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A power law tail in India's wealth distribution: Evidence from survey data

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  • Jayadev, Arjun

Abstract

This study uses survey data from India to examine the top percentile of the wealth distribution in India. Using nationally representative samples from two years, 1991 and 2002, a power law tail is found with a Pareto exponent ranging between 1.8 and 2.4. The tail is examined for three specific groupings: households in the rural areas, households in the urban areas and all households. The distribution of top households also appear to be regionally concentrated with states having the highest number of households in the top 1% in 1991 also generally having the highest number in 2002 as well.

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  • Jayadev, Arjun, 2008. "A power law tail in India's wealth distribution: Evidence from survey data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(1), pages 270-276.
    • Handle: RePEc:eee:phsmap:v:387:y:2008:i:1:p:270-276
    DOI: 10.1016/j.physa.2007.08.049
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    2. Wildauer, Rafael & Heck, Ines & Kapeller, Jakob, 2023. "Was Pareto right? Is the distribution of wealth thick-tailed?," Greenwich Papers in Political Economy 38597, University of Greenwich, Greenwich Political Economy Research Centre.
    3. Edouard Augustin Ribes, 2021. "What is the impact of introducing productivity tools for wealth management professionals? A case study for the french market," Working Papers hal-03494465, HAL.
    4. Wildauer, Rafael & Kapeller, Jakob, 2019. "Rank Correction: A New Approach to Differential Nonresponse in Wealth Survey Data," Greenwich Papers in Political Economy 26010, University of Greenwich, Greenwich Political Economy Research Centre.
    5. Chatterjee, Arnab & Chakrabarti, Anindya S. & Ghosh, Asim & Chakraborti, Anirban & Nandi, Tushar K., 2016. "Invariant features of spatial inequality in consumption: The case of India," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 169-181.
    6. Wang, Yuanjun & You, Shibing, 2016. "An alternative method for modeling the size distribution of top wealth," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 443-453.
    7. Jakob Kapeller & Rafael Wildauer, 2019. "Rank Correction: A New Approach to Differential Non-Response in Wealth Survey Data," ICAE Working Papers 101, Johannes Kepler University, Institute for Comprehensive Analysis of the Economy.
    8. Wildauer, Rafael & Kapeller, Jakob, 2022. "Tracing the invisible rich: A new approach to modelling Pareto tails in survey data," Labour Economics, Elsevier, vol. 75(C).
    9. Jakob Kapeller & Rafael Wildauer, 2019. "A Comment on Fitting Pareto Tails to Complex Survey Data," ICAE Working Papers 102, Johannes Kepler University, Institute for Comprehensive Analysis of the Economy.
    10. Aloys Prinz, 2016. "Do capitalistic institutions breed billionaires?," Empirical Economics, Springer, vol. 51(4), pages 1319-1332, December.
    11. Benedikt Fuchs & Stefan Thurner, 2014. "Behavioral and Network Origins of Wealth Inequality: Insights from a Virtual World," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-13, August.
    12. Anindya S. Chakrabarti & Arnab Chatterjee & Tushar Nandi & Asim Ghosh & Anirban Chakraborti, 2018. "Quantifying invariant features of within-group inequality in consumption across groups," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(3), pages 469-490, October.
    13. Tomson Ogwang, 2011. "Power laws in top wealth distributions: evidence from Canada," Empirical Economics, Springer, vol. 41(2), pages 473-486, October.
    14. Ogwang, Tomson, 2013. "Is the wealth of the world’s billionaires Paretian?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 757-762.

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