ACCESS to higher education has been a long-standing policy concern in India. Reservation for different social groups at the central and state levels has been a typical policy response. With the implementation of reservation for OBCs in the centrally aided higher education institutions, the debate on reservation has picked up again. Among other things, the policy of reservation in higher education is based on the premise that participation of persons from the reserved category is uniformly low and reservation would result in significantly higher participation. The discussion on issues relating to the measurement of participation in higher education and the ‘deficits’ experienced by different groups has, however, been inadequate. It is argued here that an appropriate measure of ‘deficits’ should inform the nature and scope of affirmative action. Such an effort may also make the policy initiative more ‘acceptable’ across various population segments.

 

Measuring participation in higher education: The first issue that needs to be resolved while defining a measure is whether we should focus on ‘attainment’ or ‘enrolment’. While the former captures the segment that has completed graduation or postgraduate education, the latter focuses on the segment that is currently studying for graduation or higher courses. In addition, while ‘attainment’ is a stock measure and carries the ‘burden of history’, enrolment is a flow measure that captures the current situation and provides indications for the future.

Given this background, three measures can be defined for any population segment:

1. Share of graduates (and postgraduates) in the 20+ age population which characterizes an all generation stock measure of attainment/participation in higher education; a higher share signifying higher participation. Alternatively, one can compare a group’s share in the 20 years plus population with its share in the number of graduates. Broadly, if the population share is higher than the share in graduates, the group suffers from a ‘deficit’ in terms of participation.

2. Share of graduates in 20-30 age population that measures the current generation stock measure. As in the case of the first measure, difference in population share and share among graduates measures the ‘deficit’.

3. Share of currently studying persons in the 18-25 years population that provides a current generation flow measure of participation in higher education. This measure can also be seen converted into a deficit measure as in the case of the two measures above.

If participation rates of specific groups have improved in recent years (generations), one would expect the current generation flow measure to show much lower ‘deficits’ as compared to stock measures, especially the all generation stock measure.

There is one more aspect of measurement that needs to be recognized. Eligibility requirements for enrolment in an undergraduate course are that the participant has finished school education. Thus, instead of focusing on the entire population in the relevant age group, measures of participation can also focus on the segment that has crossed the threshold of higher secondary education. Accordingly, the three measures described above can be defined for eligible population. A sharper focus on the eligible population brings the links between secondary and tertiary education explicitly into the analytical discussion. We shall come back to this issue later.

 

Defining socio-economic groups: Having defined measures of participation in higher education, the next step is to ascertain the groups for which these measures can be compared. Given the history of affirmative action in India and current debates on the issue, it is imperative that we define categories that capture caste, religion and economic status. Based on the availability of data from the National Sample Survey Organization (NSSO), the following socio-religious and economic categories have been defined:

Socio-religious categories: (i) Hindu – Scheduled Caste (H-SC); (ii) Hindu – Scheduled Tribe (H-ST); (iii) Hindu – Other Backward Classes (H-OBC); (iv) Hindu – Upper Castes (H-UC); (v) Muslim – Other Backward Classes (M-OBC); (vi) Muslim – General (M-G); (vii) Other minorities (OM).

Economic categories: (i) Extremely poor (< 0.75 Poverty Line - PL); (ii) Poor (< 0.75 PL - PL); (iii) Marginally poor (PL - 1.25 PL); (iv) Vulnerable (1.25 PL - 2 PL); (v) Middle class (2 PL - 4 PL); (vi) High income (> 4 PL).

Unfortunately, the representation of high per capita expenditure households is not adequate in the NSSO data and as a result we are not able to distinguish between different categories of ‘well-off’ households.

 

Participation in higher education by socio-religious groups: Table 1 provides the six estimates of participation for each socio-religious group defined above. As expected, the participation rates are lower for the marginalized groups (SC, ST, OBC and Muslims) than the average. However, one striking feature of the estimates is that underprivileged groups do significantly better in terms of participation rates when we move from stock to flow measures. This improvement is visible even when one compares all generation stocks with current generation stock measures.

Moreover, the gap between high participation groups (e.g., Hindu-upper castes) and others declines dramatically when we consider measures using eligible population. For example, participation rate for Hindu-upper castes (16.3%) is more than the double the rate of Hindu-OBCs (7.3%) when the entire population in the age group 28-25 is considered. But the difference narrows down considerably (64.3% vs. 53.3%) when we consider only the eligible population in this age group.

Table 2 brings out the ‘deficits’ across socio-religious groups more sharply. There are deficits for all socio-religious groups except upper caste Hindus and other minorities. Once again, deficits for the underprivileged groups are lower when flow measures are considered and decline dramatically when we consider only the eligible population. Take for example, the OBC group which                                                                                                                                                                                                                                                                                                                              will now benefit from reservation in higher education. Of the total population in the age group 18-25, this group has a share of about 34%; the group’s share in the eligible population in this age group is 30% while their share in the currently studying population is more than 28%.

Participation in higher education by economic categories: Since the flow measure provides the most current picture, we focus on the flow estimates here. It is evident from Table 3 that participation (even in terms of flow) is significantly lower for the poor. But once the threshold is crossed these differences decline, as was the case for the marginalized groups in the socio-religious categories. In other words, the ‘conversion rates’ to graduates are quite high for the poor once the eligibility is taken care of or once they pass school examinations.

A large variety of factors influence participation in higher education. We have so far looked at only the socio-religious background and some economic factors. It is important to ascertain if socio-religious background continues to be an important determinant of participation in higher education after controlling for location (state, rural/urban), household expenditures, gender and so on. A preliminary analysis undertaken by the Sachar Committee suggested that the role of socio-religious factors declines dramatically once locational and economic factors are controlled for.

In order to explore this further we analyzed how the probability of a person currently participating in higher education changes with various individual, household and regional characteristics.¹ The focus was on the impact of socio-religious affiliation, once other factors are controlled for. At the individual level, we control for age and gender; per capita household expenditure is controlled for at the household level; and state and urban/rural areas are controlled for through dummies. Interestingly, the results show that the role of socio-religious affiliation is not significant for OBCs once these factors are controlled for.

 

Among other marginalized groups, the impact is high for SCs and Muslims. The location effects are quite strong; location in southern states and in urban areas enhances participation quite significantly. Not surprisingly, the impact of socio-religious affiliation becomes insignificant in most cases once eligibility is controlled for. That is, once eligible, participation in higher education is often not significantly different across socio-religious categories. SCs and Muslims are still somewhat disadvantaged but OBC and to some extent ST status does not seem to affect participation in higher education in any significant way.

Broadly, three analytical questions emerge from this simple analysis of the National Sample Survey (2004-05) data. One, in the discussion on higher education, how should one deal with the issue of eligibility. Deficits for the underprivileged are significantly lower among the eligible population. Interestingly, more detailed estimates (not all of which are reported here) also suggest that conversion rates are reasonably high for all the marginalized groups in terms of caste (higher for SC/ST), religion (higher for Muslims), income (higher for poor), region (higher for rural) and gender (higher for women). Thus, once individuals from these groups cross the school threshold, the chances of their going to college are quite high.

 

Thus, a better understanding of the constraints on school education is critical if participation in higher education is to be enhanced. Therefore, should the higher education policy also focus on ensuring that the threshold is crossed, even when one is thinking about participation in higher education? Arguably, reservation in higher education is an incentive to cross the threshold. Similarly, one can argue that job reservation can enhance the incentives to participate in higher education. Are these adequate? To what extent have these worked? Do we have other options for affirmative action?

Two, should affirmative action (e.g., policies of reservation) be linked to the deficits that different groups have? If one goes by the estimates presented here, the deficits for OBCs are not very high, particularly when one looks at the eligible population. The share of Hindu-OBCs is more than 26% even among the total graduates in the age group 20-30 years; their share is even higher (28.3%) among the currently studying persons. Should this be considered while deciding the need and extent of reservation?

Three, to what extent should socio-religious affiliation be a focus of affirmative action? Since the role of socio-religious affiliation in influencing the participation in higher education declines dramatically once other household, individual and location specific characteristics are controlled for, an exclusive focus on such affiliation for affirmative action seems inappropriate. The importance of economic background as well as that of location highlights the role of supply side factors in affecting the participation of various groups in higher education.

 

Recent discussions on higher education in India have raised a variety of very interesting policy related and other issues. Unfortunately, the empirical underpinnings of this discussion have been rather weak. This is not to argue that issues of higher education can only be resolved through empirical analysis but to suggest that a better understanding of empirical reality would facilitate a more informed debate on the relevant issues. It is in this context that we argue that a more detailed analysis of the above kind might be useful.

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* This paper is part of an ongoing project at the Observer Research Foundation on issues relating to higher education in India. It builds upon the analysis contained in Rakesh Basant, ‘Reservation in Higher Education’, Seminar 565, September 2006; K. Sundaram, ‘Fair Access to Higher Education Revisited: Some Results for Social and Religious Groups from NSS 61st Round Employment Unemployment Survey 2004-05’, Working Paper No 163, Centre for Development Economics, Delhi School of Economics, October 2007; and in Chapter 4 of the Sachar Committee report.

Footnote:

1. It must be pointed out that the data is still tentative. Nevertheless, the relationships posited should hold.