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Patterns of Wealth Disparities in India during the Liberalisation Era

This paper examines patterns of wealth disparities in India using the all-India debt and investment surveys (1991 and 2002). We find that there have been increases in wealth levels in the country across virtually all groupings, accompanied by a small but perceptible rise in the level of interpersonal wealth inequality, whether examined by summary measures such as the Gini coefficient or by centile shares of wealth. We examine differences in wealth holdings by state and income in the two surveys as well as disparities according to socio-economic categories in 2002. There have been sharp differences in the growth rates of wealth holdings in the middle and upper income states on the one hand and poor states on the other, suggesting divergence in wealth outcomes. Faster growing states have seen larger increases in wealth inequality. Finally, there are large differences in the levels of wealth holdings according to socio-economic categories.

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Patterns of Wealth Disparities in India during the Liberalisation Era

This paper examines patterns of wealth disparities in India using the all-India debt and investment surveys (1991 and 2002). We find that there have been increases in wealth levels in the country across virtually all groupings, accompanied by a small but perceptible rise in the level of interpersonal wealth inequality, whether examined by summary measures such as the Gini coefficient or by centile shares of wealth. We examine differences in wealth holdings by state and income in the two surveys as well as disparities according to socioeconomic categories in 2002. There have been sharp differences in the growth rates of wealth holdings in the middle and upper income states on the one hand and poor states on the other, suggesting divergence in wealth outcomes. Faster growing states have seen larger increases in wealth inequality. Finally, there are large differences in the levels of wealth holdings according to socio-economic categories.

ARJUN JAYADEV, SRIPAD MOTIRAM, VAMSI VAKULABHARANAM

A
critical task that has occupied economists over the last two decades has been to examine and explain the nature and causes of distributional changes in the era of market-oriented reforms. Researchers examining this question in the case of India have tended to focus primarily on the evolution of income and consumption.1 In doing so, they have, by and large, overlooked an important manner in which disparities appear to have manifested themselves – in increasing levels of wealth and in changing concentrations of wealth across households, individuals, regions and groups. This is an important gap in the literature. The distribution of wealth has been recognised to play an important role in the development process by having a significant impact on productivity, public good provision and occupational choice [Banerjee and Newman 1993; Bardhan et al 2000; Bardhan et al 2006]. In addition, many theoretical models have considered the impact of markets on wealth inequality with differing conclusions and policy implications [Stiglitz 1969; Banerjee and Newman 1993; Galor and Zeira 1993; Aghion and Bolton 1997; Piketty 1997; Matsuyama 2000; Ghatak and Jiang 2002; Ljungqvist 1993; Mookherjee and Ray 2003, 2006]. As such, apart from complementing research on income inequality, examining wealth inequality is valuable in itself.

In recent times, there is a heightened interest in the issue of wealth distribution within and between countries. For example, a widely cited recent report by the United Nations World Institute for Development Economics Research (WIDER) [Davies et al 2006] measures the world distribution of wealth by collating household level balance sheet data from several countries. However, in the Indian case, to the best of our knowledge, there have only been two recent reports, which have examined this issue in some detail. The first of these is the National Sample Survey (NSS) summary report [NSS 2005] and the second is an exploratory piece by Subramanian and Jayaraj (2006b), who use the NSS summary report for an analysis of the latest available wealth survey and micro-data for the previous survey (which was conducted over 1991-92).2 In this paper, we seek to bolster this sparse literature by comprehensively analysing the microdata from these last two surveys. These surveys provide detailed data on the value and composition of household assets across several axes. Using these data, we provide summary measures for per capita net worth and asset holdings, and inequality patterns across various axes such as state, sector, caste, and religion, while benchmarking these against the national level computations.

We examine the wealth distribution at the per capita level and focus on interpersonal and group-wise changes in the mean values and distribution indices provided in the 1991-92 and 2002-03 surveys. Focusing on per capita as opposed to household level distribution has the advantage of taking household size into account. This approach is not without limitations3 but as pointed out by Sierminska and Smeeding (2005: 4), ignoring household size:

…implies that households are assumed to have perfect returns to

scale in the use of wealth or that access to wealth of one member

of the household has no effect on the access of other members, as

wealth is a public good within the unit.

Moreover, it is important to treat the individual as the unit of reference, since welfare ultimately resides in individuals and not in households [Deaton 1997: 153 for a discussion of this issue].

We briefly analyse the wealth distribution between 1991 and 2002, and use our results to throw independent light on growth/ accumulation and its distribution during the era of economic reforms by bringing in another dimension, viz, wealth. We do not explicitly analyse the causal linkages between reforms and the changing nature of wealth distribution but rather focus on the changes during the period of reforms. Given the richness of the

EPWRF–AD

demographic information in these surveys, we are also able to do a preliminary analysis of how different occupational and social groups have fared during the period of analysis.

A précis of our results suggests that there have been increases in wealth across virtually all axes, and that the interpersonal wealth distribution has become somewhat more unequal. Regional and other group-wise indicators show sharp divergences. In the following section we discuss the survey data used and its limitations. In Section II, we present various summary measures of wealth across individuals and groups (means and inequality indices). In Section III, we discuss these results and draw some tentative conclusions.

I NSS All-India Debt and Investment Survey Data

As mentioned above, the micro-data used in this paper are extracted from the all-India debt and investment survey (AIDIS) (in 1991-92 and 2002-03) collected by the National Sample Survey Organisation (NSSO). We focus on two different measures of wealth: household per capita net worth and household per capita assets. The NSS defines total household assets as comprising “physical assets like land, buildings, livestock, agricultural machinery and implements, non-farm business equipment, all transport equipment, durable household goods and financial assets like dues receivable on loans advanced in cash or in kind, shares in companies and cooperative societies, banks, etc, national saving certificates and the like, deposits in companies, banks, post offices and with individuals” [NSS 2005: 5]. Net worth is defined as the total household assets net of the indebtedness of households (also provided in the surveys). Debt is defined by the NSS as consisting of cash loans payable as of June 30, 2002 and June 30, 1991 for the two years respectively and subsequently, net worth is total assets less debt.

At the outset, however, it is important to provide a caveat to the use of these data and the inherent difficulties faced by researchers. Some of these problems have been summarised in Subramanian and Jayaraj (2006a) but bear repeating. We identify below problems with the asset and indebtedness data that are available in these surveys.

The NSS followed a stratified two-stage sampling methodology for the AIDIS. The first stage units were the census villages and urban blocks in rural and urban areas, respectively. The second stage units were the households. A detailed description of the sampling methodology is presented in the NSS reports and it is not worth repeating here [NSS 1998, chapter 2 and NSS 2005, appendix B]. However, it is worthwhile to point out that the sampling methodology does not make an adequate attempt to oversample the wealthy. This is a problem because a common feature of wealth distributions is the tendency for large concentrations of wealth at the top end of the population (India is no exception in this regard) and unless conscious efforts are made to over-sample the very wealthy, wealth concentration tends to be underrepresented in the actual sample [Brandolini et al 2004]. This is particularly problematic since a few very large values at the upper tail of the distribution can often impact the summary measures significantly. This is one reason why we present various summary measures (e g, mean and median) since different measures often differ in their sensitivity to outliers and large values.

A second problem that has been identified in various wealth studies and reiterated in the Indian case by Subramanian and Jayaraj (2006a) has to do with a general tendency among all respondents to under-report their wealth holdings. This problem is compounded further by the fact that this bias tends to increase in the wealth holdings, so that the problem is more severe at higher wealth levels. For example, two assets where under-reporting is serious are land and gold [Subramanian and Jayaraj 2006a]. It is obvious that these assets (especially gold) are likely to be held in larger quantities (and values) by wealthier groups. In fact, the ultra-poor are likely to own almost no land or gold. A consequence of the above is that inequality in asset holdings tends to be underestimated in the survey.

A third problem is the failure to correctly value the reported assets. This is because of the difficulty in obtaining market prices for various kinds of assets. Even if the reported prices are based on recent transactions, they tend to be underplayed so that the reported values turn out to be lower than the market values. Also, given the absence of explicit wealth based deflators [Vaidyanathan 1993], in order to make real comparisons between wealth holdings at two different points in time, consumption based deflators need to be used. In this paper, for 2002-03, we use the consumer price index (CPI) for agricultural workers in order to deflate wealth data in rural areas and the CPI for urban workers to deflate urban data. This implies two problems. First, wealth deflation may be biased in 2002-03. Compounding the problem, our estimates may not be able to fully capture the different rates of change across different income/wealth groups given the fact that we are deflating based on the consumption of relatively poorer groups.

There are other problems such as the prevalence of illegitimate but effective ownership (such as encroachment of common properties, and ‘benami’ landownership) that leads to under-counting of the assets owned by the wealthy. Moreover, as Subramanian and Jayaraj (2006a) and Davies et al (2006) point out, there is strong evidence to suggest that liabilities are severely underreported in India (by a factor of nearly 3). The fact that the correlation between net worth and total assets is very high (at over 0.995), suggests that this may be the case, and as such, considerations of asset valuation aside, net worth values are probably overstated. Given all these difficulties, we believe that the measures of inequality that we are presenting below underestimate the true extent of wealth inequality in India.

II Summary Measures of Wealth Distribution

Means and Medians

Table 1 provides some basic summary statistics of the level and distribution of wealth4 at the per capita level in 1991 and 2002.5 Per capita assets have gone up by about 35 per cent from Rs 22,833 in 1991 to Rs 31,018 in 2002. Per capita net worth has gone up by similar levels. While there are substantial differences in levels, the growth rates in asset holdings in rural and urban areas are very similar. Growth in per capita net worth, however, was faster in urban areas, reflecting greater reported indebtedness in rural areas. The ratio of average per capita assets in urban areas to average per capita assets in rural areas was therefore, relatively constant at 1.5 between 1991 and 2002. However, the ratio of average per capita net worth in urban areas to average per capita net worth in rural areas rose substantially from 1.37 in 1991 to 1.5 in 2002. The median values of per capita assets and net worth went up from Rs 10,459 and Rs 10,169 in 1991 to Rs 13,587 and Rs 13,055 in 2002, respectively. The implied annual real growth rates are slightly smaller than for the mean values.

While there is an increase in most components of asset holdings, in real terms there is a decline in the average values of livestock assets and durable assets in the urban areas. This anomaly may reflect several underlying causes including the problems mentioned earlier as to the lack of readily available and consistent price deflators for all categories, changing market prices for livestock and the continued use of labour-intensive methods rather than household durables for maintaining the household.

The sharpest growth rates have occurred for financial assets (shares and deposits/others) with these categories growing by 22 per cent and 7 per cent annually overall. The growth of financial markets and a culture of investing, especially in urban areas, are likely to be behind these changes. While rural growth rates in these categories are also impressive, the initial levels seem very low. It should also be noted that figures for financial assets are more readily comparable between 1991 and 2002 since they do not face the problem of appropriate price deflators.

For most Indians who possess some wealth, asset holdings are concentrated in land and buildings. Table 2 shows the proportion of overall per capita assets disaggregated by the main categories of holdings for rural and urban areas (land, buildings and durables) in both 1991 and 2002. There are important differences according to the sector, with durables accounting for a much larger proportion of the asset holdings of urban individuals as compared to rural individuals in both periods. Likewise, land is the primary asset of rural individuals, accounting for nearly half of total per capita assets for both periods. Other assets (the sum of non-farm equipment, agricultural machinery, transport vehicles, deposits, loans, shares, etc) constitute only about 10 per cent of total per capita assets.

Table 3 provides an indication of the ownership rates (proportion of the population owning an asset) of these assets. The ownership rates for the biggest categories – land and buildings – have remained roughly the same. By contrast, there is less ownership of livestock and agricultural machinery and more ownership of non-farm assets, perhaps reflecting a movement of the rural rich from agriculture to non-agriculture as agriculture becomes relatively less profitable over this period. The most striking rise has been in the ownership of deposits, with over 90 per cent of the respondents having some deposits in 2002 compared with less than 25 per cent in 1991. A puzzling feature of the data is the fact that the ownership rates of shares have actually declined, from 9.15 per cent to 7.33 per cent of the population, a finding that runs contrary to both the received wisdom and other studies, which have found that share and debenture ownership in India has expanded considerably [see for example, SEBI-NCAER 2000, 2003]. While these studies cannot be used to

Table 2: Major Categories of Total Assets

(Percentage distribution)

1991 2002 Overall Rural Urban Overall Rural Urban

Land 43.4 49.4 24.7 43.1 48.6 26.9 Buildings 29.7 29.5 30.7 32.8 32.7 33.2 Durables 15.2 10.4 29.9 13.5 9.8 24.2 Others 11.7 10.7 14.7 10.6 8.9 15.8 Total 100 100 100 100 100 100 N 3,01,658 2,00,179 1,01,479 7,09,291 4,56,571 2,52,720

Source: Authors’ calculations from NSS CDS for 1991 and 2002 AIDIS.

Table 3: Ownership Rates by Asset

Ownership Rate (1991) Ownership Rate (2001)

Total 99.88 99.97 Land 86.92 89.61 Buildings 88.21 89.45 Livestock 60.86 49.95 Agricultural machinery 68.64 64.66 Non-farm assets 15.58 20.12 Transport 50.34 58.62 Durables 99.75 99.86 Shares 9.15 7.33 Deposits 23.29 90.48 Loans 2.10 2.38

Source: Authors’ calculations from NSS CDS for 1991 and 2002 AIDIS.

Table 1: National Averages of Wealth Holdings 1991 and 2002

(in Rs)

1991 2002 Implied Annual Growth Rate (Per Cent)
Overall Rural Urban Overall Rural Urban Overall Rural Urban
Means
Average per capita net worth 21,553 19,885 27,215 30,137 26,735 40,113 3.1 2.7 3.6
Average per capita assets 22,833 20,352 30,505 31,018 27,515 41,293 2.8 2.8 2.8
Average per capita land 12,529 13,075 10,838 16,966 17,359 15,813 2.8 2.6 3.5
Average per capita livestock 551 688 129 453 578 87 -1.8 -1.6 -3.5
Average per capita building 6,223 4,355 12,001 8,767 6,462 15,526 3.2 3.7 2.4
Average per capita agricultural machinery 363 455 79 428 544 91 1.5 1.6 1.3
Average per capita non-farm 159 65 449 216 96 567 2.8 3.6 2.1
Average per capita durable 1,769 1,197 3,535 1,944 1,420 3,481 0.9 1.6 -0.1
Average per capita transport 411 246 923 686 381 1,581 4.8 4.1 5.0
Average per capita deposits 702 243 2,123 1,439 622 3,836 6.7 8.9 5.5
Average per capita shares 85 16 297 755 279 2,149 22.0 29.6 19.7
Average per capita loans and others 41 12 132 51 29 116 2.1 8.9 -1.1
Median
Median per capita net worth 10,169 9,988 10991 13,055 12,720 14,569 2.5 2.5 2.9
Median per capita assets 10,459 10,251 11348 13,587 13,184 15,233 2.7 2.6 3.0
N (1991): overall/rural/urban 3,01,658/2,00,179/1,01,479
N (2002): overall/rural/urban 7,09,291/4,56,571/2,52,720

Note: 2002 values deflated to 1991 values by the CPI for industrial workers for urban areas and for agricultural workers in rural areas. Source: Authors’ calculations from NSS CDS for 1991 and 2002 AIDIS.

Economic and Political Weekly September 22, 2007 benchmark the AIDIS, this divergence suggests that one should be cautious when drawing conclusions on share ownership and distribution when utilising the AIDIS.

Inequality

The data also show an increase in the degree of inequality across several axes. The Gini coefficients for total per capita assets and per capita net worth are presented in Table 4. The Gini coefficient for per capita net worth has seen an increase of about 2 percentage points, which is notable. The corresponding figure for per capita assets is at about 1 percentage point. It should be noted that for reasons stated above, these increases are almost certainly underestimates of the true levels of wealth inequality since the extremely wealthy are not properly sampled.

Table 5 provides Gini coefficients for each type of asset and we can see that inequality of these assets has been largely stable. Hence, there is no one asset (or a subset of assets) that is driving the overall pattern of changes in inequality. One point bears mentioning however. As is evident, the ownership of shares and loans (what one might term broadly as financial assets and liabilities) is highly concentrated with Gini coefficients in the order of

0.99. As such, this finding suggests that the tremendous focus given to the health of the stock market and the movement of

Table 4: Distributional Measures

Distribution 1991 2002 Overall Rural Urban Overall Rural Urban

Gini coefficient (total assets) 0.64 0.61 0.70 0.65 0.61 0.69 Gini coefficient (net worth) 0.64 0.61 0.70 0.66 0.62 0.69

Source: Authors’ calculations from NSS CDS for 1991 and 2002 AIDIS.

Table 5: Gini by Category

Gini 1991 Gini 2002

Per capita assets 0.64 0.65 Per capita land 0.73 0.73 Per capita livestock 0.72 0.77 Per capita building 0.71 0.68 Per capita agricultural machinery 0.93 0.93 Per capita non-farm 0.98 0.97 Per capita durable 0.67 0.64 Per capita transport 0.92 0.93 Per capita deposits 0.93 0.92 Per capita shares 0.99 0.99 Per capita loans and others 0.99 0.99

Source: Authors’ calculations from NSS CDS for 1991 and 2002 AIDIS.

corporate asset, values in the media as well as to its political importance reflects the interests of a very narrow constituency. Although there is evidence that there is a larger and more widespread holding of corporate assets it is still a component of very few portfolios.6 Even if one were to impute indirect holdings of shares and debentures, the concentration would likely to continue to be very high.

Two striking features of the data are the huge disparities in wealth concentration and the relative stability of the wealth shares over the decade. Since we are not able to track individuals or households across the time span (and thus, cannot measure wealth mobility), we examine the shares and cumulative shares by decile for both the total per capita assets and the per capita net worth to get at the question of concentration. Table 6 shows that the top 10 per cent of individuals possess a little over half of the total wealth (whether measured in terms of assets or net worth) in the country, while the bottom 10 per cent possess a mere 0.4 per cent of the total wealth. The bottom 50 per cent of the population own less than 10 per cent of the total wealth. The wealthiest have tended to consolidate between the two surveys (the top 10 per cent owned 51.94 per cent of wealth in 2002 versus 50.79 per cent in 1991), while the asset-poor, i e, bottom 10 per cent have only lost their share (0.21 per cent in 2002 versus 0.22 per cent in 1991).

Table 7 performs a similar exercise, by looking at the average wealth holdings by decile of mean per capita monthly expenditure7. The growth rate in asset accumulation is highest in the top decile. By contrast, the growth rate of assets in the bottom decile is the lowest. In other words, there is a stronger picture of divergence in asset holdings as the rich have pulled away from the poor in asset accumulation.

This narrative is further strengthened when one examines the very top end of the wealth distribution. Table 8 provides an indication of sharply increased holdings at the very top end of the distribution. The ratio of assets held by the individual at the 95th percentile to the assets held by the median individual rose from 758 per cent to 814 per cent, while the corresponding ratio for net worth rose from 766 per cent to 824 per cent. When we examine these figures with the reference point of the individual at the 99 percentile, the ratio rose from 1851 per cent to 1958 per cent for assets and 1886 per cent to 2012 per cent for net worth. It should be noted that the idea of rapidly increasing wealth at the very top end of the income/wealth distribution is broadly in agreement with another examination of the very rich in India [Banerjee and Piketty 2005].

Table 6: Share of Assets and Net Worth by Decile

(Per cent)

1991 2002
Total Assets Net Worth Total Assets Net Worth
Wealth Decile Share Cumulative Share Share Cumulative Share Share Cumulative Share Share Cumulative Share
0-10 0.37 0.37 0.22 0.22 0.40 0.40 0.21 0.21
10-20 1.07 1.44 1.00 1.23 1.08 1.48 1.01 1.22
20-30 1.86 3.30 1.80 3.02 1.79 3.26 1.72 2.94
30-40 2.78 6.08 2.72 5.75 2.62 5.88 2.57 5.51
40-50 3.91 9.99 3.87 9.61 3.67 9.56 3.64 9.15
50-60 5.37 15.36 5.34 14.96 5.06 14.62 5.02 14.17
60-70 7.37 22.73 7.35 22.3 7.02 21.63 7.00 21.17
70-80 10.47 33.20 10.47 32.77 10.22 31.85 10.22 31.39
80-90 16.41 49.61 16.44 49.21 16.64 48.49 16.67 48.06
90-100 50.39 100.00 50.79 100.00 51.51 100.00 51.94 100.00
Source: Authors’ calculations from NSS CDS for 1991 and 2002 AIDIS.
Economic and Political Weekly September 22, 2007 3857

One of the peculiarities of wealth data compared to income or consumption data is the presence of a substantial number of legitimate zero or negative values. This raises certain complications in computing and presenting inequality using conventional tools. For example, measures involving logarithms (e g, variance of logs) cannot be computed in the usual way. The Gini, since it involves the absolute values of pair-wise differences, can be computed in the presence of zeros and negative values8 but two problems arise. First, the usual lower and upper bounds (0 and 1) need not be valid. Second, if the mean wealth is zero, then the Gini is undefined.

In our case, as seen from Table 1, the mean net worth is positive for both the years. Moreover, as seen from Table 4, the Gini for net worth is well within the usual bounds. However, it is still worthwhile to explore this issue further. Jenkins and Jantti (2005) present an excellent survey of the tools and expositional methods that can be used in the context of wealth inequality. We draw upon their study and to throw more light on inequality, present Lorenz curves and absolute Lorenz curves in Figures 1 and 2.

From Table 6, we can see that for every decile (except, of course for the last one), the cumulative share of net worth per capita in 1991 is higher than (although close to) the corresponding figure for 2002. This would indicate that if we construct Lorenz curves based on deciles, the Lorenz curve for 1991 would be close to but lie above the Lorenz curve for 2002. We constructed Lorenz curves for 1991 and 2002 based upon cumulative shares of per capita net worth held by 5 per cent, 10 per cent, etc, of the population. These curves and the corresponding data are shown in Figure 1.

We can observe that the Lorenz curve for 1991 lies above the Lorenz curve for 2002, although the curves are close, with the difference being more prominent in the middle. This is much more apparent if we look at the data used to generate the curves. We also used percentiles of the population (1 per cent, 2 per cent

Table 7: Average Per Capita Wealth by Expenditure Decile

(in Rs)

1991 2002 Implied Annual Monthly Average Average Average Average Growth Rate Per Capita Per Per Per Per (Per Cent) Expenditure Capita Capita Capita Capita Deciles Assets Net Worth Assets* Net Worth*

0-10 8,257 8,075 8,982 8,691 0.8 0.7 10-20 10,197 9,965 12,151 11,814 1.6 1.6 20-30 11,187 10,956 15,052 14,639 2.7 2.7 30-40 13,362 13,106 17,428 16,913 2.4 2.3 40-50 16,116 15,817 19,859 19,272 1.9 1.8 50-60 17,955 17,578 24,119 23,518 2.7 2.7 60-70 22,898 22,459 29,539 28,698 2.3 2.3 70-80 27,536 26,982 35,011 33,938 2.2 2.1 80-90 36,287 35,493 51,109 49,722 3.2 3.1 90-100 76,683 75,175 1,11,007 1,07,801 3.4 3.3

Note: * Deflated values. Source: Authors’ calculations from NSS CDS for 1991 and 2002 AIDIS.

Table 8: Increasing Concentration of Wealth at Upper End of Wealth Distribution

1991 2002
(as Per Cent of Median) (as Per Cent of Median)
Percentile Total Assets Net Worth Total Assets Net Worth

90 479 482 515 522 95 758 766 814 824 99 1851 1886 1958 2012

Source: Authors’ calculations from NSS CDS for 1991 and 2002 AIDIS.

Cumulative Share of Per Capita Net Worth (per cent)

Figure 1: Lorenz Curves for Per Capita Net Worth

100 90 80 70 60 50 40 30 20 10 0 -10

0 10 20 30 40 50 60 70 80 90 100 Cumulative Share of Population (per cent) 1991 2002 Equality

Cumulative Share Cumulative Share of Cumulative Share of of Population Per Capita Net Worth Per Capita Net Worth (Per Cent) (Per Cent) 1991 (Per Cent) 2002

0 0 0 5 -0.02 -0.04 10 0.22 0.21 15 0.64 0.63 20 1.23 1.22 25 2.02 1.98 30 3.02 2.94 35 4.26 4.11 40 5.75 5.51

  • 45 7.52 7.18
  • 50 9.61 9.15 55 12.08 11.45 60 14.96 14.17 65 18.34 17.36 70 22.3 21.17 75 27.03 25.77 80 32.77 31.39 85 39.88 38.52 90 49.21 48.06 95 62.93 62.21 100 100 100
  • Source: Authors’ calculations from NSS CDS for 1991 and 2002 AIDIS.

    etc) to construct Lorenz curves and found the same result.9 Overall, the Lorenz curves indicate that there is an unambiguous increase in inequality from 1991 to 2002. This is also reflected in the above comparison of the Ginis.

    In the context of wealth inequality, another tool that is useful is the absolute Lorenz curve, which can be derived from the standard Lorenz curve.10 Figure 2 presents the absolute Lorenz curves for per capita net worth for 1991 and 2002 and we can observe that the absolute Lorenz curve for 1991 lies above the same for 2002. This indicates an unambiguous increase in inequality from 1991 to 2002 according to all standard absolute inequality measures.11 We can also note that the difference across these two points in time between the absolute Lorenz curves is much more pronounced compared to the difference between the standard Lorenz curves.

    However, comparisons of absolute Lorenz curves have to be interpreted with caution since they are not unit-free (unlike the standard Lorenz curves) and are therefore, sensitive to the deflators used, a problem already discussed earlier. Notwithstanding this caveat, we can conclude that interpersonal wealth inequality has risen between 1991 and 2002.

    Figure 2: Absolute Lorenz Curves of Per Capita Net Worth Another axis along which there have been sharp differences is in the wealth holdings by state. Tables 9A and 9B provide a break up of average per capita asset holdings and per capita net worth

    Absolute Lorenz Ordinate (nw Wealth)–15000 –10000 –5000 0 –15000 –10000 –5000 0

    0 .2 .4 .6 .8 1

    by state in 1991 and 2002, respectively. There is a wide range in per capita holdings among states. Among the major states, for example, the per capita asset holdings in the most wealthy state (Punjab) was Rs 77,051 per person in 2002, about four times as much as the per capita holdings in the least wealthy major state (Bihar), which has a per capita wealth of Rs 19,718. There have been substantially different growth rates among states. Bihar, for example, has seen a growth rate of about 0.9 per cent per annum in per capita asset holdings while Kerala has seen the fastest growth rate at about 4.9 per cent per annum (again, comparing only among major states). The tables also provide a break up of asset levels in 1991 and 2002 and the implied growth rates by an often used classification of the 14 major states as poor (Bihar, Orissa, Uttar Pradesh, Madhya Pradesh and Rajasthan), middle income (Andhra Pradesh, Kerala, Karnataka and West Bengal) and rich (Tamil Nadu, Haryana, Gujarat, Punjab and Maharashtra). The numbers tell a stark story, with the middle income and rich states experiencing much faster asset growth rates annually than the poor states. This is reflective of the growing disparities

    Cumulative population shareamong states, that has been commented upon in several recent1991 – – – – 2002

    studies [Dreze and Sen 2002; Purfeld 2006; Kochhar et al 2006]. Source: Authors’ calculations from NSS CDS for 1991 and 2002 AIDIS. This finding may perhaps reflect greater incentives and ability to

    Table 9A: State-wise Break-up of Total Assets

    State/Region 1991 (in Rs) 2002 (in Rs) Implied Annual Growth Rate (Per Cent)
    Overall Rural Urban Overall Rural Urban Overall Rural Urban
    Andamans 16,617 15,357 19,500 34,905 32,393 40,215 7.0 7.0 6.8
    Andhra 15,216 13,324 21,194 22,800 17,093 38,521 3.7 2.3 5.6
    Arunachal Pradesh 12,224 13,058 6,122 12,980 13,163 11,682 0.5 0.1 6.0
    Assam 11,941 10,875 24,580 15,962 14,632 28,554 2.7 2.7 1.4
    Bihar 17,827 17,623 19,177 19,718 18,843 27,312 0.9 0.6 3.3
    Chandigarh 31,449 17,525 33,562 68,712 35,404 71,464 7.4 6.6 7.1
    Chhattisgarh 20,254 19,002 28,067
    Dadra/Nagar Haveli 14,427 13,548 26,557 30,335 29,058 45,530 7.0 7.2 5.0
    Daman/Diu 34,700 34,187 36,201 24,660 17,187 39,123 -3.1 -6.1 0.7
    Delhi 70,893 76,069 70,305 56,254 26,465 63,030 -2.1 -9.2 -1.0
    Goa 51,249 50,643 52,062 58,371 48,345 70,458 1.2 -0.4 2.8
    Gujarat 23,443 19,203 32,132 37,011 33,513 43,638 4.2 5.2 2.8
    Haryana 52,146 57,810 32,468 68,744 71,120 61,879 2.5 1.9 6.0
    Himachal Pradesh 26,790 25,620 42,162 55,366 54,649 62,320 6.8 7.1 3.6
    J and K 28,887 26,765 43,034 62,398 53,820 94,878 7.3 6.6 7.5
    Jharkhand 17,177 15,567 23,970
    Karnataka 21,061 19,299 25,539 30,326 26,187 39,961 3.4 2.8 4.2
    Kerala 37,897 35,784 44,697 64,288 59,204 79,288 4.9 4.7 5.3
    Lakshadweep 64,949 51,044 76,845 61,121 51,259 66,544 -0.6 0.0 -1.3
    Madhya Pradesh 18,420 17,127 23,245 27,549 23,715 39,905 3.7 3.0 5.0
    Maharashtra 24,165 18,394 34,193 33,966 27,731 42,762 3.1 3.8 2.1
    Manipur 16,961 16,353 18,653 23,369 18,836 35,669 3.0 1.3 6.1
    Meghalaya 13,984 10,474 30,754 31,818 24,497 72,280 7.8 8.0 8.1
    Mizoram 10,822 7,861 20,698 33,564 15,372 66,016 10.8 6.3 11.1
    Nagaland 16,725 14,362 20,861 76,688 85,427 55,651 14.8 17.6 9.3
    Orissa 9,816 8,906 17,120 12,831 10,957 25,217 2.5 1.9 3.6
    Pondicherry 25,701 16,463 30,972 39,444 26,243 46,808 4.0 4.3 3.8
    Punjab 56,342 57,629 53,483 77,051 87,189 55,239 2.9 3.8 0.3
    Rajasthan 29,318 28,246 33,197 35,482 33,044 44,262 1.7 1.4 2.6
    Sikkim 25,894 26,879 17,257 26,083 23,422 48,028 0.1 -1.2 9.8
    Tamil Nadu 19,685 14,552 29,062 29,050 24,490 38,446 3.6 4.8 2.6
    Tripura 12,576 10,939 33,135 13,530 10,810 33,050 0.7 -0.1 0.0
    Uttar Pradesh 25,103 24,118 29,070 29,284 29,249 29,415 1.4 1.8 0.1
    Uttaranchal 40,933 40,104 44,474
    West Bengal 14,554 11,748 22,868 20,453 16,267 34,663 3.1 3.0 3.9
    N 3,01,658 2,00,179 1,01,479 7,09,291 4,56,571 2,52,720
    Poor 21,346 20,290 26,145 25,929 24,346 32,788 1.8 1.7 2.1
    Middle 19,218 17,034 25,673 28,895 24,015 42,841 3.8 3.2 4.8
    Rich 29,965 28,367 33,318 44,264 43,782 45,284 3.6 4.0 2.8

    Source: Authors’ calculations from NSS CDS for 1991 and 2002 AIDIS.

    save and invest and consequently, more rapid accumulation in Figure 3: Output Growth and Changes in Wealth Inequality the middle and high income states. It is interesting to note too

    12

    Orissathat growth in asset holdings has been fastest in the urban areas

    of the middle income states, regions which include dynamic ur

    10

    Karnataka

    ban centers such as Hyderabad and Bangalore. Figure 3 provides

    a scatter plot of the growth rate in real per capita state domestic

    product (SDP) and the change in the wealth Gini by state. As is

    evident, there is a definite positive correlation (correlation

    coefficient, r=0.3) between these variables and if Orissa (which

    is an outlier) is removed, this correlation goes up to 0.66. This

    implies that income growth and wealth inequality have become

    significantly intertwined during the period of liberalisation.

    Tables 10A and 10B provide means according to other divi

    sions available for 1991 and 2002 data. There are substantial dif-

    Percentage change in wealth Gini

    8

    Andhra Pradesh

    West Bengal

    6

    Gujarat

    Punjab Haryana4 Kerala

    2

    Bihar

    0 1

    2 2 3 3

    4 4 5 5 6 6 Rajasthan

    Uttar Pradesh

    -2

    Madhya Pradesh

    Maharashtra

    Tamil Nadu

    -4

    ferences (as might be expected) among caste and religious groups. While a direct comparison according to the 2002 defini

    -6

    tions of social groups is not possible for all groups using 1991 Annual Real Growth Rate of SDP (1991-2002) Source: Authors’ calculations from NSS CDS for 1991 and 2002 AIDIS.

    data, there continues to be a significantly different level of asset

    Data for real net SDP from CSO.

    holdings for scheduled castes/scheduled tribes (SC/STs) vs others. For the 2002-03 survey that collects data on other back-Equally, the 2002 survey data shows that there are large differward classes (OBCs) and “others” apart from SC/STs, there are ences in wealth holdings among religious groups. Muslims with expected differences in wealth holdings across these groups with average per capita asset holdings of about Rs 20,250 are the “others” being the wealthiest, SC/STs being the poorest, and poorest, compared to the somewhat wealthier Hindus with per OBCs falling in the middle. The wealth hierarchy matches the capita asset holdings of about Rs 30,500 and Jains with caste hierarchy. Rs 1,03,900 being the wealthiest.

    Table 9b: State-wise Break-up of Net Worth

    State/Region 1991 (in Rs) 2002 (in Rs) Implied Annual Growth Rate (Per Cent)
    Overall Rural Urban Overall Rural Urban Overall Rural Urban
    Andamans 15,693 15,041 17,184 34,551 32,060 39,816 7.4 7.1 7.9
    Andhra 14,511 12,727 20,152 21,253 15,757 36,394 3.5 2.0 5.5
    Arunachal Pradesh 12,196 13,030 6,096 12,938 13,118 11,662 0.5 0.1 6.1
    Assam 11,866 10,830 24,161 15,883 14,568 28,338 2.7 2.7 1.5
    Bihar 17,698 17,509 18,943 19,450 18,569 27,091 0.9 0.5 3.3
    Chandigarh 30,897 17,134 32,986 67,792 35,299 70,476 7.4 6.8 7.1
    Chhattisgarh 19,800 18,613 27,206
    Dadra/Nagar Haveli 14,285 13,484 25,340 30,084 28,859 44,658 7.0 7.2 5.3
    Daman/Diu 34,323 33,888 35,593 24,301 16,960 38,507 -3.1 -6.1 0.7
    Delhi 69,078 75,593 68,339 56,079 26,214 62,872 -1.9 -9.2 -0.8
    Goa 50,227 49,108 51,728 57,316 47,855 68,722 1.2 -0.2 2.6
    Gujarat 22,974 18,890 31,345 35,711 32,310 42,154 4.1 5.0 2.7
    Haryana 51,477 57,057 32,090 67,517 69,895 60,645 2.5 1.9 6.0
    Himachal Pradesh 26,530 25,389 41,530 54,542 54,064 59,178 6.8 7.1 3.3
    J and K 28,683 26,584 42,674 62,239 53,723 94,486 7.3 6.6 7.5
    Jharkhand 17,000 15,452 23,532
    Karnataka 20,489 18,843 24,672 29,315 25,219 38,851 3.3 2.7 4.2
    Kerala 37,133 35,108 43,652 61,847 56,929 76,356 4.7 4.5 5.2
    Lakshadweep 64,376 50,605 76,155 60,499 50,812 65,826 -0.6 0.0 -1.3
    Madhya Pradesh 18,098 16,834 22,816 26,547 22,816 38,570 3.5 2.8 4.9
    Maharashtra 23,587 17,972 33,344 32,607 26,594 41,092 3.0 3.6 1.9
    Manipur 16,932 16,335 18,593 23,234 18,721 35,479 2.9 1.2 6.1
    Meghalaya 13,973 10,471 30,702 31,789 24,487 72,137 7.8 8.0 8.1
    Mizoram 10,667 7,794 20,248 33,040 15,136 64,977 10.8 6.2 11.2
    Nagaland 16,564 14,350 20,441 76,641 85,387 55,587 14.9 17.6 9.5
    Orissa 9,564 8,701 16,489 12,307 10,556 23,878 2.3 1.8 3.4
    Pondicherry 25,147 16,106 30,306 36,258 24,705 42,703 3.4 4.0 3.2
    Punjab 55,510 56,905 52,410 75,645 85,599 54,230 2.9 3.8 0.3
    Rajasthan 28,695 27,612 32,617 34,437 31,936 43,445 1.7 1.3 2.6
    Sikkim 25,826 26,839 16,932 25,756 23,195 46,875 0.0 -1.3 9.7
    Tamil Nadu 18,844 13,975 27,739 27,748 23,241 37,033 3.6 4.7 2.7
    Tripura 12,287 10,654 32,805 13,235 10,559 32,446 0.7 -0.1 -0.1
    Uttar Pradesh 24,826 23,860 28,716 28,860 28,801 29,077 1.4 1.7 0.1
    Uttaranchal 40,757 39,990 44,035
    West Bengal 14,258 11,494 22,450 19,993 15,926 33,801 3.1 3.0 3.8
    Poor 21,054 20,022 25,745 25,384 23,826 32,132 1.7 1.6 2.0
    Middle 18,670 16,569 24,879 27,699 22,968 41,220 3.7 3.0 4.7
    Rich 29,256 27,818 32,273 42,958 42,499 43,927 3.6 3.9 2.8

    Source: Authors’ calculations from NSS CDS for 1991 and 2002 AIDIS.

    Educational and occupational differences also are strongly correlated with average wealth holdings. Unsurprisingly, wealth levels rise with the educational level of the head of the household with individuals from households with a graduate head having about twice the average wealth as those from one with a head who has a secondary school certificate (Rs 91,200 vs Rs 49,500)

    Table 10a: Asset Holdings by Household Characteristics

    (in Rs)

    1991 2002 Overall Rural Urban Overall Rural Urban

    Scheduled caste 10,336 9,976 12,114 14,293 13,520 17,480 Scheduled tribe 10,754 10,399 14,687 15,677 14,725 25,414 Other backward castes 28,161 26,975 32,291 Others 27,436 24,928 33,793 48,761 44,030 56,772 Buddhist 18,377 16,168 23,179 Christian 49,525 43,722 62,622 Hindu 30,597 26,576 43,608 Jain 1,03,990 97,407 1,05,852 Muslim 20,250 20,021 20,672 Other 24,304 23,992 26,668 Parsee 65,236 20,498 83,320 Sikh 1,00,272 1,03,622 90,976 Illiterate 19,107 Middle school 26,380 Secondary school 49,565 Graduate 91,282 Self-employed in

    non-agriculture 20,610 Agricultural labour 8,728 Other labour 13,695 Self-employed in agriculture 42,638 Others 34,669 Self-employed 47,534 Regular wage earner 39,608 Casual labour 10,961 Others 69,688

    Source: Authors’ calculations from NSS CDS for 1991 and 2002 AIDIS.

    Table 10b: Net Worth by Household Characteristics

    (in Rs)

    1991 2002 Overall Rural Urban Overall Rural Urban

    Scheduled caste 10,013 9,693 11,588 13,750 13,024 16,746 Scheduled tribe 10,575 10,233 14,352 15,277 14,380 24,450 Other backward castes 27,255 26,137 31,149 Others 26,907 24,512 32,977 47,583 42,963 55,408 Buddhist 17,725 15,618 22,305 Christian 48,064 42,403 60,838 Hindu 29,685 25,790 42,287 Jain 1,02,385 94,827 1,04,523 Muslim 19,771 19,538 20,197 Other 24,169 23,853 26,567 Parsee 65,226 20,498 83,305 Sikh 98,595 1,01,786 89,738 Illiterate 19,107 Middle school 26,380 Secondary school 49,565 Graduate 91,282 Self-employed in non-

    agriculture 13,696 20,610 Agricultural labour 6,198 8,728 Other labour 9,496 13,695 Self-employed in

    agriculture 30,880 42,638 Others 21,262 34,669 Self-employed 33,761 47,534 Regular wage earner 27,872 39,608 Casual labour 8,847 10,961 Others 60,666 69,688

    and nearly five times that of individuals from a household with an illiterate head.

    Individuals in households classified as self-employed in agriculture in rural areas enjoy the largest amount of wealth by occupational category in this sector, with an average wealth of Rs 42,000. In contrast, individuals from households classified as performing agricultural labour have an average wealth of only Rs 8,700. In urban areas, the self-employed and “others” have the highest average wealth, while those individuals who are classified as providing casual labour have the lowest average wealth.

    III Discussion and Conclusions

    Section II brings out two patterns in the Indian wealth distribution that apply for most classifications. First, the majority of the population, divided along axes such as caste, size-distribution or occupation has, on an average, witnessed increases in its absolute wealth levels during the period of liberalisation. Second, these impressive increases in wealth levels have been unequal across different groups and axes. Even with significant underreporting and under-sampling problems at the very top, there seems to be a clear trend of the wealthiest 20 per cent diverging away from the rest of the population. In particular, the top 1 per cent is making solid gains relative to the rest of the population. These add up to the inference that the first decade of reforms witnessed an impressive increase in wealth as well as a rise in its concentration, especially at the upper end.

    Wealth Increases

    With a per capita net worth annual growth rate close to 3 per cent between 1991-92 and 2002-03, it can be inferred that the growth experience of liberalisation has also translated into rapid accumulation of household assets across the country. A notable feature is the growth explosion of financial assets and transportation. As wealthy elites in Indian towns and cities deepen their engagement in the financial sector (notably, formal banking and stock markets), this is to be expected. Similarly, even casual observation in urban India suggests that household private transportation skyrocketed during the liberalisation period.12

    This story is also replicated across caste, occupational groups, rural and urban groups and so forth. Each group has witnessed accumulation of household net assets even if the rates are different across groups. However, these results hide the fact that about 1-2 per cent of the studied population has zero or negative net worth. This group not only does not witness wealth increases but, in fact, it might be in serious distress with no productive assets or collateral to count on during trying times. The proportion of individuals in this category has risen from 1 per cent of the sample population in 1991 to 1.5 per cent of the sample population in 2002.

    Wealth Disparities

    The Gini figures of over 0.64 for per capita net worth for the entire population show a significant wealth concentration in India in both these surveys. At the same time, there is also a per-

    Source: Authors’ calculations from NSS CDS for 1991 and 2002 AIDIS. ceptible trend of increasing disparities as the Gini rose from 0.64

    National Interest?

    to 0.66 between 1991 and 2002. As we pointed out above in Section I, this increase is most likely to be understated.

    One can tell a cautious story of divergence between the upper tail of the interpersonal distribution and the rest. From Table 6 on the decile share of wealth, it can be observed that the share of wealth of the bottom 80 per cent of the population seems to have been either stagnant or suffered mild declines whereas the upper 20 per cent has tended to consolidate its holdings. This indicates that India is shining more brightly on 20 per cent of its population, a phenomenon noted by several observers.

    With the agrarian distress escalating in different parts of the country, there is a concern regarding the rural-urban gap that is widening over time. Our results capture this phenomenon wherein the ratio of urban net wealth to rural net wealth in per capita terms has risen from 1.37 in 1991-92 to about 1.5 in 2002-03 (Table 11). It is also notable that the urban Gini for net worth remains somewhat stable over this period since among other factors, there is also considerable distress migration into urban areas during this period. Indeed this may be behind some of the seemingly anomalous results at the state level (for example, Delhi’s decline in average per capita assets).

    The state level data might throw some light on why the overall Gini for net worth is moving somewhat sluggishly. The rich and the poor states seem to be clearly diverging in terms of the overall accumulation of assets. However, the middle income states such as Karnataka, Andhra Pradesh and Kerala with their phenomenal urban growth seem to have witnessed an impressive accumulation of household assets, although this phenomenon needs to be understood more clearly through further disaggregated analysis. At the same time, some of the largest increases in within-state wealth inequality have happened in these states (Figure 3). We surmise at this juncture that the sluggish nature of the Gini hides a complex story of convergence in average household wealth terms between the rich and middle income states and divergence between these the poor ones.

    While a longitudinal comparison cannot be made among religious, educational and detailed caste groups because of the unavailability of data, the picture of stark disparities among these groups is evident. These data point to a strong social basis for economic exclusion and perpetuation of poverty among the asset poor communities such as SC and ST, Muslims, and the uneducated.

    While we did not trace causal implications between changes in economic policies and trends in wealth distribution in the above analysis, there are several plausible linkages that can be examined separately. For example, has increased openness to international trade and capital flows altered patterns of wealth creation

    Table 11: Comparison of Rural-Urban Ratios of Assets and Per Capita Net Worth

    1991 2002

    Ratio of urban per capita assets to rural per capita assets 1.50 1.50 Ratio of urban per capita net worth to rural per capita net worth 1.37 1.50

    Source: Authors’ calculations from NSS CDS for 1991 and 2002 AIDIS.

    Table 12: Comparison of Gini of Per Capita Net Worth for India, China and US

    India (2002) China (2002) US (2001)

    0.66 0.55 0.78

    Sources: UNDP (2005), Table 2.12, Davies and Shorrocks 2005, Table 2.

    in ways that have led to the observed divergences of outcomes between states? Has financial liberalisation and increased capital mobility from abroad contributed to the uneven asset appreciation, which we observe in the data? These and many other questions need to be asked and considered with the AIDIS and other newly available data.

    In conclusion, we present a comparative picture from Table 12 of wealth inequalities from two other contexts. In the US, in 2001, the Gini index for wealth inequality is much higher than the same for India. In China, in 2002, the Gini for wealth inequality is considerably lower than the same for India. In the former case, this reflects the extreme wealth concentration after more than two centuries of unfettered accumulation by the elites. In the latter, it reflects the socialist past of China that has imposed a fetter until now, although recent trends indicate that it may not be so for too long. The Indian case during liberalisation reflects a trend of consolidation of already concentrated wealth among the elites, a trend that might have significant consequences for the economy, society and culture of India in the decades to come.

    EPW

    Email: arjun.jayadev@umb.edu

    Notes

    [For their comments on an earlier draft, we thank Ajit Zacharias of Levy Economics Institute, Ozgur Orhangazi and participants of the Eastern Economics Association meetings in New York in 2007. We also thank D Jayaraj and S Subramanian for sharing their work with us. We would also like to thank the referee for his or her comments on the paper.]

    1 Some useful efforts are Deaton and Dreze (2002), Sen and Himanshu (2004 a,b), Topalova (2005), Aghion et al (2005) and Kochhar et al (2006). It should be mentioned that the findings from this research have been somewhat controversial and bedeviled by issues of comparability of surveys between 1993-94 and 1999-2000 [see Deaton and Dreze (2002), Sen and Himanshu (2004 a) for an overview of this issue]. The most recent survey appears to allow for comparable results between 1993-94 and 2004-05 [Himanshu 2007; Dev and Ravi 2007].

    2 We are not alone in our estimation (and surprise) that these datasets have been underused. In an overview of wealth data in developing and transition countries, Davies and Shorrocks (2005, p 15) note that “While the results of the AIDIS are available in detailed publications, our searches have not produced much evidence of scholarly or public discussion of the survey or its results. This is puzzling and contrasts markedly with China”.

    3 Heterogeneity within the household is ignored. For instance, adults and children are treated as equivalent. Economies of scale within the household are inadequately captured and might lead to underestimation of the welfare of larger households [Sierminska and Smeeding 2005]. The per capita approach can be thought of as using the simplest possible equivalence scale. Unfortunately the literature on wealth inequality does not provide better equivalence scales that can be used in the Indian context.

    4 Researchers have used various measures of wealth in their analyses including total wealth, net worth, total fungible wealth and net fungible wealth, depending in part on the question that they wish to pose [see e g, Wolff and Zacharias 2006]. The categorisation of asset holdings in India does not allow for a clean distinction between fungible and non-fungible assets. As a result, we report our results only for total assets and net worth.

    5 The values were calculated by dividing household level asset holdings for each individual by the household size and using the household level survey weights. It should be noted that consistent with the NSS methodology, we treat households which do not report assets as holding zero assets in any category.

    6 The Society for Capital Market Research and Development carried out three surveys of household investors from 1990 to 1997, showing an increase in the total number of share owners from about 10 million to 20 million over the period (or about 1-2 per cent of the population). In another survey done by the SEBI and NCAER [SEBI-NCAER 2000, 2003] the proportion of all households in India who invest in the stock market was about 7.5 per cent, with about 15 per cent in urban areas and 4 per cent in non-urban areas investing in shares. The percentage of the population which invested in mutual funds was higher at about 10 per cent of all households

    7 These data are also available in the wealth surveys. 1 N N

    8 Note that the formula for the Gini is ——— ¾¾ | Wi – Wj |, 2N2 ¾ i=1 j=1

    where Wi is the wealth of individual i, N is the total number of individuals and μ is the mean wealth. 9 These results are not shown but are available upon request from the authors.

    10 See Moyes (1987) and Jenkins and Jantti (2005) for details. If LR(p; x) is the standard (or relative) Lorenz curve of distribution x, then the absolute Lorenz curve LA(p; x) can be derived from the standard Lorenz curve through the relationship: LA (p; x) = ¾(x) [LR (p; x) – p] ∀p ¾ [0.1] where ¾(x) is the mean of the distribution [Moyes 1987, p 205].

    11 See Moyes (1987) for a discussion of absolute inequality measures. These measures are invariant to equal absolute increments to all incomes (or wealth holdings). Note that the standard Gini does not satisfy this property since if all the incomes (or wealth holdings) are equally incremented, the mean increases but the absolute values of pair-wise differences remain the same. However, the absolute Gini which is the standard Gini multiplied by the mean, satisfies the above property.

    12 Only in the case of a few assets such as livestock and consumer durables have wealth levels declined in absolute value. Especially in the case of the latter, i e, consumer durables, this reported decline is puzzling given the apparent rapid accumulation of these assets among the middle class across the country during the 1990s.

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