case study on poverty in india for project class 10

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I.  Introduction

Ii.  the expenditure surveys, iii.  nsso versus nas expenditure estimates, iv.  the official poverty lines, v.  controversies regarding poverty lines 3, vi.  poverty at the national level, vii.  poverty in the states: rural and urban, viii.  poverty in the states by social group, ix.  poverty in the states by religious group, x.  inequality, xi.  concluding remarks, a comprehensive analysis of poverty in india.

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Arvind Panagariya , Megha Mukim; A Comprehensive Analysis of Poverty in India. Asian Development Review 2014; 31 (1): 1–52. doi: https://doi.org/10.1162/ADEV_a_00021

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This paper offers a comprehensive analysis of poverty in India. It shows that regardless of which of the two official poverty lines we use, we see a steady decline in poverty in all states and for all social and religious groups. Accelerated growth between fiscal years 2004–2005 and 2009–2010 also led to an accelerated decline in poverty rates. Moreover, the decline in poverty rates during these years has been sharper for the socially disadvantaged groups relative to upper caste groups so that we now observe a narrowing of the gap in the poverty rates between the two sets of social groups. The paper also provides a discussion of the recent controversies in India regarding the choice of poverty lines.

This paper provides comprehensive up-to-date estimates of poverty by social and religious groups in the rural and urban areas of the largest 17 states in India. The specific measure of poverty reported in the paper is the poverty rate or headcount ratio (HCR), which is the proportion of the population with expenditure or income below a pre-specified level referred to as the poverty line. In the context of most developing countries, the poverty line usually relates to a pre-specified basket of goods presumed to be necessary for above-subsistence existence.

In so far as prices vary across states and between rural and urban regions within the same state, the poverty line also varies in nominal rupees across states and between urban and rural regions within the same state. 1 Similarly, since prices rise over time due to inflation, the poverty line in nominal rupees in a given location is also adjusted upwards over time.

The original official poverty estimates in India, provided by the Planning Commission, were based on the Lakdawala poverty lines, so named after Professor D. T. Lakdawala who headed a 1993 expert group that recommended these lines. Recommendations of a 2009 expert committee headed by Professor Suresh Tendulkar led to an upward adjustment in the rural poverty line relative to its Lakdawala counterpart. Therefore, while the official estimates for earlier years were based on the lines and methodology recommended by the expert group headed by Lakdawala, those for more recent years were based on the line and methodology recommended by the Tendulkar Committee. Official estimates based on both methodologies exist for only two years, 1993–1994 and 2004–2005. These estimates are provided for the overall population, for rural and urban regions of each state, and for the country as a whole. The Planning Commission does not provide estimates by social or religious groups.

In this paper, we provide estimates using Lakdawala and Tendulkar lines for different social and religious groups in rural and urban areas in all major states and at the national level. Our estimates based on Lakdawala lines are computed for all years beginning in 1983 for which large or “thick” expenditure surveys have been conducted. Estimates based on the Tendulkar line and methodology are provided for the three latest large expenditure surveys, 1993–1994, 2004–2005, and 2009–2010.

Our objective in writing the paper is twofold. First, much confusion has arisen in the policy debates in India around certain issues regarding poverty in the country—for instance, whether or not growth has helped the poor (if yes, how much and over which time period) and whether growth is leaving certain social or religious groups behind. We hope that by providing poverty estimates for various time periods, social groups, religious groups, states, and urban and rural areas, this paper will help ensure that future policy debates are based on fact. Second, researchers interested in explaining how various policy measures impact poverty might find it useful to have the poverty lines and the associated poverty estimates for various social and religious groups and across India's largest states in rural and urban areas readily available in one place.

The literature on poverty in India is vast and many of the contributions or references to the contributions can be found in Srinivasan and Bardhan ( 1974 , 1988 ), Fields ( 1980 ), Tendulkar ( 1998 ), Deaton and Drèze ( 2002 ), Bhalla ( 2002 ), and Deaton and Kozel ( 2005 ). Panagariya ( 2008 ) provides a comprehensive treatment of the subject until the mid-2000s including the debates on whether or not poverty had declined in the post-reform era and whether or not reforms had been behind the acceleration in growth rates and the decline in poverty. Finally, several of the contributions in Bhagwati and Panagariya ( 2012a , 2012b ) analyze various aspects of poverty in India using the expenditures surveys up to 2004–2005. In particular, Cain, Hasan, and Mitra ( 2012 ) study the impact of openness on poverty; Mukim and Panagariya ( 2012 ) document the decline in poverty across social groups; Dehejia and Panagariya ( 2012 ) provide evidence on the growth in entrepreneurship in services sectors among the socially disadvantaged groups; and Hnatkovska and Lahiri ( 2012 ) provide evidence on and reasons for narrowing wage inequality between the socially disadvantaged groups and the upper castes.

To our knowledge, this is the first paper to systematically and comprehensively exploit the expenditure survey conducted in 2009–2010. This is important because growth was 2–3 percentage points higher between 2004–2005 and 2009–2010 surveys than between any other prior surveys. As such, we are able to study the differential impact accelerated growth has had on poverty alleviation both directly, through improved employment and wage prospects for the poor, and indirectly, through the large-scale redistribution program known as the National Rural Employment Guarantee Scheme, which enhanced revenues made possible. In addition, ours is also the first paper to comprehensively analyze poverty across religious groups. In studying the progress in combating poverty across social groups, the paper complements our previous work, Mukim and Panagariya ( 2012 ).

The paper is organized as follows. In Section II , we discuss the history and design of the expenditure surveys conducted by the National Sample Survey Office (NSSO), which form the backbone of all poverty analysis in India. In Section III , we discuss the rising discrepancy between average expenditures as reported by the NSSO surveys and by the National Accounts Statistics (NAS) of the Central Statistical Office (CSO). In Section IV , we describe in detail the evolution of official poverty lines in India, while in Section V we discuss some recent controversies regarding the level of the official poverty line. In Sections VI to Section IX , we present the poverty estimates. In Section X , we discuss inequality over time in rural and urban areas of the 17 states. In Section XI , we offer our conclusions.

The main source of data for estimating poverty in India is the expenditure survey conducted by the NSSO. India is perhaps the only developing country that began conducting such surveys on a regular basis as early as 1950–1951. The surveys have been conducted at least once a year since 1950–1951. However, the sample had been too small to permit reliable estimates of poverty at the level of the state until 1973–1974. A decision was made in the early 1970s to replace the smaller annual surveys by large-size expenditure (and employment–unemployment) surveys to be conducted every 5 years.

This decision led to the birth of “thick” quinquennial (5-yearly) surveys. Accordingly, the following 8 rounds of large-size surveys have been conducted: 27 (1973–1974), 32 (1978), 38 (1983), 43 (1987–1988), 50 (1993–1994), 55 (1999–2000), 61 (2004–2005), and 66 (2009–2010). Starting from the 42nd round in 1986–1987, a smaller expenditure survey was reintroduced. This was conducted annually except during the years in which the quinquennial survey was to take place. Therefore, with the exception of the 65th and 67th rounds in 2008–2009 and 2010–2011, respectively, an expenditure survey exists for each year beginning 1986–1987.

While the NSSO collects the data and produces reports providing information on monthly per-capita expenditures, it is the Planning Commission that computes the poverty lines and provides official estimates of poverty. The official estimates are strictly limited to quinquennial surveys. While they cover rural, urban, and total populations in different states and at the national level, estimates are not provided for specific social or religious groups. These can be calculated selectively for specific groups or specific years by researchers. With rare exceptions, discussions and debates on poverty have been framed around the quinquennial surveys even though the other survey samples are large enough to allow reliable estimates at the national level.

For each household interviewed, the survey collects data on the quantity of and expenditure on a large number of items purchased. For items such as education and health services, where quantity cannot be meaningfully defined, only expenditure data are collected. The list of items is elaborate. For example, the 66th round collected data on 142 items under the food category; 15 items under energy; 28 items under clothing, bedding, and footwear; 19 items under educational and medical expenses; 51 items under durable goods; and 89 in the other items category.

It turns out that household responses vary systematically according to the length of the reference period to which the expenditures are related. For example, a household could be asked about its expenditures on durable goods during the preceding 30 days or the preceding year. When the information provided in the first case is converted into annual expenditures, it is found to be systematically lower than when the survey directly asks households to report their annual spending. Therefore, estimates of poverty vary depending on the reference period chosen in the questionnaire.

Most quinquennial surveys have collected information on certain categories of relatively infrequently purchased items including clothing and consumer durables on the basis of both 30-day and 365-day reference periods. For other categories, including all food and fuel and consumer services, they have used a 30-day reference period. The data allow us to estimate two alternative measures of monthly per-capita expenditures that refer to the following: (i) a uniform reference period (URP) where all expenditure data used to estimate monthly per-capita expenditure are based on the 30-day reference period, and (ii) a mixed reference period (MRP) where expenditure data used to estimate the monthly per-capita expenditure are based on the 365-day reference period in the case of clothing and consumer durables and the 30-day reference period in the case of other items.

With rare exceptions, monthly per-capita expenditure associated with the MRP turns out to be higher than that associated with the URP. The Planning Commission's original estimate of poverty that employed the Lakdawala poverty lines had relied on the URP monthly per-capita expenditures. At some time prior to the Tendulkar Committee report, however, the Planning Commission decided to shift to the MRP estimates. Therefore, while recommending revisions that led to an upward adjustment in the rural poverty line, the Tendulkar Committee also shifted to the MRP monthly per-capita expenditures in its poverty calculations. Therefore, the revised poverty estimates available for 1993–1994, 2004–2005, and 2009–2010 are based on the Tendulkar lines and the MRP estimates of monthly per-capita expenditures.

We note an important feature of the NSSO expenditure surveys at the outset. The average monthly per-capita expenditure based on the surveys falls well short of the average private consumption expenditure separately available from the NAS of the CSO. Moreover, the proportionate shortfall has been progressively rising over successive surveys. These two observations hold regardless of whether we use the URP or MRP estimate of monthly per-capita expenditure available from the NSSO. Figure 1 graphically depicts this phenomenon in the case of URP monthly per-capita expenditure, which is more readily available for all quinquennial surveys since 1983.

NSSO Household Total URP Expenditure Estimate as % of NAS Total Private Consumption Expenditure

Precisely what explains the gap between the NSSO and NAS expenditures has important implications for poverty estimates. For example, if the gap in any given year is uniformly distributed across all expenditure classes as Bhalla ( 2002 ) assumes in his work, true expenditure in 2009–2010 is uniformly more than twice of what the survey finds. This would imply that many individuals currently classified as falling below the poverty line are actually above it. Moreover, a recognition that the proportionate gap between NSSO and NAS private expenditures has been rising over time implies that the poverty ratio is being overestimated by progressively larger margins over time. At the other extreme, if the gap between NSSO and NAS expenditures is explained entirely by underreporting of the expenditures by households classified as non-poor, poverty levels will not be biased upwards.

There are good reasons to believe, however, that the truth lies somewhere between these two extremes. The survey underrepresents wealthy consumers. For instance, it is unlikely that any of the billionaires, or most of the millionaires, are covered by the survey. Likewise, the total absence of error among households below the poverty line is highly unlikely. For example, recall that the expenditures on durables are systematically underreported for the 30-day reference period relative to that for 365-day reference period. Thus, in all probability, households classified as poor account for part of the gap so that there is some overestimation of the poverty ratio at any given poverty line. 2

The 1993 expert group headed by Lakdawala defined all-India rural and urban poverty lines in terms of per-capita total consumption expenditure at 1973–1974 market prices. The underlying consumption baskets were anchored to the per-capita calorie norms of 2,400 and 2,100 in rural and urban areas, respectively. The rural and urban poverty line baskets were based on different underlying baskets, which meant that the two poverty lines represented different levels of real expenditures.

State-level rural poverty lines were derived from the national rural poverty line by adjusting the latter for price differences between national and state-level consumer price indices for agricultural laborers. Likewise, state-level urban poverty lines were derived from the national urban poverty line by adjusting the latter for price differences between the national and state-level consumer price indices for industrial laborers. National and state-level rural poverty lines were adjusted over time by applying the national and state-level price indices for agricultural workers, respectively. Urban poverty lines were adjusted similarly over time.

Lakdawala lines served as the official poverty lines until 2004–2005. The Planning Commission applied them to URP-based expenditures in the quinquennial surveys to calculate official poverty ratios. Criticisms of these estimates on various grounds led the Planning Commission to appoint an expert group under the chairmanship of Suresh Tendulkar in December 2005 with the directive to recommend appropriate changes in methodology for computing poverty estimates. The group submitted its report in 2009.

In its report, the Tendulkar committee noted three deficiencies of the Lakdawala poverty lines (Government of India 2009 ). First, the poverty line baskets remained tied to consumption patterns observed in 1973–1974. But more than 3 decades later, these baskets had shifted, even for the poor. Second, the consumer price index for agricultural workers understated the true price increase. This meant that over time the upward adjustment in the rural poverty lines was less than necessary so that the estimated poverty ratios understated rural poverty. Finally, the assumption underlying Lakdawala lines that health and education would be largely provided by the government did not hold any longer. Private expenditures on these services had risen considerably, even for the poor. This change was not adequately reflected in the Lakdawala poverty lines.

To remedy these deficiencies, the Tendulkar committee began by noting that the NSSO had already decided to shift from URP-based expenditures to MRP-based expenditures to measure poverty. With this in view, the committee's first step was to situate the revised poverty lines in terms of MRP expenditures in some generally acceptable aspect of the existing practice. To this end, it observed that since the nationwide urban poverty ratio of 25.7%, calculated from URP-based expenditures in the 2004–2005 survey, was broadly accepted as a good approximation of prevailing urban poverty, the revised urban poverty line could be anchored to yield this same estimate using MRP-based per-capita consumption expenditure from the 2004–2005 survey. This decision led to MRP-based per-capita expenditure of the individual at the 25.7 percentile in the national distribution of per-capita MRP expenditures becoming the national urban poverty line.

The Tendulkar committee further argued that the consumption basket associated with the national urban poverty line also be accepted as the rural poverty line consumption basket. This implied the translation of the new urban poverty line using the appropriate price index to obtain the nationwide rural poverty line. Under this approach, rural and urban poverty lines became fully aligned. Applying MRP-based expenditures, the new rural poverty line yielded a rural poverty ratio of 41.8% in 2004–2005 compared with 28.3% under the old methodology.

It is important to note that even though the method of pegging the national urban poverty line in the manner done by the Tendulkar committee left the national urban poverty in 2004–2005 originally measured at the Lakdawala urban poverty line unchanged, it did impact state-level urban poverty estimates. The methodology required that the state-level rural and urban poverty lines be derived from the national urban poverty line by applying the appropriate price indices derived from the price information within the sample surveys. In some cases, the state-level shift was sufficiently large to significantly alter the estimate of urban poverty. For example, Lakdawala urban poverty line in Gujarat in 2004–2005 was Rs541.16 per-capita per month. The corresponding Tendulkar line turned out to be Rs659.18. This change led the urban poverty estimate in 2004–2005 to jump from 13.3% based on the Lakdawala line to 20.1% based on the Tendulkar line.

An important final point concerns the treatment of health and education spending by the Tendulkar Committee in recommending the revised poverty lines. On this issue, it is best to directly quote the Tendulkar Committee report (Government of India 2009 , p. 2):

Even while moving away from the calorie norms, the proposed poverty lines have been validated by checking the adequacy of actual private expenditure per capita near the poverty lines on food, education, and health by comparing them with normative expenditures consistent with nutritional, educational, and health outcomes. Actual private expenditures reported by households near the new poverty lines on these items were found to be adequate at the all-India level in both the rural and the urban areas and for most of the states. It may be noted that while the new poverty lines have been arrived at after assessing the adequacy of private household expenditure on education and health, the earlier calorie-anchored poverty lines did not explicitly account for these. The proposed poverty lines are in that sense broader in scope.

We address here the two rounds of controversies over the poverty line that broke out in the media in September 2011 and March 2012. The first round of controversy began with the Planning Commission filing an affidavit with the Supreme Court stating that the poverty line at the time had been on average Rs32 and Rs26 per person per day in urban and rural India, respectively. Being based on the Tendulkar methodology, these lines were actually higher than the Lakdawala lines on which the official poverty estimates had been based until 2004–2005. However, the media and civil society groups pounced on the Planning Commission for diluting the poverty lines so as to inflate poverty reduction numbers and to deprive many potential beneficiaries of entitlements. For its part, the Planning Commission did a poor job of explaining to the public precisely what it had done and why.

The controversy resurfaced in March 2012 when the Planning Commission released the poverty estimates based on the 2009–2010 expenditure survey. The Planning Commission reported that these estimates were based on average poverty lines of Rs28.26 and Rs22.2 per person per day in urban and rural areas, respectively. Comparing these lines to those previously reported to the Supreme Court, the media once again accused the Planning Commission of lowering the poverty lines. 4 The truth of the matter was that whereas the poverty lines reported to the Supreme Court were meant to reflect the price level prevailing in mid-2011, those underlying poverty estimates for 2009–2010 were based on the mid-point of 2009–2010. The latter poverty lines were lower because the price level at the mid-point of 2009–2010 was lower than that in mid-2011. In real terms, the two sets of poverty lines were identical.

While there was no basis to the accusations that the Planning Commission had lowered the poverty lines, the issue of whether the poverty lines remain excessively low despite having been raised does require further examination. In addressing this issue, it is important to be clear about the objectives behind the poverty line.

Potentially, there are two main objectives behind poverty lines: to track the progress made in combating poverty and to identify the poor towards whom redistribution programs can be directed. The level of the poverty line must be evaluated separately against each objective. In principle, we may want separate poverty lines for the two objectives.

With regard to the first objective, the poverty line should be set at a level that allows us to track the progress made in helping the truly destitute or those living in abject poverty, often referred to as extreme poverty. Much of the media debate during the two episodes focused on what could or could not be bought with the poverty-line expenditure. 5 There was no mention of the basket of goods that was used by the Tendulkar Committee to define the poverty line.

In Annex E of its report (Government of India 2009 ), the Tendulkar Committee gave a detailed itemized list of the expenditures of those “around poverty line class for urban areas in all India.” Unfortunately, it did not report the corresponding quantities purchased of various commodities. In this paper, we now compute these quantities from unit-level data where feasible and report them in Table 1 for a household consisting of five members. 6 Our implicit per-person expenditures on individual items are within Rs3 of their corresponding expenditures reported in Annex E of the report of the Tendulkar Committee.

Source: Authors’ calculations using unit-level data (supplied by Rahul).

We report quantities wherever the relevant data are available. In the survey, the quantities are not always reported in weights. For example, lemons and oranges are reported in numbers and not in kilograms. In these cases, we have converted the quantities into kilograms using the appropriate conversion factors. The main point to note is that while the quantities associated with the poverty line basket may not permit a comfortable existence, including a balanced diet, they allow above-subsistence existence. The consumption of cereals and pulses at 50.9 kilograms (kg) and 3.5 kg compared with 48 kg and 5.5 kg, respectively, for the mean consumption of the top 30% of the population. Likewise, the consumption of edible oils and vegetables at 2.7 kg and 23.9 kg for the poor compared with 4.5 kg and 35.5 kg, respectively, for the top 30% of the population. 7 This comparison shows that, at least in terms of the provision of two square meals a day, the poverty line consumption basket is compatible with above-subsistence level consumption.

We reiterate our point as follows. In 2009–2010, the urban poverty line in Delhi was Rs1,040.3 per person per month (Rs34.2 per day). For a family of five, this amount would translate to Rs5,201.5 per month. Assuming that each family member consumes 10 kg per month of cereal and 1 kg per month of pulses and the prices of the two grains are Rs15 and Rs80 per kilogram, respectively, the total expenditure on grain would be Rs1,150. 8 This would leave Rs4,051.5 for milk, edible oils, fuel, clothing, rent, education, health, and other expenditures. While this amount may not allow a fully balanced diet, comfortable living, and access to good education and health, it is consistent with an above-subsistence level of existence. Additionally, if we take into account access to public education and health, and subsidized grain and fuel from the public distribution system, the poverty line is scarcely out of line with the one that would allow exit from extreme poverty.

But what about the role of the poverty line in identifying the poor for purposes of redistribution? Ideally, this exercise should be carried out at the local level in light of resources available for redistribution, since the poor must ultimately be identified locally. Nevertheless, if the national poverty line is used to identify the poor, could we still defend the Tendulkar line as adequate? We argue in the affirmative.

Going by the urban and rural population weights of 0.298 and 0.702 implicit in the population projections for 1 January 2010, the average countrywide per-capita MRP expenditure during 2009–2010 amounts to Rs40.2 per person per day. Therefore, going by the expenditure survey data, equal distribution across the entire country would allow barely Rs40.2 per person per day in expenditures. Raising the poverty line significantly above the current level must confront this limit with regard to the scope for redistribution.

It could be argued that this discussion is based on data in the expenditure survey, which underestimates true expenditures. The scope for redistribution might be significantly greater if we go by expenditures as measured in the NAS. The response to this criticism is that the surveys underestimate not just the average national expenditure but also the expenditures of those identified as poor. Depending on the extent of this underestimation, the need for redistribution itself would be overestimated.

Even so, it is useful to test the limits of redistribution by considering the average expenditure according to the NAS. The total private final consumption expenditure at current prices in 2009–2010 was Rs37,959.01 billion. Applying the population figure of 1.174 billion as of 1 January 2010 in the NSSO 2009–2010 expenditure survey, this total annual expenditure translates to daily spending of Rs88.58 per person. This figure includes certain items such as imputed rent on owner-occupied housing and expenditures other than those by households such as the spending of civil society groups, which would not be available for redistribution. Thus, per-capita expenditures achievable through equal distribution, even when we consider the expenditures as per the NAS, is likely to be modest.

To appreciate further the folly of setting too high a poverty line for the purpose of identifying the poor, recall that the national average poverty line was Rs22.2 per person per day in rural areas and Rs28.26 in urban areas in 2009–2010. Going by the expenditure estimates for different spending classes in Government of India ( 2011a ), raising these lines to just Rs33.3 and Rs45.4, respectively, would place 70% of the rural population and 50% of the urban population in poverty in 2009–2010. If we went a little further and set the rural poverty line at Rs39 per day and the urban poverty line at Rs81 per day in 2009–2010, we would place 80% of the population in each region below the poverty line. Will the fate of the destitute not be compromised if the meager tax revenues available for redistribution were thinly spread on this much larger population?

Before we turn to reporting the poverty estimates, we should clarify that while we have defended the current poverty line in India for both purposes—tracking abject poverty and redistribution—in general, we believe a case exists for two separate poverty lines to satisfy the two objectives. The poverty line to track abject poverty must be drawn independently of the availability of revenues for redistribution purposes and should be uniform nationally. The poverty line for redistribution purposes would in general differ from this line and, indeed, vary in different jurisdictions of the same nation depending on the availability of revenues. This should be evident from the fact that redistribution remains an issue even in countries that have entirely eradicated abject poverty. 9

Official poverty estimates are available at the national and state levels for the entire population, but not by social or religious groups, for all years during which the NSSO conducted quinquennial surveys. These years include 1973–1974, 1977–1978, 1983, 1987–1988, 1993–1994, 2004–2005, and 2009–2010, but not 1999–2000, as that year's survey became noncomparable to other quinquennial surveys due to a change in sample design. The Planning Commission has published poverty ratios for the first six of these surveys based on the Lakdawala lines and for the last three based on the Tendulkar lines. These ratios were estimated for rural and urban areas at the national and state levels.

In this paper, we provide comparable poverty rates for all of the last five quinquennial surveys including 2009–2010 derived from Lakdawala lines. For this purpose, we update the 2004–2005 Lakdawala lines to 2009–2010 using the price indices implicit in the official Tendulkar lines for 2004–2005 and 2009–2010 at the national and state levels. We provide estimates categorized by social as well as religious groups for all quinquennial surveys beginning in 1983 based on the Lakdawala lines and for the years relating to the last three such surveys based on the Tendulkar lines at the national and state levels.

While we focus mainly on the evolution of poverty since 1983 in this paper, it is useful to begin with a brief look at the poverty profile in the early years. This is done in Figure 2 using the estimates in Datt ( 1998 ) for years 1951–1952 to 1973–1974. The key message of the graph is that the poverty ratio hovered between 50% and 60% with a mildly rising trend.

The Poverty Ratio in India, 1951–1952 to 1973–1974 (%)

This is not surprising, as India had been extremely poor at independence. Unlike economies such as Taipei, China; the Republic of Korea; Singapore; and Hong Kong, China, the country then grew very slowly. Growth in per-capita income during these years had been a mere 1.5% per year. Such low growth coupled with a very low starting per-capita income meant at best limited scope for achieving poverty reduction even through redistribution. As argued above, even today, after more than 2 decades of almost 5% growth in per-capita income, the scope for redistribution remains limited. 10

We are now in a position to provide the poverty rates for the major social groups based on the quinquennial expenditure surveys beginning 1983. The social groups identified in the surveys are scheduled castes (SC), scheduled tribes (ST), other backward castes (OBC), and the rest, which we refer to as forward castes (FC). In addition, we define the nonscheduled castes as consisting of the OBC, and FC. The NSSO began identifying the OBC beginning 1999–2000. Since we are excluding this particular survey due to its lack of comparability with other surveys, the OBC as a separate group begins appearing in our estimates from 2004–2005 only.

In Table 2 , we provide the poverty rates based on the Lakdawala lines in rural and urban areas and at the national level. Four features of this table are worthy of note. First, poverty rates have continuously declined for every single social group in both the rural and urban areas. Contrary to common claims, growth has been steadily helping the poor from every broad social group escape poverty rather than leaving the socially disadvantaged behind.

FC = forward castes, NS = non-scheduled, OBC = other backward castes, SC = scheduled castes, ST = scheduled tribes.

Source: Authors’ calculations.

Second, the rates in rural India have consistently been the highest for the ST followed by the SC, OBC, and FC in that order. This pattern also holds in urban areas but with some exceptions. In particular, in some years, poverty rates of scheduled tribes are lower than that of scheduled castes, but this is not of great significance since more than 90% of the scheduled tribe population live in rural areas.

Third, with growth accelerating to above 8% beginning 2003–2004, poverty reduction between 2004–2005 and 2009–2010 has also accelerated. The percentage point reduction during this period has been larger than during any other 5-year period. Most importantly, the acceleration has been the greatest for the ST and SC in that order so that at last, the gap in poverty rates between the scheduled and nonscheduled groups has declined significantly.

Finally, while the rural poverty rates were slightly higher than the urban poverty rates for all groups in 1983, the order switched for one or more groups in several of the subsequent years. Indeed, in 2009–2010, the urban rates turned out to be uniformly higher for every single group. This largely reflects progressive misalignment of the rural and urban poverty lines with the former becoming lower than the latter. It was this misalignment that led the Tendulkar Committee to revise the rural poverty line and realign it to the higher, urban line.

Table 3 reports the poverty estimates based on the Tendulkar lines. Recall that the Tendulkar line holds the urban poverty ratio at 25.7% in 2004–2005 when measuring poverty at MRP expenditures. Our urban poverty ratio in Table 3 reproduces this estimate within 0.1 of a percentage point.

The steady decline in poverty rates for the various social groups in rural as well as urban areas, which we noted based on the Lakdawala lines in Table 2 , remains valid at the Tendulkar lines. Moreover, rural poverty ratios turn out to be higher than their urban counterparts for each group in each year. As in Table 2 , the decline had been sharpest during the high-growth period between 2004–2005 and 2009–2010.

Finally and most importantly, the largest percentage-point decline between these years in rural and urban areas combined had been for the ST followed by the SC, OBC, and FC in that order. Given that scheduled tribes also had the highest poverty rates followed by scheduled castes and other backward castes in 2004–2005, the pattern implies that the socially disadvantaged groups have achieved significant catching up with the better-off groups. This is a major break with past trends.

Next, we report the national poverty rates by religious groups. In Table 4 , we show the poverty rates based on Lakdawala lines of rural and urban India and of the country taken as a whole. Three observations follow. First, at the aggregate level (rural plus urban), poverty rates show a steady decline for Hindus, Muslims, Christians, Jains, and Sikhs. Poverty among the Buddhists also consistently declined except for 1983 and 1987–1988. With one exception (Muslims in rural India between 1987–1988 and 1993–1994), the pattern of declining poverty rates between any two successive surveys also extends to the rural and urban poverty rates in the case of the two largest religious communities, Hindus and Muslims.

Second, going by the poverty rates in 2009–2010 in rural and urban areas combined, Jains have the lowest poverty rates followed by Sikhs, Christians, Hindus, Muslims, and Buddhists. Prosperity among Jains and Sikhs is well known, but not the lower level of poverty among Christians relative to Hindus. Also interesting is the relatively small gap of just 5.8 percentage points between poverty rates among Hindus and Muslims.

Finally, the impact of accelerated growth on poverty between 2004–2005 and 2009–2010 that we observed across social groups can also be seen across religious groups. Once again, we see a sharper decline in the poverty rate for the largest minority, the Muslims, relative to Hindus who form the majority of the population.

This broad pattern holds when we consider poverty rates by religious groups based on the Tendulkar line, as seen in Table 5 . Jains have the lowest poverty rates followed by Sikhs, Christians, Hindus, Muslims, and Buddhists. With one exception (Sikhs in rural India between 1993–1994 and 2004–2005), poverty had declined steadily for all religious groups in rural as well as urban India. The only difference is that the decline in poverty among Muslims in rural and urban areas combined between the periods 2004–2005 and 2009–2010 had not been as sharp as that estimated from the Lakdawala lines. As a result, we do not see a narrowing of the difference in poverty between Hindus and Muslims. We do see a narrowing of the difference in urban poverty but this gain is neutralized by the opposite movement in the rural areas due to a very sharp decline in poverty among Hindus, perhaps due to the rapid decline in poverty among scheduled castes and scheduled tribes.

Before we turn to poverty estimates by state, we should note that in this paper, we largely confine ourselves to reporting the extent of poverty measured based on the two poverty lines. Other than occasional references to the determinants of poverty such as growth and caste composition, we make no systematic effort to identify them. Evidently, many factors influence the decline in poverty. For instance, the acceleration in growth between 2004–2005 and 2009–2010 also led to increased revenue that made it possible for the government to introduce the National Rural Employment Guarantee Scheme under which one adult member of each rural household is guaranteed 100 days per year of employment at a pre-specified wage. The employment guarantee scheme may well have been a factor in the recent acceleration in poverty reduction.

In a similar vein, rural–urban migration may also impact the speed of decline of poverty. Once again, rapid growth, which inevitably concentrates disproportionately in urban areas, may lead to some acceleration in rural-to-urban migration. If, in addition, the rural poor migrate in proportionately larger numbers in search of jobs, poverty ratios could fall in both rural and urban areas. In the rural areas, the ratio could fall because proportionately more numerous poor than in the existing rural population migrate. In the urban areas, the decline may result from these individuals being gainfully employed at wages exceeding the urban poverty line. Migration may also reinforce the reduction in rural poverty by generating extra rural income through remittances. Evidence suggests that this effect may have been particularly important in the state of Kerala.

We now turn to the progress made in poverty alleviation in different states. Though our focus in this paper is on poverty by social and religious groups, we first consider poverty at the aggregate level in rural and urban areas. India has 28 states and 7 union territories. To keep the analysis manageable, we limit ourselves to the 17 largest states. 11 Together, these states account for 95% of the total population. We exclude all seven union territories including Delhi; the smallest six of the seven northeastern states (retaining only Assam); and the states of Sikkim, Goa, Himachal Pradesh, and Uttaranchal. Going by the expenditure survey of 2009–2010, each of the included states has a population exceeding 20 million while each of the excluded states has a population less than 10 million. Among the union territories, only Delhi has a population exceeding 10 million.

A.  Rural and Urban Populations

We begin by presenting the total population in each of the 17 largest states and the distribution between rural and urban areas as revealed by the NSSO expenditure survey of 2009–2010 (Table 6 ). 12 The population totals in the expenditure survey are lower than the corresponding population projections by the registrar general and census commissioner of India (2006) as well as those implied by Census 2011. 13 Our choice is dictated by the principle that poverty estimates should be evaluated with reference to the population underlying the survey design instead of those suggested by external sources. For example, the urban poverty estimate in Kerala in 2009–2010 must be related to the urban population in the state covered by the expenditure survey in 2009–2010 instead of projections based on the censuses in 2001 and 2011. 14

As shown in Table 6 , 27% of the national population lived in urban areas, while the remaining 73% resided in rural areas in 2009–2010. This composition understates the true share of the urban population, revealed to be 31.2% in the 2011 census. The table shows 10 states having populations of more than 50 million (60 million according to the 2011 census). We will refer to these 10 states as the “large” states. They account for a little more than three-fourths of the total population of India. At the other extreme, eleven “small” states (excluded from our analysis and therefore not shown in Table 6 ) have populations of less than ten million (13 million according to the Census 2011) each. The remaining seven states, which we call “medium-size” states, have populations ranging from 36 million in Orissa to 22 million in Chhattisgarh (42 million in Orissa to 25.4 million in Chhattisgarh, according to the 2011 census).

Among the large states, Tamil Nadu, Maharashtra, Gujarat, and Karnataka, in that order, are the most urbanized with a rate of urbanization of 35% or higher. Bihar is the least urbanized among the large states, with an urbanization rate of just 10%. Among the medium-size states, only Punjab has an urban population of 35%. The rest have urbanization rates of 30% or less. Assam and Orissa, with an urban population of just 10% and 14%, respectively, are the least urbanized medium-size states.

B.  Rural and Urban Poverty

We now turn to the estimates of rural and urban poverty in the 17 largest states. To conserve space, we confine ourselves to presenting the estimates based on the Tendulkar line. We report the estimates based on the Lakdawala lines in the Appendix. Recall that the estimates derived from the Tendulkar line are available for 3 years: 1993–1994, 2004–2005, and 2009–2010. Disregarding 1973–1974 and 1977–1978, which are outside the scope of our paper, estimates based on the Lakdawala lines are available for an additional 2 years: 1983 and 1987–1988.

Table 7 reports the poverty estimates with the states arranged in descending order of their populations. Several observations follow. First, taken as a whole, poverty fell in each of the 17 states between 1993–1994 and 2009–2010. When we disaggregate rural and urban areas within each state, we still find a decline in poverty in all states in each region over this period. Indeed, if we take the 10 largest states, which account for three-fourths of India's population, every state except Madhya Pradesh experienced a consistent decline in both rural and urban poverty. The reduction in poverty with rising incomes is a steady and nationwide phenomenon and not driven by the gains made in a few specific states or certain rural or urban areas of a given state.

Second, acceleration in poverty reduction in percentage points per year during the highest growth period (2004–2005 to 2009–2010) over that in 1993–1994 to 2004–2005 can be observed in 13 out of the total 17 states. The exceptions are Uttar Pradesh and Bihar among the large states and Assam and Haryana among medium-size states. Of these, Uttar Pradesh and Assam had experienced at best modest acceleration in gross state domestic product (GSDP) during the second period while Haryana had already achieved a relatively low level of poverty by 2004–2005. The most surprising had been the negligible decline in poverty in Bihar between 2004–2005 and 2009–2010, as GSDP in this state had grown at double-digit rates during this period.

Finally, among the large states, Tamil Nadu had the lowest poverty ratio followed by Andhra Pradesh and Gujarat. Tamil Nadu, Karnataka, and Andhra Pradesh—all of them from the south—made the largest percentage-point improvements in poverty reduction among the large states between 1993–1994 and 2009–2010. Among the medium-size states, Kerala and Haryana had the lowest poverty rates while Orissa and Jharkhand made the largest percentage-point gains during 1993–1994 to 2009–2010.

It is useful to relate poverty levels to per-capita spending. In Table 8 , we present per-capita expenditures in current rupees in the 17 states in the 3 years for which we have poverty ratios, with the states ranked in descending order of population. Ideally, we should have the MRP expenditures for all 3 years, but since they are available for only the last 2 years, we report the URP expenditures for 1993–1994. Several observations follow from a comparison of Tables 7 and 8 .

MRP = mixed reference period, URP = uniform reference period.

First, high per-capita expenditures are associated with low poverty ratios. Consider, for example, rural poverty in 2009–2010. Kerala, Punjab, and Haryana, in that order, have the highest rural per-capita expenditures. They also have the lowest poverty ratios, in the same order. At the other extreme, Chhattisgarh and Bihar have the lowest rural per-capita expenditures and also the highest rural poverty ratios. More broadly, the top nine states by rural per-capita expenditure are also the top nine states in terms of low poverty ratios. A similar pattern can also be found for urban per-capita expenditures and urban poverty. Once again, Kerala ranks at the top and Bihar at the bottom in terms of each indicator. Figure 3 offers a graphical representation of the relationship in rural and urban India in 2009–2010 using state level data.

Poverty and Per-capita MRP Expenditure in Rural and Urban Areas in Indian States, 2009–2010

One state that stands out in terms of low poverty ratios despite a relatively modest ranking in terms of per-capita expenditure is Tamil Nadu. It ranked eighth in terms of rural per-capita expenditure but fourth in terms of rural poverty in 2009–2010. In terms of urban poverty, it did even better, ranking a close second despite its ninth rank in urban per-capita expenditure. Gujarat also did very well in terms of urban poverty, ranking third in spite of the seventh rank in urban per-capita expenditure.

Finally, there is widespread belief that Kerala achieved the lowest rate of poverty despite its low per-capita income through more effective redistribution. Table 8 entirely repudiates this thesis. In 1993–1994, Kerala already had the lowest rural and urban poverty ratios and enjoyed the second highest rural per-capita expenditure and third highest urban per-capita expenditure among the 17 states. Moreover, in terms of percentage-point reduction in poverty, all other southern states dominate Kerala. For example, between 1993–1994 and 2004–2005, Tamil Nadu achieved a 27.4 percentage-point reduction in poverty compared to just 19.3 for Kerala. We may also add that Kerala experienced very high inequality of expenditures. In 2009–2010, the Gini coefficient associated with spending in the state was by far the highest among all states in rural as well as urban areas.

In this section we decompose population and poverty by social group. As previously mentioned, the expenditure surveys traditionally identified the social group of the households using a three-way classification: scheduled castes, scheduled tribes, and nonscheduled castes. However, beginning with the 1999–2000 survey, the last category had been further subdivided into other backward castes and the rest, the latter sometimes referred to as forward castes, a label that we use in this paper.

We begin by describing the shares of the four social groups in the total population of the 17 states.

A.  Population Distribution by Social Group within the States

Table 9 reports the shares of various social groups in the 17 largest states according to the expenditure survey of 2009–2010. We continue to rank the states according to population from the largest to the smallest.

Source: Authors’ calculations from the NSSO expenditure survey conducted in 2009–2010.

Nationally, the Scheduled Tribes constitute 9% of the total population of India according to the expenditure survey of 2009–2010. In past surveys and the Census 2001, this proportion was 8%. The scheduled castes form 20% of the total population according to the NSSO expenditure surveys, though the Census 2001 placed this proportion at 16%. The OBC are not identified as a separate group in the censuses so that their proportion can be obtained from the NSSO surveys only. The figure has varied from 36% to 42% across the three quinquennial expenditure surveys since the OBC began to be recorded as a separate group.

The scheduled tribes are more unevenly divided across states than the remaining social groups. In so far as these groups had been very poor at independence and happened to be outside the mainstream of the economy, ceteris paribus, states with high proportions of ST population may be at a disadvantage in combating poverty. From this perspective, the four southern states enjoy a clear advantage: Kerala and Tamil Nadu have virtually no tribal populations while Andhra Pradesh and Karnataka have proportionately smaller tribal populations (5% and 9% of the total, respectively) than some of the northern states which had high concentrations.

Among the large states, Madhya Pradesh, Gujarat, and Rajasthan have proportionately the largest concentrations of ST populations. The ST constitute 20%, 17%, and 14% of their respective populations. Some of the medium-size states, of course, have proportionately even larger concentrations. These include Chhattisgarh, Jharkhand, and Orissa with the ST forming 30%, 29%, and 22% of their populations, respectively.

Since the traditional exclusion of the SC has meant they began with a very high incidence of abject poverty and low levels of literacy, states with high proportions of these groups also face an uphill task in combating poverty. Even so, since the SC populations are not physically isolated from the mainstream of the economy, there is greater potential for the benefits of growth reaching them than the ST. This is illustrated, for example, by the emergence of some rupee millionaires among the SC but not the ST during the recent high-growth phase (Dehejia and Panagariya 2012 ).

Once again, at 9%, Kerala has proportionately the smallest SC population among the 17 states listed in Table 9 . Among the largest 10 states, West Bengal, Uttar Pradesh, Bihar, Rajasthan, and Madhya Pradesh have the highest concentrations. Among the medium-size states, Punjab, Haryana, and Orissa in that order have proportionately the largest SC populations.

The SC and ST populations together account for as much as 40% and 35%, respectively, of the total state population in Madhya Pradesh and Rajasthan. At the other extreme, in Kerala, these groups together account for only 10% of the population. These differences mean that, ceteris paribus, Madhya Pradesh, and Rajasthan face a significantly more difficult battle in terms of combating poverty than Kerala.

The ST populations also differ from the SC in that they are far more heavily concentrated in rural areas than in urban areas. Table 10 illustrates this point. In 2009–2010, 89% of the ST population was classified as rural. The corresponding figure was 80% for the SC, 75% for the OBC, and 60% for FC.

An implication of the small ST population in the urban areas in all states and in both rural and urban areas in a large number of states is that the random selection of households results in a relatively small number of ST households being sampled. The problem is especially severe in many of the smallest states where the total sample size is small in the first place. A small sample translates into a large error in the associated estimate of the poverty ratio. We will present the poverty estimates in all states and regions as long as a positive group is sampled. Nevertheless, we caution the reader on the possibility of errors in Table 11 that may be associated with the number of ST households in the 2009–2010 survey.

B.  Poverty by Social Group

We now turn to poverty estimates by social groups. We present statewide poverty ratios based on the Tendulkar line for the ST, SC, and nonscheduled castes in Table 12 . We present the ratios for the OBC and FC in Table 13 . As before, we arrange the states from the largest to the smallest according to population. Separate rural and urban poverty estimates derived from the Tendulkar lines and Lakdawala lines are relegated to the Appendix.

NS = non-scheduled, SC = scheduled castes, ST = scheduled tribes.

FC = forward castes, OBC = other backward castes.

With one exception, Chhattisgarh, the poverty ratio declines for each group in each state between 1993–1994 and 2009–1010. There is little doubt that rising incomes have helped all social groups nearly everywhere. In the vast majority of the states, we also observe acceleration in the decline in poverty between 2004–2005 and 2009–2010 compared to between 1993–94 and 2004–2005. Reassuringly, the decline in ST poverty among scheduled tribes and scheduled castes and SC poverty has sped up recently with the gap in poverty rates between these groups and the nonscheduled castes narrowing.

The negative relationship between poverty ratios and per-capita expenditures that we depicted in Figure 3 can also be observed for the social groups taken separately. Using rural poverty estimates by social group in the Appendix, we show this relationship between SC poverty and per capita rural expenditures in the left panel of Figure 4 and that between the ST poverty and per capita rural expenditures in the right panel. Figure 4 closely resembles Figure 3 . The fit in the right panel is poorer than that in the left panel as well as those in Figure 3 . This is partially because the ST are often outside the mainstream of the economy and therefore less responsive to rising per-capita incomes. This factor is presumably exacerbated by the fact that the number of observations in the case of the ST has been reduced to 11 due to the number of ST households in the sample dropping to below 100 in six of the 17 states.

Scheduled Caste and Scheduled Tribe Poverty Rates and Per-capita MRP Expenditures in Rural Areas, 2009–2010

For years 2004–2005 and 2009–2010, we disaggregate the nonscheduled castes into the OBC and FC. The resulting poverty estimates are provided in Table 13 . Taking the estimates in Tables 12 and 13 , one can see that on average poverty rates are at their highest for the ST followed by SC, OBC, and FC in that order. At the level of individual states, ranking of the poverty rates of scheduled castes and scheduled tribes is not clear-cut, but with rare exceptions, poverty rates of these two groups exceed systematically those of other backward castes, which in turn exceed rates of forward castes.

An interesting feature of the poverty rates of forward castes is their low level in all but a handful of the states. For example, in 2009–2010, the statistic computed to just 3.9% in Punjab, 5.9% in Kerala, 6.5% in Haryana, 6.9% in Tamil Nadu, and 10.5% even in Rajasthan. In 14 out of the largest 17 states, it fell below 25%. The states with low FC poverty rates generally also have low OBC poverty rates making the proportion of the SC and ST population the key determinant of the statewide rate.

This point is best illustrated by a comparison of poverty rates of Punjab and Kerala. Poverty rates for the nonscheduled caste population in 2009–2010 was 7.3% in Punjab and 10.4% in Kerala, while those for scheduled castes stood at 29.2% and 27.4%, respectively, in the two states. But since scheduled castes constitute 39% of the population in Punjab but only 9% in Kerala, statewide poverty rate turned out to be 15.8% in the former and 12% in the latter.

The caste composition also helps explain the differences in poverty rates between Maharashtra and Gujarat on the one hand and Kerala on the other. In 2009–2010, statewide poverty rates were 24.8% and 23.2%, respectively, in the former and 12% in the latter (Table 10 ). In part, the differences follow from the significantly higher per-capita expenditures in Kerala, as seen from Table 11 . 15 But Maharashtra and Gujarat also face a steeper uphill task in combating poverty on account of significantly higher proportions of the scheduled tribe and scheduled caste populations. These groups account for 17% and 11%, respectively, of the total population in Gujarat, and 10% and 15% in Maharashtra. In comparison, only 1% of the population comprises scheduled tribes in Kerala, while just 9% comprise scheduled castes (Table 9 ).

Finally, we turn to poverty estimates by religious group in the states. India is home to many different religious communities including Hindus, Muslims, Christians, Sikhs, Jains, and Zoroastrians. Additionally, tribes follow their own religious practices. Though tribal religions often have some affinity with Hinduism, many are independent in their own right.

Table 14 provides the composition of population by religious group as well as the rural–urban split of each religious group based on the expenditure survey of 2009–2010. Hindus comprise 82% of the population, Muslims 12.8%, Christians 2.3%, Sikhs 1.7%, Jains 0.3%, and Zoroastrians 0.016%. The remaining comprises just 0.3%.

Together, Hindus and Muslims account for almost 95% of India's total population. With 34% of the population in urban areas compared with 26% in the case of Hindus, Muslims are more urbanized than Hindus. Among the other communities, Jains and Zoroastrians are largely an urban phenomenon. Moreover, while Muslims can be found in virtually all parts of India, other smaller minority communities tend to be geographically concentrated. Sikhs cluster principally in Punjab, Christians in Kerala and adjoining southern states, Zoroastrians in Maharashtra and Gujarat, and Jains in Gujarat, Rajasthan, Karnataka, and Tamil Nadu.

Given their small shares in the total population and their geographical concentration, random sampling of households in the expenditure surveys yields less than 100 observations for minority religious communities other than Muslims in the vast majority of the states. Indeed, as Table 15 indicates, only 13 out of the 17 largest states had a sufficiently large number of households even for Muslims to allow poverty to be reliably estimated. Orissa, Haryana, Punjab, and Chhattisgarh each had fewer than 100 Muslim households in the survey. Thus, we attempt poverty estimates by religious groups in the states separately for Hindus and Muslims only. We do provide estimates for the catch-all “other” category but caution that, in many cases, these estimates are based on less than 100 observations and therefore subject to large statistical errors.

As before, we present the estimates for statewide poverty of the religious groups using the Tendulkar line, placing the more detailed estimates for rural and urban areas and estimates based on the Lakdawala lines in the Appendix. Table 15 reports the estimates for Hindus, Muslims, and other minority religion groups for the years 1993–1994, 2004–2005, and 2009–2010.

Religious groups replicate the broad pattern seen in the context of poverty by social group. Poverty has fallen in every single state between 1993–1994 and 2009–2010 for Hindus as well as for Muslims, though the change is not always monotonic. While the level of poverty in 2009–2010 is higher for Muslims than Hindus in the majority of the states, the reverse is true in Bihar, Tamil Nadu, Madhya Pradesh, and Karnataka. An anomaly is the marginal increase in the poverty rate between 2004–2005 and 2009–2010 in Bihar for Hindus and in Gujarat for Muslims. The observation is particularly surprising since we simultaneously observe a significant decline in poverty during the same period for Muslims in Bihar and for Hindus in Gujarat. Interestingly, as documented in the Appendix, poverty rates for both Hindus and Muslims decline in both states based on the Lakdawala lines between 2004–2005 and 2009–2010.

Although the focus of this paper is on poverty, we find it useful to briefly report the evolution of inequality at the state and national levels in rural and urban areas. At the outset, it is important to note that the issue of inequality is complex partly because it can be measured in numerous ways. 16 The potential list of measures is almost endless, and there is no guarantee that these different measures will move in the same direction. Therefore, it is quite easy to show simultaneously that inequality has risen as well as fallen depending on the choice of measure.

In this paper, we use one measure of overall inequality based on the same expenditure survey data we used to report poverty measures in the previous sections: specifically, the Gini coefficient of household expenditures in rural and urban areas in the 17 states and in India as a whole using URP expenditures in 1983, 1993–1994, 1999–2000, 2004–2005, and 2009–2010. Table 17 and Table 18 report the Gini coefficient in rural and urban areas, respectively. As before, we arrange the states in descending order of population size.

Source: Planning Commission website (accessed 4 February 2013).

An immediate observation from Tables 17 and 18 is that, with rare exceptions, rural inequality tends to be lower than urban inequality. At the national level in 2009–2010, the Gini coefficient was 0.291 in rural areas and 0.382 in urban areas. These values reflect a difference of 9 percentage points. This is not surprising. The vast majority of the villagers are small farmers or wage laborers. As a result, variation in their incomes and therefore expenditures are not large. In contrast, cities serve as home to much of the industry and formal sector services as well as to a large informal sector which attracts migrant workers. This results in greater variation in incomes and expenditures.

The tables show no clear trend in the Gini in rural areas but do show a tendency for it to rise in urban areas. At the national level, rural Gini fell between 1983 and 1999–2000, rose between 1999–2000 and 2004–2005, and fell again between 2004–2005 and 2009–2010, with a small net decline over the entire period. In contrast, the urban Gini has climbed steadily.

This is hardly surprising since rapid growth, which can produce increased inequality, is concentrated in urban areas. In the Indian case, a dualism of sorts exists within urban areas. Output growth has been concentrated in the formal sector, while employment has been disproportionately concentrated in the informal sector. Unlike the Republic of Korea and Taipei, China in the 1960s and 1970s and the People's Republic of China more recently, employment in the formal sector has not grown in India due to the poor performance of labor-intensive sectors. Growth in India has been concentrated in skilled labor and capital-intensive sectors.

The data do not support the hypothesis that high levels of poverty reflect high levels of inequality. At least in the Indian case, the two outcomes are at best unrelated and at worst negatively associated. For example, at the national level, rural inequality has remained more or less unchanged and urban inequality has risen, while both rural and urban poverty have steadily and significantly declined over time.

Looking at a cross section of the data, Kerala offers the most dramatic example. In 2009–2010, it had the lowest levels of rural and urban poverty and by far the highest rural and urban Gini coefficients. At the other extreme, Bihar had the second lowest rural Gini coefficient but the highest rural poverty ratio during the same period.

At a more aggregate level, the left panel in Figure 5 plots the rural Gini against the rural poverty ratio, while the right panel plots the urban Gini against the urban poverty ratio. The exponential trend line has a negative slope in each case, though the fit is poor. In other words, there is no evidence of a positive relationship between poverty and inequality, but there is some evidence of a negative relationship.

Gini Coefficients and Poverty Ratios in Rural and Urban Areas in Indian States, 2009–2010

In this paper, we have provided a comprehensive analysis of poverty in India along six different dimensions: across time, across states, between rural and urban areas, across social and religious groups, and based on two different poverty lines (Lakdawala and Tendulkar). To keep the exposition manageable, we have concentrated on estimates based on the Tendulkar line except when we discuss poverty at the national level. In the latter case, we report estimates in rural and urban India derived from both the Lakdawala and Tendulkar lines. Our detailed estimates by social and religious groups, by rural and urban areas, and by state based on both the Lakdawala and Tendulkar lines are provided in the Appendix.

The following are some of the key conclusions of the paper. First, poverty has declined between 1993–1994 and 2009–2010 along every dimension. Indeed, poverty has fallen for every social and religious group in every state and in rural and urban areas, separately as well as jointly. Estimates based on the Lakdawala line show that the decline can be observed steadily since 1983 for all social and religious groups in all 17 large states.

Second, acceleration in growth rates between 2004–2005 and 2009–2010 has been accompanied by acceleration in poverty reduction. Poverty rates have fallen rapidly for all major social and religious groups at the national level. This phenomenon also holds true for most states across various social and religious groups.

Third, for the first time, poverty reduction between 2004–2005 and 2009–2010 has been larger for the scheduled castes and scheduled tribes than the upper caste groups. Thus, the gap in poverty rates between the socially disadvantaged and upper caste groups has narrowed over time. This pattern provides clear evidence to refute the claim that reforms and growth have failed to help the socially disadvantaged or that they are leaving these groups behind. A continuation of this trend, bolstered by further reforms and higher growth rates, would help eliminate the difference in poverty rates between the historically disadvantaged and the privileged.

Fourth, interstate comparisons reveal that the states with large scheduled castes and scheduled tribe populations face a steeper climb in combating poverty. The point is most forcefully brought out by a comparison of Punjab and Kerala. When we compare poverty rates in 2009–2010 by social group, the two states have very similar poverty rates. But because the poverty rates for the scheduled castes are higher than those for the nonscheduled castes in both states and the scheduled castes account for a much larger proportion of the population, the aggregate poverty rate in Punjab turns out to be significantly higher.

Finally, we find that in the case of India, there is no robust relationship between inequality and poverty. Indeed, to the extent that such a relationship exists, this would suggest that more unequal states enjoy lower levels of poverty. Kerala offers the most dramatic example. It has had one of the highest Gini coefficients for rural as well as urban areas and also one of the lowest poverty ratios for both regions. In 2009–2010, its Gini coefficients were by far the highest among the large states in both rural and urban areas, while its poverty ratios were the smallest.

Given space limitations, we have deliberately limited ourselves to providing one specific indicator of poverty—the headcount ratio—in different states and for different social and religious groups based on the two official poverty lines. There are at least two broad complementary directions in which the work in this paper can be extended.

First, it may be desirable for certain purposes to estimate alternative indicators of poverty such as the poverty gap or its close cousin, the Foster-Greer-Thorbecke index. Such an index allows one to gauge the resources needed to bring all those below the poverty line to a level above it. In a similar vein, we have focused on progress in combating poverty among social and religious groups that are more vulnerable. Alternatively, we could focus on a different dimension of vulnerability such as male-headed versus female-headed households and evaluate the progress in combating poverty among female-headed households.

The second direction in which the work of this paper could be extended is towards explaining the determinants of poverty. Within this broad category, we have left many questions unanswered. For instance, it would be useful to separate the contributions of growth and redistribution policies in explaining the decline in poverty. Likewise, we may want to know what role, if any, rural-to-urban migration may have played—directly as well as through remittances. Similarly, we might ask what role the division of population among various social and religious groups plays in determining the progress in combating poverty. Finally, we might also wish to study the role that education plays in bringing down poverty. The recent work by Hnatkovska and Lahiri ( 2012 ) shows that education has indeed been pivotal in bridging the wage gap between scheduled castes and scheduled tribes on the one hand and nonscheduled castes on the other. This suggests an important role for education in eradicating poverty.

a Calculated by adjusting the 2004–2005 lines using the index implicit in the official Tendulkar lines for 2004–2005 and 2009–2010.

Source: Planning Commission, Government of India, Data Tables.

SC = scheduled castes, ST = scheduled tribes, URP = uniform reference period.

a Delhi is 95% urban. The SC and ST estimates in this case are based on too few households and therefore subject to substantial sampling errors.

FC = forward castes, NC = nonscheduled castes, OBC = other backward castes.

a Only 5% of Delhi by population is rural. SC and ST estimates in this case are based on too few households and therefore subject to substantial sampling errors.

FC = forward castes, NS = nonscheduled castes, OBC = other backward castes, URP = uniform reference period.

MRP = mixed reference period, SC = scheduled castes, ST = scheduled tribes.

Source: Authors’ calculations

FC = forward castes, MRP = mixed reference period, NS = nonscheduled castes, OBC = other backward castes.

URP = uniform reference period.

MRP = mixed reference period.

The views expressed in the paper are those of the authors and not of the World Bank. We thank an anonymous referee, P. V. Srinivasan, and participants of the first 2013 Asian Development Review conference held on 25–26 March 2013 at the Asian Development Bank headquarters in Manila, Philippines.

Prices could vary not just between urban and rural regions within a state but also across subregions within rural and subregions within urban regions of a state. Therefore, in principle, we could envision many different poverty lines within rural and within urban regions in each state. To keep the analysis manageable, we do not make such finer distinctions in the paper.

We do not go into the sources of underestimation of expenditures in NSSO surveys. These are analyzed in detail in Government of India ( 2008 ). According to the report (Government of India 2008 , p. 56), “The NSS estimates suffer from difference in coverage, underreporting, recall lapse in case of nonfood items or for the items which are less frequently consumed and increase in nonresponse particularly from affluent section of population. It is suspected that the household expenditure on durables is not fully captured in the NSS estimates, as the expensive durables are purchased more by the relatively affluent households, which do not respond accurately to the NSS surveys.” Two items, imputed rentals of owner-occupied dwellings and financial intermediation services indirectly measured, which are included in the NAS estimate, are incorporated into the NSSO expenditure surveys. But these account for only 7–9 percentage points of the discrepancy.

This section is partially based on Panagariya ( 2011 ).

See, for example, the report by the NDTV entitled “Planning Commission further lowers poverty line to Rs28 per day.” Available: http://www.ndtv.com/article/india/planning-commission-further-lowers-poverty-line-to-rs-28-per-day-187729

For instance, one commentator argued in a heated television debate that since bananas in Jor Bagh (an upmarket part of Delhi) cost Rs60 a dozen, an individual could barely afford two bananas per meal per day at poverty line expenditure of Rs32 per person per day.

We thank Rahul Ahluwalia for supplying us with Table 1 . The expenditures in the table represent the average of the urban decile class including the urban poverty line. Since the urban poverty line is at 25.7% of the population, the table takes the average over those between the 20th and 30th percentile of the urban population.

The consumption figures for the top 30% of the population are from Ganesh-Kumar et al. ( 2012 ).

These amounts of cereal and pulses equal or exceed their mean consumption levels according to the 2004–2005 NSSO expenditure survey.

Recently, Panagariya ( 2013 ) has suggested that if political pressures necessitate shifting up the poverty line, the government should opt for two poverty lines in India—the Tendulkar line, which allows it to track those in extreme poverty, and a higher one that is politically more acceptable in view of the rising aspirations of the people.

The issue is discussed at length in Bhagwati and Panagariya ( 2013 ).

Although Delhi has its own elected legislature and chief minister, it remains a union territory. For example, central home ministry has the effective control of the Delhi police through the lieutenant governor who is the de jure head of the Delhi government and appointed by the Government of India.

Our absolute totals for rural and urban areas of the states and India in Table 6 match those in Tables 1A-R and 1A-U, respectively, in Government of India ( 2011b ).

The Planning Commission derives the absolute number of poor from poverty ratios using census-based population projections. Therefore, the population figure underlying the absolute number of poor estimated by the Planning Commission are higher than those in Table 6 , which are based on the expenditure survey of 2009–2010.

This distinction is a substantive one in the case of states in which the censuses reveal the degree of urbanization to be very different from that underlying the design of the expenditure surveys. For example, the expenditure survey of 2009–2010 places the urban population in Kerala at 26% of the total in 2009–2010, but the census in 2011 finds the rate of urbanization in the state to be 47.7%.

This is true in spite of significantly higher per-capita GSDP in Maharashtra presumably due to large remittances flowing into Kerala. According to the Government of India ( 2011a ), one in every three households in both rural and urban Kerala reports at least one member of the household living abroad.

For instance, inequality could be measured as the ratio of the top 10% to bottom 10% of the population, the ratio of rural to urban per-capita incomes, the ratio of skilled to unskilled wages (or formal and informal sector wages), and through the Gini coefficient (nationally or across states).

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