Adequate availability of food grains is crucial for achieving zero hunger globally. However, fluctuations in food grain availability vis-à-vis prices of other production factors pose a significant challenge to sustainable hunger eradication. Such asymmetrical price movements in food markets contribute to instability in food production. On this backdrop, the study investigates the supply response of major food grains in India from 1981 to 2024 using CS-ARDL and PSTR models. Results reveal nonlinear relationships between price, production and area. CS-ARDL analysis indicates that area has a substantial long-term impact on production, while price affects the production in short-term. Further, the post-estimation of PSTR analysis suggests a negative association in the low-price regime and a positive one in the high regime between production and price. Moreover, an inverse relationship is found below a certain area threshold, while a positive relationship emerges beyond it with food production. This threshold signifies conditions where changes in arable land use and prices positively influence food grain production. Additionally, panel non-Granger causality test re-confirms that both area and price are Granger-cause production. These insights are critical for policy formulation, enabling policymakers to anticipate price and area fluctuations due to external shocks, thereby offering appropriate incentives to farmers.
This paper investigates the role of neighbourhood effects in shaping the spatial concentration of manufacturing and service establishments across Indian districts, using unit-level data from the 2013–14 Economic Census. We apply the spatially weighted Ellison–Glaeser index (EGSPAT) developed by Guimarães et al. (2011) to account for inter-district spillovers in measuring industrial agglomeration. At the national level, incorporating spatial dependence does not significantly alter concentration patterns: highly agglomerated industries remain confined to a small number of districts, with limited spillovers to neighbouring areas. In contrast, state-level analysis for India’s three largest employment states—Maharashtra, Uttar Pradesh, and West Bengal—reveals substantial neighbourhood effects. The EGSPAT estimates exceed the standard Ellison–Glaeser index by 8–23 per cent, and districts with excess employment exhibit clear spatial contiguity. These findings highlight the importance of geographic scale in identifying agglomeration dynamics, as national-level measures mask significant within-state spatial dependence. The paper contributes novel evidence for India that spatial spillovers in industrial concentration are primarily a subnational phenomenon, and it underscores the policy relevance of state-level industrial strategies in leveraging localised agglomeration economies.
This paper analyzes the structure of inequality of opportunity in early child health in India using a nationally representative dataset. We use three anthropometric Z-scores which serve as indicators for tracking the progress of countries towards the Sustainable Development Goals. Using a novel Machine Learning algorithm that uses hypothesis tests to partition the data, we uncover granular opportunity structures for three commonly used anthropometric Z-scores as a complex interaction across multiple socio-economic, demographic and behavioral characteristics. We find that the most important circumstances determining inequality of opportunity in child health are household wealth, religion, region, maternal education, and household sanitation practices. We also find that the indicator measuring long run child health displays higher inequality of opportunity relative to indicators capturing short run child health. The results have implications for the creation of targeted social safety net policies.
The disintegration and diffusion of production process across the world, termed as Global Value Chains (GVCs), has enabled various developing countries to participate in world trade. In this scenario, India, with its abundant labour and manufacturing capability has the potential to become a key player in international commerce. The current study based on Koopman et al.’s (2010, 2014) disintegration of gross exports into several value-added components, attempts to assess their individual impacts on labour productivity of India, and determine the importance of GVC networks towards boosting the country’s growth prospects. Panel Vector Autoregression (PVAR) approach is used to analyse annual data from 16 manufacturing industries for the period 2001–2019. The corresponding Orthogonalized-Impulse-Response-Functions (OIRFs) display short-run effects of FVA, while the aggregate DVA fails to impact labour productivity significantly. The OIRFs further document fluctuating effects of shocks to DVA in final and intermediate exports absorbed by direct importers, which persist for some time. The effects of DVA in intermediate exports re-exported to third countries and those returning home, are short lived. The Forecast-Error-Variance-Decomposition (FEVDs) in line with the OIRF plots, identify the DVA in intermediate exports absorbed by direct importers, to explain the maximum variation in labour productivity. The study contributes to the existing literature by identifying that GVCs, in its different stages of production integration, can have differing impacts on the economy. Therefore, analysing GVCs in aggregate may not permit targeted policy formulation for utmost benefit of an economy.
Trade boosts productivity; hence, decomposing aggregate productivity based on major trade theories is essential to understanding the relative significance of each channel on productivity-led growth and its effects on living standards and real income of the nations. This paper uses the most recent control function estimation approaches to provide a more consistent and efficient estimate of the Indian manufacturing firms’ Total Factor Productivity (TFP). Then, the article decomposes aggregate productivity loss of India’s manufacturing due to the decline in trading activities into the inter-industry effect, the technology effect, the continuing-firm effect, and “the joint effects of entering and exiting firms”. When India actively participates in trade, the primary drivers of increased aggregate productivity in the manufacturing sector are the technology and intra-industry effects, with the inter-industry effect contributing by around 24
