Reconciling Estimates across Samples and Exposure Measures. Table 7 presents employment estimates across the Homebase and OI samples and compares estimates using the PPPE and predicted PPPE instruments. We estimate IV regressions for each sample and instrument, focusing on the local projection estimates in week 23 (June 21–27), the week with the largest coefficient in the Homebase sample, and pooled across all weeks. The endogenous variable is the fraction of establishments in an area that received PPP as of the end of the first round. We also present an industry-reweighted Homebase analysis. The Homebase industry categories are coarsely defined and do not have a one-to-one mapping with NAICS industry categories. Thus, each Homebase category can potentially span multiple two-industry NAICS industries. We use the self-reported industry category of each PPP applicant recorded in their PPP applications to create a mapping between the Homebase industry and the two-digit NAICS industries. Following DiNardo et al. (1996), we then reweight observations to match the less-than-500-worker-establishment-count distribution across industries in the Census SUSB data. Concretely, this reweighting downweights bars and restaurants relative to other industries. All specifications include state (OI) or state-by-industry (Homebase) fixed effects and pre-policy controls. Statistical tests within a sample are computed via simultaneous GMM with standard errors clustered at the state level. Confidence intervals report the difference in coefficients across samples, computed using bootstrapped coefficient distributions. ***, **, and *, represent statistical significance at 1%, 5%, and 10% levels, respectively.