In India, various policy instruments such as output price supports, procurement operations, and input price subsidies have historically been used to promote adoption of technologies and improved cultivation methods. However, the keystone of policy initiative has been output price support. In its flagship program the government fixes minimum support prices (MSP) for several food and non-food crops, with an assurance of their procurement in case their market prices fall below the MSP. The success of India’s price support policies depends on whether, and to what extent, producers respond to the incentives provided. For example, do responses exhibit low price elasticity? If so then price instruments cannot be solely relied upon to stimulate production. It is equally important to compare short-run and long-run output elasticities. A large difference between the two would indicate binding constraints on producers’ response in the short run. Public investments should then focus on removing these constraints. Similarly, if elasticities with respect to non-price factors, such as irrigation and roads, are high, then price instruments should be complemented by investment in non-price factors. This paper assesses how wheat and paddy producers allocate agriculture land and how their production decisions are influenced by changes in price, policy, and additional non-price factors.
The findings of this study offer useful insights for policy makers seeking evidence to support and improve food security through a better management of public resources. Our results show that farmers respond positively to higher farm harvest prices of rice and wheat. The response is stronger when prices are assured. Thus, we show that ignoring public procurement of rice and wheat in estimating supply response leads to an underestimation of price elasticity. The extent of this estimation bias differs across yield and acreage response, but the magnitude exceeds 6% in all cases.
This paper, uses a panel of district-level data for the period 2001-2015 to assess whether or not the spatial interconnectedness and structural transformation influence convergence in economic growth in India. To the best of our knowledge, ours is one of the few studies that examine spatial effects on convergence in economic growth in India using such a highly spatially disaggregated dataset. Our findings show an absolute convergence in economic growth across districts, a finding contrary to the widely reported evidence of divergence across states. More importantly, our findings show a strong spatial effect on economic growth, leading to a significant acceleration in its speed of convergence. Structural transformation also matters in growth convergence. Services sector, the main driver of India’s economic growth, does not influence much the speed of convergence, while agricultural sector generates positive spill-overs on convergence.