Is the caste Census a useful exercise?

Syllabus: GS2/ Polity and Governance

Context

The demand for a caste Census has gained momentum with support from political parties, NGOs, and organizations like the Rashtriya Swayamsevak Sangh (RSS).

A caste Census involves collecting data on the population size and socio-economic conditions of various caste groups.
Advocates believe it can be instrumental in providing equitable distribution of government jobs, land, and other resources based on caste proportions.

First detailed caste Census conducted in 1871-72 across major regions like Bengal and Madras.
- However arbitrary classification led to confusion, as noted by W. Chichele Plowden in the 1881 Census report.
1931 Caste Census: It identified 4,147 castes, exposing challenges like different identities claimed by the same caste in different regions.
Post-Independence: 2011 Socio-Economic and Caste Census (SECC) identified over 46.7 lakh castes/sub-castes with significant errors.

Understanding Demographic Realities: It helps policymakers identify gaps in resource allocation and ensure targeted interventions.
Revisiting Reservation Policies: Existing reservation policies are based on outdated data from the 1931 Census.
- A fresh caste Census can rationalize and update the quota system, ensuring fairness and proportional representation.
Empowering Marginalized Groups: Recognizes the presence of underrepresented castes and sub-castes in political and economic structures.
Political Representation: Accurate caste data can guide delimitation of constituencies and ensure equitable representation in legislative bodies.

Upward Caste Mobility Claims: Respondents claim higher social status due to the prestige associated with certain castes. Between the 1921 and 1931 Censuses, some groups reclassified themselves as higher castes.
Downward Caste Mobility Claims: Post-independence, benefits of reservation policies have encouraged some to report themselves as belonging to lower social categories.
Caste Misclassification: Similar-sounding surnames across regions often result in errors. Such inaccuracies lead to misrepresentation and inequitable resource allocation.
Multiplicity of Caste Claims Within Communities: Communities with the same name may claim different varna or caste identities across regions.
- Example: Sonars in one region identified as Kshatriya or Rajput, while in another, they identified as Brahmin or Vaishya.
Enumerators’ Subjectivity: Census officials inadvertently misclassify respondents due to lack of knowledge or preconceived notions about caste hierarchies.
- Example: During the Bihar caste Census in 2022, controversies arose over the inclusion of ambiguous categories like ‘hijra’ and ‘kinnar’ in caste classifications.

Establish a robust and transparent framework to conduct the caste Census, incorporating technological solutions like AI and machine learning for data accuracy.
Conduct awareness campaigns to educate respondents about the importance of providing accurate information.
Standardize caste categorization across states to resolve confusion over similar-sounding surnames and overlapping identities.
Introduce mechanisms for periodic review and validation of collected data to correct inaccuracies over time.

Source: TH

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