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Human Development in India: Levels and Inequalities

  • Raghbendra Jha
Chapter

Abstract

This chapter first develops the concept of the Human Development Index and analyzes India’s recent performance with regard to this criterion. The incidence of poverty and inequality and constituent states is then analyzed in some detail. Results using both the traditional as well as the Tendulkar committee report’s notion of poverty are also analyzed. Two controversies that have come up recently in the area of measurement of poverty in India are discussed, as are the links between hunger, inequality and poverty.

Keywords

Human Development Index (HDI) Poverty Inequality Controversies in measurement of poverty Hunger 

3.1 Introduction

Living standards and inequality thereof form important constituents of the welfare of a country’s citizens. The United Nations Development Program (UNDP ) has developed a Human Development Index (HDI ) to formalize the notion of human development in a manner that makes it comparable over time and across countries. The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, rather than economic growth alone. It can also be used to question national policy choices, asking how two countries with the same level of gross national income (GNI ) per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. There are three broad constituents of the HDI: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The health dimension is assessed by life expectancy at birth, the education dimension is measured by mean of years of schooling for adults aged 25 years or more, and expected years of schooling for children of school entering age. The standard of living dimension is measured by GNI per capita. The HDI uses the logarithm of income to reflect the diminishing importance of income with increasing GNI. The scores for the three HDI dimension indices are then aggregated into a composite index using geometric mean. The HDI simplifies and captures only part of what human development entails; it does not reflect on inequalities, poverty, human security and empowerment, for example. The Human Development Report Office offers the other composite indices as broader proxy on some of the key issues of human development, inequality, gender disparity and human poverty. The UNDP publishes its report on global HDI in its Human Development Reports.

There are many aspects of human development that the HDI ignores, including human security, gender justice, empowerment and inequality. Nevertheless, it does provide a good aggregate measure of human development.

In this and some subsequent chapters we will pay close attention to India’s recent performance relating to measures of human development. In Sect. 3.2 I trace India’s ranking on the HDI in recent times. In Sect. 3.3 I provide some basic results on poverty and inequality in the Indian context. In Sect. 3.4 I discuss two recent controversies around the measurement of poverty in India. In Sect. 3.5 I discuss the links between hunger, inequality and poverty in India, and Sect. 3.6 concludes.

3.2 The HDI and India’s Performance on It

The HDI was first developed by Mahbub-ul-Haq in 1990 and then refined by Amartya Sen. It is published annually by the UNDP as the Human Development Report.

Components of the HDI are shown in Fig. 3.1. The first component of each rectangle is the dimension captured in the HDI; for example, a long and healthy life. The second component denotes the indicator; for example, mean years of schooling (MYS) and expected years of schooling (EYS). The third component is the indicator; for example, the GNI Index.
Fig. 3.1

Components of the HDI

The three indicators, Life Expectancy Index, Education Index and GNI Index, are combined to yield the HDI.

Life expectancy at birth is measured as the number of years a new-born infant could be expected to live if prevailing age-specific mortality rates at birth remain the same as at this infant’s birth.

Mean years of schooling is the average years of schooling obtained by people aged 25 and older. Expected years of schooling is the number of years of schooling a child entering school can be expected to have if current age-specific enrolments persist.

Let us study an example in Table 3.1.
Table 3.1

Example for calculating HDI

Dimension

Observed maximum

Minimum

Life expectancy

83.4 (Japan 2011)

20

Mean years of schooling

13.1 (Czech Republic 2005)

0

Expected years of schooling

18.0 (capped at)

0

Combined education index

0.978 (New Zealand 2010)

0

Per capita income (PPP∄)

107,721 (Qatar, 2011)

100

Take a country with the characteristics listed in Table 3.2.
Table 3.2

Characteristics of countries

Life expectancy at birth (years)

75.2

Mean years of schooling (years)

5.5

Expected years of schooling (years)

10.4

Per capita income (PPP∄)

2805

$$ {\displaystyle \begin{array}{l}\mathrm{Life}\kern0.28em \mathrm{Expectancy}\kern0.28em \mathrm{Index}=\left(\mathrm{actual}-\mathrm{minimum}\right)/\\ {}\kern11.76em \left(\mathrm{maximum}\kern0.28em \mathrm{across}\kern0.28em \mathrm{countries}-\mathrm{minimum}\right)\\ {}\kern10.92em =\left(75.2-20\right)/\left(83.4-20\right)=0.870\end{array}} $$
$$ {\displaystyle \begin{array}{l}\mathrm{Mean}\kern0.28em \mathrm{years}\kern0.28em \mathrm{of}\kern0.28em \\ {}\mathrm{schooling}\kern0.28em \mathrm{index}=\left(\mathrm{actual}-\mathrm{minimum}\right)/\\ {}\kern8.68em \left(\mathrm{maximum}\kern0.28em \mathrm{across}\kern0.28em \mathrm{countries}-\mathrm{minimum}\right)\\ {}\kern7.84em =\left(5.5-0\right)/\left(13.1-0\right)=0.478\end{array}} $$
$$ {\displaystyle \begin{array}{l}\mathrm{Expected}\kern0.28em \mathrm{years}\kern0.28em \mathrm{of}\kern0.28em \\ {}\mathrm{schooling}\kern0.28em \mathrm{index}\kern1.68em =\left(\mathrm{actual}-\mathrm{minimum}\right)/\\ {}\kern10.08em \left(\mathrm{maximum}\kern0.28em \mathrm{across}\kern0.28em \mathrm{countries}-\mathrm{minimum}\right)\\ {}\kern9.24em =\left(10.4-0\right)/\left(18-0\right)=0.576\end{array}} $$
$$ \mathrm{Education}\kern0.28em \mathrm{Index}=\left(\sqrt{0.478\times 0.576}-0\right)/\left(0.978-0\right)=0.503 $$
$$ {\displaystyle \begin{array}{l}\mathrm{Income}\kern0.28em \mathrm{Index}=\left\{\ln (2805)-\ln (100)\right\}/\left\{\ln \left(107,721\right)-\ln (100)\right\}\\ {}\kern7.00em =0.478\end{array}} $$
$$ \mathrm{HDI}=\sqrt[3]{0.87\times 0.503\times 0.478}=\mathrm{0.593.} $$

Typically the observed maxima and country characteristics are for the same year.

The HDI ranges between 0 and 1 with 1 being the highest possible HDI and 0 the lowest. India’s HDI scores between 1980 and 2015 are indicated in Table 3.3. There has been substantial improvement in the HDI score since 1980, but much ground has to be covered before India can be counted among the relatively high HDI countries in the region, including China and many countries of Southeast Asia.
Table 3.3

India’s HDI score 1980–2015

Year

HDI score

1980

0.362

1985

0.397

1990

0.428

1995

0.462

2000

0.496

2005

0.539

2010

0.586

2011

0.597

2012

0.6

2013

0.604

2014

0.609

2015

0.609

Source: Author’s compilation from UNDP’s Human Development Reports, various years

In 2015 India was classified as a low middle HDI group country and her HDI rank was 130th out of 188 countries. Between 2009 and 2014 India’s rank improved by 6 points and then moved up 5 points to 130th out of 188 countries in 2015. Between 1990 and 2000 average annual growth rate of India’s HDI was 1.49%, between 2000 and 2010 it was 1.67%, between 2010 and 2014 it was 0.97% and between 1990 and 2014 it was 1.48%.

In addition to the HDI, recent UNDP reports include a gender development index (GDI ). The GDI is the ratio of the HDIs calculated separately for females and males using the same methodology as in the HDI. In 2014 the value of India’s GDI was 0.795, indicating that human development for females was less than 80% of that for males. Details of the breakdown of India’s HDI for 2014 are as follows: the HDI for females (males) was 0.525 (0.660). Life expectancy at birth was 69.5 years for females and 66.6 years for men. Expected period of schooling was 11.3 years for females and 11.8 years for males. Mean period of schooling was 3.6 years for females and 7.2 years for males and estimated GNI per capita was ∄2116 PPP∄ for women and 8656 PPP∄ for women. Hence, the biggest gaps between females and males in India are in the areas of education and, particularly, income. These are urgent areas for policy reform in India. Females outperform males in the area of longevity.

While the HDI is a summary measure of the welfare attainments by a country, it is often regarded as being incomplete. Some of the reasons for this are as follows: (1) GDP per capita does not give an indication of income distribution. (2) GDP does not show how the income is spent by the government. Some countries may spend more on the military than on healthcare. (3) Poverty is ignored in the computation of the HDI. (4) Longevity, as an average for the whole population, does not consider how healthy the population is. (5) The education index does not say much about the quality of the education or whether such education has imparted to the pupils enough skills to be gainfully employed.

3.3 India’s Performance with Regard to Poverty and Inequality: Some Basic Results

While the HDI provides an overarching view of the level of well-being in society, some other measures of well-being need to be studied in depth in the case of a country, such as India, that has a long history of mass deprivation. The first of these is poverty.

In India the study of poverty is closely tied to the notion of the head count ratio (HCR ) of poverty measured as the proportion of the population with consumption below an exogenously fixed minimum consumption.1

In Fig. 3.2 the cumulative percentage of households/individuals is measured on the y-axis and per capita consumption or income in ≠ are measured on the x-axis. The curve on the left depicts the cumulative distribution of households by per capita income or consumption. OR represents the poverty line of per capita income or consumption. If a household’s per capita consumption or income is below this level then it is deemed to be poor. In the case of India accurate data on household income is not available but consumption data is available from the Central Statistical Organization’s National Sample Survey data. Their website is http://mospi.nic.in/ (accessed November 7, 2016). So we will measure consumption per capita on the x-axis.
Fig. 3.2

Visualizing poverty

By the definition of the poverty line the percentage of the population OA (=RA’) is poor. The area above the curve OA’ measures the depth of poverty. Figure 3.2 also plots a second cumulative distribution function to the right of the first one as a result of economic growth, for example. Following from this, the proportion of the population that is poor falls from RA’ to RB’. The depth of poverty also declines. The shift in Fig. 3.2 is distributionally neutral. However, if the distribution had become more equitable, that is, if a larger proportion of income was going to poorer households in comparison to the richer households, the new cumulative distribution function would have flattened out and the drop in poverty would have been greater.

At this point it is useful to formalize the notion of distribution of household income or consumption. A simple way in which to visualize the distribution of consumption is through the Lorenz Curve depicted in Fig. 3.3. The x-axis measures the percentage of the population, with 100% as indicated. The y-axis measures the percentage of income/consumption. If income/consumption was perfectly equally distributed in society, we would be along the 45° line, where x% of the population had x% of the income and this was true for all x:
$$ 0\#x\#100. $$
Fig. 3.3

The Lorenz Curve and the Gini coefficient

The actual distribution of income/consumption is along the curved line where distribution of income/consumption is not equal. In the example in Fig. 3.3, the Lorenz Curve, which represents the actual distribution of income/consumption in a country, shows how the poorest 20% of the population only earn 5% of the national income/consumption in this population. While in a case of perfect equality, the poorest 20% of the population would earn 20% of the income/consumption. The more bowed out a Lorenz Curve is, the greater the inequality of income/consumption in the country.

In Fig. 3.3, a simple measure of inequality is the Gini coefficient, which is measured as the area labeled inequality gap/(total area of the lower triangle). By construction the Gini coefficient is bounded between 0 and 1. The lower the inequality gap the lower the Gini.

Alternative measures of inequality have been used in the literature including share of top 1% or top 10% of the population, share of top 10% divided by share of bottom 10% and so on. However, the Gini coefficient is the most widely used measure of inequality in academic and policy research.

The poverty measures used in this chapter are all drawn from the Foster-Greer-Thorbecke class of functions. This is written as:
$$ {P}_{\alpha }=\sum \limits_{y_i<z}{\left[\left(z-y\right)/z\right]}^{\alpha /n}, $$
where y i is the consumption of the ith household or the ith class of household, z is the poverty line, n is the population size, and α is a non-negative parameter. The headcount ratio, H, given by the percentage of the population who are poor, is obtained when α = 0. The poverty gap index (PG) given by the aggregate income shortfall of the poor as a proportion of the poverty line and normalized by the population size is given by α = 1, and the Foster–Greer–Thorbecke (P2) measure is obtained when α = 2.

A key determinant of poverty is the poverty line. In the Indian context this has been a highly controversial measure . Srinivasan (2007) points out that the original poverty line for India developed by Dadabhai Naoroji in the late nineteenth century was based on calorie requirements.2 After independence the norm for using a poverty line based on calorie requirement was recast in 1961 and extended over time. In 1973–1974, based on a survey of consumer behavior (NSS), a consumption basket was proposed that would ensure, on an average, 2100 calories per person per day in urban areas and 2400 calories per person per day in rural areas. The poverty lines established for 1973–1974 were Rs. 49 per person per month for rural areas and Rs. 57 per person per month for urban areas. These poverty lines were regularly updated using the Consumer Price Index for Agricultural Laborers (CPIAL ) for rural areas and the Consumer Price Index for Industrial Workers (CPIIW ) for urban areas. State level poverty lines were also determined and poverty rates were routinely calculated using these measures.

Measures of poverty and inequality for the 1950s and 1960s are available in Jha (2004). The rural Gini coefficient dropped significantly in the late 1950s and early 1960s possibly as a result of the scrapping of retrograde arrangements such as the zamindari system of land tenure. A further downward trend in the rural Gini coefficient began in the late 1980s. The HCR fell sharply during the 1980s but rose in the 1990s. The poverty gap and Squared Poverty Gap (SPG) followed similar trends. The index of Real Mean Consumption (RMC) fell during the 1960s because of poor crops following droughts. It rose steadily, but at a slower rate during the 1980s, and then fell again in 1992, following an economic crisis.

The urban Gini is routinely higher than the rural Gini and shows no particular trend. Urban poverty has fallen much more sharply than rural poverty. Average urban RMC was 20% higher than the level in 1973–1974. At its peak RMC in the urban sector was 35% higher in 1986–1987 than its level in 1973–1974. Hence, the urban sector has had greater inequality, lower poverty and higher consumption growth than the rural sector.3

More recent information on poverty trends in India is given in Table 3.4. Using the old methodology the rural HCR fell from 56.4% in 1973–1974 to 39.1% in 1987–1988. During this period 30 million people in the rural sector were lifted out of poverty. It is to be noted that the drop in urban poverty was smaller (less than 9%) and the number of urban poor actually rose by 15 million. The national HCR fell from 54.9 to 38.9 and the total number of poor fell by approximately 14 million. Hence, this period has rightly been considered the golden age of poverty (particularly rural poverty) reduction in India. Poverty lines for subsequent years were set using the recommendations of the Tendulkar Committee Report (TCR), chaired by the late Prof. Suresh Tendulkar, and include estimates of essential expenditure on health and education (see next section).
Table 3.4

Percentage and number of poor using Tendulkar methodology

Year

Poverty ratio (%)

Number of poor (million)

Rural

Urban

Total

Rural

Urban

Total

1973–1974

56.4

49.0

54.9

261.3

60.0

321.3

1977–1978

53.1

45.2

51.3

264.3

64.6

328.9

1983

45.6

40.8

44.5

252.0

70.9

322.9

1987–1988

39.1

38.2

38.9

231.9

75.2

307.0

1993–1994

50.10

31.8

45.3

328.6

74.5

403.7

2004–2005

41.8

25.7

37.2

326.30

80.80

407.10

2009–2010

33.8

20.9

29.8

278.21

76.47

354.68

2011–2012

25.7

13.7

21.9

216.50

52.80

269.30

Annual average decline from 1993–1994 to 2011–2012

2004–2005 from 1993–1994 (expert group)

0.82

0.61

0.77

2.10

−0.41

1.70

2009–2010 from 2004–2005 (expert group)

1.60

0.96

1.48

9.62

0.87

10.48

2004–2005 to 2011–2012 (expert group)

2.32

1.69

2.18

15.69

4.00

19.69

Source: Author’s computation based on data from NSS and Planning Commission documents

Note: The change in methodology to that of TCR was applied from 1993–1994—hence the jump in the HCR

Using the new methodology between 1993–1994 and 2011–2012, the rural HCR of poverty was nearly halved and the urban HCR fell by more than half. The national HCR was more than halved as well. The number of rural poor fell from 328.6 million to 216.50 million and that of urban poor from 74.5 million to 52.80 million. Thus, the total number of poor fell by more than 130 million during this 18 year period. Once again there was a larger drop in rural poverty than urban poverty. Percentage annual rates of decline of poverty are noted in the lower panel of Table 3.4. Hence, higher economic growth and associated policy reforms have had a very significant impact on poverty.4

However, there is considerable variation in the incidence of poverty in India. Table 3.5 reports on these trends for the rural, urban and aggregate HCRs across 15 major Indian states. After a long period of relative stability there is a clear upward trend in all three coefficients of variation from the late 1990s. This could be a reaction to the liberalization policies of the early 1990s. Figure 3.4 shows this trend for the poverty gap. Both Table 3.5 and Fig. 3.4 use the old measure of the poverty line.
Table 3.5

Coefficients of variation of the HCR in the rural and urban sectors and in the aggregate

Time period

CVR

CVU

CVA

September 1957–May 1958

0.24

0.17

0.168

July 1958–June 1959

0.25

0.21

0.17

July 1959–June 1960

0.30

0.21

0.208

July 1960–August 1961

0.31

0.24

0.19

September 1961–July 1962

0.24

0.17

0.17

February 1963–January 1964

0.17

0.19

0.13

July 1964–June 1965

0.18

0.16

0.14

July 1965–June 1966

0.19

0.17

0.16

July 1966–June 1967

0.19

0.17

0.14

July1967–June 1968

0.17

0.18

0.13

July 1968 to June 1969

0.26

0.20

0.17

July 1969–June 1970

0.22

0.20

0.157

July 1970–June 1971

0.26

0.20

0.18

October 1972–September 1973

0.27

0.18

0.18

October 1973–June 1974

0.18

0.15

0.13

July 1977–June 1978

0.27

0.17

0.20

January 1983–December 1983

0.28

0.17

0.23

July 1986–June 1987

0.23

0.23

0.20

July 1987–June 1988

0.29

0.20

0.21

July 1989–June 1990

0.33

0.22

0.26

July 1990–June 1991

0.27

0.25

0.24

January 1992–December 1992

0.34

0.29

0.27

July 1993–June 1994

0.24

0.26

0.25

July 1994–June 1995

0.36

0.25

0.26

July 1995–June 1996

0.36

0.23

0.25

July 1996–June 1997

0.37

0.25

0.30

1999–2000

0.59

0.35

0.50

Source: Author’s calculations based on National Sample Survey data

Fig. 3.4

Coefficient of variation of poverty gap. Source: Author’s calculations based on National Sample Survey data. Note: Traditional poverty line is used here

The World Bank estimates using the poverty line of ∄1.90 per person PPP (2011 dollars) that 270 million Indians were poor in 2012.

Seven low income states [Uttar Pradesh (60 million), Bihar (36 million), Madhya Pradesh (24 million), Odisha (14 million), Jharkhand (13 million), Chhattisgarh (10 million) and Rajasthan (10 million)] are home to 62% of India’s poor but only 45% of India’s population.

80% of India’s poor live in rural areas. Poverty rate in rural areas is 25%, in urban areas 14% and 20% in aggregate (Table 3.6).
Table 3.6

Concentration of poverty in India

Small village (pop. 0–4999)

Big village (pop. 5000+)

Small town (pop. to 1 million)

Big cities (pop. 1 million +)

27%

19%

17%

6%

Source: Author’s compilation from World Bank, World Development Indicators, 2017 and World Bank (2011)

Poverty is highest among scheduled tribes (living in remote areas of the country).

3.4 Two Controversies Relating to the National Poverty Line

Poverty lines of the sort used for data for the period 1973–1974 to 1987–1988 have been heavily criticized for restricting themselves to food consumption norms and only to calories, with no allowance being made for expenditure on health, education and other basic needs. The TCR submitted in 2009 was geared towards addressing this shortcoming. Poverty rates using the Tendulkar methodology

Prior to that, however, there was another controversy around the data related to the 55th Round of the NSS for 1999–2000. This survey marked a departure from earlier practice in two important ways. First, food items were canvassed on a dual recall period of seven days as well as the standard 30 days. Second, items on which only infrequent expenditures are made, such as medical goods, were only canvassed on a 365 day recall instead of the dual 30 day and 365 day recall in the previous thick round.5 There arose the possibility that poverty rates using such data could be biased in a downward direction. This change came at an inopportune time, as reliable statistics were required to gauge the impact of India’s 1991 economic reforms on the poor. As would be expected, a large literature evolved around correcting this data. For a review of this see Morten (2006) and, particularly , Deaton and Kozel (2005). In the next thick round of the NSS (the 61st Round conducted in 2004–2005), recall periods were set to previous norms, making results from this round comparable to those from those earlier than the 55th.

Reacting to the TCR, the Planning Commission fixed the poverty line for data relating to the 66th Round of the NSS, conducted in 2009–2010, for the country at Rs. 672.8 per person per month (i.e. Rs. 22.42 per person per day) for rural areas and Rs. 859.6 per person per month for urban areas (i.e. Rs. 28.65 per person per day) for urban areas. In 2011 this was revised to Rs. 26 per day for rural areas and Rs. 32 per day in urban areas in view of price inflation between 2009–2010 and 2011.

When these figures were released there was an immediate outcry from both the press and economists, as they did not even guarantee bare subsistence. The ensuing course of events is well known, with the Government of India appointing a new committee (under the chairmanship of C. Rangarajan) to construct a new poverty line to replace the recommendations of TCR. The newly installed government of Prime Minister Narendra Modi has basically abandoned the TCR and revisited the formulation of a poverty line for the country.

As indicated above, subsequent developments have implied that we do not yet have a widely acceptable measure of the extent and depth of poverty in India since we do not have a widely accepted poverty line.

To recall the TCR methodology was designed to address the deficiencies in the traditional approach—in particular the neglect of health and education expenditures. TCR argues that the poverty line for the 2004–2005 survey, suitably adjusted, was adequate to meet the then requirements for nutrition, education and health in both the rural and urban sectors.

TCR was an attempt to address the deficiencies of the traditional approach. It suggests that the old urban poverty line for 2004–2005, suitably adjusted, was adequate to meet the requirements, both rural and urban, with respect to nutrition, education, and health for 2009. The TCR then constructed new price indices to facilitate comparison of rural and urban price levels and state level price indices with the national price indices. TCR then used these price indices and the 2004–2005 poverty line to calculate a new poverty line for 2009–2010. This raised the poverty line for that year, and therefore larger proportions of the population were deemed to be poor in both rural and urban sectors. However, there were two major problems with this approach. First, it did not anchor the computation of poverty on nutritional norms, thereby divesting it of any policy significance, and second, it seriously underestimated the health, education and nutritional requirements of household members.

Thus, both generalizations of the traditional poverty line by TCR are deeply flawed and, therefore, misleading. In reality the TCR methodology actually underestimates the number of poor (Jha and Sharma 2014; Swaminathan 2010).6

3.5 Hunger, Inequality and Poverty in India

Against this background we are justified in asking how calorie deprivation (hunger)—the original motivation behind constructing the poverty line—evolved . Deaton and Dreze (2009) have shown that despite rapid economic growth in India, per capita calorie intake has declined, as has the intake of many other nutrients. More than three-quarters of the population live in households whose per capita calorie intake is less than 2100 in urban areas and 2400 in rural areas—calorie intakes regarded as minimum requirements in India. Anthropometric indicators tell an equally dismal story, and some of these are the worst in the world. For instance, according to the National Family Health Survey the proportion of underweight children remained virtually unchanged between 1998–1999 and 2005–2006—from 47 to 46% for the age-group 0–3 years. Yet until recently, the government allowed thousands of tonnes of foodgrains to rot.

Apart from its nutritional and human costs, such hunger also has strong implications for productivity, wages and economic growth. If workers are nutritionally deprived they will have low productivity, which means they will receive low wages. Low wages, in turn, will mean that workers will be unable to buy the entire nutritional intake they need, which will then complete this vicious cycle. In the economics literature this is called the poverty nutrition trap (PNT). Jha et al. (2009) establish the existence of a PNT for rural India relating to calories and various micronutrients for various types of agricultural operations by male and female workers in rural India. In particular, women more than men are subject to the PNT.

In the poorer districts of India the incidence of hunger is much more acute. For 2007–2008 Jha et al. (2011a) collected primary data for 500 households each in Rajasthan, Maharashtra and Andhra Pradesh, and show that the incidence of deprivation of both macronutrients (calories and protein) as well as a number of micronutrients is very high, in several cases 80% or above. Hence, there is a huge hunger crisis in India which is not being picked up by “falling” poverty—no matter which poverty line is used. This reaffirms the case for rooting poverty lines on nutritional norms to discover what the people of India probably already know—that nutritional deprivation is widespread and deep, and probably worsening!

Against this background, Table 3.7 depicts the evolution of the inequality, measured by the Gini coefficient, in the rural and urban sectors of major individual states and the country as a whole between 1993–1994 and 2009–2010. Comparable figures for 1993–1994, 2004–2005 and 2009–2010 are given, and states and the country are ranked according to increasing inequality. Figures for 1999–2000 are also given as well as those for 2004–2005, which are comparable to the 1999–2000 magnitudes. In the rural sector for the country as a whole the Gini rose between 1993–1994 and 2004–2005 and stayed unchanged in 2009–2010. For the urban sector, however, there was a steady rise in the Gini coefficient from 0.33 in 1993–1994 to 0.38 in 2004–2005 and 0.40 in 2009–2010. Therefore, the rise in urban inequality in the five year period 2004–2005 to 2009–2010 was comparable to that in the eleven year period 1993–1994 to 2004–2005. Hence, urban inequality has become a concern.
Table 3.7

Gini coefficient of distribution of consumption for select states and all India

1993–1994 (50th round)

2004–2005 (61st round) Uniform reference period (comparable with 1993–1994)

2009–2010 (66th round) (comparable with 1993–1994 and 2004–2005 uniform reference)

State/India

Rural

State/India

Urban

State/India

Rural

State/India

Urban

State/India

Rural

State/India

Urban

Assam

0.18

Haryana

0.28

Assam

0.19

Jammu & Kashmir

0.24

Bihar

0.23

Jammu & Kashmir

0.31

Bihar

0.22

Jammu & Kashmir

0.28

Bihar

0.2

Gujarat

0.31

Rajasthan

0.23

Assam

0.33

Jammu & Kashmir

0.23

Punjab

0.28

Jharkhand

0.22

Assam

0.32

Jammu & Kashmir

0.24

Bihar

0.34

Gujarat

0.24

Assam

0.29

Jammu & Kashmir

0.24

Himachal Pradesh

0.32

Karnataka

0.24

Gujarat

0.34

Orissa

0.24

Gujarat

0.29

Rajasthan

0.25

Uttarakhand

0.32

West Bengal

0.24

Karnataka

0.34

West Bengal

0.25

Rajasthan

0.29

Karnataka

0.26

Bihar

0.33

Assam

0.25

Tamil Nadu

0.34

Punjab

0.26

Orissa

0.3

Gujarat

0.27

Jharkhand

0.35

Gujarat

0.26

Haryana

0.37

Rajasthan

0.26

Bihar

0.31

Madhya Pradesh

0.27

Orissa

0.35

Maharashtra

0.27

Madhya Pradesh

0.37

Karnataka

0.27

Andhra Pradesh

0.32

West Bengal

0.27

Haryana

0.36

Orissa

0.27

Uttar Pradesh

0.37

Himachal Pradesh

0.28

Karnataka

0.32

Orissa

0.28

Karnataka

0.36

Tamil Nadu

0.27

Punjab

0.38

Madhya Pradesh

0.28

Uttar Pradesh

0.32

Punjab

0.28

Tamil Nadu

0.36

Uttar Pradesh

0.27

Andhra Pradesh

0.39

Uttar Pradesh

0.28

Madhya Pradesh

0.33

Uttarakhand

0.28

Andhra Pradesh

0.37

Andhra Pradesh

0.29

Rajasthan

0.39

India

0.28

West Bengal

0.33

Andhra Pradesh

0.29

Maharashtra

0.37

Madhya Pradesh

0.3

West Bengal

0.39

Andhra Pradesh

0.29

Kerala

0.34

Chhattisgarh

0.29

Rajasthan

0.37

Punjab

0.3

India

0.39

Kerala

0.29

Tamil Nadu

0.34

Uttar Pradesh

0.29

Uttar Pradesh

0.37

India

0.3

Orissa

0.4

Haryana

0.3

India

0.34

Himachal Pradesh

0.3

India

0.37

Haryana

0.31

Himachal Pradesh

0.41

Maharashtra

0.3

Maharashtra

0.35

India

0.3

West Bengal

0.38

Himachal Pradesh

0.31

Maharashtra

0.42

Tamil Nadu

0.31

Himachal Pradesh

0.43

Maharashtra

0.31

Madhya Pradesh

0.39

Kerala

0.44

Kerala

0.52

Jharkhand

 

Jharkhand

 

Haryana

0.32

Punjab

0.39

Jharkhand

 

Jharkhand

 

Chhattisgarh

 

Chhattisgarh

 

Tamil Nadu

0.32

Kerala

0.4

Chhattisgarh

 

Chhattisgarh

 

Uttarakhand

 

Uttarakhand

 

Kerala

0.34

Chhattisgarh

0.43

Uttarakhand

 

Uttarakhand

 

Source: Author’s computation from NSS data, various years

Table 3.7 also shows wide spatial variation in the evolution of inequality. Thus in 1993–1994 rural inequality was higher than the national Gini in Andhra Pradesh, Kerala , Haryana , Maharashtra and Tamil Nadu whereas it was below the national Gini in Assam , Bihar , Jammu and Kashmir , Gujarat , Orissa , West Bengal , Punjab , Rajasthan and Karnataka . Urban inequality was higher than the national Gini in Himachal Pradesh and Maharashtra and below the national Gini in Haryana , Jammu and Kashmir , Punjab , Assam , Gujarat , Rajasthan , Orissa , Bihar , Andhra Pradesh , Karnataka , Uttar Pradesh , Madhya Pradesh and West Bengal . In 2004–2005 rural inequality was lower than the national Gini in Assam, Bihar, Jharkhand, Jammu and Kashmir , Rajasthan, Karnataka, Gujarat, Madhya Pradesh , West Bengal , Orissa, Punjab, Uttarakhand, Andhra Pradesh , Chattisgarh and Uttar Pradesh . Urban inequality was higher than the national Gini in Maharashtra, Haryana, Tamil Nadu and Kerala. In 2009–2010 rural inequality was lower than the national Gini in Bihar, Rajasthan, Jammu and Kashmir , Karnataka, West Bengal , Assam, Gujarat, Maharashtra, Orissa, Tamil Nadu, Uttar Pradesh and Andhra Pradesh , whereas it was higher than the national HCR in Haryana, Himachal Pradesh and Kerala. Urban inequality was below the national Gini in Jammu and Kashmir , Assam, Bihar, Gujarat, Karnataka, Tamil Nadu, Haryana, Madhya Pradesh , Uttar Pradesh and Punjab, whereas it was higher than the national Gini in Orissa, Himachal Pradesh and Maharashtra.

Most of the less well off states have lower values of the Gini coefficient than India. By and large, the same states have higher Gini coefficients than India in successive rounds of the household level data.

The Gini coefficient data do not give information on inequality between the rural and urban sectors. Further, they give an indication of consumption inequality and not income or wealth inequality, essentially because household level income and wealth data for India are mostly unavailable and notoriously unreliable. Typically, income and wealth inequality are higher than consumption inequality.

Table 3.8 reports on the evolution of poverty in India using the traditional poverty line and identifies basic trends. The HCR fell in all three periods of 1957–1963, 1963–1964 to 1989–1990 and 1990–1991 to 1997. However, the Gini coefficient rose in the last of these three periods. Table 3.9 reports Planning Commission data on HCR for 2004–2005 and 2009–2010 using the TCR criterion. All poverty rates are reported according to increasing magnitudes.
Table 3.8

Profile of inequality and poverty in India

Rural

Gini

HCR

Growth: real wage/real GDP/per capita NNP

Food availability/agricultural growth

Inflation, per CPIAL

1957–1963

↓ (−4.73)

↓ (−6.63)

n.a. / ↑ / n.a

↑ / n.a.

n.a.

1963–964 to 1989–1990

↓ (−0.73)

↓ (−14.23)

n.a. / ↑ / n.a.

↑ / ↑

n.a.

1990–1991 to 1997

↑ (+2.40)

↓ (−2.21)

↑ / ↑ / ↑

↑ / ↑

Urban

G

HCR

Growth: real wage/real GDP/per capita NNP

Food availability/industrial growth

Inflation, per CPIIIW

1957–1963

↓ (−0.6)

↓ (−2.92)

n.a.

n.a.

n.a.

1963–1964 to 1989–1990

↓ (−0.95)

↓ (−11.43)

n.a. / ↑ / n.a.

↑ / ↑

n.a.

1990–1991 to 1997

↑ (+2.17)

↓ (−4.18)

↑ / ↑ / ↑

↑ / ↑

Source: Author’s computation from NSS data, various years

Notes: G, Gini; H, headcount RATIO; NNP, NET NATIONAL PRODUCT. Inflation refers to percentage in CPIAL (for agricultural laborers) and CPIIW (for industrial workers). n.a., data unavailable

Table 3.9

Poverty in India and states 2009–2010 and 2004–2005 using TCR methodology

Rural

Urban

Total

State/India

Percentage of person

No. of persons (lakhs)

State/India

Percentage of person

No. of persons (lakhs)

State/India

Percentage of person

No. of persons (lakhs)

2009–2010

Puducherry

0.2

0

A & N Islands

0.3

0

A & N Islands

0.4

0

A & N Islands

0.4

0

Puducherry

1.6

0.1

Puducherry

1.2

0.1

Delhi

7.7

0.3

Lakshwadweep

1.7

0

Lakshwadweep

6.8

0

Jammu & Kashmir

8.1

7.3

Sikkim

5

0.1

Goa

8.7

1.3

Himachal Pradesh

9.1

5.6

Goa

6.9

0.6

Chandigarh

9.2

1

Chandigarh

10.3

0

Chandigarh

9.2

0.9

Jammu & Kashmir

9.4

11.5

Goa

11.5

0.6

Tripura

10

0.9

Himachal Pradesh

9.5

6.4

Kerala

12

21.6

Mizoram

11.5

0.6

Kerala

12

39.6

Punjab

14.6

25.1

Kerala

12.1

18

Sikkim

13.1

0.8

Uttarakhand

14.9

10.3

Himachal Pradesh

12.6

0.9

Delhi

14.2

23.3

Meghalaya

15.3

3.5

Jammu & Kashmir

12.8

4.2

Punjab

15.9

43.5

Sikkim

15.5

0.7

Tamil Nadu

12.8

43.5

Meghalaya

17.1

4.9

Haryana

18.6

30.4

Delhi

14.4

22.9

Tamil Nadu

17.1

121.8

Nagaland

19.3

2.8

Andhra Pradesh

17.7

48.7

Tripura

17.4

6.3

Tripura

19.8

5.4

Dadra & Nagar

17.7

0.3

Uttarakhand

18

17.9

Tamil Nadu

21.2

78.3

Gujarat

17.9

44.6

Haryana

20.1

50

Lakshwadeep

22.2

0

Punjab

18.1

18.4

Nagaland

20.9

4.1

Andhra Pradesh

22.8

127.9

Maharashtra

18.3

90.9

Andhra Pradesh

21.1

176.6

Karnataka

26.1

97.4

Karnataka

19.6

44.9

Mizoram

21.1

2.3

Arunachal Pradesh

26.2

2.7

Rajasthan

19.9

33.2

Gujarat

23

136.2

Rajasthan

26.4

133.8

India

20.9

764.7

Karnataka

23.6

142.3

Gujarat

26.7

91.6

West Bengal

22

62.5

Maharashtra

24.5

270.8

West Bengal

28.8

177.8

Madhya Pradesh

22.9

44.9

Rajasthan

24.8

167

Maharashtra

29.5

179.8

Haryana

23

19.6

Arunachal Pradesh

25.9

3.5

Mizoram

31.1

1.6

Chhattisgarh

23.8

13.6

West Bengal

26.7

240.3

India

33.8

2782.10

Meghalaya

24.1

1.4

India

29.8

3546.80

Daman & Diu

34.2

0.2

Arunachal Pradesh

24.9

0.8

Daman & Diu

33.3

0.8

Orissa

39.2

135.5

Nagaland

25

1.4

Madhya Pradesh

36.7

261.8

Uttar Pradesh

39.4

600.6

Uttarakhand

25.2

7.5

Orissa

37

153.2

Assam

39.9

105.3

Orissa

25.9

17.7

Uttar Pradesh

37.7

737.9

Jharkhand

41.6

102.2

Assam

26.1

11.2

Assam

37.9

116.4

Madhya Pradesh

42

216.9

Jharkhand

31.1

24

Jharkhand

39.1

126.2

Manipur

47.4

8.8

Uttar Pradesh

31.7

137.3

Dadra & Nagar

39.1

1.3

Bihar

55.3

498.7

Daman & Diu

33

0.5

Manipur

47.1

12.5

Dadra & Nagar

55.9

1

Bihar

39.4

44.8

Chhattisgarh

48.7

121.9

Chhattisgarh

56.1

108.3

Manipur

46.4

3.7

Bihar

53.5

543.5

2004–2005

Lakshwadeep

0.4

0

A & N Islands

0.8

0

A & N Islands

3

0.1

Daman & Diu

2.6

0

Nagaland

4.3

0.2

Lakshwadeep

6.4

0

A & N Islands

4.1

0.1

Himachal Pradesh

4.6

0.3

Nagaland

8.8

1.7

Nagaland

10

1.5

Mizoram

7.9

0.4

Daman & Diu

8.8

0.2

Meghalaya

14

2.9

Puducherry

9.9

0.7

Chandigarh

11.6

1.1

Jammu & Kashmir

14.1

11.6

Chandigarh

10.1

0.9

Delhi

13

19.3

Delhi

15.6

1.1

Jammu & Kashmir

10.4

2.9

Jammu & Kashmir

13.1

14.5

Kerala

20.2

42.2

Lakshwadweep

10.5

0

Puducherry

14.2

1.5

Punjab

22.1

36.7

Delhi

12.9

18.3

Mizoram

15.4

1.5

Puducherry

22.9

0.8

Daman & Diu

14.4

0.1

Meghalaya

16.1

4.1

Mizoram

23

1.1

Dadra & Nagar

17.8

0.1

Kerala

19.6

62

Haryana

24.8

38.8

Kerala

18.4

19.8

Punjab

20.9

53.6

Himachal Pradesh

25

14.3

Punjab

18.7

16.9

Himachal Pradesh

22.9

14.6

Goa

28.1

1.8

Tamil Nadu

19.7

59.7

Haryana

24.1

54.6

Sikkim

31.8

1.5

Gujarat

20.1

42.9

Goa

24.9

3.4

Andhra Pradesh

32.3

180

Assam

21.8

8.3

Tamil Nadu

29.4

194.1

Arunachal Pradesh

33.6

3.2

Goa

22.2

1.7

Andhra Pradesh

29.6

235.1

Chandigarh

34.7

0.2

Haryana

22.4

15.9

Sikkim

30.9

1.7

Uttarakhand

35.1

23.1

Tripura

22.5

1.5

Arunachal Pradesh

31.4

3.8

Rajasthan

35.8

166.4

Andhra Pradesh

23.4

55

Gujarat

31.6

171.4

Assam

36.4

89.4

Arunachal Pradesh

23.5

0.6

Uttarakhand

32.7

29.7

Karnataka

37.5

134.7

Jharkhand

23.8

16

Karnataka

33.3

186.5

Tamil Nadu

37.5

134.4

West Bengal

24.4

60.8

West Bengal

34.2

288.3

West Bengal

38.2

227.5

Meghalaya

24.7

1.2

Assam

34.4

97.7

Gujarat

39.1

128.5

India

25.5

814.1

Rajasthan

34.4

209.8

Manipur

39.3

6.7

Maharashtra

25.6

114.6

India

37.2

4072.20

India

42

3258.10

Karnataka

25.9

51.8

Manipur

37.9

9

Uttar Pradesh

42.7

600.5

Sikkim

25.9

0.2

Maharashtra

38.2

392.4

Tripura

44.5

11.9

Uttarakhand

26.2

6.6

Tripura

40

13.4

Maharashtra

47.9

277.8

Chhattisgarh

28.4

13.7

Uttar Pradesh

40.9

730.7

Jharkhand

51.6

116.2

Rajasthan

29.7

43.5

Jharkhand

45.3

132.1

Madhya Pradesh

53.6

254.4

Uttar Pradesh

34.1

130.1

Madhya Pradesh

48.6

315.7

Chhattisgarh

55.1

97.8

Manipur

34.5

2.3

Dadra & Nagar

49.3

1.3

Bihar

55.7

451

Madhya Pradesh

35.1

61.3

Chhattisgarh

49.4

111.5

Orissa

60.8

198.8

Orissa

37.6

22.8

Bihar

54.4

493.8

Dadra & Nagar

63.6

1.1

Bihar

43.7

42.8

Orissa

57.2

221.6

Source: Author’s computation from NSS data various years and Planning Commission documents

Notes:

 1. Population as on March 1, 2010 has been used for estimating number of persons below poverty line (interpolated between 2001 and 2011 population census). For population in 2004–2005, data as on March 1, 2005 has been used for estimating number of persons

 2. Poverty line of Tamil Nadu is used for Andaman and Nicobar Island; urban poverty line of Punjab is used for both rural and urban areas of Chandigarh

 3. Poverty line of Maharashtra is used for Dadra and Nagar Haveli; poverty line of Goa is used for Daman and Diu

The HCR has fallen steadily from 1993–1994 to 2004–2005 and more rapidly between 2004–2005 and 2009–2010 and again 2010–2011. In the rural sector in 1993–1994 Maharashtra, Madhya Pradesh , West Bengal , Uttar Pradesh , Assam Orissa and Bihar had HCR higher than the national HCR, whereas Punjab, Andhra Pradesh , Gujarat, Kerala, Rajasthan, Haryana, Karnataka, Himachal Pradesh , Jammu and Kashmir , and Tamil Nadu had lower HCR than the national average. In the urban sector in 1993–1994 Bihar, Maharashtra, Uttar Pradesh , Andhra Pradesh , Tamil Nadu, Karnataka, Orissa and Madhya Pradesh had higher poverty than the national HCR, whereas Assam, Himachal Pradesh , Jammu and Kashmir , Punjab Haryana, West Bengal , Kerala, Gujarat and Rajasthan had lower poverty than the national average.

In the rural sector in 2004–2005 Jammu and Kashmir , Punjab, Himachal Pradesh , Andhra Pradesh , Kerala, Haryana, Rajasthan, Gujarat, Karnataka, Assam and Tamil Nadu had lower poverty than the nation as a whole whereas West Bengal , Maharashtra, Uttar Pradesh , Madhya Pradesh , Chattisgarh, Uttarakhand, Bihar, Jharkhand and Orissa had higher poverty than the national average. In the urban sector in 2004–2005 Assam, Himachal Pradesh , Punjab, Jammu and Kashmir , Gujarat, West Bengal , Haryana, Jharkhand, Kerala and Tamil Nadu had lower poverty than India as a whole, whereas Andhra Pradesh , Uttar Pradesh , Maharashtra, Karnataka, Rajasthan, Bihar, Uttarakhand, Chattisgarh, Madhya Pradesh and Orissa had higher poverty than the national average.

In 2009–2010 in the rural sector Jammu and Kashmir , Punjab, Andhra Pradesh , Himachal Pradesh , Kerala, Rajasthan, Haryana, Gujarat, Tamil Nadu, Karnataka and Maharashtra had lower poverty than the national HCR, whereas West Bengal , Madhya Pradesh , Assam, Uttar Pradesh , Orissa and Bihar had higher poverty than the national HCR. In the urban sector in 2009–2010 Punjab, Assam, Jammu and Kashmir , Himachal Pradesh , Haryana, West Bengal , Gujarat, Andhra Pradesh , Kerala, Rajasthan and Tamil Nadu had lower poverty than the national HCR, whereas Maharashtra, Karnataka, Uttar Pradesh , Madhya Pradesh , Bihar and Odisha (previously called Orissa) had higher poverty than the national HCR.

As many as 354,680,000 Indians (278,210,000 in the rural sector and 76,470,000 in the urban sector) were poor in 2009–2010 according to the Planning Commission. Jha et al. (2012) indicate that, at least in rural India, there is a very high incidence of transient poverty with only 10% of the population chronically poor. Thus, covariate and idiosyncratic shocks form a good proportion of the explanation for poverty.

Our analysis here has shown that aggregate GDP growth matters in poverty alleviation, measured traditionally or with the TCR, but agricultural growth matters more (despite a sharp reduction in its contribution to GDP) since most of the poor are in agriculture. Agricultural growth makes four contributions to poverty alleviation: its direct growth component, its indirect growth component, the participation of the poor in the growth of this sector and its size in the overall economy.

Finally, Table 3.10 provides summary measures of poverty for India and constituent states for 2011–2012. The states are ranked according to increasing HCRs. The number of poor persons in each state and in India as a whole are also indicated.
Table 3.10

Number and percentage of population below poverty line by states 2011–2012: Tendulkar methodology

States

Number and population below poverty line by States, 2011–2012, TCR methodology

Rural

States

Urban

Percentage of persons

 

No. of persons (100,000s)

Percentage of persons

 

No. of persons (100,000s)

Lakshwadeep

0

Andhra Pradesh

61.8

Chandigarh

0

Andhra Pradesh

16.98

Puducherry

0

Arunachal Pradesh

4.25

Puducherry

3.44

Arunachal Pradesh

0.66

Chandigarh

1.57

Assam

92.06

Sikkim

3.67

Assam

9.21

Dadra Nagar Haveli

1.64

Bihar

320.4

Goa

4.09

Bihar

37.75

Goa

6.81

Chattisgarh

88.9

Himachal

4.33

Chattisgarh

15.22

Punjab

7.66

Delhi

0.5

Kerala

4.97

Delhi

16.46

Himachal

8.48

Goa

0.37

Andhra Pradesh

5.81

Goa

0.38

Kerala

9.14

Gujarat

75.35

Andaman and Nicobar

6.3

Gujarat

26.88

Sikkim

9.85

Haryana

19.42

Mizoram

6.36

Haryana

9.41

Andhra Pradesh

10.95

Himachal

5.29

Tamil Nadu

6.54

Himachal

0.3

Jammu and Kashmir

11.54

Jammu and Kashmir

10.73

Jammu and Kashmir

7.2

Jammu and Kashmir

2.53

Uttar Pradesh

11.62

Jharkhand

104.09

Tripura

7.42

Jharkhand

20.24

Haryana

11.64

Karnataka

92.8

Maharashtra

9.12

Karnataka

36.96

Meghalaya

12.53

Kerala

15.48

Punjab

9.24

Kerala

8.46

Delhi

12.92

Madhya Pradesh

190.96

Meghalaya

9.26

Madhya Pradesh

43.1

Tamil Nadu

15.83

Maharashtra

150.56

Delhi

9.84

Maharashtra

47.36

Rajasthan

16.05

Manipur

7.45

Gujarat

10.14

Manipur

2.78

Tripura

16.53

Meghalaya

3.04

Haryana

10.27

Meghalaya

0.57

Andaman and Nicobar

17.06

Mizoram

1.91

Uttar Pradesh

10.48

Mizoram

0.37

Nagaland

19.93

Nagaland

2.76

Rajasthan

10.69

Nagaland

1

Gujarat

21.54

Odisha

126.14

Lakshwadeep

12.62

Odisha

12.39

West Bengal

22.52

Punjab

13.35

All India

13.7

Punjab

9.82

Maharashtra

24.22

Rajasthan

84.19

West Bengal

14.66

Rajasthan

18.73

Karnataka

24.53

Sikkim

0.45

Karnataka

15.25

Sikkim

0.06

All India

25.7

Tamil Nadu

59.23

Daman & Diu

15.38

Tamil Nadu

23.4

Uttaranchal

30.4

Tripura

4.49

Nagaland

16.48

Tripura

0.75

Assam

33.89

Uttar Pradesh

8.25

Odisha

17.29

Uttar Pradesh

3.35

Bihar

34.06

Uttaranchal

479.35

Arunachal Pradesh

20.33

Uttaranchal

118.84

Mizoram

35.43

West Bengal

141.14

Assam

20.49

West Bengal

43.83

Odisha

35.69

Andaman and Nicobar

0.69

Madhya Pradesh

21

Andaman and Nicobar

0.55

Madhya Pradesh

35.74

Chandigarh

0.04

Dadra Nagar Haveli

22.31

Chandigarh

0

Manipur

38.8

Dadra Nagar Haveli

0

Chattisgarh

24.75

Dadra Nagar Haveli

2.34

Arunachal Pradesh

38.93

Daman & Diu

1.15

Jharkhand

24.83

Daman & Diu

0.28

Jharkhand

40.84

Lakshwadeep

0

Uttaranchal

26.06

Lakshwadeep

0.26

Chattisgarh

44.61

Puducherry

0

Bihar

31.23

Puducherry

0.02

Daman & Diu

62.59

All India

2166.58

Manipur

32.59

All India

531.25

Source: Author’s computation from NSS data various years

3.6 Conclusions

This chapter has provided a broad overview of welfare in India in terms of the HDI as well as indicators of inequality and poverty. Broadly, inequality has increased marginally over time and poverty has fallen. Tens of millions of people have been lifted out of poverty but there is still a considerable amount of slack to be picked up (see World Bank 2011).7

There is also a strong regional concentration of inequality and, in particular, poverty. Broadly, higher economic growth has been associated with lower poverty (a theme we return to in vol. II chapter  10.). Had inequality not worsened, the fall in poverty would have been steeper. Much work needs to be done, therefore, to increase economic growth and keep inequality in check.

Footnotes

  1. 1.

    In India income data at the household level is generally unavailable, hence consumption per capita is used in computations of poverty.

  2. 2.

    Srinivasan (2010) and Reddy and Pogge (2010) provide insightful critiques of the World Bank’s so-called ∄1 a day poverty line. See also Srinivasan (2007).

  3. 3.

    For a comprehensive discussion of inequality in India see Kurosaki (2011).

  4. 4.

    For the poverty alleviating impact of social welfare programs see Khera (2011), Jha et al. (2009, 2011a, b), Jha and Gaiha (2012) and Jha (2014).

  5. 5.

    A thick round is characterized by large sample size—in excess of 100,000 households. In the Indian context such household data are typically collected every five years. In the intermediate years samples are thin; that is, sample sizes are much smaller.

  6. 6.

    See also Subramanian (2011).

  7. 7.

    For a broad overview of the issue of food security see Jha and Gaiha (2016).

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Copyright information

© The Author(s) 2018

Authors and Affiliations

  • Raghbendra Jha
    • 1
  1. 1.Arndt-Corden Department of EconomicsAustralian National UniversityActonAustralia

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