Connect with us

Uncategorized

What Drove Racial Disparities in the Paycheck Protection Program?

Numerous studies of the Paycheck Protection Program (PPP), which provided loans to small businesses during the COVID-19 pandemic, have documented racial…

Numerous studies of the Paycheck Protection Program (PPP), which provided loans to small businesses during the COVID-19 pandemic, have documented racial disparities in the program. Because publicly available PPP data only include information on approved loans, prior work has largely been unable to assess whether these disparities were driven by borrower application behavior or by lender approval decisions. In this post, which is based on a related Staff Report and NBER working paper, we use the Federal Reserve’s 2020 Small Business Credit Survey to examine PPP application behavior and approval decisions and to study the strengths and limitations of fintech lenders in enhancing access to credit for Black-owned businesses.

An Overview of the Paycheck Protection Program

Initially authorized in March 2020 by the CARES Act, the PPP offered qualifying small businesses nonrecourse loans with standardized terms and the possibility of full or partial forgiveness. Loans were originated and underwritten by a variety of financial intermediaries, including depository institutions, fintechs, and community development financial institutions (CDFIs). The PPP imposed few eligibility requirements, as one of the program’s goals was to include the vast majority of small businesses. There were, however, documentation requirements that proved challenging for many businesses. Ultimately, the PPP provided $800 billion in loans.

Why Were Black-Owned Firms Less Likely to Receive PPP?

To demonstrate the accuracy and validity of our survey data, we first replicate the finding in prior work that Black-owned firms were less likely than white-owned firms to obtain PPP funds. In the raw survey data, we find that Black-owned firms were 25.7 percentage points less likely than white-owned firms to receive PPP loans. After using a linear regression model to control for a rich set of firm, owner, and location characteristics, we estimate that Black-owned firms were 8.9 percentage points less likely to receive PPP loans, as shown in the first blue bar in the chart below. (That is, about 17 percentage points of the difference is explained by these characteristics.) Hispanic-owned firms were also substantially less likely to obtain PPP funds than observably similar white-owned firms, a disparity we estimate to be 6.1 percentage points.

Racial Disparities in the Paycheck Protection Program

Percentage points

Sources: 2020 Small Business Credit Survey (fielded in September and October 2020); authors’ calculations.
Notes: For each group, the take-up (application) disparity is calculated as the difference between the group’s take-up rate (application rate) and the take-up rate (application rate) of white-owned firms, controlling for firm, owner, and location characteristics. For each group, the approval disparity is calculated as the difference between the application and take-up disparities.

How much of the 8.9 percentage point disparity in take-up rates between Black- and white-owned firms is driven by a disparity in the propensity to apply for PPP loans? After controlling for observable characteristics, we find that Black-owned firms were 4.9 percentage points less likely to apply for a PPP loan (first gold bar in the above chart). The application disparity can therefore explain about 55 percent (4.9/8.9) of the take-up disparity between observably similar Black- and white-owned firms, while the disparity in approval rates explains the rest.

In the paper, we show that the lower propensity of Black-owned firms to apply for PPP loans is best explained by the administrative burdens of the program, which involved a complex set of documentation requirements and loan amount calculations that were problematic for many small business owners. It is likely that Black-owned firms experienced more difficulties with these administrative burdens; data from the 2021 Small Business Credit Survey show that Black-owned firms are significantly less likely than white-owned firms to seek business advice from professionals such as lawyers, accountants, and consultants, even after controlling for detailed firm, owner, and location characteristics. Moreover, we find that Black-owned firms were more likely than observably similar white-owned firms to say they did not apply because the process was too confusing (5.8 percentage point differential), they were unaware of the program (4.7 percentage point differential), or they missed the program deadline (7.4 percentage point differential).

Why Were Black-Owned Firms Less Likely to Use Banks?

Several previous papers have found that Black-owned PPP recipients were less likely than white-owned recipients to have obtained their loans from banks and more likely to have obtained them from fintech lenders. One paper has argued that Black-owned firms were less likely to get loans from banks because they face larger disparities in approval rates at banks than at fintechs. If true, this would suggest that the automated loan processing used by fintechs helps reduce the scope for racially biased lending decisions.

Strikingly, our results show that application behavior, not differences in approval disparities, entirely explains why Black-owned PPP borrowers tend to have received their loans from fintechs and not from banks, as shown in the chart below. Black-owned firms were 9.9 percentage points less likely than observably similar white-owned firms to apply to banks (first blue bar) and 7.8 percentage points more likely to apply to fintechs (first gold bar), but racial disparities in approval rates were very similar at banks (7.4 percentage points, second blue bar) and fintechs (8.4 percentage points, second gold bar).

Disparities in Bank Usage

Percentage points

Sources: 2020 Small Business Credit Survey (fielded in September and October 2020); authors’ calculations.
Notes: For each lender type, the application disparity is calculated as the difference between the rates at which Black- and white-owned PPP applicants applied to that type of lender, controlling for firm, owner, and location characteristics. For each lender type, the approval disparity is calculated as the difference between the rates at which Black- and white-owned firms that applied to that lender type were approved by that lender type, controlling for firm, owner, and location characteristics.

Why were Black-owned firms less likely than white-owned firms to apply to banks? As shown below, we find that Black-owned firms were particularly unlikely to apply to banks located in counties in which survey responses of white residents exhibit stronger indications of explicit or implicit bias toward Black people. Numerous studies have correlated these measures of racial bias, from Harvard University’s Project Implicit, with racial disparities in a variety of contexts. Our findings suggest either that a legacy of racial discrimination by banks discouraged Black-owned businesses from approaching banks for PPP funding, or that when they approached banks, they were discouraged from applying due to the racial animus of loan officers. In contrast, due to the automated nature of fintech lending, it is unlikely that racial animus would have limited applications by Black-owned firms to fintech lenders. It is instead likely, given our evidence that Black-owned firms experienced greater administrative burdens in the application process, that Black-owned firms preferred the more streamlined application process at fintechs.

Disparities in Bank Usage Are Larger in Biased Counties

Percentage points

Sources: 2020 Small Business Credit Survey (fielded in September and October 2020); Project Implicit (Harvard University); authors’ calculations.
Notes: For each level of implicit racial bias (low, average, high), the application disparity is calculated as the difference between the rates at which Black- and white-owned PPP applicants in a county with that level of racial bias applied to banks, controlling for firm, owner, and location characteristics. For each level of implicit racial bias, the approval disparity is calculated as the difference between the rates at which Black- and white-owned bank applicants in a county with that level of implicit bias were approved by banks, controlling for firm, owner, and location characteristics.

The similarity of bank and fintech approval disparities, displayed on the right side of the chart above entitled “Disparities in Bank Usage,” is harder to explain at first glance. Precisely due to the automated nature of fintech lending, one would predict lower approval disparities at fintechs than at banks. While the right side of the above chart indicates that racial bias was related to approval disparities at banks, our analysis suggests that that there were other determinants of approval disparities at banks and fintechs, which we now discuss.

Understanding Approval Disparities at Banks and Fintechs

Just as the administrative burdens inherent in the PPP application process seem to have led to lower application rates by Black-owned firms, they may also have led to racial disparities in approval rates. Although the overwhelming majority of loan applications from Black- and white-owned firms were approved, there are numerous accounts of difficulties faced by small firms. These difficulties include documenting eligibility for the program, determining the loan amounts that could be requested under program rules, and substantiating requested loan amounts with required documentation (see this report, for example). Considerable anecdotal evidence, including from congressional testimony, also suggests that Black-owned firms faced greater challenges meeting documentation requirements and determining the loan amounts they could request under program rules. This interpretation is consistent with evidence cited above indicating that Black-owned firms are significantly less likely than white-owned firms to have access to advice from paid professionals. Furthermore, we show in our paper that Black-owned PPP recipients were much less likely than white-owned recipients to receive the full amount of funds they requested, indicating that they either requested more than they were eligible for or provided documentation that did not fully substantiate the requested amount.

Final Thoughts

The finding that PPP approval disparities were similar in magnitude at banks and fintechs raises important questions about the relationship between automation and racial disparities in access to credit more generally. In particular, while fintech automation may make it easier for firms to apply for loans, firms that need guidance through the application process may be disadvantaged because of the limited hands-on help fintechs provide. Because Black-owned businesses are less likely to have access to professional services providers to help with their applications, they may be particularly disadvantaged by the automated fintech application process. By contrast, the more hands-on approach taken by banks may better position them to help applicants resolve documentation gaps. But our evidence on racial bias suggests that such human involvement comes with a potentially significant cost: it may enable past or ongoing racial bias to discourage Black-owned businesses from applying for credit, and it may reduce the likelihood that their applications are approved.

Sergey Chernenko is an associate professor of management at Purdue University’s Krannert School of Management.

Nathan Kaplan is a research analyst in the Federal Reserve Bank of New York’s Research and Statistics Group.

Asani Sarkar is a financial research advisor in Non-Bank Financial Institution Studies in the Federal Reserve Bank of New York’s Research and Statistics Group.

David S. Scharfstein is the Edmund Cogswell Converse Professor of Finance and Banking at Harvard Business School.

 

How to cite this post:
Sergey Chernenko, Nathan Kaplan, Asani Sarkar, and David S. Scharfstein, “What Drove Racial Disparities in the Paycheck Protection Program?,” Federal Reserve Bank of New York Liberty Street Economics, June 1, 2023, https://libertystreeteconomics.newyorkfed.org/2023/06/what-drove-racial-disparities-in-the-paycheck-protection-program/.


Disclaimer
The views expressed in this post are those of the author(s) and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the author(s).

Read More

Continue Reading

Uncategorized

February Employment Situation

By Paul Gomme and Peter Rupert The establishment data from the BLS showed a 275,000 increase in payroll employment for February, outpacing the 230,000…

Published

on

By Paul Gomme and Peter Rupert

The establishment data from the BLS showed a 275,000 increase in payroll employment for February, outpacing the 230,000 average over the previous 12 months. The payroll data for January and December were revised down by a total of 167,000. The private sector added 223,000 new jobs, the largest gain since May of last year.

Temporary help services employment continues a steep decline after a sharp post-pandemic rise.

Average hours of work increased from 34.2 to 34.3. The increase, along with the 223,000 private employment increase led to a hefty increase in total hours of 5.6% at an annualized rate, also the largest increase since May of last year.

The establishment report, once again, beat “expectations;” the WSJ survey of economists was 198,000. Other than the downward revisions, mentioned above, another bit of negative news was a smallish increase in wage growth, from $34.52 to $34.57.

The household survey shows that the labor force increased 150,000, a drop in employment of 184,000 and an increase in the number of unemployed persons of 334,000. The labor force participation rate held steady at 62.5, the employment to population ratio decreased from 60.2 to 60.1 and the unemployment rate increased from 3.66 to 3.86. Remember that the unemployment rate is the number of unemployed relative to the labor force (the number employed plus the number unemployed). Consequently, the unemployment rate can go up if the number of unemployed rises holding fixed the labor force, or if the labor force shrinks holding the number unemployed unchanged. An increase in the unemployment rate is not necessarily a bad thing: it may reflect a strong labor market drawing “marginally attached” individuals from outside the labor force. Indeed, there was a 96,000 decline in those workers.

Earlier in the week, the BLS announced JOLTS (Job Openings and Labor Turnover Survey) data for January. There isn’t much to report here as the job openings changed little at 8.9 million, the number of hires and total separations were little changed at 5.7 million and 5.3 million, respectively.

As has been the case for the last couple of years, the number of job openings remains higher than the number of unemployed persons.

Also earlier in the week the BLS announced that productivity increased 3.2% in the 4th quarter with output rising 3.5% and hours of work rising 0.3%.

The bottom line is that the labor market continues its surprisingly (to some) strong performance, once again proving stronger than many had expected. This strength makes it difficult to justify any interest rate cuts soon, particularly given the recent inflation spike.

Read More

Continue Reading

Uncategorized

Mortgage rates fall as labor market normalizes

Jobless claims show an expanding economy. We will only be in a recession once jobless claims exceed 323,000 on a four-week moving average.

Published

on

Everyone was waiting to see if this week’s jobs report would send mortgage rates higher, which is what happened last month. Instead, the 10-year yield had a muted response after the headline number beat estimates, but we have negative job revisions from previous months. The Federal Reserve’s fear of wage growth spiraling out of control hasn’t materialized for over two years now and the unemployment rate ticked up to 3.9%. For now, we can say the labor market isn’t tight anymore, but it’s also not breaking.

The key labor data line in this expansion is the weekly jobless claims report. Jobless claims show an expanding economy that has not lost jobs yet. We will only be in a recession once jobless claims exceed 323,000 on a four-week moving average.

From the Fed: In the week ended March 2, initial claims for unemployment insurance benefits were flat, at 217,000. The four-week moving average declined slightly by 750, to 212,250


Below is an explanation of how we got here with the labor market, which all started during COVID-19.

1. I wrote the COVID-19 recovery model on April 7, 2020, and retired it on Dec. 9, 2020. By that time, the upfront recovery phase was done, and I needed to model out when we would get the jobs lost back.

2. Early in the labor market recovery, when we saw weaker job reports, I doubled and tripled down on my assertion that job openings would get to 10 million in this recovery. Job openings rose as high as to 12 million and are currently over 9 million. Even with the massive miss on a job report in May 2021, I didn’t waver.

Currently, the jobs openings, quit percentage and hires data are below pre-COVID-19 levels, which means the labor market isn’t as tight as it once was, and this is why the employment cost index has been slowing data to move along the quits percentage.  

2-US_Job_Quits_Rate-1-2

3. I wrote that we should get back all the jobs lost to COVID-19 by September of 2022. At the time this would be a speedy labor market recovery, and it happened on schedule, too

Total employment data

4. This is the key one for right now: If COVID-19 hadn’t happened, we would have between 157 million and 159 million jobs today, which would have been in line with the job growth rate in February 2020. Today, we are at 157,808,000. This is important because job growth should be cooling down now. We are more in line with where the labor market should be when averaging 140K-165K monthly. So for now, the fact that we aren’t trending between 140K-165K means we still have a bit more recovery kick left before we get down to those levels. 




From BLS: Total nonfarm payroll employment rose by 275,000 in February, and the unemployment rate increased to 3.9 percent, the U.S. Bureau of Labor Statistics reported today. Job gains occurred in health care, in government, in food services and drinking places, in social assistance, and in transportation and warehousing.

Here are the jobs that were created and lost in the previous month:

IMG_5092

In this jobs report, the unemployment rate for education levels looks like this:

  • Less than a high school diploma: 6.1%
  • High school graduate and no college: 4.2%
  • Some college or associate degree: 3.1%
  • Bachelor’s degree or higher: 2.2%
IMG_5093_320f22

Today’s report has continued the trend of the labor data beating my expectations, only because I am looking for the jobs data to slow down to a level of 140K-165K, which hasn’t happened yet. I wouldn’t categorize the labor market as being tight anymore because of the quits ratio and the hires data in the job openings report. This also shows itself in the employment cost index as well. These are key data lines for the Fed and the reason we are going to see three rate cuts this year.

Read More

Continue Reading

Uncategorized

Inside The Most Ridiculous Jobs Report In History: Record 1.2 Million Immigrant Jobs Added In One Month

Inside The Most Ridiculous Jobs Report In History: Record 1.2 Million Immigrant Jobs Added In One Month

Last month we though that the January…

Published

on

Inside The Most Ridiculous Jobs Report In History: Record 1.2 Million Immigrant Jobs Added In One Month

Last month we though that the January jobs report was the "most ridiculous in recent history" but, boy, were we wrong because this morning the Biden department of goalseeked propaganda (aka BLS) published the February jobs report, and holy crap was that something else. Even Goebbels would blush. 

What happened? Let's take a closer look.

On the surface, it was (almost) another blockbuster jobs report, certainly one which nobody expected, or rather just one bank out of 76 expected. Starting at the top, the BLS reported that in February the US unexpectedly added 275K jobs, with just one research analyst (from Dai-Ichi Research) expecting a higher number.

Some context: after last month's record 4-sigma beat, today's print was "only" 3 sigma higher than estimates. Needless to say, two multiple sigma beats in a row used to only happen in the USSR... and now in the US, apparently.

Before we go any further, a quick note on what last month we said was "the most ridiculous jobs report in recent history": it appears the BLS read our comments and decided to stop beclowing itself. It did that by slashing last month's ridiculous print by over a third, and revising what was originally reported as a massive 353K beat to just 229K,  a 124K revision, which was the biggest one-month negative revision in two years!

Of course, that does not mean that this month's jobs print won't be revised lower: it will be, and not just that month but every other month until the November election because that's the only tool left in the Biden admin's box: pretend the economic and jobs are strong, then revise them sharply lower the next month, something we pointed out first last summer and which has not failed to disappoint once.

To be fair, not every aspect of the jobs report was stellar (after all, the BLS had to give it some vague credibility). Take the unemployment rate, after flatlining between 3.4% and 3.8% for two years - and thus denying expectations from Sahm's Rule that a recession may have already started - in February the unemployment rate unexpectedly jumped to 3.9%, the highest since February 2022 (with Black unemployment spiking by 0.3% to 5.6%, an indicator which the Biden admin will quickly slam as widespread economic racism or something).

And then there were average hourly earnings, which after surging 0.6% MoM in January (since revised to 0.5%) and spooking markets that wage growth is so hot, the Fed will have no choice but to delay cuts, in February the number tumbled to just 0.1%, the lowest in two years...

... for one simple reason: last month's average wage surge had nothing to do with actual wages, and everything to do with the BLS estimate of hours worked (which is the denominator in the average wage calculation) which last month tumbled to just 34.1 (we were led to believe) the lowest since the covid pandemic...

... but has since been revised higher while the February print rose even more, to 34.3, hence why the latest average wage data was once again a product not of wages going up, but of how long Americans worked in any weekly period, in this case higher from 34.1 to 34.3, an increase which has a major impact on the average calculation.

While the above data points were examples of some latent weakness in the latest report, perhaps meant to give it a sheen of veracity, it was everything else in the report that was a problem starting with the BLS's latest choice of seasonal adjustments (after last month's wholesale revision), which have gone from merely laughable to full clownshow, as the following comparison between the monthly change in BLS and ADP payrolls shows. The trend is clear: the Biden admin numbers are now clearly rising even as the impartial ADP (which directly logs employment numbers at the company level and is far more accurate), shows an accelerating slowdown.

But it's more than just the Biden admin hanging its "success" on seasonal adjustments: when one digs deeper inside the jobs report, all sorts of ugly things emerge... such as the growing unprecedented divergence between the Establishment (payrolls) survey and much more accurate Household (actual employment) survey. To wit, while in January the BLS claims 275K payrolls were added, the Household survey found that the number of actually employed workers dropped for the third straight month (and 4 in the past 5), this time by 184K (from 161.152K to 160.968K).

This means that while the Payrolls series hits new all time highs every month since December 2020 (when according to the BLS the US had its last month of payrolls losses), the level of Employment has not budged in the past year. Worse, as shown in the chart below, such a gaping divergence has opened between the two series in the past 4 years, that the number of Employed workers would need to soar by 9 million (!) to catch up to what Payrolls claims is the employment situation.

There's more: shifting from a quantitative to a qualitative assessment, reveals just how ugly the composition of "new jobs" has been. Consider this: the BLS reports that in February 2024, the US had 132.9 million full-time jobs and 27.9 million part-time jobs. Well, that's great... until you look back one year and find that in February 2023 the US had 133.2 million full-time jobs, or more than it does one year later! And yes, all the job growth since then has been in part-time jobs, which have increased by 921K since February 2023 (from 27.020 million to 27.941 million).

Here is a summary of the labor composition in the past year: all the new jobs have been part-time jobs!

But wait there's even more, because now that the primary season is over and we enter the heart of election season and political talking points will be thrown around left and right, especially in the context of the immigration crisis created intentionally by the Biden administration which is hoping to import millions of new Democratic voters (maybe the US can hold the presidential election in Honduras or Guatemala, after all it is their citizens that will be illegally casting the key votes in November), what we find is that in February, the number of native-born workers tumbled again, sliding by a massive 560K to just 129.807 million. Add to this the December data, and we get a near-record 2.4 million plunge in native-born workers in just the past 3 months (only the covid crash was worse)!

The offset? A record 1.2 million foreign-born (read immigrants, both legal and illegal but mostly illegal) workers added in February!

Said otherwise, not only has all job creation in the past 6 years has been exclusively for foreign-born workers...

Source: St Louis Fed FRED Native Born and Foreign Born

... but there has been zero job-creation for native born workers since June 2018!

This is a huge issue - especially at a time of an illegal alien flood at the southwest border...

... and is about to become a huge political scandal, because once the inevitable recession finally hits, there will be millions of furious unemployed Americans demanding a more accurate explanation for what happened - i.e., the illegal immigration floodgates that were opened by the Biden admin.

Which is also why Biden's handlers will do everything in their power to insure there is no official recession before November... and why after the election is over, all economic hell will finally break loose. Until then, however, expect the jobs numbers to get even more ridiculous.

Tyler Durden Fri, 03/08/2024 - 13:30

Read More

Continue Reading

Trending