{"type":"script","doc_desc":{"producers":[{"name":"Reproducibility WBG","abbr":"DIME","affiliation":"World Bank - Development Impact Department","role":"Verification and preparation of metadata"}],"prod_date":"2024-04-29","version":"1"},"project_desc":{"authoring_entity":[{"name":"Julius Adewopo","email":"jadewopo@worldbank.org","affiliation":"World Bank"},{"name":"Bo Pieter Johannes Andr\u00e9e","affiliation":"World Bank","email":"bandree@worldbank.org"},{"affiliation":"International Institute of Tropical Agriculture","name":"Helen Peter"},{"name":"Gloria Solano-Hermosilla","email":"gloria.solano-hermosilla@ec.europa.eu","affiliation":"European Commission Joint Research Center"}],"output":[{"type":"Working Paper","title":"Comparative Analysis of AI-Predicted and Crowdsourced Food Prices in an Economically Volatile Region","description":"Policy Research Working Paper (PRWP) 10758","uri":"http:\/\/documents.worldbank.org\/curated\/en\/099430004232434765\/IDU18a5b99971429914e501ac101b25f11061d21","doi":"https:\/\/doi.org\/10.1596\/1813-9450-10758","authors":"Julius Adewopo, Bo Pieter Johannes Andr\u00e9e, Helen Peter, Gloria Solano-Hermosilla, and Fabio Mical"}],"software":[{"name":"R","version":"4.2"}],"scripts":[{"file_name":"RR_NGA_2024_125.zip","zip_package":"RR_NGA_2024_125.zip","title":"Reproducibility package (data and code) for Comparative Analysis of AI-Predicted and Crowdsourced Food Prices in an Economically Volatile Region","dependencies":"All dependencies are stored in the renv environment.","instructions":"See README in the reproducibility package.","notes":"Computational reproducibility verified by Development Impact (DIME) Analytics team, World Bank","date":"2024-05"}],"title_statement":{"idno":"RR_NGA_2024_125","title":"Reproducibility package for Comparative Analysis of AI-Predicted and Crowdsourced Food Prices in an Economically Volatile Region"},"production_date":"2024-05","geographic_units":[{"name":"Nigeria","code":"NGA","type":"Country"}],"keywords":[{"name":"Food Price"},{"name":"Crowdsourcing"},{"name":"Artificial Intelligence"},{"name":"Ground truth"},{"name":"Data"}],"topics":[{"id":"Q11","parent_id":"Q1","name":"Aggregate Supply and Demand Analysis: Prices","vocabulary":"Journal of Economic Literature (JEL)","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php?view=jel"}],"abstract":"High-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 realtime 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\u2013generated 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.\n","language":[{"name":"English","code":"EN"}],"repository_uri":[{"name":"Reproducible Research Repository (World Bank)","uri":"https:\/\/reproducibility.worldbank.org"}],"technology_environment":"Paper exhibits were reproduced in a computer with the following specifications:\n\u2022 OS: Windows 11 Enterprise, version 21H2\n\u2022 Processor: Intel(R) Xeon(R) Gold 6226R CPU @ 2.90GHz, 16 Core(s)\n\u2022 Memory available: 15.7 GB\n\u2022 Software version: R 4.2","technology_requirements":"~20 minutes runtime","reproduction_instructions":"For successful replication of this package, new users have to download the package, open the R project, recreate the R environment using renv::restore, and run the scripts in the orders detailed in the README file. ","disclaimer":"The materials in the reproducibility packages are distributed as they were prepared by the staff of the International Bank for Reconstruction and Development\/The World Bank. The findings, interpretations, and conclusions expressed in this event do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank, or the governments they represent. The World Bank does not guarantee the accuracy of the materials included in the reproducibility package.","contacts":[{"name":"Julius Adewopo","affiliation":"World Bank","email":"jadewopo@worldbank.org"},{"name":"Reproducibility WBG","affiliation":"World Bank","email":"reproducibility@worldbank.org"}],"datasets":[{"note":"Source: World Bank, downloaded on October 3, 2023 from the following link. \nLocated at: data\/WB_monthly_data.csv.\nSome manual steps are required to reach the database in the package. These steps involve manual calculations done directly in Excel. For those who wish to download the data from scratch, the README file contains clear and detailed instructions. ","name":"Commodity Food Prices Nigeria","uri":"https:\/\/microdata.worldbank.org\/index.php\/catalog\/study\/NGA_2021_RTFP_v02_M","access_type":"Published with the package"},{"access_type":"Published with the package","note":"Source: The European Commission Joint Research Center (EC-JRC)\nLocated at: data\/FPCA_all.csv.\nDownloaded from the \u201cPost-sampled Weekly Price\u201d on October 3, 2023, from the following link.\nSome manual steps are required to reach the database in the package. These steps involve manual calculations done directly in Excel. For those who wish to download the data from scratch, the README file contains clear and detailed instructions. ","name":"Food Price Crowdsourcing Africa-expansion (1\/2)","uri":"https:\/\/datam.jrc.ec.europa.eu\/datam\/mashup\/FP_NGA\/index.html?_r=1"},{"name":"Food Price Crowdsourcing Africa-expansion (2\/2)","uri":"https:\/\/data.jrc.ec.europa.eu\/dataset\/f3bc86b0-be5f-4441-8370-c2ccb739029e","access_type":"Published with the package","note":"Source: The European Commission Joint Research Center (EC-JRC)\nLocated at: data\/Raw_groundref_FPCA_0km_fnl.csv.\nDownloaded from the \u201cStep 2. FPCA\u201d data on October 3, 2023, from the following link.\nSome manual steps are required to reach the database in the package. These steps involve manual calculations done directly in Excel. For those who wish to download the data from scratch, the README file contains clear and detailed instructions. "}],"data_statement":"All data is public and contained in the reproducibility package.","license":[{"name":"Modified BSD3","uri":"https:\/\/opensource.org\/license\/bsd-3-clause\/"}],"identifiers":[{"type":"DOI","identifier":"doi.org\/10.60572\/ejpg-0a73"}]},"tags":[{"tag":"DOI"}],"schematype":"script"}