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Multidimensional Poverty: Measurement, Estimation, and Inference


  • Christopher J. Bennett and Shabana Mitra


Multidimensional poverty measures give rise to a host of statistical hypotheses which are of interest to applied economists and policy-makers alike. In the specific context of the generalized Alkire-Foster (Alkire and Foster 2008) class of measures, we show that many of these hypotheses can be treated in a unified manner and also tested simultaneously using the minimum p-value methodology of Bennett (2010). When applied to study the relative state of poverty among Hindus and Muslims in India, these tests reveal novel insights into the plight of the poor which are not otherwise captured by traditional univariate approaches.

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  • Christopher J. Bennett and Shabana Mitra, 2011. "Multidimensional Poverty: Measurement, Estimation, and Inference," OPHI Working Papers ophiwp047, Queen Elizabeth House, University of Oxford.
  • Handle: RePEc:qeh:ophiwp:ophiwp047

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    1. Maasoumi, Esfandiar & Lugo, Maria, 2006. "The Information Basis of Multivariate Poverty Assessments," Departmental Working Papers 0603, Southern Methodist University, Department of Economics.
    2. Yélé Batana, 2013. "Multidimensional Measurement of Poverty Among Women in Sub-Saharan Africa," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 112(2), pages 337-362, June.
    3. Garry F. Barrett & Stephen G. Donald, 2003. "Consistent Tests for Stochastic Dominance," Econometrica, Econometric Society, vol. 71(1), pages 71-104, January.
    4. Bhattacharya, Debopam, 2007. "Inference on inequality from household survey data," Journal of Econometrics, Elsevier, vol. 137(2), pages 674-707, April.
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    1. Maitra, Chandana & Rao, D.S. Prasada, 2015. "Poverty–Food Security Nexus: Evidence from a Survey of Urban Slum Dwellers in Kolkata," World Development, Elsevier, vol. 72(C), pages 308-325.
    2. Robano, Virginia & Smith, Stephen C., 2013. "Multidimensional Targeting and Evaluation: A General Framework with an Application to a Poverty Program in Bangladesh," IZA Discussion Papers 7593, Institute for the Study of Labor (IZA).
    3. Rolf Aaberge & Andrea Brandolini, 2014. "Multidimensional poverty and inequality," Discussion Papers 792, Statistics Norway, Research Department.
    4. Sabina Alkire, 2011. "Multidimensional Poverty and its Discontents," OPHI Working Papers ophiwp046, Queen Elizabeth House, University of Oxford.
    5. repec:spr:soinre:v:133:y:2017:i:1:d:10.1007_s11205-016-1365-7 is not listed on IDEAS
    6. Nowak, Daniel & Scheicher, Christoph, 2014. "Considering the extremely poor: Multidimensional poverty measurement for Germany," Discussion Papers in Econometrics and Statistics 02/14, University of Cologne, Institute of Econometrics and Statistics.
    7. Sabina Alkire & James Foster, 2011. "Understandings and misunderstandings of multidimensional poverty measurement," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 9(2), pages 289-314, June.
    8. David Lander & David Gunawan & William E. Griffiths & Duangkamon Chotikapanich, 2016. "Bayesian Assessment of Lorenz and Stochastic Dominance Using a Mixture of Gamma Densities," Department of Economics - Working Papers Series 2023, The University of Melbourne.
    9. David Lander & David Gunawan & William Griffiths & Duangkamon Chotikapanich, 2017. "Bayesian Assessment of Lorenz and Stochastic Dominance," Department of Economics - Working Papers Series 2029, The University of Melbourne.
    10. David Lander & David Gunawan & William Griffiths & Duangkamon Chotikapanich, 2017. "Bayesian assessment of Lorenz and stochastic dominance," Monash Econometrics and Business Statistics Working Papers 15/17, Monash University, Department of Econometrics and Business Statistics.

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