Lower mobility, in theory, lowers the number of cases and may also possibly affect the likelihood of imposing restrictions. This should induce a downward bias to both coefficients on the right-hand side and our results should be taken as a lower bound. As we are working with a larger group of countries, we do not attempt to instrument which would not be feasible in most, but we lag both explanatory variables 1 period. The results change modestly in magnitude, with even more lags, but the overall patterns remain consistent.

**Table 1: Mobility, COVID Cases and NPIs, United States**

|<br>|(1)<br>Workplace|(2)<br>Workplace|(3)<br>Workplace|(4)<br>Workplace|(5)<br>Residential|(6)<br>Residential|
|---|---|---|---|---|---|---|
|Close N.E. business|-4.373***|-5.281***|-2.071|-3.075***|2.047***|0.830*|
||(1.235)|(0.689)|(2.006)|(1.051)|(0.356)|(0.463)|
|Log cases|-4.502***|-1.291***|-2.904***|-1.284***|0.551***|0.577***|
||(1.153)|(0.437)|(0.915)|(0.385)|(0.185)|(0.161)|
|Log national cases|-2.671**|-3.038***|-2.193**|-2.837***|0.957***|0.875***|
||(1.063)|(0.425)|(0.860)|(0.383)|(0.225)|(0.177)|
|Close K-12|||-11.975***|-0.866||-0.092|
||||(1.704)|(1.169)||(0.407)|
|Stay home/SIP|||-3.289<br>(2.630)|-3.855***<br>(1.134)||2.144***<br>(0.485)|
|Constant|24.030***|10.503***|18.981***|9.574***|-4.472***|-3.986***|
||(5.191)|(1.756)|(4.526)|(1.509)|(1.250)|(0.968)|
|Time FE|No|Yes|No|Yes|Yes|Yes|
|Day of the week FE|Yes|No|Yes|No|No|No|
|State FE|Yes|Yes|Yes|Yes|Yes|Yes|
|# of States|51|51|51|51|51|51|
|Obs.|1189|1189|1189|1189|1189|1189|
|R-squared<br>|0.836|0.963|0.875|0.964|0.956|0.959|

Notes: Regression of Google measure of work/residential related mobility on NPIs, the log of cases, the log of national cases, state, days of the week/time fixed effects. Robust clustered errors are in parenthesis. *** p<0.01, ** p<0.05, *<0.1 Table 1 suggests that both effects are at work although with surprising relative contributions. Columns 1-2 present the impact on mobility of just business closure restrictions, the log of local cases and the log of national cases with and without time fixed effects. Of the roughly 60-point decline in mobility seen in Figure 1, roughly 5 points appear due to official workplace closures. This is of the order of magnitude identified in previous studies on other measures of mobility. However, the component due to case incidence, both national and local appears to be able to account for much of the fall in mobility by itself. For instance, with the 10-log point increase in local cases in Figure 1, roughly 43 points (2/3) of the fall in mobility are accounted for, and more without FE by “voluntary” self-restriction.

Columns 3 and 4 introduce two other NPIs- school closures for K-12 and stay-at-home/shelter in place orders. The impact of imposing restrictions on businesses falls significantly, suggesting that, as expected, it was picking up the effects of other correlated measures. The three together can account for almost 8 points of the fall in mobility. This remains dwarfed by the roughly 40% arising from the number of local and national cases whose impact stays roughly the same. Hence, it appears that in the United States, the largest effect is due to protective 6