The stark disparity in caste representation in professional success is not mere happenstance. In empirical terms, caste is the most statistically significant determinant of professional success in Indian society. (Express Photo)
It is necessary to reorient policy discourse to reflect social reality, to lay foundation for a new social justice programme
Written by Praveen Chakravarty
May 10, 2023 07:00 IST
Rahul Gandhi, in an election speech in Karnataka, coined a slogan, “Jitni abaadi utna haq” (JAUH). It means the share of rewards for an identity group in a society should be commensurate with their share of the population. The principle that Gandhi articulated is: If 70 per cent of Indians belong to the OBC/SC/ST castes, then their representation in various professions and sectors should also roughly be 70 per cent.
That is, in Gandhi’s JAUH India, 8,000 out of the 11,310 senior officers in public sector banks should be from the OBC/SC/ST castes. But in reality, only 3,000 are. Or 70 out of the 104 startup unicorns should have been founded by people from OBC/SC/ST castes. But none are. Or there should be 225 joint secretaries and secretaries in the government of India from these castes. Only 68 are. Or 30 of the top 50 companies in the National Stock Exchange should be headed by people from these oppressed castes. None are. The list goes on. On the other hand, 80 per cent of the 154 million workers in the MGNREGA programme are OBC/SC/STs. All the 60,000 Indians who do manual scavenging are Dalits or tribals — 75 per cent of the 44,000 safai karmacharis in the are from these
The stark disparity in caste representation in government professional success is not mere happenstance. In empirical terms, caste is the most statistically significant determinant of professional success in Indian society.
One standard response to such evidence of caste inequality is that professional success is a function of education and merit, which is agnostic to caste. It is laughable to argue that all “merit” is concentrated in the top 30 per cent of the population by caste, while the bottom 70 per cent have none. If education is the stepping stone to professional success, then that stone is not easily accessible to children from oppressed castes. All the 175 donors who fund Ashoka University, a reputed private institution that produces future leaders and policymakers, are from upper castes. Not surprisingly, only 6 per cent of their undergraduate students come from oppressed castes vis-a-vis 35 per cent in public institutions like IITs. The idea of a meritocratic society being devoid of identity bias is a hoax.
If such harsh caste inequality stares us in the face and is arguably the most pressing social issue, why doesn’t our policy discourse reflect this adequately? Perhaps one reason could be that the discourse is dominated by people not from these oppressed caste groups. As an example, 96 per cent of the 600 authors who wrote opinion articles in the last four months in leading English language publications — The Indian Express, The Hindu, Hindustan Times, The Times of India, The Print, and NDTV — are from upper castes, including this author, and ostensible beneficiaries of their caste privilege. To be sure, one does not necessarily have to belong to an oppressed caste to champion their cause, but it is highly likely that when 96 per cent of influential discourse is shaped by upper-caste writers, they are unlikely to be about the travails of the oppressed.
While the class divide between the rich and the poor is well acknowledged, caste is the more significant factor in determining livelihood outcomes. To put it starkly, in the birth lottery of Indian society, a child born into a slightly richer Dalit family has a lower chance of success than a child born into a marginally poorer upper-caste family. There is abundant theoretical and empirical scholarship that shows factors such as social networks, bias and stigma play a big role in shaping the outcomes of the birth lottery. In which case, these questions arise: How deep is India’s caste cleavage? How much of a skew is acceptable in a civilised society? Should there be proactive efforts to correct it and if yes, what are the possible remedial solutions? In short, while perfect equality may be a mirage, how close can and should we get to a JAUH society?
The first step in embarking on a neo-social justice mission is to understand the extent of the problem. This is why caste census data is needed. Caste census will give us information not just about the share of population by caste but also details about income, education, health, and social mobility of various castes, which is the foundation for any new social justice programme.
It is both premature and intellectually lazy to immediately call for higher reservation or expand quotas as solutions to the caste problem. To offset inequalities imposed by a “divine” birth lottery, countries such as the US, Sweden and the Netherlands have experimented with social lottery solutions for education, skills and jobs. It is time to discuss these in India too. In the 21st century, it is myopic to confine ourselves to just the framework of reservation and affirmative actions as plausible solutions for caste inequalities. An open, vibrant public debate and discourse can generate creative solutions for the Indian context. Before that, the first step is to have detailed data and information of the problem through the caste census.
The demand for caste census to be made public and a call for JAUH is to reshape and reorient our policy discourse to reflect social reality. It does not immediately mean advocacy of higher reservation or Mandal 3.0. The reticence of the Narendra Modi government to not make the caste census information public for fear of a social backlash in the absence of an immediate and elegant solution is an understandable but not a justifiable excuse. Hiding the information does not make the problem disappear. Our nation is strong enough to confront our social reality and find a nuanced and acceptable solution for all sections of our society.
The writer is chairman of Data Analytics of Congress Party
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