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Missing men, migration and labour markets: evidence from India


How do labour markets function when a large part of the able-bodied male workforce is absent due to out-migration? This question holds great significance as it affects regions covering over 200 million people in India and many other parts of the world. This paper analyses individual and district level data on internal and international migration, remittances, sex ratios and labour market variables in India from the perspective of the migrant’s source region and finds that the ‘missing men’ phenomenon is associated with (a) Feminisation of the agricultural workforce; (b) Higher levels of male employment in the construction and rural non-farm services sectors; and (c) Higher rural wages for males due to tighter labour markets. It is argued in the paper that these associations are likely to be causal in nature through an instrumental variable strategy that employs historic migration networks which evolved in the late nineteenth century as instruments for current migration.

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Fig. 1

Source: Author’s estimates based on NSS 2007-08 data at the district level, with sampling weights

Fig. 2

Source: Sex ratio data from Census 2001 Table C-14 and migration data computed from unit level 64th Round NSS data (2007-08), with sampling weights

Fig. 3

Source: Tumbe (2012b, Figure 7.28), based on Census 1901 data


  1. 1.

    For India, as explained in Section II; see Lokshin and Glinskaya (2009) for Nepal; Raphael (2013) for Mexico; Boserup (1970); Murray (1981); O’Laughlin (1998) for regions in southern Africa and CFO (2013) for Philippines. Also, see Golini and Birindelli (1990) for male-dominated emigrations in historical Italy, and Gabaccia and Zanoni (2012) on historical migrations.

  2. 2.

    See Murray (1981), Gulati (1993), Desai and Banerji (2008), and many other studies cited later in this paper. The term ‘missing men’ in the migration context can be attributed to O’Laughlin (1998) and Bose (2000). While this paper focuses on male-dominated migration streams, the arguments of this paper extend symmetrically to female-dominated migration streams.

  3. 3.

    See Mishra (2007) and Dustmann et al. (2012), among others.

  4. 4.

    The figure of 200 million refers to the sum total of the district populations in India where the percentage of households receiving remittances exceeds 15 per cent [estimates based on the National Sample Survey (NSS) 2007-08 data]. Section 2 describes the migration figures and the link between remittance-based migrations and sex ratios.

  5. 5.

    By choosing districts as the units of analysis, we follow Raphael (2013)’s study on the impact of sex-selective emigration on socio-economic outcomes in Mexico, though our outcome variables and methodologies are substantially different.

  6. 6.

    Marriage migration in response to skewed child sex ratios in other regions (Kaur, 2004) can potentially affect sex ratios in the selected regions of India. However, our analysis suggests a very low association between female migration rates and sex ratios in the age group of 20-49 years.

  7. 7.

    The large literature on sex ratios and ‘missing women’ in India has focused on ‘juvenile’ sex ratios in order to cut out-migration noise.

  8. 8.

    See Mazumdar et al. (2013, Table 1). The discrepancy between the two sets of estimates is likely to be wider because the indirect estimate does not count permanent migration. The number of women migrant workers is usually under-reported as they are recorded as migrants due to reasons such as ‘marriage’ or ‘moved with family.’ However, even after counting all women workers who belong to a family with a male migrant worker as ‘migrant workers’, the broad conclusion of heavily male-dominated migration streams for work holds true (author’s estimates from NSS 2007-08 data).

  9. 9.

    The gender split is computed by using information from Zachariah and Irudaya Rajan (2008) and NSS data.

  10. 10.

    NSS defines short-term migrants as persons who stay away from their usual place of residence for more than one month and less than six months in a year, for employment or for search of employment, and the nature of the question elicits responses in the source regions of migration.

  11. 11.

    There are also entire households that out-migrate, which are not captured in the out-migration data. The figure for internal migration is about 2 per cent of the households (captured from in-migration data). The NSS captures less than half of the total international emigrants (Tumbe, 2011). The missing sample is likely to be the migration of high-skilled workers to countries like the USA where the migrations are relatively more gender-balanced and sourced from urban areas, and thus unlikely to affect the analysis in a significant manner.

  12. 12.

    Seasonal migrations are for much shorter durations and are thus unlikely to have a major impact on the labour markets in the source region.

  13. 13.

    As female in-migrants outnumber female out-migrants within India by a large proportion due to the peculiarities associated with marriage migration, only non-marriage related migrations have been taken into account to compute the female migration variables.

  14. 14.

    Hatton and Williamson (1998) characterise mass migrations when out-migrant to population ratios exceed 5 per cent.

  15. 15.

    The rise in wages also pushes up labour costs for labour-hiring households and does not uniformly benefit all households in the source regions.

  16. 16.

    The low sex ratios for Haryana are due to sex-selective abortion and sex differentials in childhood mortality. However, migration continues to have a notable impact on inflating the sex ratio of Mahendragarh relative to Bhiwani.

  17. 17.

    The first All-India Census was conducted in 1872 and since 1881, the Census has been conducted without interruption on a decennial basis, with the most recent having been conducted in 2011.

  18. 18.

    This map compares favourably with a map on overseas emigration for the same period, available in Tinker (1974, p. 40).

  19. 19.

    There were less than 300 districts in 1901 and more than 500 in 2001. The districts have been matched by using sub-district level data in 1901, as given in the District Census Handbooks of 2001. The data is also available in the CensusInfo Version 2.0 CD of Census 2001 data. In future work, we hope to update the data that is missing for some districts.

  20. 20.

    The modern-day states have been taken to compute the regional averages. The results do not differ if we take historic provinces for the regional average.

  21. 21.

    For example, GoI (1923, p. 82) noted that the people of Hoshiarpur district in the Jalandhar region “depend very largely on earning of service outside the district” and this district has always had the highest recorded female to male sex ratio in Punjab in the 20th century.”

  22. 22.

    The OLS regression results are almost identical to those obtained from Probit regressions. The results also do not differ substantially between the principal and usual status work definitions.

  23. 23.

    These include female WPRs, urbanisation levels, and the percentage of Muslims, Scheduled Tribes (STs) and Scheduled Castes (SCs), respectively, in the district. The correlation between remittance-based migration and the female WPR is small at the aggregate district level because it includes the urban sector and off-setting factors of an income effect with the impact on agricultural households documented earlier. Further, there is low association between urbanisation and remittance-based migration because we include both internal and international migrations.

  24. 24.

    We assume that the regional variation in out-migration in 2007-08 was largely the same as in 2001, which is highly plausible under conditions of migration persistence (Tumbe, 2012c). The Census 2011 data on occupations has not yet been released.

  25. 25.

    The other five sectors are: Agricultural Allied Activities (Plantation, Livestock, etc.); Mining and Quarrying; Manufacturing (Household Industry); Electricity, Gas and Water Supply; and Financial Intermediation.

  26. 26.

    Rodgers and Rodgers (2001, p. 1980) document a similar scenario in rural Bihar.

  27. 27.

    Jose (2013) observes the wage–productivity link. We can rule out any major impact of NREGS on rural wages in 2007-08 as the scheme had only just come into place in many districts and the initial response was slow. The correlation between agricultural productivity and the out-migration variable is negligible and insignificant, which in itself is an important finding that deserves further research.

  28. 28.

    Female mobility has also risen considerably during the past two decades (Mazumdar et al. 2013).


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Research grant from the South Asia Research Network’s (SARNET) programme on ‘Labour Markets, Employment and Inclusive Growth in South Asia,’ is gratefully acknowledged. The author would like to thank Janine Rodgers and participants of the SARNET workshop in Delhi in December 2013 for their valuable comments on this paper.

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Tumbe, C. Missing men, migration and labour markets: evidence from India. Ind. J. Labour Econ. 58, 245–267 (2015).

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  • Migration
  • Remittances
  • Sex ratio
  • Labour markets
  • Gender