Regional Disparities and Convergence in the Agricultural Sector: Evidence from Indian States in the 21 st Century

Abstract

This study investigates regional convergence in India’s agricultural sector from 2001 to 2020, focusing on whether states with lower initial levels of per capita agrarian income are catching up with more affluent counterparts. Using dynamic panel data pooled in five-year intervals for 18 major states, the analysis employs fixed-effects estimation to assess absolute and conditional β-convergence. The methodology incorporates initial income levels, lagged income growth, and structural controls, including population growth, electricity usage, irrigation density, fertiliser input, and human capital, while addressing potential endogeneity through the augmented Solow framework. The study aims to examine the existence of absolute β-convergence in per capita agricultural income, evaluate conditional convergence by accounting for structural and demographic influences, and explore how incorporating dynamic factors such as lagged growth affects convergence patterns. The findings reveal weak evidence of absolute convergence, with marginal statistical significance, but demonstrate strong support for conditional convergence once key variables are included. Electricity usage consistently shows a significant positive effect on growth, while population growth and price instability exert adverse effects. Traditional inputs, such as irrigation and fertiliser, display either weak or adverse effects, suggesting inefficiencies in their deployment. An accompanying σ-convergence analysis shows a decline in income disparities between 2001 and 2012, followed by increasing divergence through 2020, indicating that while some convergence is occurring, it is neither widespread nor sufficient to reduce overall regional inequality. These results emphasise that various structural and demographic factors conditionally influence convergence in Indian agriculture.