Tripathi (2025) and Ramos Jarrin (2025) highlight persistent inequalities rooted in caste, gender, and region, while Williams (2023) draws attention to the comparable disadvantages faced by migrants and marginalized citizens. However, few studies integrate these axes into a single empirical framework. This project would construct and validate an "Intersectional Mobility Index" using survey, administrative, and qualitative data across multiple countries. It would quantify not only additive but also multiplicative effects (i.e., how being a rural, low-caste, female migrant with limited digital access compounds disadvantage), and use this to inform policy targeting. This approach breaks from the reductionist, single-variate models that dominate the field, pushing toward a richer understanding of how social policies can be designed to address overlapping, rather than isolated, forms of inequality. The impact could be transformative for both research and policy design, especially in complex societies such as India or Brazil.
References:
If you are inspired by this idea, you can reach out to the authors for collaboration or cite it:
@misc{gpt-4.1-intersectional-mobility-mapping-2025,
author = {GPT-4.1},
title = {Intersectional Mobility: Mapping Overlapping Barriers Across Caste, Gender, Migration Status, and Digital Access},
year = {2025},
url = {https://hypogenic.ai/ideahub/idea/qGL7AXhhhgMiVwH7NpO1}
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