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Gender-Based Wage Discrimination in Indian Urban Labour Market: An Assessment

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Abstract

This study attempts at verifying the pattern of the wage gap between gender in India’s urban labour market using NSS 50th (1993–1994), 61st (2004–2005), and 68th (2011–2012) Employment and Unemployment Surveys. The wage gap between sexes in the urban labour market is verified among the regular and casual workers over a period of two decades (1993–1994 to 2011–2012). Using Blinder–Oaxaca decomposition as well as Recentered Influence Function (RIF) quintile decomposition analysis, it is observed that there is a male bias in wages in both the categories, namely, regular and casual workers. Female workers are also at a disadvantaged position via-a-vis male counterparts, and there is considerable disparity exists with regards to employment and earning standard between sexes. The decomposition exercise shows that the role of the discrimination component effect is larger than that of the endowment component across the regular and casual workers. Controlling for characteristic homogeneity, it is observed that female workers have a systematic wage disadvantage against their male counterparts in the urban labour market of India.

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Fig. 1

Source: authors own estimation based on NSSO 50th, 61st, and 68th employment and unemployment surveys

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Notes

  1. As quoted in the study of Gregory (2009) on “Gender and Economic Inequality”.

  2. Unit-level data on 50th, 61st and 68th employment and unemployment rounds. These surveys were undertaken by the NSSO, Mospi.

  3. NCO 2004 one-digit codes are divided into White Collar—legislator, professional and technicians, Pink Collar—clerk and service-related workers, Agriculture—skilled agriculture, Blue Collar—craft-related work and plant and machinery, Elementary—elementary occupation.

  4. NIC-2004 one-digit codes are divided into, Agri—agriculture, MME—manufacturing and mining, CNN—construction, THR—trade, hotel and restaurant, TSC—transport, storage and communication, RES—real estate and others, PDD—public community services and others.

  5. For details regarding computation of population projection, kindly refer to the report no-554, 68th NSSO employment and unemployment survey. The census adjustment has been done on the basis of census and NSSO employment data sets. First, the weighted NSSO population figure has been estimated from the concerned NSSO employment and unemployment rounds for both rural–urban and male–female differently; then, the given figures are divided by the concerned census population figures. After getting the ratios, they are multiplied with the multiplier figures to get the census-adjusted weights.

  6. We did this exercise of data trimming given in the Abraham (2007) analysis.

  7. For details regarding computation of population projection, kindly refer to the report no-554, 68th NSSO employment and unemployment survey. The census adjustment has been done on the basis of census and NSSO employment data sets. First, the weighted NSSO population figure has been estimated from the concerned NSSO employment and unemployment rounds for both rural–urban and male–female differently; then, the given figures are divided by the concerned census population figures. After getting the ratios, they are multiplied with the multiplier figures to get the census-adjusted weights.

  8. AS the populations may have different distributions in three time points, this particular issue was dealt with by introducing a year dummy to allow aggregate changes over time.

  9. This study has followed the model as per the study of Khan (2016). The model explanations are also given as per the given study.

  10. The wage equations are same for the regular and casual workers but estimated differently in this analysis.

  11. This is in line with the study of Khan (2016).

  12. Through technological change.

  13. The estimates of the descriptive statistics were not presented in the main text, and they can be presented on request.

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Authors and Affiliations

  1. Centre for the Study of Regional Development (CSRD), Jawaharlal Nehru University, New Delhi, India

    Balakarushna Padhi

  2. Centre for Development Studies, Trivandrum, Kerala, India

    Udaya S. Mishra

  3. North Orissa University, Baripada, Odisha, 757003, India

    Urmi Pattanayak

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  1. Balakarushna Padhi
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  2. Udaya S. Mishra
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  3. Urmi Pattanayak
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Correspondence to Balakarushna Padhi.

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Padhi, B., Mishra, U.S. & Pattanayak, U. Gender-Based Wage Discrimination in Indian Urban Labour Market: An Assessment. Ind. J. Labour Econ. 62, 361–388 (2019). https://doi.org/10.1007/s41027-019-00175-8

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