A Tribute to Tagore in the Time of COVID-19

In this time of COVID-19 let us recall that poem of Tagore which was a clarion call for fearlessness, adherence to reason, universal humanism and empathy for “Others” who are not us.  

In this tribute to Tagore I have elaborated Tagore’s original lines (in red, italics and underlined) to emphasize that nothing much has changed in the world. George Floyd’ death in USA and Jamlo Makdam’s death in India brings out the bitter truth that Tagore’s lament is still valid.

Let us have a world

Where the mind is without fear and the head is held high;

Where any nation state does not lord over others

Where aggression is not justified by patriotism

Where visas are not used to deny a human being to meet a loved one

                      Where knowledge is free;

Where science is not locked down in private enclosures

Where books are not burned by priests of “Other Gods”

Where beliefs do not banish logics

Where the world has not been broken up into fragments by narrow domestic walls;

Where race, caste, tribes, color, language and gods do not create strangers to us

Where words come out from the depth of truth;

Where truth evolves and not handed down

             Where tireless striving stretches its arms towards perfection;

Where innovation rules, ideas confront ideas, paradigm changes;

Where the clear stream of reason has not lost its way into the dreary desert sand of dead habit;

Where a child is not to afraid call out an Emperor naked

Where the mind is led forward by thee into ever-widening thought and action

Into that heaven of freedom, my Father, let my country awake

Where humans can reach for space and brings and an end to its childhood on Earth


The last line is a tribute to Arthur C Clarke’s Childhood’s End

Cry Jamlo Makdam Cry

In a heartbreaking tragedy, a 12-year-old child labour – Jamlo Makdam died on 20th April after walking for 150 km from her workplace Bhupalpally in Telengana to her native place, Bijapur district in Chattisgarh. She was working in Chilly fields in Kannaiguda village.

see here

I have written a poem in her memory.

Cry not -my beloved country- Cry not

                Save your tears

                for Jamlo, the chilly-picker,

She needs them plenty

to keep her walking.

Only a mile afar

 mother waiting to hug her

quench her thirst -before she moves to a land unknown.

Running away from coronavirus, with week’s hunger in belly,

100 rupees tucked in her skirt, bedecked with chilly flakes,

a mere 150 kilometers to walk,

no marathoners to accompany

she is walking, walking, and walking.

On a lonely road

Sun blistering above

With no helpful winds to blow away the heat

She is walking, walking, and walking.

Thirsty blood, tearless eyes

Saliva-less tongue

Still her dream dies hard

home, sweet home and mother awaiting – her final resting place.

Hunger, her best friend, she is not afraid of,

because she must walk, walk, and walk.

Stars are shining

in their AC cooled rooms,

cutting hubby’s hair short, sweeping floors – a first time in life

singing paeans to Lockdown, Lockdown and Lockdown

A 100K like in Instagram -no wonder.

Leaders are busy in their virtual world

With Mask on

Conferring on matters of life and death

gravitas overflowing

may be talking about Michelangelo

and heart beating about Jamlos at large.

But our Jamlo is not even a footnote.

Which country owned Toba Tek Singh?

Gods only know.

Which state owns Jamlo for her to receive some succor?

The answer is blowing in the wind

To her mother’s arm that is the only place on earth she belongs to.  

Corona Pandemic- One size does not fit all

“Blessed are the Meek, for They Will Inherit the Earth” (Matthew 5:5). 

Corona virus -COVID-19- started its journey in an industrial city of China sometimes in late 2019. Within a short span of 3 to 4 months it has enveloped the entire earth with its foot print, thus qualifying it to be designated as a pandemic attack by a tiny microorganism that can multiply only when it can find a human cell as a host , a springboard for jumping to its next victim. It is said that a virus is agnostic about the socio-economic profiles of its victims; it does not care as to what economic strata as person belongs, to what god a person kneels. At the same time credible evidence is there that age, sex and existing conditions do have a bearing on the survival probability of a corona infected person. For example, a study1 of 6839 corona deaths in New York City shows that 72.3% of them belonged to the age group 65 and above, 75% had underlying conditions like Diabetes, Lung Disease, Cancer, Immunodeficiency, Heart Disease etc. 62% of the victims were males.  In another study of 44,000 cases from China, deaths were at least five times more common among confirmed cases with diabetes, high blood pressure or heart or breathing problems2.

So, there are factors that create an enabling conditions for COVID-19 to thrive and kill its victims. But rate of incidence of corona cases and consequent deaths also varies between countries.  One Indian internal medicine expert has stated why incidence of corona is relatively low in India. He has identified 3 factors for a virus’ spread — the “agent or the virus itself, the host and the environment”.  According to him India’s relatively higher temperature and humidity slows down the march of the virus3.

It has also been reported that a US government study has also confirmed the role of ambient temperature and humidity in killing the virus on surfaces and air4.

Even after controlling all the above noted factors there is cultural a dimension that also determines how the virus would affect a given society. David Kelvin, a Canadian microbiologist has pointed out that the practice of Italians greeting “each other with an embrace and kisses” increases the probability pf passing the virus “on a more dangerous dose of COVID-19.”5

Religious faith also sometimes determines a society’s willingness to accept scientific approach to handling of any epidemic disease. For example, only 72.2% of children aged 19 to 35 months in the United States were fully vaccinated in 20156.

A major global survey published in June 2019, covering 140,000 people aged 15 and older in more than 140 countries found people in higher-income countries were among the least confident in vaccine safety — particularly in North America and Europe. Meanwhile, vaccine trust was highest in countries where preventable diseases still spread, such as Bangladesh and Rwanda.7

The above brief review of various plausible determining factors for country wide variations in incidence of corona virus and subsequent death provides a possible direction to further research that would help countries to identify deficiencies in their health infrastructure and attitudinal bottlenecks of the people at large to contain and minimize the effect of a virus like corona , in current time as well as in future. Pending that it may not be irrelevant to look at available data that provides some clues to the factor that are driving the variations in country wise impact of corona virus. Our aim is to carry out a descriptive statistical analysis without trying to build any model for conducting statistical hypothesis. 

Data and Study Variables: For this article we have used data that are available in public domain and put out by multilateral organizations like World Bank, World Health Organization., Worldometer and Pew Research Centre. Main data on COVID-19 is collected from Worldometer, a reference website.  Pew Research Centre brought out a report in October 2017 analyzing religious change and its impact on societies around the world. Covering 199 countries and territories around the world, the study identified countries which favor a specific religion either as an official government sponsored religion or by according a special status to one specific religion over all other faiths. Income data is taken from World Bank website. Expenditure on health data is taken from WHO website. (the further details are available in a table given at the end).

Intensity of infection of a virus can be estimated by the number of virus afflicted persons with symptoms. But a corona infected person may not show any symptom for several days, extending up to 14 days. These pre-symptomatic cases cannot be detected unless a country either carries out a random tests of enough size or for all citizen or at least of all persons in selected age groups. It is also possible that many persons with COVID-19 symptoms remain un-documented because many covid-19 infected persons with mild symptoms recover without hospitalization.  So, the number of cases reported by a country may also depend on the number of tests carried out by that country. However, we consider the number of reported cases as the primary variable of study. To account for the effect of population size we have taken normalized variables- that is cases/tests/deaths per million of population. 

Regarding health infrastructure we have considered the “government expenditure on health as percentage of government expenditure / GDP” as the discriminating variable across countries. To convert this numerical variable to a categorical variable, we have divided countries into 4 groups based on their percentile ranks; 4 groups based on 25 percentile, Median, 75% and maximum amount. The corresponding groups are named as Lower, Lower Middle, Upper Middle and High spenders.

Regarding “Religious Status” variable, every country is put into one of the three categories- (1) Having official State Religion, (2) Having a preferred religion, (3) No official religion. Countries which have declared atheism as official doctrine, we have designated “Capitalist Communism” as its state religion. China, Vietnam. Incidentally, Russia has a preferred state religion- Christianity.


Country Coverage:  This study is based on 123 countries having a total population of 7.2 billion as on 2019. The latest US Census Bureau estimates world population at 7.58 billion as on June 2019, a coverage of 95% of world population8

Income Group: The total number of cases of these countries was 2,32,37,82 or around 2.3 million. If we had included cases of all countries which have reported COVID-19 cases, this number would have been 2.33 million. So, analysis that follows would be representative of the world scenario.  Top ten countries in terms of number of cases accounted for 1.8 million cases, that is 78.26 percent of total cases covered. The income group wise profile of COVID-19 and its proximate determinants is given in the table below.

Table 1 here:

Table 2 here

The descriptive details of various measures of incidence of COVID-19 across income groups and its covariates given above leads to one conclusion – the richer countries with higher proportion of older people are more likely to fall prey to COVID-19 and once infected most likely to die also. The best possible health infrastructure does not provide any protection against these silent and invisible killer.

A sharper picture emerges if one looks at the top ten countries in terms of incidence of COVID-19. The following table gives the relevant details.

Table 3 here            

One obvious outlier in this group most affected countries is Germany. Despite having a high share of older people and a moderate level of public expenditure on health it could achieve much better performance in containing death rate of affected persons. The fact that Germany conducted tests of relatively larger number of persons may not be a good explanation because Spain and Italy also have tested a similar proportion of its people. S

Health Infrastructure:

The quality of health infrastructure of a country is positively correlated with the government allocation of resources for this purpose. Many physical indicators like number of doctors per million people etc. would depend more on government initiative than on private one. To establish the relationship between quality of health infrastructure and other COVID-19 related measures we have converted two numerical indicators of Government health expenditure into qualitative measures based on their percentile ranks. The resulting 4 quality levels are based on quartiles. These 4 levels in ascending order are Low, Lower Middle, Upper Middle, High. The tables below are expected to provide some clues about the importance this factor in determining the intensity of COVIS-19 in different countries.

Table 4 here


 It is obvious that, the countries in highest income bracket with high rate of government expenditure on health suffered disproportionately more due to COVID-18 pandemic. This group of countries accounting for 10.7 percent of the world population recorded 65.4% of death due to COVID-19.  Both China and India, two countries that account for near about 40% of world population and both spending relatively much less than their peer countries in their respective income groups account for only 4 % of the share of cases and 3 % of total deaths. In China, a plausible reason for this could be that the government at a early stage could segregate the district where the virus first struck.  In India, demographic profile of the population as well as peoples’ inherent immunity due to their constant exposure to highly un-hygienic living conditions could be one factor.  I believe people intuitively understand this- the fact that migrant workers are risking their lives to go out of their metropolitan workplaces to remote villages without any worthwhile medical facilities only corroborates what our data is showing above. It is the rich who should be more scared of COVID-19 than the poor.

Age Structure:

Table 5 here

Table 6 here

Table 7 here

The following chain of hypothesis emerges from the data presented above:

  1. the prosperity results in longer life span of people of high-income countries
  2. better health infrastructure increases survival probabilities of older people with heightened co-morbidities
  3. when a new virus like COVID-19 emerges on the horizon, these are the people who are most likely to succumb to the new killer.
  4. in the low-income countries with rickety health infrastructure expected life span is shorter
  5. high child mortality and un-hygienic environment of living for the poor masses create a built-in capability to survive in a hostile environment.

Blessed are the poor for whom poverty is an enabling condition that better prepares them to face the vagaries of nature; otherwise they would have died young. Cursed are the rich who are shielded by their wealth from various known morbidities but make them ill-prepared to face an unknown one.

Societal Culture 

Wikipedia defines culture as “an umbrella term which encompasses the social behavior and norms found in human societies, as well as the knowledge, beliefs, arts, laws, customs, capabilities, and habits of the individuals in these groups” 9.  As mentioned above forms of greeting a person through hugging vs handshake vs bowing reflects “culture” of a group of persons. Religious beliefs or faiths provide the overarching framework of culture of most of the countries, even in 21st century. Such beliefs do matter in the mundane task combating a pandemic. In many Islamic Societies, women cannot go out without wearing burqa or hijab, a kind of mask.  Wearing mask or covering face with simple clothes has been made mandatory in many countries reeling under COVID-19. So, women are much better protected in a conservative Islamic society. An obvious testable hypothesis would be that women to men infection ratio would be much less in an Islamic country that a non-Islamic one.

Religion could be another major factor in determining the intensity COVID-19 infection in social groups opposed to vaccination. Many low-income or lower-middle income countries have implemented universal immunization policy. But in many developed countries it is legally permitted by parents to deny vaccination to their children invoking religious sanction against vaccination.  For example, in USA, 45 states and Washington D.C. have allowed religious exemptions for people who have religious objections to immunizations. 15 states now allow philosophical exemptions for those who object to immunizations because of personal, moral or other beliefs. The Wellcome Trust survey cited above found that some of the world’s top anti-vaccine countries are in Europe. In France 1 in 3 persons disagreed that vaccine is safe. Till a few years back many Catholics were opposed to vaccination because “genetic source material made to develop most vaccines come from aborted fetuses”.  It may be noted that more than 80% of Italian citizens were Catholics. In Spain around 68% are roman Catholics.

Thus, religion can be considered another factor that may affect the progress of COVID-19 in any country. When a state declares a religion as a state religion or a preferred religion, the world view of that religion would guide, direct and probably compel any citizen to be incompliance with the edicts of that religion. The following table may not confirm or reject, prima facie, the role of religion in creating a relatively smooth passage of the onward march of COVID-19 across the globe, but it should ignite a more structured examination of the issue.  We may point out here that countries which have Christianity as an official religion belong to either High or Upper-Middle Income group. The shares of countries in these two income groups among all countries with Christianity as declared religion are respectively 41.4% and 50.3% respectively. So, there is confounding effect between these two factors, namely income status and religious status. It is neither attempted nor possible to disentangle the impact of these two factors on intensity of COVID-19 spread in different countries10, 11.

Table 8 here

Note: Capitalist Communism is taken as state religion for China and Vietnam as atheism(or rather no organized religion)  is declared as state policy.

The table above clearly points out that high per capita income does not ensure lower risk for a citizen getting infected by COVID-19, even though the country has built the best possible health care infrastructure. One caveat is due here. It has been reported that incidence of COVID-19 among poor African Americans are much higher as compared to US average. More data will be needed to address such intra-country issues like incidence of COVID-19 by race, gender and income group.

Concluding Observations:

Governments across the world have reacted to the COVID-19 pandemic in a way that reminds me of what Bertrand Russel famously said-   “Collective fear stimulates herd instinct, and tends to produce ferocity toward those who are not regarded as members of the herd”12.  Only one solution – that is Lockdown and Social Distancing – has been offered by our medical experts and their political bosses without any effort to calibrate its implementation with due regard to social differentiation in terms of prosperity, access to habitable shelter, presence of co-morbidities.  A government which in normal times cannot organize delivery of adequate nutrition to millions of children is taking upon itself to feed hundreds of thousands of migrant wage laborer because they were not allowed to return to their home villages. The irony of such policies is that while migrants would have financed their own journey if they could proceed to their home before imposition of Lockdown, now they will have to be provided with shelter and food at government’s cost.

This essay has been written to highlight the fact that COVID-19 does not affect all countries and even all social groups within a country equally. Of late, politicians across democracies are taking help of Data Science to understand voter’s behavior   – who is more likely to vote in their favors and who are on fence etc.  The electoral strategy is based on such data analysis. But we are yet to see any country that has used Data Science to calibrate its response to COVID-19. For example, in India there are many large industries which are in a relatively segregated place. Most of its workers are residents in the campus. Irrespective of the goods produced there, it is madness to impose complete Lockdown in such places. Many University campuses are also far away from large habitations. It should be possible to work out modalities of functioning of such campuses with appropriate precautionary measures.  These are only few examples.


  1. https://www.worldometers.info/coronavirus/coronavirus-age-sex-demographics/
  2.  https://www.bbc.com/news/health-51674743.
  3. https://economictimes.indiatimes.com/industry/healthcare/biotech/healthcare/india-did-have-an-innate-natural-shield-against-coronavirus-after-all/articleshow/74453719.cms?from=mdr
  4. https://in.news.yahoo.com/sunlight-heat-above-35-degrees-043448286.html
  5. https://nationalpost.com/news/why-the-covid-19-death-rate-varies-dramatically-from-country-to-country.
  6.   The state of the antivaccine movement in the United States: A focused examination of nonmedical exemptions in states and countries:  by  

Jacqueline K. Olive, Peter J. Hotez, Ashish Damania, Melissa S. Nolan : https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002578

7. https://www.vox.com/2019/6/19/18681930/religion-vaccine-refusal

8. https://worldpopulationreview.com/

9. https://en.wikipedia.org/wiki/Culture  

   10 https://catholicethics.com/forum/dealing-with-the-coronavirus/ 

11. https://www.buzzfeednews.com/article/peteraldhous/global-survey-vaccine-safety-measles-outbreaks


For all tables see the file from the Google Drive: follow the link below


Poor as a Commodity

This is a blog that I wrote on 21st April 2010 for my earlier site . I am tempted to reproduce it without any revision today in the wake of Prof. Abhijit Banerjee getting his Nobel for his work on poor of the world. Although his work is extremely valuable and revolutionary from methodological point, I am still skeptical about the obsession of social scientists and politicians with poverty , particularly absolute poverty.

Counting tigers and poor have become a national pastime of India’s leisure class. While the population of tigers we want to protect, we would like to number of poor to decline to zero.  We are failing in both, some would say miserably.

 The practice of counting number of poor in a country goes back to the second half of nineteenth century when Charles Booth carried out a remarkable survey of living conditions in London. Booth wanted to contest the results of an 1885 report that claimed that 25% of Londoners were living in abject poverty.  Booth and his team visited every street of London and estimated that the incidence of poverty at 31% initially and then at 35%.  In the first decade of 21st century and after 62 years of independence we can not claim to be in a better position.

The reason for obsessive preoccupation with a precise headcount of poor on the part politicians and economists is not difficult to understand. The Indian government has a huge budget for a variety of poverty alleviation programs. Every state vies for a share of the cake and it depends on the number of poor. There is a turf war between the Ministry of Rural Development (MORD) and the Planning Commission with regard to this counting tussle. A footnote in the Expert Committee Report of the MORD is quite candid about it. It says-

Which Ministry in GOI has the best control over the district collectors, CEO Zilla Parishads and Panchyats? The obvious answer is the Ministry of Rural Development (MoRD). , because it transfers huge funds to DRDAs and to panchayats, runs NREGA, BRGF and TSC, and ever since their creation panchayats have always regarded MoRD as their mentor.  Hence MoRD is the only Ministry in GOI that can make the field officials and the panchayats take its guidelines seriously. Therefore the task of overseeing preparation of the new BPL lists has been rightly given to the MoRD

Another very interesting thing that this report brings into focus the practice of fixing number of BPL (below poverty line) families to the limit fixed by the planning commission estimated poverty ratio. Thus BPL certificate becomes a badge of honor like a caste certificate. Only difference is that BPL certificate can become a tradable commodity. In fact Mr. P. Sainath, a member of the expert group has put it succinctly

  • In many regions like the KBK, with millions extremely poor, you will find that most of the BPL cards in a village are with the local moneylender. The poor owe him money and he takes their cards as collateral. You can find one man with 400 cards.

He also notes that

Dharavi , the biggest slum in all the world  and with a population of over a million ended up home to just 141 BPL cards. If that’s all the poor there are in that slum, then India is poverty-free.

The expert group estimates the number of poor in India as close to 50% as compared to 28.3%.  With this order of variation coming from two arms of the same government, what sanctity is there in these numbers?

Apart from the exegesis of official experts, we have a whole industry of Poverty Research mostly funded by multilateral agencies and grant giving foundations. The route to stardom is well laid out – from JNU / Delhi school to Cambridge on both side of Atlantics or some other ivy league schools and then to the portal of the World bank / UN organizations. India which is estimated to be home of the largest number of poor in the world has also produced the maximum number of researchers on poverty.

And the debate on what is the best way, statistically speaking, to estimate incidence of poverty some times assumes surrealistic proportion.  One just has to recount how, long back, two highly qualified statisticians and professors engaged themselves in a fierce debate about how to take into account inter-person variation in calories intakes and consequently how to correctly measure the incidence of poverty using a minimum level of calorie intake recommended by nutritionists.

What is the real purpose of the debate? The real motive is political – which set of policy measures is good for poverty reduction. So if your prior belief is that economic reform is bad for the country then get a suitable measure of poverty index to demonstrate that poverty has increased in the post reform period. If one’s prior belief is opposite then get hold of another measure. It is said in statistics that if you beat some data sufficiently you can always reject a null hypothesis.

I can not better the opening sentences of Charles Elliott’ book Patterns of Poverty in the Third World in this regard-

The basic configuration of world poverty is well known. Although the detailed statistics are unreliable, the services of a statistician are not required to establish that the majority of mankind is ill-fed, ill-housed, under-educated, and prey to preventable disease.

Do we really need to count the number of poor so accurately as if it is gravitational constant on which depends the trajectory of a missile?  Poverty is ugly and de-humanizing. It is ugly more in a relative sense than in an absolute sense. A poor is not treated as a full citizen in any country- developed, under-developed, capitalist or socialist. The greatest suffering a poor has when she is made to feel as a lesser human being, a person deserving only piety from others. The tears of universal humiliation are much more real and enduring than the tears of hunger. It does not matter whether she is a singleton or numerous.

Adequacy of Reserve and Economic Capital Framework for RBI

How much forex reserve should RBI have? How much capital should RBI have?  One simple answer to both these questions is- “it depends’.  The obvious follow-up question is – it depends on what?  And there is the rub.  Is it given for a central bank to “die, to sleep – to sleep, perchance to dream” of a tranquil crisis free state of economy when reserves are a luxury, a framework for economic capital for all contingent situations can be worked out. Politicians always seek simple solutions to complex problems. In today’s world, most of the national economies are highly interconnected and are subject to “butterfly effect”. When flap of wing of a butterfly in Mexico engenders a hurricane in China, we call it a “butterfly effect”. The mathematical discipline, called Chaos Theory that deals with such complex interconnected non-linear systems, is based on the assumption that such systems are inherently unpredictable.   There is thus neither any theoretical nor any empirical basis to expect that a central bank like RBI can  predict with a certain measure of uncertainty the capital required to tide over any severe shock in next one or two year.

It is even debatable whether the concept of economic capital is applicable to a central bank. The economic capital of a firm is the amount of capital that would be required by the firm to remain solvent. The capital adequacy norm for a bank is a regulatory requirement towards that effect. The central banks, however, are not banks in ordinary sense. Although a central bank does function like a bank for government and banks, it is also an integral part of sovereign so far as it has unlimited power to issue risk free liabilities in its own currency. This prerogative of a central bank enables it to become the lender of last resort. Since, theoretically, a central bank can work with even negative capital, it is difficult to work out a threshold level of minimum capital that a central bank would require to remain solvent. Some recent evidences prove this point.

In January 2015, the Swiss National Bank abandoned its pegged currency regime and allowed Swiss franc to float. Resulting appreciation in EUR/CHF rate led to a massive loss in SNB’s foreign currency portfolio. The bank’s estimated loss of CHF41 billion in the following 3 months period till March 2015 came to be about 6.5% of Swiss GDP.

Another example of a technically insolvent central bank is the Czech National Bank (CNB).  CNB was operating, at the end of 2007, with an accumulated loss of CZK200 billion, which formed 57% of the central bank currency in circulation and 6.7% of the country’s nominal GDP. The bank’s own negative capital stood at CZK 176 billion.

The following graph shows even for emerging countries, some central banks continued to function even after registering negative capital for extended periods.

Even the Federal Reserve of USA registered a steep dip in its capital-to asset ratio – 0.77% at the end of 2013 from 3.54% at the end of 2006, the year preceding the onset of global financial crisis. It is nobody’s argument that the capital requirement of Fed can be a benchmark for any other central bank, as US dollar is the primary reserve currency of the world. However, the fact remains that even for Fed, resolution of a crisis is much more important than maintaining any debatable target capital adequacy ratio of a central bank.

Since the main component of RBI’s capital is its reserve, search for an optimal capital adequacy ratio for RBI would boil down to a search for adequacy of its reserve. To a large extent the asset counterpart of RBI’s reserve (on the liability side) is its Foreign Exchange Reserve. In my earlier blog post I have provided the relevant numbers for RBI (here) . In this post I want to dwell on the IMF framework for assessment of FOREX reserve of a central bank.

While building the framework, IMF’s main emphasis has been on the “key distinguishing characteristic of reserves- their availability and liquidity for potential balance of payment needs” (emphasis original). The global financial crisis has woken up all central banks, including those of advanced countries, to the critical role that availability of reserve plays in maintaining financial stability of a country. The IMF study has noted that most emerging market countries have “ accumulated more reserves in recent years than suggested by standard rules of thumb, with the median coverage ratio among EMs being around six months of imports, 200 percent of short-term debt, and 30 percent of broad money in 2009”. Analyzing the costs and benefits of reserves under macro-economic scenarios, IMF has worked out a new metric to assess adequacy of reserve. The metric for emerging market economies comprises four components- export income, broad money, short-term debt and other liabilities.  Computed reserve adequacy, based on this metric, for selected countries including India shows that India is not an outlier in terms of forex reserve it is currently holding.

Finally, we hope that search for an optimal capital adequacy framework for a RBI would not turn out to be an exercise in futility. Let it not be : tale / Told by an idiot, full of sound and fury, /Signifying nothing.

Table: Actual Forex Reserve maintained as percentage of required           

2010 179% 129% 175% 94% 118% 197%
2011 174% 156% 159% 144% 117% 175%
2012 163% 159% 143% 90% 112% 160%
2013 151% 159% 144% 123% 114% 155%
2014 225% 155% 151% 126% 118% 137%
2015 264% 192% 156% 122% 124% 120%
2016 248% 165% 155% 128% 121% 106%
2017 265% 162% 159% 128% 106% 97%

Source:            http://www.imf.org/external/datamapper/ARA/index.html

Table: Balance Sheet of Federal Reserve of USA

Source: Carpenter, Seth et. Al; The Federal Bank’s Balance Sheet and Earnings: A Primer and Projections, International Journal of Central Banking March 2015

IMF:  Assessing Reserve Adequacy February 2011

Reserve Bank without its Reserve

Reserve Bank without its Reserve


This is not the best of times for an economist to don the mantle of the governorship of the Reserve Bank of India. Whether it is the worst of the times- that only future can tell. Be that as it may, the public spat between the political executive and RBI management needs an objective assessment of their respective positions.  Whether RBI needs more or less autonomy is an issue that can be debated “ad nauseam” but the facts need to marshalled and evaluated before the central bank gets pilloried on the altar of political expediency.

The issues in dispute are quite clear. Firstly, per the estimation of the central government, RBI’s reserves are way above the prudential level that is required to be maintained in accordance with the international best practices.  The sovereign, being the owner, has legitimate claim on the excess reserve which must be transferred to the government on demand. It is rightly argued that a central bank has two primary sources of income, namely seigniorage and a tax on the banking system. The authority to collect seigniorage and tax revenue always rests with the sovereign. RBI earns these incomes only as an agent of sovereign. Other incomes of a central bank, at least most of them, are returns on investments made out of these two incomes.

The second issue clearly falls within the statutory remit of RBI.  In 2002, RBI introduced a supervisory framework called Prompt Corrective Action (PCA). Under PCA,   RBI has identified 3 parameters, namely capital to risk weighted assets ratio (CRAR), net non-performing assets (NPA) and return on assets (ROA) and has specified certain thresholds for each of them. RBI can initiate, at its discretion, punitive action against any commercial bank, found in breach of these thresholds. A bank under PCA faces severe restriction on sanctioning of new credit to borrowers below certain rating grades. Today 11 public sector banks are under PCA.  RBI has also tightened the extant rule for classification of NPA. In 2001, RBI had allowed certain relaxation in classification norm for loans under corporate debt restructuring mechanism.  This forbearance was withdrawn with effect from April, 2015. Through another circular issued on February 2018, RBI has made it mandatory to initiate a resolution plan for stressed asset as soon as a loan gets classified as NPA. There has been huge outcry from corporate borrowers as well the government demanding relaxation of the stringent norms specified in this circular. It has been argued that RBI’s rigid approach is starving the real sector of much needed credit.

Let us first take up the issue of transfer of fund from RBI reserves to government.  Any transfer would entail a corresponding sale of asset. As on June 30, 2018, 73% of RBI’s assets were held in foreign currencies. Sale of domestic assets would immediately suck liquidity out of the market, putting upward pressure on yield- obviously not an outcome desired by the government.  So the only option available to RBI is to sell foreign assets. RBI’s foreign assets are recorded in two separate books of account, namely that of issue department and banking department. The foreign assets recorded in the book of issue department are maintained as backing of RBI’s monetary liability. A dilution of this backing would severely undermine people’s trust in Indian currency. So it can be safely presumed that the government would expect reserve fund transfer by sale of foreign assets of the banking department. As on June 30, 2018, the foreign assets of the banking department stood at around 8 trillion INR or 117 billion USD. How large is this reserve? The following table gives various indicators of a reserve’s adequacy.

Table 1: Adequacy of Foreign Exchange Reserve of RBI

Sr. No Particulars 2016-17 2017-18
1 Forex asset of Banking department of RBI (in billion  INR) 9320 7984
2 The amount at 1 in billion USD* 144 117
3 Banking Departments Forex asset as% of Short term external debt 163.7% 114.2%
4 Banking Departments Forex asset as% of Yearly Import 0.04% 0.03%
5 RBI Balance sheet size (in billion INR) 33040.94 36175.94
6 RBI BS in billion USD 511.31 528.89
7 External liabilities (billion USD) 905 1037.3
8 6 as % of 7 56% 51%
9 RBI BS to Indian  GDP 22.5% 20.3%
  • RBI balances are as on June 30.
  • Using June 30 exchange rate.

It is apparent from the above data that the discretionary component of RBI’s foreign exchange assets has registered a significant decline during the accounting year 2017-18.  The coverage of India’s short –term external debt by RBI’s discretionary foreign assets has also declined significantly during 2016-17.  At the end of December 2017, the banking department’s forex asset formed only 53.9% of India’s external liability on account of portfolio investment.  The portfolio investment is very sensitive to the interest rate changes in USA as  it affects risk adjusted dollar rate of return on investment.  High redemption of portfolio investment would put pressure on USD-INR rate and RBI has to ensure a smooth and calibrated movement in the exchange rate. Given the declared policy stance of US FED, RBI can ill afford to be complacent about the adequacy of its foreign exchange reserve held by the banking department.  Thus any liquidation of RBI’s foreign exchange assets to finance transfer of fund to the government would be a criminal dereliction of fiduciary duty that RBI is entrusted with.

As regards adequacy of capital of central banks, it would be wrong to compare a central bank with commercial banks. For a commercial bank, the capital is the last buffer between solvency and insolvency, absorbing losses as they occur. For a central bank, this is ruled out by definition. As long as the domestic public is ready to hold central bank currency, there is no outside limit to a central bank’s power to create domestic liquidity. The recent “quantitative easing” policy of US FED and some other OECD countries is a testimony to this power of central banks.  However, a central bank has no inherent power to create foreign currency liquidity.  A central bank of an emerging market economy like India needs to build up foreign exchange reserve to assure foreign lenders/ investors about the capability of the bank to defend the value of central bank currency. Although “central banks need not have capital nor even positive net worth to function in a technical sense” ,loss of trust in central banks may lead to hyperinflation and  downgrading of country rating. Central banks of many advanced countries maintain very little capital, despite having a very strong balance sheet. Since these countries are financially strong enough to borrow in their own currencies internationally, there are no economic compulsions for the central banks of these countries to maintain a high capital to asset ratio. But for emerging market economies external currency risk can be a binding constraint on a central bank’s ability to maintain stability of the financial sector.

The following tables provide a cross-country perspective about how central banks of other emerging countries are managing their balance sheet in regards to its size and currency composition.


Net Foreign Assets of Central Bank as % of Total Assets of Central Bank Central Bank Asset Size as % of GDP at market price
Country 2016 2017 2018 June 2016 2017
Indonesia 75.0% 76.0% 73.5% 16.4% 16.7%
Korea 72.2% 65.6% 66.6% 29.3% 27.2%
Malaysia 90.4% 91.4%   36.6% 33.6%
Mexico 92.5% 89.6% 94.1% 19.5% 17.4%
Brazil 41.3% 39.9% 42.9% 45.7% 46.8%
China 99.3% 99.2% 99.5% 0.3% 0.3%
Thailand 89.5% 86.4% 84.8% 46.3% 47.6%
Russian Federation 72.7% 73.2% 75.1% 35.7% 36.2%
South Africa 77.7% 78.3% 78.8% 18.0% 16.1%


Based on above data, it would be difficult to argue that RBI is pursuing an overtly conservative policy in regard to managing its balance sheet size and composition. For a country like India, where the commercial banking sector is largely owned and controlled by the government, capital adequacy of a central bank alone is of little consequence.  If both the central bank and public sector banks are owned by the government, then capital adequacy has to be assessed at the consolidated level rather than at stand-alone level.  In the absence of such a consolidated balance sheet we can look at a surrogate measure namely, capital adequacy at the consolidated banking sector level. The BIS data, given below, in this regard is quite revealing.


Country Total Equity ( Asset-Liability) of the banking sector  data as on June 2018
India 0.52%
UK 6.97%
USA 12.00%
France 6.35%
Germany 6.59%
Italy 8.15%
Ireland 8.46%
Canada 6.27%
Turkey 11.32%
Spain 7.96%
Australia 7.20%

It is obvious that Indian banking sector is lagging way behind the banking sector of developed countries in respect of capital adequacy. In fact, the government must be made to understand that undercapitalization and not over capitalization is the bane of the Indian banking sector. Any debate on optimal economic capital for RBI would be an exercise in futility unless the broader problem of undercapitalization of the government owned commercial banks is addressed.

As regards the second issue of adoption of PCA framework and a revised stress asset resolution framework by RBI and its stringent implementation by RBI, it can be argued that regulatory forbearance cannot be discretionary otherwise it would led to a chaotic and arbitrary regulatory regime.  If dues on a loan are not paid in time, there is a 90 day window available to the lender as well as the borrower to prevent the loan being classified as NPA. To ask RBI to be flexible about period this 90 day period would be a travesty of regulatory rule making. A rule becomes rule only when it is enforced.  Any deviation must also be specified and allowed under the rule itself. A regulator would turn out to be a toothless tiger if it makes rules and then allows it to be broken by regulated entities as they please.

Although it is too early to say whether a future Dickens will describe the current time as the “age of wisdom” or “age of foolishness”, central banking in India today stands at a historical cross-road.  Either it will carry the can to the darkness of ignominy or it will uphold the high standard of professional integrity that is expected from an Institution created for this purpose.

Data Localization- Mercantilism in a Networked World

Data Localization- Mercantilism in a Networked World

Economic ideas do not die. They resurface again and again, repackaged and refurbished, when the time suits it. Mercantilism is such an idea that politicians and policy makers love to espouse, albeit periodically. The basic premise of this doctrine is that international trade is a zero-sum game. The role of state is to protect domestic industries by building tariff and non-tariff barriers and encouraging export. The obvious appeal of this doctrine to general people is not difficult to understand. It gels well with the notion of national security, national pride and preservation of national assets. It was expected that in the age of internet, such ideas would be considered anachronistic and would lose their currency. But that is not to be.

Data is considered to be the most valuable asset of the 21st century. The most valuable companies of the world are those which are primarily engaged in data crunching. The business of Google, Uber and Amazon would come to a standstill if they could not access, process, and analyze data across time and geographies. Once nation-states realize that the most valuable assets of their citizens and territories are available for commercial exploitation freely, clamor for protection of these assets naturally arises. While individual citizens are rightful owners of their “personal data”, its exploitation without the consent of the concerned persons is a serious infringement of privacy of the person. The European Union (EU) has been in the forefront of creating a stringent legislative framework to protect the “personal data” of its “data subjects”. The EU’s General Data Protection Regulation (GDPR) ( see here is the most comprehensive regulation enacted so far by any competent authority anywhere. But the overarching requirement of privacy protection does not necessarily imply that all data originating within a nation’s jurisdiction are to be considered as national assets. If “data subjects” are given national tags, it follows that nations-states would consider within their right to create barriers to cross-border data flow. The recent “data localization” policy of RBI needs to be analyzed from this perspective.

The term “data localization” is meaningful and relevant mainly in regard to data flow over the Internet, which is a network of computing devices without any single point of failure and consequent enabling of universal communication capability between all nodes. The internet service providers are not expected to control and be aware of what data flows through internet. Data localization, in essence, is a negation of this architectural construct of Internet. There are two forms of data localization. The first one localizes storage of data. It means that internet service providers must store data originating in a nation-state within the territorial boundary of that nation-state. The second form of data localization policy stipulates that routing of data packets must be confined within the country specific network. This form of localization is also called localized data routing. This is the most restrictive localization policy. Countries adopting data localization policy mostly adopt the first form of data localization. Chander and Le have identified following variants of this form of localization policy: ( see here  here )
1. preventing information from being sent outside the country
2. rules requiring prior consent of the data subject before information is transmitted across national borders
3. rules requiring copies of information to be stored domestically
4. a tax on the export of data

Data localization policies are being adopted by many countries because of the genuine concern of many national governments about the disproportionate capability of USA to access sensitive data pertaining to the respective countries’ national security available on data stores of Internet service providers, many of which are located outside the national boundaries of the concerned states. The Snowden episode confirmed the existence of a nexus, probably forced, between US security establishment and US technology firms including Google and Yahoo. Subsequent to Snowden revelations the German Interior Minister declared that, “whoever fears their communication is being intercepted in any way should use services that don’t go through American servers.” (  Hill 2014)  The concerned ministers of France and Brazil unequivocally lent their support to data localization policy. It is beyond doubt that one of the important factor of data localization policy of non-US countries is their desire to minimize “their comparative disadvantage in Internet data hosting” vis-à-vis US and “their comparative disadvantage in Internet signals intelligence”. ( Selby 2017) ) Thus data localization policy is being adopted by countries cutting across political regimes as a comprehensive review by Chander and Le shows. 14 countries studied by them are: – Australia, Brazil, Canada, EU, France, China, Germany, Indonesia, Malaysia, Nigeria, Russia, South Korea and Vietnam, besides India.

Accepting that above concerns of national governments is legitimate and requires to be addressed by the proponents of open and neutral Internet, a more informed and rigorous analysis is required to evaluate costs and benefits of data localization policy. It must be stated to the credit of the US technology giants that they are more concerned to uphold the sanctity of Internet than succumbing to the narrow national interest of US governments. For example, Microsoft, Google, Apple, Facebook and other technology firms successfully fought U.S. government in court “to gain legal authority to provide the public greater detail on the information the U.S. government collects from them”. (Hill 2014)  Many companies are taking steps to diversify their data center locations to escape stranglehold of US intelligence agencies.

Tying data to territorial boundary, also termed as “Data Sovereignty”, is a natural extension of the concept of sovereignty to the virtual world. Sovereignty connotes supreme authority within a territory. The term authority refers to, in the words of philosopher R.P.Wolff, “the right to command and correlatively the right to be obeyed” (see here). In a modern democracy this authority is derived from a set of principles, objectives, practices and code of conducts called constitution. “Data Sovereignty” means that this supreme authority can be enforced on data originating and /or pertaining to the people subjected to this authority. But despite their best efforts, the modern nation states have not been able to quarantine their national data in their entirety. This diminishing effect to a sovereign’s authority over data of their citizen is the driving factor of data localization policies of different countries. Even a country specific domain name like www.abc.co.in does not indicate the physical location of the server which hosts the website and provides information or services. Thus Internet is indifferent to physical location of computing devices comprising the cyberspace. So defining “Data Sovereignty” in terms of territorial authority is a non sequitur.

Recognizing the futility of transcribing laws enacted and bounded by physical space to cyberspace, Johnson and Post has called for “distinct laws” for this virtual space (see here). For example, how do we apply anti-trust laws to companies which operate only on cyberspace? The landline based telecom companies fought to restrict Internet based voice call (VOIP) but failed miserably. Digital currencies are being resisted by all central banks but there is no doubt that in the long run the central banks have to fall in line and adopt some form of central bank digital currency. Applicability or otherwise of country specific copyright laws to the cyberspace is another example of distinctive nature of cyberspace. A subscription based access to copyrighted contents on Internet has materially changed the consumers of these contents and its producers, resulting significant benefit to consumers in terms of reduced cost.
The proponents of free trade policy have pointed out how data localization policy is reincarnation of mercantilism in a virtual world. Treating data as an asset, nation-states wants more of such assets to flow to its own territory while for cyberspace location of an asset is of no consequence.

Let us, for example, suppose that Google is forced to store all data pertaining to Indian users of Gmail in India. First of all, how does Google identify a user as Indian? Presumably, it can be done by identification of IP address of the user. But what is to be done in regard to an Indian trying to register as Gmail user from abroad? Or when a foreigner is registering using an Indian IP? If an elaborate e-KYC norm is imposed on Internet users, it would be so cumbersome and costly for Internet service providers that the present practice of free Internet would be a casualty. Furthermore, if all data of a citizen are stored within the national boundary, the citizen might face difficulty to access her own data from abroad, if no mirror dataset is available in some other geography.
Internet works on routing of messages. This works on Domain Names identification and resolution of address within a domain. Today there are about 330 million domains. Even if a sovereign authority blocks access of its citizens to some domains, new domains can be created within no time to bypass such blocking. China is reported to have created the most restrictive firewall for access to Internet by its citizens. This might have helped in creating some of the world’s largest Internet enterprises like Baidu, Tencent and Alibaba. But it will also prove to be the greatest hurdle to realization of China’s dream of becoming the world’s dominant superpower. It is doubtful whether world population at large would like to share the fate of Chinese citizen – described as “world’s biggest prison for netizens.”
The hypothesis that data localization would prevent a foreign government’s ability to snoop on sensitive personal data of citizen of a nation-state is not borne out by some recent cyber-attacks, allegedly orchestrated by foreign governments. The alleged Russian interference in USA presidential election shows that in a networked world the security of data is not enhanced by creating physical access barriers to such data. The recent example of malware driven data hacking of Core Banking System of Cosmos bank of India is an example of the false assurance that location provides guarantee that data would be secure. It has been reported that NSA of USA has “even scaled the Great Firewall of China”. Thus data localization does not serve its primary purpose.

From a technological point of view, data localization is not a very efficient solution for running any cloud based application. A massively large database must be partitioned and stored in distributed databases. Today one type of partitioning known as “sharding” is followed by most large databases. Sharding breaks down very large databases into smaller databases to manage data retrieval very fast. Even a single record can be sharded into smaller parts. Database sharding allows maintaining very large data in less expensive commodity servers. A cloud based application cannot scale up if it maintains large databases in one place. The cost of maintaining data can increase exponentially because such large database would require high-end computers.

RBI’s data localization policy
RBI in a circular dated 6th April 2018, instructed all payment system providers “to ensure that the entire data relating to payment systems operated by them are stored in a system only in India. This data should include the full end-to-end transaction details / information collected / carried / processed as part of the message / payment instruction. For the foreign leg of the transaction, if any, the data can also be stored in the foreign country, if required”. (see here)
RBI’s data localization policy is driven by its intention to get unfettered access to payments data originating in India for surveillance purpose. RBI argues that such access is an absolute necessity for effective detection and prevention of any money laundering activity. The purported reason for requiring data storage “only in India” is that, in the event of any conflict with the country hosting Indian payment data of a service provider, Indian regulator may be prevented from accessing such data.
Although such an eventuality cannot be ruled out, in today’s interconnected world no country can unilaterally deny access to payment data pertaining to citizens of another country. Many countries including India now share financial and taxation data with other countries through bilateral or multilateral agreements. For example, India has signed bilateral agreement with US Tax authority to identify, document, and report U.S. accounts to comply with the U.S. Foreign Account Tax Compliance Act known as FATCA. A U.S. account is an account maintained by a U.S. person (whether individual or entity) or by a foreign entity with U.S. ownership of more than 10% of the capital, whether directly or indirectly. OECD countries are signing similar financial data sharing agreements amongst themselves and with other non OECD countries under the Automatic Exchange of Information (AEoI) initiative of G20 countries. Obviously such sharing cannot be a one way traffic. India being a member of G20 can direct the payment service providers to store data with such countries with which it has such data sharing agreement. If such an agreement is made on reciprocal basis, outright denial of access to India’s own payment data can be of remote possibility. India can mandate payment service provider to share all cross-border transactions with RBI through a FATCA type agreement with the host country storing Indian payment data.
As regards money-laundering and terrorist financing, India is a member of the Financial Action Task Force (FATF) and has implemented its recommendations. Data localization is not a recommendation of this international body. Additionally a government can enter into Mutual Legal Assistance Treaties (“MLATs”) with other countries to access data stored in another jurisdiction but needed for its own lawful investigative purposes.

Data sans Frontier
The pervasive effort towards data localization by nation-states is a reflection of deep insecurity that the nation states are feeling in a networked world. It is not understood that the rules of games have changed forever with the introduction of a radically different communication and workflow management architecture – that is Internet- that encompasses the entire world. The allurement of Mercantilism to the general public lied in its apparent pragmatism and simplicity. It ignored the feedback effect of such a policy and long-term consequences. The same is true of digital mercantilism that is driving the data localization policy.
Internet was lapped up by nation-states when it appeared to be a mere new form of message transfer. It was not understood how the new technology is going to undermine the basis of nation-states- that is the sanctity of the national frontier. “America First” is a vacuous and anachronistic concept when the most valuable US incorporated firms produce goods and services cutting across the national boundaries.
Let me conclude by referring to the reactions of policy makers when Galileo introduced his telescope to the policy makers. A senator in the Bretolt Brecht’s drama “Galileo” exclaimed- “the contraption lets you see too much. I’ll have to tell my women they can’t take baths on the roof any longer”. Galileo then attacked their materialist attitude saying: “These people think they’re getting a lucrative plaything, but it’s a lot more than that”. I am afraid that our policy makers are no better than these senators of Galileo’s time.


Castro Daniel(2013)  The False Promise of Data Nationalism  paper published by The Information Technology & Innovation Foundation (ITIF)

Drake William J  (2016)   Data Localization and Barriers to Transborder Data Flows:   Background Paper for World Economic Forum conference (http://www3.weforum.org/docs/Background_Paper_Forum_workshop%2009.2016.pdf)

Hill Jonah Force (2014): The Growth of Data Localization post Snowden: Analysis and Recommendations for US Policymakers and Industry Leaders in Lawfare Research Paper series  July 2014

Selby John (2017): Data localization laws: trade barriers or legitimate responses to cybersecurity risks, or both? in International Journal of Law and Information Technology, 2017


India’s Job Crisis- Myth or Reality?

Prof Arvind Panagariya  (AP) in his 2nd May article (here) in the Times of India edit page has argued that the number of new job seekers on an annual basis cannot be more than 7.8 million between 2016 and 2021. His article is a rebuttal to the claim made by India’s main opposition party that around 12 million new job seekers are entering the Indian labour force every year. He has used Labour Force Participation Rate (LFPR) and the projected incremental population per annum in the age group 15 and above to arrive at this number.  Disregarding the issue of applicability of LFPR for the purpose at hand, his computation suffers from an obvious mistake- that is to compute flow from change in stocks between start and end of points of a time period. The measure of population is a stock measure as on a date.  To work out new entrants or inflow to this inventory we also need to measure the outflow of people from this inventory- that is death.  The overall death rate for Indian population is estimated to be 7.3 per 1000 of population.   Although death in the age group above 15 could be higher than this, let us apply the same to the initial stock of person – that is 928.6 million. So the number of person entering this age group ( 15 and above) would  stand corrected to 21.6 million instead of 15 million worked out by AP. Applying LFPR of 503 per 1000 persons , the estimated number of job seekers works out to around 11 million.

It must be also noted that LFPR is not a parameter that results from the behavioral characteristics of the population. In the jargon that AP would be comfortable with, I would call it an endogenous variable, decided by the job prospect, income level and many other characteristics.  Its use as a predictor of number of job seekers is questionable. In fact he himself has underscored the conundrum of very low and declining LFPR of rural females.  The 5th Annual Employment-Unemployment Survey, conducted by Labour Bureau in the year 2015, puts the female LFPR at a measly 23.7 % at all India level.  For China, the comparable figure is 63.9. But for males, the LFPR figures for these two countries are close to each other.  Cultural and social mores cannot explain such huge difference in female LFPR, when women from the poor households are always ready to work, provided they get regular employment.  Self-employment cannot be an acceptable option for young females in many cases because of lack of safe environment for them.  The gender gap in self-employed workers under “Usual Principal Status” is little more than 8% at all India level.

Instead of using population data and LFPR, we can look into some of other hard data. For example, let us consider the Gross Enrolment data by level of schooling, given in the annual publication- Educational Statistics at a Glance- by the Ministry of Human Resources Development. The latest publication gives the total enrolment for undergraduate studies in the year 2014-15 as 27 million students. Let us assume that average period for undergraduate studies as 4 year.  Thus we may expect that every year an inflow of around 6.8 million young educated Indian entering the job market.  Even if we assume 60% of this 6.8 million enters the job market this would imply the number of UG qualified job seekers would not be less than 4 million very year. So AP must provide more robust statistics to conclude that a figure of 11 or 12 million jobseekers can be considered as overestimation by a “solid 50%”. He needs a more solid ground for making that statement.


Note: Labour Force Participation Rate – This is defined as the number of person /person days in the labour force per 1000 persons/person days.  The labour force comprises both employed persons and job seekers (unemployed). A person is included in the labour force if he or she is either engaged in economic activity for a relatively longer part of the  reference period ( usually one year) or making “tangible effort” to seek “work” or available for “work”.  Full time students are not considered as part of labour force.  There are five categories of employment; self-employed, regular wage/ salaried employee,  contract worker or a casual labour. In our calculation we have taken LFR rate (50.3%) from the Fifth Annual Employment Unemployment Survey of the Labour Bureau.

NSSO report  Labour Bureau Report   Female LFPR study



Cryptocurrency Markets- concentrated and top heavy

Distrust in  fiat currency, controlled by a state, was one of the principal motivations in designing of  the Bitcoin protocol. It was designed  to be a decentralized system of creation of new money by a transparent computational algorithm.  Any person participating in the currency’s ecosystem can run this algorithm on a computer and generate new money. It is supposed to be a currency created by the people for the people and therefore a currency of the people. It is a currency of future when true democracy will prevail. here  here  here  here  here

But what is the reality? Who owns the bulk of these virtual currencies? To get an answer to this question, I looked into data about the distribution of these currencies amongst the participants in this technology game. The result of this exercise is truly revealing.

Data: We have collected data from the website https://bitinfocharts.com/top-100-richest-bitcoin-addresses.html which gives “Rich List” of some selected 9 currencies. We collected data as on 11th April, 2018. The market cap of these 9 currencies was 58.8 per cent of total market capitalization in terms of US dollar on that date.  On the data date, the total market capitalization of virtual currencies (excluding tokens) was 261 billion US dollar. So one can say collected data is adequately representative of the virtual currency ecosystem. The website has grouped data by value of coins  held against each address. An address having , say 0.001 bitcoin (BTC), would be grouped in the bucket “0 to 1 BTC” bucket.  For some cryptocurrencies , the number of coins held in an address may be very large as their market value is much smaller as compared to that of Bitcoin.  So the number of class intervals for coins held would be higher than a highly valued cryptocurrency like Bitcoin. To keep the results compact we have collapsed crypto wise class intervals into a common 3 classes. The following table gives a summary of distribution of value in US dollar and number of addresses across these class intervals.

Table 1: The distribution of addresses in terms of value of coin held and number of addresses for various class intervals of value of coins for each address.


Coin Name Market Cap


Share of each group of addresses below  in total number of addresses Share of each group of addresses  in total market  value of outstanding coin in USD Average


(USD) per address

<=1 full Coin per address 1 -100 coins per address More than 100 coin less than or equal to1Coin 1-100 coins per address > 100 coins
Bitcoin 130.3 4.06% 34.29% 61.7% 96.8% 3.1% 0.1% 5991
Bitoin cash 12.1 2.5% 27.6% 69.9% 97.1 2.8 0.1 737
Litecoin 6.8 0.4% 13.8% 96.7% 71.5% 27.1% 1.4% 2721
Dash 2.7 0.7% 9.4% 89.9% 82.0% 16.9% 1.2% 4094
Bitcoin Gold 0.8 2.9% 30.1% 67.0% 97.2% 2.7% 0.1% 38
Dodgecoin 0.4 0.0% 0.0% 100.0% 16.5% 42.2% 41.3% 183
ReddCoin 0.1 0.0% 0.0% 100.0% 14.8% 18.9% 66.3% 1317
Verticoin 0.1 0.0% 3.3% 96.7% 38.1% 47.1% 14.8% 715
Peercoin 0.0 0.0% 1.3% 98.7% 56.4% 32.0% 11.6% 934


It is obvious from the above table that only few addresses, each having more than 100 coins per address account for the bulk of total market capitalization of each currency. Bitcoin which the highest market capitalization of all circulating coins is also concentrated in a small number of addresses. The table 2 below clearly indicates how a few big market players have completely taken over each currency market.

The US tax authorities as well as Commodity Future Trading Commission have designated as “commodity” and not currency. From that perspective, this commodity market is highly monopolistic and susceptible to market manipulation by few large traders.  It is high time that anti-trust authorities in the developed economies wake up to this reality and take appropriate actions in the interest of average participant in these markets.

Table 2: The number of addresses and value held by top bracket by number of coins held per address

Coin Name Number of addresses in the highest bracket Market value of coins held by addresses in the top bracket (million USD) Share of these addresses in outstanding market value of the respective Coin
Bitcoin 3 3292 2.50%
Bitcoin Cash 7 954 7.86%
Litecoin 66 2776 40.67%
Dash 34 241 8.77%
Bitcoin Gold 12 93 12.38%
Dodgecoin 16 129 31.91%
Reddcoin 3 32 21.71%
Vertcoin 3 17 17.01%
Peercoin 2 8 18.10%

see here



The Next Battle- State versus Technology Giants

Two unrelated events hogged the headlines in the last month. On 22nd February Amazon became the third most valuable company in the world, overtaking Microsoft. On 25th February the official news agency Xinhua announced that the ruling communist party is proposing to remove the constitutional provision of “no more than two consecutive terms” for the country’s President and Vice-president. This would pave the way for the incumbent president to continue in the helm of power indefinitely.

The first event is a precursor of the future shape of the world economy while the second one is a forewarning of demise of liberal democracy as we understand today. These two events are also interrelated in the sense that their future trajectories will determine the denouement of the wrestling match that goes on between market power and state power.

Let us first have a closer look of the import of the first event. The Fortune magazine publishes a list of top 500 companies in the world, ranked by their revenues. Financial Times publishes a list of 500 top world companies ranked by their market capitalization. The latest Fortune data pertains to the year 2016 while market capitalization data is up to date as of 31st December 2017. For understanding the trend the time gap between two datasets has no bearing.

Top 10 Companies by Revenue and by Market capitalization:

Top Ten by market capitalization (M-Cap) Industry Top Ten by revenues Industry
Apple Technology Walmart Retail
Alphabet Technology State Grid Electric utility
Microsoft Technology Sinopeck Group Petro Chemical
Amazon.com Technology Retailer China National Petroleum Oil & Gas
Facebook Technology Toyota Motor Car
Tencent Technology Volkswagen Car
Berkshire Hathaway Conglomerate Royal Dutch Shell Energy & Petro-Chemical
Alibaba Group Technology Retailer Berkshire Hathaway Conglomerate
Johnson & Johnson Pharma Manufacturing Apple Technology
J P Morgan Chase Banking and Financial services Exxon Mobil Oil & Gas

The most interesting feature of the above two rankings is that while still infrastructure and manufacturing industries dominate the top rung of the current corporate behemoths, the future potential behemoths are  growing up in the technology sector. M-Cap is an indicator of market’s prediction of future growth potential of a company. Amazon’s price-to-earnings ratio, a measure of how expensive a stock is in comparison to its current period earning, is 323 as compared to average ratio of only 22 for S&P 500 companies.

Ignoring Berkshire which is essentially a conglomerate, the only non-technology firm appearing in the top ten companies by M-Cap is JPMorgan, a financial service company.  So the market is predicting that the future of market economy lies with companies which are technology driven, technology enabled and most importantly innovation focused. On the contrary, the top companies ranked by currents revenues are enjoying benefits of their access to natural resources protected by concession arrangements with the state. So these companies have to work in close cooperation with the states giving concessions. Managing the states is critical to their existence and profitability. This is in sharp contrast of the business model followed by the emerging giants.  These companies using technology are creating an ecosystem of production of goods and services that are beyond the control of the geographically bounded nation states. In fact, these companies have much better understanding and access to actions and thoughts of the citizen of a state, particularly its younger ones than the any nation state has. Facebook was aware of any Russian meddling of US election, if any, than FBI could possibly have. Today, Google has much more information about its Indian users than the Indian Federal Government can ever have, Aadhaar notwithstanding.  In 2017, 46.8% of the global population accessed the internet and by 2020 this figure is projected to grow to 53.7%. It is obvious that this growth will benefit much more technology companies than utility, retail and infrastructure companies. We cannot expect exponential growth of companies which are organically linked with exploitation of natural resources.

These technology companies are gradually increasing their footprints beyond their original areas of operations. One study forecasts that the combined market share of Apple, Samsung, and Google (via Android Pay) is expected to reach a user base exceeding 500 million for mobile contactless payments by 2021. Amazon has started its lending business by offering to fund its suppliers. China’s e-commerce giants including Alibaba, Tencent’s and others are now running a lending portfolio over $12 billion. Apple owned US Treasury bonds ($52.6 billion) by the end of July 2017 and ranked 23rd in the list of US Treasury bond holders ahead of Netherlands and Turkey. These technology companies may gradually cut out intermediaries like banks and insurance companies by using Artificial Intelligence and Blockchain technology. Since data is the fuel of 21st century, the owners of data will have more power than any nation state.

How this development is related to the China’s decision to consolidate power of state in a monarchial coterie formed around the current incumbent?

The Chinese communist party has been able to put the country on a sustained high growth trajectory in the last three decades. The country is expected to become the world’s largest economy by 2030. China has used foreign capital and technology liberally in creating its manufacturing base.  A World Bank report of 2010 mentioned that “China received about 20 percent of all FDI to developing countries over the last 10 years and over $100 billion in 2008.  In terms of share of GDP and investment, FDI accounted for some 2.5 percent of GDP on average over the last five years”.   While welcoming FDI, Chinese ruling dispensation did not allow domestic private capital to capture the “commanding height” of the economy. 9 out of top 10 Chinese companies appearing in The Forbes 2000 list of 2017 are all state owned. If China has to establish its position as the first among equals in the international distribution of power it cannot afford to destabilize its own internal economic system built under the watch of party and the state. Till now, USA has been benign bystander, if not an active facilitator, of China’s rise as a global economic power. It was expected that economic growth and prosperity along with greater integration with world economy would slowly but steadily chip away the ideological foundation of the present Chinese political system. But this expectation of US policy makers has been belied. After disintegration of Soviet Russia, the world is witnessing, not the rise of liberal democracy, but rise of two dictatorial regimes under two most focused men who want their nations to occupy the high table of international power structure.

So we have on the one hand two authoritarian states (China and Russia) that carry the legacy of failed communism and other hand we have technology giants who are not fettered to any nation state nor bound by any geography.  David Ignatius, associate editor of Washington Post wrote in an op-ed piece that “China is racing to capture the commanding heights of technology and trade.”(here) The forces that will confront China are not the usual suspects- USA, UK or European Union states. This time the war will be fought in cyber space for capturing data about and of the people and the technology companies will have to fight for withering away of states as we know it now. We may recall that Marx desired withering away of states as the final goal of communism. In that sense these privately owned technology companies, rather ironically, would stand for one of the goal of communism as against the ex-communist regimes will stoutly defend the right of nation states. As some author wrote – the last battle will be between communists and ex-communists.

See here    here    here   here   here   here   here