We use the high-frequency, decentralized implementation of Stay-at-Home orders in the U.S. to disentangle the labor market effects of SAH orders from the general economic disruption wrought by the COVID-19 pandemic. We find that each week of SAH exposure increased a state's weekly initial unemployment insurance (UI) claims by 1.9% of its employment level relative to other states. A back-of-the-envelope calculation implies that, of the 17 million UI claims between March 14 and April 4, only 4 million were attributable to SAH orders. We present a currency union model to provide conditions for mapping this estimate to aggregate employment losses.

We provide a guide to using autoregressive distributed lag models for impulse response estimations with an identified structural shock or an external instrument for the shock. We illustrate how specifications widely used in practice can lead to inconsistent and inefficient estimators. We further review empirical results from previous papers and show that some results appear to be statistical artefacts. We propose a simple method to avoid such a false conclusion from biases or large standard errors and obtain consistent and precise estimates.