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Invariant features of spatial inequality in consumption: the case of India

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  • Arnab Chatterjee
  • Anindya S. Chakrabarti
  • Asim Ghosh
  • Anirban Chakraborti
  • Tushar K. Nandi

Abstract

We study the distributional features and inequality of consumption expenditure across India, for different states, castes, religion and urban-rural divide. We find that even though the aggregate measures of inequality are fairly diversified across states, the consumption distributions show near identical statistics, once properly normalized. This feature is seen to be robust with respect to variations in sociological and economic factors. We also show that state-wise inequality seems to be positively correlated with growth which is in accord with the traditional idea of Kuznets' curve. We present a brief model to account for the invariance found empirically and show that better but riskier technology draws can create a positive correlation between inequality and growth.

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  • Arnab Chatterjee & Anindya S. Chakrabarti & Asim Ghosh & Anirban Chakraborti & Tushar K. Nandi, 2015. "Invariant features of spatial inequality in consumption: the case of India," Papers 1507.04236, arXiv.org, revised Sep 2015.
  • Handle: RePEc:arx:papers:1507.04236
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    References listed on IDEAS

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    1. A. Chatterjee & B. K. Chakrabarti, 2007. "Kinetic exchange models for income and wealth distributions," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 60(2), pages 135-149, November.
    2. Giorgio Fagiolo & Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2010. "On the distributional properties of household consumption expenditures: the case of Italy," Empirical Economics, Springer, vol. 38(3), pages 717-741, June.
    3. Mizuno, Takayuki & Toriyama, Masahiro & Terano, Takao & Takayasu, Misako, 2008. "Pareto law of the expenditure of a person in convenience stores," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(15), pages 3931-3935.
    4. Arnab Chatterjee & Bikas K. Chakrabarti, 2007. "Kinetic Exchange Models for Income and Wealth Distributions," Papers 0709.1543, arXiv.org, revised Nov 2007.
    5. Inoue, Jun-ichi & Ghosh, Asim & Chatterjee, Arnab & Chakrabarti, Bikas K., 2015. "Measuring social inequality with quantitative methodology: Analytical estimates and empirical data analysis by Gini and k indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 184-204.
    6. Anand Banerjee & Victor M. Yakovenko, 2009. "Universal patterns of inequality," Papers 0912.4898, arXiv.org, revised Apr 2010.
    7. Mishra, Padmaja & Parikh, Ashok, 1992. "Household Consumer Expenditure Inequalities in India: A Decomposition Analysis," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 38(2), pages 225-236, June.
    8. Anindya S. Chakrabarti & Bikas K. Chakrabarti, 2010. "Inequality reversal: effects of the savings propensity and correlated returns," Papers 1005.3518, arXiv.org.
    9. Sinha, Sitabhra, 2006. "Evidence for power-law tail of the wealth distribution in India," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 359(C), pages 555-562.
    10. 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.
    11. Drăgulescu, Adrian & Yakovenko, Victor M., 2001. "Exponential and power-law probability distributions of wealth and income in the United Kingdom and the United States," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 213-221.
    12. Erich Battistin & Richard Blundell & Arthur Lewbel, 2009. "Why Is Consumption More Log Normal than Income? Gibrat's Law Revisited," Journal of Political Economy, University of Chicago Press, vol. 117(6), pages 1140-1154, December.
    13. Richard Blundell & Luigi Pistaferri & Ian Preston, 2008. "Consumption Inequality and Partial Insurance," American Economic Review, American Economic Association, pages 1887-1921.
    14. Chakrabarti, Anindya S. & Chakrabarti, Bikas K., 2010. "Inequality reversal: Effects of the savings propensity and correlated returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(17), pages 3572-3579.
    15. Eliazar, Iddo & Cohen, Morrel H., 2014. "On social inequality: Analyzing the rich–poor disparity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 148-158.
    16. Ghosh, Abhik & Gangopadhyay, Kausik & Basu, B., 2011. "Consumer expenditure distribution in India, 1983–2007: Evidence of a long Pareto tail," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(1), pages 83-97.
    17. Victor M. Yakovenko & J. Barkley Rosser, 2009. "Colloquium: Statistical mechanics of money, wealth, and income," Papers 0905.1518, arXiv.org, revised Dec 2009.
    18. Angle, John & Nielsen, Francois & Scalas, Enrico, 2009. "The Kuznets Curve and the Inequality Process," MPRA Paper 16058, University Library of Munich, Germany, revised 29 Jun 2009.
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    Cited by:

    1. Chatterjee, Arnab & Ghosh, Asim & Chakrabarti, Bikas K., 2017. "Socio-economic inequality: Relationship between Gini and Kolkata indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 583-595.
    2. Kiran Sharma & Anirban Chakraborti, 2016. "Physicists' approach to studying socio-economic inequalities: Can humans be modelled as atoms?," Papers 1606.06051, arXiv.org.

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