Caste Census: Beyond Reservations and Quotas

Public discourse on India’s proposed caste census has largely revolved around reservations—how many jobs or university seats each group ought to receive. But that framing misses a deeper imperative. A caste-disaggregated census is not merely a mechanism for revisiting quotas; it is a critical tool for advancing inclusive growth, directing welfare spending, and bridging long-standing inequalities in access to public goods and services. Put plainly, granular caste data helps the state govern more fairly—for all citizens, not just those eligible for reservations.

Caste continues to shape life outcomes in India in ways that elude a binary reserved/unreserved distinction. National surveys consistently reveal that historically marginalized communities face disproportionately higher poverty rates: around 50.6% of Scheduled Tribes (ST), 33.3% of Scheduled Castes (SC), and 27.2% of Other Backward Classes (OBC) live below the poverty line, compared to just 15.6% among others. These disparities correlate with differences in geography, employment, education, and access to state services. To ignore caste in policymaking is to overlook the very structure of inequality in India.

While some political parties have anchored their support for a caste census in slogans like “jitni aabadi, utna haq” (rights in proportion to population), this article argues for a broader view. The real value of caste data lies not in quota recalibration, but in its potential to serve as a diagnostic tool—an X-ray of the economy’s social fractures. It offers policymakers the evidence they need to design more precise interventions: to improve schools and scholarships, expand credit for underserved entrepreneurs, and repair public infrastructure where neglect runs deep. In the sections that follow, we outline how caste-disaggregated data can inform policy in three critical domains: education, finance, and infrastructure.

Education: Seeing and Closing the Gaps

Caste remains a powerful determinant of educational attainment, especially beyond the primary level. While enrollment has equalized at younger ages, the dropout cliff remains steepest for marginalized groups. According to government data, the gross enrollment ratio (GER) in higher education stands at 28.4% nationally, but drops to 25.9% for SC students and just 21.2% for ST students. Secondary school dropout rates for SCs and STs are 12.5% and 16.6%, respectively—far higher than for other communities.

A caste census can help policymakers zero in on these disparities at a granular level—tracking enrollment and completion rates by caste and geography, identifying specific communities with alarming attrition, and allocating educational support accordingly. In District X, if 40% of adolescents from a particular caste drop out by age 16—double the district average—targeted responses could include new secondary schools, scholarships, or public awareness campaigns for that community. The impact of such interventions can be tracked over time, enabling data-driven policy iteration.

Furthermore, existing programs—midday meals, post-matric scholarships, hostel schemes—can be evaluated for reach and effectiveness. If SC girls in a given region continue to drop out despite these supports, the problem may lie deeper: in social attitudes, school quality, or discriminatory environments. Caste-disaggregated data is not just descriptive; it enables diagnosis.

Equally important is the equitable allocation of resources. If Dalit-majority blocks have fewer secondary schools or worse pupil-teacher ratios, census data can support redistributive investments. Mobile labs, bridge courses, or mentoring initiatives can be strategically deployed. Over time, this brings policymaking closer to India’s constitutional promise of equal opportunity.

Credit: From Financial Access to Financial Justice

Access to credit remains a critical constraint for India’s marginalized groups, who are often forced into informal borrowing due to exclusion from formal banking systems. Detailed caste data can shed light on who remains unbanked, underfunded, or structurally excluded. For instance, opening bank branches in underserved areas has been shown to reduce poverty significantly, especially for SC households—suggesting they were previously trapped in exploitative credit relationships.

The government has launched several flagship initiatives to expand financial inclusion: the Jan Dhan Yojana, Mudra loans, and Stand-Up India. But without caste-disaggregated data, it’s hard to assess whether these schemes have effectively reached their intended beneficiaries. If the census reveals that OBC women in rural districts remain underrepresented among Mudra borrowers, or that SC entrepreneurs receive fewer loans than their demographic share, the government can tailor outreach and tweak eligibility norms.

Even regulatory mandates can benefit. The RBI requires that 12% of lending go to weaker sections (including SC/ST), and recent data shows banks exceeding that threshold. Yet disaggregated census insights could further refine benchmarks, inform credit deployment to underserved districts, and support the rise of cooperative banking models in marginalized communities. Over time, a caste census offers both a roadmap and a feedback loop: revealing bottlenecks, informing program design, and measuring whether efforts to democratize credit are succeeding.

Public Goods: Mapping Infrastructure with Equity

Public goods—from water and electricity to sanitation and roads—are unequally distributed in India, often following lines of caste-based settlement. A caste census can expose this embedded inequality, enabling planners to overlay maps of caste concentration with infrastructure gaps.

Studies have found that over 48% of Dalit villages reported being denied access to common water sources, and the sanitation gap between SC/ST and other households remains 20–25 percentage points wide. While national schemes like Jal Jeevan Mission and Swachh Bharat have made strides, inequities persist beneath the surface. Detailed caste data can inform more equitable targeting—ensuring that resource flows respond to historic deficits.

Whether it’s identifying SC-dominated settlements lacking electricity or tribal hamlets still disconnected from all-weather roads, granular census data can sharpen the allocation of infrastructure grants and expedite service delivery. This is not favoritism—it’s a long-overdue correction of inherited neglect.

Urban planning too stands to benefit. Dalit and OBC neighborhoods, often on the fringes of cities, receive fewer municipal services. A caste census can support targeted interventions—more streetlights, clinics, drainage systems—turning broad development schemes into finely tuned engines of inclusion.

A Tool for Smarter, Fairer Governance

A national caste census is not an exercise in division, but one in diagnostic clarity. It is not about recalibrating quotas for the few, but about recalibrating development priorities for the many. In a country where caste continues to intersect with access to opportunity, ignoring it in data collection is neither neutral nor progressive.

Done right, the census will provide policymakers with an updated, granular map of disadvantage—across education, finance, and infrastructure. It will enable precision in allocating public funds and evaluating welfare programs. It will offer researchers, journalists, and civil society a sharper lens with which to monitor state performance and hold it accountable. Above all, it will help us see who is being left behind—and chart a path to bring them along.

India has the ambition to go much beyond a $5-7 trillion economy. It cannot afford to leave entire communities behind. A caste census, grounded in rigorous data and deployed in service of equitable policy, may be the most important development instrument the country hasn’t used in nearly a century. The time to change that is now.