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Education–Occupation Mismatch and Dispersion in Returns to Education: Evidence from India

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Abstract

Using a national level sample survey on labour market in India, we analyze the role of education–occupation (mis-)match (EOM) in explaining within-group dispersion in returns to education. Applying a double sample selection bias correction and Mincerian quantile wage regression estimation, the analysis reveals interesting findings. First, on average, overeducated workers suffer a wage penalty of 7% and undereducated workers do not receive a wage reward as compared to their adequately educated counterparts. Second, the inclusion of match status reduces within-education group dispersion in returns. The finding highlights that ignoring EOM and thus, adopting a restrictive view of similarity across workers may lead to overestimation of the within-education group dispersion in returns. This study argues for focusing on EOM to increase both pecuniary and social benefits of education in terms of productivity gains and wages as well as to reduce wage dispersion.

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Notes

  1. Strictly speaking, the concepts of wage inequality and wage dispersion are slightly different (see, Salverda and Checchi (2015) for conceptual discussion). However, this study uses these terms interchangeably.

  2. A detailed discussion related to the sample would follow in the data and descriptive statistics section of the paper.

  3. One important point to note here is that in the relevant literature, researchers have used dispersion in returns as an indicator of dispersion in wages but this may not always be true. This is because wage dispersion considers the absolute wage differences while dispersion in returns consider the premium rate which market pays to get one higher level of education.

  4. Economic activity is defined as an activity that results in the production of goods and services which, in turn, leads value addition to the national product (NSSO 2014).

  5. This assumption ensures that workers are working in a particular occupation by choice rather than due to shortage of jobs that suits their respective education.

  6. The mode is not used to avoid the problem of multi-mode in case of some occupations.

  7. The number of observations in different occupation titles ranges from 14 to 36,041.

  8. No formal schooling corresponds to zero years of formal schooling, below primary corresponds to 3 years of formal education and completion of primary corresponds to 5 years of formal schooling. The corresponding years for middle, secondary, and higher secondary are eight, ten, and twelve respectively. Lastly, workers with graduate degree have been assigned 15 years of formal education and workers with postgraduate degree have been assigned 17 years of formal education.

  9. In absolute terms, around 27 million women with graduation or above education are either unemployed or out-of-labour force. Further, 22 million women have graduate or above degree as compared to 61 million men.

  10. These are the nominal average daily wages that is, they are not adjusted for urban–rural price differences.

  11. The plausible reason could be the lower representation of undereducated workers among graduates or above category. Undereducated, by definition, are workers working in an occupation that requires more education than their attained education. Therefore, graduates or above (which includes graduates, postgraduates or above) can be undereducated only when they are working in the occupations which requires postgraduate or above degree.

  12. See, Heckman et al. (2003) for detailed discussion on Mincerian wage equation.

  13. Standard models in the literature have considered only the employment selection, and thus ignored this aspect. A few notable exceptions are Agrawal and Agrawal (2019), Dolton and Makepeace (1990), among others.

  14. Inverse Mills ratio is defined as the ratio of the probability density function to the cumulative distribution function.

  15. This is done by using snp command in STATA v15. See De Luca (2008) for its detailed application in STATA.

  16. The study considers absolute number of dependent members irrespective of the size of the household. This is because household size is controlled as a separate variable.

  17. Land holding is estimated by considering the maximum of land owned and land possessed.

  18. The study uses both mills ratio and its square term in the wage equation following Buchinsky (1998, 2002).

  19. We also estimated the results using returns at every fifth percentile from 5th to 95th percentile. The results were similar to the presented analysis.

  20. To be consistent with job competition model, we also estimated the solitary impact of job characteristics on wages. For the sake of brevity, the JCM estimation and corresponding results are not provided, but are available on the request.

  21. Duncan and Hoffman (1981) provide another approach to estimate the impact of EOM on wages. The authors used continuous variable for education that is, years of education and divided this into two parts: required years of education and over/under years of education. However, the problem in including continuous years of education is that it does not allow the consideration of level of education and hence fails to solve the purpose of this study which is to estimate the within-education group dispersion in returns.

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  1. Indian Institute of Management Indore, Rau-Pithampur Road, Prabandh Shikhar, Indore, M.P, 453556, India

    Shweta Bahl & Ajay Sharma

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  1. Shweta Bahl
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Appendix

Appendix

See Tables 11, 12, 13, 14, 15, 16, 17, 18 and 19.

Table 11 Returns to wage-determining characteristics for wage/salaried employed workers in the working-age group (15–59 years)– ordinary least square (OLS) estimates.
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Table 12 Returns to wage-determining characteristics for wage/salaried employed workers in the working-age group (15–59 years).
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Table 13 Returns to wage determining characteristics for wage/salaried employed workers in the working-age group (15–59 years)—Job characteristics model.
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Table 14 Education and occupation (mis-)match for wage/salaried employed workers in the working-age group (15–59 years): by human capital variables.
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Table 15 Education and occupation (mis-)match for wage/salaried employed workers in the working-age group (15–59 years): by job characteristics.
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Table 16 Education and occupation (mis-)match for wage/salaried employed workers in the working-age group (15–59 years): by other characteristics.
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Table 17 Average daily wages (in Indian rupees) for wage/salaried employed workers in the working-age group (15–59 years) across match status by region
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Table 18 Returns to education and EOM for wage/salaried employed workers in the working-age group (15–59 years)—Ventiles (5th–50th).
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Table 19 Returns to education and EOM for wage/salaried employed workers in the working-age group (15–59 years)—Ventiles (55th–90th).
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Bahl, S., Sharma, A. Education–Occupation Mismatch and Dispersion in Returns to Education: Evidence from India. Soc Indic Res 153, 251–298 (2021). https://doi.org/10.1007/s11205-020-02483-9

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