The long-term consequences of the global 1918 influenza pandemic: A systematic analysis of 117 IPUMS international census data sets

Sebastian Vollmer and Juditha Wójcik ()

No 1721, Working Papers from Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz

Abstract: Several country-level studies, including a prominent one for the United States, have identified long-term effects of in-utero exposure to the 1918 influenza pandemic (also known as the Spanish Flu) on economic outcomes in adulthood. In-utero conditions are theoretically linked to adult health and socioeconomic status through the fetal origins or Barker hypothesis. Historical exposure to the Spanish Flu provides a natural experiment to test this hypothesis. Although the Spanish Flu was a global phenomenon, with around 500 million people infected worldwide, there exists no comprehensive global study on its long-term economic effects. We attempt to close this gap by systematically analyzing 117 Census data sets provided by IPUMS International. We do not find consistent global long-term effects of influenza exposure on education, employment and disability outcomes. A series of robustness checks does not alter this conclusion. Our findings indicate that the existing evidence on long-term economic effects of the Spanish Flu is likely a consequence of publication bias.

Keywords: Spanish Flu; 1918 Influenza Pandemic; Fetal Origins Hypothesis (search for similar items in EconPapers)
JEL-codes: I15 N30 O57 (search for similar items in EconPapers)
Pages: 84 pages
Date: 2017-12-06
New Economics Papers: this item is included in nep-hea and nep-his
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Citations: View citations in EconPapers (5)

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https://download.uni-mainz.de/RePEc/pdf/Discussion_Paper_1720.pdf First version, 2017 (application/pdf)

Related works:
Working Paper: The Long-term Consequences of the Global 1918 Influenza Pandemic: A Systematic Analysis of 117 IPUMS International Census Data Sets (2017)
Working Paper: The long-term consequences of the global 1918 influenza pandemic: A systematic analysis of 117 IPUMS international census data sets (2017)
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