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The Political Economy of the BRICS Countries


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      From Tables 1 and 2, we can observe that bipolarization has been stable in rural areas, but increased in urban areas since the 1990s.

      While the above measures are based upon consumption expenditure, the NSSO also conducts an All-India Debt and Investment Survey, which can be used to estimate inequality in wealth. This survey is conducted at less frequent intervals, and for the period that we are interested in, three years are relevant: 1991, 2002, and 2012. Despite the availability of these data, wealth inequality in India has been underexplored (particularly compared to consumption expenditure inequality). Jayadev et al. (2007), Subramanian and Jayaraj (2013), and Anand and Thampi (2016) are some exceptions. Anand and Thampi (2016) present the most recent analysis and show that the RG of wealth as measured by total assets has increased from 0.65 to 0.74. If wealth is measured in terms of net worth, the RG increased from 0.66 to 0.75.

      A serious controversy has erupted in recent times on poverty measurement in India. The official poverty lines recommended by two official committees (Tendulkar and Rangarajan to be artificially low and based upon methodologies that are indefensible.10 However, as the CDFs discussed above reveal (and as noted by Motiram and Naraparaju, 2015), irrespective of the poverty line one uses, there was an unambiguous ranking of poverty rates in both rural and urban areas in 2011–12.11 The Head Count Ratio (HCR) of poverty for the scheduled groups is the highest, followed by the same for OBCs, and then for the Others. Studies that have examined poverty in previous years using official poverty lines (e.g. Motiram and Vakulabharanam, 2011) have come to a similar conclusion.

      What about poverty of the entire population? In Table 3, I present (from Motiram and Naraparaju, 2015) the various quantiles of real consumption and their growth in the period 2004–2005 to 2011–2012. As we can observe, all the quantiles, including the poorest, have experienced growth. This implies that poverty has fallen, irrespective of the poverty line that is used. However, the poorer groups have grown at slower rates compared to the middle and richer groups; this is particularly pronounced in the urban areas. Motiram and Naraparaju (2015) also show that the poor among disadvantaged caste groups have grown at slower rates compared to the overall average person (median). Motiram and Vakulabharanam (2012) carry out a decomposition exercise to examine whether inequality between scheduled and non-scheduled groups in consumption has increased or not.12 In this exercise, an inequality measure that belongs to the single-parameter entropy family of inequality indices (e.g. Log-Mean Deviation or Theil, see Shorrocks and Wan, 2005) is decomposed into two components: inequality between groups and inequality within groups. The former, and its contribution to overall inequality, can be used to understand whether group-based inequality has increased. After rising during the period 1993–1994 to 2004–2005, inequality between scheduled and non-scheduled groups has fallen since.

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      Notes: Data are expressed in 2011–2012 prices. Real values are computed using price indices implicit in official poverty lines.

      Source: Motiram and Naraparaju (2015).

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