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Working women and caste in India: A study of social disadvantage using feature attribution

Kuhu Joshi, Chaitanya K. Joshi

arXiv 27 Apr 2019 · Econometrics

arXiv:1905.03092 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Women belonging to the socially disadvantaged caste-groups in India have historically been engaged in labour-intensive, blue-collar work. We study whether there has been any change in the ability to predict a woman's work-status and work-type based on her caste by interpreting machine learning models using feature attribution. We find that caste is now a less important determinant of work for the younger generation of women compared to the older generation. Moreover, younger women from disadvantaged castes are now more likely to be working in white-collar jobs.

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1IIPS (2017) National family health survey (nfhs-4), 2015-16: India0.64422100%
2Nitya Rao, Arjan Verschoor, Ashwini Deshpande, and Amaresh Dubey (2010) Gender, caste and growth assessment–india (report to uk department for international development)0.64422100%
3Mukesh Eswaran, Bharat Ramaswami, and Wilima Wadhwa (2013) Status, caste, and the time allocation of women in rural india0.51121100%
4Susan Bayly (2001) Caste, society and politics in India from the eighteenth century to the modern age, volume 30.40511100%
5Marianne Bertrand and Emir Kamenica (2018) Coming apart? cultural distances in the united states over time0.40511100%
6Surjit Bhalla and Ravinder Kaur (2011) Labour force participation of women in india: some facts, some queries0.40511100%
7Maria De-Arteaga and Artur Dubrawski (2017) Discovery of complex anomalous patterns of sexual violence in el salvador0.40511100%
8Maria De-Arteaga, William Herlands, Daniel B Neill, and Artur Dubraw… (2018) Machine learning for the developing world0.40511100%
9Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong M… (2017) Lightgbm: A highly efficient gradient boosting decision tree0.40511100%
10Stephan Klasen and Janneke Pieters (2012) Push or pull? drivers of female labor force participation during india's economic boom0.40511100%

Showing the top 10 of 20 scored citations.