UA Professor Discovers Mergers Don’t Help the Largest Banks

TUSCALOOSA, Ala. — To merge or not to merge? If you’re a bank, that’s a question you may want to ask The University of Alabama’s Dr. Daniel Henderson.

Henderson, professor of economics in UA’s Culverhouse College of Commerce, recently co-authored a study on bank mergers. Henderson’s research finds that most banks can benefit from mergers, but the largest banks don’t benefit because they have exhausted their potential gains.

Henderson said the majority of banks right now are operating under increasing returns-to-scale, meaning that they can shed costs as they get larger. This means that average operational costs of the bank will go down, leading the bank to become more profitable as it grows.

“This is consistent with the majority of the literature which has been used to argue in favor of bank mergers,” Henderson said. “In other words, this implies that bank mergers may be beneficial to most banks.”

What Henderson and his colleagues have discovered, however, is that this principle does not apply to the largest banks. Those banks have already maxed out their gains in efficiency and are operating at what is known as constant returns-to-scale. This is important because the prospect of increasing efficiency is often used to argue for bank mergers and in policies limiting bank size.

This discovery may shed light on the “too big to fail” scenario that plagued the recent recession. Economists now have information that shows the largest mergers need to be looked at with caution, since they might not be as beneficial as the smaller ones before them.

Henderson and his team attained these results by using a new cross-sectional cost system method to analyze US commercial banks in 2010. They used smooth coefficients, something the previous papers did not, which allowed for a more accurate analysis of returns-to-scale.

The study, titled “Smooth Coefficient Estimation of a Seemingly Unrelated Regression,” will be published in the November 2015 issue of the Journal of Econometrics.