THIS PAGE INCLUDES OUR ORIGINAL ESTIMATES. WE HAVE NOT UPDATED THIS PAGE WITH ADDITIONAL DATA OR ANALYSIS SINCE THE SPRING.
We use this spreadsheet to estimate the potential size of the Small Business Administration's (SBA's) Paycheck Protection Program. We use payroll data from the Statistics of US Businesses (here) and sole proprietorship net income from the IRS's Statistics of Income (here). Download the spreadsheet to see our detailed assumptions and calculations and make your own assumptions.
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WE POSTED A WHITE PAPER WITH A MORE FORMAL DISCUSSION, ESTIMATES, AND TESTS. FIND IT HERE. THE ANALYSIS IN THIS PAPER SUPERSEDES THE WEBSITE CONTENT FOR NOW.
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*UPDATED TO REFLECT LOAN APPROVALS TOTALING $342 BILLION AS OF APRIL 16 REPORT LOCATED HERE.
*UPDATED APRIL 22 TO ADD NEW SCENARIOS TO CONSIDER NEW INFORMATION AND CONFIDENCE INTERVALS AROUND ESTIMATES AND ASSUMPTIONS.
--> Spreadsheet *here*. Click the link and then download a local copy if you want to work with the spreadsheet.
IMPORTANT NOTES - PLEASE READ: The primary purpose of this tool is to help provide a framework for understanding the potential magnitude of the PPP, not to say how big the program "should be." The estimates require multiple very important assumptions, each with wide confidence intervals. We encourage you to download the spreadsheet and make your own assumptions.
We updated the estimates on April 19th based on the SBA update of PPP applications processed through April 16 (found here). The primary adjustments to our estimates relate to which industries are eligible. The original version of our estimates assumed that firms in industries 52 (finance and insurance), 53 (real estate), and 55 (management of companies) would be ineligible based on SBA lending rules (found here). However, the SBA is accepting (at least some) applications from businesses in these industries, so we now have changed the assumed percentages to 50%, 50%, and 100%, respectively. We are assuming a figure lower than 100% for industries 52 and 53 under the previously maintained assumption that some businesses in these industries are ineligible, or at least do not apply to receive the funds. We have previously called this our maximum value of the PPP and it totals approximately $730 billion if all firms apply and are funded to their maximum amounts. Below we also provide a potentially bigger scenario with other assumptions.
Based on (i) potential updates to the proposed program (e.g., increasing the PPP by $310 billion); (ii) updated insights/views on the potential firms that could be receiving funds; and (iii) a desire to provide some scenarios which illustrate the sensitivity of the values to certain outcomes, we now provide two new scenarios: a "bigger program" amount which reaches almost $800 billion and a "what it would take" amount which provides a set of assumptions which sum to the proposed $660 billion allocation. Before discussing each scenario's assumptions, here is a list of some of the important assumptions/factors/unknowns in attempting to estimate any value:
Factors which could push the value higher:
1) Our starting point is a static model based on 2017 SUSB data, wherein we assume that firms do not react to policy changes. But firms could react to the policy intervention to make it more likely that they fit into the program. Essentially, a concern is how many companies have found ways to game the system? For example, have companies found ways to dis-aggregate into subsidiaries to look smaller? Our assumption is that this is difficult to do if companies have to show their 2019 payroll records (including number of employee counts), but firms can potentially be clever. If large firms break themselves up into smaller entities, this would increase participation, which is something not reflected in any of the estimates.
3) For simplicity, we used the 500 employee threshold across all industries, but some industries have alternative thresholds which make bigger firms eligible. Re-calibrating the model to adjust for these amounts increases the potential amount by $12 billion according to analysis Joost Sijthoff. This additional amount is possibly too much to add because it does not consider the $10 million loan cap (which may be more binding in these larger companies), but we are unable to estimate how binding this might be.
4) We assumed 50% participation for Finance & Insurance and Real Estate industries under the assumption that some of these entities were ineligible or not willing to participate. The run-rate for Finance & Insurance through the first round seems to support this assumption, but Real Estate industries are receiving funds at a higher rate through the first round. So the question is how intensive are these firms going to participate in the second round? Were they quick to apply so there are fewer companies in these industries for the next round or are they going feel public relations concerns from having done this (thus, in both cases the application rate will be lower in the second round) or is this rate going to be just as high or even higher? We do not know how large the participation rate for these industries is.
5) We assumed a 5% inflation factor from 2017 to 2019. This could be insufficient to pick up job and wage increases through 2019 and therefore could be higher.
Factors which could push the value lower:
1) On April 23, Treasury emphasized "economic need" in the FAQ document. If companies re-assess their economic need seriously, this could (perhaps substantially) reduce the value.
2) Affiliation is a significant issue that we cannot address. The very aggregated data cannot account for firms disqualified for affiliation rules. Specifically, we may be including payroll for firms in the SUSB which are ineligible because they are controlled by other entities which, when aggregated, surpass the 500 employee threshold (e.g., companies backed by private equity). Note this is the counter-point to the above question about companies gaming the system by appearing to look smaller.
3) SUSB data aggregates firms up to the state level, not national. So, for example, a company that operates establishments in multiple states could look like multiple smaller firms in the data set. The full consolidated company is ineligible to apply (if totaling more than 500 employees across all establishments) but their payroll in the SUSB data set would be included in the smaller firm size categories, in our understanding. If true, this inflates the payroll in the smaller firm size categories in the SUSB, potentially overstating the eligible payroll.
4) We assumed all companies apply but some aren’t going to, either because they don’t feel it’s right, don’t have employees any more, are frustrated by the process, etc. One study indicates that 13% of small businesses do not intend to participate. If the participation rate is ultimately less than 100%, this will decrease the figure. More broadly, how are the results of the first round of funding going to affect the behavior of the second round applicants? Are some bigger firms going to avoid the public relations issues and apply less aggressively? Will they see that their peers did it and decide to do so more aggressively? Will there be new paperwork put in place which discourages applicants? Are applicants frustrated? Have some more companies already lost their employees and we see less intensive demand in the second round?
5) Because the SUSB does not provide company level or wage level data, we cannot account for the limits in wages ($100K per employee) and loan amounts ($10 million per firm) in calculating PPP loan amounts because we do not have firm level data. Presumably these constraints are binding for some companies which will result in the actual number being less than what is predicted. We do not know the magnitude of this issue.
6) We essentially included all sole proprietor net income, but at least some individuals who appear as sole proprietors have regular other jobs and the sole proprietor income is side work (e.g., consulting) for which these individuals will not apply for PPP (possibly). Therefore, some portion of this will not be claimed. But more generally, there the extent to which sole proprietor income will be claimed is very much unknown.
7) Firms owned by individuals with criminal backgrounds or have delinquent SBA loans are ineligible and we do not know the extent of this issue.
Assumptions for the "bigger program" scenario:
100% participation rates for all industries (i.e., moving Finance and Insurance and Real Estate to 100%)
Increases inflation from 5% to 7%.
Includes an additional $12 billion to account for the industry-specific SBA size thresholds.
NOTE: While this includes some assumptions which we know are not likely correct (e.g., the assumption that no company is constrained by the $10 million loan threshold) it still does not account for possible gamesmanship by companies.
Assumptions for the "what it would take to hit $660 billion" scenario:
Reduces company participation rates to 90% (except Finance and Insurance and Real Estate, which stay at 50%) and sole proprietor participation rates to 80% for all industries. This assumes a slightly lower participation rate for sole proprietors under the assumption that more of them are only side operations and do not apply. Participation rates less than 100% could be driven by companies not choosing to apply or companies receiving less than full allocation because of the $100K per employee and $10 million per company cap.
Inflation remains at the 5% rate.
Includes an additional $12 billion to account for the industry-specific SBA size thresholds.
See related opinion and discussion:
Becker-Friedman Institute: https://bfi.uchicago.edu/insight/blog/key-economic-facts-about-covid-19/#ppp-totals
Additional economic analysis surrounding COVID-19:
https://bfi.uchicago.edu/insight/blog/key-economic-facts-about-covid-19/
https://review.chicagobooth.edu/collections/covid-19-crisis
Here are some other resources and articles (unaffiliated and not endorsed):
Website collecting data from companies applying for PPP loans: www.covidloantracker.com.
Forbes:
Tony Nitti discussing potential issues reconciling loan forgiveness: https://www.forbes.com/sites/anthonynitti/2020/04/15/ten-things-we-need-to-know-about-paycheck-protection-program-loan-forgiveness/#10e18b233291
Bruce Brumberg, a contributor at Forbes has a number of articles.
More analysis and predictions about the SBA PPP: https://www.sbalenders.com/conclusions-drawn-349-billion-sba-loan-program/
Comments? Questions? Suggestions? Email: michael.minnis [AT] chicagobooth.edu
Co-developers: John Barrios, Petro Liswosky, Michael Minnis, and William Minnis