Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/204638 
Year of Publication: 
2019
Series/Report no.: 
ZEF Discussion Papers on Development Policy No. 271
Publisher: 
University of Bonn, Center for Development Research (ZEF), Bonn
Abstract: 
With increasing Internet user rates across Africa, there is considerable interest in exploring new, online data sources. Particularly, search engine metadata, i.e. data representing the contemporaneous online-interest in a specific topic, has gained considerable interest, due to its potential to extract a near real-time online signal about the current interest of a society. The objective of this study is to analyze whether search engine metadata in the form of Google Search Query (GSQ) data can be used to improve now-casts of maize prices in nine African countries, these are Ethiopia, Kenya, Malawi, Mozambique, Rwanda, Tanzania and Uganda, Zambia and Zimbabwe. We formulate as benchmark an auto-regressive model for each country, which we subsequently augment by two specifications based on contemporary GSQ data. We test the models in in-sample, and in a pseudo out-of-sample, one-step-ahead now-casting environment and compare their forecasting errors. The GSQ specifications improve the now-casting fit in 8 out 9 countries and reduce the now-casting error between 3% and 23%. The largest improvement of maize price now-casts is achieved for Malawi, Kenya, Zambia and Tanzania, with improvements larger than 14%.
Subjects: 
Food Security
Maize Prices
Search Query Volume
Internet
Early Warning Systems
JEL: 
Q18
Q11
O33
Document Type: 
Working Paper

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