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Wage Inequality

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Abstract

In this chapter, Borooah considers wage inequality in India at a point in time (2011–12) with particular reference to inequality in wages between male and female workers and between workers from different social groups—the Scheduled Tribes, the Scheduled Cates, the non-Muslim Other Backward Classes, Muslims, and the Forward Castes. The thrust of the analysis in this chapter is to decompose the difference in wages between men and women, and between the Forward Castes and the other social groups, into a part that can be “explained” by employer bias and that which is due to differences in employee attributes. The analysis of this chapter extends earlier analyses of wage inequality in India to include social groups; methodologically, it seeks an explanation for inter-group inequality in terms of employer bias and (differences in) employee attributes. The analysis in this chapter uses data from two independent sources to analyse the phenomenon of inter-group wage inequality: the 68th round of the National Sample Survey pertaining to the period July 2011–June 2012 and the Indian Human Development Survey pertaining to 2011.

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Notes

  1. 1.

    Where the latter include Christians, Sikhs, and Jains who are not from the ST/SC/OBC-NM.

  2. 2.

    This chapter does not address the issue of the evolution of wage inequality over time, a topic which has been extensively discussed by Dutta (2005), Chamarbagwala (2006), Kijima (2006), Mazumdar and Sarkar (2008), Sarkar and Mehta (2010), and Mazumdar et al. (2017a, b).

  3. 3.

    By definition, an unemployed person did not work on any day of the week.

  4. 4.

    I am grateful to Ajaya Kumar Naik for advice on calculating wage rates from NSS and IHDS-2011 data.

  5. 5.

    https://data.worldbank.org/indicator/SI.POV.GINI?locations=IN (accessed 22 January 2019).

  6. 6.

    An indifference curve shows the different combinations of \( {W}_R,{W}_S \) which yield the same level of welfare. It is obtained by holding Q constant in Eq. (6.4) and solving for the different \( {W}_R,{W}_S \) which yield this value of Q.

  7. 7.

    Because of concavity, an egalitarian transfer from R to S will increase welfare: the gain in utility to S will exceed the loss to R. Welfare will be maximised when no further net gain is possible, that is, when \( {W}_R={W}_S \).

  8. 8.

    Stata’s margin command performs these calculations. See Long and Freese (2014).

  9. 9.

    The latter were obtained by computing the average wage after applying, respectively, the male coefficient to the male subsample and the female coefficient to the female subsample.

  10. 10.

    See Chap. 3 for a more detailed discussion of this decomposition.

  11. 11.

    Note that in this case, both B = ₹6769 and C = ₹5989 are positive. This means that both men and women in P&E occupations had attributes that were superior to the collective of 54,702 persons in the estimation sample but the relative superiority of men was greater so that B – C = ₹780 > 0.

  12. 12.

    The latter were obtained by computing the average wage after applying, respectively, the FC coefficient to the FC subsample and the SC coefficient to the SC subsample.

  13. 13.

    Note that in this case, both B = ₹8021 and C = ₹4470 are positive. This means that both FC and SC persons in P&E occupations had attributes that were superior to the collective 24,043 persons in the estimation sample but the relative superiority of FC persons was greater so that B – C = ₹3551 > 0.

  14. 14.

    Note that in this case, both B = ₹5564 and C = ₹1312 are positive. This means that both FC and SC persons in the public sector had attributes that were superior to the general sample but the relative superiority of FC persons was greater so that B – C = ₹4252 > 0.

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Authors and Affiliations

  1. School of Economics & Politics, University of Ulster, Belfast, UK

    Vani Kant Borooah

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Borooah, V.K. (2019). Wage Inequality. In: Disparity and Discrimination in Labour Market Outcomes in India. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-16264-1_6

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  • DOI: https://doi.org/10.1007/978-3-030-16264-1_6

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  • Publisher Name: Palgrave Macmillan, Cham

  • Print ISBN: 978-3-030-16263-4

  • Online ISBN: 978-3-030-16264-1

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