinery, labour, electricity, rent, etc., and become pivotal in MSMEs’ day-to-day operations. Therefore, the government by identifying the right target area was able to help sustain the operations of these MSMEs.
Impact on Social Sector
Keeping aside industries for a moment, it cannot be refuted that the general public was affected mostly by the pandemic. Masses of people got devoid of basic amenities like food, shelter, and health with the slowdown in economic activity and many also lost their jobs as a consequence of the slowdown with businesses trying to lower costs due to dampened demand. All this even led to large number of migrants to reverse migrate. Therefore, the government had a large problem at hand to not only stop the reverse migration process but to also help million others who got devoid of the basic amenities. Against this backdrop, the government launched the Pradhan Mantri Garib Kalyan Package for the poor to help them fight the battle against the atrocities of pandemic. The package essentially had two components shown in the figure below. Be it in terms of health services, livelihoods, shelter, or food.
The Pradhan Mantri Garib Kalyan Yojana (PMGKY) was a comprehensive scheme including multiple initiatives ranging from Jan Dhan Accounts to PM Kisan to EPFO contribution, among others. Excluding Ann Yojana, the PMGKY amounted to ₹68,820 crores ($ 9.7 billion). On the other hand, the Pradhan Mantri Garib Kalyan Ann Yojana was a scheme encompassing the distribution of free food grains to people over and above what they had already received. This latter involved the distribution of free food grains to approximately 80 crores (800 million) people (for details, see figure below). Considering the schemes as fiscal stimulus, we apply the fiscal expenditure multiplier of 0.7830 to get the economic impact of this fiscal stimulus, i.e.,
68,820 crore * 0.78 = ₹ 53,679,60,00,000 (approx. ₹ 53,679 crores) or $ 7.57 billion for PMGKY, and 2,38,495 crore * 0.78 = ₹ 1,86,026,10,00,000 (approx. ₹ 1,86,026 crore) or $ 26.24 billion for PMGKAY.
29 National Sample Survey 73rd Round (2015-16).
30 “Rebalancing Monetary and Fiscal Policies Post-Pandemic” (2022), Reserve Bank of India. Available at https://m.rbi.org.in/scripts/PublicationsView.aspx?id=21036
In terms of recession/slowdown the paper considers the fiscal multiplier to be 0.78. Being a robust estimate, we insert this multiplier to calculate the economic impact.
Pradhan Mantri Garib Kalyan Package* 31 32 33
Note – * Rupees (₹) figures are in crores
With a comprehensive package that encompassed people belonging to old age, women, farmers, and the working class, the PMGKY ensured that the impact on the livelihoods of these people was moderated to an extent, whereas, on the other side, by distributing free food grains to poor people under the PMGKAY, the government ensured that no one slept hungry. Of course, one can delve deeper into the analysis of this and bifurcate the schemes to calculate the economic impact in a more robust and nuanced way, but this can be considered a starting point to comprehend the economic impact of the welfare schemes.
Impact on Employment Generation
As highlighted in the above section, the government had to not just deal with the problem of reverse migration but sustain them in a way by providing them with economic opportunities so that livelihoods could be ensured. Moreover, this should not be confused with just migrant workers. Instead, the aim of the intended scheme(s) was to encompass all people whose jobs or employability got affected due to COVID-19. In this vein, the government launched programmes – two of which we have tried to map for their economic impact. The details of the same have been given in the figure below: –
31 The Pradhan Mantri Garib Kalyan Yojana details have been taken till September 2020, whereas the Pradhan Mantri Garib Kalyan Ann Yojana has been taken till March 2022.
Employment Generation Schemes and Beneficiaries34 35
Whereas the former entailed 24% contribution towards EPFO to establishments employing up to 1000 employees, the latter provided immediate employment & livelihood opportunities to migrant workers of 116 districts belonging to Bihar, Jharkhand, Madhya Pradesh, Odisha, Rajasthan and Uttar Pradesh. Therefore, the economic impact of the above two schemes can be calculated as: –
For Aatmanirbhar Bharat Rozgar Yojana, the benefit was availed by 59,84,256 beneficiaries. We can assess the economic impact by assuming a minimum wage earned in a year, i.e.,
59,84,256 * 26936 * 31237 = ₹ 50,224,66,37,568 (approx. ₹ 50,225 crore) or $ 7 billion and;
Through PM Garib Kalyan Rozgar Yojana, 50.78 crore man days were provided. Considering that the scheme offers 125 days of employment for one person, the number of beneficiaries can be calculated as 50.78 crore man days/125 days. Thus, beneficiaries of the scheme become 40,62,400.
Looking at the minimum wage approach, the economic impact can be calculated as: –
40,62,400 * 269 * 312 = ₹ 34,094,91,07,200 (approx. ₹ 34,095 crore) or $ 4.81 billion.
Although the exact number of people who lost their jobs or livelihoods cannot be mapped altogether, the government made some efforts to provide economic opportunities through the mentioned schemes. The schemes might not have encompassed all the people, but they certainly did help in preventing job losses and the consequent economic loss that the nation would have otherwise witnessed. What remains a learning, though, is that the COVID relief package did reach millions in securing a livelihood and such schemes with an enlarged scope became necessary in the long run to safeguard against such pandemics.
Impact on Agriculture
Being one of the oldest and largest employers in the country, the agriculture sector assumes its importance when it comes to relief package. It is not unknown that agriculture is one of the most informal sectors of the economy and encompassing their diversity under one scheme is not
36 If the median of the minimum wages in different states is drawn, it would be ₹269/day in the country. See https://pib.gov.in/PressReleseDetailm.aspx?PRID=1705410. More on this in the next chapter.
37 We take 26 days as the working days in each month as per Section 26 of The Minimum Wages (Central) Rules, 1950. See https://labour.gov.in/sites/default/files/TheMinimumWages_Central_Rules1950_0.pdf for more details.
possible. The government thus announced a slew of measures to assist the agriculture sector. These measures varied from providing loans to subsidies to seed funds and covered farmers, fishermen, and beekeepers, among others. The table below highlights some of the schemes that were launched.
2 3 4 5 6
Starting from the first, emergency capital funds through NABARD was a scheme that provides additional financial support in terms of loans to small and marginal farmers. Under the scheme ₹30,000 crore ($4.23 billion) was sanctioned from which ₹25,000 crore ($3.52 billion) was disbursed to cooperatives, district cooperatives and regional rural banks.38 This scheme becomes important because Indian agriculture is characterised by a large number of small and marginal farmers who often fall short of capital and rely on loans to resolve their working capital requirements. Therefore, to sustain their operations, the government announced this emergency capital fund over and above the ₹90,000 crores ($12.70 billion) already approved earlier by NABARD.
The second scheme, Pradhan Mantri Matsya Sampada Yojana, was a scheme that provided “livelihood and nutritional support for socio-economically backward traditional fisher’s families during the fishing ban/lean period”. The scheme entailed the upgradation of fisheries infrastructure and addressed issues like disease, sustainability of marine fisheries, sanitary and phytosanitary matters that impacted the competitiveness of India’s exports. Under the scheme, more than 8 lakh beneficiaries had enrolled themselves, and an amount of almost ₹361 crores ($0.05 billion) was spent for the welfare of the fishermen. This was pivotal given the sector provides livelihood to about 16 million fishers and fish farmers at the primary level and almost twice the number along the value chain.39
The third scheme, the agriculture infrastructure fund for farm gate infrastructure, was essentially a medium to long-run debt financing scheme for investment in postharvest management infrastructure and community farming projects. This scheme was critical since it realised that making agricultural products available to the market involves assistance not just at the time of sowing but also at the time of harvesting. Such assistance not only helps to utilize the harvest
38 https://www.pib.gov.in/PressReleseDetailm.aspx?PRID=1660691 39 https://pib.gov.in/PressReleaseIframePage.aspx?PRID=1842803
Amt. (₹ crore)
Amt. Availed/Disbursed (₹ crore)
Emergency Capital Funds through NABARD
Pradhan Mantri Matsya Sampada Yojana
Agriculture Infrastructure Fund
Kisan Credit Card
Operation Green Scheme
National Beekeeping & Honey Mission
optimally but also helps in providing a fair deal to the farmers. For example, due to the limited infrastructure connecting farmers to markets, 15-20% of yield is wasted which is relatively higher compared to other countries where it ranges between 5-15%.40 Against this backdrop, the scheme assumed importance since it helped about 10,394 projects with an overall assistance of ₹7,677 crore ($1.08 billion).41
The fourth scheme, Kisan Credit Card, was a scheme which envisaged to provide universal access to institutional concessional credit to all farmers including animal husbandry, dairy and fisheries farmers with a special focus on coverage of PM-KISAN42 beneficiaries. The scheme included small farmers, marginal farmers, sharecroppers, oral lessees, tenant farmers, and SHGs. As per the latest figures, the scheme – with an outlay of about ₹2,00,000 crore ($28.21 billion) covered about 2.5 crore beneficiaries with a sanctioned credit limit of about ₹1,35,000 crore ($19.04 billion), i.e., out of ₹2 lakh crores more than two-third amount was sanctioned for disbursement as loans.43
The fifth scheme, the operation green scheme, aims to protect the growers of fruits and vegetables from making distress sales due to lockdown and reduce post-harvest losses. It involved a subsidy @50% of the cost of the two components namely; transportation of eligible crops from the surplus production cluster to the consumption centre; and hiring of appropriate storage facilities for eligible crops (for a maximum period of 3 months). Before the pandemic, the scheme covered tomatoes, onions and potatoes; however, given the pandemic the scheme expanded to 41 notified fruits and vegetables (short run) and 22 fruits and vegetables (long run).44 In terms of numbers, the scheme got an allocation of ₹38.22 crore ($5.39 million) of which ₹38.21 crores ($5.38 million) was disbursed.
The sixth scheme, the national beekeeping & honey mission, was a scheme under the broad schema of promoting and developing scientific beekeeping in the country. Beekeeping is considered with the potential to produce honey and associated high-value products like bee wax, propolis, royal jelly, etc. Being a commercially viable product, India’s export of honey has increased by about 110% between 2013-14 to 2019-20. Therefore, to achieve the broader goal of its promotion and development, 45 projects with an amount of ₹88 crores ($12.41 million) were sanctioned.45
With the above schemes, and others not mapped due to data gaps, the government intended to ensure sustained livelihoods and improved capacity at various levels for farmers across classes and sizes. These schemes varied from DBTs to loan extensions to subsidies to financing projects. Thus, it would not be wrong to say that schemes pertaining to agriculture and the allied sector did have an impact on the sector. Although the exact impact at a granular level is beyond the scope of this paper, an attempt to know its direction gives a positive indication.
Although we have tried to track the impact of some of the components of relief package, it goes without saying that this is not all that the government came with. As mentioned earlier, we’re
42 Under the PM-Kisan, the government directly transfers ₹6000 (in 3 instalments each) to eligible farmers. 43 https://pib.gov.in/FactsheetDetails.aspx?Id=148600
restricted by data limitations and, therefore, only took those schemes on which a complete set of information was available via government sources. This chapter thus tried to assess the economic impact of the relief package by bifurcating it into four components. As noticed, the relief package tried to provide active support to various sectors of the economy like MSMEs, agriculture, social sector, among others. In this regard, the package brought respite to relatively lower strata of society in terms of taking care of their basic requirements and helping small businesses manage their inter- temporal budget constraints.
Although the impact on one particular person/business might appear to be relatively small, the overall impact turns out to be massive. This was evident when we assessed the economic impact on MSMEs, which was about 5.38% of the GDP. Similarly, the overall success of the relief package can also be ascertained by the number of enrolled beneficiaries. This hints at two things; first, the pandemic brought about with itself a dire need to come up with some kind of assistance which the government provided in the form of a relief package; second, the package(s) were well received given the number of beneficiaries that got enrolled to the same. Although the number of beneficiaries might not look great in relative terms, this might be more to do with our overall population size. However, that does not imply that the package(s) were not well received. Its phase- wise implementation suggests that they were well received and required multiple extensions, which the government was keen to provide.
Moreover, as highlighted earlier, there were some other measures that we haven’t discussed in this chapter. For example, the relief package introduced by RBI in the form of moratoriums and reducing interest rates. The central bank reduced the repo rate, reverse repo rate and cash reserve ratio to inject liquidity into the system. The measures aimed to relieve the immediate liquidity crunch that the pandemic might have induced on loans of different sizes. Moreover, they sought to provide credit at a friendly rate. Likewise, other government schemes like fertiliser subsidy, infra debt financing, and income tax relief, among others,46 had some impact on their targeted sectors, something beyond the scope of the current paper and open to future research.
Having discussed the first two sets of measures; containment and relief package, we now turn to one of the most significant interventions the world awaited in the face of the pandemic, i.e., vaccination. As was mentioned in the introduction of this paper, one of the most effective tools in combating the virus was the development of a vaccine. With persistent efforts at the global level, the world finally developed vaccines to combat the virus. The effect of vaccination in preventing the loss of lives and livelihoods is much discussed among scholars. The next chapter also attempts to add to the literature by calculating the potential income generation of the lives saved due to Covid-19 vaccination in the Indian context.
India’s COVID-19 Vaccination
The Indian government announced its first nationwide Covid-19 lockdown on 25th March 2020.
As discussed in the previous chapters, the initial idea was to somehow contain the spread of the
virus which was later transcended to striking a balance between lives and livelihoods. Although
containment and relief package(s) brought some respite within the economy, the larger remedy
lied in developing a vaccine (see box 2 below). To this end, the nation saw its first COVID-19
Vaccination on 16th January 2021. With this, the world’s largest vaccination programme was set
off to a start. Implemented in a phase-wise manner, the journey which started in early 2021, soon
took off in a way that it became one of the largest vaccination programmes of the world. In July
2022, the country crossed a global milestone of administering over 2 billion cumulative COVID-
19 vaccination doses. Progressing through different age brackets, the pace of the vaccination
campaign was commendable. Divided into phases, each of which expanded vaccination eligibility
to additional segments of the population, the government program started with the elderly,
healthcare workers and frontline workers, moving to those above 45 years of age and subsequently
including the younger age groups. The table below gives a snapshot of the phases of vaccination
60 and above and 45 years and
above (with co-morbidities)
45 years and above
18 years and above
15-18 age group
12 years and above
18 years and above (Precautionary
Dose – 3rd dose)
The graph shows the state-wise vaccine doses administered in the country. Moreover, we compare
India’s vaccination doses with some selected countries in the figure that follows. As can be
observed, the pace of vaccination differed across states, where almost 70% of the states/UTs
received at least one dose by December 2021. With time, the country could vaccinate an even
larger population, with a larger number of people getting at least two doses.
State-wise Vaccination Doses Administered Per 100 People in India till 30 Dec 2021
Meghalaya Nagaland Jharkhand Manipur Tripura Arunachal Pradesh Chhattisgarh Punjab Bihar Uttar Pradesh Assam West Bengal Puducherry Tamil Nadu Maharashtra Rajasthan Odisha Telangana Mizoram Haryana Gujarat Karnataka Delhi Uttarakhand Madhya Pradesh Kerala Ladakh Jammu And Kashmir Andhra Pradesh Andaman And Nicobar Chandigarh Goa Himachal Pradesh Sikkim Lakshadweep Dadra And Nagar Haveli And Daman And Diu
88 91 92
94 95 96
109 110 112
115 116 118
118 121 122
165 166 166
0 50 100 150 200 250
The international comparison shows that by the end of 2021, India with 102 doses was closer to
the global average of about 115 doses per 100 people. 47 Moreover, India had one of the largest
vaccine drives in the world with total doses being administered tallying up to more than 1.44 billion
by the end of 2021.
Vaccination Doses Administered Per 100 People till 31 Dec 2021 (some selected countries)
* South Africa data is till 29 Dec 2021
Total Vaccination Doses Administered till 31 Dec 2021 (some selected countries)
Finland Israel South Africa* Italy United Kingdom Russia Germany Brazil United States India China World
9.62 mn 16.79 mn 27.90 mn
111.37 mn 133.31 mn 146.36 mn 153.97 mn
331.27 mn 521.13 mn
* South Africa data is till 29 Dec 2021
47 At the time when India achieved the 2 billion vaccination mark, it administered about 140 doses per 100 people in the country.
Box 2: Developing a Cure for the Virus – The Vaccine Development
The global hunt for developing a vaccine to combat the virus began right after the pandemic hit the ground. Time demanded developing a vaccine in lesser time which was a challenge both at the global and the country level. For India, setting up of a high-level National Task Force in April 2020 for COVID-19 related works in the domain of science and vaccine development was the beginning of the journey. The task force was keen in developing an indigenous and was also open for global tie-ups. Moreover, as the nodal agency, the Department of Biotechnology (DBT) was tasked with identifying a pathway for vaccine development. This included identifying partner agencies, monitoring their progress, and undertaking the multi- stakeholder facilitation at the government level.
National efforts towards the development of the COVID-19 vaccine were led by Indian Council of Medical Research (ICMR) in association with Bharat Biotech International Limited (BBIL). While BBIL deployed the human resources and financial resources for the development of the vaccine, ICMR provided technical guidance and helped with preclinical studies and clinical trials. In June 2020, their vaccine (COVAXIN) got approved for phase 1 & 2 human clinical trials. The positive results and expedited regulatory process helped get them a nod for Covaxin to receive approval for restricted use in January 2021.
As for global tie-ups, the Serum Institute of India (SII) took the lead in this endeavour through its Oxford-AstraZeneca tie-up for manufacturing of the vaccine in India with the brand name ‘Covishield’. Indigenous manufacturing process and knowledge transfer helped development of the vaccine. Showing positive results, Covishield got the nod to conduct phase 2 & 3 clinical trials in India in August 2020. An unprecedented move taken by SII was its decision to begin production of the Covishield vaccine post technology transfer from AstraZeneca/ Oxford University for phase 2 & 3 trials in India, even as phase 3 clinical trials continued in the United Kingdom (UK). The decision gave the manufacturer head start on vaccine production and building inventories which paid off as the vaccine candidate received approval for restricted use in January 2021.
In this way, India was able to come-up with two vaccine candidates and roll-out a vast vaccination drive. After the success of both the vaccine candidates, others like Sputnik V, a Russian based vaccine – developed by Gamaleya Research Institute of Epidemiology and Microbiology and manufactured by Dr. Reddy’s Laboratories in India – emerged as the third candidate in April 2021. The development of all these vaccines helped fight the country the pernicious attack of the virus and not only inoculate a large number of people but also decrease the burden on healthcare system. A brief timeline is presented in the figure below.
Source: IFC, 2021
The challenge of the vaccination program derived not only from the scale, as India is home to
about 17% of the world population, but also from the population’s heterogeneity across diverse
linguistic, cultural, and socioeconomic groups. Nevertheless, the country also emerged as a
significant global vaccine supplier. India supported the international community through Vaccine
Maitri, under which more than 7.23 crore (1 crore = 10 million) doses of COVID-19 Vaccine were
exported to 94 countries and 2 UN Agencies by the end of November 2021.48 This was further
extended to about 11.46 crores by the end of FY22.49
The UNDP defines Vaccine Equity as vaccine allocation across all countries based on needs
regardless of their economic status. It associates access and allocation with the ‘right of every
individual to enjoy the highest attainable standard of health without distinction of race, religion,
political belief, economic, or any other social condition’ (Mathivathanan, 2021). Vaccine
distribution, however, is influenced by social, political, economic, and health-related matters. There
are multiple obstacles to attaining vaccine equity, from vaccine shortage, wastage, vaccine
hesitancy, and vaccine efficacy, to lack of storage facilities for imported vaccines and soaring
vaccine prices in private hospitals (ibid.). There were concerns about the uneven worldwide
vaccine roll-out, with high-income countries experiencing a higher rate of inoculation and lower-
income countries witnessing a relatively slower pace (Bajaj et al., 2022).
For India, vaccinating the people was a mammoth task given factors like large population,
geographical reach, cold storage capacities, and a smaller number of healthcare facilities. However,
India set an example of how emerging nations can expand vaccination coverage across the length
and breadth of the country. With a strategic prioritised vaccination drive and efficient coordination
among the stakeholders, India proved that expanding vaccination coverage was not an
insurmountable goal despite a range of challenges. For instance, adopting a phase-wise approach
didn’t lead to the crowding of vaccination sites. However, given the size of the population, the
fear that crowding could lead to chaos was still there. To tackle this, vaccination sites were
segmented into different areas – registration, waiting, vaccination, and observation along with
segmented entry and exit points. Moreover, to track the vaccination status of each individual, the
vaccination process was routed through the CoWIN app, an interface that mapped the complete
vaccination journey – both for the people and the authorities. Lastly, to support the native cold
storage capacity of the country, 29000 cold chain points, 240 walk-in coolers, 70 walk-in freezers,
45000 ice-lined refrigerators, 41000 deep freezers and 300 solar refrigerators were used for the
Covid-19 vaccine storage.50 It was through persistent efforts at multiple levels with constant co-
ordination that India’s vaccination drive became successful (IFC, 2021).
From developing vaccine manufacturing capacities, ensuring adequate supply across different
regions, to creating a ground-level ecosystem for administering the vaccine – the vaccination
campaign involved multiple stages and actors working together. Challenges appeared and had to
be solved at each of these stages. However, one significant challenge in vaccine administration was
vaccine hesitancy among people. Frontline workers such as Accredited Social Health Activists
(ASHA) from inside each community played an important role in countering vaccine hesitancy
through exhaustive door-to-door awareness campaigns (see box 3 below). The public health
strategy adopted by India involved strategic and extensive action in getting vaccination to the
people, and the people to vaccination. A systematic study by Robertson et al. (2021) showed a
strong relationship between vaccine hesitancy and socioeconomic variations. Chandani et al.
(2021) deployed an online survey investigating vaccination willingness, hesitancy among
individuals, and sociodemographic characteristics. The authors pointed out a that a majority of
Indians would accept the vaccine, but considering the size of the population, hesitancy among a
small pool of individuals could still render a substantial number unvaccinated, to the extent that
“herd immunity” throughout the population would not be achieved. Thus, tackling vaccine
hesitancy among people was essential. Sharma (2022) overviews the major reasons behind vaccine
hesitancy in India. In addition to fear of side effects and misinformation due to lack of awareness
or inaccessibility to sources of credible information, especially in rural areas, there were other
barriers to accessing reliable information, such as linguistic and religious differences, and there
were challenges in physically accessing vaccination centres. Sharma (2022) also pointed out that in
India, vaccination as a practice was still considered limited to infants and children, and vaccinating
the adults was also a contributing factor preventing its adoption.
Box 3: The Role of ASHA Workers as Frontline Workers in Vaccination Drive
Making Covid vaccination successful was not an easy task. Given the diversity of a country like India, workers from all sectors involved in conducting the exercise undertook huge efforts to make the drive a success. A special mention, however, needs to be given to the health team community including ASHAs, ANMs and AWWs who took it upon themselves to ensure that everyone was covered across all sections of the society. Their role was pivotal given the myths and hesitancy that was associated with vaccination along with reaching to places where conventional health services took time to reach.
Before making people understand the process of getting vaccinated, they had to make people come on board to get vaccinated. This involved instilling confidence and faith by first taking the dose themselves and making people believe that there were no adverse effects. The second task was to constantly counter fake news that was being spread time and again about the vaccination drive. After dealing with these challenges, did they actually succeeded in getting people vaccinated. However, this is way easier said than done.
Right from getting people registered for vaccination, to mobilizing people to the health centres, from reporting any adverse event post vaccination, to follow-up after a couple of weeks post vaccination, and to making people understand the importance of getting the second dose as well, were some of the many responsibilities that these front-line workers shouldered with flying colours. The key here was being part of the local community, that really helped in instilling the confidence and mobilising people which augmented the vaccination drive.
The role that ASHAs played in the vaccination drive is a testimony of their importance in the healthcare system especially at the local level. They served as the point of contact between the authorities and the masses. Their role can only be applauded in how they dealt with the humongous task of getting people vaccinated despite all odds. To this end, they were recognised at the global level where the WHO honoured about 1 million ASHA workers for their commitment and persistent efforts in making vaccination drive a success and in dispensing their duties round the clock towards regional health issues.
Source: IFC and NHSRC, 2022.
Vaccination is more than the immediate solution to put countries back on track to recovering from
a contagious disease. Successful and expansive vaccination coverage brings along a myriad of
benefits that surpass mere recovery to pre-pandemic lifestyles such as the resumption of education
institutes, physical retail, and other economic/social activities that require physical attendance.
Thus, there is a need to view the benefits of vaccination through an expansionary lens. We turn to
the literature to get a sense of this expansionary lens.
Assessing the impact of vaccination pertaining to different diseases has been a subject of study for
years. COVID-19 vaccination’s impact has been a subject that has seen multiple studies using
different methods. Most studies have been done at the national level. There was a growing
realisation among policymakers, researchers, and the scientific community that awareness of the
benefits of vaccination must reach the masses to tackle any form of mistrust or hesitancy that
would hamper vaccine uptake. Instilling confidence among the masses concerning vaccination is
critical, given the significance of vaccination as a tool to tackle the onslaught of the pandemic.
Vaccination was not just considered to be a potent tool in terms of protecting an individual but
also in terms of preventing its spread to others. Reduced transmission, therefore, became the
broader goal, along with helping the burdened health infrastructure. In recent years, scholarly work
has called attention towards understanding the benefits of vaccination from a broad perspective.
The focus has shifted from a narrow understanding of vaccine impact to a broader one that
accounts for vaccination’s long-lasting economic and social impact. The Economic Survey of India
emphasized vaccination as a macroeconomic indicator. Moreover, economists pointed to the
importance of distributing vaccines worldwide for economies to return to normalcy. A study by
Chang et al. (2018) projects that, because vaccinations help families avoid heavy healthcare costs
associated with various diseases, between 2016 and 2030, around 24 million people will be saved
from slipping into poverty.
Wang et al. (2021) studied the economic impact of mass vaccination against COVID-19 in Israel.
The study followed the vaccinated cohort till day 180 using a susceptible-infectious-recovery (SIR)
model. They also applied the model to a counterfactual scenario where Israel would not have
implemented the vaccination program. The study highlights the importance of vaccination not just
to save lives but also to reduce productivity loss. The study uses a probabilistic approach to provide
a cost-utility ratio and benefit/cost (B/C) ratio of three vaccines compared to no vaccination. They
conclude that, from a payer’s perspective, a dollar invested in vaccination would have $2.79, $4.77,
and $7.21 in return for Moderna, Pfizer, and AstraZeneca, respectively. Additionally, they
conducted a cost-benefit analysis for the COVID-19 vaccines adopting four approaches. As
mentioned before, the first benefit-cost ratio utilised a payer’s perspective, wherein the benefit was
the saving on COVID-19-related medical costs, considered as the direct cost of the vaccine. The
second benefit-cost ratio was calculated as the direct cost of medical expenditure saved and the
indirect cost divided by the direct vaccine cost. Here, the indirect cost was considered of
productivity loss due to COVID-19-related hospitalization, a half-day course for vaccine jab, and
two-day sick leave if vaccination had adverse effects. For the third ratio, the calculation was cost
saving on the medical cost, plus the indirect cost and the economic impacts in terms of productivity
and education loss, divided by the investment on the vaccine. The investment cost was measured
with the vaccine price and administration fee regardless of vaccination coverage. For the fourth
benefit-cost ratio, the study took the product of the lives saved from vaccination and the value of
statistical life (VSL) ($2.7 million) divided by the investment on the vaccine. The study concludes
that mass vaccination against COVID-19 with three current available vaccines is a cost-saving
endeavour in terms of the gains incurred through lives saved.
Similarly, Deb et al. (2022) analysed a daily cross-country database of vaccinations and certain high-
frequency indicators of economic activity to examine the economic effects of the COVID-19
vaccine empirically. The study analyses 46 countries over the period from December 2020 to June
2021, using high-frequency indicators such as nitrogen dioxide (NO2) emissions, carbon
monoxide (CO) emissions, and Google mobility indices. The data collected on nitrogen dioxide
(NO2) and carbon monoxide (CO) emissions at a daily frequency come from The World Air
Quality Index project. The paper posited that an increase in vaccination per capita brings a
significant increase in economic activity. The paper also suggested that country-specific features
such as the stringency level in containment measures and the pandemic outbreak’s severity
influence vaccination’s impact on economic activity.
Demiralp et al. (2021) showed that the global GDP loss resulting from a situation of no COVID-
19 vaccinations across countries as compared to a counterfactual scenario of global vaccinations
could be greater than the cost of distributing vaccines globally. The study used an economic-
epidemiological framework, for a sample of 65 countries and 35 sectors, which combined the SIR
(Susceptibility-Infection-Recovery) model with international production and trade networks. The
paper highlighted that increasing the supply of vaccines would result in significant economic
benefits for the world economy, the benefits of which far outweigh the costs. They further pointed
out the importance of an equitable vaccination distribution by estimating the costs of inequitable
vaccine distribution through a SIR-multi-sector macro framework for 65 countries. By accounting
for the economic interdependence of the economies, they concluded that up to 3% of advanced
countries’ pre-pandemic GDPs will be borne by the vaccinated countries through their trade
relationships with unvaccinated countries. To put it simply, the paper suggested if country A is
fully vaccinated and wants to export to country B, which is not fully vaccinated, the exports of
country A will be lower compared to the counterfactual where country B is also inoculated. This
study is a significant contribution to understanding the significance of an equitable distribution of
On the other hand, Plotkin & Rodrigues (2020) looked at vaccination’s economic and social impact
but in a descriptive format. They highlighted the cost savings and productivity gains as two major
aspects of the economic impact, whereas improvement in life expectancy, opportunity, and
strengthening of health care infrastructure were major aspects of the social impact that improved
due to vaccination.
Another major factor that is considered by multiple studies valuing the importance of vaccination
is R0, i.e., transmission rate. Orenstein & Ahmed (2017) discuss the importance of the same. In
mathematical models, one can estimate how many people a typical transmitting case can infect if
it comes into contact with susceptible individuals. This is what R0 stands for. The paper points to
the immunity threshold needed within the population to end the transmission. It can be calculated
in percentage (%) as (R0 − 1)/R0 × 100. This is useful in setting targets for vaccination coverage.
Scholars have also calculated the health impacts on the population caused due to Covid. For
instance, Office for National Statistics (2020) conducted a study in the context of England. The
study utilises data from the pandemic’s initial months to model future scenarios. It studied the total
health impacts of COVID-19 in terms of excess morbidity and excess mortality across the four
categories of impacts – Health impacts from contracting COVID-19, Health outcomes for
COVID-19 worsened because of lack of NHS critical care capacity, Health impacts from changes
to health and social care made to respond to COVID-19, and Health impacts from factors
affecting the wider population. The study considered the number of deaths at a constant level of
900 per week from June 2021 to March 2021. The study also estimated the years of life lost (YLL)
to COVID-19. They calculate YLL as a product of the number of excess deaths and the life
expectancy of COVID-19 victims. The life expectancy of COVID-19 victims was calculated using
mortality rates based on the hazard rates in Banerjee et al. (2020). The paper presented a model
where the health impact was seen in a scenario with mitigations. The authors suggested that in the
absence of mitigations, the number of COVID-19 deaths would have been much higher, and the
quality-adjusted life-year (QALY) impact would have been more than three times for the scenario
However, all methodologies differ in their technique and their targeted objective. For instance,
Odihi et al. (2020) discuss the usual measures taken by economists to assess the economic value
of vaccination measures. They suggest that assessing the costs related to disease prevention is a
primary factor in multiple analyses. To understand the positive impact of vaccinations, costs
averted can be a useful lens. Costs generally include medical costs that an individual bears, the
health system, or society, including insurance reimbursement payments and out-of-pocket
payments for items such as diagnostic tests, medications, hospitalization, and transportation to
and from healthcare facilities. More often than not, studies include indirect costs due to
productivity loss, lost wages, disability, or premature death. There is a need to explore the broader
effects on the economy and society beyond assessing the health costs. Apart from analysing costs
incurred due to COVID-19, delayed elective procedures, forgone routine health services during
lockdown periods, accessibility issues faced by patients due to overburdened healthcare facilities,
and delayed care-seeking due to fear of the pandemic are some of the additional costs that need to
be factored in. While traditional cost-effectiveness or cost-of-illness analysis are helpful in these
assessments, many benefits of preventing or treating COVID-19 extend beyond the health area.
Odihi et al. (2020) suggest that these broader impacts need economists to predict economic
scenarios simulating the absence of COVID-19 or vaccination. A significant contribution to the
impact evaluation of the vaccination field comes from the study conducted by Watson et al. (2021)
across 185 countries. They estimated the number of lives that would have been lost in a scenario
of no vaccination. The study concluded that based on reported COVID-19 deaths, vaccinations
averted 14·4 million deaths across 185 countries.
Bloom et al. (2014) described a theoretical framework outlining the full economic benefits of
vaccination, going beyond the benefits typically assessed by economists in economic evaluations
of vaccinations. Concerning the economic valuation method, the authors suggested that while the
cost-effectiveness analysis (CEA) stands to be the most widely used method for assessing the
economic impact of vaccination, the benefit-cost analysis (BCA) lens could capture the range of
benefits and costs more comprehensively. The method presented direct insight pertaining to the
desirability of a health intervention in the form of the estimated benefit-cost ratio. As Nandi et al.
(2016) highlighted, considering the range of benefits that can be derived from vaccinating
individuals, it is hard for any one methodology to capture all the benefits, especially the non-health-
related ones. In this context, it is important to utilise multiple approaches to move towards a fuller
understanding of vaccine gains.
Thus, studies assessing the economic impact of vaccination have done so using different
methodologies. Major studies have used econometric modelling to simulate a counterfactual
scenario of no vaccination and compared it to the observed one where vaccination was introduced.
The economic variables used for these studies have been different – from high-frequency indicators
such as NO2 and CO2 emissions, google mobility indices, to employment and GDP. Various
studies have conducted cost-benefit analyses of vaccinating the population to make a strong case
for investing in vaccination programmes for producers and consumers. Re-emphasising the need
to expand the approach to assessing the benefits of vaccination, there is still a vast scope to do so
holistically, using different methodologies. Our study is a step in this direction. This study offers
an aggregated view of India’s potential income generation from the lives saved due to COVID-19
vaccination. The approach followed is described below.
As mentioned above, different studies vary in their methodologies to calculate the economic
impact. One of the most common techniques used to calculate the economic impact is the direct-
indirect-induced one. The direct-indirect-induced impact analysis is typically utilised to assess
changes brought about by an initial change through subsequent rounds of expenditure. It is a
commonly used methodology in economic impact analysis (Bhatia et al, 2007; Carrera et al. 2014).
However, most studies use this method to assess the impact on a specific industry or event. For
instance, Lemma (2014) analyses the impact on the tourism industry. The author suggests that
direct impacts accrue to the tourism enterprises, expenditure on food and beverage suppliers to
tourism enterprises, for instance, is an indirect contribution, and induced impact happens through
what those working in the tourism sector spend within the local economy. The study highlights
the different activities that play out at the three levels of impact.
Carrera et al. (2014) use the method to study the impact of a flood event. The subject areas where
the assessment of direct and indirect impacts have been undertaken are more industry-specific, or
event specific. Diersen et al. (2002) assess the impact of drought on South Dakota’s economy
using a direct-indirect-induced vantage point. The study divides the total effect of the drought on
the South Dakota economy into three separate facets – the direct effect calculated as loss in
agricultural income; the indirect effect, or the effect on businesses related to agriculture, using an
indirect multiplier; and the induced effect to capture the effect on local consumers. While such
studies utilise this approach in a technical fashion, other work adopts the direct-indirect impact
lens to understand how effects manifest in phases. Similarly, Kapoor & Goyal (2021), in their study
on the development of the Kevadia region as an eco-tourism centre, establish this development’s
direct and indirect economic impact on the local community, allied industries and regional
economy. The study pegs the cumulative economic impact generated through the Kevadia
ecosystem (direct and indirect combined) at around ₹10,630 crores. The method highlights that
impact plays out at multiple levels in areas beyond the immediate effect.
Our study assesses the impact of vaccination, taking the lives saved due to vaccination and
assessing their potential income-generating contribution to the economy through direct and total
impact. It uses the Keynesian multiplier approach to estimate the total impact using an income
approach. Multipliers are summary measures that show the total effects of a project in relation to
its direct effects. For instance, a multiplier of 1.60 shows that for every one rupee of value
generated directly by the project at maturity, another 60 would get added as downstream effects.
Bleaney et al. (1992) calculate the multiplier effect on gross output and income of having a
University (University of Nottingham). The authors found that the disposable income and gross
output multipliers were more than unity. Similarly, Janeczko et al. (2002) studied the impact of the
tourism sector on the Snowy Region of Australia by estimating the multiplier and studying its
impact on the gross regional product of the Snowy Region. They estimate that for each $100 of
visitor expenditure in the region, there is $39.80 generated of total regional income. Moreover, to
measure the economic impact of any interjection, the impact generated may be categorised into
primary (direct) and secondary (indirect and induced). Furthermore, it may be noticed that as the
size of the interjection increases, the indirect and induced effects eventually disappear, and we are
left with the multiplier effect (Hsu, 2019; Janeczko et al., 2002).
Against the above backdrop, this chapter assumes that incomes earned as a result of life saved due
to Covid vaccination can be accounted for in two ways; one being the ‘direct impact’, i.e., how
much the people who got saved by Covid vaccination would earn; and second, the ‘total impact’,
i.e., how much economic impact would their earned incomes create, in turn, as a result of the
‘multiplier’ effect. We estimate direct impact through the potential income earned by the lives
saved. Under this, three approaches are used – Minimum Wage51, GDP per capita52, and GDP per
employed53. The first method assumes a minimal approach, wherein the assumption is that the
lives saved earn a minimum wage. The second method assumes a scenario wherein the lives saved
contribute to the economy in terms of GDP per capita, while in the third scenario, we do so in –
the GDP per person employed.
At the direct impact level, we can gauge the first-level impact or the effect in the first rung in the
economy from a particular change. In the case of vaccination, the first-rung impact of the lives
saved can be seen in their potential contribution to the economy as ‘earners’ in different scenarios.
The total impact derives from the change in the direct impact. The study assesse
s the total impact
through the consumption multiplier applied on the income earned subsumed in the direct impact.
In gauging the economic impact of India’s vaccination strategy, we first take the number of
COVID-19-related deaths averted due to vaccination from Watson et al. (2022) – ‘Global impact
of the first year of COVID-19 vaccination: a mathematical modelling study’, published in ‘The
Lancet Infectious Diseases’. Based on the officially reported death rate, the study estimates deaths
averted due to COVID-19 vaccination in India. The number of deaths averted goes on to
contribute significantly to the economy and society in general.
Additionally, it must be understood that the world has made significant efforts to extend general
vaccination coverage to the world. The scientific community, policymakers, researchers and
governments have undertaken tremendous efforts to showcase how vaccine benefits outweigh its
costs. The emphasis has long been on showing, through various means, that vaccines be perceived
as an investment with large-scale impacts. The study is an attempt to build a complete
understanding of the gains from saving lives due to COVID-19 vaccination by incorporating both
the direct and the total impact of the deaths averted. However, it should be highlighted that saving
lives has value far beyond the economic value generated. The idea this paper espouses is not to
limit the impact to an economic sense since the value that a saved life adds to society is incalculable.
51 Minimum wage is the lowest wage that an individual is entitled to by the law.
52 GDP per capita is broadly defined as GDP of a country divided by its total population.
53 GDP per employed is broadly defined as GDP of a country divided by its employed population.
Approaching it from an economic perspective is just one way to incorporate the effect of
Income Generating Potential of Lives Saved through COVID-19 Vaccination
A Lancet modelling study estimates that in India around 34,22,00054 deaths were prevented by
vaccination in the year 2021, an estimate based on officially reported deaths in India. We base the
income generation potential of vaccination on the estimated number of lives saved or deaths
averted as per the cited study.
I. Minimum Wage Method
1) Direct Impact
per day (or INR 9,750 per month)55 as of July 2018, as the level of a single national minimum wage
at an all-India level, irrespective of sectors, skills, occupations and rural-urban locations for a family
comprising of 3.6 consumption unit. The report also recommended national minimum wages for
different geographical regions of India, taking into account different region-specific realities and
the labour market conditions. The report has clubbed all states into five different groups and set
different minimum wage levels. Another report by ILO titled “Global Wage Report 2020-21:
Wages and Minimum Wages in the Time of COVID-19” took the national floor level minimum
wage as the average wage for India at ₹176/day. However, actual wages are far higher. If the
median of the minimum wages in different states is drawn, it would be ₹269/day in the country56.
Moreover, applying the workforce participation rate of India, i.e., 38.2% (PLFS 2019-20)57, we
calculate the potential economic impact as: –
𝐷𝑖𝑟𝑒𝑐𝑡 Impact = Lives saved due to vaccination × minimum wage × 312* 58 – Eq. (1)
Lives saved due to vaccination WFPR (%) Minimum wage Impact
Table 1 – Direct Impact – MWM
₹ 1,09,71,10,17,312 (approx. 11,000 crore) $ 1,547,470,518 (approx. $ 1.5 billion) **
*To account for 1 year
**Average exchange rate of 2019-20 taken 70.89759
54 (Watson et. al., 2022). Global impact of the first year of COVID-19 vaccination: a mathematical modelling study. The LANCET Infectious diseases.
57 We use all India WPR of 38.2%. For details see Periodic Labour Force Survey (PLFS) 2019-20.
58 We take 26 days as the working days in each month as per Section 26 of The Minimum Wages (Central) Rules, 1950. See https://labour.gov.in/sites/default/files/TheMinimumWages_Central_Rules1950_0.pdf for more details. 59https://rbidocs.rbi.org.in/rdocs/Publications/PDFs/137T_15092022C8DE38B03F9541259D5539E0D1AFCD6 7.PDF
The Ministry of Labour and Employment constituted an expert committee in 2017 which set ₹375
The benefit side should be viewed parallel to the cost side. Although attaching a cost to a life is
not something we wish to do, incorporating government expenditure on the roll-out of the
vaccination is something that can be done. As per the government’s data, the vaccination
expenditure incurred for the year 2021 turned out to be about 19,675 crores.60 61 . Comparing Cost
with the Impact in Eq. (1) implies: –
Net Benefit = Impact – Cost of Vaccination – Eq. (2)
2) Total Impact
As mentioned earlier, total impact captures the economy wide broader change due to an initial
change in income level. The Reserve Bank of India conducted an econometric investigation over
the period of 1970 to 2000, of the real private final consumption expenditure in relation to real
GDP at factor cost and estimated the marginal propensity to consume with respect to current
income level then of 0.60. This gives a multiplier value of 2.5, which implies that a one per cent
increase in an initial change in expenditure would raise income by 2.5 per cent. Applying this
multiplier to the direct impact at the first level, we get,
𝑇𝑜𝑡𝑎𝑙 𝐼𝑚𝑝𝑎𝑐𝑡 = Direct Impact in terms of Income earned × Multiplier – Eq. (3)
Direct Impact Expenditure incurred on Vaccination
Net Benefit / Loss
Table 2 – Net Benefit / Loss – MWM
₹11,000 crores ₹ 19,675 crores
₹ (-) 8,675 crores
$ (-) 1.2 billion (approx.) (-) minus sign implying a net loss
Direct Impact in terms of Income earned
Table 3 – Total Impact – MWM
₹ 27,427,75,43,280 (approx. ₹ 27,400 crore)
$ 3,868,676,295 (approx. $ 3.87 billion)
Using Eq. (2) –
* Multiplier taken from RBI62
II. GDP Per Capita
1) Direct Impact
Under this approach, we calculate the potential economic impact at a GDP per capita level at
constant and current prices.
𝐼𝑚𝑝𝑎𝑐𝑡 = 𝐿𝑖𝑣𝑒𝑠 𝑠𝑎𝑣𝑒𝑑 𝑑𝑢𝑒 𝑡𝑜 𝑣𝑎𝑐𝑐𝑖𝑛𝑎𝑡𝑖𝑜𝑛 × 𝐺𝐷𝑃 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎 − 𝐸𝑞. (4)
Total Impact Expenditure incurred on Vaccination
Net Benefit / Loss
Table 4 – Net Benefit / Loss -MWM
₹27,400 crores ₹19,675 crores
₹ 7,725 crores
$ 1.09 billion (approx.) no (-) minus sign implying a net benefit
Table 5 – Direct Impact – GDP per capita
GDP per capita
GDP per capita
Net Benefit / Cost
GDP per capita
₹ 3,71,78,31,90,000 (approx. ₹
$ 5,243,990,437 (approx. $ 5.2
$ 2.44 billion
at current prices
₹ 51,932,27,20,000 (approx. ₹
$ 7,325,030,960 (approx. $ 7.3
$ 4.55 billion
63 Constant prices are adjusted for inflation. Here the base year is 2011-12. Current prices are not adjusted for inflation.
For details see https://www.rbi.org.in/scripts/PublicationsView.aspx?id=20407 64 https://www.rbi.org.in/scripts/PublicationsView.aspx?id=20407
2) Total Impact
𝐼𝑚𝑝𝑎𝑐𝑡 = 𝐷𝑖𝑟𝑒𝑐𝑡 𝐼𝑚𝑝𝑎𝑐𝑡 𝑖𝑛 𝑡𝑒𝑟𝑚𝑠 𝑜𝑓 𝐺𝐷𝑃 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎 ∗ 𝑀𝑢𝑙𝑡𝑖𝑝𝑙𝑖𝑒𝑟 − 𝐸𝑞. (5)
Table 6 – Total Impact – GDP per capita
GDP per capita
Net Benefit /
GDP per capita
(approx. ₹ 92,000 crore)
$ 10.20 billion
GDP per capita
$ 13,109,976,092 (approx.
$ 13.1 billion)
at current prices
(approx. ₹ 1,29,000 crore)
$ 15.42 billion
$ 18,312,577,401 (approx.
$ 18.3 billion)
III. GDP Per Employed
1) Direct Impact
The GDP per employed is a measure of the overall productivity in the economy. Among the three
approaches used here, this measure offers a best-case scenario wherein we calculate the GDP per
employed as total GDP divided by the total workforce for the year 2019-20. The total GDP (at
constant prices) was
1,45,15,958 crore, and the total population was 1341 million65. Applying the
WFPR66 of 38.2% as per Periodic Labour Force Survey (PLFS)67 2019-20, we get the total workforce or total employed as 51,22,62,000 (about 5.1 million). This gives GDP per employed (at constant prices) as: –
1,45,15,958 crore / 51,22,62,000 = ₹ 2,83,370
Similarly, for GDP per employed (at current prices), we get, 2,00,74,856 crore / 51,22,62,000 = ₹ 3,91,887
66 Workforce participation rate (WFPR) essentially means the number/percentage of employed in the country.
67 Annual edition from Government of India that maps the employment and unemployment scenario of India.
As done earlier, applying the WFPR of 38.2% to the lives saved due to vaccination, we get the
direct impact as: –
𝐼𝑚𝑝𝑎𝑐𝑡 = 𝐿𝑖𝑣𝑒𝑠 𝑠𝑎𝑣𝑒𝑑 𝑑𝑢𝑒 𝑡𝑜 𝑣𝑎𝑐𝑐𝑖𝑛𝑎𝑡𝑖𝑜𝑛 × 𝑊𝐹𝑃𝑅 × 𝐺𝐷𝑃 𝑝𝑒𝑟 𝑒𝑚𝑝𝑙𝑜𝑦𝑒𝑑 − 𝐸𝑞. (6)
Table 7 – Direct Impact –
GDP per person
GDP Per Employed
Net Benefit / Cost
(approx. ₹ 37,000 crore)
(approx. $ 5.2 billion)
$ 2.44 billion (approx.)
(approx. ₹ 51,200 crore)
(approx. $ 7.2 billion)
$ 4.44 billion (approx.)
𝐼𝑚𝑝𝑎𝑐𝑡 = 𝐷𝑖𝑟𝑒𝑐𝑡 𝑖𝑚𝑝𝑎𝑐𝑡 𝑖𝑛 𝑡𝑒𝑟𝑚𝑠 𝑜𝑓 𝐺𝐷𝑃 𝑝𝑒𝑟 𝑒𝑚𝑝𝑙𝑜𝑦𝑒𝑑 × 𝑀𝑢𝑙𝑡𝑖𝑝𝑙𝑖𝑒𝑟 – Eq. (7) Table 8 – Total Impact –
GDP Per Employed
Net Benefit / Cost
₹ 92,605,59,93,700 (approx.
₹ 92,600 crore)
$ 13,061,991,251 (approx. $
$10.28 billion (approx.)
₹ 1,28,069,06,34,870 (approx.
₹ 1,28,000 crore)
₹1,08,325 crores (approx. ₹ 1083.25 billion)
$ 15.28 billion (approx.)
$ 18,064,101,935 (approx.
$ 18 billion)
The figure below gives a snapshot of the cost-benefit analysis using the above approaches.
Direct Impact ($ billion)
8 7 6 5 4 3 2 1 0
GDP per Capita (constant)
GDP per Capita (current)
GDP per employed (current)
8.00 6.00 4.00 2.00 0.00
GDP per employed (constant)
Total Impact ($ billion)
Direct impact can be captured via all three scenarios (GDP per employed, GDP per capita and the minimum wage).
13.00 14.00 12.000.200.28 10.00
GDP per Capita (constant)
GDP per Capita
GDP per employed
GDP per employed
The difference is bound to be large since GDP per employed worker comes out to be the maximum. At the other
end, we have a scenario wherein a person earns a minimum wage. Thus, the difference in direct and total impact
across the working method is evident. The study has the potential to gain more nuance with an age-wise
disaggregation of the lives saved due to COVID-19 vaccination. In reality, the lives saved number
would have variation not just in terms of age but also in terms of income-earning potential.
Moreover, if within the working-age bracket, there are a higher number of individuals aged under
30-35, the individual lives saved due to vaccination would have a greater number of years to
contribute to the economy through income generation. Using the COVID-19 age-group-specific
death rate, the study aims to make this analysis more nuanced.
Thus, age distribution would help us build a more nuanced understanding of vaccination’s impact on people’s lives. To do this, one way would be to distribute the same according to the age profile of the number of deaths that happened, i.e., if we’re to get the age profile of the number of deaths that happened in India as per official estimates, we could use that with the assumption that the age profile of the number of lives saved due to vaccination would be similar to those who died due to COVID. To this end, the age profile of the number of deaths that happened by August 2020 are available. This is shown in the table below.
Table 9a – Age Profile of Total Deaths
Total Deaths = 56,288
0-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 90 & above
% Of people
0.50% 0.70% 2.60% 6.10% 13.40% 25.30% 28.60% 17% 5.30% 0.50%
Using the above age-profiles, one can see that 47.4% of the people who died were in the age group 21-60, the broad working age group68. However, we may caution that this is a broad assumption where the actual age distribution (as data gets available) might differ from what is suggested above. Therefore, by applying the above age profiles to the number of lives saved due to vaccination, we get,
68 Generally, 15-59 is considered to be the working age group in the Indian Context. However, since there is only a small %age of people belonging to 0-20 age group according to the reported figures, we can broadly consider 21-60 as 15-59 and 0-20 as 0-15 to make the comparisons parallel to the level of ones on which WFPR is calculated. All other ages are considered 60 & above.
Table 9b – Percentage of People in Different Age Profiles
60 & above
% Of Lives
No. of Lives Saved (out of
Less than 15
Following the same methodology as above, we get,
I. Minimum Wage Method
Table 10 – Direct and Total Impact – MWM
This would imply that out of the total lives saved due to vaccination, those belonging to the
working age category (15-59) earned a minimum wage of 269 and thus the potential economic
impact by these people would be:
𝐼𝑚𝑝𝑎𝑐𝑡 = 𝐿𝑖𝑣𝑒𝑠 𝑠𝑎𝑣𝑒𝑑 𝑑𝑢𝑒 𝑡𝑜 𝑣𝑎𝑐𝑐𝑖𝑛𝑎𝑡𝑖𝑜𝑛 (15 − 59) × 𝑊𝐹𝑃𝑅 (15 − 59) ×
1) Direct Impact
2) Total Impact
Net Benefit/ Loss ₹12,375 crores
$ – 1.74 billion (approx.)
$ – 0.2 billion (approx.)
₹ 7,337,59,92,065 (approx. ₹ 7,300 crore)
$ 1,034,966,107 (approx. $ 1.03 billion)
₹18,343,99,80,163 (approx. ₹ 18,300 crore)
$ 2,587,415,267 (approx. $ 2.58 billion)
Since we are now dealing with only working-age individuals, we would not use the GDP per capita
approach and rather skip to GDP per person employed.
II. GDP Per Person Employed
1) Direct Impact
𝐼𝑚𝑝𝑎𝑐𝑡 = 𝐿𝑖𝑣𝑒𝑠 𝑠𝑎𝑣𝑒𝑑 𝑑𝑢𝑒 𝑡𝑜 𝑣𝑎𝑐𝑐𝑖𝑛𝑎𝑡𝑖𝑜𝑛 (15 − 59) × 𝑊𝐹𝑃𝑅 (15 − 59) ×
𝐺𝐷𝑃 𝑝𝑒𝑟 𝑒𝑚𝑝𝑙𝑜𝑦𝑒𝑑 – Eq. (9)
Table 11 – Direct Impact –
GDP Per Employed
GDP per person
Net Benefit /
(approx. ₹ 24,700 crore)
$ 0.71 billion
$ 3,494,403,713 (approx.
$ 3.49 billion)
(approx. ₹ 34,200 crore)
$ 2.05 billion
$ 4,832,591,268 (approx.
$ 4.8 billion)
2) Total Impact
The figure below gives a snapshot of the cost-benefit analysis by using the above approaches,
after age-profiling, i.e., for the working-age group (15-59).
Table 12 – Total Impact –
GDP per person
GDP Per Employed
Net Benefit / Cost
employed at constant
GDP per person
employed at current
(approx. ₹ 61,900 crore)
(approx. $ 8.7 billion)
$5.95 billion (approx.)
(approx. ₹ 85,600 crores)
(approx. $ 12 billion)
$ 9.29 billion (approx.)
Total Impact ($ billion)
14 12 10
4 2.58 2
GDP per Capita
GDP per Capita (constant)
2 1.03 1
0 -1 -2 -3
GDP per Capita (constant)
GDP per Capita (current)
For the elderly, i.e., above the 60-year age bracket, the analysis cannot be done in this particular
way since they are generally not fully financially independent. However, the elderly constitute a
large section that is more prone to the virus. Therefore, the fewer people who fall ill, the less the
burden on healthcare services. For instance, an analysis conducted by Reed et al. (2012) to quantify
the potential public health impact of a QIV vaccine strategy in the United States estimated that
over 10 years (from 1999 to 2009), QIV could have prevented up to 2.7 million cases of influenza,
more than 21,000 hospitalisations, and about 1300 influenza-related deaths. Surely, preventing
hospitalisation leads to a reduced burden on the healthcare system.
It is common knowledge that the global health systems came under tremendous pressure due to
the sudden surge in the number of COVID cases. Even developed economies like Spain, Italy,
US, among others, found it overwhelming to deal with the sheer amount of pressure that the virus
brought with its spread. Therefore, for the elderly, vaccinating them led to easing the burden on
the healthcare system since that led to a smaller number of cases and, in turn, lesser
Since the number of elderlies that got saved by vaccination was 17,58,908, it can be argued that
hospital beds got saved, which in turn, got used in dire need of the same. Although there might be
a case that out of these 17,58,908 some still contracted COVID and required hospitalisation, we
leave out that possibility for simplicity and also due to the fact that such hospitalisation was
required due to some co-morbidities or other critical illness (Aggarwal et al. 2021). As per the
Ministry of Health and Family Welfare, there were 4,94,720 oxygen-supported isolation beds69 in
the country in December 2021. Since 17,58,908 lives were saved due to vaccination in the elderly
age group, it can be presumed that it led to the prevention of occupancy of oxygen-supported
isolation beds. Thus, vaccination did prevent healthcare infrastructure from being overwhelmed.
Therefore, vaccination not only saved lives but also indirectly assisted in providing treatment to
those who required oxygen-supported isolation beds.
To account for the lifetime income generating potential of a person, we apply the value of statistical
life formula used by scholars like Abelson (2008) in order to get the lifetime potential income
generation of the lives saved of people by vaccination: –
VSL (a) = VLY/(1+r) + VLY/(1+r)2 + … … … + VLY/(1+r)n
Where ‘a’ is the age. Here we take the median age of the working age group (15-59), which turns
out to be about 37; ‘r’ is the discount rate. Here we take CPI (General) for the pre-pandemic year
2019 as the discount rate (7.74)70 to account for the reduction in the value of money earned over
time, and ‘n’ is the remaining working-age years. Here ‘n’ would go up to 23 since 60 is the
retirement age. Value of Life Years (VLY) is the numerator, essentially the wage earned over one
year. This minimum wage is enhanced based on CPI-IW as per the central labour commissioner
office71. Scrutinising data for 2019 reveals that the CPI-IW for 2019 was about 7.49 after
considering the base effect.72 Applying the above rates to the VSL formula, we get,
69 https://pib.gov.in/PressReleseDetailm.aspx?PRID=1787361. We have taken number of oxygen supported beds with the presumption that elderly population generally requires oxygen support in combating COVID given they are immune compromised and suffer other comorbidities. This is also evidenced in Aggarwal et al. (2021) where it is observed that out of total deceased persons in the elderly age group, majority suffered breathlessness or Acute Respiratory Distress Syndrome (ARDS).
70 We take the percentage increase from January 2019 to December 2019 (base year 2012).
71 See https://clc.gov.in/clc/min-wages for more details.
72 Since CPI (General) is available at the base year 2012 as compared to CPI-IW which is available at a base year 2001, we recalculated the CPI-IW for 2019 at a base year for 2012 using simple base change formula. We take the percentage increase from January 2019 to December 2019 for CPI-IW as well (with new base year 2012).
VSL (37) = 83,928/(1.0774) + 90,214/(1.0774)2 + 96,971/(1.0774)3 + … … … +
Where each numerator is a product of the number of working days (312) and minimum wage
(269), in which minimum wage is enhanced by 7.49% each year.
VSL (37) = ₹ 17,46,672 (approx. $ 24,637) for one person over his/her lifetime.
This implies that for 8,74,273 employed persons out of 16,22,028 lives saved (15-59), the
cumulative potential income generation would be: –
VSL (37) = 8,74,273 * 17,46,672
VSL (37) = ₹ 1,52,706,81,69,456 (approx. ₹ 1,52,000 crore) Or = $ 21,539,249,467 (approx. $ 21.5 billion)
The Social Aspect
One cannot completely comprehend the impact without underlining the social aspect pertaining
to vaccination. Health and socioeconomic aspects of a region share a bidirectional association. A
healthy population is able to contribute to the economy productively, while greater economic
growth helps a society improve its investment in health. These benefits of vaccinations and other
public health interventions, including sanitation, clean water, and antibiotics, are essential for social
and economic reasons. The emphasis of this paper is to understand the benefits of vaccination
from a long-term perspective. Through various public health interventions, individuals are enabled
to participate in society in a better way. Vaccinating children would go a long way in contributing
to a burgeoning adult workforce in the future. When an adult suffers from COVID-19, research
has shown how it substantially affects the economy via lost office hours, absenteeism, and the
concomitant loss in wages (Faramarzi et al., 2021). Vaccination protects individuals from suffering
an illness and allows individuals to avoid the costs attached to that illness. COVID-19 vaccination
helped prevent severe illness and, thus, contributed to healthcare expense savings. Tarazi et al.
(2022), based on Medicare FFS claims data and country-level vaccination rates, show a positive
association between COVID-19 vaccinations and reduced hospitalizations, estimating savings of
about $2.6 million in early 2021.
Besides savings, COVID-19 vaccination, by allowing people to return to office spaces, also led to
productivity gains. Gorlick (2021) discusses Economist Nicholas Bloom’s research on productivity
losses brought about by work-from-home mode during COVID-19. Their research points to
individuals facing a loss of motivation, feeling of loneliness and isolation due to extended periods
of working from home. Vaccinating individuals at a rapid pace was essential to opening up public
spaces and allowing free movement of people, which in turn helped individuals return to a work
mode they were more comfortable with. Additionally, the lack of vaccination restricts access to
multiple spaces. It can limit one’s access to employment opportunities and a lot of physical spaces
(Mathivathanan, 2021). Thus, the social impact of vaccination reflects in a range of spheres. To
quote Bloom et al. (2005), ‘Vaccination is not purely a health sector issue—it has resonance for
wider economic planning and for long-term economic progress’ the resonance goes beyond an
economic impact and encompasses society in general.
Apart from the methodology we have applied in construing the potential income generation, there
might be other techniques to do the same. As mentioned before, for example, the age profile of
deaths averted, based on a much larger dataset, can be one such addition. Moreover, approaching
the same in a more disaggregated way, say at the state level, can be another way to approach things.
However, such formal and detailed analysis requires quantifiable and consistent data – something
we found lagging in the Indian landscape, at least at the current juncture. The availability of the
same at a future point in time can open avenues for more nuanced research with more
disaggregated policy responses. This would ensure more informed decision-making based on more
robust research. In the Indian context, an age-stratified fatality comparison is important to arrive
at the lives saved from ‘vaccine as a health intervention’. It is desirable to calculate Quality Adjusted
Life Years (QALY); however, it would require disaggregated COVID-19 case and death data which
is not available for India at the national and state level. It would also require age stratification of
vaccine rollout %age among citizens to understand vaccine efficacy as an intervention. Moreover,
in calculating the lifetime earnings on the minimum wage method, we have taken CPI-IW and CPI
(General) rates that can change in future, given the sensitivity of prices over months and years
which in turn depend upon the multitude of national and international factors. Also, we have only
discounted the income based on CPI (General), whereby additional factors such as borrowing cost
could be incorporated further. Nonetheless, the assumption allows us to map a potential income
generation effect and take us closer to the approximate income that might have been lost had the
number of people not been saved due to vaccination.
Apart from making an attempt, our paper is also a call for more research in this domain, given the
paucity of existing work in the Indian context. This paper, therefore, tries to study the impact of
various interventions (vaccine, containment, and relief package) to understand the broad impact
of the interventions in combating the pernicious virus and thus try to build an understanding
towards the bigger question of whether each of these interventions were useful in combating the
forms of roadblocks that the virus brought with its onset. Our analysis reveals that the three
interventions were useful in combating the virus. Although the exact impact – both economic and
social – is hard to capture, our paper is an attempt in the direction to map the effects of the
interventions that India undertook.
There is a significant surge in the discussions around the world on building a resilient health infrastructure at a global level. Countries have realised the importance of collaboration and cooperation in facing challenges like COVID-19. While forces of globalisation and digitalisation deepened the relationships between nations by reducing barriers, the need of the hour is to ensure preparedness and resilience in the face of challenges that may arise in future. This would require a deeper understanding of countries’ strategies in combating COVID-19. This study forms a significant contribution towards these efforts by estimating the impact of strategies employed in combating COVID-19 in India and lays the ground for assessing the effects of the strategies employed by other countries.
In building our pandemic preparedness, how various regions tackled COVID-19 is a trove of experience and resources that should be understood well. They are not mere learnings from a crisis, but are essential resources for building strategies to combat global uncertainties in the future. This study highlighted how disaggregated data, age-wise and region wise at a sub-national level, can help bring more nuance in assessing the positive economic impact of vaccination. Availing age-group-wise COVID-19 death rate in India can help make the study more robust. This might be complemented with more data like hospitalizations, available beds, oxygen, etc. at a disaggregated level. Thus, the calculations that are done in this paper are by no means plenary and adds to literature by highlighting some of the ways in which the objective of this paper can be approached.
Additionally, the impact of a localized containment strategy, whereby states and districts actively engaged in demarcating red from green and orange zones, restricting the spread of the virus, can be seen better with more granular data at a district-level over the entire localized containment period. Containing the virus in a nation as populous and diverse as India is a highly arduous task. It was successful due to the strategy adopted by different regions engaging multiple actors. The study aims to get a deeper understanding of how to assess this better. Furthermore, the study highlights the lack of work on the assessment of social measures or relief packages’ impact on society. It attempts to open up further discussion on adopting different methodologies to dig deeper into the impact of India’s COVID-19 relief package. Each of the three strategies – Vaccination, Containment, and the Relief Package, was equally important and in conjunction with one other, brought the nation to a post-pandemic era.
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