Evaluating Amazon Effects and the Limited Impact of COVID-19 With Purchases Crowdsourced from US Consumers

TL;DR

This paper analyzes a crowdsourced dataset of US Amazon purchases from 2018 to 2022 to quantify online purchasing growth, its demographic heterogeneity, and the impact of COVID-19. By introducing metrics for online purchase frequency and product diversity and validating them against external e-commerce indicators, the authors reveal that online purchasing activity rose substantially before and during the pandemic, with COVID-19 inducing only a temporary lift that reverted to pre-existing trends by 2022. The study also documents demographic differences in online shopping responses and demonstrates substitution effects between online and offline retail in books, shoes, and groceries, including a notable shift in grocery shopping during COVID as seen in mobility data. Overall, the work shows how disaggregated, crowdsourced purchase data can yield actionable economic insights beyond aggregate statistics, while highlighting limitations in geography, timespan, and causality that call for further data and analysis.

Abstract

We leverage a recently published dataset of Amazon purchase histories, crowdsourced from thousands of US consumers, to study how online purchasing behaviors have changed over time, how changes vary across demographic groups, the impact of the COVID-19 pandemic, and relationships between online and offline retail. This work provides a case study in how consumer-level purchases data can reveal purchasing behaviors and 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 and dominance 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.

Figures (12)

• Figure 1: Quarterly Amazon net sales (North America segment) reported for investor relations and census e-commerce sales data, compared to metrics computed from our sample. Vertical blue lines indicate months Amazon Prime Day occurred. The orange line indicates March 2020, when COVID-19 had a major impact on US consumption. The sample metrics are scaled and shifted for legibility and should not be interpreted numerically.
• Figure 2: Distribution of monthly metrics across users (n=4115) for Q1 of each year. Boxplots show the medians (lines), means (triangles), first and third quartiles, and whiskers indicate the 1.5 x IQR. Outliers are omitted (see SI Tables \ref{['tab:s6']}-\ref{['tab:s8']}).
• Figure 3: Graphical event study estimating change in purchase frequency (purchase days per month) over time. Solid lines display coefficients with 95% CIs. The dashed line displays the trend estimated over the pre-pandemic period (2018-01 to 2020-02). The orange section indicates the first year of COVID (2020-03 to 2021-02). Vertical blue lines indicate months Amazon Prime Day occurred.
• Figure 4: Coefficient estimates reporting relative impact of consumer demographics on (left) purchase frequency for 2018 and 2022, and (right) change in purchase frequency from 2018 to 2022, and from one year prior to COVID to the first year of COVID. Bars indicating statistically significant values (p$<$0.05) are outlined in black.
• Figure S1: Quarterly e-commerce retail sales from the Census Bureau compared to the quarterly expenditure from our sample data, extending beyond our study period.