Heterogeneity among migrants, education–occupation mismatch and returns to education

ABSTRACT

Using nationally representative data for India, this paper examines the incidence of education–occupation mismatch (EOM) and returns to education and EOM for internal migrants while considering the heterogeneity among them. In particular, it considers heterogeneity arising because of the reason to migrate, demographic characteristics, spatial factors, migration experience and type of migration. The analysis reveals that there is variation in the incidence and returns to EOM depending on the reason to migrate, demographic characteristics and spatial factors. The study highlights the need of focusing on EOM to increase the productivity benefits of migration. It also provides the framework for minimizing migrants’ likelihood of being mismatched while maximizing their returns to education.

KEYWORDS:

• education–occupation mismatch
• internal migration
• heterogeneity
• India

JEL:

• I21
• I26
• O15
• R23

View correction statement:

Correction

ACKNOWLEDGEMENTS

We are thankful to the seminar participants at the UNU-WIDER Conference 2019 and ICEF 2020 for their helpful comments.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Correction Statement

This article was originally published with errors, which have now been corrected in the online version. Please see Correction (http://dx.doi.org/10.1080/00343404.2023.2168373)

Notes

1. In this study, an internal migrant is defined based on their current place of residence (a village, town or city) that is different from their last usual place of residence at the time of the survey, and the duration of such mobility is at least six months or more (which is the cut-off for domicile status identification) within the administrative boundaries of a country (National Sample Survey Office (NSSO), 2010). This study focuses on India, and hence the definition provided comes from the Census of India and NSSO. The definition is also used by the Government of India for administrative and public policy purposes. Other countries also use similar definitions with slight variation in the duration considered.

2. EOM refers to the incongruity between the attained education of a worker and the required education by her occupation (Duncan & Hoffman, 1981).

3. Although this phenomenon is also present in the case of international migration, it is suppressed by the negative impact of imperfect portability of human capital.

4. It has been argued in the literature that widening the labour market of a worker can improve the returns to education. Thus, in the past studies it was hypothesized that migration will lead to better returns to education as this would enable the individuals to have access to a larger labour market. However, in this paper we argue that not all migrants are the same and may still face geographical limitations which may lead to suboptimal returns to education.

5. We categorize a migration to be work related if the reason to migrate is stated as any of the following: in search of a job, to take up a confirmed job, business, transfer of service/contract or proximity to place of work.

6. See https://economictimes.indiatimes.com/news/politics-and-nation/over-93000-candidates-including-3700-phd-holders-apply-for-peon-job-in-up/articleshow/65604396.cms?from=mdr/ (last accessed on 31 October 2021).

7. For more detailed descriptive statistics for migrants and EOM, see Table A1 in Appendix A in the supplemental data online.

8. Section A1 in Appendix A in the supplemental data online discusses the sensitivity of these estimates to various threshold cut-offs being taken for measuring incidence of EOM.

9. The age and age squared term variables are used to capture the effect of learning on the job, and experience in the labour market, especially when no explicit indicators of these aspects of human capital are available in the labour market surveys. We are thankful to an anonymous reviewer for highlighting this aspect.

10. The NSSO provides occupation codes at three-digit levels, which can be integrated into nine broad categories. While we use occupation codes at three-digit level to measure the EOM, we are controlling for broad categories in our estimation.

11. For the complete list, see http://mospi.nic.in/sites/default/files/main_menu/national_industrial_classification/nic_2004_struc_detail.pdf/.

12. This indicator is estimated at the state level to avoid the measurement error (attenuation bias) due to very low sample size at the district level. We are thankful to an anonymous reviewer for this suggestion.

13. India is a federal country with three tiers of administrative bodies, which affect human mobility through various place-based policies, that is, district, state and central government. Based on this identification of boundaries, we can identify three types of migrants in our survey data. One, intra-district migrants who migrate within the boundaries of the district which is the smallest administrative unit observable in the data used. Second, inter-district (within-state) migrants who cross the district boundaries but remain within the state in the process of migration. Third, inter-state migrants who cross the state boundaries. Based on these three definitions, generally, intra-district migrants travel the least distances while inter-state migrants travel the most distances.

14. Interestingly, in the literature on EOM, studies have never discussed the concern of potential correlation between required, surplus and deficit years of education. We are thankful to an anonymous reviewer for raising this concern. In our dataset we have observed that the pairwise correlation of required years of education with surplus and deficit years of education is −0.08 and −0.09, respectively, which is small. However, the pairwise correlation between surplus and deficit years of education is 0.5. This will increase the standard error of the estimated coefficient by the variation inflation factor ($\mathrm{VIF}=1(1-r2)$, where r is a pairwise correlation) of 1.33. Thus, the concern of multicollinearity in this case is not present.

15. Although the employment decision is highly gender specific, in our model we do not run separate probit models for males and females because the sample of female migrants is quite small in our data. However, we control for gender and other demographic variables in the model to address this issue.

16. Card (1993) had past information about the geographical location of the household, which was used to identify the corresponding distribution of schools for the households. However, such information is not available in our survey.

17. For the results for the full set of controls, see Tables A5–A10 in Appendix A in the supplemental data online.

18. Section A4 in Appendix A in the supplemental data online also provides the estimates for returns to education for other than work-related migrants, which include education, forced migration (due to natural disaster, socio-political problems and displacement for development projects), marriage, tied movers (due to mobility of parents/family members) and others.

19. The district is the smallest geographical unit at a subnational level. In 2007–08, India comprised 35 states and union territories, 87 National Sample Survey (NSS) regions and 588 districts.

20. We have also analyed the returns to education and returns to EOM for married migrants and by gender. The results are discussed in sections A5 and A6, respectively, in Appendix A in the supplemental data online.

21. We are thankful to an anonymous reviewer for highlighting this important aspect of labour markets in the developing countries.