The Iniquity of Money-Metric Poverty in India

D Jayaraj; S Subramanian

doi:10.1515/bis-2016-0005

Published by De Gruyter June 13, 2017

The Iniquity of Money-Metric Poverty in India

D Jayaraj and S Subramanian

From the journal Basic Income Studies

https://doi.org/10.1515/bis-2016-0005

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Abstract

This paper is concerned to make three points about money-metric poverty in India: first, that the standard poverty-line approach to measuring poverty considerably underestimates poverty, and that the particular protocols by which India’s official poverty lines are determined are arbitrary and misleading; second, that a view of poverty in which the achievement of a satisfactory level of income is seen as a valuable end in itself, and which is captured in something like Kaushik Basu’s ‘quintile income statistic’, suggests a high order of income-poverty in the country which belies the relatively encouraging trends exhibited by headcount ratios based on official poverty lines; and third, that the continued co-existence of large amounts of poverty with large amounts of inequality needs to be redeemed by some deliberate redistributive strategy aimed at providing something like a guaranteed basic minimum income to every citizen of the country—for reasons, at least, of self-interest, if not justice.

Keywords: poverty line; quintile income statistic; unaccounted income; guaranteed basic income

JEL Classification: B40; D30; D31; D63; I32; O15.

Acknowledgements

We are sincerely grateful to two anonymous referees and the Editor of this journal for their detailed comments and suggestions on earlier versions of this paper. The usual caveat applies.

Appendix

A Does first order stochastic dominance necessarily imply an unambiguous reduction in poverty?

If invariance of the poverty standard is sought in the space of ‘real incomes’, then a property of cumulative distribution functions (CDFs) called ‘stochastic dominance’ furnishes a useful guide to the unambiguous poverty ranking of income distributions in terms of the headcount ratio (see Foster & Shorrocks, 1988). A cumulative distribution function is simply a plot of the cumulative proportion of the population with incomes below a given level, for every level of income in the distribution under review. Clearly, the CDF is an increasing (or at least non-decreasing) function of income: the ordinate corresponding to zero income would be zero (that is, zero percent of the population has income less than zero), and the ordinate corresponding to the highest income in the distribution is unity (that is, one hundred percent of the population would have incomes less than the highest income in the distribution). One distribution will be said to first-order stochastically dominate another, if the first distribution lies somewhere below and nowhere above the second distribution. And if this happens, then no matter what poverty line we choose, the headcount ratio for the first distribution will be somewhere lower and never higher than for the second distribution. Under these circumstances, we could say that poverty, as measured by the headcount ratio, is unambiguously lower for the first distribution than for the second.

As a broad rule, one can take it as something of an empirical regularity in the Indian setting, that the distribution of consumption expenditure tends to first-order stochastically dominate an earlier time-period’s distribution as we move forward in time. Secondly, and as we have seen in the text, a ‘calorie drift’ such as is observed in India implies, equivalently, that the (real) poverty line might be expected, over time, to lie further and further above the officially determined poverty line. These two contingencies are captured in Figure 1, which displays the (hypothetical) cumulative distribution functions of consumption expenditure in two successive years, of which the first year is the officially designated ‘reference’ or ‘base’ year, and z1 and z2 are the presumed (real) poverty lines in year 1 and year 2 respectively: in keeping with the stylized facts we have just reviewed, the year 2 cumulative expenditure distribution function has been portrayed as first-order stochastically dominating the year 1 function, and z2 has been taken to be greater than z1.

The official approach to determining the poverty line obliges us to treat z1 as the poverty line for both years 1 and 2: this is compatible with an inter-temporal decline in the headcount ratio of poverty fromH1toH2 in Figure 1, and captures, in essence, the official story of steadily declining money-metric poverty. A question which immediately arises is: is there any reason for treating the pattern of consumption expenditure in year 1 as somehow normatively privileged, for us to treat year 1 as the reference year? The answer, clearly, is ‘no’. This then leaves us open to designating year 2 as the reference year, with an order of arbitrariness which is neither more nor less than the order that presided over the earlier designation of year 1 as the reference year. In such an event, the poverty line would have to be taken to be z2, and we would have to conclude from Figure 1 that poverty has declined fromH1′in year 1 toH2′in year 2. Neither the magnitudes of the headcount ratios, nor the precise rate of decline in the ratio, are the same when year 2 is the reference year as when year 1 is the reference year, although no matter whether we employ z1 or z2 as the poverty line, we observe a decline in poverty in year 2 vis-à-vis year 1 (the usual ‘stochastic dominance’ argument).

Figure 1:

Headcount ratios of poverty for alternative poverty lines.

Note: Figure here 1 is the same as Figure 1 in Subramanian (2014).

But what now if each of the two years were to be treated as a ‘reference year’, so that z1 is taken to be the poverty line valid for year 1, and z2 as the poverty line valid for year 2? This, in fact, is the procedure that would be normally compatible with the Food Energy Intake method of determining the poverty line, and the one which Utsa Patnaik (2004, 2007) insists on. In such an event, Figure 1 assures us that we would have to conclude that in transiting from year 1 to year 2, the headcount ratio has actually increased fromH1toH2′.

We thus see that a number of inferences are available to us on what has happened to poverty over time, and in the absence of any reasonable criterion which will enable us to privilege any one inference over any of the other inferences, we are left in an indeterminate limbo of widely differing and equally (im)plausible logical outcomes. This surely cannot be a reasonable approach to measuring poverty! And this conclusion emerges from taking seriously the notion that if the poverty line in income terms is a means to the end of escaping deprivation in functionings space, then one should allow for the possibility of a poverty standard which is absolute in the space of functionings while being variable in the space of real incomes.

B What understated poverty lines can obscure

What constitutes the necessary means of subsistence in any society at any time is, as Marx observed in Capital, a matter that is always ‘practically known’. It is such practical knowledge that led the National Commission for Enterprises in the Unorganized Sector (Government of India, 2009) to suggest a pragmatic order of magnitude for the poverty line in India of Rs. 20 per person per day, or Rs. 600 per person per month, for 2004–05. With this poverty line (which many would argue is itself a very modest figure), one finds that the poverty line exceeds the rural average per capita consumption expenditure (as reflected in the relevant National Sample Survey data) of Rs. 564 per person per month in 2004–05. What is the implication of a poverty line which exceeds the average income?

A situation in which the mean income of the reference society is less than the poverty line is one that is not often explicitly encountered in the measurement literature (though for important exceptions, the reader is referred to Lewis & Ulph, 1988; Vaughan, 1987). The problem has been considered earlier by one of the present authors (Subramanian, 1989, 2003). He shows that if H is the proportion of the population in poverty (the head-count ratio), μ is the average income of the distribution under consideration, and z is the poverty line, then, in a situation in which μ<z, it will be the case that the minimum attainable value of the headcount ratio—call it H∗—will be given by the following expression:

(1)H*=1−μ/z.

Briefly, the ‘man overboard’ solution to the utilitarian’s ‘life-boat ethics dilemma’ consists in requiring that a proportion H∗(≡1−μ/z) of the population be effectively ‘thrown overboard’ from the‘life-boat’: by assigning an income of zero to each of nH∗ persons, one ensures that the headcount ratio of poverty is minimized (here n is the size of the total population). Since people presumably cannot survive without any resources whatever, the requirement, by implication, is to simply allow nH∗ individuals to perish in the cause of minimizing the headcount measure. H∗ may, with justice, be called the ‘triage headcount ratio’: according to the Mirriam-Webster Dictionary (also cited in Jayaraj & Subramanian, 1999; Subramanian, 2003), triage is ‘the sorting of and allocation of treatment to patients and especially battle and disaster victims according to a system of priorities designed to maximize the number of survivors.’ The ‘triage headcount ratio’ H∗ brings into relief the fact that the protocols of conventional poverty measurement are informed by a perverse morality which dictates mortality as a solution to poverty—a theme that has been addressed most notably in the work of Kanbur and Mukherji (2007). This solution, indeed, is what Koopmans (1957) has referred to as the ‘hardboiled’ solution to the ‘survival problem’ in General Equilibrium analysis. When sufficiently under-stated, poverty lines can point in the direction of the‘hardboiled solution’, a fact that is obscured unless it is explicitly brought into relief.

If we accept a poverty line of Rs. 20 per person per day, then in terms of eq.(1), the ‘triage headcount ratio’ H∗ for rural India in 2004–05 works out to 5.88 %. This is another way of saying, given India’s 2004–05 rural population, that if only some 46 million villagers in the country had obligingly curled up and died, rural poverty in the country might have been avoided. (Quantitatively, this would be like wiping Spain, with a population of 43 million, off the global map in 2004–05.)

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Published Online: 2017-6-13

The Iniquity of Money-Metric Poverty in India

Abstract

Acknowledgements

Appendix

A Does first order stochastic dominance necessarily imply an unambiguous reduction in poverty?

B What understated poverty lines can obscure

References

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