Thinking Rationally

Author: Ashok Nag

  • Who is afraid of Babasaheb Ambedkar

    Every storm runs out of rain eventually- Maya Angelou

    A storm has erupted across the streets of India following an invoking of the name of Babasaheb Ambedkar in a derogatory manner by the Home Minister. This happened when a discussion was going on in Rajya Sabha to commemorate the 70th anniversary of the Indian Constitution. Babasaheb, to recall, was the chairman of the drafting committee of the independent India’s constitution.  No doubt, this storm will also pass. However, the idea and vision of Babasaheb will remain unread, gathering dust in the library of the parliament. Ironically, both the ruling party and the opposition are earnestly praising a man by attributing to him ideas and beliefs that stand diametrically opposed to those of the Babasaheb who aspired to create a truly democratic and secular India after colonial rule.

    To understand what Babasaheb Ambedkar stood for, we need to read a definitive exposition of his thoughts in the text of the speech he composed but could not deliver. In December 1935, the “Jat-Pat-Todak Mandal” (translated as the Society for the Breakup of the Caste System), a reformist organization in Lahore, invited Babasaheb to deliver a speech on the Indian caste system at their annual conference scheduled for January 1936 in Lahore. After he shared the first draft of the speech  (Ambedkar 1935) with the conference organizers, a difference of opinion arose over certain views expressed in that draft. Since neither the author nor the organizers were willing to compromise on their positions, Babasaheb withdrew the speech. The title of the speech was “Annihilation of Caste,” and ironically, the inviting organization, while opposed to the caste system, advocated for breaking the barriers between castes rather than its complete annihilation, a goal for which Babasaheb was fighting. To understand the intellectual underpinnings of this dispute and the current controversy, we must recognize the critical difference between these two views.

    Why social reform is necessary for political reform? (Section 2 of the speech)

    Babasaheb Ambedkar was clear about the pre-eminence of social reform over political reform. By “social reform” he meant elimination of “mischiefs wrought by evil customs” prevailing in the Hindu society which was not “in a state of efficiency”, and “ceaseless efforts must be made to eradicate these evils”.  The goal of political reform is to eradicate “the weak points in the political organization in the country”. Babasaheb was of the opinion that without the prior occurrence of social reform, political reform would be a non sequitur, rendering it of no value to the people of the country. He pointed out that two organizations, namely National Congress and Social Conference, were twins at their birth – the first one to spearhead political reform while the other one to social reform. However, “in the course of time the party in favour of political reform won, and the Social Conference vanished and was forgotten.”  For Babasaheb, it was a conscious move by Hindu liberals. To underscore this point, he highlighted the following lines from a speech delivered by the president of the National Congress at its eighth session held in Allahabad in 1892:

    “I for one have no patience with those who say we shall not be fit for political reform until we reform our social system. I fail to see any connection between the two.  Are we not fit (for political reform) because our widows remain unmarried and our girls are given in marriage earlier than in other countries? because our wives and daughters do not drive about with us visiting our friends? because we do not send our daughters to Oxford and Cambridge? (Cheers [from the audience])”.

    To reinforce and hammer away his contention that without social reform, political reform would provide no succor to the people who have been treated as animals for millennia, Babasaheb referred to a variety facts about the inhuman treatment that the untouchables of Hindu society receive from the upper caste people. He emphasized that by social reform, he does not mean the reform of the ‘Hindu family’, like abolition of child marriage, allowing widow marriage etc.  He was seeking the reform of the fundamental architecture of the Hindu society- that is abolition of the Caste System.  

    Why social reform is necessary for economic reform (section 3 of the speech)

    In this section, Babasaheb argues that even a socialist revolution in India would fail without effecting social reform prior to it. To prove his point that political policymaking can have “value and permanence” if and only if it is in conformity with existing social practice within a society, he referred to many such instances from the history. However, more importantly, he referred to the “Communal Award” which was created on 16 August 1932 by the British government of India, to extend separate electorate to Depressed Classes (called Scheduled classes in Independent India) and other minorities. This shows that the British Government understood that without this limited social reform, even a small political reform would be a non-starter. Although Mahtma Gandhi and National Congress was vehemently opposed to eking out a separate electorate from Hindu community, Babasaheb welcomed heartily this policy of the British government of India. It is worthwhile to quote what Mahatma Gandhi wrote about this British policy.

    They do not realize that the separate electorate will create division among Hindus so much so that it will lead to bloodshed. Untouchable hooligans will make common cause with Muslim hooligans and kill caste Hindus. Has the British Government no idea of all this? I do not think so.  (Duncan Ira , 2022, also see Helen M. Nugent (1979))

    Babasaheb was unconcerned about the merits or demerits of socialism because his entire life was singularly focused on eradicating the most inhuman evil of Hindu society—caste. To quote him- “This is only another way of saying that, turn in any direction you like, Caste is the monster that crosses your path. You cannot have political reform, you cannot have economic reform, unless you kill this monster.”

    It is more than evident that Dr. Ambedkar’s view about the fundamental and unchangeable social hierarchy of the Hindu society was widely different from the views of the Congress leadership. Despite this, he agreed to be the one of the main architect of the independent India’s constitution because the Congress leadership agreed to provide a separate electorate for Scheduled Castes and Tribes.  In other words, as a pragmatic leader, he believed that a bird in the hand is worth two in the bush. This act of him does not imply that he had given up his lifelong struggle for annihilation of caste.

    Caste is not just a division of labour, it is a division of labourers (section 4 of the speech)

    Apologists of the caste system argue that it should be viewed, ignoring its etymological past, as another name for the modern division of labor, which is an integral part of any industrial society. Babasaheb , while accepting the division of labour as one of the attribute of the caste system,  points out that the caste system of the Hindu society  also determines an hierarchy in that division of labour, condemning those at the bottom as not worthy to be treated as an independent human being. The division of labor based on skill, dexterity, and judgment does not, in principle, determine the purported division at the time of a person’s birth. This sui generis division of labor is essentially a division of laborers, argues Babasaheb. The age old Indian division of labour based on caste system has thus led to “subordination of man’s natural powers and inclinations to the exigencies of social rules.”

    Caste cannot preserve a nonexistent “racial purity”( section 5)

    In today’s world the “racial purity” is just a fantasy , harbored by many but followed by none. Maintenance of “racial purity” could have been a motivation for the authors of Manusmrity, but to hallucinate it today is a non-sequitur. In this regard, we may look at the statement issued by the American Association of Physical Anthropologists in the year 2019. “pure races, in the sense of genetically homogenous populations, do not exist in the human species today, nor is there any evidence that they have ever existed in the past.” . In view of this declaration, one cannot but agree with Babasaheb’s view about the claim of racial purity for themselves by the upper caste Hindus:   “[the caste system] embodies the arrogance and selfishness of a perverse section of the Hindus who were superior enough in social status to set it in fashion, and who had the authority to force it on their inferiors.” (Last para )

    Caste prevents Hindus from forming a real society or nation ( section 6)

    As a critique of Hinduism, Babasaheb has no equal so far. His criticism is not merely a scholarly investigation into a social construct, unwrapping of interplay of power, property and social status, layer by layer. Rather, it is the result of felt agony of being an untouchable in a highly fractured and rigidly hierarchical society. Nevertheless, he actively participated in the nation building effort of independent India by providing his deep knowledge about Indian society, modern jurisprudence and structure of governance in advanced countries. Therefore, the point arises, why he should be so skeptical about the possibility of independent India becoming a modern nation without any spec of ignominy of untouchability for any section of the society.  In this regard, the following quote, a lengthy one, is good enough to understand the rationale behind his views.  

    In every Hindu the consciousness that exists is the consciousness of his caste. That is the reason why the Hindus cannot be said to form a society or a nation.

    Men do not become a society by living in physical proximity, any more than a man ceases to be a member of his society by living so many miles away from other men

    The similarity in habits and customs, beliefs and thoughts, is not enough to constitute men into society

    Men constitute a society because they have things which they possess in common. Parallel activity, even if similar, is not sufficient to bind men into a society

    The Caste System prevents common activity; and by preventing common activity, it has prevented the Hindus from becoming a society with a unified life and a consciousness of its own being.

    This is a very radical view about what constitutes a society or a nation. If we consider all the above points with regard to a country like USA, it would have failed to qualify as a nation until mid-20th century. The Rosa Park event happened on December 1, 1955 at the capital of Alabama.  City busses in that city followed the law of segregation- the front seats for whites and the rest for blacks.  Ms. Park, after a busy day boarded a city bus and sat in the middle, just behind the front “white” section. When incoming passengers filled up the front white section, the bus driver ordered the black passengers in the middle row to vacate the seats and stand. Rosa Park refused. She was arrested and convicted for defying the segregation law.  It is a different matter that this event led to quashing of segregation law.

    Let us recall the “ I have a dream “ speech of Martin Luther King that he delivered on the steps of  Lincoln Memorial of Washington DC on August 23, 1963. To recall, around 100 years back, the US president Abraham Lincoln had signed the Emancipation Proclamation freeing the slaves.

    I have a dream today.

    I have a dream that one day down in Alabama, with its vicious racists, with its governor having his lips dripping with the words of interposition and nullification; that one day right down in Alabama little black boys and black girls will be able to join hands with little white boys and white girls as sisters and brothers.I have a dream that one day this nation will rise up and live out the true meaning of its creed. We hold these truths to be self-evident that all men are created equal

    By  Babasaheb’s yardstick, USA could not be called a nation.

    In fact, inequality is all pervasive. The only difference between India and USA is that the dominant religion in USA does not discriminate between white and black people, per se. No Church would disallow a black to pray in its sanctuary. However, an untouchable is routinely denied entry into the sanctuary of many famous Hindu temple- called Garbhagriha.   

    Although majority of Indians are Hindu and Hindus by themselves do not form a society for reasons enumerated byBabasaheb Ambedkar in this section of his speech.  But for that reason India does not cease to be a nation. However, Babasaheb. Ambedkar’s argument will become valid, if India ceases to be a secular country and Indian Constitution is amended to declare it as Hindu Rashtra.

    The worst feature of the Caste System is an anti-social spirit (section 7)

    The anti-social spirit is a phenomenon that cuts across castes, religions and languages. In every nation or society, it would be difficult not to find a small community or a group that live at the margin of the society and called anti-social. Babasaheb himself has written this in the second para of this section: “This anti-social spirit, this spirit of protecting its own interests, is as much a marked feature of the different castes in their isolation from one another as it is of nations in their isolation.”    

    Similarly, the British Government of India enacted Criminal Tribes Act (CTA) 1871 to notify certain tribes as criminal and kept them under continuous surveillance, thus labelling about 200 communities in several provinces “criminal” communities under this act (Devy 2013 Ram Singh). 

    In Great Britain, a large survey of ethnic minority groups was carried out in 2021.  According to a Guardian report the survey has revealed that ethnic minorities Roma, Gypsy and Traveller face extremely high levels of racial assault, poor health, precarious employment and socioeconomic deprivation. The research found that 62% of Gypsy or Traveller people had experienced a racial assault. In other words, although anti-social spirit is present in all nations, Indian system of caste may make it more pervasive  ( see the link in reference section below)

    Caste prevents the uplift and incorporation of the aboriginal tribes ( section 8)

    This section essentially repeats section 7, with the only difference being that it focuses on aboriginal tribes, referred to as ‘scheduled tribes’ in the Indian Constitution. He criticizes the Hindu society for ignoring 13 million people still “living in the midst of civilization … in a “savage state”. The reason for this apathy of a Hindu towards anyone born outside the caste system is that Hindus are more concerned about protecting the purity of his or her caste. Unlike a Christian missionary, Hindu priests rarely engage themselves in proselytization, as it would be difficult to assign any particular caste to the converted person because caste is determined at birth only. That is why, a number Hindu groups are against worshiping Sai Baba, because he was born a Muslim.

    Remaining sections

     The higher castes have conspired to keep the lower castes down (section 9)

    Caste prevents Hinduism from being a missionary religion (section 10)

    Caste deprives Hindus of mutual help, trust, and fellow-feeling (11)

    Caste is a powerful weapon for preventing all reform (section 12)

    Caste destroys public spirit, public opinion, and public charity (section13)

    I want to address all five sections in one go because they are interrelated and repetitive, highlighting one of the most important shortcomings of Hinduism: its rigidity and the consequent barrier to conversion from other faiths to Hinduism. Dr. Ambedkar views Hinduism as a constellation of castes, where internal unity is highly fragile and contingent upon any dire external threat. This exclusivity of Hinduism is not on

    ly directed at non-Hindus but also manifests within its own people by creating an insurmountable hierarchy of status, privileges, occupation, and socialization among believers. 

    The following quotes from Dr. Ambedkar’s un-delivered lecture proves the point.

    The Hindus criticise the Mohammedans for having spread their religion by the use of the sword. [But] Hindu would not spread the light,…would endeavour to keep others in darkness, [and], would not consent to share his intellectual and social inheritance” with others who are ready to consider conversion to Hinduism. I have no hesitation in saying that if the Mohammedan has been cruel, the Hindu has been mean; and meanness is worse than cruelty. (Last para of section 9 )

    Hindu Society being a collection of castes, and each caste being a closed corporation, there is no place for a convert(Last para of section 10)

    With the Hindu Gods all-forbearing, it is not difficult to imagine the pitiable condition of the wronged and the oppressed among the Hindus. Indifferentism is the worst kind of disease that can infect a people. Why is the Hindu so indifferent? In my opinion this indifferentism is the result of the Caste System, which has made Sanghatan and co-operation even for a good cause impossible.(Last para of section 11)

    Caste in the hands of the orthodox has been a powerful weapon for persecuting the reformers and for killing all reform. (Last line of section 12)

    The capacity to appreciate merits in a man, apart from his caste, does not exist in a Hindu. There is appreciation of virtue, but only when the man is a fellow caste-man. The whole morality is as bad as tribal morality. (Last para of section 13)

    I believe that the above summary of the five paragraphs faithfully presents the views of the respected doctor. Despite having experienced inhuman treatment at the hands of upper-caste Hindus, Babasaheb, setting aside his personal rancor, has provided a succinct and accurate description of the caste system in Hinduism. However, criticism is a lazy exercise for any great thinker of the stature of Babasaheb. People would like to know: What is the road ahead? Will it be possible to annihilate the caste system in Hindu society within the next hundred years?

    In this respect, Babasaheb has only left for his followers only a dream—a dream similar to the one Martin Luther King articulated in 1963. King’s dream has largely remained unfulfilled, and fate has so far played the same game with Babasaheb. In this speech, Babasaheb outlines his vision for a society based on liberty, equality, and fraternity. The great French revolutionary Robespierre suggested that these words be inscribed on the flags of France in 1790. They were denied, and after a few failed efforts, they were incorporated into the French Constitution of 1948.

    After spelling out his ideal, in the next 11 sections of the speech, Dr. Ambedkar gave detailed reasons for the impossibility of Hindu society accepting and implementing his ideals. So far, efforts to bring reform from within the Hindu society, keeping the caste system intact, with some marginal tweaking, has failed as it was inevitable given the basic structure of Hindu religion. In this respect, Babasaheb was right. In the last section, he expressed his frustration through the title of the section itself- The struggle is yours; I have now decided to leave the Hindu fold.

    Babasaheb wrote this speech in December 1935 and Dr. Ambedkar adopted Buddhism on October 14, 1956.  It took him two decades to take the plunge because he could not or did not want to be a Godless person.  To be an atheist and leader of any community in India, even if that community is untouchable to its other communities worshiping the same God, is next to impossible. This is the same reason for accepting a key position in the committee for drafting of the Indian constitution. He did not want to give any leeway to other members of drafting committee to incorporate Hinduism in that precious document. In this respect at least, he had Pandit Nehru as a co-believer. I believe it was Pandit Nehru’s masterstroke to bring in the Doctor as the head of the drafting committee. Otherwise ,there was a possibility that Mahtma Gandhi’s view about the eternal  sanctity of Hindu Dharma could have found place in the constitution.

    When Babasaheb took upon himself to publish his speech, Mahatma Gandhi entered into a debate with the doctor by pointing out fallacies in Babasheb’s train of argument. A perusal of text of that debate clearly shows that there was no meeting ground between the two. 

    The article so far has pointed out the deep divergence between the understanding of Congress about Hindu Dharma and that of Babasaheb. What about BJP’s view on this subject? I can only say that Babasaheb Dr. Bhimrao Ambedkar must be laughing in the heaven, subject to its existence, if BJP is ready to chant his name in praise. May be, the sun has started rising in the west.

    Bhimrao Ambedkar must be laughing in the heaven, subject to its existence, if BJP is ready to chant his name in praise. May be, the sun has started rising in the west.

    Finally, we must accept that an overwhelming majority of Indian people are deeply religious, and very few are willing to renounce their ancestral religion. At the same time, a significant section of Hindus is not ready to follow all dictates of the Shastras or Manusmriti and would actively support any effort towards the annihilation of caste. We must find a middle path to gradually break the stranglehold of Brahmins and high-caste people on the practice of Hindu religion. A list of low-hanging fruits is given below.

    1. For any government document, the caste title like Sharma, Bhat, Upadhyay, Chatterjee,  Iyer, Shastri, Chattopadhayay, Bagchi, Pandit etc. will be forbidden. Mother’s given name should follow every person’s given name and nothing more. Father’s name must not be part of this naming convention.
    2. This naming convention would apply to all government documents including property registration document, birth certificate, passport etc.
    3. UPSC should prepare a list of qualified priests, based on open examination. All temples must select priests from this list. People should consider Priesthood as any other job which, requiring specific skill- like knowledge of Satras etc. Every university should have a degree in priesthood also.

    Implementation of the above list of aspirational activities may not be easy and vehement opposition to its’ introduction will defiantly break out. However, Rome was not built in one day.

    References:

    Ambedkar, Bhimrao Ramji (B.R) , 1935 The Annihilation of Caste, Columbia Centre for Teaching and Learning https://ccnmtl.columbia.edu/projects/mmt/ambedkar/web/readings/aoc_print_2004.pdf

    Duncan Ira 2022, Ambedkar and British Policy on the Communal Award: A Response to Sujay Biswas , Studies in People’s History, 9, 2 (2022): 224–240       :  quoted from M.K. Gandhi, ‘Appendix: Discussion on the Communal Award, 21 August 1932’, CWMG, Vol. 56, p. 466

    Fuentes A, Ackermann RR, Athreya S, et al. AAPA statement on race and
    racism. Am J Phys Anthropol 2019;169:400–402.

    Helen M. Nugent (1979) The communal award: The process of decision‐making, South Asia: Journal of South Asian Studies, 2:1-2, 112-129

    https://www.theguardian.com/world/2023/apr/09/social-barriers-faced-by-roma-gypsies-and-travellers-laid-bare-in-equality-survey

  • Global Distribution of Wealth

    A brief summary based on two datasets

    Ashok Nag

    Adam Smith began his magnum opus “An Inquiry into the Nature and Causes of the Wealth of Nations”, with the following line:The annual labour of every nation is the fund which originally supplies it with all the necessaries and conveniences of life which it annually consumes. (Smith- 1776; EBC edition 2001: Book 1 page 12)

    For a given amount of labour, according to Smith, the wealth of a nation will on the productivity of that labour. While there is no inherently intrinsic level of productivity of a human being, it can be worked upon and enhanced by appropriate organizational structure, technological initiatives and incentives. According to Smith, the “greatest improvement in the productive powers of labour, and the greater part of the skill, dexterity, and judgement with which it is anywhere directed, or applied, seem to have been the effects of the division of labour.”(op.cit. page 17)   It is not that the nature’s bounty – land, water, and environment- does not give a head start to a particular nation, but it would not make a nation wealthier if the “skill, dexterity, and judgment” and “division of labour” are not well developed and do not become an integral part of the production system.

    Notwithstanding Smith’s identification of “labour” as the source of all wealth, he was pragmatic enough to understand that a market economy is not designed to bring the maximum benefit to providers of labour that creates wealth. In fact, he was aware that a market economy, per se, has no institutional mechanism for reduction of inequality in distribution of a nation’s wealth, and, therefore, it cannot bring about more equality in distribution of initial endowment of wealth that provides enormous advantage only to a small minority of people. His main concern was the growth of an economy- a growth that critically depends on the increase in productivity of labour. He had no illusion about the antagonistic nature of relation between wage earners and profit earners. Smith, an astute observer of social power structure and author of Theory of Moral Sentiments, understood the real mandate of any civil government:  

    Civil government, so far as it is instituted for the security of property, is in reality instituted for the defence of the rich against the poor, or of those who have property against those who have none at all (Smith: op.cit. Book 5 page 953)

    This article shows that, even after 250 years of Smith’s assertion that labour is the ultimate source of wealth, the share of a nation’s wealth remains concentered in the hands of rich. The remit of all civil governments and international organizations remain the same- to enforce and maintain the inequality in wealth and power within and without a nation.

    Definition of wealth

    Etymologically, the English word “wealth” traces its ancestry to the old English “weal” and before that to “wel”. Both these words referred to a general state of “wellbeing “.  The circularity in this definition notwithstanding, for people at large, possession of material “wealth” is both necessary and sufficient condition for a person’s wellbeing. In order to measure wealth in possession of an individual, family or a community, we need to identify valorized components of wealth. Irving Fisher, in his book, “The nature of capital and income” , defined and elucidated the term “wealth” in a market economy in the following manner.  

    The term ” wealth” is used in this book to signify material objects owned by human beings. According to this definition, an object, to be wealth, must conform to only two conditions: it must be material, and it must be owned. To these, some writers add a third condition, namely, that it must be useful. But while utility is undoubtedly an essential attribute of wealth, it is not a distinctive one, being implied in the attribute of appropriation; hence it is redundant in a definition. (Fisher 1906,page 3).

    For Fisher ownership of material goods is a necessary qualification of a material object to be considered as a part of a legal entity’s wealth. He clarified this with following examples:

    Rain, wind, clouds, the Gulf Stream, the heavenly bodies — especially the sun, from which we derive most of our light, heat, and energy — are all useful, but are not appropriated, and so are not wealth as commonly understood. (op.cit page 3)

    More than one hundred years have passed since Fisher gave his definition of wealth. Today, even clean air is not only a desirable but a precious material object too. According to UN Environmental Program, air pollution is the greatest environmental threat to public health globally and accounts for more than 8 million premature deaths every year. A number of recent empirical studies have shown that polluted air has a negative impact on labour productivity and thereby on human capital component of a nation’s wealth (see Chen and Zhang 2021). Thus, measured degradation in the quality of environment needs to be considered as a liability that must be deducted from the value of asset. It also highlights the complexity and contradictions in the concept of ownership , especially when accounting for environmental impact. For example, access to natural resources like living and non-living useful objects in sea has been demarcated with a national boundary based on international agreement and are always prone to conflicts between nations. Similarly, the question of whether possession equates to ownership is a complex legal issue. When Smith or even Fisher wrote about wealth, they did not have to deal with the issue of ‘knowledge as a source of wealth”. For them “skill, dexterity” of individual workers, combined with “judgement” of entrepreneurs created wealth.

    It is quite evident that the concept of wealth is as fuzzy as its equivalent concept of wellbeing. The definitional issues of ‘wealth” is beyond the scope of this article, although measurement of “wealth” of a nation would depend on the demarcation of the underlying definitional boundary. Since there is no globally accepted definition of wealth of a nation, for this study, we have used two well recognized datasets on national wealth- one  by the World bank  that provides wealth data from 1995 to 2018 and another one by the  Credit Suisse (now UBS) that provides data on household wealth since 2000.  Credit Suisse (now UBS) is a globally active financial institution having a very large wealth management practice.  Although we take a quick over view of wealth measured across nations, our focus is on three largest nations- China, India, and USA. (Reference part of this article gives the details of all reports of these two agencies)

                                                                                    Box 1                        
    Data Quality

    The two datasets that we have used for our evaluation of distribution of wealth, across the nations and within a nation, are subject to many qualifications.  A brief discussion of the most important ones follows. The World Bank calculates the present value of future flows of produced outputs generated by land, labour and capital – that is a country’s GDP- by using a time-independent discount factor uniformly for all nations. This approach may introduce inaccuracies since each country’s economic conditions vary from country to country. On the other hand, the Credit Suisse /UBS evaluates household wealth, which includes “financial assets and real assets (principally housing)” using market value, wherever available, (see Notes on concepts and methods page 19 of 2023 UBS report).  However, due to the volatility of financial markets, price movements in one country may not align with those in others, leading to inconsistent valuations across countries. At the country level, domestic currency is typically used for valuation of assets, but for international comparisons, a conversion to a common currency is required. The World Bank does this by using market exchange rates in constant 2018 US dollars. The Bank also looked at how using Purchasing Power Parity (PPP) based exchange rates affects this measure. When using PPP, the share of global wealth for low-income and lower-middle income countries rises from 7.3% to 15.8%. The Credit Suisse/UBS used end period market exchange rate to convert local currency estimates into US dollar-based estimates. The price volatility, degree of market dominance by a few large corporates etc. are key features that vary from market to market. Such variations is far from negligible and in the absence of any normalization of data across markets, its impact on the wealth distribution data remains unaddressed. Apart from measurement issues, wealth data even at national level is fraught with a number of conceptual issues, particularly concerning definitional boundary of Natural Capital and Human Capital. For example, the assessment of a country’s natural wealth depends on available knowledge about resources located within its recognized borders. These borders are determined according to international agreements, and any limitations in resource knowledge can affect wealth calculation.  In spite of having many data such quality issues, the two data sets used in this article are the only ones that provide complete coverage of wealth of nations over a reasonably long time.

    Estimation methodology of National Wealth by the World Bank:

    Components of wealth:

    Where is the wealth of nations? (World Bank 2006) was the first attempt by the Bank to provide “comprehensive snapshot of wealth for 120 countries at the turn of the millennium”. Since 2006, the Bank is publishing a yearly report titled The Changing Wealth of Nations: Managing Assets for the Future (CWON). This article uses data published in the 2021 report, the latest available.

    To measure a nation’s wealth, the World Bank relies on two closely related international standards for valuing economic activity within national borders. The first standard is the System of National Accounts (SNA), which focuses on national income measurement. The United Nations Statistical Commission (UNSC) released the first version of SNA in 1953, with the latest update in 2008. The SNA bases its accounts on transactional data related to production, consumption, and the accumulation of assets. The institutional units participating in these exchanges within a market economy generate this data.

    The System of National Accounts (SNA) defines an asset as “a store of value.” Any rent or profit generated from this asset must accrue to the “economic owner” in the future. Following the principles of Irving Fisher, the SNA does not consider any store of value without identifiable economic ownership as an asset. (page 39  para 3.5 SNA 2008). Following Irving Fisher, SNA also does not recognize any store of value that has no identifiable economic ownership, as an asset.

    The second standard, introduced in 2012 and used by the Bank, is the System of Environmental-Economic Accounting (SEEA), which is the accepted international standard for environmental-economic accounting. The SEEA framework acknowledges the inherent link of every production system to its surrounding environment, and its significant impact on all production activities by human beings. These environmental impacts include the depletion of natural, non-produced resources, such as forests, minerals, and air, as well as a reduction in the quality of environment, which ultimately undermines the production process itself. The concept of sustainability in growth stems from the recognition that the environment may eventually be unable to sustain such production levels. In terms of accounting principles, conventions, and table structures, the SEEA aligns with the SNA.

    The core premise of the World Bank’s methodology is that a nation’s wealth consists of three major types of assets or capital: produced assets, natural capital, and human resources. Human resources include raw labor, human capital, and the intangible yet essential element known as social capital (World Bank 1997, Page 19).  An outline of the definitions and boundaries for each of these components follows.

    Produced Assets:

    SNA 1993 defined “produced assets” as “non-financial assets that have come into existence as outputs from processes that fall within the production boundary of the SNA; produced assets consist of fixed assets, inventories and valuables.” Thus, human capital, and natural resources without any identified owner are excluded (Paragraphs 10.7 and 13.14, see also SNA 2008 page 48 para 3.49)

    Three types of produced non-financial assets are:

    1. Fixed assets,
    2. Inventories, and
    3. Valuables, which include items like precious metals, antiques, and art objects.

    Non-produced assets comprises of three sub-groups:

    1. Natural resources,
    2. Contracts, leases, and licenses, and
    3. Goodwill and marketing assets.

    Natural Capital

    Natural capital comprises of three principal categories: natural resource stocks, land and ecosystems. Natural resources are non-renewable resources like oil, natural gas, coal and mineral resources. Land includes cropland, pastureland, and forested areas.  Ecosystem assets are those assets, which provide ecosystem services that are essential for sustainability of any human society. In environmental accounting, ecosystem incorporates both living organizations and the physical environment encompassing them in a specific area comprising landscape as well as seascape (see Estelle Dominati et al 2010, United Nations 2003). The CWON 2011 report provides further details about these Ecosystem Services and measurability of them. (CWON 21 page 22)

    Human Capital

    The three attributes of labour, namely “skill, dexterity, and judgement” are the main drivers of labour productivity. The income that a laborer earns in her lifetime can be considered as “flow of rents (or economic profits) in the future.”(page 3 CWON 2011). Accordingly, the Bank measures Human Capital as the present value of all future labour income (2021 CWON report page 144).

                                                                            Box 2                                                                 
    Genuine Savings
    Wealth, in the form of produced assets, increases through savings and investment. Net national savings are calculated by subtracting the consumption of fixed capital from the gross national savings, as per the System of National Accounts (SNA). Consumption of fixed capital reflects the depreciation of productive capacity during the estimation period. This measure of net national savings, however, does not account for the sustainability of economic growth, as it overlooks changes in natural resource bases and environmental quality besides produced assets (as highlighted in “Expanding the Measure of Wealth,” page 8). Consequently, the value depletion of natural resources like energy, metals, minerals, and net forest stocks is estimated and subtracted from the net national savings. Since education enhances human capital, current expenditures on education are added to the net national savings. This adjusted figure is known as genuine savings. Many natural resource-dependent countries exhibit low or negative genuine savings, indicating long-term sustainability issues.  

    Measurement of wealth

    From the perspective of sustainability of current economic growth, measurement of wealth at a time t needs to be the sum of all future consumption, appropriately discounted on future consumption path. Symbolically, this has been written as:   where Wt   is the present value of future flows of income in the year t, C(S) the is the consumption in a future year s , and r is the social return to investment or the discount factor for future benefits. The Bank has taken 24 years as the time horizon for its wealth computation. As regards the social discount factor, 4 per cent has been used. The Bank has not provided any counterfactual study with regard to these assumptions. However, comparison of change in wealth across nations may not suffer from gross errors, provided that the degree of errors is bounded across nations.   Similarly, a nation’s income as measured by GDP represents the cash flows generated from the produced wealth of the nation. Essentially, estimating a nation’s produced wealth becomes straightforward with an assumed income (i.e. GDP) growth rate and a given discount factor. The World Bank data is available from 1995 to 2018. For a given year, the total wealth is the sum of the following components:

    Total wealth = renewable natural capital + nonrenewable natural capital+ produced capital + human capital + net foreign assets. Data given in 2021 CWON is in constant USD 2018, at market exchange rate. All data has been sourced from https://datacatalog.worldbank.org/dataset/wealth-accounting

    Estimation methodology of Household Wealth by Credit Suisse/ UBS

    Credit Suisse, a Switzerland-based global investment bank, was established in 1856. Since 2010, the bank has been publishing the Global Wealth Report (GWT), which provides profiles of household assets across various nations. An accompanying publication, titled the Global Wealth Databook, offers detailed data that underpins the main report. In March 2013, Credit Suisse was acquired by another Swiss bank, UBS, in an all-stock deal orchestrated by a joint effort of the Swiss government and the Swiss Financial Market Supervisory Authority, to prevent the potential fallout from a Credit Suisse bankruptcy. UBS has continued publishing the Global Wealth Report since 2023. The latest report was published in July 2024.

    In contrast to the World Bank’s approach, GWT estimates wealth solely at the household level. At this level, net worth, or ‘wealth,’ is defined as the market value of financial and non-financial assets owned by a household, minus any debt. Real assets consist primarily of housing properties owned by households. This approach does not include human capital, natural capital, or other elements considered in the World Bank’s method for measuring national wealth. Additionally, government or community-owned assets are excluded (see Davies et al). To ensure consistency in measuring wealth across nations, all asset valuations are converted to US dollars using the end-period exchange rate. For assessing the wealth of individuals at the top end of the distribution in each country, the data is adjusted using wealth estimates given by in the Forbes list of billionaires.

    Wealth of Nations – World Bank estimates

    Between 1995 and 2018, global wealth increased from 603.5 trillion USD to 1152.5 trillion USD- indicating a compounded growth rate of 3.4 %. Only in two years- 2006 and 2007, the growth rates were more than 4%.  The composition of wealth remained more or less the same during this period. During the same period, the Global GDP at constant 2015USD increased by a compound growth rate of 3.1%.  The average shares of three major components, Produced Capital, Natural Capital and the Human Capital – remained at around 51.5%, 6.7% and, 62% respectively (see annexure table A2.1 and A2.2). 

    The global wealth is concentrated in two high-income group of countries, namely OECD and non-OECD countries. Non-OECD high-income countries, such as the UAE and Bahrain, have a very low population share, only 1% in 2018. As a sub-group, their share in total wealth remained between 2% to 3%.  Although high-income OECD countries continue to hold the largest share of the world’s total wealth, their share declined from 74.3% to 58.3% between 1995 and 2018. During the same period, their corresponding population share also declined modestly, from 17.6% to 15%. Lower middle-income countries, including India, accounted for the highest share of the world’s population in 2018 (39.7%) but held a very modest share of the world’s total wealth (5.7%). Human capital was the largest source of wealth for most countries, except for high-income non-OECD and low-income countries. The share of natural capital in total wealth was the lowest for high-income OECD countries, at a mere 2.1%. This suggests that the bounty of nature is neither necessary nor sufficient for the generation of wealth (see Table A2.3). Data on country wise shares in wealth and population shows that the top ten countries in terms of their wealth in 2018 have hardly changed between 1995 and 2018. The entry of China in this club of rich countries has compensated for the decline in the share of High-income OECD countries.  These top ten countries accounted for around 72% of total global wealth and around 55 percent of the world’s population. The most important takeaway from these estimates of national wealth is the phenomenal rise of China. In 1995, China’s wealth was only 22 percent of USA’s wealth and by 2018, it has reached to 85%. During this period, the ratio of China’s population to that of USA remained almost the same- 4.5 in 1995 to 4.3 in 2018 (see Table A2.6).  The shares of China, India, and USA in the total wealth of the world, given below, shows how China has leapfrogged to become the wealthiest country of the world.

    Table 1: Wealth Share versus Population Share of countries

             Year 2000     Year 2010      Year   2018
    National Wealth BandShare in World PopulationShare in Wealth of the WorldShare in World PopulationShare in Wealth of the WorldShare in World PopulationShare in Wealth of the World
    less than 1 trillion13.5%2.7%13.8%2.6%12.4%2.3%
    1-5 trillion21.0%10.9%20.9%9.9%18.4%6.9%
    5-10 trillion7.1%7.2%1.8%4.2%7.4%0.0%
    10 – 50 trillion29.7%29.6%36.5%33.8%35.0%27.7%
    More than 50 trillion28.8%49.6%27.0%49.5%26.8%56.8%
    World Bank Data

    China’s increasing footprint in the world economy is not only consistent with its growth in its national wealth but also with the growing prosperity of its average citizen. Thus, the per capita GDP growth rate of China during the period 1995 -2023 remained well above the corresponding growth rates of India as well as USA. The data given below corroborates this.

    Table 3:  Growth in per capita GDP of China/India/US

    Per capita GDP- Compound Growth Rate
    ChinaIndiaUSA
    1960-19702.4%3.0%5.7%
    1970-19805.6%9.1%9.2%
    1980-19905.0%3.3%6.6%
    1990-200011.7%1.8%4.3%
    2000-201016.8%11.8%3.0%
    2010-20208.6%3.6%2.8%
    2020-20236.6%9.1%8.3%
    2023 GDP as ratio of US GDP0.150.031
    World Bank Data

    Wealth of Nations – Credit Suisse/ UBS estimates

    The 2024 Global Wealth report estimates 4.2 % increase in global wealth ( in USD) in 2023 as compared to a decline of (-)3% in 2022. The report has highlighted that in the last 15 years of publication of this report only 3 times there has been a decline in the estimated global wealth in USD term- “during the financial crisis of 2008, in 2015 and once again in 2022, when both equities and bonds dropped across all major markets” ( page 5).

    Table 4 Distribution of Global Household Wealth- 2022 

    Wealth range ( in thousand USD)No of adults (million)No of adults (%)Total wealth  in Trillion)Wealth percentage
    less than 10K281852.55.31.2
    10K-100K184434.461.913.6
    100K-1million64212.0178.939.4
    >1 million USD59.4  1.1208.345.8
    Source:Global Wealth Report 2023 page 22 and Table 3-1: Wealth pattern within markets, 2022  in Global Wealth Databook 2023

    The wealth distribution for China, India, and USA, countries in focus of this article shows that India stands apart from the other two most populous countries.

    Table 5: Wealth Distribution of Adults for the China, India , and USA -2022

    Wealth range ( in thousand USD)-Distribution of no of adults (%)
     ChinaIndiaUSA
    less than 10K19.3%73.8%17.5%
    10K-100K65.6%24.0%30.3%
    100K-1million14.5%2.1%43.2%
    >1 million USD0.6%0.1%9.0%
    Gini Coefficient of wealth distribution70.9%82.5%83.0%
    Note: 10K USD would be little less than 10 lac Indian rupees. A person with even 1 acre of agricultural land in rural India would have more than 10 lac worth wealth.

    Finally, we find that acute wealth-deprivation of 90% of Indian people has made no adverse impact on the rising wealth of 1% of people at the top of the pyramid of wealth. In fact, between 2002 and 2018 the share of the top 1 % of India’s wealth owners have increased from   15.7% to 18.3%. At the same time, the share of the bottom 90% of wealth owners hovered around 47%. For both China and USA, there has been similar increase in inequality in distribution of wealth.(see the table A2.13 ).

    In a market economy, financial wealth is the dominant form of total wealth of households. In China, the share of financial wealth has increased from 36.4% to 45.4 % between 2000 and 2022. During the same period, the share of the financial wealth in the gross wealth held by Indian households decreased from 24.1% to 21.0%. The share of financial wealth in the gross wealth of households in USA hovered around 68% during the same period.(see Table A2.14)

    The regional distribution of household wealth shows that world continues to remain highly unequal. In the year 2000,   Africa, Latin America and India together accounted for 33.9% of the adult population of the world and only 4.2% of household wealth of the world. In the same year, the corresponding shares of Europe and USA together were 20% and 66.2 percent. By 2022, the share in total adult population did not change much for the first group, but its share in the wealth almost doubled from 4.2% to 8%. For the second group, both the shares declined to 15.7% and 53.8% respectively (see the Annexure Chart 1). The most remarkable fact about the changing distribution of the world’s wealth is the phenomenal rise of China’s wealth share. It increased from 3.6% in 2000 to 18.6% in 2022- a fivefold increase.  During the same period, China’s share in adult population decreased from 23.2% to 20.8%.

    One noteworthy feature of growth of household wealth in this millennium is the drastic fall in growth rate between two decades for some selected markets. The Annexure table A2.10 shows that annual compounded growth rates for household wealth has more than halved for many important countries between 2000-10 and 2010-23. For example, the household wealth growth rate (compounded annually) of Russia declined from 20% in the first decade to only 4% in the next period of 13 years.  The corresponding figures for China and India were (19,8) and (14,7) respectively. The comparable growth rate for USA was 4 and 6 respectively.

    Billionaires of the world with special reference to  China. India and USA

    Forbes, an USA based business magazine, publishes every year a global list of US dollar billionaires. The magazine published its first list in 1987. Between 1987 and 2024, the number of billionaires in the world have increased from 470 to 2781, with their net worth having increased from $898 billion to $14.9 trillion. A comparison with total household net wealth published by UBS shows that the share of billionaires in total household wealth increased from 1.42% in 2005 to 2.8% by 2022. (See the chart).

    In India, 166 billionaires own 4.9% of total household wealth of India in the year 2022, while the corresponding numbers for China and USA are 2.3% and 3.4% respectively.

    UBS in collaboration with PWC has also been publishing a report on billionaires of the world since 2015.  covering mostly 43 markets in the Americas, Europe, the-Middle East and Africa (EMEA)  and Asia-Pacific. In 2023, UBS/PWC study covered 2544 billionaires as against 2376 in the previous year. The report provides an interesting insight into the persistence of wealth in an already wealthy family. During the study year 2023, 53 multi-generation billionaire inherited USD 150.8 Billion, while 84 new first generation billionaires were worth of USD 143.7 Billion. ( UBS 2023, page 24 Section 2 )

    The main conclusion that we can safely draw from the data assembled in this article is that the distance between elites and the poor in terms of opportunity dominance within a given society of Homo sapiens has not changed noticeably, from the times when our ancestors started accumulating wealth. Annexure 3 provides a brief discussion of inequality prevailing in such ancient human societies that existed between 11000 to 2000 years ago.

    Annexure 1 Charts

    Chart1: Share of Regions and 3 most Populous countries in the world’s adult population and wealth (in %)

    Source : Global Wealth Databook 2023  UBS

    Chart 2: Number of Billionaires of the World- and their share in the World’s Wealth

    Source : https://www.kaggle.com/datasets/guillemservera/forbes-billionaires-1997-2023

    Annexure 2:     Tables

    Based on the World Bank Data

    Data source for all World Bank Data: The Changing Wealth of Nations 2021:  Managing Assets for the Future and https://datacatalog.worldbank.org/dataset/wealth-accounting

    Table A2.1: Income group wise share in global wealth and population

    Year199520102018
    Income GroupShare in total wealthShare in populationShare in total wealthShare in populationShare in total wealthShare in population
    High income: non-OECD2.18%0.77%2.82%0.96%2.64%1.05%
    High income: OECD74.32%17.64%64.02%15.88%58.26%15.01%
    Low income0.49%5.78%0.53%7.24%0.59%8.26%
    Lower middle income4.98%36.29%6.11%38.77%6.73%39.72%
    Upper middle income18.04%39.52%26.52%37.14%31.78%35.96%

    Table A2.2 : Share of various wealth types for different income groups– Year 2000

    Income GroupShare of Produced  CapitalShare of Natural CapitalShare of Human CapitalShare of Net Foreign Assets
    High income: non-OECD18%34%38%10%
    High income: OECD34%2%65%0%
    Upper middle income27%13%60%-1%
    Lower middle income27%19%58%-3%
    Low income26%39%39%-3%
    Note: Row percentages are in relation to total wealth of the
    corresponding income group.

    Table A2.3: Share of various wealth types for different income groups– Year 2010

    Income GroupShare of Produced  CapitalShare of Natural CapitalShare of Human CapitalShare of Net Foreign Assets
    High income: non-OECD17.7%42.2%31.9%8.2%
    High income: OECD35.9%2.8%62.0%-0.6%
    Upper middle income23.6%13.9%62.4%0.0%
    Lower middle income24.5%18.3%59.3%-2.1%
    Low income25.8%34.2%41.7%-1.8%
    Note: Row percentages are in relation to total wealth of the corresponding income group.

    Table A2.4: Share of various wealth types for different income groups– Year 2018

    Income GroupShare of Produced  CapitalShare of Natural CapitalShare of Human CapitalShare of Net Foreign Assets
    High income: non-OECD23.2%30.8%33.6%12.4%
    High income: OECD35.0%2.1%63.8%-0.8%
    Upper middle income25.8%7.9%66.2%0.1%
    Lower middle income27.2%13.5%62.1%-2.8%
    Low income27.7%25.6%50.0%-3.3%
    Note: Row percentages are in relation to total wealth of the corresponding income group.

    Table A2.5: Average growth rate of total wealth for five-year period by regions

    YearEast Asia & PacificEurope & Central AsiaLatin America & CaribbeanMiddle East & North AfricaNorth AmericaSouth AsiaSub-Saharan AfricaWorld
    1995-20003.911.752.402.503.754.600.493.01
    2001-20054.081.632.555.221.715.223.272.51
    2006-20105.731.733.876.571.056.146.943.05
    2011-20155.341.293.473.751.785.464.263.04
    2016-20184.311.761.31-2.311.715.901.852.54
    Note: South Asia includes India.  East Asia and Pacific includes China. North America includes USA. Middle East and North Africa includes Saudi Arabia, Iran and Israel. Total wealth is calculated as the sum of produced capital, natural capital, human capital, and net foreign assets.  Values are measured at market exchange rates in constant 2018 US dollars, using a country-specific GDP deflator.

    Table A2.6 Share in total global wealth and population for top ten countries in terms of wealth in 2018.

    Country1995200020102018 
    Share in WealthShare in PopulationShare in WealthShare in PopulationShare in WealthShare in PopulationShare in WealthShare in Population
    United States30.10%4.89%31.41%4.84%27.05%4.69%24.73%4.74%
    China6.69%22.11%8.49%21.48%15.20%20.22%21.07%20.75%
    Japan10.52%2.28%9.68%2.16%7.25%1.92%6.14%2.02%
    Germany6.45%1.49%5.86%1.40%5.09%1.21%4.84%1.30%
    France4.44%1.09%4.25%1.04%3.66%0.98%3.29%1.01%
    United Kingdom3.63%1.06%3.72%1.00%3.17%0.95%2.85%0.98%
    India1.50%17.84%1.66%18.26%2.26%18.81%2.83%18.52%
    Canada2.95%0.54%2.85%0.53%2.79%0.52%2.64%0.52%
    Russian Federation2.99%2.69%2.56%2.48%2.61%2.15%2.17%2.30%
    Brazil2.53%2.99%2.35%3.01%2.45%2.97%2.13%2.98%
    Total71.80%56.98%72.83%56.21%71.54%54.42%72.69%55.11%

    Table A2.7: Comparison of three selected countries in terms of per-capita wealth

    Country NamePer capita 2000RankRatio to Median2010 per capitaRankRatio to Median2018 per capitaRankRatio to Median
    United States779093618.4804679.9513.3872400613.7
    China47046721.1104563.4521.7174365412.7
    India109721210.316875.81180.3241021100.4
    Note: Rank and Median are based on per capita wealth of 146 countries.

    Table A2.8:    Share in Total Wealth of the World by three largest countries in terms of population

    Country1995200020102018
    China6.7%8.5%15.2%21.1%
    India1.5%1.7%2.3%2.8%
    USA30.1%31.4%27.0%24.7%
    Total38.3%41.6%44.5%48.6%

    A2.9: Average growth rate of total wealth for 5 year period by regions

    YearEast Asia & PacificEurope & Central AsiaLatin America & CaribbeanMiddle East & North AfricaNorth AmericaSouth AsiaSub-Saharan AfricaWorld
    1995-20003.911.752.402.503.754.600.493.01
    2001-20054.081.632.555.221.715.223.272.51
    2006-20105.731.733.876.571.056.146.943.05
    2011-20155.341.293.473.751.785.464.263.04
    2016-20184.311.761.31-2.311.715.901.852.54
    Source- World Bank. 2021. The Changing Wealth of Nations 2021

    Credit Suisse / UBS Data

    Table A2.10 Comparison of wealth growth rates over time

    Country2000-102010-23
    Russian Federation204
    Mainland China198
    UAE164
    Brazil153
    India147
    Indonesia136
    South Africa132
    France102
    Italy7minus 0
    Germany63
    Japan6minus 2
    UK54
    USA46

    Table A2.11: Wealth per adult

    CountryGDP per adult (USD)-2021 Wealth per adult  2000 USD) Wealth per adult  (USD) 2022Total wealth 2022                   USD BillionShare in global wealth 2022
    China15,6244,24775,73184,48518.6
    India3,5612,64316,50015,3653.4
    USA100,380215,146551,347139,86630.8
    UBS :  Global Wealth Databook 2023    page 20-22

    Table A2.12 : Distribution of wealth

                            Share in the county’s wealth (%)
    BottomTop
         
    Country / Year80%90%10%5%1%
    China
    200245.462.937.1NANA
    201334.551.648.4NANA
    India
    200230.147.152.938.315.7
    2018NA47.652.4NA18.3
    United States
    200122.139.260.849.325.4
    202015.730.369.757.831.4
    202317.832.068.057.030.6
    Source:   Global Wealth Databook 2023   Table 1-5: Wealth shares for markets with wealth
    distribution data:   Page 15

    Table A2.13: Share of Financial Wealth in Total wealth of selected countries (in %)

    YearWorldChinaIndiaUSA
    200055.436.424.168.1
    200551.337.424.163.0
    201050.640.924.270.1
    201553.143.421.672.2
    202053.944.223.973.1
    202153.644.721.872.2
    202251.145.421.067.6
    Source:  Global Wealth Databook 2023; Table 2-2 (by year) Wealth estimates by market 2000–22

    Table: A2.14: Share of selected regions and countries in the world’s total adult population and household wealth.

    2000201020202022
    Region/ CountryAdult Population Wealth Adult Population Wealth Adult Population Wealth Adult Population Wealth
    Africa10.10.611.21.112.81.313.21.3
    Asia-Pacific22.324.22322.423.718.123.817.2
    Europe14.629.312.931.811.3251123
    Latin America8.12.38.33.58.62.78.63.3
    North- America0.62.20.62.70.62.50.62.5
    China23.23.122.610.121.117.520.818.6
    India15.71.316.42.717.2317.43.4
    USA5.436.9525.74.829.94.730.8
    Source:  Global Wealth Databook 2023; Table 2-2 (by year) Wealth estimates by market 2000–22

    Table A2.15:  Wealth of Billionaires in China, India and USA

    2010202020222022
    CountryNoWealthNoWealthNoWealth% of wealth of the country
    China64133.23871177.55391962.52.3%
    India49222.1102312.6166749.84.9%
    USA4031349.36152948.77354701.13.4%
    Source: Forbes Billionaires Evolution (1997-2024); https://www.kaggle.com/datasets/guillemservera/forbes-billionaires-1997-2023.  Wealth data of 2022 – Credit Suisse/ UBS Databook 2023

    Table A2.17: Number of Millionaires (in thousands)

    Country20202022Percentage of the World-2020Percentage of the World-2022
    United States7,64222,71052.0%38.2%
    Mainland China396,2310.3%10.5%
    France4042,8212.7%4.7%
    Japan2,4722,75716.8%4.6%
    Germany6222,6274.2%4.4%
    United Kingdom7162,5564.9%4.3%
    Canada2692,0321.8%3.4%
    Australia1131,8400.8%3.1%
    Italy4271,3352.9%2.2%
    Korea901,2540.6%2.1%
    Netherlands2571,1751.7%2.0%
    Spain1721,1351.2%1.9%
    Switzerland1951,0991.3%1.9%
    India378490.3%1.4%
    Taiwan1107650.7%1.3%
    Hong SAR1186300.8%1.1%
    Belgium1075360.7%0.9%
    Sweden544670.4%0.8%
    Brazil334130.2%0.7%
    Russia174080.1%0.7%
    World14,69559,391  
    Source : UBS Global Wealth Databook 2023 Table 4-1: Summary details for regions and selected markets, 2022

    Annexure  3

    Inequality in terms of ownership of assets, and future income from this wealth is not exclusive to market economies. Nor was it a universal characteristic across all previous societies of Homo sapiens. It has been agued by many archeologists that hierarchy of status and dominance of few over many began with agricultural revolution in the last phase of Neolithic society, also known as Neolithic revolution.   

    A research paper published in Nature in the year 2017 compared and quantified the degree of inequality in 63 Neolithic society using Gini coefficient. For computing this coefficient, the distribution of household size was considered as a proxy for wealth. The referenced period of the study, called Old World, ranged from 11000 to 2000 years ago. The study also covered a period termed New World, which ranged from around 3,000 to about 300 years ago. The study shows that as compared to hunter-gatherers, wealth inequality generally increased when human beings started domestication of plants and animals. Ownership of large land areas became economically valuable only with the ability to harness plough animals like oxen (Kohler Timothy (2017).

    In 2019, another study using data from 39 Neolithic-Iron Age sites, found that farming per se did not result in enduring and significant wealth inequality in ancient agricultural societies. Such a phenomenon emerged only in societies where land was a relatively scarce asset as compared to labour-scarce societies (Bogaard  Amy et al. 2019)

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    World Bank. 1997. Expanding the Measure of Wealth: Indicators of Environmentally Sutainable Development. Environmentally Sustainable Development Studies and Monographs Series No. 17. Washington, DC: World Bank

    World Bank (2006), Where Is the Wealth of Nations? Measuring Capital for the 21st Century. Washington DC World Bank

    World Bank 2011.  The Changing Wealth of Nations, Measuring Sustainable Development in the New Millennium , Washington DC

    World Bank. Database             https://databank.worldbank.org/metadataglossary/world-development-indicators/series/NY.GDP.MKTP.KD?fbclid=IwAR3VlnTX31huRXCibX_YcTsylgjPg3YHqi6O0N0hXrW8isbchSFlLEUDzZo World Bank. 2021. The Changing Wealth of Nations 2021: Managing Assets for the Future. Washington, DC: World Bank.     See also https://datacatalog.worldbank.org/dataset/wealth-accounting

  • Indian Official Statistical System- through a looking glass erroneously

    “if you torture the data enough, nature will always confess” – Ronald Coase

    Introduction

    Recently a debate took place on the pages of Indian Express (starting on July 7 ,2023 edition) about the quality of survey-based data released by the National Sample Survey Organizations (NSSO). Before that, doubts about quality of Indian official statistics data and methodology used for estimating important social developmental indicators were aired through two working papers issued by Economic Advisory Council of the Prime Minister (EAC-PM). When a couple of eminent economists of impeccable credentials raises serious doubt about the quality and accuracy of some key statistical products that are supposed to reflect, directly or indirectly, the performance of the economy, it is incumbent on the statistical professionals to take these criticisms seriously and put out their non-partisan views to the public at large

    The present article aims to examine the veracity of the criticisms raised by two members of the EAC-PM regarding the quality of estimation of key policy performance indicators by NSSO surveys, who in the past have used NSSO survey data on Employment and Unemployment (Gundimeda, Sanyal & Others, 2006) and NSSO data on Morbidity (Ravi 2016) without indicating any reservation about data quality and sampling design of NSSO surveys.

    The statistical issues have been raised by the following two working papers of EAC-PM.

    1. Reversing the Gaze:  Re-examining Estimates of India’s Development Indicators by International Organisation:  EAC-PM Working Paper Series EAC-PM/WP/14/2023; March 2023 Authors: Sanjeev Sanyal, Aakanksha Arora, and Srishti Chauhan; henceforth, referred to as WP-1.
    2. Assessing the National Surveys for its Representativeness:   An Analysis of the Data Quality of the National Sample Survey (NSS)- July 2023 Authors: Shamika Ravi, Mudit Kapoor, and S V Subramanian; henceforth, referred to as WP-2.

    Section I:  Criticisms of methodologies adopted for computation of different indicators in WP-1

    This working paper deals with accuracy and soundness of “three widely used data-driven development indicators” which according to the authors “seem to stagnate or even deteriorate despite a rise in per capita income in India” (Executive summary pp3). These three indicators are” Childhood Stunting”, “Female Labour Force Participation Rate (FLFPR)”, and “Life Expectancy at Birth”.

    Indicator No-1 Stunting

    Two important measures of deleterious effect of childhood malnutrition are  wasting and stunting. Wasting is defined as low weight-for-height. Stunting is defined as low height-for-age. The prevalence of stunting amongst Indian children in the age group of 0-5 years has been declining over the years. The authors have quoted the following trend- 35.5% in 2019-21(rural 37.3%; urban 30.1%)- down from 38.4% In 2015-16 and 48% in 2005-06. The source of this data is National Family Health Survey (NFHS). The estimates of stunting have been worked out using the WHO’s global standard for this measure. The authors have questioned the methodology adopted by WHO in deriving this standard and, furthermore, its applicability in the Indian context.

    Issues raised:

    1. The authors of WP-1 are of the view that given India’s economic growth, it is highly unlikely that “over one-third of Indian children suffer from Stunting”.
    2. Physical status like height of people vary significantly between and within any geographical region. It is well known that even “the best-fed Indian child” is shorter than the average children from the developed countries and so they are not comparable. Any international standard, therefore, is most likely to overestimate prevalence of stunting amongst Indian children below 5 year of age.
    3. Like the children, Indian mothers are “substantially shorter than their counterpart” from the developed countries. The authors have approvingly quoted a paper by Kalsson et al. (2021) in which, after factoring in maternal height, “India’s stunting prevalence was revised drastically downwards from 38% to 25% using Demographic and Health Survey data 2019-21.” (all quotes above are from WP-1)

    The above issues cannot be critically analysed unless we have a clear understanding of what is being measured and how. According to WHO, children are stunted if their length/height is below (-)2 standard deviation (SD) from the WHO Child Growth Standards median for the same age and sex. It should be noted that the standard is for “Growth” and not actual height of children of a particular reference population. It then follows that the criticism at point no 2 above is invalid because it is referring to actual heights of children of different reference population. In fact, the objective of the study that WHO conducted between 1997 and 2003 for arriving at the required standard is clear from the title of the study- Multicentre Growth Reference Study (MGRS) (de Onis et al 2004).

    The critics may still argue that the choice of the 6 countries, namely, Brazil, Ghana, India, Norway, Oman, and the United States, for this study was arbitrary and cannot be considered a representative sample of the world at large. According to Mercedes de Onis, Study Coordinator for MGRS, “sampling frame was selected on the basis of scientific and health advocacy considerations” (De Onis 2006 ). To validate this sampling strategy and to evaluate the appropriateness of pooling data for the purpose of constructing a single international growth standard, an ANOVA analysis of MGRS data was carried out to measure the contribution to total volatility in linear growth as between sites and within sites. The result of this analysis clearly points out that between-sites volatility contributes only 3% to the total volatility of growth measure.  The study concludes – “these analyses document the strong similarity in linear growth from birth to 5 y in major ethnic groups living under relatively affluent conditions. They also support the inclusion of all six MGRS sites for the purpose of constructing a single international standard.”

    As regards choice of sites within a selected country, independent study of the chosen sites was carried out to “determine whether affluent population” in the chosen site had a growth performance like that in developed countries” (Bhandari Nita et al 2002, Bhandari Nita et al 2004). This study’s concluding observation is given in its title itself which reads as “Growth performance of affluent Indian children is similar to that in developed countries”.

    The last point of criticism refers to the impact of maternal height on a child’s height and thereby on the required measure of prevalence of stunting among their children.  Omar Karlsson in his 2021 paper has proposed a new measure called “maternal height-standardized prevalence of stunting (SPS)” as another measure of stunting and compared it with the standard measure of stunting used in WHO studies. Karlsson has defined SPS as follows:

    Where cps is the crude prevalence of stunting at each centimeter (cm) of maternal height in the sample and pd is the probability density for each cm of maternal height for the reference population. The result obtained by Karlsson et al. is critically dependent on their use of a specific probability density which is the distribution of mother’s height (measured in centimeter) .  The following pedagogical example proves the point

    Let us assume of incidence of stunting is more among mothers of lesser heights and the number of children bore by such mothers are more than the rest. It would be then a simple arithmetic calculation to demonstrate, that factoring of maternal height in the way Karlsson et al have done, would lead to significant decline in the computed prevalence of stunting.

    We assume two distinct heights of mothers.  The next assumption is that mothers of lesser heights, if such factoring of maternal height is recursively followed back, would be poor, bearing more child with stunted growth. Let the size of reference population of mother be one thousand with 30% of them being poor and having lesser height. This group of poor mothers have 600 children with age less than 5 and while the rich mothers have only 300 such children. I am assuming 80% of these poor children are suffering from stunting while 20% of the rich children are found to be stunted. Weighted average of prevalence of stunting will depend on which weights are used as can be seen below.

    Weighted Average % of stunted children using share of each children type as weight = 60%

    Weighted average % of stunted children using share of mother type as weight = 38%”

    It is quite clear from the above example that reported decline of prevalence of stunting by factoring in maternal height is a false narrative. The concept of maternal-height adjusted prevalence of stunting, as described by Addo et al. (2013), indeed aims to capture intergenerational influences on growth and the extent to which children can attain their genetic height potential, especially in low- and middle-income countries. Statistically, the true measure of effect of mother’s height on a child’s growth would be an estimate of conditional probability of child being stunted given a height of mother.

    Finally, there is no creditable evidence that by constructing country specific synthetic growth curve would lead to drastic change in measured prevalence of stunting. For a large country like India, with multiple ethnic groups with different mean height and weight of children at various age group, construction of a single standard for optimal growth of children will face the same issues that are now being raised against WHO standard. Some details about efforts that are underway to build country specific growth curve called Synthetic Growth Curve are provided in the end note[i].

    In conclusion, a comparative position of India among some selected countries in terms of this indicator on stunting, per the WHO standard, should be of greater importance to policy makers.

    see the footnote below

    [i] A new “synthetic growth curve” has been proposed and applied to a small group of countries like Indonesia, Malwai and a few more. There is a confusion about the use of CDC growth curve in USA as against WHO growth curve. But CDC has clarified the purpose of introducing CDC growth reference curve is not to create a new standard (see Grummer-Strawn et al 2010).

     The idea of synthetic growth curve was proposed by Michael Hermanussen and others in a 2015 paper (Hermanussen Michael et al 2015). In the Indian context few researchers have sought to estimate prevalence of stunting using India specific reference growth curve using this methodology. All of them used methodology proposed by Hermanussen and his collaborators(see Khadilkar et al 2019 , Khadilkar 2025, and Mehta et al 2022 ). A perusal of growth curve construction methodology shows that the synthetic references were not for “optimal” growth but for average growth of selected age-groups.

    End of footnote

    Table 1.1: Trend in prevalence of stunting among children under age 5 for selected countries.

          Prevalence of stunting, height for age (% of children under 5)
    Country Starting yearData for starting year End YearData for end YearNumber of years between two data pointsAverage Rate of  Yearly Reduction
    Bangladesh200051.120192819-3.2%
    Brazil1996132007711-5.6%
    China200017.820174.817-7.7%
    India199954.2201734.718-2.5%
    Indonesia200042.4201830.818-1.8%
    Malaysia199920.7201921.8200.3%
    Pakistan200141.4201837.617-0.6%
    South Africa199930.1201721.418-1.9%
    Sri Lanka200018.3201617.316-0.4%
    Vietnam200043.2202019.620-4.0%
    Note to the table 1.1: Start year is the nearest year from 2000 for which data is available. End year is that year for which data is available and is closest to 2022. Average yearly reduction is computed by log of ratio of start data to end data divided by total number of elapsed years between these two years.
     Source: https://ourworldindata.org/grapher/share-of-children-younger-than-5-who-suffer-from-stunting

    Indicator No 2. Female Labour Force Participation Rate (FLFPR)

    The second section of WP1 is titled – “Flaws in Female Labour Force Participation Estimation: ILO’s Unworkable Maths”.  Research on this subject on low FLFPR of Indian female in rural areas has been underway since India’s independence. In the World Population Conference held at Belgrade in 1965, J.N.Sinha of India, suggested a U-type movement in FLFPR as per capita income increases “The rate’s decline with an increase in income up to $500, but begin to rise with further gains in income.  ..  Within the rural areas, the rates vary inversely with agricultural prosperity and the proportion of non-agricultural work force. Labour force participation of women also declines with literacy, but female education above the matriculation level favours higher rates of employment (Sinha 1965, see also Reddy D. Narasimha 1979).”

    Bhalla and Kaur (2011) made one interesting extension to the computation of labour force by adding students in the age group 15-59 to the labour force. They called it adjusted labour force and demonstrated the varying trends of both ‘adjusted’ and ‘unadjusted’ FLFPR in rural and urban areas. (Table 2.1)

    Table 2.1 Percentage of female population in Labour Force and adjusted labour Force

    AreaYear->   19831993/94 1999/002004/052007/08
    RuralUnadjusted FLFPR 45.1 53.145.244.737.6
    Adjusted FLFPR 46.855.949.550.444.4
    UrbanUnadjusted FLFPR 2323  22.524.319.7
    Adjusted FLFPR 30.533.333.435.532.3
    Source: page number 7 of Bhalla and Kaur(2011)
    Steven Kapsos and his two fellow ILO economists expanded the definitional boundary of labour force used by NSSO by including those “people who were engaged in domestic economically gainful activities such as collection of firewood, poultry etc.” (Kapsos 2014). Based on this expanded size of labour force, they computed a new LFPR, termed as ‘Augmented LFPR’. The table 2.2 gives their computation of ‘Augmented LFPR’ for both sexes and regions.

    Table 2.2: Labour force participation rates (%), UPSS and augmented definition, by sex and area

    UPSS DefinitionAugmented definition
    Area1994200020052010201219942000200520102012
    Rural-Female4945.449.437.835.880.87776.370.166.8
    Urban-Female23.820.824.419.420.545.238.539.135.932.1
    All areas-Female42.738.942.732.631.271.866.866.460.356.4
    Rural Male87.685.385.982.581.387.985.386.282.881.6
    Urban Male80.178.779.276.276.480.278.779.376.376.4
    All-areas Male85.683.48480.679.885.983.484.380.980
    UPSS: usual principal and subsidiary status; Age group is 15+.

     

    The expanded definition of labour force did not result in any substantial difference in the trend observed in the NSSO defined rural female labour force participation rate. This result of augmentation is not unexpected given the substantial share of persons engaged in domestic activities in rural India- see Table 2.3 below.

    Table 2.3: Shares of the working-age population engaged in domestic duties.            

    GenderRegion19942000200520102012
    FemaleRural42.443.939.849.449.9
    FemaleUrban60.761.959.362.161.1
    MaleRural0.40.40.40.50.5
    MaleUrban0.50.40.40.60.3

    The ILO economists identified 4 key factors for changes in female participation rates- “increased attendance in education”, “increased household consumption levels”, changes in measurement of economic activities”, and “changes in employment opportunities” and carried out a statistical exercise to estimate the relative contribution of these factors towards the decline of FLFPR in rural areas. Their conclusion at the end is worth reproducing.

    “The econometric results indicate that religion and social perceptions of women, women’s level of education, household size and income, and the presence of young children in the household all influence the likelihood of India’s women to participate in the labour market. We find that structural characteristics in the labour market have played a more important role than changes in the underlying characteristics of the female working-age population in influencing participation rates.” (Kapsos 2014, page 31, emphasis added).

    Sonal Das & others carried out a regression analysis to identify determinants of female labor force participation in India in both urban and rural areas (Das Sonali 2015). Amongst the 5 “stylized facts” noted by the authors based on 5 NSSO EUS between 1993-94 and 2011-12, two are about causal factors for the observed declining trend in rural FLFPR:

    • There is a U-shaped relationship between education and labor force participation rates of women
    • Income has a dampening effect on female labor force participation rates

    It is now beyond doubt that the issue of declining FLFPR in rural India is a known and well researched issue. Everyone who has dealt with the issue are aware of the differences in definitional boundary of “economic activity” adopted by NSSO surveys as compared to that of national income accounting.  The difference is spelt out in the 68th round of NSSO EU survey.

    Although production of any good for own consumption is considered as economic activity by UN System of National Accounts, production of only primary goods for own consumption was considered as economic activity by NSSO for the purpose of the survey. While the former considers activities like own account processing of primary products as economic activities, processing of primary products for own consumption was not considered as economic activity in the NSS surveys (NSS 68th Round Employment-Unemployment Survey (2011-12) , Para no 1.8.11.1 page no A-12)

    This deliberate exclusion of domestic work related to “processing of primary products for own consumption” from the scope of “economic activity” is justified from the perspective of estimation of real employment opportunities available to rural female, particularly those are “poor” and outside any formal educational system. By giving such females, the stamp of “worker” is a meaningless glorification towards the satisfaction of policy makers. In this connection, the following quote from Dantawala committee report of 1970, which provided the framework for NSS Employment-Unemployment survey, is still relevant for the Indian scenario of female participation in labour force:

    On the basis of our present knowledge, it is difficult to predict with any accuracy either direction of the change in the labour participation rate or the kind of transformation that may take place in the pattern of labour supply. All one can say is that it would be a serious mistake to proceed on the assumption that the entire labour force is of a homogenous character and make estimates of increases in the labour force simply on the basis of population projections and aggregate sex-age specific participation in data relating to the past. (Planning Commission (1970) page 20, para 3.30)

    It is nobody’s case that there is no shortcoming of the methodology and implementation practice of NSSO surveys. Issues that are being raised by statistical community itself about these shortcomings are significant and substantial. Since NSSO surveys deal with the subjects – poverty, unemployment, malnutrition etc.- that are considered subjects of utmost important by policy makers, it is natural that such shortcomings will be looked with a glass politically tinted, and one need not be squeamish about that. Angus Deaton and Valerie Kozel, while discussing the utility of NSSO survey data for estimation of poverty in India provided the most balanced view about these surveys.

    “Inevitably, mistakes will be made, surveys will be compromised by internal or external factors, so that poverty assessments will have to be made using imperfectly comparable surveys. The Indian experience illustrates the possibility of repair to enhance the credibility of estimates. But that experience also made it clear that repairs, however creative, are a poor substitute for the collection of clean, credible, and comprehensive data. What are convincing assumptions to one can be absurd to another, and people’s political positions seem to play a role in the assumptions that they are prepared to make. Nevertheless, the Indian debate has shown that discussion and advance is possible, even among those with very different preconceptions, and that the balance of opinion can be changed by well-reasoned and transparent argument (Deaton & Kozel 2004 page 43)”

    Keeping the issue of definition of “work/employment” aside, the authors’ computation makes one point crystal clear- rising female full-time participation in education would be one possible factor for declining FLFPR in rural areas.

    Indicator 3: Decline in Life Expectancy at Birth: An Untenable Narrative

    The Human Development Index (HDI) has been introduced by UN to provide an aggregate measure of human development of its member countries. HDI is computed as a geometric mean of 3 constituent indices reflecting 3 dimensions of human development, namely, a long and healthy life, access to knowledge and a decent standard of living. The HDI is the geometric mean of normalized indices for each of these three dimensions.  The proxy for “a long and healthy life” is the indicator “Life Expectancy at Birth” which accounts for 35% of the overall HDI (UNDP 2021/22)

    Life Expectancy at any given age (age is 0 for a newborn) is the residual years that a person is expected to live. The HDI uses, “Life Expectancy at Birth”. There are two variants of this measure- cohort life expectancy and period life expectancy.  

    For the first variety, one identifies a group of people born in a particular year and track their deaths over time. It is then simple to work out the average number of years lived by that group. Since it is not possible to estimate the cohort life expectancy in this way unless all members of the cohort have died, a statistical estimate is worked out by combining the past mortality rates of the cohort at a given point of time and a projected mortality rate for the future.   

    For the “period life expectancy” measure, one first estimates mortality rate of the current period (year) for a particular group of people defined by any attribute (say age group by sex) and then assumes that rate to remain constant over time. Under this assumption, it would be possible to work out average life expectancy for that group for that specific year. Since mortality rate of a given population changes over time, this measure of Life Expectancy would also change. More importantly, one needs to estimate mortality rates based on the deaths that have occurred in a particular year and so it would change if there were a spike in death rates, say due to a pandemic, in a particular year. It appears that authors of the working paper of EAC-PM have overlooked this issue. As soon as death rates fall in subsequent years, we will again see changes in opposite direction for that year. The published figures for 2023 by WHO confirm this.

    Table 3. 1 Year wise Life Expectancy for India

    YearBoth sexesFemaleMale
    201066.968.665.3
    201167.469.165.8
    201267.969.666.3
    201368.570.167
    201469.170.667.7
    201569.671.168.3
    201670.171.568.8
    201770.571.969.2
    201870.772.169.4
    201970.972.469.5
    202070.171.868.6
    202167.268.965.8
    202267.769.466.3
    20237273.670.5

    Once we understand that the estimated decline in Life Expectancy at Birth for India does not suffer from any conceptual flaws per se, we need to examine the computational aspect of mortality rates for different segments of the Indian population that WHO have used for the pandemic years. This issue has attracted sharp criticism from the authors of WP-1 because of its impact on estimation of life expectancy. We must emphasise here that WHO has not undertaken any new adjustment to the officially reported All Cause Mortality (ACM) for Indian population. What has been revised is the officially declared mortality due to COVID-19. Estimation of Life Expectancy during the pandemic years critically depends on a correct estimate of death due to  COVID-19 for the simple reason that such deaths were much higher in the adult age-groups. Any spike in the mortality of higher age groups would then bring down the life expectancy at birth. Bhattacharya et al has pointed out how social stigma associated with segregation of COVID-19 patients led to a situation of “creating a fear among the public and is acting as a deterrent to the effective management of the disease, particularly in the urban setup (Bhattacharya Prama et al 2020).” So, there is a distinct possibility of under reporting of COVID related deaths.

    Since COVID-19 is a new phenomenon, death due to COVID-19 would only add to number of deaths that would have happened in the absence of COVID-19. WHO has defined this excess death as “the mortality above what would be expected based on the non-crisis mortality rate in the population of interest”.

    WHO has identified two countries (India and Indonesia) for which ACM data is available only at sub-regional level and not at national level. For these two countries, WHO constructed “a multinomial model, based on the assumption that the fractions of deaths in sub-regions remain approximately constant over time.” (Knutson Victoria et al 2023). Excess death estimated by WHO’s method has been compared with the results of few other methods and differences are within reasonable bounds.

    Table 3.2 Estimates of Excess Death due to COVID-19 by different approaches

    ApproachEstimate (10^6)95% Confidence intervalPeriod
    Naïve5.04(4.48,5.59)Jan20-Dec21
    WHO4.74(3.31,6.48)Jan20-Dec21
    The Economist4.86(1.70, 8.47)Jan20-Dec21
    IHME4.07(3.71, 4.36)Jan20-Dec21
    Naïve4.29(4.00,4.59)June 20-June21
    WHO4.33(2.85,6.13)June 20-June21
    Jha et. al.3.23(3.06, 3.39)June 20-June21
    Naïve3.96(3.62, 4.29)April20-June21
    WHO3.99(2.40, 5.95)April20-June21
    Anand et.al. 2021 Method 13.4 April20-June21
    Anand et.al. 2021 Method 24 April20-June21
    Anand et.al. 2021 Method 34.9 April20-June21

    Note:     The estimate by Jha et al. (2022) is for excess COVID-19 deaths. The naive estimates are based on the ACM estimates  where Yt,1 is the observed ACM from the available states, and s the estimated fraction of deaths available in month t. The estimate by Jha et al.  is based on a nationally representative telephone survey, a government survey that covers 0.14 million adults and the Government of India’s data from facility-based deaths and CRS deaths in 10 states. Anand et al. (2021) use three methods: Indian States’ CRS (method 1), international age-specific infection fatality rates applied to Indian demography (method 2) and seroprevalence and a household survey (method 3) (quoted from Knutson Victoria 2023 ).

    A few researchers have dealt with this issue of estimating death rates directly attributable to COVID-19 using micro level sample data. I would like to refer two studies for their innovative use of covid related death rates of well demarcated community of people to work out a national level estimate of death due to COVID-19. The first study was by Christophe Z. Guilmoto (2022) and the second one is by Acosta et. al. (2022) . Both the studies estimated COVID-19 related deaths at a much higher rate as compared to Indian official estimates. Some of the details of these two studies are given in end note2[i] . Although many journalistic observations exist about actual death count due to COVI-19 in India, it would be difficult to arrive at a robust and reliable estimate of the actual number. ( see articles in New York Times, BBC). Many assumptions are made by WHO and other researchers to arrive at an estimate of COVID-19 death in India, but these are bonafide assumptions and not to be colored with any ulterior motives.

    To sum up, there is neither unquestionable evidence to accept the India’s Life Expectancy as estimated by WHO for the two pandemic years nor are there enough supporting materials to unequivocally accept the official ones. The fact that Life Expectancy estimation for India by WHO has resumed the pre-COVID trend after the two pandemic years of 2021 and 2022 should assure Indian policy makers about the unbiased approach of WHO. The suggestion by the authors of Reversing the Gaze, “that the Registrar General of India should timely publish life expectancy estimates every year” is highly recommended but not for the alleged unfounded observation that “one-sided adjustments and circular references are routinely done to India-related data by international agencies.”

    see the footnote below

    [i] Guilmoto identified four different samples of Indian populations, having complete data on their demographic structure and death occurrence. Selection of the four samples were largely subjective and guided by the availability of full data and meeting three criteria for sample selection. These were – reliability of death estimation, regional   representativeness, and demographic characteristics. The author’s estimate for correction factor to be applied to official estimate for the country was 7 and 8.6 for two samples.

    Acosta et. al. (2022) have estimated excess mortality in the State of Gujarat, India, during the COVID-19 pandemic (March 2020-April 2021) by using data from civil death registers from a convenience sample of 90 municipalities across the state of Gujarat. This study found that “the largest increases in mortality occurred in the second wave of the pandemic, across all municipalities”. It was further found that “over 80% of the municipalities experienced increases in mortality over 100% in all demographic groups except the two youngest age cohorts. Lastly, in over 50% of the municipalities, males and the 40 to 65 years groups experienced an increase of over 500%.

    end of footnote

    Section 2: Analysis of Methodological criticisms of WP-2

    This paper’s stated objective is to provide a “quantitative analysis of the data quality of the National Sample Survey (NSS) in terms of three estimates, (i) the proportion of the rural population, (ii) the proportion of the Scheduled Caste (SC) population, and (iii) the proportion of the working-age (age between 15 and 59 years) population”.

    Although the above noted estimates are published in the reports of these surveys, these are neither the focus of any NSS survey nor are these used in any survey report from this perspective.  On the contrary, population census data is used for allocation of total sample to various stages of selection of survey households at village and urban block level[1]. To be consistent with survey design, it is necessary to estimate population from the selected households and work out various ratios like labour force participation rate, distribution of persons of each sector of each State/UT over 12 classes of MPCE (monthly per capita consumption expenditure).

    Notwithstanding the above, drawing direct comparison between the estimated rural and urban population data obtained from this survey and the corresponding figures from the 2011 census data requires careful consideration due to several factors. Firstly, the census figures were collected as on a particular day- 31st March 2011, whereas this survey was conducted in 4 sub-round of 3 months each, spanning July 2011 to June 2012. The figures from the survey are not expected to match with that of census data. Secondly, there are homeless people, convicted prisoners in jail, floating populations who are not covered by NSS surveys. Such people would be more likely to be in urban areas and this would negatively impact share of urban population. Nevertheless, there is no straight forward answer to the impact that these exclusions would have on the target measures like rate of unemployment or incidence of poverty at sub-national levels.

    Keeping the above caveat in perspective, the following paragraphs explain that the application of the new concept of Total Sample Error (TSE) is a completely misguided use of Meng’s formulation that have been specially developed for examining  sampling issues related to Big data and/or large non probabilistically obtained administrative data.

    Professor Meng has clarified that his paper is focussed on population inferences from Big Data (Meng 2018). Dr. Ravi and her colleagues have used the formula given in the Meng’s paper to compute sampling bias- the difference between the sample average of a variable of interest and its population average . This has been worked out, in Meng’s paper, as a product of three terms: (1) a data quality measure, (2) a data quantity measure, and (3) a problem difficulty measure. In case of NSS survey, the data quality is to be ascertained at the final stage sampling process- from a village or an urban block because final estimates are built up from that level. There is no evidence so far that NSS surveys suffer from a data quality issue because respondents are non-cooperative or deliberately providing wrong numbers. Furthermore, implementation of interpenetrating subsample and use of two sets of investigators (central sampling and state sampling) would clearly bring out any abnormal variation in estimation of target variables. In fact, Dr. Meng agrees that “under a genuine probabilistic sampling, the chance that a particular value of G (i.e., study variable, my addition) is recorded/reported or not should not depend on the value itself. Consequently, ρR,G should be zero on average.” So would be the sampling bias.

    In another paper on this subject, the same inference has been drawn for a probabilistic sample survey, – “random sampling or any sample design that provides constant inclusions probabilities, the term for data quality ER(𝜌RX) is 0 and thus the bias is also 0 (Biemer and Amaya 2018)

    Unfortunately, the authors of WP-2 have mechanically used Dr. Meng’s approach developed for Big data/Administrative data to assess quality of NSS survey data, without critically reviewing the NSS sampling design. The way to have an independent assessment of any sampling estimate was nicely brought out with an example by none other than founding fathers of NSS – Prof P. C. Mahalonobis and D. B. Lahiri. In their paper (1961) “Analysis of Errors in Censuses and Surveys with Special Reference to Experience in India” they compared the “results obtained on the basis of complete enumeration and sample survey against a third, but extremely reliable, figure. This could be carried out for two consecutive years – 1944-45 and 1945-46- for Jute Crop of Bengal” ( emphasis added and see end note 3 for details[i]).

    Nevertheless, the author’s use of Meng’s approach to measuring of Total Sampling Error has inadvertently brought into focus the need for using “found data” in conjunction with the usual probabilistically sampled data for estimation of many social and economic indicators of a country as large as India. Administrative data are a type of found data which gets routinely generated as a part of the official activities like getting a subsidy, registering of birth and death, registering of vehicles, getting unemployment benefit, income tax data etc.   Many official statistical authorities in advanced countries are already exploring big data or found data integration with survey data to achieve better quality of statistical estimates of important economic and social indicators. For example, Statistics New Zealand has established an Integrated Data Infrastructure (IDI) , sourcing data from government agencies, Stats NZ surveys, and non-government organisations (NGOs). Statistics Netherlands has established a Center for Big Data Statistics in 2016 “with the primary purpose of leveraging new and existing (big) data sources and techniques in order to arrive at better information about social themes such as the labour market, mobility, health, the energy transition and smart farming.” (Unique collaboration for big data research (cbs.nl) ). Statistics Canada uses administrative data to complement or to replace survey data.

    Finally, it would be amiss if Dr. Ravi’s stringent criticism of NSS sampling design in her article “The sample is wrong” published in the Indian Express 7th July edition. Three areas of her concerns are: availability of data, transparent and robust statistical analysis, and data quality.

    The first area of concern- more frequent and timely availability of survey data to policy makers for evaluating any policy intervention at national level- is indeed a crucial one, and it requires a coordinated effort between statistical authorities and government decision-makers to address it effectively. As a member of the EAC-PM the author can play a significant role in advocating for the importance of timely and frequent survey data.

    As pointed out in the discussion on WP-2 above, the data quality measure used therein is meant for non-probabilistic data or for an integrated dataset of administrative data and a probabilistic sampling data. Thus, the author has not given any statistically valid argument to declare that “Sample is wrong”.

    Dr. Ravi’s criticism about the quality of Indian Official Statistics, however methodologically flawed it might be, needs deep appreciation because it has highlighted the absence of any innovation in statistical methodology used in production of the critical social and economic indicators. in the last two decades. Thus, to paraphrase Agatha Christie, the Indian Statistical Mirror as shown to us by the official statistical system might not have “crack’d from side to side”, but it is gradually becoming covered with dust of obsolescence.

    [1] NSSO follows a stratified multi-stage design for its surveys. The first stage units (FSU) are the 2001 census villages (Panchayat wards in case of Kerala) in the rural sector and Urban Frame Survey (UFS) blocks in the urban sector. For the rural sector, the list of 2001 census villages constitutes the sampling frame. For the urban sector, the list of UFS blocks (2007-12) is considered as the sampling frame

    [i] Jute being a cash crop of international importance, accurate export trade figures were available after a gap of 15 months of harvest. So, there was a need for a quick estimation of production. The official approach to meet this demand was to carry out a plot-to-plot enumeration. Indian Statistical Institute undertook two sample surveys- one for estimation of acreages and subsequently another for post-harvest yields. Surveys were conducted using two interpenetrating sub-samples (IPNS) which were covered by different parties of investigators. These two independently computed values were compared with the trade figures. The result was follows.

    ” in both the years the official forecasts based on complete count were both very much out whereas the sample survey estimates were quite close to the trade figures. The two sub-sample (IPNS) estimates in both years agreed with the trade estimates within roughly 3 per cent while the estimates based on the so-called complete count differed from the trade figures by 27.2 per cent in 1944-45 and 16.6 per cent in 1945-46” (Mahalanobis  and Lahiri (1961) page 328-329.

    Quoted References:Acosta Rolando J , Biraj Patnaik ,and others, All-cause excess mortality in the State of Gujarat, India, during the COVID-19 pandemic (March 2020-April 2021) medRxiv preprint January 19, 2022

    https://doi.org/10.1101/2021.08.22.21262432

    Bhandari N, Bahl R, Taneja S, de Onis M, Bhan MK. Growth performance of affluent Indian children is similar to that in developed countries. Bull WHO 2002;80: 189–95

    Bhandari Nita, Sunita Taneja,and others, Implementation of the WHO Multicentre Growth Reference Study in India Food and Nutrition Bulletin, vol. 25, no. 1 (supplement 1) 2004, The United Nations University

    Bhattacharya Prama, Debanjan Banerjee, TS Sathyanarayana Rao, The “Untold” Side of COVID-19: Social Stigma and Its Consequences in India  , Indian Journal of Psychological Medicine | Volume 42 | Issue 4 | July 2020

    Biemer Paul P. and Ashley Amaya, Total Error Frameworks for Found Data in Big Data Meets Survey Science: A Collection of Innovative Methods, First Edition. Edited by Craig A. Hill and Others, 2021

    Childs, Jennifer. H., Fobia, Aleia C., King, R. and Morales, G. (2019). Trust and Credibility in the U.S. Federal Statistical System. Survey Methods: Insights from the Field. Survey Insights: Methods from the Field. Retrieved from https://surveyinsights.org/?p=10663

    Coase Ronald (1994) (1994) , “How should economists choose?”, page 27 in the book  “Essays in economics and economists” University of Chicago Press

    Das Sonali, Sonali Jain-Chandra, Kalpana Kochhar, and Naresh Kumar , Women Workers in India: Why So Few Among So Many? IMF Working Paper, March 2015

    Deaton Angus and Valerie Kozel, Data and dogma: the great Indian poverty debate,  World Bank, September 2004

    Debroy Bibek, The Janus of India’s official statistics, The New Indian Express, June 17, 2023

    de Onis Mercedes, Cutberto Garza, Cesar G. Victora, Adelheid W. Onyango, Edward A. Frongillo, and Jose Martines, The WHO Multicentre Growth Reference Study: Planning, study design, and methodology   Food and Nutrition Bulletin, vol. 25, no. 1 (supplement 1) 2004, The United Nations University

    de Onis Mercedes, Adelheid Onyango, and Others, Worldwide implementation of the WHO Child Growth Standards, Public Health Nutrition: 15(9), 1603–1610 April 2012

    De Onis Mercedes  as Coordinator of (WHO Multicentre Growth Reference Study Group) , Assessment of differences in linear growth among populations in the WHO Multicentre Growth Reference Study, Acta Pædiatrica, Suppl 450, page 56-65, 2006.

    Gundimeda Haripriya , Sanjeev Sanyal & others , 2007 Estimating the value of educational capital formation in India, ,TERI Press New Delhi 2006

    Hermanussen, M., Stec, K., Aßmann, C., Meigen, C., & Van Buuren, S. (2015). Synthetic growth reference charts. American Journal of Human Biology, 28(1), 98–111

    Guilmoto CZ (2022) An alternative estimation of the death toll of the Covid-19 pandemic in India. PLoS ONE 17(2): e0263187.

    Kapsos Steven, Evangelia Bourmpoula, and Andrea Silberman   Why is female labour force participation declining so sharply in India : ILO Research Paper No. 10 July 2014 ,International Labour Office

    Khadilkar V, Khadilkar AV, Kajale N. Indian growth references from 0‑18‑year‑old children and adolescents ‑ A comparison of two methods. Indian J Endocr Metab 2019;23:635-44

    Khadilkar V, Ekbote V, Gondhalekar K, Khadilkar A. Comparison of nutritional status of under-five indian children (NFHS 4 Data) using WHO 2006 charts and 2019 Indian synthetic charts. Indian J Endocr Metab 2021;25:136-41

    Knutson Victoria, Serge Aleshin- Guedel, and others, Estimating global and Country-Specific Excess Mortality during COVID-19 Pandemic, Ann. Appl. Stat. 17(2): 1353-1374, June 2023

    Laurence M. Grummer-Strawn, Chris Reinold and Nancy F. Krebs Use of World Health Organization and CDC Growth Charts for Children Aged 0-59 Months in the United States Author(s): Source: Morbidity and Mortality Weekly Report: Recommendations and Reports, Vol. 59, No. RR -9 (September 10, 2010), pp. 1-14

    Mahalanobis P. C. and D. B. Lahiri, Analysis of errors in censuses and surveys with special reference to experience in India, Sankhyā: The Indian Journal of Statistics, Series A (1961-2002), Vol. 23, No. 4 (Nov. 1961), pp. 325-358

    Mehta S, Oza C, Karguppikar M, Khadilkar V, Khadilkar A. Field testing of synthetic growth charts in 1–60‑month‑old Indian children. Indian J Endocr Metab 2022;26:180-5

    Meng  Xiao-Li , statistical paradises and paradoxes in Big Data (i): Law of Large Populations, Big Data Paradox, and the 2016 US Presidential Election, The Annals of Applied Statistics 2018, Vol. 12, No. 2, 685–726

    NSS Report No. 554 (68/10/1): Employment and Unemployment Situation in India, 2011-12 Para 2.10.1 Page 12 January 2014

    NSS 68th Round Employment-Unemployment Survey (2011-12)- Instructions to Field Staff, Vol. I

    Planning Commission, Government of India (1970), Report of the Committee of Experts on Unemployment Estimates. ( also known as Dantwala committee report)

    Ravi Shamika, Rahul Ahluwalia,and Sofi Bergkvist,   Health and Morbidity in India (2004-2014), Brookings India Research Paper, New Delhi  2016

    Reddy D. Narasimha , Female Work Participation in India: Facts, Problems, and Policies, Indian Journal of Industrial Relations, Vol. 15, No. 2 (Oct., 1979), pp. 197-212

    Sinha J. N., Dynamics of female participation in economic activity in a developing economy in Proceedings of the World Population. Conference, 1965 Volume IV: UN New York 1967

    UNDP, Technical Notes: Human Development Report 2021/22

    hdr.undp.org/system/files/documents/technical-notes-calculating-human-development-indices.pdf

    United Nations, Resolution adopted by the Economic and Social Council on 24 July 2013 E/RES/2013/21

    World Health Organization (2023) Methods for estimating the excess mortality associated with the COVID-19 pandemic World Health Organisation

    Other References

    Bardhan Pranab, The State of Indian Economic Statistics: Data Quantity and Quality Issues, University of California at Berkeley

    Bhattacharya Pramit, India’s Statistical System: Past, Present, Future , Carnegie Endowment for International Peace, 2023

    Biemer Paul P., Edith de Leeuw and Others (Editors), Total Survey Error in Practice  2017 John Wiley & Sons

    de Onis Mercedes and Francesco Branca, Childhood stunting: a global perspective, Maternal & Child Nutrition (2016), 12 (Suppl. 1), pp. 12–2

    Instructions to Field Staff, Vol. I: NSS 68th Round Employment-Unemployment Survey; July 2011 – June 2012

    Mehrotra, S., & Parida, J. K. Why is the Labour Force Participation of Women Declining in India? World Development 2017   http://dx.doi.org/10.1016/j.worlddev.2017.05.003

    Xiao-Li Meng & Xianchao Xie (2014) I Got More Data, My Model is More Refined, but My Estimator is Getting Worse! Am I Just Dumb? Econometric Reviews, 33:1-4, 218-250,

    Neff, Daniel; Sen, Kunal; Kling, Veronika (2012) : The Puzzling Decline in Rural Women’s Labor Force Participation in India: A Re-examination, GIGA Working Papers, No. 196

    Prabhat Jha, Yashwant Deshmukh, and others,  COVID mortality in India: National survey data and health facility deaths Science, 375 (6581)

    Rao Talluri, Official Statistics in India: The Past and the Present, Journal of Official Statistics 26(2):215-231 , June 2010

    World Health Organization and the United Nations Children’s Fund (UNICEF), Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old, 2019

  • Viksit Bharat@2047- Utopia or Doable?

    In 1963, at the “March of Washington”, Martin Luther King gave his famous “ I Have a Dream” speech in which he recalled the promise that architects of US constitution had made- “that all men—yes, black men as well as white men—would be guaranteed the unalienable rights of life, liberty and the pursuit of happiness”.    The architects of Indian constitution had also promised to all Indian citizens that the republic would secure to all its citizens – Justice (social, economic and political), Liberty, Equality, and Fraternity. After 75 years of independence, it is observed that instead of making an unbiased assessment of the progress made in fulfilling these commitments. the governing elite of the country is focussed on spinning of a new dream- that is of Viksit Bharat@2047. It is being claimed that India has stepped into a period of golden age, called Amritkal, or the “ Era of elixir”. By 2047, it is predicted that India will become a “Developed “or Viksit economy. According to the Hindu mythology, Amrit, the elixir of immortality, was churned out from the depth of ocean of milk by the combined effort of Devas (Gods) and Asuras (demons). In today’s India, where one’s adherence to traditional values and social norms define his or her position within the extant political spectrum, assignment of the labels of Devas and Asuras to anyone would also depend on that position.  

    However, whether such predictions are based concrete foundation or are merely optimistic projections influenced by various factors including political, economic, and social agendas, is subject to debate. Confucius’ wisdom offers a cautionary reminder that success isn’t merely a result of lofty aspirations; it requires meticulous preparation and groundwork. Setting ambitious goals is undoubtedly important, but without the necessary groundwork, these aspirations may remain unfulfilled. Confucius warns against the danger of indulging in mere wishful thinking, likening it to the creation of a utopia that lacks substance.

    “Success depends upon previous preparation, and without such preparation, there is sure to be failure.” – Confucious

    The ongoing narrative surrounding India’s aim to become a “Developed” or “Viksit” economy by 2047 overlooks a critical aspect. It is not the size of an economy that is used by the UN or World Bank for classification of a country on the scale of development. The per capita Gross National Income (GNI) is often considered a more accurate indicator of development. For any nation, if development does not significantly change the quality of life of an average citizen, then the nation cannot qualify to be called a developed country.

    For the year 2022, the World Bank has defined low-income economies as those with a GNI per capita US$1,135 or less ; lower middle-income economies are those with a GNI per capita between $1,136 and $4,465; upper middle-income economies are those with a GNI per capita between $4,466 and $13,845; high-income economies are those with a GNI per capita of $13,846 or more. Having per capita GNI of USD 2390 in 2022, India still falls into the lower middle-income group. The annualized growth rates required for India to transit from a lower middle-income country to an upper middle-income country and more ambitiously to cross-over to a developed or high-income country are worked out below.

    India’s per capita GNI, as computed by the World Bank, has risen from $1590 in the year 2015 to $2390 in the year 2022; that is at the yearly compounded growth rate of 6.0 percent. If this compound growth rate continues, for the next 25 years, India’s per capita GNI should reach around $10450. The lower threshold for inclusion in the middle-income group of countries is expected to reach $6411 by the year 2047, on the assumption that the observed growth rate of the lower threshold of this income group would continue to rise in the same rate as was observed between the year 2015 and 2022. In other words, India is expected to become a middle-income country. But the upper threshold of this income group is expected to rise during the same period to $20086. In other words, India will still be far away from the membership of the high-income group of countries. To be included into the high-income group, India’s per capita GNI must grow by a compound growth rate of 8.89%. Whether achieving such a growth rate is feasible or not is anybody’s guess and outside the purview of this article.  However, it is quite possible to examine whether the current political regime can achieve such a growth rate. Towards this end the compounded annual growth of India’s per capita USD GNI during the earlier regime (2004 to 2013) is compared with that of the current regime (2015 to 2022). The respective figures are 10.7% and 5.6%. To gatecrash into the elite club of developed countries, India’s per capita GNI ( in USD) must grow annually, by an average of  9% , till 2047.  Since faith can move mountain, devotees are free to bet on the possibility of India becoming a Viksit country by 2047.

    Data:Boundaries of per capita GNI (current US $)

    Upper middle income group boundariesYear 2015Year 2022Implied Growth RateProjected to 2047
    lower bound of upper middle-income group403644661.46%6411
    Upper bound of upper middle-income group12475138451.50%20087

    India’s per capita GNI( current US $)
    India’s GNI (USD)20152022Growth rate (compounded annually; in %)
     159023906%

    Possible growth rates and per capita GNI of India in 2047

    Growth Rate assumption about GNI (USD)Projected GNI (USD) in 2047Crossed lower boundary of “upper middle-income” groupCrossed upper boundary of “upper middle-income”
    6%10258YesNo
    7%12972YesNo
    8%16368YesNo
    9%20609YesYes
    Data Sources:

    https://data.worldbank.org/indicator/NY.GNP.PCAP.CD

    https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups#:~:text=For%20the%20current%202024%20fiscal,those%20with%20a%20GNI%20per

    https://blogs.worldbank.org/en/opendata/new-country-classifications-2016

  • An Ode to Abraham’s Children

    How many children must die in Gaza
    To satiate the hunger of retribution of Abraham’s children?
    How many mothers have to search  for their children’s blasted remains
    before the thirst for blood gets quenched?
    The answer is being written on Sahara’s sand
    by two disciples of Yahweh
    Living in Washington and Jerusalem.

    How many years must a child should live

    In the world’s largest open air prison

    To become an assassin of innocents,  living on the other side?

    How many bombs must be dropped in Gaza

    To make “Hamas” kneel down before the followers of Ten Commandments?

    The answer is being written on Sahara’s sand

    by two disciples of Yahweh

    Living in Washington and Jerusalem.

    How much blood the Gazian’s must shed

    To wash away the cruelty of Hamas from the floor of Beit HaMikdash?

    The three Gods of Jerusalem, knowing the answer,  

    may shake hands with each other,

    Sipping burgundy wine of Unicorn vintage,

    Will sing together

    Every world                                                    
    Is everybody’s world (all will survive)
    Any world
    Is everybody’s world (all will survive)

    [based on the song Ordinary World by Duran Duran]

        Picture from Al jazeera

  • Israel- Why it is so important to USA?

    Let us look at some basic data about Israel:

    Land mass- 21,937 Sq km (0.67% of Indian landmass); world rank 152 lower than Taiwan

    Population- 9,043,387 (2023 est.) Population of Mumbai 17 million (2023 est.) World rank-98

    Population break up-73.5% Jewish, 21.1% Arab; By religion- Jewish 73.5%, Muslim 18.1%, Christian 1.9%

    Real GDP (purchasing power parity) 394 Billion (1921 est.) data are in 2017 dollars Rank -48 ; Real GDP Per capita(2017 US dollar) -42,100 (2012 est.

    All data are from https://www.cia.gov/the-world-factbook/countries/israel/#economy

    So , by all measures, Israel is a small country. But it is very important for a variety of reasons- mostly geo-political. However, the reason that drives every thing else is Israel’s ethnic feature. It is the only country with overwhelming Jewish majority. This is the community that leads the world finance and technology to a large extent. Let the following data speak for itself.

    US Financial Sector Top Guns- Jews

    Michael Rubens:  majority owner, co-founder and CEO of Bloomberg 

    Lloyd Craig Blankfein – senior chairman of Goldman Sachs since 2019, and chairman and CEO from 2006 until the end of 2018.

    Gary David Cohn – chief economic advisor to US presisdent-2017 to 2018;

    president and COO of Goldman Sachs. vice-chairman of IBM on January 5, 2021

    David Scott Blitzer-a senior executive at the private equity firm Blackstone 

    George Soros–  Investor – net worth of US$6.7 billion (as of October 2023)

    Jonathan Scott Lavine – co-managing partner of Bain Capital, chief investment officer of Bain Capital Credit;

    Richard Severin Fuld Jr– was the final chairman and CEO of Lehman Brothers.

    Henry R. Kravis – KKR & Co.-a leading global investment firm.

    Michael Robert Milken – indicted for securities fraud in 1989 ; net worth of US$6 billion as of 2022

    Ronald Owen Perelman – banker, and investor

    Bruce Jay Wasserstein -mergers and acquisitions specialist

    Sanford I. “Sandy” Weill -former chief executive and chairman of Citigroup.

    James Harris Simons-described as the “greatest investor on Wall Street”,

    Stephen Allen Schwarzman– founder chairman of the Blackstone Group also briefly chairman of President Donald Trump’s Strategic and Policy Forum.

    Forbes Israel (2022) presents: 267 billionaires of Jewish origin with a combined net worth of 1.7 trillion dollars. The top 20 of them are given below.

    Person– Net worth (Billion USD) –Company

    1 Larry Ellison   102.9     Oracle

    2 Larry Page       85.2        Google

    3 Sergey Brin     81.8        Google

    4 Steve Ballmer 78.9        Microsoft

    5 Michael Bloomberg    76.8 Bloomberg

    6 Michael Dell   52.0        Dell Technologies

    7 Mark Zuckerberg          42.7 Facebook

    8 Len Blavatnik 31.6        Music, Energy, Real Estate

    9 Alain Wertheimer       31.2 Chanel

    10 Gerard Wertheimer 31.2 Chanel

    11 Stephen Schwarzman 30.0     investments

    12 Jeff Yass         30.0        trading, investments

    13 Miriam Adelson  29.7 casinos 

    14 Jim Simons   28.1        hedge funds

    15 Leonid Mikhelson     24.4 gas, chemicals

    16 Leonard Lauder 20.3 Estee Lauder

    17 Carl Icahn     18.6        investments      

    18 Daniel Gilbert 18.5    Quicken Loans

    19 David Tepper 18.5     hedge funds      

    20 Steve Cohen 17.5 hedge funds

    Finally let us list the top positions in Biden administration occupied by Jewish persons.

    Ron Klain– Chief of Staff (2021-2023), replaced by Jeffrey Zients.

    Janet Yellin– Secretary of Treasury

    Alejandro Mayorkas -Secretary of Homeland Security

    Tony Blinken-Secretary of State

    Merrick Garland– Attorney General

    Jared Bernstein -Council of Economic Advisers

    Mandy Cohen-Director Centers for Disease Control and Prevention (2023)

    Wendy Sherman-Deputy Secretary of State

    Anne Neuberger-Deputy National Security Adviser for Cybersecurity

    Jeffrey Zients-COVID-19 Response Coordinator (2021-2023), Chief of Staff (2023)

    David Kessler-Co-chair of the COVID-19 Advisory Board and

    Head of Operation Warp Speed

    David Cohen-CIA Deputy Director

    Avril Haines-Director of National Intelligence

    Rachel Levine-Deputy Health Secretary

    Jennifer Klein-Co-chair Council on Gender Policy

    Jessica Rosenworcel-Chair of the Federal Communications Commission

    Stephanie Pollack -Deputy Administrator-The Federal Highway Administration

    Polly Trottenberg -Deputy Secretary of Transportation

    Mira Resnick-State Department Deputy Assistant Secretary for Regional Security

    Roberta Jacobson-National Security Council “border czar”

    Gary Gensler-Securities and Exchange Commission (SEC) Chairman*

    Genine Macks Fidler– National Council on the Humanities

    Shelley Greenspan-White House liaison to the Jewish community

    Thomas Nides-U.S. Ambassador to Israel

    Eric Garcetti-U.S. Ambassador to India

    Amy Gutmann-U.S. Ambassador to Germany

    David Cohen-U.S. Ambassador to Canada

    Mark Gitenstein-U.S. Ambassador to the European Union

    Deborah Lipstadt-Special Envoy to Monitor and Combat Anti-Semitism

    Jonathan Kaplan-U.S. Ambassador to Singapore

    Marc Stanley-U.S. Ambassador to Argentina

    Rahm Emanuel-U.S. Ambassador to Japan

    Sharon Kleinbaum-Commissioner of the US Commission on International Religious Freedom

    Dan Shapiro-Adviser on Iran (2021-2023), Senior Advisor for Regional Integration (2023)

    Alan Leventhal-U.S. Ambassador to Denmark

    Michael Adler-U.S. Ambassador to Belgium

    Michèle Taylor-U.S. Representative to the United Nations Human Rights Council

    Jonathan Kanter– Assistant Attorney General in the US Dept. of Justice Antitrust Division

    Jed Kolko -Under Secretary at the Department of Commerce

    Aaron Keyak-Deputy Envoy to Monitor and Combat Anti-Semitism

    Stuart Eizenstat-Special Adviser on Holocaust Issues

    Steven Dettelbach-Director of the Bureau of Alcohol, Tobacco, Firearms and Explosives

    Amos Hochstein-Bureau of Energy Resources Special Envoy

    Eric Lander-Science and Technology Adviser

    Ned Price-State Department Spokesperson

    Ellen Germain-U.S. Special Envoy for Holocaust Issues

    Edward Siskel– White House Counsel