Spatial growth and convergence in Indian agriculture

Abstract

The economic growth convergence between advanced nations and the rest of the world has been found to reduce global income inequality. However, less is known about convergence within nations, especially in developing economies with substantial regional heterogeneities. Using the neoclassical framework, this study tests for farm income convergence in India, where agriculture employs over 200 million workers. Household farm income is estimated in 599 districts using three pan‐India situation assessment surveys (2003, 2013, and 2019). The conditional convergence model—augmented to capture spatial spillovers—shows farm incomes are converging across Indian districts but at slower rates in recent years and among farmers in the left‐tail of the income distribution. Irrigation infrastructure, crop diversification, and distance to the urban market explain farm income growth, and hence, spatial income disparities. Returns to these factors are high for middle‐income farmers than for the rich or poor. Spatial spillovers, likely from technological change, captured in residuals also had a positive impact on farm incomes. Results remain unchanged when farm income was replaced by its value added. Connecting the poorest to the markets, strengthening infrastructure and promoting diversification for the middle‐income groups, and nudging high‐income farmers toward value chains could help equitable economic development.