Can patterns of household purchases predict the outcome of US presidential elections?

Sabina Crowe, Michael Gmeiner and Sebastian Ille

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: We use NielsenIQ US retail scanner data to show that changes in sales patterns can be used to predict US presidential election results at the county level. Using a probit model, we regress 2016 election results against sales of various products six months prior to the election. We employ the results and the sales data for 2020 to forecast presidential election results in the same year. Comparison to actual election outcomes shows that our work correctly predicts election results in 86.47% of cases across 2, 602 US counties. We further study how changes in the consumption of certain goods influences voter turnout as well as Democrat and Republican votes.

Keywords: elections; prediction; preferences; consumption; permission request sent to publisher (search for similar items in EconPapers)
JEL-codes: D70 E20 (search for similar items in EconPapers)
Pages: 7 pages
Date: 2024-09-30
New Economics Papers: this item is included in nep-pol
References: View complete reference list from CitEc
Citations:

Published in Economics Bulletin, 30, September, 2024, 44(3), pp. 1181 - 1187. ISSN: 1545-2921

Downloads: (external link)
http://eprints.lse.ac.uk/126511/ Open access version. (application/pdf)

Related works:
Journal Article: Can patterns of household purchases predict the outcome of US presidential elections? (2024)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:126511

Access Statistics for this paper

More papers in LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library LSE Library Portugal Street London, WC2A 2HD, U.K.. Contact information at EDIRC.
Bibliographic data for series maintained by LSERO Manager ().