local shocks. we plot the distribution of flexibility measures as well as number of states each chain is in across stores by store type in Appendix Figure O11. Both drug and merchandise stores belong to a few large chains that price rigidly, whereas a large amount of grocery stores belong to chains that price flexibly or chains that are located only in a few states. Over 50 grocery chains are in the sample whereas both drug and merchandise stores come from around five retail chains each.<sup>66</sup> This implies most grocery stores are engaging in local pricing, whereas drug and merchandise stores are not.

# **I Firm Dynamics and Market Structure**

To investigate the impact of SNAP-benefit changes on firm dynamics and market structure, we use two additional data sets. First, we use FNS data on a yearly panel of retail stores that participate in SNAP from 2006 to 2015. For each store, we observe the years in which it is contained in the sample, that is, registered with the FNS to sell to SNAP consumers. We also observe the exact authorization date, the name and address of the store including the county it is located in, and the store type. To focus on the grocery industry, we restrict our sample to stores that are classified as grocery stores, supermarkets, or superstores. We then construct five county-level outcomes of interest from the data: (1) authorization rate, the number of newly authorized stores per month divided by the total number of stores, (2) entry rate, the number of new stores in the sample each year divided by the total number of stores, (3) exit rate, the number of stores that exit the sample the next year divided by the total number of stores the previous year, (4) reallocation rate, the sum of entry and exit rates, and (5) log count per population, the log of the total number of stores per population. Second, we use County Business Patterns (CBP) data from the Census Bureau to obtain two additional annual outcomes at the county-level: (6) log establishments per population, the log of the number of establishments per population, and (7) HHI, the sum of squared market shares calculated using employment size as a measure of firm size.<sup>67</sup> We then estimate equation (3) at the county-month level using the seven outcomes, again using data from 2007 to 2010 to focus on the periods in which SNAP benefits were increased.

As shown in Appendix Table N11, the estimated effect on entry and exit is positive but almost all of the estimates are statistically insignificant except the reallocation rate, suggesting that SNAP-benefit changes had little impact on entry and exit margins and market structure. A 10% increase in benefits per population increases the reallocation rate by 0.01, compared to a mean of around 0.17 for grocery stores in the SNAP panel. One possible reason for these small effects is that most grocery stores already accepted SNAP before our sample period. Many non-grocery stores also started accepting SNAP at the chain level over this time period so the competitive landscape may have changed in ways not fully captured in our analysis.

> 66Data from the Economic Census also show the grocery store industry is much less concentrated and less dominated by chains than the drug and merchandise store industries. For example, market share of the 4 largest firms is 30.7%, 54.4%, and 73.2% for grocery stores, drug stores, and merchandise stores respectively in 2007.

> 67Since the CBP only provides employment size in 13 size brackets, we use the mid-point of each bracket to construct the HHI.

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