Improving farmers' and consumers' welfare in agricultural supply chains via data-driven analytics and modeling : from theory to practice
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, September, 2020
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2021
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| Online Access: | https://hdl.handle.net/1721.1/129083 |
| _version_ | 1826211966638620672 |
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| author | Singhvi, Somya. |
| author2 | Retsef Levi and Yanchong Zheng. |
| author_facet | Retsef Levi and Yanchong Zheng. Singhvi, Somya. |
| author_sort | Singhvi, Somya. |
| collection | MIT |
| description | Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, September, 2020 |
| first_indexed | 2024-09-23T15:14:14Z |
| format | Thesis |
| id | mit-1721.1/129083 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T15:14:14Z |
| publishDate | 2021 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1290832021-01-07T03:06:33Z Improving farmers' and consumers' welfare in agricultural supply chains via data-driven analytics and modeling : from theory to practice Singhvi, Somya. Retsef Levi and Yanchong Zheng. Massachusetts Institute of Technology. Operations Research Center. Massachusetts Institute of Technology. Operations Research Center Operations Research Center. Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, September, 2020 Page 236 blank. Cataloged from PDF version of thesis. Includes bibliographical references (pages 223-235). The upstream parts of the agricultural supply chain consists of millions of smallholder farmers who continue to suffer from extreme poverty. The first stream of research in this thesis focuses on online agri-platforms which have been launched to connect geographically isolated markets in many developing countries. This work is in close collaboration with the state government of Karnataka in India which launched the Unified Market Platform (UMP). Leveraging both public data and platform data, a difference-in-differences analysis in Chapter 2 suggests that the implementation of the UMP has significantly increased modal price of certain commodities (5.1%-3.5%), while prices for other commodities have not changed. The analysis provides evidence that logistical challenges, bidding efficiency, market concentration, and price discovery process are important factors explaining the variable impact of UMP on prices. Based on the insights, Chapter 3 describes the design, analysis and field implementation of a new two-stage auction mechanism. From February to May 2019, commodities worth more than $6 million (USD) had been traded under the new auction. Our empirical analysis suggests that the implementation has yielded a significant 4.7% price increase with an impact on farmer profitability ranging 60%-158%, affecting over 10,000 farmers who traded in the treatment market. The second stream of research work in the thesis turns to consumer welfare and identifies effective policies to tackle structural challenges of food safety and food security that arise in traditional agricultural markets. In Chapter 4, we develop a new modeling framework to investigate how quality uncertainty, supply chain dispersion, and imperfect testing capabilities jointly engender suppliers' adulteration behavior. The results highlight the limitations of only relying on end-product inspection to deter EMA and advocate a more proactive approach that addresses fundamental structural problems in the supply chain. In Chapter 5, we analyze the issue of artificial shortage, the phenomenon that leads to food security risks where powerful traders strategically withhold inventory of essential commodities to create price surge in the market. The behavioral game-theoretic models developed allow us to examine the effectiveness of common government interventions. The analysis demonstrates the disparate effects of different interventions on artificial shortage; while supply allocation schemes often mitigate shortage, cash subsidy can inadvertently aggravate shortage in the market. Further, using field data from onion markets of India, we structurally estimate that 10% of the total supply is being hoarded by the traders during the lean season. by Somya Singhvi. Ph. D. Ph.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center 2021-01-06T17:38:54Z 2021-01-06T17:38:54Z 2020 2020 Thesis https://hdl.handle.net/1721.1/129083 1227096918 eng MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582 236 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Operations Research Center. Singhvi, Somya. Improving farmers' and consumers' welfare in agricultural supply chains via data-driven analytics and modeling : from theory to practice |
| title | Improving farmers' and consumers' welfare in agricultural supply chains via data-driven analytics and modeling : from theory to practice |
| title_full | Improving farmers' and consumers' welfare in agricultural supply chains via data-driven analytics and modeling : from theory to practice |
| title_fullStr | Improving farmers' and consumers' welfare in agricultural supply chains via data-driven analytics and modeling : from theory to practice |
| title_full_unstemmed | Improving farmers' and consumers' welfare in agricultural supply chains via data-driven analytics and modeling : from theory to practice |
| title_short | Improving farmers' and consumers' welfare in agricultural supply chains via data-driven analytics and modeling : from theory to practice |
| title_sort | improving farmers and consumers welfare in agricultural supply chains via data driven analytics and modeling from theory to practice |
| topic | Operations Research Center. |
| url | https://hdl.handle.net/1721.1/129083 |
| work_keys_str_mv | AT singhvisomya improvingfarmersandconsumerswelfareinagriculturalsupplychainsviadatadrivenanalyticsandmodelingfromtheorytopractice |