{"doc_desc":{"title":"WLD_2020_PFC_v01_M"},"study_desc":{"title_statement":{"idno":"WLD_2020_PFC_v01_M","title":"Predicting Food Crises 2020","sub_title":"Dataset for reproducing working paper results"},"authoring_entity":[{"name":"Bo Pieter Johannes Andree","affiliation":"World Bank"},{"name":"Andres Chamorro","affiliation":"World Bank"},{"name":"Aart Kraay","affiliation":"World Bank"},{"name":"Phoebe Spencer","affiliation":"World Bank"},{"name":"Dieter Wang","affiliation":"World Bank"}],"production_statement":{"funding_agencies":[{"name":"State and Peace-Building Trust Fund","abbreviation":"SPF","role":""}],"grant_no":"TF0A7049 and TF0A5070"},"distribution_statement":{"contact":[{"name":"Andres Elizondo","affiliation":"World Bank","email":"achamorroelizond(at)worldbank.org","uri":""}]},"version_statement":{"version_date":"2020-09"},"study_info":{"keywords":[{"keyword":"Famine","vocab":"","uri":""},{"keyword":"Food Insecurity","vocab":"","uri":""},{"keyword":"Extreme Events","vocab":"","uri":""},{"keyword":"Unbalanced Data","vocab":"","uri":""},{"keyword":"Cost-sensitive learning","vocab":"","uri":""}],"topics":[{"topic":"C01 - Econometrics","vocab":"Journal of Economic Literature (JEL)","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php"},{"topic":"C14 - Semiparametric and Nonparametric Methods: General","vocab":"Journal of Economic Literature (JEL)","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php"},{"topic":"C25 - Discrete Regression and Qualitative Choice Models - Discrete Regressors - Proportions - Probabilities","vocab":"Journal of Economic Literature (JEL)","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php"},{"topic":"C53 - Forecasting and Prediction Methods - Simulation Methods","vocab":"Journal of Economic Literature (JEL)","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php"},{"topic":"O10 - Economic Development - General","vocab":"Journal of Economic Literature (JEL)","uri":"https:\/\/www.aeaweb.org\/econlit\/jelCodes.php"}],"abstract":"Globally, more than 130 million people are estimated to be in food crisis. These humanitarian disasters are associated with severe impacts on livelihoods that can reverse years of development gains. The existing outlooks of crisis-affected populations rely on expert assessment of evidence and are limited in their temporal frequency and ability to look beyond several months. This paper presents a statistical foresting approach to predict the outbreak of food crises with sufficient lead time for preventive action. Different use cases are explored related to possible alternative targeting policies and the levels at which finance is typically\nunlocked. The results indicate that, particularly at longer forecasting horizons, the statistical predictions compare favorably to expert-based outlooks. The paper concludes that statistical models demonstrate good ability to detect future outbreaks of food crises and that using statistical forecasting approaches may help increase lead time for action.","time_periods":[{"start":"2007","end":"2020","cycle":""}],"coll_dates":[{"start":"2007","end":"2020","cycle":""}],"nation":[{"name":"Afghanistan","abbreviation":"AFG"},{"name":"Burkina Faso","abbreviation":"BFA"},{"name":"Chad","abbreviation":"TCD"},{"name":"Congo, Dem. Rep.","abbreviation":"COD"},{"name":"Ethiopia","abbreviation":"ETH"},{"name":"Guatemala","abbreviation":"GTM"},{"name":"Haiti","abbreviation":"HTI"},{"name":"Kenya","abbreviation":"KEN"},{"name":"Malawi","abbreviation":"MWI"},{"name":"Mali","abbreviation":"MLI"},{"name":"Mauritania","abbreviation":"MRT"},{"name":"Mozambique","abbreviation":"MOZ"},{"name":"Niger","abbreviation":"NER"},{"name":"Nigeria","abbreviation":"NGA"},{"name":"Somalia","abbreviation":"SOM"},{"name":"South Sudan","abbreviation":"SSD"},{"name":"Sudan","abbreviation":"SDN"},{"name":"Uganda","abbreviation":"UGA"},{"name":"Yemen, Rep.","abbreviation":"YEM"},{"name":"Zambia","abbreviation":"ZMB"},{"name":"Zimbabwe","abbreviation":"ZWE"}]},"method":{"data_collection":{"coll_situation":"Data compiled from multiple sources, including surveys and satellite imagery"}}},"schematype":"survey"}