Smart, Responsible, and Upper Caste Only: Measuring Caste Attitudes through Large-Scale Analysis of Matrimonial Profiles

By Ashwin Rajadesingan, Ramaswami Mahalingam, David Jurgens. 2019

25 citations recorded by the source corpus.

Discriminatory caste attitudes currently stigmatize millions of Indians, subjecting individuals to prejudice in all aspects of life. Governmental incentives and societal movements have attempted to counter these attitudes, yet accurate measurements of public opinions on caste are not yet available for understanding whether progress is being made. Here, we introduce a novel approach to measure public attitudes of caste through an indicator variable: openness to intercaste marriage. Using a massive dataset of over 313K profiles from a major Indian matrimonial site, we precisely quantify public attitudes, along with differences between generations and between Indian residents and diaspora. We show that younger generations are more open to intercaste marriage, yet attitudes are based on a complex function of social status beyond their own caste. In examining the desired qualities in a spouse, we find that individuals open to intercaste marriage are more individualistic in the qualities they desire, rather than favoring family-related qualities, which mirrors larger societal trends away from collectivism. Finally, we show that attitudes in diaspora are significantly less open, suggesting a bi-cultural model of integration. Our research provides the first empirical evidence identifying how various intersections of identity shape attitudes toward intercaste marriage in India and among the Indian diaspora in the US.

Subfields: Gender Studies, Sociology and Political Science, Safety, Robustness & Interpretability

How researchers see this paper

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In Gender Studies

1. Ben Hutchinson
2. Vinodkumar Prabhakaran

In Sociology and Political Science

1. Vinodkumar Prabhakaran
2. Ben Hutchinson
3. Nithya Sambasivan
4. Sunipa Dev

In Safety, Robustness & Interpretability

1. Vinodkumar Prabhakaran
2. Ben Hutchinson
3. Sunipa Dev
4. Shachi Dave
5. Partha P. Talukdar
6. Shaily Bhatt

Citations in the corpus

• Re-imagining Algorithmic Fairness in India and Beyond (2021, background): “predictive policing in New Delhi [127], caste [202] and ethnicity [15] biases in job applications, call-center job callbacks [27], castebased wage-gaps [124], caste discrimination in agricultural loans decisions [116], and even in online matrimonial ads [157].”
• Re-contextualizing Fairness in NLP: The Case of India (2022, background): “…to have low literacy rates, misrepresentation, poverty, low technology access, and exclusion in language data (Deshpande, 2011; Kamath, 2018; Krishna et al., 2019).7 Caste-based prejudices have been documented in matrimonial ads (Rajadesingan et al., 2019) and social media (Vaghela et al., 2021).”