Gender Disparities in Wages among Casual Labourers and Agricultural Workers in India: A Statistical Analysis in the Context of SDG 5

Background: Gender-based wage disparities remain a critical challenge to achieving Sustainable Development Goal 5 (SDG 5), which advocates for gender equality and the empowerment of women. In India, despite substantial economic growth, women often remain underpaid compared to their male counterparts, particularly in casual labour and agricultural sectors. Understanding the magnitude and trends of these disparities is crucial to addressing systemic inequalities and informing policy reforms.
Methods: This study utilized secondary data obtained from the Ministry of Statistics and Programme Implementation (MOSPI) for the years 2017-2021. Descriptive statistics were employed to examine wage distributions and test for normality. Nonparametric methods, including the Mann-Whitney U test, were applied to evaluate gender differences in labourer and agricultural wages. The Kruskal-Wallis test was further used to compare the magnitude of wage disparities across years. Data were analysed separately for male and female workers across occupational categories.
Results: The analysis revealed consistent and statistically significant gender-based wage disparities. Male casual labourers reported a higher mean rank wage (239.87) compared to females (131.13), with p < 0.001. Similarly, male agricultural workers (225.69) earned significantly more than female workers (145.31), also with p < 0.001. The Kruskal-Wallis test confirmed that wage gaps persisted across years, though the magnitude of disparities widened over time in both labourer and agricultural work, particularly in 2020 and 2021. Regional trends further highlighted variations, with certain states exhibiting larger wage differences.
Conclusion: The findings underscore persistent gender wage inequality in India's labour and agricultural sectors, which poses a substantial barrier to achieving SDG 5. The consistent statistical significance of disparities across years highlights structural imbalances in the labour market. Policymakers must prioritize targeted interventions-such as equal pay legislation, social protection measures, and skill development programs-to reduce the wage gap and foster gender-inclusive economic growth.

Gender wage gap, casual labour, agricultural wages, sustainable development goals, wage inequality, labour market disparities.

1. The table 4 presents the results of the Ordinary Least Squares (OLS) multiple linear regression analysis examining the influence of agricultural wages, labourer wages, age, and education on yearly and monthly wage outcomes separately for female and male workers. Prior to model estimation, the wage variables were logarithmically transformed to satisfy the normality assumption required for OLS regression. [Google Scholar] [Crossref]

2. Among female workers, agricultural wages showed a positive association with both yearly (beta = 0.129) and monthly (β = 0.092, p < 0.05) wage outcomes. Although the coefficient for yearly wages was not statistically significant, the significant monthly coefficient indicates that improvements in agricultural wages contribute positively to women's monthly earnings. Labourer wages also exhibited positive coefficients for yearly (beta = 0.396) and monthly (beta = 0.26) earnings; however, these relationships were not statistically significant, suggesting that labourer wages alone did not independently explain wage variation after adjusting for age and education. [Google Scholar] [Crossref]

3. Age emerged as a significant positive predictor of female wages in both yearly (β = 0.025 p < 0.01) and monthly (β = 0.027, p < 0.01) models, indicating that older women generally earned higher wages than younger women, possibly reflecting greater work experience and labour market participation. Education also demonstrated a statistically significant positive effect on both yearly (β = 0.005, p < 0.001) and monthly (β = 0.013, p < 0.01) wages, highlighting the importance of educational attainment in improving women's earning potential. [Google Scholar] [Crossref]

4. For male workers, agricultural wages were positively associated with yearly (β = 0.086, p < 0.05) and monthly (β = 0.081, p < 0.05) wage outcomes, indicating that higher agricultural wages significantly contributed to improvements in male earnings. Labourer wages showed positive but statistically non-significant coefficients for yearly (beta = 0.321) and monthly (beta = 0.15) wages, suggesting a comparatively weaker independent contribution after accounting for other explanatory variables. Similar to the female models, age remained a significant determinant of male earnings for both yearly (β = 0.018, p < 0.01) and monthly (β = 0.029, p < 0.01) wages. Education was likewise positively and significantly associated with yearly (β = 0.006, p < 0.001) and monthly (β = 0.015 p < 0.01) wage outcomes. [Google Scholar] [Crossref]

5. Overall, the findings indicate that age and education consistently influence wage outcomes for both genders, whereas agricultural wages exhibit a modest but significant positive association, and labourer wages do not retain statistical significance after controlling for other variables. [Google Scholar] [Crossref]

6. CONCLUSION [Google Scholar] [Crossref]

7. The study provides compelling evidence of persistent gender disparities in wages among both casual labourers and agricultural workers in India between 2017 and 2021. Men consistently earned significantly higher wages compared to women, and this gap persisted across states and years, despite overall increases in wages for both groups. The findings underscore structural inequalities in labour markets that disadvantage women, thereby slowing progress toward the achievement of SDG 5, particularly the target of ensuring equal pay for work of equal value. Policymakers must address these disparities by strengthening wage protection laws, ensuring enforcement of equal remuneration policies, and designing gender-sensitive labour reforms. Bridging this wage gap is essential not only for gender equity but also for fostering inclusive economic growth in India. [Google Scholar] [Crossref]

8. Funding: Not applicable. [Google Scholar] [Crossref]

9. Conflict of interest: The authors declare no conflict of interests. [Google Scholar] [Crossref]

10. Data source: This study utilized a secondary dataset downloaded from MoSPI (Ministry of Statistics and Programme Implementation) website. [Google Scholar] [Crossref]

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