Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/216657
Authors: 
Farzanegan, Mohammad Reza
Feizi, Mehdi
Sadati, Saeed Malek
Year of Publication: 
2020
Series/Report no.: 
Joint Discussion Paper Series in Economics No. 17-2020
Abstract: 
Soon after the first identified COVID-19 cases in Iran, the spread of the new Coronavirus has affected almost all its provinces. In the absence of credible data on people's unfiltered concerns and needs, especially in developing countries, Google search data is a reliable source that truthfully captures the public sentiment. This study examines the within province changes of confirmed cases of Corona across Iranian provinces from 19 Feb. 2020 to 9 March 2020. Using real-time Google Trends data, panel fixed effects, and GMM regression estimations, we show a robust negative association between the intensity of search for disinfection methods and materials in the past and current confirmed cases of the COVID-19 virus. In addition, we find a positive and robust association between the intensity of the searches for symptoms of Corona and the number of confirmed cases within the Iranian provinces. These findings are robust to control for province and period fixed effects, province-specific time trends, and lag of confirmed cases. Our results show how not only prevention could hinder affection in an epidemic disease but also prophecies, shaped by individual concerns and reflected in Google search queries, might not be self-fulfilling.
Subjects: 
Google Trends
COVID-19
Iran
epidemic disease
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.