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Education and earning linkages of regular and casual workers in India: a quantile regression approach

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Abstract

The central idea of the present study is to re-establish the importance of human capital variables (education and experience) at all India level and also at the disaggregated level of gender (male/female) across regular and casual workers using NSS 68th round. This is the latest employment–unemployment unit-level records pertaining to year 2011–2012. The paper examines the impact of human capital variables, household factors, job-related factors, individual characteristics and locational factors on earnings of an individual. Separate augmented Mincerian equations have been used for regular and casual workers, further subdivided at the level of male and female. The method of quantile regression has been used to estimate the augmented Mincerian equation at the above-mentioned disaggregated levels. The present study showcases that human capital variables, household factors, job-related factors, individual characteristics and locational factors impact regular and casual workers differently, the variation being further pronounced when disaggregated at the level of gender. Interestingly, human capital variables impact the earnings of regular workers (male and female) and casual (male and female) workers positively. Factoring the growing informalisation (not being entitled to social security benefits) in the regular form of employment, the study showcases a wide disparity within the regular workers. Thus, an attempt has been made to unfold primarily the interplay between education and earnings at various disaggregated levels.

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Notes

  1. NSS 68th round unit-level records do not provide whether workers are in the organise/unorganised sectors, and it furnishes information whether they are in casual/regular employment, self-employed or unemployed.

  2. Classification on similar logic was done in Dutta (2006) using 1999 NSS survey. The findings revealed 57 % regular workers employed in enterprises that were public, semi-public or otherwise in the registered or organised sector but only 11.4 % of casual workers were so employed.

  3. Figures are author’s calculation using NSS 68th round unit-level records.

  4. Nine one-digit occupational classifications have been made using NCO 2004 codes.

  5. Those workers receiving any form of social security benefits have been classified as formal and those without social security benefits have been classified as informal (this classification has been done in case of regular workers). I am thankful to the anonymous referees for suggesting the above classification.

  6. A regular wage salaried worker is a person who works in others’ farm or non-farm enterprises (household and non-household) and in return received salary or wages on a regular basis, while a casual worker is a person who is engaged in others’ farm or non-farm enterprises (household and non-household) and in return, received wages according to the terms of the daily or periodic work contract.

    Casual labour = individuals working as: casual wage labour in public works other than Mahatma Gandhi NREGS public works; casual wage labour in Mahatma Gandhi NREGS public works; and casual wage labour in other types of works (comprising of codes 41, 42, 51)

    Regular workers = worked as regular salaried/wage employee (comprising of codes 31, 71, 72).

  7. Studies by Duraisamy and Duraisamy (1995), Madheswaran (1998), Lakshmansamy and Ramaswamy (1999), Madheswaran and Shroff (2000) found a contrasting result, in which it was seen that private wages were higher than public sector wages.

  8. The figures for the other quantiles can be interpreted similarly.

  9. In the studies of Dutta 2006 and Madheswaran and Attewell 2007, age and age square have been taken as a proxy for actual experience. These studies specifically used the augmented Mincerian equation for regular and casual workers.

  10. The present study marks a departure wherein a subsequent higher level of education is associated with higher earnings even for casual workers.

  11. The description of the variables is mentioned in Table 1. Household head’s level of education has not been incorporated owing to the fact that two-thirds of all households are single-worker household and nuclear family constitutes a substantial portion of single regular workers’ household. In such cases, there is greater possibility of spurious correlation between workers’ education variable and educational level of the head of the household variable. I am thankful to the anonymous referee for the suggestion.

  12. For casual workers in the lower quantiles, caste affiliation does not play a significant role; it proves to be disadvantageous in the higher quantiles.

  13. For both regular and casual workers, the wage disadvantage increases with higher quantiles.

  14. From the above-mentioned studies, only Azam’s (2009) study has done a quantile regression. None of the studies have taken casual workers.

  15. Though this paper does not deal with calculation of wage gap, the raw figures suggest existence of sticky floor effect, a phenomenon shown in the works of Khanna (2012), Duraisamy and Duraisamy (2016).

  16. These studies have not done a regular/casual analysis.

  17. Azam and Prakash (2010) have used quantile regression. It is seen in the literature, Schager (1993); Katz and Krueger(1993); Elliott and Duffus (1996), the importance of assessing the public/private enterprise wages across the entire distribution as average results can be misleading.

  18. Elementary occupation comprises of sales and services, agricultural, fishery and related labourers, labourers in mining, construction, manufacturing and transport.

  19. Skilledagr comprises of market-oriented skilled agriculture and fishery workers, subsistence agriculture and fishery workers.

  20. This concept of embodied social capital was based on a study in Trinidad of low-wage workers in which it was seen that individuals do not see differences of ethnicity and gender as a social construction, rather accept them as factual true realities created by biology and heredity.

  21. The studies by Blom et al. (2001) in Brazil, Riboud et al. (2007) in India, Pakistan and Sri Lanka, Mehta et al. (2007) in Thailand, Philippines, Fiszbein et al. (2007) in Argentina, Behrman et al. (2003) in 18 Latin American countries, and (Topel 1999) in Brazil, Chile, Mexico, Korea, Taiwan, Indonesia, Thailand, China showed that the rate of return for higher education and higher secondary education started rising in the 1990s and that of primary and lower secondary education dropped. None of the studies referred above and in the main text used the formal/informal dummy and therefore the rate of return figures vary across quantile groups.

  22. In 1990, 1999 and 2007, there was an increase in supply of workers with completed primary education to the tune of 75, 80 and 86 % of the age group, respectively (Colclough et al. 2010).

  23. The rates of return on OLS coefficients(not reported due to lack of space) shows increase in rates of return with consecutive higher levels of education, it is 2.15, 6.75, 6.98 and 9.96 %, respectively, for elementary, secondary, higher secondary and graduation and above. Disaggregation at the level of quantile showcases a different result.

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    Anuneeta Mitra

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Mitra, A. Education and earning linkages of regular and casual workers in India: a quantile regression approach. J. Soc. Econ. Dev. 18, 147–174 (2016). https://doi.org/10.1007/s40847-016-0029-4

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