Affiliation:
1. Economic Research, Federal Reserve Bank of San Francisco, San Francisco, California 94105
Abstract
I apply a novel machine-learning based “weather index” method to daily store-level sales data for a national apparel and sporting goods brand to examine short-run responses to weather and long-run adaptation to climate. I find that even when considering potentially offsetting shifts of sales between outdoor and indoor stores, to the firm’s website, or over time, weather has significant persistent effects on sales. This suggests that weather may increase sales volatility as more severe weather shocks become more frequent under climate change. Consistent with adaptation to climate, I find that sensitivity of sales to weather decreases with historical experience for precipitation, snow, and cold weather events, but—surprisingly—not for extreme heat events. This suggests that adaptation may moderate some but not all the adverse impacts of climate change on sales. Retailers can respond by adjusting their staffing, inventory, promotion events, compensation, and financial reporting. This paper was accepted by Rajesh Chandy, Special Section of Management Science on Business and Climate Change. Funding: This work was supported by the National Science Foundation [Grant 0903551]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4799 .
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Subject
Management Science and Operations Research,Strategy and Management
Cited by
4 articles.
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