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Gender differences in work-schooling decisions in rural North India

Abstract

Based on a rural sample of North Indian children and adolescents, this paper addresses the determinants of participation in work and schooling. The empirical model includes market and domestic work as separate alternatives to schooling in a trivariate probit framework, allowing also for combinations of these activities as well as idleness. This differentiation sheds new light on gender differences in the work-school decisions in North India. While more traditional determinants (like wealth or parental education) mostly affect the trade-off between schooling and the gender specific work activity (market work for boys and domestic work for girls), monetary incentives (wages and schooling costs) are more closely related to market work for both sexes. Girls are also more likely to work for the market if their economic contribution can be made within the family (for instance if the household owns animals). Proxies for cultural factors turn out to play especially a role for participation in the gender non-specific work activities; for instance, overall female labor force participation shifts girls’ activities from domestic towards market work.

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Notes

  1. Substitution may be less than perfect: Ravallion and Wodon (2000) find that hours of child work decrease by less than the increase in their school participation as a response to a food subsidy in Bangladesh.

  2. Working as a child can also be expected to have negative long-term consequences, for instance in terms of adult earnings or health outcomes (Rosati and Straub 2007), although its medium-run effects are less clear-cut (Beegle et al. 2012).

  3. The literature on the trade-off between work and schooling of children started with Canagarajah and Coulombe (1997), Cartwright (1998), Grootaert (1998), Nielsen (1998); for the first study on India see Duraisamy (2000).

  4. This point is also emphasized by Levison et al. (2001) who compare the trade-off between school and market work or school and all types of work in Mexico, and find that the first procedure underestimates the trade-off for girls to a large extent.

  5. For instance, in the often applied multinomial logit framework [addressing the school only, work only, combine school and work, and stay idle alternatives, e.g., Levison et al. (2001, Maitra and Ray (2002), Bacolod and Ranjan (2008)], disregarding domestic work might violate the “Independence of Irrelevant Alternatives” assumption, which presupposes that the relative probabilities of any two alternative categories are not influenced by the existence of other alternatives.

  6. Several studies show that schooling responds to the returns to education in India: schooling increased strongly when technological change in agriculture raised its returns (Foster and Rosenzweig 1996), while the arising labor demand effects tended to decrease schooling of children from landless households (Foster and Rosenzweig 2004). Kochar (2004) also finds that the probability of rural boys completing middle school in India increases with urban wage growth of the higher skilled (but decreases with middle skilled wages).

  7. Schooling in rural India decreases when households are hit by adverse income shocks, even more so if these shocks are not anticipated (Jacoby and Skoufias 1997).

  8. Kambhampati and Rajan (2006) document that in India state level growth performance went along with increasing market work participation and reducing school enrolment of children which they attribute to labor demand effects. By contrast, Edmonds et al. (2010) interpret the smaller increases in schooling in those rural Indian districts that were more exposed to trade liberalization as a sign that trade liberalization failed to reduce poverty.

  9. Drèze and Kingdon (2001) also find that the schooling of Indian girls depends stronger on monetary incentives (school meals) and school quality variables than that of boys.

  10. This broad definition of market work is useful as it captures better the economic contribution of children. Globally only a relatively small fraction of children works for wages; most children are employed by their own parents and are working on family farms or in family businesses (Edmonds and Pavcnik 2005).

  11. This distinction between market and domestic work serves solely the purpose of distinguishing between activities that are at least potentially market oriented and activities that only produce goods and services for the household. This terminology labels most typically female activities as domestic, but does not pertain value judgements towards their relative productivity.

  12. As pointed out by a referee, some of these electric appliances (like a freezer, fan, heater, petromax, sewing machine, or a cooker) can be also used in household production and thus proxy not only for wealth but might also directly substitute (or even complement) domestic child labor. In order to address these concerns I also reran the regressions distinguishing between domestic appliances and other goods, and found that especially for domestic goods, wealth and substitution effects cannot be disentangled (cf. fn. 20).

  13. Edmonds (2006) shows that in Nepal the comparative advantage of older girls in household chores changes with younger siblings’ number, gender, and birth spacing. On the role of the gender aspects of household and sibling composition see also Parish and Willis (1993) and Morduch (2000).

  14. This measure might overestimate the true costs of schooling if school choice is endogenous to the households’ willingness to pay for education, or if school costs are positively correlated with unobservable school quality. This might counteract the expected negative effect of school costs on education; in this case the negative effect found in Sect. 5 gives an upper bound estimate of the true effect.

  15. These effects might be partly counteracted by the rising incomes and decision making power of females. If females are more concerned about child work and schooling, their economic power will shift the work-school trade-off in favor of more schooling.

  16. Only male wages are included in both girls’ and boys’ regressions because information on male wages is the one which is most consistently available throughout the villages.

  17. Kambhampati and Rajan (2008) specify a multivariate probability model for Indian girls with four equations for the outcomes work, school, combine, and idle. However, this type of specification in a multivariate framework results in a degenerate participation probability space and less reliable estimates (cf. Edmonds 2007).

  18. Estimations have been implemented with Stata, using the mvprobit , mvnp and mdraws routines of Cappellari and Jenkins (2003, 2006). The D = 300 random draws result in stable simulated parameters \(\widehat{\boldsymbol{\beta}}\) and \(\widehat{\rho}\).

  19. All regressions report robust standard errors that are clustered on the village level, allowing for correlation between unobserved characteristics of children within the same village.

  20. As only around half percent of boys combines market with domestic work, marginal effects for this joint category cannot be meaningfully computed due to convergence issues and are thus not reported.

  21. The above wealth proxies might also capture substitution effects to some extent as some of the durable goods have a productive use in domestic work, and might especially substitute for girls’ and boys’ domestic work. In regressions that distinguished between durable goods of domestic use (freezer, fan, heater, cooker, sewing machine, petromax) and other durable goods (car, bicycle, motor, television, radio, camera), only the domestic goods proxy was significantly related to boys’ work and schooling, whereas domestic goods also had an overall larger effect on girls’ work and schooling outcomes. However, as ownership of domestic and other durable assets are closely related in this sample, there might be not enough variation to disentangle their effects.

  22. This is also the reason why I do not present and interpret the marginal effects on joint trivariate probabilities for these variables.

  23. These findings support those of Kambhampati and Rajan (2008) who find similar patterns of caste-based differences among all Indian girls. They argue that this reflects the less patriarchal cultural norms among the lowest castes, which put less restrictions on the work of girls outside the household.

  24. This result is in line with some other studies on the role of local labor demand (see Duryea and Arends Kuenning 2003 on Brazil) although there is also contrasting evidence finding that the income effects of local wages outweigh substitution effects (see Kambhampati and Rajan 2006 on India or Wahba 2006 on Egypt).

  25. Although ability of the children is not measured, idleness can also be expected to crucially depend on individual abilities. As demonstrated by Bacolod and Ranjan (2008) for the Philippines, in a family the least able children are the ones to stay idle, especially among the relatively richer families.

References

  • Bacolod, M. P., & Ranjan, P. (2008). Why children work, attend school, or stay idle: The roles of ability and household wealth. Economic Development and Cultural Change, 56(4), 791–828.

    Article  Google Scholar 

  • Baland, J.-M., & Robinson, J. A. (2000). Is child labor inefficient? Journal of Political Economy, 108(4), 663–679.

    Article  Google Scholar 

  • Basu, K., Das, S., & Dutta, B. (2009). Child labor and household wealth: Theory and empirical evidence of an inverted-U. Journal of Development Economics, 91(1), 8–14.

    Article  Google Scholar 

  • Basu, K., & Van, P.H. (1998). The economics of child labor. American Economic Review, 88 (3), 412–427.

    Google Scholar 

  • Becker, G. S., & Lewis, H. G. (1973). On the interaction between the quantity and quality of children. Journal of Political Economy, 81(2), 279–288.

    Article  Google Scholar 

  • Beegle, K., Dehejia, R. H., & Gatti, R. (2006). Child labor and agricultural shocks. Journal of Development Economics, 81(1), 80–96.

    Article  Google Scholar 

  • Beegle, K., Dehejia, R., & Gatti, R. (2012). Why should we care about child labor?: The education, labor market, and health consequences of child labor. Journal of Human Resources, 47(2), 364–396.

    Google Scholar 

  • Behrman, J. (1988). Intrahousehold allocation of nutrients in rural India: Are boys favored? Do parents exhibit inequality aversion? Oxford Economic Papers, 40(1), 32–54.

    Google Scholar 

  • Bhalotra, S., Heady, C. (2003). Child farm labor: The wealth paradox. World Bank Economic Review, 17(2), 197–227.

    Article  Google Scholar 

  • Canagarajah, S., & Coulombe, H. (1997). Child labor and schooling in Ghana, policy research working paper series 1844. Washington DC: The World Bank.

  • Cappellari, L., & Jenkins, S. P. (2003). Multivariate probit regression using simulated maximum likelihood. The Stata Journal, 3(3), 278–294.

    Google Scholar 

  • Cappellari, L., Jenkins, S. P. (2006). Calculation of multivariate normal probabilities by simulation, with applications to maximum simulated likelihood estimation. The Stata Journal, 6(2), 156–189.

    Google Scholar 

  • Cartwright, K. (1998). Child labor in Colombia. In: C. Grootaert & H. A. Patrinos (Eds.), The policy analysis of child labor: A Comparative Study, Chap. 4. London: St Martin’s Press.

    Google Scholar 

  • Chamarbagwala, R., & Tchernis, R. (2010). Exploring the spatial determinants of childrens activities: Evidence from India. Empirical Economics, 39(2),593–617.

    Article  Google Scholar 

  • Cigno, A., & Rosati, F. C. (2005). The economics of child labour, Chap. 5. Oxford University Press, Oxford, pp. 83–103.

    Book  Google Scholar 

  • Drèze, J., & Kingdon, G. G. (2001). School participation in rural India. Review of Development Economics, 5(1),1–24.

    Article  Google Scholar 

  • Duraisamy, M. (2000). Child schooling and child work in India, Econometric society world congress 2000 contributed papers 0837. Econometric Society.

  • Duryea, S., & Arends Kuenning, M. (2003). School attendance, child labor and local labor market fluctuations in urban Brazil. World Development, 31(7), 1165–1178.

    Article  Google Scholar 

  • Edmonds, E. V. (2006). Understanding sibling differences in child labor. Journal of Population Economics, 19(4), 795–821.

    Article  Google Scholar 

  • Edmonds, E. V. (2007). Child labor. In: T. P. Schultz & J. A. Strauss (Eds.), Handbook of development economics, Vol. 4 of North Holland handbooks in economics (Chap. 57, pp. 3607–3709). Amsterdam: Elsevier.

  • Edmonds, E. V., & Pavcnik, N. (2005). Child labor in a global economy. Journal of Economic Perspectives, 8(1), 199–220.

    Article  Google Scholar 

  • Edmonds, E. V., Pavcnik, N., & Topalova, P. (2010). Trade adjustment and human capital investments: Evidence from Indian tariff reform. American Economic Journal: Applied Economics, 2(4), 42–75.

    Article  Google Scholar 

  • Emerson, P. M., & Souza, A. P. (2008). Birth order, child labor, and school attendance in Brazil. World Development, 36(9), 1647–1664.

    Article  Google Scholar 

  • Ersado L. (2005). Child labor and schooling decisions in urban and rural areas: Comparative evidence from Nepal, Peru, and Zimbabwe. World Development, 33(3), 455–480.

    Article  Google Scholar 

  • Foster, A. D., & Rosenzweig, M. R. (2004) Technological change and the distribution of schooling: Evidence from green-revolution India. Journal of Development Economics, 74(1), 87–111.

    Article  Google Scholar 

  • Foster, A., & Rosenzweig, M. (1996). Technical change and human-capital returns and investments: Evidence from the green revolution. American Economic Review, 86(4), 931–953.

    Google Scholar 

  • Greene, W. H. (2003). Econometric analysis (5th ed.). Prentice Hall, Upper Saddle River, New Jersey.

    Google Scholar 

  • Grootaert, C. (1998). Child labour in Cote d’Ivoire. In: C. Grootaert & H. A. Patrinos (Eds.), The policy analysis of child labor: A Comparative Study, chapter 3. London: St Martin’s Press.

    Google Scholar 

  • Heady, C. (2003). The effect of child labor on learning achievement. World Development, 31(2), 385–398.

    Article  Google Scholar 

  • Jacoby, H. G., & Skoufias, E. (1997). Risk, financial markets, and human capital in a developing country. Review of Economic Studies, 64(3), 311–335.

    Article  Google Scholar 

  • Kambhampati, U. S., & Rajan, R. (2006). Economic growth: A Panacea for child labor? World Development, 34(3), 426–445.

    Article  Google Scholar 

  • Kambhampati, U. S., & Rajan, R. (2008). The ’nowhere’ children: Patriarchy and the role of girls in India’s rural economy. Journal of Development Studies, 44(9), 1309–1341.

    Article  Google Scholar 

  • Kochar, A. (2004). Urban influences on rural schooling in India. Journal of Development Economics, 74(1), 113–136.

    Article  Google Scholar 

  • Levison, D., Moe, K., & Knaul, F. (2001). Youth education and work in Mexico. World Development, 29(1), 167–188.

    Article  Google Scholar 

  • Maitra, P., & Ray, R. (2002). The joint estimation of child participation in schooling and employment: Comparative evidence from three continents. Oxford Development Studies, 30(1), 41–62.

    Article  Google Scholar 

  • Meyerhoefer, C., & Chen, C. (2011). The effect of parental labor migration on children’s educational progress in rural China. Review of Economics of the Household, 9(3), 379–396.

    Google Scholar 

  • Morduch, J. (2000). Sibling rivalry in Africa. American Economic Review, 90(2), 405–409.

    Article  Google Scholar 

  • Nielsen, H. S. (1998). Child labor and school attendance: Two joint decisions, CLS Working Paper 98–015, University of Aarhus.

  • Pal, S. (2004). How much of the gender difference in child school enrolment can be explained? Evidence from rural India. Bulletin of Economic Research, 56(2), 133–158.

    Article  Google Scholar 

  • Parish, W. L., & Willis, R. J. (1993). Daughters, education and family budgets: Taiwan experiences. Journal of Human Resources, 28(4), 863–898.

    Article  Google Scholar 

  • Psacharopoulos, G., & Patrinos, H. A. (1997). Family size, schooling and child labor in Peru—An empirical analysis. Journal of Population Economics, 10(4), 387–405.

    Article  Google Scholar 

  • Ravallion, M., & Wodon, Q. (2000). Does child labour displace schooling? Evidence on behavioural responses to an enrollment subsidy. Economic Journal, 110(462), C158–C175.

    Article  Google Scholar 

  • Rosati, F., & Straub, R. (2007). Does work during childhood affect the health of Guatemalan adults? Review of Economics of the Household, 5(1), 83–94.

    Google Scholar 

  • Rose, E. (1999). Consumption smoothing and excess female mortality in rural India. Review of Economics and Statistics, 81(1), 41–49.

    Article  Google Scholar 

  • Rosenzweig, M. R., & Schultz, T. P. (1982). Market opportunities, genetic endowments, and intrafamily resource distribution: Child survival in rural India. American Economic Review, 72(4), 803–815.

    Google Scholar 

  • Sen, A. K. (1992). Missing women. British Medical Journal, 304(6827), 1–17.

    Article  Google Scholar 

  • Wahba, J. (2006). The influence of market wages and parental history on child labour and schooling in Egypt. Journal of Population Economics, 19(4), 823–852.

    Article  Google Scholar 

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Correspondence to Krisztina Kis-Katos.

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Kis-Katos, K. Gender differences in work-schooling decisions in rural North India. Rev Econ Household 10, 491–519 (2012). https://doi.org/10.1007/s11150-012-9153-x

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Keywords

  • Child labor
  • Schooling
  • Domestic work
  • India
  • Multivariate probit

JEL Classification

  • J22
  • J13
  • O15