Abstract
India has experienced large changes, both relative and absolute, in the education of her population in recent decades. Literacy levels have been steadily rising. During the last decade alone, it is estimated that the number of out-of-school children has been reduced by as much as 17 million. However, not least due to sheer population size, India continues to contribute a large share to global levels of non-enrolment and other areas of concern in global educational development. In particular, while socio-economic and regional inequality are of course also present in other countries, the educational development trajectory of India is something of an outlier among large emerging economies in terms of its persistent and deep polarisation. The present study attempts to trace India’s educational development during recent decades by juxtaposing statistical data from a wide variety of sources, including census aggregates, administrative school statistics, the school census, and various household surveys. With their different underlying education measures (literacy, enrolment, attainment) and different methodologies, complete agreement between these sources cannot be expected. Nevertheless, analysing their points of agreement as well as their inconsistencies can only improve our understanding of the underlying educational trends. Where possible, micro-data are analysed within a household and/or family context, to gain insights on intergenerational transmission of education, and its evolution over time. Results from the most recent 2011 national census are analysed in this context of existing trends. This includes census estimates of literacy rates and their gender differentials.
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Notes
- 1.
Literacy by itself is a highly limited measure (Ramachandran 2006); this is all the more true of “census literacy” which is literacy of household members as reported by the household head. In theory, interviewers are instructed to test actual literacy if in doubt, but there exists no data on how often this is actually done in practice. Indeed, an ancillary analysis shows that reported literacy correlates almost perfectly with whether the individual has ever attended school. This may suggest that respondents view “is literate” and “has attended school” as virtually synonymous, even though in reality, both acquiring literacy as an adult and attending school without learning to read or write is both possible and—in the latter case—unfortunately not uncommon. Moreover, even having attended school and acquired minimal literacy, it is possible to lapse back into effective illiteracy if the skill is not maintained through active use. However, the implications for data quality may be overestimated. The IHDS figures do differ from the NSS estimates at the level of individual states, but considering the variation across states as a whole, they are remarkably consistent. This is despite the fact that the IHDS in theory consistently applied the “read a sentence” test for literacy, and does not rely on self-reports. The same is true of the DHS, if partial literacy is included. This means that, even though the ability to read parts of a sentence is a very narrow understanding of “literacy”, at least it appears to be fairly reliably self-reported.
- 2.
Specifically, the split at each node is chosen statistically to maximise a deviation-based discrimination criterion. Unless otherwise stated, the potential classification criteria included poverty (specifically the dichotomous household poverty indicator calculated by IHDS based on monthly per-capita consumption and the official Planning Commission poverty line that varies by state and urban/rural residence), caste, urban/rural current residence, EAG state, gender, caste, and literacy of the household head. The robustness of the estimated trees was assessed by comparing the trees generated on 100 weighted bootstrap samples of the original data. All results shown or mentioned below were highly robust in terms of their agreement with the modal tree structure across samples.
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Acknowledgement
The author wishes to acknowledge the Indian Ministry of Statistics and Programme Implementation that provided the data for the Integrated Public Use Microdata Series, International: Version 6.3 [Machine-readable database]. Minneapolis: Minnesota Population Center, University of Minnesota, 2014.
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Barakat, B. (2016). Education Trends in India: Recent Census Results in Context. In: Guilmoto, C., Jones, G. (eds) Contemporary Demographic Transformations in China, India and Indonesia. Demographic Transformation and Socio-Economic Development, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-24783-0_11
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