Poverty patterns can sharpen
Source: By Varsha S. Kulkarni: Mint
The Nyuntam Aay Yojana (NYAY), an election battle cry of the Congress, has evoked mixed responses, many hostile but some cautiously optimistic. What is common to most, however, is that they are broad-brush and involve leaps of faith.
Many have defended the NYAY scheme from the Rawlsian perspective of justice. One of the two main principles of justice is that “...Social and economic inequalities are to be arranged so that they are... to the greatest benefit of the least advantaged". Amartya Sen rejects this on the ground that such an allocation of primary goods (including “rights, liberties, opportunities, income and wealth") is not synonymous with just outcomes (capabilities to do this or that, which include freedom from hunger, for example). While acknowledging this formidable critique, we believe that the transfer of ₹6,000 per month to the poorest 20% families envisaged under the NYAY scheme is a step in the right direction.
Our analysis is limited to three issues. First the income shares of the poorest and its variation. Two, how mobile are the poorest across income ranges? And three, what are the factors associated with the persistence of extreme poverty and whether the insights yielded can help target NYAY better. We address these issues with a unique all-India panel survey of income distribution for 2005 and 2012, reported in the India Human Development Survey 2015, conducted jointly by the University of Maryland and National Council of Applied Economic Research (NCAER). Three per capita income categories are considered: (i) ≤20%, (ii) 21-50%, and (iii) above 50%.
In 2005, category (i) accounted for 3.7% of total income, category (ii) for 13.6%, and category (iii) for 82.7% of total income. These shares changed with slight reductions in the shares of (i) the poorest and (ii) moderately better-off, and a rise in the share of (iii) the more affluent.
We have identified states with the highest and lowest income shares of poorest households in state income in 2005 and 2012. The states with highest shares were Chhattisgarh, Uttarakhand and Delhi in 2005 and those with lowest shares were Assam, Jammu and Kashmir and Karnataka. Those with highest shares in 2012 included Odisha, Uttarakhand and Tamil Nadu, and those with the lowest included Gujarat, Haryana and Andhra Pradesh.
The four poorest states in terms of share of the poorest in India in 2005 were Uttar Pradesh, Bihar, Madhya Pradesh and Odisha, and they accounted for about 47% of the poorest households. Their share, however, fell to 44% in 2012.
All (median) per capita incomes rose more than moderately from 2005 to 2012: of the poorest by 2.9 times; of the moderately better-off by 1.5 times; and of the more affluent by 1.06 times. So, if the median income of the poorest continues to rise, the lump sum transfers required to meet the minimum income threshold are likely to be substantially lower over time.
Of the poorest in 2005, more than one-third remained poorest, a slightly larger share moved up into the moderately better-off and a little over a quarter became more affluent in 2012. Glimpses into downward mobility are offered by the facts that a little over a quarter of the moderately better-off became the poorest while a much lower share of the more affluent descended to this lowest income status. The important point here is that income mobility ought not to be overlooked. Unfortunately, it is as if projections of the fiscal burden are being made on the basis of a fixed, or, more likely, notional income distribution. Such projections are not just misleading but also deeply flawed.
Our statistical analysis examines the factors underlying this income mobility pattern. Our focus here though is confined to factors associated with the poorest remaining poorest over 2005 to 2012. Our principal findings are that Scheduled Tribes were likely to be in this category, as also those with no education; among occupations of the household’s head, cultivators were highly likely to remain poorest; as also the poorest themselves in 2005, an indication of state dependence.
Perhaps equally important is the state environment: state affluence measured in terms of net state domestic product per capita, and inequality measured as the share of the top 1% in the state’s income distribution a la Thomas Piketty. The greater the state affluence, the lower is the probability of the poorest remaining so in that state. In the absence of state-wise wealth distribution data, we approximate the Piketty measure of inequality by share of the top 1% in the state income distribution. The association is positive, suggesting that greater income inequality is associated with a greater probability of remaining poorest.
While extrapolation is often risky, it is riskier to conjecture without firm evidence. Moreover, the insights gained not just in terms of household characteristics but also the state environment may contribute to a better design and implementation of NYAY, a landmark scheme. Specifically, even with limited income distribution data, better targeting is feasible and benefits to the poorest could be enhanced through growth acceleration and reduction in overall income inequality. In conclusion, the denial of a modicum of justice through the NYAY scheme would be an injustice, an anyaay.
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