national sales in food and drug stores and 32% of national sales in mass merchandise stores. The finest location of each store is given at the county level. We only use stores that appear throughout the entire sample period such that store entry and exit do not affect results. Among the stores in the sample in 2006, 84% remain throughout the entire sample period. A large number of products from all Nielsen-tracked categories are included in the data: 2.6 million universal product codes (UPCs) in total aggregated into around 1,100 product modules, which are further aggregated up to 125 product groups.

Although alternate price indices released by government agencies do exist, they have limitations that render them less suitable for our analysis, especially due to sampling error. These limitations are outlined in Beraja et al. (2019). Therefore, this paper uses price indices constructed from micro data.

The advantage of using the retail scanner data as opposed to the Nielsen Consumer Panel is that a wider range of goods is observed at higher frequencies and quantities. Scanner price indices are constructed as in Beraja et al. (2019). We briefly describe the approach they adopt in Appendix Section D and refer interested readers to their paper for details. Leung (2021) also investigates the behavior of the constructed indices and finds grocery store price indices are quite similar to the CPI city-level food-price indices, which are published for around 20 sample areas in the US. We construct a range of additional price indices using alternative methods, which give nearly identical results.

Retail stores need to apply to the FNS to accept SNAP payments. We obtain a panel of SNAP participating retail stores from the FNS and are able to match this panel with Nielsen stores. Our string-matching algorithms match over 90% of Nielsen stores to SNAP participating stores across store types, which implies that almost all stores in our data participate in SNAP.<sup>23</sup> However, we focus on results from grocery stores for several reasons. First, as shown in DellaVigna and Gentzkow (2019) (hereafter DVG) and Leung (2021), not only do drug and merchandise chains mostly implement chain-level pricing, but also the chains tend to locate stores across the entire US, such that local price responses to local policy changes are negligible. We show this is indeed the case in Section 4.1. On the other hand, we show in Appendix Section H that a large amount of grocery stores belong to chains that price flexibly or chains that are located only in a few states, which implies that most grocery stores engage in regional pricing.<sup>24</sup> Second, grocery stores derive the highest proportion of revenue from food, at around 77% based on the retail scanner data.

> 23Since the string-matching algorithm is fuzzy, this implies that the unmatched stores may still participate in SNAP. This would make the sample of non-SNAP stores even smaller, making it difficult to use it as a control group.

> 24Specifically, Leung (2021) finds that 72% of grocery stores in his sample belong to chains that have a flexibility measure above or at the median across all chains or are located in one or two states.

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