JOURNAL ARTICLE

Unpacking Caste and Intergenerational Occupational Mobility: A Novel Approach Through Occupational Prestige in West Bengal, India.

  • Published In: Journal of Asian & African Studies (Sage Publications, Ltd.), 2025, v. 60, n. 5. P. 3061 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Mondal, Sandip 3 of 3

Abstract

This article investigates intergenerational occupational mobility in West Bengal through the lens of occupational prestige, employing statistical methods such as conditional probability matrices, ordinary least squares (OLS) regression, and structural equation modelling (SEM). Using data from the India Human Development Survey II (IHDS-II) and the Standard International Occupational Prestige Scale (SIOPS), the study reveals significant caste-based disparities: the general caste (GC) group exhibits higher rates of upward mobility compared to other backward classes (OBCs) and scheduled castes and tribes (SCs/STs). The research further identifies caste as a moderating factor influencing the impact of a father's occupational status and a son's educational attainment on the son's occupational success, with GC sons more effectively translating education into occupational prestige. Overall, the findings underscore the persistent and multifaceted role of caste in shaping occupational mobility in West Bengal, highlighting the need for targeted policies to address these inequalities.

Additional Information

  • Source:Journal of Asian & African Studies (Sage Publications, Ltd.). 2025/08, Vol. 60, Issue 5, p3061
  • Document Type:Article
  • Subject Area:Political Science
  • Publication Date:2025
  • ISSN:0021-9096
  • DOI:10.1177/00219096231225954
  • Accession Number:186747049
  • Copyright Statement:Copyright of Journal of Asian & African Studies (Sage Publications, Ltd.) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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