Evaluating Amazon effects and the limited impact of COVID-19 with purchases crowdsourced from US consumers - Alex Berke; Dana Calacci; Alex Pentland; Kent Larson (PLoS ONE, 2025)
We leverage a recently published dataset of Amazon purchase histories, crowdsourced from thousands of US consumers, to study changes in online purchasing behaviors over time, how changes vary by demographics, the impact of COVID-19, and relationships between online and offline retail. This work provides a case study in how consumer-level purchases data can reveal purchasing trends beyond those available from aggregate metrics. For example, in addition to analyzing spending behavior, we develop new metrics to quantify changes in consumers’ online purchase frequency and the diversity of products purchased, to better reflect the growing ubiquity of online retail. Between 2018 and 2022 these consumer-level metrics grew on average by more than 85%, peaking in 2021. We find a steady upward trend in individuals’ online purchasing prior to COVID-19, with a significant increase in the first year of COVID, but without a lasting effect. Purchasing behaviors in 2022 were no greater than the result of the pre-pandemic trend. We also find changes in purchasing significantly differ by demographics, with different responses to the pandemic. We further use the consumer-level data to show substitution effects between online and offline retail in sectors where Amazon heavily invested: books, shoes, and grocery. Prior to COVID we find year-to-year changes in the number of consumers making online purchases for books and shoes negatively correlated with changes in employment at local bookstores and shoe stores. During COVID we find online grocery purchasing negatively correlated with in-store grocery visits. This work demonstrates how crowdsourced, open purchases data can enable economic insights that may otherwise only be available to private firms.