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Weights and substitution degree in multidimensional well-being in China

Author

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  • Esfandiar Maasoumi
  • Tong Xu

Abstract

Purpose - – The purpose of this paper is to combine multidimensional welfare analysis and entropy metrics to derive not only the best relative weights but also substitution degree among different attributes to construct multidimensional indices of well-being with Chinese Household Income Project Survey 2002 data. Design/methodology/approach - – The authors follow Maasoumi’s two-step measures of multivariate inequality to calculate the inequality for three social groups in China, urban residents, migrants, and rural residents. The two-step approach provides an aggregation formula which is numerically identified in this paper based on a metric entropy distance measure between the distribution of the aggregate well-being functions, on the one hand, and the distribution of the self-reported “happiness” indicator. The authors compare the differences in relative weights and substitution degree for the three groups, and link them to some institutional factors. Findings - – The authors find that incorporating substitution among attributes, and taking into consideration group heterogeneity are very important in multidimensional analysis of well-being. Originality/value - – The two-step approach provides an aggregation formula which is numerically identified in this paper based on a metric entropy distance measure between the distribution of the aggregate well-being functions, on the one hand, and the distribution of the self-reported “happiness” indicator.

Suggested Citation

  • Esfandiar Maasoumi & Tong Xu, 2015. "Weights and substitution degree in multidimensional well-being in China," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 42(1), pages 4-19, January.
    • Handle: RePEc:eme:jespps:v:42:y:2015:i:1:p:4-19
    DOI: 10.1108/JES-04-2014-0055
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    Citations

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    Cited by:

    1. Philipp Poppitz, 2017. "Can subjective data improve inequality measurement? A multidimensional index of economic inequality," Working Papers 446, ECINEQ, Society for the Study of Economic Inequality.
    2. Philipp Poppitz, 2019. "Can Subjective Data Improve the Measurement of Inequality? A Multidimensional Index of Economic Inequality," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(3), pages 511-531, December.
    3. Junhua Chen & Shufan Ma & Na Liu, 2023. "Multi-dimensional Housing Inequality Index: The Provincial Evidence from China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 165(2), pages 633-654, January.
    4. Chae, Seyoung & Heshmati, Almas, 2017. "The Effects of Lifetime Work Experience on Incidence and Severity of Elderly Poverty in Korea," IZA Discussion Papers 10909, Institute of Labor Economics (IZA).
    5. Deniz Sevinc, 2020. "How Poor is Poor? A novel look at multidimensional poverty in the UK," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 149(3), pages 833-859, June.
    6. 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.
    7. Mekonnen Bersisa & Almas Heshmati, 2021. "A Distributional Analysis of Uni-and Multidimensional Poverty and Inequalities in Ethiopia," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 155(3), pages 805-835, June.

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