Impact of COVID-19 on agricultural markets: Assessing the roles of commodity characteristics, disease caseload and market reforms

This paper assesses the impact of the spread of COVID-19 and the lockdown on wholesale prices and quantities traded in agricultural markets. We compare whether these impacts differ across non-perishable (wheat) and perishable commodities (tomato and onion), and the extent to which any adverse impacts are mitigated by the adoption of a greater number of agricultural market reform measures. We use a granular data set comprising daily observations for 3 months from nearly 1000 markets across five states and use a double- and triple- difference estimation strategy. Expectedly, our results differ by type of commodity and period of analysis. While all prices spiked initially in April, they recovered relatively quickly, underscoring the importance of time duration for analysis. Wheat prices were anchored in large part by the minimum support price, while tomato prices were lower in some months. Supply constraints began easing in May with greater market arrivals perhaps reflecting distress sales. Market reform measures did help in insulating farmers from lower prices, but these effects are salient for the perishable goods, and not so much for wheat where the government remained the dominant market player. Taken together, these results point to considerable resilience in agricultural markets in dealing with the COVID-19 shock, buffered by adequate policy support.PRIFPRI3; CRP4; 3 Building Inclusive and Efficient Markets, Trade Systems, and Food Industry; 4 Transforming Agricultural and Rural Economies; 5 Strengthening Institutions and GovernanceA4NH; SARCGIAR Research Programs on Agriculture for Nutrition and Health (A4NH

Social networks, heterogeneity, and adoption of technologies: Evidence from India

This study examines the role of caste-based affiliations in the smallholders’ social network interactions for adoption choices. In particular, whether lower-caste, namely Scheduled Castes/Scheduled Tribes, farmers rely more on social networks for information than their counterparts. We further explore whether social network effects are more pronounced when farmers interact within their caste than otherwise. Finally, the study tests whether the effects (intra-caste and inter-caste) vary by caste—SC/ST versus non-SC/ST farmers. The study uses a survey of 478 mustard farmers in Rajasthan, India. Econometric concerns related to unobserved heterogeneity are addressed by employing specifications with village fixed effects and a series of robustness tests. Simultaneity concerns are addressed by analyzing the social network effects in a dynamic adoption framework. Results show that the adoption choices regarding hybrid mustard seeds are more pronounced for the lower-caste farmers than for their counterparts. Findings reveal that social network effects are significant in intra-caste but not in the case of inter-caste. Finally, the result shows that the likelihood of accepting advice in technology adoption is higher when SC/ST farmers interact with non-SC/ST network members than when non-SC/ST farmers interact with SC/ST network members.PRIFPRI3; ISI; 1 Fostering Climate-Resilient and Sustainable Food SupplySA

Identifying Purchase Intent from Social Posts

In present times, social forums such as Quora and Yahoo! Answers constitute powerful media through which people discuss on a variety of topics and express their intentions and thoughts. Here they often reveal their potential intent to purchase - 'Purchase Intent' (PI). A purchase intent is defined as a text expression showing a desire to purchase a product or a service in future. Extracting posts having PI from a user's social posts gives huge opportunities towards web personalization, targeted marketing and improving community observing systems. In this paper, we explore the novel problem of detecting PIs from social posts and classifying them. We find that using linguistic features along with statistical features of PI expressions achieves a significant improvement in PI classification over 'bag-of-words' based features used in many present day social-media classification tasks. Our approach takes into consideration the specifics of social posts like limited contextual information, incorrect grammar, language ambiguities, etc. by extracting features at two different levels of text granularity - word and phrase based features and grammatical dependency based features. Apart from these, the patterns observed in PI posts help us to identify some specific features