Madhya Pradesh has witnessed substantial agricultural expansion from 2011–12 to 2024–25, but the extent to which this progress has been shared across districts remains an important empirical issue. This study examines agricultural development and inter-district inequality in Madhya Pradesh by using a Composite Agricultural Development Index (CADI) and Principal Component Analysis. District-level agricultural capability is assessed using multiple productive indicators while changes in inter-district inequality are investigated by using Gini coefficient, Theil index and coefficient of variation. Panel regression models are used to examine the association between selected agricultural factors and district-level agricultural output while Bai–Perron structural-break analysis yields additional evidence on changes in selected agricultural indicators over time. The results indicate overall improvements in agricultural capability, coupled with a modest reduction in inter-district inequality. Gini coefficient of CADI decreased from 0.2228 in 2011 to 0.2163 in 2024, while Theil index decreased from 0.0832 to 0.0801 and coefficient of variation dropped from 0.4066 to 0.3982. These changes indicate limited sigma-convergence rather than rapid equalisation of agricultural capability across districts. The panel estimates suggest that mechanisation has the most consistent positive association with district-level agricultural output across the estimated specifications while the relationships for irrigation, institutional credit, labour productivity and technology-related variables are less uniform across the models. Structural-break analysis detects shifts in crop yield, labour productivity, CADI and institutional credit, but these breaks are interpreted descriptively rather than as evidence of causal effects of particular policies or shocks. Overall, agricultural expansion in Madhya Pradesh has been accompanied by only gradual narrowing of inter-district disparities, underscoring the need to take district-specific approaches to productive infrastructure, irrigation, finance, technology, mechanisation and market access as well as policies aiming to increase aggregate agricultural output.
No takes yet. Share an insight, caveat, or question.
Noor et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: