IDEAS home Printed from https://ideas.repec.org/p/qeh/ophiwp/ophiwp047.html
   My bibliography  Save this paper

Multidimensional Poverty: Measurement, Estimation, and Inference

Author

Listed:
  • Christopher J. Bennett and Shabana Mitra

Abstract

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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://workingpapers.qeh.ox.ac.uk/pdf/ophiwp/OPHIWP047.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. Garry F. Barrett & Stephen G. Donald, 2003. "Consistent Tests for Stochastic Dominance," Econometrica, Econometric Society, vol. 71(1), pages 71-104, January.
    3. Maasoumi, Esfandiar & Lugo, Maria, 2006. "The Information Basis of Multivariate Poverty Assessments," Departmental Working Papers 0603, Southern Methodist University, Department of Economics.
    4. Bhattacharya, Debopam, 2007. "Inference on inequality from household survey data," Journal of Econometrics, Elsevier, vol. 137(2), pages 674-707, April.
    5. Yele Batana, 2008. "Multidimensional Measurement of Poverty in Sub-Saharan Africa," OPHI Working Papers ophiwp013, Queen Elizabeth House, University of Oxford.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:qeh:ophiwp:ophiwp047. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (IT Support). General contact details of provider: http://edirc.repec.org/data/qehoxuk.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.