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.

More Research From These Scholars

BFI Working Paper May 15, 2018

Occupational Licensing and Accountant Quality: Evidence from the 150-Hour Rule

John Barrios
Topics:  Employment & Wages, Financial Markets, Fiscal Studies, Monetary Policy, Industrial Organization
BFI Working Paper Apr 6, 2020

Risk Perception Through the Lens of Politics in the Time of the COVID-19 Pandemic

John Barrios, Yael V. Hochberg
Topics:  COVID-19
BFI Working Paper May 28, 2020

Civic Capital and Social Distancing during the COVID-19 Pandemic

John Barrios, Efraim Benmelech, Yael V. Hochberg, Paola Sapienza, Luigi Zingales
Topics:  COVID-19