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Binary data, hierarchy of attributes, and multidimensional deprivation

Author

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  • Shatakshee Dhongde

    (Georgia Institute of Technology)

  • Yi Li

    (Slippery Rock University)

  • Prasanta K. Pattanaik

    (University of California, Riverside)

  • Yongsheng Xu

    (Georgia State University)

Abstract

Empirical estimation of multidimensional deprivation measures has gained momentum in the last few years. Several existing measures assume that deprivation dimensions are cardinally measurable, when, in many instances, such data is not always available. In this paper, we propose a class of deprivation measures when the only information available is whether an individual is deprived in an attribute or not. The framework is then extended to a setting in which the multiple dimensions are grouped as basic attributes that are of fundamental importance for an individual’s quality of life and non-basic attributes which are at a much lower level of importance. Empirical illustrations of the proposed measures are provided based on the estimation of multidimensional deprivation among children in Ethiopia, India, Peru and Vietnam.

Suggested Citation

  • Shatakshee Dhongde & Yi Li & Prasanta K. Pattanaik & Yongsheng Xu, 2016. "Binary data, hierarchy of attributes, and multidimensional deprivation," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 14(4), pages 363-378, December.
    • Handle: RePEc:spr:joecin:v:14:y:2016:i:4:d:10.1007_s10888-016-9336-4
    DOI: 10.1007/s10888-016-9336-4
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    Cited by:

    1. Bénédicte Apouey & Jacques Silber & Yongsheng Xu, 2020. "On Inequality‐Sensitive and Additive Achievement Measures Based on Ordinal Data," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 66(2), pages 267-286, June.
    2. Sabina Alkire and James Foster, 2016. "Dimensional and Distributional Contributions to Multidimensional Poverty," OPHI Working Papers ophiwp100_2.pdf, Queen Elizabeth House, University of Oxford.
    3. Xinhua Qi & Shilin Ye & Yecheng Xu & Jing Chen, 2022. "Uneven dynamics and regional disparity of multidimensional poverty in China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 159(1), pages 169-189, January.
    4. Shatakshee Dhongde & Prasanta K. Pattanaik & Yongsheng Xu, 2019. "Well‐Being, Deprivation, and the Great Recession in the U.S.: A Study in A Multidimensional Framework," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 65(S1), pages 281-306, November.
    5. World Bank, 2022. "A Welfarist Theory Unifying Monetary and Non-Monetary Poverty Measurement," Policy Research Working Paper Series 10076, The World Bank.
    6. Vito Peragine & Maria G. Pittau & Ernesto Savaglio & Stefano Vannucci, 2021. "On multidimensional poverty rankings of binary attributes," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 23(2), pages 248-274, April.
    7. Suman Seth and Gaston Yalonetzky, 2018. "Assessing Deprivation with Ordinal Variables: Depth Sensitivity and Poverty Aversion," OPHI Working Papers ophiwp123.pdf, Queen Elizabeth House, University of Oxford.
    8. Suman Seth & Gaston Yalonetzky, 2021. "Assessing Deprivation with an Ordinal Variable: Theory and Application to Sanitation Deprivation in Bangladesh," The World Bank Economic Review, World Bank, vol. 35(3), pages 793-811.
    9. Asis Kumar Banerjee, 2018. "Multidimensional Indices with Data-driven Dimensional Weights: A Multidimensional Coefficient of Variation," Arthaniti: Journal of Economic Theory and Practice, , vol. 17(2), pages 140-156, December.

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