Assessing the Payroll Protection Program: A Framework and Preliminary Results - John M. Barrios; Michael Minnis; William Minnis; Joost Sijthoff (University of Chicago Booth School of Business, Stigler Center for the Study of the Economy and the State, 2020)
We develop a simple model to predict requests for the Payroll Protection Program (PPP) and compare these predictions to the actual allocations. The model suggests the amount of requested funds could total 750 billion, though this is likely a high watermark conditional on several assumptions. The model also generates expectations by industry, state, and firm size, allowing us to assess model performance in the cross-section. The model performs reasonably well. Through the May 1 funding, the state-level cross-sectional model has an R 2 of 99.3% and the average absolute prediction error across states is 6.4%. Interestingly, the prediction errors from the first funding round are significantly negatively correlated to the errors in the second funding round, revealing that the allocations were systematically different in the two rounds. Ultimately, the results suggest that the payroll-based model predicts PPP allocations well and that the funds were allocated as designed. One potential inference from these results is that critique about PPP allocations should be focused on program design rather than program execution. This analysis should be useful for subsequent studies assessing the performance of the PPP.
Keywords: 330; Coronavirus; Öffentliches Kreditprogramm; KMU; USA