A Study of Multidimensional Poverty in Northeast India

Part of the India Studies in Business and Economics book series (ISBE)


The primary objective of this chapter is to compute a multidimensional poverty index (MPI) for each state and for each district in northeast India. This index covers three dimensions—Knowledge, Health and Living condition. We have taken illiteracy rate and financial illiteracy rate as deprivation indicators under knowledge dimension. Health dimension includes the use of unsafe drinking water and no access to improved sanitation as indicator of deprivation. The dimension of living condition is comprised of four indicators viz. households having dilapidated residence, no census assets, no access to electricity or solar energy for lighting and no access to improved fuel for cooking. The MPI has been calculated gauging the normalised inverse ‘Euclidian distance’ of the observed vector of the indicators of deprivation from the vector indicating worst state of multidimensional poverty. This study distributes weight equally across the selected dimensions and equal weight has been consigned with each indicator within a dimension. The study has mainly used the data published by Directorate of Population Census of India 2011. We have observed that Meghalaya is the most deprived state in northeast India while Mizoram, Tripura are in relatively better-off position among the northeastern states. This study has explored that the Kurung Kumey district belonging to Arunachal Pradesh is the poorest district among the 86 districts. However, among the ten most deprived districts eight are not located in Meghalaya. None of the districts in Mizoram, Tripura and Sikkim come in the ten most multidimensionally poor districts. On the other hand, Aizawl district of Mizoram is the least deprived among the districts in North-East India. No one of the ten least multi dimensionally poor districts belong to the state of Meghalaya. The disparities among the states and among the districts in terms of the indicators under consideration have also been revealed. However, there is no straightforward relation between MPI of the states and percentage of population live below poverty line income.


Normalised inverse Euclidian distance Multidimensional poverty index Population census States in northeast India 


  1. Alkire, S., & Santos, M. E. (2010). Acute multidimensional poverty: A new index for developing countries. United Nations Development Programme Human Development. Reports Research Paper, July, 2010, Working paper no. 38.Google Scholar
  2. Alkire, S., Santos, M. E., & Seth, S. (2014). Multidimensional poverty index 2014: Brief methodological note and results. The Oxford Poverty and Human Development Initiative (OPHI). Oxford Department of International Development, University of Oxford. Contact details:
  3. Bagli, S. (2013). A study on measuring housing deprivation in India. International Journal of Development Studies, 5(1), 173–177.Google Scholar
  4. Bagli, S. (2015a). Multidimensional poverty of SC and ST households: An empirical study in Bankura district. A paper presented in a National Seminar at NIRD. Available at
  5. Bagli, S. (2015b). Multidimensional poverty: An empirical study in Bankura District, West Bengal. Journal of Rural Development, 34(3), 242–327.Google Scholar
  6. Bhattacharya, G., & Halder, S. (2014). Trend, differential and determinants of deprivation of reproductive and child health in the districts of West Bengal, India. Journal of Health Management, 16(1), 93–112.CrossRefGoogle Scholar
  7. Government of India. (2011). Population census report, 2011. Registrar General of India.Google Scholar
  8. Mehta, K. A., & Shah, A. (2003). Chronic poverty in India: Incidence, causes and policies. World Development, 31(3), 491–511.CrossRefGoogle Scholar
  9. Planning Commission. (2014). Government of India, report of the expert group to review the methodology for measurement of poverty. Available at
  10. United Nations Development Programme. (2010). Human development report, 2010. New York: Palgrave Macmillan.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Presidency UniversityKolkataIndia

Personalised recommendations