Somya Singhvi

Despite their vital role in the global rural economy, and as a major source of employment for women in the developing world, artisanal supply chains continue to be plagued by low productivity and high poverty levels. Identifying effective and implementable solutions to improve artisan productivity is a challenging task due to high fragmentation in upstream parts of the supply chain, where artisans often work from their individual households. This study presents research conducted in close collaboration with one of the leading exporters of handmade rugs in India. Leveraging insights from the field visits and analysis of detailed supply chain data, we provide robust empirical evidence that frequent supervisor visits can play a crucial role in improving artisans' productivity. Our results from Instrumental Variables analysis indicate that a one-day decrease in the average number of days between supervisor visits to remote weavers can decrease weaving times by 13.1%-14.1%, which can lead to a 15%-17% increase in monthly income for weavers. Our analysis also suggests that this impact is heterogeneous, with visits to difficult-to-weave rugs, and visits that are more consistently scheduled, leading to maximum productivity gains for the weavers. To capitalize on these insights, we propose a novel predict-then-optimize framework for optimizing supervisor visits in the supply chain. Finally, we demonstrate that the proposed framework can significantly increase weaver productivity even after accounting for various operational and scheduling constraints. This research offers valuable insights into other distributed supply chains and highlight how supply chain considerations can play a critical role in improving the productivity of the workforce in resource-constrained settings.

To improve the welfare of smallholder farmers, multiple countries (e.g., Ethiopia and India) have launched online agri-platforms to transform traditional markets. However, there is still mixed evidence regarding the impact of these platforms and more generally how they can be leveraged to enable more efficient agricultural supply chains and markets. This paper describes work conducted in close collaboration with the state government of Karnataka, India, to design, implement, and assess the impact of a new two-stage auction on the state's online agri-platform, the United Market Platform (UMP). To ensure implementability and protect farmers' revenue, the auction design is guided by practical operational considerations as well as semi-structured interviews with a majority of the traders in the field. A new behavioral auction model informed by the field insights is developed to determine when the proposed two-stage auction can generate a higher revenue for farmers than the traditional single-stage, first-price, sealed-bid auction. The new auction mechanism was implemented on the UMP for a major market of lentils in February 2019. By the end of May 2019, commodities worth more than $6 million (USD) had been traded under the new auction. A difference-in-differences analysis demonstrates that the implementation has yielded a significant 3.6% price increase (corresponding to a 55%--94% profit gain), benefiting over 10,000 farmers who traded in the treatment market. The results from this paper offer tangible lessons on how innovative price discovery mechanisms could be enabled by online agri-platforms in resource-constrained environments. Importantly, the success of these designs critically depends on careful considerations of systemic operational and behavioral factors that affect trades in the physical markets.

Problem definition: Price surge of essential commodities despite inventory availability, due to artificial shortage, presents a serious threat to food security in many countries. To protect consumers’ welfare, governments intervene reactively with either (i) cash subsidy, to increase consumers’ purchasing power by directly transferring cash; or (ii) supply allocation, to increase product availability by importing the commodity from foreign markets and selling it at subsidized rates. Academic/practical relevance: This paper develops a new behavioral game-theoretic model to examine the supply chain and market dynamics that engender artificial shortage as well as to analyze the effectiveness of various government interventions in improving consumer welfare. Methodology: We analyze a three-stage dynamic game between the government and the trader. We fully characterize the market equilibrium and the resulting consumer welfare under the base scenario of no government intervention as well as under each of the interventions being studied. Results: The analysis demonstrates the disparate effects of different interventions on artificial shortage; whereas supply allocation schemes often mitigate shortage, cash subsidy can inadvertently aggravate shortage in the market. Furthermore, empirical analysis with actual data on onion prices in India shows that the proposed model explains the data well and provides specific estimates on the implied artificial shortage. A counterfactual analysis quantifies the potential impacts of government interventions on market outcomes. Managerial implications: The analysis shows that reactive government interventions with supply allocation schemes can have a preemptive effect to reduce the trader’s incentive to create artificial shortage. Although cash subsidy schemes have recently gained wide popularity in many countries, we caution governments to carefully consider the strategic responses of different stakeholders in the supply chain when implementing cash subsidy schemes.

To improve smallholder farmers’ welfare, several governments have led reforms in improving market access for these farmers through online agri-platforms. This paper empirically evaluates the impact of such a reform—the Unified Market Platform (UMP) in Karnataka, India—on market prices and farmers’ profitability. The analysis shows an average 5.1%, 3.6%, and 3.5% increase in the modal prices of paddy, groundnut, and maize due to UMP. However, the analysis also indicates a lack of statistically significant impact on the modal prices of cotton, green gram, and tur. We provide evidence that commodities with a significant price increase differ from those without systemic features related to logistical challenges, bidding efficiency, in-market concentration, and the price discovery process used.

Economically motivated adulteration (EMA) is a serious threat to public health. In this paper, we develop a modeling framework to examine farms’ strategic adulteration behavior and the resulting EMA risk in farming supply chains. We study both “preemptive EMA,” in which farms engage in adulteration to decrease the likelihood of producing low-quality output, and “reactive EMA,” in which adulteration is done to increase the perceived quality of the output. We fully characterize the farms’ equilibrium adulteration behavior in both types of EMA and analyze how quality uncertainty, supply chain dispersion, traceability, and testing sensitivity (in detecting adulteration) jointly impact the equilibrium adulteration behavior. We determine when greater supply chain dispersion leads to a higher EMA risk and how this result depends on traceability and testing sensitivity. Furthermore, we caution that investing in quality without also enhancing testing capabilities may inadvertently increase EMA risk. Our results highlight the limitations of only relying on end-product inspection to deter EMA. We leverage our analyses to offer tangible insights that can help companies and regulators to more proactively address EMA risk in food products.