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4. IGNORING INEQUALITYThe distribution or division of the desirable things in any society—such as wealth, income, good health, status, opportunity, upward mobility, and access—depends on the depth or extent of the divisions in society and its geography. Or, as I have written elsewhere, “on the joint outcome of changes to social inequality and spatial inequality.” Social inequality refers to the differences in average life conditions and opportunities that are associated with social identity. Brahman and Dalit are different not because of their biology—their genes cannot be distinguished in a lab—but because, on average, they have unequal starting points and opportunities in life. Similarly, one’s place of birth creates unequal starting points. Being born in Gurgaon district in Haryana, for instance, provides an average starting point that is far ahead of a birth in Nabrangpur district in Odisha. This gap can be thought of in terms of spatial or geographical inequality.In this chapter, I show that the most important features of material reality and inequality in India—about income, wealth, and social mobility—are effectively unknown. We have some basic information about geographical inequality, but little that is useful about income or wealth from government data. These conditions are unknown because they are not measured, or measured poorly, or disputed, or denied, or ignored. There are solid indicators from non-government sources, and they show that inequality in India is very high and increasing. This is true of all forms of inequality—between families, between social groups, and between places. The level of income inequality in India may even be the highest in the world. But the available indicators are often deliberately misread by experts and not used at all by politicians. I argue that as meticulously as the social divisions in India were established through information gathering and categorization in British and independent India, just as zealously have the most meaningful manifestations of these divisions not been counted in either British or independent India. These are the very same inequalities that have been the source of the most significant social divisions (between Forward and Backward castes and tribes, and between religions) and have given rise to the most extensive social policies (on reservations) and politics (the caste- and religion-based party formations that dominate much of India). If it were not real, this situation—in which we appear to know least about the very thing we profess to care most about—would be considered farcical.*****India’s social and geographical divisions arguably have more dimensions and are deeper than in any other country. Most countries are not divided by religion, and neither are they as divided by language; in fact, religious and/or linguistic homogeneity are often the key bases along which national identity is created. And no other country is divided by caste or anything resembling caste. At the same time, geographical differences in the quality of life in India—between states (like Bihar and Goa, for instance) or districts (between Nabrangpur and Gurgaon)—are arguably larger than in any other country. As a result, it is possible that India is the most divided or diverse country in the world, and in some ways, the most unequal. (This is not an overstatement, as I show later in this chapter.) Most citizens are aware of India’s diversity. Some feel pride in it, some are antagonistic. But most citizens are either unaware of or oblivious to the other side of the coin of diversity—that is, inequality. From the miserable backwaters of predominantly Adivasi districts in eastern and central India to the gleaming plushness of parts of Mumbai, Bangalore, and Delhi, the differences in wealth, income, consumption, health, and education are so vast as to be unmatched. They are unmatched geographically (that is, in comparison to other countries in the world) and through time (because this is without doubt the peak of inequality in Indian history; it has never been this acute, not even under foreign rule). The social divisions of India—by religion, caste, and tribe—have become institutionalized. There is a vast bureaucratic and legal apparatus at the central and state government levels that assigns social identities and fine tunes the rights available to those social identities. This apparatus is by no means a finished product because the politics of identity, especially at the state level, is oriented primarily toward negotiating both these identities and rights. This is exactly the reason for the Jat arakshan sangharsh and the Kanhaiya Kumar Dalit andolan that created the uproar in Delhi in February, 2016, where this book began. But, those struggles over rights and their fine-tuning by governments takes place with little to no knowledge of their effects. As we shall see in this chapter, we do not know what individuals or households earn in India because income has never been measured by the government. So, there are no official data on Dalit, Brahman, Muslim, or Adivasi incomes. Though there are official data on wealth, they are so inadequate as to be less than useless; they are misleading and counterproductive. What little knowledge there exists on income, wealth, and inequality is confined to tiny expert circles and, at the same time, disputed among them. As a result, there is very little official or agreed upon knowledge about the true extent of income or wealth or social inequality today. There is even less knowledge on how these inequalities have changed in recent decades while the population grew well over three-fold after independence and the per capita gross domestic product grew six-fold.This chapter is an exposition and indictment of this paradoxical condition in which the rhetoric on social inequality is far in excess of information on its manifestations. For example, so paltry is the information on inter-caste inequality—say on the difference in income or wealth between Brahmans or upper caste groups and Dalits—that the discourse on social inequality often becomes one about humiliation and dignity. A prominent recent case in point is Ramnarayan Rawat and K. Satyanarayana’s compendium Dalit Studies (and Gopal Guru’s essay in the same volume), that begins by locating both Indian nationalist and Dalit political consciousness at the same source—“the categories of humiliation and dignity.” Little is known about the extent of inter-caste inequality of income or wealth or any other measure with a more objective standard than humiliation or dignity. Even less is known about intra-caste (or within-caste) inequality. Whereas it is obvious that all Brahmans are not well-off nor are all Dalits poor, there is, as far as I can tell, almost no statistical accounting of this reality—that is, how many Brahmans are poor and how many Dalits are well-off (the so-called “creamy layer”). The same state of ignorance exists in the domain of inter- and intra-religion inequality. There is a reality of inequality in India. Just because much of it appears to be unknown does not mean it is not real. In fact, it is possible to piece together an incomplete but reasonable account that shows the extent of these inequalities. That is, it is possible to dig deep into expert domains, especially non-governmental sources, and unearth some indicative information (as I do in this chapter and Appendix 3). But, there is no agreement on this information among experts; and among non-experts this information does not seem to have any existence. This unaccounted reality, I argue, is the source of much right-wing political mobilization that includes the demand for reservations by dominant groups like Jats in Haryana and Patels in Gujarat and the systematic efforts by middle and upper caste groups to subvert Dalit politics.An explanation is needed. Why would a nation that appears to care much for social inequality—a concern that is demonstrated openly in its policies and politics—care so little to find out how much inequality there is or whether its supposedly progressive redistributive policies are working? That is, whether reservations and other social policies are doing the job they are meant to do? Whether the benefits of economic growth are reaching all social groups more or less equally? Whether the post-liberalization growth of the economy has been “inclusive?” The fact that we do not know the answers to these questions raises the larger question: Why do we not know the answers? Why remain in this state of ignorance? What purpose or whose agenda does this ignorance serve? Is there a deep conspiracy at work or is there something about the nature of information or the nature of politics that explains this curious absence of what should be vital political information? Later in this chapter I show that the nature of inequality information may have a lot to do with ignorance about it. The inequality information, as it is currently available, may be too complicated to use (which may make this chapter too complicated to read). The right kind of inequality information—that is simple, at the right scale, and usable by non-experts—is not available. But that does not let the politics of information off the hook. I argue that this state of ignorance is not accidental, neither is it the result of lack of government capacity or competence, nor because it is too difficult to obtain this information, but because it serves a political purpose. The absence of information allows every interested party to make whatever claim they wish to make. It is convenient for them to not have the facts because the absence of facts allows them to appeal to whatever constituency they wish to target. In short, purposeful ignorance on inequality in India serves the political purpose of all political actors. The same reason is behind the purposeful avoidance of collecting caste demographic data; and having collected them, for the first time in 80 years in 2011, refusing to divulge them. In this era of increased political competition, true information on the economic conditions attached to social identity is a powder keg. If it explodes it can smash indiscriminately; no one is safe because no one controls the narrative. On the other hand, the absence of information is an opportunity for all to shape whatever narrative serves their purpose. Ignorance on inequality is political bliss for all. A Primer on InequalityBefore we proceed further, and at the risk of explaining things that are known or obvious to many readers, let us begin with some basic ideas on inequality. Inequality is a multidimensional phenomenon that is also conceptualized in several distinct ways. As a result, the broad swathe of the multiple dimensions and conceptualizations of inequality forms what is very likely the biggest subject of analysis among social scientists. In order not to get bogged down in these fundamental issues, they are placed as a separate discussion in Appendix 3. Readers interested in these basics should find Appendix 3 useful. A summary of some of the key ideas below should be sufficient for us to move forward with the main arguments of this chapter.Inequality is another word for disparity or unevenness. We understand inequality by measuring outcomes on variables that matter. In other words, inequality is a multidimensional and multi-conceptual phenomenon that only becomes real when the conception is operationalized—that is, when a fuzzy idea is converted to quantifiable phenomena and the phenomena are then measured. Inequality becomes real through quantification or measurement. If a dimension cannot be quantified—such as happiness—it is not possible to analyze inequality for that dimension. Quantification produces information in the form of data. Because of this, almost all inequality research takes the form of data collection and analysis. These masses of data have to be simplified in order for all people—from the researchers themselves to other interested parties—to make sense of it. In fact, the need to reduce complexity in inequality research is no less important than in any of the other phenomena we have given attention to so far. As a result, inequality researchers from different fields have developed what may be called simple measures of inequality. We will pay attention to some of the simplest of these measures in the following pages.A word about the different conceptualizations of inequality may be useful here (more details are in Appendix 3). These different conceptualizations exist largely because of differences in the knowledge systems and methods used in the different social science disciplines. There is some overlap, of course, because the boundaries between academic disciplines are not watertight, and many methodologies are common between them. But, by and large, it is possible to associate specific academic disciplines with specific conceptualizations of inequality. To simplify, let us think of three distinct conceptualizations and their associated academic disciplines: income distribution or income inequality in economics, social inequality in sociology and anthropology, and spatial inequality in geography.Perhaps the best description of income inequality is the one provided by Jan Pen in his parade of dwarfs and a few giants. Let us say that it was possible to arrange a parade of all income earners in a society where each person’s height is proportional to her income; that is, an average income earner would be of average height, say about five and a half feet. If such a parade were to last for one hour, starting with the lowest income earner and ending with the highest, one would “see” the income distribution of a given territorial space in dramatic light. The parade would begin with individuals walking on their hands, representing negative income earners. Using 1978-79 data for the United Kingdom, Anthony Atkinson summarizes the rest of the parade:Next come old age pensioners (with) the height of the pensioners not much over a foot. After them come low paid workers, with…the rule of women first for each occupation… The slowness with which the height increases is one of the striking features of the parade… It is only when we pass the average income (with twenty-four minutes to go) that events begin to speed up, but even when we enter the last quarter hour (the top 25 percent), the height of marchers is only some 7’. But then they begin to shoot up. Police superintendents are 11’ tall. The average doctor or dentist is some 14’. Around 20’ come senior civil servants, admirals and generals. The chairman of a medium sized company may be 35’ and for larger companies his height could be 35 yards. Indeed, the highest paid directors are…over 70 yards tall. They are not, however, the last, since the final part of the parade is made up of people of whom Pen says ‘their heads disappear into the clouds and probably they themselves do not even know how tall they are’.Keeping that vivid image in mind, consider an illustration of the different conceptualizations of inequality in Figure 4.1 which combines the “Pen’s parade” insight with different ways of organizing information about a society that is divided into two groups. Let us call the groups “grey” and “black.” One can imagine these two groups in any way one likes—Forward and Backward caste, Hindu and Muslim, vegetarian and non-vegetarian, etc.. Let us also assume, like Pen and Atkinson, that the height of each individual is proportional to his or her income. Figure 4.1a shows a random arrangement of 50 individuals—25 each from the groups grey and black. Because they are randomly arranged, it is not possible to say much about the overall distribution other than what is obvious: that both the grey and black groups have some tall (or high income) individuals, some short (low income) individuals, and some individuals of medium height (middle income).053594000When we sort these individuals by height and arrange them by rank (in Figure 4.1b), we are able to see Pen’s Parade. This curve represents inequality in this full population. The properties of this curve—such as, how much it sags away from the diagonal—can be estimated (using methods that range from simple to complicated) and summary calculations of inequality derived from it. This curve is analogous to income inequality in the full population of grey and black individuals in this hypothetical distribution. Economists are primarily interested in this distribution.Now, the same exercise can be done with the grey and black populations separately. We can sort and rank the black group (Figure 4.1c) and grey group (Figure 4.1d) separately and estimate the inequality within these groups by analyzing their separate curves of inequality. These can be thought of as “within-group” inequalities (analogous to inequality within Forward castes and within Backward castes separately). Now, each group (grey and black) has an average height (or income). In this illustration the grey average is higher than the black average. The difference between these averages is analogous to “between-group” inequality; that is, the inequality between Forward and Backward castes (or, as I show below, between Forward and Backward states or districts).So, the distribution of income can be studied using an abstract method in which everyone in India—from the most destitute to Mukesh Ambani—is ranked without reference to anyone’s social identity (this is the common method used by economists). Or, it can be done by grouping society by social identity and looking at the differences within and, in particular, between groups. This “within” and “between” distinction is important. We know that an average (or mean) is merely one representation of a group. This is illustrated by the “Bill Gates walks into a bar” story: before he enters the bar, the average wealth of its occupants may be USD 100,000; after he enters it could be a billion dollars or more (depending on how many people are in the bar). All groups have internal differences—highs and lows within the groups that are not captured by an average. So, it goes without saying, that all Brahmans do not have a higher income or a better starting point than all Dalits; conversely, all Dalits do not have a lower income or inferior starting point than all Brahmans. This complication is captured by the idea that group inequality can be conceptualized along two dimensions—between-group inequality and within-group inequality. The former—between-group inequality—is what is typically what we mean by social inequality: these are the differences in averages between pairs like Hindu vs. Muslim, or Forward vs. Backward caste. But the average tells us nothing about the “poor Brahman” or “rich Dalit” situation. There are ways to calculate this. Economists have developed a number of “decomposable” measures of inequality (such as the Theil Index and the decomposable Gini) which calculate the contribution of between-group and within-group inequality to total inequality. As a general rule, within-groups inequalities contribute more to total inequality than between-group inequalities. But, as I show later in this chapter, there is little useful information on within-group inequality: that is, inequality between Dalits or between Muslims, etc.. So, important as it is, we are unable to investigate this in any detail.Geographical Inequality Let us begin our exploration of inequality in the domain in which we have more information. Geographical (or spatial) inequality is a distinct form of group inequality. Here, the groups are not organized by social identity but by location. In some ways, this is the most obvious form of inequality and its most obvious manifestation is when the location (or scale) is the nation. The one unquestionable fact of international development is that there is a steep hierarchy of national incomes: the averages range from below USD 500 per year in some landlocked countries of central Africa to USD 60K in the U.S. to USD 100K in Luxembourg. This difference in average incomes may be the driving force of politics and economics in the world. Location matters. The social identity of a person at birth frequently combines with the location of that birth to have extraordinary influence on how the rest of that person’s life will go. To give an international example:A child born in a village far from Zambia’s capital, Lusaka, will live less than half as long as a child born in New York City—and during that short life, will earn just $0.01 for every $2 the New Yorker earns. The New Yorker will enjoy a lifetime income of about $4.5 million, the rural Zambian less than $10,000.The range in India is not quite as large as that (after all, the variance inside India cannot be larger than the variance in the world as a whole), but India has deep spatial divisions. They could be deeper than in any other country. One reason for it is India’s size—because the bigger a country, the larger the range of possibilities in it. But the variation in living standards in India go beyond what could be considered “normal” for a large country (like China or Brazil). Geographical inequality in India refers to the fact that spatial units such as states, districts, and cities have different average incomes, so their residents have different average opportunities. As with social inequality, geographical inequality too has between-group and within-group components. For example, the average resident of Goa has an income that is seven times higher than his counterpart in Bihar; but at the same time, many residents of Bihar (from the upper end of Bihar’s income distribution) have incomes higher than many residents of Goa (from the lower end of Goa’s income distribution). Despite an average seven-fold difference, all Goans are not richer than all Biharis; some Biharis are richer than some Goans.This idea that a geographical average does not capture the range of possibilities within a geographical space is especially true of large spaces, like big cities. There are great numbers of people who live far above and far below the averages of such places. The average income of a metropolis in India includes incomes of the wealthy owner of multiple flats and his maid, cook, driver, durwan, and nanny. People who can pay crores of rupees for an apartment live alongside people who defecate in the open, sometimes just outside the walls of the gated estates in which these apartments are ensconced. The latter clearly do not have the same starting point as the former. At the scale of the state there are massive and, in many cases, growing differences in average income (more accurately, the Net State Domestic Product per capita), poverty, and other measures of welfare. For example: as mentioned above, the average income difference between the highest-income and lowest-income states (Goa to Bihar) is more than seven-fold. This gap between the top and bottom has grown significantly after independence. The leading states then (West Bengal and Punjab) had incomes that were 2.5 times higher than Bihar’s; by the late 1990’s this ratio had grown to 4, and has kept increasing thereafter. Average farm size is about twenty-fold higher in Punjab than Kerala (over nine acres in the former, and barely 0.5 acres in the latter), and female literacy rates are almost twice as high in Kerala than Rajasthan or Bihar (close to 100 percent in Kerala and around 50 percent in the latter two). The poverty rate in the mid-2000’s in Odisha and Bihar was five times larger than in Punjab (around 45 percent compared to 8 percent); by the mid-2010’s, despite the fact that overall poverty had declined in the country, perhaps quite significantly, the poverty rate in states like Jharkhand and Chhattisgarh was about eight times higher than in Goa (37-40 percent compared to 5 percent). If the state-level differences are high, the district-level differences are considerably higher. For instance, in Nabrangpur district in Odisha, which the Indian Express named “District Zero” (as the least developed in the country), the poverty level in the mid-2000’s was over 80 percent. There are very significant differences at the scale of districts for poverty and other indicators of welfare (such as infant mortality, longevity, maternal mortality). In fact, just as the language of being Backward is deeply embedded in the discussions of caste and social inequality, the same language is part and parcel of the language of district-level development. The Planning Commission created lists of Backward districts on an irregular basis: in 2002 there was a list of 100 and in 2005 a list of 177 such districts. Individual states have their own lists of Backward districts and create incentives, quite unsuccessfully, to attract private investment into them. Bibek Debroy and Laveesh Bhandari created a list of 69 lagging districts using their own metrics, and Jyostna Jalan and Martin Ravallion have written extensively about “spatial poverty traps” in Indian districts. My own work on industrialization has identified clusters of districts that receive little or no industrial investment. There is no doubt that variance in development indicators (on income or poverty or any of the other variables mentioned above) is considerably higher at the district level than at the state level. This is to be expected, but the scale of difference is remarkable. For instance, in 2010-11, the per capita income of the richest district in Haryana (Gurgaon at Rs. 4.5 lakh) was ten times higher than that of the poorest district in the state (Mewat at Rs. 46,000). A ten-fold difference existed within the same small state. Across states, Gurgaon’s average income was 30 times higher than in District Zero, Nabrangpur in Odisha (Rs. 15,000). It is worth noting that in the 2011 census, of Nabrangpur’s 1.22 million residents, 56 percent were categorized as scheduled tribe and 15 percent as scheduled caste; that is, over 70 percent of the population belonged in the category of marginalized (or Backward) minorities. In Gurgaon, on the other hand, only 13 percent of the 1.5 million residents were categorized as scheduled caste and there was not a single person classified as scheduled tribe (because there is no official recognition or schedule of tribes in Haryana). This is in line with the conclusion of Sonalde Desai and her associates that “a poor, illiterate Dalit labourer in Cochi or Chennai is likely to be healthier, and certainly has better access to medical care than a college graduate, forward caste, large landowner in rural Uttar Pradesh.” The simple data shown here starkly illustrate how inequality in India is manifested by the intersection of location and social identity. When both are classified as “backward,” as in Nabrangpur, the combination yields the most abject living conditions in the country. The question arises: why use the label Backward—which is suggestive of a condition that is ancient and unchangeable—instead of a term like “lagging”—which suggests a condition that is temporary and changeable. To the best of my knowledge, the term Backward is not used in any other country to identify either its regions or social groups that are measurably behind the leading regions or groups. The term “backward region” is simply not used anywhere other than India. Large countries like Brazil and China have large regional differences, but they do not use the word Backward to describe their low income regions. In other countries that are divided by social identity (like South Africa, Brazil, and the U.S.), the condition of being low on the development or income scale is associated with skin pigmentation, hence the language of inequality tends to be racialized—leading to the use of census categories like branco (white), pardo (brown), preto (black), and amarelo (yellow) in Brazil, or black, colored, white, and Indian in South Africa. It is impossible to imagine that any of these groups or American blacks could be officially classified as “backward.” The demand for a status or label that gives a group preferential access to government patronage is not limited to India, of course. But it is only in India that lagging social groups and regions are called “backward.” The use of this language may signal a deeply paternalistic and patronizing attitude among the elite—the government leaders who create categories and labels—but it does not appear to bother the groups who demand to be categorized as Backward. It is possible that the word has lost its original bite through overuse and normalization. That is, in India, backward no longer means what it does in the rest of the English-speaking world: which is retarded, stupid, ignorant. Like “passed out” or “good name” or “history-sheeter,” backward in India may have created its own meaning, which is probably something like “deprived” (more so than “depressed” which was the label used in the early twentieth century by the British Indian government). Hence, the purpose of reservations for “backward classes” or special policies for “backward districts” is to mitigate deprivations. The question is: have these policies worked? The answer, which I outlined in Chapter 1 and explain now, is that we do not know for sure (because we do not know what would have happened in the absence of these policies), but the likely answer is negative.Economic and Social InequalitiesIn this section I discuss the reality of inequality in India using the best available information and data. First I consider economic inequality and the three different ways it is conceptualized: by expenditure (what people spend), by income (what people earn), and by wealth (what people own). Following that, I consider the available information on social inequality; that is, inequality between social groups. The sources of the analyses include official data (produced by the government) and unofficial data (produced by non-government institutions). The data presentation itself is in Appendix 3. Some of the material is technical (though I have attempted to simplify it as much as I can) and may not be of interest to all readers. The discussion in the following pages incorporates some of that data presentation, primarily by summarizing the key findings. To keep the data discussion simple, the only measure of inequality used is the Gini Index. It is not a perfect measure, but there is no perfect measure of inequality (there is a brief explanation for it in Appendix 3). It is nonetheless the most widely used measure of inequality, most likely because it is intuitively easy to understand. It is a number between 0 and 100 (or 0.0 and 1.0 for purists) in which higher numbers indicate higher inequality. 0 means that everyone has an equal amount (of income, wealth, land, or whatever distribution is of interest), 100 means that one person (or unit) has all of it (income or wealth or land, etc.). Therefore, a Gini Index of 40 indicates higher inequality than a Gini index of 30. The number 40 also means that 40 percent of the resource being studied (income or wealth or land etc.) has to be redistributed to make the Gini Index 0, that is, equal.To put the magnitude of Gini income inequality in perspective: the lowest Gini indexes for income in the world are in the mid to high 20’s. These low inequalities can be found in countries reputed for their high tax and high redistribution regimes (as in Scandinavian countries like Iceland, Finland, Sweden, and Norway) or in post-Soviet societies in central Europe (like Ukraine, Slovenia, Slovakia, the Czech Republic, and Belarus) that have retained some or much of the egalitarian ideology and apparatus of the Soviet years. The highest Gini indexes in the world are in the lows 60’s. The most egregiously high levels are in southern Africa (specifically South Africa, Namibia, and Botswana), in regimes that are deeply divided, especially by racial groups or extractive classes where the key is control of gems and precious minerals. Broadly, the story of inequality in India that emerges from the available resources and studies is one of high and growing economic inequality, a story that is at odds with the official narrative on inequality in India—that it is low and unchanging. The argument I make is not an isolated one. It is one that is supported by all serious scholars of inequality in India. Why then is there such a fundamental difference between the official and scholarly conclusions? The simple answer is that the official position in India is based on information on expenditure, whereas the rest of the world studies income (and, increasingly, wealth). There are other, deeper explanations, but we can discuss those only after we have gone over the basics. Branco Milanovic, one of the leading scholars of inequality in the world, writes: “How unequal is India? The question is simple, the answer is not.” That is largely because, in India, we can say nothing about income inequality from official data because income has never been officially measured. This seems like an outrageous statement, but it is true. This is not because the Indian government does not measure social conditions. Quite the contrary. The Indian system for gathering social statistics—led by the National Sample Survey Organization (NSSO)—is considered among the most sophisticated and professional in the developing world. But the NSSO does not estimate income in any of its national surveys. It estimates consumption or expenditure. That is, it estimates what households spend rather than what they earn. As a result, the estimates of inequality in India are for expenditure rather than income.Expenditure inequality is, however, not considered an adequate measure of inequality of condition. Households at lower income levels tend to spend all they earn; in fact, they often have to borrow to meet unexpected expenditures (like illness), or sell assets (like land and gold, if they have any), or rely on remittances (money sent by close relatives working somewhere else). Higher income households, on the other hand, are able to save; that is, they do not spend all they earn, and instead put the additional money into assets like stocks, gold, and property. Their unspent income is converted into wealth. As a result, expenditures do not capture the true range of quality of life conditions, and expenditure inequality does not provide a good sense of the true inequality of quality of life (or opportunity or access to value-producing resources). Expenditure, by definition, is narrower in range than income, and, by definition, expenditure inequality is lower than income inequality. Some analysts have estimated the gap between income and expenditure inequality for the Gini Coefficient/Index to be around 5-6 points. As we shall see, the gap in India is considerably larger. It is so large that the measurement of expenditure inequality may be meaningless in India.The origins of this choice (to measure expenditure rather than income) goes back to the early post-independence years when basic decisions were being taken on a number of issues (including this one). The focus then was more on poverty than inequality. In fact, inequality did not become a serious issue to study or fight until after the mid-1970’s, after some development economists began to discover that economic growth did not automatically mitigate poverty or improve the lives of populations at the bottom of the income distribution. At very low levels of income (as India had in the post-independence years), expenditure (rather than income) was rightly considered to be the superior measure of poverty. As a result, from its very first surveys in 1951, the NSS (as it was named then) was geared to measuring how much people spend (to understand, among other things, how many calories they intake), in order to understand the depth and breadth of poverty in the country. The expectation was that policies to mitigate poverty would be based on these data. That method (of measuring expenditure rather than income) continues to be used to the present day. *****As detailed in Appendix 3, the magnitude of expenditure inequality in rural India is in the high 20’s (using the Gini Index) and appears to be more or less unchanged in four decades. The magnitude of expenditure inequality for urban and all-India is roughly 35-36 (using the Gini Index); this is possibly a little higher now than it was in the early-2000’s (when the Gini was in the low 30’s). If these figures were true, that is, if they represented the reality of distribution, then inequality in India would be among the lowest in the developing world and among the most stable and unchanging. In international comparisons of inequality, the low official Gini Indexes of the NSSO are usually taken at face value. In the absence of official data on income in India, there is a widespread conflation between income and expenditure inequality. They are assumed to be the same—which leads to the misleading conclusion that India is a low inequality country with a stable Gini hovering in the low to mid-thirties for decade after decade. The confusion is evident in many international documents: for example, in the World Development Report of the World Bank which mentions that “India had fairly low income inequality,” in the United Nations Development Program which reports that the “income gini coefficient” in India is 33.9, and in policy papers by the International Monetary Fund which use the same figures. Today, in early 2018, the websites of the World Bank and IMF that list inequality for all countries show India’s income Gini Index to be 35.1, which we know is India’s expenditure (not income) inequality level.This problem that official surveys in India do not report income have been tackled in two different ways that have led to different income inequality estimates, both of which are significantly higher than the official expenditure inequality estimates. First, income data have been collected and analyzed by the India Human Development Survey (IHDS, details in Appendix 3) for 2004-5 and 2011-2; the income Gini Index for both years is around 54. Second, S. Chandrasekhar and K. Naraparaju and I have studied two surveys of the NSSO in which income data were collected, but for the agricultural sector alone (but not the urban sector, nor all-India), and calculated the Gini Index to be around 60 between 2003 and 2013. Other analysts have gone further based on the justifiable argument that household surveys almost always fail to capture the very top end of the income distribution. Hence, inequality calculations based on household surveys always underestimate inequality. This happens because survey personnel are often denied access to upper income households. This problem is quite acute in India. For example, in the IHDS 2004-5 survey, the individual with the highest income out of 41,000 families earned less than Rs. 22 lakh per year (about USD 48,000 at the exchange rate at that time). It seems obvious that the IHDS survey missed the top one percent of earners. Even more troubling are the NSSO expenditure surveys. For the 2011-2 round, their highest spending group, the top five percent of urban India, averaged expenditures of Rs. 123,000 per year (less than USD 2,300). This is roughly what government college professors earn per month. It is clear again that the NSSO also missed more than the top one percent (perhaps the top 3-5 percent) of consumers.This means that the NSSO surveys severely underestimate expenditure inequality to begin with; had the NSSO tried to measure income, it would have also failed to get information on the highest income households. The main reason is that survey data are useless to investigate the upper tail of income or wealth. Surveyors are never able to enter the houses and gated apartments in which the Upper and Proto Upper Class live and ask them about their income or wealth. Even if by some miracle some survey did manage to do so, there is no reason to expect that they will be told the truth. How to get income information on the high income household without having access to them? One attempt has been made by Luke Chancel and Thomas Piketty. They supplement household survey data (from the NSSO and IHDS) with tax data to conclude that the top one percent of income earners captured 22 percent of the national income in 2012, the highest share since income taxes have been collected in India. Laurence Chandy and Brina Seidel use a different approach (that utilizes the gap between survey data and national accounts statistics) to calculate India’s income Gini Index in 2012 to be 56 (rather than 36, as calculated from NSSO’s expenditure surveys).0-52260500Wealth inequality is expected to be higher than income and expenditure inequality everywhere and the best available evidence shows that to be true in India too. Ishan Anand and Anjana Thampi estimate the Gini Index of assets and net worth to be 74 and 75 respectively in 2012, having risen from 65 and 66 in 1991 (and about the same levels in 2002). These estimates are based on the NSSO’s All India Debt and Investment Survey (AIDIS) which suffers from serious problems that significantly underestimate wealth inequality. First, the NSSO is unable to get asset information on the richest households (just as it is unable to get expenditure information from them). Second, the NSSO uses an inadequate method of estimating the value of land and buildings (which make up 85 percent of total assets according to their own calculations). The problems are discussed in detail in Appendix 3. Some corrections to these problems have been made in reports from Credit Suisse which show the Gini Index of wealth inequality in India to be 83 in 2016, among their list of the most unequal in the world. The condition of inequality (as calculated from available data) in India is summarized in Figure 4.2. NSSO survey based Expenditure inequality, which is often cited as a “true” measure of inequality in India, is low by global standards. Income inequality—following the IHDS data (in which income is measured, unlike the NSSO data) and corrections to it using national accounts—is considerably higher. If correct, this would place India’s income inequality in a cluster of high-inequality countries (many in Latin America), but not the very highest in the world. Wealth inequality is even higher than income inequality (as is to be expected) and increasing. If correct, this would place India among countries with the most unequal wealth distribution (a little less unequal than countries like the U.S. and Switzerland on the one hand and Gabon and Central African Republic on the other). However, it is quite likely that because of inadequacies of household survey methods—including limited access to high income households, erroneous assumptions about stocks and land, and a general opacity about the identity, income, and wealth of the top one percent—all of these calculations of expenditure, income, and wealth underestimate the true condition of inequality in India.*****The condition of social inequality (that is, the gaps between the averages of the Forward and Backward groups) is not systematically studied in India (more on which soon), but it is possible to collate a range of diverse works and sources on the subject. The conclusion are stark. By all measures—expenditure, income, and wealth—the gaps between Forward and Backward groups is very large. Moreover, the gaps have been growing in recent decades for all the variables for which comparable temporal data are available. Consider the evidence (the details are in Appendix 3). The average urban individual who was neither Dalit nor Adivasi spent almost twice as much as the average rural Dalit or Adivasi in 1983. A quarter century later the former (the urban non-Dalit, non-Adivasi person) spent about 2.3 times as much as the latter (the rural Dalit or Adivasi). All the other gaps on expenditure widened during the same period: between the rural marginalized and the rural majority, and between the urban marginalized and the urban majority. There is unambiguous evidence of a large and growing gap in expenditure between the socially marginalized and the rest of the population. In income data from the agriculture sector (from the NSSO) we see large gaps between the non-marginalized and Backward groups, and a growing gap in income between Dalits and the non-marginalized. The income data from IHDS show large gaps between Forward and Backward group averages. Brahman average incomes were twice as large as average Dalit and Adivasi incomes. The average incomes of OBC and Muslim families were about 20 to 30 percent higher than Dalit and Adivasi incomes. Other studies show that the Forward castes progress up the income ladder most rapidly. There is income growth among Dalit and Adivasi households too; but Dalits had the least upward mobility (experienced by 30 percent of Dalit families) and the largest downward mobility (experienced by 41 percent of Dalit families). In short, there is “higher occupational mobility among forward castes than among SCs and STs…[and] a much higher prevalence of sharp descents among SC and ST sons.” The wealth scenario is even more stark and deteriorated sharply in 2002-2012. The Dalit and Adivasi share of national wealth had each been roughly half their population share till the early 2000’s but dropped to 40 percent in 2012. The per capita wealth of the general population (non-Dalit and non-Adivasi) in 2012 was almost five-fold higher than that of the Backward population. The wealth gap between the Backward and non-marginalized populations had roughly doubled in two decades. These numbers are quite remarkable. If we look at other important issues—such as education, poverty, and health—there are vast and often growing gaps between the Forward and Backward groups. For example, Brahmans, the most educated group, have twice as many years of education, are four-fold as likely to matriculate from school, and seven-fold more likely to hold a college degree than the least educated group (Adivasis). Adivasis are half as likely to be in college as non-marginalized groups, and Muslims are even further behind, only one-fourth as likely to be in college as the non-marginalized Hindu groups. Rural poverty was three- and two-times higher in the Adivasi and Dalit populations compared to non-marginalized groups. Urban poverty was about three-times higher for both. Malnutrition was almost twice as high for Adivasis compared to “upper” castes, and in the 1990’s, had declined more slowly; that is, the gap was growing larger. *****Location is an explanation for many of these gaps. For example, Backward groups are more likely to live in Backward regions. These are typically rural settings where low incomes are common (because agriculture does not pay; it is the lowest value-added activity in India and the world) and land is valued less (because “backward” region land is in least demand). Therefore location alone would lower the income and wealth of the Backward groups even if they had as much land (Adivasis have more land per head, but of poor quality; Dalits have the least land of all social groups). Location, just by itself, would therefore also increase poverty. These same “backward” rural places also have inferior education and health infrastructure. That would lead to inferior outcomes on education and health. We can speculate on the effects of location, but, absent analyses that begins from a clear understanding of inequality, we cannot do much more than guess at this point.Finally, it is necessary to give some attention to the subject of within-group inequality. The evidence is clear that there are significant between-group differences when we compare the averages of marginalized or Backward groups with dominant or Forward groups. But what of the distributions inside these Backward and Forward groups? Recall that this question is at the heart of the political agitations by leading caste groups like Jats in Haryana and Patels in Gujarat; a version of “poor Brahman” problem—the argument being that all Brahmans are not well-to-do and therefore deserve special opportunities. The data we have access to seems to show no pattern in these internal distributions within groups like Brahmans, Forward caste, Backward caste, etc. Within-group inequality levels for all social groups tend to correspond to the money variable being studied—they are lowest for expenditure, high for income, and highest for wealth. This is true for both within-backward and within-forward groups. One would expect that inequalities within Forward groups would be higher than within Backward groups, and it is quite possible that if good data were available on the top of the distribution (which is undoubtedly occupied by Forward groups) there would exist undeniable evidence on higher inequalities within Forward groups. But with the information and analyses available now it is not possible to make a strong claim on this issue. The bottom line is: there are significant levels of inequality within Forward and Backward groups with little discernible difference between them in the available data. Ignorance is BlissThese are the facts of inequality in India as best as they can be identified from the existing data and studies: Very little is known “officially” because the official statistics estimate either expenditure (a variable that is quite inadequate to study inequality) or wealth (a variable that is appropriate for studying inequality but is poorly surveyed and calculated). Government (and non-government) surveys have generally been unable to capture the top end of India’s income and wealth distribution. To remedy these inadequacies, several attempts have been made to piece together official and “unofficial” data—a patchwork quilt of sorts—to generate more accurate or representative profiles of inequality in India. These patched together data suggest that income inequality in India is very high and growing rapidly. It is certainly among the highest in the world, and, if realistic data from the top one percent were incorporated, may even be the very highest. Wealth inequality is significantly underestimated because of inadequacies in surveying and calculating. Despite these flaws, India’s wealth inequality estimates are among the highest in the world and growing rapidly.India’s social inequalities—the gaps between the marginalized and non-marginalized groups—are also very large, and to the extent they can be measured over time, appear to be growing. The expenditure gap and wealth gap between the Forward and Backward groups have grown in recent decades: this is demonstrably true of Dalits and Adivasis, but not so for OBC’s. The income gaps are also very large and growing. And there are massive gaps in educational attainment, poverty, and health indicators (like malnutrition). However, there are significant inequalities within every group, Forward or Backward, and all groups include families that are far above and far below the group averages.These findings—of high and rising income and wealth inequalities—summarize the strongest work done by scholars who study inequality in India. But, among the thought-leaders of the Indian state, there is either little acknowledgment or outright denial of both realities—that the inequalities that matter (of income and wealth) are both very high and increasing. The position on social inequality is more complicated, and I will deal with that separately, a few pages later.The denial of the reality of inequality of income and wealth is not limited to any one ideology or political party. Experts identified as left-of-center are as likely to deny it as those identified to be right-of-center. Consider the words of Montek Singh Ahluwalia, who worked at the World Bank and International Monetary Fund and was Deputy Chairman of the Planning Commission of India under the Congress-led UPA regime. It is not far-fetched to suggest that Mr. Ahluwalia was one of the principal architects of Congress economic policy for a decade, if not longer. As Deputy Chairman of the Planning Commission he wrote:The perception of concentration of wealth and widening disparities is sharpened by the tendency of the media, including especially the electronic media which now has very wide reach, to publicise success at the top end, including the conspicuous consumption with which it is often associated, while simultaneously focusing attention on the depth of poverty at the other end. Both extremes are understandably viewed as newsworthy, but in focusing disproportionately on them, the steady improvement in living standards of the very substantial population in the middle, and the associated rise of a growing middle class receives much less attention than it should. Dr. Surjit Bhalla, a highly accomplished economist and important policy figure inside the Delhi Ring Road, both when the Congress-UPA was in power and when it was not (as member of the Prime Minister’s Economic Advisory Council under the BJP-NDA), is just as dismissive about concerns about inequality. He wrote:Often in the polemical debate about poverty and policy, and the poverty of policy, the facts (unfortunately) become irrelevant…what is revealing is that to-date, there has been little variation in real inequality in India…While comparative data needs to be explored, it is likely the case that this near constancy is unusual given the “buzz” of the conventional wisdom that inequality increases with growth and/or that Indian inequality has sharply worsened.And Professor Jagdish Bhagwati, a renowned economist at Columbia University who is strongly associated with the BJP-NDA regime, wrote: The fact is that several analyses show that the enhanced growth rate has been good for reducing poverty while it has not increased inequality measured meaningfully, and that large majorities of virtually all underprivileged groups polled say that their financial situation has not worsened and significant numbers say that it has improved.To paraphrase these experts: inequality in India is neither high nor increasing because the expenditure data say so; even if it has grown a bit recently, the people do not mind because they told us so; and all of this has been blown up by the media because they only juxtapose the extremes of conspicuous consumption and poverty. Let us say we accept that media has a propensity to focus on extremes, but to propose that the Indian media focuses “disproportionately” on inequality seems to suggest that there is another media out there that I do not have access to. There is more to say on the media in the next chapter and we will tackle the issue of what is covered by it and why at that point.But, Ahluwalia, Bhalla, and Bhagwati are bona fide experts and should know better. In fact, they do know better. Their stellar track records and demonstrated mastery of the subject of inequality prove that they know better. Actually, one does not have to be an expert economist at their level to know that expenditure inequality tells us almost nothing about inequality of economic condition. One does not have to be an expert economist at their level to know that a society in which everyone is becoming better-off may, at the same time, be turning more unequal. That is the very point of paying attention to inequality—because a more progressive distribution provides more welfare at the same level of national income or growth. That is precisely why growing inequality is a matter of serious concern in very high income societies. Getting out of absolute, caloric poverty is not the issue in those societies, justice is, and fairness. Consider that the poverty line for a household of four is about USD 25,000 per year in the U.S., which is roughly fifteen-fold India’s GDP per capita by exchange rates; which means that almost no one in the U.S. is poor by Indian standards, but almost everyone in India is poor by American standards. This does not mean that there is no discourse of inequality in the U.S. Quite the contrary. It is hard to believe that these experts do not know all this, or are deceived by what “official” expenditure statistics say, or are completely unaware of the studies of income and wealth. So the question arises: why do accomplished, eminent people make claims that they must know are incorrect?The most likely explanation, I believe, is ideology, which I have shown (in Chapter 1 and Appendix 1) to be a version of confirmation bias. Let us recall that definition here: “Confirmation Bias, also called Myside Bias (to underline its self-serving property), is the tendency to look for, interpret, favor, and remember information (‘selective recall’ or ‘confirmatory memory’) so as to confirm one’s preexisting beliefs, while being dismissive of or denying information that is contradictory or could offer different explanations and possibilities (to avoid ‘cognitive dissonance,’ which the human mind finds hard to handle).” It is doubtful that any of these experts ordinarily suffers from “cognitive dissonance.” On the other hand, it is very likely that they, like everyone else, tend to “look for, interpret, favor, and remember information” that supports what they already believe or what is convenient for them.The ideology these experts from the left and right share, their common belief (which happens to be convenient for their personal and professional ambitions) is support for economic growth. Let me be clear that this is a very common condition: the belief in or desire for economic growth is one of the most widely-shared features among politicians, experts, and laypersons the world over. In the minds of many, growth is ephemeral, even magical; it is not guaranteed nor fully understood; if by some chance or action it happens, one should ride it—like a tiger by its tail—as long as possible, without asking too many questions, without disturbing the flow of magic. Sustained growth is transformative: in one generation it can reduce absolute poverty to single digit levels in a very poor society; in two generations it can transform a low income developing nation into a developed one. Witness China.This line of thinking—that growth and egalitarianism are enemies, that redistribution is a drag on strong economic performance, that inequality is inevitable with growth—is one that has been in existence in decades. It has proven impossible to kill, despite the almost unanimous conclusion of professional economists that it is wrong. Arthur Okun argued that there is a tradeoff between equality and efficiency, and that redistribution was akin to carrying water from the rich to the poor in a “leaky bucket.” Simon Kuznets suggested that inequality increases in the early decades of development and declines later; this became the famous Kuznets inverted-U curve of development. These ideas have been empirically examined dozens of times, including by Montek Ahluwalia, and have been found so wanting that Gary Fields wanted to give them a “decent burial.” Other scholars like Alberto Alesina and Dani Rodrik have argued for the reverse causality—that inequality itself is a drag on growth. Yet, the regressive ideas persist. Surjit Bhalla’s quote above includes a statement about “the conventional wisdom that inequality increases with growth.” He knows, as does Ahluwalia, that there is no such conventional wisdom.For some, it may be difficult to admit that inequality is increasing, as if acknowledging that fact would delegitimize growth and the policies and political parties that are associated with growth. For others, it may be useful to conflate social identities and geographies: if India as a whole is growing, then one need not worry about whether Dalit and Adivasi incomes (or Bihari or Rajasthani incomes) are growing apace or catching up. “Grow first, redistribute later.” This conflation between India and all its social groups and regions may be politically necessary so that the “left behinds” and other dissidents do not begin to make electoral gains.Is it coincidental that the expert class in India is almost exclusively comprised of members from dominant social groups—“upper castes,” Brahmans, Jains, Sikhs (with perhaps some representation from selected OBC communities in recent years)—the ones that have benefitted “disproportionately” from economic growth in recent years? Is it surprising that the groups that get to “speak” and create “text” (books, papers, policies) also interpret reality in ways that benefit themselves? That they see what they wish to and ignore what is inconvenient. We have seen in Chapters 2 and 3 how India’s social structure was constructed through “text” by groups with the power to create or interpret them. I suggest that the current obsession with the growth of the Indian economy in expert circles (and the media) is a continuation of similar forces at work. The “official” data on (low and stable) expenditure inequality may simply happen to be convenient for deflecting or redirecting attention away from unpleasant and inconvenient distributional issues.*****But, that is not a sufficient explanation for why the statistical information on inequality is not visible in the political discourse in meaningful ways. After all, what Ahluwalia, Bhalla, and Bhagwati write (or I do) is only accessible by a miniscule section of Indian society. In a political sense, what they write (or I do, or almost any scholar cited in this book does) does not matter. It might as well be gibberish. This is expert discourse that has not been simplified for the masses. It has not gone through the process of what I called “second-order simplification” in Chapter 1. There I wrote that “second-order simplification, however, is rarely done by experts. Very few technical experts have the translation skill—the ‘common touch’—that is needed to simplify expert knowledge for non-expert understanding. Others do this work of translation. Politicians, journalists, public intellectuals, priests, and teachers.” Where are those politicians, journalists, public intellectuals, priests, and teachers that should be talking about the truth of inequality—if not income and wealth inequality, at least social inequality? These translators should exist. The Indian system of representative democracy has seats reserved for socially marginalized groups. Relatively new political formations like the Bahujan Samaj Party and Samajwadi Party have emerged in north India and been electorally successful for exactly that reason. In states like Maharashtra and Tamil Nadu, Dalit politics are less monolithic but have deep roots. Adivasis constitute between one-fifth and one-third of the populations of large states like Odisha, Madhya Pradesh, Jharkhand, and Chhattisgarh. One would imagine that the measured reality of social inequality would be of great interest to these groups, a mobilizing principle. One would imagine that there would be political demands for a proper accounting of income and wealth by marginalized social groups and that on finding out that they were far behind to begin with (which they knew already) and have fallen further behind (which they suspect but do not know for sure), and that Forward castes have five-fold the wealth they hold (and that too is likely to be an underestimate), there would be outrage and political consequences. A delusional rationalist could even imagine that there would also be some critical examination of the fact that there is very high inequality within the Dalit population. But there is none of this. To the best of my knowledge, the “facts” of social inequality derived from official and unofficial statistics never make it to the public speeches of Dalit or Adivasi political leaders, nor are they discussed or debated in parliament or state assemblies by their elected representatives. In fact, these figures—even the easily available (if grossly inadequate) expenditure data—are hard to find in the highest-quality academic texts written by leading Dalit scholars. As I wrote in the beginning of this chapter, for many leading Dalit scholars, the focus is squarely on “humiliation,” not statistical inequality. Other scholars have studied symbolic changes on status and social distance—such as diet, marriage ostentation, seating arrangements, etc.—to examine the question of inequality. The question that rises for us is: why do the numerical “facts” of inequality seem not to matter to the groups at the bottom of the ladder? This is serious question and I submit three possibilities as answers. The first possibility is that non-expert stakeholders are largely uninformed about the statistical facts of inequality. This could happen because the inequality information has not been sufficiently simplified for it to provide cognitive utility among the general populace. Given that I felt compelled to have a “primer on inequality” in this chapter (which means that I thought it was needed) and have had to devote many pages to lay out the evidence on inequality (underlining many gaps and caveats in the evidence), most of which I have placed in an appendix rather than the main body, it is probably not hard to conclude that the discourse on statistical inequality remains confined to the expert domain. The “second-order simplification” of this multidimensional and complex issue has not been done yet, at least with statistics. As a result, this is not yet, and perhaps never will be, the stuff of the street theater, parody, and musical comedy that one sees in Dalit political meetings in Mumbai. A second possibility is that the available inequality information is at the wrong scale (national) and that there is little or no usable inequality information at the needed or appropriate scale (local). The difficulty with making political use of inequality statistics is compounded by the fact that data are never available at the scale that most people can comprehend or that matters to them. If inequality is itself an abstract idea that people have difficulty with, scaling it up to the nation or world makes it even less substantial. It is a view from far above. It has little relationship to the ground, the few square kilometers around their living space that most people can see (in a social and political sense) and seek to understand or change. For the Dalit in Mumbai, the person attending a musical revue making fun of Brahmans all dressed up in their caste marks and superstitions, does it matter what the Forward caste average income is in Bengal or Andhra or even Nagpur? What relevance does it have to his life or political identity? This problem of scale can take on ominous dimensions when it is compounded by the reality that every group in India—Forward and Backward—includes very large numbers of poor. The relatively high average income and wealth of the Forward caste group is likely to provide little comfort to the poor from the Forward castes who may feel, or made to feel, that reservations for Backward groups discriminate against them. Consider an American parallel: in 2015, more than one-third of Black households (about 6 million in number) earned less than USD 25,000, at the same time that less than one-fifth of White households (about 16 million in number) did the same. That is, Blacks were significantly overrepresented in the low income population, but low income Whites were significantly more numerous than low income Blacks. It is precisely this reality about inequality—that low income is not perfectly matched to race or caste or religion regardless of the histories of oppression and discrimination—that enables a political backlash. Like Trumpism in the U.S., the backlash is based on identity-based mobilization of the low income among the forward groups. These political mobilizations are distinctly geographical (for example, the red state-blue state dyad in the U.S.) because the manifestation of this other dimension of inequality (the “backward among the forward”) is clearly visible at local scales. People can see or instinctively understand that there is great inequality within all groups: Forward and Backward, Upper and Lower. All Dalits (or American Blacks) are not poor, nor are all Forward caste families (or Whites in America) well-to-do. Therefore, even if they are known, the statistical facts of social inequality—that some groups have been systematically deprived and are significantly worse off—have little or no political meaning for the low income among the “upper” groups. In short, people choose the inequalities that matter to them. Inequality exists by reference, through comparison. Experts may refine these comparison mechanisms as best they can using the most sophisticated tools they possess, but people choose the comparisons that matter to their lives.This leads to the third possible explanation for why information on statistical inequality does not seem to matter. It may be because the absence of simple and agreed upon inequality information benefits all political agents and parties; because the information vacuum allows all agents and parties to make claims that are convenient for them. Consider the issue of “reservations”. The basic claim in India is that reservations provide benefits for the reserved groups. Therefore, those that have reservations should seek to keep or expand them and those that do not should seek to get them. This, in essence, is one of the core principles of Indian politics. Rarely is the question asked: How many specific individuals or families benefit from reservations, or what proportion of the reserved groups actually receives a reservation benefit? These too are statistical questions without satisfactory answers. Let us try to generate some rough estimates. In 2011, there were 17.5 million public sector jobs in India; if 20 percent were held by Dalits and Adivasis, there were 3.5 million jobs for them at the same time that there were about 305 million people classified as Dalit or Adivasi (201 million Dalit + 104 million Adivasi). If we assume that not a single Dalit or Adivasi person would have received a public sector job without reservations, we can conclude that about 1.1 percent of the Dalit and Adivasi population were direct beneficiaries of employment reservations. If each direct beneficiary was from a different family—that is, there was no “creamy layer” problem or nepotism or cronyism or corruption in getting public sector jobs—they could each have created four more indirect beneficiaries (usually family members). Using these rather generous assumptions it is possible that up to 5 percent of the Dalit and Adivasi population currently benefits from employment reservations. Is this a figure a political leader can boast about to his followers? Is a one-in-hundred chance of getting a public sector job worth setting oneself or one’s public transportation system on fire? Or do ordinary people even know what the odds are of getting a public sector job through reservation? One has to conclude that they do not. Certainly there is little incentive for the established leaders of Backward groups to acknowledge that their primary demand—for reservations—has failed to deliver on many of its promises. In general, statistics and quantitative information on reservations appear to have little relevance for the affected people and their political leaders. Facts—which are valid, reliable, and verifiable information—may have nothing to do with belief. It is possible to launch many a theoretical missile to attack this patent problem of irrationality, but not if the very foundation of rationality is shaky. And following the discussions in Chapter 1 (and Appendix 1 and 2)—Daniel Kahneman’s fast and slow thinking brain, the human tendency to cognitive ease and confirmation bias, and the principle of simple information—we should be skeptical about rationality itself. We should be most deeply skeptical about the idea that rational individuals process all information fully and objectively. This is an impossible burden because it is clear that in the real world many decisions—perhaps most political decisions—are taken without any information in the form of “facts” whatsoever. There is information, alright, but not what passes for such among experts. Information exists in the form of categories, labels, stereotypes, and stories—but not data. In fact, as I have argued above, data may be unnecessary or useless. In the absence of data it is possible to stick ever more closely to categories, labels, stereotypes, and stories—that is, what one already knows, one’s comfort zone, the lazy “system 1” part of the brain that is self-affirming and doubt-free. The more information there is, the more facts there are, the more they bombard the brain, the more comfort and ease there is in ignoring them or slotting them into predetermined categories, labels, stereotypes, and stories. We are left in a very troubling position. The best available data and analyses from independent scholars suggest that income and wealth inequality levels in India are very high and increasing. If properly measured, they are the highest in the world or close to it. Yet, in official and quasi-official expert circles there is a strong tendency to deny this reality by pointing at other things that seem relevant but actually are not—such as, the low and steady expenditure inequality, declining poverty, and, worst of all, opinion polls. The existence of comforting information, especially on expenditure inequality, provides some plausible deniability about the truth about inequality in India. That deniability is strengthened by the failure of expert discourse on inequality to produce usable information for the general population. This failure serves the purpose of all political parties, which, in theory, should represent the interests of all sections of Indian society, including its marginalized groups. But, these political agents do not have much use for inequality information either. All the while, the truth about inequality in India is disagreeable and getting worse. Ignorance about it—real or feigned—benefits everyone.This book began by identifying two key features of India’s existential debate. The first is the struggle over social identity—heterogeneity vs. homogeneity, complexity vs. simplification. The second is about material reality, which, I argue, is best understood through the concept of inequality. Whether or not Indian society is heterogeneous or homogenous is best understood not by making unverifiable claims about religion and identity but examining the evidence. Are there gaps in opportunity and achievement between India’s social groups and among the citizenry in general? How big are they? Have they been growing or closing in recent decades? The answers to these questions speak more clearly to the issue of heterogeneity vs. homogeneity than bombastic claims by politicians. We have seen the best available answers to these questions in this chapter, and they should be deeply worrisome to most people. But, what may be even more worrisome is the manner in which this vital information is received—with ignorance, obfuscation, or denial. As a result, India has entered the information age—and its politics of polarization—without much information on a fundamental feature of politics: its inequalities.INEQUALITY DATAMeasuring InequalityInequality is another word for disparity or unevenness. It is a multidimensional phenomenon. Scholars study inequality of income, wealth, education, health, access, assets, housing, and other variables. Inequality is also conceptualized in several distinct ways in the different disciplines that study the issue. In economics, the focus is often on studying distributions in whole populations; in sociology and anthropology, the focus is on studying groups and their differences; in geography, the focus is on differences between spatial units (like nations, states, cities etc.). We understand inequality by measuring outcomes on dimensions or variables that matter. Inequality measurement is a vibrant and active sub-field in economics, as is, in sociology, the measurement of sociological conceptualizations of inequality (such as segregation, isolation, etc.). There are hundreds of measures of inequality. However, only a handful of measures are used in practice; as a result, the choice is not as difficult as it could be.Among the multiple dimensions along which inequality is studied—income, wealth, assets, educational attainment, health outcomes (longevity, infant mortality, maternal mortality, etc.)—the primary focus here is on income with a secondary focus on wealth. Income has a direct relationship to welfare and opportunity and as a result it is doubtless the most commonly studied variable among inequality researchers. Wealth is also important, but is generally much more difficult to measure because the wealthy have many ways to hide and obfuscate their holdings. Some analysts—especially those associated with the Human Development approach—argue that the focus on income takes attention away from other important markers of welfare, such as education and health. I do not dispute that education and health are very important, but suggest that income is most important because it is the primary determinant of education and health outcomes and it is income inequality (along with government failures to provide adequate public goods) that leads to inequalities in education and health. I do provide some information on education later in this appendix, but as I show there, these figures probably hide as much as they reveal. In fact, the clinching argument in favor of focusing on income is that so little is known about it despite its overwhelming significance. Chapter 4 has been written precisely because so little is known about the different inequalities of income. Economists tend to analyze the world in terms of the individual (person, firm, or institution), whereas other social scientists, especially from sociology and anthropology, typically think in terms of groups. Mark Granovetter, a prominent sociologist, suggested that economics as a discipline is “undersocialized” whereas sociology is “oversocialized.” A nation, in the economic framework, is a collection of individuals, each one serving his own individual interest. Their social identities or spatial locations do not matter in this “abstract” form of inequality. But in the other social sciences, the most important unit of analysis is usually not the individual but the group or location. As a result, the social world is understood through the concepts of in-group cooperation and out-group derogation or conflict (see Appendix 2). Depending on the context, group identity and interest can either be less important or significantly more important than individual identity and interest. Consider, for example, the contrasting self- and group-interests of Wall Street bankers (“greed is good”) vs. soldiers (“band of brothers”) or Bollywood stars vs. the builders of the sets on which they frolic. Let us think of group identity in terms of social identity. It is fair to say that investigations of social identity and inequality form the core of the field of contemporary sociology. For example, if a society is composed of two groups—black and white, or Forward caste and Backward caste, or Hindu and Muslim—the only way to understand whether they differ as groups is to measure things that say something about the quality of their lives and see whether there is any difference, and, if there is, how much it is. In other words, the extent of division in any social system is understood by measuring or quantifying the extent of difference or inequality between the divisions. The difference should be over something that matters. To say that black (or Backward caste) has darker skin pigmentation than white (or Forward caste) is beside the point. The question is, whether meaningful outcomes for the group called black (or Backward caste) are measurably different from the group labeled white (or Forward caste) on scales that most reasonable people can agree on? There are intricacies of measurement and making meaning from measurements—and some of those are discussed below—but the basic point must be clear. If social divisions are real, then at some level they are measureable. They will show up as differences in things like income, wealth, assets, longevity, infant mortality, years of education, and so on. The extent of difference is social inequality.In economics, the primary area of interest is in the distribution of income and the distribution of human capital (simply: education); wealth distribution is also studied, but to a lesser extent, because it is harder to track and crack. Some of the most important contributions to our understanding of income and human capital inequality have come from notable economists like Anthony Atkinson, Gary Becker, Ronald Bénabou, Gary Fields, Branco Milanovic, Thomas Piketty, and Amartya Sen, who have discussed ways of measuring inequalities in income distributions, the ideology and ethics of different distributions and their measurements, and the meanings and consequences of such inequalities for growth and economic development.The key question economists ask is how income (or wealth or education) is distributed in a population? An useful visual analog for an income distribution was provided by Jan Pen (that is described in Chapter 4). He imagined a parade in which every individual in a society walks in order of his or her income and where their heights are proportional to their incomes. This Pen’s Parade traces a curve of income distribution, from the lowest-income microscopic people (who have to walk on their hands because they have negative incomes) to giants with their heads soaring above the clouds. There is much interest among economists in calculating the properties of the curve traced by this parade and to create summary measures—simple measures—that capture in a single number a sense of the inequality in a distribution. Often these calculations are done by grouping the population into equal sizes—for example, broken into 10 segments of 10 percent of the population each (called deciles) or five segments of 20 percent of the population each (called quintiles) ranked by income. How much of the national income does the poorest decile earn? How much does the richest quintile earn? What is the ratio of the income share of the richest (decile or quintile) to the poorest? What is the income share of the superrich—the top one percent, or the top one percent of the top one percent? There are two alternative approaches in comparing different income distributions—whether to include the complete distribution (including all income earners) or simply compare the top and the bottom of the distribution. The former approach accounts for everyone whereas the latter approach is useful for investigating changes at the extremes of a given distribution. Using the latter suggests that the investigator is interested in studying income polarization. If the full distribution is to be used, certain measurement properties are considered desirable. Discussions on these desirable properties are available at many sources, the most accessible of which is on the World Bank’s website. In general there are five key axioms or principles that inequality measures should follow: The Pigou-Dalton transfer principle, the axiom of income scale independence, the principle of population, the axiom of anonymity, and the principle of decomposability.These axioms are not, however, value-free. Consider the second axiom of income scale independence: that if every individual’s income increases by the same proportion (say everyone receives a five percent increase in income), a proper inequality measure should not change. However, since the rich will receive higher absolute income increases than the poor, this is at best a status quo condition that can also be considered regressive. If we believe the utilitarian argument that each successive marginal income increase produces less utility or welfare (since the first lakh rupee one earns is valued more highly than say the tenth lakh), then an equal proportional increase in all incomes produces less overall utility or welfare than when the same total income increase is distributed more heavily among the lower income groups. Partly in response to such normative anomalies in supposedly value-free inequality measures, a group of explicitly normative or welfarist measures have been created. Among inequality scholars, Anthony Atkinson’s measure based on explicit choices of “inequality aversion” is well known. In keeping with the spirit of this book, we will avoid these complicated measures. Instead, for a summary measure, we will use only the Gini Coefficient. It is an useful visual analog of both the Pen’s Parade and the distribution of income by groups like deciles or quintiles. There is much information on the Gini Coefficient on the net. The Wikipedia page is as useful as any. As used in this Appendix, the Gini can take a value between 0 and 100. When 0, everyone has the same income (or wealth, or whatever is being measured); when 100, one rich person has everything. Wherever possible, I will use information that is even simpler than the Gini.Expenditure, Income, and Wealth Inequality ExpenditureTable A3.1 lists the Gini Index estimates of inequality of expenditure or consumption in rural, urban, and all India from three sources (Himanshu, Subramanian and Jayaraj, and NSSO) for the last four decades. The estimates are not identical because different analysts tend to use slightly different assumptions and methods for calculating the Gini Index; but the underlying data for all three sets of estimates are the same: all were collected by the NSSO. Let us not focus on the minor differences between the different estimates (which are meaningless), nor the more important finding that expenditure inequality in urban India is consistently higher than in rural India (it is not particularly meaningful because the phenomenon of higher urban than rural inequality is seen all over the world). Let us focus instead on the magnitude of inequality and its consistency. The magnitude of Gini inequality in rural India is seen to be in the high 20’s and it appears to be more or less unchanged in four decades. The magnitude of Gini inequality is roughly 35-36 for urban and all-India, with a possible small uptick from the low 30’s after the early-2000’s. If these figures were true, that is, if they represented the reality of distribution, then inequality in India would be among the lowest in the developing world and among the most stable and unchanging. Table A3.1: Expenditure Inequality in India, 1973-74—2011-12Source: HimanshuSource: Subramanian & JayarajSource: NSSORural GiniUrban GiniAll-India GiniRural GiniUrban GiniRural GiniUrban Gini1970-7128.934.71972-7330.734.51973-7428.130.21977-7834.234.833.634.5198327.131.429.831.633.929.732.51987-8830.235.71993-9425.831.93028.634.428.234.01999-200026.334.726.034.22004-0528.136.434.730.537.626.634.82009-1028.438.135.829.939.327.637.12011-1228.737.735.928.036.7Table A3.1: Expenditure Inequality in India, 1973-74—2011-12Source: HimanshuSource: Subramanian & JayarajSource: NSSORural GiniUrban GiniAll-India GiniRural GiniUrban GiniRural GiniUrban Gini1970-7128.934.71972-7330.734.51973-7428.130.21977-7834.234.833.634.5198327.131.429.831.633.929.732.51987-8830.235.71993-9425.831.93028.634.428.234.01999-200026.334.726.034.22004-0528.136.434.730.537.626.634.82009-1028.438.135.829.939.327.637.12011-1228.737.735.928.036.7IncomeBut, as explained in Chapter 4, there are few serious analysts of inequality who would consider the NSSO expenditure data and the Gini Indexes calculated from them to represent the reality of inequality in India. Consider what we know from one of the most important alternative sources of large scale survey data—the India Human Development Survey (IHDS)—that is also the one major “unofficial” but reliable source of income inequality data in India. The IHDS is a nationally representative survey of about 41-43K households that has been carried out in two rounds so far: in 2004-5 and 2011-12. The IHDS calculations show that income inequality is considerably higher than expenditure inequality: in the range of Gini 54 in 2004-5 and 2011-2. These results bolster the innovative findings of Luke Chancel and Thomas Piketty who combine household survey data (from NSSO and IHDS), national accounts statistics, and tax data to argue that income inequality in India is very high, perhaps the highest it has ever been, primarily because the share of national income accruing to the top one percent of income earners is 22 percent of the total income, the highest level in a century, far above the 6 percent it was in the early 1980’s; a visual representation of Chancel and Piketty’s findings is shown later in this chapter in Figure A3.2.Along with two colleagues (S. Chandrasekhar and Karthikeya Naraparaju), I have studied some aspects of income distribution over the last decade in rural India. We analysed the Situation Assessment Surveys of Farmers/Agricultural Households undertaken by the NSSO in 2003 and 2013. We found that there was a very large difference between the two measurement concepts—income vs. expenditure inequality—where the Gini Indexes of per capita income and expenditure were around 60 and 30 respectively during this study period. We argue that while our findings are narrow in coverage (being limited to the agricultural sector, that covers roughly half the population) that narrowness itself leads to greater robustness. Therefore, the startling gap of 30 Gini points between expenditure and income inequality should be taken seriously. Added to the findings of IHDS and Piketty and his associates, these findings should conclusively burst the mythical balloon of low inequality in India.In fact, I argue that the true level of income inequality in India is higher than anything calculated by any analyst so far. There are several reasons for taking this position. First, most inequality calculations are unlikely to include the very top and bottom ends of the income distribution. For example, our own work on rural India misses the population that has little or no income from agricultural activities; much of this group is likely to be the landless population that may comprise more than 40 percent of rural households. The far bigger problem is that most income data derived from surveys are likely to miss or have unreliable figures on the very top end of the income distribution. The Indian upper middle class is notoriously difficult to survey. Even if a survey team can make it to their doors (which is very hard to do in the gated housing estates in which the upper middle class tends to live), it is usually refused entry. The upper class is, of course, well and truly beyond questioning by anyone. For example, in the IHDS 2004-5 survey, the individual with the highest income out of the 41,000 plus families surveyed earned less than Rs. 22 lakh per year. It seems obvious that the IHDS survey missed the top one percent of earners. Even more troubling are the NSSO expenditure surveys. For the 2011-2 round, their highest spending group, the top five percent of urban India, averaged expenditures of merely Rs. 1.2 lakh per year. This is roughly what government college professors earn per month. I have no doubt that the NSSO also missed the top one percent (perhaps the top 2-3 percent) of consumers. On top of this is the well-known tendency of the poor to over-report and the rich to under-report their incomes. These problems with survey-based inequality calculations are beginning to become widely recognized. Laurence Chandy and Brina Seidel write: “Missing top incomes in household surveys is a long established problem in both developed and developing economies…The more new information we uncover about top incomes, the less faith we have in traditional survey-based inequality measures, and the less knowledge we can claim to have about the distribution of income across an economy’s entire population.” They “use the missing income between surveys and national accounts as a proxy for missing top incomes in surveys” following a method suggested by Christoph Lakner and Branco Milanovic. The new calculations of Chandy and Seidel show large increases in Gini Indexes for several countries—the average increase is from 39 to 48. One of the largest increases is for India, where the Gini goes from 36 (calculated from official expenditure data) to 56 for the early 2010’s.That too may be an underestimate. If the Gini Index of agricultural income alone is 60 (as my work with Chandrasekhar and Naraparaju has shown), there is almost no doubt that the Gini index is significantly higher at the national scale. There are two reasons to justify this claim. First, we know that urban inequality is higher than rural inequality by 5-8 Gini points even using the flawed NSSO expenditure data. The gap between urban and rural inequality is likely to be higher with income data. Second, we know that average urban incomes are at least twice as high as average rural incomes for every size subgroup (decile or quintile) of the population. Hence, if we add the two distributions—rural and urban—and it is possible to assess the income of the top one to two percent and bottom decile of households with any reasonable accuracy, a strong argument can be made that income inequality in India is among the most extreme in the world. It would not be a surprise if the true level of income inequality in India was in the range of Gini 65, on par with or higher than the highest known level of inequality in South Africa.WealthThe recently published figures on wealth inequality in India strongly suggest that the worst-case scenarios may indeed be true. There have been a spate of such publications in recent years, spurred by the annual Global Wealth Reports produced by Credit Suisse beginning in 2010. The tone of the Credit Suisse reports is largely celebratory, but the U.K.-based NGO Oxfam produces an annual Global Inequality report (based on the same wealth data) whose tone is anything but. For example, Oxfam’s 2017 report argued that the richest eight billionaires in the world (Bill Gates, Amancio Ortega, Warren Buffett, Carlos Slim Helú, Jeff Bezos, Mark Zuckerberg, Larry Ellison, and Michael Bloomberg) had as much wealth between themselves as the poorest 50 percent of the world’s population put together, and that the richest one percent of the world had as much wealth as the remaining 99 percent. The situation was “beyond grotesque,” the Oxfam report said. For India, the Credit Suisse report stated that the richest 10 percent possessed 73 percent of the nation’s wealth, whereas Oxfam stated that 73 percent of the wealth generated in 2016-7 in India went to just the richest one percent. According to the latest available Credit Suisse report, the Gini Index of wealth inequality in India is 83, among their list of the highest in the world.It is obvious that neither Credit Suisse nor Oxfam has the resources or ability to actually study wealth in India by themselves…and they do not. The primary data source for both is the decennial All India Debt and Investment Survey (AIDIS) carried out by the NSSO (last undertaken in 2012-3). One should be hesitant to rely on distant sources like Credit Suisse and Oxfam which may be tweaking the raw data from NSSO in ways that are not visible to observers (which would seem to be the case if new findings are generated every year though no new AIDIS data are available). Their audience is global whereas we need to stay closer to the ground. Therefore, it may be better to look at the findings of scholars who have looked at the AIDIS data directly and carefully. The most recent of these is a paper by Ishan Anand and Anjana Thampi, in which the Gini Index of assets and net worth are shown to be 74 and 75 respectively in 2012, having risen from 65 and 66 in 1991 (and about the same levels in 2002).Note that the AIDIS data itself is open to serious criticism. It suffers from some of the main problems of the NSSO expenditure surveys; most notably, the difficulties with getting good data on the top of the distribution. For example, in the period that the Indian stock market boomed (the last decade), the NSSO data show that the weight of shares/stocks actually went down to 0.13 percent of total wealth in its survey sample. That is simply not credible. Given that the market capitalization of all stocks on the BSE had almost equaled the country’s gross domestic product in early 2018 (Rs. 135 trillion in stocks compared to Rs. 150 trillion in GDP), the AIDIS sample clearly has missed almost all of India’s upper middle class, and, of course, the entire upper class. left2408Figure A3.2: Change in Expenditure, Income, and Wealth Inequality over TimeSources: A. As shown in figure; B. Calculated from data in Luke Chancel and Thomas Piketty, 2017, Indian Income Inequality, 1922-2014.00Figure A3.2: Change in Expenditure, Income, and Wealth Inequality over TimeSources: A. As shown in figure; B. Calculated from data in Luke Chancel and Thomas Piketty, 2017, Indian Income Inequality, 1922-2014.In addition, the AIDIS has an unusual finding: more than 90 percent of India’s wealth is shown to be in land and buildings. About 70 percent of rural wealth and a little under half of urban wealth is shown to be in land alone. As a result, much of the calculations (of wealth and inequality) depend on how accurately land is valued. There is serious case to be made that it is generally undervalued, especially given the five-fold increase in land prices across the country in the period 2000-2013, and is evidenced by the AIDIS calculation that in urban areas the value of land is roughly the same as the value of buildings. That too is simply not credible. Depending on the city and location, the value of land in total property is much above 50 percent, and for the upper class it easily surpasses 95 percent. Therefore, it is very likely that the very high levels of wealth inequality calculated from AIDIS data are nonetheless significant underestimates because the survey was unable to capture the two main sources of wealth for the Indian upper middle and upper classes—stocks and land.That possibility is highlighted by the findings of Chancel and Piketty in the second part of Figure A3.2, that show the long-term trajectories of income earned by the top one percent and the bottom 50 percent of families. If correct, this should be a severe indictment of, if nothing else, the absence of a serious discourse on inequality by government after government (more on this in Chapter 4).Social InequalitiesAs discussed earlier in this Appendix (and Chapter 4), social inequality is conceived, in a sociological sense, as the average difference between social groups. In our case, the social groups under consideration are those that have been identified as marginalized from pre-independence India (Scheduled Castes and Scheduled Tribes, to be called Dalit and Adivasi in the remainder of this discussion), new groups that have been brought into consideration for reservation or affirmative action after the Mandal Commission recommendations (Other Backward Classes), and others (who, depending on the data available, may be called “Forward Castes” or “Brahmans” plus “Other Forward Castes”). The explicit assumption of the Indian system of reservations is that there are sizable gaps between the Backward and Forward groups, and the explicit goal of the reservations is to narrow those gaps. So the question before us is: What do we know about: (a) how far apart these groups are from each other, and (b) whether the gaps between them now are narrower or wider than before? These are questions of fact and can only be answered with data. As we have seen above, the official data-gathering system in India does not collect some critical information for anyone (that is, income) and what it does collect surely does not include households at the top, which are very likely to be dominated by the Forward groups. We do not even know how much of the “top” is missing in surveys; the top one percent almost certainly, and perhaps as much as the top 2-3 percent. As a result, we know little about their income or wealth (that is, their land and stocks). Perhaps just as crucially, we do not know their social identities either (that is, what religion or caste they belong to). So, if our goal is to measure the gap from the “low” groups to the “top” groups, it is necessary to recognize from the very outset that it cannot be done, at least not easily, and not without violating some privacy barriers (such as those that protect the identities of tax payers from public scrutiny). It is possible to tease out some indicators of what may have been happening to social inequality using the available official data from NSSO and unofficial data from IHDS. Several of the scholars who have been cited above have included sections on caste inequality in their larger studies of inequality. These studies are not uniform because they all use different formulations of social groups: in some studies, Dalits (Scheduled Castes) and Adivasis (Scheduled Tribes) are combined; some studies separate out Other Backward Classes or Forward castes or Brahmans, some do not; some allow the identification of religious identity; most do not. Therefore, these studies are not comparable. They do not have the same definitions of low, middle, and high, do not use the same data over the same time period, and, above all, are flawed for the same reasons that all these studies are flawed—they measure inequality without knowing much about the top of the distribution. It is not the researchers’ fault. They have to work with what they have, and what they have is flawed.These indicative figures on social inequality are combined in a set of graphics in Figure A3.3 that provide some information on expenditure, income, wealth, and education over some period of time. This allows us to see the extent of the gaps between Forward and Backward groups and the changes in the conditions and their trajectories over recent decades. Note that if we had access to information on the uppermost section, these gaps would likely have been larger, and, crucially, growing over time.As it is, the data show that the gaps between the averages of the Forward and Backward groups are considerable. Moreover, they have been growing over time for all the variables for which comparable temporal data are available. Consider expenditure (in Figure A3.3a-A), which we know is the primary welfare information collected by the NSSO and have seen earlier is the least meaningful marker of quality of life as far as inequality is considered. The highest-spending group is, as expected, the urban non-Dalit non-Adivasi population and the lowest-spending is the rural Dalit and Adivasi population. The ratio of their expenditures has increased from 1.9 to 2.3 from 1983 to 2010. That is, the average urban non-Dalit non-Adivasi person spent almost twice as much as the average rural Dalit or Adivasi in 1983; a quarter century later the former spent about 2.3 times as much as the latter. All the other gaps on expenditure widened during the period: between the rural Backward and the rural majority and between the urban Backward and the urban majority. There is unambiguous evidence of a large and growing gap in expenditure between the socially marginalized and the rest of the population.\s\s\sThe income data from the agriculture sector (also from NSSO surveys) are more fine-grained and show somewhat better outcomes for the marginalized. Here, it is possible to differentiate between Dalit and Adivasi incomes, and between OBC and everyone else. Again we see large gaps between the non-marginalized “Others” and the Backward groups, but the gap is smaller than for expenditure (above). We see a growing gap between Dalit income and “Others”, but the gap between “Others” income and both Adivasi income and OBC income, though large, narrowed between 2003 and 2013. The income data from IHDS shown in Figure A3.3a-C are available for a single year, and they show, again, large gaps between Forward and Backward group incomes. Brahman average incomes (identifiable only in IHDS data) are twice as large as average Dalit and Adivasi incomes. The average incomes of OBC and Muslim families is about 20 to 30 percent higher than Dalit and Adivasi incomes.Because the IHDS surveyed the same set of households at two time periods (2004-5 and 2011-2), it has become possible to analyze change—or income mobility—at the household-level (in addition to the usual population-level). The surveys cover a short period (7 years), but that was also a time of great economic change. The findings in a study by Ranganathan, Tripathi, and Pandey are generally negative. Forward castes are of course heavily represented in the top income group (two to three times more heavily than their population weight) and Backward castes least represented. The progress of Forward castes up the income ladder is also the most rapid. There is income growth among the Dalit and Adivasi households too; close to one-third experienced upward mobility. But among all social groups studied in the paper, Dalits had the least upward mobility (30 percent of families) and most downward mobility (41 percent of families). Using the same IHDS data sets, Iversen, Krishna, and Sen find that there is “higher occupational mobility among forward castes than among SCs and STs…[and] a much higher prevalence of sharp descents among SC and ST sons.” The wealth scenario (in Figure A3.3b) is even more stark and appears to have deteriorated sharply in the last decade. The Dalit and Adivasi share of national wealth had each been roughly half their population share till the early 2000’s but dropped to 40 percent in the last debt and investment survey of the NSSO. The wealth share of the OBC also dropped from 90 to 80 percent of population share in the same time. In contrast, the wealth of the general (non-marginalized) population was 20 percent above its population share in 1991 and almost 90 percent above in 2012. The general (non-Dalit non-Adivasi) population’s wealth per capita in 2012 was almost five-fold higher than that of the Backward population. The wealth gap between the Backward and non-marginalized populations was large to begin with and had roughly doubled in two decades. It is important to remember that almost much of this “wealth” is notional rather than real; it is derived from land ownership and the assumed value of land. Hence, it is possible that what these data really reveal are differences between where people live—the marginalized on marginal/remote and less valuable land, the non-marginalized on more urban and generally more valuable land. It is also possible that since the NSSO has seriously undervalued urban land and has almost no account of the stock market, the wealth gap (notional or real) between the marginalized and non-marginalized is considerably higher than five-fold. The graphic on the distribution of assets by religion shows the affluence of the small minorities (Jains, Sikhs, Christians) and the poverty of the large minority (Muslims). Not only do the small minorities have significantly greater assets than average, but their shares grew over the decade 2002-12. Jains, already the wealthiest religious group by far, saw their asset share more than double in a decade, during the same time that Muslims saw their asset share shrink measurably. Finally, we look at some information on educational attainment by social group. The reasoning is simple. Education is “the hard core of the ‘hard core’ of human capital.” It is the key to income generation, intergenerational mobility, and social status, not to mention citizenship and awareness of self and rights. If the educational gaps between social groups do not close, the material gaps between them will not either. Education and educational inequality in India are big subjects. Contributions to it range from articles in well-known journals like Nature to technical expert analyses. This minimal discussion here does no more than scratch the surface of a deep problem in which the major issues include access, quality, and cost (by social identity, location, and income class). There is general agreement that some aspects of educational inequality have improved in the preceding decades. Notably, there have been big gains in literacy and school attendance among the young (including girl children) in all segments of society, and a general surge in college attendance (which nonetheless remains biased toward Forward castes). At the same time, many analysts recognize that the education market has become increasingly segmented, which means there are significant differences in quality (all “literates” are not the same, and neither are all college degrees) and that the Backward continue to fall behind in quality (even if they are catching up in quantity, having started from a very low base).The graphics in Figure A3.3c highlight the significant differences in educational attainment by social identity among the adult population in India. There are vast differences between the most educated group (Brahmans) and the least educated (Adivasis): the former have twice as many years of education, are four-fold as likely to matriculate from school, and seven-fold more likely to hold a college degree. Dalits and Muslims are also very far behind Brahmans and other “high caste” groups. A snapshot of current college enrollees shows that, while some gaps may be closing, very large differences remain between social groups. Adivasis are still half as likely to be in college as non-marginalized groups, and the Muslim population is far behind, only one-fourth as likely to be in college as the non-marginalized Hindu groups. These differences in averages exist all along the economic and social spectrum. A recent article in The Economist graphed the gaps between India’s social groups on poverty and malnourishment. They called the gaps “unconscionable.” In 2010-11, the poverty rates for Forward, OBC, Dalit, and Adivasi groups were respectively 12.5 percent, 20.7 percent, 29.4 percent, and 43 percent. Rural poverty was three- and two-times higher in the Adivasi and Dalit populations respectively compared to non-marginalized groups. Urban poverty was about three-times higher for both. Malnutrition was almost twice as high for Adivasis compared to “upper” castes, and in the 1990’s, had declined more slowly; that is, the gap was growing larger. In an innovative new paper, Diane Coffey, Payal Hathi, Nidhi Khurana, and Amit Thorat document, among other issues, the extent of prejudice against Dalits—more than half their rural survey respondents (in Rajasthan and Uttar Pradesh) practiced untouchability and were in favor of having laws banning inter-caste marriages. The numbers speak for themselves. No editorial commentary is needed. ................
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