Poverty in India and Its Decompositions: A Critical Appraisal of the New Method

Transcription

1 Indira Gandhi Institute of Development Research, Mumbai From the SelectedWorks of Srijit Mishra January 1, 2013 Poverty in India and Its Decompositions: A Critical Appraisal of the New Method Durgesh C Pathak Srijit Mishra, Indira Gandhi Institute of Development Research Available at:

2 Poverty in India and Its Decompositions: A Critical Appraisal of the New Method 1 Durgesh C Pathak, Srijit Mishra This Version: 6 June 2012 Abstract This paper has two objectives. First, it critically discusses the new method of measuring poverty. In doing so, it raises some concerns implicit in the report - (a) the need to go beyond calories to have an understanding of nutritional requirement, which could not be adequately addressed in the new method, (b) the need to incorporate expenditure on health, education and sanitation, as these are not being adequately provided by the state, which also raises serious apprehensions on whether India is a welfare state, and (c) the need to come up with multi-dimensional measures of poverty. Nevertheless, the paper contends that the approach followed in the report is pragmatic, but some additional information could have been put in the public domain to take the debate beyond the poverty line. Second, we use the new poverty lines to compute incidence, depth and severity and inequality across states and some other subgroups as also sectoral and growth-inequality decompositions at the aggregate all India level for and Among the old hotspots of BiMORU, Rajasthan and Uttaranchal have moved out and Assam has come in, Madhya Pradesh and Odisha show considerable reductions, but it is the increase of incidence in Bihar (which otherwise seems to have done well during this period) that raises curious eyebrows. The seven major states with high incidences are referred to as CABMOUJ (pronounced kab mouj, which in Hindi means when to relax ). There is an increase in urbanisation of poverty. Four Northeast states and two smaller union territories have also shown some reversals in poverty reductions. Analysis across caste, occupations, religion, land size, and gender show the usual expected patterns, but of concern is greater vulnerability of children (0-14 age group) and increasing poverty levels at higher education groups. Appropriate planning and implementation will require complementing this information with quantitative and qualitative information at the district and sub-district level. Key words: Decompositions (sectoral, growth-inequality), Growth incidence curve, Inequality, Poverty measures (head count/incidence, poverty gap/depth, poverty gap squared/severity). JEL Codes: I32 1 This is a pre-print version of a paper published in S Mahendra Dev (ed) India Development Report , Oxford University Press, New Delhi, 2013, pp and Tables A12.3 and A12.4 in the Statistical Appendix. This has been prepared for India Development Report Comments from BK Chandra Kiran, Vikas Chitre, KL Krishna, Manoj Panda, VM Rao, and SL Shetty were helpful. Page 1

3 Poverty in India and Its Decompositions: A Critical Appraisal of the New Method Durgesh C Pathak, Srijit Mishra 1. Introduction The Planning Commission recently released poverty estimates across states for rural and urban areas of India for (Government of India, 2012). A matter of concern raised in the media is the low poverty lines leading to a social experiment of living in 32 rupees per day by young persons. 2 This has revived the need to critically evaluate the Report of the Expert Group to Review the Methodology for Estimation of Poverty (Government of India, 2009). The purpose of this paper is two-fold. First, we will raise some issues with regard to the new method, which would also borrow from existing literature including some earlier work of ours (Pathak and Mishra, 2011; also see Mishra, 2012). 3 Second, we use the poverty lines provided by the Planning Commission for rural and urban areas separately to compute incidence, depth and severity of poverty and inequality at the aggregate all India level as also across states, social groups, religious groups, occupational groups, education-wise and gender-wise. We also analyse the poverty reduction between and at the aggregate all India level as also for some sub-groups of population by looking into sectoral and growth-inequality decompositions. The differential impact of the growth process between this two time points on poorer and richer sections of society is also visualized through growth incidence curves for rural and urban India. 2. The New Method: Some Issues In Pathak and Mishra (2011), we had identified five issues with regard to the new method of poverty. They are: the doing away with a calorie norm, which is how poverty estimates were being computed in India till ; the use of median expenditure on health and education will be underestimates; difficulty in reproducing the estimates; the calibration of estimates to arrive at 25.7 per cent poor for urban India; and the political economy of changing poverty shares. We will reiterate some of these and also point out some other concerns. 2.1 A Pragmatic Start The Expert Group had three important considerations. First, the calorie norm pegged at may not be appropriate because of changes in age, sex and occupational patterns and that 2 See (1) and (2) 3 A number of papers debating the pros and cons of the new method got published in a special issue of the Indian Journal of Human Development in 2010 (see Alagh, 2010; Breman, 2010; Datta, 2010; Kannan, 2010; Raveendran, 2010; Shah, 2010; and Swaminathan, 2010). Also see Subramanian, In recent times, there have been some discussions in the pages of the Economic and Political Weekly (see the EPW Editorial, 2012; as also Krishnaji, 2012; Manna, 2012; and a letter by Motwani, 2012). Independent of the poverty line debate, also see a discussion on the right not to be poor from of political perspective by Chandhoke (2012). Page 2

4 one should go beyond calories to have a deeper understanding of nutritional requirement. In any case, the subsequent updating of the poverty lines does not adhere to the calorie norm. Second, the earlier computations of the poverty line were based on the assumption that education, health and sanitation requirements would be provided by the state, which is no more appropriate. This is serious observation raising the question, is India a welfare state? Third, the consumption expenditure collected through uniform recall period does not appropriately represent low frequency items like clothing, footwear, consumer durable, education and institutional health. The Expert Group was in an easy wicket for the last concern. The 61st round of National Sample Survey (NSS) in collected expenditure data for such items with recall of both 30 days and 365 days. The Expert Group used the adjusted values of the latter to make them comparable with 30 days recall of other items. With regard to the calorie requirement, the Expert Group implicitly conceded the need for nuanced nutritional basis. However, in the absence of any norm they kept those concerns aside and delinked the poverty measure from any calorie norm. One hopes that the new technical committee dwells upon such issues. Now, the Expert Group had to start somewhere. It is perhaps this pragmatic consideration that led them to begin with the poverty estimate for urban India, as computed from the already existing method (see Alagh, 2010; Datta, 2010), as given, that is, a poverty ratio of 25.7 per cent. Hence, they could not bring in health and education components into the poverty line (also see Kannan, 2010; Shah, 2010; Subramanian, 2010; Swaminathan, 2010). At the most, what it says is that under this poverty ratio or around its associated poverty line these are the budget shares of health and education expenditure. The multi-dimensional notion of poverty measurement is still open and this also needs to be given consideration by the new technical committee that has been set up. In short, this is a pragmatic start, but one that has opened up a number of unfinished tasks. One such aspect is ideal index prices. 2.2 The Ideal Index Prices: A Black Box The poverty ratio for urban India will have an associated poverty line. The corresponding monthly per capita expenditure (MPCE) decile class would give the budget shares or expenditure of commodity-groups. These along with the median prices will give the corresponding quantities (excluding rent and conveyance). Median prices will address problems of missing observation and outliers but will be underestimates for education and health, as poor are likely to spend less and that too when the expenditure distribution is skewed (Subramanian, 2010; Swaminathan, 2010). All India quantities and the state-specific median prices give an MPCE value/class and used to compute state-specific budget shares and quantities. The two sets of prices and quantities are used to compute Fisher Ideal Index (FII) of state relative to all India. If this computed index falls in the MPCE class we began with then this is the poverty line. Otherwise, we Page 3

5 continue an iterative process till it matches. Subsequently, imputations for conveyance and rent are added to get the poverty lines. Similar exercise is done for all India rural relative to all India urban and state-specific rural relative to all India rural. In , updating of poverty lines has been done for rural and urban areas of each state separately. Planning Commission has given us the poverty lines, number of poor and also the poverty ratios and shares of poor (see Tables A and B provided in the Statistical Appendix to this volume). It would be difficult for others to replicate the poverty lines (Ravendraan, 2010), and it is this that makes it a black box. Of course, once the poverty lines are given, using unit level data one can compute other aspects of poverty, but before that we introduce some standard measures and concepts. 3. Measures and Concepts The measures introduced here are of poverty, inequality, sectoral decomposition of change in poverty, growth-inequality decomposition and growth incidence curve. Poverty is estimated using Foster, Greer and Thorbecke (1984; hereafter FGT), an additively decomposable class of measure, ఈ = ଵ σ ଵ ቀ ௭ ቁ ఈ ߙ, where if the poverty gap ݕ ݖሺ ) )ሻ for an individual with expenditure ݕ, z is the poverty line, is the total number of individuals in the population, and ߙ is a measure of sensitivity such that = ߙ 0,1, and 2 refers to head count ratio (incidence), poverty gap (depth), and severity. This measure can be decomposed by subgroups as, ఈ = σ ቀ (2) 0 ߙ, ఈ ଵ ቁ where and ఈ are the ௧ subgroups population share and poverty measure respectively. A subgroup poverty risk is the ratio of its share of poor to its share of population, (1) = Τ Τ = ఈ / ఈ Gini coefficient is used as an inequality measure, (3) + ଵ ቀ ଶ 1 = ܩ మቁ σ ଵ (ሺ + ݕ 1)ሻ where is the population, is the mean expenditure, ݕ is the expenditure of person. We also introduce two measures of decomposition. First is the sectoral decomposition. If the economy is divided into two sectors, rural and urban, then economic changes within the sector will impact changes in poverty (intra-sectoral effects, one for each sector). At the same time, migration between the sectors will also impact changes in poverty (population shift effects). In addition, there will also be an interaction effect. Following Ravallion and Huppi (1991), the measure is, ఈ௧ ଵ = ఈ ௧ ଵ ௧ + ఈ ௧ ଵ ௧ + σ ௧ ଵ ఈ ௧ + σ ఈ (4) ௧ ଵ ௧ ଵ (5) Page 4

6 where denotes change over time such that ఈ௧ ଵ = ఈ௧ ଵ ఈ௧ indicate change in poverty at + ݐ 1 from ݐ ; (ሺ ఈ௧ ଵ 100)ሻ is percentage point change and (ሺ(ሺ ఈ௧ ଵ )ሻ/ ఈ௧ 100)ሻ is percentage change; =, denotes urban and rural respectively. There have been various approaches to growth-inequality decomposition (Dutt and Ravallion, 1992; Jain and Tendulkar, 1990; and Kakwani and Subbarao, 1990). In all these, poverty is represented as, ఈ௧ =,ݖሺ ) ௧, ܩ ௧ )ሻ where ఈ௧, ௧, ܩ ௧ denote measure of poverty, mean expenditure and Gini coefficient respectively at time,ݐ and ݖ is poverty line. Given,ݖ and using Datt and Ravallion (1992) the change in poverty, ఈ௧ ଵ, can be decomposed to, ఈ௧ ଵ ௧ ଵ = (ሺ ఈ ఈ௧ ௧ ଵ (7).ܧ + )ሻ )ሻ + (ሺ ఈ ఈ௧ In the right hand side, is the growth component, the second is the inequality or redistribution component and the third, E, is an interaction between the two. A final concept that we would like to introduce is the growth incidence curve (GIC). It graphically portrays the impact of the growth process across quintiles or percentiles (Haughton & Khandekar, 2009). An upward sloping GIC indicates that the rich gained more relative to the poor, i.e., the inequality worsened. Now, we take up a discussion on recent trends and patterns. (6) 4. Recent Estimates and Patterns 4.1 Number of Poor and their Share across States Before analysing, a few caveats on the usage of NSS data are its inadequate representation of higher expenditure groups, possibility of incorrect information from households, and comparability over time among others (Vaidyanathan, 1986). Despite these limitations, we use the same in our poverty analysis of recent years. In , crore persons were poor of which 81.5 per cent were in rural areas. The number of poor increased to crore person in and this was largely on account of an increase in the number of poor in urban areas whose share of poor increased from 18.5 per cent to 20 per cent. This trend in urban India was reversed in and with a continuing decline in rural areas the total number of poor decreased to crore persons of which 78.4 per cent were in rural areas. The share of poor has been decreasing at a lower rate than their share of population in rural India and as a result their relative risk has been increasing and was greater than unity by 10.9 per cent in , 12.9 per cent in and 13.4 per cent in As a corollary, it is implicit that the relative risk of poverty in urban areas will be lower than unity. The changes in the number of poor as also the changes in shares of poor across states could be linked to allocation and transfer of funds through the centrally sponsored schemes for Page 5

7 ameliorating poverty (Rao, 2010). Any whittling down could be counterproductive because some of the reductions are because of the existing schemes. Keeping this in the background, we compare the share of poor for with The share of poor has increased in rural, urban and at the combined level in Assam, Bihar, Chhattisgarh, Jharkhand, Manipur, Meghalaya, Mizoram, Nagaland, Uttar Pradesh, Dadra & Nagar Haveli and Daman & Diu (Figure 1). It comprises most of the tribal regions, which also includes five of the Northeast states as also Chhattisgarh and Jharkhand and also the poorer states of Bihar and Uttar Pradesh. If one excludes the four smaller Northeast states and two union territories whose combined share of poor is less than one per cent, the rest comprise 46.4 per cent of the total poor from among 32.4 per cent of total population. From the smaller entities, Manipur also has a higher poverty risk. In all these states the total number of poor has increased, except for Jharkhand when the number of poor decreased in total but it increased for urban areas by eight lakhs, which was the highest across states; the number of urban poor decreased for Chhattisgarh and Dadra & Nagar Haveli. This period witnessed some positive policy changes in Bihar, but they do not show in poverty reductions. In fact, the number of poor increased by 47.7 lakh persons, accounting for 60 per cent among states where rural poor increased. Inequality also increased considerably in both rural and urban areas. The reasons behind this curious case of Bihar are beyond our comprehension. In Uttar Pradesh, the number of poor increased by 0.1 lakh and seven lakh persons in rural and urban areas respectively. 4 The share of poor have decreased in the rural sector but increased in the urban sector and at the combined level in Arunachal Pradesh, Delhi and Haryana. In Arunachal Pradesh and Haryana the poverty risk for urban areas also increased. In all the three states, the total number of poor has increased. After, Jharkhand and Uttar Pradesh, Delhi and Haryana account for the maximum number of increase in urban poor and the increase in these two states is higher than that of Jharkhand. The increase of urban poverty in Delhi and Haryana is of concerned because it is being accompanied by an increased growth and economic expansion. Do these have implications on growing crime and violence that one hears in these areas? It is only in Lakshadweep that there has been a decrease in the share of poor in urban areas, but an increase in rural and also at the combined level. All these four states poverty risk in rural areas is lower than unity. The number as also share of poor has decreased in rural, urban and combined level in the states of Andhra Pradesh, Goa, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Puducherry, Rajasthan, Sikkim, Tamil Nadu, Tripura, and Andaman & Nicobar Islands. Excluding the last one where incidence is below five per cent, all the other states have reductions in incidence of poverty that has been higher than the all Indian average reduction of 7.4 percentage points. It is more than 20 percentage points in Tripura and Odisha (does this explain the popularity of these governments), more than 15 percentage points in Sikkim and Goa, and more than 10 percentage points in Maharashtra, Puducherry, Tamil Nadu and 4 For a discussion of poverty in Uttar Pradesh during and using the old method see Pathak (2011). Page 6

8 Madhya Pradesh. Despite the reductions, Odisha and Madhya Pradesh continue to have a poverty risk greater than unity. Despite an increase in the number and share of urban poor, one observes a decrease in the rural and combined level in the states of Gujarat, Himachal Pradesh, Jammu & Kashmir, Punjab, Uttaranchal, West Bengal, and Chandigarh. The decline in incidence has been higher than the national average for Uttaranchal, Himachal Pradesh, Gujarat and West Bengal. The increase in urban poverty, as in the case of Delhi and Haryana, needs greater scrutiny for designing appropriate public policy.,, Arunachal Pradesh, Delhi, Haryana, Gujarat, Himachal Pradesh, Jammu & Kashmir, Punjab, Uttaranchal, West Bengal, Chandigarh Assam, Bihar, Chhattisgarh, Jharkhand, Manipur, Meghalaya, Mizoram, Nagaland, Uttar Pradesh, Dadra & Nagar Haveli, Daman & Diu Andhra Pradesh, Goa, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Rajasthan, Sikkim, Tamil Nadu, Tripura, Andaman & Nicobar Islands, Puducherry Lakshadweep,, Source: Authors computations; also see Table A12.3 in Statistical Appendix. Note: Encircled indicates increase in share at the combined level also. Figure 1: Change in Poverty Shares ( to ) 4.2 Incidence, Depth and Severity of Poverty The above discussion on the number and share of poor across states has in some sense already discussed about the incidence of poverty. Now we bring in the discussion on incidence along with depth and severity as also inequality (Table 1). Comparing to , we have the following observations. Page 7

9 In both rural and urban areas, the poverty risk for is greater than unity for incidence, depth and severity in the states of Chhattisgarh, Assam, Bihar, Madhya Pradesh, Odisha, Uttar Praddesh and Jharkhand (CABMOUJ, pronounced kab mouj). If one recalls BIMARU (rather, BIMORU), some of these seem to be the traditional pockets of poverty with the addition of Assam and the notable exclusion of Rajasthan and Uttaranchal. All these seven states do not have the same patterns with regard to poverty reductions, as discussed earlier about the curious case of Bihar or the increasing urban poverty in Jharkhand or substantial reductions in Odisha and Madhya Pradesh among others. Excluding these last two states, poverty risk in is greater than for both rural and urban sectors with regard to incidence, depth and severity in the remaining five of the CABMOUJ states. Excluding Assam, a part of CABMOUJ and Tripura where one observed reductions in poverty, the other five Northeast states indicate the following. There was an increase in incidence, depth and severity for both rural and urban sectors in Manipur, Mizoram and Nagaland, for rural sector in Meghalaya and for urban sector in Arunachal Pradesh. In addition, Meghalaya also indicated an increase in depth and severity in the urban sector. Poverty risk was greater than unity for rural sector in Manipur and for urban sector in Arunachal Pradesh, Manipur, Meghalaya and Nagaland. Further, the poverty risks also increased for the three measures in both the sectors in Manipur, Meghalaya, Mizoram, Nagaland, and for urban sector in Arunachal Pradesh. These are matters of concern because an earlier analysis point out that these states did better when it came to amelioration of poverty (Radhakrishna and Ray, 2005), but then are those traditional advantages being lost. Poverty risk increased for the urban sector with regard to incidence, depth and severity in Gujarat, Himachal Pradesh, Haryana, Jammu & Kashmir, Punjab, Uttaranchal, and West Bengal and for incidence alone in Chandigarh. This reiterates our earlier highlighted concern on the urbanization of poverty. This seems to be serious and needs to be appropriately addressed at the policy level. The smaller entities of Dadra & Nagar Haveli, Daman & Diu, and Lakshadweep also have their own concerns. Them may not matter much in terms of aggregate statistical level, and hence, may fall out of focus from the policy parameter. However, they may fail an ethical test of neglecting the poorest, which go against the tenets of a welfare state. Some notable attainments with regard to the reduction of poverty risk are Maharashtra in both the rural and urban sectors, Gujarat and Tripura in the rural sector and Karnataka, Rajasthan and Sikkim in the urban sector. Broadly speaking, one observes some variations like the increase in poverty risks in some parts of the Northeast states or the smaller union territories, or the increasing vulnerabilities in some urban pockets, or the reductions in poverty in Madhya Pradesh, Odisha and Tripura or reduction in poverty risk in Maharashtra among others. These suggest that public policy suggestions for these different groups of states should be different. It requires further probing and going down to details at the NSS regional or at a district or sub-district level, but then that should be done at the planning and implementation stages and with use of information that goes beyond NSS data. Now, we look into sectoral as also growth-inequality decompositions at the all India level. Page 8

10 Table 1: Incidence, Depth and Severity of Poverty and Inequality across States in India, and States Rural Urban α=0 α=1 α=2 Gini α=0 α=1 α=2 Gini α=0 α=1 α=2 Gini α=0 α=1 α=2 Gini Andhra Pradesh Arunachal Pradesh Assam Bihar Chhattisgarh Delhi Goa Gujarat Himachal Pradesh Haryana Jammu & Kashmir Jharkhand Karnataka Kerala Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Nagaland Odisha Puducherry Punjab Rajasthan Sikkim Tamil Nadu Tripura Uttar Pradesh Uttaranchal West Bengal Andaman & Nicobar Islands Chandigarh Dadra & Nagar Haveli Daman & Diu Lakshadweep All India Source: Author's calculations using unit level data. Page 9

11 4.3 Sectoral and Growth-Inequality Decompositions Sectoral Decomposition As per the unit level data, rural sector comprised of 74.7 per cent of the population in and it decreased to 73.0 per cent of the population in The poverty risk for incidence in both the years is greater than unity by 11 per cent and 30 per cent in rural and urban areas respectively. Inequality increased in both the sectors, but has been higher in the urban sector for both the years. The average monthly per capita consumption expenditure (hereafter, average MPCE) for the urban sector is nearly twice that of the rural sector (` and `579.2 respectively in ` and `1856 and `953 respectively in ). Keeping this in the background, let us look into the urban-rural sectoral decomposition of change in poverty between and for India (Table 2). Table 2: Sectoral Decomposition of Change in Poverty between and (Percentage of total change in poverty) Poverty Components of Change in Poverty Measure Intra-Sectoral Effect Inter-Sectoral Population Interaction Urban Rural Shift Effect ଵ ଶ Note: The total intra-sectoral effect is the sum of the effects in rural and urban sectors. For P it is (= ). Source: As in Table 1. For all three poverty measures, both the sectoral gains and population shift between sectors resulted in poverty reduction. The interaction effect, being negative, dampened these gains. In case of incidence,, both the sectors gained; the rural gaining comparatively more. The inter-sectoral effect is also positive indicating that a declining proportion of the population lived in relatively poorer rural sector in Overall, gains to the rural sector had a major share in reducing aggregate poverty Growth Inequality Decomposition The growth-inequality decomposition of change in poverty with regard to incidence, depth and severity between and for rural, urban and all India level are given in Table 3. In all scenarios, the growth component and interaction effect were negative indicating that they helped in the reduction of poverty while the redistribution component was positive indicating that they have halted the decline in poverty reduction. In rural India, the major part of reduction in incidence of poverty came from the growth component, but for depth and severity it was the interaction affect that dominated. At the same time, the countervailing impact of the redistribution component also became increasingly stronger. The patterns were somewhat similar for urban India. It is the growth component that was stronger than interaction effect in reducing the incidence of poverty, but interaction was Page 10

12 stronger for depth and severity. Compared to rural India, the impacts on incidence, depth and severity were much stronger both by the redistribution components for halting the reduction and by the interaction component for reducing poverty The all India level result is closer to the rural because of the greater population share. Overall, the results are indicative that those further away from the poverty line would have benefitted relatively less. In short, mean incomes of the poor have not increased as much as the increase in inequality. These are reiterated by the growth incidence curves (Figures 2a and 2b) for rural and urban India. Table 3: Growth-Inequality Decomposition of change in Poverty at all-india level Sector Poverty Measure Total Change in Poverty Growth Component Redistribution Component Interaction Effect Rural ଵ ଶ Urban ଵ ଶ Total ଵ ଶ Source: As in Table 1. Figure 2a: Growth Incidence Curve for Rural India ( and ) As evident from Figure 2a, in rural India the average income of the poorest actually declined and then it increase but was lower than the average for the poorest 30 per cent. The richest Page 11

13 five per cent also witnessed a decline in the average growth of their income. A plausible reason may be failure to make productive investments or that the consumption expenditure schedule fails to capture their investments. The growth for those in the percentile was around the average. In urban India (Figure 2b), the poorest gained very little compared to the richest as shown by the positive slope of the growth incidence curve. From this all India analysis, now we propose to look into some patterns across various sub-groups of population. Figure 2b: Growth Incidence Curve for Urban India ( and ) 4.4 Analysis across Sub-groups At the all India level, the incidence, depth and severity of poverty as also inequality for and of sub-groups based on household characteristics like social group (caste), household type (occupation), religion, and size-class of land owned (rural) and of sub-groups based on individual characteristics like gender, age and education is given in Table 4. Some of the observations are as follows Social Groups (Caste) Across social groups, Scheduled Tribes (STs) have the highest incidence of poverty in rural India for both the years. The highest percentage point decline as also percentage change in poverty was also for them. They also had the least average MPCE in and (`446.5and `777.3 respectively). In , the rural population share for STs and SCs was 10.8 per cent and 22.2 per cent respectively whereas their share of rural poor was 15.4 per cent and 28.2 per cent respectively. Poverty risk was the highest for the STs followed by that of Scheduled Castes (SCs) and then the Backward Classes (BCs) and then others. The least percentage change in poverty was for BCs, a group whose poverty risk increased for all the three measures. It also increased for the SCs for depth and severity measures. Page 12

14 In urban India, average MPCE for , is the least for SCs (`794.2and ` respectively) and the highest for others (` and ` respectively). As is generally the case, SCs have the highest poverty levels, followed by the STs, BCs and others respectively. In the SCs, STs and BCs constitute 15.1 per cent, 3.5 per cent and 38.5 per cent of the urban population but comprise 24.5 per cent, 5.0 per cent and 44.8 per cent of the urban poor respectively. At the incidence level the percentage point decline for BCs was higher than that of STs, but for depth and severity the percentage point decline for STs was higher than that of the SCs also. In fact, this is also reflected in poverty risk, which increased for STs with regard to incidence but decreased for depth and severity. The poverty risk for BCs increased for depth and severity and that of SCs increased for incidence and depth Household Type (Occupation) Analysis by household type (that is, by major occupation of household) indicates that in rural India the highest levels of poverty in all three measures is with agricultural labour, the next in line are other labour (manual non-agriculture) followed by those self-employed in nonagriculture activities, and then by those self-employed in agriculture and then by others. In , agriculture labour and other labour constitute 25.1 per cent and 14.7 per cent of the population but comprise 37.2 per cent and 17.5 per cent of the poor respectively in rural India. Poverty risk is highest for the agricultural labour and least for the others in both the years. In and , other labour had the minimum average MPCE (`434.7 and `735.5 respectively) and others had the maximum average MPCE (`852.6 and ` respectively). Between and , inequality declined for those self-employed in non-agriculture and others. Agricultural labourer has the lowest Gini coefficient for both years, but the increase in inequality is the maximum for this group (6.5 per cent). This indicates a serious fact: not only is the agricultural labourer the poorest as a group, the distribution of expenditure is also becoming more unequal for this group. Casual labour is the poorest occupation group in urban India in both and and its average MPCE was the lowest (`598.9 and ` respectively) and was more than onethird of the average MPCE for others in Further, in casual labour and selfemployed constitute 14.1 per cent and 42.0 per cent of the urban population but comprise 31.9 and 44.3 per cent of the urban poor. Thus, casual labour share, in both years, is almost twice the share of poor. Self employed also have a poverty risk greater than unity. Regular wage/salary earners exhibited have the least poverty risk. Inequality increased for all occupation groups in urban India. Others showed the highest increase in inequality in consumption expenditure followed by the self-employed (7.7 per cent and 3.8 per cent respectively). Page 13

15 Table 4: Incidence, Depth and Severity of Poverty and Inequality across Sub-groups in India, and Sub-groups Rural Urban α=0 α=1 α=2 Gini α=0 α=1 α=2 Gini α=0 α=1 α=2 Gini α=0 α=1 α=2 Gini Scheduled Tribe Scheduled Caste Backward Classes Others HHT HHT HHT HHT HHT Hinduism Islam Christianity Sikhism Buddhists Others Landless Marginal Small Semi-Medium Medium Large Male Sex Female >= Illiterate Up to Primary Up to Secondary H. Secondary > H. Secondary Note: HHT1 through HHT9 denote self-employed in non-agriculture, agriculture labour, other labour, self-employed in agriculture and others respectively in rural India and selfemployed, regular wage & salaried employee, casual labour and others respectively in urban India. Source: As in Table 1. Caste Occupation Religion Land size Age Education Page 14

16 4.4.3 Religion Religion wise analysis shows that Buddhists (with a very small population share) have the highest incidence of poverty in rural India. Among the other religions, Muslims have the highest incidence with Hindus following close behind. In , the population share of Buddhists, Muslims and Hindus was 0.52 per cent, 11.6 per cent and 83.6 per cent and their share of poor was 0.69 per cent, 12.7 per cent and 84.1 per cent in rural India. The least incidence of poverty is for Sikhism, a group which also registered the highest percentage decline. Sikhs have the lowest poverty risk. Inequality is highest among Christians and lowest among Muslims. In urban India, Muslims are the poorest group and also with the highest poverty risk followed by that of Buddhists. In , the population share of Muslims and Buddhists was 15.9 per cent and 0.9 per cent and their share of poor was 25.8 per cent and 1.4 per cent respectively. Incidence of poverty decreased for all groups, but increased for Sikhism. Gini coefficient has also increased by about 15 per cent for Sikhs. This is commensurate with increasing poverty in urban Punjab and Chandigarh, which has a substantial amount of Sikh population Size-class of Land Size-class of land, as expected, suggest a secular decline in incidence, depth and severity of poverty with an increase in land size in rural India. The landless class has the highest incidence of poverty, followed by marginal landowners. They constitute 41.8 per cent and 31.9 per cent of the population and comprise 47.4 per cent and 33.4 per cent of the poor in The highest decline in poverty between and occurred for those with large size-class of land. Inequality declined for landless (4 per cent) and small sized land owners (0.7 per cent) while it increased by 8.1 per cent for large land owners, by 3.1 per cent for semi-medium land owners, 1.4 per cent for marginal land owners and 1.3 per cent for medium sized land owners. The inverse relationship between size-class and poverty seems to hold in urban India for if one excludes the landless. However, it is only the marginal group that constitutes an incidence that is higher than the overall average. Given that almost 88 per cent of the population in urban India are landless one should be cautious in extending the size-class analysis to urban areas Gender So far we were discussing household characteristics. Now, we get into analysing individual characteristics - gender, age and education. With regard to gender, we do give some information on female-headed households also (a household characteristic), as has been traditionally done and also at the individual level. Across gender, females have a slightly higher incidence of poverty than that of males for both the years under consideration in both rural and urban India. Correspondingly, poverty risk is Page 15

17 higher for females. For and , in rural India, incidence of poverty among female-headed households (41.6 per cent and 29.4 per cent respectively) was lower than that of male-headed households (41.8 per cent and 33.6 per cent respectively) whereas incidence of urban poverty in female-headed households (28 per cent and 22.1 per cent respectively) was higher than that for the male-headed households (25.5 per cent and 20.7 per cent respectively). For the same two years, when one takes into consideration widow/widower or divorcee status of the head of household then incidence of poverty in rural India was 41.8 per cent and 32.7 per cent respectively for female-headed households and 39.9 per cent and 32.9 per cent among male-headed households. In urban India, it was 30.6 per cent and 25.4 per cent for female-headed households and 33.2 per cent and 27 per cent for male-headed households Age-wise Classification A curious observation about age wise classification is that incidence, depth and severity of poverty is the highest in the 0-14 age-group. This is true for both the time periods and for both rural and urban India. In fact, this is the only age-group for which poverty risk is greater than unity. In , for rural and urban sectors this age-group constitutes 32.2 per cent and 20.9 per cent of the population respectively but comprises 40.1 per cent and 36.6 per cent of the poor respectively. Though this information is based on an individual characteristic, yet it does suggest that households with this category of dependent population are at a greater risk, perhaps because of health and education requirements. Of course, this age-group would be living in the same household with members in other age-groups, which is likely to be spread across. But, in rural areas, it is possible that they reside with the age-group for whom we have a poverty risk that is less than unity but relatively higher than the other age groups. Again, in both rural and urban areas the poverty risk is relatively higher for 60+ agegroup of population, another dependent category. Both these vulnerable groups will have nutritional and health implications, which is beyond the scope of the current paper Education-wise Classification Like age-wise classification, the education-wise classification also seems to have some broad patterns that are similar for both rural and urban sectors. There are four broad observations. First, in both the years there is an inverse relationship between incidence, depth and severity of poverty and educational attainment; for a discussion on increasing inequality, particularly in urban Indian, and its link with increasing returns to education to some sections during and see Cain et al (2010). Second, in , poverty risk was greater than unity only for the illiterates but in , it is also greater than unity for those literate up to primary level. This could be associated with the vulnerability of the 0-14 age-group discussed above, but this was also the case in The other plausibility is that adults who are literate up to primary level are losing out in the economy. Third, poverty risk has increased for all categories. This is because the poverty risk of the category for which information on educational attainment is missing has reduced. Last, but not the least, incidence, depth and severity of poverty has reduced only for illiterates (and also for up to primary in rural areas Page 16

18 only) and has increased for the rest. This reiterates our contention that some people are losing out in the economy. 5. Concluding Remarks This paper points out that there are some unfinished tasks that the Report of the Expert Group to Review the Methodology for Estimation of Poverty (Government of India, 2009) has implicitly mentioned. A nuanced approach is needed to go beyond calories in our understanding of nutritional norms to link them to poverty. This also assumes importance for the fact that there exists an agriculture-nutrition disconnect in India. A much more serious question that the report raises is that the state is no more being able to provide some basic facilities to its citizens - be it education, health care needs or sanitation. These raises a question, does India continue to be a welfare state? There is also a case to articulate the understanding of poverty by bringing in additional dimensions (see Krishnaji, 2012; Radhakrishna, Ravi and Reddy, 2012), but it should not be in the form of a unidimensional money-centric measure that Guruswamy and Abraham (2006) attempted. Further, given a larger debate in the country on transparency, it is imperative of the Planning Commission to give all important data computed at the state and sector level in the public domain through a spreadsheet. In particular, we are referring to the price indices, the budget shares and quantities among others. This would have taken the debate in the popular as also academic discourse beyond the poverty line and helped replication in some limited sense. In its absence, the computation of the poverty line remains a black box. In our discussion on shares of poor, particularly its link with grants from the centre for some welfare schemes should be carefully evaluated. This is required, particularly so, because of the claims by the government that the Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS) could have helped in ameliorating poverty. If true, then any reduction of grants because of reduction of poverty can be counterproductive because the scheme that helped reduce poverty will get withdrawn. Of course, implementation of programmes under the scheme should integrate wage-based income with asset generation that are locally relevant. The state-wise analysis pointed out the curious case of Bihar, the increasing incidence of poverty in some states including among the Northeast, the urbanisation of poverty with serious implications on law and order in Delhi and adjoining areas as also other places. At the same time, we should not ignore the concerns of smaller entities. There should be specific programmes addressing their problems, as their exclusion, though statistically irrelevant, raise ethical concerns. In sectoral decompositions, there is a positive impact at the intra-sectoral level for both the rural and urban areas, with rural gaining relatively more, and the inter-sectoral effect also shows a positive impact due to population shift. The growth-inequality decompositions show that the growth and interactions components reduce poverty whereas the redistribution component counteracts it, which becomes stronger as we go from the analysis of incidence to Page 17

19 that of depth and severity. These call for specific programmes for those who are far below the poverty line. Our sub-group analysis pointed out, as expected, the following: greater vulnerabilities among Scheduled Tribes and Scheduled Castes across social groups, the former needing greater emphasis in rural and the latter in urban; the agricultural labour and other labour in rural and casual labour and self-employed (read petty traders and hawkers) in urban across occupation types of households; Buddhists (with a small share of population), and Muslims (highest incidence in urban) with greater poverty risk across religions; the inverse relationship with land size and educational attainment; greater incidence of females, particularly divorcee and widows; and the vulnerability of the dependent population comprising children and the old. All these point to a lot of variations in patterns. However, they need to be complemented with other quantitative and qualitative information at the district and sub-district level for appropriate planning and implementation of poverty alleviation programmes. Page 18

20 Table A: Poverty Line and Number of Poor in Rural and Urban Areas across States: , and States Poverty Line (in Rupees) Number of Poor (in Lakhs) Rural Urban Rural Urban Andhra Pradesh Arunachal Pradesh Assam Bihar Chhattisgarh Delhi Goa Gujarat Haryana Himachal Pradesh Jammu & Kashmir Jharkhand Karnataka Kerala Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Nagaland Odisha Puducherry Punjab Rajasthan Sikkim Tamil Nadu Tripura Uttar Pradesh Uttaranchal West Bengal Andaman & Nicobar Is Chandigarh Dadra & Nagar Haveli Daman & Diu Lakshadweep All India Note and Source: Number of poor for are estimated based on head count ratio population computed from the estimates of number of poor as per the earlier method of the Planning Commission and obtained from, Head count ration for 31 states and union territories is from the Report of the Expert Group to Review the Methodology for Estimation of Poverty, Planning Commission, New Delhi (Chairperson: Suresh D. Tendulkar), For the remaining four union territories of Andaman & Nicobar Islands, Chandigarh, Dadra & Nagar Haveli, Daman & Diu and Lakshadweep we used incidence provided by C. Ravi through a personal communication. The population of undivided states of Bihar, Madhya Pradesh and Uttar Pradesh was divided based on their share as per the National Sample Survey region unit level data of , which is an overestimate for Uttaranchal because the relevant region had one district (Bareily) which is not part of the new state. For updates of and for we used Press Note on Poverty Estimates, , Planning Commission, New Delhi, Page 19