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Polarization in Indian Education: An Ordinal Variable Approach

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Abstract

This paper aims to see the direction in which the polarization of education in India is changing over time. The study is based on the five latest quinquennial rounds of survey data collected by the National Sample Survey Office (NSSO). NSSO provides information on different categories of educational attainment, which is an ordinal variable. Our approach is different from the existing literature in the sense that we use indices designed specifically for ordinal variables. We observe that polarization in education has increased at the all-India level and in rural India. Furthermore, polarization is increasing for disadvantaged groups like individuals from the poorest quantile, scheduled castes, scheduled tribes, females, etc. On the contrary, polarization has either decreased or remains more or less unchanged over time for privileged groups like individuals from the richest quantile, forward castes, males, etc.

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Fig. 1

Source: Author’s calculation. Data Sources: NSSO consumer expenditure rounds

Fig. 2

Source: Author’s calculation. Data Sources: NSSO consumer expenditure rounds

Fig. 3

Source: Author’s calculation. Data Sources: NSSO consumer expenditure rounds

Fig. 4

Source: Author’s calculation. Data Sources: NSSO consumer expenditure rounds

Fig. 5

Source: Author’s calculation. Data Sources: NSSO consumer expenditure rounds

Fig. 6

Source: Author’s calculation. Data Sources: NSSO consumer expenditure rounds

Fig. 7

Source: Author’s calculation. Data Sources: NSSO consumer expenditure rounds

Fig. 8

Source: Author’s calculation. Data Sources: NSSO consumer expenditure rounds

Fig. 9

Source: Author’s calculation. Data Sources: NSSO consumer expenditure rounds

Fig. 10

Source: Author’s calculation. Data Sources: NSSO consumer expenditure rounds

Fig. 11

Source: Author’s calculation. Data Sources: NSSO consumer expenditure rounds

Fig. 12

Source: Author’s calculation. Data Sources: NSSO consumer expenditure rounds

Fig. 13

Source: Author’s calculation. Data Sources: NSSO consumer expenditure rounds

Fig. 14

Source: Author’s calculation. Data Sources: NSSO consumer expenditure rounds

Fig. 15

Source: Author’s calculation. Data Sources: NSSO consumer expenditure rounds

Fig. 16

Source: Author’s calculation. Data Sources: NSSO consumer expenditure rounds

Fig. 17

Source: Author’s calculation. Data Sources: NSSO consumer expenditure rounds

Fig. 18

Source: Author’s calculation. Data Sources: NSSO consumer expenditure rounds

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Notes

  1. The achievement gaps among students of different social and ethnic backgrounds are also relevant in the developing economies (see Schofield 2010, who highlights the problems in the context of the United States of America)). Shavit and Blossfeld (1993) observes that such disparities in educational outcomes have more or less remained unchanged over time.

  2. An ordinal variable is a categorical variable with more than one categories following a well-defined ordering rule, where one category is strictly better than the other. However, the distance between categories is not important.

  3. One important point to be noted is that GEL data are not based on learning outcomes, but are based levels of certification. Learning outcomes are not always identical to the level of certification. For example, in India, only about 38% of students in standard ‘V’ can read text meant for standard ‘II’ (see Banerji et al. (2013)). Our focus is not on learning outcomes, but on the levels themselves, which signify the number of students in the formal education system and the length of their stay thereafter.

  4. Some papers treat the approach of Allison and Foster (2004) as a contribution towards studying polarization, examples include Apouey (2007), Kobus (2015), Chakravarty and Maharaj (2015), etc. Other papers like Naga and Yalcin (2008); Lazar and Silber (2013); Kobus and Miłoś (2012); Dutta and Foster (2013); Naga and Stapenhurst (2015); Wang and Yu (2015); Lv et al. (2015), etc., treat the same as a contribution towards inequality ordering for ordinal variables. Recently two new approaches significantly different from that of Allison and Foster (2004) have evolved that study the problem of inequality ordering of ordinal variables. One is the status-based inequality indices introduced in Cowell and Flachaire (2017), and the other is the inequality ordering based on the notion of Hammond’s transfer by Gravel et al. (2021).

  5. We are thankful to an anonymous reviewer for suggesting this interesting exercise.

  6. The Indian Constitution specifies the list of castes and tribes and accords that the SC and ST entitled to special treatment in terms of affirmative action quotas in state and central legislatures, the civil service and government-sponsored educational institutions (see Revankar 1971). SC corresponds to the castes at the bottom of the hierarchical order of the Indian caste system and was subject to social exclusion in the form of untouchability at Indian Independence (August 15, 1947), while the ST corresponds to the indigenous tribal population scattered all over India in different pockets. NSSO classifies the social groups: scheduled tribe, scheduled caste, other backward class (OBC), and others, only from the 55th round. Till the 50th round, the social group has been divided into three categories, SC, ST, and Others. OBC was not taken as a separate category. The Others category is not a monolithic category, it encompasses many heterogeneous castes and classes (see Deshpande 2000, for details). However, a large section in this category is Hindu Upper Castes. In this paper we refer to this category as Forward Caste (FC)

  7. The social attitudes and community prejudices of the socially dominant upper castes towards the individuals of lower castes have led to a sharp divide between the categories. The deprivation of the socially backward groups in education has been widely documented in Ramachandran et al. (2005); Borooah and Iyer (2005); Velaskar (1990); Tilak (2015); Thomas et al. (2001). The caste dynamics also has implications on the Mid Day Meal Program (discussed in the next section) where the parents of the dominant caste take measures to ensure their children do not eat food touched or served by Dalits (see Thorat and Lee (2005) for further detail.)

  8. The average Indian’s attitude towards female education is often based on a complex cost-benefit analysis (see Wu et al. (2007)). According to National Sample Survey Office (NSSO), reportedly 21.8% of girl children have not been enrolled because of their engagement in domestic activities (See Statement 3.20b:, page no 38, NSSO report Government of India (2014)). 43.6% of the females have discontinued education for marriage or engagement in domestic activities (See Statement 3.20a, page no 38, NSSO report Government of India (2014)).

  9. Note that framing policies on controlling distribution always have some distortionary effects. In the context of income inequality, this issue has been discussed in Gupta et al. (2018) and Marjit et al. (2019). Such distortions may also evolve while controlling polarization of education, which is left as a topic for future research.

  10. Sarva Shiksha Abhiyan means education for all. There are also similar programs like education schemes for children aged 3–5 years have been introduced as “Early Childhood Care and Education” (ECCE) services under the “Integrated Child Development Service” (ICDS) through ‘Anganwadis’ or village courtyards. The total number of children receiving preschool education in Anganwadis has increased by 112% (from 16.7 million to 35.3 million) during the period 2001–2002 to 2012–2013. Girls constitute 49% (17.3 million) of the total number of children who received preschool-school education during the year 2012–2013 (see page 55, Government of India 2015).

  11. In the 66th and 68th rounds of the consumer expenditure survey, two types of schedules of inquiry, namely, Schedule 1.0 Type 1 and Schedule 1.0 Type 2, were used to collect data. The schedules differed only in the recall periods for reporting the monthly per capita expenditure.

  12. Earlier (Agrawal 2014) has used individuals with age 15 years or above. However, some individuals, say those within the age group of 15–20 years, can not belong to the category “graduates and above” especially because of their age. Hence, including such individuals may affect our analysis of educational inequality. While studying inequalities in the context of educational opportunities (Asadullah and Yalonetzky 2012) have considered individuals aged above 25 years.

  13. Note that NSSO considers expenditure as a proxy for income data. In order to formulate the quantiles we use the MPCE based on the ‘Uniform Recall Period (URP)’. In URP all items are reported on a recall period of 30 days basis.

  14. Note that in the supplementary material some other Apouey ’s indices like \(IA_{0.25}\), \(IA_{0.5}\), and \(IA_{0.75}\), and Lazar and Silber ’s indices like \(ILS_{1,1}\), \(ILS_{1.5,1.5}\) are also made available. The direction of the trends remains more or less the same. Hence for the sake of not abusing the graphs, we present only some of the indices.

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Acknowledgements

We are grateful for the insightful comments and suggestions made by an anonymous referee on an earlier draft.

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  1. School of Economics, XIM University, Bhubaneswar, India

    Debasmita Basu & Sandip Sarkar

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Basu, D., Sarkar, S. Polarization in Indian Education: An Ordinal Variable Approach. J. Quant. Econ. 21, 569–591 (2023). https://doi.org/10.1007/s40953-023-00356-9

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