One finding of my research on small businesses earlier this year is that they are better than their larger competitors at responding to so-called “soft” information. In larger firms, information often must be communicated through several hierarchical layers to a centralized decision maker; in small businesses, the decision maker is typically more closely linked to the direct gathering of this information.

One obvious place to test this hypothesis is the banking industry. When determining whether to make a commercial loan (as well as the interest rate to charge), a bank looks at hard data on a business’s finances and credit history. But a loan officer at a bank branch also takes in information more difficult to quantify.

The loan officer might know people around town who have dealt with the business, or even observe the body language of the business owner when discussing the loan. This is precisely the information that is difficult to pass up a chain of management. If such information is important, we might expect to see differing behavior and outcomes in commercial lending for small versus large banks.

In a 2002 paper, a group of authors looked at lending decisions by small and large banks to businesses with varying degrees of financial documentation. They found that large banks were significantly less likely to lend to businesses without formal financial records than small banks. This result is consistent with the idea of soft information discussed above. If small businesses can take in and respond to soft information more efficiently than large ones, their need for hard data may be less. These findings are echoed in a 2004 paper that finds small banks utilize more information on a potential borrower’s “character” than larger banks.

While the evidence that soft information is a greater influence on small banks’ lending decisions is compelling, the ultimate test would compare commercial lending outcomes for small and large banks. Consider a business that wants a loan and, in terms of hard financial data, is right on the threshold of what banks consider credit worthy. If small banks respond to soft information more efficiently, we would expect to see a lower default rate than for large banks among these threshold cases. I’m currently in the preliminary stages of research on just this question, and I am looking for the best data sources to shed light on lending outcomes.

In addition to testing theories on the lending side for small and large banks, the results above might be important for small businesses doing the borrowing. Numerous sources say that lending to small business has not kept pace with the overall recovery since the Great Recession. To the extent that credit-worthy small business owners can better understand the right banks with which to do business, their odds of getting much-needed loans may increase. Look in this space in the coming months for more updates on this research.

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