The US experience shows that rather than Mandal II, Indian education needs to offer nuanced admission policies and support for disadvantaged students
Many countries around the world have introduced policies of positive discrimination in an effort to reduce historically persistent lags in the social, political and economic standing of disadvantaged communities. Positive discrimination (PD) can be defined as the provision of some amount of preference, in processes of selection to desirable positions in a society, to members of groups that are under-represented in those positions. The preference may be provided in various forms — reserved seats in separate competitions, or preferential boosts in a single competition; but it always has the effect of increasing the number of members of an eligible under-represented group selected to a desirable position.1
PD policies should be understood not as a frontal assault on severe socioeconomic inequalities, but primarily as an effort to integrate the upper strata of a society — by increasing the access of members of highly disadvantaged and under-represented communities to respected occupations and responsible positions. This kind of integration of society’s elite helps to promote a variety of benefits, including: greater legitimacy of the political system; better performance of jobs involving familiarity with and understanding of disadvantaged communities; fairer access by ordinary members of those communities to resources and to jobs; and greater motivation for youths from such communities to work hard to better their future prospects.
In India positive discrimination has from the beginning taken the form of reservations, ie, quotas of reserved seats or positions to which eligible candidates can gain access without competing with candidates from non-eligible groups. The size of the quotas are set according to the proportion of the eligible group in the relevant overall population; but quotas for the most desirable positions are usually only partially filled, because an insufficient number of eligible candidates meet the minimum qualifications set for such positions.
In this paper I will focus on reservations in admissions to Indian higher educational institutions, which have recently become the subject of much public debate in the context of proposals to extend to ‘Other Backward Classes’ (OBCs) reserved seats in elite national institutes. In past work I have surveyed and compiled much of the available empirical evidence on the consequences of reservations in Indian higher education. Here I will summarise that evidence and then discuss its implications for the present debate. Before doing so, however, it will be useful to address some theoretical issues in the analysis of PD admission policies.
Because there are both potential benefits and potential costs associated with positive discrimination, I believe that evaluation of the consequences of a PD policy should best proceed within a benefit-cost framework. It follows from this understanding that the way in which a PD policy is structured and implemented can be crucial to its effectiveness — and thus to whether or not it generates net benefits.
The overall success of a PD policy depends on a variety of factors; but one is particularly important. The net benefits of any PD policy are significantly and positively correlated with the (average) quality of performance by beneficiaries in the institutions or organisations to which they gain preferential access. Good beneficiary performance strengthens the magnitude of anticipated benefits, and it weakens the magnitude of potential costs.
In the case of positive discrimination in admissions to higher educational institutions, the net benefits of a PD policy hinge on the number of beneficiaries who have a successful educational experience —e.g., they complete the degree program — as compared with the number of beneficiaries who are unsuccessful. A PD policy in educational admissions provides for the admission of applicants from disadvantaged groups who have weaker conventional qualifications than is required for admission of applicants from other groups. One hopes in this way to select applicants from PD-eligible groups who are formally under-qualified, but nonetheless have the potential to succeed in a higher educational institution, while avoiding the selection of those who would be incapable of overcoming their weaker formal qualifications.
The key elements of a PD admissions policy that affect the accuracy of the selection process (in the above sense) are the following: the magnitude of the preference given to members of beneficiary groups, the sensitivity of the admissions process and the extent of support for PD beneficiaries by the educational institution into which they are admitted. I will discuss each of these briefly in turn.
Magnitude of preference
The preference magnitude involved in any given PD policy is easy to measure for admissions procedures that are quantitative, in the sense that applicants’ qualifications are summarized in an overall point score. In the case of a reserved quota system, the preference magnitude is the difference between the point score of the marginal applicant selected in the general competition and that of the marginal applicant selected in the reserved competition (whether or not the quota is filled). In the case of a quantitative preferential-boost system, it is simply the number of additional points granted to beneficiary group members. When an affirmative action selection procedure is qualitative, involving consideration of a variety of qualifications that are not scored and aggregated into a single overall point score for each applicant, the average amount of preference extended to affirmative action beneficiaries is implicit in the process and much harder — but not impossible — to estimate.
The preference magnitude is especially important, because ceteris paribus the benefits and costs of a PD policy will vary systematically with this magnitude, as follows:
the larger the magnitude, the greater the number of beneficiaries admitted, hence the less frequent the instances of failure to admit a successful candidate;
but the larger the magnitude, the greater the percentage of beneficiaries who will be unsuccessful, hence the more frequent the instances of admission of an unsuccessful candidate.
There must be some (positive) level of the preference magnitude at which the costs associated with admitting unsuccessful candidates will start to exceed the benefits associated with admitting successful ones.
In principle, with full information about the consequences of different preference magnitudes, one could estimate both the number of PD beneficiaries and the overall net benefits associated with each preference magnitude. For any given under-represented group the net benefits from PD would presumably rise initially, as the magnitude of the preference was raised from zero, because at low magnitudes PD beneficiaries could be expected to perform about as well as other marginal applicants. After a certain point, however, the additional net benefits from a higher preference magnitude would turn negative because, at ever higher magnitudes, an ever smaller proportion of additional PD beneficiaries admitted would be able to perform well.2 The magnitude of the preference at that turning point could therefore be identified as the optimal one, which maximises the expected net benefits from a PD policy favouring the given group.
In practice, of course, decision-makers will never have access to sufficient information to determine optimal preference magnitudes in such a systematic and precise manner. Instead, they will have to mix available information with educated guesses and rely on their own best judgment to determine the magnitude of preference appropriate for a group deemed PD-eligible.
Thomas E. Weisskopf is Professor of Economics at the University of Michigan. A dissenting economist and a founder of the Union for Radical Political Economics, he has lived in India in the 1960s, teaching at the Indian Statistical Institute. He has worked extensively on affirmative action in the US and India