AI-imputed and crowdsourced price data show strong agreement with traditional price surveys in data-scarce environments
Julius Adewopo, Bo Pieter Johannes Andrée, Helen Peter, Gloria Solano-Hermosilla, Fabio Micale, Pradeep Paraman, Pradeep Paraman, Youssef El Khatib, Youssef El Khatib
TL;DR
AI and crowdsourcing can reliably track food prices in data-scarce regions like northern Nigeria, matching traditional methods.
Contribution
Demonstrates that AI-imputed and crowdsourced price data are reliable alternatives to traditional surveys in data-scarce environments.
Findings
Crowdsourced prices strongly correlate with enumerator-collected prices (r = 0.94–0.96 for maize and rice).
AI-imputed prices align closely with crowdsourced data (r = 0.99 for maize, r = 0.94 for rice).
Discrepancies between data sources are consistent with measurement error, not actual market differences.
Abstract
Continuous access to up-to-date food price data is crucial for monitoring food security and responding swiftly to emerging risks. However, in many food-insecure countries, price data is often delayed, lacks spatial detail, or is unavailable during crises when markets may become inaccessible, and rising prices can rapidly exacerbate hunger. Recent innovations, such as AI-driven data imputation and crowdsourcing, present new opportunities to generate continuous, localized price data. This paper evaluates the reliability of these approaches by comparing them to traditional enumerator-led data collection in northern Nigeria, a region affected by conflict, food insecurity, and data scarcity. The analysis examines crowdsourced prices for two staple food commodities, maize and rice, submitted daily by volunteers through a smartphone application over 36 months (2019–2021), and compares them…
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Taxonomy
TopicsCOVID-19 Pandemic Impacts