Minnesota data suggests that the Paycheck Protection Program (PPP) failed to effectively reach ALANA (African Latino Asian and Native American) businesses, including ALANA females. The formal banking system failed to provide adequate access to ALANA businesses. Nonprofit lenders (CDFIs and CDCs) with strong ties to the ALANA communities were key in assisting ALANA businesses access the program. Banks with strong partnerships with ALANA based nonprofit lenders showed better lending outcomes. Large banks were largely absent from ALANA lending.
Congress needs to act urgently to repurpose PPP dollars to flow directly to ALANA based CDFIs and CDCs to remedy the failure of the PPP program from to support ALANA businesses hurt badly by the Covid crisis, civil unrest and an economic depression. The $60 billion ALANA economy in Minnesota is on the verge of collapse without any serious policy or programs in place.
Analysis of Minnesota data of the PPP program provides some evidence that the program failed to effectively reach ALANA businesses. In order to assess the program impact, we analyze Scale (number of loans), Depth (dollar value of loans) and Intensity (percentage of loans) of the PPP program on ALANA businesses.
This analysis is greatly hampered by the lack of data and the way the data is formatted. Only 20 percent of PPP borrowers revealed their race or ethnicity. One data set included loan amounts (loans up to $150,000) and no business names, the other (loans over $150,000) included business names but had loan ranges instead of actual amounts. In total Minnesota disbursed $11 billion in loans to over 98,000 businesses and nonprofits.
As a result of the above data challenges, this report analyzes the subset of data which consists of ALANA and White borrowers and the lending institutions connected to them, to draw insights on the program’s effectiveness. The focus for the most part will be borrowers with loans under $150,000. We call this data, the Race Dataset of 1097 ALANA businesses in a pool of 19001 businesses who identified their race or ethnicity and $2.5 billion in loans disbursed to this group (out of a Minnesota total of $11.5 billion). ALANA businesses were only 5.8 percent of the businesses represented in this dataset and received an estimated $168 million.
This data, with all its limitations, is the best possible insight into the effectiveness of the PPP program in reaching ALANA businesses.
Key Findings
Numbers and Access
There was a very small number of ALANA businesses who accessed the PPP program. In the data set of 93 958 loans, only 19,001 reported race or ethnicity. Out of this group of 19001, only 1097 were ALANA businesses or 5.8 percent of those who revealed their race or ethnicity. Only 2 percent of this group were ALANA female businesses (312 businesses).
In perspective, 1097 ALANA businesses receiving PPP loans were only around 2 percent of all ALANA businesses in Minnesota (Survey of Business Owners, 2012). 312 ALANA female businesses receiving PPP loans represented around 2 percent of all ALANA female business in Minnesota (Survey of Business Owners, 2012).
Differences Within ALANA Groups
The PPP race data set, also revealed differences within ALANA groups accessing the PPP programs. In general Black and American Indian businesses had lower access and Asian and Latino businesses had better access. The same pattern was for ALANA female businesses. This could be connected to the sector, size, location of businesses, and access to the banking system. 45 percent of the ALANA loans were to Asian businesses, 30 percent to Latino businesses, 17 percent to Black businesses and 7 percent to American Indian businesses.
Lender Analysis
Analyzing lending to the ALANA businesses is done at three levels – Scale (number of ALANA loans), Depth (dollar value of loans) and Intensity (percentage of loans to ALANA businesses). This analysis is restricted to the Race dataset and only on banks with at least 10 loans to ALANA businesses. Out of a total of 218 lenders to ALANA businesses only 22 lenders met these criteria (see table).
Scale (Number of ALANA Loans)
Top lenders in this category were a nonprofit lender (CRF), a Fintech company (Choice Financial) and a community bank (Old National Bank).
Depth (Dollar Value of Loans)
Top lenders in this category were a Fintech company (Choice Financial), a community bank (Sunrise Bank) and a nonprofit lender (CRF).
Intensity (Percentage of ALANA Loans)
Two of the top lenders here were nonprofit lenders (MEDA and NDC) and a business development company, Newtek.
A striking but not unsurprising fact is that nonprofit lenders with deep ties to the ALANA communities provided the best access to ALANA businesses. In the case of CRF, it offered its lending platform to smaller ethnic based CDFIs and CDCs and that is reflected in the numbers. Nonprofit lenders played also a critical role in lending to Black and ALANA female businesses. Sunrise Bank had partnering relationships with a nonprofit lender and got ALANA referrals through that channel.
Large national banks like Wells Fargo and US Bank do not show up in this list of 22 lenders with at least 10 loans to ALANA businesses in the Race dataset.
In the case of loans over $150,000 the only two lenders who provided at least 10 ALANA loans were Vibrant Credit Union and Choice Financial Group. Vibrant Credit Union is based outside Minnesota and does “shared co-op branch banking” and Choice Financial Group is a Fintech company.
Borrowers that reported race and ethnicity were served by 487 lenders (for loans under $150,00). Here also patterns differ – White borrowers had loans with 478 lenders, Asian firms had access to 144 lenders, 120 lenders for Latinos, 79 lenders for Blacks and 62 lenders for American Indians.
There were 90 lenders to ALANA borrowers (for loans over $150,000) compared to 328 lenders for White borrowers.
Analysis of lenders reveal that ALANA businesses are yet to effectively penetrate the established financial system. Yet government programs, like the PPP program, flow through those channels. It is not surprising to see poor access and the greater reliance on nonprofit lenders.
The reliance of Fintech and non traditional lenders also needs more scrutiny, especially with a rise in predatory lending in ALANA communities.
One final caveat – as the table on top lenders for the PPP program (under $150,000) in Minnesota shows, many of the lenders including the 22 lenders on this list, made less than 5 percent of the loans to the ALANA businesses in the Race Dataset. So, we do not have a good overall picture of PPP lending, just an insight from the analysis of the Race Dataset.
There is a large group of ALANA businesses who could not access the system due to many reasons, such as not having a relationship with a banker, lack of paperwork and ability to provide documents, need for more technical assistance in applying for the loan, language barriers, or not being eligible for these loans. They are largely out of the system and their only recourse were lotteries for grants. You cannot get equity by lottery, especially if these lotteries are not structured right. Priority should be given to Black and American Indian businesses as they are having a tougher time accessing capital.
Case Study: 10 Businesses during the Covid Crisis
As the news about the EIDL and PPP program were being released on a first come, first served model, I knew this would not work for ALANA businesses as they did not have established financial relationships with the banking sector, many did not have good book keeping systems or a business plan. I called around 10 businesses in retail, auto, furnishing, health care, fitness, and the restaurant sector. Most did not know about the programs so I texted them application details. They did not make it in the first round of PPP for reasons mentioned above and were frustrated. Some had filled up EIDL applications and were waiting to get a response which never came. It was only when the SBA suddenly opened their 7-hour window for small lenders that things began to change. They got connected with nonprofit lenders, some used their own platform like NDC, others used CRF’s platform, such through AEDS. Two got it through their own bank. All who tried got in that day. Most only knew about the 7-hour window because I called them as soon as I heard the announcement that day via the District SBA office. On their part, the businesses had paperwork that was ready to go. Staff at NDC and AEDS worked through the night to get people in. Access met opportunity and the results were positive. Nonprofit lenders were accessible, they answered the phone and email and patiently worked with the businesses.
These ten businesses also applied for various lotteries for grants. This was a frustrating experience as they would fill up many applications but not hear back. Only in two cases were actual lists of grantees made public. At least 3 of the 10 received a grant via a lottery. The best structured lottery is one where there are separate tracks for ALANA and other micro businesses so that they have a better chance of success. Priority should be given to them as their access to capital via the financial system is limited.
One insight from this case study is that with active community outreach programs can reach businesses without access provided there is an infrastructure in place, in this case, ALANA based nonprofit lenders.
Proposal to Congress
Congress needs to act urgently to repurpose the PPP fund so that they can be accessed by ALANA businesses. Two major things are needed – funds to increase the capacity of nonprofit lenders to serve ALANA businesses and dedicated and flexible capital so that ALANA businesses can access a lifeline to survival. The proposal by the ALANA Community Brain Trust is for the PPP funds to flow through two channels as provided below:
- $10 billion to flow directly to ALANA CDFIs towards business development and capacity building of CDFIs. ALANA CDFIs serve ALANA businesses more effectively than other lenders.
- $10 billion to flow directly to neighborhood-based CDCs in diverse neighborhoods. Neighborhood based CDCs provide more patient capital and patient technical assistance than other business development organizations. We need to build up their loan pools, give them more flexibility in the use of capital and invest in their capacity to serve ALANA businesses.
- Repurpose $1 billion of PPP to the SBA’s Microlending program and modify the program to become more effective and accessible to ALANA microlenders.
The vision for this proposal is that in every city in America with ALANA population over 100,000, there would be at least 5 community based nonprofit lenders to serve them.
Proposal to Banks and Lending Institutions
If the $60 billion ALANA economy in Minnesota collapses so will the banks and lending institutions. ALANA households contribute $175 million every month in rental payments to property owners (who in turn have taken mortgages with your banks), ALANA households own $27 billion in residential real estate in Minnesota (whose mortgages are with your banks), ALANA businesses provide jobs and income and turbo charge the local neighborhood economies, providing a customer base for your banks. Here are some recommendations:
- Partner with nonprofit lenders and ALANA community groups to help you get the cultural intelligence needed to effectively service ALANA businesses. The PPP program success of certain community banks occurred because of their strong partnerships with community nonprofit lenders who referred them clients in a win-win situation.
- Partner with the ALANA Community Brain Trust to develop an ALANA Capital Platform to provide the critical economic development infrastructure in ALANA neighborhoods.
- Ensure that ALANA lending follows the principles articulated in the Small Business Borrowers’ Bill of Rights so that responsible lending occurs with ALANA businesses
