Do returns to education matter to schooling participation? Evidence from India

Abstract

While it might be expected that demand for schooling will depend positively on the economic returns to education (ER) in the local labor market, in fact there is theoretical ambiguity about the sign of the schooling–ER relationship when households are liquidity‐constrained. Whether the relationship is positive or negative depends on which effect dominates – the positive substitution effect of an increase in ER on years of education, or the negative income effect. For India, we find a positive relationship between the rate of return to education for adults in the local labor market and school attainment of girls and non‐poor boys. The size of the effect of ER on years of education acquired is large for some groups. However, for poor boys the negative income effect dominates the positive substitution effect. Thus, while improved economic incentives for acquiring education have a positive impact on educational attainment of girls and non‐poor boys, they worsen the educational attainment of poor boys. Policy implications are discussed.

Keywords:

• schooling attainment
• market returns to education
• child labor
• India

Acknowledgements

The authors are grateful to Kenneth Swinnerton for helpful comments on the paper and to Måns Söderbom for helpful discussions on some of the issues in the paper. Any errors are the authors’ own.

Notes

1. The model and notation used here stem from Gormly and Swinnerton (2003).

2. Note that discounting has been excluded for clarity.

3. The National Sample Survey (NSS) does not collect information on hours worked but rather records the number of half‐days worked in the past week. We have ascribed four hours’ work to each ‘half‐day’ worked for two reasons. Firstly, because it seemed the most reasonable assumption for the sample of waged workers, which is the subsample we work with. While hours worked may be significantly longer for self‐employed workers, among the sample of waged workers, the assumption of a eight‐hour day seems reasonable (especially given the six‐day week, rather than the five‐day week prevalent in most developed countries). Secondly, we ascribed four hours to a ‘half‐day’ because other studies using Indian NSS data have also assumed this (for example, Vasudeva‐Dutta 2006).

4. The National Sample Survey Organization covers all 78 state‐regions defined by it for the 55th round; however, one state‐region the Jhelum Valley in Jammu & Kashmir is not covered in 1993 and hence is excluded from analysis in 1999.

5. Wage earners are those for whom a wage is recorded and whose activity status is recorded to be wage employment.

6. Measurement of the economic benefits of education has a long history, starting with Mincer’s (1974) semi‐logarithmic framework. A series of reviews by Psacharopoulos (1985, 1994), Psacharopoulos and Patrinos (2004), and Card (2001) document the large number of studies in the field. While accurate estimation is difficult due to ability bias, Mincerian returns are a widely used measure of the economic benefits of education and yield estimates not too different from those obtained from IV and twin studies (Card 2001).

7. See Kingdon (1998) and Kingdon and Unni (2001). While Duraisamy (2002) and Vasudeva‐Dutta (2006) report returns to different levels of education for India, they do not report the marginal return to each extra year of education.

8. For example, a child dropping out of school to earn a wage will raise household expenditure, meaning the coefficient on pce could be downward biased; equally, unobserved family endowments may raise both pce and child schooling attainment, implying that the coefficient on pce could be upwardly biased.

9. ER ₁₉₉₃ will not be correlated with shocks that occur after 1993 and that may affect both ER ₁₉₉₉ and EDYRS ₁₉₉₉.

10. Income and wealth are widely used as proxies for the absence or presence of liquidity constraints (see, for example, Johnson, Parker, and Souleles 2006; Centeno and Novo 2007). Since consumption is smoothed income, it also serves as a proxy for the extent of liquidity constraints. This is reinforced by the fact that people with low incomes in India find it harder to borrow money due to a lack of assets/collateral.

11. We do not presume that our per‐capita expenditure categories are exogenous. However, there is no clear way of addressing the potential endogeneity of pce category.

12. This is compatible with the existence of pro‐male bias in education in India. Kingdon (2005) finds evidence of significant gender bias in household education expenditure allocations in India.