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1 – 1 of 1This study examines the impact of caste on salary levels and job positions within Kerala’s Information Technology sector, aiming to challenge the meritocratic perception of this…
Abstract
Purpose
This study examines the impact of caste on salary levels and job positions within Kerala’s Information Technology sector, aiming to challenge the meritocratic perception of this critical area for India’s economic growth.
Design/methodology/approach
The study utilises a dataset of 24,590 employees from 21 IT firms, classified by ownership into Indian and foreign firms. Caste-based disparities are analysed by identifying employees with upper-caste surnames and distinguishing between Kerala Upper Caste and non-Kerala Upper Caste. Generalised Linear Model (GLM) are used to quantify salary disparities and provide deeper insights into how caste, gender and ownership influence salaries.
Findings
The findings reveal significant wage gaps, with individuals bearing upper-caste surnames earning more than their non-upper-caste counterparts, especially in Indian-owned firms. Kerala Upper Caste employees enjoy a salary premium, which is reduced in foreign-owned firms. Moreover, upper-caste individuals are likelier to hold senior roles, indicating potential barriers for non-upper-caste employees.
Research limitations/implications
These results highlight the need for targeted policies to address caste-based inequalities, promoting inclusiveness and fairness in the IT workplace. Wage-setting practices and promotion criteria, particularly the recent trend of employee recommendations for recruitment, may risk amplifying existing disparities if not carefully managed. Industry leaders must recognise and mitigate these risks to ensure equitable employment practices. Limitation: The study’s reliance on surname-based caste identification may underestimate the extent of caste disparities. Further, the absence of additional human capital controls, and focus on Kerala limit the generalizability of findings.
Originality/value
This study is the first to analyse caste dynamics within the IT sector using a rich dataset of corporate firms and wages, offering a novel methodological approach to understanding how social identity intersects with economic outcomes.
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