The Peninsula Foundation

Category: COVID-19

  • The Economics of Clean Energy: Transitioning to Renewables in a Post-COVID Era

    The Economics of Clean Energy: Transitioning to Renewables in a Post-COVID Era

    “the climate emergency is a race we are losing, but it is a race we can win” – Antonio Guterres, UN Secretary General

    Even without a global health pandemic, our world is still facing a crisis of staggering proportions.  In the 21st century the threat of climate change has outweighed almost all the other threats put together. Such is the pressing nature of the issue that it has even prompted re-branding of nomenclature from ‘climate change’ to ‘climate crisis’ – because that is what it is, a crisis. But as the UN secretary general António Guterres points out, “the climate emergency is a race we are losing, but it is a race we can win”.

    In this light, it is high time a discourse on transition to clean energy systems takes centre stage. With climate change progressing at an alarming rate, the need for clean energy has only been compounded.  At a time of great disruption for the world owing to an unprecedented health crisis with severe economic and social ramifications, a transition to renewables could be the way forward. As governments around the world lead COVID-19 recovery efforts, the verdict is clear that we cannot go back to our old systems – a transition to clean energy must be on the forefront of national agendas.  While the road to recovery is long and might take years, it is also the perfect opportunity for governments to accelerate clean energy adoption by putting this transition at the heart of post-COVID-19 social and economic recovery plans.

    While COVID-19 has certainly slowed down this transition by disrupting and delaying several renewable energy expansion and installation projects, the outlook on clean energy still looks very promising. In Q1 2020, global use of renewable energy in all sectors increased by about 1.5% relative to Q1 2019, while the overall share of renewables in global electricity generation jumped to nearly 28% from 26% in Q1 2019. While this does not reflect the impact of COVID-19 on capacity expansion, as the increase in use is largely due to expansion efforts in the preceding years, it is still a positive sign.

    Solar PV has had the most remarkable fall during this period, with the levelized cost of electricity (LCOE) falling almost 82% over the last decade. Closely following are CSP and On-shore Wind, both of which have fallen 47% and 38% respectively

    Even without factoring in the current global scenario, the rationale for transition has never been more compelling. Over the past decade, the cost of renewables has fallen to record lows (as shown in Figure 1), making it more attractive than ever before to invest in clean energy. Solar PV has had the most remarkable fall during this period, with the levelized cost of electricity (LCOE) falling almost 82% over the last decade. Closely following are CSP and On-shore Wind, both of which have fallen 47% and 38% respectively. Batteries, which have been appraised as one of the key enabling technologies in accelerating the shift to clean energy, have also recorded significantly lower costs in the past couple of years. Battery technologies such as Lithium-ion and Vanadium-flow have long been considered the missing link in ensuring continuity of supply for Wind and Solar generated power, which often depend on the vagaries of the weather. The LCOE for Lithium-Ion batteries has fallen by 35% since 2018, owing to advancements in technology. The only increases in cost have been recorded by Geothermal and Hydropower.

    With the cost of renewables falling, fossil fuel options are looking more and more expensive. According to IRENA (International Renewable Energy Agency), by 2020 Solar PV and onshore wind will be less expensive than the cheapest fossil fuel alternative. In the past, one of the key reasons why fossil fuels such as oil and gas were considered attractive options was because they were highly subsidized and incentivized. The true cost of these non-renewable sources minus the subsidies may well be much higher. The conventional cost of fossil fuels also does not factor in the environmental costs associated with carbon emissions. The extraction and use of these resources are often accompanied by several negative externalities associated with environmental degradation, pollution and global warming. This failure to account for the emissions and their impact has been termed by many as one of the greatest market failures the world has seen.

    Thus, falling costs of renewables coupled with the growing pressure on fossil fuels has presented the world with a unique opportunity to accelerate the adoption of clean energy. As governments pump more money into economies as part of COVID recovery efforts, the same level of investments can now yield greater returns owing to falling costs. Globally, investments in renewable capacity and technology have been on the rise and have shown remarkable growth, especially for Solar and Wind. Investments in Solar PV (Utility) in particular have shown astounding growth, increasing over 200% since 2010 to reach $69.4 billion in 2019. Total investments across renewables stands at $253.6 billion, having grown 21% in the last decade.

    While renewable capacity and investments have been growing, so has the demand for electricity. This growth in demand has somewhat offset the impact of transition to renewables. While mainstream adoption of clean energy is still progressing in the right direction, policy makers are worried that the pace of transition is not fast enough to offset growing demands. Unless renewable technology can scale up quickly and bridge the demand-supply gap, this excess demand will inevitably have to be met by fossil fuels.

    The IRENA estimates that investments in clean energy could boost global GDP by close to $98 trillion by 2050

    Despite several roadblocks still existing for large-scale adoption of clean energy to be made feasible, governments and institutions are putting climate action at the forefront now more than ever before. Post COVID-19, as economic recovery consolidates, we cannot afford to put clean energy on the back burner. Across the world, clean energy technologies such as electric vehicles, solar and wind energy are becoming increasingly mainstream. According to a UN report, global investment in renewables is set to triple in the next 10 years. If governments continue to sustain this momentum, the benefits are manifold. The IRENA estimates that investments in clean energy could boost global GDP by close to $98 trillion by 2050. Thus, the rationale is clear and more compelling than ever for a shift to clean energy. The robustness and resilience of economies to future global shocks will be determined by how quickly and effectively they transition to renewables and reduce dependence on fossil fuels.

     

    References

    [1] The Climate Crisis – A Race We Can Win. (2020). United Nations.

    https://www.un.org/en/un75/climate-crisis-race-we-can-win

    [2] Renewables 2019 – Global Status Report. Ren 21. Retrieved from: https://www.ren21.net/wp-content/uploads/2019/05/gsr_2019_full_report_en.pdf

    [3] Global Energy Review 2020. (2020, April). IEA.

    https://www.iea.org/reports/global-energy-review-2020/renewables

    [4] Renewable Power Generation Costs Report 2019. (2020, June). IRENA. https://www.irena.org/publications/2020/Jun/Renewable-Power-Costs-in-2019

    [5] Henze, V. (2019, March 26). Battery Power’s Latest Plunge in Costs Threatens Coal, Gas. Bloomberg NEF. 

    Battery Power’s Latest Plunge in Costs Threatens Coal, Gas | BloombergNEF (bnef.com)

    [6] Sinha, S. (2020, September 23). How renewable energy can drive a post-COVID recovery. World Economic Forum.

    https://www.weforum.org/agenda/2020/09/renewable-energy-drive-post-covid-recovery/

     

    Image Credit: AZoCleantech.com

  • (Part-II) Proposing a Legal Framework for Distribution of the COVID-19 Vaccination

    (Part-II) Proposing a Legal Framework for Distribution of the COVID-19 Vaccination

    I.   Reassessing Vulnerabilities During a Pandemic

    A general problem across all conventional models is their failure to understand that vulnerabilities during a pandemic are created and compounded by socio-economic factors too. Therefore, there is a need to adopt approaches that holistically assess the correlation between socioeconomic factors and vulnerability during a pandemic.[1]

    The Syndemics Approach

    Under this approach, pandemics are understood as an interaction of that disease with other diseases and the socio-economic and political factors that increase the risk of vulnerability.[2] All these factors synergistically interact to impact the health of individuals and society. Through these risk factors, it identifies the overlapping health and socio-economic problems that increase vulnerability (‘syndemic vulnerabilities’). The socio-economic risk factors are influenced by social determinants of health, i.e., the conditions of housing, food, employment, healthcare, and education.[3] Therefore, the utility of this approach lies in its holistic conception of socio-economic factors that impact the formation, clustering, and progression of diseases.[4] Using this approach, I argue that the COVID-19 pandemic has synergistically interacted and exacerbated the existing diseases and socio-economic conditions of marginalized groups across countries.

    Higher Risks of Infection, Transmission, and Mortality: Typically, due to historic discrimination and denial, marginalized communities have a greater number of pre-existing diseases like diabetes and asthma,[5] which in turn elevates their risk of infection and mortality. Moreover, there is unequal access to healthcare among marginalized communities due to the high costs of medical care and the absence of health insurance.[6] Marginalized communities are also disproportionately poor,[7] which affects their ability to mitigate the impact of the pandemic.

    Typically, marginalized communities are housed in crowded neighbourhoods with smaller houses that lack outside space.[8] They also have higher population densities, especially in urban areas, and lower access to communal green space.[9]Due to historic discrimination, marginalized communities are over-represented in essential services, including low-wage healthcare sectors and sanitation jobs.[10] This reduces their ability to work from home, and thus increases their risk of infection and transmission. Marginalized communities are more likely to take public transportation,[11] which further increases their risk of infection and transmission.

    These syndemic vulnerabilities have increased the risk of mortality among these marginalized communities. For instance, in America, the mortality rate of African-Americans and Indigenous/Latino communities is 3.4 times and 3.3 times higher than a non-Hispanic White person.[12] Evidence from past epidemics/pandemics shows that the rates of infection and mortality are always disproportionately higher among marginalized communities.[13]

    Greater Socio-Economic Disruption: Due to a lack of quality education, members of marginalized communities tend to work in lower-wage jobs in the informal sector, which has been worst hit by the pandemic.[14] The percentage fall in employment for marginalized communities has been far greater, indicating that education was a protective factor in the first wave of job losses.[15] Consequently, there has also been greater housing evictions among these communities.[16]The access to quality education for children in marginalized communities has also been severely impacted because they lack access to the internet,[17] affecting their ability to access education. Moreover, low literacy among adults in marginalized communities indicates their inability to assist their children with any form of home learning.[18]

    Therefore, the increased syndemic vulnerabilities of marginalized communities and the consequent disproportionate socio-economic disruptions of the pandemic on them necessitate a greater strive for their inclusion in distributing the vaccine. Early access to such vaccines allows these groups the opportunity to proportionately mitigate these vulnerabilities and disruptions.

    Intersectionality

    Presently, vulnerabilities among individuals are dominantly viewed from a single-axis framework. This ignores the multiple layers and experiences of vulnerability, resulting from an interplay of power structures and different social identities, held by one individual. This ignorance is avoided when using intersectionality, which is an analytical framework that explains how different social, economic, and political identities overlap to create different modes of discrimination and privilege.[19] Thus, it explains how certain individuals in the population are relatively more disadvantaged than others.[20] Intersectionality not only provides a multi-layered understanding of vulnerabilities during a pandemic but also helps prioritize distribution within an identified category, given the scarcity of vaccines.

     

    II.   Proposing a Multi-Value Ethical Framework

    Given its rational criteria, incorporating utilitarianism’s clinical risk factors is quite valuable. However, as argued, vulnerability during a pandemic is also determined by socioeconomic risk factors. Therefore, there is a need to adopt a multi-value approach that incorporates both clinical and socio-economic risk factors. I propose to do so by simultaneously prioritizing the values of ‘collective wellbeing’ and ‘justice’.

    Borrowed from utilitarianism is the value of ‘collective wellbeing’, which aims at maximizing benefits and minimizing harms. Flowing from a syndemic conception of COVID-19 is the value of ‘justice’, which aims at reducing health inequities and treats like people alike. These values are not necessarily always distinct, but their overlap over one parameter indicates a stronger justification. They can be operationalized using an ‘intersectional multi-parameter weighted framework’.

    Operationalizing Values

    The framework is constructed through three layers: (1) for each risk parameter, there is (2) a value-based justification, along with (3) its extent of weightage. The risk parameters are viewed from an intersectional power axis, with value justifications sourced from clinical and syndemic vulnerabilities. The weightage typically connotes a three-point scale, where 3 indicates the highest priority, and 1 indicates the lowest. The priority order is based on the greatness of one’s total score. The lottery method should only be used as a tie-breaker when the score is the same, and no more doses are presently available.

    Age:    Older people are at a significantly higher risk of infection and severe morbidity or mortality due to physiological changes associated with ageing. Globally, more than 95% of COVID-19 deaths were among individuals aged 60 and above. Even among older people, more than half of all deaths occurred in people aged 80 and above.[21]

    Therefore, in descending order, weightage must be given to individuals above 80 years, individuals between 60-80 years, and individuals between 40-59 years.

    Comorbidities:          Depending on the country, between 48-75% of COVD-19 deaths are associated with existing comorbidities. Those with comorbidities are also at moderately higher risk of infection.[22]

    The prioritization has to be categorized based on the severity of the comorbidity, in contracting the infection and causing death. Therefore, in descending order, higher weightage must be given to severe comorbidities, moderate comorbidities, and mild comorbidities. The severity in infection and mortality is different for countries due to distinct socio-economic realities and evolutionary biology. Therefore, this identification and classification need to be uniquely undertaken. However, as a general rule, it is almost universal for HIV, cancer, and most cardiovascular diseases to be severe comorbidities.[23]

    Profession:     Prioritizing frontline healthcare, sanitation, and defence workers are justified because they engage in services, whose absence has the greatest negative societal impact- whether on health, safety/security, or economy. They are also in constant contact with areas and people having the greatest risk of infection. Therefore, protecting them has a multiplier effect, in that their ability to remain uninfected protects the health of others and minimizes societal and economic disruption. Since the state obligates these workers to work in risk conditions, while everyone else is working from home, it is further obligated to protect them.

    Therefore, in descending order, priority must be given to frontline workers, workers in other essential sectors, and workers in non-essential sectors.

    Income:          One’s economic status affects their ability to access healthcare, thus results in higher rates of mortality and severe morbidity.[24] The syndemic approach reveals that poverty compounds one’s syndemic vulnerability.

    Therefore, in descending order, priority must be given to individuals with low-income, middle-income, and high-income.

    Ethnic Identity:         The syndemic approach reveals that marginalized communities are at a greater risk of infection, transmission, and mortality. They are also worst affected by the pandemic, which further compounds their vulnerability. Given these vulnerabilities, prioritized vaccine access to marginalized communities also helps reduce all three risks among the general population.

    The prioritization criteria would depend on the marginalized communities within a country and the extent of their syndemic vulnerabilities. For instance, in America, the syndemic vulnerabilities are greatest for African-Americans, followed by the Indigenous/Latinos communities, and then Pacific Islanders.

    Conclusion

    The conventional models of vaccine distribution are unethical towards disadvantaged groups. While neoliberalism completely ignores the distributive function of law, utilitarianism, lottery, and FCFS at least acknowledge this. However, their criterion of distribution ignores socio-economic vulnerabilities. This ignorance can be addressed using a syndemics approach and intersectionality.

    The syndemics approach explains the socio-economic risk factors that disproportionately disadvantage marginalized communities, both medically and socio-economically. Intersectionality provides a layered understanding of how vulnerabilities affect people, even those in the same group, differently. Using these approaches, I propose a multi-value ethical framework that balances the pragmatic considerations of medical utilitarianism with greater social inclusion. It operationalizes the values of these ethical systems through the priority order generated under an ‘intersectional multi-parameter weighted framework’.

     

    Notes:

    [1] While each country has different marginalized groups, the patterns of vulnerability explored are similar. Thus, marginalized groups have been generally analyzed hereinafter.

    [2] Merrill Singer, Nicola Bulled, et al, ‘Syndemics and the biosocial conception of health’ (2017) 389 Lancet 941, 941-943.

    [3] Clare Bambra, Ryan Riordan, et al, ‘The COVID-19 pandemic and health inequalities’ (2020) 1 J Epidemiol Community Health 964, 965.

    [4] Singer (n 23) 948.

    [5] Harleen Kaur, ‘Indirect racial discrimination in COVID-19 ethical guidance’ (BMJ Blog, 27 August 2020) <https://blogs.bmj.com/covid-19/2020/08/27/indirect-racial-discrimination-in-covid-19-ethical-guidance/> accessed 8 January 2021.

    [6] Bambra (n 24) 965-966.

    [7] Melanie Moses, ‘A Model for a Just COVID-19 Vaccination Program’ (Nautilus, 25 November 2020) <http://nautil.us/issue/93/forerunners/a-model-for-a-just-covid_19-vaccination-program> accessed 8 January 2021.

    [8] Tonia Poteat, ‘Understanding COVID-19 Risks and Vulnerabilities among Black Communities in America: Syndemics’ (2020) 47 Annals of Epidemiology 1, 3.

    [9] Bambra (n 24) 966.

    [10] National Academies (n 16) 30-31.

    [11] ‘Beyond the data: Understanding the impact of COVID-19 on BAME groups’ (2020) Public Health England Report, 22-23 <https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/892376/COVID_stakeholder_engagement_synthesis_beyond_the_data.pdf> accessed 8 January 2021.

    [12] Harald Schmidt, ‘Is It Lawful and Ethical to Prioritize Racial Minorities for COVID-19 Vaccines?’ (2020) 324 JAMA <https://jamanetwork.com/journals/jama/fullarticle/2771874> accessed 8 January 2021.

    [13] Bambra (n 24) 967.

    [14] Shruti Srivastava, ‘Millions Escaped Caste Discrimination. Covid-19 Brought It Back’ (Bloomberg Quint, 21 August 2020) <https://www.bloombergquint.com/politics/millions-escaped-caste-discrimination-covid-19-brought-it-back> accessed 8 January 2021.

    [15] Ashwini Deshpande, ‘Differential impact of COVID-19 and the lockdown’ (The Hindu, 22 August 2020) <https://www.thehindu.com/opinion/lead/differential-impact-of-covid-19-and-the-lockdown/article32416854.ece> accessed 8 January 2021.

    [16] Schmidt (n 33).

    [17] Deshpande (n 36).

    [18] Ibid.

    [19] Olena Hankivsky, ‘An intersectionality-based policy analysis framework’ (2014) 13(119) Intl J Equity in Health 1, 2.

    [20] Ibid.

    [21] ‘Supporting older people during the COVID-19 pandemic’ (WHO, 3 April 2020) <https://www.euro.who.int/en/health-topics/health-emergencies/coronavirus-covid-19/news/news/2020/4/supporting-older-people-during-the-covid-19-pandemic-is-everyones-business> accessed 8 January 2021.

    [22] Awadhesh Kumar, ‘Impact of COVID-19 and comorbidities on health and economics’ (2020) 14(6) Diabetes Metab Syndr 1625, 1626-1627.

    [23] Ibid.

    [24] National Academies (n 16) 68-77.

     

    Image Credit: One India

  • Proposing a Legal Framework for Distribution of the COVID-19 Vaccination [Part I]

    Proposing a Legal Framework for Distribution of the COVID-19 Vaccination [Part I]

    Introduction

    Distributing the COVID-19 vaccination has been touted as the biggest policy decision in 2021. This stems from the utility and efficacy of vaccines in immediately addressing pandemics. Specifically, the COVID-19 vaccination not only protects the injected person, with a 70%-95% efficacy[1] but also provides ‘herd immunity’.[2] That is, the non-injected population is also benefited due to a reduced risk of transmission and infection, so long as 70% of individuals in society are vaccinated. Therefore, access to the vaccine determines how much and for whom the adversity of the pandemic is mitigated.

    Currently, most vaccine developers are in the final two phases of clinical trials, with some, like Pfizer/BioNTech’s and Oxford University/AstraZeneca’s, already receiving ‘emergency use authorization’ from multiple countries. Most countries have prepared a ballpark action plan for distribution, while the United Kingdom has already vaccinated more than 3.5 million people.[3]

    In this paper, I evaluate the most ethical framework for distributing COVID-19 vaccinations, amongst the population of one country, by its government. I address this question from the perspective of marginalized communities, using the approaches of realism, syndemics, and intersectionality. In Part I of this article, I will evaluate the conventional models for vaccine distribution. In Part II, I will provide an alternative framework for reassessing vulnerabilities during a pandemic, and propose a multi-value ethical framework.

    1. Evaluating the Conventional Models for Vaccine Distribution

    The decision to distribute COVID-19 vaccines is inherently ethical because it involves allocating an important resource in a resource-scarce world. Thus, determining who can pre-maturely mitigate the pandemic’s adversity. There are four models in conventional discourse that have sought to answer the distribution question. In this section, under each model, I will critically evaluate the role of law in distribution and the ethical values that guide prioritized distribution.

    Neoliberalism

    Neoliberalism is characterized by a strict separation between the state, society, and the market.[4] The objective of all economic activity in the markets is wealth and efficiency maximization.[5] To this end, greater involvement of the private sector in the economy is justified because the market allocation of resources is more efficient. Any state intervention beyond a minimum supporting role is conceived as inefficient because rent-seeking, corruption, and capture by special interests are inevitable.[6]

    The diminished role of the state in securing redistribution means that individuals are responsible for their welfare and income. Therefore, individuals would themselves be responsible for ensuring access to the vaccination, notwithstanding their socio-economic status. They must attain this access by successfully competing in the “free market”, through instruments like price point discovery.[7] The underlying rules of competition create a level playing field where fair bargaining over market transactions can occur, so long as the requisite effort is made. This is because the rules are universal in their applicability, and create a distinct economic space, free from state coercion.[8] Therefore, access to the vaccine is determined by one’s ability to pay for it.

    State intervention is only justified when there is a market failure, but even then, preference is accorded to non-state solutions like direct public action or self-regulation.[9] Neoliberalism addresses equity concerns, like non-access to the vaccine, through safety nets and income transfers rather than through market regulation.[10] Otherwise, inefficiencies are introduced into the system, which distorts market incentives, and thus undermines the goal of economic growth.[11] This means that vaccine developers would lose the incentive to undertake expedient and mass production.

    Critique:         Neoliberalism denies that any redistribution to disadvantaged groups is covered by legal reforms. There is no focus on how economic gains are distributed, and the effect of reforms on vulnerable social groups.[12]Neoliberalism’s refusal to acknowledge the distributive function of legal regulation is flawed because rules necessarily always operate to distribute resources and powers to various groups and actors in particular ways.[13] The neoliberal machinery devises a particular allocation of risks, resources, powers, costs, burdens and benefits among different market actors. The effect is that the existing propertied class receive greater entitlement, whilst others are disadvantaged.[14] This perpetuates the inequalities already in status quo, impacting accessibility to the vaccine. Therefore, the relevant question is not whether distributive concerns must be considered, but rather their manner of incorporation in the process of market reform. To this end, the state, which guarantees the regulatory underpinnings of a market economy, must inherently play a greater role in regulating the distribution of economic gains from the market.

    The idea to distribute vaccines based on personal purchasing power is flawed because it ignores the fact that vaccines possess inelastic demand. Therefore, given short supply at short-term and medium-term levels, the price will continually go up to unaffordable rates. This increased price does not encourage new suppliers because the intellectual property rights and R&D is held only by a few developers.[15]

    Utilitarianism

    Utilitarianism assesses the morality of a decision based on its consequences, whether it maximizes benefits and/or minimizes harms. Under this rationale, priority is accorded based on the greatest clinical risks and greatest utility to social functioning. The clinical factors consider the risk of severe morbidity and mortality, risk of infection, and risk of transmission.[16] The greatest utility to society is measured in terms of the risk of negative societal impact, i.e., the public utility of one’s occupation/social role to society and other individuals’ lives and livelihood.[17]

    Therefore, in this pandemic, utilitarianism would prioritize age (above 50/60 years) and associated comorbidities (identified set of diseases) based on the risk of morbidity/mortality and infection, followed by occupation (healthcare and frontline workers) based on the risk of negative societal impact and risk of infection.[18]

    Critique:         Unlike neoliberalism, there is limited value in the utilitarian model because it recognizes the distributive role of law in allocating benefits. Moreover, it pursues this based on a rational objective criterion.

    However, its main problem lies in assessing vulnerabilities through only a clinical lens. It ignores that socio-economic factors also contribute to overall vulnerability during the pandemic, as I argue in the next section. Additionally, it doesn’t acknowledge that even within the identified categories, some are more vulnerable than others. Therefore, it has the effect of compounding existing socio-economic inequalities.

    Lottery

    This approach prioritizes distribution through a random selection of names. This is premised on the assumption that such selection is egalitarian and impartial, and also overcomes the inherent moral relativity/ambiguity of human reasoning.[19]

    Critique:         Random lotteries acknowledge the role of law in distributing benefits, but they lack any rational prioritization to effectively and immediately address the pandemic. While absolute objectivity is unattainable, avoiding moral reasoning altogether is merely “an easy method to avoid hard decisions”.[20] The assumption that everyone’s life is equally important fails to acknowledge the differential disparities that differentially threaten such lives.[21]

    First Come First Serve

    Like lotteries, this approach is premised on avoiding moral decisions and the assumption that everyone has an equal opportunity to access the vaccine.[22]

    Critique:         While this approach acknowledges the role of law in distributing benefits, it is completely blind to the socio-economic realities. Given scarcity, it is inevitable that access will be confined to those with better connections, access to information, communication, and transportation. All these factors are, in turn, tied to one’s socio-economic status. Thus, there is disproportionate denial to disadvantaged communities.

     

    References:

    [1] James Gallagher, ‘Covid vaccine update’ (BBC, 30 December 2020) <https://www.bbc.com/news/health-51665497> accessed 8 January 2021.

    [2] Rebecca Weintraub, ‘A Covid-19 Vaccine Will Need Equitable, Global Distribution’ (HBR, 2 April 2020) <https://hbr.org/2020/04/a-covid-19-vaccine-will-need-equitable-global-distribution> accessed 8 January 2021.

    [3] Lucy Rodgers & Dominic Bailey, ‘Covid vaccine: How will the UK jab millions of people?’ (BBC, 23 January 2021) <https://www.bbc.com/news/health-55274833> accessed 24 January 2021.

    [4] Manfred Steger & Ravi Roy, Neoliberalism (OUP 2010) 3-4.

    [5] Kerry Rittich, Recharacterizing Restructuring (Kluwer Law International 2002) 50-52.

    [6] Rittich (n 4) 55-59.

    [7] Sahil Deo, Shardul Manurkar, et al, ‘COVID19 Vaccine: Development, Access and Distribution in the Indian Context’ (2020) Observer Research Foundation Issue Brief No. 378, 6 <https://www.orfonline.org/research/covid19-vaccine-development-access-and-distribution-in-the-indian-context-69538/> accessed 8 January 2021.

    [8] Rittich (n 4) 131.

    [9] Rittich (n 4) 74-76.

    [10] Ibid.

    [11] Steger (n 4).

    [12] Rittich (n 4) 130.

    [13] Steger (n 11)

    [14] Rittich (n 4) 158-160.

    [15] Deo (n 7).

    [16] National Academies of Sciences, Engineering, and Medicine, Framework for Equitable Allocation of COVID-19 Vaccine (National Academies Press 2020) 102-105.

    [17] National Academies (n 16) 8.

    [18] Ibid.

    [19] Richard Zimmerman, ‘Rationing of influenza vaccine during a pandemic’ (2017) 25 Vaccine 2019, 2023.

    [20] Ibid.

    [21] Erica Moser, ‘Many ethical questions involved in prioritizing groups for vaccine distribution’ (The Day, 13 December 2020) <https://www.theday.com/article/20201213/NWS01/201219766> accessed 8 January 2020.

    [21] Ibid.

    [22] Zimmerman (n 19).

     

    Image Credit: Crowd Wisdom 360

  • Is MGNREGA a Sustainable Employment Option for Migrants?

    Is MGNREGA a Sustainable Employment Option for Migrants?

    Covid-19 certainly has kindled a renewed focus on healthcare systems, sanitation, and most importantly, employment in the rural areas of the country. The pandemic has thrown light on the huge inadequacies and challenges of our healthcare structure that the government and the citizens had not foreseen. Millions of skilled and unskilled migrants moved across the country in droves to their hometowns in the absence of income and work and means to sustain their life. Around 30 Million (3 Crore) or 15-20% of the total urban workforce left for their hometowns, accounting for the largest ever reverse migration trend in the country, exclusive of intra-state migration. The World Bank in its report mentioned that a whopping number of 40 million internal migrants were harshly affected by the lockdown. Now that the country is just a few steps from opening up in full, concerns about workers moving back in search of work remain in the air. The Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), which has a mixed track record in sustaining the livelihood of people in distress by providing guaranteed employment and considerate wages might be the only way out for the worst of the worst-affected. But, will the scheme be a viable and sustainable employment option for the days and years to come? This article aims to answer the question of efficiency, significance, and sustainability of MGNREGA in rural employment in the country.

    What is MGNREGA?

    MGNREGA, the world’s largest guarantee work programme, is the legitimised pioneer of the fundamental ‘Right to Work’. The scheme does that by providing a time-bound guarantee of work for 100 days a year, with considerate fixed wages. Workers under the scheme are assigned to agriculture and related capacity building projects thus ensuring sustainable development for all, as advocated by Gandhi. The scheme has reasonable success stories to its credit, all across the country. A study by Parida (2016) at Odisha proves that MGNREGA has played an important role in the agricultural off-season by providing work to the needy, the poor, and the socially marginalised communities. In various villages in Sikkim, families under MGNREGA were more self-reliant and less dependent on government programmes for a livelihood, according to the results of an evaluation conducted by the Tata Institute of Social Sciences (2017).

    The Ministry of Finance announced Rs. 40,000 crore fund allocation to MGNREGA on the onset of the fourth phase of lockdown in May, while under the Atmanirbhar Bharat Abhiyan, the government plans in creating jobs for 300 Crore persons, and the national average wages of workers also saw an increase from Rs. 182 per day per person to Rs. 202, with effect from April 1st, 2020. All of these might come off as a huge sigh of relief to the worst affected, but in many states, the scheme wage rates are lower than the minimum wages in the respective states. So, this increase in wages does not hold huge significance in reality.

    Unemployment and Work Allocation Concerns

    Reverse Migration Trends and Unemployment:      Unemployment has always been a perennial problem for a developing country like India, especially in times of crisis. The unemployment rate of the country reached an all-time high of close to 24% in April, while the rate of unemployment is expected to reach 8-8.5% in 2020-21, which may increase owing to the reverse migration trends. According to the Former Chief Statistician of India, rural unemployment is now a double-edged sword, given the impact of different migration trends. The reverse migration trends have altered the demand-supply dynamics in rural India significantly. Areas that previously had negative net migration rates are now expected to experience labour surplus, while the locations that may need workers might lack supply. The trends in reverse migration and its impact on local employment in states are visible, with Uttarakhand topping the charts in both the number of reverse migrants and the unemployment rate at around 22.3% as of September. The state is followed by Tripura at 17.4% and Bihar at 11.9%. Thus a strong correlation can be inferred between the amount of reverse migration and the unemployment rate in a given state.

    Putting together numbers of short-term and long-term vulnerable workers gives us a total of about 13 Crore (130 million) workers, who are deeply affected by the Covid-19 crisis.

    Another trend that is recognisable from literature is that migration is no longer a one-way street. Seasonal and circular migration continues to grow and take various forms (Conell et.al., 1976). Amongst these, vulnerable circular migrants are termed as the most distressed section of migrants, which include both Short-term seasonal and long-term occupationally vulnerable workers. Srivastava (2020) has estimated the number of 5.9 crore short-duration circular migrant workers in the year 2017-18. In the same study, vulnerable long-term circular migrants have been identified at 6.9 crores in the same period. Putting together numbers of short-term and long-term vulnerable workers gives us a total of about 13 Crore (130 million) workers, who are deeply affected by the Covid-19 crisis.

    Work Allocation Concerns:     Besides, The Taskforce for Eliminating Poverty constituted by Niti Aayog in the year 2015 (Occasional Paper,2016) has noted that most beneficiaries under the MGNREGS have been on an average get only 50 days of work. This shows that the scheme requires a better mechanism that recommends better targeting of the poorest of the poor and gets them guaranteed work for 100 days. Additionally, if 50-60% of the migrant workers in urban India (2018 above) return to their home destinations, then the scheme has to accommodate between 5.5 – 6.6 crore new workers, which will add 50 – 60% weight on people to be accommodated under the scheme. This exerts additional pressure on the already drying up state funds, which means catering to the huge number of migrants might not be economically sustainable for a long period.

    Wages and Work Efficiency under MGNREGA

    The wage rate in MGNREGA has been a huge concern for policymakers across India. While the recent increase in wages seemed quite positive at the onset, the wage hike is lesser than the minimum wage rate in certain states. Wage rates in the year 2019 seemed to be on the same trajectory, with the MGNREGA wage hike being lesser than the minimum wages in 33 states. Long payment delays also with meager wages add to the burden on workers under the scheme. Another important loophole in the scheme is the availability of work for such a huge number of workers seeking work under the scheme. In most cases, work is inadequate for such a huge number of workers. The standing committee report on rural development for the year 2012-13 also mentioned a significant decline in annual work completion rates (%). According to the report, work completion rates have taken a deep plunge consecutively in the years after 2011, with work completion rates of 20.25% for the year 2012, and 15.02% for the year ending 2013. Such dismal performances also throw light on the lack of productive allocation of work under the scheme. All of these certainly are results of the weakening of the act.

     CONCLUSION

     While MGNREGA fails in addressing a lot of important issues, COVID-19 certainly allows it to fit the dynamic changes in employment and work conditions. Making amendments to the act can be the only way out if the act needs to be sustainable in the long term. MGNREGA gives a rights-based framework to migrants seeking skilled and unskilled labour opportunities but lacks in giving enough benefits to the workers. Work under the scheme should be allocated efficiently, as per the project needs. While COVID-19 put a halt to a lot of existing projects, a lot of new projects are on the anvil. Catering to the needs arising on account of the pandemic including sanitation infrastructure building projects and infrastructure and rehabilitation projects can help the scheme diversify its project base, thus increasing employment opportunities to the migrants. Agriculture, the only positive contributor to the GDP of the country should be taken advantage of in the situation. A strong work evaluation setup should be made sure of, that would efficiently track work completion records thus giving opportunities for workers to complete the incomplete projects. This will yield benefits in both completion of a project and increased workdays and consequently increased wages for a worker.

    Cash-based transactions can be a game-changer in this scenario. Instead of reliance on Aadhar, the unbanked should be remunerated regularly by the means of cash.

    Need for Cash-Based Wage Transfer:      While cash crunch and plunging aggregate demand are looming over the country’s economy, MGNREGA can be used as a tool to put money in the hands of the needy. The propensity to consume of a rural worker is way higher than that of an urban employee. Cash-based transactions can be a game-changer in this scenario. Instead of reliance on Aadhar, the unbanked should be remunerated regularly by the means of cash. Bank and Post office ways of remunerating workers surely did have an impact on corruption, but irregular payments and lack of access to formal banking systems are a common testimony among the migrants. Reverse migration is also the beginning of people bringing themselves into the formal cycle of work, with their enrolment under MGNREGA. Tapping the untapped potential and better engagement and benefits to workers under the scheme will largely increase its base and efficiency. If states learn from their past mistakes and amend the working system of the act, then surely it may do wonders in rural employment in the country.

    Image Credit; The Quint

  • Forecasting Unemployment Rate during the Pandemic

    Forecasting Unemployment Rate during the Pandemic

    Forecasting
    Forecasting, in simpler terms, is a process of predicting future values of a variable based on past data and other variables that are related to the variable being forecasted. For example, values of future demand for tickets for a particular airline company depend on past sales and the price of its tickets.
    Time-series data is used for forecasting purposes. According to Wikipedia ‘A time series is a series of data points indexed in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus, it is a sequence of discrete-time data.’ An example of time series data for monthly airline passengers is given below:

    Figure 1


    More technically, it is modelled through a stochastic process, Y(t). In a time series data, we are interested in estimating values for Y(t+h) using the information available at time t.  
    Unemployment rate
    Unemployment is the proportion of people in the labour force who are willing and able to work but are unable to find work. It is an indicator of the health of the economy because it provides a timely measure of the state of labour market and hence, overall economic activities. In wake of the impact of Covid-19 on economic activities throughout the world, unemployment rate analysis and forecasts have become paramount in assessing economic conditions.
    In India, unemployment rates have been on the higher end in recent times. According to data released by Statistics Ministry, unemployment rate for FY18 was 6.1%, the highest in 45 years. It is no co-incidence that GDP rates have also been declining successively for the past few years. The shock that Covid-19 has given to the economy has only worsened our situation. The unemployment rate rose to 27.1% as a whopping 121.5 million were forced out of work.
     

    Figure 2


    Source: CMIE
    Methodology
    The data used to forecast unemployment rates was sourced from CMIE website, which surveys over 43,000 households to generate monthly estimates since January 2016. The data has 56 monthly observations ranging from January 2016 to August 2020, data before 2016 was not available.
    Four popular econometric forecasting models (ARIMA, Naïve, Exponential Smoothing, Holt’s winter method) were used and the best performing model was chosen to forecast unemployment till December 2020.
    The forecasting models were programmed in R. The relevant codes are available upon request with the author. The Dicky-Fuller test and the Chow test for structural breaks were conducted using STATA, results of which are presented further in the article.
    Before beginning the analysis, I believe that the limitations of the analysis should be mentioned:

    • The sample size of 56 observations is not sufficient for a thorough analysis, ideally the sample size should have been 2-3 times larger than the available data. Smaller sample sizes lead to skewed forecasting results which are prone to errors.
    • The unemployment data from CMIE is an estimate and is a secondary source. In India, primary data is only collected once in 3-4 years, thus the forecasting results are only as good as the source of the data.
    • This is a univariate analysis, an Okun’s law based analysis of Unemployment rate as a function of GDP (output) and past trends would have been more suitable. However, since GDP data is only available quarterly and there are only 56 monthly observations available, it would have rendered the analysis insignificant with only 19 quarterly observations.
    • Forecasting being based on past trends, is prone to errors. The negative shock provided by Covid-19 to the economies worldwide has made it all the more difficult to forecast. A Bloomberg study analysed over 3,200 forecasts by IMF since 1999 and found that over 93% of the forecasts underestimated or overestimated the results with a mean error of 2 percentage points.

     
    Checking the stationarity of data
    In order to model build a model, we need to make sure that the series is stationary. For intuitively checking the stationarity, I plotted the data over time as indicated in Figure 2 above. I also plotted the correlograms (autocorrelations versus time lags) as shown in Figure 8 and 9 in appendix. The plot of data over time indicate varying mean, variance and covariance. The ACF and PACF plot show that autocorrelations function are persistent indefinitely.
    We perform the Augmented Dickey Fuller test at 2 lags. Result of the ADF test is shown in Table 1 below. The test statistic is insignificant at 5 per cent and the p-value is 0.1709, which is more than the accepted benchmark of 0.05. We fail to reject the null hypothesis of non-stationarity. We conclude that our series is non-stationary.

    Dicky-Fuller test on raw data

    Table 1

    		 —– Interpolated Dickey-Fuller —–
    Test statistic 1% critical value 5% critical value 10%critical value
    Z(t) -2.303 -3.576 -2.928 -2.599

     
     
     
     
     

    MacKinnon approximate p-value for Z(t) = 0.1709

    Converting the non-stationary series into stationary

    In order to transform the non-stationary series into stationary, we use differencing method (computing difference between consecutive observations).
    We plot the data over time, ACF and PACF again as shown in Figure 5 below and figure 10 and 11 in appendix, respectively. From the figures, we can intuitively say that the transformed series is stationary. Further, we used Augmented Dickey-Fuller tests to ascertain the stationary of our series. Table 2 shows the result of the ADF test. The test statistic is significant at 1,5 and 10 per cent levels and the p-value is less than 0.05. We reject the null hypothesis of non-stationarity of our series. The tests confirm that the series is stationary.
     

    Dicky-Fuller test on first difference data

    Table 2

    —– Interpolated Dickey-Fuller —–
    Test statistic 1% critical value 5% critical value 10%critical value
    Z(t) -5.035 -3.576 -2.928 -2.599

    MacKinnon approximate p-value for Z(t) = 0.0000
     
     

    Figure 3


     
    Naïve model
    Naïve models are the simplest of forecasting models and provide a benchmark against which other more sophisticated models can be compared. Thus, a Naïve model serves as an ideal model to start any comparative analysis with. In a naive model, the forecasted values are simply the values of the last observation. It is given by
    y^t+h|t=yt.
    Forecast results from Naïve method are presented below in figure 4 and table1.
     

    Figure 4

     

     

    Table 1

     
         		 Point forecast Lo 80 High 80 Low 95 High 95
    Sept 8.35 4.861900 11.83810 3.0154109 13.68459
    Oct 8.35 3.417081 13.28292 0.8057517 15.89425
    Nov 8.35 2.308433 14.39157 -0.8897794 17.58978
    Dec 8.35 1.373799 15.32620 -2.3191783 19.01918

     
    Box-Jenkins Approach
     

    1. Identification of ARIMA (p, d, q) model

     
    The data was split into training and testing dataset in 80:20 ratio. The training data was used for estimating the model, while the model was tested on the remaining 20 percent data. This is done in order to forecast the future values of the time series data.
    p, d and q in (p, d, q) stand for number of lags, difference and moving average respectively.
    The model best fitting the data was (0,1,3) as its Akaike Information Criterion (AIC) was the lowest amongst all the possible combinations of the order of the ARIMA model.
    The residuals from Arima model were found to be normally distributed, with a mean of 0.09 and zero correlation. This causes a bias in the estimates. To solve the problem of bias, we will add 0.09 to all forecasts. The ACF and line graph of residuals is attached in the appendix.
    After identification and estimation, several diagnostic tests were conducted to check if there were any uncaptured information in the model. Results of the diagnostics tests have been omitted from the article in interest of length.
     

    1. Forecasting

     
    The model that has been constructed was used to forecast unemployment rates for the next four months. The results are presented below in figure 5 and table 2.
     

    Figure 5

     

     
    Table 2

     
         		 Point forecast Lo 80 High 80 Low 95 High 95
    Sept 9.04 5.978858 11.93987 4.401073 13.51765
    Oct 9.77 5.183039 14.1951 2.797671 16.58054
    Nov 10.3 5.364191 15.06267 2.797157 17.62971
    Dec 10.3 5.280182 15.14668   2.668678   17.75819

     
    Exponential Smoothing method
    It is one of the most popular classic forecasting models. It gives more weight to recent values and works best for short term forecasts when there is no trend or seasonality in dataset. The model is given by:
    Ŷ(t+h|t) = ⍺y(t) + ⍺(1-⍺)y(t-1) + ⍺(1-⍺)²y(t-2) + …
    with 0<⍺<1
    As observed in the model, recent time periods have more weightage in the model and the weightage keeps decreasing exponentially as we go further back in time.
    The ⍺  is the smoothing factor here whose value was chosen to be 0.9 since it had the lowest RMSE among all other values.
    The forecast results are presented below:
     

    Figure 6


    Table 3

     
         		 Point forecast Lo 80 High 80 Low 95 High 95
    Sept 8.30 4.739288 11.87260 2.8512134 13.76068
    Oct 8.30 3.507498 13.10439 0.9673541 15.64454
    Nov 8.30 2.532806 14.07908 -0.5233096 17.13520
    Dec 8.30 1.700403 14.91149 -1.7963595   18.40825

     
    Holt Winters’ method
    The simple exponential function cannot be used effectively for data with trends. Holt-Winters’ exponential smoothing method is a better suited model for data with trends. This model contains a forecast equation and two smoothing equations. The linear model is given by:
    yt+h = lt + hbt
    lt  = αyt + (1-α)lt-1
    bt = β(lt-lt-1)+ (1-β)bt-1
    where, lt is the level (smoothed value).
    h is the number of steps ahead.
    bt is the weighted average of the trend.
    Just like the simple exponential smoothing method, lt shows that it is a weighted average of yt
    The α  is the smoothing factor here whose value was chosen to be 0.99 and  the β  value 0.0025 since they had the lowest RMSE among all other values.
    The forecast results are presented below:
     

    Figure 7


     
    Table 4

     
         		 Point forecast Lo 80 High 80 Low 95 High 95
    Sept 8.34 4.749288 11.9326 2.84121 13.84
    Oct 8.33 3.24 13.4243 0.54541 16.11977
    Nov 8.32 2.0800 14.5678 -1.2253 17.87316
    Dec 8.31 1.0963 15.53419 -2.725103   19.35565

     
    Evaluation
    To compare the models the two parameters chosen are:

    • Root mean square error (RMSE)
    • Mean absolute error (MAE)

    MAE is a measure of mean error in a set of observations/predictions. RMSE is the square root of the mean of squared differences between prediction and actual observation. RMSE is more useful when large errors are not desirable and MAE is useful otherwise.
    RMSE and MAE statistics for all the models are presented below:

    Naive ARIMA Exp Smoothing Holt Winters’
    RMSE 2.72 2.24 2.73 2.7
    MAE 1.05 1.034 1.06 1.05

     
    From the table it is clear that ARIMA/Box Jenkins method has both the lowest RMSE and MAE among the models under consideration while Exponential smoothing method has the highest MAE and RMSE among all.
    Therefore, the unemployment rate forecasts as per the Box Jenkins method for the next four months are:
     

    Sept 9.04
    Oct 9.77
    Nov 10.3
    Dec 10.3

     
    The way ahead?

    • The unemployment rate is expected to rise in the coming months. This is a bad sign for an economy that is already suffering.
    • With GDP forecasts getting lower and lower for the current financial year, the govt needs to act quick to mitigate the potential damage.
    • It is impossible to correctly ascertain the total impact of covid-19 on the economy and the range of the impact, but it is safe to say that we will be seeing the effects for a long time to come in some form or other.
    • We might see more and more people slip into poverty, depression, increased domestic violence and with potentially long term impact on human development parameters like child mal-nutrition, enrolment rates etc among other things.

    Some possible solutions

    1. Expansionary monetary policy: It is a common tool of dealing with high unemployment rate in the short term. Under expansionary monetary policy, the central bank reduces the rate of interest on which it lends money to the banks, subsequently the banks lower their rates which leads to a higher amount of loans being taken by business owners. This extra capital helps businesses to hire more workers and expand production, which in turn reduces unemployment rate.
    2. Expansionary fiscal policy: Under expansionary fiscal policy the government increases its spending, particularly in the infra-structure sector. It spends more money to build dams, roads, bridges, highways etc. This increased spending leads to an increase in employment as these projects require labour.
    3. Expand the scope of NREGS to urban areas permanently and a higher minimum wage for all : NREGS has proved to be really effective in alleviating poverty, improving quality of life and decreasing unemployment rate in rural areas. Given the unprecedented circumstances, the govt can consider expanding its scope to urban areas, so that it could provide employment to the millions of unemployed workers there. This increase in expenditure could also help the govt revive consumer demand, which is essential if we want to help the GDP get back on track.
    4. A stimulus package aimed at putting money into the hands of the poor :

    The govt should also consider providing at least a one-time transfer of funds to people just like the US govt did. Such a transfer of putting money directly into the hands of the poor is the most effective way of reviving consumer demand in the economy and many economists around the world have been calling for such a plan to be implemented. There is no better way of increasing consumer expenditure other than putting money into the hands of cash-starved people.
     
    Appendix:
     

    Figure 8


     

    Figure 9

     

    Figure 10

     

    Figure 11

     

    Figure 12

     

    Figure 13

     
     

  • Vietnam: Economic Prospects in Post Second Wave Covid-19

    Vietnam: Economic Prospects in Post Second Wave Covid-19

    The global community is into the ninth month of the COVID-19 pandemic and international efforts to develop a vaccine are at advanced stages.  Meanwhile in Russia over 250 Moscow residents received a dose of Sputnik V[i] and the Chinese Center for Disease Control and Prevention (CDC) has announced that the vaccine will be ready by November this year.[ii]Similarly, many American, British, European, and Indian companies are developing the vaccine which is at different levels of trials.  While the above progress is very encouraging, the global COVID-19 infections continue to rise and as on 13 October, according to the World Health Organisation (WHO), the total confirmed cases of COVID-19 were 37,601,848 people including 1,077,799 deaths.[iii] The top four countries with the highest infections were the US, India, Brazil and Russia.

    Vietnam’s COVID response since 23 January 2020, when the first case was detected, has been noteworthy. It successfully contained the spread of the virus by instituting local quarantine measures in early stages, declaring a state of emergency in February, and banning flights to and from China. For the next two months, Vietnam maintained strict COVID related measures including national lockdown and it was only in late April that some restrictions were removed in localities if they had contained the virus; but non-essential public services remained suspended. The opening up continued slowly with the resumption of flights to select destinations and cross-border travel restrictions were lifted. Meanwhile, Vietnam registered to buy a Russian Covid-19 vaccine as also developing vaccine on its own.

    In August, the second largest COVID-19 outbreak (after Danang) was reported in Quang Nam Province. The ‘second wave’ has now been successfully controlled.

    However, in July, Danang, a tourist hotspot, reported several new cases and a massive evacuation of nearly 80,000 tourists was undertaken. In August, the second largest COVID-19 outbreak (after Danang) was reported in Quang Nam Province. The ‘second wave’ has now been successfully controlled. As of 15 September, in Vietnam (total population 95,540,000) there were 1063 cases; 35 deaths; 261,004 tests had been conducted, and 11cases per million was recorded.[iv]

    Vietnam’s economic outlook in the ‘post-COVID Second Wave’ is a mixed bag of opportunities and challenges. There are at least four issues which merit attention. First, the Vietnamese economy, like any other global economies, suffered due to the pandemic. The 2020 first-half growth was about 1.8% compared with 7% in 2019 (year-on-year), but the Vietnamese economy has shown enormous resilience when compared with major global economies who have recorded negative growth. This is due to the proficient handling of the pandemic and the country is now on a quick and steady recovery path. The HSBC has revised Vietnam’s 2020 growth forecast from 1.6% to 3.0%.[v]

    It is also important to mention that the Vietnamese government has offered attractive incentives to multinational investors to help them “move up the value chain” and build supply chains in the country.

    Second, there are clear signs that Vietnam continues to be an attractive destination for foreign investments. This trend is not only due to global conglomerates moving out of China and seeking new destinations with attractive options for setting up of their businesses, but Vietnamese handing of the pandemic has provided them enormous business confidence in the country. According to the Ministry of Planning and Investment, total foreign investment in the first half was worth US$18.47 billion.[vi] Japanese (Panasonic), South Korean (LG Electronics), US (Foxconn; Apple) and the European (Heineken) companies moved to Vietnam. It is also important to mention that the Vietnamese government has offered attractive incentives to multinational investors to help them “move up the value chain” and build supply chains in the country.

    Third, is about renewable energy. Vietnam’s current energy generation mix is skewed towards coal (18,516 MW) and hydrocarbons (8,978 MW). Notwithstanding the COVID-19, the country’s average electricity consumption per day during the first few months of 2020 was 615 million KWh, an increase of 7.5 per cent compared with 2019.[vii] It is estimated that “Vietnam’s energy demand will increase by over 10 per cent by the end of 2020, followed by an eight per cent growth per year in 2021 to 2030.” The “government wants to reduce its greenhouse gas emissions by eight per cent by 2030” for which investments in renewable sources of energy such as solar and wind would have to be made,

    Fourth, is about immersion in Industry 4.0 technologies. There are now clear trends of widespread digital transformation across the globe and is impacting every aspect of the industry from commercial operations, technology management, use in fintech to support banking and financial services, new business models through analytics, and human resource management.  These technologies can potentially boost productivity and improve Vietnam’s GDP. For that innovative national policies for growth are needed. Also, the human resource would require ‘up-skilling, reskilling and retooling’ to embrace these technologies.  The industry leaders too have to recognize the importance of educating themselves and using new technologies as also adopting innovative models for their operations.

    Vietnam should build upon its successes of handling the COVID-19 pandemic and ‘build back better’ by offering long-term stimulus for investments and accord high priority to zero-carbon technologies to spur inclusive and resilient growth.

    Finally, Vietnam should build upon its successes of handling the COVID-19 pandemic and ‘build back better’ by offering long-term stimulus for investments and accord high priority to zero-carbon technologies to spur inclusive and resilient growth. It must adopt strategies for investments in technologies, products and services as also create new jobs tailored for Industry 4.0.

     

    Notes

    [i] “Russia Covid-19 vaccine: Over 250 people in Moscow get inoculated, says report”, https://www.livemint.com/news/world/russia-covid-19-vaccine-over-250-people-in-moscow-get-inoculated-says-report-11600085464168.html  (accessed 16 September 2020).
    [ii] “China coronavirus vaccine may be ready for public in November: Official”, https://www.hindustantimes.com/world-news/china-coronavirus-vaccine-may-be-ready-for-public-in-november-official/story-1DzVCBrdOwleJXxuw0wvyI.html  (accessed 16 September 2020).
    [iii] “WHO Coronavirus Disease (COVID-19) Dashboard”, https://covid19.who.int/?gclid=CjwKCAjwzIH7BRAbEiwAoDxxTlG5T6XZYiHVHBesW5cmAa9DKUytaVgH01haDH10TpmFA3OP-2s_phoCk9sQAvD_BwE  (accessed 16 September 2020).
    [iv] “Southeast Asia Covid-19 Tracker”, https://www.csis.org/programs/southeast-asia-program/southeast-asia-covid-19-tracker-0#National%20Responses  (accessed 16 September 2020).
    [v] “Vietnam’s positive growth in Q2 defies market expectations: HSBC”, http://hanoitimes.vn/vietnam-positive-growth-in-q2-defies-market-expectations-hsbc-313035.html  (accessed 16 September 2020).
    [vi] “Vietnam expects imminent new wave of foreign investment”, https://www.nationthailand.com/news/30392781?utm_source=homepage&utm_medium=internal_referral  (accessed 15 September 2020).
    [vii] “Assessing Vietnam’s Economic Prospects for Foreign Investors After COVID-19”, https://www.vietnam-briefing.com/news/assessing-vietnams-economic-prospects-foreign-investors-after-covid-19.html/  (accessed 15 September 2020).

    Image: Ho chi-min City

  • Living Next to China: India’s Economic Challenge

    Living Next to China: India’s Economic Challenge

    Abstract

    Hampered by declining economic growth, India needs to take bold and practical economic measures to overcome the adverse impact of the coronavirus pandemic, compounded by past economic blunders such as the demonetisation and the haphazard implementation of the GST regime. Mohan Guruswamy analyses that the seeds of the current economic slide were sown by the UPA II regime by its populist measures that were wasteful, unproductive, and reduced capital expenditure. Non action by the NDA governments on these issues has made it worse. He argues that India must not shy away from recourse to deficit financing to overcome the current unprecedented challenges faced by the economy on account of the Covid-19 disruption. India needs to increase its stimulus package from a mere 0.3% of the GDP to at least 10% to boost economic revival and growth. India’s reserves of $490 billion ($530 billion as of recent figures) is available to be tapped for economic revival. The measures must focus on addressing the severe impact on weaker sections of the society such as the poor, lower middle-class, and the farmers.

    The Covid2019 shock hit all world economies and has caused a serious contraction in all of them. Ironically, in the advanced economies like the USA, UK, Japan, and others, it exposed their intrinsic strengths with highly evolved social security systems by and large being able to absorb the labor displacement and the ability to quickly put together a fiscal fight back plan. Even China has been able to quickly recover its pole position as the worlds leading exporter and industrial production center. In India, Covid2019 exposed our co-morbidities, and has further opened the traditional faultlines, with the large unorganized labor cohort bearing the brunt of the costs. At last count the CMIE estimates over 130 million daily wagers in the urban centers being rendered jobless and homeless.[i] India’s economy which has been in distress for most of the last decade in now seriously stricken.

    When India’s economic history is written in some future date, and when a serious examination is done of when India lost its way to its ‘tryst with destiny’, the decade of 2010-20 will be highlighted.

    When India’s economic history is written in some future date, and when a serious examination is done of when India lost its way to its ‘tryst with destiny’, the decade of 2010-20 will be highlighted. The facts speak for themselves. India’s real GDP growth was at its peak in March 2010 when it scaled 13.3%.  The nominal GDP at that point was over 16.1%. The nominal GDP in September 2019 was at 6.3%, it’s lowest in the decade. Since then the downward trend is evident and we are now scraping the bottom at about a real GDP growth rate of 4.5%, this too with the push of an arguably inflationary methodology. Our previous CEA, Arvind Subramaniam, estimated that India’s GDP growth is overestimated by at least 2.5%. BJP MP and economist Subramaniam Swamy was even more pessimistic. He estimated it to be 1.5%.

    The decline in the promise is amply evident by the change in the make up of the economy during this decade.  In 2010 Agriculture contributed 17.5% of GDP, while Industry contributed 30.2% and Services 45.4%.  In 2019 that has become 15.6%, 26.5% and 48.5% respectively.  The share of industry has been sliding.  This is the typical profile of a post-industrial economy.  The irony of India becoming post-industrial without having industrialized must not be missed.

    Decline in Capital Investment

    The most significant cause for the decline of growth is the decline in capital investment.  It was 39.8% of GDP in 2010 and is now a good 10% lower.  Clearly without an increase of capital investment, one cannot hope for more industrialization and hence higher growth.  What we have seen in this decade is the huge increase in Services, which now mostly means increase in Public Administration and informal services like pakora sellers.

    In 2010 it seemed we were well on track.  But now we are struggling to get past $3 trillion, and the $5 trillion rendezvous that Modi promised by 2024 will have to wait longer.

    At the turn of the century, as China’s GDP began its great leap forward (from about $1.2 trillion in 2010 to $14.2 trillion in 2019), was also a heady moment for India whose GDP of $470 billion began a break from the sub 5% level of most of the 1990’s to the rates we became familiar with in the recent past (to hit a peak stride of 10.7% in 2010). At that point in time, if growth rates kept creeping up, we could have conceivably gone past $30 trillion by 2050. But for that the growth rate should consistently be above 7%. It seemed so feasible then.  In 2010 it seemed we were well on track.  But now we are struggling to get past $3 trillion, and the $5 trillion rendezvous that Modi promised by 2024 will have to wait longer.

    To be fair to Modi and the NDA, the decline began early in the second term of the UPA when capital expenditure growth had begun tapering off.  Dr. Manmohan Singh is too canny an economist to have missed that.  But UPA II also coincided with the increasing assertion of populist tendencies encouraged by the Congress President and her extra-Constitutional National Advisory Council. The decline in the share of capital expenditure was accompanied by a huge expansion in subsidies, most of them unmerited.  Instead of an increase in expenditure on education and healthcare, we saw a huge expansion in subsidies to the middle and upper classes like on LPG and motor fuels. Even fertilizer subsidies, which mainly flow to middle and large farmers with irrigated farmlands, saw a great upward leap.  Clearly the money for this came from the reduction in capital expenditure.  Modi’s fault in the years since 2014 is that he did nothing to reverse the trend, and only inflicted more hardship by his foolish demonetization and ill-conceived GST rollout.

    The realities are indeed stark.  The savings/GDP ratio has been in a declining trend since 2011 and Modi has been unable to reverse it.  Consequently, the tax/GDP ratio and the investment/GDP ratio have also been declining.  The rate of economic growth has been suspect and all objective indicators point to it being padded up. The drivers of economic growth such as capital expenditure is dismal.  Projects funded by banks have declined by over half since 2014 to less than Rs.600 billion in 2018-19.  Projects funded by the market have dropped to rock bottom.  Subsequently the manufacturing/GDP ratio is now at 15%.  Corporate profits/GDP ratio is now at a 15-year-old low at about 2.7%.  You cannot have adequate job creation if these are dipping.  Declining rural labor wage indices testify to this.

    Between October 2007 and October 2013 rural wages in the agricultural and non-agricultural sectors grew at 17% and 15%, respectively.  Since November 2014, however, agricultural and non-agricultural sector wages grew at only 5.6% and 6.5%, respectively. In 2019 average rural wage growth has further fallen to 3.1%.[ii]

    Bharat and India Divide

    It is very clear now that the urban lane has been moving well in India.  Indeed, so well that an Oxfam study revealed that that as much as 73% of the growth during the last five years accrued to just 1% of the population.[iii] This does not mean it is just the tycoons of Bombay and Delhi who are cornering the gains.  Government now employs close to 25 million persons, and these have now become a high-income enclave.  The number of persons in the private and organized sector is about another ten million. In all this high-income enclave numbers not more than 175-200 million (using the thumb rule of five per family).  Much of the consumption we tend to laud is restricted to just these.

    The simple fact that the share of Agriculture is now about 15.6% of GDP and falling, while still being the source of sustenance for almost 60% of the population reveals the stark reality.  A vast section of India is being left behind even as India races to become a major global economy.

    Agriculture is still the mainstay of employment.  Way back in 1880 the Indian Famine Commission “had observed that India had too many people cultivating too little land”.  This about encapsulates the current situation also.  While as a percentage the farmers and farmworkers have reduced as a part of the work force, in absolute terms they have almost tripled since 1947.  This has led to a permanent depression in comparative wages but has also led to a decline in per farmer production due to fragmentation of holdings.  The average farm size is now less than an acre and it keeps further fragmenting every generation.[iv] The beggaring of the farming community is inevitable.  The only solution to this is the massive re-direction of the workforce into less skilled vocations such as construction.

    The simple fact that the share of Agriculture is now about 15.6% of GDP and falling, while still being the source of sustenance for almost 60% of the population reveals the stark reality.  A vast section of India is being left behind even as India races to become a major global economy.

    As the decade ends, the Bharat and India divide have never been more vivid.  Our social scientists are still unable to fix a handle to this because the class, cultural and ethnic divides still eludes a neat theoretical construct.  Yet there can be little disagreement that there are two broad parts to this gigantic country and one part is being left behind.  The distance between the two only increased from 2010 to 2020.  This is indeed the lost decade.  Recovering from this will take long and will be painful.  If we take too long, we might have used up a good bit of the ‘demographic dividend’ and the demographic window of opportunity.  The ageing of India will be upon us by 2050[v].

    Covid-19 Impact – Increasing Economic Disparities 

    In the recent months the onslaught of the Covid2019 induced lockdown has been quite relentless.  From 2004-2014 India’s GDP grew at an average of 7.8%.  At its peak it went past 10% in 2010-11 Then it started slowing down.  The new government was unable to return to the old growth rates because it did not care to learn from the experiences of the previous regime, which began to spend more on giveaways, misguidedly thinking it was welfare economics, and took the accelerator off capital expenditure.  Even though capital expenditure is driven in India by government spending, this government spending is very different from subsidies and giveaways.  Subsidies generally tend to be misdirected with the already well-off garnering most of it.  Minimum Support Prices (MSP) are a huge annual subsidy[vi]and 90% of it accrues to the states of Punjab, Haryana, and the coastal region of Andhra Pradesh.  Fertilizer subsidies tend to accumulate to the advantage of large and medium farmers or to about a quarter of all land holdings.  Ditto for free power.  The only welfare expenditure to benefit farmers is investment in irrigation, rural infrastructure, and social welfare like education and health.  Unfortunately, this has been on the decline.  This has exacerbated disparities, both local and regional.  With capital expenditures declining, job creation suffered and the inevitable slowdown of GDP growth happened.  As we started diving, the government inflicted the so-called Demonetization adding to our woes.  Just as things began to look up, the Covid2019 pandemic overtook us.

    Now the only dispute on national income is how much will be the contraction.  The Finance Ministry hopes there won’t be any. The IMF has officially said it will be 4.5%.  The rating agencies predict a contraction of 6.8%, while many more are suggesting something closer to 10%.  How do we deal with is now?  The government of India has tended to be “conservative” in its outlook and has made no serious suggestion on economic stimulus.  What it calls a stimulus is actually not a stimulus. The problem is more philosophical.

    The divide between the Keynesians and the Chicago school is as intense and often antagonistic as the Sunni-Shia, Catholic-Protestant or Thenkalai-Vadakalai Iyengar divides.

    Keynesian economics is a theory that says the government should increase demand to boost growth. Keynesians believe consumer demand is the primary driving force in an economy.  As a result, the theory supports expansionary fiscal policy.  The Chicago School is a neoclassical economic school of thought that originated at the University of Chicago in the 1930s.  The main tenets of the Chicago School are that free markets best allocate resources in an economy and that minimal or zero government intervention is best for economic prosperity.  They abhor fiscal deficits.

    Inadequate Stimulus Package 

    The instruments used to beat countries like India into submission are ratings agencies such as Moody’s, which just downgraded India.  We shouldn’t lose too much sleep over it.  India is a hardly a borrower abroad and is more of a lender holding $490 billion as reserves.

    The only reason why the actual stimulus package is only Rs.63K crs is the obsession with fiscal deficits by Chicago economists such as Raghuram Rajan and his former student the hapless Krishnamurthy Subramaniam, the present CEA. They are true disciples of the Washington Consensus to judge countries like India by the fiscal deficit size.  The instruments used to beat countries like India into submission are ratings agencies such as Moody’s, which just downgraded India.  We shouldn’t lose too much sleep over it.  India is a hardly a borrower abroad and is more of a lender holding $490 billion as reserves.

    That is why the CEA when asked about a big stimulus said: “There are no free lunches!” That’s exactly what Milton Friedman said. But they quite happily ignore the biggest deficit financed economy in the world is the USA.  Raghuram Rajan told Rahul Gandhi on his videoconference that a stimulus of Rs.65K crores would suffice in the present situation[vii]. The Nobel Laureate Abhijit Bhattacharya and former CEA Arvind Subramaniam suggest a stimulus package like the USA or Japan[viii].  The USA has just announced a stimulus of over $3.5 trillion or over 15% of GDP.  Modi’s stimulus is a mere 0.3% of GDP.

    What is ‘Fiscal Deficit?’ A fiscal deficit occurs when a government’s total expenditures exceed the revenue that it generates, excluding money from borrowings.  Deficit differs from debt, which is an accumulation of yearly deficits.

    Many serious economists regard fiscal deficits as a positive economic event.  For instance, the great John Maynard Keynes believed that deficits help countries climb out of economic recession.  On the other hand, fiscal conservatives feel that governments should avoid deficits in favor of balanced budgets.

    India’s debt/GDP ratio is by contrast a modest 62% and yet it intends to pump in a mere 0.3% of GDP as stimulus.

    The fastest growing economies in the world, and now its biggest – USA, China, Japan and most of Western Europe – have the highest debt/GDP ratios.  Japan’s debt/GDP is over 253% before the latest stimulus of 20% of GDP.  China’s debt is now over 180% of its GDP.  The USAs debt/GDP is close to 105% yet it is raising $3 trillion as debt to get it out of the Covid2019 quagmire.  India’s debt/GDP ratio is by contrast a modest 62% and yet it intends to pump in a mere 0.3% of GDP as stimulus.

    Pump priming the economy by borrowing per se is not bad.  It is not putting the debt to good use that is bad.  Nations prosper when they use debt for worthwhile capital expenditure with assured returns and social cost benefits.  But we in India have borrowed to give it away as subsidies and to hide the high cost of government.  To give an analogy, if a family has to make a choice of borrowing money to fund the children’s education or to support the man’s drinking habit, the rational choice is obvious. The children’s education will have a long-term payback, while the booze gives instant gratification. But unfortunately, our governments have always been making the wrong choices.

    If borrowed money is used productively and creates growth and prosperity, it must be welcomed.  What we want to hear from the government is not about fiscal deficit targets, but economic growth, value addition, employment, and investment targets.  Our governments have hopelessly been missing all these targets.

    Modi’s Options – Need for Bold Decisions

    So, what can Modi do now to get us out of this quagmire?  If the regime abhors a stimulus financed by deficit financing there are other options that can be exercised.  But he is hamstrung with a weak economic management team with novices as the two key players, the Finance Minister and RBI governor.

    India has over $490 billion nesting abroad earning ridiculously low interest.  Even if a tenth of this is monetized for injection into the national economy, it will mean more than Rs.3.5 lakh crores.  At last count the RBI had about Rs.9.6 lakh crores as reserves.  This is money to be used in a financial emergency.  We are now in an emergency like we have never encountered or foresaw before. Even a third of this or about Rs.3.2 lakh crores is about five times the present plan.

    There is money in the trees, and all it needs is a good shake up to pick the fruits. The pain of the lockdown must not be borne by the poor alone.  The government can easily target 5% of GDP or about Rs.10L crores for the recovery fund as an immediately achievable goal.

    There are other sources of funds also, but tapping these will entail political courage and sacrifices. Our cumulative government wages and pension bill amounts to about 11.4% of GDP.  After exempting the military and paramilitary, which is mostly under active deployment, we can target 1% of GDP by just by cancelling annual leave and LTC, and rolling back a few DA increases.

    The government can also sequester a fixed percentage from bank deposits, say 5% of deposits between Rs.10-100 lakhs and 15-20% from bigger deposits for tax-free interest-bearing bonds in exchange.  The ten big private companies alone have cash reserves of over Rs.10 lakh crores[ix].

    There is money in the trees, and all it needs is a good shake up to pick the fruits. The pain of the lockdown must not be borne by the poor alone.  The government can easily target 5% of GDP or about Rs.10L crores for the recovery fund as an immediately achievable goal.

    This money can be used to immediately begin a Universal Basic Income scheme, by transferring a sum of Rs.5000 pm into the Jan Dhan accounts for the duration of the financial emergency; fund GST concessions to move the auto and engineering sectors in particular; begin emergency rural reconstruction projects to generate millions of new jobs and get our core infrastructure sectors like steel, cement and transportation moving again.

    Getting money to move India again is not a huge problem.  What comes in between are the philosophical blinkers.  Call it Chicago economics or the Gujarati mindset.

    Notes

    [i] https://www.businesstoday.in/sectors/jobs/india-unemployment-rate-hits-26-amid-lockdown-14-crore-lose-employment-cmie/story/401707.html

    [ii] https://www.financialexpress.com/economy/farm-wages-growth-fell-to-a-four-quarter-low-in-q3-fy-20/1789235/

    [iii] https://economictimes.indiatimes.com/news/economy/indicators/wealth-of-indias-richest-1-more-than-4-times-of-total-for-70-poorest-oxfam/articleshow/73416122.cms?from=mdr#:~:text=Wealth%20of%20India’s%20richest%201%25%20more%20than%204%2Dtimes%20of,total%20for%2070%25%20poorest%3A%20Oxfam&text=The%20Oxfam%20report%20further%20said,particularly%20poor%20women%20and%20girls.

    [iv] https://www.prsindia.org/policy/discussion-papers/state-agriculture-india

    140 million hectares of land is used as agricultural area, as of 2012-13.  Over the years, this area has been fragmented into smaller pieces of land.  As seen in Table 3, the number of marginal land holdings (less than one hectare) increased from 36 million in 1971 to 93 million in 2011.  Marginal and small land holdings face several issues, such as problems with using mechanization and irrigation techniques.

    [v] https://economictimes.indiatimes.com/news/politics-and-nation/demographic-time-bomb-young-india-ageing-much-faster-than-expected/articleshow/65382889.cms

    [vi] https://www.thehindubusinessline.com/opinion/all-you-wanted-to-know-about-minimum-support-price/article7342789.ece

    [vii] https://www.hindustantimes.com/india-news/in-video-conversation-with-rahul-rajan-suggests-65k-crore-aid-for-poor/story-CtrtvW6HErR16L9m1t9wHP.html

    [viii] https://economictimes.indiatimes.com/news/economy/policy/rahul-gandhi-in-conversation-with-abhijit-banerjee-india-needs-a-bigger-stimulus-package-like-us-japan-to-revive-economy/videoshow/75549770.cms

    [ix] https://www.screener.in/screens/2551/Cash-Rich-Companies/

     

    Image credit: Adobe Stock

  • Vietnam: Bright Economic Outlook post-COVID

    Vietnam: Bright Economic Outlook post-COVID

    COVID-19 is truly a ‘Black Swan’ event and its impact is being felt across the globe. There is widespread worry about the future of economic growth in the post-pandemic period and the World Bank has observed that the pandemic caused the deepest global recession since Second World War. [i] There are at least three reasons which triggered and added to the current crisis. First, it has involved the US and China in a trade war since July 2018, when US President Donald Trump imposed wide-ranging tariffs on China for its alleged unfair trade practices. In August 2019, Trump ordered U.S. companies to “immediately start looking for an alternative to China, including bringing your companies home and making your products in the USA.”[ii] China responded in a similar manner with counter tariffs on US goods. Since then numerous negotiations between them have been held, the last in June 2020 at Hawaii, did not yield any breakthrough. This revengeful tariff war has now blown into a full-fledged trade war and President Trump aggravated with the renewed threat of a “complete decoupling from China.”

    There is widespread worry about the future of economic growth in the post-pandemic period and the World Bank has observed that the pandemic caused the deepest global recession since Second World War.

    Second, amid the trade war, the Corona-19 pandemic made matters worse for the two protagonists. The US accused China of withholding information about the Wuhan virus which was detected in December 2019 and Beijing did not make public the information till January 2020 after which it spread across the globe from Europe to the US. The pandemic has caused massive disruptions in supply chains and some countries have decided to shift businesses out of China. For instance, Prime Minister Shinzo Abe government announced US $2.2 billion stimulus package to help companies shift production out of China back to Japan or elsewhere.[iii]

    Third, the new security law in Hong Kong has triggered an exodus by several companies to move out of China. The Law “targets acts of secession, subversion, terrorism and collusion with foreign forces, with life in prison for those committing the most serious offences”[iv] has scared common people. Many technology companies, startups, entrepreneurs are now confronted with uncertainty and are exploring alternative destinations.[v]

    many companies are being forced to shut down their operation in China and rethink-reevaluate-reinvest in new destinations to remain buoyant for the time being and slowly make their networks more resilient across sectors for the future.

    Furthermore, the pandemic exposed the weaknesses and susceptibilities of many organizations, business houses and industries particularly those that are intimately connected and dependent on China to fulfil their need for raw materials or finished products. Consequently, many companies are being forced to shut down their operation in China and rethink-reevaluate-reinvest in new destinations to remain buoyant for the time being, and slowly make their networks more resilient across sectors for the future. According to a leading business research and advisory company, “tariffs imposed by the U.S. and Chinese governments during the past years have increased supply chain costs by up to 10% for over 40% of organizations” and “popular alternative locations are Vietnam, India, and Mexico.” [vi]

    Vietnam and Thailand have a very good scorecard in their fight against COVID-19 and are rearing to attract investments and kick start the economy.

    Even before COVID-19 pandemic crisis, in 2019, five Asian countries i.e. Malaysia, India, Thailand, Indonesia and Vietnam (MITI-V) or “Mighty Five” had been identified as “up-and-coming players” with high potential for being world’s next manufacturing hubs.[vii] Among these, Vietnam and Thailand have a very good scorecard in their fight against COVID-19 and are rearing to attract investments and kick start the economy.

    According to the World Economic Forum, Vietnam’s economic rise is marked by trade liberalization, domestic reforms through deregulation, lowering the cost of doing business and investments made in human resource development.[viii] During the first six months of the current year, FDI commitments was at over US$15 billion which is a positive outlook for the country. In fact, Vietnam has attracted FDI from 136 countries and territories with nearly 32,000 projects with a combined value of US$378 billion. Among these Japan is the second largest investor with over US$60 billion. Last month, Vietnam’s Ministry of Planning and Investment, Embassy of Japanese at Hanoi, Japan External Trade Organization (JETRO), and Japan Bank for International Cooperation (JBIC) held a virtual conference to explore FDI investments “especially in the context of Japanese government providing a US$2.3 billion aid package for Japanese firms to diversify their supply chains”.[ix]

    Vietnam has many common export products from China such as broadcasting equipment, and could emerge as the “top exporter of broadcasting equipment to developed countries” but is constrained by “smaller GDP and workforce”; but its   progresses in infrastructure could potentially make it a more appealing option.[x]

    Vietnam has attracted FDI from 136 countries and territories with nearly 32,000 projects with a combined value of US$378 billion. Among these Japan is the second largest investor with over US$60 billion.

    Besides, there are other contenders such as Thailand and India to attract FDI and these two countries offer attractive FDI policies and manufacturing infrastructure. In mid-2019, as many as 200 American companies were planning to move their manufacturing base from China and were looking at India.[xi] Similar trends have been reported from South Korea [xii] and Japan [xiii] who could migrate to “production-conducive economies like India, Vietnam and Thailand”.[xiv]

    According to one estimate, FDI “across the globe may decline by 40% this year due to the Covid-19 crisis”[xv], but by all counts and accounts, Vietnam is a resounding success story.  It is a stable economy, possesses necessary infrastructure and facilities, and above all it enjoys “multilateral and bilateral agreements with foreign countries”[xvi], which makes it a popular destination in the post-COVID economic revival outlook.

    Notes

    [i] “Global Economic Prospects”, https://www.worldbank.org/en/publication/global-economic-prospects (accessed 16 July 2020).
    [ii] “Trump says he’s ordering American companies to immediately start looking for an alternative to China”, https://www.cnbc.com/2019/08/23/trump-says-hes-ordering-american-companies-to-immediately-start-looking-for-an-alternative-to-china.html (accessed 30 July 2020).
    [iii] “Coronavirus Impact: Japan to offer $2.2 billion to firms shifting production out of China”, https://www.businesstoday.in/current/world/coronavirus-impact-japan-to-offer-22-billion-to-firms-shifting-production-out-of-china/story/400721.html (accessed 30 July 2020).
    [iv] “Hongkongers contemplate a second exodus”, https://www.scmp.com/week-asia/politics/article/3093517/home-and-away-after-national-security-law-hongkongers (accessed 30 July 2020).
    [v] “Tech Firms Begin to Abandon Hong Kong over Security Law”, https://webcache.googleusercontent.com/search?q=cache:tmQW3Yjx5vcJ:https://www.bloomberg.com/news/articles/2020-07-20/tech-firms-begin-to-abandon-hong-kong-because-of-security-law+&cd=13&hl=en&ct=clnk&gl=in (accessed 30 July 2020).
    [vi] “Gartner Survey Reveals 33% of Supply Chain Leaders Moved Business Out of China or Plan to by 2023”, https://www.gartner.com/en/newsroom/press-releases/2020-06-24-gartner-survey-reveals-33-percent-of-supply-chain-leaders-moved-business-out-of-china-or-plan-to-by-2023 (accessed 30 July 2020).
    [vii] “5 China Sourcing Alternatives In Asia”, https://www.intouch-quality.com/blog/5-alternatives-to-sourcing-from-china (accessed 30 July 2020).
    [viii] “Vietnam races ahead of China in economic growth: opportunities and challenges for Vietnam in the post-COVID- 19 period”, https://timesofindia.indiatimes.com/blogs/ChanakyaCode/vietnam-races-ahead-of-china-in-economic-growth-opportunities-and-challenges-for-vietnam-in-the-post-covid-19-period/ (accessed 30 July 2020).
    [ix] Ibid.
    [x] “COVID-19: Developing countries and shrouded opportunities”, https://www.orfonline.org/expert-speak/covid-19-developing-countries-and-shrouded-opportunities/ (accessed 30 July 2020).
    [xi] “About 200 US firms aim to move manufacturing base from China to India post-general election: USISPF”, https://www.businesstoday.in/current/economy-politics/about-200-us-firms-aim-to-move-manufacturing-base-from-china-to-india-post-general-election-usispf/story/341011.html ( 30 July 2020).
    [xii] “Korean companies keen to move out of China to India”, http://timesofindia.indiatimes.com/articleshow/75130387.cms?utm_source=contentofinterest&utm_medium=text&utm_campaign=cppst (30 July 2020).
    [xiii] “Global firms look to shift from China to India”, https://www.livemint.com/industry/manufacturing/global-firms-look-to-shift-from-china-to-india-11587494725838.html  (30 July 2020).
    [xiv] “India isn’t ready yet for foreign companies that want to quit China”, https://theprint.in/opinion/india-isnt-ready-yet-for-foreign-companies-that-want-to-quit-china/415040/ (accessed 30 July 2020).
    [xv] “1,000 Japanese firms looking for investment opportunities in Vietnam”, http://hanoitimes.vn/1000-japaneses-firms-looking-for-investment-opportunities-in-vietnam-313133.html (accessed 30 July 2020).
    [xvi] “Vietnam races ahead of China in economic growth: opportunities and challenges for Vietnam in the post-COVID- 19 period”, https://timesofindia.indiatimes.com/blogs/ChanakyaCode/vietnam-races-ahead-of-china-in-economic-growth-opportunities-and-challenges-for-vietnam-in-the-post-covid-19-period/ (accessed 30 July 2020).

     

    Image: Ho Chi Minh city and Saigon River – Credit: Adobe Stock

  • The Catalysing Effect of Covid-19 on the Changing World Order

    The Catalysing Effect of Covid-19 on the Changing World Order

    Contrary to the realist belief, international states co-exist in a world order of hierarchy rather than anarchy. Ikenberry presents this hierarchical world order and the cyclical rise and fall of hegemonic powers. Early 20th century witnessed the shift from Pax-Britannica to Pax-Americana that was complete by 1945, from which point the US defended its position during the Cold War with the erstwhile USSR. It exercised its hegemonic influence even more aggressively after the Cold War. However, US dominance of the world order has been diminishing owing to the Trump administration’s isolationist approach to foreign policy, and the increasing influence of China in world politics. This article examines the catalysing effect of Covid-19 and the rise of China on the current World Order.

    Trump’s policy of disregarding multilateralism and imposing its unilateralism on the world has catalysed into an involuntary retreat, protectionism, and isolationism for the USA with dire consequences for its foreign policy effectiveness.

    Trump’s policy of disregarding multilateralism and imposing its unilateralism on the world has catalysed into an involuntary retreat, protectionism, and isolationism for the USA with dire consequences for its foreign policy effectiveness. The net result is that the world is witnessing an abdication of leadership by America in a world disrupted by the Covid-19 pandemic. A clear pattern of isolationism can be seen in various actions of the Trump Administration since it’s assumption of the Office. In 2017, the US withdrew from the Paris Agreement, in 2018 it unilaterally reneged from the JCPOA, re-imposed sanctions on Iran and threatened sanctions on allies who supported Iran. In 2019, it withdrew troops from Syria, which led to subsequent Turkish incursion on Rojava Kurds, and in early 2020 it negotiated with the Taliban to enable withdrawal of US troops from Afghanistan. With the onset of Covid19 global pandemic, the Trump administration has accused the WHO of protecting China. In a unilateral action not endorsed by its allies, USA first stopped its funding for WHO and then terminated its relationship with the UN institution. This comes as a blow to multilateralism since the US was WHO’s largest donor, contributing about $440 million yearly. In addition to this, the US has failed to provide the lead in the global response to tackle the virus despite its initiatives in the past pandemics such as H1N1, Ebola and the Zika virus. The US was absent from the WHO initiative – Global Coronavirus Response Summit (before its withdrawal from the association). In addition, the US has been unable to provide external aid to combat the virus due to domestic shortages, which explains its restraint to guide an international response in the absence of a coherent domestic plan of action. Thus, the coronavirus pandemic has acted as a catalyst in increasing the pace of US isolationism from world politics.

    China has turned the tide on its previous missteps in containing the virus by publicising its governance model as the most effective way to combat the pandemic.

    Meanwhile, the pandemic has established firmly China’s rise in the international stage. Though China is facing backlash for suppressing details about the virus, it is battling to overcome this criticism by providing international aid and stepping up to lead a global response using Beijing’s success as a template to overcome the novel virus. China has contributed significantly to the global response by providing materials such as ventilators, respirators, masks, protective suits and test kits to Italy, Iran, Serbia, and the whole of Africa. Grabbing its opportunities to lead international responses, China hosted Euro-Asia conference, participated in the Global Coronavirus Summit where it pledged an emergency funding of $20 million to WHO, and pledged $ 2 billion to the WHO (equalling its annual budget) to be disbursed over the next two years, thus contrasting sharply with the US behaviour of withdrawing from the WHO. China has turned the tide on its previous missteps in containing the virus by publicising its governance model as the most effective way to combat the pandemic. It continues to highlight the inadequacies and shortfalls in healthcare systems of the western world as against the success of its governance model, Beijing Consensus, and variations of it in East Asia. It is clear that China has seized the Covid-19 pandemic as a huge opportunity to establish its global leadership.

    Taking advantage of the global disarray due to the pandemic, China has taken strong actions to deflect global criticism of its initial handling of the virus. Two prominent examples of this being, European Union watering down the report on Covid19 disinformation owing to pressure from Beijing, and the passing of the controversial Hong Kong security law. While the US has taken initiative in cracking down on China by repealing the special privileges to Hong Kong, other countries were cautious in retaliating against China significantly and limited their actions to sympathetic support for pro-democracy protestors. The exception to this was Britain, which offered UK citizenship to British National Overseas Passport holders in Hong Kong, despite seriously offending China. Despite the global backlash against Chinese diplomacy in the form of generous aids, international actors have expressed limited concerns through action against Chinese domination. This is due to the circumstantial mismatch in global balancing against China’s rise. The US uses unilateral actions and ‘expects’ its allies to follow, while its allies despite their serious concern over China’s rise, remain vary of following in the American footsteps. This is because US allies treat coronavirus as an immediate threat as opposed to China’s rise. The US being a status quo power is more threatened by China’s rise since it posits as a revisionist state. However, in view of China’s proactive efforts in leading global contributions to battle the coronavirus, US allies remain tolerant of China’s dominance.

    The passive and fractured response to China’s aggressive exploitation of the pandemic to establish its global leadership is a concern for India. The recent setting up of Chinese military camps in Indian controlled territory of Ladakh is a manifestation of China’s complex strategy. India has, true to its traditional policy, opted out of involving the United Statesin the ‘bilateral issue. However, it would be beneficial to be united in balancing against China’s rise. While it is necessary to work together to utilise Global Supply Chains (GSC) during the pandemic to battle the coronavirus pandemic, it is equally important to look at global balancing against China to ensure its compliance to rules-based world order. Since China’s power is derived from its economic strength, balancing strategy against China should focus on trade and economy. Chinese foreign policy depicts a pattern of economic coercion to reward or punish its counterparts. This can be tackled through concerted global action. India is, as one of the largest producer of pharmaceuticals, playing a crucial role in global efforts to fight the pandemic by providing Hydroxychloroquine globally. However, given that most raw materials are sourced from China, balancing against China requires a favourable movement of GSC diversification. US-China trade war has, encouraged companies to move production out of China and into Asian countries such as Vietnam and Taiwan. As a result of the coronavirus crisis and the global backlash, companies look to further diversify their resources and supply chains. India and other Asian countries could benefit from this if they adapt their policies suitably.

    Global backlash against China’s handling of the virus in Wuhan is still a challenge for China’s geopolitical strategy. Its foreign policy is seen more as displaying aggressive and coercive approach than persuasive diplomacy.

    It is difficult to estimate whether China would aspire for hegemonic leadership. Global backlash against China’s handling of the virus in Wuhan is still a challenge for China’s geopolitical strategy. Its foreign policy is seen more as displaying aggressive and coercive approach than persuasive diplomacy. Given the current volatile scenario most countries have, in the absence of US leadership, increased their dependence on China as it is now the largest provider of aid. While all this tips the scale in China’s favour, it’s hegemonic ambitions can be countered through trade strategies as its weakness stems from the fact that it is a hugely export driven economy. Global diversification of supply chains would reduce the world’s increasing dependency on Chinese manufacture and products. The world will need to be cautious as the pandemic has provided China an opportunity to tighten its grip on the global economy as the world’s workshop and technology provider. Here on, international efforts to bandwagon or balance will become a decisive factor in determining China’s rise to apex position in the world order.

     

  • Freedom of Speech and Right to Information amidst the Covid-19 Pandemic

    Freedom of Speech and Right to Information amidst the Covid-19 Pandemic

    The global pandemic hit India in March 2020 and Prime Minister Modi announced a 21 day lockdown beginning on 25th March 2020. Since then the lockdown has been extended multiple times as the country grapples with a major public health crisis. Media houses have been on their feet, both literally and metaphorically, as they cover new stories, cases and most importantly, the state response towards the pandemic. The citizenry relies on news reportage to learn more about their government’s approach towards handling this unconventional situation. Media is often regarded as the fourth pillar of democracy meaning it is a supporting figure for democracy to persist and flourish. The pandemic has exposed some paramount inadequacies in the government’s handling of the situation such as lack of a robust public health infrastructure and other issues. The reportage on such instances has often faced backlash from the government resulting in legal notices against the journalists and media houses. India also dropped two places in the World Press Freedom Index making it 142nd in position citing the curfew in Jammu and Kashmir. The watchdog has also issued a warning about the implications of the pandemic, “the looming health crisis could serve as an excuse for governments to take advantage of the fact that politics are on hold, the public is stunned and protests are out of the question, in order to impose measures that would be impossible in normal times” (Scroll Staff, 2020).

    Media is often regarded as the fourth pillar of democracy meaning it is a supporting figure for democracy to persist and flourish.

     Two patterns can be observed with regards to media freedom in India during a time like this; furthering a certain narrative through misinformation and misrepresentation and carrying out state-sponsored propaganda. In this lockdown, the state wants a narrative which eulogizes their efforts during the lockdown and overall handling the situation. However, there are major loopholes in the measures taken by the government which has led to the system failing its most vulnerable class of individuals; the marginalized and the poor. The state has also taken this time to strike upon certain civil liberties and advance their propaganda by curbing dissent.

     Misinformation and misrepresentation of certain communities has been rampant during this time. Nabeela Khan, in an article called Trends in Covid19 misinformation in India for Health Analytics Asia categorizes the spread of misinformation in four waves. First, misinformation about the origin of the virus, this has been debated not just in India but worldwide where they have accused China of producing this virus in a lab and spreading it to use to its advantage. There have also been multiple other theories available online related to consumption of certain meats in China. Second circulation of old images and videos to create fear, in this case the Tablighi Jamaat incident was highlighted immensely and videos from before the pandemic were used to show that ‘Muslims’ in India spread the virus. Third, on ‘cures’ and prevention techniques which is particularly famous on several WhatsApp groups where home-made remedies of lemon, honey, turmeric or any other ‘Ayurvedic’ cures are posted every day. And fourth, on lockdowns in India, where the news of lockdown being extended were spread even before the official announcements were made. Increasingly, there has been excess confusion over the surging numbers in India and whether or not governments give out the exact figures. Additionally, there is no clarity about government aid and funding towards the poor such as the internal migrants in the country.

    Kaye makes an important point as he says that the governments are using the pandemic as a smokescreen to carry forward their agenda and take actions that they have wanted to take for a long time.

    The UN Special Rapporteur David Kaye, talks to The Lawfare Podcast about his latest UN report Disease, pandemics and the freedom of opinion and expression. Kaye makes an important point as he says that the governments are using the pandemic as a smokescreen to carry forward their agenda and take actions that they have wanted to take for a long time. He gives an example from Hungary where the Press is under strict scrutiny of the government. Since the coronavirus is a recent occurrence, there is not a very large body of information available on it. The information keeps changing as cases increase or decrease, as there are multiple waves of it so the orders issued by the government are subject to change. He also particularly criticizes India for its treatment of Jammu and Kashmir since August 2019 and calls it “a real misuse of the situation”.

     Journalists and activists across the world have been arrested during this lockdown and India is no exception to this trend of suppressing free speech. Siddharth Varadarajan, Gautam Navlakha, Anand Teltumbde, Safoora Zargar, Umar Khalid, Dhaval Patel, Supriya Sharma among many others have either been arrested or served notice by the government during the lockdown. These journalists have either been arrested on the grounds of their reportage of the pandemic, during the pandemic or incidents that took place before the pandemic.

     An FIR was lodged against Siddharth Varadarajan, one of the founding editors of The Wire, an acclaimed media house, on the grounds of making unverifiable claims. Varadarajan tweeted on March 31st saying that UP Chief Minister Yogi Adityanath had given a go-ahead for the Ram Navami festival to be held from March 25th to April 2nd, in the middle of the lockdown and Yogi also said that “Lord Rama would protect the devotees from coronavirus”. As a matter of fact, it was Acharya Paramhans who gave out this statement and not CM Adityanath and Varadarajan tweeted a clarification the following day. On April 10, police from Ayodhya showed up at his doorstep in Delhi to serve him notice and his wife Nandini Sundar explained this instance elaborately in her tweets. However, this act only suggests the government’s misuse of power and tactics to pursue a culture of intimidation. It could be argued that the journalist was peddling unverified claims but CM Adityanath in fact supported the decision to have a Ram Navami mela. The Wire has published an elaborate FAQs list on this matter explaining every detail of it. It has also been condemned by the Editors’ Guild of India who have called this episode “an overreaction and an act of intimidation”.

     Journalists and activists such as Gautam Navlakha, Anand Teltumbde, Safoora Zargar, Umar Khalid, Sharjeel Imam etc. have been booked under the UAPA, Unlawful Activities Prevention Act. This Act was formulated as a law in 1967 to prevent any ‘unlawful’ activities or any measures which threatened the integrity and sovereignty of India. In 2004, the UPA government expanded on it further to target terrorist outfits or any organizations harming the state but not individuals. The 2019 Amendment of the Act has entrusted the government with identifying individuals who might be harming the integrity of the state, the definition of which the government decides. The contemporary term used for such people on social media and other platforms is ‘urban naxals’. Student activists and journalists have been booked under this act for protesting against oppressive government laws which promotes a narrative that dissent is by its very nature, ‘anti national’. There have also been cases where activists have been arrested, then granted bail and arrested again based on some other complaint. Safoora Zargar’s case is a particularly complex one in this regard where she was arrested after she was granted bail and was granted bail again recently on humanitarian grounds. Zargar is five months pregnant which was the basis of her bail but the discourse around this has been to release her not because of her pregnancy because dissent is a fundamental right.

    The moment we no longer have a free press, anything can happen. What makes it possible for a totalitarian or any other dictatorship to rule is that people are not informed; how can you have an opinion if you are not informed? If everybody always lies to you, the consequence is not that you believe the lies, but rather that nobody believes anything any longer. This is because lies, by their very nature, have to be changed, and a lying government has constantly to rewrite its own history – Hannah Arendt

     Praveen Swami makes a compelling argument in a FirstPost article about hate speech and freedom of speech. He opines that the response to hate speech is not censorship but plurality where opinions are allowed to coexist. In India, a large part of the Press is controlled and supported by the government leading them to produce streamlined biased news. According to him, alternatives need to come up for hate speech where the dominant narrative does not remain unchallenged.

     To conclude, Hannah Arendt’s cautioning words on freedom of press and misinformation are very relevant today and sounds the alarm bells:

    “The moment we no longer have a free press, anything can happen. What makes it possible for a totalitarian or any other dictatorship to rule is that people are not informed; how can you have an opinion if you are not informed? If everybody always lies to you, the consequence is not that you believe the lies, but rather that nobody believes anything any longer. This is because lies, by their very nature, have to be changed, and a lying government has constantly to rewrite its own history”

     

    Reference

    Bakshi, Asmita (2020, May 31) From Pinjra Tod to Kashmiri Journalists: What’s the Deal with UAPA?. Livemint. Retrieved from https://www.livemint.com/mint-lounge/features/from-pinjra-tod-to-kashmiri-journalists-what-s-the-deal-with-uapa-11590915249625.html

     

    Chakma, Suhas (2020, June 22) FIR Against Supriya Sharma is Emblematic of how the Law is Abused to Throttle Press Freedom. The Wire. Retrieved from https://thewire.in/media/supriya-sharma-fir-abuse-law-press-freedom

     

    Goldsmith, J (Host) (2016, May 16). The Lawfare Podcast: David Kaye on Free Speech During a Pandemic. (Audio podcast episode). In Lawfare. Retrieved from https://www.lawfareblog.com/lawfare-podcast-david-kaye-free-speech-during-pandemic

     Khan, Nabeela (2020, June 12) Trends in Covid19 misinformation in India. Health Asia Analytics. Retrieved from https://www.ha-asia.com/trends-in-covid-19-misinformation-in-india/

     Scroll Staff (2020, April 21) Covid-19: India drops 2 places on World Press Freedom Index, as watchdog warns of pandemic impact. Scroll.in. Retrieved from https://scroll.in/latest/959816/covid-19-india-drops-2-places-on-world-press-freedom-index-as-watchdog-warns-of-pandemics-impact

     Scroll Staff (2017, December 4) Top ten things that Hannah Arendt said that are eerily relevant in today’s times. Scroll.in. Retrieved from https://scroll.in/article/856549/ten-things-hannah-arendt-said-that-are-eerily-relevant-in-todays-political-times

     Swami, Praveen (2020, April 27) Hate speech in the time of a pandemic: Answer to malevolent incendiary language is plurality, not censorship. Firstpost. Retrieved from https://www.firstpost.com/india/hate-speech-in-the-time-of-a-pandemic-answer-to-malevolent-incendiary-language-is-plurality-not-censorship-8295271.html

     The Wire Analysis (2020, April 19) FAQ: What are the UP Police FIRs Against The Wire Actually about? The Wire. Retrieved from https://thewire.in/media/faq-up-police-fir-siddharth-varadarajan

     

    The views expressed are the author’s own.

    Image Credit: Rhy Design and medium.com