Regional estimates of multidimensional poverty in India

Dehury, Bidyadhar; Mohanty, Sanjay K. Working Paper Regional estimates of multidimensional poverty in India Economics Discussion Papers, No. 2015-34 Provided in Cooperation with: Kiel Institute for the World Economy – Leibniz Center for Research on Global Economic Challenges Suggested Citation: Dehury, Bidyadhar; Mohanty, Sanjay K. (2015) : Regional estimates of multidimensional poverty in India, Economics Discussion Papers, No. 2015-34, Kiel Institute for the World Economy (IfW), Kiel This Version is available at: https://hdl.handle.net/10419/110308 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/3.0/ Received April 27, 2015 Accepted as Economics Discussion Paper April 30, 2015 Published May 6, 2015 © Author(s) 2015. Licensed under the Creative Commons License - Attribution 3.0 Discussion Paper No. 2015-34 | May 06, 2015 | http://www.economics-ejournal.org/economics/discussionpapers/2015-34 Regional Estimates of Multidimensional Poverty in India Bidyadhar Dehury and Sanjay K. Mohanty Abstract Using unit data from the Indian Human Development Survey (IHDS), 2004-05, this paper estimates and decompose the multidimensional poverty dynamics in 84 natural regions of India. Multidimensional poverty is measured in the dimensions of health, knowledge, income, employment and household environment using ten indicators and Alkire-Foster methodology. The unique contributions of the paper are inclusion of a direct economic variable (consumption expenditure) to quantify the living standard dimension, decomposition of MPI across the dimensions and the indicators and provide estimates at sub-national level. Results indicate that about half of India's population are multidimensional poor with large regional variations. More than 70% of the population are multidimensional poor in the Mahanadi Basin, the southern region of Chhattisgarh and the Vindhya region of Madhya Pradesh, while it is less than 10% in the coastal regions of Maharashtra, Delhi, Goa, the mountainous region of Jammu and Kashmir, the Hills region and Plains region of Manipur, Puducherry and Sikkim. The decomposition of MPI indicates that economic dimension alone accounts for about one-third of multidimensional poverty in most of the regions of India. Based on these analyses, the authors suggest target based interventions in the poor regions to reduce poverty and inequality, and achieve the Millennium Development Goals in India. JEL I J Z Keywords Multidimensional poverty index; decomposition; regions; India Authors Bidyadhar Dehury, International Institute for Population Sciences (IIPS), Mumbai Sanjay K. Mohanty, International Institute for Population Sciences (IIPS), Mumbai, [email protected] Citation Bidyadhar Dehury and Sanjay K. Mohanty (2015). Regional Estimates of Multidimensional Poverty in India. Economics Discussion Papers, No 2015-34, Kiel Institute for the World Economy. http://www.economics-ejournal.org/ economics/discussionpapers/2015-34 3 ! 1.1 Introduction During the first four decades of development studies (1950-90), poverty was primarily measured in money metric form, either from household income or consumption expenditure. The limitation of money-metric poverty to capture the multiple deprivations of human life and the development of the capability approach (Sen, 1985) led to growing interest to measure poverty in a multidimensional space. The evolution of the human development paradigm in 1990 led to a strong theoretical foundation to measure multidimensional poverty. The United Nations Development Programme (UNDP) in its annual publications devised a set of composite indices, the Capability Poverty Measure (CPM), the Human Poverty Index 1 (HPI 1) and the Human Poverty Index 2 (HPI 2) to measure multidimensional poverty (UNDP 1996, 1997) using aggregate data. The Millennium Declaration has outlined eradication of poverty in its all forms - hunger, ill health, illiteracy. The goals, targets and indicators of the Millennium Development Goals (MDGs) are included in national and local planning (United Nations, 2000). In recent years, the UNDP has disseminated the multidimensional poverty index (MPI) for 104 countries (UNDP, 2010). While the HPI measures poverty at the macro level, the MPI is unique as it identifies individuals (at the micro level) deprived in overlapping multiple dimensions and captures both the extent and intensity of poverty (Alkire and Santos, 2010). Following the UNDP’s work, several researchers have contributed towards measurement of multidimensional poverty (Anand and Sen, 1997; Chiappero-Martinetti, 2000; Bourguignon and Chakravarty, 2003; Gordon et al., 2003; Qizilbash, 2004; Alkire and Foster, 2007; Antony and Rao, 2007; Calvo, 2008; Wagle, 2008; Alkire and Santos, 2010; Alkire and Foster, 2011; Mohanty, 2011). Most of these studies used the dimensions of education, health and standard of living and a few studies included subjective well-being such as fear of facing hardship (Calvo, 2008) in defining multidimensional poverty. However, these studies differed in measuring multidimensional poverty, for instance in fixing the poverty cut-off point of each dimension, weighting the dimensions, deprivation cut-off point in separating the poor from the non-poor and so on. With respect to measurement, some researchers considered the union (poor in any dimension) approach (Bourguignon and Chakravarty, 2003) while others have used the intersection approach (poor in two or more dimension) (Gordon et al., 2003) or relative approach (Wagle, 2008) in defining the poverty line. 4 ! While earlier studies used aggregate data, recent studies estimated multidimensional poverty using micro level data. Based on the counting approach, Alkire and Foster (2007; 2011) developed a new methodology in estimating multidimensional poverty. Following the Alkire- Foster method, some studies estimated multidimensional poverty (Alkire and Santos, 2010; Coromaldi and Zoli, 2012; Alkire et al., 2013; Batana, 2013; Battiston et al., 2013; Santos, 2013; Yu, 2013). Alkire and Santos (2010) provided estimates of multidimensional poverty for many developing countries using Demographic and Health Survey (DHS) and other large scale survey data. However, their analysis was restricted to three dimensions and had data constraints. Santos (2013) measured multidimensional poverty reduction in Bhutan from 2003 to 2007 using the Bhutan Living Standard Survey. Consumption expenditure with other indicators was used in measuring multidimensional poverty. The reduction in multidimensional poverty was observed irrespective of indicators weights, deprivation cut-off and identification criterion of the poor. A significant poverty reduction was found due to reduction in the proportion of poor accompanied by the intensity of poverty among those who were less intense poor. Batana (2013) measured multidimensional poverty among the women in Sub-Saharan countries using four dimensions - assets, health, schooling and empowerment. Multidimensional poverty estimates when compared with Human Development Index (HDI), Income poverty, Asset poverty and Gender Development Index (GDI) show a different picture in country rankings. This suggests that inclusion of additional dimensions in multidimensional measure changes the rankings of countries. The decomposition analysis reveals that deprivations in schooling and lack of empowerment among women contribute to poverty. Battiston et al (2013) measured multidimensional poverty in six Latin American countries by combining indicators from two traditional measures of poverty: income based and unsatisfied basic needs (UBN) approach and used Alkire-Foster and Bourguignon- Chakravarty (BC) measures of poverty. While measuring poverty, both income based and UBN indicators are relevant and useful in targeting the poor. Mohanty (2011; 2012), using the unit data from NFHS 3, linked multidimensional poverty with health and health care utilisation. Children belonging to multidimensional poor households are more likely to be deprived of health care and lower survaival. Alkire and Seth (2013b) suggested a new method using binary scoring method, which can be updated periodically, to target BPL households in India. 5 ! 1.2 Aim and Rationale Though eradication of multidimensional poverty has been at the centre stage of development agenda, there are only a few studies that estimated multidimensional poverty in India. This paper aims at providing estimates of multidimensional poverty at disaggregated level; in the regions of India, and decomposing multidimensional poverty dynamics across dimensions and regions. This is an improvement on existing literature as we have measured multidimensional poverty by including direct economic variables rather than economic proxies, incorporated the missing dimensions of work/employment and household environment, provided estimates for 84 regions of India, and disaggregated across dimensions, indicators and regions. We put forward the following rationale in support of the study. First, the regions of India are classified baed on agro-climatic conditions and homogenous with respect to economic, social, cultural and demographic variabls. On the otherhand variation in the socio-economic development among regions of India are large. Regional estimates of multidimensional poverty will be helpful in identifying the backward areas for policy intervention. Second, earlier studies in India (Alkire and Seth, 2013a; Mohanty 2011) used economic proxies rather than direct economic variables in measuring living standard and were restricted to three dimensions - health, knowledge and living standard. We have included some of the key missiing dimension such as consumption expenditure, work/employment and household environmental dimensions in estimating multidimensional poverty. Third, for the first time, we provide the estimates of multidimensional poverty fat disaggregated level (for 84 regions of India) and decomposed the MPI by indicators, rural and urban, regions and states to understand the relative contribution of factors in explaining multidimensional poverty. 1.3 Data The Indian Human Development Survey (IHDS), 2004-05, conducted by the University of Maryland and the National Council of Applied Economic Research (NCAER), New Delhi is used for the analyses. The IHDS survey interviewed 41554 households and covered 215754 individuals from 1503 villages and 971 urban blocks of India. The advantage of using the IHDS survey in estimating multidimensional poverty is that it provides comprehensive information on key dimensions of income, consumption expenditure, health, wealth and 6 ! work/employment. It provides comprehensive information on income, consumption expenditure, employment, education, fertility, reproductive health, child health, morbidities, gender relations, social capital and cognitive development of children. The details of the survey design, sampling instrument, variables and constructed variables, and various codes used are available in the national report (Desai et al., 2008). Households with missing information were small and we have excluded missing values from analyses. 1.4 Methods 1.4.1 Dimensions and Indicators In measuring multidimensional poverty, five dimensions have been selected, namely health, education, economic, work/employment and household environment. These five dimensions comprise a total of ten indicators. The description of dimensions, indicators and the weight to each indicator is shown in Table 1. Two indicators included in the health dimension are household experience of any child or adult (<60 years) death in the year preceding the survey, and if any ever married woman (15-49 years) in the household was undernourished (BMI less than 18.5). The two indicators considered in the education dimension are school enrolment and years of schooling. The household is considered deprived in the school enrolment indicator if at least one school going child aged 6-14 years in the household currently are not enrolled in the school. Similarly, a household is deprived in years of schooling indicator if no adult member aged 15 years and more in the household has completed five years of schooling. In the economic dimension, the monthly per capita consumption expenditure and the revised official state level poverty line cut-off for 2004-05 is used to define consumption poverty (GOI, 2011). With respect to work/employment two indicators, occupation and employment are used. A household is said to be deprived in occupation if the household’s annual per capita income is less than 5000 rupees and, either the household belongs to labour class households or low paid non-farm business or has low land holdings with less than 2.5 acres. The three indicators used in the household environment dimension are access to clean drinking water, adequate sanitation and clean cooking fuel. 7 ! Table 1: Dimensions, indicators and weights used in computation of multidimensional poverty index (MPI) in India Sn Dimensions Description of Indicators Weights 1 Health Mortality (V1): Any child or adult (<60years) death occurred in the household in the year preceding the survey date 1/10=0.1 Nutrition (V2): If the household has any undernourished (BMI <18.5) ever married women (15- 49 years) 1/10=0.1 2 Education School Enrolment (V3): At least one child in the school going age (6-14 years) in the household currently not enrolled in school 1/10=0.1 Years of Schooling (V4): No adult member (15 years and above) in the household has completed five years of schooling 1/10=0.1 3 Economic Consumption Expenditure (V5): If the household falls below the consumption expenditure threshold limit (official poverty line) 2/10=0.2 4 Work and Employment Occupation (V6): If the per capita annual income is less than 5000 rupees and the household belongs to either low paid non-farm business, or labour class household, or low land holding (<2.5 acre) 1/10=0.1 Employment (V7): No one in the household (15-59 years) has worked for more than 240 hours in one activity in the year preceding the survey date 1/10=0.1 5 Household environment Water (V8): No access to clean drinking water 1/15=0.67 Sanitation (V9): No access to adequate sanitation 1/15=0.67 Cooking fuel (V10): No access to clean cooking fuel 1/15=0.67 1.4.2 Measurement of Multidimensional Poverty We measured the multidimensional poverty index (MPI) using the dual cut-off method based on the counting approach developed by Alkire and Foster (2007; 2011). This method is gaing popular and disseminated by UNDP in the Human Development Report (HDR) 2010 (UNDP, 2010). The Alkire and Foster assigns equal weight to each dimension and equal weight to each indicator within each dimension. An individual gets a weighted deprivation score according to his/her number of weighted deprivations. The total weighted deprivation score ranges 0-1 and a household is identified as multidimensional poor if the weighted deprivation score is greater than 0.33, which is one-third of the total weighted deprivation 8 ! score. To derive multidimensional poverty, the Head count ratio (H) and intensity of poverty (A) are computed. The headcount ratio is the proportion of the population who are multidimensional poor. The headcount ratio is computed as: H= Where, q is number of multidimensional poor, n is total population. The intensity of poverty (A) or the breadth of deprivation captures the average weighted count of deprivations experienced by the multidimensional poor. The intensity of poverty (A) is computed as A= Where, c is the total weighted deprivations experiences by the poor. The multidimensional poverty index (MPI) is the product of headcount ratio (H) and the intensity of poverty (A). It is also referred as adjusted headcount ratio. The MPI is computed as: MPI= H * A 1.4.3 Decomposition of MPI We have further decomposed the MPI by its component indicators. The censored headcount ratio is first identified to decompose MPI into each indicator. The censored headcount ratio is defined as the proportion of multidimensional poor deprived in the given indicator to the total population. The contribution of deprivation of a particular indicator is computed as: Contribution of Indicator i to MPI = * 100 Where wi is the weight of ith indicator and CHi is the censored headcount ratio of ith indicator. 9 ! The contribution of each region to overall poverty is computed by using the following formula: Contribution of region i to MPI = * 100 Where ni is the population of ith region and n is the total population. MPIi is the MPI of ith region. We prepared state and region maps of multidimensional poverty index using ArcGIS software package (ArcMap 10) to show the spatial variation of multidimensional poverty . 1.5 Results 1.5.1 Multidimensional Poverty in the States of India Multidimensional poverty at the national level was estimated at 45% and it is close to the estimates of Alkire and Seth (49%) (Alkire and Seth, 2013a). The correlation coefficient of our estimates with Alkire-Foster estimates is 0.77. Among the bigger states of India (states with population of more than 10 million), our estimate of multidimensional poverty is maximum in Chhattisgarh (71.3%) followed by Odisha, Bihar, Jharkhand and Uttar Pradesh. All these states are also marked red in Map 1. It is minimum in the state of Jammu and Kashmir followed by Himachal Pradesh and Punjab. Among smaller states, the variation in multidimensional poverty estimates is large, from 52% in Dadra & Nagar Haveli to less than 5% in Goa. 1.5.2 Poverty Estimates at the Regional Level The multidimensional poverty index (MPI) is the product of two measures, headcount ratio (H) and intensity of poverty (A). The headcount ratio is the proportion of multidimensional poor to the total population. The intensity of poverty is the average weight of deprivations experienced by the multidimensional poor at a time. Table A.1 provides eight columns, starting with the serial number of the regions, name of the state and region, the estimated headcount ratio, intensity of poverty, MPI, rank of regions by MPI, share of MPI in the region and the percentage of population in the region. The estimates for a total of 29 states and 84 regions are presented in the Table A.1. The estimated headcount ratio varies largely among the regions, from as high as 93% in the southern regions of Chhattisgarh and 72% in 16 ! 1.7 References Alkire, S., Conconi, A., & Roche, J.M. (2013). Multidimensional poverty index 2013: brief methodological note and results. Oxford Poverty and Human Development Initiative (OPHI), Oxford Department of International Development, University of Oxford. Alkire, S., and Foster, J. (2007). Counting and multidimensional poverty measures. Oxford Poverty and Human Development Initiative (OPHI), Working Paper 7, Oxford Department of International Development, University of Oxford. Alkire, S., & Foster, J. (2011). Counting and multidimensional poverty measures. Journal of Public Economics, 95, 476-87. Alkire, S., & Santos, M. E. (2010). Acute multidimensional poverty: A new index for development countries. Oxford Poverty and Human Development Initiative (OPHI) Working Paper 38, Oxford Department of International Development, University of Oxford. Alkire, S., & Seth, S. (2013a). Multidimensional poverty reduction in India 1999-2006: where and how? Oxford Poverty and Human Development Initiative (OPHI), Working Paper 60, Oxford Department of International Development, University of Oxford. Alkire, S., & Seth, S. (2013b). Selecting a targeting method to identify BPL households in India. Social Indicators Research, 112, 417-446. Anand, S., & Sen, A. (1997). Concepts of human development and poverty: a multidimensional perspective, Human Development Papers. Antony, G. M., & Rao, K. V. (2007). A composite index to explain variations in poverty, health, nutritional status and standard of living: use of multivariate statistical methods. Public Health, 121, 578-587. Batana, Y. M. (2013). Multidimensional measurement of poverty among women in Sub- Saharan Africa, Social Indicators Research, 112, 337-362. Battiston, D., Cruces, G., Lopez-Calva, L.F., Lugo, M.A.,& Santos, M.E. (2013). Income and beyond: multidimensional poverty in six Latin American countries. Social Indicators Research, 112, 291-314. Bourguignon, F., & Chakravarty, S. (2003).The measurement of multidimensional poverty”, Journal of Economic Inequality, 1, 25–49. Calvo, C. (2008). Vulnerability to multidimensional poverty: Peru, 1998-2002. World Development, 36(6), 1011-20. Chiappero-Martinetti, E. (2000). A multidimensional assessment of well-being based on Sen's functioning approach. Rivista Internazionale di Scienze Sociali, 108(2), 207-239. Coromaldi, M. & Zoli, M. (2012). Deriving multidimensional poverty indicators: methodological issues and an empirical analysis for Italy. Social Indicators Research, 107, 37-54. Desai, S., Dubey, A., Joshi, B. L., Sen, M., Shariff, A., &Vanneman, R. (2008). India Human Development Survey (IHDS). University of Maryland and National Council of Applied Economic Research, New Delhi. 17 ! Gordon, D., Namdy, S., Pantazis, C., Pemberton, S., &Townsend, P. (2003). The distribution of child poverty in the developing world. Bristol: Centre for International Poverty Research. Government of India, Planning Commission (2011). Press Note on Poverty Estimates. Mohanty, S. K. (2011). Multidimensional poverty and child survival in India. PLoS ONE, 6(10), doi. e26857. Mohanty, S. K. (2012). Multiple deprivation and maternal care in India. International Perspective on Sexual and Reproductive Health, 38 (1), 6-14. Qizilbash, M. (2004). On the arbitrariness and robustness of multi-dimensional poverty rankings.UNU-WIDER Research Paper (2004/37). Santos, M. E. (2013). Tracking poverty reduction in Bhutan: Income deprivation alongside deprivation in other sources of happiness. Social Indicators Research, 112, 259-290. Sen A. (1985). Commodities and Capabilities. North-Holland. UNDP (1996). Human Development Report 1996. New York: Oxford University Press. UNDP (1997). Human Development Report 1997. New York: Oxford University Press. UNDP (2010).Human Development Report 2010. New York: Palgrave Macmillan. United Nations (2000).UN Millennium Declarations, Resolutions A/RES/55/2. United Nations, New York. Yu, J. (2013). Multidimensional poverty in China: Findings based on the CHNS. Social Indicators Research, 112, 315-336. Wagle, U. R. (2008). Multidimensional poverty: an alternative measurement approach for the United States? Social Science Research, 37, 559-580. 18 ! 1.7 Appendix Table A.1: Head count ratio (H), intensity of poverty (A), multidimensional poverty index (MPI) and decomposition of MPI value at state and regional level in India, 2004-05. Sl no (col 1) Regions (col. 2) Headcou nt Ratio (H) (col. 3) Intensity of Poverty (A) (col. 4) MPI (col. 5) Rank of regions by MPI (col. 6) Contribu tion to MPI (%) (col. 7) % of popul ation (col. 8) INDIA 45.1 46.0 0.207 100 100 Andhra Pradesh 37.5 42.3 0.158 5.7 7.5 1 Coastal Northern 36.1 39.1 0.141 29 0.8 1.2 2 Coastal Southern 41.7 44.4 0.185 43 1.1 1.2 3 Inland North Eastern 43.5 41.2 0.179 41 1.3 1.5 4 Inland North Western 36.7 43.3 0.159 37 1.6 2.1 5 Inland Southern 30.4 42.8 0.130 23 0.9 1.5 6 Arunachal Pradesh 44.5 44.3 0.197 47 0.1 0.1 Assam 47.0 45.3 0.213 2.4 2.4 7 Cachar Plain 29.3 46.6 0.137 25 0.1 0.1 8 Plains Eastern 25.0 41.6 0.104 17 0.1 0.3 9 Plains Western 51.0 45.5 0.232 58 2.2 2.0 Bihar 57.8 45.2 0.261 8.5 6.8 10 Central 52.9 46.0 0.243 65 3.7 3.1 11 Northern 62.0 44.6 0.277 73 4.9 3.7 12 Chandigarh 5.9 40.2 0.024 6 0.0 0.1 Chhattisgarh 71.3 49.4 0.353 4.8 2.8 13 Mahanadi Basin 72.0 49.5 0.356 83 3.7 2.2 14 Northern 54.3 50.2 0.273 72 0.5 0.4 15 Southern 93.1 48.4 0.451 84 0.5 0.2 16 Dadra & Nagar Haveli 52.3 49.9 0.261 71 0.0 0.0 17 Daman & DIU 38.0 44.1 0.167 39 0.0 0.0 18 Delhi 8.5 42.1 0.036 8 0.2 1.3 19 Goa 4.6 42.5 0.020 3 0.0 0.2 Gujarat 36.5 45.7 0.167 4.1 5.1 20 Dry areas 53.0 44.9 0.238 62 0.2 0.1 21 Kachchh 41.4 44.7 0.185 44 0.2 0.2 22 Plains Northern 31.8 44.9 0.143 30 1.3 1.9 23 Saurashtra 33.2 46.7 0.155 35 0.8 1.1 24 South Eastern 42.3 46.0 0.195 46 1.6 1.7 Haryana 26.8 42.5 0.114 1.0 1.9 25 Eastern 30.8 42.2 0.130 22 0.7 1.2 26 Western 20.4 43.1 0.088 14 0.3 0.7 19 ! Himachal Pradesh 21.6 41.9 0.090 0.3 0.6 27 Central 17.5 40.8 0.071 13 0.1 0.4 28 Trans Himalayan & Southern 27.4 42.9 0.118 21 0.2 0.3 Jammu & Kashmir 14.0 40.8 0.057 0.3 1.1 29 Jhelum Valley 13.3 39.8 0.053 11 0.2 0.8 30 Mountainous 9.3 41.5 0.039 9 0.0 0.2 31 Outer Hills 24.6 43.7 0.108 18 0.1 0.1 Jharkhand 57.4 49.1 0.282 5.1 3.8 32 Hazaribag Plateau 46.7 50.7 0.237 61 1.7 1.5 33 Ranchi Plateau 64.3 48.4 0.311 78 3.4 2.3 Karnataka 40.4 43.6 0.176 3.9 4.6 34 Coastal and Ghats 25.9 45.0 0.117 20 0.2 0.4 35 Inland Eastern 35.6 43.8 0.156 36 0.3 0.4 36 Inland Northern 49.3 44.4 0.219 54 2.1 2.0 37 Inland Southern 34.5 41.9 0.145 33 1.2 1.8 Kerala 35.2 40.6 0.143 2.2 3.1 38 Northern 35.7 39.1 0.140 26 0.5 0.8 39 Southern 35.0 41.2 0.144 31 1.6 2.3 Madhya Pradesh 57.1 47.4 0.271 6.9 5.2 40 Central 47.6 47.1 0.224 57 0.5 0.4 41 Malwa 47.8 46.2 0.221 56 1.7 1.6 42 Northern 54.3 44.1 0.239 63 0.8 0.7 43 South 58.2 48.0 0.280 75 0.6 0.4 44 South Western 58.1 48.5 0.282 76 1.1 0.8 45 Vindhya 71.1 49.0 0.348 82 2.3 1.4 Maharashtra 40.0 46.1 0.184 9.3 10.4 46 Coastal 10.5 41.9 0.044 10 0.5 2.2 47 Eastern 53.8 47.6 0.256 70 0.8 0.7 48 Inland Central 50.1 48.8 0.245 67 2.0 1.7 49 Inland Eastern 42.1 47.3 0.199 48 1.7 1.8 50 Inland Northern 50.4 46.5 0.235 60 1.4 1.3 51 Inland Western 47.8 43.9 0.210 53 2.9 2.9 Manipur 6.6 34.2 0.023 0.0 0.3 52 Hills 3.8 40.0 0.015 2 0.0 0.1 53 Plains 7.5 33.3 0.025 7 0.0 0.2 54 Meghalaya 43.4 47.1 0.205 51 0.2 0.2 55 Mizoram 5.5 41.9 0.023 5 0.0 0.1 56 Nagaland 23.8 39.3 0.094 16 0.1 0.2 Odisha 65.9 49.3 0.325 6.3 4.0 57 Coastal 67.5 49.5 0.335 81 2.7 1.7 58 Northern 64.7 48.5 0.314 79 1.6 1.1 59 Southern 64.7 49.6 0.321 80 2.0 1.3 60 Puducherry 5.6 39.2 0.022 4 0.0 0.1 20 ! Punjab 18.9 42.0 0.079 0.9 2.4 61 Northern 16.4 42.5 0.070 12 0.4 1.2 62 Southern 21.3 41.6 0.089 15 0.5 1.2 Rajasthan 42.7 45.7 0.195 4.8 5.1 63 North-Eastern 36.6 45.0 0.165 38 1.5 1.9 64 Northern 41.2 44.7 0.184 42 1.0 1.2 65 South-Eastern 50.1 48.1 0.241 64 0.7 0.6 66 Southern 53.0 46.8 0.248 68 0.5 0.4 67 Western 47.8 45.8 0.219 55 1.0 1.0 68 Sikkim 3.7 38.2 0.014 1 0.0 0.1 Tamil Nadu 35.7 44.1 0.158 4.7 6.2 69 Coastal 42.6 44.8 0.191 45 1.6 1.8 70 Coastal Northern 33.5 43.0 0.144 32 1.2 1.7 71 Inland 33.2 45.4 0.151 34 0.8 1.1 72 Southern 32.3 43.3 0.140 28 1.1 1.6 73 Tripura 28.4 48.0 0.136 24 0.2 0.3 74 Uttarakhand 43.2 46.4 0.201 50 1.7 1.8 Uttar Pradesh 55.3 46.4 0.256 18.2 14.7 75 Central 53.8 46.7 0.251 69 3.2 2.6 76 Eastern 61.2 46.9 0.287 77 9.8 7.1 77 Northern Upper Ganga Plain 46.2 45.0 0.208 52 2.5 2.5 78 Southern 40.3 44.3 0.178 40 0.7 0.8 79 Southern Upper Ganga Plains 52.9 46.0 0.244 66 2.1 1.8 West Bengal 46.6 47.5 0.222 8 7.4 80 Central Plains 42.4 47.0 0.200 49 1.7 1.8 81 Eastern Plains 57.7 48.1 0.278 74 3.9 2.9 82 Himalayan 45.8 51.0 0.234 59 1.3 1.1 83 Southern Plains 32.6 42.9 0.140 27 1.1 1.6 84 Western Plains 24.7 45.6 0.113 19 0.0 0.1 21 ! Table A.2: Decomposition of multidimensional poverty index by dimensions and indicators in states and regions of India, 2004-05 Health Education Incom e Work Household environment MPI State/ Region Any death Unde rweig ht Scho ol enrol ment Year s of schoo ling Consu mption poor Work and emplo yment Occu patio n Source of drinki ng water Sanit ation facili ty Cook ing facili ty Andhra Pradesh 1.7 10.0 2.4 13.1 18.8 0.6 21.6 2.2 14.5 15.0 0.158 Coastal Northern 2.5 11.6 2.5 14.7 10.5 0.9 23.1 2.9 15.0 16.3 0.141 Coastal southern 0.6 6.3 3.0 9.8 27.0 0.7 21.2 3.7 13.1 14.6 0.185 Inland North Eastern 3.7 15.6 1.3 14.3 7.4 0.4 22.5 3.2 15.5 16.1 0.179 Inland North Western 1.3 8.1 2.6 16.0 21.1 0.4 20.8 0.9 14.2 14.6 0.159 Inland Southern 0.6 8.9 2.6 8.5 28.1 0.5 21.1 0.8 15.1 13.8 0.130 Arunachal Pradesh 0.9 0.0 1.1 5.4 39.2 2.0 19.6 3.5 15.1 13.3 0.197 Assam 1.4 1.8 5.0 7.2 36.1 1.9 18.4 0.8 13.9 13.4 0.213 Cachar Plain 2.0 7.2 2.0 3.8 35.7 4.6 18.0 2.4 11.8 12.4 0.137 Plains Eastern 0.3 0.6 2.0 4.7 38.8 11.0 17.3 0.5 14.1 10.6 0.104 Plains Western 1.4 1.8 5.3 7.4 36.0 1.3 18.5 0.7 14.0 13.6 0.232 Bihar 2.3 8.0 3.2 11.4 24.8 1.2 19.8 1.3 14.3 13.7 0.261 Central 2.2 6.2 4.1 9.0 27.5 1.2 19.5 2.6 13.9 13.9 0.243 Northern 2.5 9.4 2.5 13.2 22.8 1.2 20.1 0.2 14.6 13.5 0.277 Chandigarh 0.0 0.0 0.0 6.2 49.8 0.0 19.9 0.0 16.6 7.5 0.024 Chhattisgarh 0.9 7.4 1.7 6.6 38.3 0.2 14.6 3.8 13.3 13.3 0.353 Mahanadi Basin 1.0 7.4 1.9 6.5 37.8 0.1 15.0 3.8 13.3 13.3 0.356 Northern 0.6 6.4 0.9 7.3 38.7 0.5 14.3 5.3 13.0 13.3 0.273 Southern 0.0 8.6 1.4 6.8 41.3 0.0 12.2 2.5 13.8 13.5 0.451 Dadra & Nagar Haveli 1.4 7.6 3.4 7.6 38.8 0.2 16.4 0.0 13.2 11.3 0.261 Daman & DIU 1.3 10.4 0.0 2.9 40.1 0.0 16.7 0.9 12.6 15.1 0.167 Delhi 0.0 4.2 4.1 7.3 42.3 4.2 16.4 2.3 10.8 8.5 0.036 Goa 2.1 7.4 1.4 0.8 42.4 3.2 14.3 2.3 14.1 12.0 0.020 Gujarat 0.5 10.3 3.2 7.7 31.2 0.5 17.4 3.9 12.3 13.0 0.167 Dry areas 0.0 1.7 9.4 15.7 30.3 0.0 9.4 4.7 13.9 14.9 0.238 Kachchh 0.0 4.3 3.4 12.9 27.3 0.0 18.3 6.4 12.7 14.9 0.185 Plains Northern 0.5 13.1 2.6 5.7 27.5 0.6 20.4 5.2 11.3 13.1 0.143 Saurashtra 0.4 7.1 3.8 9.2 33.7 0.7 16.4 4.6 13.1 11.1 0.155 South Eastern 0.6 11.2 2.7 7.3 33.5 0.4 16.2 2.0 12.6 13.6 0.195 Haryana 1.4 7.2 2.5 7.2 30.9 1.0 19.6 1.2 14.9 14.1 0.114 Eastern 1.7 7.1 2.6 7.7 29.8 1.1 19.3 1.2 15.0 14.5 0.130 Western 0.8 7.3 2.1 6.1 33.6 0.9 20.1 1.3 14.7 13.2 0.088 Himachal Pradesh 2.5 11.0 1.2 4.2 26.7 0.2 20.9 2.7 15.3 15.3 0.090 Central 2.6 11.9 1.1 3.4 25.7 0.1 21.3 2.2 16.2 15.5 0.071 Trans Himalayan & Southern 2.5 10.3 1.3 4.9 27.6 0.2 20.5 3.1 14.5 15.1 0.118 Jammu & Kashmir 3.4 9.2 4.6 10.6 15.4 1.7 21.3 5.8 15.4 12.6 0.057 Jhelam Valley 4.9 7.0 5.2 11.8 17.4 1.4 21.2 4.4 15.4 11.3 0.053 Mountainous 1.4 7.3 1.7 7.7 22.4 4.1 22.4 2.9 15.4 14.7 0.039 22 ! Outer Hills 0.0 16.8 4.3 8.7 5.8 1.1 21.4 11.4 15.3 15.3 0.108 Jharkhand 0.4 7.0 2.4 5.3 36.8 0.7 15.0 6.2 12.7 13.4 0.282 Hazaribag Plateau 0.6 9.9 2.4 6.0 32.9 0.1 16.8 5.4 12.8 13.1 0.237 Ranchi Plateau 0.2 5.6 2.5 5.0 38.7 1.0 14.1 6.6 12.7 13.6 0.311 Karnataka 1.5 10.2 2.5 8.2 27.4 0.7 18.9 1.7 14.6 14.3 0.176 Coastal and Ghats 0.4 12.3 0.5 3.7 27.8 2.8 20.4 7.4 11.6 13.3 0.117 Inland Eastern 1.7 10.6 1.6 7.7 28.4 1.0 19.9 2.8 13.7 12.6 0.156 Inland Northern 1.4 8.9 3.3 9.2 28.3 0.6 17.3 1.8 14.5 14.8 0.219 Inland Southern 1.7 11.9 1.8 7.4 25.4 0.4 21.2 0.3 15.7 14.0 0.145 Kerala 1.3 3.8 0.0 0.7 42.8 2.1 19.9 10.0 5.1 14.4 0.143 Northern 0.5 1.9 0.0 0.2 48.2 2.2 21.4 13.3 2.3 10.0 0.140 Southern 1.5 4.4 0.0 0.8 40.9 2.1 19.4 8.9 6.1 15.9 0.144 Madhya Pradesh 0.6 7.2 2.0 6.9 35.7 0.2 15.8 4.7 13.1 13.8 0.271 Central 0.6 8.2 3.1 10.1 27.5 0.2 17.7 5.7 12.9 14.1 0.224 Malwa 0.8 7.4 2.1 8.2 34.4 0.4 16.4 3.5 13.3 13.6 0.221 Northern 0.6 6.1 2.3 5.5 38.3 0.0 18.0 1.3 12.9 15.0 0.239 South 0.5 6.5 1.7 5.9 41.3 0.4 12.3 4.3 13.7 13.4 0.280 South Western 0.3 7.6 1.9 7.5 38.0 0.3 16.0 2.6 12.2 13.7 0.282 Vindhya 0.7 7.1 1.7 5.8 34.9 0.1 15.1 7.7 13.4 13.5 0.348 Maharashtra 0.8 9.6 1.9 4.5 38.3 0.3 14.1 3.5 14.1 12.8 0.184 Coastal 0.1 10.6 1.7 2.2 43.3 0.9 15.6 2.4 15.3 7.9 0.044 Eastern 1.6 10.7 0.9 4.4 33.0 0.1 16.8 4.8 14.0 13.5 0.256 Inland Central 0.9 8.6 3.5 7.0 36.5 0.4 13.3 3.7 13.4 12.8 0.245 Inland Eastern 0.6 9.0 2.8 5.7 35.0 0.1 15.0 4.1 14.1 13.6 0.199 Inland Northern 0.7 9.2 1.1 5.1 39.0 0.5 14.1 3.9 13.7 12.7 0.235 Inland Western 0.6 10.4 1.0 2.2 41.8 0.2 13.2 2.7 14.7 13.1 0.210 Manipur 14.6 4.2 5.5 6.6 0.0 2.6 25.0 5.4 16.7 19.5 0.023 Hills 25.0 0.0 0.0 0.0 0.0 0.0 25.0 16.7 16.7 16.7 0.015 Plains 12.7 5.0 7.7 6.5 0.0 3.1 25.0 3.3 16.7 20.0 0.025 Meghalaya 0.8 3.8 4.1 9.3 32.4 3.3 18.5 5.8 13.5 8.6 0.205 Mizoram 0.0 0.0 6.1 4.1 47.7 3.9 8.0 6.5 15.9 7.9 0.023 Nagaland 0.0 0.0 0.0 0.6 49.6 6.1 5.3 7.6 13.8 17.0 0.094 Odisha 1.3 8.1 2.7 6.4 33.7 0.5 17.1 3.7 13.4 13.0 0.325 Coastal 1.4 9.0 2.9 5.3 33.2 0.5 17.0 4.2 13.4 13.1 0.335 Northern 0.7 6.0 1.7 5.1 37.6 0.3 17.1 4.2 13.7 13.5 0.314 Southern 1.5 8.7 3.4 9.0 31.1 0.7 17.3 2.5 13.3 12.6 0.321 Pondicherry 1.6 7.9 3.7 10.5 21.9 0.0 21.8 0.0 15.6 17.0 0.022 Punjab 2.2 5.9 2.8 9.0 30.1 1.3 20.7 0.2 12.5 15.3 0.079 Northern 3.3 5.9 2.4 9.8 26.1 1.6 22.2 0.3 13.7 14.7 0.070 Southern 1.3 6.0 3.0 8.4 33.0 1.1 19.7 0.2 11.6 15.7 0.089 Rajasthan 0.8 7.3 3.2 7.6 33.9 0.4 16.5 3.1 13.3 14.0 0.195 North-Eastern 0.5 4.0 3.1 6.7 36.4 0.2 16.9 4.1 14.1 13.9 0.165 Northern 1.9 11.5 2.6 6.8 33.2 0.6 14.7 2.0 12.1 14.5 0.184 South-Eastern 0.6 6.2 3.0 10.3 33.0 0.2 17.9 2.4 12.9 13.3 0.241 Southern 0.2 11.0 2.7 9.5 26.7 0.4 18.7 3.2 13.8 13.8 0.248 Western 0.6 6.7 4.1 7.0 34.6 0.4 15.9 3.2 13.4 14.1 0.219 Sikkim 6.6 0.0 0.0 0.0 52.4 0.0 23.5 0.0 0.0 17.5 0.014 Tamil Nadu 4.1 8.2 1.6 6.7 29.2 1.4 20.9 1.6 13.4 13.0 0.158 Coastal 3.9 8.3 1.4 7.4 26.8 1.6 20.5 1.4 14.6 13.9 0.191 23 ! Coastal Northern 9.0 12.5 2.0 4.5 21.6 1.1 21.5 0.4 13.5 13.8 0.144 Inland 1.9 7.0 1.2 11.4 30.8 0.8 20.4 0.9 14.1 11.5 0.151 Southern 0.6 4.0 1.7 4.4 39.8 1.8 21.0 3.6 11.0 11.9 0.140 Tripura 1.3 3.5 2.5 13.0 32.8 1.6 14.6 5.6 12.6 12.6 0.136 Uttar Pradesh 1.9 8.1 2.8 6.9 32.0 0.7 18.5 1.1 13.8 14.1 0.256 Central 2.5 8.6 1.8 4.7 34.7 1.0 17.9 1.2 13.8 13.9 0.251 Eastern 2.0 8.7 2.4 6.7 31.7 0.8 18.5 1.3 13.9 14.1 0.287 Northern upper Ganga Plain 1.0 5.6 5.0 10.3 30.9 0.6 19.2 0.0 13.2 14.2 0.208 Southern 1.5 8.5 5.0 7.9 24.1 0.7 19.9 3.1 14.6 14.7 0.178 Southern Upper Ganga Plains 1.8 7.5 3.2 7.2 32.9 0.3 18.1 1.1 13.8 13.9 0.244 Uttarakhand 0.3 12.6 1.3 3.8 35.1 0.0 18.7 3.1 10.9 14.0 0.201 West Bengal 0.9 8.8 3.1 12.1 28.4 0.6 18.5 1.8 12.7 13.2 0.222 Central Plains 1.2 10.1 3.4 12.7 26.9 0.5 18.0 0.5 13.2 13.5 0.200 Eastern Plains 1.1 9.5 3.0 13.0 27.0 0.3 18.7 0.6 13.2 13.5 0.278 Himalayan 0.3 7.4 2.1 13.2 26.8 0.2 16.7 8.4 12.0 12.9 0.234 Southern Plains 0.1 5.6 4.0 6.8 38.0 2.3 20.4 0.0 11.2 11.5 0.140 Western Plains 2.3 16.3 2.3 4.7 32.7 0.0 18.7 0.0 9.6 13.4 0.113 India 1.4 8.2 2.6 7.7 31.9 0.7 17.8 2.8 13.4 13.6 0.207 Please note: You are most sincerely encouraged to participate in the open assessment of this discussion paper. You can do so by either recommending the paper or by posting your comments. Please go to: http://www.economics-ejournal.org/economics/discussionpapers/2015-34/ The Editor © Author(s) 2015. Licensed under the Creative Commons Attribution 3.0.