Читать книгу The Political Economy of the BRICS Countries - Группа авторов - Страница 55

After following a model of planned development for almost four and half decades, in the wake of a serious balance of payments crisis, India embarked upon a set of economic reforms in the early 1990s.¹ These reforms aimed to radically restructure the Indian economy. Broadly speaking, the role of the state was reduced, and markets (both national and global) were provided a larger role.² India has been growing rapidly since the early 1990s after these reforms were initiated. Nagaraj (2013) has argued that the best growth phase for India was during the period 2003–2008, when the average annual growth rate of real gross domestic product (GDP) was 8.9%. During the period 1991–2008, the corresponding figure was 6.5%. There is considerable debate on the reasons for accelerated growth and when Indian growth really took off, in particular whether it took off in the 1980s or after the reforms.³ There is also a recent controversy on the new estimates put forward by the Central Statistical Office (CSO) which has rendered the official growth figures problematic.⁴ In my opinion, these debates and controversies do not distract from the broad idea that Indian economy has displayed unprecedented growth by its own historical standards and has been one of the fastest growing economies in the world in recent decades. Also, what is relevant for us is the distributional changes associated with this growth process. We will first focus on changes in the interpersonal distribution.

Many scholars and policymakers have used the surveys on consumption expenditure conducted by the National Sample Survey Organization (NSSO) to understand the Indian interpersonal distribution.⁵ These are large, nationally representative surveys that are cross-sectional in nature (i.e. not panels) and usually conducted every five years, in various rounds. The relevant rounds for us are: 50th (1993–1994), 55th (1999–2000), 61st (2004–2005), 66th (2009–2010), and 68th (2011–2012).⁶ The limitations of these surveys are well known and have been discussed in the literature (e.g. Jayadev et al., 2007; Motiram and Vakulabharanam, 2012) viz., the rich are underrepresented and their consumption is undervalued. These limitations are likely to result in an underestimation of the level of inequality and its increase over time. The consumption expenditure survey in 1999–2000 (55th round) differed in its methodology from previous surveys, and therefore many recent studies ignore it.⁷

Most studies of inequality in India have used relative measures of inequality (e.g. the Relative Gini (RG) or Theil). These measures are unaffected if the consumption expenditure of every one is scaled up or down by the same factor (e.g. doubled or halved). This requirement, referred to as scale invariance, facilitates comparisons. An important insight provided by Kolm (1973a, 1973b) is that while relative measures have the virtue of convenience, they come with their own ethical baggage. He termed these as ‘rightist’ measures and compared them with ‘leftist’ (absolute) measures and illustrated his point by considering the case of strikes by workers in France in the late 1960s. An analogous hypothetical example can be given from the Indian context: suppose the daily wages of workers and managers, which stand at Rs. 100 and 1000, respectively, are doubled. Inequality, as measured by a relative measure remains unchanged, but since the managers are earning much more than the workers now, some could argue that inequality has actually increased.⁸ Although still sparse, a recent literature has emerged in the Indian context that draws upon these insights and goes beyond relative measures. I will draw upon Motiram (2013) to provide a brief and non-technical description of the relevant concepts and ideas. A more elaborate and technical description is presented in Kolm’s work cited above and Subramanian and Jayaraj (2013). Let c_i denote the consumption expenditure of an individual i (= 1, 2, … , N) and μ denote the mean consumption. The RG is as follows:

Note that the RG is unit-less. Absolute inequality measures embody the principle of translation invariance, which requires that they are unaffected if all incomes increase or decrease by the same amount. Examples of absolute inequality measures are the Absolute Gini (AG) and Standard Deviation. The AG is calculated as follows:

For the above hypothetical example, it is quite easy to see that the AG increases when the incomes of the workers and managers double. However, the AG is not unit-less. Can we preserve the convenience of relative inequality measures while avoiding the ethical problem that they embody? Intermediate measures try to achieve this trade-off by incorporating the principle of unit consistency, which requires that inequality ranking between two distributions is unaffected if both are scaled by the same factor, i.e. inequality comparisons do not depend upon the units in which distributions are expressed. Examples of intermediate measures are the Intermediate Gini (IG) and the product of Standard Deviation and Coefficient of Variation. The IG is determined as follows:

Note that IG satisfies unit consistency since RG is unaffected by scaling, and if distributions are scaled by a factor, AG simply gets multiplied by this factor.

In my opinion, it is useful to examine inequality using different measures, and I therefore present various estimates in Tables 1 and 2. As we can observe (from column (1)), RG for nominal consumption expenditure has increased in both rural and urban areas, although the increase in urban areas is more pronounced. Column (2) presents the RG for real consumption expenditure, and the trends are roughly similar. Both AG and IG show increases in rural and urban areas. India is a large country with substantial variation in prices that different households face. Mishra and Ray (2011) take this into account and show that inequality has increased even after doing so.

Table 1:Inequality in Monthly Per-Capita Expenditure (MPCE), rural India.

Notes: RG: Relative Gini, AG: Absolute Gini, IG: Intermediate Gini, W: Wolfson Index.

Table 2:Inequality in Monthly Per-Capita Expenditure (MPCE), urban India.

Notes:

1.Estimates for RG (Nominal) are author’s computations from NSS data.

2.Estimates for RG (Real) are from Dubey and Thorat (2012).

3.Absolute and IG are from Subramanian and Jayaraj (2013). These are for real MPCE computed by using Consumer Price Index for Agricultural Laborers (CPIAL) in rural areas and Consumer Price Index for Industrial Workers (CPIIW) in urban areas.

4.Estimates for Wolfson Index are for nominal MPCE and from Motiram and Sarma (2014).

A growing body of research has emerged recently that argues that we should move beyond traditional notions and measures of inequality, particularly if we want to understand conflict. This literature on ‘polarization’ is discussed in greater detail in Chakravarty (2009) and Motiram and Sarma (2014). One important concept in this literature is ‘bipolarization,’ which is based upon the understanding that a decline in the share of the middle could have negative implications for stability and could accentuate the possibility of conflict. Measures of bipolarization are derived by conceptualizing the middle in terms of the median and replacing the Dalton–Pigou principle used in traditional measures of inequality (e.g. RG) with two other principles: Increasing Spread and Increased Bipolarity. The Dalton–Pigou principle holds that a regressive transfer (from a poorer to a richer person) should increase inequality and a progressive transfer should decrease inequality. The principle of Increasing Spread holds that bipolarization increases under the following circumstances: a rich person becomes richer or a poor person becomes poorer with the median being unaffected; or a transfer occurs from a poor person to a rich person across the middle (median). On the contrary, Increased Bipolarity holds that bipolarization increases if a richer and poorer person on the same side of the median are brought together due to a transfer from the former to the latter — this process is linked to the formation of poles on either side of the median. One popular index that has been proposed in the literature is the Wolfson Index. Let m and L(0.5) denote the median and the share of consumption held by the bottom half of the population, respectively.⁹ The Wolfson’s index is denoted as follows:

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.

Having examined interpersonal inequality, we will now move to group-based inequality. Caste is one of the important forms of social stratification in the Indian context. The ‘caste system’ is incredibly complex, defying easy understanding and generalization, and continues to be the subject of considerable debate and controversy today. In the interest of space, it is not possible to go into these debates, but readers can refer to the following studies (and the references therein): Chatterjee (1998), Gupta (2000), Dirks (2001), and Rawat and Satyanarayana (2016). I will examine the broad caste groupings that secondary statistical data like the NSS allow us to analyze: Scheduled Castes (SC), Scheduled Tribes (ST), Other Backward Classes (OBC), and Others. The first three groups (particularly SCs and STs) have been historically disadvantaged against and lag behind on many dimensions: income, education, etc. In fact, the scheduled groups are referred to in this manner because they are given a special place (schedule) in the Indian constitution. Motiram and Naraparaju (2015) presented the cumulative distribution functions (CDFs) of consumption expenditure for these four groups in 2011–2012 and showed that the CDFs of the disadvantaged caste groups lie above that of Others in both rural and urban areas. The average consumptions are lower for the scheduled groups compared to OBCs, who themselves have lower average consumption compared to Others.

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.

Table 3:Growth of rural and urban quantiles between 2004–2005 and 2011–2012, India.

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).

Class is another important cleavage in the Indian context. Motiram and Naraparaju (2015) use the NSS data on consumption expenditure to divide rural India into seven classes based upon their ‘household type’¹³ and land possessed: Large farmers (greater than 10 hectares), Medium farmers (between 2 and 10 hectares), Small farmers (between 1 and 2 hectares), Marginal farmers (between 0 and 1 hectare), Self-employed in non-agriculture, Agricultural and other laborers, and Others. They show that the rankings of poverty rates and average consumption are as expected, with the lower classes characterized by higher poverty rates and lower average consumption. In urban areas, Motiram and Naraparaju (2015) use the household type (Self-employed, Regular Wage, Casual Labor, and Others) to divide the population. They show that the poverty rates show a clear ranking from highest to lowest: Casual Labor, Self-employed, and Regular Wage. Average consumption shows the reverse ranking. They also show that consumption of the poor among Casual Laborers has grown at a slower pace compared to the overall average (median). Vakulabharanam (2010, 2014) developed a rigorous class-schema based upon landownership and occupations to classify the Indian population into various classes. He used NSS consumption data and performed a decomposition exercise based on the Gini index to show that class-based inequality has been rising since the 1990s.¹⁴ Overall, during the high-growth period since early 1990s, India has witnessed an increase in interpersonal inequality in consumption and wealth. This is robust to the way we conceptualize and measure inequality (traditional: relative, absolute, and intermediate; polarization) although the results look much stronger with absolute and intermediate measures. We should also appreciate the (highly likely) possibility that the real increase in inequality is starker than what the data reveals, given the limitations that we discussed above. Class-based inequality has also increased. Caste-based inequality has come down in consumption and other domains (e.g. education) although there are still stark differences among caste groups.¹⁵ Having examined the Indian context, we now move to the Brazilian context.