Comparative Analysis of AI-Predicted and Crowdsourced Food Prices in an Economically Volatile Region

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dc.creatorAdewopo, Julius
dc.creatorAndrée, Bo Pieter Johannes
dc.creatorPeter, Helen
dc.creatorSolano-Hermosilla, Gloria
dc.creatorMicale, Fabio
dc.date2024-04-23T19:13:16Z
dc.date2024-04-23T19:13:16Z
dc.date2024-04-23
dc.date.accessioned2026-07-01T00:34:10Z
dc.descriptionHigh-frequency monitoring of food commodity prices is important for assessing and responding to shocks, especially in fragile contexts where timely and targeted interventions for food security are critical. However, national price surveys are typically limited in temporal and spatial granularity. It is cost prohibitive to implement traditional data collection at frequent timescales to unravel spatiotemporal price evolution across market segments and at subnational geographic levels. Recent advancements in data innovation offer promising solutions to address the paucity of commodity price data and guide market intelligence for diverse development stakeholders. The use of artificial intelligence to estimate missing price data and a parallel effort to crowdsource commodity price data are both unlocking cost-effective opportunities to generate actionable price data. Yet, little is known about how the data from these alternative methods relate to independent ground truth data. To evaluate if these data strategies can meet the long-standing demand for real-time intelligence on food affordability, this paper analyzes open-source daily crowdsourced data (104,931 datapoints) from a recently published data set in Nature Journal, relative to complementary ground truth sample. The paper subsequently compares these data to open-source monthly artificial intelligence–generated price data for identical commodities over a 36-month period in northern Nigeria, from 2019 to 2022. The results show that all the data sources share a high degree of comparability, with variation across commodity and market segments. Overall, the findings provide important support for leveraging these new and innovative data approaches to enable data-driven decision-making in near real time.
dc.formatapplication/pdf
dc.formattext/plain
dc.identifierhttp://documents.worldbank.org/curated/en/099430004232434765/IDU18a5b99971429914e501ac101b25f11061d21
dc.identifierhttps://hdl.handle.net/10986/41467
dc.identifier10.1596/1813-9450-10758
dc.identifier.urihttp://hdl.handle.net/123456789/406336
dc.languageEnglish
dc.languageen_US
dc.publisherWashington, DC: World Bank
dc.relationPolicy Research Working Paper; 10758
dc.rightsCC BY 3.0 IGO
dc.rightshttps://creativecommons.org/licenses/by/3.0/igo/
dc.rightsWorld Bank
dc.subjectZERO HUNGER
dc.subjectSDG 2
dc.subjectFOOD SECURITY
dc.subjectFOOD COMMODITY PRICES
dc.subjectARTIFICIAL INTELLIGENCE
dc.subjectINDUSTRY, INNOVATION AND INFRASTRUCTURE
dc.subjectSDG 9
dc.titleComparative Analysis of AI-Predicted and Crowdsourced Food Prices in an Economically Volatile Region
dc.typeWorking Paper

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