data from the previous year to set the SUA in the coming year. We do not find any states setting the SUA based on predictions of future utility expenses. We are also able to verify for nearly half of the states that update their base that they indeed use lagged regional CPI data on fuel and utilities to set the SUA.

Because shelter expenses account for a substantial amount of deductions and the SUA accounts for over 50% of shelter expenses, the SUA has a particularly strong effect on state-level variation in benefits per population. We plot the relationships between changes in synthetic SNAP benefits per population, SUA per population, energy usage per population and prices for the residential sector by different fuel types, and temperature by state during the Farm Bill in Appendix Figure O8. We focus on this period because the SUA only changes in October and not in April when the ARRA led to a rise in maximum benefits. We also focus on the heating and cooling SUA (HCSUA) because households predominantly use the HCSUA and the HCSUA has the largest value relative to other types of the SUA, such as telephone expenses. We see that changes in the SUA are highly positively correlated with changes in synthetic benefits per population and that these changes are also correlated with drops in temperature and rises in total energy usage from heating using sources such as electricity, LPG, and natural gas, and less so for changes in fuel prices. These figures also show variation in synthetic benefits per population that is not explained by the HCSUA, since maximum benefits are also changing during the Farm Bill. Therefore, we show our results are robust to controlling for a vector of energy usage and prices at the state-level in Table 2, because these variables could have an impact on retail prices directly. Residual policy variation should be driven by prediction errors by states in setting the SUA since most of them use lagged data, as well as idiosyncratic state-level differences in methodologies used to set the SUA. We show that results are nearly identical for states using either SUA methodology in Table N5. Since we can fully observe how states that use lagged CPI are setting the SUA and we control for contemporaneous energy prices and quantities consumed, the fact that states that recalculate the SUA using utility data also have similar price responses suggests that any unobservable variation in the SUA does not pose significant endogeneity concerns.

# **C.2 Other Adjustments to the SNAP Formula Made by Individual States**

Second, states have the flexibility to relax the federal eligibility rules through a policy called Broad-Based Categorical Eligibility (BBCE).<sup>59</sup> For instance, in the case of the gross income limit in 2015, 11 states followed the federal 130% limit, whereas 14 states extended the limit to 200%, with other states choosing a limit in between. In the case of the asset limit, 24 states waive it altogether; 11 states waive it for most households, but if a household cares for an elderly or a disabled member and the household’s gross income exceeds 200% of the federal poverty line, the states apply an asset limit of $ 3,250; New Hampshire waives the asset limit for households with children; New York waives it for households with dependents or households with earned income. All states except four allow for at least one vehicle per household to be excluded from countable assets. This implies that heterogeneity exists in the composition of SNAP participants across states.

59BBCE allows states to equate their SNAP eligibility rules with TANF eligibility rules, over which states have some jurisdiction.

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